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 |
|---|---|---|---|---|---|
Biomolecular pathways are built from diverse types of pairwise interactions , ranging from physical protein-protein interactions and modifications to indirect regulatory relationships . One goal of systems biology is to bridge three aspects of this complexity: the growing body of high-throughput data assaying these interactions; the specific interactions in which individual genes participate; and the genome-wide patterns of interactions in a system of interest . Here , we describe methodology for simultaneously predicting specific types of biomolecular interactions using high-throughput genomic data . This results in a comprehensive compendium of whole-genome networks for yeast , derived from ∼3 , 500 experimental conditions and describing 30 interaction types , which range from general ( e . g . physical or regulatory ) to specific ( e . g . phosphorylation or transcriptional regulation ) . We used these networks to investigate molecular pathways in carbon metabolism and cellular transport , proposing a novel connection between glycogen breakdown and glucose utilization supported by recent publications . Additionally , 14 specific predicted interactions in DNA topological change and protein biosynthesis were experimentally validated . We analyzed the systems-level network features within all interactomes , verifying the presence of small-world properties and enrichment for recurring network motifs . This compendium of physical , synthetic , regulatory , and functional interaction networks has been made publicly available through an interactive web interface for investigators to utilize in future research at http://function . princeton . edu/bioweaver/ .
The complexity of cellular activity is driven not only by interactions among genes and gene products , but also by the timing and dynamics of these interactions , the conditions under which they occur , and the many forms that they can take . Proteins interact in many different functional manners with multiple partners - physically in complexes[1] and through modifications[2] , [3] , synthetically when employed in parallel pathways[4] , and in regulatory roles as activators or repressors[5] - and these interaction types combine to form complete molecular pathways . Functional assays such as gene expression , localization , and binding each capture individual aspects of this molecular activity at a global level , but translating the vast amount of resulting genomic data into specific hypotheses at the molecular pathway level has proven challenging . The heterogeneity of gene interactions within each pathway has compounded this difficulty by preventing any one assay from providing a complete biological picture . It is thus critical to integrate large genomic data collections to describe not only the membership of gene products within pathways , but also their construction from the building blocks of individual types of biomolecular interactions . In this work , we provide the means for investigators to study complete molecular pathways at a whole-genome level as generated from integrated functional genomic data . First , we relate 30 general and specific biomolecular interaction types , such as transcriptional regulation , ubiquitination ( and other post-translational modifications ) , or protein complex formation , in an ontology of interaction types . This ontology is hierarchical , in that a phosphate transfer is perforce a covalent post-translational modification , which is in turn by definition a transient physical interaction , and so forth . Next , we combine this ontology with Bayesian hierarchical classification methodology [6] , enabling the simultaneous prediction of genome-wide interaction networks of all of these 30 types from integrated heterogeneous experimental data . Finally , we apply this method to a compendium of ∼3 , 500 Saccharomyces cerevisiae experimental conditions , experimentally validating several of the resulting predictions in glucose utilization , DNA topological maintenance , and protein biosynthesis as described below . This methodology ensures that investigators can take advantage of all available data to accurately identify the entire range of functional interaction types within specific pathways and across an organism's genome . It is important to contrast this genome-wide system for predicting diverse biomolecular interaction types with previous work predicting specific individual interaction networks . A variety of methodologies have been proposed for inferring regulatory networks [7]–[10] , physical interaction networks [11] , [12] , synthetic interaction networks [13] , [14] , and other interaction types [15] , generally from their respective primary data types ( ChIP-chip and -seq , proteomics , double knockouts/knockdowns , etc . ) Likewise , other methods have been proposed for heterogeneous genomic data integration [16]–[24] , but these almost uniformly focus on either general functional interactions or on specific bimolecular interaction types . This work combines the strengths of these two bioinformatic areas , providing a simultaneous platform with which all data available for a system can be integrated and focused onto specific interaction types , genome-wide and for individual gene products . We first applied our yeast network compendium to explore two cellular processes , carbon metabolism and cellular transport . This generated many promising interactions involving Snf1 , Cmk2 , Glc7 , Adr1 and Gph1 supported by recent published work . We also suggest several novel pathway connections , such as the interplay between the glycogen breakdown and glucose utilization pathways , by systematically layering multiple different interaction types . To experimentally validate a collection of our predicted yeast interactions , we focused on the synthetic lethal interactions , where double knockouts result in lethality , predicted among proteins involved in DNA topological change and regulation of protein biosynthesis . Highly ranked 20 protein pairs , 10 pairs from each pathway , were hypothesized to be synthetically lethal , and we experimentally confirmed 14 of these pairs ( 70% ) . Furthermore , we evaluated our posttranslational modification predictions based on recent experimental results on 173 protein pairs , resulting in a prediction AUC over 0 . 8 . In an analysis of the systems-level global and local network structures of our interactomes , we observed differential usage of several recurring subgraphs , providing insight into the functional design principles of pathway components . Finally , we provide a web-based interface to explore all 30 yeast interaction networks at http://function . princeton . edu/bioweaver . This will allow investigators to interactively survey and generate hypotheses from the diverse interaction types comprising the S . cerevisiae cellular circuitry .
We predicted a compendium of biomolecular interaction networks by integrating diverse yeast genomic data using a multi-label hierarchical classification system ( [6] , Figure 2A ) . As briefly outlined in Figure 1 , we first independently predict each interaction type using specifically trained SVM classifiers . Next , it is desirable to avoid making inconsistent interactome predictions due to noisy data , e . g . predicting that two genes share a regulatory relationship without occurring within the same pathway . In order to share information among classifiers for related interaction types in a principled manner , each SVM's predictions are treated as noisy observations . The final set of labels for each gene pair is then derived by finding the maximum likelihood assignment of interaction labels by integrating these observations in a Bayesian graphical model . Based on ∼30% heldout test data , our average AUC over all 30 interaction types was 0 . 79 , with minimal variations in performance across the interaction ontology ( Figure 2A , Figure 1 in Text S1 ) . The most general interaction type , functional relationship , also incurred the lowest AUC of 0 . 63 , which remains comparable to state-of-the-art functional interaction prediction systems [25] . In order to quantify the contribution of our hierarchical Bayesian system relative to predicting disparate biomolecular interaction types in isolation , we compared the accuracy of each individual SVM classifier to that of the complete system . For all 30 predicted interactomes , the Bayesian hierarchy showed increased AUC scores , averaging +0 . 076 and ranging from a minimum of +0 . 011 to a maximum of +0 . 166 . For example , posttranslational regulation improved from 0 . 61 to 0 . 77 , while phosphorylation increased from 0 . 67 to 0 . 79 . ( full ROC curves for all interaction networks can be found in Text S1 ) . In combination , these two evaluations suggest that this methodology can accurately leverage large genomic data collections to simultaneously infer a diversity of genome-wide interaction networks . Many gene interactions are directional and thus asymmetric , e . g . phosphorylation or ubiquitination , in which the two interactors take on distinct source and target roles . It is thus important to correctly infer not only the presence or absence of these directed interactions , but also the correct directionality . Specifically , for each directed interaction type , we constructed a list of all edges ranked by predicted probability; we then compared the rank of the correct interaction direction relative to the incorrectly flipped interaction between the same two genes ( Figure 2 in Text S1 ) . Using this as a true- and false-positive rate criterion , we were able to predict the correct direction of gene interactions with average AUC of 0 . 85 over the 12 directed networks ( maximum 0 . 94 , minimum 0 . 70 ) . This indicates that this methodology can accurately recover not only overall pathway structure , but also the upstream and downstream effects of individual gene products within molecular pathways . Simultaneous inference of biomolecular networks for many different interaction types allows the generation of very specific novel hypotheses . As a first example , we detail a combination of transcriptional , genetic , post-translational , and metabolic interactions among gene products coordinating glycogen breakdown and glucose utilization in yeast . As shown in Figure 3 , Adr1 is an important transcription factor involved in carbon metabolism in Saccharomyces cerevisiae . It has many known regulatory inputs [26] , one of which is the glucose-responsive kinase Snf1 , and what proteins transmit this regulatory information has been under investigation for some time . By examining different classes of predicted interactions with Adr1 and other proteins not in our gold standard ( Figure 3A ) , we first identified regulatory and genetic interactions between the protein phosphatase Glc7 and Adr1 . Specifically , our prediction of a synthetic alleviating interaction between Glc7 and adr1 mutants places it upstream of Adr1 in this pathway . This combination of interactions is almost always associated with an upstream inhibitory regulator , consistent with the known biological role of Glc7 as a protein phosphatase that removes activating phosphorylations [27] . The predicted yeast networks also hypothesized post-translational regulatory interactions between Cmk2 and both Adr1 and Gkc7 ( Figure 3A ) . This three-protein network creates a feed-forward regulatory motif in which Cmk2 simultaneously activates Adr1 as well as its inhibitor Gkc7 , creating a time-delayed inactivation of Adr1 . These interactions are supported by a recently published paper [26] linking the calmodulin- and Snf1-dependent pathways to Adr1 regulation . Our predicted pathway takes these results a step further by identifying which of the three calmodulin-dependent kinases ( Cmk2 ) is responsible [28] . Finally , a novel metabolic interaction was predicted between Adr1 and Gph1 , the only high scoring interaction of its type for Adr1 . Like Adr1 , Gph1 is involved in glucose metabolism by glycogen breakdown , and both are regulated by the metabolites glucose and cAMP [29] . A metabolic interaction between Adr1 and Gph1 , combined with the known regulation of these genes by glucose and cAMP , suggests that coordinated regulation is occurring between the glycogen breakdown and glucose utilization pathways and is transcriptionally controlled by Adr1 . Protein sorting and trafficking is an essential function of eukaryotes and requires numerous multi-subunit complexes to ensure the proper localization and secretion of proteins ( Figure 3C , [30] ) . At the early stages of this process , the two major transport pathways from the endoplasmic reticulum ( ER ) to the Golgi are governed by the SNARE and COPI complexes [30] . We predicted synthetic interactions between these pathways ( e . g . synthetic aggravation for Arf1-Sec18 and synthetic alleviation for Sec27-Uso1 ) that are supported by known genetic interactions[31] , [32]; Arf1 and Arf2 are a representative example , as they are considered functionally redundant GTPases , and each COPI complex contains either Arf1 or Arf2 [33] . Later in the pathway , Bch1 is a member of the ChAP family of proteins , which direct cargo bound to COPI complexes in the Golgi to their destinations such as the plasma membrane [34] . We predict a physical interaction between Bch1 and the COPI complex that is well established in the literature but was not part of our gold standard . Likewise , Vps1 serves a similar function for vacuole targeting [35] , and our predictions of its physical and shared pathway interactions with COPI are supported by the literature [34] . Novel hypotheses in Figure 3C include the predicted physical interaction between Bch1 and Vps1 , suggesting competition between the Sec27-Arf1 and Vps1 complexes for the Bch1 sorting function ( also supported by a metabolic interaction between Sec27-Arf1 and Vps1 ) . Both Vps1 and Arf1 are GTPases that must hydrolyze GTP to perform their roles in protein sorting [33] . Thus , this predicted pathway hypothesizes a competition between the Arf1 GTPase and Vsp1 GTPase for Bch1 that is likely regulated by GTP availability . Similarly , the uncharacterized membrane-bound protein YDL012c is placed in the same pathway as Vps1 , suggesting that the former may be involved in regulating Vps1 activity . By highlighting just a few of our predicted interactions in the protein sorting pathway , we demonstrate the potential for generating hypotheses used to drive novel biological discoveries . To experimentally evaluate the accuracy of a subset of our predicted interactions in a directed manner , we focused on the DNA topological change and protein biosynthesis regulation processes in S . cerevisiae [36] . 20 synthetic lethality interactions predicted with high probability were experimentally tested using SGA technology [4] , [13] , with the results summarized in Figure 4 . 14 gene pairs ( 70% ) were validated , 8 involved in DNA topological change and 6 in the regulation of protein biosynthesis . Several of the remaining 6 unconfirmed interactions may be synthetic lethal under different conditions . For example , GCS1 and SLT2 deletions both individually decreased resistance to ethanol stress [37] , and similar conditions might elicit synthetic lethality . Based on a total of ∼100 , 000 pairs estimated to have been synthetically lethal in yeast of a possible ∼18 million ( 0 . 05% ) [13] , our predictions are a clear improvement over the baseline rate for novel discovery . As an additional evaluation , we collected 24 recent publications containing a total of 173 experimentally confirmed post-translationally regulated protein pairs ( see Text S2 for the list of publications ) . None of these interactions was present in our training standard . Evaluating on this set , our Bayesian hierarchical system achieved an AUC of 0 . 802 , demonstrating its ability to accurately predict novel , experimentally verifiable post-translational regulation interactions on a whole-genome scale . This accuracy is comparable to our initial cross-validation AUC of 0 . 778 , indicating that our evaluation provides a reasonable estimate of the expected experimental verification rate for novel predictions . This rich compendium of inferred interaction types provided an opportunity to analyze systems-level network features genome-wide at multiple levels of biomolecular activity . In particular , we examined the network structural characteristics that potentially help to define the functional roles of each interactome . Biological networks have been proposed to exhibit a scale free topology [38] , implying a power-law degree distribution . Previous studies have detected such distributions based on partial networks and single interactomes [39] . Here ( Figure 5A ) , we observe a scale-free degree distribution very robustly in all 30 interaction types . However , the high-degree hubs in each interactome do differ , reflecting the distinct functional activities carried out by each network type . To verify this , we analyzed the extent of the overlap of high-connectivity genes ( in the top 5% of the degree distribution ) between the networks for each pair of interactomes ( Figure 5B; directed interactomes were divided into separate in- and out-degree comparisons ) . The major clusters show distinct functional similarity , correctly reflecting the similarities captured by our interaction ontology: transient and nontransient physical interactions each group together , synthetic interactions cluster , and so forth . Beyond confirming the structure of the ontology , this also captures relationships such as the sharp divide between regulatory in- and out-degree ( the most regulated genes are not themselves high-level regulators with many targets ) and a tendency for regulatory hubs to incur more synthetic interactions than expected . Degree distribution captures a global description of each network , while analysis of small recurring subgraphs has been proposed to describe local network properties [40] , [41] . We investigated the enrichment of two types of subgraphs , network motifs and graphlets , in our interactomes . First , network motifs are small directed subgraphs that have been found to recur in a growing number of organisms [42]–[44] . In our 12 directed interaction networks , the feed forward loop motif showed significant enrichment ( relative to a random background; see Text S1 ) consistent with previous studies on the yeast transcription factor network [41] . Feed forward loops are known to accelerate or delay the response of a input signal [45] , suggesting in this context a much wider usage of dynamic information processing than has been previously reported in regulatory networks[46]–[48] . A second approach to exploring the local structure of biological networks is to examine graphlet degree distributions [40] . Graphlets are small non-isomorphic subgraphs , and a graphlet's degree for a given node is defined as the number copies of that graphlet to which it is incident . For example , the number of triangle motifs touching a particular node represents its 3-node graphlet degree . Compared to network motifs , for which enrichment can be difficult to detect due to the complexity of the baseline null distribution[49] , graphlet analysis may have a higher sensitivity towards infrequent subgraphs . Thus , as a complementary analysis , we computed the graphlet degree distributions for all two to five node graphlets for the 13 specific leaf node interactomes in our interaction ontology ( Figure 5C ) . We compared the graphlet degree distributions between these interactomes , demonstrating a clear divergence in the local network structure between subclasses of metabolic , regulatory and synthetic interactions . Unlike the comparison of high-degree genes , this also captures unexpected similarities between disparate interaction types: phosphorylation and ubiquitination , for example , are siblings in the interaction ontology and represent comparable mechanisms of post-translational modification . The former's local network topology is more similar to that of synthetic interactions , however , while ubiquitination is more strongly regulatory . This differentiating pattern between ubiquitination and phosphorylation provides a base for intriguing network hypotheses for further investigation . One potential explanation could be due to the differing mechanistic activities where ubiquitination is most often employed exclusively as a regulatory mechanism to degrade active proteins , whereas phosphorylation serves both regulatory and dynamic information processing roles [50] .
The increasing abundance of genomic data has opened up countless new possibilities for systems-level biological perspectives , but its increasing complexity impedes the understanding of specific cellular circuitry at a mechanistic level . Here , we provide a method with which very large experimental data compendia can be integrated to predict 30 specific biomolecular interaction types at a genome-wide scale . By applying this to more than ∼3 , 500 experimental conditions in yeast , we have evaluated these predictions at an average AUC of 0 . 79 , validated 70% of experimentally tested synthetic lethal interactions , and proposed novel transcriptional , genetic , post-translational , and metabolic interactions in the yeast carbon metabolism and cellular transport pathways . As described above , the investigation of specific S . cerevisiae biology in the processes of glucose utilization and protein trafficking demonstrates the use of these interactomes to reconstruct complete pathways . In many instances , experimental biologists are faced with the task of designing experiments to target a specific set of genes . By simultaneously hypothesizing all types of biomolecular interactions in which a group of gene products may be involved , this methodology can be used to select both the proteins to be assayed and the assays that may be most informative . Prior approaches inferring these interaction types in isolation mask this information and may even be inconsistent; how might a biologist interpret predictions that two proteins physically interact , but that they are not part of the same pathway ? Such inconsistencies are avoided by simultaneous ontology-based inference , allowing underlying experimental data to be integrated into a consistent description of a cellular system . To our knowledge , there has been no other method that simultaneously enables researchers to leverage high-throughput data in an interaction-type-specific manner within an ensemble setting . Successful focused attempts to predict specific interaction types have shown comparable AUCs to our results [51] , [52] , which could be incorporated into a framework like this as base classifiers during future work ( instead of the SVMs utilized in this study ) . Recent “functional coupling” predictions [20] are also related , but fall short of pathway-level interaction predictions , mainly due to a lack of the crucial directional information needed to infer bimolecular pathways . These frameworks typically also do not resolve inconsistencies among predicted interaction type labels that can hinder pathway reconstruction and experimental follow up . Ultimately , compendia of inferred interaction networks can be used to explicitly construct and understand distinct cellular pathways . By investigating and confirming different interaction types suggested by our system , investigators can stitch together both new pathways and new interconnections between existing ones . This process can be applied in any organism for which diverse genome-scale data is available - a situation that is only becoming more common . We believe that our work can leverage this diversity of experimental results that might otherwise be underutilized , helping to spur new functional discoveries in organisms beyond yeast . Finally , all of our predicted networks are made publicly available through an interactive tool at http://function . princeton . edu/bioweaver for investigators to explore their own biological areas of interest .
We constructed an interaction ontology focused on categorizing gene pair relationships . This is similar in spirit to the Gene Ontology ( GO ) [36] , which curates individual proteins' molecular functions , biological roles , and subcellular localizations . Our interaction ontology contains a total of 124 terms and integrates information from existing interaction catalogs [53] , [54] , the EBI [55] , and SGD [56] . The ontology's three major branches are metabolic , interaction pathway , and physical interactions . Metabolic interactions describe protein pairs linked in metabolic pathways , such as isoenzymes or enzymes that catalyze adjacent reactions . Physical interactions include covalent or non-covalent binding , e . g . stable complexes or transient post-translational modifications . Pathway interactions include more conceptual relationships between genes in a pathway , such as regulation or synthetic interactions . We selected the 30 nodes in our interaction ontology with more than 70 annotations ( as described below ) to include in this evaluation , and the complete ontology with descriptions of each term is provided in Text S1 and Text S3 . There exists no comprehensive curated gold standard repository for all types of gene pair interactions . For the 30 interactomes evaluated here , we assembled a gold standard for each type from various sources . SGD interaction labels were used for all terms under the physical and pathway interaction terms [56] . Additional transcriptional regulation annotations were obtained from the high confidence set from [57] . Co-complex annotations were obtained from gene pairs in the GO Slim term PROTEIN_COMPLEX [58] . Pairs included in terms under metabolic interaction were obtained from reactions in the KEGG database [59] . For the topmost node , functional relationships , we used positive examples from the biological process branch of GO [60] . When possible , we further manually curated gene pairs to more specific terms based on literature examination . Manual curation was performed to annotate ubiquitination interactions based on SGD curated interaction publications and also to cross annotate experimentally validated covalent modification branch examples to regulatory interaction branch terms . The directionality of the gold standards was derived directly from the inherent high throughput experiments ( e . g . kinases to targets ) . All gene pairs annotated to a term were propagated such that they were included as positive interactions for all ancestor terms . This resulted in a total of 1 , 333 , 014 unique positive labels across 30 terms ( individual terms are detailed in Text S1 ) . This process established positive interactions for each term in our interaction ontology . For supervised machine learning ( such as our SVM-based method described below ) , negative examples are also required . As protein interactions are sparse , we randomly selected a number of negative gene pairs for each term's gold standard equal to the number of positive interactions [61] . Additionally , to assess the accuracy of our directed interaction predictions , we used negative gene pairs identical to the positive examples but with inverted directionality . Finally , for evaluating predictions on new post-translational regulation completely unrelated to our training gold standard , we selected 173 additional gene pairs from 24 recent publications ( see Text S1 ) . Evaluation was performed by randomly excluding ∼%30 of the genes for each interaction type during training . That leads to a group of genes that are not in the training set and established a test set of interactions containing at least one gene from this exclusive gene set . The remaining pairs were used for SVM training and for parameter estimation in the Bayesian network . We used area under the receiver operator characteristic ( ROC ) curve ( AUC ) for evaluation as detailed in Text S1 . As training data for each interaction type , we used subsets of a data compendium consisting in total of microarray , colocalization , protein domains , transcription factor binding sites , and sequence similarity . For each interaction type to be predicted , experimental data closely related to the output was excluded ( e . g . TF binding sites for regulatory relationships ) . 78 yeast microarray datasets were included , comprising 3 , 516 conditions ( see Text S2 ) . Missing values in these datasets were imputed using KNNImpute [62] with k = 10 , and genes with more than 30% missing values were removed . For machine learning , one feature was constructed per expression condition as follows . For directional gene pair interaction types such as phosphorylation , we evaluated various methods and found xi-xj to provide optimal performance , where xi and xj are the expression values of gene i and j in condition x . When predicting non-directional interaction types such as physical interaction , we used |xi-xj| , the absolute value of the subtracted expression values . Colocalization data for 22 different cell compartments [63] and automatically determined protein family information from Pfam B [64] were both included as binary features ( true if both genes in a pairs shared localization or a protein family ) . TRANSFAC data [65] was incorporated using the Euclidian distance between the two gene's binding site profiles across 211 transcription factors . Sequence similarity between the two genes in each pair's 1 , 000 bp upstream and 1 , 000 bp downstream was scored as the sequence alignment E-values from all-against-all BLAST outputs . We developed an integrated method for predicting diverse protein interactions , based on a multi-label hierarchical classification formulation we have previously applied to gene function prediction in both yeast and mouse [6] , [66] . First , for each interaction type , we trained 10 separate SVM classifiers . We use bagging ( bootstrap aggregation , [67] ) to combine these and improve generalization , training each individual SVM classifier on a bootstrapped subsample of its interaction type's complete gold standard . We thus begin with a total of 300 SVM classifiers for our 30 interaction types in yeast , and each interaction type's group of 10 SVM outputs were averaged ( bagged ) to produce a non-hierarchically-resolved predicted interactome . Next , a Bayesian network was constructed based on the structure of the interaction ontology . First , we modeled each interaction type's bagged SVM output i as a random event Yi taking discrete values binned by five standard deviations above or five below the training set mean . Each SVM's predictions in isolation were treated as a noisy observation of a latent event Xi representing the true , binary interactions and non-interactions of each type i . Each Yi was considered to be dependent only on its corresponding Xi , and each Xi was dependent only on its set of children {Xj , . . . , Xk} in the interaction ontology , resulting in the “decorated tree” Bayesian network structure seen in Figure 1 and in [6] . Given this structure , conditional probability table parameters for P ( Yi|Xi ) were learned using maximum likelihood from interaction type i's training data . Finally , parameters for P ( Xi|Xj , . . . , Xk ) were fixed to constrain the hierarchical semantics of the ontology . If a pair is annotated to any child in {Xj , . . . , Xk} , it must also be of interaction type i , making P ( Xi = 1|Xj = 1 ) = . . . = P ( Xi = 1|Xk = 1 ) = 1 . The remaining parameters P ( Xi = 1|Xj = 0 , . . . , Xk = 0 ) were inferred using maximum likelihood by counting the corresponding training labels . Finally , Laplace smoothing was used to improve parameter robustness . All 30 interactomes were converted into binary interaction networks by setting a threshold of 5 standard deviations above the mean edge probability , retaining ∼1% of all edges . The degree of each gene was counted in this binarized network . The overlap between each pair of interactomes' high-connectivity genes was computed as the probability of a gene g being in the top 5% of interactome N1's degree distribution ( Qi ( Nj ) , defined as genes in the top i percent degree distribution of interactome Nj ) given that it was in N2's: P[g in Q0 . 05 ( N1 ) |g in Q0 . 05 ( N2 ) ] . For each of the 30 interactomes N2 , we generated a sorted gene list by edge degree; for directed interactomes , separate lists were generated for in- and out-degree . Next , we counted the number of shared genes in the top 5% of edge degree in the target interactome N1 . Finally , hierarchical clustering was used to generate clusters of shared high degree genes . Network motif enrichment analysis was carried out using FANMOD [68] . Searches were conducted for 3-node motifs using a sampling method with probability parameters of 0 . 6 , 0 . 5 , 0 . 4 and compared to 500 random networks generated using an edge swapping process preserving each gene's degree . Computational complexity precluded analysis of 4-node motifs . Graphlet degree distributions were calculated using GraphCrunch [69] . For each interactome , 73 graphlet degree distributions were generated , each representing a unique distribution of 2-5 node graphlets . Comparison between graphlet distributions was performed using the GDD agreement metric , defined as the average normalized distance to provide robust comparisons [40] , [69] . All software was implemented using the Sleipnir library [70] , which interfaces with the SVMperf software [71] for linear kernel SVM classifiers ( the error parameter C was set to 20 for these experiments ) . Bayesian network inference used the Lauritzen algorithm [72] as implemented in the University of Pittsburgh SMILE library [73] . 20 gene pairs predicted to synthetically interact [56] with high probability were selected from the DNA topological change and regulation of protein biosynthesis pathways in yeast ( as defined by GO [36] ) . Synthetic Genetic Array ( SGA ) technology [4] , [13] was applied to these pairs by combining either non-essential gene deletion mutants or conditional alleles of essential genes in haploid yeast double mutants . The query mutant strain for each pair of genes ( harboring SGA-specific reporters and markers ) was crossed to the complementary single mutant strain . Mating to the non-essential gene deletion collection was followed by meiotic recombination and selection of haploid meiotic progeny , resulting in an output array of double mutants grown in rich medium . Fitness was assessed by comparing this double mutant colony size to the sizes of single mutant colonies , which were assessed for significance as described in [4] , [13] . A p-value threshold of 0 . 05 was used to determine the final confirmed synthetic lethal pairs ( the full table of p-values can found in Text S1 ) . | To maintain the complexity of living biological systems , many proteins must interact in a coordinated manner to integrate their unique functions into a cooperative system . Pathways are typically constructed to capture modular subsets of this dynamic network , each made up of a collection of biomolecular interactions of diverse types that together carry out a specific cellular function . Deciphering these pathways at a global level is a crucial step for unraveling systems biology , aiding at every level from basic biological understanding to translational biomarker and drug target discovery . The combination of high-throughput genomic data with advanced computational methods has enabled us to infer the first genome-wide compendium of bimolecular pathway networks , comprising 30 distinct bimolecular interaction types . We demonstrate that this interaction network compendium , derived from ∼3 , 500 experimental conditions , can be used to direct a range of biomedical hypothesis generation and testing . We show that our results can be used to predict novel protein interactions and new pathway components , and also that they enable system-level analysis to investigate the network characteristics of cell-wide regulatory circuits . The resulting compendium of biological networks is made publicly available through an interactive web interface to enable future research in other biological systems of interest . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"computational",
"biology/systems",
"biology",
"computational",
"biology/genomics",
"computational",
"biology"
] | 2010 | Simultaneous Genome-Wide Inference of Physical, Genetic, Regulatory, and Functional Pathway Components |
Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks . Especially their ability to model persistent activity , a property of many neurons involved in decision-making , distinguishes them from other approaches . Stable decision attractors are , however , counterintuitive to changes of mind . Here we demonstrate that a biophysically-realistic attractor network with spiking neurons , in its itinerant transients towards the choice attractors , can replicate changes of mind observed recently during a two-alternative random-dot motion ( RDM ) task . Based on the assumption that the brain continues to evaluate available evidence after the initiation of a decision , the network predicts neural activity during changes of mind and accurately simulates reaction times , performance and percentage of changes dependent on difficulty . Moreover , the model suggests a low decision threshold and high incoming activity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation , which up to now lacked evidence for physiological relevance . Thereby , we further affirmed the general conformance of attractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor from linear diffusion models .
In our lives , we constantly are required to make decisions . Some of these decisions are irretrievable , while others are not binding and can be adjusted if we change our mind . The brain processes leading to decisions , have occupied neuroscientists during the last decades ( reviewed in: [1] , [2] ) . Perceptual decision-making paradigms , like the random-dot motion ( RDM ) task [3]–[5] , were designed to study decision-making behavior and brain activity of decision-associated brain areas , like the dorsolateral prefrontal cortex and lateral intraparietal ( LIP ) cortex , in the simplest context . Traditionally , the decision process is regarded as a decision variable evolving in time , until a termination criterion is reached . Firing rates of LIP neurons gradually increase during motion-viewing in the RDM task and correlate with subjects' choices and reaction times [3] , [6] , making LIP activity a possible candidate for a neural decision variable . Recently , more complex aspects of decision-making received increasing attention , involving multiple choices [6] , [7] or confidence [8] and also: What happens in our brains if we change our mind ? To elucidate this question , Resulaj et al . [9] developed a psychophysical RDM task , where humans had to indicate their choice by moving a handle towards a left or right target ( Fig . 1A ) . Because this hand movement is continuous , contrary to ballistic saccades or pressing a button [10] , changes of mind could be directly observed by recording the handle traces . Changing improved the overall accuracy , but depended on task difficulty: most correcting changes were observed at intermediate levels , while erroneous changes increased monotonically with difficulty . These findings pose a challenge for a class of models that implement decision-making by diffusion in a nonlinear landscape of stable fixed points , which act as decision-attractors . Once a decision-attractor is reached , this state will persist except for high levels of noise or perturbations and is thus rather counterintuitive to a change of mind . On the other hand , due to the stable attractors , those models account for persistent activity frequently observed in decision-related neurons . Moreover , biophysically-realistic attractor models , as introduced by Brunel and Wang [11] , successfully simulate animal behavior and neural activity of LIP neurons during various versions of the RDM task [2] , [12]–[14] . Here we show that changes of mind ( after a first decision ) are entirely consistent with attractor dynamics . In particular , they arise naturally during the itinerant transients following sensory perturbation , if the system lies close to a bifurcation ( or phase boundary ) that separates a neuronal state of categorical decision-making from a multi-stable region . There , the decision process is impeded by a second attractor , where both populations encoding the possible alternatives fire at high rates . This facilitates changes of mind . Moreover , by replicating the psychophysical data of Resulaj et al . [9] with a biophysically-realistic attractor network with spiking neurons , we gained neurophysiological predictions on neural firing rates during the change process . In all , our results offer testable predictions on the attractor concept and general principles of decision-making like the speed-accuracy trade-off and a fixed decision threshold .
Fig . 2A shows the simulated behavioral data . In the experiments the reaction time was set by the initiation of the hand movement . Accordingly , the simulated reaction time is composed of the time of first threshold crossing , plus the non-decision time tND = 380 ms . The reaction times and percentages of correct choices fit the experimental results well ( Fig . 2 , left and middle panel , for further comparison see [9] ) . Moreover , the model also replicated the frequency of changes observed experimentally ( Fig . 2 , right panel ) . Taking the changes of mind into account improves the performance ( Fig . 2A , left panel , red line ) , as changes from wrong to correct choice are more frequent for all coherence levels , but especially for intermediate difficulty . Changes to the wrong alternative , however , are most frequent for low motion strengths and do not occur for high motion coherence . In comparison to the experimental findings , the model predicts slightly more changes to correct and less to the wrong choice , which also explains the larger difference of performance with and without changes ( see discussion ) . Resulaj et al . [9] further noted that a seemingly optimal strategy to opt for or against a change would be to always wait until the end of tND after the first decision and , thus , to consider all possibly available evidence . This , however , was not consistent with their experimental observations . Along that line , we analyzed the time distribution of changes of mind in the attractor model ( Fig . 3 ) . In the simulations , the changes are broadly distributed across tND , with the exception that hardly any changes occur during the first 50 ms after the first decision . The distribution peak depends on the motion coherence level , with earlier changes for higher coherences ( Fig . 3B ) . Interestingly , the time difference between threshold crossings for erroneous changes is not considerably shorter than for correcting changes , although there is more evidence in favor of changing in the case of an initially wrong choice . Erroneous changes just become overall less frequent with increasing coherence . Moreover , in the simulation in at most 1 . 6% of the trials two changes occurred during tND ( Fig . 2 right panel , dashed line ) . The second change was then neglected . Notably , these double-changes were indeed occasionally found in the experiments ( M . N . Shadlen , personal communication ) . In summary , although we did not aim for a perfect quantitative fit to the experimental data , the psychometric functions obtained by our model simulations match the experimental observations very well in all relevant aspects . In Fig . 4A and E single trial examples of network simulations are displayed with and without changes of mind . In the trials with identical inputs to both selective pools ( 0% motion coherence ) , the decision which population activity will rise or decay is stochastic due to the Poisson inputs and finite-size noise fluctuations . The general temporal structure of the network activity matches single neuron recordings of primate LIP neurons [6] , [8] , [15] with a high response to the target signals , a subsequent dip of activity and a build-up of the firing rate after the onset of the moving dots , which is steeper with higher motion coherence ( Fig . 4C , D average of correct trials at first threshold crossing ) . Except for the highest motion coherence , this firing rate build-up is biphasic: after an initial steep increase independent of motion strength , the slope of the ramping activity decreases with lower motion coherence . To obtain sufficient changes of mind in the model simulations , the decision threshold was set relatively close to the divergence of the mean build-up activities for different motion coherences , which led to rather small differences in reaction times between the easiest and more difficult trials ( see discussion ) . Nevertheless , the firing rate slopes clearly diverge with motion strength already before the threshold is reached ( Fig . 4D ) . In Fig . 4F we averaged all simulation trials with changes of mind , aligned to the first threshold crossing , which , if a constant non-decision time is assumed , corresponds to aligning to reaction time in the experiments . Thus , we show that the predicted rise and fall of activity during changes of mind might actually be observed experimentally , even if neural activities obtained in single cell recordings need to be averaged over trials to obtain reliable firing rates . In fact , even for a normally distributed non-decision time with moderate standard deviation , the switch in firing rates should still be discernible in neurophysiological experiments ( see Fig . S2H ) . As most of the dots in the experimental RDM stimulus are moving randomly , the actual momentary level of coherent motion towards the target direction fluctuates around the set mean coherence . A measure of these stimulus fluctuations with respect to the monkeys' choices , the “motion energy” , was found to support the initial decisions as well as the change of mind [9] . More precisely , the fluctuations in the first 150 ms after stimulus onset acted as additional evidence in favor of the first decision ( positive motion energy ) . In change trials the motion energy subsequently became negative , indicating that stimulus fluctuations played a causal role in switching through weakening or even reversing the preceding evidence in favor of the initial choice . In the model simulations , the Poisson noise around the mean input rate corresponds to the experimental stimulus fluctuations . Fig . 5 shows the variation from mean input difference of the selective populations aligned to first threshold crossing and changes of mind ( insets ) . In line with the experimental motion energy , the average input fluctuations across all change trials became negative after the first threshold crossing . Input fluctuations thus act as evidence against the initial choice . Note however , that for high coherence levels the changes do not depend on random fluctuations of the input , since it is mostly initial errors that are reversed by the designated input bias to the correct selective population . Interestingly , the fluctuation strength necessary to reverse a decision is in general not substantially higher than that causing the initial decision . While the model can match the experimentally obtained reaction times and performances for a large range of selective inputs , if the threshold is adapted accordingly ( Fig . S3 ) , the feasible range of network inputs is greatly reduced by the additional constraint to match the changes of mind . Using a mean-field approximation of the model [11] , we analyzed the dynamical behavior of the network as a function of the selective input amplitude for the parameters that fit the changes of mind . Simulating populations of individual and realistic neurons as described above is necessary to simulate realistic neuronal dynamics , physiological responses and behavior . However , to understand the underlying attractor and dynamical structures prescribing the behavior of population dynamics , we had to use a simpler model that summarized the average activity of these populations . The number of integration variables in the mean-field approximation is reduced to one for each neural population . Thus , it can be solved much more quickly and the parameter space can be scanned ( Fig . 6A ) . Clearly , this obliged us to check the consistence of the mean-field calculations with the simulated activity of the full spiking network . We did this by running both sorts of simulations with the same parameters at key points in their parameter space ( see below ) . By solving the mean-field equation for a set of initial conditions ( here the initial firing rates of each neural population ) one obtains the approximated average firing rate of each pool , when the system has settled into a stationary state . These stationary states correspond to the stable states or attractors of the system ( Fig . 6A , thick black lines ) . The unstable fixed points denote the border of the “basins of attraction” of the stable states ( Fig . 6A , dotted black lines ) . The present model has three qualitatively different dynamical regions across the range of symmetric inputs to the selective populations from 0 to 200 Hz , which are separated by fixed-point bifurcations ( where a stable fixed point becomes unstable or vice versa ) . For small inputs the spontaneous state ( ↓↓ ) , where both selective pools fire at low firing rates , is still stable ( Fig . 6A , blue shaded region ) . At about 20 Hz the system crosses the first bifurcation and the spontaneous state becomes unstable . The network then operates in a region of categorical decision-making , where one selective pool will settle at the upper branch and the other will decay to the lower one . With sufficiently high selective inputs ( >125 Hz ) a symmetric “double-up” state becomes stable ( ↑↑ ) , where both selective populations fire with intermediate , elevated rates . Because of the strong recurrent connections within the selective populations , the decision state is stable over the whole range of inputs shown and the spontaneous- and symmetric-state bifurcations are “subcritical pitchfork bifurcations” . The above conclusions still hold if , instead of symmetric selective inputs as in Fig . 6A , biased inputs are applied , favoring one selective population against the other . In that case the double-up state still exists , but the pool with positive bias will fire at a higher rate than the one with negative bias . The higher the bias , the more will the firing rates of the two selective populations differ in the double-up state . In addition , the basin of attraction of the decision state grows for the favored population at the expense of the other , making wrong choices less likely [16] , [21] . The mean-field approximation in general provides an accurate qualitative picture of the attractor landscape . Nevertheless , also quantitative conclusions can be drawn from the analysis . However , there is typically a shift of the predicted fixed-points in comparison to the attractors of the spiking network [11] , [22] . To obtain a measure for this discrepancy , we performed network simulations to determine the fixed points of the full spiking model for some discrete selective input amplitudes ( see methods ) , shown as blue crosses in Fig . 6A . At 150 Hz selective inputs the symmetric state was first found to be stable for more than 3 , 000 ms in 9 out of 100 trials . The real second bifurcation point of the spiking network is thus shifted by about 25 Hz to higher inputs ( i . e . to the right ) with respect to the mean-field predictions . The input amplitude of the spiking simulation for which changes of mind can be obtained with the attractor model ( 155 Hz ) lies close to this second bifurcation point . Note that in the spiking simulation the dip of firing activity at motion onset marks the start of the transition to the decision state . The initial firing rates of the selective populations ( about 25–30 Hz ) are therefore located close to the symmetric attractor . As a consequence of the proximity to the symmetric attractor , the decision process is prolonged [21] , [23] , making changes of mind more probable . A change of mind is possible until one pool crosses the unstable fixed point ( Fig . 6 , dotted line between symmetric state and the decision branches ) and falls too deep into the basin of attraction of the decision state , where only strong input fluctuations can pull it out again . Taking the shift between the mean-field and spiking-network attractors into account , the decision threshold of 44 Hz coincides approximately with the unstable fixed point and thus with the border between the basins of attraction of the double-up and the decision state . A change of mind can consequently be interpreted as a transient that comes very close to or even surpasses the unstable fixed point , but , because of contrary evidence or fluctuations , does not escape towards the upper decision state and eventually loses the competition . Although the above-presented notion of changes of mind is consistent with the mean-field attractor picture , the accuracy of the approximation is known to be especially weak close to bifurcation points [11] , [22] . The mean-field conclusions on the frequency of changes of mind thus have to be validated by simulations with the full spiking network . Therefore , we performed spiking simulations for all coherence levels for different selective inputs ( Fig . 6B , C , yellow and orange lines in Fig . 6A ) to further demonstrate the importance of the system's proximity to the symmetric-state bifurcation . All network parameters and the motion input were kept identical to the simulations presented above . The selective inputs were changed by varying the target input after motion onset . The decision thresholds were adjusted so that the model with altered selective inputs fit the experimental reaction times and performances ( Fig . S3 ) . For 25 Hz target input ( and thus a total selective input of 95 Hz at 0% motion coherence ) , considerably less changes of mind were obtained , especially for low motion strength , despite the low decision threshold of 30 Hz . By contrast , with a target input of 125 Hz the model predicted too many changes at low motion coherence . More importantly , in most of the low coherence trials with high target input the selective pools did not leave the symmetric state ( Fig . 6C , Fig . S3B ) . Contrary to the concept of using the attractor states to determine the decision outcome , here , even large fluctuations do not necessarily lead to a transition towards the decision attractors . By contrast , close to the bifurcation point , fluctuations will eventually lead to an escape from the symmetric state . These additional simulations also justify the use of tND as a timeout for changes: Turning the motion stimulus off with movement initiation would correspond to stopping the motion input in the simulations at tND after the first decision . The remaining symmetric target input of 85 Hz would be even lower than the selective inputs in the 95 Hz simulations with symmetric inputs ( Fig . 5B ) . Thus , even if changes of mind were possible after tND they would be very unlikely . Apart from the input to the selective populations , changing other network parameters will affect the location of the bifurcations . The general shape of the attractor landscape , however , is robust to gradual parameter changes . For example increasing ( decreasing ) the inhibitory connectivity ωI shifts the whole attractor landscape to the right ( left ) , which has a similar effect as decreasing ( increasing ) the selective inputs ( Fig . 6 ) and likewise leads to fewer ( more ) changes ( Fig . S4 and S5 ) . This further confirms the crucial role of the symmetric state bifurcation for changes of mind in the attractor network . As shown above , the frequency of changes of mind , as well as the simulated reaction times and performance of the attractor model , depend on the amount of common external input applied to both selective populations ( Fig . 6 ) . In Fig . 7 we give a more detailed analysis of simulated behavior with respect to common and biased external inputs , if the decision threshold is fixed at the standard decision criteria ( 44 Hz , 10 Hz difference ) . More precisely , we performed additional network simulations starting from various levels of equal external baseline inputs to both selective pools , indicated by different colors in Fig . 7: from 120 Hz in steps of 8 . 75 Hz to 155 Hz ( the standard input close to the second bifurcation , used above to model the experimental changes of mind ) . On top of that , we varied the bias between the selective populations , again in steps of 8 . 75 Hz from 0 to 43 . 75 Hz ( abscissa ) . In this input scheme , the pink and red dots correspond ( approximately ) to the standard input parameters used above at 0% and 25 . 6% ( here actually 25% ) motion coherence . Increasing the baseline inputs leads to faster reaction times and overall more changes . Performance is less affected , but still decreases uniformly regardless of input bias . An experimental equivalent for higher inputs to both selective populations might be obtained by increasing the overall dot density or with bidirectional random-dot motion , similar to the three-alternative experiment by Niwa and Ditterich [7] . Independent coherent motion in two opposed directions allows comparing differences in the total sensory input while keeping the bias fixed . As an example , in the case of 10% dots moving to the right and 20% to the left , fewer changes , larger reaction times and higher performance would be expected than for 30% of dots to the right and 40% to the left . Such an experiment should generally help to distinguish the nonlinear attractor model from linear diffusion models as used by Resulaj et al . [9] , which implement the accumulation of evidence as a single decision variable , encoding only the difference in sensory evidence , but not the absolute value for each direction . Still , changes in the input variance might affect the diffusion model in a similar way as changes in the baseline input affect the attractor network ( Fig . 8 ) . Less variance in the input to the diffusion model leads to fewer changes , higher reaction time and better performance . Thus , to unambiguously distinguish the two types of models based on behavioral data , the experimental stimulus fluctuations should be controlled for . Nevertheless , the two scenarios , input variation in the attractor model versus variance changes in the diffusion model , also differ in their predictions on the variance of the output firing rates across trials ( compare Fig . 7D with Fig . 8D ) . While the variance across trials in the diffusion model intuitively increases with increasing input variance , in the attractor model it actually decreases with higher baseline inputs to the selective populations . The reason is again the approximation to the second bifurcation , which impedes the escape to the decision attractors more the higher the inputs , leading to smaller variation in firing rate across trials . Neurophysiological recordings could thus distinguish the two mechanisms based on this higher order measure .
The presented attractor model offers a simple , yet biologically detailed , explanation for changes of mind with predictions on physiological recordings and the dynamical state of the brain region involved in the decision-making process . As in the bounded-accumulation model of Resulaj et al . [9] , a threshold crossing determines the initial choice , which can then be reversed by further processing of the remaining available information . Importantly , the linear accumulator model is not a reduced one-dimensional version of the attractor model . The mechanism behind the changes of mind is quite different . The attractor model is highly nonlinear: once the transient falls into the basin of attraction of the decision state , it is captured by the attractor and a change of mind is no longer possible , except for very strong fluctuations . One requirement for intrinsic changes of mind in the attractor model is a relatively low ( first ) decision threshold . A low threshold implies fast reaction times and comparatively low performance and thus corresponds to an emphasis on speed against accuracy [10] , [25] , [26] . Indeed , Resulaj et al . [9] suggest that time pressure induces changes of mind , as fewer changes were observed when participants were instructed to perform more slowly . Moreover , a low threshold in the attractor model leads to the experimental prediction of a bimodal build-up of the mean firing rates ( Fig . 4C ) . After an initial uniform ramping activity that terminates already close to the threshold , the slopes of the average firing rates diverge rapidly for the various motion coherences . As coherence-dependent differences in mean ramping activity only set in near the decision threshold , differences in reaction time with motion strength are rather small . The reaction times of the three participants from Resulaj's experiments are in fact very fast and differ by less than 150 ms between 0% and 51 . 2% motion strength in comparison to over 400 ms in previous studies with well-trained monkeys [3] or human subjects without explicit instructions on speed or accuracy [10] . More generally , neurophysiological recordings along the lines of our predictions in Fig . 4F could yield further experimental evidence on the existence and value of an absolute decision threshold in LIP . Apart from the decision boundaries , the speed-accuracy trade-off can , theoretically , be controlled by a second mechanism: Roxin and Ledberg [23] showed that , in a reduction of the attractor model to a one-dimensional nonlinear diffusion equation , higher common inputs to both selective populations lead to a decrease in performance and reaction times ( Fig . 7 , see further Note 1 in Text S1 ) . Supporting experimental evidence comes from several recent fMRI studies , where an increase in the activity of neural integrators was observed with speed emphasis ( reviewed in: [27] ) . The mean-field analysis and complementary simulations with different selective inputs ( Fig . 6 ) revealed that , in order to explain the frequency of changes found by Resulaj et al . [9] , high common inputs to the selective pools are required in addition to a low threshold . Therefore , we suggest that , physiologically , both mechanisms to implement a speed emphasis are essential to explain the experimentally observed changes of mind: high selective inputs and a low decision threshold . Previous analyses of the binary attractor model for decision-making [14] , [21] , [22] all focused on a region in the vicinity of the first bifurcation , where the spontaneous state becomes unstable . There , performance is high and reaction times are rather long , because of long stimulus-integration times . Recently , also the “double-up” symmetric state gained relevance in connection with target presentation [12] , [13] , [16] , since consistent experimental evidence was found for high firing rates just before stimulus presentation [3] , [6] , [8] , [15] . Assuming high selective inputs with target onset , the double-up state can explain neural activity prior to the decision-making period . Furthermore , in Soltani and Wang [28] cue inputs that arrive while the system is in the symmetric up-state add up to determine the network's starting point for subsequent decision-making , thereby implementing probabilistic inference . If neural activity in decision-related areas actually evolves according to an attractor landscape , as proposed by this and previous studies ( reviewed in: [2] ) , the dynamical system has to cross a bifurcation in order to switch between the double-up state , effective during target presentation , and the decision-making regime , during random-dot motion . Yet , experimental indications that would suggest any physiological relevance of this second bifurcation for brain dynamics during decision-making have been lacking . In this study , we found that the attractor model best captures the behavioral data and changes of mind observed in the experiments of Resulaj et al . [9] , if the system lies in the proximity of the second bifurcation . We thus proved that all input regimes of the binary attractor model are consistent with particular aspects of the decision-making process and thereby confirmed the suitability of the attractor model to describe neural dynamics . Consequently , we predict that the brain operates over the whole range of inputs that enable decision-making , dependent on the pressure for speed or accuracy , instead of switching between two discrete input levels for decision-making and target representation . This could be tested pharmacologically by gradually blocking inhibition in the decision-related brain areas: decreasing inhibition shifts the working point of the system closer to the bifurcation ( Fig . S5 ) . Thus , decreasing reaction times , lower accuracy and more changes would be expected , until the double-up symmetric state becomes stable , where decision making might consequently be impaired completely for low coherence levels . Taken together , we showed that changes of mind arise naturally in an attractor model of perceptual decision-making by emphasizing reaction speed against accuracy . We suggest that this speed-accuracy trade-off is physiologically implemented by both , threshold adaptation and increasing symmetric inputs . Moreover , we found evidence for the physiological relevance of a so far unregarded bifurcation in the binary attractor model and thereby confirmed the general accordance of attractor networks with neural processes . Finally , we provided predictions on a new experimental paradigm , which might help to distinguish between nonlinear attractor and linear diffusion models .
The network consists of NE = 800 ( 80% ) excitatory pyramidal neurons , NI = 200 ( 20% ) inhibitory interneurons and is all-to-all connected . Single neurons are modeled as leaky integrate-and-fire neurons [29] with conductance-based synaptic responses , characterized by their sub-threshold membrane potential ( V ) dynamics:with resting potential VL , membrane capacitance Cm and membrane leak conductance gm . A spike is emitted , when the membrane potential reaches the firing threshold Vth . Consequently , V is reset to Vreset with an absolute refractory period τref . Isyn denotes the total synaptic current flowing into the cell . It is composed of excitatory recurrent post-synaptic currents ( EPSCs ) , mediated by fast AMPA ( IAMPA . rec ) and slow NMDA glutamate ( INMDA . rec ) receptors , and inhibitory post-synaptic currents ( IPSCs ) , mediated by GABAA receptors ( IGABA ) . External inputs are assumed to be driven only by AMPA receptors ( IAMPA , ext ) . In summary:Please see Table S1 for the mathematical description of the receptor kinetics following Brunel and Wang [11] . The parameters for neuronal and synaptic capacities , time constants and conductances are mostly adopted from the original publications [11] , [14] , except for the recurrent AMPA to NMDA ratio: to better fit the relatively high neural firing rates observed in recent neurophysiological studies [6] , [8] , [15] , we decreased gNMDA by 8% and adapted gAMPA accordingly to preserve the spontaneous spiking rates of about 3 Hz for excitatory neurons and 9 Hz for inhibitory neurons [12] . The connections in the network ( Fig . 1B ) are kept fixed during the simulation and are normalized so that the overall excitatory recurrent synaptic drive remains constant if only baseline input is applied to the network ( spontaneous state ) [11] , by calculating ω− according to , where f = 0 . 2 is the fraction of excitatory neurons in one selective pool , or “coding level” . External inputs are modeled as uncorrelated Poisson spike trains . All neurons receive a background input of νext = 2 . 4 kHz , equivalent to 800 excitatory connections from external neurons firing at 3 Hz ( Fig . 1B ) . In the spiking simulation , sensory inputs evoked by the target and motion stimuli ( Fig . 1C ) are applied ( only ) to the selective pools . They are present until the end of the simulation ( 3 , 500 ms ) , starting at ttarget = 400 ms and tmotion = 1 , 300 ms plus an assumed latency of 100 ms and 200 ms , respectively , for the signal to arrive in area LIP [6] . The time course of the target input ( Fig . 1C , red ) follows the approach of Wong et al . [16]:It is in accordance with experimental findings [6] , [15] and has since been used in several models of LIP activity during the RDM paradigm [12] , [13] . The initial exponential decay τ1 = 100 ms can be explained by short term adaptation . Due to the exponential decrease of the target input with τ2 = 15 ms , starting with a latency of 80 ms after motion-stimulus onset , the target input is already decaying for 120 ms , before the motion input arrives in LIP with a latency of 200 ms . This causes the dip of firing rate in the simulations . Note that the specific parameters of the target input are irrelevant as long as , first , the initial inputs are high enough to shift the network from the spontaneous to the symmetric state with high firing rates in both selective populations and , second , the target input is reduced sufficiently with motion onset to allow competition ( Fig . 5 ) . The random-dot motion stimulus is simulated as: ( 1 ) with a time invariant rate of νmotion = 70 Hz for 0% coherence . Coherent motion thus corresponds to a positive bias to one selective pool , balanced by a reduction of the motion input to the other . We simulated six coherence levels: c = 0% , 3 . 2% , 6 . 4% , 12 . 8% , 25 . 6% , and 51 . 2% . 1 , 000 trials of 3 , 500 ms were run for each parameter set and motion coherence . We used a second-order Runge-Kutta routine with a time-step of 0 . 02 ms to perform the numerical integration of the coupled differential equations that describe the dynamics of all cells and synapses . The population firing rates were calculated by counting all spikes over a 50 ms window and dividing this sum by the number of neurons in the population and the window size . The time window was shifted with a time step of 5 ms . We filtered the external input spikes in the same way to obtain input firing rates for the fluctuation analysis of the external Poisson inputs ( Fig . 5 ) . The “variation from mean input difference” was calculated by subtracting the mean input rate across trials from each selective population . The remaining input difference between the selective populations in each trial was then signed with respect to the first pool that crossed the decision threshold . According to recent experimental findings [3] , [6] , we assumed a fixed decision threshold independent of motion coherence: a ( first ) decision was reached when one selective pool crossed a threshold of 44 Hz and surpassed the other by at least 10 Hz . The same conditions applied for a change of mind . To confirm the mean-field approximation ( see below ) , additional simulations were run for different target inputs after motion input onset ( 25 Hz and 125 Hz instead of 85 Hz ) , and also for higher and lower inhibitory weights ( ωI = 1 . 425 and ωI = 0 . 825 instead of 1 . 125 ) . The respective threshold values were: 30 Hz for 25 Hz target input , 50 Hz for 125 Hz target input , 38 Hz for the simulations with ωI = 1 . 425 and 50 Hz for ωI = 0 . 825 . All threshold values used were determined within 1 Hz accuracy in order to match the experimental reaction times and percentage of correct choices ( A threshold alteration of ±1 Hz roughly corresponds to a ±3% variation in reaction time and about 10% in the frequency of changes ) . For the simulations shown in Fig . 7 , the standard threshold parameters were used ( 44 Hz with 10 Hz difference ) . The additional condition of a minimal difference of 10 Hz between the firing rates of the two selective populations avoids occasional joint crossings to count as decisions or changes ( Fig . S2E ) . Reaction times were calculated as the time of threshold crossing plus a non-decision time tND = 380 ms , which consists of a latency of 200 ms for the motion signal to arrive in LIP [3] , [6] and 180 ms to account for movement initiation and execution [17] , [18] . tND also set the time limit for the changes of mind . The robustness of the model simulation to variations in the decision criteria and the non-decision time is shown in Fig . S2 . To obtain the stable states of the standard spiking-neuron model in comparison to the mean-field analysis ( Fig . 5A , blue crosses ) , we simulated 100 trials each , without target inputs , but for constant symmetric inputs to the selective populations , ranging from 0 to 200 Hz in steps of 10 Hz for 3 , 500 ms . The stable fixed points of the decision state were found by averaging the last 500 ms of all trials in which the decision attractor was reached . For ( very ) low and high inputs , in some ( most ) of the trials the symmetric spontaneous or double-up state was stable and no decision was formed . The mean firing rate from 1 , 000 to 2 , 000 ms of these trials determined the fixed point of the respective symmetric state . Numerical integration of the mean-field equations was performed using a second-order Runge-Kutta routine with a time-step of 0 . 1 ms . Stable fixed points were found by terminating integration when the firing rates did not differ by more than 10−8 from the mean over the last 40 ms . Unstable fixed points were determined by the boundary of the basins of attraction between two stable states , searched by iterating the initial values between two stable branches to find the change of dynamic flow towards one or the other stable state . Both , the mean-field analysis and the spiking simulations were implemented in custom-made C++ programs . Custom-made MATLAB programs were used for later analysis , fits of the simulation results and the numerical integration of the diffusion model . The results shown in Fig . 8 were obtained by numerically integrating a diffusion model with an added second threshold and timeout for changing as described in [9] . For the drift and boundary parameters , we used the average fitted value of Subject S from Resulaj et al . [9]: a drift rate , with k = 0 . 3 , a first decision bound B = 13 . 2 , tND = 324 ms and BΔ = 23 . 3 , without any bias in starting point or drift ( μ0 = 0 , y0 = 0 ) . The increments of evidence were obtained from normal distributions with several variance levels . To obtain the predictions on alterations in input variance , we simulated 10 , 000 trials for each of the six coherence levels , with input variances of 0 . 7 , 1 . 0 and 1 . 3 , respectively , at time steps of 1 ms . | A recent psychophysical experiment showed that participants do adjust their decisions ( change their mind ) based on further evidence , which was processed only after the first decision was made . The established notion of ( perceptual ) decision-making as a decision variable evolving in time until a termination criterion is reached does not incorporate these changes of mind . In the biophysically-realistic attractor model , the mean firing rates of neural populations encoding the decision alternatives act as the decision variable . In line with neurophysiological evidence from decision-related neurons in the lateral intraparietal cortex , a decision is made if a fixed firing rate threshold is crossed . We propose here that a change of mind is induced if this decision threshold is crossed a second time , namely by the neural population encoding the initially losing alternative , which thus overtakes the population that first crossed the decision threshold . Interestingly , we found this more likely to happen the further the system is pushed towards a regime where decision-making is no longer unambiguous , but both neural populations can fire at elevated rates . This , besides , corresponds to higher incoming activity and thus faster and less accurate decisions and suggests that the brain operates over the whole range of inputs enabling decision-making . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"cognitive",
"neuroscience",
"computational",
"neuroscience",
"biology",
"neuroscience"
] | 2011 | Changes of Mind in an Attractor Network of Decision-Making |
The World Health Organization ( WHO ) has set ambitious time-bound targets for the control and elimination of neglected tropical diseases ( NTDs ) . Investing in NTDs is not only seen as good value for money , but is also advocated as a pro-poor policy since it would improve population health in the poorest populations . We studied the extent to which the disease burden from nine NTDs ( lymphatic filariasis , onchocerciasis , schistosomiasis , soil-transmitted helminths , trachoma , Chagas disease , human African trypanosomiasis , leprosy , visceral leishmaniasis ) was concentrated in the poorest countries in 1990 and 2010 , and how this would change by 2020 in case the WHO targets are met . Our analysis was based on 1990 and 2010 data from the Global Burden of Disease ( GBD ) 2010 study and on projections of the 2020 burden . Low and lower-middle income countries together accounted for 69% and 81% of the global burden in 1990 and 2010 respectively . Only the soil-transmitted helminths and Chagas disease caused a considerable burden in upper-middle income countries . The global burden from these NTDs declined by 27% between 1990 and 2010 , but reduction largely came to the benefit of upper-middle income countries . Achieving the WHO targets would lead to a further 55% reduction in the global burden between 2010 and 2020 in each country income group , and 81% of the global reduction would occur in low and lower-middle income countries . The GBD 2010 data show the burden of the nine selected NTDs in DALYs is strongly concentrated in low and lower-middle income countries , which implies that the beneficial impact of NTD control eventually also largely comes to the benefit of these same countries . While the nine NTDs became increasingly concentrated in developing countries in the 1990–2010 period , this trend would be rectified if the WHO targets were met , supporting the pro-poor designation .
The term neglected tropical diseases ( NTDs ) is used to denote a diverse group of infectious diseases , which are mostly confined to ( sub ) tropical resource-constrained regions . Resources allocated towards their treatment , control and elimination have been inadequate . In spite of major advances in science , technology , and medicine , these diseases are still causing a high disease burden [1 , 2] . The concentration of NTDs in ( sub- ) tropical resource-constrained regions is caused by climatic factors in combination with poverty-associated factors that favor the spread of the diseases and prevent adequate access to prevention and care . This explains why NTDs are also viewed as diseases of poverty [3] . NTDs have become less neglected in the past decade , following increased recognition of their high disease burden and poverty-perpetuating impact , and awareness that medicines and other interventions to fight these diseases are available but largely inaccessible to populations in need . Thanks to donations of medicines from the pharmaceutical industry , together with investments from several other organizations , NTD control has entered a new phase [4 , 5] . The World Health Organization ( WHO ) has set ambitious targets for the control and elimination of NTDs by 2020 [6] . Broad international commitment to do what is needed to achieve these goals was expressed through the adoption of World Health Assembly Resolution on Neglected Tropical Diseases ( WHA66 . 12 [7] ) , and the 2014 Addis Ababa NTD Commitment by African endemic countries [8] . By endorsing the London Declaration on Neglected Tropical Diseases 2012 , pharmaceutical companies , donors , endemic country governments and non-governmental organizations involved in NTD control created a public-private partnership to ensure the necessary supplies of medicines and other interventions for the 10 diseases benefitting most from immediate targeted action [9] . For five London Declaration NTDs ( lymphatic filariasis , onchocerciasis , soil-transmitted helminths , schistosomiasis and trachoma ) rapid impact could be achieved by the expansion of preventive chemotherapy programmes , while for four other diseases ( leprosy , human African trypanosomiasis , Chagas disease , and visceral leishmaniasis ) the main impact should come from improved accessibility and individual case management , often in combination with other interventions . For Guinea worm ( dracunculiasis ) , the 10th disease , it is critical that case detection and containment is sustained to push its eradication . All this helped endemic countries to accelerate their efforts in the control and elimination of NTDs , moving towards universal access to preventive interventions and essential health services . According to the GBD 2010 study , the 10 diseases covered by the London Declaration together accounted for about 16 million DALYs and for about 63% of the overall NTD disease burden [2] . Meeting the 2020 targets for the London Declaration NTDs will have a major health impact . We estimated that this would avert about 230 million DALYs from 2011–2020 and another 363 million DALYs between 2021–2030 , through the prevention of disability and premature death [10] . Guinea worm was ignored in these calculations , since it is already close to eradication and was not covered by the GBD 2010 study . The associated economic impact of this is also high [11 , 12] . Investing in NTDs is good value for money [13 , 14] . NTD control is also advocated as a pro-poor policy , because it improves the health of poorest countries and the poorest groups within countries . Gaps in health between poorer and richer countries and between the poorest and richest groups within countries have been documented extensively [15] . NTDs are exemplary for the unequal distribution of health . WHO defines health equity as the absence of avoidable or remediable differences among groups of people , whether those groups are defined socially , economically , demographically or geographically . Inequities exist both within and across nations . Health equity is an explicit concern of public health policy , with the Commission of Social Determinants of health stating: “Where systematic differences in health are judged to be avoidable by reasonable action they are , quite simply , unfair . It is this that we label health inequity . Putting right these inequities–the huge and remediable differences in health between and within countries–is a matter of social justice” [15] . NTD control alone cannot solve these inequities , which are largely driven by social , economic and political environments , but it presents a step in the right direction . As a first step to assessing the impact of current NTD control initiatives on health equity , we study how the burden of disease from the London Declaration NTDs ( excluding Guinea worm ) varies between countries grouped by income level and how the burden and distribution over income groups have changed between 1990 and 2010 . We also look at expectations for the year 2020 . This analysis is based on Global Burden of Disease ( GBD ) data for 1990 and 2010 , and extrapolations/estimations up to 2020 .
We expressed the burden of disease in terms of disability-adjusted life years ( DALYs ) . The DALY is a measure of overall disease burden , which sums the number of years of life lost ( YLL ) due to premature mortality and the years lived with disability ( YLD ) due to clinical manifestations of infection ( sequelae ) weighted for severity [16] . Country- and disease-specific DALY estimates for the years 1990 and 2010 were directly obtained from the Global Burden of Disease 2010 ( GBD 2010 ) study , for 183 countries [1] . Five NTDs are associated with premature mortality , i . e . human African trypanosomiasis , visceral leishmaniasis , Chagas disease , ascariasis ( as one of the three soil-transmitted helminthiases ) and schistosomiasis . The other diseases ( lymphatic filariasis , onchocerciasis , hookworm and trichuriasis ( the two other soil-transmitted helminthiases considered ) , trachoma , and leprosy ) are associated with YLD only . Data sources , methods and assumptions used to estimate trends in the burden of disease towards 2020 and beyond are described in detail by De Vlas et al . [10] . Here , we briefly summarize the general approach and main assumptions . The trend in expected burden after 2010 was estimated under the assumption that the WHO targets for control and elimination of these NTDs would be met . An overview of the WHO targets and our interpretation in terms of country-specific achievements is provided by in supplementary file , S1 Table . First , we studied the time-bound WHO roadmap targets to determine what the endpoint exactly entails in terms of incidence and prevalence of NTD-caused clinical manifestations ( sequelae , in GBD terminology ) at specific time points . In doing so , we were advised by experts from the WHO and other disease experts . For some NTDs , the target entails a reduction to zero of the incidence of infection and/or associated sequelae at a future point in time ( trachoma , lymphatic filariasis , onchocerciasis , schistosomiasis ) . For other NTDs , it implies a reduction in the number of cases to low levels but not elimination , so that some cases of infection and associated reversible and irreversible sequelae would still occur after the targets are met ( e . g . visceral leishmaniasis , Chagas disease , leprosy , soil-transmitted helminthiases , human African trypanosomiasis ) . The targets can be expressed in terms of incidence of clinical manifestations , but burden estimates are based on prevalence estimates . For reversible sequelae , i . e . if patients recover due to treatment or intervention within a relatively short period of time ( within a couple of years at most ) , trends in incidence and prevalence are expected to be largely similar . However , for irreversible diseases ( like blindness through onchocerciasis or trachoma ) a decline in incidence is not immediately reflected in prevalence , since the latter measure will include people who acquired the clinical manifestations at some point in the past and cannot be cured . For irreversible diseases , the target was defined in terms of incidence density rates and prevalence was estimated in a second step . The year in which the targets are expected to be achieved can vary between countries . We used existing estimates from WHO and disease control programmes if available ( e . g . from the WHO roadmap report and disease control programme estimates [6] ) and otherwise determined the target year in discussions with experts . We assumed that the prevalence ( for reversible sequelae ) and incidence ( for irreversible sequelae ) follow a linear decline from their levels in 2010 to their time-bound endpoint . From this , we calculated the number of prevalent cases , YLD , and YLL . DALY estimates by NTD , country , and calendar year were obtained by summing YLL and YLD estimates over sequelae and age-sex groups . See De Vlas et al [10] for a more detailed description of the methods used to estimate the number of prevalent cases at each point in time for reversible diseases , and the calculation of DALYs . The countries considered are grouped into 6 regions , as identified by WHO: Region of the Americas , South-East Asia Region , European Region , Eastern Mediterranean Region , and Western Pacific Region . See the WHO website for further information [17] . We further classified countries into four income groups , based on their Gross National Income ( GNI ) per capita ( Atlas Method ) in 2010 , using the 2010 World Bank classification ( Fig 1 ) [18] . Low income countries are those with a GNI of 1 , 005 US$ or less . Lower-middle and upper-middle income countries had a GNI of 1 , 006–3 , 975 US$ and 3 , 976–12 , 275 US$ , respectively . Countries with a GNI of 12 , 276 US$ or more are classified as high-income countries . Low and lower-middle income countries are considered to be developing economies . Table 1 gives the total population ( summed over countries ) by income group and WHO region; in 2010 , 12% of the global population lived in low income countries , 36% in lower-middle countries , and another 36% in upper-middle income countries . In Africa and South-East Asia , respectively 89% and 96% of the population lived in low or lower-middle income countries . All data underlying the analysis are provided in S1 Datafile . The resulting health impact and the NTD-specific assumptions are also available as an open-access web-based dissemination tool ( https://erasmusmcmgz . shinyapps . io/dissemination/ ) .
Fig 2 summarizes the data , showing the absolute burden in DALYs per NTD by income group , for 1990 , 2010 , and 2020 . The same information is numerically presented in Table 2 . The burden of the nine NTDs is concentrated in low and lower-middle income countries . In 1990 , low and lower-middle income countries held 43% of the world population , but accounted for 69% of the total burden caused by the 9 NTDs . For most of these NTDs , the share of the burden held by low and lower-middle income countries was even higher ( around 80% for trachoma , schistosomiasis and leprosy; 90–100% for visceral leishmaniasis , lymphatic filariasis , onchocerciasis and human African trypanosomiasis ) . Only for the soil-transmitted helminthiases and Chagas disease , upper-middle income countries held a large share of the burden . By 2010 , low and lower-middle income countries held 48% of the world population , but accounted for 81% of the global NTD burden . The DALY distribution by income group largely intersects with the geographical distribution of the infections . Fig 3 shows the contribution of different diseases to the total burden from the nine NTDs by region and calendar year . S1 Fig presents the same data in a different way , highlighting how the burden is spread over regions , by disease and calendar year . Chagas disease is confined to Latin America; most countries in this region are classified as upper-middle income countries , explaining the peak in burden in this category . Diseases that predominantly occur in Africa ( onchocerciasis , human African trypanosomiasis , and to a lesser extent schistosomiasis ) , were clustered in low and lower middle income countries , as 86% of the African population lives in such countries . Diseases that are particularly prominent in the South-East Asian region ( lymphatic filariasis , visceral leishmaniasis , trachoma , leprosy ) , were clustered in the lower-middle income category . The soil-transmitted helminthiases are globally widespread , also in populous China , which was categorized as upper-middle income country , explaining the peak in burden in this income group in 1990 . Fig 4 shows the trend in disease burden overall and by country income group . Between 1990 and 2010 , the global burden ( in DALYs ) of the nine NTDs declined by 27% . The decline varied strongly between income groups: the relative and absolute reduction was smallest in low income countries ( 6%; 297 , 000 DALYs ) and greatest in upper-middle income countries ( 56%; 3 , 858 , 000 DALYs ) . Similar patterns were observed for the individual diseases . Between 1990 and 2010 , the burden declined for five out of nine diseases , in particular for the soil-transmitted helminthiases , visceral leishmaniasis , and human African trypanosomiasis , with much smaller declines for onchocerciasis and Chagas disease ( Table 2 ) . For these diseases , the relative reduction was usually greater each step up the income hierarchy . The 2020 burden was estimated under the assumption that the ambitious WHO roadmap targets would be met . For most diseases some burden remains in 2020 , largely explained by chronic diseases that have developed from infections acquired in the past . Favorable exceptions are the soil-transmitted helminthiases ( which are mainly associated with reversible conditions ) , visceral leishmaniasis and human African trypanosomiasis ( for which the burden is largely caused by premature death soon after acquisition of the infection ) . While low income countries ( followed by lower-middle income countries ) profited least from improvements between 1990 and 2010 , this is projected to be rectified in the 2010–2020 period . Achieving the WHO roadmap targets would imply a reduction in disease burden of around 55% in all country income groups ( apart from a zero reduction in high income countries ) . In absolute terms , the reduction would be greatest in the low ( 2 , 449 , 000 DALYs ) and lower-middle ( 4 , 824 , 000 DALYs ) income groups . In total , 81% of the reduction that would be achieved in the 2010–2020 period , would be concentrated in low and lower-middle income countries , if the WHO roadmap targets were met . For more detailed discussion of findings per disease , we refer to supplementary information in S1 Appendix . This appendix also provides additional information per disease on the 3–5 countries contributing most to the 2010 burden of disease , and the sequelae considered in the burden of disease estimates .
The burden of the nine NTDs under study is largely concentrated in low and lower-middle income countries . In 1990 and 2010 , low and lower-middle income countries accounted for 42% and 48% of the world population respectively , but for about 69% and 81% of the global burden from these NTDs . For most of these NTDs , the burden of disease was even more strongly concentrated in low and lower-middle income countries; only for the soil-transmitted helminthiases and Chagas disease a considerable part of the burden occurred in upper-middle income countries . Between 1990 and 2010 , the global burden of the nine NTDs in DALYs declined by 27% , which is largely attributable to declines in the soil-transmitted helminthiases , human African trypanosomiasis , and visceral leishmaniasis . The decline varied greatly between income groups: the reduction was as high as 56% in upper-middle income countries , but only 6% in low income countries , leading to a further concentration of the burden in low and lower-middle income countries . The slower adoption and implementation of recommended control strategies in the lowest income group may be related to multiple interrelated factors , including lack of human and financial resources , weak health systems , lack of donor support , societal unrest , and epidemiological factors . This underscores the importance of coordinated international programmes to control these NTDs worldwide and reduce the prevailing between-country health inequalities . Extra efforts from the international community will be needed to achieve the goals in the lowest income countries . The 2020 burden was calculated under the assumption that the ambitious WHO roadmap targets would be met . If the targets would be met globally , the burden caused by the nine selected NTDs is expected to decline by about 55% in all income groups ( apart from high income countries ) between 2010 and 2020 . In absolute terms , the decline would be largest in the lower-middle income group , because this group holds the largest share of the 2010 burden . 81% of the reduction that would be achieved in the 2010–2020 period , would occur in low and lower-middle income countries . Some burden would still remain by 2020 , because the burden largely arises from chronic diseases that have developed from infections acquired in the past . The targets are highly ambitious and may not be met everywhere with current strategies , e . g . due to a late start of slow scale-up of interventions [19 , 20] , epidemiological circumstances that require more intensive and/or longer duration of interventions [21–23] , or failure to reach everyone with interventions [21] . Yet , by intensifying efforts or adopting modified strategies , it may still be possible to reach the targets in many settings , thereby achieving the important impacts described in the current paper and other papers in this series . We performed an ecological analysis . Care is required when making inferences regarding the association between poverty and NTDs at the individual level . For example , our analysis shows that a non-negligible part of the NTD burden in 1990 and 2010 occurred in upper-middle income countries ( classified based on 2010 GNI per capita ) . It should be realized , though , that there can be large within-country differences in income and health within the highly populous upper-middle income countries like China and Brazil . Within these countries , the actual burden of NTDs is arguably still concentrated in the poorest subgroups , so that the clustering in the poor might even be stronger than suggested here . To get a more complete picture of the clustering of NTDs among the poor , we have also reviewed published literature on within-country differences in prevalence of NTDs . Although the amount and strength of evidence varied somewhat between diseases , overall there is considerable evidence that socioeconomically disadvantaged groups have higher infection rates [24] . We based our analysis on the disease burden estimates as calculated in the GBD 2010 study [1] . The absolute burden estimates are notably uncertain for NTDs , due to lack of data on their geographic spread and control , uncertainties about the association between acute or chronic infection and specific morbidities , and uncertainties about assigned disability weights [2] . Most GBD 1990 and 2010 estimates for NTDs show very wide confidence intervals , often ± 50% of the mean , but sometimes with an upper confidence limit up to 5 times the mean . Although considerable uncertainty exist with respect to absolute disease burden estimates for specific countries , this will probably not have strong impact on our estimates of the share of the burden in country income groups , which primarily depend on the geographic spread of the various NTDs and the distribution of the world population over the different income strata . This paper concentrates on 9 diseases named in the London Declaration on NTDs ( excluding Guinea worm ) , but there are many more . In recent reports , WHO has categorized 17 diseases as NTDs , including the endemic treponematoses ( yaws ) , human dog-mediated rabies , dengue , buruli ulcer , cutaneous leishmaniasis , taeniasis/cysticercosis and echinococcosis/hydatidosis , foodborne trematode infections . The 9 diseases considered here together constituted about 63% of the global burden caused by the 17 NTDs listed by WHO , according to the GBD 2010 study , but this proportion is almost halved if the definition of NTDs is broadened to include also other NTDs [2] . The burden caused by these other NTDs can be similarly high as from the other diseases . As yet these diseases do not have the same opportunities for control and elimination due to unavailable or compromised tools , and WHO targets include a need to validate strategies and perform pilot projects . Eventually , though , these NTDs also need to be addressed and mitigated . We did not assess the poverty-impact of these measures . This impact is probably is clearest if catastrophic health expenditures are prevented , e . g . for human African trypanosomiasis [14] . We should acknowledge the possibility that the impact of these interventions on the total disease burden , also due to other causes , might to some extent be attenuated because competing causes of morbidity and death might replace the NTDs . This would also attenuate the immediate impact of these interventions on poverty . Nevertheless , affected populations will be left in a better position to escape from the poverty trap . NTDs as a group are known to constitute a major health burden , which is largely concentrated in low and lower-middle income countries . Between 1990 and 2010 , the disease burden from NTDs has declined considerably in upper-middle income countries , but hardly declined in the low income countries . Achieving internationally agreed targets of NTD control and elimination will bring about major gains in health and reductions in human suffering , with 81% of the global reduction occurring in low and lower-middle income countries . This would reduce the prevailing between-country health inequalities . | The World Health Organization ( WHO ) has set ambitious time-bound targets for the control and elimination of neglected tropical diseases ( NTDs ) . Investing in NTDs is seen as good value for money and as a pro-poor policy . We analyzed 1990 and 2010 burden estimates from the Global Burden of Disease Study 2010 for nine selected NTDs . These data show that the NTD disease burden is strongly concentrated in low and lower-middle income countries in 1990 and 2010 . The global burden from these nine NTDs declined by 27% between 1990 and 2010 , but the reduction was only 6% in the low income countries compared to 56% in upper-middle income countries , explaining the trend of increasing concentration of the burden in the poorest countries . This trend would be rectified if the WHO targets were met , supporting the pro-poor designation of public policies against NTDs . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"helminth",
"infections",
"medicine",
"and",
"health",
"sciences",
"zoonoses",
"socioeconomic",
"aspects",
"of",
"health",
"chagas",
"disease",
"global",
"health",
"neglected",
"tropical",
"diseases",
... | 2016 | Between-Country Inequalities in the Neglected Tropical Disease Burden in 1990 and 2010, with Projections for 2020 |
The p12 protein of the murine leukemia virus ( MLV ) is a constituent of the pre-integration complex ( PIC ) but its function in this complex remains unknown . We developed an imaging system to monitor MLV PIC trafficking in live cells . This allowed the visualization of PIC docking to mitotic chromosomes and its release upon exit from mitosis . Docking occurred concomitantly with nuclear envelope breakdown and was impaired for PICs of viruses with lethal p12 mutations . Insertion of a heterologous chromatin binding module into p12 of one of these mutants restored PICs attachment to the chromosomes and partially rescued virus replication . Capsid dissociated from wild type PICs in mitotic cells but remained associated with PICs harboring tethering-negative p12 mutants . Altogether , these results explain , in part , MLV restriction to dividing cells and reveal a role for p12 as a factor that tethers MLV PIC to mitotic chromosomes .
To integrate , reverse transcribed retroviral genomes are imported from the cytoplasm to the chromosomes as part of a pre-integration complex ( PIC ) . Differences in retrovirus PIC trafficking influence their ability to infect resting and/or dividing cells . Lentiviruses , including the human immunodeficiency virus ( HIV ) , infect both dividing and resting cells . In contrast , simple oncoretroviruses , such as murine leukemia viruses ( MLV ) , are restricted to dividing cells [1] , [2] , [3] , [4] . The HIV PIC is capable of entering the nucleus through the nuclear pore complexes , allowing integration in chromosomes of resting cells . The MLV PIC is thought to get access to the chromosomes only during mitosis , upon nuclear envelope ( NE ) disassembly , as inferred from correlating kinetics of cell division and integration [3] . This dissimilarity between HIV and MLV PIC trafficking likely stems from their different composition [4] . Capsid ( CA ) , present in MLV PICs [5] , [6] , [7] and absent from HIV PICs [8] , [9] , contributes to the difference in ability to transduce nondividing cells [10] . Also , the lens epithelium-derived growth factor ( LEDGF/p75 ) , interacts with HIV PICs [11] , [12] tethers the integrase ( IN ) of HIV and other lentiviruses to chromatin [11] , [13] , [14] , [15] , [16] , but not the MLV IN [11] , [17] , for which no equivalent tethering factor has been identified . p12 , a cleavage product of MLV Gag precursor , is thought to influence MLV integration . p12 acts in the budding of assembled Gags , and mutations in this domain hamper particle morphogenesis and release [18] , [19] , [20] . Viruses with other lethal mutations in p12 showed early infection defects , with normal generation of linear genomic DNA , but no circular DNA forms [18] , [19] , [21] , [22] . The latter forms , thought to be generated by nuclear enzymes , are not substrates for IN-mediated integration but mark nuclear entry of the viral DNA . Their absence in cells infected with some p12 mutants , and the normal in vitro integration activity shown by the PIC of one of these mutants [18] , suggest that p12 functions in an unknown way after reverse transcription and before integration . p12 is a component of the MLV PIC , crucial for the progression of the PIC towards integration: p12 , observed as discrete puncta , associates with CA and the viral genomic DNA and this p12- genome association occurs in the cytoplasm and adjoining chromosomes , suggesting that p12 escorts the viral genomic DNA throughout early stages of infection [7] . p12-containing PICs accumulate on mitotic chromosomes , however this accumulation is impaired for p12 proteins with a mutation that rendered the virus integration-defective [7] . These data implied that p12 functions in directing the PIC to integration , yet its precise role has remained unknown . Here we imaged MLV-PICs in live cells and revealed that p12 tethers the MLV PIC to mitotic chromosomes .
To investigate the role of p12 in PIC trafficking we labeled the PIC with p12 fused to enhanced green fluorescent protein ( GFP ) . An in-frame insertion of GFP to the central region of p12 was lethal to the virus ( data not shown ) . Thus , we generated chimeric virions , composed of both wt and modified Gag molecules; the latter containing GFP fused to p12 N-terminus . In the modified Gag ( MA-GFP/p12-CA-NC ) , the GFP sequence was inserted in-frame , downstream of the protease-cleavable matrix ( MA ) -p12 junction , and upstream of a short , non-cleavable linker fused to p12 ( Fig . 1A ) . MA-GFP/p12-CA-NC retains wt p12-CA and CA-nucleocapsid ( NC ) cleavage sites , critical for MLV particle formation , maturation and infectivity [23] , [24] . Wt virus was co-expressed with MA-GFP/p12-CA-NC , and virions were purified by ultracentrifugation through a 25% sucrose cushion and immunoblotted with anti-GFP antibody . This revealed a major band corresponding to the GFP-p12 fusion ( ∼36 kDa ) , a fainter band of MA-GFP/p12-CA-NC precursor ( ∼88 kDa ) and traces of additional cleavage products ( Fig . 1B ) ; suggesting that MA-GFP/p12-CA-NC co-assembled with wt Gag and was processed by the viral protease . Importantly , the linker connecting GFP and p12 was protease-resistant as no free GFP was processed from MA-GFP/p12-CA-NC , unlike the control construct ( named MA-GFP-p12-CA-NC , Fig . 1B ) , in which the GFP was flanked by cleavable sites ( Fig . 1A ) . To test if co-expression of MA-GFP/p12-CA-NC with wt virus affects infectivity , a MLV vector ( pQCXIP-gfp-C1 ) , encoding for the puromycin-resistance gene and GFP was expressed together with different ratios of MA-GFP/p12-CA-NC to wt virus . Virions , normalized by reverse transcriptase ( RT ) activity , were used to infect NIH3T3 cells and GFP+ cells were counted by fluorescence-activate cell sorting ( FACS ) . A 1∶1 molar ratio resulted in only a minor reduction ( ∼15% ) in infectivity compared to the wt virus ( with no MA-GFP/p12-CA-NC ) control ( Fig . 1C ) . Counting the number of puromycin-resistant colonies in infected cultures gave similar results ( data not shown ) . Thus , 1∶1 molar ratio was used in further experiments . At 12 hr post-infection ( hpi ) of U/R cells ( human osteosarcoma U2OS cells , expressing the murine receptor for MLV; [7] ) with labeled chimeric virions ( hereinafter named wt GFP ) , discrete fluorescent puncta were detected in the cytoplasm of interphase cells , and adjacent to the condensed chromosomes of mitotic cells ( Fig . 1D ) . This appearance was identical to former images of PICs , labeled with Myc-tagged p12 proteins ( derived from the replication-competent 1xMycR clone ) [7]; in addition , 65±3% of the fluorescent puncta ( approximately 100 dots/cell in 5 cells were analyzed ) overlapped the chromosomes in mitotic cells , in good agreement with the 70% overlap between Myc-tagged p12 proteins and mitotic chromosomes , as was quantified before [7] . Immunostaining of wt GFP-infected U/R cells with antibodies against CA ( a component of the MLV PIC ) revealed extensive co-localization between GFP and CA signals ( Fig . S1A ) ; and quantification of the overlap between these two signals revealed a 71±4% overlap in interphase cells ( data was obtained from five cells , each containing ∼80 fluorescent dots ) . This number is similar to the extent of overlap measured for Myc-tagged p12 and CA in interphase U/R cells , infected with 1xMycR virus ( ∼80%; see below ) . This suggests that GFP-p12 molecules are associated with the PICs to a similar extent as p12 molecules lacking the GFP moiety . Thus , the GFP-p12 labeling system successfully marks the incoming PICs . To monitor cytoplasmic-nuclear trafficking of PICs in live cells , we labeled the NE of U/R cells by stably expressing lamin A fused to red fluorescent protein ( RFP-lamin A; U/R/RFP-laminA cells ) [25] . U/R/RFP-laminA cells , arrested before S phase by serum starvation and aphidicolin treatment [3] , were infected with wt GFP virions . At 18 hpi , GFP-labeled PICs exhibited undirected and directed movements , including towards the NE , in sharp contrast to the immobility of virions attached to the cover-slip ( Movie S1 ) . None of the PICs crossed the intact NE ( Movie S1 and see below ) , demonstrating the physical barrier that the intact NE imposes on nuclear entry of the PICs in interphase cells - a notion previously deduced from measuring integration kinetics , in respect to the cell cycle [3] , but never directly shown . To monitor the PICs in mitotic cells , U/R/RFP-laminA cells were arrested at metaphase with 2-methoxyestradiol ( 2ME2 ) and infected with wt GFP virions . 2ME2 impairs microtubule dynamics without gross microtubule depolymerization and arrests cells at the spindle assembly checkpoint [26] , [27] , [28] . Arrested cells displayed diffuse RFP-lamin A ( indicating NE disassembly [29] ) and restricted PIC motility ( Fig . 2A; Movie S2 , part A ) . In 2ME2-treated cells that did not reach metaphase ( with intact NEs ) , PICs were restricted to the cytoplasm and motile ( Fig . 2B; Movie S2 , part B ) , excluding a direct 2ME2-induced inhibition of PIC motility . To test for the docking of immobile PICs to mitotic chromosomes , we generated U/R/RFP-H2A cells [U/R cells stably expressing RFP fused to histone H2A ( RFP-H2A ) ] , infected them with wt GFP virions and imaged unsynchronized , interphase and mitotic cells . Indeed , motionless PICs were attached to the mitotic chromosomes , while cytoplasmic PICs in interphase cells were motile ( Fig . 2C , D; Movie S2 , parts C , D ) . Attachment of PICs to mitotic chromosomes was also observed for wt GFP-infected , unsynchronized mouse NIH3T3 cells expressing RFP-H2A ( NIH3T3/RFP-H2A; Movie S2 , part E ) ; and for wt GFP-infected U/R/RFP-H2A cells that were arrested at M phase by nocodazole treatment ( Movie S2 , part F ) . In the latter settings , PICs attachment to mitotic chromosomes could also be observed in cells arrested at mitosis for up to 40 hpi ( data not shown; extended time points were not tested because of apparent drug-induced cytotoxicity ) . The apparent docking of the PIC to the chromosomes may be the result of a stable association of the PIC components with the viral DNA genome that had integrated into the chromosomes . To test this , we made chimeric virions ( named D184A GFP ) , using MLV with the D184A mutation in the catalytic site of IN , which disrupts its activity [30] . D184A GFP PICs were motile in interphase U/R/RFP-H2A cells , and docked to mitotic chromosomes in dividing cells ( Fig . 2E; Movie S2 , part G ) . Thus , IN activity is not required for the docking of MLV PICs to the chromosomes . To monitor the transition between the cytoplasmic movements of the PICs to their docking to mitotic chromosomes , we viewed infected cells as they entered mitosis . Confluent U/R/RFP-H2A and U/R/RFP-laminA cells were infected with wt GFP particles and 2 hr later , the cells were trypsinized and replated at a lower density . After additional 5 hr we detected infected cells that entered mitosis as judged by the growing condensation of their chromosomes ( U/R/RFP-H2A ) , or by the progressive dissolution of the NE ( U/R/RFP-laminA ) . Docking of the GFP-labeled PICs to the RFP-labeled chromosomes could readily be detected; the time frame between the first observed docking event and the docking of the rest of the PICs was ∼2 min ( Fig . 3A; Movie S3 , part A ) . Similarly , the immobilization of PICs occurred concomitantly with the breakdown of the RFP-labeled NE ( Fig . 3B , C; Movie S3 , part B ) , in contrast to the cytoplasmic PICs that remained mobile in the same cells ( Fig . 3C; Movie S3 , part B ) . Thus , these images are consistent with the idea that the intact NE might act as a physical barrier to the PICs . To probe if PICs bind exclusively to mitotic chromosomes , provided that the physical barrier of the NE is avoided , we infected 2ME2-arrested U/R/RFP-H2A cells with wt GFP virions and imaged chromosome-docked PICs at 12 hpi . Addition of Reversine , which inhibits the Mps1 kinase , counters the spindle assembly checkpoint [31] , and reverses the 2ME2-induced blockage of the cell cycle [32] , resulted in the decondensation of the mitotic chromosomes within 1 hr ( Fig . 4A–C; Movie S4 , parts A , B ) . Remarkably , in these conditions , PICs regained their movement , which was now restricted to the nucleus ( compare Fig . 4B to C , and part A to B in Movie S4 ) . The same was also observed for unsynchronized U/R/RFP-H2A or U/R/RFP-laminA cells that naturally exit mitosis ( without 2ME2/Reversine treatment; Fig . 4D–G; Movie S4 , parts C–F ) . The regaining of PICs movement during the exit from mitosis may reflect their release from the chromatin upon completion of the integration step . However , the same dissociation occurred also with D184A GFP PICs that are unable to discharge their viral genomic DNA due to the lack of IN activity ( compare the same D184A GFP-infected U/R/RFP-H2A cells in Movie S2 part G and Movie S4 part G ) . This result provides further support for the affinity of the MLV PIC towards mitotic , and not interphase , chromosomes . Of note , a minor fraction of PICs ( integration-competent wt GFP or integration-incompetent D184A GFP ) remained associated with the chromatin following exit from mitosis , ( Movie S4; and see an example in Fig . 4E , marked with an arrowhead ) ; demonstrating that integration is not a pre-requisite for such a stable association . The above imaging employed p12 as a marker of PICs , but falls short of attributing a function to p12 . Defined mutations in p12 [such as a five-amino acids alanine block substitution , named PM14; and the S61A and S ( 61 , 65 ) A mutations] are lethal to the virus and dramatically reduce the levels of circular forms of the viral genomic DNA , suggesting a defect in nuclear entry of this genome [18] , [19] , [22] . Myc-labeled PM14 PICs are normally distributed in the cytoplasm of interphase cells , but fail to accumulate on mitotic chromosomes [7] . We next generated GFP-labeled mutant chimeric virions ( PM14 GFP; composed of PM14 virus and modified Gags with PM14 mutation ) and infected and imaged ( at 12 hpi ) unsynchronized U/R/RFP-H2A cells . In the cytoplasm of interphase cells , PM14 GFP PICs moved similarly to wt GFP PICs ( Movie S5 , part A ) . A portion of PM14 PICs reached the chromosomes in mitotic cells; however , despite their proximity to chromosomes , all were motile and none attached to the mitotic chromosomes ( Fig . 5A; Movie S5 , part B ) . The failure in docking was also observed in unsynchronized , mitotic mouse NIH3T3/RFP-H2A cells ( Movie S5 , part C ) . This strongly implies a role for p12 in the docking of MLV PICs to the chromosomes . To monitor wt and PM14 PICs in the same cell we generated chimeric virions ( named wt mCherry ) , labeled with mCherry instead of GFP ( using the MA-mCherry/p12-CA-NC construct; Fig . 1A ) . In Hoechst-stained 2ME2-arrested U/R cells , PM14 GFP PICs were motile , in contrast to the immobilization of the wt mCherry PICs on condensed chromosomes ( Fig . 5B; Movie S5 , part D ) . To quantify the spatial retention of PICs over time we calculated the percentage of overlap between the PICs in the first frame , to the PICs in the second , third and fourth frames of each movie ( Fig . 5C ) . While high retention ( ∼70 to 80% ) was observed for wt GFP and D184A GFP PICs over time; PM14 GFP PICs showed lower and decreasing retention overtime ( ∼30 to 20% ) . This low retention was comparable to that of motile wt GFP and D184A GFP PICs upon Reversine-mediated exit from mitosis ( Fig . 5C ) . For PM14 GFP , we also quantified the overlap between the signals of GFP and mitotic chromosomes in fixed cells , and found it to be approximately 16±2% ( approximately 100 dots/cell in 8 cells were analyzed ) . This value is in a good agreement with both the relative low retention exhibited by this mutant in the above real-time analysis , and the low overlap ( 11% ) measured before for Myc-tagged p12 proteins and mitotic chromosomes [7] . This relatively low overlap is in contrast to the 63% overlap , measured for wt GFP and mitotic chromosomes in fixed cells ( see above ) . Reconstitution of serial optical sections into 3D images of mitotic chromosomes of U/R/RFP-H2A cells further showed the close contacts between such chromosomes and wt GFP ( Fig . S2A ) , but not PM14 GFP ( Fig . S2B ) , PICs . Of note , chimeric PICs resulting from the expression of wt MLV genome and modified Gag containing the PM14 mutation docked to mitotic chromosomes ( not shown ) , demonstrating that the docking activity of wt p12 is dominant over the lack of such activity of PM14 GFP-p12 , and providing genetic evidence for the ability of GFP-p12 to mark the incoming MLV PIC . Viruses carrying the S ( 61 , 65 ) A mutations are phenotypically undistinguishable from the PM14 virus . However , a replication-competent revertant with an additional compensatory mutation ( M63I ) in p12 exists for this virus [22] . We introduced the S ( 61 , 65 ) A or the S ( 61 , 65 ) A/M63I to the 1xMycR clone and to the modified Gag ( Fig . 1A ) and co-expressed each cognate pair of constructs to generate chimeric virions [named S ( 61 , 65 ) A GFP or S ( 61 , 65 ) A/M63I GFP , respectively] . S ( 61 , 65 ) A GFP PICs failed to stably anchor to mitotic chromosomes or showed a very short , unstable association with the chromosomes ( Fig . 5D; Movie S5 , part E ) . The S ( 61 , 65 ) A/M63I PICs , in contrast , stably docked to mitotic chromosomes , identically to wt PICs ( Fig . 5E; Movie S5 , part F ) . A 25 and 65% overlap with mitotic chromosomes was measured for S ( 61 , 65 ) A GFP and S ( 61 , 65 ) A/M63I GFP , respectively; in good accord with the 10 and 65% overlap of PM14 and wt viruses , respectively [7] . Altogether , these results further demonstrate the correlation between the replication competence of the tested virus and the ability of its PIC to dock to the chromosomes; and provide additional demonstration for the connection between the impairment of such docking and the presence of specific mutations in p12 . To evaluate if addition of a foreign chromatin-binding element rescues the docking of mutant p12 PICs , we inserted such a module of the herpes LANA protein ( LANA31 ) , into p12 of the PM14 clone , generating the PM14/LANA31 virus . LANA31 consists of 31 residues , 23 of which bind the groove between histones 2A and 2B [33] . This module restores the tethering activity of a mutated LEDGF/p75 , resulting in the binding of HIV IN to chromatin [34] . A control virus ( named wt/LANA31 ) was also made by inserting LANA31 into p12 of wt MLV . LANA31 was inserted between the DRD and GNG residues of p12 ( Fig . 1A ) , as MLV replication tolerates insertion of a Myc tag into this location [7] . These viruses were expressed with the MA-GFP/p12-CA-NC modified Gag , resulting in chimeric viruses ( named wt/LANA31 GFP and PM14/LANA31 GFP ) . PICs of PM14/LANA31 GFP stably anchored to mitotic chromosomes in U/R/RFP-H2A cells ( Fig . 6B; Movie S6 , part A ) similarly to wt/LANA31 GFP ( Fig . 6A; Movie S6 , part B ) and wt GFP ( Fig . 2C; Movie S2 , part C ) PICs . Quantification of the overlap between the signals of GFP and mitotic chromosomes in fixed cells , revealed a 73±5% value for PM14/LANA31 GFP ( approximately 70 dots/cell in 7 cells were analyzed ) , which was similar to the overlap found for wt GFP ( 63% ) , and which was in contrast to the low overlap ( 16% ) measured for PM14 GFP ( see above ) . In addition , quantification of the spatial retention of PM14/LANA31 GFP PICs over time in mitotic cells showed high retention levels that were almost identical to the retention of wt GFP PICs in mitotic , 2ME2-treated cells ( compare Fig . 6C to 5C ) . Thus , insertion of the LANA31 peptide into p12 rescues chromatin docking of the PICs with PM14 mutations . To evaluate the effect of LANA31 insertion into p12 on virus infectivity we infected NIH3T3 cell cultures and monitored the kinetics of virus spread . Whereas wt virus spread quickly , wt/LANA31 showed much slower spreading ( Fig . 6D ) , indicating that the insertion of LANA31 greatly attenuated virus replication . This is in line with our previous observation , showing that insertion of a peptide with a similar size ( 30 residues of a triple Myc epitope ) in the same location in p12 attenuates virus replication [7] . Furthermore , sequence analysis of p12 of the viruses that spread in the wt/LANA31-infected cells revealed that LANA31 was rapidly deleted from wt/LANA31 virus ( data not shown ) , substantiating the deleterious effect of this sequence on the virus . PM14 virus showed no spreading ( here and [19] ) ; the PM14/LANA31 virus showed detectable , slow spreading ( Fig . 6D ) , indicating that insertion of LANA31 into p12 partially restored the infectivity of PM14 mutant . Sequence analysis of p12 of viruses that spread in the PM14/LANA31-infected cells showed a mixture of PM14/LANA31 sequence together with wt sequences ( no PM14 and no LANA31; data not shown ) . This likely reflects the selection that the PM14 mutation enforces on the retention of the LANA31 sequence in p12 and the parallel recombination of the PM14/LANA31 slow virus with endogenous retroviruses of the mouse genome [35] that harbor wt p12 sequences . Such recombination , which likely involves the co-packaging of the endogenous and exogenous viral genomes , and the recombination between these genomes during reverse transcription [36] , cannot efficiently occur with PM14 virus that lacks detectable replication , but may occur upon the multiple cycles of infection of the PM14/LANA31 virus . Further support for the rescue of PM14 infectivity by LANA31 came from single-cycle infection assays , in which NIH3T3 cells were infected with wt , wt/LANA31 , PM14 or PM14/LANA31 virus-like particles ( VLPs ) , harboring puromycin or neomycin -resistance markers , and normalized by RT activity ( Fig . 6E ) . Quantification of the number of drug-resistant cell colonies from three independent experiments revealed that LANA31 insertion into wt p12 reduced particles' infectivity to 85±11% of that of wt particles . A major increase in VLPs infectivity , however , was observed when LANA31 was inserted into PM14 p12: whereas PM14 VLPs had only residual infectivity ( 0 . 3±0 . 4% , compared to wt ) , PM14/LANA31 VLPs showed higher infectivity ( 16±8% , compared to wt ) . These results are in accordance with the spreading assays described above . Altogether , insertion of LANA31 into p12 compensates for PM14 effect on both virus replication and anchorage of the PIC to the chromosomes , further emphasizing the role of p12 as the tethering factor for the MLV PIC . We also tested how the exit from mitosis affects LANA31-mediated docking of the PICs to the chromosomes . In unsynchronized U/R cells co-infected with wt/LANA31 GFP and wt mCherry , and stained with Hoechst; wt mCherry PICs regained their movement upon exit from mitosis , while wt/LANA31 GFP PICs remained motionless ( Fig . 6F; Movie S6 , part C ) . This result provides further support for the affinity of the wt PICs towards mitotic but not interphase chromatin . Altogether , the above results demonstrate a role for p12 as a factor that tethers the MLV PIC to mitotic chromosomes . CA and Myc-tagged p12 co-localize in the cytoplasm of interphase cells [7] . To further study the spatial relations between CA and p12 , we compared interphase with mitotic 1xMycR-infected U/R cells , using immunofluorescence ( Fig . 7 ) . In interphase cells , the majority of the PICs were cytoplasmic with a clear overlap ( ∼80% ) between CA and p12 signals , similar to the co-localization observed in particles attached to the glass outside the cells ( Fig . 7A and see identical results in [7] ) . In contrast , in mitotic cells , p12 associated with the condensed mitotic chromosomes and almost no CA could be co-detected in these chromosome-localized p12 spots ( ∼30% overlap between p12 and CA , Fig . 7B , F; ∼70 and ∼3% overlap with the mitotic chromosomes for p12 and CA , respectively , Fig . 7G ) . Similar results were obtained when the overlap between CA and GFP-labeled PICs was measured ( 32±5% , calculated from three inspected cells , each containing ∼80 dots; Fig . S1B ) . These results , together with the fact that p12 co-localizes with the genomic viral DNA both in cytoplasmic PICs and in PICs attached to mitotic chromosomes [7] , imply that during early stages of infection gradual uncoating events occur , involving the sequential dissociation of MA and CA , and the trafficking of p12 proteins as part of the PIC , to the chromosomes in mitotic cells . Importantly , in tethering-negative PM14 and S ( 61 , 65 ) A mutant PICs , a continued CA-p12 association was observed in mitotic cells ( Fig . 7 C , D , F ) , while the S ( 61 , 65 ) A/M63I revertant showed wt levels of dissociation ( Fig . 7E , F ) . This shows that lack of CA dissociation correlates with inability of p12 mutants to tether to mitotic chromosomes .
To follow MLV PICs by live cell microscopy , we generated chimeric particles co-assembled from wt Gag molecules and Gag harboring a viral-protease resistant GFP-p12 fusion . This labeling method was applied as p12 escorts the viral genomic DNA as part of the PIC to the chromosomes [7]; and since the chimeric particles showed only a slight reduction in infectivity . The notion that GFP-p12 labeled the PICs is supported by: ( i ) the similarity between punctuate distributions of GFP-p12 and Myc-p12 [7] in infected cells; ( ii ) the cytoplasmic localization of GFP-p12 puncta in interphase cells and their accumulation on mitotic chromosomes , identical to the patterns observed with fluorescence in situ hybridization ( FISH ) -labeled MLV genomes [3] , [7]; ( iii ) the lack of chromosomal localization of MA-GFP/p12-CA-NC Gag proteins when exclusively assembled as VLPs ( without wt Gag ) and pseudotyped with the MLV ecotropic envelope ( data not shown ) ; ( iv ) the accumulation on mitotic chromosomes of GFP-p12 harboring the PM14 mutation , only when employed in the context of chimeric PICs containing wt p12 proteins; ( v ) the extent of overlap between CA and GFP-p12 signals , which was very similar to the overlap of CA and Myc-tagged p12 proteins ( derived from the 1xMycR replication-competent virus ) , in both interphase and mitotic cells . Thus , our live-cell imaging system allows monitoring of early stages of MLV infection , and complements the envelope-based system , developed to detect MLV-cell interactions before entry [37]; and the systems developed to detect HIV PICs [38] , [39] . Our main finding is the inability to dock to mitotic chromosomes of PICs derived from PM14 and S ( 61 , 65 ) A p12 mutants , which sharply contrasted to the stable docking of PICs from wt and the S ( 61 , 65 ) A/M63I revertant viruses . This was best visualized upon the co-infection of PM14 and wt viruses , which showed lack of docking , or stable chromosomal association , respectively , in the same cell . Moreover , insertion of a heterologous chromatin-binding module ( LANA31 ) into p12 of the PM14 virus rescued both its infectivity and the anchorage of its PIC to the chromosomes , strongly implying a chromosome-tethering function for p12 . The discrepancy between the full restoration of docking to the chromosomes of PM14/LANA31 PICs and the partial restoration of infectivity of this virus likely results from the adverse effect caused by the addition of 31 residues into p12 on other stages of the replication cycle; for example , we observed a reduction in assembly of the wt/LANA31virus ( data not shown ) . A similar-sized insertion ( a triple Myc epitope ) in the same location also attenuated replication [7] . Of note , while the PM14 mutation in p12 resulted in complete disruption of PIC docking to the mitotic chromosomes , S ( 61 , 65 ) A PICs showed short , unstable associations with the chromosomes; possibly explaining why revertants could be isolated for the latter mutant [22] . Importantly , although the function of p12 as a chromatin tether can be deduced from the clear differences in the ability of the PICs described above to attach to the chromosomes , our microscopic analysis cannot distinguish between functional and nonfunctional PICs in a given cell . LEDGF/p75 was identified as the factor that tethers the HIV IN to chromatin: it interacts with unknown chromatin ligands and with IN , and is essential for the chromosomal targeting of HIV IN [11] , [14] , [40] . LEDGF/p75 depletion hampers HIV integration and blocks infection of HIV and other lentiviruses; however , LEDGF/p75 does not interact with MLV IN and accordingly , its depletion does not affect MLV infection [11] , [17] , [41] , [42] , [43] . We suggest that different lentiviruses , including HIV , evolved to tether their PICs to the chromosomes through IN-LEDGF/p75 interactions; in contrast , MLV evolved to tether its PIC via a LEDGF/p75-independent way , which involves the use of p12 . The notion of different tethering mechanisms of HIV and MLV is further supported by their differences in integration site selection [42] , [43] , [44] , [45] , [46] , by similarity in the target site selection of a HIV chimeric virus expressing the MLV IN with MLV , and by the increased similarity upon replacement of the HIV Gag of this chimera with MLV Gag [47] , [48] . Our data point to p12 as a Gag protein essential for MLV integration; but fall short of dissecting a putative role for p12 in target selection . A likely scenario is that p12 tethering activity is an essential prerequisite for integration , while progression to complete integration depends on additional interactions of IN with cellular factors [49] . What kind of interactions may be involved in the p12-mediated tethering activity ? The specific recognition of mitotic chromosomes by p12-labled PICs , and their detachment upon exit from mitosis suggest that the MLV PIC recognizes chromatin features , such as post-translational modifications that are associated with cell cycle progression . For example , phosphorylation of histone H3 at Thr3 ( H3T3 ) and methylation of the adjacent Lys4 ( H3K4 ) , serve as a motif for the binding of cellular factors , such as TFIID , to mitotic chromosomes [50] . Intriguingly , strong association between the target site selection of MLV and specific chromatin modifications , including H3K4 methylation , has been described [48] . However , no chromatin binding modules that recognize these modifications [51] have been identified in p12 . Notably , p12 displays similarity to histone H5 protein [52] and may directly interact with chromatin . Alternatively , p12 may recruit a cellular factor harboring chromatin recognition domains . MLV integration peaks in cells after exit from metaphase and decondensation of chromosomes [3] . The release of the GFP-p12 complexes from the decondensed chromosomes in cells that exit mitosis may represent the release of PICs after integration . However , the released GFP-p12 puncta may represent PICs that failed to integrate . The release of D184A PICs upon exit from mitosis exemplifies the independence of this release from IN activity . Moreover , the lack of release of LANA31-tethered PICs further underscores the specific affinity of wt MLV PICs for mitotic chromosomes . Of note , a minority of the wt PICs remained immobilized at the end of mitosis . Although it can be argued that only such PICs mediate active integration , this is unlikely as the same phenomenon was observed for D184A PICs . Using immunofluorescence analysis , we demonstrated here that CA , a known component of the PIC [5] , [6] , co-localized with cytoplasmic p12 in interphase cells , but not with chromosome-docked p12 in mitosis . This result concords with the higher ratio between CA and viral genomic DNA , or CA and IN , in MLV PICs extracted from cytoplasmic fractions , compared to nuclear PICs -suggesting that CA is lost from the PIC after its entry into the nucleus [6] . Our results extend this finding and show that CA-PIC/p12 dissociations occur specifically upon mitosis , when no intact NE exists in the cell . Furthermore , the co-localization of p12 and the viral genomic DNA adjacent to the chromosomes [7] provides support for the notion that p12 , in contrast to CA , associates with the PIC till the final stages of PIC trafficking . Thus , gradual uncoating events that depend on the cell cycle can be described for MLV: first , MA , which forms the protein layer adjacent to the internal side of the virion membrane , dissolves away after fusion of this membrane with the plasma membrane [7] . Next , CA , which forms an inner protein layer in the virion and is part of the PIC , dissociates from this complex in mitotic cells . In HIV , CA is also mainly absent from the PIC [8] , [9] . Moreover , swaps between Gag domains of HIV and MLV demonstrated that CA is the dominant determinant for the difference between HIV and MLV in the ability to transduce nondividing cells and led to the suggestion that the stable association of the MLV CA with the PIC prevents the access of this complex to components of the cellular transport machinery [10] . Thus , the timely dissociation of CA from the PIC in mitotic cells , demonstrated here , may expose this complex to interactions with cellular factors , necessary for the completion of the trafficking and/or p12-mediated docking of the PIC to mitotic chromosomes . This notion is further supported by the correlations between the maintenance of CA association with the p12/PIC in PM14 and S ( 61 , 65 ) A mutants and their inability to dock to the chromosomes; and the reversal of both phenomena in the context of wt and S ( 61 , 65 ) A/M63I revertant . These data are in line with the proposed cooperative effect of p12 and CA at early stages of MLV infection [53] . In summary , we identified p12 - a PIC component essential for integration - as a factor that tethers the MLV PICs to mitotic chromosomes . MLV-based vectors have been used successfully in gene therapy trials in humans , yet with the risk of leukemogenesis [54] , [55] . Identification of factors influencing integration , such as p12 , should lead to safer MLV-derived vectors [49] . The specific docking of MLV PICs to mitotic chromosomes , the requirement for NE disassembly and the disassociation of CA from the PIC during mitosis , may all contribute to the productive integration of MLV in dividing cells .
Moloney MLV clones wt ( pNCS ) , PM14 , 1xMycR , S ( 61 , 65 ) A , S ( 61 , 65 ) A/M63I , and pQCXIP-gfp-C1 vector were described before [7] , [19] , [22] . LANA31 peptide ( of the Kaposi's sarcoma herpesvirus LANA protein; obtained from R . Sarid , Bar-Ilan University ) , or mutations S ( 61 , 65 ) A and S ( 61 , 65 ) A/M63I , were introduced into the indicated viruses , as described before for the PM14 mutation [7] . Overlapping PCR was used to generate the sequences of MA-GFP/p12-CA-NC , MA-GFP-p12-CA-NC and MA-mCherry/p12-CA-NC ( Supporting Information ) . pEF-H2AmRFP , expressing RFP-histone H2A fusion , and pRFP-lamin A , expressing RFP-Lamin A fusion [25] , were provided by M . Brandeis ( The Hebrew University of Jerusalem ) and H . J . Worman ( Columbia University ) , respectively . Culture conditions and serum starvation/aphidicolin treatment were described before [7] . 2ME2 ( 1 . 3 µg/ml; Sigma M6383 ) or nocodazole ( 15 µg/ml; Sigma M1404 ) were added 16 hr prior to imaging . Reversine ( 5 µM; Sigma R3904 ) was added to 2ME2-containing media when indicated . RFP-lamin A and RFP-histone H2A fusions were used to generate cell lines with labeled NE and chromosomes , respectively ( Supporting Information ) . Generation of , and infection with , the 1xMycR virus were described before [7] . To quantify infectivity of chimeric virions , 293T cells were co-transfected with plasmids expressing the pQCXIP-gfp-C1vector ( 2 µg ) , and the indicated molar ratios of wt MLV and MA-GFP/p12-CA-NC . A 1∶1 molar ratio represents 10 µg of pNCS and 5 µg of MA-GFP/p12-CA-NC plasmid . 48 hr post-transfection , supernatants of transfected cultures were filtered ( 0 . 45 µ ) , supplemented with HEPES ( pH 7 . 0; 50 mM final concentration ) and virus content was normalized by exogenous RT assay [56] . Supernatants with an equal RT activity were used to infect NIH3T3 cells for 2 hr in the presence of polybrene ( hexadimethrine bromide; 8 µg/ml ) . Two days post-infections the cells were analyzed by FACS for the percentage of GFP+ cells . For live-cell imaging , chimeric particles were generated by co-transfecting 293T cells with a 1∶1 ratio of the indicated virus and the modified Gag as described above . Infections were carried out as described above with the following modifications: ∼8 hr before infection , the cells were plated ( ∼10% cofluency ) in a 4-compartments-cell-view-glass-bottom-dish ( 35-mm; Greiner Bio One ) and were infected with MOI of approximately 10 ( based on the comparison of the RT activity of the samples to a standard MLV stock ) . For imaging , HEPES pH 7 . 0 ( 20 mM ) was added to growth medium . Imaged samples were maintained at 37°C and supplied with CO2 when imaging exceeded 2 hr . In some experiments , chromosomes were stained with Hoechst 33342 ( bisBenzimide H 33342 trihydrochloride , Sigma B2261; 1 µg/ml , 15 min , 37°C ) . Imaging was with spinning disk confocal ( Yokogawa CSU-22 Confocal Head ) microscope ( Axiovert 200 M , Carl Zeiss MicroImaging ) , 100× lens ( NA 1 . 45 , Zeiss ) and Evolve or HQ2 ( Photometrics ) cameras . Single-cycle infection assays were carried out with the pQCXIP-GFP-C1 or pQCXIN ( Clontech ) vectors . For VLPs carrying the wt p12 sequence the following plasmids were co-transfected into 293T cells: vector plasmid ( 10 µg ) , VSV-G expression plasmid ( 2 . 5 µg ) , pGag-PolGpt helper plasmid [57] ( 5 µg ) , and the plasmid expressing the modified Gag MA-GFP/p12-CA-NC ( 2 . 5 µg ) . The PM14 mutation was inserted to the p12 sequences of pGag-PolGpt helper plasmid ( generating pGag-PolGpt/PM14 ) and of MA-GFP/p12-CA-NC plasmid ( generating pMA-GFP/p12-CA-NC/PM14 ) . These two plasmids were used to generate PM14 VLPs as described above for VLPs with the wt p12 sequences . The LANA31 sequence was inserted into pMA-GFP/p12-CA-NC or pMA-GFP/p12-CA-NC/PM14 , generating pMA-GFP/p12-CA-NC/LANA31and pMA-GFP/p12-CA-NC/PM14/LANA31 , respectively . To generate VLPs with LANA31 module , pairs of pMA-GFP/p12-CA-NC/LANA31 and pGag-PolGpt , or pMA-GFP/p12-CA-NC/PM14/LANA31and pGag-PolGpt/PM14 , were co-transfected with VSV-G and vector plasmids , using the same plasmid ratio indicated above . NIH3T3 cells were infected with the resulting VLPs , normalized by exogenous RT assay , and drug-resistant colonies were selected with either puromycin ( 4 µg/ml ) , or G418 ( 1 mg/ml ) . For Western blotting , virions were purified through sucrose cushions using ultracentrifugation [7] and detected with anti-GFP monoclonal antibody ( Covance , MMS-118R ) . Immunofluorescence and calculations of the co-localization degree between CA and p12 , or CA and GFP-p12 , and between the chromosomes and each of these proteins were performed as in [7] . Quantification of the overlap between GFP fluorescence and chromosomes ( RFP fluorescence ) was performed as was described before for measurements of the overlap between p12 and chromatin signals [7] . Reconstitution of 3D images was performed with SlideBook software , employing MIP ( Fig . 1D ) or X-Ray ( Fig . S2 ) functions . For the calculation of the spatial retention of PICs over time , time-lapse sequences were processed [NoNeighbors deconvolution , Laplacian 2D filtering , SlideBook software ( Intelligent Imaging Innovations ) ] . Objects were identified through intensity-based segmentation , with no further selection for specific PICs . For each movie , the second , third or fourth frame were superimposed on the first frame; the areas of overlapping PICs identified , and the signal intensity in overlapping areas was presented as the percentage of total intensity of objects in the first frame . | Retroviruses , including the murine leukemia virus ( MLV ) , reverse transcribe their RNA genome to a DNA copy , which travels from the cytoplasm to the nucleus as part of a ‘pre-integration complex’ ( PIC ) , to integrate into cellular chromosomes . The viral p12 protein is a constituent of the MLV PIC , but its function in this complex has remained unknown . We developed a real-time imaging system to detect p12 and MLV PICs in live cells . This revealed that p12 tethers the MLV PIC to mitotic chromosomes . Accordingly , PICs derived from viruses with specific lethal mutations in p12 failed to attach to the chromosomes , and insertion of a heterologous chromatin binding module into p12 restored PICs attachment to the chromosomes and rescued virus replication . In addition , docking of wild type PICs to chromosomes coincided with nuclear envelope breakdown during mitosis , and detachment occurred upon exit from mitosis . Capsid - another viral component of the PIC - dissociated from wild type PICs in mitotic cells but remained associated with PICs harboring tethering-negative p12 mutants , suggesting interplay between these two proteins in regulating targeting of mitotic chromosomes by the PIC . These results highlight steps contributing to the high tropism of MLV to dividing cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"cellular",
"structures",
"viruses",
"and",
"cancer",
"retrovirology",
"and",
"hiv",
"immunopathogenesis",
"microbiology",
"host-pathogen",
"interaction",
"viral",
"vectors",
"immunodeficiency",
"viruses",
"mitosis",
"viral",
"preintegration",
"complex",
"cell",... | 2012 | p12 Tethers the Murine Leukemia Virus Pre-integration Complex to Mitotic Chromosomes |
Early events in the human airways determining whether exposure to Mycobacterium tuberculosis ( Mtb ) results in acquisition of infection are poorly understood . Epithelial cells are the dominant cell type in the lungs , but little is known about their role in tuberculosis . We hypothesised that human primary airway epithelial cells are part of the first line of defense against Mtb-infection and contribute to the protective host response in the human respiratory tract . We modelled these early airway-interactions with human primary bronchial epithelial cells ( PBECs ) and alveolar macrophages . By combining in vitro infection and transwell co-culture models with a global transcriptomic approach , we identified PBECs to be inert to direct Mtb-infection , yet to be potent responders within an Mtb-activated immune network , mediated by IL1β and type I interferon ( IFN ) . Activation of PBECs by Mtb-infected alveolar macrophages and monocytes increased expression of known and novel antimycobacterial peptides , defensins and S100-family members and epithelial-myeloid interactions further shaped the immunological environment during Mtb-infection by promoting neutrophil influx . This is the first in depth analysis of the primary epithelial response to infection and offers new insights into their emerging role in tuberculosis through complementing and amplifying responses to Mtb .
The first interactions between Mycobacterium tuberculosis ( Mtb ) and its human host occur in the lungs after inhalation of aerosolised bacteria . Approximately fifty percent of individuals exposed to Mtb remain uninfected [1] and host-determinants have been associated with this resistance to or clearance of infection in humans [2] . The absence of a detectable peripheral adaptive immune response in resistant individuals suggests that it is mediated by the local innate immune system and that the respiratory mucosa plays a role in determining the outcome of exposure [3] . It is assumed that the primary target of Mtb , an intracellular pathogen , is the alveolar macrophage in the lower airways . However , the majority of cells in the airway lining are epithelial which constitute a surface of approximately 70 m2 [4] and are thus likely to be the first point of contact for Mtb in the human host . Epithelial cells are known to significantly contribute to the immune responses in the lungs and can sense intra- and extracellular pathogens , such as viruses and bacteria , via a wide range of pattern recognition receptors ( PRR ) [5] . In contrast to this , their responses in tuberculosis ( TB ) are poorly defined and little investigated , which presents a surprising knowledge gap . Recognition by , or infection of , epithelial cells in the human airways may shape early host responses to the bacilli and act as an adjunct to the immune response against infections . Work on human airway epithelial-like cell lines and murine airway epithelial cells has identified potential mechanisms of recognition and response to Mtb-infection , including chemokine release and antimicrobial action [6–8] . However , the response of primary human airway epithelial cells to Mtb infection has barely been investigated and has so far mainly concentrated on isolated pathways rather than the global epithelial response to Mtb-infection [9–11] . The need to investigate the interactions of Mtb with primary human airway epithelial cells is further underscored by detection of Mtb in these cells during infections in vivo . Previous studies reported that in animal models of tuberculosis infection the bronchial and alveolar epithelium is infected after instillation with Mtb [12 , 13] and Mtb DNA was detected in human epithelial cells of histologically normal lung tissue in healthy latently infected individuals [14] . In this study , we hypothesised that human primary airway epithelial cells contribute to the protective early host response to Mtb-infection in the human respiratory tract . To test this , we interrogated the direct response of epithelial cells to Mtb as well as indirect effects mediated via an immune-network comprising infected myeloid cells , by measuring the global epithelial transcriptome . Through in vitro infection and transwell co-culture models , we identified primary bronchial epithelial cells to be inert to direct Mtb-infection , yet to be potent responders within an Mtb-activated immune network , mediated by IL1 β and type I IFN , with the capacity to shape the local antimicrobial and immunological environment .
In healthy human airways , epithelial cells were the major cell type lining the respiratory tract and harboured less than 10% of leukocyte subsets ( Fig 1A ) , of which macrophages , lymphocytes and neutrophils contributed on average 86 . 4% , 12 . 5% and 1 . 1% , respectively . The abundance of epithelial cells increases the likelihood that Mtb interacts directly with the epithelial lining after inhalation of aerosols . To assess whether human airway epithelial cells are permissive to intracellular Mtb-infection , primary bronchial epithelial cells ( PBECs ) recovered from bronchial brushes were expanded in vitro and infected with Mtb . The total cell-associated mycobacterial burden , after amikacin treatment , was over two logs less in PBECs than the Mtb-burden in THP-1 macrophages after 24h ( Fig 1B ) . The decreased association of Mtb with PBECs was confirmed by microscopy in the presence of an increased multiplicity of infection ( MOI ) ( S1 Fig ) . The minimal level of epithelial infection was reflected by a lack of transcriptomic changes in PBECs after exposure to different MOIs of Mtb for 24h ( Fig 1C ) . Microarray analysis revealed no significant differences in gene expression after statistical adjustment for multiple comparisons . Since PBECs were poor direct responders to Mtb exposure and infection , yet macrophages were readily infected , we reasoned that the role of PBECs during the early host response to Mtb might be as part of the Mtb-activated immune network with myeloid cells as the primary replicative niche for Mtb . After infection with Mtb , macrophages , the largest leukocyte subset in the healthy airways [15 , 16] , can mount a pro-inflammatory response . To investigate if PBECs , whilst inert to direct interaction with Mtb bacilli , are affected by these events , we established a contact-independent co-culture system to measure the global epithelial response to myeloid-derived Mtb-induced mediators ( Fig 2A ) . THP-1 monocytic cells were used to mimic the myeloid compartment in the airways and were infected with Mtb or remained uninfected . Microarrays were performed on PBECs derived from eight healthy donors exposed to these two conditions to model cross-talk in the healthy and infected lungs ( Fig 2B ) . Four-hundred-twenty-eight probesets representing 375 genes were differentially expressed in PBECs exposed to Mtb-infected THP-1 cells compared to uninfected THP-1 cells ( q-value<5% ) . Seventy of these were at least 1 . 5 fold differentially expressed ( Fig 2B , see S1 Table for full list ) . The identified expression signature included cytokines and antimicrobial peptides and 6 of these genes were selected for independent validation by RT-PCR ( Fig 3 ) : DEFB4 and S100A7A , which encode known antimicrobial peptides β-defensin 2 ( hBD2 ) and koebnerisin [17 , 18]; IL36G which is an emerging IL1-family member and may be beneficial during tuberculosis infection [19] the chemokines IL8 and C-X-C motif ligand ( CXCL ) 10; and two known type I IFN inducible targets interferon-induced protein with tetratricopeptide repeats ( IFIT ) 1 and interferon-induced protein ( IFI ) 44 . The RT-PCR validation experiment confirmed the gene expression patterns identified by microarray and showed that the signature was strongly dependent on exposure to infected THP-1 cells and was not inducible by direct infection of PBECs with Mtb , which confirmed the epithelial inertia to Mtb . While myeloid infection with virulent Mtb had profound effects on epithelial gene expression , non-pathogenic M . bovis BCG infection of THP-1 cells at an MOI of 10 only increased epithelial CXCL10 expression to 9 or 19% of the gene expression induced by Mtb in two independent experiments , respectively . THP-1 cells are a valuable model for in vitro studies of myeloid responses to Mtb , and we confirmed that our model reflected responses in the lungs through the use of primary alveolar macrophages ( AMΦ ) . For this , PBECs were exposed to infected primary AMΦs from independent donors in co-culture ( n = 2 , S2 Fig ) , which confirmed increased expression of DEFB4 , S100A7A , CXCL10 and IFIT1 . Transwell co-culture with primary macrophages could only be performed twice and cell-free supernatant of infected and uninfected AMΦs was used to confirm expression patterns in depth ( Fig 4 ) . Soluble mediators secreted by AMΦs induced similar transcriptomic responses as observed when THP-1 cells were used . Thus , the epithelial response to Mtb-induced inflammation we observed in our model mimicked paracrine AMΦ-epithelial signalling that may occur early after exposure of the human airways to Mtb . To identify the Mtb-induced inflammatory signals which drove gene expression in PBECs , we performed transcription factor binding site ( TFBS ) enrichment analysis with oPossum which revealed that the epithelial expression signature was associated with nuclear factor kappa-light-chain-enhancer of activated B cells ( NFκB ) , including RelA and the p105 subunit ( NFKB1 ) , and interferon regulatory factor ( IRF ) -family members 1 and 2 ( Fig 5A ) . Exposure of PBECs to myeloid Mtb-infection furthermore induced pathways involved in immune response signalling , including interferon-driven pathways ( Fig 5B ) . Similarly , gene ontology was enriched for terms associated with cytokine and IFN signalling ( Fig 5C ) . It is well known that macrophages secrete several inflammatory mediators in response to Mtb-infection , including IL1β and IFNβ [20–22] . We found that , IL1β levels in culture supernatants were over threefold enhanced when infected THP-1 cells were co-cultured with PBECs ( Fig 6A ) . A similar increase was not observed to the same extent when THP-1 cells were infected with BCG in the presence of PBECs , which resulted in a release of only 28 . 9–81 . 2 pg/ml IL1β during co-culture in three independent experiments . IFN signalling was indicated by the pathway enrichment analysis and we confirmed IFN β expression and release from myeloid cells to identify whether type I IFNs may have been involved in epithelial activation . We detected increases in gene expression and protein secretion during Mtb-infection in AMΦ and THP-1 cells ( Fig 6B and 6C ) . IFNβ induction co-incided with phosphorylation of STAT1 , a downstream mediator of type I IFN-signalling , in PBECs exposed to Mtb-infected THP-1 cells ( Fig 6D ) . IFNB was not induced in PBECs during co-culture ( S3A Fig ) and neither IFNγ nor IFNλ could be detected by ELISA in culture supernatants of THP-1 cells 24h after Mtb-infection ( S3B Fig ) . Stimulation with recombinant cytokines confirmed that IL1β and IFNβ were sufficient to induce DEFB4 and CXCL10 expression respectively , but not vice versa ( S3C Fig ) . To identify whether IL1 β or type I IFN could in fact mediate the epithelial signature in response to myeloid Mtb-infection , we abrogated each pathway in co-culture . Neutralising IL1β prevented upregulation of AMP genes DEFB4 and S100A7A ( Fig 7A ) as well as pro-inflammatory cytokines IL8 and IL36G ( S3D and S3E Fig ) . Neutralisation of TNF in Mtb-infected co-cultures or infected THP-1 macrophage monoculture , enhanced the IL1β mediated effects , likely reflecting autocrine activation of THP-1 cells ( S4A–S4C Fig ) . In contrast , genes associated with interferon-signalling , CXCL10 and IFIT1 , were unaffected by IL1β neutralisation ( Fig 7A ) and dependent on IFNAR2 activation ( Fig 7B ) , which mediates type I IFN signalling . Conversely , abrogation of IFNAR2 signalling did not affect the expression of the tested IL1β -dependent genes . Neutralisation of IFNγ had no effect on CXCL10 expression ( S4D Fig ) . DEFB4 and S100A7A , which were strongly expressed in PBECs in response to Mtb-driven inflammation , were not expressed by Mtb-infected THP-1 cells . Mtb-infection also failed to induce S100A7A expression in AMΦs , while DEFB4 induction ranged from 0 . 84–14 . 08 fold in three healthy donors . Since the release of AMPs may critically determine the outcome of exposure to Mtb , we tested whether the infection-induced defensins and S100A-family members control free growing Mtb . Human β -defensin 2 ( hBD2 ) , which is encoded by DEFB4 , has been previously described to diminish Mtb growth in a resazurin assay [17] and was a potent inhibitor of growth of Mtb H37Rv ( Fig 8A ) and clinical strains in our hands ( S5A Fig ) . Because , no bioactive form of S100A7A was available , we used the close homologue S100A7 [23] , known as psoriasin , which was also induced in an IL1β-dependent manner in co-culture ( S5B and S5C Fig ) and reflected the induction pattern observed for S100A7A ( Fig 3 ) . Psoriasin decreased the median Mtb burden in liquid culture significantly by 62% ( Fig 8B ) . In addition to local responses to Mtb , influx of immune cells to the site of infection can shape the human immune response in the lungs . As part of the myeloid-driven epithelial expression signature identified here , chemokines including CXCL10 and IL8 were induced through pro-inflammatory and IFN-driven pathways . Infected myeloid cells secrete chemokines which attract leukocytes [24 , 25] and in addition to this , constitutive or induced release of mediators by epithelial cells may contribute or amplify timely immune cell recruitment to the site of infection . To address this , we investigated whether the observed gene expression patterns in PBECs were associated with changes in the chemotactic environment during co-culture compared to infected THP-1 monoculture . THP-1 cells were infected with Mtb in the presence or absence of PBECs and cell free culture supernatants were harvested after 48h . The secreted protein content ( secretome ) was measured through LC-MS . We detected 654 proteins at significantly different levels in co-culture compared to infected THP-1 monocultures ( S2 Table ) . This included increased levels of IL1β in THP-1 PBEC co-culture , which confirmed our previous ELISA data ( Fig 6A ) . To identify which of these proteins were differentially expressed at the gene level in PBECs during co-culture , the gene expression signature was converted into UniProt IDs , which mapped to 341 proteins of which 18 were detected in the transcriptomic and secretome analyses ( Fig 9A ) . Three entities , the leukocyte chemoattractants IL8 , CXCL1 and CXCL10 , associated with the GO term ‘chemotaxis’ ( GO:0006935 , AmiGO 2 , Gene Ontology Consortium ) . Myeloid-epithelial cross-talk significantly induced their expression in PBECs and the subsequent release into the culture supernatant , thus directly contributing to the chemotactic environment during Mtb-infection ( Fig 9B ) . In fact , in a transwell chemotaxis assay , confirmed , that the epithelial-driven differences in the mediator environment contributed to cellular influx towards the site of infection . The largest population in peripheral blood are polymorphonuclear ( PMN ) cells ( S6 Fig ) , of which neutrophils are rapid responders during bacterial infections [26] . PMN migration towards cell free cell-culture supernatants , which was generated in the same ways as the supernatants for secretome analysis , was measured . Supernatants from infected epithelial-myeloid co-cultures attracted on average approximately 9-fold more PMNs than supernatants from infected THP-1 monocultures ( Fig 9C–9F ) , suggesting that epithelial cells contribute crucially to the leukocyte recruitment to the site of infection .
The first step in the natural history of tuberculosis infection , the host-pathogen interaction in the human airways after Mtb inhalation , is likely shaped by the two dominant cell types in the lungs: epithelial cells and alveolar macrophages . While macrophages are well known to respond and interact with Mtb , airway epithelial cells are understudied . We present the first global transcriptomic profiling of primary human airway epithelial cells in response to Mtb and Mtb-driven inflammation and found that epithelial cells contribute distinctly to the host response against Mtb through antimicrobial effectors as well as by amplifying the chemotactic environment initiated by infected macrophages to shape the local immunological environment . To interrogate the epithelium in an in vitro setting which resembles early immune interactions in the human respiratory tract more physiologically , we established a co-culture model to allow contact-independent communication between PBECs and infected myeloid cells in real time . We used THP-1 cells to model myeloid responses in the co-culture system developed here and confirmed that the epithelial expression patterns are the same in response to Mtb-infected alveolar macrophages or THP-1 cells . Through biological over-representation analysis of the epithelial transcriptome , we have uncovered the paracrine activation of epithelial cells by type I IFNs and IL1β . The interplay of type I IFN and IL1 has recently been elegantly delineated within infected macrophages and has been proposed as a point of therapeutic intervention during active tuberculosis [27] . While the cross-regulation of IL1 and type I IFN signalling in the Mtb-infected macrophage is an important well-documented rapidly-evolving area , our findings revealed for the first time that these pathways also act on the epithelial lining . In contrast to the induction patterns of IL1 and type I IFN within macrophages [20] and their respective cross-regulation , the paracrine epithelial activation induced by these two mediators occurred independently of each other with abrogation of either signalling pathway leaving gene expression of the opposite mediator untouched . The activation of epithelial cells through Mtb-driven myeloid-derived type I IFNs has not previously been reported . Despite the association of type I IFNs with neutrophil-driven disease severity of tuberculosis in humans [28] and mice [29] , the effect of type I IFNs on the outcome of Mtb-infection early after exposure is yet to be determined . Early myeloid-driven activation of IFN-signalling may be beneficial for the host as evidenced by the finding that IFN I and IFNγ are both required for optimal immune cell recruitment during the initial pre-adaptive phase of Mtb-infection in a mouse model [30] . Our findings further support this concept and showed that type I IFN signalling is required to induce epithelial transcription of the chemotactic factor CXCL10 during Mtb-driven inflammation in humans . This activation , together with IL1β-mediated chemokine expression , amplifies cellular influx initiated by infected myeloid cells . We observed that PBECs significantly enhance the neutrophil influx induced by infected myeloid cells and suggest that they may support rapid recruitment to the initial site of infection in vivo . Neutrophils are important early antimicrobial effectors with poor specificity . While they can cause substantial tissue damage and inflammation , their antimicrobial properties are crucial to control and prevent recurrent infections in humans [31] . Tissue neutrophilia is associated with pathogenesis during active TB; however , influx of neutrophils early after infection has been suggested to be beneficial for host control of the pathogen . Absence of neutrophils during early mycobacterial infection results in increased bacterial burden in mice and zebrafish [32 , 33] and growth control of mycobacteria by whole blood in vitro is strongly dependent on neutrophils [34] which may be an important function at the site of infection . Besides the release of chemokines , myeloid-derived IL1β mediated the antimicrobial responses elicited in PBECs , including induction of DEFB4 expression , in accordance with previously reported findings [9] . We further confirmed that DEFB4 was a potent direct anti-mycobacterial effector . While β-defensins are known to kill mycobacteria [17] , IL1β-induced S100-family members have not previously been considered to be active against Mtb . Whilst both S100A7A ( encoding koebnerisin ) and S100A7 ( encoding psoriasin ) are antibacterial effectors in the skin , neither were known to have antimycobacterial activity prior to our current discovery that psoriasin limited Mtb growth in liquid culture . IL1β which mediated AMP-expression , is increased in the airways during active TB [35]; and may already be elevated early after Mtb-infection of alveolar macrophages before the onset of detectable lung pathology . This would then result in the activation of epithelial antimicrobial responses at the earliest stages of the natural history of tuberculosis in the human airways . Interestingly , IL1β -dependent epithelial AMP-expression was further enhanced by TNF in PBECs during co-culture with infected myeloid cells . While augmentation of TNF signalling by IL1β has been described in macrophages before [36] , we believe that this is the first time that IL1β activation was shown to be enhanced by TNF in Mtb-infection . Epithelial antimicrobial peptides during infection may contribute to host defense against free extracellular mycobacteria thus preventing or diminishing cell to cell spread in early infection . They may also be a promising therapeutic target , since their activity is not affected by multidrug resistance of Mtb . Additionally , the identification of epithelial-specific AMP-expression with anti-mycobacterial activity may complement myeloid responses to Mtb in the airways , since monocytes and macrophages do not express high levels of the AMPs identified here [37 , 38] . Prior studies of macrophage-driven activation of epithelial cells focussed solely on specific pathways [9 , 11] , involving matrix metalloproteinases ( MMPs ) or DEFB4 . Our data provide the first global perspective on the epithelial response to Mtb infection , and reveal important new targets in the epithelial response to Mtb-infection while simultaneously confirming the previously published findings . In transwell co-culture , a large proportion of the expression signature ( 112 genes ) was associated with the innate immune response , including type I IFN and cytokine-mediated signalling . IL36G , which is an emerging IL1-family member , was dependent on myeloid-derived IL1 β . It has recently been described to have a host-protective role during Mtb-infection [39] and its epithelial release may support macrophage function further . As part of the type I interferon-inducible mediators , the known antiviral factor ISG15 was upregulated and its secretion during mycobacterial infection is involved in the appropriate induction of IFNγ in humans [40] . These are only two of several genes uncovered by the co-culture model we have established , which provide interesting targets for future investigations , but where further analysis into their function was beyond the scope of this study . In contrast to their multifaceted responses to myeloid Mtb-infection , PBECs were surprisingly inert to direct stimulation with Mtb . This was not due to a general non-responsiveness to PRR-stimulation , as PBECs mount a substantial IL8 response towards synthetic TLR-agonists and in response to Streptococcus pneumoniae ( Reuschl et al , manuscript in preparation ) . The global assessment of the transcriptomic response to direct stimulation with live Mtb revealed that no genes were significantly upregulated after 24h and this was complemented by poor invasion and adherence of Mtb to epithelial cells . This finding is consistent with reports of poor invasion of Mtb into primary tracheal epithelial cells in comparison to dendritic cells [41] . Interestingly , two days after intra tracheal Mtb-infection of mice , which results in locally very high density of Mtb bacilli , the majority of bacilli are found in macrophages and only approximately 10% of intracellular Mtb can be detected in epithelial cells [13] . Epithelial inertia to Mtb and the lack of uptake in humans may thus ensure the uptake of Mtb by local professional phagocytes , the preferred target host cell of Mtb after inhalation in the human lungs . Our co-culture model did not include endothelial cells and it remains to be seen whether interaction with an endothelial layer would augment the epithelial response to Mtb . Our findings contrast with previous studies , which described epithelial susceptibility to infection and upregulation of chemokines in A549 cells within 24h [10 , 42–44] . This most likely reflects differences between primary cells and cell lines , as A549 cells are adenocarcinoma-derived and may not truly mimic the responses of healthy human respiratory epithelium [45] . Through the recovery of PBECs from several donors , we have overcome the limitations of using a single cell line and reflected true biological variation in humans . In vivo , the airway epithelium is at an air-liquid-interface , polarised and differentiated . However , in our model , the epithelial cells were undifferentiated and in submerged culture which allowed for comparison with the existing literature . Mediator release of undifferentiated epithelium to viral infection or air pollution particles surpasses the release by differentiated epithelium [46 , 47] suggesting that the inert phenotype of PBECs in response to Mtb is not an underestimate of their in vivo response and is moreover consistent with the lack of epithelial infection observed in mice in vivo [13] . We expanded and studied human primary cells in vitro , as opposed to using respiratory epithelial cell lines or mouse models . A limitation of our study is the focus on primary bronchial epithelial cells which , unlike alveolar epithelial cells , can be obtained by bronchoscopy . The recovery of pure alveolar epithelial cells requires samples from resections of dissected lungs or post-mortem cadaveric explants which may not reflect the responsiveness of healthy epithelium , and their phenotype is difficult to maintain in vitro [48] . Primary alveolar epithelial cells should be used to address whether our findings extend to human alveolar epithelial cells . This would be of interest as differences in the responsiveness to pro-inflammatory mediators between upper and lower airway epithelial cells have been previously described [49 , 50] . Myeloid-epithelial cross-talk was independent of cell-cell contact , providing evidence that the bronchial epithelium is able to respond to Mtb-infection upon activation by secreted mediators which diffuse from the site of infection throughout the airspaces . In the future , it would be important to assess the mechanisms identified in our study in PBECs derived from patients with active pulmonary tuberculosis , where epithelial responsiveness may be altered . Most of the epithelial cells within the human airways will not be in direct contact with the small number of infected alveolar macrophages . The majority of the initial epithelial response would therefore be expected to be driven by soluble mediators . Our experimental rationale therefore focussed on contact-independent interactions between infected myeloid and primary epithelial cells . Based on our findings , future work should also include direct interactions between the myeloid and epithelial compartment during infection to assess how cell-cell contact modulates the response to Mtb . Our findings shed light on the contribution of the respiratory epithelial lining to the response against Mtb as well as the complexity of the immune response in the airways early after infection . It is known from challenge studies with rhinovirus in humans and ex vivo infection of bronchial tissue that only a limited number of epithelial cells are actively infected [51 , 52] , yet potent immune activation occurs throughout the epithelial lining . Given that infection with Mtb requires only very few aerosolised mycobacteria [53 , 54] , amplification of the macrophage-response to infection by uninfected surrounding epithelium may very well be decisive for the outcome of infection . Appropriate animal models of Mtb-infection should be employed to assess the reported epithelial responses in vivo during early infection . In summary , we show that PBECs are inert to direct early Mtb-infection , but potent responders to infected alveolar macrophages . PBECs contributed to host defense as part of an Mtb-activated immune network through pathways , which are likely protective early after infection , by creating an extracellular antimicrobial milieu and promoting early neutrophil influx . Through these cell-specific differential responses to myeloid-derived cytokines , airway epithelial cells likely fulfil a non-redundant role in the human pulmonary host response to Mtb as vital players with the potential to shape the human innate immune response to infection and influence the outcome of exposure to Mtb .
PBECs and AMΦs were collected through bronchial brushings and bronchoalveolar lavage from the airways of healthy volunteers during bronchoscopic procedure at St . Mary’s Hospital , London , United Kingdom . All volunteers were adults and recruited through advertisements or from Contact and TB clinics at Imperial College NHS Trust ( St . Mary’s Hospital ) , London North West Healthcare NHS Trust ( Ealing Hospital , Northwick Park Hospital ) and Barts Health NHS Trust ( Newham Chest Clinic ) . All samples were collected in accordance with the Human Tissue Act 2004 and written informed consent was obtained from all participants ( National Research Ethics Service approval reference 07/H0712/85+5 ) . Bronchial epithelium was recovered from the right lower lobe of healthy volunteers through cytological brushes ( Olympus Keymed , Southend-on-Sea , UK ) . PBECs were cultured from up to three brushes per volunteer and expanded in supplemented bronchial epithelial growth medium ( BEGM ) ( Lonza , Walkersville , USA ) as previously described [55] . To prevent the outgrowth of recovered contaminating bacteria or fungi , the growth medium was supplemented with gentamycin and amphothericin-B as per the manufacturer’s instruction . All cultures showed the characteristic cobblestone appearance of bronchial epithelial cells . PBECs were used in experiments after two passages . Cells were seeded at 8x104/ml in collagen/fibrinogen-coated tissue culture plates and grown to confluence . BEGM was then changed to bronchial epithelial basal medium ( BEBM ) over night before experimentation . To identify cell subsets in the epithelial lining , ex vivo differential cell counts of one separate brush was obtained . Sample sizes given in figure legends refer to biological replicates from independent donors . Alveolar macrophages ( AMΦ ) were recovered from bronchoalveolar lavage and isolated through overnight adherence to tissue culture plastic . AMΦ were cultured in RPMI with 10% human serum and 50μg/ml gentamycin . For infection experiments , medium was changed to medium with 5% human serum or BEBM . AMΦ were used at 106/ml for experiments . Peripheral blood leukocytes ( PBL ) were isolated from heparinised whole blood . Red blood cells were removed trough addition of 10 fold excess RBC lysis buffer ( Biolegend , San Diego , USA ) for 10 min . THP-1 cells were obtained from American Type Culture Collection ( ATCC ) and grown RPMI with 10% FBS , 50000 U penicillin , 50 mg streptomycin ( Sigma-Aldrich , Poole , UK ) and 0 . 05 mM β-mercaptoethanol . For macrophage experiments , THP-1 cells were differentiated with 50nM phorbol 12-myristate 13-acetate ( PMA ) for 24h , seeded in tissue culture plates and rested overnight in complete medium without PMA . THP-1 cells were used at 106/ml for experiments . Before infection experiments , culture medium was replaced with infection medium containing 5% human serum or BEBM . Mtb H37Rv , M . bovis BCG ( SSI ) or the clinical isolates Mtb CH [56] and Mtb NPH4216 [57] were cultured in 7H9 Middlebrook medium supplemented with 10% OADC , 0 . 5% glycerol , 0 . 05% Tween-80 and 10 μ g/ml amphotericin . Cultures were harvested in mid-log phase and frozen down in 15% glycerol . To standardise infection doses , cells were infected from frozen stocks and multiplicity of infections determined based on the mycobacterial counts of the stocks . For determination of colony forming units ( CFU ) , mycobacteria were grown on 7H10 agar plates supplemented with 10% OADC , 0 . 5% glycerol and 0 . 05% Tween-80 and cultured for 3–4 weeks at 37°C . Antimycobacterial effects of selected peptides were performed in 96 well plates . hBD2 ( PeproTech , London , UK ) , S100A7/Psoriasin ( a kind gift from Prof . Joachim Grötzinger [23] ) and controls were diluted in fresh 7H9 and Mtb bacilli harvested in mid-log phase was added . Plates were incubated shaking for up to 7 days at 36°C . Culture growth was monitored by measuring optical density at 595nm . OD-readings from 7H9-only controls were subtracted from all culture conditions before the data was analysed . On day 7 , cultures were plated on 7H10 for CFU enumeration . For infections , Mtb bacilli were diluted in cell culture medium , washed by centrifugation and taken up in cell culture medium for infection experiments . Mtb was sonicated for 40 seconds in an ultrasonic waterbath ( Grant ) to disperse clumps before inoculation of cell culture . For Mtb-uptake and adherence to THP-1 MΦ or PBECs . Mtb bacilli were added onto cells for 24h . Infection was determined by cell lysis with 0 . 1% Triton-X/PBS-Tween80 . Lysates were serially diluted in PBS-Tween80 and plated on 7H10 agar . Association of Mtb with PBECs was visualised for representative experiments by Kinyoun stain with a Tb-color kit ( Merck , Darmstadt , Germany ) and images were acquired with an Axio Scope . A1 microscope ( Zeiss , Rugby , UK ) . For some experiments , 200 μg/ml amikacin ( Sigma ) was added for 2h before cell lysis . To measure mediator release or gene expression , myeloid cells or PBECs were infected with Mtb at the indicated MOI and supernatants or RNA harvested at the indicated time . For transwell experiments , PBECs were seeded into tissue culture plates then 0 . 4 μm cell culture inserts ( Millipore ) were placed on top with THP-1 cells added to them . Mtb was added to either compartment as desired ( MOI5 over THP-1 ) . For blocking and neutralisation of cytokine signalling , 20 μg/ml of antibodies ( αIFNα/ β R2 ( MMHAR-2 , R&D Systems ) , α IFNγ ( K3 . 53 , R&D Systems ) , α IL1β ( 2805 , R&D Systems ) , α TNF ( 1825 , R&D Systems ) , mIgG1 ( 11711 , R&D ) , mIgG2A ( 20102 , R&D Systems ) were added to the bottom well 45 min before transwell inserts and THP-1 cells were added . Culture supernatants were harvested from the tissue culture wells after inserts were removed . PBECs were exposed to soluble mediators released from uninfected or infected AMФ . Culture supernatants from AMФ or cell-free incubation controls ± Mtb bacilli were harvested after 24h and 0 . 22 μm sterile filtered . Supernatants were diluted 1:10 in fresh BEBM , added to PBECs and total RNA was harvested after 24h . RNA was extracted with the TRIzol Plus RNA Purification Kit ( Ambion ) and treated with DNAse I ( Thermo , Epsom , UK ) to remove residual genomic DNA . For RT-PCR , RNA quality and quantity was measured by Nanodrop . RNA was converted to cDNA using Maxima reverse transcriptase and random hexamers . Solaris Gene Expression and Taqman Expression assays were used to determine gene expression levels by RT-PCR . RT-PCR reactions were performed in duplicate with 12 . 5 ng of cDNA . Expression data were analysed with Biogazelle qbase+ and normalised to ACTB and PGK according to [58] . Solaris Gene Expression assays: ACTB ( AX-003451 ) , CXCL10 ( AX-007871 ) , DEFB4A ( AX-012997 ) , IFIT1 ( AX-019616 ) , IFNB1 ( AX-019656 ) , IL36G ( AX-007959 ) , IL8 ( AX-004756 ) , PGK ( AX-006767 ) , S100A7A ( AX-027145 ) . Taqman Expression assays: S100A7 ( Hs01923188_u1 ) . For microarrays , RNA quality was assessed by Bioanalyzer ( Agilent , Stockport , UK ) ( RIN > 9 for all samples ) and RNA was quantified by Qubit ( Thermo ) . 50-100ng total RNA was prepared for GeneChip Human Transcriptome Array 2 . 0 with the GeneChip WT PLUS Reagent Kit ( Affymetrix , High Wycombe , UK ) . Samples were hybridized and scanned at the MRC Genomics Laboratory ( Hammersmith Campus , Imperial College London ) . Soluble mediators were measured in cell culture supernatants after double-filtration through 0 . 22 μm centrifuge spin filters . Human IL1β , IFNγ and IFNλ were measured by DuoSet ELISAs ( R&D Systems ) . IFNβ was measured by VeriKine Human IFN Beta ELISA kit ( pbl interferon source ) . The secretome of Mtb-infected THP-1 cells and Mtb-infected PBEC-THP-1 co-cultures was measured by liquid chromatography—mass spectrometry ( LC-MS ) . Six paired cell culture supernatants were analysed using two independent PBEC cultures . Cell culture supernatants were harvested at 48h and sterile filtered twice through 0 . 22 μm centrifuge filters . 500 μl of culture supernatants were buffer exchanged twice with 450 μl of 2 M urea , 100 mM Tris-HCL ( pH 8 . 0 ) using a 3 kDa nominal molecular weight limit cut-off spin filter ( Millipore ) . A volume of 80 μl of sample was reduced with 5 mM DTT ( Sigma ) for 30 min at 60°C and alkylated with 10 mM of iodoacetamide ( Sigma ) in the dark at room temperature for 30 min . 1 μg Trypsin/Lys-C mix ( Promega ) was added for 18 h at 37°C . Samples were adjusted to 1% ( v/v ) trifluoroacetic acid ( TFA ) ( Sigma ) and peptide digests were purified using the C18 STop And Go Extraction ( STAGE ) tips [PMID: 16602707] and resuspended in 2% ( w/v ) acetonitrile , 0 . 1% ( v/v ) fomic acid for LC-MS . Samples were analysed using an EASY-nLC 1000 Liquid chromatography system coupled to a Q-Exactive mass spectrometer . The separation column and emitter was an EASY-Spray column , 50 cm x 75 μm ID , PepMap C18 , 2 μm particles , 100 Å pore size . Buffer A was 2% acetonitrile , 0 . 1% formic acid and buffer B 100% ( v/v ) acetonitrile , 0 . 1% ( v/v ) formic acid . A gradient from 5% to 40% acetonitrile over 120 min was used to elute peptides for ionization by electrospray ionisation ( ESI ) and data dependent MS/MS acquisition consisting of 1 full MS1 ( R = 70K ) scan acquisition from 350-1500m/z , and 10 HCD type MS2 scans ( R = 15K ) . MS/MS charge targets were limited to 1E6 and iosolation window set to 2 . 0 m/z , monoisotopic precursor selection , charge state screening and dynamic exclusion were enabled , charge states of +1 , >4 and unassigned charge states were not subjected to MS2 fragmentation . Raw mass spectra were identified and quantified using Maxquant 1 . 5 . 15 using a 1% peptide and protein FDR . Searches were conducted against the Uniprot SwissProt database downloaded on 06/06/2014 . The database was supplemented with common contaminant proteins introduced during proteomic experiments . Searches were specified as tryptic with 1 missed cleavage , 7 ppm precursor ion mass tolerance , 0 . 05 Da fragment ion mass tolerance , fixed modifications of carbamidomethylation ( C ) , and variable modification of oxidation ( M ) , acetylation ( N-term , Protein ) . Protein Label free quantitation ( LFQ ) intensity measured in medium alone was subtracted from all paired samples , and LFQ values were log2 transformed . To identify both qualitative and quantitative effects of stimulation on extracellular all missing values were replaced assuming a normal distribution ( width 0 . 3 , and a down shift of 1 . 8 ) for only control/unstimulated samples as described in [59] . For protein lysates PBECs were treated with RIPA buffer ( Pierce ) supplemented with 2x Halt Protease and Phosphatase Inhibitor Cocktail ( Pierce ) and 250 U Benzonase ( Sigma ) for 15 min on ice . Lysates were centrifuged at 14000 rpm for 15 min at 4°C . Total protein concentration was measured by BCA protein assay ( Pierce ) . 12 μg of protein lysates lysates were mixed with Laemmli buffer ( BioRad , Hertfordshire , UK ) and 25 mM DTT and boiled for 7 min . Proteins separated on 4%-20% Mini PROTEAN gels ( BioRad ) and transferred onto nitrocellulose membranes by iBlot ( Invitrogen ) . Membranes were blocked in 5% milk/TBST for 1h and probed with primary antibodies ( αbeta-Actin ( D6A8 , New England Biolabs ) , αPhospho-Stat1 ( Tyr701 ) ( D4A7 , New England Biolabs ) , αStat1 ( #9172 , New England Biolabs ) ) at 1:1000 in 5% BSA/TBST over night at 4°C . HRP-linked IgG ( New England Biolabs ) ( 1:2000 ) in 5% BSA/TBST was added the next day for 1h . Membranes were developed with ECL Western blotting substrate ( Pierce ) and imaged with FUSION FX7 SPECTRA ( Vilber ) . PBLs were isolated and resuspended in PBS with 0 . 5% BSA and 2mM EDTA ( FACS buffer ) . Before staining , 10% human serum in FACS buffer was added to cells for 20 min to block Fc receptors . For surface staining , PBLs were incubated with α CD14-Brillian Violet 421 ( M5E2 , Biolegend ) , α CD15-Brillian Violet 605 ( SSEA-1 , Biolegend ) , α CD3-PE-CF594 ( UCHT1 , BD ) and α CD66b-PerCP/Cy5 . 5 ( G10F5 , Biolegend ) . Cells were treated with Cytofix Fixation buffer , resuspendend in FACS buffer and left overnight before flow cytometric acquisition using either a BD LSR II or BD Fortessa . Anti-Mouse Ig compensation beads ( BD ) were used to determine compensation parameters . Cell free culture supernatants were generated through direct co-culture of Mtb-infected THP-1 cells in the presence of absence of PBECs for 48h . BEBM was used as a background control for unspecific migration . Conditioned or control medium was added to the bottom well of a tissue culture plate and 0 . 5 μm transwell inserts ( Corning ) placed on top . The plates were placed in a humified 37°C CO2 incubator for 30 min to equilibrate . 2 . 5x105 PBLs were added to the transwell insert and the plate was placed back into the incubator for cell migration . After 3h , 2mM EDTA was added to the bottom compartment to dislodge cells from the transwell membrane . Cells were collected from the insert and bottom compartment , fixed and resuspended in equal volumes of FACS buffer . Cells in each sample were enumerated through acquisition for 120 s using a BD LSR II flow cytometer . Microarray data was normalised by Robust Multi-Array Average ( RMA ) using Partek Genomic Suite 6 . All genes annotated by NCBI Reference Sequence Database ( RefSeq ) , NIH GenBank or Ensembl were taken forward for statistical analysis . One-Way ANOVA on microarray data was performed with PGS . For SAM analysis of expression and secretome data with TIGR MultiExperiment Viewer 256 or 64 permutations were used , respectively . Pathway analysis was performed with InnateDB for over-representation analysis [60] . Transcription factor binding site enrichment on differentially expressed genes was performed using oPossum [61] . Flow cytometry data was analysed using FlowJo v10 . Statistical analysis of all other experiments was performed with GraphPad Prism 6 and is indicated where appropriate . | Mycobacterium tuberculosis ( Mtb ) is the causative agent of tuberculosis , which remains a major public health burden today . In the majority of cases , infection is acquired by inhalation of aerosolised bacteria . Mtb is thought to target alveolar macrophages in the lower airways to establish infection . However , the cells predominantly lining the respiratory tract are epithelial cells and thus are likely crucial during the early host-pathogen interactions . We recovered primary human bronchial epithelial cells from healthy volunteers to assess their global transcriptomic response to direct Mtb-exposure and exposure to Mtb-infected myeloid cells . Our analysis revealed that , while being inert to direct Mtb-infection , epithelial cells were highly responsive to soluble mediators released by infected macrophages . The epithelial response induced by this cellular cross-talk , promoted neutrophil influx in vitro as well as the increase of antimycobaterial host responses . Our data provide novel and unexpected insights into the role of the primary human airway epithelium and define a non-redundant role for epithelial cells in shaping the local immunological environment at the site of initial Mtb infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"reverse",
"transcriptase-polymerase",
"chain",
"reaction",
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"respiratory",
"infections",
"immunology",
"pulmonology",
"epithelial",
"cells",
"molecular",
"biology",
"techniques",
"bacteria",
"re... | 2017 | Innate activation of human primary epithelial cells broadens the host response to Mycobacterium tuberculosis in the airways |
Mycolactone A/B is a lipophilic macrocyclic polyketide that is the primary virulence factor produced by Mycobacterium ulcerans , a human pathogen and the causative agent of Buruli ulcer . In M . ulcerans strain Agy99 the mycolactone polyketide synthase ( PKS ) locus spans a 120 kb region of a 174 kb megaplasmid . Here we have identified promoter regions of this PKS locus using GFP reporter assays , in silico analysis , primer extension , and site-directed mutagenesis . Transcription of the large PKS genes mlsA1 ( 51 kb ) , mlsA2 ( 7 kb ) and mlsB ( 42 kb ) is driven by a novel and powerful SigA-like promoter sequence situated 533 bp upstream of both the mlsA1 and mlsB initiation codons , which is also functional in Escherichia coli , Mycobacterium smegmatis and Mycobacterium marinum . Promoter regions were also identified upstream of the putative mycolactone accessory genes mup045 and mup053 . We transformed M . ulcerans with a GFP-reporter plasmid under the control of the mls promoter to produce a highly green-fluorescent bacterium . The strain remained virulent , producing both GFP and mycolactone and causing ulcerative disease in mice . Mosquitoes have been proposed as a potential vector of M . ulcerans so we utilized M . ulcerans-GFP in microcosm feeding experiments with captured mosquito larvae . M . ulcerans-GFP accumulated within the mouth and midgut of the insect over four instars , whereas the closely related , non-mycolactone-producing species M . marinum harbouring the same GFP reporter system did not . This is the first report to identify M . ulcerans toxin gene promoters , and we have used our findings to develop M . ulcerans-GFP , a strain in which fluorescence and toxin gene expression are linked , thus providing a tool for studying Buruli ulcer pathogenesis and potential transmission to humans .
Mycobacterium ulcerans is the causative agent of Buruli ulcer ( BU ) an emerging but neglected disease found predominantly in tropical regions of the world and with an increasing incidence in West and Central Africa [1] , [2] . BU is a chronic infection of subcutaneous tissue that can result in high morbidity such as permanent scarring and functional disabilities . The combination of rifampin and an aminoglycoside for four to eight weeks leads to the healing of early lesions without radical surgery and is now the recommended standard regimen [3] . However , substantial tissue damage often necessitates surgery [4] . The social and economic burden of BU can be severe , particularly in impoverished rural regions of West Africa where the prevalence of BU is sometimes higher than that of the two most significant mycobacterial diseases , leprosy and tuberculosis . Cases of BU are usually clustered around swamps and slow-flowing water and while the mode of transmission of M . ulcerans is unknown , evidence to date suggests , fish [5] , snails [6] and certain carnivorous aquatic insects [7] , [8] can all harbour the bacterium . Recent studies in Australia suggest mosquitoes may play a role in transmission [9] , [10] . A major factor influencing the pathology of Buruli ulcer is the production by M . ulcerans of a secondary metabolite called mycolactone [11] . Mycolactone is an immunosuppressive and cytotoxic macrocyclic polyketide , characterised by a 12-membered macrolactone core appended to a highly unsaturated acyl side chain [11] , [12] . Polyketides are a class of naturally occurring compounds , some of which have potent pharmaceutical activity such as the immune suppressor rapamycin , the antibiotic erythromycin A , and the antiparasitic agent avermectin [13]–[15] . Why M . ulcerans produces mycolactone is unknown . However , studies on the effect of the molecule in cell culture and animal models have shown that in the microgram range it has cytotoxic properties , while at sub-cytotoxic concentrations it has immunomodulatory properties , most strikingly the inhibition of tumour necrosis factor production by monocytes and macrophages [16]–[18] . In mice , mycolactone has been shown to concentrate within peripheral blood monocytes [19] . Mycolactone synthesis is dependent on the pMUM megaplasmid found in M . ulcerans and closely related mycobacteria ( Figure 1 ) [20]–[23] . This plasmid contains three , very large genes ( mlsA1: 51 kb , mlsA2: 7 kb , and mlsB: 42 kb ) that encode type I polyketide synthases ( PKS ) . MlsA1 and MlsA2 synthesize the upper side chain and macrolactone core , whilst MlsB synthesizes the acyl side chain [22] . A putative beta-ketoacyl transferase encoded by another pMUM gene , mup045 , is thought to catalyse the ester linkage between the acyl side chain and the macrolactone core whilst a P450 hydroxylase , encoded by mup053 , oxidizes the side chain at C12′ ( Figure 1 ) [22]–[25] . A third gene , mup038 , is predicted to encode a type II thioesterase that might be required for removing aberrant polyketide extension products from the Mls PKS that form during synthesis . An unusual feature of the mycolactone PKS is the very high level of sequence identity between domains of the same function ( 98 . 7–100% nt identity and 98 . 3–100% aa identity ) . This observation suggested that the evolution of the locus may be recent and also prone to rearrangements that result in either loss of mycolactone production or production of new mycolactones . These hypotheses have recently gained support by studies that have shown ( i ) all mycolactone producing mycobacteria ( which includes M . ulcerans and some closely related fish and frog pathogens ) have recently evolved from a common Mycobacterium marinum ancestor by pMUM plasmid acquisition [23]–[28] , ( ii ) laboratory passaging leads to mycolactone negative mutants through spontaneous deletion of mls gene fragments [29] , and ( iii ) natural swapping of particular acyltransferase and ketoreductase domains and loss or gain of entire extension modules in some strains of M . ulcerans has led to the production of new mycolactones [30] , [31] . However , there have been very few studies of gene expression in M . ulcerans . Therefore , in this study we began by investigation of the mycolactone-associated genes mlsA1/mlsA2 , mlsB , mup045 , mup053 and mup038 . Promoter regions were mapped upstream of the above genes using a GFP reporter . Putative transcriptional start sites and promoter sequences were then identified by primer extension analysis and site-directed mutagenesis . The GFP reporter containing the promoter region of the mls genes was then used to transform M . ulcerans . This recombinant GFP M . ulcerans fluoresced brightly and was used to follow infection in both mice and mosquito larvae .
The bacterial strains and plasmids used in this study are described in Table S1 . All cloning experiments were performed in Escherichia coli DH10B , cultivated in Luria-Bertani ( LB ) broth at 37°C or on LB agar containing 50 µg kanamycin , for 16 hours at 37°C . Mycobacterial strains were grown in Middlebrook 7H9 medium ( Difco ) supplemented with albumin ( 6 . 25% ( w/v ) ) , dextrose ( 2 . 5% ( w/v ) ) , sodium chloride ( 1 . 1% ( w/v ) ) , catalase ( 5×10−4% ( w/v ) ) and 0 . 05% ( v/v ) Tween−80 at 37°C for Mycobacterium smegmatis and 30°C for M . marinum and M . ulcerans . Mycobacteria were also cultured on 7H10 agar supplemented with OADC ( Difco ) . Recombinant mycobacteria were cultivated with kanamycin at a final concentration of 25 µg ml−1 . Standard methods were used for cloning , PCR and DNA sequencing . The oligonucleotides used in this study for PCR , RT-PCR and DNA sequencing are listed in Table S2 . Genomic DNA was extracted from mycobacteria as described [23] . The broad host range , promoterless , GFP ( gfpmut3 ) vector pSM20 , that replicates in E . coli , Corynebacterium sp . and Mycobacterium sp , was used for all promoter cloning experiments [32] . PCR products derived from the upstream regions were modified using oligonucleotides described in Table S2 and ligated into the unique restriction enzyme sites immediately upstream of the gfp gene in pSM20 ( Figure 1 ) . Constructs were confirmed to be correct by DNA sequencing and then transformed into M . smegmatis mc2155 as described [33] . Electrocompetent M . marinum and M . ulcerans were prepared as described [34] and these cells were transformed with 10 µg of DNA from plasmid pJKD2893 ( Table S1 ) . The constructs were confirmed to be correct in mycobacteria by Southern hybridization and back transformation to E . coli . Acetone soluble lipids were extracted from recombinant M . ulcerans and analysed by LC-MS for the presence of mycolactones as previously described [35] . GFP expression in pSM20 and derivatives ( Table S1 ) was measured using a FLUOstar OPTIMA plate scanner ( BMG Lab Technologies ) . M . smegmatis strains were grown to an OD of 1 . 0 using a WPA CO8000 cell density meter ( Isogen Life Science ) . For each strain , 30 µl of starter culture was added to each of 16 wells of a 96-well flat-bottomed clear plate containing 150 µl of fresh 7H9 medium . Plates were incubated at 37°C for 30 mins . Each well was scanned using an excitation filter of 485 nm and an emission filter of 520 nm . Fluorescence readings were taken every 10 minutes and the average of 20 flashes per well was taken to be the measure of fluorescence . Prior to each reading , the plates were shaken for 5 minutes in an orbital motion . Replicates were averaged for each experiment and the average value for the vector-only control was taken as background and subtracted from the average at each time point . Total RNA was prepared from E . coli using the RNeasy mini kit as described and per the manufacturer's instructions ( Qiagen ) [36] . For M . ulcerans , a 0 . 5 volume of RNAlater ( Qiagen ) was added to 100 ml of late log-phase culture and allowed to stand at room temperature for 10 minutes prior to centrifugation at 4 , 600 g , for 10 minutes . The resultant cell pellet was washed in 1 ml of 0 . 5% ( v/v ) Tween-80 per 50 mg of cells ( wet weight ) , resuspended in 800 µl of RNA lysis buffer ( 0 . 12 M sodium acetate ( pH 4 . 0 ) , 9 . 6% ( v/v ) liquid Pyroneg ( Diversey ) , pH 4 . 0 ) and then added to 250 µg of glass beads ( Sigma Aldrich ) , with 600 µl of acidified phenol:chloroform ( pH 4 . 0 ) ( Sigma Aldrich ) . Cells were disrupted with a FastPrep tissue homogenizer ( Savant Instruments ) for 45 seconds , at speed 6 and chilled on ice for 5 minutes . The aqueous phase was then re-extracted with chloroform:isoamylalcohol ( 24:1 ) and precipitated with isopropanol , and 3 M sodium acetate ( pH 4 . 6 ) . Two 70% ( v/v ) ethanol washes were performed and the pellet was dried briefly under vacuum and resuspended in 100 µl of DEPC water . RNA in this preparation was then further purified using an RNeasy extraction kit , including an on-column DNase treatment , following the manufacturers recommendations ( Qiagen ) . For RNA extraction from M . smegmatis and M . marinum the following modifications to the above method were used . The cell pellet was first resuspended in 2 ml of lysis solution ( 20 mM potassium acetate ( pH 4 . 8 ) , 1 mM EDTA , 0 . 5% ( v/v ) SDS , 100 µg proteinase K ml−1 ) . One millilitre was added to 250 µg of glass beads with 700 µl acidified phenol:chloroform pH 4 . 0 . Cells were disrupted by three cycles in a FastPrep instrument at speed 5 , for 30 seconds , and then centrifuged at 17 , 900 g for 10 minutes . The aqueous phase was recovered and extracted once with 500 µl phenol:chloroform ( pH 4 . 0 ) followed by a chloroform only extraction . Nucleic acids were precipitated as above and RNA extraction proceeded as for M . ulcerans using the RNeasy extraction kit . The primer extension protocol used was modified from Lloyd et al . , [37] . Two reverse transcription reactions were performed . To the RNA-primer mix , 6 µl of 5x first strand buffer ( Invitrogen ) , 15 mM DTT ( Invitrogen ) , 1 mM dNTPs ( Promega ) , 1 U RNasin ( Promega ) and 100 U of Superscript II RNase H- reverse transcriptase ( Invitrogen ) were added . After one hour at 42°C , 2 µl of 5x first strand buffer , 1 . 5 mM dNTPs , 1 U RNasin , 15 mM DTT and 100 U of Superscript II was added and incubated for a further hour at 42°C . Ten nanograms of RNaseA ( Sigma ) was then added and allowed to incubate at 37°C for 30 minutes . The resultant cDNA was precipitated and washed once with 70% ( v/v ) ethanol , dried and stored at −20°C until analysis . Capillary electrophoresis was performed on an Applied Biosystems 3730 DNA analyzer using Liz™ 500 size standards to generate a standard curve ( Applied Biosystems ) . Genemapper® version 3 . 7 ( Applied Biosystems ) was used to analyze the sample files with automated allele calling verified by manual inspection . The sized cDNA fragments were then mapped to their respective first strand synthesis primer binding sites to identify the putative transcription start site . From the alignment of each of the SigA , C , D , E , F , H & L promoters [38] , nucleotide frequency counts were derived and used to construct a library of 110 position specific scoring matrices ( PSSMs ) for each sigma factor ( PSSMs available upon request ) . This allowed the gap between the -35 and -10 signals to vary between 14 and 23 residues , and the gap between the -10 signal and the TSP to vary between 3 and 13 residues . PoSSuM software [39] was used to scan the pMUM001 genome for high scoring hits to these PSSM libraries [40] , using a background model consistent with the G+C biased nucleotide distribution of pMUM001 . A p-value significance cutoff of 0 . 0001 was used . Splice overlap extension PCR [41] was used to alter the sequence of putative promoter motifs with oligonucleotides 1075-F and 1074-R for mlsA1/mlsB , 1667-F and 1668-R for mup045 , and 1669-F and 1670-R for mup053 ( Table S2 ) . Each PCR reaction consisted of 20 cycles of 94°C for 1 minute , 50°C for 1 minute and 72°C for 3 minutes then 94°C for 1 minute , 72°C for 10 minutes and held at 4°C . Two overlapping PCR products were obtained and 2 µl ( ∼50 ng DNA ) of each were used in a subsequent reaction using the outermost primers for each product to yield a complete fragment incorporating both products . Each product was then ligated into pSM20 as described above . Mutations were confirmed by DNA sequencing . Ten , six-week-old female BALB/c mice ( Charles River France , http://www . criver . com/ ) were injected subcutaneously into the tail with 30 µl of a suspension containing 5×104 bacteria . To favour the growth of the GFP-expressing bacilli , animals received 0 . 1 ml of a solution containing 80 mg/ml of kanamycin ( 1% w/v ) , administered by oral gavage every day . The mice were killed and their tails were collected fifty days after inoculation . Mice were maintained in the animal house facility of the Centre Hospitalier Universitaire , Angers , France ( Animal Ethics Committee , Centre Hospitalier Universitaire , Agreement A 49 007 002 ) , adhering to the institution's guidelines for animal husbandry . The tissue specimens from mice were minced with disposable scalpels in a Petri dish and ground with a Potter–Elvehjem homogeniser , size 22 , ( Kimble/Kontes , Vineland , NJ ) , in 0 . 15 M NaCl to obtain a tenfold dilution . The suspension was decontaminated to remove other bacteria using an equal volume of N-acetyl-L-cysteine sodium hydroxide ( 2% ) [42] and inoculated on 7H10 agar supplemented with OADC ( Difco ) , containing 25 µg/ml of kanamycin . For histological examination , tissues were fixed in 4% paraformaldehyde in phosphate buffer ( pH 7 . 4 ) . Decalcification of the tissue was performed for 7 days in 0 . 1 M of EDTA solution in PBS . Samples were frozen in isopentane cooled to −140°C in liquid nitrogen and stored at −80°C for subsequent histochemical analysis . Eight-micron thick transverse sections were cut at −30°C on a cryostat ( Jung-Reichert Cryocut 1800 , Cambridge Instruments , Germany ) and kept at −80°C until histochemical processing , which was done within 1 week of sectioning . For detection of GFP-expressing bacilli , tissues were counterstained with DAPI , with endogenous phosphatase activity first detected using alkaline phosphatase substrate kit I ( Vector Laboratories ) . The preparation was then mounted in Vectashield mounting medium containing DAPI ( Vector Laboratories ) and the samples were visualized using fluorescence microscopy ( Leica DM5000B ) . Hematoxylin phloxine saffron and Ziehl Nielsen staining were performed according to standard procedures . Mosquito larvae ( Aedes camptorhynchus ) between first and second instar were distributed into 4×50 ml plastic tubes ( 10 larvae per tube ) , containing 20 ml of sterile tap water . To three groups of four tubes were added 1 . 5 ml of an aqueous slurry of possum faecal material containing either 5×106 colony forming units ( cfu ) M . ulcerans-GFP , 5×106 cfu M . marinum-GFP , or possum faecal material alone . The larvae were left to feed on the material for one week at 24°C . At the end of one week and also at the end of every subsequent week up to week five , all larvae were transferred to new tubes containing 20 ml of sterilized tap water . The original tubes spiked with possum faecal material were kept at room temperature and at the commencement of each week 500 µl of water from each of these tubes was tested by IS2404 and ppk qPCR to estimate the residual quantity in the water of M . ulcerans and M . marinum respectively [43] . Results were reported as cfu by reference to standard curves for each PCR and bacterial species , correlating qPCR Ct values with cfu [43] . From weeks 2–5 the larvae were sustained with small quantities of fish food added to each tube . A larva was taken from each tube as it progressed through each instar and tested by IS2404 PCR for the presence of M . ulcerans as described [43] . A selection of 4th instar larvae were also fixed overnight in 10% formaldehyde in PBS ( v/v ) then mounted in cedarwood oil ( Matheson , Coleman and Bell ) on a glass slide for examination by fluorescence microscopy with an Olympus BX51 microscope ( Olympus , Tokyo , Japan ) with the following filter sets: DAPI ( Blue ) ex: 360–70 nm , em: 420–60 nm , FITC ( Green ) ex: 450–80 nm , em: 535 nm , TRITC ( red ) ex: 535 nm , em: 635 nm . Images were acquired using an Olympus DP-70 digital camera and merged using DP controller software ( version 1 . 1 . 1 . 71 ) or Adobe Photoshop ( version 8 ) These experiments were terminated before the insects progressed to pupal and adult developmental stages .
By cloning DNA fragments ranging from 229 bp–1646 bp located immediately upstream of mlsA1/mlsB ( these genes have a duplicated start and upstream sequence so one cloned fragment was sufficient to analyse both genes ) , mlsA2 , mup038 , mup045 and mup053 in the promoterless GFP E . coli/Mycobacterium reporter vector pSM20 ( Table S1 , Figure 1 ) we were able to discover regions containing promoter activities . The resulting plasmids were used to transform E . coli , M . smegmatis and , for the mlsA1/mlsB construct , M . marinum and M . ulcerans were also transformed . Bacteria were cultured in 96-well plates for 2 hours at 37°C and expression of GFP for each strain was assessed by continuous fluorescence measurements . E . coli expressing GFP from the strong , constitutive promoter srp ( pSM22 ) [32] and M . smegmatis expressing GFP from the sigA promoter from Mycobacterium bovis BCG ( pJKD3042 ) [44] were used as positive controls for each genus . Results were expressed as fold changes in fluorescence above the levels detected in bacteria containing the empty vector pSM20 . The results for mlsA1 and mlsB are summarized in Figure 2 and show that strains containing the construct pJKD2893 with the region 1646 bp upstream of mlsA1/mlsB , led to detectable GFP expression in E . coli , and high levels of GFP expression in M . smegmatis and M . marinum ( Figure 2A ) . A single copy version of pJKD2893 was also created where a DNA fragment spanning the 1646 bp mls upstream region and gfp gene from pJKD2893 was subcloned into the mycobacterial integrating shuttle vector , pJKD8003 resulting in pJKD3111 . M . marinum transformed with pJKD3111 expressed GFP 40-fold less than the same strain containing pJKD2893 ( Figure 2A ) . To further localize the region conferring promoter activity within the 1646 bp upstream of mlsA1/mlsB , four overlapping sub-clones of this region were prepared by PCR , cloned into pSM20 and used to transform E . coli and M . smegmatis . Comparison of GFP expression in these constructs in another time course experiment , comparing fluorescence with the full-length 1646 bp fragment and controls , clearly showed that promoter activity was restricted to a 413 bp fragment located between nucleotide positions 35996-36409 for mlsA1 and 100821-101234 for mlsB in pMUM001 ( Figure 2B ) . The region 1440 bp upstream of mup045 and 1466 bp upstream of mup053 also led to significant GFP expression in M . smegmatis , 8–15 fold above background , but these regions showed little transcriptional activity in E . coli ( Figure S1 ) . No fluorescence was observed in either E . coli or M . smegmatis for strains containing the 229 bp region upstream of mup038 ( pJKD3269 ) or the 1096 bp region upstream of mlsA2 ( pJKD3041 ) ( data not shown ) . These experiments demonstrate that the regions upstream from mlsA1/mlsB , mup045 and mup053 all harbour at least one strong promoter . To identify TSPs upstream of each gene primer extension ( PE ) analysis was performed using RNA extracted from M . ulcerans Agy99 . For mlsA1/mlsB , RNA was also extracted from M . marinum harbouring the GFP expression construct pJKD2893 . One or more 5′ 6-FAM-labeled antisense oligonucleotides were used to prime cDNA synthesis to determine the TSP for mlsA1/mlsB , mup045 , and mup053 ( Table S2 ) . Single , distinct PE products were identified for all three regions using multiple RNA preparations ( Figure S2 , Figure S3 ) . Size fragment analysis of the PE products suggested single TSPs at 533 bp ( T533 ) upstream of the mlsA1/mlsB translational start ( Figure S2 ) , 207 bp upstream of mup045 ( T207 ) , and 68 bp upstream of mup053 ( T068 ) ( Figure S3 ) . Primer extension analysis of mup038 and mlsA2 was not attempted due to the lack of promoter activity observed with the wild type sequences in the GFP reporter assays . Several studies of promoters in mycobacteria facilitated the construction of position-specific scoring matrices ( PSSMs ) to perform in silico searches for potential regulatory regions in DNA sequences [38] . We used sigma factor-specific libraries of PSSMs to scan the regions upstream of the three TSPs identified by our PE analysis . High-probability SigA-like promoter motifs were predicted in the regions upstream of mlsA1/mlsB and mup045 and a SigD-like motif was predicted upstream of mup053 ( Table 1 ) . To confirm the in silico promoter predictions , the GFP expression constructs spanning the putative -10 sequences from mlsA1/mlsB ( pJKD2893 ) , mup045 ( pJKD3040 ) and mup053 ( pJKD3039 ) were mutated by PCR ( Table 1 ) . GFP production by E . coli and M . smegmatis harbouring these constructs was assayed as before by continuous fluorescence measurements over 2 hours at 37°C . Fluorescence production was compared with the same strains containing the wild-type putative promoter sequences . Mutation of the proposed −10 boxes for both the mlsA1/mlsB and mup045 reduced fluorescence in M . smegmatis to less than 4% of the wild-type sequences , strongly suggesting these sequences are functional −10 motifs , required for proper binding of the sigma factor and RNA polymerase to initiate transcription ( Figure 2B ) . Mutation of the proposed −10 box from mup053 had no impact on GFP expression . The strength of the mls promoter led us to develop a GFP-producing strain of M . ulcerans that might be useful in studies of pathogenesis or transmission . We transformed M . ulcerans JKD8049 with plasmid pJKD2893 , resulting in highly green fluorescent M . ulcerans ( JKD8083 or M . ulcerans-GFP ) , with fluorescence expression more than 100 fold above empty vector ( Figure 2A ) . To ensure that GFP expression did not stop mycolactone production we performed cell LC-MS analysis of acetone-soluble lipids from cultures of JKD8083 and confirmed the presence of mycolactone A/B and C ( Figure S4 ) . Mouse-tail infection is a well-established animal model for studying M . ulcerans . Forty days after subcutaneous inoculation of 105 M . ulcerans-GFP oedema was observed and on the 50th day the lesion became ulcerated and the mice were killed . Histological study of the ulcerated region showed an area of necrosis consistent with wild type M . ulcerans infection ( Figure 3A , Figure 3B ) . Granulomatous inflammation was not observed . Acid-fast bacilli were localized in clumps in necrotic areas ( Figure 3C ) and expressed green fluorescent protein ( Figure 3D ) . The viability of these bacteria was demonstrated by re-isolating them in bacterial culture media . These results demonstrate that M . ulcerans-GFP is virulent in the mouse model and provokes lesions typical of M . ulcerans infection . Adult mosquitoes in some Buruli ulcer endemic regions of Australia have tested PCR positive for M . ulcerans and epidemiological evidence suggests a role for biting insects in the disease ecology of M . ulcerans [45] , [46] . These data and the presence of M . ulcerans in possum faecal material from the same endemic regions has led to the hypothesis that larval stages of mosquitoes may and probably do ingest M . ulcerans as well as other bacteria via filter feeding activity on decomposing , faecally contaminated environments [47] . We mimicked this environment by establishing simple aquatic microcosms , seeded with 1 or 2 instar Aedes camptorhynchus larvae that were then transiently fed with possum faecal material , spiked with either M . ulcerans-GFP or M . marinum-GFP ( Figure 4A ) . M . ulcerans and M . marinum were initially liberated into the water from the food source but neither bacterial species were detectable in water by week 4 ( Figure 4B ) . Analysis of 4th instar larvae at week 4 by fluorescence microscopy revealed an accumulation of M . ulcerans primarily within the larval midgut and around the mouthpart ( Figure 4C ) . Fourth instar larvae assayed by PCR for M . ulcerans had a mean bacterial load of 27 , 300±15 , 200 cfu ( n = 4 ) . The same pattern of accumulation within the insect was not seen with M . marinum-GFP with very few fluorescent bacteria observed in association with larvae ( Figure 4C ) . Neither M . ulcerans or M . marinum were detected in the microcosms containing mosquito larvae only ( Figure 4C ) . These data show that mosquito larvae in contaminated aquatic environments were able to ingest and maintain M . ulcerans within regions of the digestive tract over a significant time period .
In this study we have explored gene expression of six pMUM001 genes required or implicated in mycolactone synthesis and attempted to identify their transcriptional start sites and promoter motifs . Using a combination of primer extension and in silico analysis together with a GFP reporter system , we were able to identify a SigA-like promoter that drives expression of the mycolactone polyketide megasynthases mlsA and mlsB in M . ulcerans . Primer extension analyses with mRNA extracted from E . coli , M . smegmatis and M . marinum bearing the GFP reporter construct pJKD2893 and from wild-type M . ulcerans Agy99 all consistently demonstrated a transcription start point ( TSP ) 533 bp upstream of the mlsA1/mlsB initiation codons . The primer extension analysis was fully supported by the GFP expression data , wherein only strains containing expression constructs that spanned the TSP at T533 produced fluorescence . These results indicate the presence of a strong promoter preceding position T533 . Sequence scanning using PoSSuM of the region immediately upstream of T533 for mycobacterial consensus promoter sequences predicted a high probability SigA-like promoter ( Table 1 ) . Site-directed mutagenesis of the putative −10 box by substitution of three nucleotides completely abolished GFP expression ( Table 1 , Figure 2B ) , implicating this sequence in RNA polymerase ( RNAP ) binding . The mlsA/mlsB promoter lies between two pseudogenes that once encoded transposases . These CDS appear to be remnants of two distinct insertion sequence elements ( ISE ) as the partial transposase sequences display similarity to two different IS families ( IS3 family for MUP034/MUP042 and the IS6 family for MUP033/MUP041 ) [25] . These vestigial ISE are quite distinct to the two high copy number elements , IS2404 and IS2606 present in M . ulcerans . It is possible that the T533 promoter was once a component of an ISE . A role for ISE in altering gene expression in mycobacteria has been reported [48] . Similarly , we investigated DNA sequences upstream of mup045 and found a TSP at T207 with a potential SigA promoter element predicted by PoSSuM and confirmed by a loss of GFP expression in M . smegmatis following mutagenesis of the proposed −10 box . The principal mycobacterial sigma factor sigA is utilized by genes expressed during exponential growth [49] , thus the data from mlsA/B and mup045 fit well with our previous report that show these genes are constantly expressed during exponential growth in the heterologous host , M . marinum [35] . PoSSuM sequence scanning predicted a SigA-like promoter upstream of mup045 , a finding confirmed by mutagenesis of its putative −10 motif ( Table 1 ) . The same in silico search suggested a SigD-like promoter element upstream of mup053 . However , mutation of the putative −10 motif for this gene resulted in no significant difference in GFP production in either E . coli and M . smegmatis backgrounds compared to wild type sequence , indicating that this was not the promoter region or that the introduced mutations were not sufficiently different to the wild type sequence to alter transcription . The latter scenario seems more likely given the low complexity of SigD −10 consensus sequences ( Table 1 ) . The discovery in this study of the strong SigA-like promoter , active in diverse bacterial genera , and driving expression of the mycolactone mls PKS genes prompted us to transform M . ulcerans with a reporter plasmid with GFP under the control of the T533 mls promoter , resulting in the highly green fluorescent strain M . ulcerans JKD8049 . M . ulcerans-GFP still produced mycolactone and was capable of causing disease in a mouse-tail infection model . Interestingly , GFP expression was more than 2-fold higher in M . ulcerans than in M . marinum harbouring the same plasmid ( Figure 2A ) , suggesting additional regulatory factors might augment mls expression in M . ulcerans ( or conversely , repress gene expression from the same promoter in other mycobacteria ) . The high level of mls promoter activity and the presence of viable M . ulcerans-GFP in the ulcerated tail tissue 50 days post inoculation implies that there was sustained expression of the mycolactone PKS and presumably sustained mycolactone production by the bacteria within necrotic tissue ( Figure 3 ) . These observations demonstrate the utility of this M . ulcerans-GFP strain as a tool for following the dynamics of mls gene expression during infection and understanding the role of mycolactone in pathogenesis . We also used M . ulcerans-GFP to explore the previously reported association of M . ulcerans with Aedes camptorhynhcus mosquitoes [45] . Here , we addressed the specific question of whether or not A . camptorhynchus larvae could ingest M . ulcerans via feeding on possum faecal material and whether the bacteria could persist through the larval growth stages . Many larval mosquito species filter feed on microbial particles and detritus where they aggregate at air-water interfaces near plant stems and algal mats in lentic waters [50] , [51] and a recent report has also suggested that M . ulcerans can persist within the gut of Ochlerotatus triseriatus mosquito larvae [52] . We were also able to observe the presence of M . ulcerans within the gut contents of mosquito larvae in laboratory experiments . However , the mode of larval ingestion via possum faecal pellets that we have employed in this study , presents a natural and viable pathway that A . camptorhynchus larvae as well as other filter-feeding macroinvertebrates might become infected for a long period of time with M . ulcerans . The peritrophic matrix is a proteoglycan ‘sleeve’ that separates food sources from the gut epithelium in insects [53] and our data suggests an accumulation of M . ulcerans within this matrix through each instar ( Figure 4C ) . The significantly greater mean bacterial load of M . ulcerans-GFP found in fourth instar larvae compared to M . marinum-GFP may indicate that A . camptorhynchus larvae are able to digest and assimilate M . marinum-GFP better than M . ulcerans or that M . ulcerans is able to persist and perhaps multiply within the peritrophic matrix . Production of GFP in the mosquito larvae also indicates that the mycolactone mls genes are likely to be expressed and producing mycolactone under these conditions . Whether or not M . ulcerans can be transferred through larval , pupal and then adult insects remains to be tested . Experiments are now underway to examine vertical transmission of M . ulcerans within mosquitoes . The data presented in this study provide the first insights into gene expression within the mycolactone biosynthesis locus and the development of M . ulcerans-GFP , a strain where fluorescence and toxin gene expression are linked thus providing a tool for studying Buruli ulcer pathogenesis and potential transmission to humans . | Buruli ulcer ( BU ) is a serious skin infection of humans predominantly occurring in West and Central Africa . The disease is caused by infection with Mycobacterium ulcerans , a bacterium that produces an unusual toxin called mycolactone . There are many unanswered questions surrounding BU , particularly regarding the role of mycolactone in disease and how M . ulcerans is transmitted to humans . Here , we have partly addressed these questions by identifying genetic factors controlling the transcription of the mycolactone genes . Using a variety of experimental approaches , including green fluorescent protein ( GFP ) as a reporter of gene expression , we have identified strong promoters that drive transcription of the mycolactone genes in M . ulcerans . We then used our GFP reporters to produce highly fluorescent M . ulcerans-GFP that were readily visualized by microscopy . Mosquitoes have been proposed as a potential vector of M . ulcerans so we used M . ulcerans-GFP in feeding experiments with mosquito larvae . M . ulcerans-GFP accumulated within the insects , whereas other mycobacteria did not . This is the first report of the mycolactone gene promoters , and we have used our findings to develop M . ulcerans-GFP , a strain in which fluorescence and toxin gene expression are linked , thus providing a powerful tool for studying Buruli ulcer . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"genetics",
"and",
"genomics/gene",
"expression"
] | 2009 | Mycolactone Gene Expression Is Controlled by Strong SigA-Like Promoters with Utility in Studies of Mycobacterium ulcerans and Buruli Ulcer |
The CSB-PGBD3 fusion protein arose more than 43 million years ago when a 2 . 5-kb piggyBac 3 ( PGBD3 ) transposon inserted into intron 5 of the Cockayne syndrome Group B ( CSB ) gene in the common ancestor of all higher primates . As a result , full-length CSB is now coexpressed with an abundant CSB-PGBD3 fusion protein by alternative splicing of CSB exons 1–5 to the PGBD3 transposase . An internal deletion of the piggyBac transposase ORF also gave rise to 889 dispersed , 140-bp MER85 elements that were mobilized in trans by PGBD3 transposase . The CSB-PGBD3 fusion protein binds MER85s in vitro and induces a strong interferon-like innate antiviral immune response when expressed in CSB-null UVSS1KO cells . To explore the connection between DNA binding and gene expression changes induced by CSB-PGBD3 , we investigated the genome-wide DNA binding profile of the fusion protein . CSB-PGBD3 binds to 363 MER85 elements in vivo , but these sites do not correlate with gene expression changes induced by the fusion protein . Instead , CSB-PGBD3 is enriched at AP-1 , TEAD1 , and CTCF motifs , presumably through protein–protein interactions with the cognate transcription factors; moreover , recruitment of CSB-PGBD3 to AP-1 and TEAD1 motifs correlates with nearby genes regulated by CSB-PGBD3 expression in UVSS1KO cells and downregulated by CSB rescue of mutant CS1AN cells . Consistent with these data , the N-terminal CSB domain of the CSB-PGBD3 fusion protein interacts with the AP-1 transcription factor c-Jun and with RNA polymerase II , and a chimeric CSB-LacI construct containing only the N-terminus of CSB upregulates many of the genes induced by CSB-PGBD3 . We conclude that the CSB-PGBD3 fusion protein substantially reshapes the transcriptome in CS patient CS1AN and that continued expression of the CSB-PGBD3 fusion protein in the absence of functional CSB may affect the clinical presentation of CS patients by directly altering the transcriptional program .
Cockayne syndrome ( CS ) is a neurodevelopmental disorder most often caused by loss of functional CSB or CSA protein ( OMIM #133540 or #216400 ) [1] . CSB is a SWI/SNF2-like ATPase and chromatin remodeling protein that plays a key role in transcription-coupled nucleotide excision repair ( TC-NER ) of helix-distorting DNA lesions . When RNA polymerase II ( RNAPII ) stalls at a site of DNA damage , CSB is among the first proteins to bind [2]–[4] and is required to recruit other NER factors including CSA and the TFIIH complex containing the XPB and XPD helicases [5]–[7] . CSB is also known to activate RNA polymerase I ( RNAPI ) transcription of ribosomal RNA [8] , and to induce changes in gene expression resembling those caused by chromatin remodeling and histone modification [9] . We recently discovered a domesticated PGBD3 transposon ( piggyBac transposable element-derived 3 ) that inserted into intron 5 of the CSB gene at least 43 Mya in the common ancestor of marmoset and humans . As a result , primate CSB genes including our own now generate both full length CSB ( coding exons 2–21 ) and — by alternative splicing and polyadenylation — a CSB-PGBD3 fusion protein that joins the N-terminal domain of CSB ( coding exons 2–5 ) to the intact PGBD3 transposase [10] . CSB-PGBD3 is startlingly well conserved from marmoset to humans , whereas four other identifiable copies of the PGBD3 transposon elsewhere in the human genome have all decayed into pseudogenes ( PGBD3P1-4 ) . The PGBD3 transposon contains a 5′ splice acceptor site just upstream of the transposase ORF and a polyadenylation signal downstream of the ORF that allow alternative splicing of CSB exon 5 to the intact transposase without precluding continued expression of full length CSB ( Figure 1 ) . In fact , the insertion of PGBD3 expanded the repertoire of the CSB locus from one protein to three: full length CSB , the more abundant CSB-PGBD3 fusion protein , and most abundant of all , the intact PGBD3 transposase transcribed from a cryptic promoter near the 3′ end of CSB exon 5 [10] . Coexpression of the CSB-PGBD3 fusion protein with CSB initially suggested that the fusion protein might contribute to or modulate CS disease [10]; however , mutations that cause CS are distributed across the entire length of the CSB gene ( except in the PGBD3 transposon ) and no consistent clinical differences have been observed between CS patients with CSB mutations in coding exons 2–5 ( many of whom do not make the CSB-PGBD3 fusion protein ) and patients with mutations in exons 6–21 ( who continue to make the CSB-PGBD3 fusion protein ) [1] . Unlike the ATPase domain ( CSB exons 6–21 ) , the function of the N-terminal domain ( coding exons 2–5 ) shared by CSB and the CSB-PGBD3 fusion protein is not yet well understood ( Figure 1 ) . The only recognizable motif in exons 2–5 is a highly acidic domain between E356 and E403 containing 25 aspartates and glutamates , but this domain does not appear to be essential for recovery of RNA synthesis following UV damage [11] , [12] . Interestingly , the N-terminus autoinhibits association of CSB with chromatin in both normal and UV-irradiated cells , and ATP hydrolysis is required for relief of inhibition [13] . The isolated N-terminal domain has also been shown to interfere with transcription and repair: Truncated CSB protein expressed in the patient-derived cell line CS1AN represses elongation by RNAPI [14] and the N-terminus of CSB interacts with topoisomerase I ( Top1 ) to inhibit repair of Top1 adducts both as part of the CSB-PGBD3 fusion protein and independently [15] . We have recently shown that expression of the CSB-PGBD3 fusion protein in CSB-null UVSS1KO cells induces a strong transcriptional response dominated by an interferon-like innate antiviral immune response that may be driven by upregulation of the STAT1 , STAT2 , and IRF9 components of the heterotrimeric transcription factor ISGF3 ( interferon-stimulated gene factor 3 ) [16] . As might be expected from conservation of the CSB-PGBD3 fusion protein for over 43 My , the interferon-like response induced by CSB-PGBD3 is dramatically repressed by coexpression of full-length CSB , and is not induced by CSB alone . However , the mechanism by which the CSB-PGBD3 fusion protein induces the interferon-like response , and CSB represses it , are still unclear . The CSB-PGBD3 fusion protein may affect RNAPII gene expression through both global and local mechanisms . Globally , CSB-PGBD3 may modulate CSB functions by interacting with complexes that normally contain functional CSB; this could explain how the fusion protein modulates DNA repair without inducing or repressing transcription of known DNA repair factors [16] . CSB-PGBD3 may also affect RNAPII transcription locally by binding to dispersed DNA elements called MER85s , thereby regulating expression of nearby genes . PGBD3 , like many autonomous mobile elements , has given rise to a family of internally-deleted , nonautonomous elements that can be mobilized by the PGBD3 transposase . These 140 bp MER85 elements retain about 100 bp from the 5′ end of PGBD3 , and about 40 bp from the 3′ end , but have lost the transposase ORF along with the upstream 5′ SS and the downstream poly ( A ) site . We have identified 889 MER85 elements dispersed throughout the human genome , most of which include 13 bp terminal inverted repeats ( TIRs ) that are required by the PGBD3 transposase for excision and reinsertion into TTAA target sites . We have also demonstrated that MER85 elements bind PGBD3 and CSB-PGBD3 in vitro [16] . Thus , CSB-PGBD3 may enable MER85s to recruit the N-terminus of CSB to specific genomic loci where it can affect local chromatin structure or recruit transcription and repair factors . We wish to understand why the CSB-PGBD3 fusion protein is so well conserved , and to determine what roles it may play in health and CS disease . Here , we explore the connection between the genome-wide DNA binding profile of CSB-PGBD3 and transcriptional regulation in UVSS1KO cells . As expected , we find that CSB-PGBD3 binds directly in vivo to many MER85 elements throughout the genome . Surprisingly , we also find that CSB-PGBD3 binds indirectly to TRE motifs ( tumor promoting antigen response elements ) recognized by AP-1 family ( activating protein-1 ) transcription factors , as well as to motifs for the TEAD1 ( TEA domain family member 1 ) and CTCF ( CCCTC-binding factor ) transcription factors . We show that CSB-PGBD3 physically interacts with the AP-1 protein c-Jun , and that genes upregulated by CSB-PGBD3 correlate with binding of CSB-PGBD3 to nearby TRE motifs but not with binding to MER85 elements . We also show that CSB-PGBD3 interacts with RNAPII ( RNA polymerase II ) , and that interactions with RNAPII and c-Jun are both mediated primarily by the N-terminal CSB domain of CSB-PGBD3 . Thus despite the ability of the CSB-PGBD3 fusion protein to bind specifically to MER85s both in vitro and in vivo , binding does not appear to have widespread transcriptional consequences . In contrast , binding of the CSB-PGBD3 fusion protein to TRE motifs through protein-protein interactions with c-Jun and possibly other AP-1 family members correlates with genes involved in angiogenesis [17] , [18] , innate immunity [19] , and the Smad2/3 and TGF-beta pathways [20] , demonstrating that the CSB-PGBD3 protein modulates a preexisting AP-1-based regulatory network . Whether these regulatory effects were responsible for initial fixation of the CSB-PGBD3 fusion protein in the common ancestor of humans and marmoset 43 Mya , or whether these regulatory effects have evolved over time , remains to be seen .
Using a panel of six highly conserved MER85s with >90% identity to the Repbase MER85 consensus [21] , we found previously that both the CSB-PGBD3 fusion protein and solitary PGBD3 transposase can bind MER85 elements in vitro [16] . To extend these results to living cells , we performed ChIP-PCR ( chromatin immunoprecipitation followed by radiolabeled PCR ) using human euploid HT1080 fibrosarcoma cells , genomic primers for the same six MER85 elements , and antibodies directed against the N- or C-terminus of CSB ( Figure 2 ) . We first confirmed that in HT1080 cells , which are wild-type for CSB and CSB-PGBD3 , antibody against the N-terminus of CSB immunoprecipitated both CSB and CSB-PGBD3 , whereas antibody against the C-terminus brought down CSB alone ( data not shown ) . ChIPs with antibody against the N-terminus of CSB enriched for 5 of 6 MER85 elements in vivo including all 4 elements that shifted in the electrophoretic mobility shift assay ( EMSA ) [16]; ChIPs using antibody against the C-terminus or nonspecific antibody did not enrich for any of the six MER85s ( Figure 2 ) . To explore the DNA sequence requirements for CSB-PGBD3 binding to MER85 elements , we performed EMSAs with two strongly bound MER85 elements ( MER85-360 and MER85-427 , see Table S1 ) that contain the 13 bp TIR sequences required for transposition . Surprisingly , when the MER85s were cut in two at the unique DpnI site , only fragments containing the 5′-most 42 bp of MER85 sequence exhibited a mobility shift ( Figure 3 ) . We confirmed this result by EMSAs using synthetic 42-mers that corrected occasional mismatches between the two MER85s and the consensus MER85 sequence ( Figure S1 ) . Thus the TIR sequence is not sufficient for binding the PGBD3 transposase , and essential sequences of the transposase binding site must be located elsewhere within the 5′-most 42 bp of MER85 elements . Visual inspection of MER85 sequences revealed an imperfect 16 bp palindrome GTTCCAtTAtTGGAAC located 3 bp internal to the 5′ TIR . The PGBD3 transposon that integrated into the CSB gene contains the same palindrome at three locations: once near the 5′ TIR as in MER85s , again 59 bp upstream of the PGBD3 transposase ORF , and yet again 75 bp downstream of the ORF termination codon and 114 bp upstream from the 3′ TIR ( Figure 4; also see Figure S2 for conservation of the palindromes in PGBD3 pseudogenes ) . In MER85 elements , the sole palindrome lies 3 bp downstream from the 5′ TIR but 96 bp upstream of the 3′ TIR . Similar spacing between the 3′ most palindrome and the 3′ TIR in both the PGBD3 transposon ( 114 bp ) and MER85s ( 96 bp ) suggests that the sole MER85 palindrome may be functionally equivalent to the 3′ most palindrome in the full-length transposon , or may perhaps do double duty — functioning early in the reaction at the 5′ end and later at the 3′ end . A similar palindrome TGCGTaAAATTgACGCA , called the internal repeat , is found 3 bp downstream from the 5′ TIR and 31 bp upstream from the 3′ TIR of the piggyBac transposon from Trichoplusia ni [22] . A partial deletion of the 3′ internal repeat abolishes transposition [23] , suggesting that the palindromes are functionally important for transposition by both the moth and human piggyBac elements . To determine whether the 5′ palindrome of MER85s is required for PGBD3 binding , we examined MER85-65 in greater detail . This was the only MER85 in the panel of 6 that did not bind PGBD3 transposase in vitro or in vivo , despite being nearly identical in sequence to the other 5 elements [16] . Inspection of the 5′ end of MER85-65 revealed mismatches at 4 positions compared to the MER85 consensus: 2 in the TIR , and 2 in the palindrome ( Figure 5 ) . To test if the mutations in the 5′ TIR or the palindrome or both reduced the binding affinity , we performed EMSAs with 42 bp oligonucleotides that contained these mutations , singly and in combination , but otherwise matched the 5′ end of the MER85 consensus . Oligonucleotides with mutations that matched the MER85-65 TIR exhibited little loss of binding compared to the consensus . In contrast , oligonucleotides with mutations that matched the MER85-65 palindrome exhibited a 60% loss of binding ( Figure 5 ) , suggesting that mutations in the palindrome are likely responsible for the lack of binding to this particular element in vivo . No combination of mutations in the oligonucleotide gave as great a loss of binding as observed when the entire MER85-65 element was assayed by EMSA [16] or ChIP-seq ( Figure 2 ) , suggesting that other factors , such as sequence context or chromatin accessibility , may contribute to CSB-PGBD3 binding in vivo . To confirm the importance of the 5′ palindrome , we tested 42 bp oligonucleotides in which either the entire 5′ TIR or 5′ palindrome was replaced by random sequence . Surprisingly , deletion of either region reduced binding in vitro , but the effect was greater for the palindrome ( 80% loss ) than for the TIR ( 60% loss ) ( Figure 5 ) . The fact that 5′ MER85 sequences favor DNA binding both in vitro ( Figure 3 ) and in vivo ( Figure 2 , Table S1 ) suggests that the PGBD3 transposase alone is sufficient for initial recognition of the 5′ end of MER85 mobile elements . The ability of the moth element to function efficiently in mammalian cells further reinforces this interpretation [24]; however , host independence does not exclude the participation of auxiliary proteins that may facilitate or stabilize assembly of the transpososome [25] . CSB-null UVSS1KO fibroblasts are derived from a patient with UV sensitive syndrome ( UVSS ) and express neither CSB [26] nor CSB-PGBD3 fusion protein [10] as a result of a homozygous nonsense mutation at CSB codon 77 . We had previously generated gene expression array data for UVSS1KO cells stably expressing FLAG-HA-tagged CSB-PGBD3 fusion protein [16] . To correlate these expression array data with genome-wide CSB-PGBD3 chromatin binding profiles for the same cells , we used paired-end ChIP-seq [27] in which the cells are crosslinked with formaldehyde , sonicated , and sheared chromatin is immunoprecipitated with an antibody against the protein of interest — in this case a mouse monoclonal antibody against the N-terminal domain of human CSB . The immunoprecipitated DNA fragments are ligated to Illumina adapters , and 300–600 bp fragments are size-selected by PAGE and pre-amplified by PCR before loading onto the Illumina flow cell where one end of each captured fragment is sequenced . Synthesis of the opposite strand and cleavage of an 8-oxoguanine incorporated into the immobilized flow cell oligonucleotides then allow the fragments on the surface of the flow cell to be resequenced from the other end [27] . Paired-end sequencing greatly improves the mapping of repetitive DNA sequence elements such as MER85s because the short reads obtained from both ends of each sonicated chromatin fragment can be required to align uniquely with genomic sequences near each other and on opposite strands . More than 8 . 5 million pairs of enriched ChIP-seq reads of 36 bp were mapped to human genome build hg18 ( NCBI 36 ) using the read mapping program Bowtie [28] . Because CSB-PGBD3 binds to repetitive ( and very similar ) MER85 elements , we used stringent settings that disregard reads containing mismatches and reads that could not be uniquely mapped . The surviving reads were then analyzed for local enrichment using three independent peak-finding algorithms — Model-based Analysis of ChIP-Seq ( MACS ) [29] , Enhanced Read Analysis of Gene Expression ( ERANGE ) [30] , and Quantitative Enrichment of Sequence Tags ( QuEST ) [31] — which differ based on how the paired sequence tags are handled , as well as in the statistical methods used to determine peak enrichment ( reviewed in [32] ) . Comparison of results from each algorithm allowed us to find peaks that were consistently enriched independent of the peak-calling method . We found that 363 of 889 MER85 elements were reliably enriched and called as peaks by all 3 peak finding algorithms ( Table S1 ) . To prevent easily sheared chromatin regions and regions artefactually enriched by pre-amplification from scoring as peaks , each of our analyses included an input control consisting of ∼3 million single-end reads from the same sheared chromatin used for ChIP-seq . The 2 , 087 peaks found by all 3 algorithms were used for subsequent analysis ( Table S2 ) . We then wrote a Perl script to generate internally consistent CSB-PGBD3 binding profiles over all 2 , 087 peaks . The script converted mapped paired-end reads to the genomic coordinates of the corresponding ChIP fragments , calculated the number of fragments overlapping each position in the genome , and compiled the fragment map as a wiggle file to display and analyze CSB-PGBD3 binding profiles . A second script was used to locate the highest fragment overlap , defined as the peak summit , in each of the 2 , 087 enriched region identified by all three peak calling algorithms . We also used the Cis-regulatory Element Annotation System ( CEAS ) program [33] to show that CSB-PGBD3 peaks are significantly enriched within 3 kb of transcription start sites ( 6 . 1% , p-value 1 . 6e-20 ) , although the vast majority of peaks are either intronic ( 41 . 6% ) or in distal intergenic regions ( 47 . 8% ) ( Figure S3 ) . We located all 889 MER85 elements in the hg18 build of the human genome , and examined them individually to ensure that the boundaries of each MER85 were correctly identified even in cases where expansions or insertions altered the length of MER85 elements . All MER85 elements are given in the same orientation as the parental PGBD3 transposon ( Figure 1 ) with the 5′ and 3′ ends of the MER85s corresponding to the first ∼100 bp and last ∼40 bp of the transposon . Of these 889 MER85s , we found 813 with intact terminal inverted repeats ( TIRs ) , 13 of which had large internal insertions or repeat expansions ( >20 bp longer than normal ) and 1 of which had an internal deletion . Of the remaining 76 MER85s , 22 had incomplete 5′ ends , 49 incomplete 3′ ends , and 5 lacked both TIRs ( Table S1 ) . When all bound MER85s were aligned in the same orientation , fragment overlaps indicated preferential binding to a 40 bp region just internal to the 5′ TIR ( Figure 6 ) , consistent with EMSA experiments on representative MER85 elements ( Figure 3 ) . No MER85 element lacking the 5′ palindrome bound CSB-PGBD3 , although several elements that lacked 5′ or 3′ TIRs were reliably enriched ( Table S1 ) , further supporting our conclusion that CSB-PGBD3 binds primarily to the 5′ palindrome . Comparison of the 5′ palindrome sequences of bound and unbound MER85s revealed that 291 of 363 bound elements ( 80 . 1% ) but only 48 of 526 unbound elements ( 9 . 1% ) perfectly matched the consensus . The presence of unbound MER85s with perfect palindrome sequences suggests once again that other factors , such as chromatin accessibility , are likely to modulate CSB-PGBD3 binding in vivo . This could also explain why only 5 of the 6 MER85 elements used previously for EMSA correlated with the ChIP-PCR and ChIP-seq results ( Figure 2 ) . We also examined binding of the CSB-PGBD3 fusion protein to the PGBD3 locus within the CSB gene , as well as to PGBD3 pseudogenes . Unexpectedly , the PGBD3 locus in CSB is one of the strongest and most extensive CSB-PGBD3 binding sites in the entire genome; moreover , paired-end fragment reads overlapped most heavily near each of three copies of the imperfect 16 bp palindromic sequence in the PGBD3 transposon ( Figure 4 ) . The same was true for the PGBD3 pseudogenes ( Figure S2 ) , but only where the palindromic repeats perfectly matched those of the full-length PGBD3 insertion in CSB ( Figure S2 ) . Although CSB is thought to be expressed in all tissues , and CSB mutations are recessive , it is unclear if or how binding of the CSB-PGBD3 fusion protein to the PGBD3 transposon affects CSB and/or PGBD3 transcription , splicing , or expression . Much to our surprise , peaks over MER85 , PGBD3 , and PGBD3 pseudogenes accounted for only 367 ( 17 . 5% ) of the 2 , 087 genomic regions enriched by immunoprecipitation with CSB-PGBD3 . To determine what sequences in non-MER85 peaks were responsible for enrichment of CSB-PGBD3 , we used MEME ( Multiple Em for Motif Elicitation ) to search for overrepresented sequence motifs located within 50 bp of non-MER85 peak summits [34] . Enriched motifs were then submitted to the TOMTOM motif comparison tool to identify known binding proteins [35] . The top hit was the sequence TGANTCA found near 585 ( 28% ) of the 2 , 087 peak summits ( E-value = 6 . 4e-335 ) ( Figure 7 ) . This motif was identified by TOMTOM as the tumor promoting antigen response element ( TRE ) best known as the binding site for Activator Protein 1 ( AP-1 ) family complexes [36] . The next most highly represented motif was [AT]GGAAT[GT] where [AT] is A or T , and [GT] is G or T; this motif is found near 269 ( 13% ) of the 2 , 087 peak summits ( E-value = 3 . 1e-64 ) and resembles the binding site for the TEAD1 ( TEA domain family member 1 ) transcription enhancer protein ( Figure 7 ) . This motif is very similar to part of the MER85 palindromic region ( TGGAACG ) , and we cannot entirely exclude the possibility that it is bound directly by CSB-PGBD3 because a C>T mutation within this motif ( TGGAATG ) only slightly reduced PGBD3 binding in vitro ( Figure 5 ) . On the other hand , 199 MER85 elements in the genome have this C>T mutation , yet only 6 are bound by CSB-PGBD3 in the ChIP-seq dataset ( Table S1 ) . The third most significant motif with a known binding protein was CCA[CG][CT]AG[AG][GT]GGC , found near 58 ( 2 . 7% ) of the 2 , 087 peak summits ( E-value 1 . 6e-9 ) and was identified as the binding site for CTCF ( CCCTC-binding factor ) , a key regulator of chromatin looping and other higher-order chromatin structures [37] ( Figure 7 ) . The overrepresentation of these three motifs near the CSB-PGBD3 summits in non-MER85 peaks ( tabulated in Table S3 ) suggests that CSB-PGBD3 may interact with all three of these DNA binding factors . Consistent with this interpretation , average fragment overlap profiles centered on these motifs show sharp accumulation of CSB-PGBD3 enriched fragments over the motifs ( Figure 8 ) . Alternatively , CSB-PGBD3 might bind directly to one or more of these motifs , for example through a cryptic activity of PGBD3 DNA binding domain . We used an EMSA assay to ask whether CSB-PGBD3 can bind directly to TRE motifs , or is more likely tethered to the motif by protein-protein interactions with TRE bindng factors . As anticipated , purified CSB-PGBD3 fusion protein failed to shift 42 bp oligonucleotides containing one or two TRE motifs , although control MER85 sequences shifted cleanly and random sequences did not shift at all ( Figure S1 ) . To determine if binding of CSB-PGBD3 to TRE motifs is mediated by an interaction with a TRE binding protein , we asked whether CSB-PGBD3 would co-immunoprecipitate ( coIP ) with AP-1 proteins that are known to bind TRE motifs . AP-1 complexes are composed of many homo- or heterodimeric combinations of members of the Jun , Fos , Maf , and ATF protein families , and the combination of AP-1 family members determines the affinity of the complex for specific variants of the sequence motifs [19] , [38] . Fos and Jun bind preferentially to the TRE sites ( TGANTCA ) identified in CSB-PGBD3 peaks , and more weakly to the similar cyclic AMP response element binding site ( TGACGTCA ) . Although the binding repertoire of Jun and Fos can be expanded through interactions with several other DNA binding proteins [39] , the CSB-PGBD3 peaks contain only TRE motifs suggesting that CSB-PGBD3 interacts directly with Jun or Fos proteins . The Jun and Fos genes c-Jun , JunD , Fra1 , and Fra2 have previously been shown to be expressed in exponentially growing fibroblast cultures [40] . We were able to detect expression of Jun , JunD , and Fra2 in our UVSS1KO-derived fibroblast lines by Western blotting ( Figure S4 ) but not Fra1 ( data not shown ) . In UVSS1KO cells stably expressing FLAG-HA-tagged CSB-PGBD3 , coIPs with antibodies against c-Jun enriched for CSB-PGBD3 compared to a non-specific antibody control ( Figure 9B ) but coIPs with antibodies against JunD and Fra2 did not ( data not shown ) . Moreover , reciprocal coIPs with anti-FLAG antibodies enriched for c-Jun in cells expressing FLAG-HA-CSB-PGBD3 ( Figure 9A ) . These results suggest that CSB-PGBD3 binds to TRE sites indirectly , through a protein-protein interaction with bound c-Jun . To localize the site of interaction on CSB-PGBD3 , we repeated the coIPs in cells expressing FLAG-HA-tagged chimeric CSB-eGFP , eGFP-PGBD3 , or full-length CSB ( Figure 9B ) . Of these cell lines , only CSB-eGFP enriched for c-Jun in an anti-FLAG coIP . Thus c-Jun interacts with the N-terminus of CSB in the CSB-PGBD3 fusion protein , but not with the N-terminus of intact CSB protein . CSB may fail to bind c-Jun because the autoinhibitory N-terminal domain preferentially interacts with the C-terminal helicase domain in the intact protein [13] . We used the Genomic Regions Enrichment of Annotations Tool ( GREAT ) [41] to ask whether genes that are regulated by the CSB-PGBD3 fusion protein [16] are located near CSB-PGBD3 binding sites as determined by ChIP-seq . We previously generated expression array datasets for stable expression of CSB-PGBD3 , CSB , both proteins , or neither in CSB-null UVSS1KO cells [16] but these data had not yet been entered into a database used by the online version of GREAT . Instead , we used a local copy of the GREAT tool , Calculate Binomial P-Value , to correlate our CSB-PGBD3 expression array and ChIP-seq data . We also compared our CSB-PGBD3 ChIP-seq data to genes up- and downregulated when the CS1AN cell line , a patient-derived CSB compound heterozygote , was rescued with wild-type CSB [9] . GREAT tests for statistical enrichment of peaks in regions near a set of genes . To do this , GREAT defines “regulatory domains” that extend in both directions for a specified distance from the transcription start site ( TSS ) or to the next nearest gene . Using regulatory domains of 100 kb , 250 kb , and 1 Mb , we tested sets of genes that were up- and downregulated under each condition separately , and compared them to the set of all 2 , 087 CSB-PGBD3 peaks . We also used GREAT to correlate our expression array datasets with CSB-PGBD3 peaks over MER85 elements ( 363 peaks ) , TRE motifs ( 585 peaks ) , TEAD1 motifs ( 269 peaks ) , CTCF motifs ( 58 peaks ) , and peaks that contain none of these motifs ( 892 ) . Very few peaks contained more than one motif except for 72 peaks with both TRE and TEAD1 motifs , and for consistency these TRE+TEAD1 peaks were counted as members of both peak sets . For each comparison , 100 sets of randomized peak locations were used as negative controls and to calculate empirical false discovery rates ( FDR ) [42] . Only comparisons with an FDR of less than 1% were considered significant ( Table S4 ) . GREAT analysis revealed that peaks containing TRE motifs are significantly enriched near genes upregulated and downregulated by CSB-PGBD3 using all of the regulatory domain sizes ( orange cells in Table S4 ) . Enrichment of TRE motifs near upregulated and downregulated genes suggests that CSB-PGBD3 interacts with AP-1 proteins to modulate the expression of nearby genes . In contrast , peaks over MER85 elements did not correlate significantly with any of the UVSS1KO or CS1AN expression array datasets ( gray cells in Table S4 ) , despite enrichment of MER85 elements near specific gene ontology ( GO ) categories [10] . This suggests that regulation of gene expression by the CSB-PGBD3 fusion protein is strongly dependent on location and cooperation with other transcription factors; simple DNA binding in the vicinity of genes is not sufficient . These results support a very different model from our initial speculation that CSB-PGBD3 binding would create a MER85-based transcriptional network . Instead , it appears that CSB-PGBD3 selectively interacts with existing transcription factors to provide an additional layer of gene regulation on top of established regulatory networks . In addition to analysis of genes regulated by CSB-PGBD3 expression alone , we also compared CSB-PGBD3 binding to genes regulated by coexpression of CSB and CSB-PGBD3 in the same CSB-null cell line UVSS1KO . Importantly , this set of genes is distinct from genes regulated by CSB or CSB-PGBD3 alone , suggesting that co-regulation could be the result of direct interactions between the N-terminus of CSB-PGBD3 and CSB [13] or indirect interactions in which upregulation of certain genes by CSB-PGBD3 requires prior ( or concurrent ) chromatin remodeling by CSB [9] . GREAT analysis revealed that many genes which are upregulated by coexpression of CSB and CSB-PGBD3 , but not by either protein alone , correlate significantly with the set of all peaks bound by CSB-PGBD3 and with the subsets of peaks over TRE and TEAD1 motifs ( blue cells in Table S4 ) . These genes could in principle be regulated by the N-terminal domain of CSB , CSB-PGBD3 , or both; however , we might then have expected to see a similar correlation with genes upregulated by stable expression of CSB alone . It therefore seems more likely that this subset of genes is upregulated by CSB-PGBD3 through interactions that are enhanced by or require CSB , and thus may also be upregulated in normal , healthy individuals . In contrast , many genes that are upregulated by expression of CSB-PGBD3 in CSB-null cells are repressed by coexpression of CSB [16] . Moreover , 16 of these genes are also downregulated ( binomial p-value 6e-7 ) when CSB is expressed in CS1AN cells that continue to express the CSB-PGBD3 fusion protein despite loss of functional CSB [10] . Of these 16 genes , 8 have CSB-PGBD3 binding sites within 100 kb of the TSS ( ARHGAP29 , IGFBP7 , MGLL , PODXL , PSG1 , RGMB , RGS4 , and SERPINE1 ) suggesting that CSB can repress some , but not all genes that are upregulated by nearby CSB-PGBD3 fusion protein — perhaps depending on local context or the specific transcription factor ( s ) that tether CSB-PGBD3 to the site . Our expression array analysis was limited to several cell lines and culture conditions . To investigate the role of CSB-PGBD3 binding sites in the broader context of human biology and disease , we used the online version of GREAT to compare our binding sites to a diverse set of gene ontologies . Using the default settings , we submitted either the full set of CSB-PGBD3 peaks to GREAT , or the subsets containing the MER85 , TRE , TEAD1 , or CTCF motifs , or no recognizable motif . The MER85 and CTCF ( as well as TRE+TEAD1 ) peaks did not exhibit statistically significant overlaps with any ontology sets , but for the other peak categories we examined the top five results in the GO Biological Processes , Disease Ontology , Pathway Commons , and MSigDB Perturbation datasets ( Table S5 ) . We found that CSB-PGBD3 binding sites correlated significantly with genes related to the TGF-beta pathway , carcinogenesis , and IFN and IL-2 driven innate immune responses ( see Table S5 legend for details ) . MER85 elements are among the strongest CSB-PGBD3 binding sites in vivo , yet bound MER85 elements do not correlate with genes induced or repressed by CSB-PGBD3 expression in CSB-null UVSS1KO cells ( Table S4 ) . Thus we must consider the possibility that continued binding of the CSB-PGBD3 fusion protein to MER85s might be fortuitous or functionless . The burst of MER85 replication apparently came to an end about 35 Mya [43] , perhaps upon mutation of the conserved catalytic aspartate ( D352 ) in the PGBD3 transposase ORF to asparagine [10] , [44] . The limited sequence diversity of the surviving 889 human MER85s ( Table S1 ) , the ability of the CSB-PGBD3 binding site to tolerate point mutations and even deletions ( Figure 5 ) , and the small target size of the essential 16 bp imperfect palindrome ( Figure 5 and Figure 6 ) , are all consistent with our observation that at least 40% ( 363/889 ) of all MER85s retain the ability to bind the PGBD3 transposase ( Table S1 ) despite ongoing mutations over the past 35 My . We conclude that neutral sequence evolution could have been sufficient to account for the homogeneity and current functions of MER85 elements . Alternatively , binding of the CSB-PGBD3 fusion protein to MER85s through the PGBD3 domain may enable CSB-PGBD3-mediated chromosome looping with transcription factors bound to TRE , TEAD1 , or CTCF motifs . To test this hypothesis , we used the GREAT tools to determine if CSB-PGBD3 binding sites containing TRE , TEAD1 , or CTCF motifs ( Table S3 ) were significantly enriched within 100 kb of MER85s that are bound by CSB-PGBD3 ( Table S1 ) . Surprisingly , we found a strong correlation between CSB-PGBD3 peaks containing TRE motifs and the 363 MER85 elements bound by CSB-PGBD3 ( 36 of 585 bound TRE motifs , P-value = 7 . 9e-7 ) but not with the 529 unbound MER85 elements ( 16 of the 585 bound TRE motifs , P-value = 0 . 88 ) . Peaks containing TEAD1 or CTCF motifs , and peaks containing no identified motif , showed no enrichment near bound or unbound MER85 elements . CSB interacts with stalled RNAPII after induction of DNA damage [6] , but it also copurifies with RNA polymerase II in unirradiated cells [4] and thus may associate with transcribing RNA polymerase II ( RNAPII ) as well as with TCR complexes . To determine whether some of the genomic CSB-PGBD3 peaks might reflect interaction of CSB-PGBD3 with RNAPII , we asked if antibody against the C-terminal domain ( CTD ) of RNAPII could co-immunoprecipitate ( coIP ) CSB-PGBD3 , and vice versa . Intriguingly , coIPs with antibody against the RNAPII C-terminal domain enriched for CSB-PGBD3 but not CSB in undamaged HT1080 human fibrosarcoma cells ( Figure 10a ) . In a reciprocal coIP using UVSS1KO cells that stably express FLAG-HA-CSB , FLAG-HA-CSB-PGBD3 , or the FLAG-HA tags only , coIPs with anti-FLAG antibody enriched for RNAPII in cells expressing CSB-PGBD3 , but not in cells expressing intact CSB or tags only ( Figure 10b ) . The failure of CSB to co-immunoprecipitate RNAPII is more likely to reflect low affinity between CSB and RNAPII in the absence of DNA damage than accessibility of the tags , because anti-FLAG IPs readily pull down FLAG-HA-CSB in UVSS1KO cells ( data not shown ) . To see if interactions between CSB-PGBD3 and RNAPII could account for CSB-PGBD3 peaks that did not contain an overrepresented sequence motif , we compared regions within 50 bp of CSB-PGBD3 peaks to enriched RNAPII peaks obtained from the Yale TFBS collection in the UCSC Genome Browser database [45] , [46] . Because RNAPII binding sites vary between cell types , we analyzed 18 RNAPII genome-wide peak sets from 15 cell lines . We found 105 of 2087 CSB-PGBD3 peaks consistently overlapped at least 10 of 18 RNAPII peak sets ( Table S6 ) , and that 85 of these CSB-PGBD3 peaks did not contain a MER85 , TRE , TEAD1 , or CTCF motif . The set of 105 CSB-PGBD3 peaks that overlapped RNAPII peaks were compared to expression array datasets using GREAT as described previously . Peaks associated with RNAPII binding sites were enriched near genes upregulated by coexpression of CSB and CSB-PGBD3 , but not by expression of either protein alone ( Table S4 ) . Thus , interactions between CSB-PGBD3 and RNAPII may require regulation or remodeling of the gene by CSB [9] . We localized the region of interaction between CSB-PGBD3 and RNAPII by asking whether antibody against the CTD of RNAPII would immunoprecipitate stably expressed FLAG-HA-CSB-eGFP , FLAG-HA-eGFP-PGBD3 , or FLAG-HA-CSB-PGBD3 from UVSS1KO cells . Surprisingly , RNAPII interacts with CSB-eGFP but not with eGFP-PGBD3 ( Figure 10c ) . Thus , CSB-PGBD3 interacts with RNAPII through the N-terminal CSB domain , just as it does with c-Jun ( Figure 9 ) . The implication may be that the highly conserved SWI/SNF ATPase domain encoded by CSB exons 6–21 , although unlikely to be a generic chromatin remodeler [47] , is modulated , autoinhibited [13] , and targeted to specific chromosomal locations by the N-terminal domain ( coding exons 2–5 ) ( Figure 10 and Figure S1 ) . The ability of the N-terminal domain of CSB to interact directly with c-Jun ( Figure 9 ) and RNAPII ( Figure 10 ) , as well as the failure of CSB-PGBD3 fusion protein to affect expression of nearby genes when bound to MER85s ( Table S4 ) , suggested that CSB-PGBD3 could potentially regulate gene expression without binding directly to DNA . To test if the CSB N-terminus alone can induce the changes in gene expression caused by CSB-PGBD3 , we stably expressed two chimeric fusion proteins in UVSS1KO cells: a CSB-LacI chimera in which the C-terminal PGBD3 domain is replaced by LacI , and the reciprocal eGFP-PGBD3 chimera in which the N-terminal CSB domain is replaced by eGFP . Using quantitative PCR ( QPCR ) , we then compared the relative expression of selected genes in the stable lines expressing the CSB-LacI , eGFP-PGBD3 , and control CSB-PGBD3 constructs . We selected a panel of 23 genes for the QPCR assay: 13 genes that were upregulated ( signal log ratio SLR>1 ) when CSB-PGBD3 was stably expressed in the CSB-null UVSS1KO line [16] , 7 genes that were downregulated ( SLR<−1 ) , and 3 genes that showed no significant change in expression ( SLR between 1 and −1 ) . Most of the 23 genes exhibited similar expression changes in both expression array and QPCR experiments: 14 genes were upregulated at least 2-fold ( SLR>1 ) by CSB-PGBD3 , 6 genes downregulated at least 2-fold ( SLR<−1 ) , and 3 genes exhibited less than 2-fold changes in expression by QPCR ( Figure 11 ) . The two exceptions were the v-src sarcoma viral oncogene homolog ( SRC ) which appears elevated by QPCR but not by microarray , and the spinocerebellar ataxia 1 gene ( SCA1 or ataxin 1 ) which appeared to be less downregulated in the QPCR than in the microarray assay ( SLR of −0 . 5 and −1 , respectively ) . Thus the QPCR assays are consistent with our earlier expression array analysis [16] . Of the 14 genes upregulated by CSB-PGBD3 , 11 were also upregulated by CSB-LacI , although less so for 8 of the 11 ( Figure 11 ) . Similarly , 8 of the same 14 genes were upregulated by eGFP-PGBD3 , although less so for 6 of the 8 genes ( Figure 11 ) . These data suggest that both the N-terminal CSB domain and the C-terminal PGBD3 domain can independently upregulate genes induced by the CSB-PGBD3 fusion protein , but less effectively than when tethered together in a single protein . In contrast , the 6 genes downregulated by CSB-PGBD3 were almost unchanged by expression of CSB-LacI or eGFP-PGBD3 ( Figure 11 ) . CSB-LacI failed to downregulate any of these 6 genes by as much as the 2-fold cutoff for significance , and eGFP-PGBD3 downregulated only 1 of the 6 ( Figure 11 ) . Thus , downregulation of genes by CSB-PGBD3 requires fusion of the N- and C-terminal domains . Neither the CSB N-terminus nor C-terminal PGBD3 domain alone is capable of fully recreating the expression changes induced by CSB-PGBD3 , and fusion of the two domains results in a transcriptional response that is greater than and somewhat different from the effect of the two domains individually . This could explain why the CSB-PGBD3 fusion has been conserved despite the presence of the CSB N-terminus in intact CSB and the intact PGBD3 protein transcribed from the cryptic promoter in CSB exon 5 ( Figure 1 and [10] ) .
Genome-wide binding of transposase-derived transcription factors had been demonstrated in Arabidopsis [48] , but we provide a first look at the genome-wide binding of a transposase in transition: the PGBD3 transposase still binds strongly to related transposons , but has acquired novel functions because fusion with the N-terminus of CSB enables it to interact with previously established transcription factor networks . We had initially expected that binding of CSB-PGBD3 to MER85 elements would correlate with gene regulation induced by CSB-PGBD3 . However , we found that CSB-PGBD3 interacts with a much broader range of binding sites , including TRE , TEAD1 , and CTCF motifs , each of which is bound by factors that long predate horizontal transfer of PGBD3 to primate genomes . These results suggest that the conservation of the CSB-PGBD3 fusion protein over 43 My is due at least in part to modulation of existing regulatory networks rather than the creation of a de novo network based on insertion of MER85 elements near genes . However , the CSB-PGBD3 fusion protein also continues to bind MER85 elements , so we cannot rule out scenarios in which the fusion protein , bound to MER85 elements , regulates expression of nearby genes in specific cell types that we have not tested , or in occasional instances that would not appear statistically significant in our GREAT analysis . Thus a new protein ( or RNA ) that can modify established regulatory circuits may be able to build new functions without disrupting the old , whereas a new protein or regulatory RNA that can generate regulatory circuits de novo may be too powerful to survive because it would more likely do harm than good . Nonetheless , transposable elements can , under very special circumstances , create regulatory networks de novo . For example , placental mammals express a large network of genes driven by transcription factor binding sites in MER20 transposons that regulate differentiation of endometrial stromal cells required for embryo implantation [49] . MER20s are present in >16 , 562 copies per human genome , 42% of which are located within 200 kb of the transcription start sites for pregnancy-induced genes . Moreover , the 218 bp element contains at least 22 potential binding sites for a total of 10 transcription factors ( YY1 , p300 , C/EBPβ , CTCF , TGIF , p53 , HoxA-11 , FOXO1A , ETS1 , and PGR ) , and quite remarkably 5 of these ( C/EBPβ , PGR20 , PGR21 , FOXO1A , and HoxA11 ) are known to be important for hormone responsiveness and endometrial expression during pregnancy . The ability of MER20 to introduce a cluster of functional — and functionally related — transcription factor binding sites in a single insertional event may account for the evolutionary success of the MER20-based transcriptional network; it seems almost inconceivable that nearly identical clusters could have arisen at multiple genomic locations by neutral , stepwise mutation [50] . MER85s ( some with bound CSB-PGBD3 fusion protein ) could in principle participate in similar regulatory networks , as many MER85 elements contain binding sites for FOXA2 , GFI , HAND1 , HMGIY , HNF1A , NFE2L1 , RORA , SOX5 , and SRF ( see Figure S5 for potential MER85 transcription factor binding sites ) , but this seems less likely because MER85s are 20-fold less abundant than MER20s ( 889 versus 16 , 562 copies per genome ) . CSB-PGBD3 may also regulate genes by affecting higher-order chromatin structure and looping . Our MEME analysis revealed the distinct signature of the CTCF binding motif in 58 CSB-PGBD3 peaks ( Table S3 , Figure 7 ) . CTCF acts as a transcription activator or repressor depending on context , as a defining factor for gene insulation and silencing , and as a master regulator of long-range chromatin looping [37] . Although CTCF peaks represent only a small fraction of all CSB-PGBD3 binding sites , these peaks suggest that CSB-PGBD3 interacts directly with CTCF , perhaps mediating long-range interactions with CSB-PGBD3 bound to MER85s , TRE and TEAD1 motifs , or sites enriched for RNAPII . An interaction between CSB-PGBD3 and CTCF , through either the N-terminal CSB or C-terminal PGBD3 transposase domain , might also facilitate transposition . Intriguingly , the CTCF binding network in mammals has been shaped in part by retroposition of SINE elements that contain a CTCF motif [50] , further expanding the repertoire of mechanisms by which transposons affect the structure and function of eukaryotic genomes . CSB-PGBD3 could even play a direct role in chromosome looping ( Figure 12 ) . We found , by comparing all subsets of peaks in the ChIP-seq dataset for the CSB-PGBD3 fusion protein , that TRE motifs bound by the fusion protein are significantly enriched within 100 kb of MER85 elements that are also bound by fusion ( see Results for details ) . Although it is possible that the fusion protein binds to these pairs of peaks independently , the data are consistent with chromosome looping mediated by the bifunctional fusion protein: the C-terminal PGBD3 transposase domain would bind to the MER85 and the N-terminal CSB domain would bind to AP-1 family transcription factors bound to the TRE motif — thus linking two distant sites , both of which would generate peaks in the ChIP-seq experiment . The full-length CSB protein plays an essential role in TC-NER by recognizing stalled RNAPII and initiating assembly of the large TC-NER complex [5]–[7] . As these interactions had not yet been mapped to specific domains of CSB , we were surprised to find that both the CSB-PGBD3 fusion protein and the chimeric CSB-eGFP protein are able to interact with RNAPII ( Figure 10 ) although these proteins contain only the N-terminal domain of CSB and none of the 7 conserved ATPase motifs ( Figure 1 and [16] ) . We do not yet know whether the interaction between RNAPII and the N-terminal domain of CSB occurs on DNA or at sites of stalled RNAPII , but our co-immunoprecipitation experiments demonstrate that CSB and CSB-PGBD3 can share protein interaction partners through the common N-terminus . In fact , competition between CSB and CSB-PGBD3 for binding partners could play a role in CSB-dependent processes because expression of CSB-PGBD3 is about 4-fold higher than CSB in all cell lines we have examined [10] . The N-terminal domain of CSB has been shown to autoinhibit both normal and UV-induced association of CSB with chromatin [13] , [51] , but deletion of the N-terminal acidic tract had no obvious effect on repair of UV damage [11] , [12] . Our data suggests that the N-terminus of CSB may play a larger role in targeting CSB to specific genes or chromosomal regions . We were surprised to find that the N-terminal domain of the CSB-PGBD3 fusion protein interacts both with both RNAPII ( Figure 10 ) and c-Jun ( Figure 9 ) , and the sharp fragment accumulation profiles over TEAD1 and CTCF motifs ( Figure 8 ) suggest that the fusion protein may also interact directly with TEAD1 and CTCF transcription factors bound to DNA . It also seems likely that the N-terminus of the CSB-PGBD3 fusion protein is responsible for binding to at least some of the 892 CSB-PGBD3 peaks ( 43% of 2 , 087 peaks total ) that have no currently identifiable sequence motifs , but very likely bind transcription factors , chromosomal proteins , or enzymes such as topoisomerase I [15] involved in RNA and DNA transactions . UV irradiation and other stressors activate c-Jun through phosphorylation by c-Jun N-terminal kinases ( JNKs , also called stress-activated kinases ) such as JNK1 [52] . Activated JNK and AP-1 complexes can then affect cell proliferation and apoptosis , depending on cell type and stimulus [36] , [53] . We have previously shown that the CSB-PGBD3 fusion protein , although lacking all 7 ATPase motifs , can partially rescue UV damage repair in a host-cell reactivation assay using CSB-null UVSS1KO cells [16] . Conceivably , CSB-PGBD3 may facilitate repair by interacting with TC-NER proteins that normally associate with full-length CSB . However , the interaction between the CSB-PGBD3 fusion protein and the AP-1 family protein c-Jun ( Figure 7 , Figure 9 , Figure S1 ) near genes upregulated by CSB-PGBD3 expression ( Table S4 ) suggests an alternative scenario in which CSB-PGBD3 plays a transcriptional role in repair . After UV damage , activated AP-1 complexes could help guide CSB-PGBD3 to genes that are activated in response to UV . CSB-PGBD3 might then recruit RNAPII to these UV-activated TREs if the interactions of the N-terminal CSB domain of CSB-PGBD3 with c-Jun ( Figure 7 , Figure 9 , Figure S1 ) and RNAPII ( Figure 10 ) are not mutually exclusive . We were surprised to find that PGBD3 is strongly bound by CSB-PGBD3 near three palindromic motifs that are also present in the 5′ end of bound MER85s ( Figure 4 ) . Binding to these palindromes may autoregulate CSB transcription , CSB-PGBD3 expression , or alternative splicing and polyadenylation — perhaps by modulating the rate of RNAPII transcription or through interactions between the acidic N-terminus of CSB and phosphorylated serine/arginine-rich motifs in SR-family splicing enhancer proteins [54] . Thus it is possible that the CSB-PGBD3 fusion protein was initially retained in order to regulate CSB expression , and only secondarily acquired the ability to regulate other DNA repair , antiviral , and pathogen resistance genes . Continued binding of the CSB-PGBD3 fusion protein to MER85s may be fortuitous but need not be functionless . SETMAR ( also called Metnase ) is another domesticated transposase that exhibits continued binding to dispersed copies of the parental transposon . SETMAR consists of a SET methyltransferase domain fused to a Mariner ( Hsmar1 ) transposase domain . SETMAR has been shown to play a role in NHEJ ( nonhomologous end joining ) repair of double-stranded DNA breaks [55] as well as repairing and restarting damaged replication forks [56] , but it also retains the ability to bind Mariner transposon TIR sequences [57] . The binding affinity of SETMAR for Mariner elements appears to be regulated by interactions with a damage-regulated partner protein , Pso4 [58]; although normally bound to Mariner elements , SETMAR is released in response to DNA damage [59] . Similarly , MER85s could serve as reservoirs for excess CSB-PGBD3 , perhaps regulating the interactions of CSB-PGBD3 with AP-1 factors , or ensuring that CSB-PGBD3 is readily available throughout the genome ( Figure 12 ) . CS1AN cells are derived from a Cockayne syndrome patient with compound heterozygous CSB alleles . An early truncating mutation ( K377term ) in one CSB allele prevents expression of CSB and the CSB-PGBD3 fusion protein , but the 100 bp deletion in exon 13 of the other CSB allele [60] is located far downstream of PGBD3 and allows continued expression of the CSB-PGBD3 fusion protein in the absence of full-length CSB [10] . Surprisingly , genes downregulated by expression of full-length CSB in CS1AN cells [9] correlate strongly with CSB-PGBD3 binding sites in CSB-null UVSS1KO cells ( Table S4 ) . Thus , CSB-PGBD3 contributes to an aberrant transcriptional state in CS1AN cells by binding near , and perhaps interacting directly with , genes that are normally repressed by full-length CSB . How could the N-terminal domain of CSB in the CSB-PGBD3 fusion protein activate genes that are normally repressed by CSB ? One tantalizing but highly speculative scenario would be that for CSB-regulated genes , the autoinhibitory N-terminal domain of CSB [13] has dual checkpoint and transcriptional activation functions: Once the ATPase domain had engaged as a chromatin remodeler , the N-terminal domain would be released to activate transcription . Unconstrained in the fusion protein by the mutually autoinhibitory ATPase domain of CSB , the N-terminal CSB domain of CSB-PGBD3 would function as a constitutive transcriptional activator of CSB-regulated ( and perhaps other ) genes unless displaced by functional CSB . GREAT analysis provided considerable insight into the consequences of the interactions of CSB-PGBD3 with TRE and TEAD1 motifs: Genes downregulated by CSB rescue of CS1AN cells correlate strikingly , for all regulatory domain sizes , with the entire set of CSB-PGBD3 binding sites including those with TRE , TEAD1 , or no detectable motifs ( green cells in Table S4 ) . Thus , CSB-PGBD3 binding to each of these motifs , and even to the large number of peaks for which we could not identify a motif , correlates with upregulation of gene expression in CS1AN cells that continue to make the CSB-PGBD3 fusion protein but lack functional CSB . The correlation of CSB-PGBD3 binding sites with genes repressed by CSB in CS1AN cells suggests that the fusion protein substantially reshapes the transcriptome in CS patient CS1AN , and may do so in other CS patients whose mutations allow continued expression of the CSB-PGBD3 fusion protein in the absence of functional CSB . Just as expression of functional CSB in CS1AN cells represses genes upregulated by continued expression of the CSB-PGBD3 fusion protein [9] , so expression of the CSB-PGBD3 fusion protein in CSB-null UVSS1KO cells induces a strong interferon-related innate antiviral immune response which is dramatically repressed by coexpression of functional CSB [16] . This could be driven by CSB-PGBD3 binding to AP-1 binding motifs , which are known to play a role in upregulating pro-inflammatory cytokines [61] and chemokines such as IL-8 [62] . In normal aging , inflammation is driven by an increase in cytokine expression [63] and appears to be responsible for many age-related diseases [64] . Thus induction of AP-1 dependent inflammatory pathways by the CSB-PGD3 fusion protein may contribute to segmental aging in CS [65] , and could be responsible for parts of the innate immune response ( including IL-8 ) induced by CSB-PGBD3 expression in CSB-null UVSS1KO cells [16] . These observations suggest a previously unappreciated role for CSB in regulation of innate immunity and inflammation . Indeed , even CS patients who do not express the CSB-PGBD3 fusion protein because of mutations upstream of intron 5 ( Figure 1 ) might inappropriately activate or fail to deactivate innate immune pathways . As perceptively advocated by Brooks et al . [66] , inflammation and calcification of the brain are seen both in CS and in another childhood neurodevelopmental disease known as Aicardi-Goutiéres syndrome ( AGS ) . In AGS , loss of RNASEH2 or TREX1 nuclease activity causes accumulation of intracellular DNA and RNA fragments , counterfeiting a viral infection and triggering a constitutive type I interferon response [67] . Our data suggest that CS may also have an autoimmune component , caused both by loss of downregulation through CSB , and inappropriate upregulation by the CSB-PGBD3 fusion protein . If so , CS patients may benefit from treatment with immunosuppressive or anti-inflammatory drugs .
We previously identified the locations of 613 partial or complete MER85 elements [10] . Closer examination of these elements revealed that almost all of them are actually complete , with both 5′ and 3′ terminal inverted repeats ( TIRs ) . Additional MER85 elements in the hg18 build of the human genome were obtained from the RepeatMasker 3 . 2 . 7 track [68] in the UCSC genome browser [45] . Several additional elements were located using the BLAT tool [69] in the UCSC genome browser with the 100 5′-most bases of the PGBD3 transposon as the query . Each MER85 element was examined individually to determine the boundaries of the sequence , the orientation , and the location of the TIRs and internal palindrome motifs ( Table S1 ) . The HT1080 human fibrosarcoma cell line was maintained in Minimum Essential Medium Alpha ( MEM-α ) with 5% fetal bovine serum ( FBS ) , penicillin , and streptomycin . All UVSS1KO-derived cell lines were cultured in Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with 10% FBS , penicillin , and streptomycin . UVSS1KO cells stably expressing the pFLAG-HA-CSB , pFLAG-HA-CSB-PGBD3 and pFLAG-HA constructs have been described previously [16] . To generate analogous pFLAG-HA-CSB-eGFP , pFLAG-HA-CSB-LacI , and pFLAG-HA-eGFP-PGBD3 constructs , the indicated coding sequences were fused in frame and inserted into the same bicistronic pIREShyg3 backbone ( Clontech ) ; LacI was a gift of N . Maizels . The constructs were linearized before transfection into UVSS1KO cells ( TransIT-LT1 transfection reagent , Mirus #MIR2300 ) and selection of stable pools with 200 µg/ml of hygromycin . For ChIP-PCR , HT1080 cells were crosslinked with 1% formaldehyde for 10 minutes before quenching with 125 mM glycine . For ChIP-seq , UVSS1KO cells expressing FLAG-HA-CSB-PGBD3 were crosslinked with 0 . 5% formaldehyde for 5 min before quenching . Lower crosslinking was used for ChIP-seq to allow more thorough shearing of chromatin by sonication . Cells were then washed twice with phosphate buffered saline ( PBS ) , scraped from the tissue culture plates , and resuspended in 1 ml cell lysis buffer ( CLB , 5 mM PIPES pH 8 , 85 mM KCl , 0 . 5% NP40 ) per 2×107 cells . Cells in lysis buffer were vortexed for 10 sec , incubated on ice for 10 min , and vortexed again for 10 sec . After lysis , nuclei were pelleted by centrifugation and resuspended in 500 µl RIPA buffer ( 10 mM TrisHCl pH 8 , 140 mM NaCl , 1% Triton X-100 , 0 . 1% SDS , 0 . 1% deoxycholate , 0 . 1 mM EDTA , 0 . 05 mM EGTA ) per 2×107 cells . Glass beads ( 50 mg per 2×107 cells ) were added to assist shearing , and nuclei were broken by 6 pulses of 10 sec each from a Sonic Dismembrator ( Fisher Scientific ) at a setting of 4 W . Chromatin samples were precleared by nutation for 1 h at 4°C with crosslinked Staph A cells ( 20 µl per 2×107 cells ) . Glass beads and Staph A were removed before immunoprecipitation . Rabbit polyclonal antibodies specific for the N-terminal 240 residues and the C-terminal 158 residues of CSB were raised against fusions with bacterial GST [70] . CSB antibodies were purified from GST antibodies by passage over a GST column [10] . Rabbit anti-mouse IgG , rabbit polyclonal anti-CSB-N-terminus , rabbit polyclonal anti-CSB-C-terminus , or no antibody was added to HT1080 chromatin preparations at a dilution of 1∶200 and nutated overnight at 4°C . To precipitate bound antibodies , 0 . 1 vol Protein A-sepharose CL4B beads ( Sigma ) was added , and nutated for 1 h at 4°C . The beads were then washed 3x in RIPA buffer , 1x in RIPA wash buffer ( 10 mM Tris-HCl , pH 8 , 250 mM LiCl , 0 . 1 mM EDTA , 0 . 5% NP40 , 0 . 5% deoxycholate ) , and resuspended in 0 . 2 vol TE at pH 7 . 5 . ChIP samples were digested with pancreatic ribonuclease A , followed by Proteinase K , and decrosslinked by incubation at 65°C overnight . ChIPs were assayed using PCR primers for the 6 genomic MER85 elements ( Table S7 ) and α-32P-dCTP to body-label the products . ChIPs were performed as described for ChIP-PCR but using a 1∶200 dilution of mouse monoclonal 1B1 directed against the N-terminus of CSB ( kind gift of Hua-Ying Fan , University of Pennsylvania ) and Protein G Dynabeads ( Invitrogen ) for the pulldown . An input sample of sheared , crosslinked chromatin was set aside from the same chromatin pool used for ChIPs . The input sample was digested with RNase , protease , and decrosslinked without enrichment by ChIP . The ends of the ChIP and input samples were repaired using End-It ( Epicentre ) , A-tailed using Taq polymerase ( Invitrogen ) , and Illumina paired-end sequencing adapters were ligated using Quick T4 DNA Ligase ( NEB ) . DNA fragments ranging from 400 to 700 bp were selected and purified by PAGE , then preamplified for 9 ( input ) or 12 cycles ( ChIP ) using Illumina paired-end preamplification primers , a BioRad iTaq supermix , and the following PCR protocol: denaturation 5 min at 95°C; cycling 30 sec at 95°C , 2 sec at 55°C , 2 sec at 72°C , and extension 10 sec at 72°C . For Illumina adapter and primer sequences , see supplementary methods of [27] . The preamplified samples were purified using a Qiagen PCR Cleanup kit , and were sequenced using an Illumina Genome Analyzer II ( J . Shendure , University of Washington ) . Bases were called suing Illumina Real Time Analysis 1 . 5 software . Raw reads and processed data can be accessed at GEO study GSE37919 . The input sample generated 4 , 735 , 921 reads of 36 bp each . Reads from a single end of each sequenced fragment were aligned to the UCSC build hg18 ( NCBI36 ) using the read alignment program Bowtie v0 . 12 . 7 with settings -n 0 , -m 1 , and –best to ensure that mapped reads had no mismatches , did not match multiple locations in the genome , and were from the best stratum of alignments [28] . All together , 3 , 307 , 313 reads were successfully aligned . The CSB-PGBD3 ChIP sample generated 14 , 263 , 776 paired-end reads of 36 bp each . These reads were aligned using the same settings as for the input , but were aligned as pairs with default settings for read spacing . All together , 8 , 574 , 668 paired-end reads were successfully aligned . Alignment files were created in both Bowtie and SAM format for subsequent analysis . After using IGVTools to sort the SAM-formatted files , the paired-end SAM-formatted Bowtie output was converted using a Perl script into fragment overlap WIG files ( available at http://code . google . com/p/graylt-plosgenetics-2012/ ) for use with the Cis-regulatory Element Annotation System ( CEAS ) and for subsequent analysis of fragment overlaps . The Perl script used paired-end reads as boundaries for each sequenced fragment , looked for clusters of at least 5 overlapping fragments of a specified size , and then calculated the number of sequenced fragments overlapping each position of the genome within each fragment cluster . ChIP-seq peaks were called using three peak-calling programs . Model-Based Analysis of ChIP-seq ( MACS ) was used to call peaks using the full paired-end CSB-PGBD3 ChIP and Input datasets [29] . MACS identified 45 , 067 peaks with a p-value<1e-5 . For subsequent analysis , only the 9 , 835 peaks with a p-value<1e-12 were considered . ERANGE was also used for calling peaks after converting Bowtie map files to RDS format using the ERANGE makerdsfrombowtie Python script [30] . The ERANGE setting -minimum 2 was used to adjust the minimum enrichment threshold to 2-fold because of the disparate read depth between input and enriched samples . Using this setting , ERANGE found 3 , 743 peaks , which were used for subsequent analysis . The third peak-calling algorithm used was Quantitative Enrichment of Sequence Tags ( QuEST ) , with settings for transcription factor ChIP and custom peak calling parameters ( 20 , 10 , 3 ) [31] . QuEST identified 5 , 663 peaks , which were used for subsequent analysis . Peaks from each of the three peak-calling algorithms were compared using the Join on Genomic Intervals feature of the multi-purpose Galaxy analysis tool [71]–[73] . Comparison of peaks from all three algorithms showed 2 , 087 distinct enriched regions ( “combined peaks” ) identified using all three algorithms . The 5′ and 3′ boundaries of these regions were determined based on the outermost boundaries of overlapping peaks identified using MACS , ERANGE , and QuEST . The summit of each combined peak was determined by using a Perl script to search for the center of the deepest fragment overlap in the CSB-PGBD3 WIG file generated as described above ( available at http://code . google . com/p/graylt-plosgenetics-2012/ ) . Combined peaks and peak summits are listed in ( Table S2 ) . Galaxy's Intersect tool was used to compare the locations of 2 , 087 Combined Peaks to the locations of the 889 MER85 elements ( Table S1 ) . Peaks were found to overlap 363 MER85 elements . The cumulative fragment overlap over all 363 bound MER85 elements was calculated using a Perl script ( available at http://code . google . com/p/graylt-plosgenetics-2012/ ) to sum the fragment overlaps over each MER85 element in the whole-genome CSB-PGBD3 fragment overlap WIG file after correcting the positions of the fragments for MER85 position and orientation . The same script was used to generate fragment overlap profiles over individual PGBD3 and PGBD3 pseudogenes , as well as sets of all bound TRE , TEAD1 , and CTCF motifs . Combined peaks were filtered to remove peaks over MER85 elements using Galaxy's Subtract tool [71] . Using Galaxy's Extract Genomic DNA tool [71] , 100 bp regions around each filtered peak summit ( summit - 50 bp to summit +49 bp ) were extracted [71] and then searched using a local installation of the Multiple-Em for Motif Elicitation ( MEME ) tool with the settings -dna -mod zoops -minw 6 -maxw 12 -revcomp -nmotifs 5 [74] . The Position Specific Frequency Matrix ( PSFM ) for each of the 5 statistically significant motifs identified by MEME were submitted to the online version of Tomtom [35] for comparison to motifs in the JASPAR and TRANSFAC transcription factor motif databases . Of the 5 motifs , 3 matched known transcription factors AP-1 , TEAD1 , and CTCF . Using Galaxy's Subtract tool , 892 peaks were identified that contained none of the 3 transcription factor motifs . The summit sequences of these peaks were resubmitted to MEME to identify any additional motifs that may have been masked by the high-scoring AP-1 , TEAD1 , or CTCF motifs , but no additional motifs were identified . Probe locations for all Affymetrix Human U133plus2 . 0 array probe sets were retrieved from the HG-U133_Plus_2 Annotations , CSV format , Release 31 ( 8/23/10 ) available on the Affymetrix web site . We had previously defined probe sets that were up- or downregulated at least 2-fold by expression of FLAG-HA-tagged CSB , CSB-PGBD3 , or both compared to FLAG-HA tags alone in UVSS1KO cell lines [16] , or by CSB rescue of the CS1AN cell line [9] . Lists of Affymetrix probes for each condition and the direction of expression change were compiled , and a Perl script used to convert each list of probes to a list of 5′ ends of probe set locations for regulated probe sets . These “probe set start sites” were converted to regulatory domains using the GREAT createRegulatoryDomains program locally with settings for 1 Mb , 250 kb , or 100 kb maximum extensions [41] . The regulatory domains were then compared to sets of peaks using a Perl script that counted overlaps between the peak summits and the set of regulatory domains ( available at http://code . google . com/p/graylt-plosgenetics-2012/ ) . These counts were submitted to the GREAT calculateBinomialP program locally to obtain P-values ( Table S4 ) . False discovery rates ( FDR ) were then calculated empirically for each comparison using 100 sets of randomly selected summit locations of the same size as each peak set . P-values corresponding to a FDR below 1% were considered significant . Finally , the peak summits were compared to the regulatory domain tables that include gene names , and lists of genes with nearby CSB-PGBD3 peaks were generated for each comparison . Peak summit locations for all identified peaks , as well as for MER85 , AP-1 , TEAD1 , CTCF , and other peaks separately , were submitted to GREAT v1 . 8 . 2 ( great . stanford . edu ) using default association rule settings for the hg18 genome build . The results from each analysis , including up to 20 significantly enriched gene sets for each database with a region enrichment of 2-fold or more and an FDR<0 . 05 , were downloaded as tab-separated files . Whole cell lysates from UVSS1KO cell lines were separated by SDS-PAGE and western blotted as described previously [10] . Primary antibodies were rabbit polyclonal anti-JunD ( sc-74 , Santa Cruz Biotechnology ) , rabbit polyclonal anti-c-Jun ( sc-1694 , Santa Cruz Biotechnology ) , mouse monoclonal anti-Fra-1 ( sc-28310 , Santa Cruz Biotechnology ) , rabbit polyclonal anti-Fra-2 ( sc-13017 , Santa Cruz Biotechnology ) , mouse monoclonal ANTI-FLAG M2 ( F3165 , Sigma-Aldrich ) , and mouse monoclonal anti-actin ( A2228 , Sigma-Aldrich ) . Subconfluent cells were trypsinized , washed in PBS , and counted . Nuclei were prepared by detergent lysis in CLB ( 1 ml/107 cells ) and pelleted after 10 min on ice . Whole cells or nuclei were resuspended in IP50 buffer [75] ( 10 mM TrisHCl , 50 mM KCl , 0 . 1 mM EDTA , 10% glycerol , 1× protease inhibitor cocktail [Roche] ) at a concentration of 106 nuclei/ml and sonicated for 10 sec using a Sonic Dismembrator ( Fischer Scientific ) at 4 W . Aliquots of 0 . 5 ml containing 5×106 cells or nuclei were nutated with a 1∶200 dilution of antibody for 1 h , followed by a pulldown with Protein G Dynabeads ( Invitrogen ) . The beads were washed 3 times with IP50 buffer , then resuspended in sample buffer , denatured , and resolved by 6% SDS PAGE ( for RNAPII and FLAG-HA tagged proteins ) or 10% SDS PAGE ( for c-Jun ) . The gels were electroblotted to PVDF membranes , and blocked with 5% nonfat dry milk in Tris-buffered saline ( TBS ) . Primary antibodies were added in blocking buffer containing 0 . 1% Tween 20 , and the membrane washed 3× with TBS containing 0 . 1% Tween 20 ( TBST ) . Horseradish Peroxidase ( HRP ) -coupled secondary antibodies were added in blocking buffer with Tween , then washed 3× with TBST . The HRP signal was detected on X-ray film using ECL Plus Western Blot Detection reagents ( GE Healthcare ) . Total RNA was extracted from UVSS1KO-derived cell lines using TRIzol ( Invitrogen ) . RNAs were reverse-transcribed using random primers ( Invitrogen ) and Superscript III ( Invitrogen ) , then digested with pancreatic RNase A . cDNAs were purified on PCR Cleanup columns ( Qiagen ) , quantified with a Nanodrop spectrophotometer , and used for QPCR . QPCR was performed using SYBR Green Master Mix ( BioRad ) with 1 . 25 µM primers and 20 ng cDNA per reaction . All 4 combinations of 2 forward and 2 reverse primers were tested in the QPCR protocol . Table S8 lists primer pairs that amplified under QPCR conditions , generated a clean melting curve , and produced a single band upon gel electrophoresis of QPCR products . PGBD3 was expressed and MER85s were cloned as described in [16] . Electrophoretic mobility shift assays ( EMSAs ) were performed as described in [76] . Gels were dried , used to expose a storage phosphor screen , and scanned using a phosphorimager . Images were then quantitated using ImageJ [77] , and the difference in intensity of shifted bands was compared between adjascent lanes from samples with and without addition of PGBD3 protein . These differences were then normalized by comparison with the scrambled sequence control ( 0% ) and the MER85 consensus control ( 100% ) . Locations of MER85-39 , MER85-236 , MER85-592 , and MER85-763 were converted to hg19 coordinates using the UCSC Genome Browser Convert tool , then were submitted to the online version of MAPPER2 [78] to locate transcription factor binding sites from the JASPAR database . Results were filtered to the 90th percentile of MAPPER scores , then displayed in the UCSC Genome Browser using a feature of the MAPPER2 website . Transcription factor binding sites found in 3 of 4 MER85s were then used to search the Entrez Gene database [79] for human homologues . | Cockayne syndrome ( CS ) is a terrible and ultimately fatal childhood progeria often caused by mutations in the CSB chromatin remodeling and DNA repair protein . A piggyBac transposable element invaded the CSB gene about 43 million years ago , before humans diverged from marmosets . However , this last common ancestor “domesticated” the “selfish” invader , and our CSB locus now encodes both the original CSB protein and a novel CSB-piggyBac fusion protein joining the first third of the CSB protein to the piggyBac transposase . Although likely to be advantageous in health , expression of the conserved fusion protein in cells lacking any CSB-related protein induces a strong interferon response that may explain the brain calcifications seen in advanced CS . To determine whether continued expression of the fusion protein in CS patients affects the severity or heterogeneity of the disease , we identified all genomic binding sites for the fusion protein experimentally . We find that the fusion protein is tethered by protein–protein interactions to at least three transcription factor binding motifs on DNA ( AP-1 , TEAD1 , and CTCF motifs ) . The tethered fusion protein regulates nearby genes , including some that may induce the interferon response . Our data suggest that drugs or biologicals targeting innate immunity and inflammation may benefit CS patients . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"protein",
"interactions",
"genome",
"evolution",
"dna-binding",
"proteins",
"molecular",
"cell",
"biology",
"dna",
"immune",
"system",
"proteins",
"transposons",
"proteins",
"biology",
"evolutionary",
"genetics",
"molecular",
"biology",
"aging",
"biochemistry",
"nucleic"... | 2012 | Tethering of the Conserved piggyBac Transposase Fusion Protein CSB-PGBD3 to Chromosomal AP-1 Proteins Regulates Expression of Nearby Genes in Humans |
In bacteria , double-strand break ( DSB ) repair via homologous recombination is thought to be initiated through the bi-directional degradation and resection of DNA ends by a helicase-nuclease complex such as AddAB . The activity of AddAB has been well-studied in vitro , with translocation speeds between 400–2000 bp/s on linear DNA suggesting that a large section of DNA around a break site is processed for repair . However , the translocation rate and activity of AddAB in vivo is not known , and how AddAB is regulated to prevent excessive DNA degradation around a break site is unclear . To examine the functions and mechanistic regulation of AddAB inside bacterial cells , we developed a next-generation sequencing-based approach to assay DNA processing after a site-specific DSB was introduced on the chromosome of Caulobacter crescentus . Using this assay we determined the in vivo rates of DSB processing by AddAB and found that putative chi sites attenuate processing in a RecA-dependent manner . This RecA-mediated regulation of AddAB prevents the excessive loss of DNA around a break site , limiting the effects of DSB processing on transcription . In sum , our results , taken together with prior studies , support a mechanism for regulating AddAB that couples two key events of DSB repair–the attenuation of DNA-end processing and the initiation of homology search by RecA–thereby helping to ensure that genomic integrity is maintained during DSB repair .
Double-strand breaks ( DSBs ) are a potentially lethal form of DNA damage as incorrectly repaired or unrepaired breaks can lead to the loss of genetic information , chromosomal rearrangements , mutations , or cell death . Cells have evolved the ability to faithfully repair DSBs via homologous recombination using a sister chromatid or sister chromosome as a template [1–3] . In all domains of life , homologous recombination requires the processing of DSB ends to produce single-stranded DNA overhangs [3–5] . In bacteria this processing is carried out by a helicase-nuclease complex , such as AddAB or RecBCD , whereas eukaryotes use multiple complexes including the Rad50/Mre11 complex [3 , 4] . The single-stranded DNA overhangs produced by the helicase-nuclease complex become bound by a single-stranded DNA-binding recombinase , usually RecA in bacteria , or the homologous Rad51 in eukaryotes . The RecA/Rad51 filaments that form on ssDNA overhangs can initiate homology search and strand invasion to drive recombination and subsequent repair of the damaged chromosome [1–3] . Precisely how DSB end processing occurs and how it is regulated to ensure the generation of single-stranded DNA required for recombination without an excessive loss of genomic information is not fully understood . Biochemical studies have led to two general models . In one model , the nuclease-helicase complex initially degrades both strands of DNA until it encounters a specific DNA element called chi ( crossover hotspot instigator ) that triggers a switch to a state that drives resection of only one strand , thereby producing the necessary ssDNA overhang needed to initiate homologous recombination [6–13] . This model has emerged from biochemical studies on E . coli RecBCD , which has two independent helicase domains and a nuclease domain , and B . subtilis AddAB , which contains a helicase and a nuclease domain in AddA along with a nuclease domain in AddB ( which also carries an inactive helicase domain ) [4 , 5 , 14] . Biochemical studies of AddAB from B . subtilis show that , like RecBCD , recognition of chi sequences on the 3’-terminated strand during degradation converts AddAB from a double-stranded nuclease to a single-stranded DNA nuclease and slows its effective rate of translocation [15–18] . Structural studies have further suggested that this could be due to a conformational change in the complex following chi recognition by AddB [14 , 19] . The single-stranded DNA generated by either RecBCD or AddAB is thought to be bound by the RecA filament . In the case of RecBCD , the loading mechanism for RecA has been well-characterized , showing that RecA binding to single-stranded DNA is facilitated by RecBCD in a chi-dependent manner , via a direct interaction with RecB [8 , 20 , 21] . How RecA loading occurs in the context of AddAB remains to be determined . The alternative model for production of single-stranded overhangs posits , at least in E . coli , that RecBCD initially unwinds a DSB end but without any degradation [6] . Upon activation at a chi site RecBCD then nicks one strand with subsequent helicase activity separating the two strands to create a single-stranded overhang that can load RecA and initiate homologous recombination . Which of these two models applies in vivo to AddAB is not fully resolved . As noted , the molecular events underlying the initial processing of DSBs have been extensively studied in vitro , both in bulk and in single-molecule experiments [14–17 , 19 , 22–24] , but assessing these events and measuring the rates of AddAB or RecBCD-dependent processing of a DSB on the chromosome in living cells remains a major challenge . Genetic assays used to assess RecBCD [25 , 26] , and to some extent AddAB [27–30] activity , in vivo have provided important insights , but direct measurements of DNA processing by these helicase-nuclease complexes has been limited . The assays used often involve measuring the retention of radioactively labeled nucleotides in chromosomes subjected to UV damage [31] , which produces a large number of lesions and kills most cells , complicating the estimation of degradation rates by a helicase-nuclease complex acting on a single chromosomal DSB . Techniques such as Southern blotting [32 , 33] have also been used to probe helicase-nuclease activity in vivo , but cannot easily be used to examine DNA processing on a global level . Advances in whole genome DNA sequencing in combination with the development of systems for the controlled introduction of single DSBs [32 , 34–36] in bacterial chromosomes now offer the ability to probe the in vivo activity of helicase-nuclease complexes like AddAB with higher resolution and more precision . Caulobacter crescentus is a useful organism for probing the in vivo dynamics and mechanisms underlying DSB repair . Unlike many bacteria , Caulobacter exhibits once-and-only-once replication of its chromosome under all growth conditions , and large populations of cells are easily synchronized , enabling the isolation and study of cells with a single chromosome . DSB repair has been previously visualized at the single-cell level in Caulobacter [37] . Here , we examine the in vivo processing of site-specific DSBs introduced in the Caulobacter chromosome , which is thought to require AddAB [37 , 38] , using the endonuclease I-SceI [34 , 37] . Using a deep sequencing-based assay we measure the extent of DNA processing by AddAB around a break site and provide evidence that AddAB initially degrades both strands , but is then triggered , by putative chi sites , to resecting a single strand . We show that putative chi sites in Caulobacter attenuate the rate of AddAB-mediated DNA processing in vivo , but only with ~20% efficiency , similar to in vitro estimates for B . subtilis AddAB [16] . We find that , in the absence of RecA , AddAB translocation rates inferred in vivo are comparable to the previously measured in vitro rate of ~400 bp/s for B . subtilis AddAB [16 , 22 , 23 , 39] . Further , our results suggest that , in the presence of RecA , AddAB translocation after chi recognition is reduced ~4-fold . Successful attenuation of degradation requires the formation of a RecA filament , but not the SOS response or recombination . Collectively , our results indicate that RecA likely downregulates the translocation rate of AddAB after chi , possibly through a direct protein-protein interaction . This regulation of AddAB by RecA helps to limit DNA degradation around a break site , thus constraining the impact on transcription to a more limited region of the genome .
To assess DNA processing around a DSB induced on the chromosome , we used the I-SceI system previously developed in Caulobacter [37] ( Fig 1A ) . Briefly , a single I-SceI site was introduced +780 or +3042 kb from the origin of replication and the I-SceI enzyme , which recognizes and cleaves the I-SceI site to generate a DSB , was placed under a vanillate-regulated promoter on the chromosome [37 , 40] . This promoter is repressed by the protein VanR; addition of vanillate releases VanR from the Pvan promoter and induces gene expression [40] . DnaA , the replication initiator , was expressed from an IPTG inducible promoter to control the replication state of cells . To isolate the initial step of DSB processing from later events of homologous recombination , we conducted our experiments primarily in cells with a single chromosome , the swarmer cells of a C . crescentus population . To isolate these swarmer cells and to prevent subsequent rounds of replication , cells were grown without IPTG for 1 . 5 h to deplete DnaA and arrest cells in a G1 state . These G1-arrested cells were then isolated using Percoll density gradient centrifugation [41]; flow cytometry analysis verified that this procedure produced a population of G1-phased swarmer cells ( S1A Fig ) . I-SceI was then induced by adding vanillate for 0 . 5 , 1 , 2 , or 4 h and genomic DNA isolated and sequenced . As a control , genomic DNA from swarmer cells to which vanillate was not added was also isolated and sequenced . The fold difference in reads per kilobase per million ( rpkpm ) in the DSB-induced sample relative to the control was plotted as a function of genomic position , hereafter referred to as a DSB processing profile . In cells with a single chromosome , induction of a DSB with 500 μM vanillate at +780 kb from the origin resulted in bidirectional loss of DNA around the break site , with an ~30% and ~40% drop in reads at the DSB site after 0 . 5 h and 1 h respectively ( Fig 1B , S1B and S1C Fig ) . After 2 h , there was an ~60% drop in reads at the location of the DSB site . Similar decreases in reads were also observed upon induction of a DSB +3042 kb from the origin ( Fig 1C , S1D Fig ) . In this case , 1 h of vanillate induction resulted in an ~60% drop in reads at the site of the DSB with 2 h of induction resulting in an ~80% drop in reads . The DSB processing profiles were highly reproducible , with independent repeats yielding r values of 0 . 94 ( S1E Fig ) . To confirm that the troughs observed in the profiles ( Fig 1B and 1C ) result from DSB processing by AddAB , we repeated our assay in ΔaddAB cells . These cells did not show any significant drop in reads near the DSB site , or elsewhere in the genome ( Fig 1D ) , indicating that AddAB is , in the growth conditions tested here , the only helicase-nuclease complex that processes a DSB . We also generated a DSB processing profile for an unsynchronized , actively replicating population of cells . We first treated an asynchronous population of cells with 500 μM vanillate ( as with the synchronized G1/swarmer cells in Fig 1B ) to drive maximal induction of I-SceI , which is sufficient to drive cleavage of both chromosomes in nearly all cells with two chromosomes [37] , thereby preventing homologous recombination-based repair . However , even with maximal induction of I-SceI , a very small percentage of cells could experience only a single DSB that can be repaired . Treatment with 500 μM vanillate produced a profile almost identical to that observed with swarmer cells alone ( Fig 1E ) . This effect was not because cells entered a G1 arrest ( S1G Fig ) , indicating that AddAB activity is not significantly influenced by the replication status of the cells . We also measured the DSB processing profile for cells in which I-SceI was induced with 2 μM vanillate ( Fig 1F ) . These cells likely experience only a single DSB and thus can repair the damaged chromosome through homologous recombination-based repair , as judged by the fact that cells treated with 2 μM vanillate showed no major change in their flow cytometry profile ( S1G Fig ) , did not lose viability , and were previously shown to engage in homology-based repair [37] . The profile for these cells was similar in shape to that of synchronized swarmer cells or asynchronous cells treated with 500 μM vanillate , but the magnitude of differences was substantially reduced ( Fig 1F ) , likely because some fewer cells experience DSBs and because cells can repair a single cut chromosome . The library preparation procedure used should , in principle , only result in the sequencing of double-stranded DNA . However , DSB resection could , in principle , result in the production of some single-stranded DNA . To ensure that we were not sequencing single-stranded DNA , we treated genomic DNA extracted from a DSB-induced sample with Mung bean nuclease , which degrades single-stranded DNA . The resulting degradation profile was indistinguishable from that of a sample not treated with the nuclease ( S1F Fig ) , indicating that our method only produces reads for double-stranded DNA . Thus , our profiles likely represent total DNA loss due to the degradation of one or both strands from the DSB . This interpretation would also fit with prior biochemical studies indicating that AddAB can degrade double-stranded DNA and resect a single strand . However , as noted before , an alternative model for E . coli RecBCD posits that the helicase-nuclease complex initially unwinds the two strands and then , at chi sites will nick and again unwind but not degrade the DNA . If the initially unwound DNA reannealed , it would form double-stranded DNA that would be sequenced . But if that were occurring , we would not have seen any loss of reads near the DSB site . Alternatively , the initially unwound DNA may not anneal , remaining single-stranded , which is not captured in our sequencing . Hence , to distinguish between these possibilities , we performed quantitative PCR ( qPCR ) , which can report on total DNA , including any single-stranded DNA that may be missed in our sequencing assay . If AddAB were only unwinding the DNA flanking a DSB , then qPCR using primer pairs at loci adjacent to a DSB should yield significantly more product than a primer pair that spans the DSB site itself . However , the qPCR values for loci immediately adjacent to a DSB site at +780 kb were comparable to the value for the DSB site itself , and to the values measured by our sequencing-based profiling , supporting the notion that AddAB initially degrades both strands of DNA ( Fig 1G and 1H ) . The qPCR values increased at sites further from the DSB site , relative to the qPCR value at the DSB and relative to the values measured by our sequencing approach , likely reflecting the presence of some single-stranded DNA not detected in our sequencing assay . Thus , taking together prior biochemical studies and our own sequencing and qPCR data , we favor a model in which AddAB initially degrades both strands and then , in response to chi sites ( see below ) , switches to resecting a single strand . There is also a formal possibility that AddAB does initially just unwind the DSB end and that other nucleases in the cell degrade each strand , but such a model is less parsimonious as it invokes additional components that are not necessary in vitro . In the 1 h DSB processing profile for swarmer cells with a DSB induced at +780 kb ( Fig 1B ) the read counts were lowest at the site of cleavage and then increased progressively in both directions until they matched the read counts of the control profile . These profiles can , therefore , be used to estimate an upper bound on the speed of AddAB-dependent processing . After 1 h , the first point of separation between the DSB-induced and control profiles was ~450 kb to the left of the DSB and ~1300 kb to the right . Thus , the rate of processing ( degradation and resection ) in vivo can be , at most , ~100 bp/s to the left and ~200 bp/s to the right . However , because the profiles are not step functions and instead feature a gradual decrease from +450 and +1300 kb toward the DSB site , it implies that most cells degrade DNA more slowly than 100–200 bp/s or initiate DSB processing at different times . To test this latter possibility , we measured cell viability as a function of time after adding vanillate to induce I-SceI and a DSB . Because we induce DSBs in swarmer cells containing a single chromosome , recombination-based repair cannot occur and a DSB is lethal . Thus , if all cells experienced a DSB immediately after the addition of vanillate , we would expect a precipitous drop in viability post-induction . Instead , we found a gradual decrease in viability suggesting that cells likely experience a DSB at variable times ( S1H Fig ) . Heterogeneity in a population of cells may also arise if DNA degradation proceeds at different rates in individual cells or if DSB processing is slowed or stopped at different frequencies , possibilities explored further below . For DSBs at either +780 or +3042 kb , the global processing profile was clearly asymmetric around the break site ( Fig 1I and 1J ) . In each case , read loss extended further toward the terminus than the origin . Prior studies have shown that DNA degradation by helicase-nuclease complexes such as AddAB or RecBCD is negatively regulated by chi sequences that are highly abundant in bacterial genomes , and often with a much higher frequency on the lagging-strand template , with respect to DNA replication [2 , 42] . Because B . subtilis AddAB directionally recognizes chi sites [4] , the distribution of these sequences may underlie the asymmetry seen in our degradation profiles . The putative chi sequence in Caulobacter was previously predicted computationally to be 5’-GCGGTGGT-3’ [43] ( Fig 2A ) . To test whether this sequence element is responsible for degradation asymmetry , we first overlaid on the DSB processing profile of cells with a DSB induced at +780 kb the putative chi sequences on the leading ( Figs 2B and S2A ) and lagging strands ( Figs 2C and S2B ) that may affect AddAB translocation and degradation in the 3’->5' direction . Leading and lagging strands are defined with respect to DNA replication , which is presumed to proceed bidirectionally from the origin ( 0 kb ) to the terminus ( ~2000 kb ) ; for the sake of simplicity , we use 'leading' and 'lagging' strands to refer to the lagging- and leading-strand templates , respectively ( Fig 2A ) . Additionally , we note that the chi sequences shown in Fig 2 and throughout our study are those that match the computationally predicted chi sequence and are in an orientation that would , in principle , allow them to affect AddAB . The presence of putative chi sequences was inversely correlated with the extent of degradation , with more degradation occurring on the side of the DSB where the density of putative chi sequences was substantially less . This pattern was also observed when a DSB was induced +3042 kb from the origin ( S2C and S2D Fig ) suggesting that the asymmetry observed in the DSB processing profiles results from an effect of chi site frequency on AddAB activity . To more directly determine whether 5’-GCGGTGGT-3’ is the Caulobacter chi sequence and whether the presence of this sequence explains the asymmetry of our profiles , we inserted an array of 15 such sequences on the chromosome either +30 kb or +100 kb from the DSB site at +780 kb . This chi array was inserted either in the correct orientation for recognition by AddAB ( chifor ) or in the opposite orientation as a control ( chirev ) . After inducing a DSB , the chirev construct had no effect on degradation ( Fig 2D and 2E , S2E and S2F Fig ) . In contrast , the chifor construct significantly reduced degradation beyond the location it was inserted when compared to the control or wild-type profiles ( Figs 2D , 2E , S2E and S2F ) . Although degradation beyond the inserted array of chi sites was reduced , it was not completely eliminated . Given prior studies suggesting that chi sites switch AddAB to a mode in which it degrades only one strand to produce resected DNA [4 , 17] , we infer that ssDNA degradation likely occurs more slowly than the initial double stranded degradation . Alternatively , it is possible that the chi recognition frequency is low . These data also support the conclusion that 5'-GCGGTGGT-3' is likely the chi sequence in Caulobacter , though we have not , of course , shown whether they are sites where homologous recombination preferentially occurs . Importantly for our purposes though , these sites clearly affect DSB processing and likely explain the asymmetric degradation by AddAB from a DSB site . Notably , the chifor construct produced a clear difference in the profile , relative to the wild type and cells harboring the chirev construct . This difference was evident within ~2–5 kb of the site of insertion ( Fig 2F and 2G ) , demonstrating that our assay has a resolution of at least 5 kb and likely better . Collectively , the results presented thus far suggest that the recognition of chi sequences by AddAB results in an attenuation of AddAB-mediated processing of DNA around a break site . Next , we wondered whether the attenuation of DSB end processing after chi recognition may result , in part , from the recruitment of RecA to the ssDNA formed after AddAB encounters a chi site . To examine the effect of RecA on DSB processing , we measured the DSB processing profile of cells lacking RecA ( Fig 3A , S3C and S3D Fig ) . In sharp contrast to the wild type , the profile for ΔrecA cells exhibited significantly more extensive degradation and/or processing in both directions after a DSB ( Fig 3A ) . Additionally , the asymmetry of the degradation profile was no longer apparent in cells lacking RecA ( Fig 3B and 3C ) . After 1 h , the first point of separation between the DSB-induced and control profiles was approximately the same on both sides of the DSB , yielding an upper bound of ~400 bp/s for the rate of DNA processing in both directions in ΔrecA cells . Note that we also detected more total DNA via qPCR than in our DSB processing profiles of ΔrecA cells , again with a progressively increasing ratio of qPCR to degradation profile values away from the DSB site ( S3A and S3B Fig ) . These results indicate that RecA is important in limiting the extent of DSB end processing . The effect of RecA on DSB resection could be indirect . RecA , when bound to single-stranded DNA , can induce auto-cleavage of the transcriptional repressor LexA , resulting in the expression of genes in the SOS regulon , many of which participate in DNA damage response and repair [44 , 45] . To test whether the effect of RecA on AddAB-dependent degradation around a DSB is indirectly mediated via the SOS response , we introduced a non-cleavable mutant of LexA [46] into our DSB system and measured the profile of cells after a DSB . The profile for this non-cleavable LexA mutant strain was nearly indistinguishable from the wild-type profile ( Fig 3D ) . Thus , the effect of RecA on AddAB-dependent degradation is likely not mediated through its effect on the SOS regulon . Further , the dispensability of the SOS response for AddAB regulation suggests that basal levels of RecA are sufficient to prevent excessive DNA processing at a DSB [45] . To test whether RecA directly interacts with AddAB , we used a bacterial two-hybrid assay to screen for physical interactions [47] . Each protein was fused to a subunit of adenylate cyclase and then co-expressed in E . coli . Interaction between two fusion proteins will reconstitute adenylate cyclase , leading to production of cAMP and the subsequent activation of a reporter gene that turns colonies red on MacConkey agar . In this assay , we found that RecA interacted with AddA but not with AddB ( Figs 3E , S3E and S3F ) . We also confirmed an interaction between AddA and AddB , as expected , but not between AddA and a negative control , FtsZ . RecA or AddA also did not display interaction with empty vector controls , T18 and T25 respectively . Further , our assay indicated that RecA likely interacts with the N-terminal portion of AddA ( Figs 3F , S3E and S3F ) , where the helicase domain of AddA resides . This is in contrast to RecA’s interaction with RecB in E . coli , which occurs via the nuclease domain of RecB [20] . RecA forms a filament on single-stranded DNA that is formed by AddAB after it interacts with chi and begins degrading only one strand of the DNA [2 , 4 , 5] . To test whether this filament forming activity of RecA is necessary for it to interact with and regulate AddAB , we generated a mutant , RecA ( K83A ) , predicted to abrogate filament formation based on studies of E . coli RecA [48–50] . This mutant retained an interaction with AddA in the bacterial two-hybrid system and was expressed in vivo at levels comparable to the wild type protein ( S3G Fig ) . However , the K83A mutant was incapable of regulating AddAB-mediated DNA resection as the degradation profile of the mutant looked similar to that of ΔrecA cells ( Fig 3G ) . We conclude that RecA likely must form a filament to properly regulate AddAB and attenuate DNA degradation and processing . To test whether recombination is required for AddAB regulation , or if RecA filament formation is sufficient to slow down AddAB after chi recognition , we constructed a strain producing RecA ( N304D ) [51] , which is predicted to be recombination deficient but still capable of binding ssDNA to form a filament . This mutant was also expressed in vivo ( S3G Fig ) , but sensitive to DSBs , comparable to ΔrecA cells ( S3H Fig ) . In asynchronously growing , replicating cells induced with maximal levels of I-SceI ( 500 μM vanillate ) , the profile of this mutant was comparable to wild-type cells , including the same asymmetry around the DSB site ( Fig 3H ) . In cells treated with only 2 μM vanillate ( a concentration that allows for repair via homologous recombination in wild-type cells ) , an asymmetric global profile was still seen in the recombination-deficient mutant ( Fig 3I ) . These results suggest that recombination is likely not required for RecA-mediated regulation of AddAB . To further probe the effect of RecA on AddAB , we sought to assess the rates of AddAB-dependent processing in vivo , both before and after chi recognition . These rates have been measured previously in vitro for B . subtilis AddAB , although only in the absence of RecA [16 , 22 , 24 , 39]; the B . subtilis AddAB translocation rate before chi recognition was reported to be between 400 and 2000 bp/s , with a chi recognition probability of ~0 . 25 , and a post-chi translocation rate only ~15% slower than the pre-chi rate . To estimate the in vivo rates and chi recognition probability , we ran a simulation of DNA degradation/processing with four parameters: ( i ) the rate of DSB formation after adding inducer , which produces a distribution of times post-induction when DSB processing begins , ( ii ) the degradation rate before chi recognition , ( iii ) the probability of recognizing each chi site , and ( iv ) the DNA processing rate after chi recognition . Rates of DSB formation were estimated via simulations ( S4A and S4B Fig ) and confirmed by performing qPCR on chromosomal DNA isolated from DSB-induced ΔaddAB cells ( S4C and S4D Fig ) . Based on previous studies [22 , 39] , it was assumed that AddAB would recognize only one chi sequence and , once bound to chi , the complex would not be affected by further chi sequences encountered . With a rate of DSB formation of ~0 . 4 DSB / h , we first tested the parameters measured in vitro and found that a pre-chi degradation rate of 400 bp/s , a chi recognition probability of ~0 . 23 , and a post-chi degradation rate of 340 bp/s , produced a close fit to the in vivo symmetric DSB processing profile of cells lacking recA ( Fig 4A ) . We next considered the case of wild-type cells in which RecA attenuates AddAB-dependent degradation and combines with chi to produce an asymmetric degradation profile . A reasonable fit to the wild-type profile at 1 and 2 h was produced by simulations with a pre-chi degradation rate of 400 bp/s , a chi recognition probability of ~0 . 22 , and a post-chi processing rate of 51 bp/s . However , these parameters did not fit the measured profile at 4 h ( S5A Fig ) . At this later time point , the model predicted more extensive degradation than was observed . In fact , the profile at 4 h was not substantially different than that observed at 2 h . This could indicate that cells are dead after 4 hours , although DNA degradation can likely still occur even when cells are no longer viable as judged by plating assays . Thus , the similarity between the 2 and 4 h profiles could suggest that AddAB dissociates from the DNA at long time points . We therefore added a fifth parameter to the model , the rate of AddAB dissociation and considered two possible models: Model 1 where we ( i ) fixed the post-chi processing rate to be 15% less than the pre-chi rate , as measured in vitro in the absence of RecA [16 , 24] , and ( ii ) varied the rate of AddAB dissociation after chi recognition; Model 2 where we varied both the post-chi processing rate and the dissociation rate for AddAB ( Fig 4B ) . For each model we identified parameters that produced good fits to the degradation profiles at each time point measured ( Fig 4C and 4D , S5B and S5D Fig ) . In each case the rate of degradation pre-chi recognition was 400 bp/s and the probability of chi recognition was ~0 . 23 . In Model 1 where the post-chi processing rate was 340 bp/s , the dissociation rate for AddAB was 0 . 102 / min . In Model 2 , the post-chi processing rate was 96 bp/s with a dissociation rate for AddAB of 0 . 021 / min . To distinguish between these models , we sought to directly measure the post-chi processing rate , which differs more than 3-fold between the two models , by examining the time it takes AddAB to degrade two loci positioned at specific distances from a DSB site on the arm where chi site density is highest . For these experiments we used a strain with a DSB site located +30 kb from the origin , which enabled us to label the endogenous parS locus ~38 kb from the DSB site by expressing a fusion of the protein MipZ and YFP . MipZ binds ParB , which forms a large nucleoprotein complex at parS; thus , MipZ-YFP forms a fluorescent focus in vivo that marks the cellular position of parS [52] . We also inserted an orthogonal parS site from the plasmid pMT1 either 130 or 230 kb from the break site; expressing the cognate ParBpMT1 fused to CFP enables the in vivo tracking of this locus [53] . Using time-lapse microscopy we then measured the timing of disappearance of the MipZ-YFP and ParBpMT1-CFP foci after inducing a DSB . We infer that the disappearance of each focus reflects the degradation of either one or both strands that correspond to a given locus , as occurs during DSB processing . Mere translocation of a protein , such as AddAB , past a locus would not lead to the permanent losses in fluorescent foci seen here; for instance , MipZ foci are well known to be maintained after the passage of the replisome [52] . The model with a post-chi degradation rate of 340 bp/s ( Model 1 in Fig 4B ) predicted an interval between loss of the two fluorescent foci of ~4 or 9 min , respectively , whereas the model with a post-chi degradation rate of 96 bp/s ( Model 2 in Fig 4B ) predicted an interval of ~15 or 30 min , respectively ( Fig 4E–4J ) . Our measurements , using fluorescence time-lapse microscopy , revealed mean intervals of ~12 and 29 min ( Fig 4E–4H ) , respectively , depending on whether the second locus was 130 or 230 kb from the break site . As predicted from the above results , we also found that the frequency of loss of a marker -130 kb from the break site was higher than a marker -230 kb away ( Fig 4J ) . Thus , we favor a model in which AddAB initially drives DSB ends processing at a rate of ~400 bp/s , with an ~23% chance of recognizing each chi site during translocation , and that chi recognition in combination with RecA loading on the single-stranded DNA produced by AddAB slows subsequent processing or translocation ~4-fold , with an additional , modest rate of AddAB dissociation . Taken together , our results indicate that DSB ends are often subject to extensive degradation and processing , as the AddAB complex may not slowdown at the first chi site encountered or may slowdown but continue degrading one strand of the DNA . Thus , AddAB-dependent processing of DSB ends could affect the transcription of genes flanking a break site , as shown recently in yeast [54] . To test this possibility in Caulobacter , we performed RNA-seq on swarmer cells subjected to a single DSB . We compared the expression levels of individual genes to untreated swarmer cells . A set of genes associated with the DNA damage response in Caulobacter that are found throughout the chromosome increased significantly following a DSB . In addition , we observed a clear decrease in the RNA levels of genes nearest the DSB site ( Fig 5A and 5B ) . These transcriptional profiles correlated well with the DNA processing profiles seen after inducing a DSB . The asymmetry observed in the DNA profiles was also observed in the transcriptional profiles , with larger decreases in transcription on the arm with fewer chi sequences . The loss of transcription near a DSB was dependent on AddAB ( Fig 5C and 5D ) . Because RecA associates with DNA around the break site we also conducted RNA-seq experiments in cells lacking RecA to test the effect of RecA on global and local transcriptional changes . In contrast to the wild type , the decrease in transcription around the break site in ΔrecA cells was no longer asymmetric and was more extensive compared to recA+ cells ( Fig 5E–5F ) . This result suggests that by slowing AddAB-dependent processing of DSB ends , RecA may help prevent excessive and potentially deleterious losses in transcription .
Homologous recombination in bacteria has been extensively studied in vitro and deep mechanistic insights into the function of various protein complexes that participate in the process have been gained using ensemble biochemistry experiments as well as single molecule and structural studies [4 , 6 , 8 , 14 , 15 , 17 , 55] . Recent advances in imaging and sequencing now provide a way to also probe and dissect these mechanisms in the context of living cells and in the context of individual DSBs . Homologous recombination is likely to be profoundly influenced in vivo by the structure and organization of the chromosome , and by other concomitant cellular processes such as DNA replication and transcription . Prior efforts to examine the activities of RecBCD [25 , 26 , 31 , 32 , 56 , 57] and AddAB [27–29] in vivo have often relied on UV irradiation to create DSBs , but UV light can introduce a range of different types of lesions in the DNA and likely creates a large number of lesions simultaneously . Even in the case of limited UV doses that create only 1–2 lesions per chromosome , the location and timing of the lesions cannot be precisely determined . While the development of I-SceI-based cleavage offers an ability to precisely control the number and timing of DSBs in vivo [32 , 34 , 37] , previous assays for monitoring DNA processing have relied on techniques such as radiolabeled nucleotide incorporation , which has limited resolution , or Southern blotting , which cannot query DNA processing on a global level [31] . Here , we developed a novel in vivo assay for monitoring DSB processing by the helicase-nuclease AddAB with relatively high resolution and at a genomic level . Using this assay , we found that AddAB bidirectionally processes a DSB in an asymmetric manner , with one arm of the chromosome undergoing more degradation than the other arm . This asymmetry correlated with the asymmetric distribution of putative chi sequences on the leading and lagging strands , and ectopically inserting these putative chi sites was sufficient to significantly slow AddAB-dependent processing ( Fig 2 ) . Notably , the difference between the degradation profiles for cells with and without the chi sites inserted was detected within ~2–5 kb of the site of insertion ( Fig 2F and 2G ) , demonstrating that our assay has nearly kb resolution and is thus a powerful method for probing DSB processing and repair processes . Our mathematical modeling suggested that chi sites are recognized by and trigger a slowing of AddAB with a probability of 0 . 23 . However , the insertion of an array of 15 sites did not completely stop DNA processing beyond the site of insertion , possibly because clustered chi sites are not recognized independently , as noted previously for chi recognition by RecBCD in E . coli [36] . Regardless , our results demonstrate that the putative chi site used in these arrays has a demonstrable effect on AddAB-dependent processing of DSBs in Caulobacter . Whether these putative chi sites are also the sites of increased recombination events as with chi sites in E . coli remains to be determined . The asymmetry of AddAB-dependent degradation likely leads to an asymmetry in RecA loading , as seen in recent E . coli RecA ChIP-Seq studies [36] . The loading of RecA onto single-stranded regions of processed DNA ends triggers a significant decrease in AddAB resection or degradation rates as cells lacking recA showed more extensive DNA degradation and processing with no obvious asymmetry ( Fig 3 ) . These results are consistent with previous work showing that RecA is required to prevent excessive , or 'reckless' , DNA degradation by RecBCD in E . coli [25 , 26 , 31 , 56 , 58 , 59] , suggesting a conserved mechanism for the regulation of DSB resection in bacteria . Our bacterial two-hybrid results suggest that RecA may regulate AddA through a direct interaction , with AddA probably recruiting RecA to a DSB , as RecBCD does in E . coli [20 , 21] . The regulation of AddAB activity likely depends on formation of a RecA filament on ssDNA produced by AddAB , but does not require the SOS response and an increase in RecA levels , nor does it appear to require RecA recombinase activity ( Fig 3 ) . Collectively , our results favor a model in which AddAB activity is effectively self-limiting . In this model , AddAB generates ssDNA upon chi recognition , on which RecA can form a filament . AddA may recruit RecA to these regions of ssDNA or somehow promote RecA filament formation , and the subsequent RecA-triggered slowdown in AddAB translocation or nuclease activity [60] limits further resection . This model couples two key events of homologous recombination . The production of RecA-bound DNA , which is competent for homology search , slows or limits additional DSB end processing , enabling homologous recombination to proceed , without any additional impact on the chromosome . The pattern of our wild-type degradation profile ( Fig 2B and 2C ) suggests that there is active degradation of DNA around a DSB even before chi recognition . Our experiments further support the idea that ssDNA is generated after chi recognition as the amount of ssDNA we detect via qPCR increased further from the DSB site and as more putative chi sites were encountered by AddAB ( S3 Fig ) . The notion that RecA may directly or indirectly regulate DNA resection by RecBCD in E . coli has also been suggested previously [25 , 26 , 56 , 58] , but precisely how this occurs has been unclear . There are two general models , both of which would lead to "reckless" DNA degradation in the absence of RecA . In one model , as suggested for RecBCD in E . coli [61–66] , RecA loaded onto the ssDNA initially produced by AddAB prevents the reloading of any AddAB or any other exonuclease . In an alternative model , RecA filaments or bundles [48 , 67] physically limit or slow translocation or DNA resection by AddAB after it recognizes a chi site . These models are not mutually exclusive and it is possible that a combination of both occur in the cell . Using a mathematical model to fit our experimentally determined degradation profiles we found two sets of parameters that fit the degradation profiles of wild-type cells . In one , AddAB slowed down only slightly after chi recognition , favoring a model in which RecA primarily blocks reassociation of AddAB that has dissociated from the DNA . In the other model , AddAB slowed down more significantly after chi recognition , favoring a model in which RecA primarily attenuates AddAB-dependent DNA processing . Direct assessments of the degradation rates in vivo using single-cell fluorescence microscopy ( Fig 4E–4H ) support the latter model in which degradation slows ~4-fold after chi recognition , leading us to favor a model in which RecA primarily regulates AddAB translocation . While AddAB may also be influenced by later steps of homologous recombination and repair that were not captured in our experimental set-up , our data indicated that recombination per se is not essential for an asymmetric degradation profile in the presence of RecA ( Fig 3H and 3I ) . Whatever the case , limiting AddAB mediated DNA resection is likely important to prevent the excessive loss of DNA if the later steps of homologous recombination are delayed . As already noted , we also estimated degradation rates in individual cells by tracking the interval of time between loss of fluorescent markers at two different loci near a DSB ( Fig 4E–4H ) . These measurements supported a model in which chi recognition by AddAB and the subsequent loading of RecA onto ssDNA slows AddAB-dependent degradation to ~100 bp/s on average . Notably however , the degradation rates measured in individual cells showed much greater variability than was captured in our model ( Fig 4 ) . This variability could reflect noise in our measurements of degradation in single cells . Alternatively , it could reflect inherent stochasticity in the degradation rate , or another step of DSB processing , such as the recognition of chi sites . Such variability may also partly explain why the degradation profiles show a gradual rather than step-like increase outward from the site of a DSB , although this effect may also arise from variability in when a DSB occurs in individual cells expressing I-SceI . In sum , our study helps to reveal how AddAB , chi sites , and RecA combine to facilitate homologous recombination and maintain genome integrity . In particular , our results highlight an important additional role for RecA . In addition to promoting the pairing of homologous chromosomes , RecA helps to limit DNA resection by AddAB , which , in turn , limits the loss of genetic material and potentially deleterious decreases in gene expression ( Fig 5 ) . Given the highly conserved nature of homologous recombination , we speculate that RecA homologs , Rad51 proteins , in eukaryotes may play a similar role .
Bacterial two hybrid experiments were performed as described in [69] . Briefly , genes of interest were fused to the 5’ or 3’ end of the T18 or T25 fragments in the pUT or pKT vectors [47] . The fusion plasmids were co-transformed into E . coli BTH101 . Co-transformants were grown until saturation in M63 media with maltose , IPTG and appropriate antibiotics and 5 μL of culture was spotted on MacConkey agar ( 40 g/L ) plates with maltose , IPTG and appropriate antibiotics . Plates were incubated at 30°C for 2–3 days . Cells were pelleted and then resuspended in 1xSDS sample buffer and heated to 95°C for 5 min . Equal amounts of total protein were run on 10% Tris-HCl gels ( Bio-Rad ) at 150V for separation . Resolved proteins were transferred to polyvinylidene fluoride membranes and probed with 1:5000 dilution of primary antibodies against RecA ( Sigma ) and secondary horseradish-peroxidase-conjugated antibody ( 1:5000 ) . Blots were visualized using a FluorChem M imager ( ProteinSimple ) . Caulobacter cells were depleted of DnaA for 1 . 5 h and G1-arrested cells were then isolated by Percoll density gradient centrifugation . Swarmer cells were then released into DnaA depleting conditions ( without IPTG ) and DSBs were induced for 1 h by the addition of 500 μM vanillate . Cells were pelleted and genomic DNA was isolated using the DNeasy Blood and Tissue kit from Qiagen . For Mung bean treatment , isolated genomic DNA was incubated at 30°C for 15 min with Mung bean nuclease [70] ( NEB ) and the DNA was purified with a phenol-chloroform extraction . DNA was sent for whole-genome Illumina sequencing ( BioMicroCenter , MIT ) . Caulobacter cells were depleted of DnaA for 1 . 5 h and G1-arrested cells were then isolated by Percoll density gradient centrifugation . Swarmer cells were then released into DnaA depleting conditions ( without IPTG ) and DSBs were induced for 1 h by the addition of 500 μM vanillate . Cells were pelleted and frozen in liquid nitrogen for RNA extraction . Cells ( in pellets ) were lysed by treatment with 400 μL of 65°C-preheated Trizol ( Thermoscientific ) for 10 min on a thermomixer at 200 rpm . They were frozen at -80°C for 30 min and then centrifuged at 4°C at maximum speed for 5 min . Supernatant was aspirated and added directly to 400 μL of 100% ethanol . The mixture was applied to an RNA-extraction spin column ( Zymo Research ) . The column was then spun at 10000 rpm for 30 s and the spin column was washed with 400 μL of RNA Prewash solution twice and finally with 700 μL of RNA Wash buffer . Residual RNA Wash buffer was removed by an additional centrifugation step . RNA was eluted out with 90 μL of DEPC-treated water . DNase I treatment was carried out to remove any genomic DNA and the RNA was purified using acidic phenol-chloroform extraction . The integrity of the RNA was checked via agarose gel and submitted for Illumina sequencing ( BioMicroCenter , MIT ) . For analysis of DNA sequencing data , Hiseq 2500 Illumina short reads ( 40 bp ) were mapped to the Caulobacter NA1000 reference genome ( 4 . 01 Mbp ) ( NCBI Reference Sequence: NC-011916 . 1 ) using Bowtie 1 [71] using the following command: bowtie -m 1 -n 1 --best --strata -p 4 --chunkmbs 512 NA1000-2014-bowtie --sam * . fastq The Caulobacter NA1000 genome was divided into 4000 bins and mapped Illumina reads were allocated to their corresponding bins to quantify the number of reads in each genomic bin . For samples where the DSB was introduced at 780 kb , datasets were normalized so that the experimental and the control dataset have the same number of Illumina reads between genomic position 2800 kb and 3400 kb . This genomic region was chosen since it is far from the DSB and was not affected by AddAB-induced DNA degradation . For samples where DSB was introduced at 3042 kb , datasets were normalized to have the same number of Illumina reads between genomic position 600 kb and 1200 kb instead . The enrichment between experimental datasets ( DSBs were induced ) and control dataset ( no DSBs ) is represented as the ratio of read counts in each bin between the experiment and the control , smoothed using the Lowess function in R with the smoothing bandwidth set to 0 . 01 , and plotted against the genomic positions . For analysis of RNA-seq data , Hiseq 2500 Illumina short reads ( 40 bp ) were mapped back to the Caulobacter NA1000 reference genome ( NCBI Reference Sequence: NC-011916 . 1 ) using Bowtie 1 using the following command: bowtie -m 1 -n 1 --best --strata -p 4 --chunkmbs 512 NA1000-2014-bowtie --sam * . fastq The sequencing coverage was computed using BEDTools [72] . The general feature format ( gff ) file for Caulobacter NA1000 was downloaded from NCBI ( ftp://ftp . ncbi . nih . gov/genomes/archive/old_genbank/Bacteria/Caulobacter_crescentus_NA1000_uid32027/ ) . The normalized value of reads per kb per million mapped reads ( RPKPM ) was calculated for each gene by a custom R script to enable comparison of gene expression within and between RNA-seq datasets . The enrichment between experimental datasets ( DSBs were induced ) and control dataset ( no DSBs ) is represented as the ratio of RPKPM of each gene between the experiment and the control and plotted against the genomic positions . To estimate rates of DSB formation , qPCR was performed using one set of probes across the DSB site on chromosomal DNA isolated from swarmer or asynchronous ΔaddAB cells treated with 2 or 500 μM vanillate to induce a DSB +780 kb from the origin . This was normalized to qPCR using probes across a control region ( rpoA: +1 , 444 kb ) where no DSB is induced . As a control , qPCR across the DSB site was performed on chromosomal DNA isolated from swarmer or asynchronous cells with no DSB induction . Samples were taken 0 , 5 , 10 , 15 , 30 , 60 , 120 and 240 min after DSB induction . The rate of DSB induction was estimated by calculating the slope of the curve ( excluding the 240 min time point ) . To measure total DNA at a DSB and at flanking loci , we performed qPCR using primer pairs as indicated in Fig 1G . The qPCR value measured for each locus following DSB induction was normalized to the distal , control locus , rpoA . These normalized values were then divided by similarly normalized values , but from cells in which a DSB was not induced . The resulting ratios ( +DSB / -DSB ) for each locus were are reported in Fig 1G . The calculations of all qPCR values was done by first generating a standard curve for each oligo pair , with 3-fold dilutions of genomic DNA ( and three technical repeats ) . The average of the 3 technical repeats was then used to calculate the slope and intercept of the curve . The oligo pair was then used for the qPCR measurement described above , using genomic DNA extracted from the following experimental samples: -DSB , +DSB ( wild type ) and +DSB ( ΔrecA ) ( 3 technical repeats for each , with results averaged ) . The Ct values were converted to amounts using the following formula: 2^ ( ( average ct value for sample - intercept ) /slope ) . Fluorescence microscopy was performed on the Zeiss observer Z1 microscope with the LED-Collibri illumination system , 100x oil-immersion objective , Zeiss Temp module to maintain temperature at 30°C and a definite focus system for automatic maintenance of focus . Images were acquired via the metamorph imaging system and data analyzed on ImageJ . Swarmer cells were isolated as described above and then grown on PYE + 1 . 5% low-melting agarose pads with xylose and vanillate and imaged in a glass-bottomed petri dish . Images were acquired every 4 min for ML2402 and every 8 min for ML2401 . Rate of degradation was calculated as the number of frames it took to go from the loss of the MipZ marker until the loss of the ParBpMT1 marker . Scale bars in figure = 1 μm . Sequencing data are available in GEO , GSE86913 . | Double-strand breaks ( DSBs ) are a threat to genome integrity and are faithfully repaired via homologous recombination . The initial processing of DSB ends that prepares them for recombination has been well-studied in vitro , but is less well characterized in vivo . We describe a deep sequencing-based assay for assessing the early steps of DSB processing in bacterial cells by the helicase-nuclease complex AddAB . We find that a combination of chi site recognition and RecA loading is required to attenuate AddAB activity . In the absence of RecA , the chromosome is excessively degraded with a concomitant loss in transcription . Our results , along with prior studies , support a model for how chi recognition and RecA together regulate AddAB to maintain genome integrity and facilitate recombination . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"caulobacter",
"nucleases",
"enzymes",
"dna-binding",
"proteins",
"enzymology",
"surgical",
"and",
"invasive",
"medical",
"procedures",
"dna",
"molecular",
"biology",
"techniques",
"bacteria",
"homologou... | 2017 | Global analysis of double-strand break processing reveals in vivo properties of the helicase-nuclease complex AddAB |
Mitochondrial import of pyruvate by the mitochondrial pyruvate carrier ( MPC ) is a central step which links cytosolic and mitochondrial intermediary metabolism . To investigate the role of the MPC in mammalian physiology and development , we generated a mouse strain with complete loss of MPC1 expression . This resulted in embryonic lethality at around E13 . 5 . Mouse embryonic fibroblasts ( MEFs ) derived from mutant mice displayed defective pyruvate-driven respiration as well as perturbed metabolic profiles , and both defects could be restored by reexpression of MPC1 . Labeling experiments using 13C-labeled glucose and glutamine demonstrated that MPC deficiency causes increased glutaminolysis and reduced contribution of glucose-derived pyruvate to the TCA cycle . Morphological defects were observed in mutant embryonic brains , together with major alterations of their metabolome including lactic acidosis , diminished TCA cycle intermediates , energy deficit and a perturbed balance of neurotransmitters . Strikingly , these changes were reversed when the pregnant dams were fed a ketogenic diet , which provides acetyl-CoA directly to the TCA cycle and bypasses the need for a functional MPC . This allowed the normal gestation and development of MPC deficient pups , even though they all died within a few minutes post-delivery . This study establishes the MPC as a key player in regulating the metabolic state necessary for embryonic development , neurotransmitter balance and post-natal survival .
Pyruvate is a pivotal component in intermediary metabolism , lying at the crossroads between cytosolic and mitochondrial metabolism . The main intracellular source of pyruvate is glycolysis in the cytosol , which generates two molecules of pyruvate per molecule of glucose . Glycolysis-derived pyruvate then follows one of two major routes for energy production: conversion into lactate by lactate dehydrogenase ( LDH ) in a reaction that replenishes the cytosolic NAD+ cofactor pool , allowing maintenance of the glycolytic flux; or cytosolic pyruvate can enter the mitochondria to be oxidized to acetyl-CoA by the pyruvate dehydrogenase complex ( PDH ) , fueling the TCA cycle and oxidative phosphorylation ( OXPHOS ) . Alternatively , mitochondrial pyruvate can be used in an anaplerotic pathway through conversion to oxaloacetate by pyruvate carboxylase . In most differentiated cells , decarboxylation of pyruvate by PDH is used in order to meet the high energetic demands associated with specialized cellular processes such as the transmission of neuronal signals or muscle contraction [1] . In contrast , the strong anabolic requirements of proliferating cells are better met by high glycolytic rates , since several intermediates in this pathway serve as precursors for biomass production , including nucleotides and proteins synthesis [1 , 2] . This marked reliance on high glycolytic flux is a hallmark of highly proliferating cells , including many cancer cells in which a shift from oxidative phosphorylation to aerobic glycolysis ( the Warburg effect ) is frequently observed [2] . During embryogenesis , dynamic regulation of metabolic substrate utilization takes place , in which glucose utilization increases during the early , highly proliferative stages of development reaching a peak after embryo implantation . Vascularization and increased oxygen supply then trigger oxidative metabolism [1] allowing differentiation into different tissues [3 , 4] . Accordingly , mutations in glycolytic genes impair early post-implantation embryonic viability , while alteration in oxidative processes such as PDH activity often results in embryonic lethality at later stages ( ~E9-E11 ) , when mitochondrial metabolism becomes crucial [1] . This versatility of metabolic pathways therefore allows the developing embryo to adapt its metabolism to meet the energetic and anabolic requirements of the diverse cellular programs . In order to fuel the TCA cycle and drive oxidative phosphorylation , glucose-derived pyruvate must enter the mitochondrial matrix . To do so , it is believed to diffuse non-specifically through the outer mitochondrial membrane via porins [5] , before being taken up by a specific carrier to cross the impermeable inner mitochondrial membrane . The existence of a specific transporter has been postulated since the 1970s [6] , and its biochemical properties have been extensively studied , including its specific inhibition by chemical compounds [7 , 8] . However , the molecular and genetic identity of the mitochondrial pyruvate carrier ( MPC ) were revealed only recently by us and by others [9 , 10] . In mammals , the MPC is believed to be composed of two obligatory , interdependent subunits , MPC1 and MPC2 , which form a multimeric complex of so far unknown stoichiometry [10] to mediate pyruvate transport across the inner mitochondrial membrane . These findings have led to renewed interest in the study of the physiological and pathological importance of the MPC , since they have allowed the development of genetic and biochemical strategies to investigate and experimentally modulate its expression , regulation and activity . Recent studies in both yeast and mammalian cells have revealed a close and reciprocal relationship between MPC expression and activity and changes in cellular metabolic programs . When grown under fermentative conditions , yeast cells express a carrier composed of MPC1 and MPC2 subunits . In contrast , when grown under oxidative conditions they express MPC1 and MPC3 subunits , which results in the formation of a carrier with a higher efficiency for pyruvate import [11] . In turn , the importance of the MPC regulation as a driver of changes in cellular and whole organism metabolism is also beginning to be explored . In cell culture models , several recent studies have reported that pharmacological or genetic inhibition of the MPC resulted in a decreased contribution of glycolysis-derived pyruvate to the TCA cycle . Instead of leading to decreased TCA cycling , oxygen consumption and cell growth , the oxidative TCA cycle flux was maintained through an increase in glutamine-mediated anaplerosis and fatty acid oxidation . In addition , to compensate for the reduced pyruvate import , some pyruvate was found to be synthesized in situ , in the mitochondrial matrix , by mitochondrial malic enzymes [12 , 13] . Finally , in several cancers , a decrease in MPC activity has been observed [14–16] , which correlates with poor prognosis in multiple colon cancers [16] . Several mouse models of MPC deficiency have now been published ( MPC2 hypomorphic allele [17] , liver-specific knock-outs of MPC1 or MPC2 [18 , 19] , acute MPC inhibition by UK5099 [20] ) , which showed defects in glucose-stimulated insulin secretion or gluconeogenesis , thus demonstrating a role for the MPC in regulating whole-body glucose homeostasis . However , ubiquitous disruption of MPC2 expression in mice results in embryonic lethality , and this has not been further investigated [17] . In this study , we have generated a whole-body knock-out of the MPC1 gene and we have studied the impact of ubiquitous loss of MPC activity on mouse embryogenesis . We show that the loss of MPC1 protein results in embryonic lethality at E12-E14 , and perturbations of respiratory and metabolic profiles in mouse embryonic fibroblasts ( MEFs ) derived from mutant embryos . These changes were reversed by re-expression of a functional MPC1 gene . In addition , mutant embryos presented lesions in the pons region of the brain stem , and the metabolome of the telencephalic brain showed significant anomalies , including lactate accumulation and an imbalance in the levels of several neurotransmitters . Interestingly , both lethality and the brain metabolism defects were prevented when the pregnant dam was maintained on a ketogenic diet . Overall , our results demonstrate that the MPC is required for normal mouse embryogenesis and brain development , but that the effects of its absence can be compensated , at least until birth , by a ketogenic diet .
To investigate the physiological importance of the MPC in vertebrates , we generated MPC1 deficient mice from an ESC clone harboring a gene trap cassette in the first intron of the MPC1 gene ( MPC1gt; Fig 1A ) . This cassette contains a splice acceptor at the 5’ end followed by transcription termination signals , thus disrupting MPC1 mRNA . After breeding MPC1gt/+ mice together , the resulting genotypes of the newborns diverged markedly from expected Mendelian ratios , with no homozygous MPC1gt/gt pups being recovered ( Fig 1B ) suggesting that loss of MPC1 resulted in embryonic lethality as further described below . In contrast , heterozygous MPC1gt/+ mice appeared outwardly normal , showed no growth defect ( Fig 1C ) and were fully viable and fertile . The efficiency of our gene disruption strategy was assessed by measuring MPC1 mRNA levels in embryos at E13 . 5 . In MPC1gt/+ embryos , RT-qPCR showed that MPC1 mRNA levels decreased to about 50% compared to MPC1+/+ ( WT ) , while in MPC1gt/gt embryos , MPC1 mRNA was drastically reduced to about 5% of the WT level ( Fig 1D ) . Nevertheless , this indicates that the gene trap allele is not 100% efficient and that at low frequency , splicing events may bypass the splice acceptor site of the cassette thus producing the full length MPC1 mRNA . However , we did not detect MPC1 protein by Western blot in E13 . 5 embryos ( Fig 1E ) , showing that the amount of MPC1 protein in the MPC1gt/gt embryos is , if present , very low . MPC2 mRNA levels remained unchanged in MPC1gt/gt and MPC1gt/+ compared to WT ( Fig 1D ) . In WT adult mice , Western blot analysis showed that the level of MPC1 protein expression , after normalization to the mitochondrial marker Tom20 , varies considerably in different tissues , with the highest expression levels in the heart and liver ( Fig 1F ) . Nevertheless , it is widely expressed throughout the organism , and thus its absence could potentially affect the physiology of many vital organs . To assess the effect of loss of MPC on mitochondrial function , we used primary ( passages 1–5 ) , or spontaneously immortalized mouse embryonic fibroblasts ( MEFs ) derived from MPC1+/+ and MPC1gt/gt embryos . As found above in whole embryos , cultured MPC1gt/gt primary MEFs had very low levels of MPC1 mRNA compared to MPC1+/+ MEFs , whereas MPC2 mRNA levels were unchanged ( Fig 2A ) . Under basal conditions , permeabilized primary MPC1gt/gt MEFs showed no detectable defect in oxygen consumption rate ( OCR ) when pyruvate was provided as a respiratory substrate , but a major drop in OCR was observed when maximal respiration was evoked by the addition of fCCP ( Fig 2B and 2C ) . This defect was abolished by supplementing the medium with methyl pyruvate ( Fig 2C ) which diffuses freely across membranes and thus bypasses the requirement for the MPC [21] . Furthermore , the MPC inhibitor UK5099 decreased pyruvate-driven OCR in MPC1+/+ MEFs but had no effect on MPC1gt/gt MEFs ( Fig 2D ) , also indicating that MPC activity was severely affected in MPC1gt/gt MEFs . The presence of dichloroacetate in the experiments on permeabilized MEFs prevents inhibition of PDH by PDH kinase [22] , minimizing the possibility of a bottleneck in respiration through reduced activity of PDH . Western blotting experiments using spontaneously immortalized MEFs showed that neither MPC1 nor MPC2 could be detected in the MPC1gt/gt derived cells ( Fig 2E ) , suggesting that in the absence of MPC1 , MPC2 is unstable and is degraded , as previously proposed by others [10 , 17 , 19 , 23] . Restoration of MPC1 gene expression in MPC1gt/gt MEFs by transduction with lentiviral particles containing a MPC1-Flag fusion construct led to reexpression of both MPC1 and MPC2 proteins ( Fig 2E ) and a concomitant increase in pyruvate-driven OCR compared to the parental MPC1gt/gt MEFs ( Fig 2F and 2G ) . Taken together , these results indicate that disruption of MPC1 expression using the gene trap strategy described above significantly diminished pyruvate entry into the mitochondria and thus the ability to utilize pyruvate as a respiratory substrate . We further assessed the metabolic consequences of MPC deletion in MEFs by metabolomics . Targeted tandem mass spectrometry was used to profile glycolytic and TCA cycle intermediates in the MPC1+/+ , MPC1gt/gt and MPC1-Flag rescued MEFs ( S1 Table ) . Abolishing MPC expression caused intracellular accumulation of pyruvate , lactate and the glycolytic precursors phosphoenolpyruvate and 2/3-phosphoglycerate , as well as almost complete depletion of detectable citrate ( Fig 3A ) . This pattern is indicative of defective pyruvate uptake into mitochondria and a subsequent decrease in the pyruvate-driven oxidative TCA cycle , fully consistent with our results on pyruvate-driven OCR ( Fig 2 ) . Furthermore , a four-fold increase in aspartate and a somewhat lesser increase in malate and fumarate were observed , also indicating reduced citrate synthase ( CS ) flux and accumulation of glutamine-derived molecules . In MPC1-Flag rescued MEFs , the levels of all metabolites were restored close to those observed in MPC1+/+ MEFs ( Fig 3A ) . In order to identify the compensatory mechanisms that are established in response to MPC deletion , we performed 13C-tracer experiments ( for raw values , see S2 Table ) . In the presence of [U-13C]glucose , MPC1gt/gt cells showed a substantial decrease in the M+2 mass isotopomer fraction of TCA cycle intermediates and a corresponding increase in the unlabeled fraction ( Fig 3B ) . The M+2 mass isotopomer results from the incorporation of glycolytic pyruvate into citrate via MPC , PDH , and CS ( S1A Fig ) . The very low M+2 fraction measured in MPC1gt/gt cells indicates that this route is blocked in the absence of MPC1 . Using [U-13C]glutamine , we found that this amino acid was the main carbon source for dicarboxylic acids in the TCA cycle ( Fig 3C ) in both MPC1+/+ and MPC1gt/gt MEFs . The only appreciable difference between the two cell types was a decrease in the M+2 fraction and an increase in the M+3 fraction in MPC1gt/gt compared to MPC1+/+ cells . The lack of M+2 mass isotopomers is consistent with a lack of CS activity which in the mutant , prevents synthesis of [13C4]citrate from [U-13C]oxaloacetate ( S1B Fig ) . The higher M+3 fractions indicate an increase in reductive glutamine metabolism ( S1B Fig ) . This shift was confirmed in a labeling experiment with [1-13C]glutamine ( Fig 3D ) , which allowed us to distinguish between reductive and oxidative TCA-cycle activity ( S1C Fig ) . Overall , these data demonstrate that the contribution of glucose-derived pyruvate to TCA cycle intermediates is abolished upon MPC1 disruption , and that one compensatory mechanism established in the absence of the MPC involves glutamine-driven anaplerosis and an increase in reductive TCA cycle metabolism . The absence of MPC1gt/gt offspring from breeding heterozygous MPC1gt/+ mice indicated embryonic lethality ( Fig 1B ) . To confirm this , we conducted timed pregnancies and observed a marked drop in viability of MPC1gt/gt embryos between embryonic days E12 and E14 ( Fig 4A and 4B ) . Visual inspection of E13 . 5 MPC1gt/gt embryos did not reveal any striking morphological defects compared to their wild-type and heterozygous littermates ( Fig 4C ) , and histological analysis also showed that the overall internal morphology was quite normal , although specific lesions in the pons region of the embryonic brain stem were observed ( Fig 4D ) . We analysed this lesion in more detail by immunostaining for cleaved ( i . e . activated ) Caspase-3 and no increase in the number of apoptotic cells was observed ( S4A and S4B Fig ) . However , higher magnification images of the H&E stained paraffin sections showed a clear loss of tissue integrity , with numerous cellular bodies and processes detached from the rest of the tissue reminiscent of an ‘oedema-like’ morphology ( S5 Fig ) . Postmitotic neurons and proliferating cells were stained respectively with tubulin beta 3 antibody ( TuJ1 ) and Ki67 directed antibodies and no obvious changes in the numbers of these cell types was observed . A close examination of the lesion site in MPC1gt/gt embryos indicates that there is a major disorganization of the periventricular zone , a region rich in proliferating cells ( S4C Fig ) , although the causes underlying the morphological disruption of this region remain unclear . At this point , we are unable to say whether these lesions are responsible for the death of MPC1gt/gt embryo or whether they are simply a consequence of the metabolic alteration caused by the loss of MPC1 . Similar to the metabolic perturbations caused by PDH deficiency [24 , 25] , loss of MPC leads to lactic acidosis ( Fig 3A ) which is known to cause brain lesions [26] and might explain the death of the MPC1gt/gt embryos . Alternatively , it is possible that some embryonic tissues may be unable to compensate for MPC disruption and are strictly dependent on pyruvate-derived acetyl-CoA for normal development . Lactic acidosis resulting from PDH deficiency can be treated with a ketogenic diet , which decreases lactate overflow and provides acetyl-CoA directly into mitochondria independently of PDH and MPC [26 , 27] . To test whether such a diet could also protect MPC1gt/gt embryos , we maintained the pregnant dams on a ketogenic diet from E8 . 5 onwards . Since the ketogenic diet led to cannibalism of the pups by the mother , delivery was performed by Caesarean section at E18 . 5 just prior to the time of natural birth . Strikingly , the ketogenic diet resulted in the survival of the MPC1gt/gt embryos up until the end of gestation ( i . e . until E18 . 5 ) ( Fig 5A ) , and although the weight of MPC1gt/gt at E18 . 5 was slightly reduced compared to wild type and heterozygous littermates ( Fig 5B ) , they appeared normal ( Fig 5C ) . Furthermore , the lesions in the pons region of the brain stem observed in E13 . 5 MPC1gt/gt embryos , were also prevented by the ketogenic diet ( Fig 5D ) . Strikingly however , the MPC1gt/gt embryos failed to survive for more than a few minutes after delivery . Nevertheless , these experiments show that the lethality seen in E13 . 5 MPC1gt/gt embryos can be circumvented by a ketogenic diet , allowing these embryos to develop almost normally until the end of gestation . In an attempt to understand the metabolic changes that could explain the survival of MPC1gt/gt embryos until the end of gestation , we performed non-targeted metabolomic analyses on telencenphalic brain extracts from E13 . 5 embryos maintained on a normal or a ketogenic diet . Upon processing and annotation , a total of 222 metabolite ions could be detected ( S3 Table ) . A principal component analysis of these data revealed that the most prominent metabolic changes were specific to the MPC1gt/gt brain samples from animals maintained on normal diet , while MPC1gt/gt ketogenic diet samples clustered with the MPC1+/+ samples ( Fig 6A ) . Moreover , the predominant changes were all indicative of abnormal TCA cycle activity ( Fig 6B ) . To avoid possible bias due to differences in abundance and ionization efficiency , we performed a univariate analysis to systematically find all metabolites which varied between E13 . 5 MPC1gt/gt and E13 . 5 MPC1+/+ embryos exposed to either normal or ketogenic diets . Of the 222 metabolite ions detected , 46 were found to satisfy our criteria ( q-value < 0 . 01 , abs ( log2 ( fold change ) ) > 0 . 5 ) in at least one of the two dietary regimes ( Fig 6C and S2A and S2B Fig ) . The differences found in our analysis using MEFs ( pyruvate , lactate , and aspartate , and TCA cycle intermediates ) ( Fig 3A ) were confirmed in brain samples from animals kept on normal diet . In addition , in brain samples we found several additional changes in other pathways ( e . g . proline , pyrimidine , glutathione ) , indicating the existence of pleiotropic effects of loss of MPC1 on the metabolic state in E13 . 5 MPC1gt/gt brains . Interestingly , neurotransmitter levels were substantially affected in MPC1gt/gt brain samples , where a decrease in glutamine , glutamate and gamma-aminobutyric acid ( GABA ) and an increase in N-acetylaspartylglutamate ( NAAG ) were observed ( Fig 6C and 6D ) suggesting that impaired pyruvate metabolism also affects the balance of neurotransmitter levels during development In addition , the phosphocreatine to creatine ratio ( PCr/Cr ) was low in MPC1gt/gt brains following a normal dietary regime , indicative of an energy deficit in vivo ( S3 Fig ) . Virtually all of these effects were abolished in E13 . 5 MPC1gt/gt mice maintained on a ketogenic diet ( Fig 6C and 6D and S2B Fig ) . Under these conditions , the levels of most metabolites including amino acids , GABA , and NAAG were similar to those seen in MPC1+/+ embryonic brains samples . Levels of lactate and aspartate were still increased in MPC1gt/gt embryos under the ketogenic diet ( q < 0 . 05 ) although they were only increased by 30% compared to WT embryos ( Fig 6C and 6D and S2B Fig ) .
The aim of this study was to understand better the physiological role of the MPC in regulating metabolic homeostasis in vertebrates . In the genetic mouse model described here , we used a gene trap strategy to abolish MPC1 expression in all tissues . Homozygous disruption of MPC1 resulted in embryonic lethality between E12 and E14 ( Fig 4A and 4B ) , and this is consistent with the results on the MPC2 knock-out mice reported by Vigueira et al . [17] who observed embryonic lethality around E11 . In the latter case , a frameshift mutation was introduced two nucleotides after the initiator codon likely resulting in complete loss of the MPC2 protein . In our model , approximately 5% of mature transcripts were still present in homozygous MPC1gt/gt embryos ( Fig 1D ) , presumably because the splicing machinery may bypass at low frequency the splice acceptor site present in the gene trap . Even though no MPC1 protein could be detected by Western blotting ( Fig 1E ) , we cannot fully exclude that low levels of functional MPC may persist . Thus , differences in the gene inactivation strategies could explain the slightly earlier death of the MPC2 knock-out embryos as compared to the MPC1gt/gt embryos . Targeted metabolomics studies using MPC1gt/gt MEFs , derived from MPC1gt/gt embryos at E13 . 5 , showed that citrate was barely detectable ( Fig 3A ) , and our labeling experiments using 13C-tracers ( Fig 3B and 3C ) confirmed that the citrate synthase step of the TCA cycle is strongly inhibited in these cells . On the other hand , MPC disruption led to a compensatory increase in the use of glutamine as a substrate for reductive TCA cycle metabolism . In addition , our findings based on metabolic flux analysis in MEFs cultured with [U-13C]glucose showed that the contribution of glucose-derived pyruvate to TCA cycle intermediates is strongly inhibited in MPC1gt/gt cells ( Fig 3A and 3B ) to a greater extent than previously reported [12 , 13] . The absence of any significant residual contribution in the present study could be explained by a more complete inhibition of the MPC mediated by insertion of the gene trap cassette , compared to the approaches based on pharmacological inhibition and RNA interference used in the previous reports . This is consistent with the fact that neither MPC1 nor MPC2 could be detected by Western blotting in MPC1gt/gt MEFs ( Fig 2E ) and that no residual pyruvate import was detected making unlikely the existence of MPC-independent pyruvate import activities in these cells ( Fig 2D ) . This also indicates that in our cellular model , glucose-derived pyruvate does not enter mitochondria through alternative pathways , such as pyruvate-alanine cycling as recently proposed by McCommis et al . [19] , or through the action of malic enzymes that could convert pyruvate to malate in the cytosol , and convert it back to pyruvate after import of malate into the mitochondrial matrix [28] . However , we cannot exclude that these alternative routes or that additional pyruvate carriers may function in specific organs during embryogenesis , which could explain how embryos lacking the MPC can reach E12 to E14 stage . These alternative mechanisms leading to pyruvate synthesis within mitochondria may also explain why the phenotype of MPC-deficient embryos appears to be slightly less severe than the phenotype of PDH-deficient embryos which die at around stage E9 to E11 [29] , and in which the contribution of pyruvate to the TCA cycle is fully inhibited . The cause of the death of the MPC1gt/gt embryos has yet to be completely elucidated although we hypothesize that metabolic acidosis is at least in part responsible for the phenotype we observe . Several years ago , Brivet et al . [30] described details of a patient showing impaired mitochondrial pyruvate import which was linked to a mutation in a gene which , a posteriori , was found to be MPC1 [10] . This patient was the first child of healthy consanguineous parents and presented at birth with hypotonia , mild facial dysmorphism , periventricular cysts , marked metabolic acidosis and severe hyperlactacidemia [30] . Consistent with this study , we found increased lactic acid in the telencephalic brain of E13 . 5 MPC1gt/gt embryos ( Fig 6C and 6D ) while Vigueira et al . [17] reported increased lactate in the blood of the MPC2 hypomorphic mutant . Any impairment in pyruvate oxidation , whether it be due to a deficit in mitochondrial pyruvate import as in our study , decreased PDH activity [29] or defects in the TCA cycle or the respiratory chain [31] , would be expected to result in increased reduction of pyruvate to lactic acid by the LDH , greater release of lactic acid into the extracellular medium and consequently to metabolic acidosis . Long term metabolic acidosis results in multiple organ failure , in particular to defects in heart contractility leading to cardiac arrest [32] . The brain is also particularly at risk during periods of metabolic acidosis and , for example , the dysfunction of neurons which accompanies inhibition of the neuronal PDH activity has previously been shown to be the cause of embryonic lethality [33] . All these results argue in favor of acidosis being a major , if not the principal cause , of embryonic lethality of MPC deficient embryos . This hypothesis is further supported by the findings that a ketogenic diet provided to the pregnant female from E8 . 5 onwards rescued the embryonic lethality of MPC1gt/gt embryos and prevented lesions in the mesencephalon ( Fig 5 ) . A ketogenic diet is commonly used to treat the lactic acidosis resulting from PDH deficiency in humans [26 , 27] , and has been shown to have similar effects in experiments with zebrafish embryos [34] . Used therapeutically , the ketogenic diet reduces lactic acidosis probably by decreasing glucose uptake and aerobic glycolysis , the main pathway induced in mammalian cells to compensate for a deficiency in OXPHOS . The beneficial effects of the ketogenic diet may be immediate , through fueling the TCA cycle with acetyl-CoA , or delayed , through an epigenetic regulation of gene expression [35] in the embryos . In addition , the beneficial effects of the ketogenic diet may also be mediated through changes in the maternal metabolism thus changing the supply of metabolites and/or growth factors to the embryo . In our experiments , maintaining the pregnant dams on a ketogenic diet from E8 . 5 onwards reduced lactate accumulation allowing the MPC1gt/gt embryos to complete normal gestation ( Fig 5 ) . We suggest that this is because the diet is able to sustain efficient oxidative metabolism , which is required during the later stages of embryogenesis for cell and tissue differentiation [1 , 3 , 4] . In agreement with this is the fact that the ketogenic diet rescued the energy deficit observed in vivo in the brains of E13 . 5 MPC1gt/gt embryos ( S3 Fig ) . Moreover , in addition to the effects on lactic acid and energy balance , we observed that the ketogenic diet also normalized other metabolic parameters in the brain , including glutaminolysis which seemed abnormally elevated in untreated MPC1gt/gt embryos as evidenced by reduced glutamine and glutamate levels ( Fig 6C and 6D ) . Under the ketogenic diet , glutamine , glutamate , and GABA levels were increased compared to untreated MPC1gt/gt embryos ( Fig 6C and 6D ) whereas the level of NAAG was decreased ( Fig 6C ) . It was recently shown that GABAergic transmission in neonatal mice is essential for cortical neuron development and the establishment of a proper balance between excitation and inhibition in the adult cortex [36] . Together our results allow us to hypothesize that during embryogenesis , MPC activity is required not only for adapting energy metabolism to the needs of the developing embryo , but also in maintaining a balanced pool of major neurotransmitters and ensuring normal brain development . It is already established that PDH deficiency is associated with severe neurological phenotypes such as developmental defects , ataxia , cognitive delay and epilepsy [24–27] , the latter being caused by impaired energetic status and abnormal neurotransmitter metabolism [26] . In future experiments , it will be of interest to evaluate further the role of the MPC in modulating neurotransmitter levels and in regulating brain function . Despite the ability of the ketogenic diet to restore normal metabolism and gestation of the MPC1gt/gt embryos ( Figs 5 and 6 and S2 Fig ) , the newborn pups survive for only a few minutes post-delivery . This suggests that without nutritional support from the dam , which provides a continuous source of glucose and ketone bodies , MPC1gt/gt pups were not able independently to meet their energetic needs during the post-natal starvation state . Loss of pyruvate oxidation and ketogenic supply in the MPC1gt/gt pups may be further exacerbated by the fact that autophagy-driven gluconeogenesis , an important source of energy during the post-natal period [37] , is probably impaired in these newborn animals . Indeed , recent reports indicate that liver-specific ablation of MPC activity diminishes the gluconeogenic flux because of the relatively low efficiency of compensatory pathways such as glutaminolysis and pyruvate/alanine cycling in liver [18 , 19] . Our results show that global loss of MPC activity is incompatible with embryonic development and neonatal survival in mammals .
Mice were euthanized by CO2 inhalation . All experimental procedures were performed according to guidelines provided by the Animal Welfare Act and Animal welfare ordinance , the Rectors' Conference of the Swiss Universities ( CRUS ) policy and the Swiss Academy of Medical Sciences / Swiss Academy of Sciences' Ethical Principles and Guidelines for Experiments on Animals , and were approved by the Geneva Cantonal Veterinary Authority ( authorization number: 1027/3907/1 ) . Mice bearing the MPC1gt allele were generated by the Texas A/M institute for Genomic Medicine ( TIGM ) using the OmniBank ESC clone OST39041 . Timed matings were set up at the end of the day and the presence of a vaginal plug was checked the following morning . This time point was taken as E0 . 5 . To rescue embryonic lethality with a ketogenic diet , timed matings were set up as above and normal food was replaced at 8 dpc by a diet containing 75% fat and 10% protein ( Ketogenic diet XL75:XP10 , Kliba Nafag , Switzerland ) . Cells were grown in Dulbecco’s modified Eagle’s medium ( DMEM ) containing 25mM glucose supplemented with 2mM L-Glutamine , 10% FBS and Penicillin/Streptomycin at 37˚C , 5% CO2 . MEFs were isolated from E13 . 5 embryos as described elsewhere [38] . Briefly , the embryos were dissected out , internal organs were removed and the carcasses were minced with a razor blade and incubated in 0 . 25% Trypsin/EDTA for 15 min at 37˚C . After addition of growth medium , cells were spun down and plated . Experiments with primary cells were carried out no more than 5 passages after isolation . Immortalized MEFs appeared spontaneously in primary MEFs cultures after continued passaging . Rescued MEFs were obtained by transduction of immortalized MPC1gt/gt MEFs with lentiviral particles containing the C-terminally Flag-tagged MPC1 coding sequence under the control of the EF1-alpha promoter . Generation of this construct as well as the lentiviral transduction procedure has been described previously [39] . RNA was extracted from whole embryo homogenates or from cultured cells using TRIzol reagent ( Ambion ) according to the manufacturer’s instructions . Reverse transcription was carried out using M-MLV reverse transcriptase ( Invitrogen ) and random primers ( Promega ) from 2 μg of total RNA according to the manufacturer’s instructions . Quantitative PCR using SsoFast Evagreen supermix ( BioRad ) was performed according to the manufacturer’s protocol . The following primers were used: MPC1 F: GACTATGTCCGGAGCAAGGA; MPC1 R: TAGCAACAGAGGGCGAAAGT; MPC2 F: TGTTGCTGCCAAAGAAATTG; MPC2 R: AGTGGACTGAGCTGTGCTGA; 28S F: TTGAAAATCCGGGGGAGAG; 28S R: ACATTGTTCCAACATGCCAG . The relative abundance of the MPC1 and MPC2 transcripts in each sample was determined by normalizing to 28S rRNA using BioRad CFX manager software . Total cell lysates were prepared by lysing cells in RIPA lysis buffer for 15 min on ice and removal of insoluble material by centrifugation at 16000g for 10 min at 4˚C . Protein content was measured using the Bio-Rad protein assay and equal protein amounts in 1x Laemmli buffer were used for SDS-PAGE . For immunoblotting , proteins were transferred electrophoretically to nitrocellulose membranes and exposed to the following primary antibodies: anti-MPC1 ( HPA045119 , Sigma ) , anti-MPC2 ( home-made ) , anti-TOM20 ( sc-11415 , Santa-Cruz ) , anti β-tubulin ( T4026 , Sigma ) . Images of Western blotting were uniformly treated for contrast enhancement using Adobe Photoshop . Measurement of oxygen consumption was performed using a Seahorse XFe24 Flux Analyzer ( Seahorse Biosciences ) . 30’000 cells were seeded in XF24 cell culture microplates and grown overnight in DMEM containing 10% FBS , 2mM L-Glutamine , 25mM Glucose , and Penicillin/Streptomycin . Experiments on primary or immortalized , permeabilized MEFs were carried out at 37°C in Mitochondrial Assay Solution ( MAS , containing 70 mM sucrose , 220 mM mannitol , 10 mM KH2PO4 , 5 mM MgCl2 , 2 mM Hepes , 1 mM EGTA , 0 . 2% fatty acid free BSA; pH 7 . 2 ) . Cells were permeabilized with 1 nM XF Plasma Membrane Permeabilizer reagent ( Seahorse Bioscience ) and provided with 5 mM pyruvate , 0 . 5 mM malate , 2 mM dichloroacetate and 1 μM oligomycin one hour before the assay . Basal oxygen consumption was measured before injection . At the times indicated , the following compounds were injected: fCCP ( final concentration 0 . 4 μM ( primary MEFs ) or 2 μM ( immortalized MEFs ) ) , succinate/rotenone ( 10 mM/1 μM ) , antimycin A ( 1 μM ) . Each measurement loop consisted in 30 sec mixing , 1 min waiting , and 2 min measuring oxygen consumption . For immortalized cells , OCR data were corrected for cell number by nuclear staining with DAPI . Embryos were surgically removed at the times indicated and immediately fixed in Bouin’s fixative solution , dehydrated , paraffin-embedded , and sectioned at 4 μm . Sections were mounted on glass slides and stained with haematoxylin and eosin . For cryosections , embryos were fixed for 1 hr at room temperature in 4% paraformaldehyde , rinsed twice in PBS and cryoprotected in 15% sucrose/PBS for at least 48 hrs . After inclusion in 7 . 5% gelatin/15% sucrose/PBS , blocks containing the embryos were snap-frozen in liquid nitrogen-cold isopenthane before sectioning in a cryostat . Sections were collected on Superfrost Plus glass slides ( ThermoScientific ) , allowed to dry for ~30 min and stored at -20°C before immunostaining . For immunofluorescence studies , cryosections were rehydrated in PBS , and the excess embedding matrix was removed by 1 min incubation in 37°C pre-warmed PBS . Sections were permeabilized in 0 . 1% Triton X-100/PBS for 20 min , rinsed 3 times in PBS , and incubated for 30 min in blocking buffer containing 3% bovine serum albumin/0 . 1% Tween 20 in PBS . Incubations with primary antibodies were performed for 3 hrs at room temperature , before rinsing 3 times in PBS and incubation for 1 hr at room temperature with secondary antibodies . Nuclei were stained with DAPI , sections were mounted in FluorSave ( Millipore ) and observed using a Cytation 3 Cell Imaging apparatus . Antibodies used were Cleaved Caspase-3 ( Asp175 ) ( #9664 , Cell Signaling ) , tubulin β 3 ( TuJ1 clone , 801201 , BioLegend ) and Ki67 ( ab15580 , Abcam ) . For labeling experiments , the tracer medium was obtained by replacing the carbon substrate of interest with 13C labeled glucose or glutamine ( Cambridge Isotopes ) . Both , metabolomics and labeling experiments were performed as follows: 6–8 x 104 cells/well were seeded in a 6-well plate ( Nunc ) and allowed to attach for approximately 12 h in the presence of unlabeled DMEM . The medium was then completely removed , and cells were washed with 1x PBS . Fresh medium , labeled or unlabeled as appropriate , was added and cells were incubated for further 24 h . Three replicates per condition were performed . For sampling , the medium was removed and cells were washed twice with 75 mM ammonium carbonate buffer ( pH 7 . 4 ) and quenched by snap freezing the plate in liquid N2 . Plates were stored at -80°C until proceeding with metabolite extraction . Metabolite extraction was performed by addition of 1 . 8 mL of cold ( -20°C ) extraction solution ( acetonitrile/methanol/water ( 2:2:1 ) ) to each well . For targeted metabolomics experiments , 200 μL of a uniformly 13C labeled E . coli metabolite extract was added as internal standard [40] . For pyruvate measurements , the extraction solution above also contained 25 μM phenylhydrazine for derivatization , and 100 μL of a 5 μM [U-13C]pyruvate solution as an internal standard [41] . After 1 h incubation at -20°C , the bottom of each well was scraped and the extract was collected in a 2 mL microcentrifuge tube . Extracts were centrifuged ( 4°C , 10 , 000 rpm , 10 min ) to remove cell debris , and the supernatants were transferred to fresh tubes . For targeted metabolomics , supernatants were evaporated to complete dryness , while the pellets containing the cell debris were used to normalize metabolite concentrations to cellular protein . Pellets were incubated with CellLytic lysis reagent ( Sigma ) and protein content was quantified using the Bradford assay . Dried samples were resuspended in 100 μL deionized water , and 10 μL aliquots were injected into a Waters Acquity UPLC ( Waters Corporation , Milford , MA ) with a Waters Acquity T3 column coupled to a Thermo TSQ Quantum Ultra triple quadrupole instrument ( Thermo Fisher Scientific ) with negative-mode electrospray ionization . Compound separation was achieved by a gradient of two mobile phases ( A ) 10 mM tributylamine , 15 mM acetic acid and 5% ( v/v ) methanol , and ( B ) 2-propanol [41 , 42] . Acquisition of mass isotopomer distributions of intact and fragmented carbon backbones was done as previously described [43] . Peak integration was performed using an in-house software . Metabolites were quantified by normalizing the peak area of each compound to the respective signal from the internal standard , additionally compared to the calibration curve with known metabolite concentrations . Fractional labeling and MIDs were calculated as previously described [44] , and corrected for naturally occurring 13C [45] . The telencephalic brain was dissected from E13 . 5 embryos harvested from dams fed either a normal or a ketogenic diet . Metabolites were then extracted from the ca . 10 mg brain samples . The brain pieces were homogenized in 1 mL cold 70% ( v/v ) ethanol with a TissueLyser ( Qiagen ) . Extraction was continued with addition of 7 mL of 70% ( v/v ) ethanol preheated to 75°C for 2 min , followed by removal of cell debris by centrifugation ( 4°C , 4 , 000 rpm , 15 min ) . Extracts were stored at -20°C until mass spectrometric analysis . Non-targeted metabolomics was performed by flow injection analysis on a 6550 Agilent QTOF instrument as described previously [46] . Briefly , profile spectra were recorded in negative ionization from m/z 50 to 1000 mode at 4 GHz high-resolution mode . Ion annotation was based on accurate masses using a tolerance of 0 . 001 a . m . u . and KEGG mmu database , accounting systematically for–H+ and F- ions , sodium and potassium adducts , and heavy isotopes . The full annotated ion list is provided in S3 Table . Differences in mRNA expression levels were assessed by two-way ANOVA using the GraphPad Prism software . Statistical analysis of metabolomics data was performed by using Matlab R2015a and software developed in-house . Significance of changing metabolites between groups ( MPC1+/+ and MPC1gt/gt ) was calculated from Student’s t-test distribution , and p-values were adjusted to account for false discovery rate [47] . | The tight control of cellular metabolism and energy production plays a crucial role during embryonic development , cancer and neurodegenerative disorders . We show that mitochondrial pyruvate carrier deficiency in mice causes metabolic alterations that result in lactic acidosis , neurotransmitter imbalance , energy deficit , brain damage and embryonic lethality . Feeding the pregnant dams a ketogenic diet allowed the survival of affected embryos until birth . Our results demonstrate the importance of the mitochondrial pyruvate carrier in maintaining the metabolic program necessary to sustain normal mammalian development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"chemical",
"compounds",
"metabolic",
"processes",
"ketones",
"diet",
"pyruvate",
"developmental",
"biology",
"metabolomics",
"nutrition",
"metabolites",
"mitochondria",
"molecular",
"biology",
"techniques",
"bioenergetics",
"embryos... | 2016 | Embryonic Lethality of Mitochondrial Pyruvate Carrier 1 Deficient Mouse Can Be Rescued by a Ketogenic Diet |
The mechanisms used to coordinate uterine contractions are not known . We develop a new model based on the proposal that there is a maximum distance to which action potentials can propagate in the uterine wall . This establishes “regions” , where one action potential burst can rapidly recruit all the tissue . Regions are recruited into an organ-level contraction via a stretch-initiated contraction mechanism ( myometrial myogenic response ) . Each uterine contraction begins with a regional contraction , which slightly increases intrauterine pressure . Higher pressure raises tension throughout the uterine wall , which initiates contractions of more regions and further increases pressure . The positive feedback synchronizes regional contractions into an organ-level contraction . Cellular automaton ( CA ) simulations are performed with Mathematica . Each “cell” is a region that is assigned an action potential threshold . An anatomy sensitivity factor converts intrauterine pressure to regional tension through the Law of Laplace . A regional contraction occurs when regional tension exceeds regional threshold . Other input variables are: starting and minimum pressure , burst and refractory period durations , enhanced contractile activity during an electrical burst , and reduced activity during the refractory period . Complex patterns of pressure development are seen that mimic the contraction patterns observed in laboring women . Emergent behavior is observed , including global synchronization , multiple pace making regions , and system memory of prior conditions . The complex effects of nifedipine and oxytocin exposure are simulated . The force produced can vary as a nonlinear function of the number of regions . The simulation directly links tissue-level physiology to human labor . The concept of a uterine pacemaker is re-evaluated because pace making activity may occur well before expression of a contraction . We propose a new classification system for biological CAs that parallels the 4-class system of Wolfram . However , instead of classifying the rules , biological CAs should classify the set of input values for the rules that describe the relevant biology .
Over the past several decades , a large body of literature has addressed intracellular [1] and intercellular [2] signaling in pregnant myometrium , but comparatively little effort has been spent seeking to describe the mechanisms that coordinate contractions at the level of the whole uterus . It is clear that at the cellular level , electrical excitability properties control both the signal to contract and the mechanism to directly raise intracellular calcium . Tissue recruitment by action potential propagation is dominant at the tissue-level [2] , [3] ( in the order of centimeters ) , and it is generally believed to play a key role at the organ-level in human labor [4] , [5] ( in the order of several 10 s of centimeters ) . However , it remains unclear how the pregnant uterus coordinates a kilogram of myometrium into repetitive , synchronous contractions of normal human labor . Several mathematical simulations of organ-level functioning have been published , including action potential propagation mechanisms alone [6] , [7] , calcium waves [8] , and a previous cellular automaton approach [9] . Each has significant limitations , including failure to predict uterine behavior across the spectrum of clinical function , or direct disagreement with subsequently published data [10] . These limitations seem to reveal a poor understanding of how the uterus communicates at the organ-level . In the canonical action potential propagation model , an action potential originates at a pacemaker site , travels through the wall of the entire uterus , exciting and recruiting tissue as it propagates [4] , [5] . Despite being widely accepted , there is no direct evidence to suggest that organ-level recruitment utilizes a stable pacemaker site or a single propagating action potential . Indeed , there is good evidence to the contrary . The highest resolution electrical mapping of the pregnant uterus was reported by Lammers et al . in the guinea pig [11] . They found that the first spike of the action potential rarely activated the entire field . A given action potential burst seemed limited to about 10 cm2 , and burst propagation speeds were much slower than action potential propagation speeds . Additionally , a stable pacemaker was not identified , and the pathways of propagation were described as “tortuous” . The only high resolution mapping of the entire front of the human uterus are by Ramon [12] and Eswaran [13] , using an array of superconducting quantum interference devices ( SQUID ) . Synchronization analysis suggested that the human uterus is similar to the guinea pig uterus , as there appears to be a maximum distance of propagation for an action potential . Specifically , wave fronts of electrical activity that propagated long distances were not observed . Although recurrent electrical activities tended to occur in the same physical locations , they also failed to observe a stable pacemaker . These observations raise the possibility that despite its irrefutable importance at the tissue-level , a mechanism using only action potential propagation for tissue recruitment is insufficient , and a second mechanism may be functioning at the organ-level . In 1970 the action potential propagation model was the leading candidate for organ-level signaling , but Csapo [14] proposed an alternate mechanism - mechanotransduction by pressure-tension sensing . In brief , he proposed that increases of intrauterine pressure cause increases of wall tension ( per the Law of Laplace; T = P*r/w ) , which then initiate contractions throughout the uterus . Unfortunately , the way mechanotransduction was initially presented suggested that contractions were controlled by stretch , and that electrical activity , if present at all , played a minimal role in initiating contractions or recruiting tissue . This seemed to present a choice between two mutually exclusive mechanisms . Over the next several years it was established without question that electrical activity causes contractions [3] , and expression of an action potential is necessary and sufficient for tissue-level contractions [15] . Thus , the action potential propagation hypothesis was viewed as the winner , and over time , mechanotransduction was largely forgotten . However , given the difficulty action potential propagation alone has explaining organ-level function , we revisited Csapo's mechanotransduction mechanism . It is has long been known that quickly stretching smooth muscle can initiate a contraction , and myometrium is no exception . In the 1970s , however , Csapo was perhaps unaware that acutely stretching , or increasing tension on myometrium , initiates an action potential burst which contributes to at least part of the underlying mechanism of stretch-initiated contractions [16] . Hence , mechanotransduction and action potential propagation are complementary and not mutually exclusive . In this work we will use the term “mechanotransduction” primarily to refer to the mechanism , and “stretch-initiated” to refer to the phenomenon . In the discussion we will explain what we mean by the phrase “myometrial myogenic response” , which , we suggest , emphasizes the importance of stretch-initiated contractions to normal function . This term is also intended to highlight the similarity of myometrial and vascular smooth muscle functioning , and imply that the cellular mechanisms may overlap . The arterial myogenic response is understood in fair detail [17] , but the physiological processes following acute stretch of myometrium are only superficially understood . To build the model that incorporates both action potential propagation and mechanotransduction , we make one key assumption . That is , there is an upper limit on the distance each tissue-level action potential can travel . If true , this limitation defines functional regions of the uterine wall . The SQUID array synchronization analysis provides some evidence for the existence of these regions , and an approximate size of 8 cm×8 cm ( reference 12 , figure 8 ) . Within regions , we assume , tissue recruitment is entirely by action potential propagation . To coordinate regional contractions into organ-level contractions , we use mechanotransduction as follows: If one region contracts , the intrauterine pressure rises slightly . This pressure rise would increase tension on the other areas of the uterine wall according to the Law of Laplace ( T = P*r/w ) . Increasing tension could trigger a stretch-initiated contraction of another region . With two regions contracting , pressure would increase further , and the cycle would repeat until all regions capable of expressing a stretch-initiated contraction were recruited . A key feature of this mechanism is that it is a pressurized hydraulic system , similar to what is used for automobile braking . Hydraulic systems rapidly transmit signals over long distances , and are not limited by physical proximity to the event that initiates the signal . Therefore , because pressure is the signal used to recruit regions , coordinating a uterine contraction at the organ-level is not constrained by the speed of action potential propagation , even though tissue within regions may be wholly recruited by action potentials . Here we report a mathematical simulation of our model for creating uterine contractions of human labor . We use a variation of the cellular automaton ( CA ) technique . A CA is a system of “cells” , each of which has a state that is defined by the state of the other cells through one or more “rules” . As detailed above , our model divides the uterus into regions , where a cell represents each region ( e . g . “cell” does not refer to an individual myocyte ) . With each iteration , or time step , the status of all the cells are simultaneously updated . Classical , or elemental CAs apply one rule and consider only the state of each cell's near neighbors . Complex biological CAs use multiple rules to describe complex physiology , and the status of a cell may be influenced by cells that extend beyond the neighbors .
For human labor , the contractile state of the uterus and the intrauterine pressure are of primary concern . Since the intrauterine pressure is shared by all regions of the uterine wall regardless of relative location , we weight the nearest neighbors no more or less than the other regions . The CA rules approach to simulation is computationally efficient but , more importantly , emphasizes the physiological properties of the tissues that make up the organ . With this in mind , we define three specific rules ( see flowchart figure 1 ) : 1 ) Intrauterine pressure at each time step is calculated as a function of the regional contractile activities . 2 ) At the next time step , the intrauterine pressure sets the passive tension on each region according to the Law of Laplace . 3 ) Within regions , electrical activity creates contractile activity ( defined as the tension of a contraction ) . The tension on each region will initiate and maintain an action potential burst if it exceeds a defined threshold ( action potential threshold ) . By definition , if any part of a region experiences an action potential , the action potential travels throughout the entire region , but no farther , and the region contracts as a unit . When a region is experiencing an action potential burst , the contractile activity is calculated by multiplying the passive tension by the action potential multiplier ( a factor >1 ) . Each region can remain electrically active no more than a defined number of time steps ( burst duration ) . If a region has been electrically active for the maximum allowable number of time steps , it enters a refractory period . When a region is in the refractory period , the tension is decreased by multiplying the passive tension by a factor <1 ( refractory multiplier ) . The refractory period lasts a defined number of time steps ( refractory duration – not shown in figure 1 ) , then the region reverts back to expressing the passive tension . In figure 2A we represent a hypothetical isometric contractility experiment , except that instead of a single tissue strip , tissues A through E are mechanically linked end-to-end . The total tension on the system is T , which is the same for each tissue . If any tissue contracts , T will increase and the new T will be expressed equally across each tissue . In figure 2B we present the corresponding hydrodynamic experiment . Here there are 5 hydraulically connected identical chambers , each containing one tissue strip that is fixed at the bottom and attached to a moveable piston at the top . The entire volume is filled with an incompressible medium , because under these conditions , the volume of the system is constant . For the human uterus , this is an excellent assumption since air is never seen within the uterine cavity , even after rupture of membranes . In this hypothetical apparatus , contraction and shortening of one tissue would pull the piston downward and increase pressure throughout the system . In the chambers with the 4 non-contracting tissues , the pistons would move outward , and the stretching would increase tension on each tissue . While somewhat counterintuitive , the two arrangements in figure 2A and B are mechanically identical . Translating these models to the physical structure of the gravid uterus , figure 2C represents the “tension” descriptor that is a wrap-around of figure 2A , and figure 2D represents a Law of Laplace interpretation that corresponds to both figures 2B and C . Consider the hydraulic system in figure 2B in detail . Because of the viscoelastic properties of myometrium , the system is in hydraulic equilibrium at only two times – when no contractions are occurring , and when all tissues are maximally contracting simultaneously . At these times Pascal's Law applies , and P = Force/area in all chambers . This equation does not apply during the onset and offset of the contraction because of the viscoelastic properties of the tissues undergoing passive stretch . Assuming nearly identical tissues and identical piston areas , the peak pressure throughout the system when all the tissues are contracting simultaneously is equal to the peak pressure ( Pmax ) generated by one tissue in an isolated chamber . When no tissues are contracting , the minimum pressure equals the baseline tension , which is equally expressed on each tissue . If one tissue contracts , the pressure begins to rise in all the chambers . The elasticity of the four tissues that are not contracting slows the rate of pressure development . If another tissue contracts , the elasticity of that tissue is lost , and pressure rises faster . Therefore , we will approximate the system pressure at any time as linearly proportional to the average contractile activity of the regions . Applying this reasoning to the five chamber experiment in figure 2B , if the first tissue contracts maximally , the system pressure will rise to approximately 20% Pmax . The pistons in the other chambers move outward in response to the rising pressure , and the non-contracting tissues are stretched . A stretch-initiated contraction occurs if one of the four remaining tissues is susceptible . Because of hydraulic signaling , the susceptible tissue that contracts next does not need to be physically located adjacent to the tissue that contracted first . With two tissues contracting maximally , the pressure increases to ∼40% Pmax , which further pushes the pistons of the remaining three tissues . With the additional stretch , the next most susceptible tissue may then contract , and so on , until all tissues contract simultaneously and the system pressure equals Pmax . Because tissue contractions are caused by electrical activity , in the simulation we will use the term “activity” to mean both electrical activity and contractile activity of a region . In addition , viscoelastic tissue creates tension passively when stretched , and we will use the term “passive activity” or passive tension specifically as the force generated by a tissue that is not caused by contractile activity . Using this nomenclature: Pressure ( t ) = ∑activity of all regions ( t ) /#regions In a closed container containing an incompressible fluid , pressure ( P ) and wall tension ( T ) are related by the Law of Laplace: T = P*r/w The r/w factor accounts for the physical shape of the container , or in this case , the anatomy of the uterus . w is the thickness of the wall and r is the local radius of curvature . r can be measured in perpendicular x , y axes , where 1/r = 1/rx+1/ry . If rx = ry , a factor of 2 is introduced in the denominator , making the Law of Laplace for a sphere . If rx is infinity , r = ry , and it becomes the Law of Laplace for a cylinder . Differences of local r and w mean that there are regional variations due to local uterine anatomy that must be accounted for when calculating either T from P , or P from T . We introduce the “anatomy sensitivity factor” to account for these variations . Each region has its own multiplicative factor corresponding to r/w , and we will assume that it is the same for the entire region , and that it does not significantly change for the duration of the run . For the gravid uterus ( an oblate spheroid ) , the largest rx will be close to the “sphere radius” of the uterus , or ∼10 cm , but as short as a few cm at regions of high curvature . Thus , rx conservatively ranges over about a factor of 3 . w varies by approximately a factor of 2 [18] , and the sphere-cylinder difference ( rx may be very large for an oblate spheroid ) brings in another factor of 2 that associates with w . Using these approximations , the anatomy sensitivity multiplier will vary from 1/4 to 3/1 ( range of the numerator/range of the denominator ) . This results in a skewed distribution of this factor between . 25 and 3 , with most values near 1 . To simulate this , we define the anatomysens ( i , j ) matrix . Each ( i , j ) value of the matrix is the anatomy sensitivity multiplier of the ( i , j ) region , and it is held constant through each run of the simulation . To convert from pressure to the passive activity of the ( i , j ) region , we multiply by anatomysens ( i , j ) . When pressure is calculated from the regional activities , we will divide by the same factor . To set specific values between ∼ . 25 and 3 centered around 1 , we select pseudorandom numbers from a Weibull distribution . This distribution is shaped by three parameters , the first sets the shape , the second the scale , and the third establishes the starting location . Hence , act ( i , j ) = pressure * anatomysens ( i , j ) and pressure = ∑ ( act ( i , j ) / ( #regions * anatomysens ( i , j ) ) where act ( i , j ) is the contractile activity of the i , j region at a specific time step . Since act ( i , j ) varies over time , the time series is stored in the activity tensor , activity ( i , j , t ) . SQUID data suggest that repetitively active regions maintain the same location , at least on the time scale of the reported experiments ( several contractions ) . Hence , we will also assume the regions are physically stable over the duration of each run of the simulation . When stretched , each region will either increase tension passively , or it may contract if a propagating action potential is initiated . To simulate a stretch-initiated contraction , we assign each region a tension threshold value . An active contraction occurs if the wall tension of a region exceeds its threshold . To emphasize a key element of our model - that propagating action potentials recruit tissue within regions - we will use the more specific term “action potential threshold” to refer to the value of the tension that initiates a contraction . It is unlikely that the action potential threshold is the same for all the regions . Since this is possibly a key factor in the onset of labor , we will again allow flexibility in the simulation by pseudorandomly selecting action potential threshold values from a Weibull distribution . When a regional tension exceeds that region's action potential threshold , we simulate the contraction by multiplying the passive tension by the action potential multiplier ( an input variable >1 ) . From isometric muscle bath experiments [19] , it is well-understood that tissue relaxation begins when the burst stops , and there appears to be an upper limit to the duration of a burst . In our simulation , once a regional burst is initiated , it will remain on until either the activity falls below threshold , or the tissue reaches its maximum burst duration ( an input variable ) . We therefore calculate the ( i , j , t ) burst tensor such that each region experiences independent bursting behavior as follows: If act ( i , j ) is below threshold , then burst ( i , j ) = 1 . If act ( i , j ) is above threshold , then bursting occurs and burst ( i , j ) = action potential multiplier . However , if the Maximum burst duration is exceeded , then burst ( i , j ) = 1 Immediately following a burst , the tissue relaxes and it is relatively unresponsive to initiation of another contraction . To simulate this refractory period , we apply the refractory multiplier ( input variable , value <1 ) . The refractory period has a defined refractory duration ( input variable ) . If the Maximum burst duration is not exceeded , then refractory ( i , j ) = 1 . If the Maximum burst duration is exceeded , then refractory ( i , j ) = refractory multiplier . It the Refractory duration is exceeded , then refractory ( i , j ) = 1 . Thus , rule 3 corrects each region's contractile activity by the burst and refractory matrices during each time step of the simulation . act ( i , j ) = act ( i , j ) ×burst ( i , j ) ×refractory ( i , j ) pressure ( t ) = ∑act ( i , j ) / ( # regions * anatomysens ( i , j ) ) at the next time step: act ( i , j , t+1 ) = pressure ( t ) * anatomysens ( i , j ) * burst ( i , j ) * refractoryfactor ( i , j ) where burst ( i , j , t+1 ) = 1 , if act ( i , j , t+1 ) <threshold ( i , j ) . burst ( i , j , t+1 ) = action potential multiplier , if act ( i , j , t+1 ) >threshold ( i , j ) . burst ( i , j , t+1 ) = 1 , if duration of the burst>max burst duration . refractoryfactor ( i , j , t+1 ) = 1 , if duration of the burst< = max burst duration . refractoryfactor ( i , j , t+1 ) = refractory multiplier , if duration of the burst>max burst duration . refractoryfactor ( i , j , t+1 ) = 1 , if refractory period duration>max refractory duration . The names and descriptions of the input , calculation , and output variables are in Table 1 . Because there are a number of input variables , we have selected the following short-cut descriptors to allow changing conditions to be easily compared: X . S1YYY: weibullvar1/weibullvar2/weibullvar3; S2ZZZ: weibullvar4/weibullvar5/weibullvar6 . Here X represents a specific set of input variables for row , column , timesteps , initial pressure , minimum pressure , maximum burst duration , refractory duration , action potential multiplier , and refractory multiplier ( see Table 2 ) . S1YYY refers to the anatomy sensitivity ( anatomysens ) seed value between 1000 and 1999 . S2ZZZ refers to the action potential threshold ( burstthreshold ) distribution seed value 2000 through 2999 . Weibull1 to 3 refer to the Weibull parameters for anatomy sensitivity and Weibull4 to 6 for action potential threshold parameters .
Here we describe the behavior of the simulation for specific input values ( Table 2 ) . In figure 3A the default values , 1 . S1000:1 . 8/1/0 . 3; S2000:4/0 . 6/0 . 4 , reveal a pressure profile suggestive of patterns commonly seen using pressure catheters on women in labor . Contractions are regular , although show some variability of the period between contractions . Peak heights vary slightly . Each contraction arises spontaneously . Pressure rises slowly at first , then accelerates . There is a pseudo-plateau , then a pseudo-symmetrical falling phase . Of note , the refractory time is set at 20 iterations , yet the number of steps between some contractions is ∼40 . In figure 3B , the action potential multiplier is reduced to 2 from 3 . Only small localized contractions are seen and coordinated organ-level contractions are lost . This behavior is what might be anticipated following exposure to nifedipine , which blocks L-type calcium channels and inhibits contractions in tissue strips in a dose-dependent manner [20] . However , reducing the action potential multiplier further to 1 . 5 ( simulating increasing the concentration of nifedipine ) results in a paradoxical reappearance of coordinated contractions ( Fig . 3C ) . In isometric contractility experiments , the primary effect of oxytocin exposure is an increase of force production . To simulate this effect , we step-wise increased the action potential multiplier from 1 . 5 to 2 , then 4 , then 8 ( Fig . 4A , B , C , D , respectively ) . We also simulate a different patient by increasing the number of regions ( using 5 rows and 5 columns ) and changing the pseudorandom seeds , which assigns different anatomy sensitivity and action potential threshold matrices . When the action potential multiplier is 1 . 5 , no coordinated contractions are seen . Increasing this value to 2 resulted in the appearance of irregular and infrequent contractions . At 4 , the contractions became more regular , with increased force and frequency . At these settings , the time between contractions is near the limit of the refractory duration . Doubling the action potential multiplier to 8 resulted in only subtle changes of the contraction pattern . Next we demonstrate that it is possible for the simulation to generate contractions that are initiated by multiple pacemakers . In this run , the rows and columns are set to reflect 25 regions ( rows = columns = 5 ) and the random number seeds for anatomy sensitivity and action potential thresholds are set to 1474 and 2500 , respectively . Regular contractions are seen with only slight variations of the interval between contractions ( Fig . 5A . The color animation of the regional activities reveals that the first region to demonstrate significant activity that continues through a global contraction is not always the same ( Fig . 5 B , C , D ) . The likelihood that a region becomes active is increased with a high value of anatomic sensitivity , but a low value of action potential threshold . Therefore , to approximate the overall , or “total” , sensitivity of each region , we divided the anatomic sensitivity by the action potential threshold for each region . The most sensitive region is in row 5 , column 1 ( total sensitivity = 4 . 003 ) . The second most sensitive region is in row 3 , column 5 ( total sensitivity = 2 . 024 ) , and the third is in row 5 column 3 ( total sensitivity = 1 . 975 ) . The pacemaker for the 4th contraction ( Fig . 5B , Step 98 ) is the region in row 5 , column 3 , the pacemaker for the 5th contraction ( Fig . 5C , Step 122 ) is the region in row 5 , column 1 , and the pacemaker for the 6th contraction ( Fig . 5D , Step 151 ) is the region in row 3 , column 5 . An isolated tissue strip can be simulated with rows = columns = 1 ( Fig . 6 ) . Here we specifically set the anatomy sensitivity to a value near 1 ( 0 . 991 , using anatomy seed 1341 ) , since under isometric conditions the anatomy sensitivity equals 1 exactly . The value for threshold is 0 . 558 ( threshold seed 2987 ) . Repetitive trials reveal that the tissue will only contract if the minimum pressure is above 0 . 6 , even if the initial pressure is above threshold ( Fig . 6B ) . Therefore , minimum pressure is the key factor for determining if regional contractions will occur . When rows = 1 , columns = 2 there are two regions , which simulates two mechanically linked tissue strips ( Fig . 7 [16] . The anatomy seed and Weibull parameters are set to reflect both tissues with similar anatomy sensitivity values near 1 ( 0 . 986 and 0 . 989 , respectively ) . The left and right tissues have action potential thresholds of 0 . 428 and 0 . 670 , respectively , and the minimum pressure is set to fall between these values ( 0 . 5 ) . In Fig . 7A the tissues appear to oscillate out of phase . The activity animation reveals that with these settings , the two regions are not ever highly active simultaneously ( not shown ) – first the “left” tissue becomes active , and then the “right” . This pattern correlates well with experimental observations ( Fig . 7B ) [16] . However , when the simulation is run with the starting pressure increased to 1 ( Fig . 7C ) , the regions oscillate at the same time and the expressed pressure is large . It is also possible to coordinate the contractions by reducing the refractory duration to 10 ( Fig . 7D , the starting pressure was returned to 0 . 5 ) . In addition to the special cases where only one or two regions were simulated , changing the number of regions has important effects on the expression of contractions . To correlate the total force production as a function of the number of regions , we calculated pseudo- “Montevideo units” ( pMV = peak force * # contractions in 300 time steps ) and plotted this value as a function of the number of regions ( Fig . 8 ) . In figure 3B with 16 regions , reducing the value of the action potential multiplier to 2 from 3 , the regional oscillations fail to create enough pressure to trigger a coordinated organ-level contraction . Continuing with these input values , global contractions are expressed when the number of regions is increased to 18 ( 6 rows , 3 columns ) , and the pMV units remain high through 30 regions . At 32 regions , the pMV units drop , and remain low through 42 regions . When the number of regions is 49 and above , the Montevideo units again increase . To ensure that the fall of force production between 32 and 42 regions was not the result of a large change of sensitivity as the number of regions varied , for each run we averaged the total sensitivity of all the regions to obtain the mean total sensitivity . While mean total sensitivity varies slightly , the onset of global contractions at 18 regions and the mid-range dip of force production at 32 to 42 regions does not appear to be attributable to changing sensitivities .
It is possible to measure the apparent speed that an action potential propagates through the human uterus using surface EMG . Two or more pairs of EMG electrodes are placed on the abdominal surface , then the distance between the electrode pairs is divided by the time between the onset of electrical activity . Speeds have been reported to average in excess of 50 cm/sec [4] , which is nearly more than an order of magnitude greater than the speeds measured in rodent in vitro [11] . This discrepancy has been explained [5] by assuming the tissue-level action potential travels long distances at much slower speeds , but they are measured as faster because the initiation point is presumably in between or lateral to the electrodes . In our model , a single action potential does not travel great distances . We propose that high speeds are artifacts of measurement that arise when electrodes record from two regions that are separated by a relatively long distance , and the regions are independently recruited by mechanotransduction at nearly the same time because the regions sense the same intrauterine pressure . Recently , however , action potential propagation velocities ( speed and direction ) were measured in term pregnant women in labor [23] . Using a 2-dimensional array over a 14 cm×14 cm grid of electrode pads , the average speed over 35 contractions was 2 . 18±0 . 68 cm/sec . The maximum measuring distance was 17 . 5 cm on the diagonal , but only 3 velocities were measured near the diagonals . Most measurements were over distances between 3 . 5 and 7 cm . From our model this most likely represents measuring the action potential propagation velocity within one region . In small arteries , pressure-dependent contractions regulate local blood flow by what is referred to as the myogenic response [17] . In brief , acutely increasing intravascular pressure results in acute contraction of the artery wall , which narrows the lumen and helps maintain the flow of blood through the artery at a constant rate . In our model of the laboring uterus , acutely raising intrauterine pressure results in an acute contraction of the uterine wall , which further increases pressure and recruits more regions to participate in the contraction . In this sense , the physiological process we propose for the uterus closely parallels the myogenic response of the artery , with the exception that the artery contraction is tonic and the myometrial contraction is phasic . Despite this difference , we propose that the term “myometrial myogenic response” should be used to refer to the process of generating large intrauterine pressures by synchronizing uterine wall contractions via mechanotransduction . In a more limited sense , it can also be used to describe the mechanism of stretch-initiated contractions . The key purpose of introducing this terminology is to help differentiate the mechanisms used to rapidly coordinate uterine contractions from the mechanotransduction mechanisms used to regulate gene expression over longer time frames . Elementary CAs are well-defined mathematical constructs previously used to investigate the emergent properties of complex systems [24] . There are only 256 possible rules and Wolfram places each into one of four classes based on the expression of complex behavior . Starting values are not considered in the classification of the rule . However , biological CAs are quite different from elementary CAs . Rules are layered to integrate lower level processes into higher level function , and they are highly constrained ( or perhaps enriched ) by the physiology . After establishing the rules , the main purpose of the biological CA is to examine effect of the input values on the CA behavior , and determine how closely the higher level function is simulated . Hence , a great deal of insight can be gained on the relationship of less complex physiology to more complex biological behavior , and it is worthwhile to classify biological CAs . Therefore , we propose that the input values form the basis for classification of biological CAs . The “input-based classification” system should parallel the rule-based classification established by Wolfram . A set of input values that converges to a uniform state ( no change of activity over time ) is class 1 . An input value set that converges to a repetitive , or stable state is class 2 . A class 3 set yields a “chaotic” state , without repeating patterns or large structures . A class 4 set generates complex behavior where emergent global patterns occur . In addition , there must be two constraints on biological CAs if they are to be classifiable and yield input-based classes . First , the rules must be consistent with known relevant physiological processes , although the rules do not need to describe all known processes . For example , a CA of the pregnant uterus should not contain a rule describing long range efferent neural connections ( which are known to not exist ) . Yet it is not necessary that it contain a rule describing prostaglandin paracrine signaling , even though the physiology is well-known . Furthermore , using a rule that describes a process that is not clearly a “known relevant physiological process” , must be described as such , and input values should allow testing of the rule . It could be argued that our assumption that there are limits to the distances action potentials can propagate in the human uterus is a speculation . But since we test this by allowing the numbers of regions to vary , the CA remains classifiable . Second , the input values must be physiologically reasonable and potentially measureable , or calculable , from experiments . In formulating the rules , each input variable should be aligned as closely as possible with a measurable physiological effect . This will allow the CA to test how modifying the physiology changes function . Lastly , in order to be assigned to a class , the input values must be physiologically reasonable . As an example for this CA , any set of input values that contains 0 for the minimum pressure cannot be assigned to a class , since the uterus always maintains a non-zero baseline pressure . Using these definitions , an example of class 1 behavior is figure 6B . An example of class 2 behavior is figure 6A . Figure 3A ( the default input set ) at first glance seems to be class 2 , but because of the variation of the interval between contractions , is best placed in class 4 . Although not shown , a more obvious example of class 4 behavior occurs with input set 4/4/300/0 . 5/0 . 7/8/24/3/0 . 2 . S1246:1 . 8/0 . 6/0 . 55;S2648:4/0 . 8/0 . 1 . We were unable to find a set of input values that displayed class 3 behavior , which may be a reflection of the stability of the underlying physiological system . In addition to classes , there should be some notation regarding the relationship between the input values and the physiological relevance of the outcome . This will vary based on the biological system . For example , a uniform state without global activity ( e . g . class 1 ) aptly describes the uterus for much of the pregnancy , and examining the range of input values where this occurs is important . In other biological systems this uniform state may be physiologically less relevant ( perhaps a CA describing brain activity ) . Therefore we propose that for biological CAs an additional modifier may be added based on the relevance to function . First we propose the “N” modifier , which refers to behavior observed within the range of normal functioning . The “X” modifier , refers to behavior never observed under any conditions , and the “P” modifier for behavior with less than normal function ( i . e . pathological ) . For behavior at the transition between states , for example between normal and pathological states , the N-P descriptor could be used . Ultimately we seek to classify and study patterns of input sets that will help determine the relationship between the physiology of the components and the complex and emergent behavior of the organ . Using this terminology , the default input value set ( Fig . 3 A ) is class 4-N . Previous mathematical simulations of uterine function have seemed focused on identifying conditions that yield repetitive phasic contractions , or class 2-N . Our work suggests that forcing class 2 behavior of closed-form models may not be necessary , and may artificially narrow the boundary conditions . As discussed above , this CA offers an explanation for why a stable pacemaker has not been found in the human uterus , and we suggest that the concept of a pacemaker of the uterus should be reexamined . If the framework of our model is correct , the mode of operation of normal labor exploits the emergent properties of a complex system ( class 4 ) . Yet at any point in time there will be one region with the largest total sensitivity . That region can be reasonably called the pacemaker because the cascading events that create each contraction are largely driven by the activity of that region . By this definition , the pacemaker only needs to initiate the events that eventually progress to generate repetitive coordinated contractions . It does not need to be the trigger at the leading edge of every contraction ( Fig . 5 ) , or even participate in every contraction . Previously , attempting to identify the uterine pacemaker involved looking for the first activity at the beginning of every contraction , but we propose that looking for the region that expresses the highest frequency of activity will identify the pacemaker . This is a particularly difficult task in practice , since current EMG techniques , and even the SQUID array , are able to examine only the front wall of the human uterus . As long as there are no changes that effect the total sensitivity of the regions , it is likely that the pacemaker location will be relatively stable , perhaps over many contractions . However , the pacemaker location will change as the anatomy or excitability properties of the uterus as a whole change . Specifically , the pacemaker site will be highly dependent on tissue-level electrical interconnectivity . This is because changing the size of the regions changes two factors: the local anatomy , and ( as we demonstrate in figure 8 ) the interactions of the regions in a class 4 system . It may be possible to observe repetitive regional activity that fails to recruit other regions and is not associated with coordinated contractions . That behavior would likely be class 2 . As we proposed above , true labor arises out of class 4 behavior , so repetitive activity associated with class 2 behavior that fails to yield coordinated contractions is merely repetitive activity and not pacemaker activity . The simulation at this stage of development is not adequate to observe clinical tracings and then suggest specific treatments designed to normalize abnormal patterns . However , this is a long-range goal , and we encourage continued investigation and further refinement of the model by others . To this end , we provide the code and documentation for this simulation in supplementary information S1 and S2 . The program is free to download , modify , and investigate . In conclusion , simulation of our model successfully links cellular- and tissue-level physiology to observable organ-level functioning . This success supports our model of electrical activity and mechanotransduction synergistically combining into a dual mechanism of global uterine function . If this model withstands further investigation , we propose that the concept of a stable uterine pacemaker triggering and participating each contraction be abandoned for human labor . In our model , the distances action potentials can propagate in vivo define the size of the functional regions , and the number of functional regions is important in the expression of coordinated contractions . We propose that further work is needed to determine how these distances vary with different clinical conditions , including multiple gestation , uterine anomalies , mass lesions of the uterus , and gestational age . | How does the pregnant uterus coordinate a kilogram of smooth muscle tissue into repetitive , synchronous , organ-level contractions of human labor ? Action potential propagation recruits tissue for contraction over short distances , but a single action potential sweeping through the uterus cannot explain organ-level function . Multiple action potentials seem to arise simultaneously , and apparently spontaneously , in different “regions” of the uterine wall . We interpret the existence of regions as a consequence of there being a maximum distance a single action potential can travel . To explain the synchronization of regional contractions , we use a second mechanism: initiation of contractions by stretch . Because the uterus is pressurized , contraction of the first region raises the intrauterine pressure slightly , which stretches the entire uterine wall . The stretch recruits another regional contraction , which generates more pressure . This positive feed-back recruits most regions into simultaneous activity . With this mechanism we simulate the contraction patterns of human labor , and show how contractions emerge from complex interactions . We explain why the decades-long search for the uterine pacemaker has failed , and why drugs that stimulate or inhibit tissue contractions have enigmatic effects at the organ-level . This simulation , for the first time , successfully links tissue experiments to clinical obstetrics . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"women's",
"health",
"maternal",
"health",
"obstetrics",
"and",
"gynecology",
"pregnancy"
] | 2014 | Linking Myometrial Physiology to Intrauterine Pressure; How Tissue-Level Contractions Create Uterine Contractions of Labor |
In schistosomiasis control programmes using mass chemotherapy , epidemiological and morbidity aspects of the disease need to be studied so as to monitor the impact of treatment , and make recommendations accordingly . These aspects were examined in the community of Musoli village along Lake Victoria in Mayuge district , highly endemic for Schistosoma mansoni infection . A cross sectional descriptive study was undertaken in a randomly selected sample of 217 females and 229 males , with a mean age of 26 years ( SD ±16 , range 7–76 years ) . The prevalence of S . mansoni was 88 . 6% ( 95% CI: 85 . 6–91 . 5 ) . The geometric mean intensity ( GMI ) of S . mansoni was 236 . 2 ( 95% CI: 198 . 5–460 . 9 ) eggs per gram ( epg ) faeces . Males had significantly higher GMI ( 370 . 2 epg ) than females ( 132 . 6 epg ) and age was also significantly associated with intensity of infection . Levels of water contact activities significantly influenced intensity of infection and the highest intensity of infection was found among people involved in fishing . However , organomegaly was not significantly associated with S . mansoni except for very heavy infection ( >2000 epg ) . Liver image patterns C and D indicative of fibrosis were found in only 2 . 2% and 0 . 2% , respectively . S . mansoni intensity of infection was associated with portal vein dilation and abnormal spleen length . Anaemia was observed in 36 . 4% of the participants but it was not associated with S . mansoni infection intensity . Considering growth in children as one of the morbidity indicators of schistosomiasis , intensity of S . mansoni was significantly associated with stunting . Although organ-related morbidity , with the exception of periportal fibrosis , and S . mansoni infections were highly prevalent , the two were only associated for individuals with very high infection intensities . These results contrast starkly with reports from Ugandan Lake Albert fishing communities in which periportal fibrosis is more prevalent .
In Uganda , schistosomiasis is mainly caused by Schistosoma mansoni [1] and affects more than 10% of the population [2] , [3] . It is one of the Neglected Tropical Diseases ( NTDs ) which perpetuate poverty . The distribution of schistosomiasis has increased due to environmental changes , water development projects and migration of people from endemic to non-endemic areas , spreading to urban areas in developing countries [4] , [5] . Schistosome eggs trapped in the host tissue are the major cause of morbidity . Eggs trapped in the liver cause granulomatous reactions and lead to formation of fibrotic lesions with hepatosplenic enlargement which may in turn cause portal hypertension and hepatosplenic schistosomiasis [6] , [1] , [7] , [8]–[10] . Hepatosplenic schistosomiasis is common in children and adolescents and may affect up to 80% of the infected individuals [6] , [9] and in the Kenyan study it was exacerbated by malaria otherwise , its severity is related to the intensity of infection [11] and duration of exposure to contaminated water [8] . However , not all infected individuals experience morbidity and the level of schistosomiasis related morbidity differs among affected communities and endemic areas . Other manifestations of schistosomiasis include anaemia [12] and physical retardation [13] , [14] . However , other parasitic infections may interact on schistosomiasis-related morbidity . For instance , malaria may act synergistically with schistosomiasis in the development of hepatosplenomegaly [15]–[17] and anaemia is associated with malaria [18] and hookworm [19] infections . Previously , epidemiological and morbidity data have been reported from the Lake Albert region [1] , [11] , however such data from Lake Victoria in Uganda are few [20] . This study was carried out to describe the epidemiology of S . mansoni infection and its related morbidity among communities living along Lake Victoria .
The study was conducted in Musoli village along Lake Victoria , Mayuge district in South East Uganda . The district lies at an altitude of 1161 m above sea level , with temperatures ranging from 19–27°C and receives annual rainfall in the range of 600–1100 mm [21] . Like other lakes in Uganda , transmission of schistosomiasis in Lake Victoria is stable and intense throughout the year . Musoli village is inhabited by two ethnic groups; the Bantu and Nilotics . The level of literacy is high as compared to other fishing communities in Uganda and most children go to school . Other than subsistence farming , fishing is the major economic activity . Lake Victoria is the only source of water and this exposes the population to schistosomiasis infection . This was a baseline cross sectional descriptive part of a longitudinal study of randomly selected sample of people stratified by age and sex . Power calculations for multivariate analysis indicated that a minimum sample size of 156 individuals were required . In order to cater for loss to follow up , a higher sample size was selected . Children 6 years or below were excluded . The study included anthropometric measurements , clinical and ultrasound examinations , haemoglobin assessment and parasitological examinations for S . mansoni , hookworms and malaria . On three consecutive days , early morning stool specimens were collected from each participant . From each specimen; two slides each containing 50 mg were prepared using the modified Kato Katz thick smear technique [22] . Two experienced technicians examined the six slides under the microscope ( 10× ) within one hour of slide preparation so as to assess presence of hookworm eggs and 24 hours later slides were examined for S . mansoni . Hookworm infection was only reported as positive or negative . Intensity of S . mansoni infection , described as eggs per gram of stool ( epg ) was categorised as low: 1–99 epg; moderate: 100–399 epg and heavy: ≥400 epg [23] . A finger prick blood sample was taken from each study subject and a thick blood smear prepared and stained with Giemsa for diagnosis of malaria parasites . The slides were read on a microscope under an oil-immersion objective 40× . A blood slide was considered negative if 100 fields were read and no malaria parasites seen . Malaria parasites were counted against 200 white blood cells ( wbc ) . Assuming a wbc count of 8000/µL the parasite count was multiplied by 40 ( 8000/200 ) to calculate number of parasites/µL . Another drop of blood was absorbed into a micro-cuvette , inserted into a portable photometer ( HemoCue Hb 201+ Analyser , Quest Diagnostics Company , Norrköping - Sweden ) . Anaemia was defined as: Hb<11 . 5 g/dL for children 5–11 years; Hb<12 . 0 g/dL for children 12–14 years; Hb<12 . 0 g/dL for non-pregnant women ≥15 years; Hb<11 . 0 g/dL for pregnant women; Hb<13 . 0 g/dL for men ≥15 years , [24] . As quality control , two independent technicians read a 10% randomly selected sample of stool and malaria slides respectively . A questionnaire was developed and translated into the most commonly used language , Lusoga . The questionnaire was used to obtain information about individual water contact exposure patterns and ethnic group . For children who could not answer some of the questions , their parents or guardians provided the required information . Height was measured to the nearest 0 . 1 centimetres using a portable stadiometer and weight measured to the nearest 0 . 1 kg using a Seca portable digital scale . Z scores of height-for-age ( HAZ ) and weight-for-age ( WAZ ) were calculated using Nutritional Index Calculator , EpiInfo , Version 6 . 04 ( Centers for Disease Control and Prevention , USA ) . HAZ and WAZ values less than -2 were considered as stunting and wasting respectively as described by WHO [25] . Body Mass Index ( BMI ) of each child was calculated as weight in kilograms divided by the square of height in meters ( kg/m2 ) . In our study , BMI<15 kg/m2 was considered as underweight , otherwise BMI beyond 15 kg/m2 would have rendered all the children in our study to be underweight . Each individual was clinically and independently examined by three experienced examiners , i . e . one physician and two nurses . The obtained measurements from all the examiners were discussed and a final measurement agreed upon . If the measurements of the three examiners varied greatly , all the examiners repeated the examination . Abdominal palpations were performed as previously described [12] . Using a tape measure , the following measurements were taken: the extension of the left liver lobe beneath the sternum was measured in the mid sternal line ( MSL ) ; the extension of the right liver lobe beneath the rib cage was measured in the right mid clavicular line ( MCL ) ; the extension of the spleen below the rib cage was measured both in the left MCL and left mid axillary line ( MAL ) . The liver tenderness and its consistency as well as the spleen consistency were graded as described by Vennervald [12] . The findings were translated into an overall clinical score reflecting the degree of organomegaly . The consistency of the organs and any signs of portal hypertension were recorded . Ultrasonography was performed by two experienced ultrasonographers using a portable ultrasound machine ( SSD 500 Aloka with 3 . 5 MHz curvilinear - 60% probe ) . Each individual was examined by one ultrasonographer , who would consult another examiner in case of need . The subjects were examined in a supine position lying with their legs stretched on an examination coach . The portal vein diameter was measured at the porta hepatis at the ventral lower end of the caudate lobe as previously described [26] . Liver texture patterns were graded according to WHO guidelines [27] . Portal vein diameter values were compared with the values for the corresponding height groups from a Senegalese non-infected population and classified as normal if they were below or equal to the mean+2 SD; moderately abnormal if they were >2 SD but ≤mean+4 SD and severely abnormal if they were >mean+4 SD [28] as suggested by Richter [27] . The data were double entered in Microsoft Excel and analysis performed using Stata 11 . 0 ( Stata Corporation , USA ) . Schistosoma mansoni egg counts and malaria parasite counts were analysed in relation to various predictors using negative binomial regression [29] adjusting for clustering within households; we used generalized linear models using log-link function . The ancillary parameter was estimated using full maximum likelihood estimation [29] and was used in the generalized linear model . For this analysis S . mansoni egg counts were summed for all slides ( up to 6; less than 8% did not provide the full set of samples ) and total faeces examined ( = no . of slides*0 . 05 g ) was entered as an offset . Similarly , prevalence of infection by S . mansoni , malaria or hook worm infections and of various morbidity indicators ( anemia , portal vein dilatation , organomegaly ) was analysed using logistic regression [30] adjusting for possible clustering within households . The model building strategy was to test all potential predictors one by one after adjusting for sex and age group whether these were significant or not and the interaction between these two factors if significant . Significant predictors were then entered together with sex , age group and the interaction between the sex and age group . Insignificant predictors were then eliminated including possibly some of the indicator variables for levels in categorical variables . Model fit for count models was assessed using dispersion statistics to check for over dispersion and Anscombe residuals were used to check for outliers [29] . Logistic regression models were assessed using the Hosmer-Lemeshow goodness of fit statistic [30] . P-values less than 0 . 05 were considered significant . Ethical approval was obtained from the Higher Degrees Research and Ethics Committee of the School of Public Health , Makerere University . Ethical clearance was granted by the Uganda National Council of Science and Technology and the Danish National Committee on Biomedical Research Ethics in Denmark . Written in the local language , consent forms were used to obtain individual adult participants' consent while parents or guardians consented on behalf of participants less than 15 years . Participants suffering any minor ailment like clinical malaria , anaemia , diarrhoea and others were treated according to the Uganda national guidelines . Following the National Schistosomiasis Control Programme guidelines , all study participants were treated with 40 mg/kg body weight of praziquantel ( Distocide 600 mg ) and one tablet of albendazole ( Alzental 400 mg ) all manufactured by Shin Poong Pharmaceuticals , Seoul Republic of Korea , irrespective of their infection status . The rest of the community were treated following the National guidelines .
The overall prevalence of S . mansoni was 88 . 6% . The overall GMI for positives only was 236 . 3 epg ( 95% CI: 198 . 5–460 . 9 ) and overall proportions of heavily , moderately and lightly infected persons were 39 . 0% , 22 . 2% and 27 . 4% respectively . Males generally had higher prevalence of infection than females in all age groups , and the interaction between sex and age group was significant ( p<0 . 001 ) . Basically , all males in the age range 7 to 29 years were infected and prevalence of infection among females was high in the age range from 7 to 19 years while prevalence among females aged 20 years and above was low ( Fig . 1 ) . The following factors tested individually and after adjusting for age , sex , the interaction between sex and age group and clustering within households were significant , ethnic group ( p<0 . 001 ) , occupation ( p<0 . 05 ) and water contact activity ( p<0 . 001 ) . The significance of ethnic group was mainly due to the Bagwere all being infected ( n = 27 ) . For occupation , students were reported to have higher odds of infection while for water contact people reporting to be involved in fishing had higher odds of infection . Duration of stay was coded as 1–2 years , 3–4 years , 5–9 years , 10–14 years and 15 or more years . Odds of infection among those with a duration of stay of 1–2 years was 5 . 2% ( p<0 . 001 ) and those with a duration of 3–4 years was 11 . 9% ( p<0 . 001 ) , of those of people with a duration of stay of 15 or more years . The final model showed that gender ( <0 . 001 ) , age group ( <0 . 01 ) , the interaction between gender and age group ( <0 . 001 ) , being a student ( OR = 109 . 7; p<0 . 01 ) , having stayed in the area 1–2 years ( OR = 0 . 07 p<0 . 001 ) or 3–4 years ( OR = 0 . 2 p<0 . 01 ) compared to people having stayed 5 or more years; and engagement in fishing ( OR>1000; p<0 . 001 ) were all significant . The sample size was reduced to 381 , since some people did not report on water contacts ( frequencies and type of activity ) . Intensity of infection varied greatly among individuals with a maximum egg count of 7083 epg . Males had on average 3 . 40 times higher egg counts than females ( Fig . 2; Table 1 ) and intensity of infection varied significantly across age classes ( p<0 . 001 ) , while the interaction between sex and age groups was not significant . The following factors tested individually after adjusting for age , sex , and clustering within households were significant: occupation ( p<0 . 01; fishing being associated with higher intensities of infection ) , water contact activity ( p<0 . 001; those reporting fetching of water only had lower egg counts than those involved in all reported activities ) , frequency of visiting the lake ( p<0 . 05; with 5–7 times per day being associated with higher intensity of infection ) , and duration of stay ( <0 . 01 ) . Egg counts as percentage of egg counts among people having stayed 15 or more years were 38 . 5% ( p<0 . 01 ) , 55 . 5% ( n . s . ) and 61 . 4% for people having stayed 1–2 years , 3–4 years and 5–9 years respectively after adjusting for gender and age group and clustering within households . The final model showed that males had higher egg counts than females , and lower egg counts were associated with people who reported to have water contact 1–2 days per week , those reporting to only fetch water and those having stated only for 1–2 years in the area ( Table 2 ) . The overall prevalence of hookworm infection was 43 . 3% and did not differ significantly between sexes . Prevalence of hookworm infection increased with age group up to the age group 20–24 years while differences between this age group and those older were not significant . Odds ratios for the age groups 7–9 years , 10–14 years and 15–19 years compared to all older age groups combined were 0 . 25 ( p<0 . 001 ) , 0 . 21 ( p<0 . 001 ) and 0 . 49 ( p<0 . 05 ) , respectively . Prevalence of infection was not related to the following factors tested individually after adjusting for age effects and clustering within villages: ethnic group , occupation , frequency of water contact , water contact activities or malaria prevalence . Malaria parasitaemia was found in 291 ( 65 . 2% ) of the participants with a mean parasite density of 571 . 4 parasites/µL ( 95% CI: 430 . 8–712 . 1 ) . Prevalence of malaria parasitemia declined with increasing age ( p<0 . 001 ) from age group 7–9 years to the age group 20–24 years , while differences between the latter and the older age groups were not significant . Odds ratios for the age groups 7–9 years , 10–14 years and 15–19 years compared to all older age groups combined were 9 . 91 ( p<0 . 001 ) , 5 . 82 ( p<0 . 001 ) and 2 . 86 ( p<0 . 05 ) , respectively . Prevalence of malaria parasitemia was not related to the following factors tested individually after adjusting for age effects and clustering within households: ethnic group , frequency of water contact , water contact activities or malaria prevalence , while occupation was significant ( p<0 . 05 ) with students having slightly higher levels of infection . A total of 246 people ( 59 . 4% ) were co-infected with S . mansoni and malaria while 106 ( 23 . 8% ) had S . mansoni , hookworm and malaria . Malaria parasite density did not differ significantly between sexes when after adjusting for age group , while density decreased with increasing age from 5–9 years to the 15–19 years age group ( p<0 . 001 ) . Density did not differ significantly between the 15–19 years age group and those older . Parasite count ratios for the age groups 7–9 years and 10–14 years compared to all older age groups combined were 6 . 20 ( p<0 . 001 ) and 3 . 70 ( p<0 . 001 ) , respectively . Parasite counts in the 10–14 years age group was 60% of those in the 7–9 years age group , while in the 15–19 years age group it was 24% of that in the 7–9 years age group . Malaria parasite density of positives only was 884 . 8 parasites/µL ( 95% CI: 600 . 3–1169 . 3 ) in children aged 7–14 years and 303 . 9 parasites/µL in adults 15 years or older ( 95% CI: 226 . 4–381 . 5 ) . From clinical examinations , after analysing each organomegaly indicators separately , levels of hepatomegaly , splenomegaly and hepatosplenomegaly were moderate . Overall presence of hepatomegaly was 24 . 2% , splenomegaly 4 . 9% and hepatosplenomegaly 30 . 3% . Hepatomegaly did not differ between sexes after adjusting for age groups , while variation across age groups was significant ( p<0 . 05 ) with adults having slightly lower prevalence of hepatomegaly . None of the following factors were significant when tested individually after adjusting for age category: ethnicity , occupation , water contact activities , S . mansoni intensity category , malaria or hookworm infection . S . mansoni intensity category tested as the only predictor after adjusting within households was not significant . Length of stay was not associated with hepatomegaly when adjusting for age group . Splenomegaly did not differ between sexes or among age groups and none of the factors listed above for hepatomegaly were significant . Length of stay was not associated with splenomegaly when adjusting for age group . Hepatosplenomegaly did not differ among sexes when adjusting for age group , while it varied among age groups ( p<0 . 01 ) with age groups 15–19 years and older having less hepatosplenomegaly than the 7–9 year class . The odds ratio for adults ( 15 years and above ) compared to children was 0 . 44 . Among other factors tested individually after adjusting for age group and clustering within households only malaria infection was associated ( p<0 . 05 ) with increased hepatosplenomegaly ( OR = 1 . 75 , 1 . 04–2 . 94 ) . The odds of having hepatosplenomegaly among the very heavily infected ( n = 41 ) was 3 . 39 times higher than among people with no infection or with less than 2000 epg ( p<0 . 01 ) when adjusting for age class . This category of very heavy infections , however , was not associated with splenomegaly or hepatomegaly . Of those infected with S . mansoni and having enlarged spleens and livers , majority had firm organs ( 92 . 7% and 89 . 9% respectively ) . Length of stay was not associated with hepatosplenomegaly . Periportal fibrosis ( PPF ) was rare . A total of 423 ( 94 . 8% ) and 3 ( 0 . 7% ) had normal livers with patterns A and B respectively , while ten ( 2 . 2% ) and one ( 0 . 2% ) people had liver image patterns C and D respectively . Patterns Z and Y , associated to alcoholic-related liver damage , were observed in 1 ( 0 . 2% ) and 8 ( 1 . 8% ) people respectively . A total of 48 people ( 10 . 8% ) had dilated portal vein diameters . Portal vein dilatation differed significantly between sexes ( p<0 . 05 ) and among age groups ( p<0 . 05 ) . There was , however , a significant interaction between sex and age group ( Fig . 3 ) . Apart from the youngest age group few females had dilated portal veins in the age groups up to the 25–29 years class , while in the older classes females had higher prevalence of dilated portal vein . Among the other factors tested individually after adjusting for sex , age group , the interaction between these two factors and possible clustering within households , occupation ( p<0 . 05; with fishers having the highest prevalence ) and water contact activities ( p<0 . 01; with those involved in all forms of activity having highest prevalence ) were significant . Intensity categories for S . mansoni infection were not related to portal vein dilatation . However , the odds of having dilated portal vein among the very heavily infected ( n = 41 ) was 4 . 49 ( p<0 . 001 ) times higher than among people with no infection or with less than 2000 epg after adjusting for age . Length of stay in the villages was not associated with dilated portal vein prevalence after adjusting for age and clustering within households . Haemoglobin ( Hb ) level , ( overall value 12 . 6 g/dL , 95% CI: 12 . 4–12 . 8 ) differed between sexes and across age groups , but there was a significant interaction these two variables ( p<0 . 001 ) . None of the other factors evaluated individually after adjusting for sex , age group , the interaction between these two factors and possible clustering within villages were significantly associated with haemoglobin levels , i . e . ethnic group , occupation , water contact , S . mansoni intensity category , malaria infection and hookworm infection . Overall 36 . 4% ( 95% CI: 31 . 7–41 . 1 ) of the people were anaemic but it was not associated with sex , age group , intensity of infection by S . mansoni , malaria nor hook worm infection . Intensity of S . mansoni significantly affected the level of stunting ( p<0 . 01 ) . However , there was no evidence of the effect of schistosomiasis on wasting and underweight ( Table 3 ) .
Schistosomiasis is highly prevalent ( 88 . 6% ) with high intensity of infection in this Victoria Lake shore community and infection is related to water contact . The high infection level is typical for endemic areas around Lake Victoria [7] , [31]–[33] . Intensity of infection is higher in males than females [2] , [10] , [34] . This could be due to occupational exposure such as fishing [35] , which prolongs the duration of contact with schistosome-infested water [8] , [36] . The peak S . mansoni infection intensity occurred in the 15–19 year age group , similar to observations elsewhere [2] , [10] , [37]–[39] . Several explanations have been suggested for this trend , among which is water contact . However , exposure alone may not explain this age difference in infection . A study of a fishing community along Lake Albert , where adults were more exposed to infested water than children recorded a similar age infection pattern [10] . This pattern could be explained by slow development of acquired immunity to schistosomiasis infection . In endemic areas , people acquire immunity in response to parasite antigens and this immunity is influenced by age [40] , [41] or duration of exposure [42] , [43] . Another explanation for infection peaking in the second decade of life could be due to physiological changes at puberty [38] . Hormonal changes during puberty , such as increase in skin thickness or deposition of fat , increase resistance to S . mansoni infection by reducing cercarial penetration [42] , [44] . The most common observed morbidity indicator in our study , hepatosplenomegaly without fibrosis , concurs with observations from a Kenyan study [19] . Having more children than adults with hepatosplenomegaly , is also comparable to other studies [28] , [45] . Could the age difference in prevalence of hepatosplenomegalybe due to increased regulation of inflammatory immune responses with age [41] , [43] ? Contrary to other studies where hepatosplenomegaly was associated with prevalence of S . mansoni [46] , [47] and intensity of infection [9] , [13] , [48] , clinically detected enlarged spleens and livers in our study were not associated with prevalence of S . mansoni . Our findings are comparable to those among Kenyan school children where no significant difference in hepatosplenomegaly between S . mansoni infected and un-infected children was found [17] . The observed organomegaly is likely to have been affected by the highly prevalent malaria infection in our study as evidenced by the weak correlation we obtained between the liver size and malaria infection . However , when heavy intensity of S . mansoni infection was further categorised , hepatosplenomegaly showed an association with very heavy intensity . This is in agreement with earlier studies in Uganda [11] , [49] and else where [50] . Our study was carried out in a community with more or less similar living conditions as those in a study conducted along Lake Victoria in Tanzania [13] that had lower GMI than what we obtained and another one along Lake Albert [11] but contrary to our study , these two studies registered high levels of PPF . The low PPF levels in our study were not expected because markedly high morbidity has been reported by other studies along Lake Albert [8] , [11] and also from evaluation of the impact of the Uganda National NTD Control Programme in areas where S . mansoni infection intensity was the same as what we obtained in our study [51] . In one of the studies along Lake Albert , a lower prevalence of PPF was attributed to shorter duration of exposure to infection [8] . In our study , duration of exposure is not likely to have affected PPF since most people were born in the village and having no other source of water; all of them are exposed to infection . Probably factors like parasite genetic differences [28] could have influenced the levels of PPF we obtained . This is supported by findings from a study that revealed a genetic variation of S . mansoni parasites in Lake Albert from those of Lake Victoria [52] , more so with locally different strains within Lake Victoria [53] . Anaemia levels were mild and comparable with findings from other studies [19] , [54] . In a Kenyan study , no relationship between Hb and intensity of S . mansoni was observed and this was attributed to low intensity of S . mansoni infection [19] . Although we realised high S . mansoni intensity of infection than the Kenyan study , levels of infection had no effect on anaemia . It should be noted that schistosomiasis may not be the only predictor but other parasitic infections such as malaria and hookworms may cause anaemia [12] , [18] , [54] . Nonetheless , anaemia was neither associated with malaria nor hookworm infections in our study . This is also contrary to previous studies which report an association between malaria and anaemia [18] , [19] yet malaria parasitaemia was as prevalent in our study as it was in these studies . Other factors such as poor nutritional diet or inadequate dietary in-take without iron supplements , poverty and haemoglobinopathies may also have an impact on haemoglobin levels and lead to anaemia [18] , [55] . Our results suggest that anaemia was probably a result of other factors such as poor nutrition , haemoglobinopathies , increased haemolysis in the spleen or hookworm intensity of infection [56] , all of which were not assessed in our study . Growth parameters were computed for only children below 15 years of age because this age group is most susceptible to debilitating effects of schistosomiasis infection [14] , [57] . Other than stunting , wasting and underweight were not associated with schistosomiasis infection intensity . This is contrary to the Kenyan study where no significant relationship between stunting and S . mansoni infection was observed [58] . In conclusion , S . mansoni infection is highly prevalent in Mayuge district . Whereas there is evidence from this study that an individual's age , sex and occupation influence the level of S . mansoni intensity of infection , only severe heavy intensity of infection has an influence on morbidity . Furthermore , the schistosomiasis specific periportal fibrosis was practically absent . These results indicate that organ related morbidity cannot solely be used as an indicator of successful morbidity reduction when monitoring the impact of a schistosomisis control programme . This is clearly illustrated by this study in combination with previous reports which show that morbidity differs between endemic areas with similar exposure patterns . There is a complex relationship between intensity and organ related morbidity , with these currentresults contrasting starkly with reports from Ugandan Lake Albert fishing communities where periportal fibrosis was more prevalent . | Schistosoma mansoni infection is one of the Neglected Tropical Diseases ( NTDs ) that perpetuate poverty , especially in Sub Saharan Africa . It is associated with hepatomegaly , splenomegaly or hepatosplenomegaly , liver fibrosis and anaemia . Control of schistosomiasis is now a priority in most endemic countries in Africa as a component of integrated control of NTDs using mass drug administration ( MDA ) . Other than the new WHO strategic plan to eliminate schistosomiasis as a public health problem in WHO Africa region by 2020 , the major target in the control of schistosomiasis has for a long time been reduction of its related morbidity . Epidemiological and morbidity studies are key in monitoring the impact of an intervention . However , epidemiology of schistosomiasis and its related morbidity have been shown to vary in different endemic areas and communities . We report on the epidemiology of S . mansoni infection and related morbidity in a community in Mayuge District along Lake Victoria in Uganda . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"biology"
] | 2013 | A Very High Infection Intensity of Schistosoma mansoni in a Ugandan Lake Victoria Fishing Community Is Required for Association with Highly Prevalent Organ Related Morbidity |
Single Nucleotide Polymorphisms ( SNPs ) in genes involved in the DNA Base Excision Repair ( BER ) pathway could be associated with cancer risk in carriers of mutations in the high-penetrance susceptibility genes BRCA1 and BRCA2 , given the relation of synthetic lethality that exists between one of the components of the BER pathway , PARP1 ( poly ADP ribose polymerase ) , and both BRCA1 and BRCA2 . In the present study , we have performed a comprehensive analysis of 18 genes involved in BER using a tagging SNP approach in a large series of BRCA1 and BRCA2 mutation carriers . 144 SNPs were analyzed in a two stage study involving 23 , 463 carriers from the CIMBA consortium ( the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 ) . Eleven SNPs showed evidence of association with breast and/or ovarian cancer at p<0 . 05 in the combined analysis . Four of the five genes for which strongest evidence of association was observed were DNA glycosylases . The strongest evidence was for rs1466785 in the NEIL2 ( endonuclease VIII-like 2 ) gene ( HR: 1 . 09 , 95% CI ( 1 . 03–1 . 16 ) , p = 2 . 7×10−3 ) for association with breast cancer risk in BRCA2 mutation carriers , and rs2304277 in the OGG1 ( 8-guanine DNA glycosylase ) gene , with ovarian cancer risk in BRCA1 mutation carriers ( HR: 1 . 12 95%CI: 1 . 03–1 . 21 , p = 4 . 8×10−3 ) . DNA glycosylases involved in the first steps of the BER pathway may be associated with cancer risk in BRCA1/2 mutation carriers and should be more comprehensively studied .
Carrying an inherited mutation in the BRCA1 or BRCA2 gene increases a woman's lifetime risk of developing breast , ovarian and other cancers . The estimated cumulative risk of developing breast cancer by the age of 70 in BRCA1 and BRCA2 mutation carriers varies between 43% to 88%; similarly , between 11% to 59% of mutation carriers will develop ovarian cancer by the age of 70 [1]–[3] . These considerable differences in disease manifestation suggest the existence of other genetic or environmental factors that modify the risk of cancer development . The Consortium of Investigators of Modifiers of BRCA1 and BRCA2 ( CIMBA ) , was established in 2006 [4] and with more than 40 , 000 mutation carriers currently provides the largest sample size for reliable evaluation of even modest associations between single-nucleotide polymorphisms ( SNPs ) and cancer risk . CIMBA studies have so far demonstrated that more than 25 SNPs are associated with the risk of developing breast or ovarian cancer for BRCA1 or BRCA2 carriers . These were identified through genome-wide association studies ( GWAS ) of breast or ovarian cancer in the general population or through BRCA1- and BRCA2-specific GWAS [5]–[8] . Cells harboring mutations in BRCA1 or BRCA2 show impaired homologous recombination ( HR ) [9]–[11] and are thus critically dependent on other members of the DNA repair machinery such as poly ADP ribose polymerase ( PARP1 ) involved in the Base Excision Repair ( BER ) pathway . The BER pathway is crucial for the replacement of aberrant bases generated by different causes [12] . A deficiency in BER can give rise to a further accumulation of double-strand DNA breaks which , in the presence of a defective BRCA1 or BRCA2 background , could persist and lead to cell cycle arrest or cell death; this makes BRCA-deficient cells extremely sensitive to PARP inhibitors , as previously demonstrated [13] . We hypothesize that SNPs in PARP1 and other members of BER may be associated with cancer risk in BRCA1 and BRCA2 mutation carriers . SNPs in XRCC1 , one of the main components of BER , have been recently evaluated within the CIMBA consortium [14] , however a comprehensive study has not yet been performed of either XRCC1 or the other genes participating in BER . In the present study , we used a tagging SNP approach to evaluate whether the common genetic variation in the genes involved in the BER pathway could be associated with cancer risk in a large series of BRCA1/2 mutation carriers using a two-stage approach . The first stage involved an analysis of 144 tag SNPs in 1 , 787 Spanish and Italian BRCA1/2 mutation carriers . In stage II , the 36 SNPs showing the strongest evidence of association in stage I , were evaluated in a further 23 , 463 CIMBA mutation carriers included in the Collaborative Oncological Gene-environment Study ( COGS ) and genotyped using the iCOGS custom genotyping array .
In stage I , 144 selected Tag SNPs covering the 18 selected BER genes were genotyped in 968 BRCA1 and 819 BRCA2 mutation carriers from five CIMBA centres ( Spanish National Cancer ResearchCentre ( CNIO ) , Hospital Clínico San Carlos ( HCSC ) , Catalan Institute of Oncology ( ICO ) , Demokritos and Milan Breast Cancer Study Group ( MBCSG ) . Of those , 50 were excluded because of low call-rates , minor allele frequency ( MAF ) <0 . 05 , evidence of deviation from Hardy Weinberg Equilibrium ( p-value<10−3 ) or monomorphism . Associations with breast cancer risk were assessed for 94 SNPs , as summarized in Table S1 . The 36 SNPs that showed evidence of association at p≤0 . 05 were selected for analysis in stage II . Of the 36 SNPs successfully genotyped in the whole CIMBA series comprising 15 , 252 BRCA1 and 8211 BRCA2 mutation carriers , consistent evidence of association with breast cancer risk ( p-trend<0 . 05 ) was observed for six SNPs ( Table 1 ) . The strongest evidence of association was observed for rs1466785 in the NEIL2 gene ( HR: 1 . 09 , 95% CI ( 1 . 03–1 . 16 ) , p = 2 . 7×10−3 ) for association with breast cancer risk in BRCA2 mutation carriers . We had observed a consistent association in stage I in BRCA2 mutation carriers ( HR: 1 . 25 , p = 0 . 06 ) . The SNP was primarily associated with ER-negative breast cancer ( HR: 1 . 20 , 95%CI ( 1 . 06–1 . 37 ) , p = 4×10−3 ) , although the difference in HRs for ER-positive and ER-negative disease was not statistically significant . The evidence of association in Stage II was somewhat stronger when considering the genotype-specific models , with the dominant being the best fitting ( HR: 1 . 20 95% CI: 1 . 09–1 . 37 , p = 1×10−4 ) . The associations remained significant and the estimated effect sizes remained consistent with the overall analysis when the data were reanalyzed excluding samples used in stage I of the study ( data not shown ) . Imputation using the 1000 genomes data showed that there were several SNPs in strong linkage disequilibrium ( LD ) with rs1466785 showing more significant associations ( p<10−3 ) ( Figure 1 ) . Due to lack of power we did not perform analysis of associations with ovarian cancer in stage I . However , we performed this analysis for the 36 SNPs tested in stage II . Although they had been selected based on their evidence of association with breast cancer risk , under the initial hypothesis they are also plausible modifiers of ovarian cancer risk for BRCA1 and BRCA2 mutation carriers . We found four SNPs associated with ovarian cancer risk with a p-trend<0 . 01 in BRCA1 or BRCA2 mutation carriers ( Table 1 ) . The strongest association was found for rs2304277 in OGG1 in BRCA1 mutation carriers ( HR: 1 . 12 , 95%CI: 1 . 03–1 . 21 , p = 4 . 8×10−3 ) . The association was somewhat stronger under the dominant model ( HR: 1 . 19 , 95%CI: 1 . 08–1 . 3 , p = 6×10−4 ) . Although three other SNPs were found to be associated with ovarian cancer risk in BRCA2 mutation carriers ( p-trend<10−3 ) , these results were based on a relatively small number of ovarian cancer cases . Imputed data did not show any SNPs with substantially more significant associations with ovarian cancer risk except for rs3093926 in PARP2 , associated with ovarian cancer risk in BRCA2 mutation carriers for which there was a SNP , rs61995542 , with a stronger association ( HR: 0 . 67 , p = 4 . 6×10−4 ) ( Figure S1 ) .
Based on the interaction of synthetic lethality that has been described between PARP1 and both BRCA1 and BRCA2 , we hypothesize that this and other genes involved in the BER pathway could potentially be associated with cancer risk in BRCA1/2 mutation carriers . Several studies have recently investigated the association of some of the BER genes with breast cancer , however , no definitive conclusions can be drawn , given that some publications suggest that SNPs in these genes can be associated with breast cancer risk with marginal p-values while others rule out a major role of these genes in the disease [15]–[21] . There is only one study from the CIMBA consortium which has evaluated the role of three of the most studied SNPs in the XRCC1 gene , c . -77C>T ( rs3213245 ) p . Arg280His ( rs25489 ) and p . Gln399Arg ( rs25487 ) , ruling out associations of these variants with cancer risk in BRCA1 and BRCA2 mutation carriers [14] . However , a comprehensive analysis of neither XRCC1 nor the other genes involved in the pathway in the context of BRCA mutation carriers has been performed . In the present study we have assessed the common genetic variation of 18 genes participating in BER by using a two stage strategy . Eleven SNPs showed evidence of association with breast and/or ovarian cancer at p<0 . 05 in stage II of the experiment ( Table 1 ) . Of those , six showed a p-trend value<0 . 01 and were therefore considered the best candidates for further evaluation . Only one of those six , rs1466785 in the NEIL2 gene ( endonuclease VIII-like 2 ) showed an association with breast cancer risk while the other five , rs2304277 in OGG1 ( 8-guanine DNA glycosylase ) , rs167715 and rs4135087 in TDG ( thymine-DNA glycosylase ) , rs3093926 in PARP2 ( Poly ( ADP-ribose ) polymerase 2 ) and rs34259 in UNG ( uracil-DNA glycosylase ) were associated with ovarian cancer risk . The minor allele of NEIL2-rs1466785 was associated with increased breast cancer risk in BRCA2 mutation carriers; moreover , when considering the genotype-specific risks observed that the best fitting model was the dominant one . NEIL2 is one of the oxidized base-specific DNA glycosylases that participate in the initial steps of BER and specifically removes oxidized bases from transcribing genes [22] . By imputing using the 1000 genome data we found six correlated SNPs in strong LD with rs1466785 ( r2>0 . 8 ) , located closer or inside the gene and showing slightly stronger and more significant associations with the disease and therefore being better candidate causal variants . From those , we considered rs804276 and rs804271 as the best candidates given that they showed the most significant associations ( p = 6×10−4 and p = 8×10−4 respectively ) and there were available epidemiological or functional data supporting their putative role in cancer . SNP rs804276 has been associated with disease recurrence in patients with bladder cancer treated with Bacillus Calmette-Guérin ( BCG ) ( HR: 2 . 71 , 95%CI ( 1 . 75–4 . 20 ) , p = 9×10−6 ) [23] . SNP rs804271 is located in a positive regulatory region in the promoter of the gene , between two potential cis- binding sites for reactive oxygen species responsive transcription factors in which sequence variation has been proven to alter the transcriptional response to oxidative stress [24] . Moreover , this SNP has been proposed to partly explain the inter-individual variability observed in NEIL2 expression levels in the general population and has been proposed as a potential risk modifier of disease susceptibility [25] . Several studies have been published showing associations between SNPs in NEIL2 and lung or oropharyngeal cancer risk [26] , [27] but to our knowledge , no association with breast cancer risk has been reported . We hypothesize that the potential association observed in the present study could be explained by the interaction between NEIL2 and BRCA2 , each of them causing a deficiency in the BER and HR DNA repair pathways , respectively . This would explain why the breast cancer risk modification due to rs1466785 would only be detected in the context of BRCA2 mutation carriers and not in the general population . The strongest evidence of association found in BRCA1 carriers was between rs2304277 in the OGG1 gene and ovarian cancer risk . The association was more significant when considering the dominant model . OGG1 removes 8-oxodeoxyguanosine which is generated by oxidative stress and is highly mutagenic , and it has been suggested that SNPs in the gene could be associated with cancer risk [28]–[31] . This is an interesting result , given that to date only one SNP , rs4691139 in the 4q35 . 3 region , also identified through the iCOGS effort , has been found to modify ovarian cancer risk specifically in BRCA1 carriers [32] . SNP rs2304277 is located in the 3′UTR ( untranslated region ) of the gene and is probably not the causal variant , however , in this case imputations through the 1000 Genome did not show better results for a more plausible causal SNP . We have identified four SNPs associated with ovarian cancer risk in BRCA2 mutation carriers , rs167715 and rs4135087 in the TDG gene , rs34259 in the UNG gene and rs3093926 in PARP2 . However , these last results should be interpreted with caution given that the number of BRCA2 carriers affected with ovarian cancer is four-fold lower than for BRCA1 carriers and the statistical power was therefore more limited , increasing the possibility of false-positives . In the case of PARP2 , imputed data showed a lower p-value of association ( 4×10−4 ) for another SNP , rs61995542 , that had a slightly higher MAF than rs3093926 ( 0 . 074 vs . 0 . 067 ) ( Figure S1 ) . However , it must still be interpreted with caution due to small number of ovarian cancer cases in the BRCA2 group . It is worth noting that , four of the five genes for which strongest evidence of association was observed , are all DNA glycosylases participating in the initiation of BER by removing damaged or mismatched bases . Apart from the already mentioned NEIL2 and OGG1 , TDG initiates repair of G/T and G/U mismatches commonly associated with CpG islands , while UNG removes uracil in DNA resulting from deamination of cytosine or replicative incorporation of dUMP . We have not found strong associations with SNPs in genes involved in any other parts of the pathway , such as strand incision , trimming of ends , gap filling or ligation . It has been suggested that at least in the case of uracil repair , base removal is the major rate-limiting step of BER [33] . This is consistent with our findings , suggesting that SNPs causing impairment in the function of these specific DNA glycosylases could give rise to accumulation of single strand breaks and subsequently DNA double strand breaks that , in the HR defective context of BRCA1/2 mutation carriers would increase breast and ovarian cancer risk . The fact that the SNPs tested are located in genes participating in the same DNA repair pathway as PARP1 , make them especially interesting , not only as risk modifiers but also because they could have an impact on patients' response to treatment with PARP inhibitors . BRCA1/2 mutation carriers harboring a potential modifier SNP in DNA glycosylases could be even more sensitive to PARPi due to a constitutional slight impairment of the BER activity . This is a hypothesis that should be confirmed in further studies . The design of this study in two stages , the hypothesis-based approach adopted to select genes , and that it is based on the largest possible series of BRCA1 and BRCA2 carriers available nowadays , mean that the results obtained are quite solid However , the study still has some limitations such as the possible existence of residual confounding due to environmental risk factors for which we did not have information . In summary , we have identified at least two SNPs , rs1466785 and rs2304277 , in the DNA glycolylases NEIL2 and OGG1 , potentially associated with increased breast and ovarian cancer risks in BRCA2 and BRCA1 mutation carriers , respectively . Our results suggest that glycosylases involved in the first steps of the BER pathway may be cancer risk modifiers in BRCA1/2 mutation carriers and should be more comprehensively studied . If confirmed , these findings could have implications not only for risk assessment , but also for treatment of BRCA1/2 mutation carriers with PARP inhibitors .
Eligible subjects were female carriers of deleterious mutations in BRCA1 or BRCA2 aged 18 years or older [6] . A total of 55 collaborating CIMBA studies contributed genotypes for the study . Numbers of samples included from each are provided in Table S2 . A total of 1 , 787 mutation carriers ( 968 with mutations in BRCA1 and 819 with mutations in BRCA2 ) from the CNIO , HCSC , ICO , Demokritos and MBCSG were genotyped in the first stage of the study . Stage II included 23 , 463 CIMBA samples ( 15 , 252 with mutations in BRCA1 and 8 , 211 with mutations in BRCA2 ) . All carriers participated in clinical and/or research studies at the host institution under IRB-approved protocols . | Women harboring a germ-line mutation in the BRCA1 or BRCA2 genes have a high lifetime risk to develop breast and/or ovarian cancer . However , not all carriers develop cancer and high variability exists regarding age of onset of the disease and type of tumor . One of the causes of this variability lies in other genetic factors that modulate the phenotype , the so-called modifier genes . Identification of these genes might have important implications for risk assessment and decision making regarding prevention of the disease . Given that BRCA1 and BRCA2 participate in the repair of DNA double strand breaks , here we have investigated whether variations , Single Nucleotide Polymorphisms ( SNPs ) , in genes participating in other DNA repair pathway may be associated with cancer risk in BRCA carriers . We have selected the Base Excision Repair pathway because BRCA defective cells are extremely sensitive to the inhibition of one of its components , PARP1 . Thanks to a large international collaborative effort , we have been able to identify at least two SNPs that are associated with increased cancer risk in BRCA1 and BRCA2 mutation carriers respectively . These findings could have implications not only for risk assessment , but also for treatment of BRCA1/2 mutation carriers with PARP inhibitors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"biology",
"and",
"life",
"sciences",
"cancer",
"genetics"
] | 2014 | DNA Glycosylases Involved in Base Excision Repair May Be Associated with Cancer Risk in BRCA1 and BRCA2 Mutation Carriers |
Allocation of goods is a key feature in defining the connection between the individual and the collective scale in any society . Both the process by which goods are to be distributed , and the resulting allocation to the members of the society may affect the success of the population as a whole . One of the most striking natural examples of a highly successful cooperative society is the ant colony which often acts as a single superorganism . In particular , each individual within the ant colony has a “communal stomach” which is used to store and share food with the other colony members by mouth to mouth feeding . Sharing food between communal stomachs allows the colony as a whole to get its food requirements and , more so , allows each individual within the colony to reach its nutritional intake target . The vast majority of colony members do not forage independently but obtain their food through secondary interactions in which food is exchanged between individuals . The global effect of this exchange is not well understood . To gain better understanding into this process we used fluorescence imaging to measure how food from a single external source is distributed and mixed within a Camponotus sanctus ant colony . Using entropic measures to quantify food-blending , we show that while collected food flows into all parts of the colony it mixes only partly . We show that mixing is controlled by the ants’ interaction rule which implies that only a fraction of the maximal potential is actually transferred . This rule leads to a robust blending process: i . e . , neither the exact food volume that is transferred , nor the interaction schedule are essential to generate the global outcome . Finally , we show how the ants’ interaction rules may optimize a trade-off between fast dissemination and efficient mixing . Our results regarding the distribution of a single food source provide a baseline for future studies on distributed regulation of multiple food sources in social insect colonies .
Food sharing in social insects is a compelling example of cooperation within a population [1–7] . Ants and bees can store a considerable amount of liquids in a pre-digestion storage organ called the ‘crop’ [8–10] . The stored food can later be regurgitated and passed on to others by mouth-to-mouth feeding ( oral trophallaxis ) [10–12] . Trophallaxis is a principal mechanism of food-transfer between individuals and therefore , the crop is often referred to as a “social stomach” [8] . When food is exchanged through trophallaxis , it is stored within the crop of the recipient workers and mixed with the rest of food in the crop [13–17] . Food blending is therefore an important factor in any process mediated by trophallaxis: from nutrient transfer and the maintenance of gestalt odor to hormonal regulation and information sharing [8 , 13 , 18 , 19] . The extent to which food is blended in the colony has only been partially addressed before [3 , 14 , 20–22] and is still an open question . Food blending is especially interesting in light of the fact that most colony members do not leave the nest [5 , 14 , 16 , 23 , 24] , and all food is brought in by a a small fraction of workers called the foragers [16 , 25] . The inter-relations between food-supplies brought in by different foragers can be expected to have an important role in the nutritional regulation of the colony . Social insect colonies have a documented ability to tightly regulate both the global nutritional intake [15 , 21] and the dissemination of food to various sub-populations ( such as nurses , larvae and brood ) which may have different nutritional needs [5 , 14 , 16 , 23 , 26 , 27] . The mechanisms that underlie this regulation are , however , not fully understood [28] . Trophallactic food exchange requires physical contact between ants . The dissemination process is therefore conveniently described by a time ordered network , in which ants are the nodes and the food transfers are the ( directed ) edges . The topology of this network provides the underlying infrastructure of the food-sharing process [17 , 29–31] . In the study of social insects and other real-world networks , the topology of the network can frequently be traced while the details of particular interactions are concealed [32 , 33] . Indeed , previous studies that traced individuals in a colony have mainly focused either on the network topology [29 , 31] or on coarse grained descriptions of food dissemination [1 , 16 , 22 , 26 , 27 , 34] . In this study we use single ant identification and fluorescently-labeled food ( Fig 1 ) to measure not only the interaction network but also the flow of food over this network . For technical reasons , these experiments are conducted with a single food source . Characterization of this basic case is a first but necessary step towards more complex scenarios which include multiple sources . The flow of food is limited by capacity: As the crop of ants is of finite size , this imposes a constraint on the amount of food that can be transferred in an interaction . This physical constraint limits the rate of mixing as ants become more and more full . Therefore , a potential trade-off between fast rate of food accumulation and well mixed outcome is expected . The main objective of our study is using single ant measurement techniques to quantify how food brought in by different foragers blends as it is being disseminated across an ant colony . To this end , we use Shannon entropy to quantify the quality of mixing in an ant’s crop . The Shannon entropy provides a single quantity that reflects the relative abundances of multiple constituents [35] and therefore sets a common scale by which food homogenization can be evaluated from our empirical data . Using our detailed measurements we characterize the interaction network and the rules by which food flows across this network . We then use hybrid simulations to identify which of these characteristics function as regulators of food mixing , and which might play a lesser role . Finally , we employ a theoretical model to study the trade-offs between food dissemination and nutritional homogenization .
We studied food ( sucrose solution [80g/l] ) dissemination in Camponotus sanctus ant colonies residing in an artificial , single chamber nest and following famine relief ( see Materials and methods , Experimental Setup ) . The dissemination process begins when the foragers , a small subgroup of the ants which we label F = { 1 , 2 , … , N foragers ≡ | F | } , return to the nest with liquid food loaded at the food source . Back in the nest , the foragers transfer the food to the non-forager population , A = { N foragers + 1 , N foragers + 2 , … , N ants } , via trophallactic interactions ( Fig 1a ) . As food accumulates in the colony ( Fig 1b–1d ) it also flows between non-forager ants as they interact among themselves [17 , 36] . The amount of food held in the crop of each ant as well as the amount of food passed per interaction were directly measured by combining single ant tracking with imaging of fluorescently labeled ( Rhodamine B [0 . 08g/l] ) food ( Fig 1a , S1 Data ) [17] . We designate the total amount of food in the crop of a non-forager ant a at time t by na ( t ) and the total amount of food held by all non-forager ants by Z ( t ) = ∑ a ∈ A n a ( t ) . During the course of an experiment , the total amount of food held by the colony grows until it reaches saturation ( S1e Fig ) [1 , 36] . The fraction of the total food held by ant a by Pa ( t ) = na ( t ) /Z ( t ) is not uniform across colony members ( Fig 2a , S2 Fig ) and is restricted by variable physiological properties such as crop capacity . As a first step towards quantifying food mixing in the ant colony we took a forager-centric approach . The idea is to track how food brought in by each forager spreads across the colony ( Figs 1 ( b ) –1 ( d ) , 2 ( a ) and 2 ( b ) and S1c Fig ) and the degree to which these food flows may overlap and mix . Since our experiments included a single food source we implemented this approach using a computational procedure in which we define the type of each ‘food droplet’ by the index of the forager , f ∈ F , that had initially collected it at the food source ( see ‘Food tracking’ , Methods ) . This entails that the number of ‘food types’ in the system is taken to be equal to the number of foragers . Using the assumption that mixing of food inside the crop of an individual ant is extremely rapid when compared to the rate at which food is transferred between ants , we then tracked the trajectories of labeled food droplets as they flow through the colony ( see ‘Food tracking’ , Methods ) . This procedure allowed us to define the empirically measured probability , Pa ( t ) , described above ( Pa ( t ) can be viewed as the probability that a randomly chosen ‘food-droplet’ is found within the crop of ant a ) , and consider the inferred joint probability Pf , a ( t ) = nf , a ( t ) /Z ( t ) , which represents the probability that food , originally collected by forager f , is located in the crop of ant a at time t . To quantify the degree to which different foragers contributed to the total foraging effort we calculate the total amount of food of type f that has accumulated in the colony up to time t as P f ( t ) = ∑ a ∈ A P a , f ( t ) . This probability function may be associated with an entropy , which we refer to as the types-entropy ( Htypes ) , and which quantifies the relative abundance of the different food types ( for all entropy definitions refer to SI , ‘Mathematical Framework’ and ‘Table B , S1 Text’ ) . It is defined by: H types ≡ H ( F ) = - ∑ f ∈ F P f log ( P f ) ( we suppress the explicit notation of time from here onward ) . Our measurements show that Htypes increases as a function of time ( Fig 2c ) and quickly approaches the upper bound of log ( | F | ) . This upper bound can only be saturated if all foragers bring in equal amounts of food . As discussed below , Htypes sets a limit on the total level of mixing in the colony . The degree to which food of a given type , i . e food brought in by a single forager—f , spreads across the colony can be quantified by the conditional distribution P ( a|F = f ) = Pf , a/Pf . We found that the food initially collected by each and every forager reaches , practically , all members of the colony ( Fig 2b ) . This degree of dissemination dictates overlapping food flows such that the crops of non-forager ants must hold a mixture of food of several types ( Fig 1b–1d ) . Mixing was assessed by tracking the differently labeled food droplets as they flow , via the trophallactic network , from ant to ant . The conditional distribution P ( f|A = a ) = Pf , a/Pa signifies the mixture of food-types in the crop of a specific ant a ( Fig 2 ) . Since each non-forager ant receives its load from multiple interactions with both foragers and non-foragers [17 , 31] the food composition in her crop , P ( f|A = a ) , contains a mixture of differently labeled ‘droplets’ . The level of blending in the crop of each individual ant , a , can be defined by the crop entropy: h mix a ≡ H ( F | A = a ) = - ∑ f ∈ F P ( f | A = a ) log [ P ( f | A = a ) ] . The range of individual crop entropy , h mix a , is [ 0 , log ( | F | ) ] where zero crop entropy indicates that all food in the ants crop originates in a single forager while log ( | F | ) indicates that food in the crop is equally divided among all possible food types . We find that the ( non-weighted ) average mixing entropy ( Fig 2d ) takes an intermediate value of 0 . 79 of the maximal possible mixing . While the actual components that mix to create the crop of each ant vary greatly ( Fig 2 ) we find that the degree of mixing is actually quite uniform across the colony ( standard deviation of 0 . 2 · log ( | F | ) , Fig 2d ) . Mixing within the entire colony , as a whole , can be quantified by the conditional entropy , H ( F|A ) . This global mixing entropy is defined as the weighted average over individual crop entropies , h mix a , where each ant is weighted by its relative load , Pa [35]: H mix ≡ H ( F | A ) = ∑ a ∈ A P a · h mix a . Mixing entropy is bounded from below by zero , a value which signifies no mixing ( this can happen if the food in the crop of any ant originates from a single forager only ) . An upper bound on mixing is obtained by the general rule H ( F|A ) ≤ H ( F ) ( conditioning reduces entropy [35] ) which , in our notation , translates into the fact that the mixing entropy is smaller or equal to the types entropy ( Hmix ≤ Htypes ) . Equality signifies perfect blending and occurs only when all ants have identical crop-load compositions that exactly match the concentration-distribution of food types across the entire colony . We find that as the number of interactions grows so does the mixing entropy , Hmix ( Fig 2c , S1 Fig ) . However , while the crop composition of a typical ant contains food that originated from each of the foragers , the relative proportions of these food types differ from ant to ant and do not match the proportions of food types flowing into the system ( Fig 2c , S1 Fig ) . In other words , even though the types entropy ( Htypes ) designating the partition of the total food in the colony into types , does approach the maximal bound of log ( | F | ) , the mixing entropy ( Hmix ) designating a similar partition on an individual level within each crop , is lower during the entire course of the experiment and reaches Hmix/Htypes = 0 . 8 ± 0 . 02 ( mean ±std over three experiments ) at the end of the experiments ( Fig 2c ) . If the mixing entropy does eventually reach the upper bound of the types entropy the time for this to occur is very long . To discern the causes of these intermediate mixing levels we next focus on the underlying dynamics of food exchange . In the following sections , we characterize the pairwise interactions via which food spreads through the colony and study their implications on mixing . The flow of food across the colony can be described by focusing on two processes: 1 ) The interaction network which is the time-ordered depiction of the pairs of ants that engage in trophallaxis . 2 ) The interaction volume which depicts food exchange during an interaction in terms of both direction and volume . Next , we briefly characterize these two components . Different aspects of the trophallactic interaction may limit food mixing in different ways . One way in which mixing levels may be reduced stems from the details of the interaction rule . As an extreme example: if the crop capacity of all ants was about equal and in any trophallactic event ants would transfer as much food as possible this would lead to pure food loads that are simply relayed between the ants and therefore minimal mixing . Decreased mixing may also be the result an interaction network which is topologically segregated into several disjoint communities with limited food flows between them ( as reported for other ant species [4] ) . In this section , we describe hybrid simulations , which preserve some of the empirically measured data while replacing others by simulated values ( for details see SI , ‘Simulations’ ) , to separately examine the effects of the different aspects of the interaction details on overall mixing . Finite crop size naturally impacts an ant’s ability to mix food . Mixture composition can significantly change only if an ant receives a large enough portion relative to her present load . Therefore , as ants become more satiated , their free storage space ( i . e . , the difference between her capacity and her current load ) becomes smaller and the ability to mix ( the potential mixing rate ) declines . Consequentially , a fast accumulation rate might interfere with the mixing process . As implied by the empirical interaction rule , in a receiving interaction , an ant is provided with a random volume of food that follows an exponential distribution , with an average that is proportional to her free storage space . This means that on average , an ant receives food in a series of decreasing volumes with a parameter δ . The parameter δ can thus be expected to have opposite effects on the accumulation and mixing of the food: the larger the value of δ the higher the accumulation rate and the lower the mixing rate ( and vice versa ) . We used a simple model to explore the possible trade-offs between the rate at which food accumulates within the colony and the extent to which it is mixed . For simplicity , the model assumes that all ants have the same capacity , that foragers and non-foragers use the same δ ˜ ( in a deterministic version of the original food-transfer rule , see SI , ‘Simulations’ ) and that interactions occur randomly . Furthermore , for the purpose of the model , we defined the amount of food held by a forager at time t = 0 to equal the total amount of food she collects at the food source during the entire course of the experiment . This definition sets the amount of food summed over all colony members , M , as a quantity that is conserved over time . Considering the entire colony we now define the probability P ˜ a = n a ( t ) / M as the fraction of total amount of food held by any ant , forager or non-forager . Using these definitions entails that at t = 0 all food is held by the foragers being , therefore , completely non-mixed while at later times , as food flows into the colony , it mixes within the crops of non-forager ants . This interplay between food accumulation and food mixing can be captured by considering the mixing entropy over all ants in the colony: H mix overall = ∑ a ∈ A ∪ F P ˜ a h mix a Note that since foragers receive almost no food from other workers ( see above ) we can approximate P ( f′|a = f ) ≈ 1 for f′ = f and zero otherwise . This means that h mix f = 0 for f ∈ F and leads to a second representation of H mix overall ( see SI , ‘Trade-off model’ ) : H mix overall = P colony · H mix where P colony = ∑ a ∈ A P ˜ a is the colony’s satiation level which starts off at 0 and saturates at 1 as food flows into the system [17] and Hmix is the mixing entropy over all non-forager ants , as defined above . This representation neatly separates the dissemination behavior into a component which quantifies the extent at which food is accumulated and a second component which quantifies the extent at which it is mixed . We simulated an approximation to this model ( see SI , ‘Simulations’ ) to study the relative effects of these terms as a function of the parameter δ ˜ . The interactions of the simulations approximate the empirical data by keeping the average number of interactions per ant and the ratio between forager to non-forager and non-forager to non-forager interactions . As may be expected , larger values of the parameter δ ˜ lead to larger transfer of food into the colony ( Pcolony indicated by the green line in Fig 5a ) . However , due to the finite capacity of an ant’s crop , larger values of δ ˜ also hamper mixing among non-foragers ( Hmix indicated by the blue line in Fig 5a ) . The compromise between these two factors is captured by their product , the total mixing entropy H mix overall . This entropy exhibits a maximum for an intermediate value of δ ˜ . These results demonstrate a robust process: as long δ ˜ does not approach the extremes , both the mixing and the accumulation are comparable for a given number of interactions ( Fig 5a ) . Surprisingly , even though higher δ ˜ will result in a higher accumulation rate , the ants seem to function at smaller δ ˜ values ( red bars in Fig 5a ) . A potential benefit of smaller δ ˜ values is the maintenance of similar mixing levels across all ants in the colony ( Fig 5b ) . This stands in agreement with our empirical evaluation of the variance in mixing levels across the colony ( Fig 2d ) .
It is well known that social insects manage their nutrient resources on the collective level and also on finer scales because the colony channels foods with different nutritional composition to different sub-populations . In this paper , we put forward the idea that this intricate regulation relates to the interplay between food dissemination and food mixing within the colony . High levels of dissemination are important as they ensure that any food type is available to any ant . On the other hand , high dissemination induces mixing and this reduces the required variety of nutritional choices within the colony . A main finding of this work is that , despite repeated trophallactic interactions between the ants , food in the colony does not become evenly mixed . Quantifying mixing using entropy measures we showed that , compared to what was theoretically possible , mixing is slow to rise and levels up at around 80% of the full mixing potential . The logarithms in the definition of entropy make the significance of this number difficult to assess . For intuition , in the case of only two food sources , the maximal mixing entropy ( 1 bit ) corresponds to each crop holding equal parts of the food sources ( 1: 1 ) while 80% of this ( 0 . 8 bits ) corresponds to , a far from perfect , 3: 1 partition of food sources . This imperfect mixing offers the possibility for receiving ants to choose from a wide spectrum of nutritional compositions when the donors provide different blends . Such choices can allow ants within the nest to reach their nutritional target using feeding schemes similar to those described by the geometrical framework for food foraging [42] . We further explored the mechanisms that allow for intermediate levels of food blending . Using hybrid simulations , we found that the interaction network over which food flows does not pose any limits on mixing levels . Rather , it is the interaction rule employed by the ants that regulates the extent to which food blends . This is reminiscent of several examples in which cellular pathways with identical architecture can achieve starkly different regulatory behaviors depending on actual rate coefficients [43 , 44] . Regulation by interaction rules rather than by meeting patterns is an intriguing possibility for social insects in which different collective functions often reside over very similar interaction networks [29] . For example , while proximity is required for both food sharing and disease transmission [45] different interaction rules may ensure that one of these is enhanced while the other is suppressed . Quantifying a large number of trophallactic interactions , we directly measured the food-transfer rule ( see also [36] ) used by the ants . We stress several important aspects of this rule . First , the rule respects the physical limits on crop size of the ants . Broadly speaking , this limit along with the fact that ants receive a substantial fraction of their free crop space per each interaction imply that an ant may become relatively full following her first few interactions . Thus , an ant’s mixing entropy is , to a large extent , determined by a small number of large events . Since these events are random both in order and in volume it is likely that mixing entropies will not saturate their maximal upper limit ( see SI , ‘Entropy by largest events’ , S5 Fig ) . Second , we show that the interaction rule is most likely stochastic in nature and , therefore , does not entail any strong requirements on ant cognition or communication . Finally , the fact that in trophallactic interactions the recipients fill only partially ( Fig 3 ) is in agreement with a model in which , similar to animals foraging in the environment , ants in the nest regulate their nutritional income by feeding off of multiple partners each with a different mixture of the available ‘food types’ . We explored the interplay between food dissemination and mixing using a simple model of food flow that is based on our empirical observations . We find that the intermediate levels of mixing , as measured , can viewed as a compromise between the requirements to quickly unload incoming food and the requirement to disseminate different food types to all parts of the colony . We show that this process is robust over a wide range of δ values and that the actual measured parameter ensures that all ants in the colony are equally well mixed ( although each holds a different particular mixture ) . Finally , we wish to highlight the limitations of this study . Due to current technological availability , this work was performed using a single food source labeled by a single dye . The ants may behave differently in terms of both interaction network and food-transfer-rule when several food sources with different nutritional values are available [4] . For example , ants may modulate the amount of food they receive in a trophallactic interaction according to its nutritional value . Such modulation , which can be captured in an extension of our current model , can allow the ants to differentially regulate the flow of different nutritional types across the colony . Further , our artificial setup contained a single chamber nest . More realistic , multi-chambered , nest structure may induce interaction networks that are more clustered than the one measured here . This may hold important consequences for nutrition dissemination . Last , is our choice to measure mixing by labeling food types by foragers . While arbitrary , this is a reasonable choice since , as we have shown , foragers are responsible for a large part of the mixing ( Fig 4b ) . Taking all these limitations into account we view our findings as a baseline to which future results , where multiple food sources are provided and tracked may be compared to . Overall , our finding that the interaction rule takes precedence over the interaction schedule manifests both the robustness of collective processes within the ant colony and the large extent to which individual behaviors may modulate global outcomes .
The experimental setup consisted of an IR-sheltered artificial nest chamber ( ~100 cm2 ) , neighboring an open area which served as a yard . The setup was recorded by two cameras ( details in [17] ) : the top camera images were used to extract ant identities , coordinates and orientations using the BugTag software ( Robiotec , Israel ) . The bottom camera images were used to detect fluorescent-labelled , using the openCV library in Python . Combining the information from both images , we associated between the identity of an ant and her appropriate fluorescent image . Thus , for each experiment a database was obtained , which included for every frame the coordinates , orientation , and measured fluorescence ( in arbitrary units of pixel intensity ) of each identified ant . The experimental trophallactic network includes a time-ordered pairwise-interaction schedule , and the volume of liquids that one ant passed ( received ) to ( from ) the other ( see S1 Data ) . Food is tracked from the moment it is acquired by a forger from the food source . We associate this volume ( food ‘droplets’ ) with the forager’s barcode identity ( ‘type’ ) , and continue tracing these droplets as they split between the ants according to the interaction schedule . To do this , we assume that in each interaction the receiver ant receives a fraction of the donor’s food in which the food type distribution is identical to that of the donor . In other words the number , n receiver ′ ( f ) of food droplets of type f in the receiver’s crop , following an interaction , is given by: n receiver ′ ( f ) = n receiver ( f ) + v V donor · n donor ( f ) where nx ( f ) is the number of food droplets of type f in an ant’s crop before the interaction and v V donor is the fraction of the donors crop content that is transmitted during the interaction . The updated distribution of food types in the receivers’ crop following the interaction is given by: P ( f | A = r e c e i v e r ) = n receiver ′ ( f ) ∑ ϕ ∈ F n receiver ′ ( ϕ ) Note that the number of food droplets per milliliter of food is arbitrary and cancels out in this calculation . The interaction networks for all three colonies including interacting ants’ identities , time , and interaction volumes can be found in the accompanying file ‘S1 Data’ . | We study how food is distributed in colonies of ants . Food collected by a small fraction of ants is distributed throughout the colony through a series of mouth-to-mouth interactions . An interesting interplay exists between food dissemination and food mixing within the colony . High levels of dissemination are important as they ensure that any food type is available to any ant . On the other hand , high dissemination induces homogenization which reduces the required variety of nutritional choices within the colony . Tracking fluorescent-labelled food and interpreting the results within the concepts of information theory , we show that food collected by each forager reaches almost every ant in the colony . Nonetheless , it is not homogenized across workers , resulting in a limited level of mixing . We suggest that the difference in food mixture held by each individuals can provide ants with the potential to control their nutritional intake by interacting with different partners . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"fluorescence",
"imaging",
"interaction",
"networks",
"sociology",
"social",
"sciences",
"animals",
"social",
"systems",
"animal",
"behavior",
"crops",
"zoology",
"animal",
"sociality",
"thermodynamics",
"research",
"and",
"analysis",
"methods",
"hymenopt... | 2019 | Colony entropy—Allocation of goods in ant colonies |
Alternative splicing generates protein diversity and allows for post-transcriptional gene regulation . Estimates suggest that 10% of the genes in Caenorhabditis elegans undergo alternative splicing . We constructed a splicing-sensitive microarray to detect alternative splicing for 352 cassette exons and tested for changes in alternative splicing of these genes during development . We found that the microarray data predicted that 62/352 ( ∼18% ) of the alternative splicing events studied show a strong change in the relative levels of the spliced isoforms ( >4-fold ) during development . Confirmation of the microarray data by RT-PCR was obtained for 70% of randomly selected genes tested . Among the genes with the most developmentally regulated alternatively splicing was the hnRNP F/H splicing factor homolog , W02D3 . 11 – now named hrpf-1 . For the cassette exon of hrpf-1 , the inclusion isoform comprises 65% of hrpf-1 steady state messages in embryos but only 0 . 1% in the first larval stage . This dramatic change in the alternative splicing of an alternative splicing factor suggests a complex cascade of splicing regulation during development . We analyzed splicing in embryos from a strain with a mutation in the splicing factor sym-2 , another hnRNP F/H homolog . We found that approximately half of the genes with large alternative splicing changes between the embryo and L1 stages are regulated by sym-2 in embryos . An analysis of the role of nonsense-mediated decay in regulating steady-state alternative mRNA isoforms was performed . We found that 8% of the 352 events studied have alternative isoforms whose relative steady-state levels in embryos change more than 4-fold in a nonsense-mediated decay mutant , including hrpf-1 . Strikingly , 53% of these alternative splicing events that are affected by NMD in our experiment are not obvious substrates for NMD based on the presence of premature termination codons . This suggests that the targeting of splicing factors by NMD may have downstream effects on alternative splicing regulation .
Alternative splicing ( AS ) is the process by which a single pre-mRNA is spliced in different ways to generate multiple mRNA transcripts . This process represents an important mechanism for post-transcriptional regulation of gene expression [1] . It has been estimated that as many as 70% of human genes undergo AS [2] , [3] . For Caenorhabditis elegans , it has been proposed that ∼10% of genes undergo alternative splicing [4] . Alternative splicing is known to be regulated in tissue-specific , cell-cycle , stress-responsive , hormone-responsive and developmental manners [5]–[7] . The regulation of AS is achieved by an interplay between proteins known as trans-acting splicing factors , that can act as activators or repressors , and intronic or exonic RNA sequences known as cis-elements , that can also be labeled as enhancers or silencers . With the development of splicing-sensitive microarrays [8] there has been a recent increase in the information about global regulation of splicing in yeast , flies , mice and humans ( for a review see [9] ) . The establishment of C . elegans by Sidney Brenner as a model system for the study of neurobiology and development has led to many important discoveries and advances in biology ( for a review of global studies see [10] ) . Analysis of heterochronic genes led to the discovery of the first microRNAs [11] and studies in this organism led to the discovery of the phenomenon of RNAi [12] . In 1998 , C . elegans became the first animal to have its genome fully sequenced [13] . The understanding of the complete cell lineage of the animal has led to important insights into development [14] . Even with the wealth of genomic information available about C . elegans , there is currently no information about the global regulation of alternative splicing during worm development . Some examples of AS events regulated during C . elegans development have been described [15]–[17] . An important question remains as to whether alternative splicing has an important role in the development of this animal . For example , in a global study of alternative splicing it was shown that in Drosophila as much as 46% of the genes show changes in patterns of exon expression during development , suggesting regulated alternative splicing or alternative promoter usage for a high percentage of the genome [18] . In particular , RNAi experiments of several splicing factors showed that some are necessary for worm development while others have redundant functions [19]–[22] . While there is currently no evidence of their targets , it is possible that some splicing factors might function as stage-specific regulators of alternative splicing . This regulation might trigger important changes in diverse cellular processes . Nonsense-mediated decay ( NMD ) of messages containing premature termination codons ( PTCs ) caused by mutation has been described in this organism , and mutations in genes that affect this process have been described as suppressors with morphogenetic effects on genitalia ( smg ) genes [23] , [24] . It has been shown that there is a dynamic interplay between alternative splicing and NMD [25]–[27] . While it is known that the inclusion or exclusion of cassette exons changes important functions for diverse proteins , it has also been suggested that the inclusion/skipping events also help the cell to regulate gene expression . Perhaps a gene can be turned off not by turning off transcription but by changing its splicing to produce an isoform of the mRNA that is a target for NMD . This scheme might allow for regulation of individual genes in polycistronic operons in C . elegans in which several genes share the same promoter [28] . Thus individual genes in the operon , which could not be regulated individually at the level of transcription , might be regulated at the level of alternative splicing leading to changes in individual mRNA stability . In mammalian cells NMD is known to follow the 55 nucleotides rule , where a PTC needs to be at least 55 bp upstream of the last splice site in order to elicit NMD [29] . It has recently been shown that NMD in C . elegans is splicing independent in that it can be elicited by premature termination codons even in intronless genes [30] . This difference in the action of NMD makes the identification of native NMD targets in C . elegans more complex . To add another layer of complexity , the regulation of several splicing factors is known to be achieved by complex interactions that include both alternative splicing and NMD . This was shown originally in C . elegans [27] and more recently found in vertebrates [25] , [26] . It was demonstrated initially that two C . elegans SR protein splicing factors contain exons with premature termination codons ( PTCs ) that target a particular isoform to NMD [27] . In this regard C . elegans presents an interesting model for the study of NMD regulation due to the viability of mutants that are defective in NMD pathways [23] . An important link between NMD and development has been recently shown for Drosophila , with the discovery that NMD is highly active during development and is required for the proper expression of dozens of genes as well as for larval viability [31] . However , the link between AS and NMD during development remains to be studied . In this paper we investigate the regulation of AS during C . elegans development , from embryos to gravid adults , and passage through each of the four different larval stages . To do this we developed a splicing-sensitive microarray with features for detection of AS for 352 cassette exons . We show that there is a global regulation of alternative splicing during development , with several events presenting significant differences in AS between different stages . We focus our analysis on events with dramatic changes in AS ( greater than four-fold changes in spliced isoform ratios ) . The highest number of significant splicing changes detected occurred at the step between embryos and the subsequent L1 stage , with 14 AS events highly regulated in this transition . One of the events with the highest change occurs in the W02D3 . 11 gene , which encodes the C . elegans ortholog of the human hnRNP H and F splicing factor proteins , which we have named hrpf-1 . The skipping isoform of hrpf-1 includes a PTC that targets a fraction of the messages with this isoform to NMD . However , the exon inclusion isoform shows a dramatic change at the level of splicing at the transition from embryos to L1 , with 65% of steady state messages in embryos including this exon but with only 0 . 1% of messages including this exon in L1s . Global analysis of splicing during development shows the complex nature of this regulation and provides initial clues towards de-convoluting these regulatory networks . We also provide evidence that more than half of the alternatively spliced isoforms of messages that are differentially stabilized in NMD mutant strains appear not to be direct substrates for NMD , suggesting the possibility that NMD regulation of splicing factors may influence downstream alternative splicing events .
We previously reported the identification of a group of 449 C . elegans genes with EST or mRNA evidence of having alternative cassette exons [32] . In order to study the regulation of alternative splicing during C . elegans development we created a splicing-sensitive microarray with features to detect alternative splicing of 302 of these cassette exons ( see Methods and Figure 1 for details ) . Although the coverage by ESTs in C . elegans is limited ( ∼300 , 000 ESTs [33] ) , algorithms to predict alternative splicing events without previous expression evidence have been reported [4] , [33] , [34] . To further expand the set of AS events detected by our array , we incorporated 50 predictions of cassette exons with AS , based on the RASE algorithm . RASE is a support vector machine-based classifier that identifies known exons with AS potential [34] . In total , our platform has features for the detection of the AS of 352 cassette exons . RNA from six different developmental times was obtained from large-scale synchronized cultures; embryo , L1 , L2 , L3 , L4 and gravid adults . To study the changes in alternative splicing during development , we determined the AS ratios for each cassette exon at each developmental stage compared to embryo ( set arbitrarily as the reference ) ( Figure 1B ) . To do this we calculated the log ratios for each one of the probes ( constitutive 1 , constitutive 2 , alternative and junction ) ( Figure 1A ) between embryo and each of the other five stages . From this embryo vs . stage AS ratio we calculated the AS ratios between all possible pairs of stages to detect all the AS events that changed greater than ±2 . 0 in log scale . For example: for F25C8 . 3 the emb vs . L1 AS ratio is 1 . 94 and the emb vs . adult AS ratio is −1 . 15 so the L1 vs . adult AS ratio is 3 . 09 . The method for calculation of the AS ratio is shown in Figure 1B . For the microarray analysis we applied a loop design with dye-swaps so that each developmental stage will have four different hybridizations corresponding to two different RNA dye labelings ( Figure 1C ) , comprising a total of 12 hybridizations [35] . We identified 62 cassette exons with AS ratios between any two developmental stages of greater than ±2 . 0 in log scale ( Table 1 and Table S1 ) . These data indicate a greater than 4-fold change in the relative abundance of stable alternatively spliced isoforms containing these cassette exons during the course of development . Clustering of these AS events shows a group of exons that are highly included in embryos ( 10 exons ) , another group with low embryonic inclusion ( 16 exons ) and smaller clusters that primarily include the exons at different larval stages ( Figure 2 ) . While several of the changes are gradual between subsequent stages , some events present drastic changes between stages ( Figure 2 ) . In order to confirm these differences in AS we analyzed , by reverse transcription followed by polymerase chain reaction ( RT-PCR ) , the changes in the relative abundance for 14 randomly selected alternative events from the 32 with the highest changes between embryo and any other stage . From this group of randomly selected events we detected AS ratios that correlate with the microarray for 10 events; two did present changes but in disagreement with the microarray , while two showed no changes according to the RT-PCR ( Figure 3 ) . This gives an array validation fraction of 10/14 ( ∼71% with a confidence interval of ±18% for the rest of the AS events with >4-fold changes ) for our splicing sensitive microarray . This validation rate is similar to one observed for a comparable microarray platform for detecting AS changes in human breast cancer , in which validation of a subset of array results by RT-PCR confirmed ∼75% of the events predicted by the microarray [36] . Among the cassette exons for which the array data were validated by RT-PCR is topoisomerase-1 ( top-1 ) , a gene that was previously shown to have an embryo-specific exon [15] . Several of the RT-PCRs show additional bands in addition to the predicted isoforms . For tag-253 we sequenced one an additional band and identified it as a novel isoform for which there was no previous EST or mRNA evidence . This isoform , which represents ∼65% of the transcripts , derives from skipping of two exons . One of them is the one targeted for detection by the microarray probes , but the other was considered a constitutive exon in the current gene model at Wormbase . This example shows evidence that in some cases , the disagreement between the microarray AS ratios and the ones from the RT-PCR validation can be due to the generation of novel isoforms by alternative splicing that are not considered in the current gene models . Seven of the 62 cassette exons with >4 fold changes in their relative levels during development were novel cassette exons predicted to be alternatively spliced by the RASE algorithm [34] ( Table 1 and Figure S1 ) . These changes in levels during development help to confirm that these are indeed alternative cassette exons . In general , these results also validate the RASE approach to alternative splicing prediction; roughly the same fraction of cassette exons showing >4-fold changes in relative usage during development were found in those predicted by RASE ( 7/50 ) , as for known cassette exons ( 55/302 ) . The set of genes showing large changes in alternative splicing during development includes some well characterized genes ( i . e . unc-16 , unc-43 , top-1 ) and several putative proteins of unknown function ( i . e . D1046 . 1 , F28E10 . 1 ) ( Table 1 ) . Among the characterized genes are two nuclear hormone receptors ( nhr-64 and nhr-65 ) . Nhr genes belong to a family of transcription factors known to have an important role in development [37] , of which 270 members are found in the C . elegans genome [38] . We noted that the alternative spliced exon of nhr-65 contains a premature termination codon ( PTC ) that possibly targets this isoform to NMD . Embryos have a higher rate of inclusion for the PTC containing exon of nhr-65 , opening the possibility that AS coupled to NMD is important for determining the relative steady-state levels of the spliced isoforms of nhr-65 in embryos . It has been previously reported that non-symmetrical alternative cassette exons ( those not divisible by three ) have a higher tendency to disrupt protein domains and to create possible substrates for NMD [39] . Almost half ( 42% ) of the cassette exons with high differences in alternative splicing are not a multiple of three in length . Therefore , inclusion or skipping of these exons by alternative splicing is likely to alter the reading frame for the rest of the transcript , possibly disrupting protein domains and leading to in-frame PTCs ( Table 1 ) . While this number ( 42% ) seems high , it is not significantly different ( Fisher Exact test , P = 0 . 42 ) from the number of exons that are not a multiple of three in the whole set of exons considered for the microarray analysis , where 34% are not a multiple of three ( see Table S3 ) . Given the high proportion of cassette exons undergoing large changes in alternative splicing during development that are not multiple of three , and that some alternative cassette exons contain PTCs , we studied how many of the splicing changes detected are indeed linked to NMD in embryos . To do this , we used the splicing-sensitive microarray to detect the levels of splicing in two strains carrying mutants in NMD genes , smg-1 ( r861 ) and smg-2 ( e2008 ) . SMG-1 is a member of the family of phosphatidylinositol 3-kinase ( PI3K ) -related protein kinases ( PIKKs ) and is required for NMD [40] . SMG-1 phosphorylates SMG-2/UPF1 , the key component of the NMD pathway , and this phosphorylation is required for NMD to occur [41] . RNA was prepared from smg-1 and smg-2 mutant embryos and tested on the array in comparison to WT embryo RNA . We found that for 4/62 ( ∼6% ) of the events with high changes during development , the steady state levels of the various isoforms are strongly affected by NMD ( greater than 4-fold changes ) ( Table 1 ) . In order to see whether there are any specific effects for mutation of either of these two components of the NMD pathway on message stability , we compared the results obtained when comparing WT against smg-1 or smg-2 embryo samples ( Table 2 ) . 28 different mRNAs showed >4 fold changes in the relative levels of the different isoforms in an NMD mutant background vs . wild type . There are only three genes that show a significant difference in alternative splicing between the two different smg gene mutants . Two of these ( akt-1 and C27A7 . 5 ) were further analyzed by RT-PCR , and we found no differences in AS according to RT-PCR , so the differences detected by the microarray are false negatives for the smg-2 sample . This result indicates that the SMG-1 kinase does not have detectable effects on message stability outside of its interaction with its known target , SMG-2 . NMD is known to regulate the mRNA stability of splicing factors isoforms , and in this way creates another level of complexity for determining the relative steady state levels of alternative splicing isoforms in an NMD mutant . This had already been noted for two of the SR protein splicing factors ( rsp-4 and rsp-6 ) [27] one of which did show changes in splicing in a NMD mutant in the present analysis ( Table 2 ) . The differences in alternative splicing between wild type and NMD mutant worms may be due to direct effects on the stability of the transcripts ( NMD ) , or to indirect effects due to the mis-regulation of splicing factors . In general we found that ∼8% of the 352 studied AS events had greater than 4-fold changes in the relative levels of alternatively spliced isoforms in embryos in the smg-1 or smg-2 mutants when compared to WT ( Table 2 and Table S2 ) . If from this 8% we exclude all the events that either contain a PTC ( according to the Wormbase gene annotations ) or that are not a multiple of three ( so they change the reading frame ) , 53% of the NMD-regulated alternative splicing events are not obvious NMD substrates . This high proportion of cassette exons which are not PTC substrates , but which are affected in an NMD mutant strain , might be secondary targets of the NMD mutations due to the mis-regulation of splicing factors whose mRNAs are direct targets for NMD . It has been recently shown that the AS of splicing factors is an important step in the global regulation of AS in mammalian cells [25] , [26] . Most of the examples reported correspond to the SR family of splicing factors , but there are also some examples of splicing regulation of hnRNPs [42] , [43] . In our analysis of AS during development we detected the AS ratio and expression changes for 10 known and putative splicing factors ( Table 3 ) . We found differences in the AS ratio of more than 2 log for just one of them , W02D3 . 11 , which encodes the C . elegans ortholog of the human hnRNP F protein . We have registered W02D3 . 11 with Wormbase as hrpf-1 . The hnRNP F/H proteins are a family of well-studied splicing factors that are known to silence exon inclusion by binding to G rich sequences [44] , [45] . Previous work in C . elegans has shown that hrpf-1 together with two other known splicing factors , unc-75 and exc-7 , are localized to subnuclear speckles . hrpf-1 is expressed in all cell types unlike unc-75 and exc-7 , which are neuronal-specific [46] . Alternative splicing of hrpf-1 by skipping exon 5 leads to a shorter transcript which still encodes two of the three RRMs but which contains a PTC in exon 6 ( Figure 4A ) . RT-PCR analysis during development shows that hrpf-1 alternative splicing has a strong regulation in L1 compared to any other stage as suggested by the microarray results ( Figure 4B ) . To further validate the effect of NMD on hrpf-1 we compared RT-PCR products of N2 and smg-1 mutant embryonic RNA for this gene . Figure 4C shows that there is a significant difference ( ∼30% in N2 vs . ∼90% in smg-1 ) in the relative steady state levels of messages showing skipping of exon 5 of hrpf-1 in embryos of NMD defective worms . This indicates that the skipping isoform is a target for NMD and that alternative splicing skips this exon more often than we can measure in the steady state mRNA level of N2 animals . In addition , since the exon inclusion isoform is not a likely substrate for NMD , its absence in L1 is indicative of a dramatic change in alternative splicing . This strong regulation of a splicing factor allows us to hypothesize that hrpf-1 may be a regulator of some of the alternative splicing differences that occur in the transition from embryos to subsequent stages . Its own alternative splicing must be tightly regulated in L1 , and an understanding of the regulation of the splicing switch of hrpf-1 will be important to pursue . In total , our analysis of the NMD defective worms show that at least three of the splicing factors studied have drastic changes in AS , hrpf-1 , swp-1 and rsp-6 ( Table 3 ) . Several other splicing factors , including other SR proteins are known to be targeted by NMD [27] . While the differences in AS detected using the microarray in the current work for hrpf-1 are not as dramatic as the ones found for swp-1 and rsp-6 , it does show a difference ( 1 . 02 log scale ) with more skipping of the cassette exon in the NMD defective worms , in accordance with the semi-quantitative RT-PCR results which show a 3-fold increase in exon skipping ( Figure 4C ) . To further characterize the developmental regulation of the AS of hrpf-1 , we asked whether this change is evolutionarily conserved by assaying the levels of splicing for the C . briggsae homolog of C . elegans hrpf-1 . It is estimated that C . elegans and C . briggsae derived from a common ancestor 100 million years ago [47] . The alternatively spliced cassette exon of hrpf-1 in the C . briggsae homolog has 70% identity with C . elegans , and is 3 bp longer than the C . elegans exon . As can be seen in Figure 4D , the same type of developmental regulation , where the inclusion isoform is down-regulated in L1s , was detected for C . briggsae . The inclusion of the alternative spliced exon goes from 67% in embryos to 48% in L1 . While this change is clearly not as dramatic as the one found in C . elegans , it does change the major isoform at this developmental stage from exon inclusion to skipping . There are three homologs of the mammalian hnRNP F/H splicing factor proteins in C . elegans; hrpf-1 , sym-2 , and Y73B6BL . 33 ( Figure 5A ) . All three contain three highly conserved RNA recognition motif ( RRM ) type RNA binding domains . sym-2 was identified by its synthetic lethality with mutations in another splicing factor , mec-8 [48] . RNAi of Y73B6Bl . 33 results in embryonic lethality , suggesting that this factor regulates the splicing of an essential gene [49] . In order to investigate the role of hnRNP F/H splicing factors in the regulation of alternative splicing , we analyzed global alternative splicing using the splicing-sensitive DNA microarray comparing RNA extracted from wild type and sym-2 ( mn617 ) mutant embryos . We chose to study splicing in sym-2 mutant embryos for two reasons . First , the availability of a viable mutant meant that we could obtain the large quantities of synchronized RNA necessary for dye-labeling for the microarrays . Second , because sym-2 shows higher expression levels in embryos than in L1s [50] , alternative splicing changes in the mutant embryos might allow for a correlation with some of the dramatic alternative splicing changes we observed in the embryo to L1 transition in wild type animals . In comparing N2 and sym-2 ( mn617 ) embryo RNA on the DNA microarray , we observed 65 changes in AS ratios that were greater than 2-fold ( Table S3 ) . One striking result was that 6 of the 14 AS events with >4-fold changes in AS during the embryo to L1 transition showed changes in sym-2 mutant embryos relative to wild type embryos that mimicked the wild type L1 splicing ( Table 4 ) . These changes were all in the same direction and strikingly close in magnitude as measured by the array . For example , twk-31 changes +2 . 41 ( log scale ) between embryos and L1 in wt animals and it changes +2 . 34 between N2 embryos and sym-2 ( mn617 ) embryos . For these six genes , expression of wild type sym-2 in embryos appears to account for the splicing difference between embryos and L1 . There are an additional seven genes whose alternative splicing both shows dramatic changes during development ( but not at the embryo to L1 transition ) and whose alternative splicing appears to be regulated by sym-2 ( Table 4 ) . Only two of these seven genes , ser-7 between embryo and L3 and D2024 . 5 between L1 and adult , have alternative splicing changes during development that move in the same direction as the sym-2 mutant embryos compared to wt embryos . Therefore 7 of the 8 genes whose alternative splicing in sym-2 mutant embryos changes in the same direction as the change during development show one of the two extremes of its developmental splicing regulation in embryos . These data are consistent with sym-2 having its most important developmental role in embryonic splicing . Among the genes regulated by sym-2 ( Table S3 ) are two splicing factors , hrpf-1 and rnp-6 . rnp-6 is the C . elegans homolog of the drosophila HALF-PINT splicing factor [51] . The changes in alternative splicing for these two genes between N2 embryos and sym-2 ( mn617 ) embryos were confirmed by semi-quantitative RT-PCR ( Figure 5B ) , consistent with a model in which splicing factors regulate the splicing of additional splicing factors during development . One of the genes with the most similar AS regulation to hrpf-1 is top-1; both show dramatic decreases in exon inclusion during the embryo to L1 transition . According to the microarray analysis of sym-2 mutant embryos , top-1 is not strongly regulated by sym-2 . We decided to ask if top-1 alternative splicing was potentially regulated by hrpf-1 . To do this we targeted hrpf-1 for reduction by RNAi feeding of N2 worms with E . coli bacteria expressing double-stranded RNA for a region of hrpf-1 . We also used an E . coli control strain for RNAi feeding that carried only the empty plasmid vector L4440 . We isolated RNA from embryos and compared the level of mRNA for hrpf-1 and a control gene , lin-10 , between the two differentially fed worm populations . We used the methods established by Caceres and colleagues to estimate the reduction in hrpf-1 mRNA levels under these RNAi conditions [20] . As shown in Figure 5C , we were able to achieve a modest 60% reduction in hrpf-1 levels relative to lin-10 under hrpf-1 RNAi conditions . We then looked at whether this change led to changes in top-1 alternative splicing . We consistently detected a 30% relative decrease in top-1 exon inclusion in hrpf-1 RNAi embryos relative to a control RNAi feeding ( a drop from inclusion in 34 . 9% of top-1 messages in the no RNAi control embryos to 24 . 3% inclusion in hrpf-1 RNAi embryos ) ( Figure 5D ) . Obtaining this modest yet consistent change in alternative splicing with partial removal of hrpf-1 messages by RNAi is consistent with a model in which hrpf-1 may play a role in splicing regulation . Changes in hrpf-1 alternative splicing to an NMD substrate isoform during the embryo to L1 transition may account for some of the alternative splicing changes at this stage of development .
Here we report the first large scale analysis of alternative splicing during C . elegans development . Previous studies had demonstrated strong regulation of AS for a small number of individual genes during C . elegans development [15]–[17] . In this present work we sought to gain further understanding of the global regulation of AS during development . To achieve this we designed a splicing-sensitive microarray to detect the splicing levels for 352 cassette exons . To validate our splicing-sensitive microarray we used RT-PCR of selected individual genes . With the assumption that the original gene models are accurate , our platform has a validation rate of ∼70% for the detection of changes in AS . This allows us to draw some general conclusions from our microarray studies . During worm development there are many morphological changes that must occur , including the generation of new tissue types and the increase in size of already existing tissues [14] . A change in the mRNA isoform proportions as detected by the array can be due to a change in the global regulation of AS for a specific gene in many cell types or overrepresentation of particular tissues at specific stages in which that gene undergoes cell type-specific alternative splicing . In both cases , the factors that regulate these splicing events will also have differential representation at the specific stages , even if their expression is limited to a particular cell type . For some alternative splicing events , a gradual change in steady state levels of AS isoforms is observed between subsequent developmental stages . For example , in sma-9 the change in emb-L1 AS ratio measured in log scale is −1 . 24 , while the overall change in AS ratio between embryo and adult is −2 . 51 . We found seven AS events in total that presented this type of gradual stepwise change between embryos and adults , and these are reminiscent of the gradual change in mutually exclusive exon usage observed for let-2 during development [16] . The other major class of alternative splicing changes that we observed showed a dramatic change in the AS ratio between two subsequent stages ( i . e . gip-1 , emb-L1 with a change in AS ratio of −4 . 74 ) . In this study we found that , for the most part , the major developmental changes in AS ratio in a single developmental step occurred between embryo and L1 ( 14 events ) . While we did not address the functional importance of these alternative splicing events , such strong regulation is highly suggestive of a specific function linked to the events . Additional experimentation to show the functions of these highly regulated distinct isoforms will provide further information about specific processes such as transcription or cell signaling ( i . e . nhr-65 and ser-7 ) . These changes do not take into account that there are indeed several morphological and molecular changes within the stages used for this study . For example , the development of embryos has several intermediate stages that include the generation of new cell types as well as important molecular events . To gain further understanding of the regulation of AS during development , and in light of findings that several splicing factors are themselves regulated by AS , we examined the 10 alternative spliced alternative splicing factors for which we had features on the microarray ( Table 3 ) . While swp-1 , etr-1 , and rsp-5 , showed greater than 2-fold changes in alternative splicing ratios during development , they paled in comparison to the dramatic 16-fold change in AS ratios observed for hrpf-1 between the embryo and L1 stages . hrpf-1 is a C . elegans homolog of the human hnRNP F/H family of splicing factors . In the worm genome there are three genes with strong identity to the hnRNP F/H family: sym-2 , hrpf-1 and Y73B6BL . 33 . We demonstrated that sym-2 has a role in alternative splicing regulation of many genes in embryos . One striking result was that for 6/14 of the genes showing dramatic changes in alternative splicing between the embryo and L1 stages , the embryo splicing pattern is dependent on sym-2 . It has previously been shown that hnRNP F/H factors form heterodimers to regulate alternative splicing of a particular target [52] , so it is possible that sym-2 also co-regulates targets with hrpf-1 or Y73B6BL . 33 . hnRNP F/H factors in mammals have either activator or repressor activity [52] , [53] and they share a similar high-affinity RNA binding sequence [44] . Regulation of splicing by hnRNP F/H family members can also be regulated by competing interactions with SR proteins , whose expression is also regulated [54] . Of the three C . elegans hnRNP F/H family members , only hrpf-1 is known to be alternatively spliced . While it is not known whether the skipping mRNA isoform , hrpf-1b , is translated into a protein , the protein it encodes would lack one of the three RNA recognition motifs . This truncated protein could potentially act to interfere with some of the functions of the full-length protein and may lead to an alteration in splicing of target genes . It was recently shown for the human hnRNP F that the C-terminal qRRM is not required for the recognition of RNA G-tracts [44] . This implies that the truncated alternative HRPF-1b protein isoform , if stably produced , may be able to bind specifically to RNA . Future experiments will be required to determine if the dramatic regulation of splicing of hrpf-1 at the embryo-L1 transition is responsible for regulation of other alternative splicing events in addition to our demonstration by RNAi that hrpf-1 has a regulatory role in top-1 splicing . Truncated human SR proteins lacking the C-terminal SR domain have been shown to bind to RNA and alter alternative splicing in vitro [55] . The NMD substrate isoforms of rsp-4 and rsp-6 similarly encode the RRMs and lack the SR domains . If these truncated isoforms are translated , the proteins produced may have a similar role in regulating alternative splicing . There will be many challenges to work out before a true understanding of the role of hnRNP and SR proteins in splicing can be deconvoluted , but it is clear that both families are important regulators who are themselves highly regulated . To study the influence of NMD on steady-state mRNA levels for some of these highly regulated AS events , we studied the effects of NMD mutants on AS ratios using the microarray . In Drosophila , mutants in the NMD pathway are lethal and it has been suggested that this toxicity in NMD-defective flies might be due to the mis-regulation of native gene expression [31] . While mutants in the NMD pathway are viable in C . elegans , our results show that NMD coupled to alternative splicing leads to >4-fold changes in the relative steady-state levels of isoforms of a high percentage of genes ( 8% of AS events ) . This result allows us to suggest that the coupling of alternative splicing with NMD is an important step in gene regulation during C . elegans development . It is also interesting that several splicing and transcription factors are regulated by alternative splicing and NMD during development and that some of the genes with the most dramatic changes in AS ratios in NMD-defective embryos are themselves not substrates for NMD . This implies that transcripts encoding splicing factors that are targets for NMD ( hrpf-1 , swp-1 , sup-12 , rsp-6 ) , may be stabilized and translated into proteins with dominant negative effects on splicing if the NMD pathway is defective ( Table 3 ) . This may explain why ∼60% of the genes whose AS ratios changed >4-fold in an NMD mutant background are not obvious substrates for NMD regulation and it is also interesting to note that many predicted substrates for NMD regulation do not show dramatic changes in the relative steady-state levels of isoforms in NMD mutant backgrounds . Previous reports have suggested that the activation of NMD in C . elegans does not require splicing and it has been suggested that rare codons may help to activate the process [30] , [56] , [57] . It is clear that more experiments need to be done to understand the rules for activation of NMD in C . elegans . Another point to consider is that there are no known phenotypes associated with early development in C . elegans NMD mutants . This seems to indicate that this organism in laboratory growth conditions , at least at the early stages of development , can tolerate changes in relative steady state levels of the isoforms of many genes with no detectable phenotypic abnormalities . One potential role of the linkage of AS and NMD in C . elegans is to alter the relative expression of genes that belong to the same operon and thus share the same promoter . For example hrpf-1 is part of an operon that includes two other genes , W02D3 . 10 and unc-37 . Results presented here show that in L1s there is a dramatic reduction in the relative levels of hrpf-1a isoform to less than 10% of the hrpf-1 transcripts . Expression microarrays from several groups show that the hrpf-1-containing operon has a reduction of around 75% between embryos and L1 [50] , [58] . While this reduction affects all three genes in the operon , the regulation of hrpf-1 is even stronger by the fact that , assuming that hrpf-1a is the functional isoform , its expression goes from ∼70% of the transcripts in embryos to ∼10% in L1 ( Figure 6 ) . This drastic reduction in the generation of hrpf-1a by AS might allow unc-37 and W02D3 . 10 to maintain basal levels of expression in L1s , while the levels of functional hrpf-1 are much more highly reduced . Developmentally controlled changes in the alternative splicing of alternative splicing factors might initiate a cascade of events that would induce a global effect on gene expression in C . elegans . This has been observed in sexual development in drosophila where the sex determination pathway depends on the regulation of alternative splicing of splicing factors at subsequent steps [59] . We demonstrate here that among the genes whose AS events show the most dramatic changes during development there is a splicing factor . We also show that this AS event is coupled to NMD and in that way the strong gene regulation during development is enhanced . Further studies of the effects that hrpf-1 has on downstream alternative splicing targets ( in addition to top-1 ) and identification of the primary and secondary effects of changes in splicing factor regulation will be the next challenges . Future work will be aided by knowledge of the preferred RNA binding sites for the regulated splicing factors on evolutionarily conserved splicing regulatory regions of the primary transcripts .
BWe previously reported 449 examples of genes with strong cDNA evidence for alternative spliced cassette exons in the C . elegans genome [32] . These , together with the 50 predictions made with the Recognition of Alternatively Spliced Exons in C . elegans algorithm , were used for the array [34] . For this work we designed microarray probes for all the cassette exons where it was possible to design a junction probe that will be specific for the skip isoforms ( i . e . single cassette exons ) , and where OligoPicker [60] was able to accurately design a probe for that region . We found 352 alternative cassette exons that pass these criteria ( Table S1 ) . Our general methodology for probe design can be seen in Figure 1 . In brief , we designed 40 bp probes to detect the cassette exon , the junction created by the skipping of the cassette exon , and two constitutive probes for each gene analyzed . The junction probes are centered on the exon-exon junction with 20 bp in each exon . Probes were resuspended in Pronto spotting solution ( Corning ) at a concentration of 50 µM and robotically spotted onto Epoxide slides ( Corning ) according to the manufacturer recommendations . Each probe was spotted either four or six times on each slide . The fact that each junction probe has half of its length with perfect complementarity to two different exons that are used in both the skipped and included isoforms creates the possibility of hybridization to both isoforms . This problem has been previously addressed and solved by other array designers . This led us to choose 40 bp oligonucleotides centered on the skipping isoform splice junction as under these hybridization conditions they can discriminate true exon-exon junctions with continuous 40 bp complementarity from skipping isoforms with two 20 bp complementary regions [61] , [62] . Probes sequence and raw data are available upon request . Bristol N2 , AF16 ( C . briggsae ) , TR1331 ( smg-1 ( r861 ) ) , CB4043 ( smg-2 ( e2008 ) ; him-5 ( e1490 ) ) and SP2230 ( sym-2 ( mn617 ) ) lines were obtained from the Caenorhabditis Genetics Center . Large quantities of mixed-stage worms were grown on egg-NGM plates with HB101 until plates were confluent; at that point worms were synchronized using 1% sodium hypochlorite and 0 . 5 M NaOH to isolate embryos . Embryo samples were taken after axenization of adults from mixed-stage cultures . Larval and adult stages were synchronized from embryos that we let hatch overnight in M9 buffer at room temperature as previously reported [63] . The next morning synchronized L1s were washed in fresh M9 and plated onto egg-NGM plates with HB101 . Samples were collected at 3 hours ( L1 ) , 12 hours ( L2 ) , 22 hours ( L3 ) , 32 hours ( L4 ) and 48 hours ( gravid adults ) at 25C . RNA samples were extracted with Trizol reagent according to the manufacturer recommendations ( Invitrogen ) , and further purified using RNeasy columns ( Qiagen ) . 20 µg of purified RNA per channel were labeled with Alexa Fluor dyes ( 555 and 647 ) using the SuperScript Indirect Labeling System ( Invitrogen ) according to the manufacturer recommendations for each of the developmental stages of N2 worms as well as for smg-1 , smg-2 and sym-2 mutant embryos . Development hybridizations were done in a loop design , with each stage hybridized a total of four times ( Figure 1 ) ( i . e . embryo vs . L1 , L1 vs . embryo , embryo vs . adult and adult vs . embryo ) . Hybridizations were done in duplicate with dye swaps . Labeled samples were hybridized to slides for 14–16 hours in 20% formamide , 5× SSC , 0 . 1% SDS and 0 . 1 mg/ml sheared salmon sperm DNA . Following hybridization , the slides were washed and dried prior to scanning with an Axon Instruments 4000 series scanners . Data were normalized and further processed using R and Bioconductor [64] . Specifically , Limma “rma” background correction was used to avoid blow out of M-values at low intensities , median normalization was used before differential expression analysis was done with lmFit [65] , [66] . Alternative splicing ratios ( AS ratios ) were calculated as described in Figure 1 , [65] and [62] . For comparisons where the RNA samples were not directly compared in the experiments ( i . e . embryo vs . L2 ) , the ratio of the probes was calculated by using Limma Contrast Matrix function [65] . Tables containing the AS ratios for all the genes in all experiments are available as Tables S1 and S2 . cDNAs from 5 µg RNA samples were synthesized in 20 µl reaction mixtures using oligo dT primers and SuperScript III enzyme according to the manufacturer recommendations ( Invitrogen ) . PCR primers to detect both the inclusion and skipping isoform were designed for the genes indicated in text ( primers available on request ) . 2 µl of the cDNA reaction mixture was used as the template in 25 µl PCR reaction mixtures . Reaction mixtures were incubated between 27–32 cycles at temperatures corresponding to each primer set . PCR products were analyzed using an Agilent Bioanalyzer 2100 , with the Agilent DNA 1000 kit . AS ratios and inclusion proportions were calculated from the molar concentrations of each isoform as reported by the Bioanalyzer 2100 software ( Agilent ) . RNAi by feeding was performed as previously described [67] . A region covering the first 1000 bp of hrpf-1 was amplified from cDNA ( Fwd primer: TCTCGAGGATCAGGCATTCT; rev primer: AGGCCACTGAACAGGAGCTA ) and later cloned into the L4440 feeding vector . Plasmids were transformed into bacterial strain HT115 . Plates containing 50 µg/ml carbenicillin and 0 . 1 mM IPTG were seeded with the corresponding transformed HT115 strain ( either empty L4440 as control or hrpf-1+L4440 ) . Worms were transferred to IPTG/Carbenicillin plates and let grown for seven days . Collected worms were synchronized as described above . RNAi efficiency was measured by comparing the levels of mRNA for hrpf-1 and a control gene , lin-10 , between the two differentially fed worm populations by using semi-quantitative RT-PCR . Molar ratios were calculated using the molar concentration obtained with a Bioanalyzer 2100 as described for the RT-PCR experiments . | Alternative splicing is a mechanism for generating more than one messenger RNA from a given gene . The alternative transcripts can encode different proteins that share some regions in common but have modified functions , thus increasing the number of proteins encoded by the genome . Alternative splicing can also lead to the production of mRNA isoforms that are then subject to degradation by the nonsense-mediated decay pathway , thus providing a mechanism to down-regulate gene expression without decreasing transcription . Examples of cell type-specific , hormone-responsive , and developmentally-regulated alternative splicing have been described . We decided to measure the extent of developmentally regulated alternative splicing in the nematode model organism Caenorhabditis elegans . We developed a DNA microarray that can measure the alternative splicing of 352 cassette exons simultaneously and used it to probe alternative splicing in RNA extracted from embryos , the four larval stages , and adults . We show that 18% of the alternatively spliced genes tested show >4-fold changes in alternative splicing during development . In addition , we show that one of the most regulated genes is itself a splicing factor , providing support for a model in which a cascade of alternative splicing regulation occurs during development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"developmental",
"biology/molecular",
"development",
"molecular",
"biology/mrna",
"stability",
"molecular",
"biology/rna",
"splicing"
] | 2008 | Alternative Splicing Regulation During C. elegans Development: Splicing Factors as Regulated Targets |
Organisms are structurally robust , as cells accommodate changes preserving structural integrity and function . The molecular mechanisms underlying structural robustness and plasticity are poorly understood , but can be investigated by probing how cells respond to injury . Injury to the CNS induces proliferation of enwrapping glia , leading to axonal re-enwrapment and partial functional recovery . This glial regenerative response is found across species , and may reflect a common underlying genetic mechanism . Here , we show that injury to the Drosophila larval CNS induces glial proliferation , and we uncover a gene network controlling this response . It consists of the mutual maintenance between the cell cycle inhibitor Prospero ( Pros ) and the cell cycle activators Notch and NFκB . Together they maintain glia in the brink of dividing , they enable glial proliferation following injury , and subsequently they exert negative feedback on cell division restoring cell cycle arrest . Pros also promotes glial differentiation , resolving vacuolization , enabling debris clearance and axonal enwrapment . Disruption of this gene network prevents repair and induces tumourigenesis . Using wound area measurements across genotypes and time-lapse recordings we show that when glial proliferation and glial differentiation are abolished , both the size of the glial wound and neuropile vacuolization increase . When glial proliferation and differentiation are enabled , glial wound size decreases and injury-induced apoptosis and vacuolization are prevented . The uncovered gene network promotes regeneration of the glial lesion and neuropile repair . In the unharmed animal , it is most likely a homeostatic mechanism for structural robustness . This gene network may be of relevance to mammalian glia to promote repair upon CNS injury or disease .
The structure of organisms is robust . Cells accommodate changes in their environment during development and throughout life by adjusting cell number and cell morphology to preserve overall organismal integrity . In the central nervous system ( CNS ) , adjustments are carried out in interacting populations of neurons and glial cells , from development to learning , ultimately enabling function . Injury and regeneration experiments and theoretical models have long been used to uncover the cellular and molecular mechanisms of how cells “sense” and maintain normal organ structure [1] . The premise is that shared mechanisms may underlie normal structural homeostasis and plasticity , and cellular responses to injury . Understanding such mechanisms is one of the frontiers in biology . It will also lead to a greater understanding of regeneration and repair of relevance to the treatment of human injury and disease . The fruitfly Drosophila is an ideal model organism for discovering gene networks , and has been successfully used to investigate cellular responses to CNS injury [2]–[6] . Here , we use Drosophila to uncover a gene network that controls the glial regenerative response to injury and promotes robustness in the normal CNS . Previous experiments had revealed two important findings about glial responses to injury in Drosophila . Firstly , enwrapping glial cells become phagocytic upon injury clearing cellular debris . This phagocytic function requires the corpse engulfment receptor Draper , which is specifically expressed in enwrapping glia , and whose function involves Simu , Src42A , and Shark [4] , [6]–[9] . Secondly , stabbing injury in the adult head [5] and neuronal ablation in the embryonic Ventral Nerve Cord ( VNC ) [10] induce the proliferation of glial cells ( including the enwrapping Longitudinal Glia ) . Similar findings had long before been observed in the cockroach [11]–[13]: surgical lesioning and chemical ablation of enwrapping glial cells induced cell transformation leading to the phagocytosis of cellular debris , and most remarkably glial proliferation . This restored glial numbers , enwrapment , and normal electrophysiological function . Insect glia may be evolutionarily distant from mammalian glia , but it can be insightful to compare them . Injury induces distinct responses in mammalian CNS glial cells . Astrocytes normally maintain ionic homeostasis , provide nutrients , and participate in synapses . Microglia are the immune cells of the brain , and are normally in a resting state . Upon injury , microglia and astrocytes phagocytose degenerating axons and other cellular debris , and they can also form a glial scar that inhibits axonal regeneration [14] . Ensheathing glia ( oligodendrocytes ) normally myelinate axons for saltatory conduction in vertebrates , maintain ionic homeostasis , and provide metabolic and trophic support to axons [15] , [16] . Oligodendrocyte progenitor cells ( OPCs ) respond to injury by dividing , and resulting oligodendrocytes remyelinate [17]–[19] . This latter response is regenerative , leading to spontaneous re-enwrapment of axons and partial functional recovery , for instance of locomotion [20]–[22] . Conditions such as spinal cord injury , stroke , and multiple sclerosis induce the proliferation of OPCs resulting in spontaneous remyelination of CNS axons , and underlie the “remission” phases of multiple sclerosis [18] , [23]–[28] . Thus , the regenerative response of ensheathing glia occurs across the animals , from insects [11] to fish [23] and humans [20] . In cockroach , fruitflies , and vertebrates , ensheathing glia proliferate upon injury , and both in insects and mammals this response can lead to limited remyelination and some recovery of function . This reveals that there is an endogenous tendency of the CNS to repair itself . Its manifestation across species may reflect a common underlying gene network . If understood , it could be harnessed to stimulate CNS repair . Here , we search for a Glial Regenerative Response ( GRR ) gene network that can promote repair after injury and confer structural robustness in the normal animal . The following factors are promising candidates to belong to this gene network . The Drosophila TNF super-family member Eiger triggers the proliferation of adult brain glia upon injury in fruitflies [5] . TNFα also triggers the proliferation of mammalian oligodendrocytes progenitors through its receptor TNFR2 upon injury [29] . While in other contexts TNFR2 is thought to function by activating NFκB [30] ( which can promote the cell cycle ) , whether this is the case for CNS glial cells and whether this activates glial proliferation are unknown . Notch maintains the undifferentiated and stem cell state in many contexts [31] . In Drosophila , Notch maintains the mitotic potential of embryonic ensheathing glia in interaction with the Jagged1 homologue , Serrate , from axons [10] , [32] . Similarly , in vertebrates , Notch1 maintains the oligodendrocyte progenitor state by interacting with its ligand Jagged1 present in axons [33] . However , the functions of Notch in the glial regenerative response remain unsolved . Notch1 is present in adult NG2+ OPCs , and it is upregulated upon injury and during regeneration , but conditional Notch1 knock-out in OPCs does not prevent the regenerative response [34] , [35] . Notch1 can inhibit the differentiation of progenitors into myelinating oligodendrocytes , preventing repair [33] , [36] , but presence of Notch1 signaling in these cells does not prevent the regenerative response either [34] , [35] . Finding out how to control Notch1 function , to enable glial proliferation , and to subsequently promote ensheathing glial differentiation is thus a key issue . The transcription factor Prospero ( Pros ) interacts with Notch in ensheathing glia in Drosophila embryos [10] , but how Pros and Notch affect each other's function is not understood . Pros appears to have opposite functions in neuroblasts and in glia . In neuroblasts Pros is a tumour suppressor , as mutations in pros result in over-proliferation [37]–[40] . Instead in glia both Pros and Notch are necessary for proliferation , and mutations in pros do not result in glial hyperplasia [10] , [32] . Unravelling the relationship between Notch and Pros may hold the key to understanding how glial proliferation and differentiation are regulated . Here , we uncover the Glial Regenerative Response gene network in Drosophila . For this , we establish a new CNS injury paradigm in the larval VNC . We use the larva because it is more accessible than the adult while it has locomotion , senses , learning , and memory , enabling the investigation of repair in the context of a fully functional CNS . We show that the ensheathing Interface Glia of the VNC respond to injury by phagocytosing and clearing cellular debris and by dividing . We reveal a gene network that controls the balance of glial proliferation and differentiation , and it is comprised of two feedback loops: one involving Pros and Notch , and a second involving Pros and Dorsal/NFκB , connected via Eiger/TNF and Wgn/TNFR signaling . By manipulating this gene network we could shift from exacerbating the damage to promoting repair of the damaged neuropile . The uncovered gene network is a homeostatic mechanism for structural robustness and plasticity .
To test how Drosophila larval glia respond to injury , dissected ventral nerve cords ( VNCs ) were stabbed with a fine needle and cultured ( Figure 1A ) . Stabbing was applied dorsally into the neuropile , which comprises the bundles of CNS axons and Interface Glia ( IG ) [41] . To obtain an overview of the injury response , we performed a time-lapse analysis . Axons were visualised with GFP driven by the protein trap line G9 , and glia with repoGAL4>DsRed ( Figure 1B , Video S1 and Video S2 ) . The wound initially expanded , and vacuoles formed within the neuropile . However , after 6 h of culture , glial processes invaded the wound , the axonal and glial wound began to shrink , and by 22 h the wound could heal considerably . This suggested that there is a natural mechanism that can promote repair . Here ( Figure 1C ) , we ( 1 ) characterised the glial responses to injury , ( 2 ) investigated the gene network controlling the regenerative potential of glia , and ( 3 ) tested whether altering the functions of this gene network could promote glial regeneration and axonal repair . To characterise the glial responses to injury , glial membranes were visualised with repoGAL4>mCD8-GFP and Interface Glia ( IG ) with anti-Glutamine Synthetase 2 ( GS2 ) ( Figure 2A , B and Figure S1 ) . This revealed the stabbing wound dorsally and an indentation ventrally ( Figure S1 , Video S3 and Video S4 ) . Although stabbing damaged to some extent surface and cortex glia ( unpublished data ) , it affected most prominently the IG , causing GS2+ glial loss ( Figure 2A , B and Figure S1 ) and GS2+ glial debris at 6 h after injury ( Figure 2B ) . We used the alrmGAL4 driver , which is restricted to the Pros+ IG ( Figure S2A ) , to visualize IG nuclei with HistoneYFP , and this showed that some IG were lost through apoptosis ( Figure 2C , intact VNCs had 0 cleaved-Caspase-3+ YFP+ IG n = 10 VNCs versus stabbed VNCs with an average of 1 . 5 cleaved-Caspase-3+ YFP+ in 57% of the VNCs n = 14 ) . In remaining IG , injury provoked an increase in the size and complexity of cytoplasmic projections ( seen with the membrane reporter mCD8GFP , Figure 2D ) . As observed in time-lapse , injury led to vacuolization of the neuropile , as holes formed within the axonal bundles ( Figure 1B and Figure 2E ) . IG projections enveloped these vacuoles ( Figure 2E ) . To further characterise these aspects , we used Transmission Electron Microscopy ( TEM ) . In wild-type wandering larvae , the IG nuclei were located outside the neuropile and their cytoplasms enwrapped the entire neuropile ( Figure 3A ) . IG processes projected into the neuropile , where they could enwrap smaller axonal bundles ( Figure 3B ) and individual axons ( Figure 3C ) . As seen with confocal microscopy , TEM confirmed that injury caused glial loss with breakdown of neuropile enwrapment after 6 h ( Figure 3D ) , and vacuoles formed within the neuropile ( Figure 3D ) . Some Interface Glia degenerated via necrosis as seen by swelling of mitochondria ( Figure 3E ) . Remaining IG expanded their cytoplasmic projections both around and within the neuropile ( Figure 3F–J ) , which was never observed in intact specimens . IG processes lined vacuoles ( Figure 3F , G ) , phagocytosed axonal fragments and other cellular debris , as revealed by phagosomes and multilamellar bodies within the glial processes ( Figure 3H–J ) . IG processes frequently wrapped around isolated axons that could be degenerating ( Figure 3K–N ) . These data show that upon injury IG phagocytose cellular debris , presumably clearing the lesion . Altogether , these data demonstrate that larval IG enwrap CNS axons , and that they are damaged by , and respond to , injury . Next , we investigate if stabbing induced IG proliferation . Based on their location , the IG are classified into dorsal ( dIG ) , lateral ( lIG ) , and ventral ( vIG ) IG ( Figure 4A ) . They are identified by co-localisation of the pan-glial marker Repo and the transcription factor Pros in nuclei , surrounded by the cytoplasmic marker Ebony ( Figure S2B , C ) . IG do not normally divide [42] , but are arrested in G1 and have mitotic potential ( Text S1 and Figure S3 ) . To examine cell proliferation , we used PCNA-GFP , a reporter with E2F binding sites that reveals GFP expression when cells go through S-phase ( Figure 4B , C , D ) [43] . PCNA-GFP+ Pros+ Ebony+ IG were rarely seen in non-stabbed controls , but stabbing increased their frequency at the lesion site ( Figure 4B ) and throughout the neuropile ( Figure 4C ) . We did not find any Ebony-negative IG with PCNA-GFP ( unpublished data ) , suggesting that Ebony+ Pros+ IG are the only IG that divide in response to injury . Normally there is one Ebony+ vIG per hemisegment , but the number of Ebony+ vIG adjacent to the wound increased significantly in stabbed larvae ( Figure 4E , F ) . Altogether these data show that stabbing causes a local increase in proliferation of Pros+ Ebony+ IG at the lesion site . A BrdU pulse experiment ( a commonly used method to visualise proliferating cells ) also revealed an overall increase in the number of dividing IG upon stabbing , to 50% of Ebony+ IG being also BrdU+ ( p<0 . 05 ) , comprising local IG at the lesions site ( Figure S4 ) and at some distance along the VNC . This suggested that neuronal damage may also affect glial cells at a distance from the original lesion , which is explained as axons extend along the whole length of the VNC . Stabbing may also affect other glial classes than IG . To take these facts into account , we purposely developed DeadEasy Glia software to automatically count in vivo all Repo+ glial cells ( Figure 4G and Figure S5 ) . After 22 h culture , stabbed VNCs had more glial cells than non-stabbed controls ( Figure 4G ) . This effect was abolished when a cell cycle inhibitor—constitutively active Retinoblastoma protein factor ( Rbf280/Rb ) —was expressed in glia ( Figure 4G ) . This demonstrates that the increase in glial number upon stabbing was due to the induction of glial proliferation . These data show that stabbing the larval VNC causes an increase in glial proliferation and a consequent increase in glial cell number . Although we cannot rule out that other glial cells might also divide , our data demonstrate that this response involves the IG . We next asked what genes might control the proliferative glial response to injury . Notch and Pros regulate the mitotic potential of embryonic glia [10]; thus , we wondered if they might be involved . prosvoila1/prosS044116 hypomorphic mutants specifically affected larvae , since embryogenesis proceeded normally but the levels of Pros dropped in IG by the third instar larval stage ( Figure S6A , B ) . In prosvoila1/prosS044116 VNCs , Ebony was downregulated , meaning that Ebony is a downstream target of Pros ( Figure S6B ) , but there were no major developmental defects as Repo and GS2 expression were normal ( Figure S6C , D ) . Expression of the Notch antagonist numb with repoGAL4 to knockdown Notch specifically in glia did not cause general developmental defects either ( see below ) . However , the glial proliferative response to injury was significantly reduced both upon the glial over-expression of numb ( Figure 5A ) and in prosvoila1/prosS044116 mutant larvae ( Figure 5A ) . In particular , IG number decreased upon stabbing in prosS044116 mutant larvae ( Figure S7 p<0 . 01 ) . These data show that Notch and Pros are required for the glial proliferative response . Drosophila Egr/TNF is required for glial proliferation in response to injury in the adult brain [5] , but how it implements this is unknown . In mammals , TNF can induce cell proliferation via the activation of NFkB [30] , but whether it does in glial progenitors upon CNS injury is unknown . Thus we asked whether in our injury paradigm , IG proliferation required Egr/TNF , Wengen ( Wgn ) /TNFR , and Drosophila NFκB , Dorsal . egr/TNF and wgn/TNFR are expressed in the VNC ( Figure S8A–D ) and Dorsal/NFkB is distributed preferentially in Ebony+ pros-lacZ+ IG ( Figure S8E ) . There were no major developmental defects in the VNC of dorsalH/dorsal1 or egr1/egr3 mutant larvae ( Figure S9 ) . However , the glial proliferative response was abolished in stabbed egr1 ( unpublished data ) , wgne00637/Df ( 1 ) Exel7463 , and dorsal1/dorsalH mutant larval VNCs ( Figure 5A ) . These data suggest that Wgn/TNFR , its ligand Egr/TNF , and Dorsal/NFκB are required for the glial proliferative response to injury . To verify this , we asked whether stabbing resulted in the activation of Dorsal/NFκB in glia . In its inactive form , Dorsal/NFκB is cytoplasmic , and upon signaling it is translocated to the nucleus to function as a transcription factor . We found intense nuclear distribution of Dorsal/NFκB in IG upon injury in wild-type ( Figure 5C p<0 . 05 ) , but not in egr1 mutants ( Figure 5C ) . This shows that stabbing induces the activation of Dorsal/NFκB in IG , which depends on Egr/TNF ( Figure 5B ) . Therefore , we sought to find out how might Pros , Notch , Eiger/TNF , and Dorsal/NFκB implement their functions in the glial proliferative response to injury . To investigate the function of Pros in glial proliferation , we generated prosJ013 null mutant MARCM clones in larval glia . The number of IG in prosJ013 mutant clones ( 1–8 cells per clone in 8 clones generated in n = 786 VNCs ) did not differ from the number of IG in wild-type clones ( 1–9 cells per clone in 15 clones in n = 1 , 254 VNCs ) . Furthermore , in wandering larvae ( 120 h AEL ) the number of glial cells in prosvoila1/prosS044116 mutants was indistinguishable from wild-type ( Figure 6A , D ) . These data demonstrate that Pros does not affect the extent of glial proliferation in the normal , non-stabbed larva . However , loss of pros function affected the timing of glial cell division ( Figure S10A , B ) . In younger ( 96 h AEL ) prosvoila1/prosS044116 mutant larvae , there were more glial cells than in wild-type ( Figure S10A ) , implying that the excess glial cells arose from faster ( but not more ) cell divisions . Cell division is speeded up by shortening the G1 phase , for instance with the up-regulation of CycE . Consistently , Pros activates the expression of the CycE repressor Dacapo ( the p21/p27 homologue ) in glia ( Figure S10C p<0 . 05 ) . To further test if Pros can halt larval glial proliferation , we over-expressed pros in larval glia using tubGAL80ts;repoGAL4 . This resulted in early larval lethality , and escapers had decreased glial number compared to controls ( Figure 6G ) , showing that Pros inhibits glial proliferation . Altogether , our data show that Pros functions as a repressor of cycE in glia and it inhibits cell cycle progression by keeping glia arrested in G1 ( Figures 6B ) . If Pros inhibits cell division , why isn't there glial hyperplasia in the mutants ? And why can't pros mutant glia proliferate upon injury ? To solve this conundrum , we wondered if Pros might interact with Notch . Notch signalling is present in larval IG , its ligand Serrate is in axons ( Figure S11A ) , and Notch maintains the expression of pros . Pros is also required for Notch signalling ( Figure S11B–G ) , like in embryonic LG [10] . Thus , Notch and Pros maintain each other in IG . In other contexts , Notch promotes cell division by regulating the G1/S transition ( Figure S10H ) [43] , [44] . We found that constitutive activation of Notch signalling—expressing the Notch Intra Cellular Domain ( NotchICD ) —in all glia increased both total glial number ( Figure 6A , Ai , D ) and the number of Ebony+ IG ( Figure 6E ) . Activation of Notch restricted to the IG only also resulted in an increase in IG cell number ( Figure 6F ) . Consistently , transient activation of Notch signalling induced PNCA-GFP expression ( Figure S10D , E ) and BrdU incorporation ( Figure S10F , G ) specifically in IG . These data show that Notch can promote glial cell division . So if Notch signalling is normally activated in IG , why don't they divide in the intact larva ? Our data show that Pros and Notch have antagonistic functions in the control of glial proliferation . Since they also maintain each other , a “tug of war” between Notch and Pros is likely to keep IG in cell cycle arrest . To test this , we asked whether cell cycle arrest could be evaded by interfering with this feedback loop . Over-expression of NotchICD in glia resulted in the up-regulation of Pros ( Figure S11E ) , which would repress cycE expression . When we expressed cycE together with NotchICD , this increased glial number and expanded VNC size ( Figure 6A , D ) . Over-expressing NotchICD in glia in prosS044116 mutant larvae further increased glial number , causing a tumourous expansion of the VNC ( Figure 6A , Ai , B ) . The increase in abdominal VNC size was not due to a non-autonomous effect on neuroblast proliferation or increased neuronal number ( Text S2 and Figure S12 and Figure S13 ) , but to increased glial divisions ( Figure 6C ) . Altogether , our data show that Notch promotes cell cycle progression in glia while Pros inhibits it , and positive feedback between Notch and Pros counterbalances the effects of each other , maintaining glial cells on the brink of dividing ( Figure 6B and Figure S10H ) . Interfering with this feedback loop has dramatic consequences in glial number and VNC size . To find out whether Notch and Pros influence IG differentiation , we visualised IG morphology using alrm>mCD8GFP upon loss or gain of function for each of these genes . To knockdown Notch function only in larvae , we used a temperature sensitive allele of Notch—Notchts1 . In Notchts1 mutant larvae , IG filopodia and lamellipodia are thinner than in wild-type controls ( Figure 7A ) . Conversely , over-expression of NotchICD in glia results in larger and rounder glial cells ( Figure 7A ) . In hypomorphic prosS044116 mutant larvae , IG hardly developed cytoplasmic projections ( Figure 7B ) . Conversely , over-expression of pros in glia induced more elaborate IG projections ( Figure 7B ) . These findings show that Notch and Pros have opposite effects on glial differentiation . To further test how loss of pros function affects IG differentiation , we analysed MARCM clones of prosJ013 null mutant IG: glial morphology was aberrant , with dramatic loss of glial projections compared to wild-type ( Figure 7C , D ) . The glial differentiation markers Ebony and GS2 were also influenced by Pros . Ebony is a glial enzyme involved in neurotransmitter recycling [45]–[48] , and it was down-regulated in prosvoila1/prosS044116 mutants ( Figure 7E and Figure S6B ) . GS2 is an enzyme involved in Glutamate recycling normally restricted to enwrapping glia [49] . Larval over-expression of pros with tubGAL80ts; repoGAL4 induced its ectopic expression in non-enwrapping glia ( Figure 7F ) . Over-expression of NotchICD did not induce Pros , Ebony , or GS2 expression in non-enwrapping glia ( Figure S14 ) . Thus , GS2 and Ebony are directly regulated by Pros but not by Notch . Altogether , these findings demonstrate that Pros controls IG differentiation . We have shown above that the glial proliferative response to injury is abolished in Egr/TNF , Wgn/TNFR , and Dorsal/NFκB mutants . The number of Repo+ glia , as well as the expression of GS2 and Ebony , were normal in egr1 and dlH/dl1 mutant wandering larvae ( Figure 8A , C and Figure S9 ) , meaning that the glial functions of Egr/TNF and Dorsal/NFκB are dormant in the normal , non-stabbed larva . To investigate if activation of Dorsal/NFkB could promote glial proliferation , we over-expressed dTRAF2 in all glia . The Drosophila TRAF6 homologue dTRAF2 binds Wgn/TNFR and induces the nuclear translocation of NFκB homologues [50]–[52] . When dTRAF2 was expressed in glia , the number of glial cells increased ( Figure 8A , C ) . This effect was rescued by expressing dTRAF2 in a dorsalH/dorsal1 mutant background ( Figure 8C ) , showing that the effect of dTRAF2 is mediated by Dorsal . Activation of Dorsal/NFκB by expressing dTRAF2 in glia resulted in an increased number of Ebony+ IG ( p<0 . 01 ) . Temporal over-expression of dTRAF2 also induced BrdU incorporation in Ebony+ IG , showing that it activated mitosis cell-autonomously ( Figure S15 ) . These data show that activation of Dorsal by dTRAF2 promotes glial proliferation . Glial number and VNC size increased further upon glial expression of dTRAF2 in pros mutants ( UASdTraf2;repoGAL4 prosS044116/prosS044116 Figure 8A , C ) , indicating that Pros antagonizes the proliferative function of Dorsal/NFkB ( Figure 8E ) . Our data show that Pros-Notch feedback keeps glia on the brink of dividing , and upon injury Egr/TNF signalling via dTRAF2 activates Dorsal/NFκB , tipping the balance towards cell division ( Figure 8E ) . Thus we asked whether these two genetic mechanisms are linked . We found that in prosvoila1/prosS044116 mutant larvae Dorsal is decreased from IG ( Figure 8B ) , suggesting that Pros is required for dorsal/NFκB expression . Since the glial response to injury critically depends on Dorsal/NFkB , this means that the ability of IG to respond to injury is regulated by Pros ( Figure 8E ) . The glial regenerative response is constrained , as it induces glial proliferation but not tumours , indicating that cell cycle arrest is restored in daughter cells . Tumorous-like over-growth was induced by expressing dTRAF2 in glia in pros mutants ( Figure 8A , C ) , as was the case when expressing NotchICD in pros mutants ( Figure 6A , Ai , D ) . This suggested that Dorsal/NFκB might activate pros expression restoring arrest . To test this , we used hypomorphic prosS044116 mutant larvae that still produce Pros at low levels in a few glial cells . Expression of dTRAF2 in glia in prosS044116 mutant larvae resulted in the up-regulation of Ebony and Pros ( Figure 8D ) . These data show that Dorsal/NFκB activates pros expression in glia . Since Pros inhibits cell cycle progression whereas Dorsal/NFκB promotes it , the “tug of war” between Pros and Dorsal/NFkB is likely to restore G1 arrest in the daughter cells ( Figure 8E ) . Thus , we have shown that a gene network involving Notch , Pros , TNF , and NFκB controls the balance between glial proliferation , arrest , and differentiation . To test whether manipulating this gene network was regenerative to enwrapping glia , we examined the glial wound . Stabbing disrupted the GS2+ Ebony+ glial mesh in the neuropile , and the area devoid of these markers was measured ( Figure 9A ) . In egr1; prosvoila1/prosS04416 double mutant larvae , in which the proliferative glial response and glial differentiation were both affected , the glial wound increased significantly compared to controls ( Figure 9C ) . In larvae expressing NotchICD in glia , resulting in over-proliferation , the glial wound was consistently significantly smaller than in controls ( Figure 9A , D ) . This indicates that either Notch itself or increased glial number is regenerative . We showed above that over-expression of NotchICD in glia also induced pros expression , and that Pros promoted glial differentiation . Thus we asked whether the regenerative function of Notch relied on Pros . When stabbing was carried out in larvae that over-expressed NotchICD in glia but were also mutant for pros ( repoGAL4 prosS044116/UASNotchICDprosS044116 ) , wound size increased significantly ( Figure 9D ) . Since glial cells proliferated in excess in this genotype ( Figure 6A , D ) , this means that glial proliferation alone is not sufficient for repair and glial differentiation is also required . To test what consequence the uncovered gene network might have on neuropile repair , we examined cell death levels . Apoptotic cells were visualized with anti-cleaved-Caspase3 antibodies , and counted in vivo automatically using purposely adapted DeadEasy Caspase software [53] . Injury increased the extent of apoptosis over non-injured controls ( Figure 9B , E ) . Expression of NotchICD in glia did not rescue baseline apoptosis , but it rescued injury-induced apoptosis ( Figure 9B , E ) . This suggests that either NotchICD itself or the resulting increase in glial cell number is protective upon injury . To test the effect of these genes in enwrapment , we used TEM . Over-expression of NotchICD in glia dramatically increased glial projections and axonal enwrapment ( Figure 9F ) . However , enwrapment was reduced when NotchICD was over-expressed in pros mutant larvae ( Figure 9F ) , indicating that Pros is required for enwrapment . Altogether , these data show that the glial response is regenerative , that both glial proliferation and differentiation are necessary for glial regeneration , and that Notch and Pros play central roles . To test what effects the glial regenerative response ( GRR ) may have on the axonal bundles , we carried out time-lapse recordings of stabbed larval VNCs , with glial cells labeled with repoGAL4> or alrmGAL4>UAS-DsRed , and all axons labeled with the GFP-protein-trap line G9 . In wild-type larvae the neuropile wound first increased in size , and numerous vacuoles formed , consistently with TEM and confocal microscopy data from fixed samples ( Figure 10A , G , H and Video S1 , refer also to Figure 2E and Figure 3D , F , G ) . Subsequently , the vacuoles might disappear and the wound might shrink ( Figure 10B , F , G , H and Video S2 ) . In Notchts1 mutant larvae , wound size in the axonal neuropile was considerably larger , had greater vacuolization than controls , and did not decrease over time ( Figure 10C , G and Video S5 ) . Similarly , when glial proliferation was prevented by over-expressing pros in IG , wound size and vacuolization were also more extensive than in controls ( Figure 10D , H and Video S6 ) . Conversely , when glial cell proliferation was increased by over-expressing NotchICD , wound enlargement and vacuolization were considerably constrained , wound size decreased over time , and even repaired ( Figure 10E , H and Video S7 ) . In both wild-type and upon over-expression of NotchICD there was a correlation between repair and presence of DsRed+ glial processes within or around the vacuoles and in areas of axonal damage ( Figure 10F and Video S2 , Video S7 ) . Together with the TEM data ( Figure 3D–N ) , the time-lapse data suggest that upon injury , glial processes engulf the vacuoles , and phagocytose axonal fragments and other cellular debris , contributing to repair . Altogether , our data show that Notch and Pros control glial proliferation and differentiation required for glial regeneration and debris clearance , and this enables neuropile repair .
Stabbing injury in normal larval VNCs caused an initial loss of IG , wound expansion , and neuropile vacuolization . Ensheathing glia extended large processes within the neuropile , phagocytosed axonal fragments and cellular debris and dissolved vacuoles , some remaining glial cells divided , and neuropile integrity could be restored . This natural mechanism was enhanced by activating Notch signalling in glia in the presence of Pros . Together , NotchICD and Pros prevented wound enlargement and vacuolization , they prevented injury induced apoptosis , increased ensheathing glial number , and promoted glial regeneration and axonal neuropile repair ( Figure 11A ) . Remarkably , the stabbing injury wound could be completely repaired in these larvae . This was achieved through the balance of glial proliferation and differentiation under the control of Notch and Pros ( Figure 11B ) . NotchICD promotes glial proliferation and Pros promotes their differentiation . In the normal intact larva , the balance between NotchICD and Pros keeps IG in the brink of dividing . Pros also promotes the expression of cytoplasmic NFκB and of the glial differentiation factors Ebony and GS2 . Upon injury NFκB shuttles to the nucleus increasing the relative levels of cell cycle activators , and glia divide . NFκB and NotchICD activate Pros expression , and as Pros levels rise , Pros halts further glial cell division and promotes glial differentiation . Pros also promotes Notch expression , thus restoring the original balance . We have shown that interfering with the functions of these genes prevents repair ( Figure 11C ) . When both glial proliferation and differentiation were inhibited as in egr-pros- double mutant larvae , the glial wound enlarged . When glial proliferation was abolished in Notchts mutants or upon over-expression of pros in glia , the glial and neuropile wounds enlarged and vacuolisation increased . Conversely , increasing glial proliferation by activating Notch signaling promoted glial regeneration . The regenerative effect of Notch not only relied on the increase in glial cell number , but also on pros . Glial regeneration was prevented if NotchICD was expressed in a pros mutant background . Pros promotes IG differentiation , increasing the complexity of cytoplasmic processes and promoting axonal enwrapment . In the absence of Pros glia have fewer filopodia and lamellipodia , and downregulate the glial differentiation marker Ebony—an enzyme involved in the recycling of neurotransmitters [45]–[48] . Conversely , upon over-expression of pros , glia have more processes and up-regulate the expression of the enwrapping glial marker GS2—and enzyme involved in the recycling of Glutamate [49] . The control of glial differentiation by Pros is conceivably required for glia to phagocytose and clear cellular debris , restore neurotransmitter homeostasis , and re-enwrap the neuropile and axons . Thus , the uncovered gene network is regenerative and Pros is the critical link in the control of glial proliferation and differentiation that enables repair . The identified gene network enables the regenerative response to injury in the following way . ( 1 ) In the normal larva it maintains glial cells arrested with mitotic potential , enabling them to respond to injury ( Figure 11D ) . Generally , a cell that has exited the cell cycle cannot divide again . Pros and Notch together prevent cell cycle exit and maintain glia in a proliferative yet arrested state . That is , the IG can divide , but do not normally do so . This state is achieved as Pros and Notch maintain each other but have antagonistic functions on the cell cycle . Pros prevents cell cycle progression by repressing cycE , and Notch promotes the G1/S transition . Their mutual maintenance counterbalances their effects on the cell cycle , and maintains glial cells on the brink of dividing . ( 2 ) The gene network enables IG to respond to injury by proliferating at the lesion site ( Figure 11E ) . This is achieved as Pros regulates the expression of dorsal/NFκB . Dorsal/NFκB is a transcription factor located in the cytoplasm in its inactive form . Upon injury , the pro-inflammatory cytokine Egr/TNF via its receptor Wgn/TNFR induces the translocation of Dorsal/NFκB to the nucleus , where it promotes cell cycle progression . This breaks the balance of the Pros-Notch loop pushing glia to divide . ( 3 ) The gene network restores cell cycle arrest , preventing uncontrolled proliferation ( Figure 11F ) . Dorsal/NFκB activates the expression of pros , which inhibits cell cycle progression . Presumably as the total input from cell cycle activators ( Notch and NFκB ) and inhibitor ( Pros ) balances out , it restores cell cycle arrest . The antagonistic function of Pros versus Notch and Dorsal/NFκB , and their mutual dependence , restricts cell proliferation . Overgrowth is induced when negative feedback breaks down , upon activation of Notch or Dorsal/NFκB in the absence of Pros . Thus the GRR gene network prevents tumourous overgrowth . ( 4 ) The gene network controls glial differentiation , which critically depends on Pros . We have shown that in the absence of Pros , IG have reduced processes , and wound size and vacuolization enlarge . This would indicate that Pros may be required for the phagocytic response of glia and lesion clearance . We have also shown that Pros is required for axonal enwrapment , an end point of repair . Thus the gene network is a homeostatic cycle by which injury triggers a response in glia that not only repairs the wound but also primes the restored glia to respond to further injury or smaller changes . This would suggest that there must be a mechanistic link between Pros and the corpse engulfment pathway of Simu , Draper , Src42A , and Shark [4] , [6]–[9] . While our data show that the IG underlie the regenerative response , we cannot rule out that other glial classes might also be involved . For instance , cortex glia have also been reported to be phagocytic and activate Draper [8] . It will be interesting to explore these uncovered research avenues in the future . Drosophila Pros and the mammalian homologue Prox1 have different effects on cell proliferation in different cell lineages [10] , [37]–[40] , [54]–[56] . In Drosophila ganglion mother cells , Pros promotes cell cycle exit by repressing cycE expression , and is a tumour suppressor [37]–[40] . However , in Drosophila embryonic glia Pros enables cell division [10] , but how this might occur remained unexplained . Our findings demonstrate that Pros functions as a repressor in cycE also in glia . However , in IG loss of pros does not result in hyperplasia because as Pros maintains Notch signaling , loss of pros leads to loss of Notch signaling , consequently reducing cell cycle activation . Although glial cell division initially occurs faster in pros mutants as the G1 phase shortens upon the de-repression of cycE , cell division soon stops due to the loss of Notch . The diverse outcomes of Pros function depend on cell type specific gene networks . We have shown that in normal larvae the IG do not divide and that loss of function mutations in the gene network genes can result in normal glial cell number and distribution of glial markers . It would appear that these glial gene functions are uncovered upon injury . However , since in the wild CNS injury most likely results in fruitfly death , this raises the intriguing question of what might this gene network be for in the non-injured fruitfly . The fact that the glial regenerative response is also found in other insects , fish , and humans might imply an underlying common genetic mechanism , but why should it be ? Our findings suggest that the GRR is a homeostatic mechanism that promotes structural robustness in the non-injured animal . Homeostasis is grounded in two features of the feedback loops . Firstly , both feedback loops result in negative feedback on cell proliferation . Injury initially causes cell loss , and glial proliferation and differentiation are subsequently induced followed by cell cycle arrest , restoring cell number and enwrapment . This is achieved as the cell cycle activators ( Notch and NFκB ) induce the expression of a cell cycle inhibitor ( Pros ) . Thus , although glial cells divide initially , as more activator protein is produced , more inhibitor is produced too , restoring normal cell number but halting further cell division ( Figure 11C ) . This homeostatic control enables glial proliferation for repair while preventing excess , which would result in tumours . Secondly , the two feedback loops limit the amounts of cell cycle regulators . The mutual maintenance between Pros and Notch , and Pros and NFκB would result in their levels forever rising . Instead , the two positive feedback loops are constrained , in different ways . Pros-Notch feedback is established spatially through interactions between enwrapping glia and the axons that express the Notch ligand Ser . Pros is a transcription factor and can directly activate the expression of Notch . However , Notch only functions as a transcription factor after it has been cleaved at the membrane as NotchICD , which then translocates to the nucleus . Thus positive feedback only takes place when Notch contacts Ser in neighbouring axons . If contacts with Ser are saturated , NotchICD is not processed and cannot activate Pros . In this way the Pros-Notch loop is stabilized relative to the amount of Ser in axon-glia contacts . Pros-NFκB feedback is constrained through time , in response to changes in the cellular environment . Although NFκB is a transcription factor , it can only activate Pros expression when it translocates to the nucleus . In the uninjured glia , NFκB is trapped in the cytoplasm and cannot activate pros . In this way , Pros-NFκB positive feedback is frozen in time and released only in an injury event . Following injury , the homeostatic feedback loops restore initial conditions . Injury is likely to compromise contact between axons and glia , perhaps causing an initial drop in Notch signalling in glia , which would consequently down-regulate Pros levels . But injury also triggers the nuclear shuttling of NFκB , inducing cell proliferation and pros expression . As Pros levels rise , it halts further cell division and promotes glial differentiation and the expression of Notch . As glial differentiation restores neuron-glia interactions , this activates Notch signalling in glia , re-establishing the Pros-Notch loop , cell cycle arrest and priming glia for future responses to injury , thus closing the cycle . In normal , non-injured larvae , the IG divide sporadically , and these divisions may represent homeostatic adjustments in glial number in response to cellular changes due to genetic variability or exogenous influences ( e . g . , changes in temperature ) . This would help clear debris and neurotransmitters , maintain axonal enwrapment and ionic homeostasis , modulate axonal growth and fasciculation , and provide trophic support to neurons . It would maintain enwrapment of the neuropile preserving architecture . This adjustment could result from fluctuations in axon-glia interactions that altered the relative levels of NotchICD and Pros . We propose that the normal function of the GRR gene network is the homeostatic regulation of glial proliferation and differentiation to provide structural robustness . The glial regenerative response to injury may “hitch-hike” on this developmental mechanism to restore structural integrity . This would explain why a gene network underlies these events and how it emerged in the course of evolution—as a mechanism that confers robustness , reproducibility , and reliability to CNS structure . We have shown that IG can enwrap the neuropile , axonal bundles , and individual axons . Similar glia in insects have been compared to mammalian oligodendrocytes [57] , [58] . However , IG do not form nodes of Ranvier , thus resembling non-myelinating enwrapping glia , such as Remak glia in the peripheral nervous system ( PNS ) and olfactory ensheathing glia of the CNS [15] . Drosophila IG also express molecules involved in neurotransmitter recycling such as Ebony and Glutamate synthetase ( GS2 ) . In vertebrates , neurotransmitter reuptake is mostly carried out by astrocytes in the CNS and Schwann cells in the PNS [59] . NG2+ OPCs and oligodendrocytes also express molecules involved in glutamate recycling , including glutamate synthetase , at non-synaptic sites [60]–[62] . Like microglia and astrocytes in mammals , and Drosophila ensheathing glia in other contexts [63]–[65] , Drosophila IG are phagocytic and engulf axonal and other debris [66] . And like Schwann cells , Drosophila Pros+ IG can function as differentiated cells but can also divide . Thus , Drosophila IG cells are neuropile glia that behave like , and carry out multiple functions attributed to , distinct glial types in mammals . The injury progression we observed—wound expansion , followed by debris clearance , glial regeneration , and neuropile repair—reproduces that documented for insect [12] , [13] and mammalian CNS injury [67] . The molecular mechanisms underlying debris clearance have been little explored in mammals [68] . Although astrocytes proliferate to some extent upon injury [18] , [19] , NG2+ OPCs are the prominent cell type to divide [18] , [19] , [69] . Replenishing ensheathing glia promotes axonal regrowth and insulation , and protects against axonal degeneration and neuronal death . Thus , transplantation of enwrapping glial progenitors is a relevant strategy for the therapeutic treatment of spinal cord injury and demyelinating diseases [20] , [22] . A critical aim has been to identify a gene interacting with Notch1 that will enable differentiation of progenitors into enwrapping oligodendrocytes . Our Drosophila findings have revealed shared functions of Notch between insect IG and NG2+ OPCs: as with IG , Notch1 maintains the mitotic state of OPCs [33]; it is present in OPCs that divide upon injury [34] , [35]; and it prevents oligodendrocyte differentiation [70] . We show here that Pros antagonizes Notch function and it has a critical role inducing glial differentiation in fruitflies . The vertebrate homologue Prox1 can promote cell cycle exit and differentiation [54] , [71] and antagonize Notch1 function in mammalian neural stem cells [72] . If a gene network similar to that uncovered here operates in human glial progenitors , its manipulation may facilitate CNS repair . The GRR gene network should also bring insights into the understanding of glioma , as Notch , NFκB , and cycE are hyper-activated in human gliomas [73] , [74] . Finally , like in Drosophila , the response to spinal cord injury by mammalian OPCs recapitulates developmental events [69] . The physiological function of enwrapping glial plasticity in the adult may be to promote re-enwrapment following focal loss and to modulate myelination also during learning [15] , [75] . Similarly , the clearance of axonal degeneration by glia during circuit remodeling shares mechanisms with that after injury [4] , [66] . Accordingly , the GRR gene network may be a common , homeostatic mechanism for structural robustness and plasticity . Supporting Information is linked to the online version of the article . Supporting Information comprises Figures S1 to S15 , Table S1 with statiscal analyses , Videos S1 to S7 and TextS1 and Text S2 with data , and Text S3 with detailed methods .
Conventional genetics was used to generate lines of flies bearing multiple mutations , drivers , and other genetic tools . For the description of genotypes and crosses , please see Text S3 . To drive gene expression in larval stages only , thus enabling normal embryogenesis , we used ( 1 ) the temperature sensitive GAL4 repressor GAL80ts driven by the general tubulin promoter in tubGAL80ts flies ( Bloomington ) [76]; GAL80ts represses GAL4 at 18°C but not at 30°C , so larval GAL4 expression is controlled by shifting larvae from 18°C to 30°C at the required time . ( 2 ) hsGAL4 ( gift of S . Brogna ) flies , where GAL4 is switched on after heat-shock at 37°C . ( 3 ) Notchts1 . For details , see Text S3 . Eggs were collected for 6 h and kept at 18°C until heat-shock was applied to the larvae . Mosaic Analysis with a Repressible Cell Marker ( MARCM ) clones were generated as described [77]; for genotypes , see Text S3 . VNCs were dissected from 96 h AEL old larvae ( unless otherwise indicated elsewhere ) in Shields and Sang M3 insect culture media ( Sigma ) . The VNCs were stabbed from the dorsal side with a fine tungsten needle ( Fine Scientific Tools ) , of 0 . 5 mm diameter at the base and 1 µm diameter at the tip . Culture of dissected injured or uninjured VNCs was done according to [78] with the indicated adaptations . Each brain was cultured separately in a well ( 24-well plate ) containing 500 µl culture medium with 7 . 5% fetal bovine serum ( Sigma ) , 1% Penicillin , and streptomycin ( Sigma ) for 18 to 22 h at 25°C . Control VNCs were dissected and cultured in the same way , without stabbing injury . Following culture , VNCs were fixed and stained as normal . Immunohistochemistry , in situ hybridisations to mRNA and BrdU incorporation experiments of larval VNCs were done following standard procedures . Samples were mounted either in Vectashield with the nuclear dye DAPI ( Vector Laboratories ) or in 80% Glycerol PBS after staining nuclei with Daunomycin 5 µg/ml ( Sigma ) or Hoechst33342 5 µg/ml ( Sigma ) . For BrdU detection , the VNCs were treated with 2 M HCl for 20 min at room temperature after immunolabeling for other proteins . Samples were mounted either in Vectashield with DAPI ( Vector Laboratories ) or in 80% Glycerol PBS after staining nuclei with Daunomycin 5 µg/ml ( Sigma ) or Hoechst33342 5 µg/ml ( Sigma ) . For antibodies used , details on plasmids , and other , see Text S3 . Bright field , laser scanning confocal and transmission electron microscopy , and image processing were done following standard procedures ( more details in Text S3 ) . Time-lapse confocal laser scanning throughout the entire neuropile was done visualizing axons with G9 ( gift of W . Chia ) [79] , an protein-trap line with GFP in all CNS axons , and all glia ( except midline glia ) with repoGAL4/UASDsRed . Experimental genotypes were generated by crossing G9; repoGAL4/TM6B flies to the following flies: ( 1 ) UASDsRed S197Y ( gift of K . Ito ) [80] , ( 2 ) UASDsRed S197Y;UASNotchICDmyc , ( 3 ) Notchts1 UASDsRed S197Y/FM7 ( sn+ ) actGFP , and ( 4 ) by crossing G9;alrmGAL4 ( alrmGAL4 is a gift of Marc Freeman ) [64] to UASDsRed S197Y; UASpros . The stabbed VNCs were placed dorsal side down in a 35 mm glass based dish ( Iwaki ) treated with PolyLysine ( Sigma ) . Time-lapse scans ( xyzt scan ) were carried out using a Leica SP2-AOBS confocal inverted microscope with a temperature controlled chamber set to 25°C , or to 30°C for Notchts1 and its control experiments , 1- to 2-h intervals per Z-scan ( 8 time points in average ) , 20× lens with 4 times zoom , and 1 µm interval between optical slices . The obtained images were processed in ImageJ using plugins Turboreg and Stackreg to correct accidental sample movement . The Videos were arranged using ImageJ and Adobe Photoshop . To count automatically the number of all larval glia stained with anti-Repo and acquired as confocal microscopy images , we purposely wrote DeadEasy Larval Glia software in Java as an ImageJ plug-in . Confocal serial sections were obtained with the BioRad Radiance 2000 confocal microscope as images of x , y = 0 . 5665 µm/pixel and z = 1 µm/pixel dimensions . The region of interest ( ROI ) was defined as the region starting from immediately posterior to the Ebony-positive ventral IG of the last thoracic segment to the posterior tip of the VNC , hereby referred to as abdomen . Peripheral nerves exiting the VNC were excluded from the ROI . DeadEasy Larval Glia identifies the stained cells first in 2-D based on shape ( circular or elliptical ) through each confocal slice and then in 3-D throughout the stack , based on minimum and maximum volume and minimum pixel intensity . DeadEasy creates a stack of processed images , where the identified objects reproduce those of the Repo glia in the raw images , enabling easy comparisons and validation . Each cell can be uniquely identified , as placing the mouse over each cell highlights a number , with which it is possible to check if , for example , two adjacent cells are counted as one . The programme was validated using n = 997 cells out of 3 different stacks of images . The mathematical algorithm for DeadEasy Larval Glia will be published elsewhere ( Forero , Kato , and Hidalgo , in preparation ) . To count the number of dying cells ( labeled with active Caspase3-positive ) throughout the VNC , we adapted the programme DeadEasy Caspase [81] to work on larvae . Given the strong variation in background intensity in larval samples , the outlier thresholding method originally used for embryos did not provide good results under the new conditions . Entropy thresholding was used instead , which provided better results . However , some other labeled tissues could not be rejected automatically anymore , given that they did not have a particular shape or size which would have allowed one to differentiate them from the apoptotic cells . Poor signal-noise ratio due to thickness of larval VNCs also resulted in false negatives/positives in deeper slices . Therefore , we manually corrected the error by deleting false positive and adding back false negatives for the final counting , employing the ImageJ DeadEasy Manual macro ( described below ) . Anti-Ebony stained IG were counted manually with support of the ImageJ DeadEasy Manual macro , which we purposely developed to speed up manual counting and eliminate error . With this macro , Ebony positive cells in a confocal stack of images are labelled manually with a digital colour , and DeadEasy Manual software automatically counts the colour labels . Wound area was measured on longitudinal confocal microscopy images of the neuropile , using the ROI manager in ImageJ , for the glial wound on anti-Ebony and anti-GS2 stained VNCs , and for the axonal wound and vacuoles in time-lapse on G9-GFP-expressing VNC . The largest outline of the wound throughout the stack of images ( i . e . , a neuropile ) was set as the ROI and measured in µm2 . The kinetics of the area affected by the wound and vacuoles were obtained by normalising the size of affected area at each timepoint to the size of the area at 0–1 h after stabbing . For statistical tests applied to each experiment and p values , please see Text S3 and Table S1 . | The process of tissue regeneration has long been studied as a route to understanding what promotes structural robustness of cellular networks in animals . In the central nervous system ( CNS ) , neurons and glia interact throughout adult life and during learning , at the same time accommodating functional changes while preserving the structural integrity necessary for function . The mechanisms that confer this combination of structural robustness and functional plasticity in the CNS are unknown , but they may be shared with the cellular responses to injury , which also require structural changes while retaining function . The glial cells that enwrap axons respond to injury by dividing and re-enwrapping them , leading to partial recovery of function . Here , we use Drosophila genetics to uncover a gene network underlying this glial regenerative response . This gene network enables glia to divide upon injury , prevent uncontrolled proliferation , and differentiate . We find that the network also has homeostatic properties: two cell-cycle activators ( Notch and NFκB ) promote the expression of a cell cycle inhibitor ( Pros ) , providing negative feedback on cell division . Pros is also essential for glial differentiation , enabling the clearance of cellular debris and axonal enwrapment , and priming glia for further responses . By removing these genes or adding them in excess , we can shift the response to injury from prevention to promotion of lesion repair . This gene network is thus a homeostatic mechanism for structural robustness . Our findings from Drosophila may also help manipulation of glia to repair the damaged human CNS . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"cell",
"death",
"molecular",
"neuroscience",
"neurobiology",
"of",
"disease",
"and",
"regeneration",
"mitogenic",
"signaling",
"animal",
"genetics",
"cancer",
"genetics",
"genetic",
"mutation",
"gene",
"regulation",
"signaling",
"in",
"selected",
"disciplines",
"neuros... | 2011 | The Glial Regenerative Response to Central Nervous System Injury Is Enabled by Pros-Notch and Pros-NFκB Feedback |
Immunoglobulin A is a class of antibodies produced by the adaptive immune system and secreted into the gut lumen to fight pathogenic bacteria . We recently demonstrated that the main physical effect of these antibodies is to enchain daughter bacteria , i . e . to cross-link bacteria into clusters as they divide , preventing them from interacting with epithelial cells , thus protecting the host . These links between bacteria may break over time . We study several models using analytical and numerical calculations . We obtain the resulting distribution of chain sizes , that we compare with experimental data . We study the rate of increase in the number of free bacteria as a function of the replication rate of bacteria . Our models show robustly that at higher replication rates , bacteria replicate before the link between daughter bacteria breaks , leading to growing cluster sizes . On the contrary at low growth rates two daughter bacteria have a high probability to break apart . Thus the gut could produce IgA against all the bacteria it has encountered , but the most affected bacteria would be the fast replicating ones , that are more likely to destabilize the microbiota . Linking the effect of the immune effectors ( here the clustering ) with a property directly relevant to the potential bacterial pathogeneicity ( here the replication rate ) could avoid to make complex decisions about which bacteria to produce effectors against .
The digestive system has a large surface area [1 , 2] , covered by a single layer of epithelial cells , essential for nutrient absorption , but also a gateway for many pathogens . Contrary to the inside of the body , where the presence of any bacteria is abnormal , the lumen of the digestive system is home to a very important microbiota . These microbiota bacteria are present in extremely high densities [3] . Bacteria are necessary to break down and absorb certain nutrients , and can compete against potentially pathogenic intruders [4] . Inside the organism , the immune system can fight generically against any bacteria . However , in the digestive system , the host has to find alternative ways to fight dangerous bacteria while sparing beneficial ones . As closely related bacteria ( e . g . Salmonella spp . and commensal E . coli ) can show highly variable behaviors in the intestine , identifying which bacteria are good or bad is challenging . Besides , the overgrowth of any type of bacteria , even those that do not cause acute pathology , can impair the functionality of the microbiota . Thus the host needs mechanisms to maintain the gut microbiota homeostasis . The adaptive response is the only strong handle that the host has on directly controlling microbiota composition at the species level [5 , 6] . The main effector of the adaptive immune response in the digestive system is secretory IgA , an antibody . sIgA specifically bind to targets that the organism has already encountered and can be elicited by vaccination . It was observed more than 40 years ago that this prevents infection by pathogenic bacteria such as Salmonella [7] . Many studies have focused on the complex molecular and cellular pathways that trigger an immune response on the host side of the digestive surface [8] . However , we are only just beginning to understand by which physical mechanisms the immune effectors act once secreted into the intestinal lumen , which are crucial for the control of both commensal and pathogenic bacteria . The influence on bacteria dynamics of abiotic factors such as the flow in the gut has recently started being quantitatively studied [9 , 10] . We have shown that mice vaccinated with inactivated Salmonella Typhimurium do produce specific sIgA which bind to S . Typhimurium , but this neither kills them nor prevents them from reproducing [11 , 12] . The initial colonization of the intestinal lumen by S . Typhimurium is in fact unchanged in either kinetics or magnitude in vaccinated animals . These mice are nevertheless protected against pathogen spread from the gut lumen to systemic sites like lymph nodes , liver or spleen . A classic idea in immunology is that , as one antibody has several binding sites , antibodies aggregate bacteria when they collide into each other . But this effect would be negligible at realistic densities of a given bacterium in the digestive system , simply due to very long typical encounter times between bacteria recognized by the same sIgA ( see appendix A in S1 Text ) . We have shown that actually , the main effect is that upon replication , daughter bacteria remain attached to one another by sIgA , driving the formation of clusters derived from a single infecting bacterium [12] . This “enchained growth” is effective at any bacterial density . Clustering has physical consequences: the produced clusters do not come physically close to the epithelial cells . And as interaction with the epithelial cells is essential for S . Typhimurium virulence , this is sufficient to explain the observed protective effect . If sIgA was perfectly sticky , we would expect all bacteria to be in clusters of ever increasing size . In these experiments , despite observing S . Typhimurium clusters in the presence of sIgA , there are still free S . Typhimurium , and small clusters . One possibility would be that not all bacteria are coated with sIgA . But in these experiments , it has been demonstrated that they are ( extended figure 2c of [12] ) . Indeed , a gram of digestive content contains at most 1011 bacteria , and typically 50 micrograms or more of sIgA [13] , of molecular mass of about 385kD . This leads to about 800 sIgA per bacteria . sIgA may not be all bound to bacteria , and sIgA for different specific antigens may be produced in proportions not matching the proportions of antigens present in the digestive system , so that not all bacteria are coated with 800 sIgA . Nevertheless , most bacteria already encountered by the organism will be coated with many sIgA , and thus the cluster size is not limited by the number of available sIgA . Another possibility is that the sIgA-mediated links break . Such breaking has been demonstrated to be dependent on the applied forces in related systems [14 , 15] . As there is shear in the digestive system , because mixing is needed for efficient nutrients absorption , it is plausible that links break over time . Small clusters are linear chains of bacteria , bound by sIgA , with these links being broken over time by the forces induced by the flow . As bacteria are similar to each other , it is , at another scale , analogous to other physical systems [16] , such as polymers breaking under flow [17] . The main difference is that these chains grow by bacterial replication . Growth and fragmentation are competing effects , and the modelling of these chains can be viewed as statistical physics , to predict their length distribution , whether there is a typical chain length , or if large chains of ever-increasing length dominate the distribution , and how the growth in number of free bacteria depends on the bacterial replication rate . This could have very important biological consequences . To illustrate this point , let us consider a simplified model: bacteria remain enchained by sIgA when they grow ( replication time τdiv ) , and this link between 2 bacteria breaks at a specific time τbreak ( although this latter hypothesis is not realistic , we make it for now for the sake of simplicity ) . If τdiv > τbreak , then when a bacterium divides , it forms a 2-bacteria cluster , which dislocates into 2 free bacteria before the next replication steps , so that the bacteria remain in the state of free or 2-bacteria clusters and there are no larger clusters . If τdiv < τbreak , when a bacterium divides , it forms a 2-bacteria cluster , which becomes a 4-bacteria cluster before the first link breaks , so there cannot be free bacteria . In this model , the fast-growing bacteria are selectively targeted by the action of the immune system . The immune system does not need to sense which bacteria are growing faster , it only has to produce sIgA targeted to all the bacteria it has encountered , and bacteria with τdiv > τbreak are unaffected , whereas bacteria with τdiv < τbreak are trapped in clusters . That could be a simple physical mechanism to target the action of the immune system to the fast-growing bacteria which are destabilizing the microbiota , and thus to preserve microbiota homeostasis . In the following , we present different plausible models of bacteria clusters dynamics , and the methods to study them . Then we give , for each model , the resulting dynamics and chain length distribution , before putting these results in perspective with experimental data . Eventually , we discuss the results . As some biological details are unknown , studying different models enables to show which key results are robust; and differences confronted to experimental data give some indications about which are the most likely .
All animal experiments were approved by the legal authorities ( licenses 223/2010 , 222/2013 and 193/2016; Kantonales Veterinäramt Zürich , Switzerland ) and performed according to the legal and ethical requirements . We perform a new analysis on microscopy images that were produced for [12] . We analyzed images of cecal content in vaccinated mice for the early data points ( 4 and 5 hours ) of experiments starting from a low inoculum ( 105 ) , to minimize the clustering from random encounters . Further details on our analysis can be found in appendix G in S1 Text , as well as a brief description of the experiments from which the images were produced . We consider low bacterial densities , so encounters between unrelated bacteria are negligible . Thus , we consider each free bacteria and each cluster of bacteria independently of the others . Salmonella are rod-shaped bacteria , which replicate by dividing in two daughter bacteria at the middle of the longitudinal axis . Thus if the daughter bacteria remain enchained , they are linked to each other by their poles . With further bacterial replications , the cluster will then be a linear chain . This is consistent with experimental observations , in which clusters are either linear chains , with bacteria attached to one or two neighbors by their poles , or larger clusters which seem to be formed as bundles of such linear clusters ( pannel A Fig 1 ) . Our aim is to model the dynamics of these chains . A first element is the bacterial replication ( see Fig 1C ) . One way to model it is to assume that bacteria replicate every τdiv . Another way , that we will generally use , less realistic but easier for calculations , is to assume that there is a fixed replication rate r . A second element is that when bacteria replicate , they may be able to escape enchainment ( see Fig 1B ) , but likely with low probability ( see discussion in appendix B in S1 Text ) . In general , we will take the limit with perfect enchainment upon replication ( δ = δ′ = δ″ = 0 ) . A crucial element is the possibility for the links between bacteria to break . We usually assume that the breaking rate α is the same for all links and over time . We will also explore the case when the link breaking rate is force-dependent , in which case not all the links have the same breaking rate . Another crucial element , is to model what happens when the chain breaks ( see Fig 1D ) . If the subparts come in contact again at the same poles and get linked again , then this could simply be modeled by an effectively lower breaking rate . More likely , if the subparts come in contact again , they do so laterally , forming larger clusters of more complex shapes . Because in these clusters , most bacteria have more than two neighbors , and more contact surface , they are much less likely to escape . To simplify , we will consider that these clusters do not contribute anymore to releasing either free bacteria or linear chains . Thus when a link breaks , either the two subparts move sufficiently away and become two independent chains ( probability q ) ; or collide and become a more complex cluster which does not contribute anymore to both free bacteria and linear chains ( probability 1 − q ) . For simplicity , we consider that when an outermost link breaks , the single bacterium , more mobile , always escapes ( qoutermost = 1 ) , but that else q is independent of the position and the length . The simplest values to study are either q = 0 or q = 1 . As we will see , when we study the case in which q can take any value between 0 and 1 , we find that the case q = 1 is qualitatively different from other values of q . Consequently , we will take q = 0 for the base model . As digestive content leaves the digestive system , or the part of the digestive system under consideration , due to flow , we define c the loss rate of free bacteria , and c′ the loss rate of chains . We assume no death . Bacterial death would break chains . It would thus have a similar effect to a larger breaking rate α . As free bacteria have more autonomous motility , enabling them to swim towards the epithelial cells , it is likely that c′ ≥ c . We will usually take c = c′ . Crucially , in this latter case , free bacteria , and all chains are lost at the same rate . The c value has a complex effect on stochastic quantities , such as the probability to have at least one chain of a given length . However , here we study the mean numbers of free bacteria and chains of different lengths , then the case with c = c′ is equivalent to c = c′ = 0 , with all numbers of bacteria and chains multiplied by exp ( −ct ) . We start with the most basic model , with a replication rate r , bacteria perfectly bound upon replication ( δ = δ′ = δ″ = 0 ) , a fixed breaking rate per link α , and bacterial chains always binding into a more complex cluster when a link breaks ( except for the outermost links ) ( q = 0 ) . We then study variations of the model to test the robustness of the results: with an non-zero escape probability upon replication and c ≠ c′; with a replication time τ instead of a replication rate r; with the possibility for chains to escape when an inner link breaks ( q > 0 ) ; with a force-dependent breaking rate ( see Table 1 for a list of symbols ) . We consider the beginning of the process , early enough so that the carrying capacity is far from reached , and thus the replication rate is constant . We do not consider generation of escape mutants which are not bound by IgA . We consider only the average numbers of free bacteria and linear chains of different lengths , and we do not count more complex clusters , as they do not contribute to free bacteria dynamics in our model . For each model , we write the equations for the derivative of these numbers with respect to time . With N the vector of the mean number of free bacteria , linear chains of length 2 , 3 , etc . , these equations give the coefficients of the matrix M , such that dN/dt = MN . The results are obtained in part via analytical derivations and in part via numerical studies . The latter are obtained in Mathematica by numerically solving the eigensystem written for chains up to length nmax , chosen large enough not to impact the results . In the long term limit , N ( t ) → Ceλt P , with C a constant , λ the largest eigenvalue of M , and P the corresponding eigenvector , normalized such that the sum of its components is equal to 1 . λ is thus the long term growth rate of the free bacteria and the linear chains . For each model , we study how the growth of free bacteria—the ones which are capable of causing systemic infection [12]—which is λ in the steady state , depends on the bacterial replication rate . Besides , we obtain chain length distributions ( the components pi of P ) , which could be compared to experimentally observed distributions .
In this variant of the base model , bacteria divide every τ . The effective growth rate is reff such that exp ( reff t ) = 2t/τ , thus reff = log ( 2 ) /τ . We analyzed ( see appendix G in S1 Text ) microscopy images of cecal content from vaccinated mice infected with S . Typhimurium , which were acquired for our previous study [12] . Most clusters are large , and of complex shape . But smaller clusters are linear , and we obtained the distribution shown by the black line and points in Fig 4 . The model with fixed division time is a priori more realistic . The best fit is obtained with this model , with the one adjustable parameter reff/α = 4 . 1 ( red line and points ) with estimated 95% confidence interval [3 . 6 − 4 . 7] . It seems however , though there is not enough data to quantitatively ensure it , that there are less long clusters than expected ( see appendix G . 3 in S1 Text for an expanded discussion ) . The data may be biased , as longer chains may not be fully in the focal plane . That the distribution is relatively narrow could also be compatible with force-dependent breaking rates .
We started from the recent finding [12] that the protection effect of sIgA , the main effector of the adaptive immune system in the gut , can be explained by enchained growth . Because sIgA are multivalent , they can stick identical bacteria together if they encounter each other . Early in infection , bacteria of the same type are at low density , thus typical encounter times are very long , but when a bacterium replicates , the daughter bacteria are in contact and thus can remain enchained to each other by IgA . Bacteria in clusters are less motile than individual bacteria , and in particular , are not observed close to the epithelial cells . In the case of wild type S . Typhimurium , only free bacteria which can interact with the epithelial cells contribute to the next steps of the infection process . Despite the presence of sIgA , some free bacteria are observed . It could be that they escape at the moment of replication . But , along with the observation that clusters do not grow indefinitely , it could also be a sign that the links between bacteria break . It is also physically expected that the links have some finite breaking rate . If the typical time between two bacterial divisions is much larger than the typical time for the link to break , then there would be no cluster . Conversely , in the inverse case , bacteria will be very likely to be trapped in large clusters . Then , even if sIgA are produced against all bacterial types , the bacteria dividing faster will be disproportionately affected . We investigated if this qualitative idea holds with more realistic models . We started from a base model in which: bacteria replicate at a fixed rate; remain enchained upon replication; until the link between them breaks at a given fixed breaking rate , identical for all links; and considering that , because of the way bacteria such as Salmonella or E . coli divide , the early clusters are linear chains of bacteria; when the chain breaks at an outermost link , we assumed the free bacteria will escape; but if the chain breaks elsewhere , we assumed that the two resulting sub-chains encounter each other quickly and form clusters of more complex shapes from which individual bacteria do not escape . We studied this base model with a combination of analytical and numerical approaches . We also tested the robustness of our findings by studying separately several variations of the base model: a probability of escaping upon replication , loss rates , fixed replication time , non-zero probability for the subchains to escape , and force-dependent breaking-rates . For each model , we studied how the growth rate of the free bacteria varies with the replication rate ( which would be equal if there were no clusters ) , and the distribution of chain lengths . We find that , except in the very specific case in which subchains always escape upon link-breaking ( q = 1 ) , the growth rate of the number of free bacteria is lower than the replication rate . And more spectacularly , in most of the models studied ( but not if more than half the subchains escape upon link breaking , or if there is a significant probability for bacteria to escape enchainement upon replication ) , the growth rate of the number of free bacteria is non-monotonic with the replication rate: there is a finite replication rate which maximizes the growth rate of non-clustered bacteria . At very high replication rates , bacteria get trapped in more complex clusters and cannot contribute anymore to the free bacteria dynamics and thus to the next steps of the infection process . The replication rate maximizing the growth rate is of the order of the breaking rate , though its specific value depends on the details of the model . To summarize , except when q = 1 , we always find that the higher the replication rate , the higher the proportion of bacteria trapped into clusters; and in many cases , the effect is even more dramatic , with the growth rate of free bacteria that may decrease with the replication rate . The chain length distribution depends on the model ( see Fig 3 ) . In most cases , the proportion of linear chains having length k decreases as γk , with γ some constant smaller than 1 . When replication occurs at fixed time , or when breaking rates are force-dependent , the proportion of longer chains decreases faster . There are models with different chain length distributions but qualitatively similar dependence of the growth rate on the replication rate , and the opposite is true too . This shows that large clusters have little importance for free bacteria production , what matters most is the small chains dynamics . It is reassuring , as we did not consider buckling , which would make long linear chains fold on themselves and produce more complex clusters , and may bias the linear chain distribution for very large lengths . It should also be noted that with fixed division time , not only the distribution is bumpy , as chains comprising a power of two number of bacteria are more frequent than others , but the distribution is also narrower . We analyzed experimental data on clusters of S . Typhimurium in the cecum of vaccinated mice . The experimental chain length distribution is in line with the model of fixed replication time , which is indeed more realistic . There is however somewhat less large chains than expected . More data would be necessary to asses this more reliably . This could be because of possible bias in the data . This could be also compatible with force-dependent breaking rates . Additional experiments , for instance to measure the breaking rate , could help by giving additional independent information and constrain the fitting . To test the dependence of the growth rate with the replication rate , an ideal experiment would be to compare similar bacterial strains , but with differing replication rates , and compete them in the same individual . It is however very challenging to obtain bacteria that differ only by their replication rate , particularly in vivo . sIgA-enchained bacterial clusters could be studied in vitro to measure how they break . However , using in vitro results to draw conclusions on in vivo systems is limited . First , there could be chemical or enzymatic components of the lumen that could facilitate or hinder link breaking , and the non-Newtonian viscosity of the digesta could play a role in the mechanic forces felt by the links , thus a simple buffer may not mimic well the real conditions . More crucially , the exact forces felt by particles of the size of bacterial clusters are not well characterized . Most studies of the flow characteristics in the digestive system rely either on external observations of the peristaltic muscles [21] or indirect measures of times for a marker to exit some section of the digestive track [22] . More quantitative study of the digestive flow at small scales is just beginning [9 , 10 , 23–27] and in the future it may give more clues to assess to which forces bacteria are subjected to in the digestive track . The mechanism we propose is nevertheless plausible . The observation in vaccinated mice of the existence of single bacteria and small clusters , and particularly small linear chains with an odd number of bacteria , are pieces of evidence that clusters do break in these in vivo conditions . An alternative explanation could be that some bacteria escape enchainement upon replication . However , at higher bacterial densities , we have evidence of independent bacteria binding when they encounter [12] , thus sIgA coated bacteria are adhesive . When two daughter bacteria divide , they are in contact , thus if sIgA is adhesive , escape is unlikely ( see appendix B in S1 Text ) . Importantly , even though our results show that specific conditions are needed for the growth rate to decrease with high replication rates , we almost always find that the higher the replication rate , the higher the proportion of bacteria trapped in clusters . Thus , even when it does not reverse the relationship between the growth rate of the free bacteria and the replication rate , it is at least dampening this relationship , and can be a tool both to control pathogenic bacteria , but also to maintain homeostasis of the gut microbiota . It is also interesting that there are other host effectors besides sIgA that bind bacteria together: neutrophil extracellular traps for instance [28] , and there could also be an interplay between replication rates and the breaking of the links mediated by these other effectors , as the mechanism we propose here is generic . As for any mechanism to fight against bacteria , the question of how easily resistance can be evolved is crucial . On the one hand , the replication rate could evolve . But bacteria replicating slower would be less competitive with other bacteria in the absence of sIgA , and a slower growth leaves more time for further host response . On the other hand the typical link breaking time could evolve . On the host side , sIgA is thought to be mechanically very stable , and experiments about the bonding of cells by sIgA seem to point to the link failing because of the extraction of the antigen rather than because of sIgA breaking , and rather than the sIgA/antigen bond detaching [14 , 20] . In the case of IgA defficiency , there is more secretion of IgM , and microbiota is disturbed [29]: we may speculate that IgM being less powerful for microbiota homeostasis is related to these immunoglobulins being more protease-sensitive than IgA and thus cleaved on shorter time scales [30] . On the other side , bacteria could evolve surface antigens . It could be interesting to think that bacteria could produce decoy antigens with no functional value , but against which the immune system will mount an immune response , and that are more easily released from the bacteria , thus disabling the main sIgA mode of action ( being easily evolvable would also be a benefit ) . Such decoys would however be a metabolic cost for the bacteria , and when breaking , may unmask other antigens corresponding to crucial functions of the bacteria . It could be argued that the capsule around bacteria such as Salmonella spp . , and also common in pathogenic E . coli , may behave as a decoy , though it has also other functions . Evolving resistance to IgA-mediated enchainment would thus be costly . Along the same lines , we may speculate whether mechanical aspects could be a reason why sIgA against some antigens are not efficient for protection . For instance , while anti-flagella sIgA aggregate very well Salmonella Enteriditis together , they are not efficient for protection [31] . A main reason could be that as Salmonella can switch flagella production on and off , then some Salmonella will always escape these sIgA , and seed the infection [32] . An additional possibility could be that flagella may more easily break , especially as distance between bacteria bound by flagella ( long ) is likely larger than for bacteria bound by O-antigens ( on chains shorter than flagellas ) [33] , and thus the shear forces would be larger . Further , the mechanical properties of the outer sugar layer of the gram negative bacteria could vary , and thus could be used to tune interactions . However , it would add another constraint on bacteria , and the general result that the growth rate compared to the replication rate is at least dampened by the cluster formation would remain . In the crowded environment of the gut , it is hard for the host to identify the good and the bad bacteria . That vaccination with dead bacteria is sufficient to produce sIgA and protection , shows that the host does not discriminate well against which bacteria they produce sIgA , as these dead bacteria do not harm . Linking the effect ( here the clustering ) of the immune effectors with a property directly relevant to the potential bacterial pathogeneicity ( here the replication rate ) avoids to make complex decisions about which bacteria to produce effectors against . | Inside the organism , the immune system can fight generically against any bacteria . However , the gut lumen is home to a very important microbiota , so the host has to find alternative ways to fight dangerous bacteria while sparing beneficial ones . While many studies have focused on the complex molecular and cellular pathways that trigger an immune response , little is known about how the produced antibodies act once secreted into the intestinal lumen . We recently demonstrated that the main physical effect of these antibodies is to cross-link bacteria into clusters as they divide , preventing them from interacting with epithelial cells , thus protecting the host . These links between bacteria may break over time . Using analytical and numerical calculations , and comparing with experimental data , we studied the dynamics of these clusters . At higher replication rates , bacteria replicate before the link between daughter bacteria breaks , leading to growing cluster sizes , and conversely . Thus the gut could produce IgA against all the bacteria it has encountered , but the most affected bacteria would be the fast replicating ones , that are more likely to destabilize the microbiota . Studying the mechanisms of the immune response may uncover more such processes that enable to target properties hard to escape through evolution . | [
"Abstract",
"Introduction",
"Models",
"and",
"methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"microbiome",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"microbiology",
"epithelial",
"cells",
"bacterial",
"diseases",
"enterobacteriaceae",
"antibodies",
"bacteria",
"bacterial... | 2019 | Enchained growth and cluster dislocation: A possible mechanism for microbiota homeostasis |
Quorum sensing is a process of chemical communication that bacteria use to monitor cell density and coordinate cooperative behaviors . Quorum sensing relies on extracellular signal molecules and cognate receptor pairs . While a single quorum-sensing system is sufficient to probe cell density , bacteria frequently use multiple quorum-sensing systems to regulate the same cooperative behaviors . The potential benefits of these redundant network structures are not clear . Here , we combine modeling and experimental analyses of the Bacillus subtilis and Vibrio harveyi quorum-sensing networks to show that accumulation of multiple quorum-sensing systems may be driven by a facultative cheating mechanism . We demonstrate that a strain that has acquired an additional quorum-sensing system can exploit its ancestor that possesses one fewer system , but nonetheless , resume full cooperation with its kin when it is fixed in the population . We identify the molecular network design criteria required for this advantage . Our results suggest that increased complexity in bacterial social signaling circuits can evolve without providing an adaptive advantage in a clonal population .
Quorum sensing is a mechanism of bacterial cell—cell communication that relies on the production , release , and group-wide detection of extracellular signal molecules called autoinducers . Quorum sensing enables populations of bacteria to coordinate changes in gene expression [1 , 2] . Bacteria often use quorum sensing to orchestrate the release of public goods ( e . g . , enzymes or surfactants ) whose functions benefit the entire community [2] , and to direct other cooperative behaviors such as transitions to more efficient modes of growth [3] . The cooperative nature of quorum sensing is susceptible to exploitation by mutant genotypes that do not contribute to cooperation but benefit from it [2 , 4–6] . Despite their immediate advantage over the wild-type , exploiting “cheater” genotypes will be eliminated in structured populations due to their negative effect on the average fitness of the community [5 , 7–11] . In bacteria , population structure can naturally arise in biofilms , where bacteria can grow without significant mixing [10] , or during the formation of growth bottlenecks upon invasion into a new environment [11–14] . Many bacterial species employ multiple quorum-sensing systems that impinge on the activity of a shared transcriptional regulator . Each of the quorum-sensing systems encodes a specific receptor and autoinducer production gene with no or limited crosstalk [15] . In several species such as B . subtilis [16] , V . harveyi [17] , and its pathogenic relative , V . cholerae [18 , 19] , the quorum-sensing systems are arranged in a parallel , seemingly redundant , architecture . That is , all the quorum-sensing autoinducer receptors funnel information into the same signal transduction pathway . It is unclear what the adaptive benefit is of harboring multiple , rather than a single , quorum-sensing autoinducer—receptor pair when the pairs function in parallel . Here , we combine modeling and experiments in B . subtilis and V . harveyi to show that a strain that has accumulated an additional quorum-sensing system reduces its cooperative investment in the presence of its ancestor , but resumes full cooperation in a clonal population . We show that this facultative cheating strategy requires a specific system integration design criterion; the novel receptor must have a dominant repressive effect on the ancestral quorum-sensing response in the absence of the novel autoinducer . We show that , additionally , this particular network design often leads to synergistic activation of the quorum-sensing response by the different autoinducers .
We hypothesized that social interactions between different genotypes may contribute to the adaptive role of redundant quorum-sensing networks . This hypothesis can be approached by comparing the social behavior of a wild-type species possessing multiple quorum-sensing systems to the behavior of mutant strains harboring varying numbers of quorum-sensing systems . To explore this idea , we first examined the ComA-directed quorum-sensing network of B . subtilis [16 , 20–22] . This network is composed of a single ComP-ComX system and multiple paralogous Rap-Phr systems , each encoding its own specific autoinducer . The ComP receptor phosphorylates ComA when ComP is bound to the ComX autoinducer , while Rap receptors repress ComA only when their corresponding Phr ligands are not bound ( Fig 1A ) . All the autoinducers therefore positively control ComA activity , but through different regulatory interactions . The Com and Rap systems also differ with respect to their population genetics patterns . The ComP-ComX system exhibits significant genetic variability within the population and different alleles form distinct orthogonal signaling pherotypes [20] . These different pherotypes often display partial cross-inhibition , such that an autoinducer from one strain inhibits the response of another strain to its cognate autoinducer [23] . Nonetheless , only a single ComP-ComX system is encoded in each B . subtilis isolate . By contrast , all B . subtilis strains encode multiple Rap-Phr systems . The exact number varies between strains , likely due to the association of some Rap-Phr systems with mobile elements [24] . It was previously shown that deletion of any Rap-Phr system has only a small effect on ComA activity , while deletion of the phr genes or overexpression of the rap genes led to repression of quorum sensing [16 , 25] . It is therefore unclear why so many quorum-sensing systems regulate ComA , and specifically , why Rap-Phr paralogs proliferate in the genome , while ComP-ComX is unique . To address this question , we constructed strains in which we added to the wild-type strain a novel ComP-ComX system ( ExtraCom strain , comQXPRO-H-1+ [20] ) or we either added ( ExtraRap strain , rapPphrP+ [26] ) or we deleted ( MinusRap strain , ΔrapFphrF ) a Rap-Phr system ( Fig 1B , see methods and S1 File for strain construction details ) . The autoinducing signals produced by the introduced ExtraRap and ExtraCom systems differed from those made by the paralogs present in the parent strain with no cross-activation [20 , 26] ( S1 Fig ) . We note , however , that the comXRO-H-1 autoinducer cross-inhibits the endogenous ComP168 receptor [23] . We examined the behavior of these strains under surface-swarming motility conditions , which strictly require the production and release of a ComA-dependent surfactant called surfactin [27 , 28] ( Fig 1C , see methods for details on swarm motility protocol ) . Unlike the ΔcomA mutant , the above quorum-sensing variant strains exhibited robust swarming , reaching a similar cell yield as the wild-type after 48 h ( Fig 1C , S1A Fig , p = 1×10−5 , F ( 4 , 12 ) = 8 . 1 , n = 16; two-way ANOVA for difference between genotypes when including ΔcomA , p = 0 . 26 , F ( 3 , 9 ) = 8 . 1 , n = 12 without ΔcomA ) . Altering the number of quorum-sensing systems therefore does not significantly affect the fitness of the bacteria in clonal populations . Surfactin may function as a costly public good during swarming , allowing “cheater” strains to exploit the wild-type in coculture . In agreement with this possibility , we found that the ΔcomA mutant strain regained its ability to swarm when cocultured with the wild-type , and in so doing , dramatically increased its relative frequency in the population ( Fig 1D and S1B Fig; p = 10−4 , two sample t test , n = 42 , see methods for details of the competition experiments and the wild-type competition against itself in Fig 1C that was carried out as a control ) . When we performed similar coculture experiments between the wild-type and the different quorum-sensing variants , we found that the strain carrying an additional Rap-Phr system was strongly selected for over a strain lacking it . In contrast , the ExtraCom strain was out-competed by the wild-type . Moreover , the fitness advantage of the ExtraRap strain over the wild-type was similar to that of the “cheater” ΔcomA mutant at low frequency ( p = 0 . 21 , t ( 4 , 32 ) = 0 . 8 , linear regression comparison of the intercepts at zero frequency ) and approached neutrality as its frequency increased ( Fig 1D , p = 0 . 13 , t ( 2 , 16 ) = 1 . 06 , linear regression comparison of the intercepts at zero at a frequency of one ) . Similar results were obtained for wild-type exploitation of the MinusRap strain ( S1C Fig ) . In contrast to the selection of the ExtraRap strain , the ExtraCom strain remained close to neutral with respect to the wild-type at low frequency , but its competitive disadvantage increased with increasing frequency ( Fig 1D , p = 10−8 , t ( 2 , 16 ) = 11 , linear regression of slope ) . Our results are therefore in agreement with the observed population genetics data for the two systems—selection for genomic proliferation of Rap-Phr systems and against proliferation of the ComP-ComX system . To gain further insight into our results , we mathematically modeled cellular growth and quorum-sensing signaling dynamics during swarming ( Fig 2 ) . In the model , we assume a simplified ancestral strain encoding a single ComP-ComX and a single Rap-Phr system . We explored the growth and social dynamics of this ancestor and its corresponding ExtraRap- and ExtraCom-derived strains during swarming ( see methods and S1 File for description of the model and its assumptions ) . Strikingly , the model was able to capture qualitatively the experimental results we obtained above both in clonal and social conditions ( Fig 2A and S2 Fig , compare with Fig 1B and 1C ) . The findings underpin how selection depends on the particular circuit design of the two quorum-sensing systems . The model also provides simple explanations for the frequency dependence of the ExtraRap system and the difference in selection for and against the ExtraRap and ExtraCom strains , respectively . When a derived “Extra” strain is at low frequency , the concentration of the novel autoinducer it produces is very low compared to those of the ancestral autoinducers , which are produced by all the members in the population ( Fig 2A , left insets ) . In this scenario , the level of quorum-sensing response of the “Extra” strain depends on the activity of the unliganded form of the novel receptor . In the ExtraCom system , the novel ComP receptor is inactive in the absence of its cognate autoinducer . The ancestral network will therefore not be affected by the presence of the novel ComP system , which leads to equal quorum-sensing activation of the ancestral and ExtraCom strains ( Fig 2A and 2C ) . In contrast , in the ExtraRap strain , the autoinducer-free novel Rap receptor represses ComA . Repression is dominant and overpowers activation of ComA by the shared ancestral quorum-sensing system ( Fig 2A ) . Activated ComA levels will therefore be lower in the ExtraRap strain than in the ancestral strain ( Fig 2B ) , leading to selection of the ExtraRap strain due to exploitation of the ancestral strain . As the frequency of the derived “Extra” strain increases , so does the concentration of its corresponding novel autoinducer ( Fig 2A , right insets ) . In the case of the ExtraRap strain , accumulation of the novel autoinducer leads to partial de-repression of ComA to a level that approaches that of the ancestor ( Fig 2B ) , and this condition occurs as the ExtraRap strain approaches fixation . Therefore , the ExtraRap strain acts as a cheater at low frequency but returns to full cooperation when fixed in the population . In the case of the ExtraCom strain , accumulation of the novel autoinducer leads to a corresponding increase in its quorum-sensing response . In the specific experimental case we examined , the novel ComX system ( ComXRO-H-1 ) in the ExtraCom strain cross-inhibits the ancestral ComP168 receptor , leading to a strong reduction in ComA activity in the wild-type , ancestral strain ( Fig 2C ) . The ancestral strain therefore acts as a cheater with respect to the ExtraCom strain . Our modeling framework also allows us to explore a theoretical case in which no cross-inhibition occurs . In this situation , the ancestral strain maintains a constant level of ComA activity ( S3 Fig ) . The net selective effect , with or without autoinducer cross-inhibition , is against the ExtraCom system , although selection is stronger when autoinducer cross-inhibition occurs ( S3 Fig ) [9] . Thus , while autoinducer cross-inhibition naturally exists in the B . subtilis system we are studying , this feature is not strictly required for selection against accumulation of a novel quorum-sensing system ( S3 Fig ) . Our model also predicts how the novel autoinducer will act with respect to the ancestral autoinducer in that the model provides us with information about what type of regulatory input—output gate is established . An additional ComP-ComX system leads to formation of an OR-like ( additive ) regulatory gate for the two ComX autoinducers with respect to their control of ComA activity ( Fig 2E ) . Thus , a single autoinducer is sufficient to elicit a strong quorum-sensing response . In contrast , the repressive activity of an additional Rap system leads to formation of an AND-like ( multiplicative ) gate between it and the ancestral Phr or ComX system . Thus , the simultaneous presence of both autoinducers is required to elicit a strong response ( Fig 2D and S4A Fig ) . Our model predicts that the different architectures of the two quorum-sensing systems lead to differential investment in cooperative behavior by the ancestral and derived strains as well as to distinct regulatory input—output gate structures . These features result in the observed patterns of selection . To address these predictions experimentally , we introduced a YFP transcriptional reporter for ComA activity ( PsrfA-YFP ) into the wild-type , the ExtraCom , and the MinusRap strains . We cocultured each reporter-containing strain with a reporter-free counterpart and measured gene expression as a function of frequency ( Fig 3A and 3B and S5A Fig ) . To minimize the effect of changes in frequency and spatial distribution , we performed these assays in minimal medium using a surfactin production-deficient mutant of the sfp gene ( sfp− [27] ) , which has reduced quorum-sensing-associated cost . Similar results were obtained when gene expression was measured during swarming ( S5B Fig ) . The absence of surfactin in the minimal growth medium did not significantly affect expression of the PsrfA-YFP reporter construct in the cocultured strains ( S5B Fig ) . We found that , when the MinusRap and wild-type strains are cocultured , the MinusRap strain maintained a constant ComA activity , irrespective of its frequency in the population ( F ( 1 , 16 ) = 0 . 41 , p = 0 . 53 , n = 18 , linear regression of the slope ) . In contrast , at low frequency , the wild-type exhibited low level ComA activity , which increased with increasing frequency of the wild-type ( F ( 1 , 15 ) = 96 , n = 17 , p < 10−7 , linear regression of the slope ) . At high frequencies , wild-type ComA activity approached the activity level of the MinusRap strain ( Fig 3A , t test , p = 0 . 43 for interception of best-fitted lines at a frequency of 1 ) . When the ExtraCom strain was cocultured with the wild-type , ComA activity was the same in both strains when the frequency of the ExtraCom strain was low ( t test for linear regression of lines , p = 0 . 26 for interception at frequency of zero ) . ComA activity in the ExtraCom strain increased with increasing frequency ( Fig 3B , p = 10−11 F ( 1 , 14 ) = 405 , for a zero slope ) . In accordance with the expected effects of crossinhibition ( Fig 2 and S3 Fig ) , the ComA activity of the wild-type strain decreased dramatically with increasing frequency of the ExtraCom strain ( Fig 3B , p = 10−6 F ( 1 , 15 ) = 54 , for a zero slope ) . We next measured the resulting regulatory gate structure of the response of ComA to addition of multiple autoinducers . We constructed a strain constitutively expressing the rapF and rapC receptor genes but not their respective phrF and phrC autoinducer-production genes . We found that the ComA response was significant only if both the PhrF and PhrC autoinducers were present , showing an AND-like gate structure ( Fig 3C , S6A and S6B Fig ) . Likewise , an AND-like response occurred for PhrF and ComX regulation of ComA ( S5 Fig ) . In contrast , regulation of ComA by the two ComX autoinducers in the ExtraCom strain was additive as expected for an OR-like response ( Fig 3D , S6C , S6D and S6E Fig , methods ) . The experimental results support the role of social interactions in selection for accumulation of Rap-Phr systems coupled with selection against accumulation of ComP-ComX systems in B . subtilis . In order to generalize these results , we formulated a generic model of selection with respect to quorum-sensing-dependent public goods ( S1 File ) . This model suggests that two design criteria are necessary and sufficient for the invasion of a strain carrying an additional quorum-sensing system into a population lacking it: 1 ) Dominant repression: The ligand-free novel receptor should act negatively to overpower the quorum-sensing response of the ancestral system , and 2 ) Facultative operation: The addition of the novel autoinducer should restore the quorum-sensing response to levels similar to that of the ancestor . The combination of these two features allows the invading strain to perform facultative cheating—cheat the ancestor in coculture ( criterion #1 ) but resume cooperation when it is fixed in the population ( criterion #2 ) [9 , 29] . In the S1 File , we show that if repression by the novel quorum-sensing system is strong , the two autoinducers will regulate the response in an AND-like manner . We further demonstrate that formation of an AND-like gate is sufficient but not mandatory to select for acquisition of a novel quorum-sensing system . Likewise , an OR-like gate between autoinducers is sufficient but not required to select against the acquisition of a novel quorum-sensing system . The AND-like and OR-like gate structures provide an intuitive , albeit simplified , explanation for selection ( AND ) or counterselection ( OR ) of an evolved strain . If both autoinducers are necessary to activate the quorum-sensing response in the evolved strain ( AND gate ) , while the ancestral strain produces and responds to only one of the autoinducers , then the evolved strain will cease to cooperate when present as a small minority together with its ancestor . In contrast , if either autoinducer is sufficient , then the evolved strain will continue to cooperate even when it is present as a minority . We next examined whether our results also apply to another well-studied model organism in which multiple quorum-sensing systems exist and control a common output . The bioluminescent marine bacterium V . harveyi [15] has a quorum-sensing network composed of three parallel systems that regulate expression of the quorum-sensing master transcription factor LuxR , which controls multiple traits including bioluminescence emission ( Fig 4A ) . A similar architecture composed of four quorum-sensing systems exists in the related pathogen , V . cholerae [19] . While the deletion of any of the receptors does not affect the quorum-sensing response [18] , deletion of any of the autoinducer synthase genes represses LuxR-activated genes , demonstrating the dominant repressive effect of each ligand-free receptor [17] . In addition , two of the autoinducers have been shown to act multiplicatively in their regulation of LuxR [30] and to synergistically control bioluminescence [31] . We used the abundant quantitative data on this organism to construct a model of the expected social behavior of the wild-type and an ancestral-like strain deleted for any one of the quorum-sensing systems ( S1 File , S7A Fig ) . The model predicts that the wild-type will reduce its cooperative investment in the presence of such an ancestral strain , which will lead to facultative cheating under appropriate conditions . To verify the model experimentally , we constructed a putative ancestral strain deleted for the luxMN autoinducer-receptor system . We introduced a null mutation into the lux ( luciferase ) operon in the wild type and derived ancestral strains , and by mixing Lux+ and Lux− pairs ( WT/Lux+ mixed with luxMN/Lux− and WT/Lux− mixed with luxMN/Lux+ ) , we could measure the level of quorum-sensing response per cell of the Lux+ strain in each coculture ( Fig 4B and S7B Fig , methods ) . As expected from the model , we found that light production by the luxMN mutant strain remained almost constant irrespective of its frequency ( the small decrease is most likely due to the effect of the lux locus , S7B Fig ) . The wild-type showed a near 100-fold reduction in bioluminescence output compared to the luxMN mutant at low frequency ( p < 10−13 , T ( 32 ) = 12 . 7 , t test on linear regression for intersect at zero frequency ) , while approaching the same level of light production as the luxMN mutant at high frequency . Our rationale therefore also applies to the parallel quorum-sensing network of V . harveyi .
In this work , we propose that bacteria possessing multiple quorum-sensing networks that control the identical response , which are commonly found in nature , are selected through a facultative cheating process . Facultative cheating has been described in the past as a strategy by which microorganisms exploit nonkin but return to cooperation in the presence of kin [32] . Such behavior has been described in fruiting body-forming amoeba and bacteria [29 , 33] , but the underlying molecular processes that lead to it are unknown [34]; however , links to cell—cell signaling and facultative cheating have been suggested [35–37] . We predict that accumulation of multiple quorum-sensing systems requires a specific set of network design criteria , the functioning of which we explored in two diverse but well studied organisms . Specifically , the introduced novel receptor must repress the quorum-sensing response in the absence of the novel autoinducer , as occurs in the B . subtilis Rap-Phr and V . harveyi Lux quorum-sensing systems . In contrast to these systems that depend on repression , other quorum-sensing systems exist that act positively , in that the receptor functions as an activator upon autoinducer binding . Our model and experimental results explain why accumulation of parallel positively acting systems is selected against . Indeed , we do not know of any bacterium that possesses multiple activation-based quorum-sensing systems that function in parallel . Rather , activation-based systems are commonly organized in a hierarchy , in which one quorum-sensing system regulates the expression of a second system . A hierarchical network design is not fully redundant because the two quorum-sensing systems can control different genes . Further work will be required to define the benefits and possible evolutionary routes giving rise to quorum-sensing systems that function positively and are arranged as hierarchies . Beyond facultative cheating , other possible adaptive functions for possessing multiple quorum-sensing systems have been suggested . These include gains in information acquired about cell density , information about the frequency of phenotypes in the vicinal population , and access to information about physical flow conditions [38–41] . Our social selection model does not contradict those alternatives and may promote them by driving the initial fixation of the redundant network design , which can , subsequently , be further modified for other adaptive advantages . Several processes may limit the accumulation of quorum-sensing systems . First , each system contributes a signaling cost [5 , 42] . Second , the facultative return to cooperation may not be complete , leading to reduced benefit during exploitation in structured populations . Third , social selection of facultative characters is weak and can lead to variable mutation selection balance [34] . Finally , rareness of available systems and the need to integrate them appropriately into the existing network may limit the rate of accumulation . Further work will be required to define the importance of each of these mechanisms . Exploitation can also occur between species; not only between variants within species . For example , cooperative secretion of antibiotic degrading enzymes has been shown to lead to coexistence of secreting and nonsecreting genotypes , at both the species and interspecific levels [43 , 44] . Accumulation of additional quorum-sensing systems could also be used to exploit species that produce fewer signals . This ecological factor may contribute to the continuous selection for maintenance of multiple systems . More generally , our results point to the roles facultative cheating and kin recognition may have in the ecology of complex microbial communities .
Routine growth was performed in Luria—Bertani ( LB ) broth: 1% tryptone ( Difco ) , 0 . 5% yeast extract ( Difco ) , 0 . 5% NaCl . Experiments with B . subtilis were done using Spizizen minimal medium ( SMM ) : 2 g L−1 ( NH4 ) 2SO4 , 14 g L−1K2HPO4 , 6 g L−1KH2PO4 , 1 g L−1disodium citrate , 0 . 2 g L−1MgSO4∙7H2O . This was supplemented with trace elements ( 125 mg L−1MgCl2∙6H2O , 5 . 5 mg L−1CaCl2 , 13 . 5 mg L−1FeCl2∙6H2O , 1 mg L−1MnCl2∙4H2O , 1 . 7 mg L−1ZnCl2 , 0 . 43 mg L−1CuCl2∙4H2O , 0 . 6 mg L−1CoCl2∙6H2O , 0 . 6 mg L−1 Na2MoO4∙2H2O ) . Unless otherwise noted , 0 . 5% glucose was used as carbon source . Petri dishes for routine procedures were solidified using 1 . 5%agar ( Difco ) . Antibiotic concentrations: Macrolides-lincosamides-streptogramin B ( MLS; 1 μg ml−1 erythromycin , 25 μg ml−1 lincomycin ) ; Spectinomycin ( Sp , 100 μg ml−1 ) ; Tetracycline ( Tet , 10 μg ml−1 for B . subtilis ) ; Kanamycin ( Km , 5 μg ml−1 ) ; Chloramphenicol ( Cm , 15 μg ml−1 ) ; Ampicillin ( Amp , 100 μg ml−1 ) ; Carbenicillin ( Carb , 300 μg ml−1 ) . Isopropyl β-D-thiogalactopyranoside ( IPTG , Sigma ) was added to the medium at the indicated concentration when appropriate . Premeasurement Bacillus growth protocol: Prior to all measurements , an overnight colony from an LB agar plate was inoculated in 1 mL SMM liquid medium and grown for 7 h until an OD600 of 0 . 1–0 . 3 was reached . The cultures were diluted by a factor of 106 and grown overnight at 37°C . Overnight cultures were centrifuged , resuspended in PBS , and diluted to an OD600 of 0 . 01 . We find that this long incubation in minimal medium both reduced the effects of quorum sensing prior to growth and reduced the arbitrary difference in growth between two cocultured wild-type colonies . For coculture experiments , cells of different strains were mixed in appropriate ratios after overnight growth in SMM , based on relative optical density ( OD ) . The exact ratios were subsequently measured using flow cytometry . Synthetic pentapeptides PhrF ( NH2 QRGMI COOH ) and PhrC ( NH2 ERGMT COOH ) were purchased from GL Biochem ( Shanghai , China ) at >98% purity . 10 mM aliquots were prepared by resuspension of the lyophilized peptides in H2O and stored at −20°C . Samples were analyzed on a Gallios flow cytometer ( Beckman-Coulter ) , equipped with four lasers ( 405 nm , 488 nm colinear with 561 nm , 638 nm ) . The emission filters used were: BFP– 450/50 , YFP/GFP– 525/40 , mCherry– 620/30 . Events were discriminated using the forward-scatter parameter . For each run , discrimination enabled a single , well-defined population to appear in the forward-scatter ( FS ) by side-scatter plot . Gating on the fluorescent populations and inspection of the nondiscriminated forward by side-scatter plot indicated that over 99 . 9% of the fluorescent cells are present in the discriminated population . In all analyzed samples , only single cells were considered by gating on correlated time-of-flight and FS events . Gating of the different fluorescent populations was performed by inspection of the log-log FLx by FLy plots ( where x & y represent the appropriate filter number for each fluorescent marker ) , where two distinct populations were clearly visible , resulting in type-I and type-II errors of less than 0 . 05% . For each run , at least 100 , 000 cells were analyzed and the total events analyzed such that the minority population was never below 1 , 000 events . Cells were grown as described in the premeasurement growth protocol . Five microliters of diluted cultures were placed at the centers of 0 . 7% agar plates containing 25 mL of SMM medium supplemented with trace elements and 0 . 05% glucose . The plates were prepared 1 h prior to inoculation , allowed to solidify in room temperature , and dried for 5 min in a laminar flow chamber . The plates were incubated at 30°C for up to 72 h . The swarms were collected after suspension in 5 ml of PBS , the OD was measured , and the final ratios or the gene expression was determined using flow cytometry as described above . For spatial analysis of swarming , samples were taken from the centers of the plates , in addition to several samples 1 cm and 2 cm from the center . We find that a glucose concentration of 0 . 05% compared to 0 . 5% reduced the residual swarming of the comA mutant , increasing the difference in growth between the mutant and the wild-type , likely because residual production of surfactin by the mutant colony was reduced . In all experiments , YFP level was determined from the median level of the unimodal distribution of YFP expressing cells using flow cytometry . YFP level was normalized by the autofluorescence of the wild-type . For coculture experiments ( Fig 3A and 3B ) , samples were taken at several time points , and the OD600 and YFP levels were measured by flow cytometry . The expected YFP level at OD600 = 1 was calculated by interpolation . V . harveyi strains were grown at 30°C in Luria-marine ( LM ) medium with aeration . Following overnight growth , samples were diluted to OD600 = 0 . 005 with varying ratios of dark and bright strains . Following 6 . 5 h of growth , bioluminescence was measured on a Tri-Carb 2810 TR ( Perkin Elmer ) scintillation counter . Dilutions of the cultures were made and plated on LM agar plates . Plates were incubated at 30°C overnight to allow colony formation . Images of the plates were taken using an ImageQuant LAS system that detects both bioluminescence and total colony forming units ( CFUs ) . Colonies were counted using the ImageQuant TL and ImageJ programs . Values shown are calculated as ( total bioluminescence ) / ( # bioluminescent CFUs ) . The values were normalized to the bioluminescence per cell of the bright strain . All strains are detailed in S1 Table , while respective primers are provided in S2 Table . All of the mutations and constructs were transferred to PY79 by transformation [45] . Integration of amyE integration plasmids into the zjd89::amyEΩ Cm Km [46] was done as previously described [26] . Deletion of rapF-phrF , rapC-phrC , comA , and comQXP from the PY79 chromosome and their replacement with the MLS resistance cassette was performed through the long flanking homology PCR method [47] using the primers rapF-P1-P4 , rapC-P1-P4 , comA-P1-P4 , and comQXP-P1-P4 , respectively ( S2 Table ) . The rapFphrF::Cm deletion was generated using the antibiotic switching vector ece76 . rapFphrF::Cm was next used as a template to generate rapFphrF::Tet using the antibiotic switching vector ece75 ( S1 Table ) . To generate inducible zjd89:: ( Phyperspank-rapF ) and amyE:: ( Phyperspank-rapC ) constructs , a PCR product containing the relevant open reading frame was amplified using the primer pairs hsRapF-F/hsRapF-R andhsRapC-F/hsRapC-R . The PCR products were digested with the appropriate enzymes ( S2 Table ) and ligated downstream of the hyperspank promoter of the pDR111 vector containing Spec resistance [48] . Construction of sacA:: ( comQXPRO-H-1 Cm ) was performed by PCR amplification of comQXP from strain B . mojavensis RO-H-1 using the comQXP-ROH1-F and comQXP-ROH1-R primer pair . The PCR product was digested with restriction enzymes BamHI and EcoRI and ligated to the ece174 plasmid ( S1 Table ) . The resulting vector was integrated into the sacA site on the chromosome using Cm resistance for selection . Construction of sacA:: ( Psrf-3xyfp Cm ) was performed by PCR amplification of Psrf-3xyfp using AEC945 as a template and the Psrf-sacA-F/Psrf-sacA-R primer pair . The PCR fragment was digested with the appropriate enzymes ( S2 Table ) and ligated to the ece174 plasmid . The resulting vector was integrated into the sacA site on the chromosome using Cm resistance for selection . The swrA+ mutation allele is a spontaneous revertant that was selected by plating swrA−sfp+ cells on 0 . 7% LB agar plates and selecting motile variants , as was done previously [28] . The reconstituted swrA+ allele was verified by sequencing . The sfp+ allele was amplified from the undomesticated strain B . subtilis NCBI3610 and fused to a spectinomycin resistance cassette by PCR using primers sfp-P1-P4 ( S2 Table ) . The constitutive fluorescent construct P43-yfp was synthesized by Genewiz , and sub-cloned into ece137 using BamHI and EcoRI restriction enzymes . Plasmid pBB1131 ( pLAFR2/luxCDABE::Tn5 ) was conjugated into strains BB120 ( WT ) and HLS252 ( ΔluxMN ) . The luxCDABE::Tn5 region was transferred to the endogenous luxCDABE locus on the chromosome to generate the dark strains . In pBB1131 the Tn5 is located in the luxA gene . Modeling of social interactions , signaling , and growth of the different organisms was done using regular differential equations , which describe the kinetic interactions between the molecular components of the quorum-sensing signal transduction process , cell growth kinetics and its dependence on nutrient availability , and public goods production . The equations were either analytically treated or solved numerically using Matlab ( Mathworks ) . The specific equations used and a discussion of their relevance to the known biological data are provided in S1 File . | Quorum sensing is a mechanism through which bacteria communicate by producing , releasing , and detecting signal molecules encoding information about cell population density . Quorum sensing allows bacteria to synchronize their behaviors and act as collectives . Often , quorum sensing controls cooperative behaviors that benefit the entire community , such as the production and secretion of costly metabolites . Some bacteria release multiple signal molecules which , once detected , funnel information into the same cellular response . Thus , the benefit of using multiple rather than a single signal is mysterious since the signals seem redundant . Here , we combine modeling and experiments to show that the evolutionary accumulation of multiple quorum-sensing systems can be attributed to social exploitation and kin recognition . When in low abundance , a strain that has acquired an additional quorum-sensing system can avoid cooperating and can exploit its ancestor strain , which contains one less quorum-sensing system . The cheater containing the additional system returns to a cooperative behavior when it is abundant . We also identify the molecular mechanisms necessary for the acquisition of an additional signaling system . Our work demonstrates that increased complexity in bacterial social signaling circuits can evolve without providing an adaptive advantage in a clonal population . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"engineering",
"and",
"technology",
"pathogens",
"bacillus",
"microbiology",
"electromagnetic",
"radiation",
"regression",
"analysis",
"prokaryotic",
"models",
"luminescent",
"proteins",
... | 2016 | Social Evolution Selects for Redundancy in Bacterial Quorum Sensing |
Proper protein folding in the endoplasmic reticulum ( ER ) is vital in all eukaryotes . When misfolded proteins accumulate in the ER lumen , the transmembrane kinase/endoribonuclease Ire1 initiates splicing of HAC1 mRNA to generate the bZIP transcription factor Hac1 , which subsequently activates its target genes to increase the protein-folding capacity of the ER . This cellular machinery , called the unfolded protein response ( UPR ) , is believed to be an evolutionarily conserved mechanism in eukaryotes . In this study , we comprehensively characterized mutant phenotypes of IRE1 and other related genes in the human fungal pathogen Candida glabrata . Unexpectedly , Ire1 was required for the ER stress response independently of Hac1 in this fungus . C . glabrata Ire1 did not cleave mRNAs encoding Hac1 and other bZIP transcription factors identified in the C . glabrata genome . Microarray analysis revealed that the transcriptional response to ER stress is not mediated by Ire1 , but instead is dependent largely on calcineurin signaling and partially on the Slt2 MAPK pathway . The loss of Ire1 alone did not confer increased antifungal susceptibility in C . glabrata contrary to UPR-defective mutants in other fungi . Taken together , our results suggest that the canonical Ire1-Hac1 UPR is not conserved in C . glabrata . It is known in metazoans that active Ire1 nonspecifically cleaves and degrades a subset of ER-localized mRNAs to reduce the ER load . Intriguingly , this cellular response could occur in an Ire1 nuclease-dependent fashion in C . glabrata . We also uncovered the attenuated virulence of the C . glabrata Δire1 mutant in a mouse model of disseminated candidiasis . This study has unveiled the unique evolution of ER stress response mechanisms in C . glabrata .
In eukaryotic cells , the majority of secretory and transmembrane proteins are folded and modified in the lumen of the endoplasmic reticulum ( ER ) . Contingent on proper folding , they are either transported to the Golgi apparatus or degraded [1] . Impairment of these vital cellular machines can be caused by various factors , such as chemical compounds and mutations in genes involved in ER quality control , resulting in the accumulation of unfolded or misfolded proteins in the ER , collectively called ER stress [2] , [3] . Unsolved ER stress has detrimental consequences for eukaryotic cells . For example , in humans , ER stress is implicated in the pathology of various diseases including metabolic disease , inflammation , neurodegenerative disorders , and cancer [4] , [5] . It has also been revealed in several pathogenic fungi that ER quality control is important for antifungal resistance and virulence [6] , [7] , [8] , [9] , [10] , [11] . In response to ER stress , eukaryotic cells activate a signaling pathway , termed the unfolded protein response ( UPR ) , to induce a comprehensive gene expression program that adjusts the protein-folding capacity of the ER ( reviewed in [2] , [12] ) . In Saccharomyces cerevisiae , the ER-resident transmembrane stress transducer Ire1 , which contains an ER luminal sensing domain and cytosolic protein kinase/endoribonuclease domains , is responsible for detecting unfolded proteins and triggering the UPR [13] , [14] . Ire1 is activated by direct interaction with the unfolded proteins , resulting in the oligomerization of its luminal domain . Signaling is transduced to the cytosolic domain through the sequential steps of autophosphorylation and oligomerization of the endoribonuclease domain , leading to the activation of the Ire1 ribonucrease [15] , [16] . Active Ire1 cleaves the HAC1 mRNA to excise the intron , allowing translation of the basic-leucine zipper ( bZIP ) transcription factor Hac1 that subsequently induces transcription of the UPR target genes [17] , [18] . ER-stressed cells attempt to reduce the load of abnormally folded proteins in the ER by facilitating protein folding ( e . g . upregulation of genes encoding ER-resident chaperones and protein-modifying enzymes ) and by translocating misfolded proteins from the ER to the cytosol where they are degraded by the proteasome . The latter mechanism is called ER-associated protein degradation ( ERAD ) ( reviewed in [3] ) . An alternative mechanism of degradative response is autophagy , which degrades organelles including damaged ER . In addition to ER-resident chaperones and protein-modifying enzymes , many of the components that mediate ERAD and autophagy have also been identified as UPR targets [19] , [20] , [21] . In pathogenic fungi , the molecular basis of ER quality control has been poorly understood , but several recent studies in Aspergillus fumigatus and Cryptococcus neoformans have found that Ire1 and Hac1 homologs are key components of the UPR and are indeed required for the ER stress response [7] , [8] , [10] . Interestingly , these studies have also discovered that the UPR is implicated in fungal pathogenicity and antifungal resistance . In A . fumigatus , the loss of HacA/Hac1 results in increased susceptibility to caspofungin , amphotericin B , and azole antifungals , and decreased virulence in mouse models of invasive aspergillosis [10] . Recently , the necessity of IreA/Ire1 for A . fumigatus virulence has also been reported [8] . C . neoformans mutant strains lacking Ire1 or its downstream transcription factor Hxl1 display increased azole susceptibility , failure to grow at 37°C , and avirulence in a mouse model of systemic cryptococcosis [7] . It is also known in Candida albicans that Hac1 plays a role in hyphal development [11] , and a Δire1 mutant is hypersusceptible to caspofungin [6] . These observations shed light on the UPR as an attractive target for the development of novel antifungal therapies . Candida glabrata has emerged as an important fungal pathogen due in part to its intrinsic or rapidly acquired resistance to azole antifungals such as fluconazole [22] , [23] . In addition , recent surveillance data have revealed an increase of C . glabrata clinical isolates that display resistance to not only azoles , but also echinocandin-class antifungals [24] , [25] . Considering the limitations of the currently available antifungals in clinical settings , there is an urgent need to develop an effective antifungal strategy for a broad range of fungal pathogens , including C . glabrata . Since how C . glabrata cells deal with ER stress has not been explored , we first functionally characterized the C . glabrata Ire1 and Hac1 orthologs . It has been believed that the UPR mediated by the Ire1-Hac1 linear pathway is evolutionarily conserved in most eukaryotic species , but surprisingly , we found that C . glabrata Ire1 plays a role in the ER stress response in a Hac1-independent manner , despite the presence of an apparent HAC1 ortholog . The present study revealed that C . glabrata has lost the canonical Ire1-Hac1 UPR , but has developed alternative mechanisms for ER quality control . In addition , our comprehensive analyses of Δire1 mutant phenotypes revealed significant diversities of Ire1-mediated stress response mechanisms between C . glabrata and other fungi . Here , we describe the unique evolution of ER quality control systems in C . glabrata .
The ability of fungal cells to cope with ER stress was assessed by monitoring cell growth in the presence and absence of two well-known ER stress inducers that interfere with protein folding in the ER by different mechanisms: tunicamycin ( TM ) , an N-linked glycosylation inhibitor , and dithiothreitol ( DTT ) , an inhibitor of disulfide bond formation . Compared to S . cerevisiae and C . neoformans wild-type strains , C . glabrata was relatively tolerant to both TM and DTT independently of its strain backgrounds and culture media , although S . cerevisiae displayed strain dependent susceptibilities ( Figure S1 ) . Other representative Candida species also exhibited higher tolerance to these agents than S . cerevisiae and C . neoformans with intriguing exceptions: Candida krusei was highly susceptible to TM , but not to DTT , while Candida tropicalis was hypersusceptible to DTT , but not to TM . These results imply that diverse aspects of ER stress response mechanisms may exist even in closely related yeast species . In this report , the following studies focused on C . glabrata , which is phylogenetically close to S . cerevisiae , but has gained increased tolerance to ER stress and pathogenic potential to humans . The C . glabrata IRE1 and HAC1 orthologs were identified by BLASTp searches using the NCBI ( http://www . ncbi . nlm . nih . gov/BLAST/ ) and Genolevures ( http://cbi . labri . fr/Genolevures/elt/CAGL ) databases . The respective amino acid sequences of S . cerevisiae IRE1 ( YHR079c ) and HAC1 ( YFL031w ) were used as queries . The deduced amino acid sequence of C . glabrata IRE1 ( NCBI accession No . : XP_446111 , Genolevures ID: CAGL0F03245g ) displayed 53 . 6% similarity and 35 . 8% identity with that of S . cerevisiae IRE1 . As presented schematically in Figure 1A , C . glabrata Ire1 consists of typical Ire1-domain structures [16] , [26] , including an N-terminal hydrophobic signal sequence , an ER luminal domain , a transmembrane domain , and a serine-threonine protein kinase followed by a nuclease domain . The signal sequence and the ER luminal domain are important for ER localization and unfolded protein sensing , respectively [14] , [27] , [28] , [29] . The most conserved segment is the cytoplasmic C-terminal region containing the protein kinase linked to the nuclease domain ( Figure 1B ) . The protein kinase domain is required for autophosphorylation and concomitant activation of the nuclease domain , which provides the endoribonuclease activity [30] , [31] , [32] . C . glabrata IRE1 is syntenic with S . cerevisiae IRE1 situated on chromosome VIII , but the 3′ region of C . glabrata IRE1 is orthologous to S . cerevisiae chromosome IV ( Figure S2 ) . This suggests that a genomic arrangement between S . cerevisiae chromosomes IV and VIII has occurred to evolve the IRE1-surrounding region on chromosome F in C . glabrata . A BLASTp search using the NCBI and Genolevures databases also identified a single HAC1 ortholog ( NCBI accession No . : XP_448761 , Genolevures ID: CAGL0K12540g ) in the C . glabrata genome , exhibiting 36 . 9% similarity and 24 . 2% identity with the deduced amino acid sequence of the unspliced form of S . cerevisiae HAC1 . It has been known that functional orthologs of transcription factors often display limited sequence similarity outside their DNA-binding domains [11] . Indeed , C . glabrata HAC1 is not highly conserved at the overall sequence level , but contains a bZIP domain at the N-terminal region . The putative DNA binding domain of C . glabrata Hac1 displays strong sequence similarity to those of other Hac1 homologs ( Figure 2A ) . In addition , there is a clear syntenic relationship between S . cerevisiae and C . glabrata HAC1 loci ( Figure S2 ) . Based on these sequence similarities and synteny conservation , we considered the genes CAGL0F03245g and CAGL0K12540g to be C . glabrata IRE1 and HAC1 , respectively . It has been known in S . cerevisiae [33] , [34] , [35] that the uninduced form of HAC1 ( HAC1u ) contains the 252 nucleotides intron , which is excised by Ire1 to generate the induced form of HAC1 ( HAC1i ) ( Figure 2B ) . The well-conserved Ire1 recognition motifs , CNG′CNGN and CNG′AAGC at the 5′ and 3′ boundaries of the intron , respectively [36] , locate in the loop regions of the HAC1 mRNA stem-loop structures [34] , [37] . Based on the conservation of common features around the predicted intron ( Figure 2C ) , C . glabrata HAC1 mRNA is also likely to form stem-loop structures [36] . If excision of the predicted intron in C . glabrata HAC1 mRNA occurs , it changes the last 15 amino acids of the putative ORF and extends it by only 9 amino acids ( Figures 2B and 2C ) . However , C . glabrata HAC1 has mutations in the consensus sequences of the Ire1 recognition motifs at both splice sites: a C to A mutation at the 4th position of the 5′ splice site and an A to U mutation at the 5th position of the 3′ splice site ( Figure 2C ) . To examine involvement of Ire1 and Hac1 in the ER stress response in C . glabrata , we constructed a mutant of each in which the entire open reading frame ( ORF ) of IRE1 or HAC1 was deleted , and examined growth on plates containing TM and DTT ( Figure 3 ) . Both S . cerevisiae Δire1 and Δhac1 mutants were used as controls and they exhibited severe growth defects in the presence of TM or DTT . In C . glabrata , the Δire1 mutant displayed decreased tolerance to both TM and DTT compared with the wild-type strain , whereas surprisingly , the Δhac1 mutant exhibited wild-type growth in the presence of these ER stress-inducing agents ( Figure 3 ) . Southern blot analysis demonstrated that HAC1 was truly absent and there was no ectopic integration of the deletion construct in the Δhac1 mutant ( Figure S3 ) . The results indicate that Ire1 , but not Hac1 , is required for the ER stress response in C . glabrata . This is quite a contrast to S . cerevisiae , which is dependent equally on Ire1 and Hac1 to survive ER stress [38] , implying that C . glabrata may possess a unique mechanism , which is mediated by Ire1 independently of Hac1 , to cope with ER stress . Ire1-mediated splicing of HAC1/XBP1 mRNA was first uncovered in S . cerevisiae [32] , and similar mechanisms in HAC1/XBP1 homologs were later described in some fungi , including Trichoderma reesei , Aspergillus nidulans [39] , C . albicans [11] , Pichia pastoris [40] , Yarrowia lipolytica [41] , A . fumigatus [8] , [10] , and C . neoformans [7] , as well as in metazoans including Caenorhabditis elegans , mice [37] , [42] , humans [43] , and Drosophila melanogaster [44] . We examined HAC1 mRNA splicing in ER-stressed S . cerevisiae and C . glabrata cells by RT-PCR using the HAC1 specific primer pairs denoted in Figure 2B . As expected , S . cerevisiae HAC1 mRNA was spliced in response to treatment with 1 . 5 µg/ml TM in wild-type control but not in the Δire1 strain ( Figure 4A , left panel ) . As C . glabrata wild-type cells were relatively tolerant to both TM and DTT ( Figure S1 ) , they were treated with higher concentrations of TM and DTT for 1 and 3 h . However , contrary to S . cerevisiae , splicing of HAC1 mRNA was not induced in C . glabrata under any of the conditions tested in this study ( Figure 4A , right panel ) . The absence of a HAC1 splicing event was also verified by sequencing the RT-PCR products of the entire HAC1 mRNA ( data not shown ) , suggesting that Hac1 is not a downstream target of Ire1 in C . glabrata . To further confirm this aspect , we also performed Northern blot analysis using the probes directed against the 5′ region of the HAC1 ORF in S . cerevisiae and C . glabrata . In S . cerevisiae , HAC1 mRNA was efficiently spliced upon ER stress induced by 1 . 5 µg/ml TM or 5 mM DTT ( Figure 4B , left panel ) . In addition to the unspliced and spliced HAC1 mRNAs , detection of two smaller bands ( indicated with asterisks in Figure 4B ) were consistent with previous reports [17] , [45] , [46] , although it is unclear whether these bands correspond to splicing intermediates or dead-end products [17] , [46] . The C . glabrata wild-type and Δire1 cells were incubated under the normal conditions and in the presence of 10 µg/ml TM or 10 µM DTT for 3 h . Under all conditions tested , the C . glabrata wild-type strain somehow exhibited 2 bands but these bands were also detected similarly in the Δire1 mutant ( Figure 4B , right panel ) , indicating that it was not due to Ire1-mediated splicing . These results and the phenotypic differences between the Δire1 and Δhac1 mutants strongly suggest that C . glabrata Ire1 plays a role in the ER stress response in a Hac1-independent manner . To address the possibility that a HAC1-like gene encoding a bZIP transcription factor may exist as a downstream target of Ire1 in C . glabrata , we performed a low-stringent BLASTp search ( e-value = 10−2 ) using only the bZIP domain sequences of S . cerevisiae Hac1 , C . neoformans Hxl1 , and Homo sapiens Xbp1 as described previously [7] . In addition to HAC1 , the following eight putative genes were identified in the C . glabrata genome ( Table S1 ) : CAGL0H04631g ( YAP1 ) , CAGL0J06182g ( SKO1 ) , CAGL0K02585g ( YAP3 ) , CAGL0M10087g ( YAP3 ) , CAGL0L02475g ( GCN4 ) , CAGL0F03069g ( CAD1/YAP2 ) , CAGL0F01265g ( YAP7 ) , and CAGL0M08800g ( YAP6 ) . We sequenced the RT-PCR products of these mRNAs , which were obtained from C . glabrata wild-type cells treated with TM at concentrations of 1 . 5 and 5 µg/ml for 3 h , but none of them displayed evidence of a splicing event ( data not shown ) . The results suggest that another Hac1 homolog is unlikely to exist in C . glabrata . To investigate whether C . glabrata Ire1 could complement S . cerevisiae Ire1 function , C . glabrata IRE1 was expressed under the S . cerevisiae ADH1 promoter in the S . cerevisiae Δire1 mutant . After treatment with 1 . 5 µg/ml TM for 3 h , a spliced form of S . cerevisiae HAC1 was generated in the wild-type strain , but not in the Δire1 strain regardless of the presence of C . glabrata IRE1 ( Figure 4A ) . The absence of a HAC1 splicing event in the S . cerevisiae Δire1 mutant containing C . glabrata IRE1 was also verified by sequencing analyses of the RT-PCR products ( data not shown ) . As expected , C . glabrata Ire1 did not rescue the growth of the S . cerevisiae Δire1 mutant in the presence of TM ( Figure 5A ) . These results indicate that C . glabrata Ire1 is unable to initiate the UPR due to its inability to cleave the HAC1 mRNA in S . cerevisiae , accounting for the growth defects of the S . cerevisiae Δire1 mutant containing C . glabrata IRE1 in the presence of TM . Although HAC1 was dispensable for C . glabrata cells to cope with ER stress ( Figure 3 ) , the fact that the bZIP domain is conserved in this gene prompted us to investigate whether C . glabrata Hac1 behaves as a transcription factor similarly to S . cerevisiae Hac1 . To address this question , C . glabrata HAC1 was constitutively expressed under the ADH1 promoter in the S . cerevisiae Δhac1 mutants , and the transcription of representative UPR target genes , including KAR2 ( ER-resident chaperone ) , PDI1 ( protein disulfide isomerase ) , DER1 ( ER-associated degradation ) , and FPR2 ( ER protein trafficking ) , was examined . Compared to the wild-type strain , the expression levels of these genes were decreased in the Δhac1 mutant , but reversed by the heterologous expression of C . glabrata HAC1 in the mutant ( Figure 5B ) . Although a direct interaction between C . glabrata Hac1 and UPR targets in S . cerevisiae was not examined , the overexpression of C . glabrata HAC1 conferred the increased expression levels of these genes ( e . g . 3 . 5-fold induction of KAR2 expression relative to the wild-type control ) . Since sustained activation of the UPR is toxic to cells [47] , the constitutive overexpression of C . glabrata HAC1 and the induced form of S . cerevisiae HAC1 ( ScHAC1i ) similarly impaired cell growth even under normal growth conditions ( Figures 5C and 5D ) . Furthermore , growth of the S . cerevisiae Δhac1 mutant in the presence of TM was rescued by C . glabrata Hac1 to the wild-type level ( Figure 5C ) . The sequences of C . glabrata HAC1 obtained by RT-PCR from TM-treated and -untreated cells were identical ( data not shown ) . These results indicate that C . glabrata Hac1 was matured without splicing in S . cerevisiae . In agreement with this , C . glabrata Hac1 also rescued growth of the S . cerevisiae Δire1 mutant in the presence of TM ( Figure 5C ) . These results suggest that C . glabrata Hac1 retains a function as a transcription factor to activate the UPR targets; however , unlike S . cerevisiae HAC1 , the C . glabrata HAC1 mRNA does not need to be spliced by Ire1 before translation . We also examined the effects of heterologous expression of S . cerevisiae HAC1 in C . glabrata . Since C . glabrata Ire1 was unable to splice S . cerevisiae HAC1 ( Figure 4A ) , ScHAC1i was expressed under the control of the PGK1 promoter in C . glabrata . Although C . glabrata Hac1 complemented S . cerevisiae Hac1 functions ( Figures 5B and 5C ) , ScHAC1i did not rescue growth of the C . glabrata Δire1 mutant in the presence of TM ( Figure 5E ) . ScHAC1i partially rescued growth of the S . cerevisiae Δhac1 and Δire1 mutants in the presence of TM , confirming its functionality ( Figure 5D ) . Since C . glabrata Hac1 induced transcription of the UPR targets in S . cerevisiae ( Figure 5B ) , the Hac1 orthologs in C . glabrata and S . cerevisiae may share a common binding motif in the promoter regions of the target genes . These results support the idea that the impaired ER stress response of the C . glabrata Δire1 mutant is not associated with Hac1 functions . We next investigated the possibility that the cellular response to ER stress might involve potential crosstalk between Ire1 and other signaling pathways in C . glabrata . In addition to the UPR , the serine-threonine-specific protein phosphatase calcineurin and the Slt2 mitogen-activated protein kinase ( MAPK ) pathway are also required for the ER stress response in S . cerevisiae [48] , [49] , [50] , [51] . We examined whether this is the case in C . glabrata using four C . glabrata mutant strains lacking a key component of these signaling pathways , including Ire1 , the regulatory B subunit of calcineurin Cnb1 , the calcineurin-regulated transcription factor Crz1 , and the last member of the PKC1-MAPK cascade Slt2 . While all mutants displayed wild-type growth on a drug-free control plate , the Δire1 , Δcnb1 , and Δslt2 mutants exhibited decreased tolerance to both TM and DTT ( Figure 6A ) . Growth of the Δcrz1 mutant was at nearly wild-type levels in the presence of TM , but drastically impaired on the plate containing DTT . These results were in agreement with a previous proposal that C . glabrata calcineurin is involved in various stress responses via Crz1-dependent and -independent mechanisms depending on the type of stress [52] . All mutant phenotypes were recovered to wild-type levels by reintroducing the corresponding wild-type genes into the mutants ( Figure 6A ) . Taken together , the results indicate that Ire1 , calcineurin , and Slt2 are required for the ER stress response in C . glabrata , consistent with previous findings in S . cerevisiae . Next , we deleted IRE1 in the Δcnb1 , Δcrz1 , and Δslt2 backgrounds and monitored the viabilities of the single and double deletants after exposure to 1 . 5 µg/ml TM in liquid medium for up to 5 h . The double deletants displayed more rapid and greater reduction of viability than each single deletant in the presence of TM ( Figure 6B ) . The results suggest that Ire1 , calcineurin , and Slt2 function in parallel to cope with ER stress in C . glabrata . To examine the transcriptional response to ER stress in C . glabrata , the wild-type , Δire1 , Δcnb1 , Δcrz1 , and Δslt2 mutants were exposed to 1 . 5 µg/ml TM for 0 . 5 and 3 h , and genome-wide analyses were conducted using DNA microarrays . The complete dataset can be found at the NCBI Gene Expression Omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo/ ) with accession number GSE29855 . It has been reported that at least 381 genes , corresponding to more than 5% of the ORFs in the genome , were induced in response to TM and DTT under the control of the UPR in S . cerevisiae [20] . In our assay , a total of 325 genes were differentially regulated ( greater than 2 . 0-fold change ) and 75 genes of them were upregulated in the C . glabrata wild-type strain after treatment with TM for 3 h ( Figure 7 and Table S2 ) . In contrast to previous findings in S . cerevisiae [20] , the induced dataset in C . glabrata was not enriched for genes involved in folding capacity of the ER ( Table S2 ) . Therefore , to rule out the possibility that unfolded protein stress might not be induced sufficiently by the conditions used in our study , C . glabrata wild-type cells were treated with higher concentrations of TM and DTT , and then expression levels of five C . glabrata genes ( KAR2 , PDI1 , DER1 , FPR2 and HAC1 ) , which are well-known UPR targets in S . cerevisiae [20] , [53] , were examined by qRT-PCR . The induction levels of KAR2 in the presence of high concentrations of TM and DTT were similar to those observed in the presence of 1 . 5 µg/ml TM ( Figure S4A ) . In addition , expression levels of the other 4 genes were not increased even in cells treated with high concentrations of TM and DTT ( Figure S4B ) . Collectively , these results suggest that ER quality may be controlled differently in C . glabrata than S . cerevisiae , which relies primarily on transcriptional induction of genes that increase the protein folding capacity of the ER . Furthermore , in contrast to S . cerevisiae , the vast majority of genes induced in the C . glabrata wild-type strain were also similarly upregulated in the Δire1 mutant , but at less than two-fold change , or were downregulated in the Δcnb1 mutant ( Figure 7 and Table S2 ) . The loss of Crz1 and Slt2 had partial effects on the upregulation of those genes . Many of the genes induced by TM overlapped with calcineurin-regulated genes reported in a recent study of calcineurin signaling in C . glabrata [54] , indicating the activation of calcineurin signaling in response to TM . A subset of genes that displayed calcineurin-dependent upregulation in the presence of 1 . 5 µg/ml TM in our microarray experiments were further evaluated by qRT-PCR after treatment with a higher concentration of TM ( 10 µg/ml ) for 3 h ( Figure 8 ) . All the genes examined in this assay were upregulated upon TM treatment in a calcineurin- and Crz1-dependent manner but independently of Ire1 and Hac1 . In addition to calcineurin , Slt2 was also required for the induction of CAGL0I07249g , consistent with the microarray data . Although these assays cannot differentiate a direct from an indirect effect , the results suggest that calcineurin , but not Ire1 , plays important roles in transcriptional response to ER stress in C . glabrata . Transcriptional induction of the ER-resident chaperone KAR2 is a well-known marker for UPR activation in yeast [55] , [56] . In response to TM treatment , expression levels of C . glabrata KAR2 were increased in the wild-type , Δslt2 , and Δire1 strains , but not in the Δcnb1 and Δcrz1 mutants ( Figure 8 ) . These results are in contrast to previous observations in S . cerevisiae , where Ire1-Hac1 signaling is required for upregulation of KAR2 in response to ER stress [13] , [17] . A Hac1-binding site , termed the unfolded protein response element ( UPRE ) , was originally defined as a 22-bp sequence element of the S . cerevisiae KAR2 promoter and subsequently refined to a seven-nucleotide consensus , 5′-CAGNGTG-3′ [57] , [58] . Mutations in any of the six conserved nucleotides , or deletion of the central nucleotide , cause it to lose its function as an autonomous upstream activating sequence [57] . Consistent with the KAR2 expression data , the consensus UPRE sequence was absent in the 1-kb upstream region of C . glabrata KAR2 . On the other hand , it is known in S . cerevisiae and C . glabrata that the promoters of most calcineurin-dependent genes contain one to six copies of the Crz1-binding sequence , 5′-GNGGC ( G/T ) -3′ [59] , [60] . Five copies of the Crz1-binding sequence , including one copy of the full consensus sequence , 5′-GNGGCTCA-3′ [60] , were found within the 1-kb upstream region of C . glabrata KAR2 . These results support our KAR2 expression data , collectively suggesting that transcriptional induction of KAR2 in response to ER stress is mediated by the calcineurin-Crz1 pathway , but not by Ire1 signaling , in C . glabrata . In our microarray analyses , there was a subset of ∼33 genes whose mRNA abundance was decreased in the wild-type strain , but not in the Δire1 mutant at a relatively early phase ( 0 . 5 h ) of ER stress ( Figures S5 and Table S4 ) . This cluster predominantly contained genes encoding membrane proteins involved in transferase activity ( Table S5 ) . Most of these gene products are known to be ER-resident or pass through the ER before translocating to their final destinations . Further analyses were made to interpret this phenomenon , which will be discussed below . To further investigate how Ire1 is involved in the ER stress response in C . glabrata , we examined the contribution of the protein kinase and nuclease functions of C . glabrata Ire1 to cell growth in the presence of TM and DTT . In S . cerevisiae , mutations of two catalytic residues ( D797 and K799 ) in the nucleotide-binding pocket of Ire1 kinase to asparagines abolish phosphorylation , but preserve RNase activity [61] . Therefore , these mutations ( D797N , K799N ) allow uncoupling of kinase and nuclease activities of Ire1 . Since these two residues are highly conserved in the Ire1 orthologs of fungi , including C . glabrata ( Figure 1B , asterisks ) , we created C . glabrata kinase-dead IRE1 ( IRE1-KD ) harboring the corresponding mutations ( D723N , K725N ) in the Ire1 kinase domain . The growth defects of the Δire1 null mutant in the presence of TM and DTT were complemented by wild-type IRE1 , but not by IRE1-KD ( Figure 9 ) , indicating that the protein kinase function of Ire1 is required for the ER stress response in C . glabrata . Unlike in other eukaryotes , C . glabrata Ire1 did not cleave HAC1 mRNA in response to ER stress; we therefore hypothesized that the nuclease function of Ire1 would be dispensable for the ER stress response in C . glabrata . To test this hypothesis , we engineered C . glabrata nuclease-dead IRE1 ( IRE1-ND ) containing a 10 residue ( D973-Y982 ) internal deletion within the nuclease domain ( Figure 1B ) . This deleted region contains three highly conserved residues ( indicated by arrowheads in Figure 1B ) that are the nuclease active sites of S . cerevisiae Ire1 [16] . A recent study has shown that deletion of the corresponding region ( D1076-R1085 ) in A . fumigatus IreA abrogates its nuclease activity without an apparent effect on any nuclease-independent functions [8] . Contrary to our expectations , the Δire1 mutant carrying IRE1-ND displayed growth defects in the presence of TM and DTT , similar to the Δire1 null mutant in C . glabrata ( Figure 9 ) . These results suggest that both protein kinase and nuclease functions of Ire1 are required for the ER stress response in C . glabrata . It has been found in higher eukaryotes that Ire1 induces degradation of mRNAs encoding proteins that are translocated into the ER lumen [62] , [63] , [64] . This cellular response is called regulated Ire1-dependent decay ( RIDD ) and represents an Xbp1-independent posttranscriptional mechanism to selectively relieve the burden of incoming proteins on the ER [63] . In our microarray analysis , a subset of genes displayed Ire1-dependent “downregulation” in response to TM ( Figure S5 and Table S4 ) . There was a strong enrichment for genes encoding GPI-anchored cell wall and membrane proteins in this cluster ( Table S5 ) . Thus , we further analyzed the expression levels of 3 representative genes , GAS2 encoding a GPI-anchored cell wall protein that functions as β-1 , 3-glucanosyltransferase , GAS4 encoding a member of the GAS family similar to GAS2 , and ECM33 encoding a GPI-anchored membrane protein involved in apical bud growth , by qRT-PCR using C . glabrata cells treated with DTT and TM . In response to the treatment with 10 mM DTT for 2 h , RNA abundance of these genes was significantly decreased in the wild-type strain compared to the results with the Δire1 mutant ( Figure 10A ) . Similar results were obtained for cells treated with 1 . 5 µg/ml TM in the microarray analysis ( Table S4 ) and by a confirmatory qRT-PCR assay ( data not shown ) . This Δire1 phenotype was reversed by intact IRE1 , but not by IRE1-ND ( Figure 10A ) , indicating that the degradation of these mRNAs was dependent on the nuclease activity of Ire1 . C . glabrata IRE1-KD harboring the simultaneous mutations D723N and K725N had moderate effects on the mRNAs decay , implying that Ire1's nuclease activity may be partially impaired by these mutations in C . glabrata . Blocking transcription with 1 , 10-phenanthroline ( PHEN ) inhibited the transcriptional upregulation of CAGL0K11946g and CAGL0L06776g by DTT but had no or little effect on the mRNA dacay of GAS2 , GAS4 and ECM33 ( Figure 10A ) , indicating that the decrease in the mRNA abundance was not due to transcriptional repression . This was further supported by the observation that β-galactosidase activities of the C . glabrata GAS2 promoter-lacZ fusion gene were increased , not decreased , in both the wild-type and Δire1 strains in response to DTT exposure ( Figure 10B ) . Although it is unclear why the activity of the GAS2 promoter is increased in response to DTT , this paradoxical behavior suggest that transcriptional regulation and Ire1-dependent decay may not coordinately function in ER-stressed C . glabrata cells . To further investigate the roles of Ire1 in stress response in C . glabrata , we examined the growth of the Δire1 mutants under various stress conditions . Pathogenic fungi commonly encounter a variety of adverse environmental conditions , such as low oxygen availability and nutrient depletion , in the infected host . It has been reported in A . fumigatus that IreA is required for growth in hypoxia and under iron-depleted conditions , which is induced by adding the Fe2+ chelator bathophenantroline disulphonate ( BPS ) to the culture medium [8] . In C . glabrata , the loss of Ire1 had no apparent effect on cell growth under low oxygen tension ( Figure S6A ) and on plates containing BPS or the bacterial siderophore desferrioxamine ( DFO ) ( Figure S6B ) . C . glabrata cells are unable to utilize DFO , which therefore induces iron-depletion for this fungus [65] . Growth of both C . glabrata wild-type and Δire1 strains in the presence of BPS or DFO was rescued by supplementation of the media with 1 mM FeCl3 . It is also reported in several pathogenic fungi , including C . albicans , C . neoformans , and A . fumigatus , that mutant strains lacking either Ire1 or Hac1 display growth defects in the presence of cell wall-damaging agents such as caspofungin , Congo red , and calcofluor white [6] , [7] , [8] , [10] , [11] , [66] . However , in contrast to those fungi , C . glabrata strains lacking IRE1 , HAC1 , or both did not exhibit decreased tolerance to these agents at various concentrations tested ( data not shown ) , suggesting that neither Ire1 nor Hac1 is primarily involved in cell wall stress response in C . glabrata . Importantly , disruption of Ire1/IreA confers a drastic increase in azole susceptibility in C . neoformans and A . fumigatus [7] , [8] . In contrast to these findings , deletion of IRE1 alone did not affect azole susceptibility in C . glabrata ( Figure 11A ) . However , we found that the Δcnb1 Δire1 double mutant , but not the Δcrz1 Δire1 double mutant ( data not shown ) , was more susceptible to azole antifungals than either single mutant ( Figure 11A ) . The results are in agreement with our previous report that calcineurin is required for azole tolerance through a Crz1-independent mechanism in C . glabrata [52] . Furthermore , deletion of either CNB1 or IRE1 alone did not affect cell growth in the presence of caffeine and NaCl , whereas the simultaneous loss of these genes resulted in severe growth defects ( Figure 11B ) . None of these effects was observed when IRE1 was deleted in the Δslt2 background ( data not shown ) . These results suggest that calcineurin and Ire1 serve redundant roles in cell growth under certain stress conditions in C . glabrata . Collectively , these phenotypic analyses revealed that loss of Ire1 alone does not induce diverse phenotypes in C . glabrata unlike in other fungi . We have previously demonstrated in C . glabrata that calcineurin and Slt2 are required for virulence in murine models of disseminated candidiasis [52] , [67] , but the involvement of Ire1 in C . glabrata virulence has not been examined . The C . neoformans Δire1 and A . fumigatus ΔireA mutants are avirulent in murine models of systemic cryptococcosis and invasive aspergillosis , respectively , which can be explained primarily by their growth defects at 37°C [7] , [8] . In C . glabrata , the loss of Ire1 did not affect cell growth at 37°C ( generation time of the wild-type and Δire1 strains in Yeast-peptone-dextrose ( YPD ) broth was 75 and 78 min , respectively ) . Therefore , we compared the virulence of the C . glabrata wild-type , Δire1 , and IRE1-complemented strains in a mouse model of disseminated candidiasis . Immunocompetent mice infected with the Δire1 mutant displayed significantly reduced fungal burden in both kidney and spleen relative to those infected with the wild-type and Ire1-complemented strains ( Figure 12A ) . To evaluate mortality of mice with disseminated candidiasis , mice were immunosuppressed by cyclophosphamide prior to C . glabrata infection . Consistent with the results of organ fungal burden , mice infected with the wild-type and IRE1-complemented strains exhibited higher mortality rates than mice infected with the Δire1 mutant ( Figure 12B ) . These results suggest that Ire1 is important for virulence in C . glabrata .
According to the current evidence , the UPR regulated by Ire1-Hac1 signaling is highly conserved and is a key pathway to cope with ER stress in eukaryotes [2] , [12] . However , the present study demonstrates the lack of this pathway and the development of alternative mechanisms for the ER stress response in C . glabrata . The gene CAGL0K12540g , named C . glabrata HAC1 , seems to be the sole HAC1 ortholog in C . glabrata , since it is the only gene showing sequence similarity and conserved synteny to S . cerevisiae HAC1 . In addition , C . glabrata Hac1 was able to complement S . cerevisiae Hac1 functions . However , surprisingly , C . glabrata Hac1 did not necessitate Ire1-mediated splicing of mRNA before translation , and thus could recover the growth of the S . cerevisiae Δire1 mutant in the presence of ER stress . A recent study has comprehensively characterized the RNA structures of the Hac1/Xbp1 orthologs in various eukaryotic species [36] . In C . glabrata HAC1 , although the overall RNA structure is maintained , the predicted intron ( 379 nucleotides ) is much longer than those in other species and has mutations in the Ire1 recognition motifs at both splice sites , suggesting that the Ire1-dependent unconventional splicing mechanism may not be present in C . glabrata [36] . This has been experimentally confirmed in our studies: the heterologous expression of C . glabrata HAC1 complemented S . cerevisiae Hac1 functions without splicing , and C . glabrata HAC1 mRNA was not spliced by Ire1 even in ER-stressed C . glabrata cells . Furthermore , Hooks and Griffiths-Jones [36] revealed that the non-canonical Hac1/Xbp1 intron structure is not conserved in 28 out of 156 eukaryotic species searched , including several Candida species such as Candida parapsilosis , Candida lusitaniae , and Candida guilliermondii , despite the fact that the HAC1 ORFs are intact in these Candida species . These findings suggest that these species may have lost the unconventional splicing mechanism , but instead acquired an alternative mechanism to regulate the UPR . Future studies are warranted to determine whether Hac1 is involved in the ER stress response in these species . Although some Hac1-independent functions of Ire1 have been reported in some eukaryotic species [2] , [7] , [8] , [63] , mutants lacking Hac1 or its homolog always phenocopy Δire1 strains with respect to growth defects in the presence of ER stress-inducing agents ( e . g . TM and DTT ) in all eukaryotic species tested so far , and the UPR mediated by Ire1-Hac1 signaling has been believed to be a phylogenetically conserved pathway in eukaryotes [2] , [68] , [69] . However , our study demonstrated that C . glabrata Ire1 plays a role in the ER stress response in a Hac1-independent manner . Exploration of potential Ire1 targets in this fungus by a genome-wide analysis will be of interest in future investigations ( e . g . RNA-seq analysis of ER-stressed and unstressed cells of wild-type and Δire1 strains to detect Ire1-dependent spliced RNAs ) . The results of our gene expression assays suggest that Ire1 does not mediate transcriptional response to ER stress in C . glabrata , raising the question of how Ire1 is involved in the ER stress response in this fungus . In metazoans , ER stress triggers two distinct outputs of Ire1's nuclease activity , XBP1 splicing and RIDD [69] , [70] . The latter is an Xbp1-independent pathway that selectively degrades a small subset of ER-associated mRNAs and remodels the repertoire of proteins translated in ER-stressed cells [63] , [64] . This cellular response is predicted to reduce the ER load by limiting protein influx and unfolded protein load into the ER lumen . The RIDD pathway was first uncovered in D . melanogaster [64] , and later , its conservation was confirmed in mammalian cells [62] , [63] , but has yet to be fully determined in fungi . Interestingly , in S . cerevisiae , RIDD does not occur [63] , [70] , but the classic Ire1-Hac1 UPR mediates downregulation of some mRNAs encoding membrane proteins [64] , [71] . Here , we have demonstrated that mRNA abundance of a subset of genes encoding GPI-anchored cell wall and membrane proteins was diminished during the response to ER stress in the wild-type strain dependent on the nuclease activity of Ire1 . There was no overlap between these repressed C . glabrata genes and the previously identified S . cerevisiae gene that were transcriptionally downregulated by the UPR in response to TM [71] . It is thought that RIDD targets are nicked by the Ire1 endoribonuclease at sites that do not display an identifiable consensus sequence , in contrast to Ire1-mediated splicing of HAC1/XBP1 mRNA at the highly conserved splice junctions [69] . As excessive activation of RIDD seems to be detrimental to cell integrity [62] , reducing the load of proteins entering the ER must be balanced with the need to sustain synthesis of essential proteins . In contrast to Ire1 in metazoans , C . glabrata Ire1 may not need to switch between specific and nonspecific modes of cleavage . We are currently investigating how the RIDD activity is regulated in this fungus . Although the only known substrate of the Ire1 kinase is Ire1 itself in S . cerevisiae [30] , [31] , we also addressed the possibility that Ire1 may integrate with other signaling pathways in C . glabrata . In S . cerevisiae , calcineurin and Slt2 are also involved in the ER stress response through different mechanisms [48] , [49] , [50] , [51] . Slt2 plays a role in the ER stress surveillance ( ERSU ) pathway that ensures transmission of only functional ER to daughter cells during cell division [72] . Upon ER stress , the ERSU pathway delays ER inheritance and cytokinesis to prevent death of both mother and daughter cells . Calcineurin mediates the calcium cell survival pathway by regulating intracellular Ca2+ homeostasis . TM increases Ca2+ uptake by stimulating the Cch1-Mid1 high affinity Ca2+ channel , while calcineurin dephosphorylates the Cch1 subunit of the channel to inhibit Ca2+ influx and prevents nonapoptotic cell death in S . cerevisiae [48] , [51] . In C . glabrata , loss of either calcineurin or Slt2 resulted in decreased tolerance to ER stress , and the additional deletion of IRE1 in the Δcnb1 and the Δslt2 backgrounds had synthetic effects on viability loss in the presence of TM . The results suggest that Ire1 functions in parallel with the calcineurin and Slt2 MAPK pathways to cope with ER stress in C . glabrata , consistent with previous findings in S . cerevisiae [48] , [49] , [50] . However , the possibility that Ire1 might interact with other signaling pathways has not been completely ruled out . Whether the Ire1 kinase domain has an effector other than Ire1 itself remains one of the major unsolved questions in this field [69] . The term UPR is derived from a cellular response to chemicals that interfere with proper protein folding ( e . g . TM and DTT ) , but it is now known that the signaling pathway is involved in various stress responses from yeast to humans [73] , [74] . Indeed , downstream targets of the UPR include not only ER chaperones , but also genes involved in diverse functions , including protein trafficking and quality control , lipid and sterol metabolism , heme biosynthesis , and cell wall biogenesis in S . cerevisiae [20] . To extend our understanding of Ire1 functions in C . glabrata , phenotypes of the Δire1 mutant were investigated in various stress conditions in view of clinical significance . The UPR is critical for azole tolerance in C . neoformans and A . fumigatus , and thus loss of either Ire1/IreA or Hxl1/HacA leads to increased susceptibility to azole antifungals in these species [7] , [8] , [10] . In contrast , loss of Ire1 , Hac1 , or both did not affect azole tolerance in C . glabrata ( Figure 11A and data not shown ) , further supporting the idea that the classic UPR mechanism regulated by Ire1-Hac1 signaling is not conserved in this fungus . However , the additional deletion of IRE1 in the Δcnb1 background increased azole susceptibility in C . glabrata . One possible explanation is that azole antifungals induce severe ER stress in calcineurin-defective C . glabrata cells , which therefore require Ire1 to survive this stress condition . Previous studies in several yeasts and filamentous fungi have demonstrated that the UPR plays a role in maintaining cell wall integrity , and that mutants lacking Ire1 or Hac1 exhibit decreased tolerance to a variety of cell wall perturbing agents [6] , [7] , [8] , [10] , [11] , [66] . TM also induces cell wall stress , thereby explaining why certain genes implicated in cell wall biogenesis are upregulated during the UPR in TM-treated S . cerevisiae and C . albicans cells [11] , [20] , [75] . In contrast to other fungi investigated to date , the loss of Ire1 alone did not confer a cell wall-defective phenotype , and upregulation of some cell wall-related genes in response to TM exposure was mainly dependent on calcineurin , but not on Ire1 , in C . glabrata ( Table S2 ) . These results provide additional evidence that there are diversities in Ire1-dependent stress response mechanisms between C . glabrata and other fungal species . In addition , Δire1/ireA mutants of C . neoformans and/or A . fumigatus exhibit growth defects under various environmental conditions , such as growth temperature at 37°C , hypoxia , and iron limitation , which are commonly encountered by pathogenic fungi in the infected host [7] , [8] . However , none of these phenotypes were observed in the C . glabrata Δire1 mutant . In agreement with our proposal that the canonical Ire1-Hac1 UPR has been lost in C . glabrata , phenotypes of the C . glabrata Δire1 mutant were confined relative to those of UPR-defective mutants in other fungi where the UPR is involved in various stress responses . Diversity in the transcriptional induction system in response to ER stress has developed during evolution [68] . For instance , the Ire1-Hac1 pathway is required for upregulation of KAR2 in yeast while the pathway is dispensable for induction of the Kar2 homolog GRP78/BiP in mammalian cells [17] , [76] . In C . glabrata , the majority of genes , including KAR2 , induced in response to ER stress were dependent on the calcineurin-Crz1 pathway , but not on Ire1 signaling . In addition , several lines of evidence described above demonstrate that C . glabrata has lost the classic Ire1-Hac1 UPR , but instead possesses an alternative mechanism , RIDD , like metazoans . To our knowledge , these unusual characteristics have been demonstrated for the first time in any eukaryote , and perhaps they were developed in C . glabrata after its divergence from S . cerevisiae due to its ecological niche . Our results provide novel insights into ER stress response mechanisms as well as fundamental information for evolutionary biology to further understand how eukaryotic cells have developed ER quality control systems .
All animal experiments were performed in full compliance with the Guide for the Care and Use of Laboratory Animals ( National Research Council , National Academy Press , Washington DC , 2011 ) and all of the institutional regulations and guidelines for animal experimentation after pertinent review and approval by the Institutional Animal Care and Use Committee of Nagasaki University under protocol number 0904130747 . The C . glabrata and S . cerevisiae strains used in this study are listed in Table S6 . Cells were routinely propagated at 30°C in YPD , synthetic complete ( SC ) medium lacking appropriate amino acids , or synthetic defined ( SD ) medium [77] , unless otherwise indicated . The nitrogen starvation medium , SD-N , consists of 0 . 17% yeast nitrogen base without amino acids and ammonium sulfate ( BD Biosciences , Franklin Lakes , NJ ) plus 2% glucose as described previously [78] . The Anaeropack system ( Mitsubishi Gas Chemical Company Inc , Tokyo , Japan ) was used for cultures under conditions of low oxygen tension ( hypoxic ) . To induce iron-depleted conditions , the Fe2+ chelator BPS ( MP Biomedicals , Solon , OH ) or the bacterial siderophore DFO ( EMD Chemicals , San Diego , CA ) were added to SC media at final concentrations of 20 and 50 µM , respectively . The primers and plasmids used in this study are listed in Tables S7 and S8 , respectively . Transformation of C . glabrata and S . cerevisiae was performed using a lithium acetate protocol as described previously [79] . C . glabrata deletion mutants were constructed using a one-step PCR-based technique as described previously [67] . Briefly , a deletion construct was amplified from pBSK-TRP or pBSK-HIS using primers tagged with the 100 bp sequences homologous to the flanking regions of the target ORF . C . glabrata parent strains were transformed with the deletion construct , and the resulting transformants were selected by tryptophan or histidine prototrophy . PCR and Southern blotting were performed to verify that the desired homologous recombination occurred at the target locus . To generate complementation plasmids , C . glabrata and S . cerevisiae genes were amplified from the genomic DNA of CBS138 [80] and BY4742 [81] , respectively . Procedures for each plasmid construction are summarized in Table S8 . The spliced form of S . cerevisiae HAC1 mRNA was obtained from BY4742 cells treated with 1 . 5 µg/ml TM for 3 h . The extracted RNA was then reverse-transcribed using a QuantiTect Reverse Transcription kit ( Qiagen , Valencia , CA ) , and the resulting cDNA was used as a template to amplify the mature S . cerevisiae HAC1 , ScHAC1i ( “i” for induced ) . The plasmid pCgACT-PIRE was used as the template to generate pCgACT-PIRE-KD and pCgACT-PIRE-ND . pCgACT-PIRE-KD was generated by mutating two residues , D723 and K725 , in the kinase domain of C . glabrata Ire1 to asparagines using the KOD-Plus-Mutagenesis Kit ( Toyobo , Osaka , Japan ) , and mutagenic primers CgIRE1-mut-F2171 and CgIRE1-mut-R2170 . Similarly , pCgACT-PIRE-ND was created by introducing a 10 residue ( D973-Y982 ) internal deletion within the nuclease domain of C . glabrata Ire1 using mutagenic primers , CgIRE1-F2947 and CgIRE1-R2916 . All of the plasmids constructed using PCR products were verified by sequencing before use . Spot dilution tests and time-kill assays were performed as described previously [52] . Voriconazole was kindly provided by Pfizer ( New York , NY ) . Other drugs were purchased from Sigma ( St Louis , MO ) . TM , voriconazole , and caffeine were dissolved in dimethyl sulfoxide ( DMSO ) , and others were dissolved in distilled water . DMSO alone did not interfere with cell growth at the final concentrations used in this study . MICs of azole antifungals were determined by a broth microdilution test using a commercially prepared colorimetric microdilution panel ( ASTY; Kyokuto Pharmaceutical Industrial Co . , Ltd . ) [82] . All sensitivity tests were performed on at least two separate occasions to ensure reproducibility . HAC1 mRNA splicing was analyzed using a modification of a reported protocol [66] . Logarithmic-phase S . cerevisiae cells grown in SC medium lacking leucine were treated with 1 . 5 µg/ml TM or 5 mM DTT for 3 h . Logarithmic-phase C . glabrata cells grown in SC medium were treated with either TM ( 5 and 10 µg/ml ) or DTT ( 5 and 10 mM ) for 1 and 3 h . These cells were harvested , and total RNA was extracted using a FastRNA Red Kit ( Qbiogene , Carlsbad , CA ) according to the manufacturer's instructions . cDNA was synthesized from 1 µg of total RNA using a QuantiTect Reverse Transcription kit ( Qiagen ) in a final volume of 20 µl , and 5 µl of resulting cDNA were then used as the template for individual PCR . The amplified PCR products were analyzed by both electrophoresis on a 1% agarose gel and DNA sequencing . For Northern blot analysis , twenty micrograms total RNA was separated on a 2% agarose gel and transferred to a Hybond-N+ membrane ( GE Healthcare , Buckinghamshire , UK ) , which was incubated in PerfectHyb Hybridization Solution ( Toyobo ) with probes directed against the 5′ region of the HAC1 ORF . All probes were generated by PCR with primers listed in Table S7 and were labeled with [α-32P]dCTP using a Random Primer DNA Labeling Kit Ver . 2 . 0 ( Takara Bio Inc . , Shiga , Japan ) . Images were obtained using Image Reader FLA-5000 ( FUJIFILM Corporation , Tokyo , Japan ) . Total RNA extraction and cDNA synthesis were performed as described above , and 3 µl of resulting cDNA were used as the template for individual PCR with a QuantiTect SYBR Green PCR kit ( Qiagen ) . qRT-PCR was carried out in triplicate in a 96-well plate format , using a 7500 Real-Time PCR System ( Applied Biosystems , Foster City , CA ) . The mRNA abundance of the target genes was normalized to 18S rRNA ( CAGL0L13398r ) . The qRT-PCR assays were repeated at least twice on independent occasions . C . glabrata cells were grown in SC medium to exponential phase ( OD600 = 0 . 8 ) and exposed to 1 . 5 µg/ml TM at 30°C . Total RNAs were extracted using the FastRNA Red Kit ( Qbiogene ) . The quality of RNA was checked with a RNA 6000 Nano Kit and Agilent 2100 Bioanalyzer . Double-stranded cDNA was synthesized using the Invitrogen SuperScript Double-Stranded cDNA Synthesis Kit and oligo ( dT ) primers . The resulting cDNA samples were labeled with Cy3 using the NimbleGen One-Color DNA Labeling Kit and subsequently hybridized to a custom-made 4×72 K C . glabrata array ( Roche NimbleGen , Tokyo , Japan ) wherein each chip measures the expression levels of 5 , 217 genes from C . glabrata CBS138 with six 60-mer-probe pairs per gene , with two-fold technical redundancy . The arrays were washed using the NimbleGen Wash Buffer Kit and scanned with a NimbleGen MS 200 Microarray Scanner . Data were quantified using the NimbleScan v2 . 6 software and normalized as described previously [83] , [84] . Hierarchical clustering was performed using Gene Cluster 3 . 0 and visualized using Java TreeView ver . 1 . 1 . 5r2 . Gene Ontology ( GO ) enrichment was searched using the Candida Genome Database GO term finder ( http://www . candidagenome . org/cgi-bin/GO/goTermFinder ) with default parameters [85] . A 699-bp DNA fragment containing the 5′ untranslated region ( UTR ) and the first 17 codons of the C . glabrata GAS2 gene was amplified from the genomic DNA of CBS138 and fused to the lacZ reporter in pEM14 [86] to construct pEM14-GAS2 as described in Table S8 . Logarithmic-phase cells of the C . glabrata wild-type and Δire1 strains containing pEM14-GAS2 were grown in SC-ura broth , adjusted to 1×107 cells/ml , and then incubated in the presence and absence of 3 mM DTT for 2 h . β-galactosidase assay was performed as described previously [59] . The cell cultures were harvested and washed twice with ice-cold phosphate-buffered saline . Cells ( 100 µl ) were re-suspended in 300 µl Reporter Lysis Buffer ( Promega , Madison , WI ) containing 5 µl Protease Inhibitor Cocktail ( Sigma ) . Cell extracts were prepared using acid-washed glass beads ( Sigma ) and cleared by centrifugation at 14 , 000× g for 30 min at 4°C . Protein concentrations were determined by the Bio-Rad Protein Assay ( Bio-Rad , Richmond , CA ) using bovine serum albumin as a standard . β-galactosidase activities were measured using the β-Galactosidase Enzyme Assay System ( Promega ) according to the manufacturer's instructions and calculated in Miller units ( nmoles/min/mg of protein ) at 37°C [87] . All assays were performed in triplicate on separate days . Animal experiments were performed as described previously [52] , [67] . Briefly , to prepare cells for injection , logarithmic-phase C . glabrata cells were harvested , washed , resuspended in sterile saline , and adjusted to 4×108 cells/ml after counting the number of cells using a hemocytometer . The actual CFU in the inocula were confirmed by plating serial dilutions of cell suspension on YPD plates . Groups of 8 female , 8-week-old , BALB/c mice ( Charles River Laboratories Japan Inc . , Japan ) were injected with 0 . 2 ml of the C . glabrata cell suspension via the lateral tail vein . The mice were euthanized 7 days after injection and the spleen and bilateral kidneys were then excised . No mice died before euthanasia in this experiment . Appropriate dilutions of organ homogenates were plated on YPD plates . Colonies were counted after 2 days of incubation at 30°C and the CFUs per organ were calculated . Statistical analyses were performed using the Kruskal-Wallis test with Dunn's multiple comparison post-test ( GraphPad Prism 5 , La Jolla , CA ) . To examine mortality of mice with disseminated candidiasis due to C . glabrata , groups of 7 female BALB/c mice ( 20–23 g body weight , mean 20 . 6–20 . 9 g for each group ) were immunosuppressed by intraperitoneal administration of cyclophosphamide at a concentration of 200 mg/kg/day on days −3 , −2 , and −1 of infection , and housed under sterile conditions . The mice were inoculated intravenously with 0 . 2 ml of C . glabrata cell suspensions ( 1×108 and 1×107 cells/ml ) on day 0 . Checks were made for dead or moribund mice in a blinded fashion twice daily over a period of 16 days . A group of 4 immunosuppressed mice was injected with sterile saline instead of C . glabrata cell suspension , and no mice died due to immunosuppression only . Survival was plotted on a Kaplan-Meier curve for each C . glabrata strain , and the log rank ( Mantel-Cox ) test was used for pairwise comparison of percent survival ( GraphPad Prism 5 ) . A P value of <0 . 05 was considered statistically significant . These animal experiments were conducted on two separate occasions to ensure reproducibility . C . glabrata: 18S rRNA ( 9488051 ) ; ACT1 ( 2890423 ) ; CAD1/YAP2 ( 2887750 ) ; CAGL0A04081g ( 2886450 ) ; CAGL0F04829g ( 2887774 ) ; CAGL0I07249g ( 2889193 ) ; CAGL0I08591g ( 2888926 ) ; CAGL0K11946g ( 2889962 ) ; CAGL0L06776g ( 2890816 ) ; CAGL0M04191g ( 2891538 ) ; CAGL0M12320g ( 2891428 ) ; CNB1 ( 2890566 ) ; CRZ1 ( 2891693 ) ; DER1 ( 2891627 ) ; ECM33 ( 2891194 ) ; FPR2 ( 2888506 ) ; GAS2 ( 2891237 ) ; GAS4 ( 2887679 ) ; GCN4 ( 2890908 ) ; HAC1 ( 2890416 ) ; HIS3 ( 2890838 ) ; IRE1 ( 2887705 ) ; KAR2 ( 2887030 ) ; PDI1 ( 2886594 ) ; SKO1 ( 2889579 ) ; SLT2 ( 2889880 ) ; TRP1 ( 2886909 ) ; YAP1 ( 2888604 ) ; YAP3/CAGL0K02585g ( 2890264 ) ; YAP3/CAGL0M10087g ( 2891349 ) ; YAP6 ( 2891289 ) ; YAP7 ( 2887954 ) . S . cerevisiae: ACT1 ( 850504 ) ; ADH1 ( 854068 ) ; CCH1 ( 853131 ) ; DER1 ( 852500 ) ; FPR2 ( 852131 ) ; HAC1 ( 850513 ) ; IRE1 ( 856478 ) ; KAR2 ( 853418 ) ; MID1 ( 855425 ) ; PDI1 ( 850314 ) ; PGK1 ( 850370 ) ; SLT2 ( 856425 ) . Others: A . fumigatus HacA/Hac1 ( 3506096 ) ; A . fumigatus IreA/Ire1 ( AEQ59230 ) ; A . nidurans HacA ( CBF87535 ) ; C . albicans Hac1 ( 3639758 ) ; C . albicans Ire1 ( 3640823 ) ; C . elegans Xbp1 ( 175541 ) ; C . neoformans Hxl1 ( 3255200 ) ; C . neoformans Ire1 ( 3255994 ) ; D . melanogaster Xbp1 ( 44226 ) ; H . sapiens Xbp1 ( 7494 ) ; P . pastoris HAC1 ( 8199414 ) ; T . reesei HAC1 ( AJ413272 ) ; Y . lipolytica HAC1 ( 2906724 ) . | The majority of secretory and transmembrane proteins are structurally matured in the endoplasmic reticulum ( ER ) . The accumulation of misfolded proteins in the ER ( ER stress ) activates the ER-resident stress transducer Ire1 , which has two distinct outputs: the unfolded protein response ( UPR ) and regulated Ire1-dependent decay ( RIDD ) . The UPR is a transcriptional response to increase the protein folding capacity of the ER . RIDD induces degradation of ER-localized mRNAs to reduce the ER load . To date , the UPR has been believed to be an evolutionarily conserved pathway in almost all eukaryotic species , while RIDD has been found only in metazoans . Recent studies in several pathogenic fungi revealed that the UPR is implicated in antifungal resistance and virulence , and thus it has attracted attention as a therapeutic target . Here , we demonstrate that the important fungal pathogen Candida glabrata has lost the canonical UPR , but instead possesses the RIDD pathway and is relatively tolerant to ER stress . The transcriptional response to ER stress was dependent mainly on calcium signaling mediated by the protein phosphatase calcineurin in C . glabrata . Our results provide novel insights into ER quality control mechanisms and are useful for understanding evolutionary biology and the development of antifungal agents targeting the UPR . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"mycology",
"cellular",
"stress",
"responses",
"model",
"organisms",
"molecular",
"cell",
"biology",
"cell",
"biology",
"yeast",
"and",
"fungal",
"models",
"biology",
"microbiology",
"candida",
"albicans"
] | 2013 | Dissection of Ire1 Functions Reveals Stress Response Mechanisms Uniquely Evolved in Candida glabrata |
Cyclic GMP-AMP synthase ( cGAS ) senses viral DNA in the cytosol and then catalyzes synthesis of the second messenger cGAMP , which activates the ER-localized adaptor protein Mediator of IRF3 Activator ( MITA ) to initiate innate antiviral response . Human cytomegalovirus ( HCMV ) proteins can antagonize host immune responses to promote latent infection . Here , we identified HCMV UL42 as a negative regulator of cGAS/MITA-dependent antiviral response . UL42-deficiency enhances HCMV-induced production of type I interferons ( IFNs ) and downstream antiviral genes . Consistently , wild-type HCMV replicates more efficiently than UL42-deficient HCMV . UL42 interacts with both cGAS and MITA . UL42 inhibits DNA binding , oligomerization and enzymatic activity of cGAS . UL42 also impairs translocation of MITA from the ER to perinuclear punctate structures , which is required for MITA activation , by facilitating p62/LC3B-mediated degradation of translocon-associated protein β ( TRAPβ ) . These results suggest that UL42 can antagonize innate immune response to HCMV by targeting the core components of viral DNA-triggered signaling pathways .
The innate immune system is the first line of host defense against microbial infection . Upon microbial infection , cellular pattern recognition receptors ( PRRs ) recognize structurally conserved microbial components called pathogen-associated molecular patterns ( PAMPs ) , which triggers a series of signaling events that lead to the induction of type I interferons ( IFNs ) , pro-inflammatory cytokines and other downstream effectors . These effectors mediate the inhibition of microbial replication , clearance of infected cells and facilitation of adaptive immune response to eliminate infected pathogens [1–4] . Among the PRRs , cyclic guanosine monophosphate-adenosine monophosphate ( cGAMP ) synthase ( cGAS ) has been demonstrated as a general cytosolic DNA sensor in response to DNA virus infection in various cell lines and in mice [5 , 6] . cGAS recognizes double-stranded DNA ( dsDNA ) and utilizes ATP and GTP to synthesize the second messenger cGAMP [7] . cGAMP binds to the ER-localized adaptor protein MITA [8] , which is also designated as STING , ERIS , and MPYS [9–12] . The cGAMP-bound MITA traffics from the ER to the Golgi apparatus via the inactive rhomboid protein 2 ( iRhom2 ) and TRAPβ containing translocon complex [13–16] , and then further to the Sec5-containing perinuclear punctate structures [10] . During the trafficking processes , MITA recruits TANK-binding kinase 1 ( TBK1 ) and interferon regulatory factor 3 ( IRF3 ) , leading to induction of type I interferons and other antiviral effectors [17] . In addition to trafficking , the functions of MITA are also regulated by several post-translational mechanisms . The E3 ubiquitin ligases TRIM32 and TRIM56 can catalyze K63-linked polyubiquitination of MITA and promote the recruitment of TBK1 to MITA , thereby positively regulating innate immune responses [18 , 19] . In addition , the ER-associated E3 ligase AMFR mediates K27-linked polyubiquitination of MITA , providing a scaffold to recruit TBK1 and IRF3 [20 , 21] . The human cytomegalovirus ( HCMV ) , a member of the beta herpesvirus family , is a typical dsDNA virus that encodes over 200 proteins [22] . HCMV causes global epidemics and complications in AIDS patients and organ transplant recipients and is a major cause of birth defects [23] . HCMV infection is also associated with inflammatory and proliferative diseases such as certain cardiovascular diseases and cancers [24] . However , there is no vaccine to prevent HCMV infection , and the drugs currently approved for the treatment of HCMV infectious diseases suffer from low bioavailability , toxicity , and the generation of resistant viruses [25] . HCMV proteins could suppress cellular and organismal defenses , which are pivotal for establishing immune evasion and latent infection [26] . Therefore , HCMV has become an ideal model for the study of viral immune evasion due to its multiple strategies to modulate host innate and adaptive responses [27] Similar to many other DNA viruses , the cGAS-MITA axis also plays a crucial role in HCMV-induced host antiviral defense [28 , 29] . Meanwhile , HCMV have evolved various mechanisms to antagonize this signaling pathway for efficient infection and replication [30 , 31] . For example , it has been demonstrated that HCMV tegument protein UL82 contributes to HCMV immune evasion by inhibiting the cellular trafficking and activation of MITA to evade antiviral immunity [15] . UL31 inhibits DNA sensing of cGAS to mediate immune evasion [32] . UL83 inhibits gamma-interferon-inducible protein 16 ( IFI16 ) - and cGAS-mediated DNA sensing for immune evasion [33 , 34] . Whether other HCMV proteins are involved in antagonization of innate antiviral response are unclear . In this study , we identified HCMV UL42 as an inhibitor of innate antiviral response . UL42 is classified as a CMV-specific but function-unknown gene , which consists of 124 amino acids and partially localized at the trans-Golgi network and cytoplasmic vesicles [35 , 36] . In addition , UL42 has a C-terminal hydrophobic domain predicted to be transmembrane domain and two PPXY motifs in its N terminus . Our results suggest that UL42 inhibits cGAS activation and impairs the trafficking of MITA , thereby contributes to HCMV evasion of innate antiviral responses .
Previously , we performed systematic screens for HCMV proteins that can inhibit DNA-triggered activation of interferon-stimulated response element ( ISRE , which is bound by activated IRF3 ) by reporter assays and identified UL42 as a candidate protein [15] . Reporter assays indicated that overexpression of UL42 inhibited cGAS-induced activation of the IFN-β promoter ( which is driven by ISRE and κB enhancers ) and ISRE in a dose-dependent manner in HEK293T cells stably expressing MITA ( HEK293T/MITA ) ( Fig 1A ) , but did not affect IFN-β-induced activation of signal transducer and activator of transcription 1/2 ( STAT1/2 ) ( Fig 1B ) . Previously , it has been shown that the human primary foreskin fibroblasts ( HFFs ) can express downstream antiviral genes in response to HCMV infection [33 , 37] . Our results indicated that HCMV AD169 strain could infect HFF cells , but could not infect endothelial HUVEC , epithelial HEK293T and Ea . hy926 cells ( S1A & S1B Fig ) . We established HFF cell lines that stably express UL42 ( HFF-UL42 ) by lentiviral-mediated transduction ( Fig 1C ) . qPCR analysis indicated that induction of antiviral genes including IFNB1 , ISG56 and CXCL10 following infection with the DNA viruses HCMV , herpes simplex virus 1 ( HSV-1 ) , and vaccinia virus ( VACV ) was inhibited in HFF-UL42 as compared to empty vector-transduced control cells ( HFF-Vec ) ( Fig 1C ) . In addition , transcription of genes induced upon transfection of dsDNAs , including 120-mer dsDNA representing the genome of HSV-1 ( HSV120 ) , dsDNA of approximately 90 bp ( dsDNA90 ) , 70-mer dsDNA representing the genome of vaccinia virus ( VACV70 ) and 45-mer interferon stimulatory DNA ( ISD45 ) , was impaired in HFF-UL42 cells ( Fig 1D ) . Since phosphorylation of MITA , TBK1 , IRF3 , and p65 are hallmarks of cGAS/MITA-mediated signaling , we further examined the effects of UL42 on these events . Consistently , ectopic expression of UL42 dramatically inhibited phosphorylation of MITA , TBK1 , IRF3 and RelA ( p65 ) in response to HCMV and HSV120 ( Fig 1E ) . In contrast , UL42 did not have marked effects on phosphorylation of STAT1 induced by IFN-β in HFFs ( Fig 1F ) . In these experiments , MITA was down-regulated after HCMV infection and HSV120 stimulation , which is a mechanism of timely termination of innate antiviral response to avoid immune damage [13 , 38 , 39] . As previously reported , down-regulation of MITA is dependent on its activation and happens during its trafficking from the ER to the perinuclear punctate structures [13 , 30 , 40 , 41] . Notably , the down-regulation of MITA following HCMV infection was inhibited in HFF-UL42 , suggesting a role of UL42 in MITA-mediated signaling . To investigate the roles of endogenous UL42 in innate antiviral response to HCMV , we constructed two UL42-shRNA plasmids that could specifically knock down the expression of UL42 , but not other HCMV genes ( Fig 2A ) . qPCR analysis indicated that knockdown of UL42 promoted HCMV- but not HSV-1-induced transcription of IFNB1 , ISG56 , ISG54 , CXCL10 , and IL6 genes at 6 , 12 , and 24 hr post-infection in HFFs ( Fig 2B ) . These results suggest that UL42-deficiency promotes innate antiviral response . To further confirm the role of UL42 , we generated UL42-deficient HCMV ( HCMVΔUL42 ) by CRISPR/Cas9 technology . We next examined the expression of downstream antiviral genes in cells infected with wild-type HCMV or HCMV-ΔUL42 . Consistently , mRNA levels of IFNB1 , ISG56 , CXCL10 , and IL6 genes induced by HCMV-ΔUL42 were significantly higher than those induced by HCMV-WT at 6 , 12 , and 24 hr post-infection in HFFs , human primary monocyte-derived dendritic cells and macrophages ( Fig 2C ) . In addition , UV-inactivated HCMV , which does not undergo viral transcription and translation after infection , induced higher levels of IFNB1 , ISG56 , and IL6 mRNA than un-treated HCMV . In these experiments , UL42 also inhibited transcription of IFNB1 , ISG56 , and IL6 induced by UV-inactivated HCMV ( Fig 2D ) . We also examined the mRNA levels of UV-treated or untreated HCMV . qPCR assays indicated that HCMV were inactivated by UV treatment ( S2 Fig ) . Furthermore , HFFs infected with UV-inactivated HCMV-WT or HCMV-ΔUL42 showed little difference on mRNA level of IFNB1 , ISG56 , and IL6 ( Fig 2E ) , suggesting that UL42 directly affects HCMV-induced transcription of downstream antiviral genes . Consistently , knockdown of UL42 increased HCMV-induced phosphorylation of MITA , TBK1 , and IRF3 in HFFs ( Fig 2F ) . In addition , phosphorylation of MITA , TBK1 , and IRF3 was increased following infection with HCMV-ΔUL42 compared to wild-type HCMV ( Fig 2G ) . Taken together , these results suggest that UL42 plays a critical role in the inhibition of HCMV DNA-triggered induction of downstream antiviral genes . Since UL42 antagonizes innate antiviral response , we further investigated its functions in HCMV immune evasion . Overexpression of UL42 markedly enhanced the replication of HCMV and HSV-1 ( Fig 3A & 3B ) , whereas knockdown of UL42 inhibited replication of HCMV but not HSV-1 ( Fig 3C & 3D ) . Fluorescence microscopy experiments indicated that overexpression of UL42 markedly enhanced replication of GFP-tagged HCMV ( HCMV-GFP ) [42] ( Fig 3E ) , whereas knockdown of UL42 inhibited replication of HCMV-GFP in HFF cells ( Fig 3F ) . These results suggest that UL42 contributes to HCMV immune evasion . It has been shown that MITA/STING is a pivotal adaptor protein for viral DNA-induced expression of downstream antiviral genes [3 , 8 , 10 , 43] , which is also essential for innate immune response to HCMV [33] . Our results also indicated that MITA-deficiency inhibited HCMV-induced transcription of downstream antiviral genes ( S3A Fig ) . However , knockdown of RIG-I or TLR9 did not affect HCMV-induced transcription of IFNB1genes in HFF cells ( S3B & S3C Fig ) . We found that replication of HCMV-ΔUL42 was decreased in comparison with wild-type HCMV in HFF-WT cells ( Fig 3G ) , but replications of both HCMV-ΔUL42 and wild-type HCMV were identical at early phase ( 6–48 hr ) of infection in MITA-deficient cells ( Fig 3H ) . Consistently , the progeny virions of HCMV-ΔUL42 were lower than wild-type HCMV in control cells , but they were identical in MITA-knockout cells at late phase ( 3–7 d ) of infection ( Fig 3I ) . Interestingly , both wild-type HCMV and HCMV-ΔUL42 production in MITA-knockout cells was increased in comparison to wild-type cells , consistent with a critical role of MITA in innate antiviral response ( Fig 3H ) . These results suggest that UL42 plays a direct role in evasion of innate antiviral response and contribute to the replication of HCMV . Next , we investigated the molecular mechanisms on the negative regulatory role of UL42 in innate antiviral response . Reporter assays indicated that UL42 inhibited cGAS- and MITA- but not TBK1- or IRF3-5D ( an active mutant of IRF3 ) -mediated activation of the IFN-β promoter and ISRE ( Fig 4A ) . As previously described [7 , 15 , 32 , 38] , the extracts from DNA-transfected cells contain cGAMP , which trigger induction of IFNB1 , ISG56 , and CXCL10 genes . To elucidate the mechanisms on how UL42 antagonizes innate antiviral response , we firstly examined whether UL42 affects cGAMP synthesis . Overexpression of UL42 impaired both HSV120- and VACV70-induced production of cGAMP ( Fig 4B ) and phosphorylation of MITA , TBK1 and IRF3 in HFFs ( Fig 4C ) . These results suggest that UL42 is important for inhibiting viral DNA-induced cGAS activation . Interestingly , UL42 also dramatically inhibited cGAMP-induced transcription of downstream antiviral genes such as IFNB1 , ISG56 , and CXCL10 ( Fig 4D ) . cGAMP-induced phosphorylation of MITA , TBK1 , and IRF3 in HFF-UL42 cells were decreased in comparison to HFF-Vec cells ( Fig 4E ) . These results suggest that UL42 targets at steps both upstream and downstream of cGAMP in the cGAS-cGAMP-MITA signal pathway . We next determined whether UL42 is associated with signaling components in dsDNA-triggered pathways . Co-immunoprecipitation experiments indicated that UL42 was associated with both cGAS and MITA , but not TBK1 , IRF3 , IKKβ or IKKα in overexpression system ( Fig 4F ) . Domain mapping experiments indicated that the C-terminal fragment ( aa161-522 ) of cGAS interacted with UL42 , and both N-terminal fragment ( aa1-160 ) and C-terminal fragment ( 161–379 ) of MITA could independently interact with UL42 ( Fig 4G ) . Endogenous co-immunoprecipitation experiments indicated that UL42 was associated with both cGAS and MITA following HCMV infection ( Fig 4H ) . These results suggest that UL42 targets both cGAS and MITA for antagonizing innate antiviral response . Since UL42 interacts with cGAS , we next determined whether UL42 affects cGAS binding to DNA . As shown in Fig 5A , both UL42 and the RNA sensor MDA5 did not bind to HSV120 DNA in pull-down assays . However , UL42 dramatically inhibited the binding of cGAS to HSV120 DNA ( Fig 5A ) . The inhibitory effect of UL42 on cGAS binding to DNA was dose-dependent ( Fig 5B ) . In addition , levels of viral DNA bound by endogenous cGAS were higher in HFFs infected with HCMV-ΔUL42 in comparison with wild-type HCMV ( Fig 5C ) . These results suggest that UL42 impairs cGAS binding to DNA . Previously , it has been shown that cGAS self-association and oligomerization are important for its activation after binding to dsDNA [44 , 45] . Co-immunoprecipitation experiments indicated that UL42 inhibited self-association of cGAS but not MITA ( Fig 5D ) . Consistently , UL42 markedly inhibited self-association of cGAS in a dose-dependent manner in pull-down assays ( Fig 5E ) . However , overexpression of UL42 did not affect polyubiquitination of cGAS ( S4A Fig ) . Collectively , these results suggest that UL42 impairs synthesis of cGAMP by inhibiting DNA binding and oligomerization of cGAS . Previously , it has been shown that self-association and oligomerization of MITA are crucial for MITA-mediated signaling [46] . As shown above , UL42 did not affect self-association ( Fig 5D ) or polyubiquitination ( S4B Fig ) of MITA . Interestingly , confocal microscopy indicated that UL42 inhibited accumulation of MITA in perinuclear punctate structures induced by cGAMP ( Fig 6A ) , which is a key marker for MITA activation . Previous studies have demonstrated that iRhom2 and TRAPβ containing complex is critically involved in MITA trafficking after viral infection [13 , 14] . We found that UL42 was associated with TRAPβ and iRhom2 but not TRIM38 , which mediates MITA sumoylation [38] ( Fig 6B ) . Confocal microscopy confirmed that UL42 was colocalized with TRAPβ and the ER ( Fig 6C ) . Interestingly , we found that UL42 promoted degradation of TRAPβ in a dose-dependent manner in overexpression experiments , but did not affect the stability of iRhom2 , TRIM38 , TRIM32 or TRIM14 ( Fig 6D ) . Consistently , UL42-deficiency inhibited HCMV-induced degradation of TRAPβ in HFFs ( Fig 6E ) . Taken together , these results suggest that UL42 impairs the trafficking of MITA after viral infection by promoting degradation of the translocon complex protein TRAPβ . Two major systems exist for protein degradation , including the ubiquitin-proteasome and autophagy-lysosome pathways . To investigate the mechanisms responsible for UL42-mediated degradation of TRAPβ , we treated HEK293T-UL42 and HEK293T-Vec cells with various inhibitors for protein degradation pathways . The results indicated that UL42-mediated degradation of TRAPβ could be inhibited by the lysosomal inhibitor NH4Cl and the autophagic inhibitors 3MA and bafilomycin , but not the proteasomal inhibitor MG132 ( Fig 6F ) , suggesting that UL42 mediates degradation of TRAPβ via an autophagic lysosomal pathway . In co-immunoprecipitation experiments , UL42 interacted with p62 and LC3B , two essential components involved in autophagic lysosomal degradation , and increased the associations of p62-TRAPβ or LC3B-TRAPβ ( Fig 6G ) . Consistently , knockdown of p62 or LC3B inhibited UL42-mediated degradation of TRAPβ ( Fig 6H ) . These results suggest that UL42 impairs the trafficking of MITA by promoting p62-LC3B-mediated autophagic degradation of TRAPβ . Previous studies have demonstrated that UL31 and UL82 respectively targeted cGAS and MITA to inhibit HCMV-induced the transcription of downstream antiviral genes . Above results suggest that UL42 targets both cGAS and MITA to suppress HCMV-triggered host antiviral immune responses and promote HCMV immune evasion . Reporter assays indicated that UL42 collaborates with UL31 or UL82 to inhibit cGAS-MITA-induced activation of the IFN-β promoter , ISRE , and NF-κB in HEK293T cells stably expressing MITA ( HEK293T/MITA ) ( S5A Fig ) . Compared with UL42 or UL31 , the cGAMP synthesis were reduced lower by Co-expression of UL42 and UL31 in S5B Fig . Moreover , while both UL42 and UL82 inhibited cGAMP-induced transcription of downstream genes , the inhibition efficiency by co-expression of UL42 and UL82 was higher than individual expression of UL42 or UL82 ( S5C Fig ) . Conversely , UL42-deficiency collaborated with UL31- or UL83-deficiency in enhancing innate immune response following HCMV infection ( S5D & S5E Fig ) . These results suggest that UL42 cooperated with UL82 and UL31 to inhibit innate immune response against HCMV .
The innate immune system constitutes the first line of host defense against viral infection [47] . The ability of viruses to evade and modulate host innate immune response is of central importance for successful establishment and maintenance of infection [48] . As the cGAS-MITA-TBK1 axis plays a crucial role in host defense against DNA viruses [29] , the DNA viruses have evolved various mechanisms to antagonize this signaling pathway for replication and latent infection [30] . For example , HSV-1 tegument protein UL37 has been reported to deamidate cGAS which impairs the ability of cGAS to catalyze cGAMP synthesis [49]; UL41directly degrades cGAS mRNA to inhibit antiviral signaling [50]; ICP27 targets the TBK1-activated MITA/STING signalosome to inhibit antiviral response [51] . Kaposi sarcoma herpesvirus ( KSHV ) protein ORF52 and cytoplasmic isoforms of LANA counteract cGAS-mediated signaling [52 , 53]; vIRF1 inhibits antiviral gene expression by impeding interaction of MITA with TBK1 [54] . One important feature of HCMV is to establish long-term latent infection in vivo . Therefore , it is understandable that HCMV may employ multiple and even redundant mechanisms to inhibit innate immune response . Previously , it has been shown that HCMV tegument protein UL83 interacts with cGAS and IFI16 in the nucleus to inhibit type I IFN induction [33 , 34]; HCMV UL82 inhibits the translocation of MITA from the ER to perinuclear microsomes by disrupting the MITA-iRhom2-TRAPβ translocation complex , resulting the impairment of recruitment of TBK1 and IRF3 to the MITA complex [15]; HCMV UL31 inhibits DNA binding and enzymatic activity of cGAS , leading to decreased production of cGAMP and impairment of innate antiviral response [32] . In this study , we identified UL42 as a new HCMV protein that impairs cGAS activation and MITA trafficking , and contributing to evasion of innate immunity of HCMV . In light of these studies , it is possible that the different HCMV proteins , which are optimally expressed in the host cells at different time points or distinct intracellular locations after infection , may antagonize innate immune response in a temporal/spatial manner . In addition , cGAS activation or MITA trafficking themselves are involved in complicated regulatory mechanisms , the HCMV UL proteins may regulate distinct molecular events in these processes . To understand the comprehensive mechanisms on how HCMV rapidly establishes persistent infection , we systematically screened for HCMV proteins that can inhibit DNA-triggered activation of ISRE [15 , 32] . In this report , we investigated the role of HCMV UL42 , one of the non-essential genes for HCMV viral replication [35] , in antagonizing innate antiviral response . Several lines of evidence suggest that UL42 acts to antagonizing cGAS-MITA-mediated innate antiviral response . Firstly , overexpression of UL42 inhibited cGAS-induced activation of the IFN-β promoter and ISRE . Consistently , UL42 inhibited HCMV- or cytosolic dsDNA-induced transcription of downstream effector genes , whereas deficiency of UL42 increased HCMV-triggered production of type I IFNs and downstream antiviral genes . Additionally , the viral titers of HCMV-ΔUL42 were decreased in comparison with wild-type HCMV in HFFs . Although UL42 efficiently inhibits cGAS-MITA-mediated signaling , UL42-deficiency only led to a moderate increase of IRF3 activation and induction of downstream antiviral genes . The simplest explanation is that HCMV encodes multiple proteins to antagonize innate antiviral response , therefore , deficiency of one of this protein has only partial effect . Mechanistic studies suggest that UL42 inhibits innate antiviral response by targeting both cGAS and MITA . Firstly , overexpression of UL42 inhibited HCMV-triggered induction of cGAMP and cGAMP-induced transcription of downstream effector genes . Cellular and biochemical experiments indicated that UL42 interacted with cGAS and MITA following HCMV infection . Second , in vitro pull-down analysis showed that UL42 inhibited the binding of cGAS to dsDNA . In mammalian cells , overexpression of UL42 inhibited the self-association of cGAS . Third , confocal microscopy revealed that UL42 impaired the trafficking of MITA , a critical process for MITA activation . Biochemical experiments indicated that UL42 impaired the trafficking of MITA by promoting p62-LC3B-mediated autophagic degradation of TRAPβ , which is a critical component in the translocon complex . Collectively , our results suggest that UL42 antagonizes innate antiviral response by inhibiting cGAS activation , as well as promoting p62-LC3B-mediated degradation of TRAPβ and therefor impairing MITA trafficking and activation . Thus , UL42 represents a new player involved in HCMV evasion of innate antiviral response .
2’ 3’-cGAMP , and lipofectamine 2000 ( Invitrogen ) ; polybrene ( Millipore ) ; puromycin and RNase inhibitor ( Thermo ) ; dual-specific luciferase assay kit ( Promega ) ; SYBR ( BIO-RAD ) ; digitonin ( Sigma ) ; streptavidin agarose ( Solulink ) ; mouse antibodies against Flag , and β-actin ( Sigma ) , and HA ( Covance ) ; rabbit monoclonal antibodies against cGAS ( 66546S/31659S ) , MITA ( 13647S ) , phosphor-MITA ( 85735S ) , phosphor-p65 , and phosphor-IRF3 ( 4947S ) ( Cell Signaling Technology ) , phosphor-TBK1 ( ab109272 ) and TBK1 ( ab40676 ) ( Abcam ) , IRF3 ( sc-9082 ) , phosphor-Tyrosine701-STAT1 ( 9167S ) and STAT1 ( sc-346 ) ( Santa Cruz Biotechnology ) were purchased from the indicated manufacturers . Antisera against UL42 , UL82 , and UL44 were generated by immunizing rabbits or mice with purified recombinant UL42 , UL82 , and UL44 proteins . HEK293 cells and MRC5 cells were obtained from ATCC . HFFs were provided by Dr . Min-Hua Luo ( Wuhan Institute of Virology , CAS ) . MITA-/- MLF-MITA-Flag cells were previously described [13 , 52] . These cells were cultured in DMEM ( Hyclone ) supplemented with 10% fetal bovine serum ( Gibco ) and 1% penicillin–streptomycin ( Thermo Fisher Scientific ) at 37°C with 5% CO2 . All cells were negative for mycoplasma . HCMV ( AD169 ) and HSV-1-GFP were provided by Dr . Min-Hua Luo ( Wuhan Institute of Virology , CAS ) and Dr . Chun-Fu Zheng ( Suzhou University ) respectively . HCMV-GFP was provided by Dr . Dong Yu ( Washington University ) . HSV-1 ( KOS strain ) and VACV ( Tian-Tan Strain ) were obtained from China Center for Type Culture Collection , Wuhan , China . HCMV and HCMV-GFP stocks were prepared on HFFs and the virus titers were determined by standard TCID50 assays . HSV-1-GFP stock was prepared in Vero cells and the virus titers were determined by standard plaque assays . Expression plasmids for HA- , FLAG- , MyC- , RFP- or GFP-tagged UL42 , HA- , FLAG- or RFP-tagged cGAS and its truncation mutants , HA- , FLAG- or RFP-tagged MITA and its truncation mutants were constructed by standard molecular biology techniques . Expression plasmids for HA- and FLAG-tagged MDA5 , TRAPβ , TBK1 and IRF3 , p62 , LC3B , LC3A , IKKα , IKKβ , TRIM38 , TRIM32 , TRIM14 , iRhom2 and the IFN-β promoter reporter plasmids were previously described [13 , 15 , 38 , 55] . The following oligonucleotides were used to stimulate cells: ISD45: 5’-TACAGATCTACTAGTGATCTATGACTGATCTGTACATGATCTACA-3’; VACV70: 5’-CCATCAGAAAGAGGTTTAATATTTTTGTGAGACCATGGAAGAGAGAAAGAGATAAAACTTTTTTACGACT-3’; dsDNA90: 5’-TACAGATCTACTAGTGATCTATGACTGATCTGTACATGATCTACATACAGATCTACTAGTGATCTATGACTGATCTGTACATGATCTACA-3’; HSV120: 5’-AGACGGTATATTTTTGCGTTATCACTGTCCCGGATTGGACACGGTCTTGTGGGATAGGCATGCCCAGAAGGCATATTGGGTTAACCCCTTTTTATTTGTGGCGGGTTTTTTGGAGGACTT-3’ . Transfection and reporter assays were performed as previously described [46] . HEK293 cells were transfected by standard calcium phosphate precipitation method . HFFs were transfected by Lipofectamine 2000 . To ensure that each transfection receives the same amount of total DNA , the empty control plasmid was added to each transfection . To normalize for transfection efficiency , pRL-TK ( Renilla luciferase ) reporter plasmid ( 0 . 01 μg ) was added to each transfection . Luciferase assays were performed using a Dual-Specific Luciferase Assay Kit . Firefly luciferase activities were normalized on the basis of Renilla luciferase activities . Double-stranded oligonucleotides corresponding to the target sequences were cloned into the pSuper . Retro-RNAi plasmid ( Oligoengine ) . The following sequences were targeted for UL42 mRNA: #1 5’-GCTGGTGGACCTCAACAACTT-3’; #2 5’-GCCAATGGATCATGCTGTTTC-3’; The following sequences were targeted for UL82: 5’-GCTGGTGGACCTCAACAACTT-3’; The following sequences were targeted for UL31: 5’-GGACAACTTTCTCACGTCT-3’; The sequence targeted by the control RNAi plasmid is: 5’-GGAAGATGTATGGAGACATGG-3’ . The HEK293T cells were transfected with two packaging plasmids ( pGAG-Pol and pVSV-G ) together with a control , UL42- , UL82- , UL31 , RIG-I- , TLR9- , p62- , or LC3B-shRNA retroviral plasmid . Twenty-four hours later , cells were incubated with new medium without antibiotics for another 24 hr . The recombinant virus-containing medium was filtered and then added to HFF or HEK293 cells in the presence of polybrene ( 6 μg/ml ) . The infected cells were selected with puromycin ( 0 . 5–1 . 0 μg/ml ) for 10 days before additional experiments . HEK293 cells , or HFF cells were lysed in l ml NP-40 lysis buffer ( 20 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl , 1 mM EDTA , 1% Nonidet P-40 , 10 μg/ml aprotinin , 10 μg/ml leupeptin , and 1 mM phenylmethyl sulfonyl fluoride ) . Coimmunoprecipitation and immunoblot analysis were performed as previously described [56 , 57] GST-cGAS were bound to glutathione agarose beads and incubated for 3 hrs with lysates from HEK293T cells transiently expressing HA-cGAS or UL42-HA plasmid . The beads were washed three times each with lysis buffer ( 20 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl , 1mM EDTA , 1% Nonidet P-40 , 10 μg/ml aprotinin , 10 μg/ml leupeptin , and 1mM phenylmethyl sulfonyl fluoride ) , then mixed with an equal volume of 2× SDS loading buffer and boiled for 10 min . The input/elutes were resolved by SDS-PAGE and analyzed by coomassie staining and/or immunoblot analysis [52] . HEK293 cells transfected with the indicated plasmids were lysed in NP-40 lysis buffer . Lysates were incubated with biotinylated-HSV120 for 1 hour at 4°C , and then incubated with streptavidin beads for another 2 hours at 4°C . The beads were washed three times with lysis buffer and analyzed by immunoblotting with the indicated antibodies . Cells were mock-transfected or transfected with HSV120 ( 3 μg/ml ) for 4 hours . Cell extracts were then prepared and heated at 95°C for 5 min to denature most proteins , which were removed by centrifugation . The supernatants containing cGAMP were delivered to MLFs pretreated with digitonin permeabilization solution ( 50 mM HEPES pH 7 . 0 , 100 mM KCl , 3 mM MgCl2 , 0 . 1 mM DTT , 85 mM Sucrose , 0 . 2% BSA , 1 mM ATP , 0 . 1 mM GTP and 10μg/ml digitonin ) at 37°C for 30 min . Three hours later , the cells were collected for a qPCR analysis . The experiments were performed as previously described [58 , 59] . Briefly , potential guide RNAs ( gRNAs ) targeting UL42 gene were analyzed using the CRISPR Design tool . The UL42 gRNA target sequence used in this study is 5’- CGTCGTCGGGCACAGACCCA-3’ . Double-stranded oligos were cloned into the lentiCRISPRv1 vector and cotransfected with packaging plasmids into HEK293T cells . Lentiviral particles were collected and used to transduce HFFs . The HFF-gRNA cells were infected with serial dilution of HCMV in 96 well plates . Twenty days later , the viruses were collected and diluted to infect HFFs for 20 days . A single plaque was pick up to infect HFFs to produce homogenous HCMV-ΔUL42 strain , which was verified by immunoblotting analysis . Total RNA was isolated for qPCR analysis to measure mRNA levels of the indicated genes . Data shown are the relative abundance of the indicated mRNA normalized to that of GAPDH . Primer sequences for IFNB1 , ISG56 , ISG54 , CXCL10 , IL6 , Uls , and GAPDH were previously described [32 , 38 , 60] . The cells were incubated with the ER-Tracker Green or Mito-Tracker Red ( Invitrogen ) following protocols recommended by the manufacturer . The cells were then fixed with 4% paraformaldehyde for 10 minutes and observed with an Olympus confocal microscope under a 60× oil objective . | Recognition of viral DNA by the cytosolic DNA sensor cGAS and subsequent induction of type I IFNs via the cGAS-MITA signaling axis are important for host antiviral innate immunity . The human cytomegalovirus ( HCMV ) causes complications in immunodeficient populations and is a major cause of birth defects . It is known that HCMV suppresses innate immunity , which is pivotal for establishing immune evasion and latent infection . In this study , we found that HCMV protein UL42 inhibits innate antiviral responses thus promotes HCMV replication . UL42 functions by targeting cGAS and MITA through distinct mechanisms . UL42 inhibits cGAS activation by interrupting its DNA binding and oligomerization , while it targets MITA by interfering trafficking of MITA from the ER to perinuclear punctate structures , a process required for MITA activation . These findings defined an important mechanism for HCMV immune evasion , which may provide a therapeutic target for the treatment of HCMV infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"antiviral",
"immune",
"response",
"pathology",
"and",
"laboratory",
"medicine",
"molecular",
"probe",
"techniques",
"pathogens",
"immunology",
"microbiology",
"immunoblotting",
"cytomegalovirus",
"infection",
"plasmid",
"constructio... | 2019 | Human cytomegalovirus protein UL42 antagonizes cGAS/MITA-mediated innate antiviral response |
Forkhead-box protein P2 is a transcription factor that has been associated with intriguing aspects of cognitive function in humans , non-human mammals , and song-learning birds . Heterozygous mutations of the human FOXP2 gene cause a monogenic speech and language disorder . Reduced functional dosage of the mouse version ( Foxp2 ) causes deficient cortico-striatal synaptic plasticity and impairs motor-skill learning . Moreover , the songbird orthologue appears critically important for vocal learning . Across diverse vertebrate species , this well-conserved transcription factor is highly expressed in the developing and adult central nervous system . Very little is known about the mechanisms regulated by Foxp2 during brain development . We used an integrated functional genomics strategy to robustly define Foxp2-dependent pathways , both direct and indirect targets , in the embryonic brain . Specifically , we performed genome-wide in vivo ChIP–chip screens for Foxp2-binding and thereby identified a set of 264 high-confidence neural targets under strict , empirically derived significance thresholds . The findings , coupled to expression profiling and in situ hybridization of brain tissue from wild-type and mutant mouse embryos , strongly highlighted gene networks linked to neurite development . We followed up our genomics data with functional experiments , showing that Foxp2 impacts on neurite outgrowth in primary neurons and in neuronal cell models . Our data indicate that Foxp2 modulates neuronal network formation , by directly and indirectly regulating mRNAs involved in the development and plasticity of neuronal connections .
Forkhead-box protein P2 is a highly conserved vertebrate protein , belonging to an important group of transcription factors [1] . By modulating the expression of downstream target genes , forkhead-box proteins influence a diverse array of processes , including cell-cycle regulation , signal transduction , differentiation , patterning and metabolism [2] . They thereby play crucial roles during embryogenesis , in postnatal development and in the mature organism , and many have been linked to disease states [3] . The P subgroup is a divergent branch of forkhead-box proteins that share a distinctive DNA-binding domain located near the C-terminal end of the protein , as well as zinc-finger/leucine-zipper motifs that mediate dimerization , and a glutamine-rich region towards the N-terminus [4] , [5] . Functional evidence from multiple species implicates Forkhead-box protein P2 in particularly intriguing aspects of brain development and function [1] . ( Here we adopt the standard accepted nomenclature to refer to the protein in different species: FOXP2 in humans , Foxp2 in mice , FoxP2 in other chordates , with the corresponding gene names in italics [6] . ) In humans , damage to one copy of the FOXP2 gene causes a rare neurodevelopmental disorder , characterised by difficulties mastering sequences of mouth movements during speech , as well as impaired language processing [4] , [7] , [8] . Heterozygous disruptions of the mouse orthologue ( Foxp2 ) yield dramatic reductions in synaptic plasticity of cortico-striatal brain circuits , associated with deficits in learning of rapid motor skills [9] . Mouse pups with homozygous Foxp2 mutations show more severe neural effects – gross motor impairments , delayed postnatal maturation of the cerebellum and dramatic reductions in emission of ultrasonic vocalisations – against a background of reduced weight-gain and postnatal lethality [9]-[11] . In addition , the avian orthologue ( FoxP2 ) is required for normal vocal learning in songbirds [12] , [13] . Selective knockdown of the gene in a key striatal nucleus in juvenile zebrafinches leads to incomplete and inaccurate imitation of tutor songs [14] . Studies of both human FOXP2 and mouse Foxp2 identified similarly strong CNS ( central nervous system ) expression during embryogenesis , which is confined to neurons ( absent from glial cells ) and enriched in various brain structures , including deep layers of the developing cortical plate , and parts of the striatum , thalamus and cerebellum [15] , [16] . These embryonic expression patterns appear highly concordant in the different species , and show remarkable overlaps with sites of pathology identified by neuroimaging of human children and adults carrying FOXP2 mutations [16] , [17] . Neural expression of the gene continues postnatally and into adulthood [4] , [15] , and is also observed in certain non-neural tissues , most notably the distal alveolar lung epithelium , and the outflow tract and atrium of the cardiovascular system [18] . The above observations of well-conserved and specific CNS expression patterns [15] , [16] suggest that Foxp2 is likely to have important functions in neurodevelopment . Nevertheless , as data continue to accumulate regarding its impacts on the postnatal brain [9] , [11] , [14] , the specific roles of Foxp2 in the developing CNS remain largely elusive . One route for gaining insights into the biological processes controlled by a transcription factor is to define the regulatory networks that are directly downstream of it [1] . An efficient strategy for identifying direct targets exploits chromatin immunoprecipitation ( ChIP ) methods to screen the tissue of interest [19] . Two previous investigations have coupled ChIP with hybridisation to promoter microarrays ( ChIP-chip ) in order to uncover binding sites of FOXP2 in human foetal brain tissue [20] and in human neurons grown in culture [21] . Both screens were of limited scope – the microarrays in these studies comprised fragments from the 5′ ends of ∼5 , 000 loci [20] , [21] , representing a small percentage of the known gene promoters in the genome . Neither study combined ChIP data with large-scale expression analyses . A more recent report used mRNA expression profiling in human neuronal models transfected with different versions of FOXP2 to explore regulatory differences between the human and chimpanzee orthologues , but did not include any ChIP screening for direct targets [22] . In the present study , we performed a systematic large-scale in vivo ChIP-chip screen of the embryonic mouse brain , coupling Foxp2-ChIP to high-density arrays with oligonucleotides tiled across >17 , 000 promoters . We robustly established the empirical significance of our ChIP results in wild-type brains by determining the null distribution of signals generated by matched control tissue from littermates that expressed no Foxp2 protein . Under strict empirical thresholds that minimised false positive signals , we isolated a set of 264 high-confidence in vivo targets . Gene ontology ( GO ) analyses of the ChIP-chip data , as well as genome-wide expression profiling and in situ hybridisations of wild-type and mutant mice , converged on neurite outgrowth as one of the most prominent biological themes associated with Foxp2 function in the embryonic CNS . We went on to directly demonstrate , using neuronal cell models and primary neurons from the embryonic mouse brain , that Foxp2 alters expression of neurite-outgrowth targets and thereby influences neurite process length and branch number .
In vivo Foxp2-ChIP screening was carried out using brains harvested from embryonic mice . Experiments were performed with mice that were wild-type for Foxp2 , as well as homozygous littermates that do not express any Foxp2 protein ( Foxp2-S321X mutants; see Materials and Methods ) [9] . The different types of sample were screened in parallel , undergoing identical experimental manipulations and data processing . In this context , the homozygous mutant mouse tissue acts as an ideal negative control [21] . Since such samples completely lack Foxp2 protein ( see Figure S1 and [9] ) , fragments that are pulled down by Foxp2-ChIP in these cases give an unbiased empirical indication of background noise and false positive rates yielded by the procedure . Whole mouse brains from wild-type or mutant mice were harvested at embryonic day 16 ( E16 ) , corresponding to a timepoint at which particularly high levels of Foxp2 expression are observed in the developing CNS [16] . Chromatin isolated by Foxp2-ChIP was labelled and hybridised to DNA microarrays covering the promoter regions of ∼17 , 000 mouse transcripts ( Agilent Technologies ) , using total input DNA as a reference sample . Each promoter on these arrays is represented by an average of twenty-five 60-mer probes spanning ∼5 . 5 kb upstream and ∼2 . 5 kb downstream of the transcription start site , allowing peak regions of binding to be precisely defined ( Figure 1 ) . Moreover , the presence of multiple probes for each promoter scattered throughout the array gives independent enrichment values within the same promoter , which aids discrimination of real biological targets from false positive events . Specifically , since the shearing process during ChIP produces overlapping fragments of chromatin , true targets should show evidence of enrichment for multiple probes across the promoter region , while promoters with only a single enriched probe are most likely to be false positive results . In order to identify enriched promoters , Foxp2-ChIP data were analysed as per Materials and Methods . Briefly , array data from independent biological replicates ( three independent ChIP experiments hybridised to one each of three array sets ) were normalised for each genotype ( wild-type or mutant control ) separately . Normalised array data ( excluding probes with a negative average enrichment across replicate experiments ) were subjected to a sliding window analysis , using a similar method to that employed in genome-wide ChIP-chip studies of other forkhead transcription factors [23] . Each probe was assigned a value ( window-adjusted score ) based on the median fold enrichment of itself and its neighbouring probe on either side ( within 500 bp upstream and 500 bp downstream ) , and then probes were ranked based on this window score . By analysing the distribution of window scores observed in the mutant null control experiments we were able to derive an empirical threshold for significance , which could then be applied to the wild-type data . We found that window scores greater than or equal to 0 . 974 ( corresponding to ∼2-fold enrichment ) excluded 99% of the data-points in the mutant null control experiments . When we applied this threshold to data from wild-type experiments , we identified a set of 1 , 217 promoter regions that were consistently enriched by Foxp2-ChIP over 3 replicates in wild-type mouse brains ( Table S1 ) . On inspection of the locations of the enriched probes throughout the mouse genome , no positional bias was observed ( Figure S2 ) . Since some of the enriched regions lay close to the transcriptional start site ( TSS ) of more than one gene , the 1 , 217 promoter regions corresponded to 1 , 253 genes . Of note , using the same analysis parameters , only 147 genes were enriched in the mutant null controls , suggesting a low false discovery rate . Nevertheless , in order to minimize false-positive findings , we excluded any enriched genes from the wild-type dataset that also had window scores exceeding the 99% threshold in the mutant control dataset . This filtering process yielded a slightly smaller set of 1 , 164 putative targets ( Table S2 ) . When we applied stricter thresholds to the wild-type data , selecting only those promoters in which at least one probe gave a window score of ≥ 1 . 5 , we identified a shortlist of 259 promoter regions . Since a small number of peak regions lay directly between the TSSs of two different genes , these 259 promoters corresponded to a slightly higher total of 266 genes . Crucially , the same analyses of the entire mutant null control dataset identified only a single gene in the genome with a window score of ≥ 1 . 5 ( the Pigt gene ) , indicating an extremely low rate of false positives under these stricter selection criteria . We excluded two genes from the strict wild-type shortlist ( Pigt and Zfp496 ) since they contained probes that exceeded the 99% threshold ( i . e . window score of >0 . 974 ) in mutant null controls ( Figure S3 ) . The outcome of these analyses was a final curated shortlist of 264 high-confidence in vivo targets ( Table S3 ) . Given that DNA is sheared randomly during the ChIP process , we would expect a true Foxp2 binding event to be represented by a peak of enrichment at a target promoter . This peak would result from the sheared DNA forming a series of overlapping fragments , with the region closest to the binding site showing the highest degree of enrichment ( i . e . highest number of fragments pulled down during immunoprecipitation ) and with progressively less enrichment observed as the distance to the binding site increases on either side . Figure 1A gives typical examples of the enrichment peaks observed for putative targets from our Foxp2-ChIP dataset . Examination of corresponding data from mutant control experiments emphasises the relative lack of enrichment in nulls that lack Foxp2 protein , indicating that the enrichment in wild-type samples results from highly specific Foxp2-mediated interactions . Furthermore , we followed up a subset of candidates with qPCR , consistently confirming their enrichment ( Figure 1B ) . Enriched regions represented in the shortlist of high-confidence targets were assessed in silico for any over-represented sequence motifs ( see Text S1 ) . This analysis did not enforce a priori conditions of motif sequence , other than a length restriction of 8 bases . This meant that rather than limiting our search to occurrences of known patterns in the promoters , we obtained an unbiased list of motifs that were characteristic of the Foxp2-ChIP target promoters . Eight sequences ( motifs A-H ) were found to be significantly over-represented ( p<0 . 05 ) in the shortlist of high confidence target promoter sequences ( Table 1 ) . Importantly , the three most commonly identified over-represented motifs from this unbiased search ( A–C ) were partial or complete matches to well established FOX/FOXP/FOXP2 binding motifs ( RYMAAYA/TATTTRT/AATTTGT ) , providing additional strong support for the biological relevance of our findings . A further over-represented motif ( motif D ) did not match the known consensus motifs and was detected in 182 promoters out of the 247 promoter regions that could be surveyed from the Foxp2-ChIP shortlist ( See Text S1; Figure S4A ) . Thus , we reasoned that it may represent a novel putative Foxp2 binding sequence . EMSA ( Electrophoretic Mobility Shift Assay ) experiments demonstrated strong specific binding of FOXP2 to this motif ( Figure S4B ) , when located in putative Foxp2 target promoter sequences , such as those for Nrn1 , Nfat5 and Sema6d . However , not all occurrences of this motif were strongly bound by Foxp2 , suggesting that while the site is capable of being bound by Foxp2 protein , the binding is context specific – as is regularly seen for other FOX family binding sites [24] . In addition to the use of in vivo ChIP to uncover target genes that are directly bound by Foxp2 ( direct targets ) , we assessed regulatory cascades further downstream ( indirect targets ) via an expression profiling approach . Again we focused on E16 mouse brain tissue , analysing the same genotypes ( wild-type mice and their homozygous Foxp2-S321X littermates , 5 and 6 biological replicates , respectively ) on the same genomic background as used for the ChIP experiments . While ChIP identifies DNA-binding events of Foxp2-positive cells , expression profiling is expected to be more sensitive to tissue heterogeneity . Therefore we selected a key site of high Foxp2 expression with considerable prior evidence of functional relevance [9] , [14]–[17] , the ganglionic eminences ( developing striatum and pallidum ) . Analysis of genome-wide expression data ( see Materials and Methods for details ) identified 340 genes that were differentially expressed ( p<0 . 01 ) between wild-type and Foxp2-S321X homozygous mutant samples ( Table S4 ) . 180 of these genes showed reduced expression in absence of Foxp2 protein , while the remaining 160 genes showed increases ( Table S4 ) . Of these 340 genes , 19 genes ( 5 . 6% ) were found in common with the ChIP-chip target gene list ( Table S5 ) , including those with known CNS functions , such as Nell2 ( neural epidermal growth factor-like like 2 ) , Nrn1 ( neuritin ) , Cck ( cholecystokinin ) , and Alcam ( activated leukocyte cell adhesion molecule ) . Notably , the human orthologues of Nrn1 and Cck have been independently proposed as top direct targets in small-scale ChIP screens of human foetal tissue [20] . We went on to determine whether any biological themes were over-represented within the direct targets ( promoter bound by Foxp2 ) and indirect targets ( not bound by , but regulated downstream of Foxp2 ) , using unbiased GO analyses . The Foxp2-ChIP and expression profiling datasets were each assessed independently using the WebGestalt program [25] , identifying functional categories that were significantly enriched ( Figure 2 and Figure S5 ) . In the Foxp2-ChIP dataset we observed significant over-representation of genes involved in processes including cell motility and migration , chromatin architecture and assembly , synaptic transmission , and a number of categories associated with RNA metabolism such as regulation of RNA stability and mRNA processing . In the expression profiling dataset significant categories included regulation of transcription , actin cytoskeleton organisation and biogenesis , and cellular protein catabolism . Consistent with previous studies [20] , [21] , nervous system development , neurogenesis and multiple G-protein signalling categories — including G-protein coupled receptor signalling ( ChIP ) , and G-protein signalling and Wnt receptor signalling ( expression ) — were significant in both datasets . We next performed in situ hybridisation on brains from wild-type and Foxp2-S321X E16 embryos , to further assess major targets suggested by the ChIP and expression profiling screens . Consistent with previously published data [16] , in addition to the developing striatum , Foxp2 expression at this developmental stage is highest in the diencephalon ( developing thalamus ) , midbrain and cerebellar primordium ( Figure 3 ) . The in situ hybridisation data confirmed regulation of Shhrs ( also known as Dlx6as1 or Evf1/2 ) , a transcript showing greater than 200-fold increased expression levels in S321X mice . This noncoding RNA is highly specific to the ganglionic eminences in the embryo and is known to play a vital role in the control of the homeodomain transcription factors Dlx5 and Dlx6 [26] , [27] . These data illustrate that loss of Foxp2 can influence transcripts central to key neurodevelopmental processes in vivo . We then focused on target genes common to both ChIP and expression profiling datasets , to determine whether expression changes could be observed , not only in the ganglionic eminences , but also elsewhere in the developing brain ( Figure 3 ) . Indeed , Nell2 , Nrn1 and Cck all demonstrated clear increases in expression in the developing basal ganglia at E16 in the Foxp2 mutant compared to wild-type , in agreement with the array data ( Figure 3 and Figure S5 ) , providing further evidence that they are indeed direct targets , repressed by Foxp2 . Significantly , Nrn1 , a gene important for neuronal outgrowth [28] , showed strongly increased expression in mutants in additional regions where Foxp2 is typically expressed , including the developing thalamus and cerebellum ( Figure 3 ) . Similarly , Cck shows additional increases in expression in the cerebellum ( Figure 3 ) . Certain putative direct Foxp2 targets with known roles in the CNS , such as Ywhah ( tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein , eta polypeptide ) and Wasf1 ( WASP family 1 ) , are ubiquitously expressed in the developing mouse brain [29] . However , other genes are thought to have more localised and/or temporally defined patterns of expression . To ascertain whether these targets are co-expressed with Foxp2 , further in situ hybridisation was carried out at E16 . The results demonstrate that genes from the ChIP dataset with established CNS functions are found in a range of Foxp2-expressing brain regions ( Figure S6 ) . The GO analyses of independent Foxp2 target data from genome-wide ChIP and expression profiling uncovered a consistent and statistically significant over-representation of genes involved in neurite development & morphogenesis , axon extension and axon guidance pathways ( Table 2 ) . Multiple GO categories associated with such processes were significantly over-represented in at least one of the datasets , and several of these functional classes were significant in both datasets including neurogenesis , neuron projection development and axonogenesis ( Figure 2 and Table 2 ) . Furthermore , when we investigated KEGG pathways associated with these datasets , we observed enrichment of genes in a number of pathways ( Figure 4A and Table S6 ) , one of the most significant of which was the axon guidance pathway ( p = 4 . 73×10−8 and p = 3 . 00×10−4 in Foxp2-ChIP and expression profiling datasets , respectively ) . Interestingly , a number of different but interacting genes within this pathway were identified in the two datasets ( Figure 4B ) , suggesting that direct and indirect targets may represent different aspects of the same functional downstream effects of Foxp2-mediated regulation . In sum , our unbiased genomic screens for Foxp2-dependent gene networks predicted that neurite outgrowth and axon guidance should be key biological themes associated with Foxp2 function in the developing brain . We went on to test this prediction through genetic and functional analyses of neuronal cell models and primary neurons . First , we independently assessed whether differences in Foxp2 expression affect expression of putative direct target genes involved in neurite outgrowth , using a well-validated murine cell model . Neuro2a cells are neuron-derived cells that can be differentiated to take on a more neuron-like identity via exposure to retinoic acid . These cells were stably transfected with Foxp2 or with an empty vector control , and then grown in media either with or without the addition of retinoic acid . Cells that were over-expressing Foxp2 demonstrated consistent expression changes for multiple direct target genes that were identified in our E16 ChIP screen and implicated in neurite outgrowth pathways ( Figure 5A ) . Significant repression of target gene expression was observed both pre- and post-differentiation; however most of the neurite-outgrowth genes showed greater fold changes following differentiation . Next , we formally assessed the hypothesis that changes in Foxp2 levels , and the concomitant alterations in expression of neurite-outgrowth genes , yield detectable differences in the growth of neurites in these cells . After 24 and 48 hours of retinoic acid treatment ( matching the timepoints for analyses target gene expression ) we performed blind scoring of cells and observed striking qualitative differences in neurite outgrowth when cells over-expressed Foxp2 , as compared to sham-transfected controls . Cells that had been transfected with Foxp2 prior to differentiation consistently displayed increased neurite length , in a manner that was easy to distinguish from controls ( Figure 5B ) . To further assess the in vivo relevance of these findings , we examined whether there were corresponding phenotypic effects mediated by functional Foxp2 in neurons of the developing brain . We isolated primary neurons from the ganglionic eminences of E16 mouse brains , matching the region and timepoint used for our original target screening . Here , we aimed to directly test whether the Foxp2-positive neurons derived from the developing basal ganglia show altered neurite outgrowth when the gene is mutated . The assay was facilitated by availability of a mouse model ( Foxp2-R552H ) in which the protein is expressed at normal levels , but is nevertheless dysfunctional [9] . R552H mice recapitulate an aetiological mutation that causes speech and language deficits in a large human family . This change yields a substitution in the DNA-binding domain which severely impairs the transcription factor function of the mutant protein [30] , such that the overall phenotype of homozygous R552H animals is very similar to that observed for mice which completely lack Foxp2 [9]–[11] . However , unlike the Foxp2-null mice , R552H homozygotes still express detectable levels of the protein , allowing us to clearly identify Foxp2-positive cells in our primary cultures via antibody staining . This represents an important measure , given the heterogeneous nature of the dissected material used to generate the primary culture . We again observed obvious differences in neurite outgrowth associated with presence of functional Foxp2 ( Figure 6A ) . A blinded analysis revealed statistically significant changes in quantitative measures of neurite outgrowth for Foxp2-expressing neurons from wild-type embryos as compared to those from homozygous Foxp2-R552H littermates ( Figure 6B ) . In particular , the latter showed significant reductions in total outgrowth ( p = 0 . 001 ) ; mean ( p<0 . 001 ) , median ( p = 0 . 008 ) and maximum process length ( p<0 . 001 ) ; and average number of branches ( p = 0 . 003 ) . Thus , loss of Foxp2 function in striatal neurons that normally express this transcription factor yields significant reductions in multiple indices of neurite outgrowth . When Foxp2-negative cells from the wild-type cultures were compared to equivalent cells from mutants ( Figure 6C ) , it was only the total outgrowth that met significance ( p = 0 . 013 ) . These findings are strongly in agreement with differences in levels of Foxp2 expression , neurite outgrowth and correlated physiological properties between the two major subpopulations of striatal medium spiny neurons ( MSNs ) in vivo . While both striatonigral ( Drd1a ) and striatopallidal ( Drd2 ) MSNs continue to increase their dendritic area well into adulthood , Drd1a MSNs develop significantly more dendrites [31] . This dichotomy in dendritic growth contributes to key physiological differences between both MSN populations , although the underlying mechanisms remain unknown [31] . Furthermore , studies of cultured striatal neurons demonstrate that Drd1a MSNs have larger dendritic trees than Drd2 MSNs , invoking intrinsic mechanisms [31] . To study whether these intrinsic differences in dendritic growth correlate with Foxp2 expression levels , we investigated mice expressing enhanced green fluorescent protein ( EGFP ) either mainly in Drd1a or Drd2 MSNs [32] . We found that Foxp2 shows consistently high expression in striatonigral Drd1a MSNs and very low expression in Drd2 MSNs throughout the striatum ( Figure S7 ) , further supporting roles for Foxp2 in neurite outgrowth .
Although early studies of Foxp2 orthologues in multiple species suggested that it may play crucial roles in neurodevelopment [15] , [16] , the exact nature of such roles has not been established . Indeed , much of the existing knowledge regarding neuronal functions of this transcription factor instead concerns its impacts on the postnatal CNS [9] , [14] . In the present study we employed a high-throughput functional genomic strategy to shed new light on the in vivo activities of Foxp2-dependent pathways in the developing CNS . Of note , among the biological themes that we identified , our comprehensive ChIP-chip and expression profiling in midgestation brain tissue independently and consistently highlighted gene networks underlying neurite development and morphogenesis , axon extension and axon guidance . These findings drove us to specifically assess the impact of the Foxp2 gene on neurite outgrowth phenotypes in genetically manipulated neuronal cell models and primary neurons from embryos of mutant mice . Our functional experiments confirmed regulation of the highlighted gene networks and indicated that wild-type Foxp2 thus enhances multiple facets of neurite development in vivo , including outgrowth process length and branch number . The data suggest that the mode of action may be predominantly cell autonomous , since the functional effects were mainly restricted to the subset of Foxp2-expressing cells within a mixed population of neurons . This possibility of cell-autonomous effects is an interesting hypothesis that could be clarified in further studies . Our neurite outgrowth findings are in line with new evidence regarding the functional impact of evolutionary differences between FOXP2 orthologues [33] . For example , it is known that this transcription factor underwent two amino-acid substitutions on the human lineage after splitting from the chimpanzee lineage , leading to speculation that such changes may have been important for evolution of spoken language . In a recent study , researchers inserted the relevant substitutions into the endogenous Foxp2 gene of mice , and observed that striatal neurons had significantly longer dendrites and increased synaptic plasticity [33] . By contrast , we have shown that mice with loss of function of Foxp2 have statistically significant reductions in neurite outgrowth ( Figure 6 in the present paper ) and decreased synaptic plasticity [9] . Furthermore , the identification of potential regulatory links between Foxp2 and neural connectivity may be informative for wider discussions regarding the evolution of vocal learning [34] . Auditory-guided vocal learning is a rare trait that is only found in a small number of animal groups; the best understood examples include speech acquisition in humans and learning of song by certain bird species . As noted above , while human FOXP2 has been implicated in speech abilities [4] , [7] , [8] , avian FoxP2 is required for normal song-learning in songbirds [12] , [13] , supporting the view that this is a molecule with broader relevance for vocal-learning in multiple species . Intriguingly , it has been independently proposed that specific changes in patterns of neural connectivity in the brains of vocal learners account for the differences in their speech/song behaviours relative to other closely-related species that lack such abilities [34]–[36] . Perhaps evolutionary differences in FoxP2 orthologues may contribute to altered patterns of connectivity in the different species , and thereby help to explain differential capacities for vocal learning . Since we did not assess the impact of evolutionary changes in the present study , this remains an open question for future investigation using comparative functional genomics . To our knowledge , the current report represents the first large-scale in vivo characterisation of direct and indirect Foxp2 targets in the embryonic brain . It is of interest to consider how the present findings relate to published screens that used more limited ChIP surveys [19]–[21] , or that employed expression profiling [22] , [33] . The extent of direct overlap with previous datasets is complicated by three confounding factors . First , there are differences in scope of screening; the prior ChIP-chip investigations only queried a small subset of known promoters [20] , [21] . Second , there are differences in species under investigation . Previous target screens largely focused on human and/or chimpanzee FOXP2 , and the differences between the two [19]–[22] , [33] , while here we chose to comprehensively define the pathways regulated by murine Foxp2 . Mouse models offer considerable advantages for functional genomics , and careful integration of murine data with those from other species will enhance our understanding of evolutionary roles of this gene . Finally , the majority of earlier studies screened neuron-like cells grown in culture [19]–[21] , and no investigation of this transcription factor has reported integrated use of genome-wide ChIP and expression profiling to screen the same tissue . Nevertheless , many important consistencies are observed between the different datasets , particularly in the biological themes and processes that they implicate . For example neurite outgrowth pathways and synaptic plasticity are over-represented in all FoxP2 ChIP-chip datasets across different species and neuronal cell-type , in vitro and in vivo [20] , [21] . These processes are closely related during the development of neuronal networks . Genes controlling neurite outgrowth or axon guidance during early development have crucial roles in maturation and stabilisation of synaptic connectivity at later stages and eventually in activity-dependent synaptic plasticity in the mature brain throughout life ( such as neurotrophins , semaphorins and ephrins ) [37] , [38] . Hence , the strong impact of Foxp2 on neurite outgrowth during one particular stage at E16 might even reflect major Foxp2 functions that are relevant throughout the development and maintenance of neuronal networks . A case in point is provided by our data demonstrating that Nrn1 is a highly robust downstream target . The Nrn1 gene encodes neuritin , which is already expressed at embryonic stages of development and was initially identified as a downstream effector of neuronal activity and neurotrophin-induced neurite outgrowth [28] . Nrn1 not only showed one of the strongest enrichment signals in our in vivo ChIP experiments , but was independently detected as a target in our systematic expression profiling experiments of equivalent tissue and by in situ hybridisation – the corresponding human homologue was also one of the top direct targets reported in a small-scale ChIP screen of human foetal brain tissue [20] . A number of additional genes , which overlap with earlier studies , merit further comment . The Cck gene , which showed convergent evidence in our embryonic ChIP experiments , expression profiling screens and in situ hybridisation analyses , was reported as a direct target in both prior published human ChIP-chip studies [20] , [21] . Lmo4 ( Lim domain only 4 ) was found to be indirectly downregulated by Foxp2 in our analyses of embryonic brain tissue ( Table S4 ) and the human orthologue LMO4 was similarly repressed by FOXP2 in previous expression profiling studies of human neuron-like cells by Konopka and colleagues [22] . Interestingly , in that earlier study using cellular models , this indirect target was repressed both by human and chimpanzee versions of FOXP2 [22] . LMO4 encodes a transcription factor that plays important roles in cortical patterning , and is one of the few genes known to show asymmetric expression in the embryonic human brain [39] . Efnb2 ( Ephrin-b2 ) , a well-validated direct target ( Figure 1 , Figure 4 , Figure 5 ) was identified in the Konopka et al . study as one of a small number of genes that may be differentially regulated by human and chimpanzee FOXP2 orthologues [22] . This gene is of particular interest since it is implicated in neurite outgrowth and axon guidance ( and also synaptic plasticity ) in the basal ganglia and related brain structures [40] . In addition , Nell2 , a validated ChIP and expression array target ( Figure 3 ) , has also been linked to neurite outgrowth [41] , and has recently been shown to promote neuronal survival by trans-activation by estrogen [42] . Given the substantially enhanced scope of ChIP screening in the present study , we were able to identify many interesting novel targets that could not be isolated in the earlier work . For example , our high-confidence shortlist of direct targets includes Pak3 – a downstream effector of the Rho family of GTPases which plays critical roles in pathways restraining neurite growth [43]; Nptn ( neuroplastin ) – encoding a synaptic glycoprotein involved both in development/maintenance of synaptic connections [44] and in long-term plasticity [45]; Wasf1 – a gene that regulates activity-induced changes in dendritic spine morphogenesis [46] and is involved in actin remodelling during axon growth [47]; the neuronal semaphorins Sema4f [48] and Sema6d [49]; as well as Ywhah ( also known as 14-3-3 ) , which encodes an adapter protein implicated in presynaptic plasticity [50] ( Figure 1 , Figure 4 , Figure 5; Table S3 ) . Although the screening tissue was embryonic brain , many of the relevant genes have functions that go beyond this to also influence neural plasticity at later stages . Overall , this dataset will be important for directing follow-up studies of Foxp2-dependent pathways and assessing their involvement in traits such as acquisition of motor-skills [9] , vocal learning [14] , and spoken language [1] . While it is likely to be an indirect target of Foxp2 regulation , it is noteworthy that Evf1/2 ( Shhrs ) showed such highly increased expression in Foxp2-S321X mice . It has been shown that the Evf2 RNA molecule co-operates with the Dlx2 protein to activate the Dlx5/6 enhancer element [27] . Thus it is interesting that both the DLX1/2 and DLX5/6 loci have been implicated in autism via independent studies , including a common polymorphism in the DLX5/6 enhancer itself [51]–[53] . Of 340 genes showing differential expression ( p<0 . 01 ) between mutant and wild-type ganglionic eminences , only 19 ( ∼5% ) corresponded to putative direct targets of Foxp2 from the ChIP-chip screens . Thus , most of the expression differences observed in the transcriptional profiling experiments are unlikely to represent direct modulation due to Foxp2 binding , but could instead represent cascade effects further downstream ( i . e . loss of Foxp2 directly alters expression of a relatively small subset of genes , which in turn indirectly affect many others ) . Discrepancies between the ChIP-chip and expression profiling datasets may also result from our experimental design: the former could potentially detect binding events of Foxp2-expressing neurons anywhere in the brain , while the latter was targeted specifically at the ganglionic eminences , a region showing particularly high Foxp2 levels . Foxp2 target genes that are not expressed in this structure could therefore be observed in the ChIP study , but would not be detected in the expression analysis . An example of such a target is Sema3a . The promoter of this gene was bound by Foxp2 in our ChIP study ( Figure 1 ) , but its expression only overlaps with Foxp2 expression in the cerebellum ( Figure S6 ) . Nevertheless , it is not unusual in studies of transcription factor function to observe substantial differences between promoter occupancy maps and transcriptional profiling data . It is well established that transcription factors can be poised ready at particular genomic sites , awaiting important co-factors , before modulating expression of the relevant targets [2] , [54] , [55] . The present investigation queried the vast majority of known promoters in the genome , but we acknowledge that the screening strategy is unable to uncover potential regulatory sequences that lie outside classical promoter regions . In earlier work , based on low-throughput shotgun sequencing of human FOXP2-ChIP fragments , we identified a FOXP2-bound element in the first intron of CNTNAP2 ( contactin-associated-protein-like-2 ) a gene implicated in language impairments and autism [19] . Although the mouse genome contains an orthologous region to the human FOXP2-bound regulatory element of CNTNAP2 , this was not represented on the arrays used in this study , and hence it escaped detection . When we carried out ChIP-PCR experiments using the same mouse embryonic brain tissue as used for ChIP-chip we demonstrated clear Foxp2 occupancy of the orthologous region in mouse Cntnap2 . Specific enrichment was observed in the wild-type brains; while no enrichment was found in equivalent tissue from the mutant null controls ( see Figure S8 and Table S7 ) . Studies are now underway using ‘ChIP-seq’ techniques ( coupling ChIP to next-generation-sequencing ) to allow a fully unbiased view of FOXP2/Foxp2 binding throughout the genome . Among the validated direct targets of Foxp2 identified in our study there were a number of microRNA ( miRNA ) molecules , including mir-124a and mir-137 . miRNAs are an extensive class of short ( ∼18–23 nucleotide ) noncoding molecules which provide extra layers of dynamic control in networks of gene expression [56] . miRNAs are abundant in the brain and implicated in critical aspects of nervous system development and function , ranging from early neurogenesis and proliferation [57] , through neural differentiation and dendrite morphogenesis [58] , to adaptive mechanisms in mature neurons , including learning and memory [59] . They play pivotal roles in processes such as neurite outgrowth , axonal pathfinding and synaptic plasticity , mechanisms for which localised rapid control of protein synthesis is paramount [58] , [59] . In conclusion , the use of in vivo genomic screening strategies in the developing embryonic brain has proved to be a powerful approach for understanding the biology of Foxp2 , one of the most intriguing transcription factors of the CNS . This starting point led us to functional characterisation of new mechanisms of Foxp2 action , in particular the modulation of networks involved in neurite outgrowth , axonogenesis and other core aspects of neural development . Future studies will define how these regulatory networks differ between distinct species , what role miRNAs play in Foxp2-related pathways and phenotypes and will investigate whether it is possible to rescue the established neurobiological effects associated with loss of Foxp2 function , through manipulation of key targets . Ultimately , such work promises to fully uncover the functional pathways that connect Foxp2 with plasticity of the developing CNS .
In vivo Foxp2-ChIP in embryonic mouse brain tissue was performed according to the protocol previously described by Vernes and colleagues [21] . Each of the three replicates included whole brain tissue ( from the telencephalon to the brain stem at the level of the foramen magnum ) at E16 ( embryonic day 16 ) , a developmental timepoint of high Foxp2 expression [16] , pooled from 5–6 mice of matching genotype . Experiments were carried out either with wild-type embryos , or homozygous Foxp2-S321X mutants as negative controls . S321X mutants carry an early nonsense mutation that disrupts Foxp2; the resulting combination of nonsense-mediated RNA decay and protein instability leads to a complete lack of detectable Foxp2 protein in the brain [9] . The wild-type embryos and mutant controls used in these experiments were all matched littermates , backcrossed for at least ten generations into a C57BL/6J strain , maximizing the homogeneity of the genomic background . Although homozygous mutants display developmental delays and reduced cerebellar growth after birth , they show no gross anomalies in brain anatomy or development during embryogenesis [9] . All animal work was carried out conforming to the regulatory standards of the UK Home Office , under Project Licence 30/2016 . E16 mouse brains were extracted , snap frozen in liquid nitrogen and stored at −80°C until use . Each whole brain was weighed , then chopped finely with a razor on ice . Brains were pooled to achieve a total weight of between 0 . 3 and 0 . 5 g of tissue ( between 5–6 brains per replicate ) and resuspended in 5 ml PBS . A 1/10 volume ( 500 µl ) of cross-linking buffer was added prior to 15 minutes incubation with agitation at room temperature . Formaldehyde was quenched via the addition of 125 mM glycine . Cross-linked tissue was washed in PBS before brief mechanical homogenisation . Pellets were then incubated in two in vivo ChIP lysis buffers at room temperature for ten minutes each: Buffer 1 ( 50 mM HEPES-KOH pH = 7 . 5 , 140 mM NaCl , 1 mM EDTA , 10% glycerol , 0 . 5% NP-40 , 0 . 25% Triton X-100 , protease inhibitors ) ; Buffer 2 ( 200 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 10 mM Tris pH = 8 , protease inhibitors ) . After collection via centrifugation , nuclei were resuspended in 5 ml sonication buffer ( 10 mM Tris-HCl pH = 8 , 1 mM EDTA , 0 . 5 mM EGTA , protease inhibitors ) . Samples underwent 15 rounds of 20-second sonication pulses at 30% power , with 60 seconds on ice between each round ( Branson Digital Sonifier - S450D ) . Agarose gel electrophoresis was used to confirm that fragment size was 300–1000 bp . Cells were centrifuged at 10 , 000 g and 4°C for 10 minutes to remove cell debris . 10 µg of polyclonal rabbit anti-Foxp2 antibody ( C-terminal antibody , Geschwind Laboratory , UCLA ) [20] pre-coupled to 100 µl Dynal M-280 rat anti-rabbit IgG magnetic protein-A beads was added and incubated at 4°C , rotating overnight . Beads were washed five times in RIPA buffer and once in TE buffer . Chromatin was eluted from beads in TE buffer with 1% SDS at 65°C for 10 minutes with agitation . The chromatin was then incubated at 65°C overnight to reverse cross-links . Purified chromatin was amplified via Ligation Mediated PCR ( LMPCR ) according to published protocols [60] . Size and purity of DNA was assessed via spectrophotometry and gel electrophoresis . 2 µg of amplified immunoprecipitated chromatin , or total input DNA was fluorescently labelled with Cy5 and Cy3 respectively using random primers provided in the BioPrime DNA labelling system ( Invitrogen ) . The labelling reaction was allowed to proceed for 16 hours at 37°C , before purification by sodium acetate precipitation . Hybridisation to mouse promoter arrays ( Agilent Technologies , #G4490A ) was carried out by the UCLA microarray core facility , according to the manufacturer's instructions . Arrays consisted of 60-mer oligonucleotides spanning ∼8 kb ( 5 . 5 kb upstream and 2 . 5 kb downstream of TSS ) at each of ∼17 , 000 mouse promoter regions . Probes were spaced on average , between 100–300 bp apart , with approximately 25 probes for each promoter region . Three littermate matched sets of pooled wild-type or mutant control chromatin samples were applied to microarrays , each using its respective input DNA sample as the internal reference on the array . Thus , the three wild-type and three mutant control datasets represent signals obtained from a total of 34 individual mouse embryos . Array images were scanned using the Axon GenePix 4000B . Data were retrieved and initial quality control carried out using the Axon GenePix 4000B software package . All promoter coordinates and probes were mapped with reference to the NCBI m36 mouse assembly . Microarray data analysis was carried out using the mArray package for R [61] . LOESS normalisation and background correction was performed within each array . Data were normalised between arrays using quantile normalisation , and mean values were calculated from three biological replicates ( wild-type or mutant control experiments ) for each probe - called ‘probe scores’ , such that a score of 1 corresponds to 2-fold enrichment in ChIP versus total input DNA . All negative probe scores were assigned a value of zero . A ‘window-adjusted score’ for each probe was then calculated as the median value of each probe score and its nearest neighbour on either side . Neighbouring probes were only considered if they fell within 500 bp upstream or 500 bp downstream of the central probe . This window size was based on the average size of the labelled DNA fragments , estimated to be approx 1000 bp . Thus , a true binding event would likely be indicated by positive scores of multiple neighbouring probes within a 1000 bp window . In cases where there were less than three probes located within this 1000 bp window showing a signal greater than background then the window-adjusted score was set to zero . This process helps to guard against artificial skewing of enrichment values at edges of promoter regions . The use of mutant null controls enabled us to robustly assess the empirical significance of wild-type ChIP results . The data from the mutant control experiments were used to estimate a null distribution of window scores; that is , the non-specific signals produced by the Foxp2-ChIP protocol even when there is no Foxp2 protein available for pulldown . ( Note that a subset of binding events in mutant null controls could potentially be due to crossreactivity of Foxp2 antibodies with closely related proteins , such as Foxp1 or Foxp4 , that may bind to the same promoter . However , prior work with the antibody used here suggests that levels of crossreactivity are extremely low [20] . ) From this null distribution of window scores we calculated the threshold which excluded 99% of all datapoints in controls . This threshold could then be applied to the wild-type array data . Chromatin isolated during Foxp2-ChIP in mutant and wild-type mouse brains was amplified using a semi-quantitative PCR technique , as described previously [21] , using primers directed towards the peak regions of enrichment ( Table 3 ) . The β-actin housekeeping gene promoter was used as a negative control . The ganglionic eminences , sites of particularly high embryonic Foxp2 expression [16] , were dissected from E16 brains of six wild-type mice and six homozygous Foxp2-S321X mutant littermates . For each embryo , the left- and right-hemisphere ganglionic eminences were pooled in TRIzol reagent and RNA was extracted using the QIAGEN RNeasy kit , according to the manufacturer's instructions . RNA yield was measured using a NanoDrop ND-1000 spectrophotometer ( NanoDrop Technologies , Wilmington , DE ) , and its quality was assessed using RNA6000 Nano Assays on an Agilent Bioanalyzer 2100 ( Agilent Technologies , Santa Clara , CA ) . Gene expression profiling was performed using whole-genome mouse BeadChip arrays from Illumina ( San Diego , California , USA ) , which include 45 , 281 probes representing 31 , 492 mouse transcripts . In brief , 500 ng of total RNA was reverse transcribed to synthesize first- and second-strand complementary DNA ( cDNA ) . Following purification on spin columns , in vitro transcription was used to synthesize biotin-labelled complementary RNA ( cRNA ) . 1500 ng of biotin-labelled cRNA was hybridized to Mouse WG-6 V2 Expression BeadChip arrays ( Illumina Inc . , San Diego , CA ) at 55°C for 18 h . The hybridized arrays were washed and labelled with streptavidin-Cy3 according to the manufacturer's protocols before being scanned with the Illumina Bead Array Scanner . Raw data were exported from the Illumina BeadStudio software ( v3 . 4 . 0 ) for further processing and analysis using the R statistical software [62] and BioConductor packages [63] . Signal data and detection scores were extracted for each of the 12 samples . Signal data were background corrected by subtracting the average signal from the negative control probes on each array , prior to being transformed and normalised using the ‘VSN’ package [64] . Quality control analyses , including hierarchical clustering and principal component analysis ( PCA ) , identified one outlier sample ( from the wild-type group ) . This sample had very low signal compared to other samples while hybridisation and labelling metrics were normal , suggesting a sample problem rather than a technical issue . It was sufficiently outlying to remove from further analysis and the remaining 11 samples were re-normalised . The dataset was then filtered to remove probes that were not detected ( detection score <0 . 95 in all samples ) , resulting in a final dataset of 24 , 479 probes . Statistical analysis was performed using the ‘Linear Models for Microarray Analysis’ ( Limma ) package [65] . Differential expression between mutant and wild-type animals was assessed using a linear model that included effects for genotype and litter . Raw p-values were corrected for multiple testing using the false discovery rate ( FDR ) controlling procedure of Benjamini and Hochberg [66] . Fourteen probes were significant at a FDR of 5% . The larger set of 340 probes significant at p<0 . 01 was used for further biological investigation . We performed permutation tests on the genotype labels ( 11 choose 5 ) , taking litter effects into account , and found that ≥340 genes were differentially expressed at p = 0 . 01 in only 9 out of the possible 108 permutations ( ∼8% ) . Gene annotation was added to the final probe list using the relevant annotation file ( MouseWG-6_V2_0_R0_11278593_A . txt ) from the Illumina website ( http://www . illumina . com ) . Neuro2a ( murine neuroblastoma ) cells were cultured in ‘Growth media’: Modified Eagles Medium ( MEM ) ( Sigma ) supplemented with 10% Foetal Calf Serum ( Sigma ) , 2 mM L-glutamine ( Sigma ) and 2 mM Penicillin/Streptomycin ( Sigma ) . Cells were grown at 37°C in the presence of 5% CO2 . Stable Neuro2a cell-lines overexpressing Foxp2 protein or non-expressing controls , were generated via transfection with pcDNA3 . 1/Foxp2 ( mouse isoform I - untagged ) or the empty vector , using Genejuice ( Novagen ) according to the manufacturers' instructions . Cells were cultured in complete medium supplemented with 500 µg/ml G418 ( Calbiochem ) as a selective agent . Resistant single colonies were isolated 20 days after transfection , then cultured and expanded independently in the presence of G418 ( 500 µg/ml ) . Expression of recombinant Foxp2 was confirmed using qRT-PCR and Western blotting with two polyclonal antibodies recognizing different epitopes of the protein ( goat N-terminal antibody , Santa Cruz Biotechnology [30]; rabbit C-terminal antibody , Geschwind Laboratory , UCLA [20] ) . Three Foxp2-transfected clones with a high and consistent level of expression and three empty vector clones were chosen for use in further experiments . Neuro2a cells were differentiated via the addition of Modified Eagles Medium supplemented with 2% Foetal Calf Serum ( Sigma ) , 2 mM L-glutamine ( Sigma ) , 2 mM Penicillin/Streptomycin ( Sigma ) and 20 µM all-trans retinoic acid ( ‘Differentiation media’ ) . RNA was extracted from three independent clones of Neuro2a cells stably transfected either with murine Foxp2 or the empty control vector following culture in growth media or differentiation media ( for 24 or 48 hours ) . Total RNA was extracted from cells harvested in TRIzol reagent using the RNeasy kit ( QIAGEN ) according to manufacturers' instructions . Reverse transcription was performed as described previously [21] . Small molecular weight RNA was harvested using the Purelink miRNA isolation kit ( Invitrogen ) according to manufacturers' instructions . In order to assess miRNA expression levels , the small molecular weight RNA was polyadenylated prior to reverse transcription using the NCode miRNA First-strand cDNA synthesis kit ( Invitrogen ) , as per the manufacturers' protocol . PCR reactions utilised SYBR Green supermix ( BioRad ) as described [21] . Primers specific for candidate genes and the control housekeeping genes GAPDH/Gapdh ( glyceraldehyde 3-phosphate dehydrogenase ) and U6 ( small nuclear RNA ) were designed using PrimerBank [67] ( Table 4 ) . Quantitative PCR reactions were performed on the iQ5 thermal cycler real-time PCR detection system ( BioRad ) according to manufacturers' instructions . Melting curve analysis was performed to assess the specificity of the amplification . Data analysis was performed using iCycler software ( BioRad ) , and quantification was via the comparative CT method [68] . Fold changes are reported in response to Foxp2 expression compared to cells transfected with an empty vector , following normalisation to an internal control , the GAPDH housekeeping gene ( for gene expression ) or U6 small nuclear RNA ( for miRNA expression ) . Data are expressed as mean of values from three independent clones ± standard error of the mean ( SEM ) . Statistical significance was assessed using Students t-tests ( two-tailed ) . Ganglionic eminences from both hemispheres were dissected from wild-type and homozygous Foxp2-R552H E16 littermates [9] . R552H mice carry a missense mutation affecting a conserved arginine residue located in the Foxp2 DNA binding domain , matching an aetiological mutation found in a well-characterised multigenerational family with speech and language disorder ( the KE family ) [4] . R552H homozygous mice demonstrate comparable phenotypes to homozygous Foxp2 knockouts [9] . This suggests that the introduction of this mutation yields a stable , but non-functional protein product , a conclusion that is supported by in vitro functional studies [30] . Dissections were performed in dissection buffer ( 15 mM HEPES , 0 . 01% NaHCO3 , 25 mM glucose in HBSS-CMF ) and dissected regions were immediately chopped on ice and pelleted at 800 RPM and 4°C for 5 minutes . The pellet was incubated in papain solution ( 20 units/ml papain , 1 mM L-cysteine , 0 . 5 mM EDTA , 100 units/ml DNaseI , in dissection buffer ) on ice for 5 minutes then at 37°C for 10 minutes , agitating regularly . The enzymatic reaction was halted by addition of Ovo-BSA solution ( 10 mg/ml ovomucoid , 10 mg/ml BSA in dissection buffer ) . Cells were pelleted at 1000 RPM for 5 minutes at 4°C and the pellet was washed then re-suspended in complete medium ( neurobasal media ( Sigma ) supplemented by 2 mM Glutamax ( Sigma ) . 2 mM Penicillin/Streptomycin ( Sigma ) and 1X B27 supplement ) . Suspension was triturated using plastic and glass pipettes to dissociate any remaining cell clumps before passing the cell suspension through a 70 µm cell strainer . Single cell suspensions were seeded onto laminin and poly-D-lysine coated coverslips ( BD Biosciences ) at a density of 6 . 3×104 cells per well into 24 well plates and grown at 37°C in the presence of 5% CO2 in complete medium . After 4 days in culture , cells were fixed using 4% Paraformaldehyde solution for 15 minutes at room temperature and permeablised in wash solution ( 0 . 1% Triton X-100 in TBS ) . Antibodies were diluted in Blocking Solution ( 1% Fish Gelatine , 0 . 1% Triton X-100 , 5% BSA in PBS ) . Cells were co-stained at 4°C overnight , using two primary antibodies; an anti-MAP2 rabbit polyclonal antibody ( Chemicon ) and an anti-Foxp2 mouse monoclonal antibody recognising an epitope near the C-terminal end of the protein ( Gift from Prof . A . Banham ) . Detection was then facilitated via four rounds of antibody incubation , which allowed magnification of the Foxp2 signal . Cells were incubated with anti-rabbit TRITC ( Alexa Fluor 568 , Molecular Probes ) plus anti-mouse biotinylated ( BA9200 , Vector Labs ) secondary antibodies , followed by incubation with anti-rabbit TRITC plus anti-biotin FITC ( Alexa Fluor 488 , Molecular Probes ) antibodies , each for 1 hour , shaking under limited light exposure . This secondary/tertiary antibody incubation was then repeated under the same conditions . Nuclei were visualised using mounting media containing a DAPI counterstain ( VectaShield ) . Cells were viewed on a Nikon Eclipse TE2000U fluorescence inverted microscope . Images were captured using a Hamamatsu black and white C4742-95 Orca hi-sensitivity CCD camera with IPLab imaging software ( Scanalytics Inc ) , and analysed using the neurite outgrowth function of Metamorph Version 7 . 6 ( Molecular Devices ) . Statistical analyses were carried out using ANCOVA ( analysis of covariance ) for genotype and embryo , followed by post-hoc Sidak correction . Data are expressed as the mean ± standard error of the mean ( SEM ) . In situ hybridization was carried out on 10 µM frozen sections of E16 . 5 embryo heads as previously described [69] . An approximately 500 bp fragment of each target transcript was subcloned into pCR4-TOPO ( Invitrogen ) for dioxygenin-labelled riboprobe synthesis . Primer sequences for the riboprobes are available on request . Equivalent parasagittal sections were hybridized in parallel from three wild-type and three homozygous Foxp2-S321X mutant embryos and all slides were developed for 16 hours , or 6 hours in the case of Shhrs . In all cases a sense-strand negative control riboprobe gave no specific signal ( data not shown ) . | Foxp2 codes for an intriguing regulatory protein that provides a window into unusual aspects of brain function in multiple species . For example , the gene is implicated in speech and language disorders in humans , song learning in songbirds , and learning of rapid movement sequences in mice . Foxp2 acts by tuning the expression levels of other genes ( its downstream targets ) . In this study we used genome-wide techniques to comprehensively identify the major targets of Foxp2 in the embryonic brain , in order to understand its roles in fundamental biological pathways during neurodevelopment , which we followed up through functional analyses of neurons . Most notably , we found that Foxp2 directly and indirectly regulates networks of genes that alter the length and branching of neuronal projections , an important route for modulating the wiring of neural connections in the developing brain . Overall , our findings shed light on how Foxp2 directs particular features of nervous system development , helping us to build bridges between genes and complex aspects of brain function . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neurolinguistics",
"molecular",
"neuroscience",
"gene",
"networks",
"animal",
"genetics",
"neuroscience",
"gene",
"function",
"molecular",
"genetics",
"developmental",
"neuroscience",
"gene",
"expression",
"biology",
"cellular",
"neuroscience",
"genetics",
"genomics",
"gen... | 2011 | Foxp2 Regulates Gene Networks Implicated in Neurite Outgrowth in the Developing Brain |
Gene-specific , age-dependent regulations are common at the transcriptional and translational levels , while protein transport into organelles is generally thought to be constitutive . Here we report a new level of differential age-dependent regulation and show that chloroplast proteins are divided into three age-selective groups: group I proteins have a higher import efficiency into younger chloroplasts , import of group II proteins is nearly independent of chloroplast age , and group III proteins are preferentially imported into older chloroplasts . The age-selective signal is located within the transit peptide of each protein . A group III protein with its transit peptide replaced by a group I transit peptide failed to complement its own mutation . Two consecutive positive charges define the necessary motif in group III signals for older chloroplast preference . We further show that different members of a gene family often belong to different age-selective groups because of sequence differences in their transit peptides . These results indicate that organelle-targeting signal peptides are part of cells' differential age-dependent regulation networks . The sequence diversity of some organelle-targeting peptides is not a result of the lack of selection pressure but has evolved to mediate regulation .
Gene-specific , age-dependent regulation is critical for the growth and development of all organisms . The regulation is known to take place at the transcriptional and translational levels . For example , some genes are preferentially transcribed , and some mRNAs or proteins are preferentially degraded , in younger or older tissues . In contrast , the process of protein import into organelles is generally thought to be constitutive . To our knowledge , it has not been previously shown that a specific group of proteins is preferentially imported into an organelle of a certain age . However , whether protein-specific , age-dependent regulation can exist at the level of organelle protein import has not been thoroughly investigated because of difficulties in isolating organelles of different ages . Proteins imported into the endoplasmic reticulum ( ER ) , mitochondria , and chloroplasts are usually synthesized as higher molecular weight precursors containing N-terminal signal peptides for organelle targeting . Another common feature of these three organelles is the high sequence heterogeneity of their signal peptides . Even among members of a gene family , the sequences of their signal peptides are often diverse , while their mature protein regions are highly similar . Consensus secondary-structure motifs have been identified for ER and mitochondrial signal peptides , but their signal peptides still vary greatly in sequences and lengths [1] , [2] . No consensus sequence or structure has been identified for chloroplast-targeting signal peptides ( herein called “transit peptides” ) , and their length varies from 13 to 146 amino acids [3] , [4] . The reason for the sequence heterogeneity of these targeting signals is unknown . For the ER-targeting signal peptides , it has been proposed that the heterogeneity may have regulatory functions . Different sequences may interact with different accessory factors around the Sec61 translocon and thus modulate transport efficiency [5] . Protein import into chloroplasts is mediated by a translocon complex with components located in and around the two membranes of the envelope . Translocon components in the outer envelope membrane are called Toc ( translocon at the outer-envelope-membrane of chloroplasts ) proteins , and those in the inner envelope membrane are called Tic ( translocon at the inner-envelope-membrane of chloroplasts ) proteins ( for reviews see [4] , [6] , [7] ) . By importing three representative proteins into chloroplasts of different ages , Dahlin and Cline showed that very young developing chloroplasts exhibited very high import efficiency; as chloroplasts matured , their import efficiency for all three proteins decreased dramatically [8] . These findings have led to the supposition that chloroplast protein import capability is regulated globally in concert with chloroplast protein demand . As chloroplasts mature , their protein demand decreases , and their import capacity for all proteins declines accordingly . We show here that in higher plants , chloroplast precursor proteins can be divided into three age-selective groups , with each having a preference for chloroplasts of a different age . This differential age-dependent regulation is important for growth . We further define a necessary transit peptide motif for older chloroplast preference and show that different gene family members , through variations in their transit peptides , are preferentially imported into chloroplasts of different ages . This result suggests that one of the reasons for transit peptide sequence diversity among isoforms of a gene family is to achieve differential age-dependent import regulation .
Chloroplasts isolated from different leaves , from top to bottom along the stem of pea seedlings , show increasing degrees of maturity [8] . Previously , when these chloroplasts were used to investigate the import of precursors to the small subunit of RuBP carboxylase ( RBCS ) , light-harvesting complex of photosystem II ( CAB ) , and small heat shock protein Hsp21 , younger chloroplasts exhibited higher protein import efficiency [8] . We were interested in knowing the import behavior of some of the recently identified translocon components . Chloroplasts isolated from the four leaves of 16- to 18-d-old pea seedlings ( the youngest leaf is designated as leaf number 1; Figure 1A ) were tested for their ability to import prHsp93 ( “pr” prefix for precursor forms; see Table S1 for full names and accession numbers of all precursors used ) and prTic40 , with prRBCS as a control . The level of endogenous chloroplast stromal Hsp70 ( cpHsc70 ) , which remains unchanged throughout the developmental stages tested [8] ( Figure S1A ) , was analyzed by immunoblotting as a loading control . The import efficiency of prRBCS indeed declined as chloroplasts aged ( Figure 1B ) . Surprisingly , prHsp93 showed similar import efficiency into chloroplasts isolated from all four leaves , and the import efficiency of prTic40 increased in older chloroplasts ( Figure 1B ) . We then tested more precursors ( Table S1 ) . For better quantification , prHsp93 was co-imported with each precursor , and import results were normalized to the amount of mature Hsp93 imported . The amount of precursor protein synthesized by the in vitro translation system is very small , so this co-import is unlikely to affect the import of other precursors by saturating the import machinery . Indeed , the age dependency of prRBCS and prTic40 did not change when results were normalized to Hsp93 ( Figure 1C ) . However , if prHsp93 was not suitable for co-import for technical reasons ( see Materials and Methods ) , endogenous cpHsc70 was still analyzed and used for normalization . Our results showed that precursors could be divided into at least three age-selective groups . Group I precursors , which include prRBCS , prOE23 , prPC , prDJC22 ( Figure 1C ) , prDJC75 , prOE33 , and prFd-protA ( Figure S2 ) , were preferentially imported into younger chloroplasts . Group II precursors , which include prHsp93 , prPDH , prTic20 , prcpHsc70-2 , and prGlu2 ( Figure 1C ) , were imported into chloroplasts of different ages with similar efficiency . Group III precursors , which include prTic40 , prL11 , prcpHsc70-1 , and prPORB ( Figure 1C ) , exhibited increasing import efficiency as chloroplasts aged . The separation of precursors into three age-selective groups was observed when import was performed with an equal number of plastids or an equal amount of chlorophylls in each reaction ( see Materials and Methods and Figure S3 ) . Although most of the precursors we used were from Arabidopsis , some of them were from other species ( Table S1 ) , including three from pea ( prPC , prHsp93 , and prTic40 ) . We have also tested the import of rice and Arabidopsis prTic40 and Physcomitrella patens prOE23 and prL11 . Their age selectivity was the same as that of their orthologs ( data not shown ) . Thus , it is unlikely that the different import patterns we observed were due to the species origin of the precursors . To investigate which part of a precursor protein was responsible for the age selectivity , we swapped the transit peptides between prRBCS ( representative of group I precursors ) and prTic40 ( representative of group III precursors ) and imported the fusion proteins , RBCStp-mTic40 ( “tp” for transit peptide and “m” for mature protein ) and Tic40tp-mRBCS , into chloroplasts isolated from leaves of different ages . RBCStp-mTic40 had an import pattern similar to that of prRBCS ( Figure 2 ) . Tic40tp-mRBCS produced two lower molecular weight proteins after import , most likely because the prTic40 transit peptide is processed twice [9] , [10] . To make sure both proteins were within chloroplasts , after the import of Tic40tp-mRBCS , chloroplasts were treated with thermolysin to remove chloroplast-surface-bound proteins , and thermolysin-resistant mature proteins were quantified . The result showed that Tic40tp-mRBCS had age selectivity similar to that of prTic40 ( Figure 2 ) . We further fused the transit peptides of prRBCS and prTic40 to the N terminus of GST and imported the fusion proteins into chloroplasts of different ages . RBCStp-GST was preferentially imported into younger chloroplasts , just like prRBCS , and Tic40tp-GST was preferentially imported into older chloroplasts , just like prTic40 ( Figure 2 ) . Therefore , transit peptides are sufficient to determine age selectivity . If precursors are divided into three groups because of different characteristics of the transit peptides among different groups , then in import competition experiments , one precursor in excess should out-compete the import of precursors from the same group , but would have a smaller effect on the import of precursors from other groups . To test this we performed import competition experiments using Escherichia coli–produced recombinant prRBCS ( a group I precursor ) , in vitro–translated [35S]Met-labeled representative precursors from all three groups and chloroplasts isolated from 8-d-old pea seedlings . The degree of competition was indeed divided into three groups ( Figure 3 ) . At 0 . 5 µM recombinant prRBCS , the import of group I precursors prRBCS and prOE23 decreased by 50% to 60% , import of group II precursors prHsp93 and prPDH deceased by 20% , while the import of group III precursors prTic40 and prL11 was not affected . At 2 µM recombinant prRBCS , the import of the two group I precursors decreased 80% , import of the two group II precursors decreased about 40% , and the import of the two group III precursors decreased about 20% . This result indicates that different precursors in the same age-selective group most likely share the same import pathway . If the differential age-dependent regulation we observed is physiologically important , it could be expected that a group III precursor engineered with a group I transit peptide would not function optimally , which should have an impact on plant growth if the affected protein is important for growth . To test this idea , we examined the complementation of an Arabidopsis tic40 knockout mutant by the hybrid protein RBCStp-mTic40 , in which the group III prTic40 transit peptide was replaced by the group I prRBCS transit peptide . It has been shown that information for targeting and insertion of prTic40 to the inner membrane is contained within the mature region of prTic40 [9] , [10] , and replacing the prTic40 transit peptide with the prRBCS transit peptide did not affect the proper insertion and topology of Tic40 in the inner membrane [9] . We also first verified that the in vitro import efficiency of RBCStp-mTic40 and prTic40 into Arabidopsis chloroplasts was comparable ( data not shown ) . Tic40 is required for protein import into chloroplasts , and its knockout mutants have extremely retarded growth and small and pale green leaves with jagged leaf margins [11] , [12] . The RBCStp-mTic40-encoding cDNA was placed under the control of a 2-kb promoter fragment from the TIC40 gene ( Figure 4A ) . The original prTic40-encoding cDNA under the control of the same TIC40 promoter fragment was used as a control . The two constructs , designated as TIC40p:RBCStp-mTic40 and TIC40p:prTic40 , were used to transform tic40-1 , a knockout mutant caused by T-DNA insertion in the TIC40 gene [11] . As shown in Figure 4B , the TIC40p:prTic40 transgenic plants were indistinguishable from the wild type , indicating that the tic40 mutation was fully complemented . In contrast , the TIC40p:RBCStp-mTic40 transgenic plants all had a variable pale green color in between that of the tic40 mutant and the wild type , and retained the jagged-leaf-margin phenotype of the tic40 mutant , even though the steady state Tic40 protein level in the mature plants was comparable to that in the wild-type and in the TIC40p:prTic40-complemented plants ( Figure S4A ) . Fractionation of isolated chloroplasts also showed that Tic40 proteins were localized in the inner envelope membrane in both transgenic plants ( Figure S4B ) . These results indicate that replacing the prTic40 transit peptide with the prRBCS transit peptide prevented Tic40 from functioning optimally . However , we cannot exclude the possibility that there are differences other than age selectivity between the prRBCS and prTic40 transit peptides . For example , studies have shown that prRBCS cannot be imported into isolate pea root plastids [13] , although in stable transgenic wheat and maize lines , the prRBCS transit peptide can direct fusion protein import into plastids in root hair cells [14] , [15] . Thus , it is possible that the incomplete complementation is caused by failure of prRBCS transit peptide to direct Tic40 import into other cell types in roots . To begin to understand the physiological reason for the observed differential age-dependent import , we investigated whether a similar regulation exists in unicellular organisms . We used the unicellular green alga Chlamydomonas reinhardtii . Each Chlamydomonas cell contains a single chloroplast . Furthermore , cultures of Chlamydomonas can be synchronized by light/dark cycles , and ages of chloroplasts can be defined by hours of culturing in the light [16] . Thus , Chlamydomonas also has an age spectrum of chloroplasts during development but does not face the problem of having leaves/chloroplasts of different ages supported on the same stem in the same organism . If group III precursors prefer older chloroplasts solely because of chloroplast maturity , then group III precursors should also prefer to be imported into Chlamydomonas chloroplasts isolated from cells that have been cultured for longer periods in the light . Chloroplasts isolated from synchronized Chlamydomonas cells cultured for 1 , 6 , and 11 h in the light were used for in vitro import experiments . Endogenous Hsp70B of Chlamydomonas chloroplasts analyzed by immunoblots was used as a loading control , as Hsp70B level was steady in the three time points analyzed ( Figure S1B ) , similar to cpHsc70 in pea chloroplasts . We tested the import of one group I precursor ( prOE23 ) and two group III precursors ( prTic40 and prL11 ) . We also obtained cDNA encoding Chlamydomonas chloroplast precursor proteins Cr-prRBCS and Cr-prL11 ( prefix “Cr” for C . reinhardtii ) for import experiments . Similar to results reported previously [16] , import of Cr-prRBCS was highest in the 6-h chloroplasts and lower in the 1- and 11-h chloroplasts ( Figure 5 ) . All four other precursors tested showed the same pattern as Cr-prRBCS , despite the fact that they were recognized as group I ( prOE23 and Cr-prRBCS ) and group III precursors ( prTic40 , prL11 , and Cr-prL11 ) by pea chloroplasts ( Figures 1 and S5 ) . These results suggest that protein import into Chlamydomonas chloroplasts shows only a global regulation for all proteins , rather than a differential regulation according to precursor groups like in higher plants . If precursors are divided into three groups because of different characteristics of their transit peptides , there should be some sequence motif that is the determining feature for each group of transit peptides . We started with the group III transit peptides because signals that prefer older organelles represent a novel finding of our current study and may have biotechnology applications . Not surprisingly , comparing the transit peptides of the group III precursors we had identified did not reveal any consensus sequence motif . We then used the transit peptide of prTic40 as a model and made a series of alanine substitution mutants to systematically identify the regions important for older chloroplast recognition . The 72-residue prTic40 transit peptide was divided into eight blocks of nine amino acids , and each block was replaced with nine Ala residues ( Figure 6A ) . These Ala substitution mutants were imported into young and old chloroplasts isolated from the first and fourth leaves , respectively . All mutants , with the exception of A ( 10–18 ) and A ( 28–36 ) , still showed a preference for older chloroplasts ( Figure 6B and 6C ) . No imported mature protein was observed for the A ( 10–18 ) mutant in either young or old chloroplasts . To determine the location of the A ( 10–18 ) mutant precursors , after import of A ( 10–18 ) , chloroplasts were treated with trypsin , which can only penetrate the outer membrane but not the inner membrane . A ( 10–18 ) mutant precursors were completely degraded by trypsin , while imported wild-type mature Tic40 was resistant to trypsin digestion , indicating that the A ( 10–18 ) mutant precursors were outside the inner membrane ( Figure S6A ) . These results suggest that the A ( 10–18 ) mutant could bind to chloroplasts but could not be translocated into the stroma . The A ( 10–18 ) mutations most likely affected interaction of the prTic40 transit peptide with some translocon components that are required for translocation into both young and old chloroplasts . Furthermore , in all mutants except A ( 10–18 ) , we did not observe a significant increase in accumulation of precursors , suggesting that the mutations did not affect the processing of prTic40 transit peptide . The A ( 28–36 ) mutation severely reduced the import into older chloroplasts , indicating that residues 28 to 36 are important for older chloroplast recognition ( Figure 6B and 6C ) . These residues were further divided into two halves , and two more alanine substitution mutants were generated: A ( 28–31 ) and A ( 32–36 ) . A ( 28–31 ) no longer preferred older chloroplasts , while the A ( 32–36 ) mutant still did ( Figure 6B and 6C ) . Residues 28 to 31 ( Gly28-Arg29-Lys30-Ser31 ) were further individually substituted with amino acids with characteristics different from those of the original amino acids . Substitution of Gly28 with Pro ( Figure 6B and 6C ) or His or Ala ( data not shown ) , or substitution of Ser31 with Ala ( Figure 6B and 6C ) , did not change the age selectivity of prTic40 . In comparison , substitution of the positively charged Arg29 or Lys30 with Glu , or substitution of both residues with Ala , reduced preference for older chloroplasts . Substitution of both residues with Glu almost knocked out the import into older chloroplasts . Adding back Arg29 and Lys30 into the A ( 28–36 ) and A ( 28–31 ) mutants , which had severely reduced import into older chloroplasts , restored the preference of prTic40 for older chloroplasts ( Figure 6B and 6C ) . These data indicate that the two consecutive positive charges of Arg29 and Lys30 are important components for older chloroplast recognition in the prTic40 transit peptide . We further confirmed this result using another group III precursor , prL11 . Its transit peptide has one pair of consecutive positive charges , at residues 44 and 45 . Mutating Lys44 and Lys45 to Glu ( the prL11[KK4445EE] mutant ) severely knocked down import into older chloroplasts without affecting import into younger chloroplasts ( Figures 6B , 6C , and S6B ) , confirming that having two consecutive positive charges is a determining feature of group III transit peptides . However , two consecutive positive charges are also present in some group I and group II transit peptides . Thus , this motif is only one necessary feature . Other yet unknown structural motifs are required to work together with the two positive charges to build a sufficient group III signal . Many genes have duplicated from single genes in unicellular organisms into gene families in multicellular organisms . This duplication is often attributed to the need for tissue-specific regulation at the transcriptional level . However , for gene families encoding organelle proteins , the organelle-targeting signals are often diverse , despite having highly similar mature regions . The reason for this high sequence heterogeneity in the targeting signals is not known . Among the chloroplast precursors we tested , cpHsc70-1 and cpHsc70-2 provide one such example ( Figure S7A ) . While the mature regions of the two isoforms are 93% identical , the transit peptide region has only 75 . 5% identity . Interestingly , in green algae , including Chlamydomonas , chloroplast Hsp70 is encoded by a single gene , and the gene has duplicated since moss [17] . Our data show that cpHsc70-1 is a group III precursor and cpHsc70-2 is a group II precursor ( Figure 1C ) . We thus hypothesize that one of the reasons for transit peptide diversification after gene duplication is to achieve differential age-dependent import regulation through sequence differences in the transit peptides . To test our hypothesis , we found three more examples in which the protein is encoded by a single gene in Chlamydomonas but by a two-gene family in higher plants: chloroplast Cpn60α , Cpn10 , and BCCP ( sequences shown in Figure S7B–S7D ) . Import into chloroplasts of different ages showed that prCpn60α1 is a group I precursor and prCpn60α2 is a group II precursor , while prBCCP-1 is a group II precursor and prBCCP-2 is a group I precursor ( Figure 7A ) . The prCpn10-1 protein has the initiation methionine as its single methionine , and thus the mature protein could not be observed after the removal of the transit peptide when the protein was labeled by [35S]Met . We thus added two methionine residues at its C terminus and created the protein prCpn10-1MM . Import of prCpn10-1MM and prCpn10-2 into chloroplasts of different ages showed that prCpn10-1MM is a group I precursor and prCpn10-2 is a group II precursor . We also tested a family in which the proteins are only present in higher plants , not in Chlamydomonas . The small J-domain containing protein DJC23 is encoded by a three-gene family , DJC23 , DJC24 , and DJC66 , in Arabidopsis . DJC23 and DJC24 are more similar to each other than to DJC66 ( Figure S7E for sequences ) . Import of the three precursor proteins into chloroplasts of different ages showed that prDJC23 and prDJC24 are group I precursors and prDJC66 is a group III precursor . However , not all gene family members fall into different age-selective groups . All three isoforms of Arabidopsis protochlorophyllide oxidoreductase are group III precursors ( prPORA , prPORB , and prPORC; Figures 1C and S2 ) . If different isoforms fall into different age-selective groups because of sequence differences in their transit peptides , then changing the sequences should change the age-selective group of the precursor . One of the regions that is different between the transit peptides of cpHsc70-1 and cpHsc70-2 is around residues 23 and 24 of cpHsc70-1 , where the two positively charged amino acids Lys23-Arg24 in cpHsc70-1 are replaced with Thr-Lys at the corresponding position in cpHsc70-2 ( Figure 7B ) . Since we have shown that two consecutive positive charges is a necessary motif for group III transit peptides , we mutated Lys23 and Arg24 in cpHsc70-1 to Thr and Lys to resemble the sequence in cpHsc70-2 and created the mutant prcpHsc70-1 ( KR2324TK ) . Import of prcpHsc70-1 ( KR2324TK ) into chloroplasts of different ages showed that it was changed to a group II precursor , just like prcpHsc70-2 ( Figure 7B ) .
We have identified a new level of differential regulation residing at the stage of protein import into chloroplasts . We have further identified a necessary transit peptide motif specifying a preference for older chloroplasts . To our knowledge , this is the first functional motif identified for chloroplast-targeting transit peptides [18] , [19] . Most likely specific motifs are also present for the other two age-selective groups . These motifs are actually one set of tools available to multicellular organisms for differential age-specific regulation . The lack of differentially regulated import in Chlamydomonas suggests that differential regulation is not just used to select for proteins that are needed in young versus mature chloroplasts . Because of the limited number of precursors tested , it is difficult to generalize the function of proteins in each age-selective group . However , upright growth of higher plants results in the unique situation of having leaves of different ages on the same stem , exposed to different environmental and nutrient conditions . In addition , mature chloroplasts are maintained for a longer period of time . Group I proteins may be more important for chloroplast functions during rapid cell division and expansion under normal or high light conditions . Group II proteins may be the true housekeeping proteins that are needed at a similar level under all conditions , and Group III proteins , which in unicellular algae such as Chlamydomonas would be down-regulated like other chloroplast proteins in anticipation for the next cell division , may need to be maintained at a higher level in higher plants because they are important for long-term maintenance of chloroplasts under lower light conditions . How the three groups of transit peptides are decoded by receptors on the chloroplast surface remains to be investigated . They may interact with the same receptor with different affinities , or each may interact with a specific receptor . In Arabidopsis , the major receptor Toc159 is encoded by a four-gene family that is separated into three subfamilies , Toc159 , Toc132 , and Toc90 . Analyses of grape , rice [20] , soybean , and Medicago truncatula ( Figure S8 ) genomes indicate that their Toc159 families are also separated into the same three subgroups , suggesting that these three Toc159 subfamilies are most likely also present in pea . Based on small-scale analyses , different Toc159 subfamily members were shown to have different substrate preferences , and it was proposed that in Arabidopsis , the atToc159 ( “at” for A . thaliana ) subfamily functions as a selective receptor for photosynthetic proteins and the atToc132 subfamily functions as a selective receptor for housekeeping proteins [21]–[24] . However , recent large-scale proteomic and transcriptomic analyses of attoc159 mutants revealed an unexpected client protein promiscuity of atToc159 , indicating that the original separation of photosynthetic versus housekeeping is too simplistic [25] . Nonetheless , two transit peptides , those of prRBCS and of prFd , were shown directly to preferentially bind to atToc159 [22] , and both precursors are group I precursors from our study . One transit peptide , that of prPDH , was shown directly to preferentially bind to atToc132 [24] , a group II protein from our study . Proteomic analyses of the attoc159 and attoc132 mutants also support that Toc159 may be the receptor for group I precursors and Toc132 may be the receptor for group II precursors . Of the 44 atToc159-dependent proteins identified [25] , only one , prRBCS , was tested by us , and it is indeed a group I precursor . Of the 308 Toc159-independent proteins identified , we have tested nine precursors , and eight are in group II or III . The only exception is prCpn10-1 ( At2g44650 ) , which is classified as an atToc159-independent protein but is a group I protein in our analyses . Cpn10 is encoded by two highly similar genes , CPN10-1 and CPN10-2 , and the two proteins share 79 . 4% identity . Cpn10-2 is a group II protein . It is possible that some of the peptides from these two proteins were not distinguished . Of the 19 proteins whose abundance was increased or not changed in the attoc132 mutant [21] , we and others [8] have tested five , and all five are group I or group III precursors , suggesting that atToc132 is less important for the import of group I and III precursors . However , the overlap between the proteins identified by the proteomic approaches and the proteins tested by us is small . More work is needed to identify the receptors for the three groups of precursors . Our preliminary data suggest that the receptor for group III precursors is a thermolysin-sensitive chloroplast surface protein that is present in increasing amounts as chloroplasts age , but its identity remains to be uncovered . We also cannot exclude that some post-translational modifications that increase with age on one of the Toc159 family members are responsible for the increased import of group III precursors in older chloroplasts . The two consecutive positive charges in group III transit peptides may interact with some negatively charged modifications , like phosphorylations , on one of the receptors . More and more evidence indicates that organellar protein import is specifically regulated . The biogenesis and functions of three mitochondrial outer-membrane translocon proteins have recently been shown to be affected by phosphorylations through two cytosolic kinases [26] . Furthermore , during mitochondrial stress , protein import into mitochondria is reduced , allowing the protein ATFS-1 ( activating transcription factor associated with stress-1 ) to be imported into the nucleus , which is important for activation of the mitochondrial unfolded protein response [27] . In addition , the presence of a specific degradation system in the cytosol for chloroplast precursor proteins [28] also supports our finding that protein import into chloroplasts is regulated . In vitro , phosphorylation and exogenously added calmodulin inhibitor and redox reagents have also been shown to affect the function and association of some chloroplast translocon components [29]–[31] . Whether these modifications take place in vivo remains to be investigated . It has long been puzzling why the signal peptides for protein targeting to ER , mitochondria , and chloroplasts are so diverse at the amino acid sequence level . It has been proposed for ER-targeting signal peptides that the heterogeneity in sequences has regulatory functions [5] . It has been shown that during acute ER stress , translocation of ER proteins is attenuated in a signal-peptide-selective manner [32] . In addition , the intrinsic inefficiency of the prion protein signal peptide is required for pathogenesis of some prion mutations . Therefore , different signal peptide efficiencies , resulting from differences in sequences , can have a significant impact on organism physiology [33] . Here we show that sequences of chloroplast transit peptides determine the age selectivity of precursor proteins . Furthermore , different members of a gene family often belong to different age-selective groups because of sequence differences in their transit peptides . Therefore , the sequence diversity of these transit peptides has evolved to mediate age-selective regulation . Multi-gene family members of ER and mitochondrial proteins also share the property that they display high sequence similarity in the mature protein region but have more diverse signal sequences . Thus , it is likely that diversity in signal peptide sequences among gene family members has evolved to mediate protein transport regulation in ER and mitochondria as well .
Pea ( Pisum sativum cv . Little Marvel ) seedlings were grown on vermiculite in growth chambers under a 12 h light/12 h dark cycle at 20°C . Chloroplasts were isolated from pea seedlings as described [34] except that a blender , instead of a Kinematica Polytron homogenizer , was used . The cell-wall-deficient strain of C . reinhardtii , CC-400 cw15 , was inoculated in TAP medium [35] at a density of 4×104 cells ml−1 and grown under continuous light . After 2 d , the cell cultures were switched to 12 h light/12 h dark cycles . Chloroplasts were isolated from cultures harvested 1 , 6 , and 11 h after the start of the third light cycle as described [36] , except that a Dounce tissue grinder ( Wheaton ) was used to break the cells . Arabidopsis plants were grown on MS synthetic agar medium with 2% sucrose as described [11] . Chloroplast numbers were counted under an Olympus BH2 phase-contrast microscope with a hemocytometer . All precursor proteins were translated in vitro by the TNT-wheat germ or reticulocyte lysate system ( Promega ) in the presence of [35S]Met . A typical import reaction with pea chloroplasts was performed in a 60-µl reaction containing 20–25 µl of in vitro–translated precursor proteins , chloroplasts ( 20 µg of chlorophylls , see below ) , 3 mM Mg-ATP in 1× import buffer ( 330 mM sorbitol , 50 mM HEPES-KOH [pH 8 . 0] ) for 25 min ( see below ) at room temperature . For most precursors , prHsp93 was co-imported for quantification normalization . For some precursors , because of the position of their mature protein or their lower import efficiency , signals from hemoglobin from the reticulocyte lysates or the imported Hsp93 degradation fragments may interfere with quantification , and endogenous cpHsc70 was analyzed by immunoblotting for confirmation and/or normalization . Competition experiments using recombinant prRBCS were performed as described [37] , [38] . In brief , import was performed with [35S]Met-labeled precursors and various concentrations of recombinant prRBCS at room temperature for 15 min . All reactions had the same concentration of urea as the reaction with the highest amount of recombinant proteins . After import , an excess amount of ice-cold 40% Percoll in 1× import buffer was added to stop the import reaction , and intact chloroplasts were pelleted and washed . Where indicated , chloroplasts were further treated with 200 µg ml−1 thermolysin as described [34] . Import into Chlamydomonas chloroplasts was performed as described [16] . In brief , [35S]Met-labeled precursors were incubated with isolated chloroplasts in the presence of 10 mM Mg-ATP in import buffer ( 250 mM sorbitol , 1 . 5 mM MgCl2 , 1 mM MnCl2 , 0 . 1 mM Na2HPO4 , 2 mM Na2-EDTA , 35 mM HEPES-KOH [pH 7 . 8] ) for 20 min at room temperature . For the import of prTic40 and prOE23 into Chlamydomonas chloroplasts , we found that import efficiencies were much higher when import was performed in the import buffer for pea chloroplasts ( 330 mM sorbitol , 50 mM HEPES-KOH [pH 8 . 0] ) containing 5 mM Mg-ATP , and still showed the same pattern of developmental regulation . Thus , for these two precursors data obtained using the pea chloroplast import buffer are presented . Chloroplasts of different ages have several different characteristics . For example , older chloroplasts have more chlorophylls per chloroplast and are also larger in size and have larger surface area [8] , [39] and possibly more receptors per chloroplast . We therefore used two methods for normalization during import . Import reactions were performed with either an equal number of chloroplasts or an equal amount of chlorophyll in each reaction . The three groups of age-selective import patterns were obtained with both methods ( Figure S3A ) . However , we found that chlorophyll measurements were more accurate and consistent , and significant variations were often obtained with chloroplast number counting from different counts and dilutions . We therefore used an equal amount of chlorophyll per reaction for further characterizations . The same age-selective import patterns were also observed when import was performed for 10 min ( Figure S3B ) . Protein samples were analyzed by SDS-PAGE using the NuPAGE gel system ( Invitrogen ) , exposed to X-ray films , and quantified using a phosphorimager ( Fuji FLA-5000 imaging system ) . Some samples were additionally analyzed by immunoblotting as described [40] . Immunoblots were quantified using the Luminescent Image Analyzer LAS1000 Plus and Image Gauge version 4 . 0 software ( Fujifilm ) . The antibodies against glutamine synthetase 2 ( AS08 296 ) , CAB ( AS01 004 ) , and Chlamydomonas chloroplast stromal Hsp70B ( AS06 175 ) were purchased from Agrisera . The antibody against mitochondrial porin was produced by cloning the gene encoding Arabidopsis mitochondrial porin ( At3g01280 ) into the pGEX-5X-1 vector . The GST-porin fusion protein was purified from E . coli and used as antigen for immunization in rabbits . The antibody was used at a dilution of 1∶1 , 000 . Antibodies against Toc159 [38] , Tic110 [38] , Toc75 [38] , Tic40 [11] , and cpHsc70/S78 [41] were produced and used as described . The TIC40p:prTic40 and TIC40p:RBCStp-mTic40 transgenes were cloned into the binary vector pPZP221 [42] , introduced into Agrobacterium tumefaciens GV3101 and transformed into the tic40-1 mutant [11] using the floral spray method [43] . Independent transgenic lines containing at least one copy of the transgene were selected from subsequent generations and used for further analyses . Arabidopsis chloroplast isolation and fractionation were performed as described [44] . The full names of precursors used in this study , and their accession numbers and species , are listed in Table S1 . EST clones of C . reinhardtii Cr-prRBCS and Cr-prL11 were obtained from Kazusa DNA Research Institute [45] . Primers used for building fusion constructs are listed in Table S2 . For the construction of RBCStp-mTic40 , the RBCStp-mTic40 fragment was amplified by two-step PCR . The first rounds of PCR were performed to generate the soybean RBCS transit peptide ( RBCStp ) from prRBCS [46] and the Tic40 mature region from an Arabidopsis Tic40 cDNA that was subcloned into the pSP72 plasmid ( pSP72-atTic40 ) . The RBCStp-mTic40 fragment was then amplified by a second round of PCR using the two PCR products from the first rounds as the template and with a forward primer adding a BamHI site and a T7 primer as the reverse primer . The fragment was cloned into the BamHI/EcoRI site of pSP72 , creating the plasmid pSP72-RBCStp-mTic40 . For Tic40tp-mRBCS , the Tic40 transit peptide ( Tic40tp ) fragment was amplified by PCR from pSP72-atTic40 , with a forward primer adding an XhoI site and a reverse primer adding an SphI site . The amplified fragment was subcloned into the XhoI/SphI site of the plasmid pSP72-mSS [47] . For RBCStp-GST , the pea prRBCS transit peptide fragment was amplified by PCR from a plasmid containing the pea prRBCS cDNA cloned into the pSP64 plasmid , with the forward and reverse primers both adding an EcoNI site to the PCR fragment . The fragment was digested with EcoNI and subcloned into the EcoNI site of the vector pGEX-5X-1 [48] . The RBCStp-GST fragment was then amplified by PCR , with a forward primer adding a BamHI site and a reserve primer adding an EcoRI site , and the amplified fragment was subcloned into the BamHI/EcoRI site of pSP72 , resulting in the plasmid pSP72-PsRBCStp-GST . The soybean prRBCS transit peptide was excised from the soybean–pea hybrid prRBCS [46] using HindIII and SphI and cloned into the HindIII/SphI site of pSP72-PsRBCStp-GST to replace the pea prRBCS transit peptide . The resulting plasmid was called pSP72-RBCStp-GST and was used to produce RBCStp-GST for import assays . For Tic40tp-GST , the Tic40 transit peptide fragment was amplified by PCR from a plasmid containing the pea prTic40 cDNA cloned into pBluescript , with a T7 primer and a reverse primer adding an SphI site . The fragment was then subcloned into the XhoI/SphI site of pSP72-PsRBCStp-GST , replacing the pea prRBCS transit peptide . For TIC40p:prTic40 and TIC40p:RBCStp-mTic40 , the two fragments were amplified by two-step PCR . The first rounds of PCR were performed to generate the TIC40 2-kb promoter fragment from Arabidopsis leaf genomic DNA and the Tic40 full-length cDNA from pSP72-atTic40 , or the RBCStp-mTic40-encoding fragment from pSP72-RBCStp-mTic40 . The second rounds of PCR were performed to generate the TIC40p:prTic40 and TIC40p:RBCStp-mTic40 fragments using the first rounds of PCR products as templates and with a forward primer adding a KpnI site and a reserve primer adding an XbaI site to the PCR fragment . The amplified fragments were subcloned into the KpnI/XbaI site of the transformation vector pPZP221 [42] . For the nine-amino-acid alanine block scanning mutations of prTic40 transit peptide , a PCR approach was used to introduce mutations into the transit peptides [49] . For each mutant , one pair of complementary primers was designed . The primers consisted of mutated residues in the central region , flanked by wild-type sequences . With the wild-type template DNA and these primers , including the complementary pair , N-terminal , and C-terminal primers , the first round of PCR was performed to generate two fragments , the 5′ and 3′ segments . The second round of PCR was performed with the 5′ and 3′ segments as templates , and the N-terminal and C-terminal primers . The PCR products were subcloned and then sequenced . For amino acid changes , the QuikChange Site-Directed Mutagenesis Kit was used ( Agilent Technologies ) . | It is well known that some genes are preferentially transcribed in young tissues and others are specifically expressed in aging tissue , but protein import into organelles is generally thought to be constitutive and independent of age . In this study , we find that , contrary to expectation , in higher plants the import of proteins into chloroplasts is indeed dependent on the age of the organelle . We find that chloroplast precursor proteins can be divided into three age-selective groups , with each having a preference for chloroplasts of a different age . The age-selective signal is located within the signal peptide of each protein that controls organelle import , and we further identify a motif that is necessary to make a signal peptide target older chloroplasts preferentially . We show that different members of a gene family often belong to different age-selective groups , and that changing the sequence motifs within a protein's signal peptide can change its age selectivity . These results indicate that organelle-targeting signal peptides are one set of tools available to multicellular organisms for differential age-specific regulation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"chloroplast",
"cell",
"biology",
"plant",
"cell",
"biology",
"membranes",
"and",
"sorting",
"biology",
"molecular",
"cell",
"biology"
] | 2012 | Differential Age-Dependent Import Regulation by Signal Peptides |
Anaphase onset is an irreversible cell cycle transition that is triggered by the activation of the protease Separase . Separase cleaves the Mcd1 ( also known as Scc1 ) subunit of Cohesin , a complex of proteins that physically links sister chromatids , triggering sister chromatid separation . Separase is regulated by the degradation of the anaphase inhibitor Securin which liberates Separase from inhibitory Securin/Separase complexes . In many organisms , Securin is not essential suggesting that Separase is regulated by additional mechanisms . In this work , we show that in budding yeast Cdk1 activates Separase ( Esp1 in yeast ) through phosphorylation to trigger anaphase onset . Esp1 activation is opposed by protein phosphatase 2A associated with its regulatory subunit Cdc55 ( PP2ACdc55 ) and the spindle protein Slk19 . Premature anaphase spindle elongation occurs when Securin ( Pds1 in yeast ) is inducibly degraded in cells that also contain phospho-mimetic mutations in ESP1 , or deletion of CDC55 or SLK19 . This striking phenotype is accompanied by advanced degradation of Mcd1 , disruption of pericentric Cohesin organization and chromosome mis-segregation . Our findings suggest that PP2ACdc55 and Slk19 function redundantly with Pds1 to inhibit Esp1 within pericentric chromatin , and both Pds1 degradation and Cdk1-dependent phosphorylation of Esp1 act together to trigger anaphase onset .
Cell survival requires faithful inheritance of genetic material between generations . This is ensured through the regulation of sister chromatid cohesion during metaphase and sister chromatid separation at anaphase onset . We define anaphase onset as the two events needed for accurate segregation of the genome into daughter cells: the coordinated dissolution of sister chromatid cohesion and the rapid elongation of the mitotic spindle . Cohesion of sister chromatids is mediated by the Cohesin complex , which consists of the four core subunits Mcd1 , Mcd3 , Smc1 and Smc3 [1–3] . Each Cohesin complex forms a ring ~40 nm in diameter that connects the sister chromatids by topological linkages [4–7] . Cohesin mediates cohesion of sister chromatids along their length but is concentrated within the pericentromere ( defined in yeast as 15–20 kb on either side of the centromere ) where the correct orientation of sister kinetochores is essential for bipolar attachment to the mitotic spindle [8–10] . During mitosis , Cohesin within the pericentromeres of all chromosomes organizes into a bi-lobed barrel structure with clusters of kinetochores capping each lobe and spindle microtubules running along the core of the barrel [11] . Despite the high concentration of pericentric Cohesin , sister chromatids within this region are not directly cohesed , at times being separated by as much as 1 μm [12 , 13] . This observation has led to the model that the Cohesin barrel is formed by intra-chromatid linkages that form an elastic chromatin network between sister kinetochores to distribute force and resist microtubule-based extensional forces [11 , 14] . Cohesin mediated linkages are removed at anaphase onset by Separase ( Esp1 in budding yeast ) , a protease that cleaves the Mcd1 subunit of Cohesin [15] . In metaphase , Separase is inhibited by Securin ( Pds1 in budding yeast ) [16 , 17] and its regulated destruction by the anaphase promoting complex ( APC ) , an E3 ubiquitin ligase , triggers Separase activation and Mcd1 cleavage [18 , 19] . Work in several organisms has shown that Securin is also a positive regulator of Separase function: Securin promotes Separase nuclear localization , loading onto the mitotic spindle and stability [17 , 20 , 21] . These opposing functions of Securin can lead to phenotypes that appear paradoxical: the most extreme example occurs in the fission yeast , Schizzosaccharomyces pombe , in which a cut2 mutant ( the fission yeast Securin ) has the same phenotype as a cut1 mutant ( the fission yeast Separase ) and blocks sister chromatid separation because Cut1 does not become active [17 , 18] . In budding yeast , Esp1 has also been shown to cleave the spindle midzone and kinetochore-associated protein Slk19 [22 , 23] . Slk19 and Esp1 are believed to regulate a variety of spindle functions including centromere elasticity , kinetochore clustering , and spindle stability [22 , 24–26] . In meiosis , deletion of SLK19 causes two rounds of chromosome segregation on the anaphase I spindle , leading to the proposal that Slk19 may also destabilize the spindle [27 , 28] . It is poorly understood if these phenotypes reflect a single Esp1/Slk19-regulated process . Securin is essential in fission yeast , but in budding yeast and metazoans Securin mutants are viable and Cohesin cleavage only occurs during mitosis , suggesting that Separase is regulated by additional mechanisms [29–33] . In vertebrates , Separase is inhibited by Cdk1 phosphorylation and binding [34 , 35] and this regulation has been shown to act redundantly with Securin inhibition of Separase . PP2A also interacts with human Separase and this interaction has been shown to both promote and inhibit Separase function [36 , 37] . Cdk1 activity activates anaphase onset [38–40] , and current models suggest Cdk1 promotes anaphase only by activating the APC and initiating sister separation via proteolysis of Securin [41 , 42] . However , in budding yeast , Cdk1 activity is needed for triggering anaphase in cells lacking Pds1 [43] . pds1Δ cells arrested by the spindle assembly checkpoint ( SAC ) , which monitors attachment of kinetochores to the mitotic spindle and arrests cells with high Cdk1 activity , prematurely dissolve sister chromatid cohesion [44] . In contrast , during morphogenesis checkpoint activation , which monitors cell size and triggers Wee1-dependent inhibition of Cdk1 , pds1Δ cells remain arrested in metaphase , despite the assembly of a mitotic spindle and the generation of pulling forces on sister chromatids [43 , 45] . These results suggest that Cdk1 activates an event downstream of APC activation and Pds1 degradation . The PP2A regulatory subunit Cdc55 may regulate a similar event . CDC55 is essential in the absence of PDS1 , and loss of both genes leads to premature Mcd1 cleavage and anaphase onset [46] . Below we show that Cdk1 phosphorylates Esp1 in vivo and in vitro , and this phosphorylation activates Esp1 function and anaphase onset . Depleting Pds1 in cdc55Δ , slk19Δ or ESP1 phospho-mimetic mutants triggers immediate anaphase spindle elongation . This premature spindle elongation is accompanied by changes in the timing of Mcd1 proteolysis , and a dramatic loss of pericentric Cohesin upon mitotic entry . Our results suggest that Slk19 functions to inhibit Esp1 within the pericentric Cohesin barrel , and this inhibition is promoted by PP2ACdc55 and opposed by Cdk1 phosphorylation of Esp1 .
In vivo metabolic labeling with 32P-orthophosphate followed by immunoprecipitation of a tagged Esp1 revealed that Esp1 is phosphorylated in vivo in mitotically arrested cells ( Fig 1A ) . This phosphorylation depends on Cdk1 activity , because after induction of the yeast Wee1 kinase ( GAL-SWE1 ) , a treatment that inhibits Cdk1 but maintains a mitotic arrest [45] , Esp1 phosphorylation is reduced . Esp1 has six minimal Cdk1 phosphorylation sites ( S/TP ) distributed into three groups; two sites near the N-terminus ( termed ‘N-terminal’ ) , three sites near the N-terminal end of the protease domain ( termed ‘central’ ) and a single site close to the C-terminus ( termed ‘C-terminal’ ) ( Fig 1B ) . To determine if these sites are phosphorylated by Cdk1 , we mutated each group individually , and in combination , to unphosphorylatable alanine residues ( S/T to A ) . These Esp1 mutants are all expressed at endogenous levels ( S1A Fig ) , interact with Pds1 normally ( S1B Fig ) and support viability in otherwise wild-type cells . We also created a set of Esp1 mutants that substitute two phospho-mimetic aspartic acid residues at each potential phosphorylation site ( SP/TP to DD ) . These Esp1 mutants are also expressed at endogenous levels and support full viability in otherwise wild-type cells ( S1C Fig ) . In vivo metabolic labeling using 32P-orthophosphate demonstrates that Esp1 phosphorylation is lost only in cells lacking the three central Cdk1 sites ( Fig 1C ) , suggesting that Cdk1 phosphorylates Esp1 in the central region . Esp1 with the central phosphorylation sites mutated to alanine migrates more quickly than wild-type Esp1 or other alanine-substituted Esp1 mutants ( S1A Fig ) , while mutating these central sites to aspartate slightly retards Esp1 mobility ( S1C Fig ) . To confirm this phosphorylation , lysates from cells synchronized in G1 and released into the cell cycle were examined on Phos-Tag polyacrylamide gels that retard the mobility of phosphorylated proteins [47 , 48] . Wild-type Esp1 is phosphorylated on at least two residues following release from G1 , and this phosphorylation peaks at the same time as the mitotic B-type cyclin , Clb2 , when both Pds1 and Mcd1 levels are falling during anaphase ( Fig 1D ) . Esp1 phosphorylation is detected 30 minutes after release from G1 , before Clb2 appearance , suggesting that other mitotic cyclins , like Clb5 , may also phosphorylate Esp1 in vivo . No mobility shift on the Phos-Tag gel is detected in the esp1-3A mutant , in which the three central Cdk1 sites are mutated , and the protein resolves as a single unphosphorylated form , demonstrating that the mobility shift of phosphorylated Esp1 depends on these sites . To determine if Cdk1 can phosphorylate these residues directly , we incubated immunoprecipitated wild-type and mutant Esp1 proteins with purified Cdk1Clb2 and Cdk1Clb5 complexes in an in vitro phosphorylation reaction ( Fig 1E and S1D Fig ) . Both Cdk1Clb complexes phosphorylate Esp1 in vitro and mutation of the three central sites prevents Cdk1Clb2 phosphorylation . No phosphorylation of Esp1 is observed when Cdk1Clb2 or Cdk1Clb5 is omitted from these reactions , demonstrating this phosphorylation is not due to a co-precipitated kinase ( S1D and S1E Fig ) . Taken together , these data show that Cdk1 phosphorylates Esp1 in vivo and in vitro on sites in the central region of the protein . Purified PP2ACdc55 dephosphorylates several Cdk1 substrates in budding yeast [45 , 49] , and is also able to dephosphorylate immunoprecipitated Esp1 that has been phosphorylated in vitro by purified Cdk1Clb2 ( Fig 1F ) . Mitotic cdc55Δ cells also display increased in vivo phosphorylation of Esp1 relative to wild-type cells , and relative to cells deleted for the second PP2A regulatory subunit in yeast , RTS1 , the homologue of the vertebrate B56 subunit of PP2A ( Fig 1C ) . esp1-3A and ESP1-3D cells exhibit no apparent differences in cell cycle progression , the timing of anaphase onset , or the behaviour of the mitotic spindle as compared to wild-type cells ( S1F–S1I Fig ) , suggesting that if Esp1 phosphorylation regulates anaphase it may act redundantly with Pds1 function . We therefore constructed strains expressing an auxin-inducible degron tagged PDS1 ( PDS1-AID ) and the rice , Oryza sativa , F-box protein Tir1 ( OsTir1 ) [50] . Treating these cells with the plant hormone auxin ( indole-3-acetic acid ) causes rapid degradation of Pds1 to 10–20% of its normal levels ( S2A Fig ) . This reduction inhibits the growth of PDS1-AID cells on plates containing auxin ( Fig 2A ) . We observe that the esp1-3A mutant , which prevents Esp1 phosphorylation , partially suppresses the growth defect caused by Pds1 depletion . In contrast , the ESP1-3D allele , which mimics Esp1 phosphorylation , exacerbates this defect ( Fig 2A ) . These results suggest a model in which Pds1 and phospho-regulation of Esp1 work redundantly to regulate the essential function of Esp1 , with destruction of Pds1 and phosphorylation of Esp1 both acting to activate Esp1 , and preventing phosphorylation , in the esp1-3A allele , inhibiting Esp1 activity . These interactions are partially dominant when tested in diploids that express ESP1-3D or esp1-3A and one wild-type copy of ESP1 ( Fig 2B ) , or in haploid cells that contain wild-type ESP1 on a low-copy centromeric plasmid ( Fig 2C ) . Simply increasing the copy number of wild-type ESP1 in PDS1-AID cells causes a greater growth defect on auxin , which suggests that the dominant phenotypes of ESP1-3D and esp1-3A are caused by an increase or decrease of Esp1 activity , respectively ( Fig 2C ) . We observe similar negative genetic interactions between pds1Δ and ESP1-3D cells ( S2B and S2C Fig ) , but were surprised that esp1-3A is synthetically sick when combined with pds1Δ , the opposite phenotype to that observed in esp1-3A PDS1-AID cells ( Fig 2A and S2C Fig ) . We observe additional differences between pds1Δ and PDS1-AID cells ( presented below ) , and hypothesize that they are caused by the requirement of Pds1 for effective nuclear import and activation of Esp1 [20] . Unlike pds1Δ cells that produce no Pds1 protein , PDS1-AID cells treated with auxin retain some Pds1-AID protein ( S2A Fig ) , suggesting that this residual protein may be sufficient to fulfill Pds1’s positive function on Esp1 , but insufficient to fully inhibit Esp1 activity after mitotic entry . To understand the lethality of ESP1-3D PDS1-AID cells we analyzed mitotic progression using live cell imaging of an endogenously tagged spindle pole body ( SPB ) protein ( SPC42-eGFP ) and directly measured the length of SPB separation as a correlate for spindle elongation ( [45]; Fig 2D ) . The formation of a short mitotic spindle ( 1–2 μm in length ) , caused by the rapid separation of SPBs , marks entry into mitosis . Wild-type cells spend an average of 22 . 4 minutes with a short mitotic spindle before SPBs and sister centromeres rapidly separate at the onset of anaphase ( S2D Fig ) . In the presence of auxin , both PDS1-AID and ESP1-3D PDS1-AID cells display striking defects in spindle elongation , with many cells undergoing continual elongation of the spindle as soon as SPB separation occurs ( Fig 2D ) . To categorize the behaviour of these cells we quantified spindle dynamics: cells whose spindles do not elongate to 2 and 2 . 5 μm within the first 10 and 15 minutes , respectively , are classified as “normal metaphase spindle formation” ( or “normal”; black traces ) , and those that elongate beyond 2 and 2 . 5 μm within these time-intervals are classified as “immediate spindle elongation” ( or “immediate”; green traces ) ( Fig 3 ) . We used these two rules to identify cells that are difficult to score , and among all cells treated with auxin ( Fig 3 ) , 15% of the cells produce conflicting scores ( i . e . , immediate/normal or normal/immediate in the 10/15 minute intervals ) . These cells were manually curated ( see Materials and Methods for details ) . Of the wild-type , pds1Δ , and PDS1-AID cells not treated with auxin , 97% score as “normal” ( Fig 3 , S2D and S3 Figs ) , showing this metric differentiates between auxin-treated and -untreated cells . In a small number of cells the spindle fails to elongate to 6 μm in the 60 minutes of observation , and these are scored as “failed anaphase” ( red traces ) . Of the PDS1-AID cells treated with auxin , 40% undergo immediate spindle elongation , and this number increases to 70% of ESP1-3D PDS1-AID cells ( Fig 2D ) . The rate of spindle elongation varies among these “immediate” cells , but it is faster than the rate of spindle elongation during metaphase in wild-type cells ( which lengthens from ~ 1 to 2 μm during 20 minutes ) , and slower than the rapid anaphase elongation that occurs at the metaphase-to-anaphase transition in untreated cells ( compare “immediate” cells in Fig 2D to “normal” cells in S3 Fig ) . A subset of “immediate” cells also display uncharacteristic shortening of the spindle after a period of continual spindle lengthening . These behaviours are not observed in untreated ESP1-3D PDS1-AID or PDS1-AID cells , which undergo spindle formation , maintain a short metaphase spindle and then abruptly initiate anaphase spindle elongation in a manner indistinguishable from wild-type cells ( S2D and S3 Figs ) . Together these results suggest that the combination of mimicking phosphorylation on Esp1 and reducing Pds1 allows precocious anaphase spindle elongation which may explain their synthetic effects on viability . PDS1-AID cells grown in the presence of auxin have strikingly different behaviour than pds1Δ cells which undergo relatively normal spindle formation and then delay in initiating anaphase spindle elongation ( S2D Fig ) . This difference , like the differences in the viability of pds1Δ esp1-3A and PDS1-AID esp1-3A ( Fig 2A and S2C Fig ) , may be caused by residual Pds1-AID protein in PDS1-AID cells ( S2A Fig ) . Past work has shown that cells lacking both Pds1 and Cdc55 are inviable and undergo premature Mcd1 cleavage and anaphase onset [46] . Changes in Esp1 phosphorylation and activity could explain this phenotype . cdc55Δ PDS1-AID cells are inviable when grown in the presence of auxin ( Fig 4A ) and 88% of the cells undergo immediate spindle elongation ( Figs 3 and 4B ) , a phenotype more severe than observed in ESP1-3D PDS1-AID cells . PP2ACdc55 has been shown to inhibit Swe1 , the budding yeast Wee1 kinase , and activate Mih1 , the budding yeast Cdc25 phosphatase [49 , 51 , 52] . Swe1 phosphorylates and Mih1 dephosphorylates a conserved tyrosine on Cdk1 ( Y19 on yeast Cdk1 ) that when phosphorylated inhibits Cdk1 activity before and during mitosis . cdc55Δ mutants have increased inhibitory tyrosine 19 phosphorylation on Cdk1 [52] , so we tested if the immediate spindle elongation observed in cdc55Δ mutants depends on SWE1 and Cdk1 inhibition . cdc55Δ swe1Δ PDS1-AID cells are inviable on media containing auxin ( Fig 4A ) , and 85% of the analyzed cells undergo immediate spindle elongation ( Fig 4B ) . A similar percentage of cells undergo immediate spindle elongation in the absence or presence of SWE1 showing the immediate spindle elongation in cdc55Δ PDS1-AID cells is not caused by increased inhibitory tyrosine phosphorylation on Cdk1 , but through a different mechanism . However , spindle elongation in cdc55Δ swe1Δ PDS1-AID cells is less variable , with few dramatic spindle shortening events , suggesting that sustained high levels of inhibitory phosphorylation on Cdk1 in cdc55Δ cells affects spindle dynamics . Immediate spindle elongation may be caused by premature separation of sister chromatids and a similar phenotype has been observed in fixed cdc55Δ cells with reduced Pds1 , and in mcd1 and mcm21 mutants [46 , 53 , 54] . Additionally , spo11Δ diploids , which lack meiotic recombination between homologous chromosomes , initiate anaphase I of meiosis prematurely [55] . We observed sister chromatid cohesion and SPB movement directly using a lacO array on the arm of chromosome V at the URA3 locus in cells expressing lacI-GFP and Spc42-mCherry ( Fig 4C and 4D ) . In cdc55Δ PDS1-AID cells treated with auxin , the lacO array separated , on average , 6 . 71 minutes after SPB separation compared to 13 . 85 minutes in auxin treated PDS1-AID control cells . In some cdc55Δ PDS1-AID cells , the lacO arrays separate prior to SPB separation , an event not observed in controls ( Fig 4C and 4D ) . Consistent with our observation that many PDS1-AID cells treated with auxin display immediate spindle elongation , these cells also advance the timing of chromosome V separation compared to untreated cells ( 13 . 85 vs . 17 . 65 minutes ) . Although ESP1-3D PDS1-AID cells display more severe spindle elongation defects than control PDS1-AID cells ( Fig 2D ) , the timing of chromosome V separation is not significantly different in these two strains ( 15 . 89 vs . 13 . 85 minutes; Fig 4C ) . The advanced timing of sister separation in cdc55Δ PDS1-AID cells may cause severe chromosome segregation defects . When we follow the fate of the lacO arrays during anaphase , cdc55Δ PDS1-AID cells treated with auxin segregate the lacO arrays randomly to the two daughter cells ( Fig 4E ) and this defect may explain the potent lethality observed in these cells after auxin treatment . Although control PDS1-AID cells also show high rates of chromosome mis-segregation , their defect is less severe . Importantly , in PDS-AID and cdc55Δ PDS1-AID cells both chromatids remain attached to a SPB ( though often the same one ) , suggesting that neither the immediate spindle elongation , nor the chromosome segregation defects , are caused by a failure in kinetochore attachment to microtubules . Because PP2ACdc55 can dephosphorylate Esp1 in vitro ( Fig 1F ) and deletion of CDC55 increases Esp1 phosphorylation in vivo ( Fig 1C ) , we tested whether blocking Esp1 phosphorylation , in the esp1-3A mutant , suppresses the lethality of cdc55Δ PDS1-AID . Although esp1-3A partially suppresses the growth defect of PDS1–AID ( Fig 2A ) , we see no suppression in esp1-3A cdc55Δ PDS1-AID or esp1-3A cdc55Δ swe1Δ PDS1-AID cells ( Fig 5A ) . Additionally , esp1-3A has no impact on the immediate spindle elongation we observe in cdc55Δ swe1Δ PDS1-AID cells treated with auxin ( S3 Fig ) . In budding yeast , Cdc5 ( the budding yeast Polo kinase ) phosphorylation of Mcd1 promotes its cleavage , and this phosphorylation is essential in the absence of Pds1 [30 , 56] . PP2ACdc55 has also been shown to dephosphorylate Mcd1 in vitro , and deletion of CDC55 increases phosphorylation of Mcd1 in vivo [57] . We therefore tested whether phosphorylation of both Esp1 and Mcd1 work redundantly to promote sister chromatid separation . Mutation of ten phosphorylation sites in Mcd1 , in the mcd1-10A mutant [56] ( note that the mcd1-10A mutation has been named scc1-10A in previous reports , but for clarity we use the standard name ( http://www . yeastgenome . org/locus/S000002161/overview ) ) , suppresses growth defects of PDS1-AID cells treated with auxin , but neither mcd1-10A , nor the double mutant esp1-3A mcd1-10A , suppress the lethality of cdc55Δ PDS1-AID cells ( Fig 5A ) . Cdk1-dependent phosphorylation of the APC , which targets Pds1 for degradation , is also regulated by PP2ACdc55 in vivo and in vitro , and mutation of twelve Cdk1 sites on three APC subunits can partially suppress the SAC defect of cdc55Δ cells [45] . Combining these APC mutations with esp1-3A and mcd1-10A does not increase this suppression ( manuscript in preparation ) . PP2ACdc55 and Esp1 also function in the Cdc Fourteen Early Anaphase Release ( FEAR ) pathway [58 , 59] . Though not essential , this pathway promotes release of the Cdc14 phosphatase from the nucleolus early in anaphase to activate the essential mitotic exit network ( MEN ) in late anaphase . Cdc14 release from the nucleolus in early anaphase has been proposed to be an important trigger of anaphase onset [60–62] . We therefore examined if the lethality and premature spindle elongation of cdc55Δ swe1Δ PDS1-AID cells correlate with earlier Cdc14 release from the nucleolus . To carefully monitor any changes in FEAR activation we correlated release of Cdc14-GFP to spindle length ( S4A Fig ) and found that early degradation of Pds1-AID does not cause Cdc14 release at shorter spindle lengths ( Fig 5B ) . This result suggests that premature FEAR activation is not responsible for the lethality , and the premature spindle elongation and sister chromatid separation in cdc55Δ PDS1-AID cells . Supporting this data , deletion of SPO12 , a component of the FEAR pathway , delays Cdc14 release [58] , but does not suppress the growth defects of cdc55Δ PDS1-AID or ESP1-3D PDS1-AID cells when grown on auxin ( S4B and S4C Fig ) . In conclusion , we find no evidence that the premature spindle elongation and sister chromatid separation in cdc55Δ PDS1-AID cells are caused by increased phosphorylation on Esp1 , Mcd1 and the APC , or by premature activation of the FEAR network . While examining whether disruption of the FEAR pathway suppresses the lethality of cdc55Δ PDS1-AID cells ( S4B and S4C Fig ) , we tested mutants in SLK19 , a spindle-associated protein that is also a component of the FEAR pathway and a substrate of Esp1 [23 , 58] . Unlike spo12Δ cells , which are insensitive to Pds1 depletion , slk19Δ cells are as sensitive to Pds1 depletion as cdc55Δ cells ( Fig 6A and 6B ) , and when combined with cdc55Δ PDS1-AID or ESP1-3D PDS1-AID do not suppress their growth defects . High throughput synthetic lethal screening has also identified synthetic interactions between pds1Δ and slk19Δ [63 , 64] . Because slk19Δ cells are sensitive to reduced Pds1 protein , we imaged slk19Δ PDS1-AID cells in mitosis . Similar to cdc55Δ and ESP1-3D cells , depletion of Pds1 in slk19Δ cells cause 95% of cells to undergo immediate spindle elongation ( Figs 3 and 6C ) . slk19Δ PDS1-AID cells also have a severe defect in the segregation of a lacO array integrated on chromosome V ( Fig 6D ) . In this experiment , performed on fixed cells , we score whether sister lacO arrays segregate to opposite poles or to the same pole 120 minutes after release from a G1 arrest , when most cells have completed anaphase . Segregation of chromosome V is nearly random in slk19Δ PDS1-AID cells ( Fig 6D ) , while cdc55Δ PDS1-AID cells mis-segregate chromosome V in 25% of divisions , a defect less severe than our measurements of chromosome V mis-segregation in live cells ( Fig 4E ) . PDS1-AID and ESP1-3D PDS1-AID cells have less severe defects , mis-segregating chromosome V in 10% of divisions . To determine if premature spindle elongation in ESP1-3D PDS1-AID , cdc55Δ swe1Δ PDS1-AID and slk19Δ PDS1-AID cells is caused by premature activation of Esp1 , we monitored cleavage of the Esp1 substrates Mcd1 and Slk19 by western blot following release from a G1 arrest . In the absence of auxin , control , ESP1-3D , cdc55Δ swe1Δ and slk19Δ cells show similar kinetics of Pds1 and Mcd1 proteolysis and Slk19 cleavage ( Fig 7 ) . The behaviour of these cells differs very little from wild-type cells without PDS1-AID . In the presence of auxin , little Pds1 accumulates as cells transition through the cell cycle , and Mcd1 proteolysis occurs 10 to 20 minutes earlier in control PDS1-AID cells ( beginning at 90 minutes following G1 release compared to 110 minutes ) . In cdc55Δ swe1Δ PDS1-AID and slk19Δ PDS1-AID cells , Mcd1 proteolysis occurs an additional 10 to 20 minutes earlier ( 80 and 70 minutes after G1 release , respectively ) ( Fig 7A and 7B ) . Although Mcd1 proteolysis may initiate slightly earlier in ESP1-3D PDS1-AID cells , the kinetics of Mcd1 disappearance is very similar to PDS1-AID cells . In addition to changes in Mcd1 proteolysis , very little full length Slk19 accumulates prior to mitosis and its cleavage is advanced relative to cells grown in the absence of auxin ( Fig 7A ) . This difference is very similar in all cells examined , and the defect occurs early in the cell cycle , suggesting that Pds1 inhibition of Esp1 normally allows full-length Slk19 to accumulate . The mitotic cyclin , Clb2 , accumulates similarly in both the absence and presence of auxin in all mutants ( Fig 7 ) , indicating that premature Esp1 activation is not due to premature mitotic entry . The destruction of Clb2 is blocked in ESP1-3D PDS1-AID cells treated with auxin ( Fig 7A ) and we hypothesize that defects in anaphase onset may activate the SAC , causing a delay in APC activation . cdc55Δ swe1Δ PDS1-AID cells only partially stabilize Clb2 , but cdc55Δ mutants are defective in the SAC [52 , 65] . slk19Δ mutants activate the SAC [66] and irrespective of auxin addition we see stabilization of Clb2 , Clb5 and Pds1-AID ( only in the absence of auxin ) ( Fig 7B ) . Cohesin is bound along the length of paired sister chromatids , but is concentrated within the pericentromere where it forms a barrel structure [8 , 11] . Cleavage of Cohesin triggers sister chromatid separation , and the local cleavage of Cohesin within the pericentromere is essential for the separation of kinetochores at anaphase onset [14 , 54] . Because the premature proteolysis of total Mcd1 is subtle in ESP1-3D , cdc55Δ swe1Δ and slk19Δ cells ( Fig 7 ) , we wondered if these mutants might have a specific defect in the cleavage of pericentric Cohesin . We imaged pericentric Cohesin by tagging the Smc3 subunit of Cohesin with GFP . When control and mutant PDS1-AID cells are released from a G1 arrest in the absence of auxin , the Smc3-GFP barrel forms normally , persists during metaphase and rapidly disappears at anaphase onset ( Fig 8 ) . Prior to anaphase , the pericentric Cohesin barrel fluorescence is ~1 . 5-fold over the non-barrel nuclear fluorescence , which represents the binding of Cohesin along chromosomes arms . When cells are released in the presence of auxin , Smc3-GFP barrels form in control PDS1-AID cells , though with significantly decreased intensity compared to wild-type cells [11] . Strikingly , Cohesin barrels do not form in ESP1-3D , cdc55Δ , cdc55Δ swe1Δ and slk19Δ cells depleted of Pds1 and Smc3-GFP localization between the SPBs is reduced to background levels . When untreated or auxin-treated cells are imaged prior to mitosis , when SPBs have not yet separated , Smc3-GFP localization is similar in all strains , forming a focus adjacent to the paired SPBs ( Fig 8 ) . This pre-mitotic localization is consistent with our observation that total Mcd1 accumulates normally early in the cell cycle in both untreated and auxin-treated cells ( Fig 7 ) . We began investigating Cdk1-dependent Esp1 phosphorylation as a possible mechanism to explain the metaphase arrest of pds1Δ cells grown in latrunculin A ( LatA ) , a treatment that activates a Swe1-dependent checkpoint characterized by low Cdk1 activity . In contrast , sister chromatids separate in pds1Δ cells grown in nocodazole , a treatment that activates the SAC and maintains high Cdk1 activity . Using PDS1-AID cells we confirmed that Pds1 is not required for the maintenance of Mcd1 in LatA-arrested cells , but it is required for the maintenance of Mcd1 in nocodazole-arrested cells ( Fig 9 ) . Strikingly , Mcd1 is destabilized in LatA arrested ESP1-3D PDS1-AID and slk19Δ PDS1-AID cells soon after auxin addition , suggesting these mutants bypass the Pds1-independent block to sister chromatid separation ( Fig 9 ) . During this bypass , Swe1 remains stabilized and Y19 phosphorylation on Cdk1 is unchanged , showing that this bypass occurs downstream of Cdk1 inhibition . In contrast to the behaviour of Mcd1 , Slk19 is cleaved during the first 15 minutes after auxin addition in both nocodazole and LatA arrested cells .
In this study we made use of cells in which Pds1 is fused to an auxin-inducible degron ( PDS1-AID ) in order to induce the rapid degradation of Pds1 [50] . Like pds1Δ cells grown at permissive temperature , PDS1-AID cells grow poorly in the presence of auxin [29] ( Fig 2A ) . We were surprised , however , that these cells advance proteolysis of Mcd1 and often trigger immediate anaphase onset ( Figs 2D and 7 ) , phenotypes not seen in pds1Δ cells [16 , 30] . pds1Δ cells are thought to delay Mcd1 cleavage both via Cdc5/PP2A phosphoregulation , and also because of a defect in Esp1 nuclear localization [20 , 30 , 57 , 67] . Although some Esp1 must enter the nucleus in pds1Δ cells , it is insufficient to trigger premature anaphase , and in fact causes delays in anaphase onset ( S2D and S2E Fig ) [30] . Although most Pds1-AID is degraded in our experiments , we observe some Pds1-AID accumulation prior to mitosis ( Fig 7 and S2A Fig ) , and speculate that this Pds1 is responsible for the increased Esp1 activity and advancement of anaphase onset in these cells . Past work has shown that depletion of Pds1 in cells deleted for CDC55 led to the initiation of anaphase soon after mitotic entry [46] . We have extended this study using live cell imaging and confirmed that more than half of ESP1-3D , cdc55Δ and slk19Δ cells depleted for Pds1 initiate anaphase soon after mitotic entry ( Figs 2D , 3 , 4B and 6C ) . In many of these cells spindle elongation is initiated immediately after mitotic entry , and sister chromatids separate and segregate to a pole of the spindle , indicating that anaphase onset occurs prematurely . cdc55Δ and slk19Δ cells display nearly random segregation of chromosome V which would occur if spindle elongation began prior to the formation of bipolar attachments to the mitotic spindle , and may indicate that these mutants also have bi-orientation defects caused by premature Cohesin loss from the pericentromere . Similar bi-orientation defects can be observed in mcm21Δ mutants , which are defective in pericentric Cohesin loading , and load similar amounts of Cohesin within the pericentromeres as on chromosome arms [68] . Unlike mutants in kinetochore components that prevent kinetochore attachment to the spindle and prematurely elongate their spindles [69] , we see no evidence for attachment defects , as sister chromatids separate and then segregate ( or mis-segregate ) to one pole or the other . We hypothesize that premature anaphase onset in these mutants is caused by the absence of the pericentric Cohesin barrel in early mitosis ( Fig 8 ) . The Cohesin barrel , and the pericentric chromatin contained within it , has been proposed to be an integral component of the mitotic spindle [11 , 14 , 70 , 71] that functions to orient sister kinetochores towards opposite poles and to resist the pulling forces of the spindle . Although immediate spindle elongation is uninterrupted in many of these cells , the rate of elongation is slower than during normal anaphase onset , and a subset of cells contract their spindles ( Figs 2D , 4B and 6C and S3 Fig ) . These differences from normal anaphase onset may be caused by persistent sister chromatid linkages outside the pericentromere that slow ( and in some cases reverse ) spindle elongation . We have shown that budding yeast Esp1/Separase , as in vertebrates , is phosphorylated in vitro by Cdk1 , and its in vivo phosphorylation depends on Cdk1 activity and on three central phosphorylation sites ( Fig 1 ) . Two of these three Cdk1 sites ( S1027 and T1034 ) are conserved in related yeasts , and a recent crystal structure of the Esp1/Pds1 complex reveals that these sites lie in a region that forms part of the substrate binding domain of Esp1 [72] , raising the possibility that phosphorylation of these sites could affect substrate binding . We think it is unlikely Esp1 phosphorylation stimulates its catalytic activity because the activity of immunopurified Esp1 doesn’t vary during the cell cycle , and Cdk1 phosphorylation of Esp1 doesn’t increase Mcd1 cleavage in vitro ( [30] and Frank Uhlmann , personal communication ) . Several lines of evidence suggest that phosphorylation stimulates Esp1 activity in vivo: 1 ) Blocking phosphorylation in esp1-3A and mcd1-10A cells suppress the growth defects of PDS1-AID cells ( Figs 2A and 5A ) , 2 ) the ESP1-3D mutant acts semi-dominantly to cause synthetic growth defects in combination with PDS1-AID , and exacerbates the immediate spindle elongation phenotype of PDS1–AID ( Fig 2B–2D ) , 3 ) increasing the dosage of esp1-3A , ESP1 and ESP1-3D increases the growth defects of PDS1-AID cells , and form an allelic series in the strength of this effect ( Fig 2C ) , 4 ) ESP1-3D cells lacking Pds1 prevent the assembly of the pericentric Cohesin barrel ( Fig 8 ) , and 5 ) ESP1-3D cells share phenotypes with cells lacking PP2ACdc55 , which can dephosphorylate the same residues that Cdk1 phosphorylates in vitro ( Figs 4 and 1F ) and regulates Esp1 phosphorylation in vivo ( Fig 1C ) . PP2ACdc55 has also been shown to dephosphorylate Mcd1 in vitro , and deletion of CDC55 increases phosphorylation on Mcd1 in vivo [57] . However , we were surprised that the esp1-3A and mcd1-10A mutants have no effect on the lethality and spindle morphology of cdc55Δ PDS1-AID cells ( Fig 5A ) . These results suggest either that PP2ACdc55 dephosphorylates and inhibits other targets that promote anaphase onset , or that the physical interaction of PP2ACdc55 to a known substrate plays a more important role than its dephosphorylation . We favor the latter model because previous work has shown that PP2ACdc55 can stably bind Esp1 , and this interaction is reduced after anaphase onset [59] . We have identified SLK19 as an inhibitor of anaphase onset in vivo . A function for Slk19 in protecting pericentric Cohesin may provide a mechanism for previous observations that slk19Δ cells have defects in kinetochore clustering and change the elasticity of pericentromeric chromatin [24 , 25] . We propose a model in which Slk19 acts redundantly with Pds1 to inhibit Esp1 ( Fig 10 ) . Phosphorylation of Esp1 relieves inhibition by Slk19 , while dephosphorylation and binding of PP2ACdc55 enhances Slk19 inhibition . Past data has shown stable physical interactions of Esp1 to both PP2ACdc55 and Slk19 . Consistent with this model , Slk19 binding to Esp1 occurs throughout most of the cell cycle , and like the interaction between Cdc55 and Esp1 , is reduced after anaphase onset [23 , 59 , 73] . Our finding that ESP1-3D and slk19Δ cells bypass the Pds1-independent arrest caused by LatA ( Fig 9 ) provides additional evidence that phosphoregulation of Esp1 , and Slk19 , function redundantly with Pds1 to regulate Mcd1 proteolysis . This model , however , does not provide an explanation for why ESP1-3D has milder phenotypes than slk19Δ and cdc55Δ . We speculate that either the aspartate residues do not fully mimic Esp1 phosphorylation , or an additional regulator of this process is also regulated by Cdk1 phosphorylation . Slk19 itself is phosphorylated by Cdk1 on several sites that are adjacent to the Esp1 cleavage site [74] . ESP1-3D , cdc55Δ and slk19Δ cells all share a defect in the formation of a pericentric Cohesin barrel in mitosis ( Fig 8 ) and this defect is more penetrant in all three mutants than is the advancement in bulk Mcd1 proteolysis ( Fig 7 ) . This observation suggests that this regulatory network is involved in the control of pericentric cohesion . Modeling of the Cohesin barrel suggests it may create an outward pushing force along the spindle axis at anaphase onset and may therefore assist in the initial movement of chromosomes towards the spindle poles [70 , 71] . In this model , ordered loss of Cohesin from the barrel may be needed to transform this outward force into movement of sister chromatids toward the spindle poles . Both Esp1 and Slk19 localize to the kinetochore and at anaphase onset move together to the central spindle [21 , 22 , 75] , providing a mechanism to localize Separase along the axis of the Cohesin barrel . We speculate that this re-localization is accompanied by relief of Esp1 inhibition by Slk19 and PP2ACdc55 , leading to cleavage of pericentric Mcd1 from within the Cohesin barrel . The localization of Esp1 and Slk19 along the spindle axis is interdependent [75] , so like Pds1 , Slk19 may play a positive function delivering Esp1 to its site of action , but also maintain inhibition of Esp1 until Cdk1 activity reaches maximal levels at the metaphase-to-anaphase transition [16 , 20 , 67 , 76] . Although we see premature proteolysis of Mcd1 and loss of pericentric Cohesin when Pds1 is depleted in cdc55Δ and slk19Δ cells , Mcd1 still accumulates normally and localizes properly in pre-mitotic cells ( Figs 7 and 8 ) . Cdc5/PP2ACdc55 regulation of Mcd1 phosphorylation [30 , 57] is likely the mechanism for Mcd1 protection early in the cell cycle because Cdc5 activity only rises in mitosis [77–79] . An alternative mechanism may involve a poorly understood function of the mitotic cyclins Clb5 and Clb6 , which function redundantly with Pds1 during S-phase to prevent loss of pericentric cohesion [80 , 81] . In vertebrates , Cdk1 also phosphorylates the central region of Separase , but this phosphorylation is inhibitory , and works in parallel with Securin inhibition [34 , 82] . Although this is opposite to the regulation we have identified in this work , the mechanism by which Cdk1 inhibits Separase is poorly understood . Separase inhibition may be caused both by the stable binding of Cdk1/Cyclin B1 to Separase , as well as phosphorylation-dependent Separase aggregation [35 , 83 , 84] . Although phosphorylation itself triggers aggregation , recent work has shown that stable binding of Cdk1/Cyclin B1 to Separase prevents aggregation [85] , indicating that Cdk1 also promotes Separase activity in vertebrates . PP2A associated with its B56 regulatory subunit ( homologous to Rts1 in yeast ) interacts directly with human Separase and this interaction has also been shown to both promote and inhibit Separase function [36 , 37] . Similar to our model for PP2ACdc55 function ( Fig 10 ) , stable binding of PP2AB56 , rather than dephosphorylation , regulates Separase function . Slk19 homologues have not been clearly identified outside of budding yeasts . A few reports have speculated that mitosin/CENP-F or fission yeast alp7 may share homology with the C-terminal coiled-coil domains of Slk19 [26 , 86–88] . Although these regions may mediate Slk19 interaction with microtubules and depletion of CENP-F causes cohesion defects at the kinetochore [89] , our proposed mechanism suggests that proteins with homology to the N-terminus of Slk19 , which contains the Esp1 cleavage site , may be more relevant to understanding whether vertebrate Separase is regulated by a similar mechanism . If this function were conserved it could be mediated by an unidentified Slk19 homologue , a different Separase substrate , or perhaps even Separase itself , which has three internal autocleavage sites [90] . A recent cryo-EM structure of the C . elegans Separase/Securin complex suggest that the autocleavage sites are accessible to the catalytic site of Separase [91] and persistent binding between the protease domain and these sites , as seen in Slk19 binding to Esp1 , would be an effective mechanism to inhibit Separase activity . Mutation of the Separase autocleavage sites in vivo causes poorly understood delays in G2 [92] , but when this mutant is expressed in wild-type cells it causes premature loss of centromeric cohesion and separation of sister chromatids [37] , suggesting these sites may indeed regulate the initiation of anaphase .
This study was performed in strict accordance with standards for animal care and use outlined in the Canadian Council on Animal Care Standards . The University of Ottawa is a registered research facility under the Province of Ontario's Animals for Research Act . The protocol was approved by the University of Ottawa Animal Care Committee ( Permit Number: BMI-113 ) . All surgery was performed under sodium pentobarbital anesthesia and every effort was made to minimize suffering . S1 Table lists the strains used in this work and S2 Table lists the strains used in each figure . All strains are derivatives of the W303 strain background ( W303-1a; see S1 Table for complete genotypes ) . All deletions and replacements were confirmed by immunoblotting , phenotype or PCR . Strains were constructed by genetic cross and transformation . The sequences of all primers used in this study are available upon request . Phusion polymerase ( NEB ) was used for all PCR reactions . The bacterial strains TG1 and DH5α were used for amplification of DNA , and Rosetta ( Novagen ) was used for protein purification . SPC42-mCherry-NATR was constructed in the following manner . The mCherry coding sequence ( BBa_K165004 ) was obtained in the vector BBa_J63009 ( iGEM ) . mCherry was amplified by PCR as a PacI/AscI fragment and cloned into pKT127[93] , resulting in pAR733 . mCherry along with the KANR marker was amplified by PCR off pAR733 and integrated at SPC42 . SPC42-mCherry-KANR was switched to SPC42-NATR using pAG25 [94] . SPC42-eGFP-KANR and SPC42-eGFP-Sphis5+ were constructed by amplifying eGFP and either the KANR marker or the Sphis5+ marker from pKT127 and pKT128 , respectively [93] , and integrating the resulting PCR product at SPC42 . SPC42-eGFP-NATR and SPC42-eGFP-HYGR were constructed by switching KANR for NATR or HYGR using pAG25 and pAG32 , respectively [94] . CDC14-eGFP- Sphis5+ was created by amplifying GFP and the Sphis5+ marker from pKT128 and integrating the resulting PCR product at CDC14 . SMC3-GFP-URA3 was made using the plasmid pLF639 ( A . Strunnikov , National Institutes of Health , Bethesda , MD ) cut with Hpa1 . SPC29 was tagged with RFP by PCR amplifying a SPC29-RFP-HYGR fragment from yeast strain KBY4999 , or a SPC29-RFP-NATR fragment from ADR9045 . his3::pCUP1-GFP12-lacI-12::HIS3 was made by integrating pSB116 [95] . To create the ura3::240lacO-URA3 allele the lacO array was cut out of pLAU43 ( a gift from D . Z . Rudner , Harvard Medical School , Boston , MA ) [96] with XbaI/BamHI and cloned into pRS406 [97] to make pAR615 . pAR615 was cut with StuI and transformed into yeast to integrate the lacO array at URA3 . cdc55Δ::HIS3 was created using pJM6 [52] . pds1Δ::HYGR , cdc55Δ::HYGR and esp1Δ:: HYGR were constructed by amplifying HYGR off pAG32 and deleting PDS1 , CDC55 or ESP1 , respectively . slk19Δ::NATR and spo12Δ::NATR were constructed by amplifying NATR off pAG25 and deleting SLK19 and SPO12 , respectively . BAR1 was deleted using pJGsst1 ( J . Thorner , University of California , Berkeley , CA ) . MIH1 was deleted using pIP33 ( P . Sorger , Harvard Medical School , Boston , MA ) . swe1Δ::TRP1 strains were made by crossing JM449 ( J . Minshull , Atum , Newark , CA ) to the appropriate strains . The 2μ-pGAL-CLB2-TAP-URA3 ( pAR546 ) and 2μ-pGAL-CLB5-TAP-URA3 ( pAR547 ) plasmids were created as follows . The CLB2 or CLB5 ORF was amplified and the resultant PCR , designed to have overlapping homology , was co-transformed into yeast along with pRS-AB1234 ( C . Carroll and D . O . Morgan , UC San Francisco , San Francisco , CA ) cut with BamH1 and HindIII . CEN-PDS1-URA3 was constructed by PCR amplifying PDS1 with upstream and downstream regions and cloning the PCR fragment into pRS316 digested with EcoR1/BamH1 to create pAR1060 . PDS1-AID-KANR was constructed by amplifying AID and KANR from pAID1 [50] and integrating the resulting PCR product at PDS1 . PDS-AID-NATR was made by switching KANR to NATR using pAG25 . leu2::pGPD1-OsTIR1-LEU2 was constructed by digesting pTIR4 [50] with PmeI to integrate it at LEU2 ( plasmids were gifts of T . Eng and D . Koshland , UC , Berkeley , Berkeley , CA ) . The mcd1::pGAL-MCD1-18myc-URA3 , trp1::pMCD1-MCD1-3HA-TRP1 , trp1::pMCD1-mcd1-10A-HA3-TRP1 and leu2::pMCD1-mcd1-10A-3HA-LEU2 alleles were derived from strains Y1287 , Y1288 and Y1296 ( gifts of N . Hornig and F . Uhlmann , The Francis Crick Institute , London , UK ) [56] . ESP1-13myc-KANR was created by amplifying 13myc-KANR from pFA6a-13Myc-kanMX6 [98] and integrating the resulting PCR product at ESP1 . The ESP1-18myc-TRP1 allele was derived from K7024 ( a gift of F . Uhlmann , The Francis Crick Institute , London , UK ) . A 5 kb region containing the ESP1 ORF was amplified and cloned into pRS316 between XhoI and NotI resulting in pAR745 ( CEN-ESP1-URA3 ) . The XhoI/NotI fragment from pAR745 was cloned into pRS315 and pRS313 resulting in pAR797 ( CEN-ESP1-LEU2 ) and pAR800 ( CEN-ESP1-HIS3 ) , respectively . The esp1-2A-NATR , esp1-3A-NATR , esp1-1A-NATR , esp1-2A+3A-NATR , esp1-2A+1A-NATR , esp1-2A+3A+1A-NATR , esp1-2D-NATR , ESP1-3D-NATR , esp1-1D-NATR , ESP1-2D+3D-NATR , esp1-2D+1D-NATR and ESP1-2D+3D+1D-NATR alleles were constructed in the following manner: six DNA sequences were synthesized ( DNA2 . 0 ) corresponding to each of the N-terminal ( containing SP13 and TP16 ) , central ( containing TP1014 , SP1027 and TP1034 ) and C-terminal ( containing SP1280 ) sites and surrounding residues with each site mutated to either alanine or tandem aspartic acid residues . Restriction sites were engineered within or adjacent to the mutated codons for later identification . Each mutated region was amplified and cut with restriction sites found in the ESP1 gene ( AvrII/MscI for esp1-2A and esp1-2D; SpeI/NheI for esp1-3A and ESP1-3D; and SalI/AatII for esp1-1A and esp1-1D ) . These fragments were then cloned into pAR797 to make pAR873 ( CEN-esp1-2A-LEU2 ) , pAR875 ( CEN-esp1-3A-LEU2 ) , pAR871 ( CEN-esp1-1A-LEU2 ) , pAR872 ( CEN-esp1-2D-LEU2 ) , pAR874 ( CEN-ESP1-3D-LEU2 ) and pAR870 ( CEN-esp1-1D-LEU2 ) . pAR875 was subsequently used to make pAR902 ( CEN-esp1-2A+3A-LEU2 ) ; pAR873 to make pAR924 ( CEN-esp1-2A+1A-LEU2 ) ; pAR871 to make pAR886 ( CEN-esp1-3A+1A-LEU2 ) ; pAR872 to make pAR903 ( CEN-ESP1-2D+3D-LEU2 ) ; pAR870 to make pAR915 ( CEN-esp1-2D+1D-LEU2 ) ; pAR874 to make pAR901 ( CEN-ESP1-3D+1D-LEU2 ) ; pAR886 to make pAR1005 ( CEN-esp1-2A+3A+1A-LEU2 ) ; and pAR915 to make pAR927 ( CEN-ESP1-2D+3D+1D-LEU2 ) . To add a marker to pAR797 , pAR873 , pAR875 , pAR871 , pAR872 , pAR874 , pAR870 , pAR902 , pAR924 , pAR886 , pAR903 , pAR915 , pAR901 , pAR1005 and pAR927 , each plasmid was cut with SnaBI and co-transformed into yeast with a PCR product containing the NATR cassette amplified from pAG25 and ends overlapping the cut backbone plasmid . Plasmids were rescued and confirmed by restriction digest . The resulting plasmids were pAR906 ( CEN-ESP1-NATR-LEU2 ) , pAR936 ( CEN-esp1-2A-NATR-LEU2 ) , pAR905 ( CEN-esp1-3A-NATR-LEU2 ) , pAR904 ( CEN-esp1-1A-NATR-LEU2 ) , pAR921 ( CEN-esp1-2D-NATR-LEU2 ) , pAR922 ( CEN-ESP1-3D-NATR-LEU2 ) , pAR920 ( CEN-esp1-1D-NATR-LEU2 ) , pAR990 ( CEN-esp1-2A+3A-NATR-LEU2 ) , pAR992 ( CEN-esp1-2A+1A-NATR-LEU2 ) , pAR930 ( CEN-esp1-3A+1A-NATR-LEU2 ) , pAR991 ( CEN-ESP1-2D+3D-NATR-LEU2 ) , pAR932 ( CEN-esp1-2D+1D-NATR-LEU2 ) , pAR931 ( CEN-ESP1-3D+1D-NATR-LEU2 ) , pAR1006 ( CEN-esp1-2A+3A+1A-NATR-LEU2 ) , and pAR994 ( CEN-ESP1-2D+3D+1D-NATR-LEU2 ) . ESP1 mutants along with the NATR cassette were amplified from these plasmids and transformed into wild-type yeast . Presence of mutated phosphorylation sites was verified by amplifying the mutated region and digesting the amplified product with the appropriate restriction enzyme . ESP1-3D-HYGR and esp1-3A-HYGR were constructed by switching NATR for HYGR using pAG32 . esp1-3A-ADE2 and ESP1-3D-ADE2 were constructed by amplifying ADE2 from pRS412 [97] and using it to replace NATR and HYGR respectively . 3FLAG tagged ESP1 mutants were constructed in the following manner . The ESP1 ORF was amplified off pAR745 and cloned into pBS-KS ( Stratagene ) between SalI and NotI to make pAR868 . A BglII site was inserted downstream of the ESP1 stop codon in pAR868 using site-directed mutagenesis to make pAR877 . ESP1-BglII was amplified from pAR877 and co-transformed into yeast along with pAR797 cut with SnaBI and NcoI , and rescued to create pAR888 . pAR888 was then cut with BglII and co-transformed into yeast with 3FLAG-KANR amplified off pDAM278 [99] and rescued to create pAR911 . 3FLAG-KANR was then cut out of pAR911 with BsgI and NotI and cloned into pAR797 , pAR873 , pAR875 , pAR871 , pAR902 , pAR924 , pAR886 and pAR1005 to create pAR911 ( CEN-ESP1-3FLAG-KANR-LEU2 ) , pAR965 ( CEN-esp1-2A-3FLAG-KANR-LEU2 ) , pAR968 ( CEN-esp1-3A-3FLAG-KANR-LEU2 ) , pAR973 ( CEN-esp1-1A-3FLAG-KANR-LEU2 ) , pAR971 ( CEN-esp1-2A+3A-3FLAG-KANR-LEU2 ) , pAR964 ( CEN-esp1-2A+1A-3FLAG-KANR-LEU2 ) , pAR966 ( CEN-esp1-3A+1A-3FLAG-KANR-LEU2 ) and pAR975 ( CEN-esp1-2A+3A+1A-3FLAG-KANR-LEU2 ) . These plasmids were then transformed into the appropriate yeast strain . Unless noted in the figure legend , cells were grown in yeast extract peptone media + 2% dextrose ( YEPD ) at 25°C or 30°C . Cells cycle arrests were performed with 10 μg/mL nocodazole ( Sigma-Aldrich ) or 100 ng/mL α-factor ( Biosynthesis ) for 3 hours . Auxin ( indole-3-acetic acid , Sigma-Aldrich ) was used at 500 μM in liquid and solid media . The morphogenesis checkpoint was activated using 2 . 5–5 μM LatA ( Sigma-Aldrich or Tocris Biosciences ) . LatA efficacy varied between batches and suppliers so the amount needed to induce a fully Swe1-dependent checkpoint arrest was determined empirically . Plate-based viability assays were performed using a multi-pronged serial dilution fork ( DAN-KAR ) . Liquid culture viability assays were performed by diluting cultures 1000X and/or 10000X into YPD and sonicated to disrupt cell adhesion . Viability was calculated relative to viability at t = 0 . Dilutions were adjusted to ensure that > 100 colonies grew at each timepoint . For fixed cell microscopy , ~ 2 . 0 x 106 cells were harvested and fixed with 4% paraformaldehyde in PBS pH 7 . 5 for 15 minutes . Cells were washed with 100 mM KPO4/1 . 2 M sorbitol pH 7 . 5 , sonicated to break cell adhesions and resuspended in KPO4/1 . 2 M sorbitol . Samples were imaged using a Nikon TI microscope ( Nikon ) with a Nikon Plan Apo 60X 1 . 4 NA objective and FITC and/or TRITC filter sets ( FITC ( 41001 ) ; TRITC ( 41002c ) , Chroma ) at room temperature . Images were obtained using a Photometrics CoolsnapHQ2 camera ( Photometrics ) and NIS-Elements software ( Nikon ) . Unless otherwise noted a minimum of 200 cells were visually scored per data point . Spindles were measured in three dimensions using a stack of 17 fluorescence images spaced every 0 . 5 μm , covering the entire height of the cell . All measurements were made using NIS-Elements software ( Nikon ) . Imaging pads were made by adding 25% Gelatin ( w/v ) to SC or YEPD media at 55–60°C , pipetting 50 μL between two microscope slides and allowing it to cool . 1–2 μL of cultures were pipetted onto live imaging pads , covered by a coverslip and sealed with 1:1:1 vaseline:lanolin:petroleum jelly ( VALAP ) . Strains were imaged at 25°C for 2 hours using brightfield and FITC and/or TRITC filter sets ( FITC ( 41001 ) ; TRITC ( 41002c ) , Chroma ) on a Nikon TI microscope ( Nikon ) with a Nikon Plan Apo 60X 1 . 40 NA objective and a Photometrics CoolsnapHQ2 camera ( Photometrics ) . 17 Z-slices , spaced every 0 . 5 μm were imaged at each timepoint . Fluorescence excitation was attenuated using neutral density filters and 100–200 ms exposure times were used for GFP , mCherry and brightfield acquisition . Measurements were made using NIS-Elements software ( Nikon ) . Look up tables were manually adjusted linearly . Example images were prepared using ImageJ software ( National Institutes of Health ) . Spindle behaviour was classified using three rules . Cells with “normal metaphase spindle formation” did not elongate their spindle more than 2 μm in the first 10 minutes after SPB separation and more than 2 . 5 μm in the first 15 minutes after SPB separation . Cells whose spindles elongate more than 2 and 2 . 5 μm in these time-intervals display “immediate spindle elongation . ” A small number of cells ( 15% for cells treated with auxin , and 11% for untreated cells ) produce conflicting scores using these two rules ( i . e . , immediate/normal or normal/immediate in the 10/15 minute intervals ) , and we categorized these cells manually . For immediate/normal cells: If the spindle elongated to a length greater than 2 μm in a single time point and there was a clear inflection point that defined anaphase onset , these cells were classified as “normal . ” If there was no clear anaphase onset inflection point and there was spindle shortening between 10 and 15 minutes , these cells were classified as “immediate . ” For normal/immediate cells: If rapid anaphase spindle elongation began between 10–15 minutes with a clear inflection point , these cells were classified as “normal . ” If there was no clear inflection point and the spindle elongated at a slow continuous rate , these cells were classified as “immediate” . Cells with “failed anaphase” do not elongate their spindles to 6 μm in the 60 minutes after SPB separation . Smc3-GFP fluorescence as cells progress from metaphase to anaphase was quantified according to the method described in Hoffman et al . and Yeh et al . [11 , 100] . Live-cell images were obtained from cells immobilized on 25% gelatin/media slabs . Five plane Z sections at 200 nm steps through the cell were acquired at 1 min intervals . The microscope used for wide-field imaging was a Nikon Eclipse TE2000E stand ( Nikon ) with 100 PlanApo NA 1 . 4 objective with a Hamamatsu Orca ER camera ( Hamamatsu ) . Images were acquired at room temperature with MetaMorph imaging software ( Molecular Devices ) . In brief , a computer-generated 5 x 5 and 6 x 6 pixel regions were centered over the region of interest , and the total integrated fluorescence counts were obtained for each region . Inner- and outer-region data were transferred into Microsoft Excel ( Microsoft ) with the use of the MetaMorph Dynamic Data Exchange function . The measured value for the 5 x 5 pixel region includes both cohesin fluorescence and local background fluorescence . The background component was obtained by subtraction of the integrated value of the 5 x 5 pixel region from the larger 6 x 6 pixel region . This result was scaled in proportion to the smaller area of the 5 x 5 pixel region and then subtracted from the integrated value of the 5 x 5 pixel region to yield a value for cohesin fluorescence . These methods have been described previously [45] . Immunoprecipitations of wild-type and mutant Esp1 and Esp1-FLAG were performed in APC lysis buffer ( 50 mM Hepes-KOH pH 7 . 8 , 700 mM NaCl , 150 mM NaF , 150 mM Na-β-glycerophosphate pH 8 . 3 , 1 mM EDTA , 1 mM EGTA , 5% glycerol , 0 . 25% NP-40 , 1 mM DTT , 1 mM PMSF , 1 mM Na3VO4 , 1 mM benzamidine , and leupeptin , bestatin , pepstatin A and chymostatin all at 1 mM ) . 1–2μl of α-Esp1 and α-FLAG-M2 ( F1804 , Sigma-Aldrich ) were used in each immunoprecipitate . The following antibodies were used for Western blots and immunoprecipitations: The use of 9E10 ascites ( BabCO ) , α-Swe1 , α-Clb2 , α-Pds1 , α-Clb5 , α-Cdk1 , α-P-Cdc2-Y15 ( #9111 , Cell Signaling Technology ) antibodies have been described previously [45 , 101] . Rabbit polyclonal α-Esp1 , α-Mcd1 , α-G-6-PDH antibodies ( A9521 , Sigma-Aldrich ) were used at 1:1000 , and α-Slk19 at 1:2000 , in TBS-T with 4% Fat Free Milk Powder , 5% glycerol , 0 . 02% NaN3 . An autoclaved solution of 5% milk was used to make the 4% milk dilution buffer to increase the longevity of the antibody solution . Membranes were pre-blocked with TBS-T with 4% Fat Free Milk Powder before incubation with all primary antibodies . α-Esp1 antibodies were generated as follows: coding sequences for the truncated protein Esp1230–414 was amplified using PCR and cloned into pHIS-parallel2 [102] as a BamHI/SalI fragment to create pAR882 . Denatured His6-Esp1230–414 protein was purified on Ni-NTA columns , dialyzed and ~0 . 5 mg of precipitated protein was injected into rabbits every 4 weeks for 8 to 16 weeks ( uOttawa animal facility ) . Rabbit serum was harvested and the α-Esp1 antibodies purified on Affigel-15 ( Bio-rad ) columns coupled to purified His6-Esp1230–414 that had been solubilized in 0 . 3% SDS . α-Mcd1 antibodies were generated as follows: coding sequence for the truncated protein Mcd1201–301 was amplified by PCR and cloned into pGEX6P-1 ( Promega ) as a BamHI/EcoRI fragment to create pAR742 . GST-Mcd1201–301 was purified and 1 mg of the fusion protein was injected into rabbits every 4 weeks for 8 to 16 weeks ( uOttawa animal facility ) . Rabbit serum was harvested , and the α-Mcd1 antibodies purified on an Affigel-10 ( Bio-rad ) column coupled to purified malE-Mcd1201–301 . malE-Mcd1201–301 was expressed from the plasmid pAR1117 which contains Mcd1201–301 cloned as a BamH1/Sal1 fragment into pMAL-c2 ( NEB ) . α-Slk19 antibodies were generated as follow: coding sequence for the truncated protein Slk19700–817 was amplified by PCR and cloned into pGEX6P-1 ( Promega ) as a BamHI/EcoRI fragment to create pAR1230 . GST-Slk19700–817 was purified and 1 mg of the fusion protein was injected into rabbits every 4 weeks for 8 to 16 weeks ( uOttawa animal facility ) . Rabbit serum was harvested , α-GST antibodies were removed on an Affigel-10 ( Bio-rad ) column coupled to GST , and the α-Slk19 antibodies were purified on an Affigel-10 ( Bio-rad ) column coupled to purified GST-Slk19700–817 . HRP-conjugated α-rabbit and α-mouse secondary antibodies ( Bio-rad ) were used at a 1:5000 dilution in TBS-T + 4% Fat Free Milk Powder for 30 min to 1 hr . , washed with TBS-T and incubated in Western Lightning Plus-ECL ( PerkinElmer ) . Signal detection was done on HyBlot CL ( Harvard Scientific ) autoradiography film . 3 . 0 x 107 cells were harvested for immunoblotting and cell pellets were washed twice with 50 mM HEPES pH 8 . 0 . Cell extracts were prepared by bead beating frozen cell pellets in a Mini-Beadbeater ( BioSpec Products ) in 1X urea sample buffer ( 2% SDS , 65 mM Tris-Cl pH 6 . 8 , 10% glycerol , 4 M Urea , 0 . 02% bromophenol blue , 5% betamercaptoethanol , and 1mM PMSF ) and an excess of acid washed glass beads ( BioSpec Products ) for 60 sec . Samples were run on 10% acrylamide gels with 100 μM Phos-tag reagent ( Wako ) , 200 μM MnCl2 and 0 . 1% SDS . Gels were run for 5 h at 200 V and 25 mA . Following electrophoresis , gels were washed 2 x 10 min in transfer buffer with 1 mM EDTA , 1 x 10 min in transfer buffer and transferred to nitrocellulose using standard wet-transfer protocol at 60 V and 500 mA for 2 hr . at 4°C . 10 x 107 cells were harvested and labeled in 2 mL phosphate-free medium containing 0 . 5–1 mCi 32PO4 ( PerkinElmer ) as described previously [45] . Uptake of label was monitored by scintillation counting ( TriCarb 2910TR; PerkinElmer ) of the cells and media , and exceeds 98% . Esp1-myc13 or Esp1 was immunoprecipitated using 9E10 or α-Esp1 antibodies , respectively . Cdk1/Clb2-CBP and Cdk1/Clb5-TAP complexes were purified from cells containing pAR546 or pAR547 , respectively ( 2μ-pGAL-CLB2-TAP and 2μ-pGAL-CLB5-TAP , described above ) . Clb2-TAP was overexpressed by growth in galactose . Cdc55-CBP complexes were purified from asynchronously growing CDC55-TAP ( ADR5465 ) cells . Protein complexes were purified as described previously [45 , 103] . To phosphorylate Esp1 , Esp1 was immunoprecipitated with α-Esp1 antibody and treated with purified Cdk1/Clb2-CBP complexes . Kinase reactions were performed with 1 μCi γ-[32P]ATP as previously described [45] . Dephosphorylation of Esp1 was measured by incubating in vitro phosphorylated Esp1 , still bound to beads , with TAP purified PP2ACdc55 complexes as previously described [45] . Okadaic acid ( LC laboratories ) was used at 2 nM . Phosphatase assays were quantified using a Typhoon Trio Phosphorimager and ImageQuant software ( GE ) . | The fidelity of chromosome segregation is essential for the survival of cells after cell division . Mis-regulation of chromosome segregation can lead to aneuploidy which is associated with many cancers , and may initiate the formation of a cancerous cell . Chromosome segregation is triggered by the cleavage of one subunit of the Cohesin complex , a ring-shaped protein complex that topologically links replicated sister chromatids . The regulation of Cohesin cleavage relies on several redundant pathways that together decrease the chance that Cohesin is cleaved prematurely . We have identified a novel pathway that regulates Cohesin cleavage in the pericentromere , which lies between the bi-oriented sister centromeres , where microtubule attachments are built . Spatial and temporal control of Cohesin cleavage in the pericentromere is important because Cohesin creates intra-chromosomal crosslinks that protect chromosomes from the forces that pull them to poles , and at anaphase onset , the dissolution of pericentromeric cohesion triggers chromosome segregation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"phosphorylation",
"anaphase",
"cell",
"cycle",
"and",
"cell",
"division",
"metabolic",
"processes",
"cell",
"processes",
"chromatids",
"hormones",
"plant",
"science",
"immunoprecipitation",
"plant",
"hormones",
"proteolysis",
"molecular",
"biology",
"techniques",
"resear... | 2018 | Cdk1 phosphorylation of Esp1/Separase functions with PP2A and Slk19 to regulate pericentric Cohesin and anaphase onset |
Echinocandins are a new generation of novel antifungal agent that inhibit cell wall β ( 1 , 3 ) -glucan synthesis and are normally cidal for the human pathogen Candida albicans . Treatment of C . albicans with low levels of echinocandins stimulated chitin synthase ( CHS ) gene expression , increased Chs activity , elevated chitin content and reduced efficacy of these drugs . Elevation of chitin synthesis was mediated via the PKC , HOG , and Ca2+-calcineurin signalling pathways . Stimulation of Chs2p and Chs8p by activators of these pathways enabled cells to survive otherwise lethal concentrations of echinocandins , even in the absence of Chs3p and the normally essential Chs1p , which synthesize the chitinous septal ring and primary septum of the fungus . Under such conditions , a novel proximally offset septum was synthesized that restored the capacity for cell division , sustained the viability of the cell , and abrogated morphological and growth defects associated with echinocandin treatment and the chs mutations . These findings anticipate potential resistance mechanisms to echinocandins . However , echinocandins and chitin synthase inhibitors synergized strongly , highlighting the potential for combination therapies with greatly enhanced cidal activity .
In fungi , two covalently cross-linked polysaccharides , β ( 1 , 3 ) -glucan and chitin , form a primary scaffold that is responsible for structural integrity and shape of the cell wall [1]–[4] . Other β-linked polysaccharides and glycosylated proteins are attached to this glucan-chitin core , thus modifying the properties of the wall . The integrity of the cell wall scaffold must , however , be monitored and regulated constantly to ensure cell viability . This is not a trivial challenge since surface expansion during growth and cellular morphogenesis requires a delicate balance to be maintained between the rigidity and the flexibility of the cell wall . The cell wall must be able to expand under the outwardly directed and variable force of cell turgor , whilst maintaining sufficient rigidity to prevent cell lysis . This balance between plasticity and rigidification must also be achievable in the presence of extrinsic factors such as inhibitory molecules and enzymes in the environment that may attack the integrity of the cell wall . Responses to cell wall damage involve a sophisticated homeostatic mechanism that is mediated via a signalling network which communicates information about physical stresses at the cell surface to the biosynthetic enzymes that orchestrate cell wall synthesis and repair . The signalling pathways and transcription factors that mediate this repair response are termed the cell wall salvage or cell wall compensatory mechanisms [5]–[8] . Echinocandins are a new class of antifungal agent , which are non-competitive inhibitors of β ( 1 , 3 ) -glucan synthase [9] . Caspofungin is the first echinocandin to be approved for clinical use and is fungicidal for Candida albicans , and other Candida species , and fungistatic for Aspergillus fumigatus [10] , [11] . It is active against isolates of Candida spp . that are resistant to other antifungals such as fluconazole [12] . Deletion of both copies of the FKS1 gene is lethal in C . albicans , although point mutations in FKS1 can arise that result in reduced susceptibility to caspofungin [9] , [13]–[15] . FKS1 point mutations associated with resistance accumulate in two hot spot regions that encode residues 641–649 and 1345–1365 of CaFks1p in C . albicans and other species [14]–[17] . Fungi that are inherently less susceptible to echinocandins , have a tyrosine at residue 641 compared to phenylalanine in that position in CaFks1p [16] , [18] , suggesting sequence divergence around the hot spot regions may contribute to reduced echinocandin susceptibility . In Saccharomyces cerevisiae deletion of ScFKS1 is not lethal and inhibition of β ( 1 , 3 ) -glucan synthesis or damage to β ( 1 , 3 ) -glucan results in increased levels of chitin synthesized by ScChs3p [7] , [19] . Scchs3Δ mutants are hypersensitive to caspofungin [20] and ScCHS3 and ScFKS1 are synthetically lethal [21] , [22] suggesting that Scfks1Δ mutants depend on chitin synthesis for their survival . In addition microarray analyses have shown that CaCHS2 expression increases in response to caspofungin treatment [23] , [24] . Treatment of S . cerevisiae [25] and Cryptococcus neoformans [26] with caspofungin results in activation of the PKC cell integrity pathway via phosphorylation of the mitogen activated protein kinase , ScSlt2p/ScMkc1p . C . albicans MKC1 expression has been found to increase in response to caspofungin treatment [27] and deletants in ScSLT2 are hypersensitive to caspofungin [20] , [25] . Damage to the cell wall involves cell wall protein sensors which transmit signals that lead to activation of the ScRho1p GTPase , which activates β ( 1 , 3 ) -glucan synthase as well as ScPkc1p and hence the PKC cell integrity MAP kinase cascade [8] . The downstream target of this cell wall salvage pathway is the ScRlm1p transcription factor , which activates transcription of cell wall related genes [28] . The Ca2+-calcineurin pathway has also been implicated in the regulation of cell wall biosynthesis [29]–[31] . C . albicans calcineurin mutants are hypersensitive to caspofungin , suggesting that the calcineurin pathway is involved in the response to cell wall damage caused by caspofungin [32] . Combined treatment with caspofungin and the calcineurin inhibitor , cyclosporin A , prevents the paradoxical effect of increased survival that is sometimes seen at echinocandin concentrations well above the typical minimal inhibitory concentration ( MIC ) [27] . The calcineurin inhibitors , FK506 and cyclosporin A , have also been shown to act synergistically with caspofungin against Aspergillus fumigatus and Cryptococcus neoformans [26] , [33]–[35] . A primary response of fungi to cell wall damage is to up-regulate chitin synthesis . In C . albicans there are four chitin synthase enzymes , CaChs1p , CaChs2p , CaChs3p and CaChs8p . CaChs1p is an essential class II enzyme that synthesizes the chitin of the primary septum and contributes to lateral wall integrity [36]; CaChs2p and CaChs8p are two class I enzymes that account for almost all measured chitin synthase activity in vitro [37]–[39] and CaChs3p is a class IV enzyme that synthesizes the majority of cell wall chitin , including the septal chitin ring [40] . While CaChs3p is predominantly regulated at the post-transcriptional level , CaCHS1 , CaCHS2 , and CaCHS8 can all be transcriptionally activated in response to stimulants of the PKC , Ca2+-calcineurin and HOG signalling pathways [31] . Here we show that pre-treatment of cells with activators of these pathways activates CaCHS transcription and leads to the selection of cells with increased cell wall chitin that survive otherwise lethal concentrations of caspofungin . We also show that activation of the cell wall compensatory pathways can induce the synthesis of a novel salvage septum even in the absence of CaChs3p and CaChs1p which are normally required for septum formation and viability . Rescue of such cells was strictly dependent on chitin synthesis from residual class I enzymes and combinations of echinocandins and chitin synthase inhibitors exhibited synergy in the killing of C . albicans .
To test whether exposure to echinocandins induced chitin synthesis we first used a lacZ-reporter system to measure the response of the four C . albicans CHS promoters to echinocandins at concentrations below their MICs . Caspofungin ( Figure 1 ) and echinocandin B , cilofungin and anidulafungin ( data not shown ) activated expression of CHS1 , CHS2 and CHS8 . The level of expression of the class IV CHS3 was only increased significantly with anidulafungin ( data not shown ) . Previously we showed that the PKC , Ca2+-calcineurin and HOG pathways all regulated CHS expression [31] . We then used reporter constructs to establish which signalling pathways were required to activate these transcriptional responses to echinocandins . The mutants tested had the following genes deleted; HOG1 encoding the MAP kinase of the HOG pathway , MKC1 encoding the MAP kinase of the PKC pathway , and the calcineurin catalytic subunit CNA1 . Mutations in these pathways affected both the basal level of gene expression and the response to caspofungin . Mutant strains with deletions in the HOG pathway ( hog1Δ ) showed no increase in expression of CHS1 , CHS2 and CHS8 when caspofungin was applied ( Figure 1 ) . Up-regulation of CHS1 was not seen in cna1Δ mutants after caspofungin addition , therefore the Ca2+-calcineurin pathway was involved in the regulation of CHS1 . Equivalent analyses showed that the PKC pathway contributed to the up-regulation of the expression of CHS2 and CHS8 upon exposure to caspofungin . Caspofungin treatment of cells also led to a 2 . 5-fold increase in specific chitin synthase activity measured in mixed membrane preparations ( Figure 2A ) , and a near 3-fold increase in the chitin content of the cells ( Figure 2B ) . The measured stimulation of chitin synthase activity was dependent on the presence of two class I enzymes Chs2p and Chs8p and on the Ca2+-calcineurin , PKC and HOG pathways ( Figure 2A ) . The total chitin content stimulated by caspofungin was largely dependent on Chs3p and this also required a functional PKC pathway and the presence of calcineurin ( Figure 2B ) . The HOG pathway also had a significant influence on the stimulation of chitin content by caspofungin . The effect of point mutations in FKS1 was also determined by measuring cell wall chitin content ( Figure 2B ) . Strain NR3 , which was resistant to caspofungin as a result of homozygous point mutations in the β ( 1 , 3 ) -glucan synthase gene FKS1 [9] had an almost three-fold increase in chitin content and lost the stimulation of chitin synthesis by caspofungin ( Figure 2B ) . We further implicated the PKC pathway in the response to caspofungin by showing phosphorylation of Mkc1p in wild type cells treated with caspofungin ( Figure 2C ) . We also quantified an increase in Chs3p upon caspofungin treatment by western analysis using anti-GFP antibodies and a strain engineered with a C-terminal YFP tag fused to Chs3p [41] ( Figure 2D ) . Therefore , the HOG , PKC and Ca2+-calcineurin signalling pathways were found to mediate the elevation of chitin synthase gene expression , chitin synthase activity and chitin content in response to caspofungin . Having shown previously that Calcofluor White ( CFW ) and Ca2+ are activators of the cell wall compensatory signalling pathways that could stimulate chitin synthesis [31]; we determined whether pre-treatment of cells with such agonists could protect cells from the cidal affects of caspofungin . Inocula of wild type or various mutant strains of C . albicans were grown in YPD , with and without added CaCl2 and CFW , before washing , dilution and plating onto agar containing caspofungin and other supplements ( Figure 3 ) . The homozygous fks1 point mutant , strain NR3 , which was greatly reduced in sensitivity to caspofungin [9] , [14] was included as a control . Under normal growth conditions , the cna1Δ , mkc1Δ and chs3Δ mutants were hypersensitive to a low concentration of caspofungin ( 0 . 032 µg/ml ) compared to wild type cells . The strains did not show significant sensitivity to 100 µg/ml CFW alone but CFW was found to act synergistically with 0 . 032 µg/ml caspofungin to enhance killing ( Figure 3 ) . Only the fks1 point mutant was able to grow at a higher caspofungin concentration ( 16 µg/ml ) . Pre-treatment of the inoculum by growth in CaCl2 and CFW ( rows marked with asterisks ) significantly reduced the efficacy of 16 µg/ml caspofungin against wild type cells and was dependent upon MKC1 , HOG1 , CNA1 , and CHS2 , CHS3 and CHS8 ( Figure 3 ) . At lower caspofungin concentrations less dependency on these genes was found . These results suggest that stimulation of chitin synthesis accounted for decreased caspofungin sensitivity and inhibition of chitin assembly increased caspofungin toxicity . Combining FK506 with caspofungin phenocopied the effects of the cna1Δ mutation ( data not shown ) . Experiments were also carried out using CaCl2 or CFW pre-treatments alone . Priming cells with CaCl2 alone conferred more caspofungin protection than treatment with CFW alone ( data not shown ) . Pre-growing cells in CaCl2 and CFW supplemented medium was also found to protect cells against caspofungin in liquid culture on YPD or RPMI 1640 . Using the CLSI method we determined that pre-growing cells with CaCl2 and CFW significantly increased the MIC for caspofungin by up to 6 doubling dilutions ( Figure 4 ) . The MIC to anidulafungin and micafungin was also increased by pre-treatment of wild type strains with CaCl2 and CFW however , MIC to fluconazole , amphotericin B , terbinafine and 5-flucytosine remained unchanged ( data not shown ) . Growth of S . cerevisiae on glucosamine–supplemented medium has been shown to stimulate chitin synthesis [42] . Therefore , C . albicans yeast cells were pre-grown on glucosamine-supplemented YPD to establish whether this would also lead to protection against caspofungin . Cells grown in YPD supplemented with 23 mM glucosamine had an almost two-fold increase in chitin content compared to the control cells ( data not shown ) and glucosamine-grown cells were considerably less sensitive to caspofungin ( Figure 5 ) . This protection did not require Chs3p , Cna1p and Mkc1p . Therefore , this compensatory mechanism could occur even in mutants with deletions in individual signalling pathways of the cell wall compensatory response and in the absence of Chs3p - the chitin synthase responsible for synthesizing the majority of chitin in wild type cells . Wild type cells were grown in media containing CaCl2 and CFW , and then washed , diluted and plated onto YPD agar containing 16 µg/ml caspofungin . Colonies emerged that contained punctate rich zones of growth within a lawn of cells that when re-grown were resistant to 16 µg/ml caspofungin ( Figure 6A , left panel ) . In contrast to the inoculum these resistant cells stained brightly with CFW indicating higher chitin content ( Figure 6B ) and they excluded the vital dye propidium iodide indicating they were viable ( Figure 6C ) . In contrast , sensitive cells surrounding these rich zones of growth were susceptible to caspofungin , stained poorly with CFW , and were propidium iodide-sensitive and non-viable ( Figure 6B and 6C ) . However , when inocula taken from parts of the colony outside the rich zones of growth were plated onto caspofungin-agar a few colonies arose which contained cells that were caspofungin insensitive and of high chitin content ( Figure 6A , right panel ) . All colonies emerging on such plates could be propagated indefinitely on caspofungin-containing agar . When such cells were grown without caspofungin selection , they reverted quickly to become caspofungin-sensitive , and the reverted cells stained poorly with CFW ( Figure 6D ) . Pre-treatment with CaCl2 and CFW stimulated the emergence of resistant colonies at a higher rate than occurred when cells were pre-treated with sub-MIC levels of caspofungin . For example , when cells were pre-treated with CaCl2 and CFW the rate at which resistant colonies emerged was approximately 1 in 120 cells . This compared to the emergence of resistant colonies from approximately 1 in 1 . 3×106 cells that were pre-treated with 0 . 032 µg/ml caspofungin . The intensity of CFW fluorescence of yeast cells was found to be an accurate reflection of the relative chitin content . Within a population of cells the average chitin content was found to be stimulated by treatment with CaCl2 + CFW or by sub-MIC concentrations of caspofungin ( Figure 6E ) . Cells that were pre-treated with CaCl2 and CFW and then cultured in sub MIC concentrations of caspofungin had the highest levels of chitin ( Figure 6E , and Figure S1 ) . Thus exposure to CaCl2 and CFW and to caspofungin led to both an increase in the average chitin content of cells ( Figure S1 ) and the selection of a sub-population of caspofungin resistant cells that formed zones of rich growth within colonies . When chitin-rich , caspofungin-insensitive cells were transferred to fresh YPD medium lacking caspofungin their chitin content declined to control unstimulated levels within 4–5 h equivalent to approximately 3–4 generations ( Figure 6F ) . Thus the activation of chitin in response to caspofungin was a transient adaptation and upon removal of the drug chitin content returned to wild type levels . Having established that treatment with echinocandins leads to a compensatory up-regulation of chitin synthesis , we next used a panel of single and double chs mutants to determine which chitin synthase enzymes were required to rescue the cells from the effects of echinocandins . By measuring the pattern and relative amount of CFW fluorescence we observed that Chs3p was responsible for synthesising the majority of chitin induced by caspofungin treatment ( Figure 7 panels 1–16 ) . Pre-growing the wild-type , chs2Δchs8Δ , chs2Δ and chs8Δ mutants in CaCl2 and CFW ( Figure 7 panels 17 , 19 , 21 & 24 ) led to an overall increase in cell wall chitin content . Significant amounts of chitin accumulated at the poles of the chs3Δ and chs2Δchs3Δ mutants when pre-grown in CaCl2 and CFW ( Figure 7 panels 20 & 23 ) suggesting that pre-treatment stimulates the remaining chitin synthase enzymes to synthesize salvage chitin in the absence of Chs3p . Likewise , the chs1Δ and chs1Δchs3Δ mutants had concentrated areas of chitin at the septum after pre-treatment with CaCl2 and CFW ( Figure 7 panels 18 and 22 ) . In all cases , pre-treatment and then exposure to caspofungin stimulated production of salvage chitin ( Figure 7 panels 25–32 ) via activation of multiple Chs enzymes . Moreover , mutants lacking Chs3p and Chs1p were able to survive in the presence or absence of caspofungin when pre-grown in CaCl2 and CFW-containing medium ( Figure 7 panels 22 and 30 ) . This is remarkable in view of the fact that CHS1 is essential for C . albicans and that Chs3p has been thus far been considered to be the key chitin synthase of the cell wall salvage pathway in S . cerevisiae [7] . In this double mutant , the cells grown in CaCl2 and CFW-containing medium had unusually bright CFW-staining and thickened septa that formed proximal to the normal location at the mother-bud neck region ( Figure 7 panel 22 and Figure 8A–C ) . These salvage septa also stained with Wheat Germ Agglutinin ( WGA ) -Texas Red indicating that they were chitin rich ( data not shown ) . Chs3p and Chs1p normally collaborate to form the chitin ring and primary septal plate of wild type septa respectively , but these salvage septa were formed in the absence of these two chitin synthases . The salvage septum was able to restore the capability for cell division , so that the formation of septum-less chains of cells and subsequent cell lysis normally associated with the lack of Chs1p was abrogated and viability was restored ( Figure 8D , E and Figure 8F ) . Abrogation of these phenotypes associated with the chs mutations was entirely dependent upon chitin synthesis and could be inhibited completely by treatment with nikkomycin Z ( Figure 8F ) . In pre-treated wild type cells or the fks1 point mutant , inhibition of chitin assembly by CFW or chitin synthesis by nikkomycin Z was strongly synergistic with caspofungin in killing cells even under conditions that maximally induce cell wall compensatory mechanisms ( Figure 9 ) . Treatment with RO-09-3143 , a selective chitin synthase inhibitor developed by Roche against the class II enzyme Chs1p [43] , phenocopied all the effects of the chs1 conditional mutation ( data not shown ) . These observations strongly support the conclusion that the Chs2p and/or Chs8p class I chitin synthases are responsible for synthesizing the chitin in the salvage septum that rescues the cells under these conditions .
The echinocandins are proving to be a safe and efficacious new class of antifungal drug for the treatment of systemic mycoses [11] , [44] . Laboratory-generated point mutations in the Fks1p target around the 645Ser hotspot region alter the affinity for these non-competitive inhibitors and results in reduced susceptibility [9] , [15] . However , there are a few recorded cases of failed echinocandin therapy in the treatment of Candida infections caused by C . albicans [45]–[48] , C . glabrata [49] , C . parapsilosis [50] , [51] and C . krusei [52] . In two cases , the decreased echinocandin sensitivity of recovered C . albicans isolates was shown to be due to mutations in FKS1 [47] , [48] . In addition , the emergence of a C . krusei isolate with decreased caspofungin susceptibility [53] was found subsequently to have a heterozygous mutation in the FKS1 hotspot region [17] . It has also been suggested that an increase in cell wall chitin may explain the so-called “paradoxical effect” whereby some clinical isolates exhibit decreased sensitivity to increased concentrations of caspofungin [54] . Collectively these observations suggest that the sensitivity of a strain of Candida may relate in part to aspects of fungal physiology other than the affinity of the Fks1p target protein for echinocandins . We have shown by in vitro experiments that C . albicans can rapidly respond to the presence of echinocandins by elevating chitin content , and that this response protects the cells from cell wall damage due to inhibition of β ( 1 , 3 ) -glucan synthesis . This may occur either by selection of a sub-population of naturally occurring chitin-rich cells , and/or by induction of the cell wall compensatory mechanisms that activate chitin synthesis . Our data predict that elevation in chitin content can offset the loss of cell wall integrity caused by echinocandin treatment . Although no direct measurements of the mechanical robustness of the cell wall have been devised in fungi , we show that survival against high levels of echinocandins is chitin synthesis-dependent and that the class I enzymes , Chs2p and Chs8p play vital roles in this respect . All treatments and conditions that led to elevation of chitin content also increased the MIC to echinocandins . It is also formally possible that changes in the cell wall , other than induction o chitin synthesis , also contribute to the changes in sensitivity to caspofungin that we have observed . As demonstrated previously [31] , at least three signal transduction pathways participate in the compensatory responses - PKC , Ca2+-calcineurin and HOG . Of these , the Ca2+-calcineurin pathway plays a key role in activating class I chitin synthases that are important for the compensatory response to caspofungin . Caspofungin treatment activated these pathways and led to increased transcription of CHS1 , CHS2 and CHS8 and increased levels of Chs3p in cells . The class II enzyme of C . albicans , Chs1p , is normally essential for viability and is responsible for synthesis of the primary septum and for stabilizing lateral cell wall integrity [36] . As in S . cerevisiae , the class IV enzyme Chs3p synthesizes the chitin ring around the rim of the primary septal plate and makes 80–90% of the total cell wall chitin of both yeast and hyphal cells [38] , [40] . Remarkably , when pre-treated with CaCl2 and CFW , the conditional double chs1Δchs3Δ mutant grown under restrictive conditions for CHS1 expression was viable , had a normal morphology and was able to construct a chitin-containing septum that enabled cell division . In these cells , the only enzymes available for chitin synthesis were the two class I enzymes , Chs2p and Chs8p , which thus far have not been considered to be relevant for septum formation . Reinforcing this , nikkomycin Z which is selectively active against class I chitin synthases , prevented salvage septum synthesis and synergized strongly with caspofungin in killing the fungal cells . C . albicans is relatively insensitive to these inhibitors under normal conditions and a class II ( CaChs1p ) inhibitor has been shown to be cidal in a genetic background that lacks CaChs2p . We observed potent synergistic effects when chitin assembly and synthesis , were inhibited , even partially , by CFW and nikkomycin Z in the presence of β ( 1 , 3 ) -glucan synthesis inhibitors . This underlines the potential for new combination treatments , which inhibit both β ( 1 , 3 ) -glucan and chitin synthesis . Cidal combinations of chemotherapeutic agents can also be devised by using inhibitors of β ( 1 , 3 ) -glucan synthesis along with agents that block the cell wall compensatory pathways of fungi [35] . These experiments point simultaneously to the remarkable robustness and potential vulnerability of fungal cell wall biosynthesis to chemotherapeutic intervention .
C . albicans strains used in this study are listed in Table S1 provided in the Supporting Information section . C . albicans cultures were maintained on solid YPD medium ( 1% ( w/v ) yeast extract , 2% ( w/v ) mycological peptone , 2% ( w/v ) glucose , 2% ( w/v ) agar ) and yeast cell cultures were grown at 30°C in YPD with shaking at 200 rpm . Hyphae were induced by growing cells in RPMI-1640 at 37°C . The MRP1p-CHS1/chs1Δ conditional mutant was maintained in medium containing maltose and grown in YPD to repress expression of CHS1 [36] . Cells were grown in YPD supplemented with the following antifungal agents: 0 . 032 µg/ml to 16 µg/ml caspofungin ( Merck Research Laboratories , New Jersey , USA ) dissolved in sterile water , 1 . 6 µg/ml cilofungin ( Eli Lilly Laboratories , Indianapolis , USA ) dissolved in 100% ethanol , 0 . 3 µg/ml echinocandin B ( Eli Lilly Laboratories ) dissolved in 50% ( v/v ) methanol , 10 µM nikkomycin Z ( Bayer , Chemical Co . , Leverkusen , Germany ) dissolved in sterile water and 0 . 032 µg/ml anidulafungin ( Pfizer , Sandwich , Kent ) dissolved in 100% DMSO . In some experiments the inoculum was pre-treated by growing in YPD containing 0 . 2 M CaCl2 and 100 µg/ml CFW . Cultures were incubated at 30°C overnight with shaking at 200 rpm . Caspofungin was incorporated into YPD plates at 0 . 032 µg/ml and 16 µg/ml . In some experiments caspofungin was used in combination with 100 µg/ml CFW ( Sigma-Aldrich , UK ) . Yeast cells were grown to late log phase in YPD and serially diluted to generate suspensions containing 1×106 to 1000 cells/ml in fresh YPD . Plates were inoculated with 5 µl drops of each cell suspension and incubated for 24 h at 30°C . Minimum inhibitory concentrations were determined by broth micro-dilution testing using the CLSI ( formerly NCCLS ) guidelines M27-A2 [55] . Drug concentrations ranged from 2 ng/ml to 16 µg/ml for caspofungin , anidulafungin and micafungin , 0 . 032 µg/ml to 16 µg/ml for amphotericin B , terbinafine and itraconazole and 0 . 13 µg/ml to 64 µg/ml for fluconazole and flucytosine . Each drug was serially diluted with sterile water in flat bottomed 96 well plates . Exponentially grown cultures were diluted and 20 µl of a 1×106 culture was inoculated in either 11 ml 2× RPMI-1640 or 2× YPD and 100 µl of culture was added to each well . Plates were incubated for 24 h at 30°C for YPD plates and 37°C for RPMI-1640 plates . After incubation each well was mixed thoroughly and optical densities were read in a VERSAmax tunable microplate reader ( Molecular Devices , California , USA ) at 405 nm for RPMI-1640 plates and 600 nm for YPD plates . Plasmid placpoly-6 was used for the lacZ promoter reporter system [56] ( Uhl and Johnson , 2001 ) . A 1 kb upstream region from the ATG start codon of each CHS1 , CHS2 , CHS3 and CHS8 ORF was cloned into the PstI-XhoI sites of placpoly-6 generating pCHS1plac , pCHS2plac , pCHS3plac , pCHS8plac respectively as described previously [31] . C . albicans cultures were grown overnight on YPD at then inoculated into fresh YPD medium for 4 h , with or without echinocandins ( 0 . 032 µg/ml caspofungin and the others at the concentrations stated above ) . Cells were harvested after 4 h incubation , with shaking , at 30°C . Quantification of β-galactosidase activity was determined using the method previously described [31] . Specific β-galactosidase activities were expressed as nmol ο-nitrophenol produced min/mg/protein . Statistical significant differences in the assay results were determined with SPSS software using ANOVA and Post Hoc Dunnett's T-test , P<0 . 05 . When the results displayed unequal variance the Dunnett's T3 test was applied . Mixed membrane fractions ( MMF ) were prepared from exponential phase yeast cells and their chitin synthase activities were measured as described previously [31] . Cell walls were prepared from exponential C . albicans yeast cultures grown in YPD and the chitin content was measured as described previously [31] . Overnight cultures of yeast cells of NGY477 ( Chs3p-YFP ) and BWP17 ( untagged ) were diluted 1∶100 into 50 ml YPD supplemented with uridine and 0 . 032 µg/ml caspofungin and incubated with shaking for 4 h at 30°C . BWP17 [57] , the untagged parent strain of NGY477 , provided a negative control for the anti-GFP antibody [41] . After treatment , cells were harvested by centrifugation ( 1 500×g , 2 min , 4°C ) , washed in 1 ml cold Lysis Buffer ( 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 0 . 5% NP40 , 2 µg/ml Leupeptin , 2 µg/ml Pepstatin , 1 mM PMSF ) and finally resuspended in 250 µl cold Lysis Buffer . Cells were broken using a FastPrep machine and acid-washed glass beads . The extracts were clarified by centrifugation ( 16 000×g , 5 min , 4°C ) . Protein samples ( 15 µg each ) were separated by SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) using the XCell SureLock™ Mini-Cell system with NuPAGE®Novex Bis-Tris 4-12% pre-cast gels in NuPAGE® MOPS-SDS Running Buffer containing NuPAGE® Antioxidant ( Invitrogen Ltd , Paisley , UK ) as per the manufacturer's instructions . The proteins were transferred to Invitrolon™ PVDF Membranes ( Invitrogen ) following the manufacturer's instructions . The membranes were then rinsed in PBS , blocked in PBS-T+10% BSA ( PBS , 0 . 1% Tween-20 , 10% ( w/v ) BSA ) for 30 min at RT and incubated overnight at 4°C in PBS-T+5% ( w/v ) BSA ( PBS , 0 . 1% Tween-20 , 5% ( w/v ) BSA ) containing a 1∶2000 dilution of anti-GFP Antibody ( Roche , Basel , Switzerland ) . The membranes were washed five times for 5 min in PBS-T ( PBS , 0 . 1% Tween-20 ) and then incubated for 1 h at RT in PBS-T+5% ( w/v ) BSA containing a 1∶4000 dilution of anti-mouse IgG , ( Fab specific ) peroxidase conjugate Antibody ( Sigma-Aldrich ) . The membranes were washed three times for 5 min in PBS-T and the signal was detected using LumiGLO™ Reagent and Peroxide ( Cell Signaling Technology , Massachusetts , USA ) as per the manufacturer's instructions . Western blots were carried out as above with the following modification; a 1∶1000 dilution of Phospho-p44/42 Map Kinase ( Thr202/Tyr204 ) Antibody ( Cell Signaling Technology ) was used as the primary antibody . The secondary antibody was Anti-rabbit IgG , HRP-linked Antibody ( Cell Signaling Technology ) diluted 1∶2000 . Samples were fixed in 10% ( v/v ) neutral buffered formalin ( Sigma-Aldrich ) and examined by phase differential interference contrast ( DIC ) microscopy . Cells were stained with 25 µg/ml CFW to visualize chitin . Nuclei were stained by overlaying samples with mounting media containing 1 . 5 µg/ml DAPI ( Vector Laboratories , Peterborough , UK ) . Cell membrane integrity was determined by staining cells with 2 µg/ml propidium iodide ( Sigma-Aldrich ) . All samples were examined by DIC and fluorescence microscopy using a Zeiss Axioplan 2 microscope . Images were recorded digitally using the Openlab system ( Openlab v 4 . 04 , Improvision , Coventry , UK ) using a Hamamatsu C4742- 95 digital camera ( Hamamatsu Photonics , Hamamatsu , Japan ) . CFW fluorescence was quantified for individual yeast cells using region of interest measurements . Mean fluorescence intensities were then calculated for at least 35 individual cells . In some experiments the exposure time for a series of fluorescence images was fixed so the intensity of fluorescence relative to a control of known chitin content was shown . Yeast cultures were harvested by centrifugation and the pellets were fixed in 2 . 5% ( v/v ) glutaraldehyde in 0 . 1 M sodium phosphate buffer ( pH 7 . 3 ) for 24 h at 4°C . Samples were encapsulated in 3% ( w/v ) low melting point agarose prior to processing to Spurr resin following a 24 h schedule on a Lynx tissue processor ( secondary 1% OsO4 fixation , 1% Uranyl acetate contrasting , ethanol dehydration and infiltration with acetone/Spurr resin ) . Additional infiltration was provided under vacuum at 60°C before embedding in TAAB capsules and polymerising at 60°C for 48 h . 0 . 5 µm semi-thin survey sections were stained with toluidine blue to identify areas of best cell density . Ultrathin sections ( 60 nm ) were prepared using a Diatome diamond knife on a Leica UC6 ultramicrotome , and stained with uranyl acetate and lead citrate for examination with a Philips CM10 transmission microscope ( FEI UK Ltd , Cambridge , UK ) and imaging with a Gatan Bioscan 792 ( Gatan UK , Abingdon , UK ) . Gene nomenclature is defined at the Candida genome database ( http://www . candidagenome . org/ ) and NCBI http://www . ncbi . nlm . nih . gov/sites/entrez ) . CHS1 ( orf19 . 5188 , XM_711849 ) ; CHS2 ( orf19 . 7298 , XM_711340 ) ; CHS3 ( orf19 . 4937 , XM_712573 ) ; CHS8 ( orf19 . 5384 , XM_712667 ) ; FKS1/GSC1 ( orf19 . 2929 , XM_716336 ) ; MKC1 ( orf19 . 7523 , X76708 ) ; HOG1 ( orf19 . 895 , XM_715923 ) ; CNA1 ( orf19 . 6033 ) . | Fungal pathogens are increasingly important agents of human disease and are also difficult to treat since few antifungal agents kill the invading organism . The cell wall of a fungus is essential for its viability and this can be attacked by a new generation of antifungal antibiotics called echinocandins . Echinocandins such as caspofungin are normally cidal for the human pathogen Candida albicans . These inhibit the synthesis of β ( 1 , 3 ) -glucan , a major strength-imparting polysaccharide in the cell wall . Treatment of C . albicans with echinocandins in vitro stimulated the formation of a second cell wall polysaccharide—chitin , which rescued the cells . Treatments that increased the chitin content of the C . albicans cell wall reduced the efficacy of echinocandins and could even induce the formation of novel structures such as a salvage septum that enabled the cells to continue to undergo cell division under otherwise lethal conditions . Combined treatments with echinocandins and chitin synthase inhibitors synergized strongly , highlighting the potential for potent combination therapies with enhanced fungicidal activity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"cell",
"biology/microbial",
"physiology",
"and",
"metabolism",
"molecular",
"biology/post-translational",
"regulation",
"of",
"gene",
"expression",
"infectious",
"diseases/fungal",
"infections",
"cell",
"biology/cell",
"growth",
"and",
"division",
"cell",
"biology/microbial"... | 2008 | Stimulation of Chitin Synthesis Rescues Candida albicans from Echinocandins |
Obesity is characterized by accumulation of excess body fat , while lipodystrophy is characterized by loss or absence of body fat . Despite their opposite phenotypes , these two conditions both cause ectopic lipid storage in non-adipose tissues , leading to lipotoxicity , which has health-threatening consequences . The exact mechanisms underlying ectopic lipid storage remain elusive . Here we report the analysis of a Drosophila model of the most severe form of human lipodystrophy , Berardinelli-Seip Congenital Lipodystrophy 2 , which is caused by mutations in the BSCL2/Seipin gene . In addition to reduced lipid storage in the fat body , dSeipin mutant flies accumulate ectopic lipid droplets in the salivary gland , a non-adipose tissue . This phenotype was suppressed by expressing dSeipin specifically within the salivary gland . dSeipin mutants display synergistic genetic interactions with lipogenic genes in the formation of ectopic lipid droplets . Our data suggest that dSeipin may participate in phosphatidic acid metabolism and subsequently down-regulate lipogenesis to prevent ectopic lipid droplet formation . In summary , we have demonstrated a tissue-autonomous role of dSeipin in ectopic lipid storage in lipodystrophy .
Lipids are major membrane components as well as the main source of cellular energy . Cells have developed precise homeostatic mechanisms to tightly regulate lipid uptake , synthesis , storage , and usage [1] , [2] . Abnormalities in lipid metabolism often lead to disease states: excess body fat can lead to obesity while loss or absence of body fat results in lipodystrophy [3] . In vertebrates , white adipose tissue is the main lipid storage organ; however , ectopic lipid storage in non-adipose tissues such as muscle , pancreas , and liver , is often observed in disease states such as obesity and lipodystrophy . Lipotoxicity as a result of ectopic lipid storage in these diseases is thought to be a major cause of severe pathological conditions including insulin resistance , pancreatic β-cell failure , and hepatic steatosis [4]–[6] . Ectopic lipid storage could be due to cell-extrinsic effects or cell-intrinsic effects . Extrinsically , overflow of excess lipids that can no longer be stored in adipose tissues leads to lipid droplet formation in non-adipose tissues [7] , [8] . In one study , surgical implantation of normal adipose tissue back into lipoatrophic mice reversed ectopic lipid accumulation in the liver , suggesting a tissue-non-autonomous mechanism [9] . On the other hand , defects within non-adipose tissues have also been postulated to contribute to ectopic lipid storage intrinsically , suggesting a positive role of non-adipose tissue in lipid storage . For example , removal of the transcription factor PPAR-δ specifically from cardiomyocytes results in decreased fatty acid oxidation and severe lipid storage in the heart [10] . Currently , the contribution of extrinsic and intrinsic mechanisms to ectopic lipid storage in non-adipose tissues in various diseases conditions remains to be determined . Berardinelli-Seip Congenital Lipodystrophy ( BSCL ) , the most severe form of lipodystrophy in humans , is caused by mutations in either BSCL1 or BSCL2 . BSCL1 encodes acylglycerol phosphate acyltransferase 2 ( AGPAT2 ) , which is involved in triacylglycerol ( TAG ) biosynthesis , while BSCL2 encodes Seipin [11] , [12] . Although BSCL1 and BSCL2 patients exhibit similar disease pathology , the causal link between Seipin and AGPAT2 is unclear . BSCL2 patients are born with nearly no adipose tissue and have ectopic lipid storage in muscle and liver , implying that human Seipin ( hSeipin ) may be required for adipocyte survival or differentiation [13] , [14] . Within cells , lipids are stored in specialized organelles called lipid droplets . The surface of the lipid droplet is a monolayer of polar lipids in which are embedded coat proteins that may be important for lipid homeostasis . The core of the lipid droplet contains neutral lipids , predominantly TAG and sterol esters [15]–[17] . Recent studies in both yeast and human cells suggested that Seipin may regulate the morphogenesis of lipid droplets . Yeast Seipin mutant cells contain irregular clustered lipid droplets and sometimes giant lipid droplets , while human BSCL2/Seipin mutant fibroblasts were found to contain numerous small lipid droplets [18] , [19] . Nevertheless , the exact role of Seipin in human and yeast remains obscure . In addition , there is no plausible explanation for the cause of the ectopic lipid storage within non-adipose tissues in human Seipin patients . The lack of adipose and non-adipose tissues in yeast makes it impossible to uncover the mechanisms of ectopic lipid storage using yeast mutants . Therefore , it is important to utilize suitable model organisms that have both adipose and non-adipose tissues to study Seipin function and gain insights into the underlying mechanisms of ectopic lipid storage and lipid droplet formation . As a multi-cellular model organism , Drosophila and its cell lines have been widely used to study conserved mechanisms of lipid metabolism [20]–[25] . Here we report that Drosophila Seipin ( dSeipin ) mutants have reduced lipid storage in the fat body , an adipose tissue , while exhibiting ectopic lipid droplets in the salivary gland , a non-adipose tissue . We also found that dSeipin functions tissue-autonomously in both tissues . Further genetic and lipidomic analyses revealed that in the salivary gland dSeipin genetically interacts with lipogenic genes likely at the level of phosphatidic acid ( PA ) . Our studies uncover an unexpected tissue-autonomous mechanism in ectopic lipid storage and provide an attractive model system with which to dissect lipid metabolism in vivo .
A single Drosophila homolog of human Seipin , CG9904 , was identified through Blast sequence comparisons and named as dSeipin ( Figure 1A and Figure S1 ) . The middle portion of dSeipin ( aa 45–277 ) , including two potential transmembrane domains , shares 40% identity and 63% similarity to hSeipin . To study the function of dSeipin , we examined the expression profile of dSeipin mRNA using quantitative-RT-PCR ( qRT-PCR ) . We found that dSeipin was widely expressed in many tissues with the highest expression in the fat body and moderate expression in the salivary gland , midgut and muscle ( Figure 1B ) . We also examined the expression of dSeipin mRNA through in situ hybridization . At late embryonic stages , dSeipin mRNA is highly expressed in the hindgut . At larval stages , dSeipin mRNA is expressed in the fat body , anterior midgut and salivary gland ( Figure S1 ) . As an entry point to explore the function of dSeipin in vivo , we analyzed the subcellular localization of YFP-tagged dSeipin using the UAS-Gal4 system , where the target gene downstream of the UAS element is activated by the transcriptional activator Gal4 . We confirmed that the YFP-tagged dSeipin protein is functional ( data not shown ) . We then used a fat body- and salivary gland-specific Gal4 driver , ppl-Gal4 [26] , to express dSeipin-YFP in the fat body . dSeipin-YFP is colocalized with the ER marker PDI-GFP ( Figure S1 ) . In addition , dSeipin-YFP forms some puncta which might represent specific subdomains of the ER ( Figure S1 ) . The similar subcellular localization patterns of dSeipin to its human and yeast counterparts [18] imply that dSeipin may perform conserved roles in the regulation of lipid droplet formation and lipid storage . We next examined the role of dSeipin through mutant phenotypic analysis . We generated a deletion allele of dSeipin using an ends-out gene targeting approach . This allele lacks the whole genomic region of dSeipin and is a null allele as confirmed by RT-PCR ( Figure 1A and 1C ) . dSeipin mutants are viable and fertile with no noticeable behavior defects . We used the neutral lipid stain Nile red to examine the morphology of the fat body , which is the adipose tissue of insects and also has liver-like activity due to its detoxification function . The larval fat bodies of dSeipin mutants have significantly reduced lipid storage , in contrast to wild-type fat bodies which have many large lipid droplets ( Figure 1D ) . Similarly , in fat cells from young adults , dSeipin mutants have smaller lipid droplets compared to wild type . The small lipid droplet phenotype is more severe under starvation conditions ( Figure 1D ) . This lipid droplet phenotype is similar to that of hSeipin mutant fibroblasts , which also contain small lipid droplets [18] . Further genetic rescue experiments supported the idea that the lipid storage phenotype is indeed due to the deletion of dSeipin ( see below ) . These results indicate that dSeipin is required for proper lipid storage in the fat body . Except for the aberrant lipid droplets , the overall morphology of the fat body in the mutants appears normal . Interestingly , the fat bodies from dSeipin mutants sink to the bottom of 2% sucrose solution while control fat bodies float on top ( Figure 1E ) . This phenomenon is likely due to reduced lipid storage in the mutant fat bodies . We examined the total glyceride content and found that the glyceride level in dSeipin mutants is greatly reduced compared to control animals ( Figure 1F ) . This phenotype can be rescued by ubiquitous expression of a UAS-dSeipin transgene ( Figure 1F ) . dSeipin mutants are viable and fertile , but their average weight is lower than control flies ( Figure 1G ) . The weight reduction phenotype can be fully rescued by expressing UAS-dSeipin ubiquitously with tub-Gal4 and partially rescued by expressing dSeipin in the fat body and salivary gland with ppl-Gal4 ( Figure 1G ) . What is the cause of reduced lipid storage in dSeipin mutants ? Both increased lipolysis and reduced lipogenesis could potentially lead to the same reduced lipid storage phenotype . Results from the following experiments rule out the former possibility . We firstly investigated the levels of circulating lipids , which are elevated if lipolysis is increased . It has been previously reported that oenocytes ( larval secretory cells ) can be used to monitor the levels of circulating lipids [27] . Under fed conditions in wild type , oenocytes are weakly stained with the neutral lipid dye Oil Red O because of low circulating lipids and low levels of TAG biosynthesis in oenocytes . However , under starved conditions , lipolysis is stimulated in the fat body , resulting in high levels of circulating lipids and strong Oil Red O staining of oenocytes . Moreover , under fed conditions , increasing lipolysis by overexpression of BMM , the Drosophila homolog of mammalian adipocyte triglyceride lipase ( ATGL ) [28] , leads to strong Oil Red O staining in oenocytes [27] ( Figure 2A ) . We found that under fed conditions , there is no difference between Oil Red O staining of oenocytes in wild type and dSeipin mutants ( Figure 2A ) . Furthermore , in starved dSeipin mutants , although the Oil Red O staining signal is higher than in fed mutants , it is still much lower than that of wild type , reflecting the fact that less lipid is stored in the fat body of dSeipin mutants ( Figure 2A ) . To rule out the possibility that loss of dSeipin in oenocytes prevents them synthesizing TAG , we specifically expressed dSeipin in oenocytes with Cyp4g1-Gal4 in dSeipin mutants . We found that under starved conditions the Oil Red O staining signal is much lower than that of wild type ( Figure S3 ) . These results indicate that circulating lipids in dSeipin mutants are not elevated under normal fed conditions . Moreover , we also measured the circulating glyceride levels and found it is significantly lower in dSeipin mutants than wild type , supporting the idea that lipolysis is not increased in dSeipin mutants ( Figure 2B ) . LSD-2 is one of the two Drosophila lipid droplet surface PAT domain-containing proteins . Lsd-2 mutants may have slightly increased lipolysis [29] , but dSeipin;Lsd-2 double mutants exhibit a similar phenotype to dSeipin single mutants , suggesting that dSeipin likely does not genetically interact with Lsd-2 ( Figure 2C ) . Taken together , these results indicate that the reduced lipid storage in dSeipin mutant fat bodies is unlikely to be due to increased lipolysis . To test whether the reduced lipid storage phenotype is due to reduced lipogenesis in the mutants , we examined the genetic interaction of dSeipin with several known lipid storage regulators in Drosophila . Drosophila midway ( mdy ) encodes a diglyceride acyltransferase ( DGAT ) , which is involved in the last step of TAG biosynthesis [30] . In a partial loss-of-function DGAT/mdyqx25 mutant alone , there is little effect on fat body lipid storage ( Figure 2C and 2D ) . However , double mutants of dSeipin and DGAT have greatly reduced lipid storage compared to either single mutant ( Figure 2C and 2D ) . Moreover , overexpression of DGAT or SCAP ( SREBP cleavage activating protein ) , key positive regulators of lipogenesis [24] , suppresses the reduced lipid storage phenotype in dSeipin mutants ( Figure 2C and 2D ) . Taken together , these results suggest that dSeipin mutants likely have reduced lipogenesis in the fat body . Since stored lipid is an important energy supply during starvation stress , reduced lipid storage might be deadly during nutrient deprivation . We therefore tested whether dSeipin mutants are sensitive to starvation . Under starved conditions , more than half of wild type or controls survive for at least 48 hours , while 100% of dSeipin mutants die within 42 hours , indicating that dSeipin mutants are hypersensitive to starvation ( Figure 2E ) . The reduced lipid storage and the increased sensitivity to starvation in dSeipin mutants raise the possibility that dSeipin mutants are always in a starved state under normal fed conditions . In wild-type flies , starvation can trigger autophagy in the fat body , so if dSeipin mutants are always in a starved state , the autophagy program should be active under normal culturing conditions . We used the conventional lysosomal dye lysotracker to detect autophagic cells; however , we found no difference in lysotracker staining between wild type and dSeipin mutants . Fat bodies from both genotypes were positively stained under starved conditions and negatively stained in normal conditions , suggesting that under normal feeding conditions dSeipin mutants are not limited in nutrition ( Figure S2 ) . Besides reduced lipid storage in adipose tissue , another prominent phenotype of lipodystrophy is ectopic lipid storage in non-adipose tissues , such as muscle and liver . Therefore , we also performed lipid staining in other tissues from dSeipin mutants , including wing disc , gut , brain , muscle , epidermis and salivary gland . Among these tissues , gut and wing disc can store lipids under normal or fasting conditions [27] . Similar to the fat body , dSeipin mutants have reduced lipid storage in the wing disc ( Figure S2 ) . We did not find excess lipid storage in the brain , muscle and epidermis of dSeipin mutants ( Figure S2 ) . Interestingly , we found that the proventriculus had large lipid droplets in dSeipin mutants compared to small lipid droplets in wild type ( Figure 3A ) . In the anterior midgut region , dSeipin mutants had much more stored lipid than wild type ( Figure 3A ) . Moreover , we found that dSeipin mutants exhibit ectopic lipid droplets in the salivary gland , which normally lacks any visible lipid droplets and has never been found to serve as a lipid storage organ under any conditions . In wild-type salivary glands , Nile red staining is diffused in the cytoplasm , while in mutants many small Nile red-positive puncta were found ( Figure 3B ) . The ectopic puncta in mutant salivary glands are bona fide lipid droplets because the lipid droplet surface marker LSD-1-mCherry forms typical ring-like structures surrounding them ( Figure 3C ) . Together , our data show firstly that the Drosophila dSeipin mutation results in reduced lipogenesis and lipid storage in adipose tissue , and secondly that it causes ectopic lipid droplets in some non-adipose tissues . Since the ectopic lipid droplet phenotype has not been reported before in Drosophila , we decided to take a genetic approach to tackle the underlying mechanisms using the salivary gland and the gut as models . First , we asked whether the ectopic lipid storage in dSeipin mutants is caused by intrinsic tissue-autonomous mechanisms or extrinsic tissue-non-autonomous mechanisms . To test in which tissue dSeipin function is required , we used the UAS-Gal4 system to express wild-type dSeipin in a tissue-specific manner in an otherwise dSeipin mutant background . Tissue-specific expression of dSeipin was also verified by qRT-PCR ( Figure S3 ) . UAS-dSeipin driven by ppl-Gal4 ( which is highly active in salivary gland and moderately active in fat body ) rescued both lipid storage defects in the fat body and ectopic lipid storage in the salivary gland ( Figure 4A , 4C and Table S1 ) . dSeipin expression driven by the fat body-specific Gal4 , lsp2-Gal4 [31] , rescued the defects in the fat body but not in the salivary gland or in the gut ( Figure 4A , 4C and Figure S3 ) . In contrast , dSeipin expression driven by a salivary gland-specific Gal4 , sgs3-Gal4 ( which is expressed at late L3 stage ) [32] , fully rescued the ectopic lipid droplet phenotype in the salivary gland but not the lipid storage defects in the fat body or the ectopic lipid storage in the gut ( Figure 4A , 4C and Figure S3 ) . We were unable to obtain a suitable proventriculus- or anterior midgut-specific Gal4 line for tissue-specific rescue . Nevertheless , these results indicate that the ectopic lipid droplets in the salivary gland and the midgut and the lipid storage defects in the fat body of dSeipin mutants are likely due to tissue-autonomous requirements of dSeipin . Furthermore , it implies that reduced lipogenesis in the fat body of dSeipin mutants did not lead to ectopic lipid storage in the salivary gland . We further confirmed the tissue-autonomous requirements of dSeipin using a tissue-specific RNAi approach . The tissue-specific knockdowns of dSeipin were also confirmed by qRT-PCR ( Figure S3 ) . UAS-dSeipin RNAi driven by ppl-Gal4 caused ectopic lipid storage in the salivary gland with mild lipid storage defects in the fat body , supporting the tissue-autonomous role of dSeipin ( Figure 4B and 4C ) . However , sgs3-Gal4-driven dSeipin RNAi did not result in ectopic lipid storage in the salivary gland . We reasoned that sgs3-Gal4 may act too late in the L3 larval stage to generate the RNAi effect . Indeed , UAS-dSeipin RNAi driven by either 48Y-Gal4 or elav-Gal4 , which is expressed in salivary gland much earlier than sgs3-Gal4 ( Table S1 ) , caused ectopic lipid droplet formation in the salivary gland ( Figure 4B and 4C ) . Taking the data together , we concluded that dSeipin is required tissue-autonomously for preventing ectopic lipid droplet formation in salivary gland . The tissue-autonomous role of dSeipin prompted us to further explore the intrinsic mechanism of ectopic lipid droplet formation in dSeipin mutant salivary glands . Both increased lipogenesis and reduced lipolysis could theoretically result in lipid droplet formation . Which pathway is altered in dSeipin mutants and what is the function of dSeipin in that particular pathway ? bmm is a key positive regulator of lipolysis [28] . bmm loss-of-function mutants have reduced lipolysis , and display progressive obesity with increased synthesis of TAG , while overexpression of bmm results in a lean phenotype [21] . We found that bmm mutants have no ectopic lipid storage , suggesting that the ectopic lipid storage in salivary glands is not due to decreased lipolysis ( Figure 5A and 5B ) . In addition , we found no ectopic lipid droplets in the salivary gland of Lsd-2 mutants ( Figure 5C ) , which also have a lean phenotype . Overexpression of Lsd-2 with ppl-Gal4 did not result in the ectopic lipid droplet phenotype either ( data not shown ) . We next examined the lipogenic pathway . There are many enzymatic steps involved in the biosynthesis of TAG from fatty acids ( Figure 5I ) [33] . Firstly , fatty acids are converted to fatty acyl-CoA by acetyl-CoA synthetase ( ACS ) . Fatty acyl-CoA has two different fates , either fatty acid oxidation to provide energy , or conversion to lysophosphatidic acid ( LPA ) by glycerol-3-phosphate acyltransferase ( GPAT ) . Thus GPAT regulates the first committed step in lipogenesis and is likely a rate-limiting factor in lipogenesis [33] , [34] . Acylglycerol phosphate acyltransferase ( AGPAT ) then adds another acyl chain to LPA to generate PA . PA can be converted to cytidine diphosphate diacylglycerol ( CDP-DAG ) by CDP diglyceride synthetase ( CDS ) or alternatively to DAG by Lipin , a PA phosphatase . CDP-DAG is the precursor of phosphatidylinositol ( PI ) and phosphatidylglycerol ( PG ) . The last step in TAG biosynthesis , conversion of DAG to TAG , is catalyzed by DGAT . DAG can also be metabolized to phosphatidylcholine ( PC ) by choline phosphotransferase ( CPT ) or to phosphatidylethanolamine ( PE ) by ethanolamine phosphotransferase ( EPT ) . All these enzymes are conserved in Drosophila ( Table S2 and data not shown ) . We verified by qRT-PCR that all the corresponding genes are expressed in salivary gland ( Figure S4 ) . Since GPAT is the rate-limiting enzyme , we reasoned that overexpressing GPAT could lead to increased lipogenesis . Indeed , we found that overexpression of GPAT1 ( CG5508 ) using either 48Y-Gal4 or ppl-Gal4 in wild-type flies caused an ectopic lipid droplet phenotype , indicating that increased lipogenesis can lead to ectopic lipid storage ( Figure 5D , 5E and data not shown ) . To examine whether dSeipin is involved in the lipogenesis pathway , we next analyzed the genetic interaction of dSeipin with GPAT1 and DGAT , two key genes in the lipogenic pathway . Overexpression of GPAT1 enhances the ectopic lipid storage phenotype of dSeipin mutants , consistent with the hypothesis that dSeipin mutants may have increased lipogenesis in non-adipose tissues ( Figure 5D , 5E , 5F and 5J ) . Furthermore , although overexpression of DGAT using either 48Y-Gal4 or ppl-Gal4 in wild type causes only mild lipid storage in salivary glands , overexpression of DGAT in a dSeipin mutant background results in a massive lipid storage phenotype in the salivary glands ( Figure 5D , 5G , 5H , 5J and data not shown ) . Under these conditions , the salivary gland appears more like a lipid storage organ . We also verified the genetic interactions between dSeipin and DGAT with Bodipy , another lipid dye ( Figure S4 ) . Such strong synergistic genetic interactions in the metabolic pathway imply that in dSeipin mutants , DAG levels are likely increased in the salivary gland . Together , these results indicate that dSeipin probably negatively affects lipid storage in salivary gland . We next investigated at which point dSeipin acts in the lipogenesis pathway ( Figure 5I ) . We examined mutants of dSeipin that also had loss-of-function mutations of the main lipogenic genes including GPAT , AGPAT , Lipin , and DGAT . We found that the ectopic lipid storage phenotype in dSeipin mutants was fully suppressed by a DGAT mutation or by RNAi of either DGAT or Lipin using either 48Y-Gal4 or ppl-Gal4 ( Figure 6A , 6B , 6C , 6D , 6M and data not shown ) . These results indicate that dSeipin may act upstream of Lipin and DGAT . In contrast , a partial loss-of-function mutant of GPAT1 and RNAi of either AGPAT1 or AGPAT2 , two AGPAT homologs , could not suppress the dSeipin phenotype ( Figure 6E , 6F , 6G , 6M and data not shown ) . Simultaneous RNAi of AGPAT1 and AGPAT2 also failed to suppress the dSeipin phenotype ( data not shown ) . Due to the gene redundancy of GPAT and AGPAT , these results can't pinpoint the specific interaction between dSeipin and GPAT1 or AGPAT . It is possible that dSeipin may not interact with GPAT1 and AGPAT , or alternatively , dSeipin may act downstream of GPAT1 and AGPAT . Since the connection point between AGPAT and Lipin is PA ( Figure 5I ) , it is possible that dSeipin may affect the metabolism of PA so that dSeipin mutants have altered levels of PA , which subsequently leads to increased DAG and lipid storage . To test our hypothesis genetically , we examined the salivary glands of two other mutants . Cct1 is the rate limiting enzyme in PC biosynthesis , and loss of function of Cct1 may lead to increased DAG ( Figure 5I ) . Cct1 RNAi was found to produce large lipid droplets in the S2 cell line [35] and Cct1 mutants contain large lipid droplets in the fat body ( data not shown ) . However , the salivary glands of Cct116919 mutants do not show ectopic lipid storage , suggesting that the Cct116919 mutation alone is insufficient to cause ectopic lipid droplets ( Figure 6H and 6M ) . CdsA is the sole Drosophila homolog of human CDS and a partial loss-of-function CdsA1 mutant has increased levels of a single species of PA ( PA 16∶0/18∶2 , also called PA 34∶2 ) ( Figure 5I ) [36] . In contrast to Cct116919 mutants , we found that Drosophila CdsA1 mutants exhibit a similar ectopic lipid droplet phenotype to that of dSeipin mutants ( Figure 6I and 6M ) . Therefore , we conclude that dSeipin may participate in PA metabolism . We further checked the relationship between dSeipin and CdsA through double mutant analysis . CdsA1;dSeipin double mutants exhibit a strong synergistic phenotype compared to either single mutant ( Figure 6J and 6M and Figure S4 ) , indicating that dSeipin may function in parallel with CdsA . However , since CdsA1 is a weak loss-of-function mutant , it is still possible that dSeipin functions in the same pathway as CdsA . In addition , overexpressing CdsA fully suppressed the ectopic lipid storage phenotype of dSeipin mutants ( Figure 6K , 6L and 6M ) . Moreover , in Drosophila S2 cells , dSeipin also synergizes with CdsA in the formation of large lipid droplets ( Figure S4 ) . Consistent with the dSeipin RNAi result , UAS-CdsA RNAi driven by 48Y-Gal4 or elav-Gal4 , but not sgs3-Gal4 , resulted in ectopic lipid storage in the salivary gland , reflecting a tissue-autonomous role of CdsA and a tissue-autonomous mechanism of ectopic lipid droplet formation ( data not shown ) . Taken together , these results suggest that dSeipin may be involved in the metabolism of PA and alteration of PA levels in dSeipin mutants may contribute to lipid droplets in salivary glands . To directly analyze whether PA levels are indeed increased in dSeipin mutants , we performed a lipidomic analysis of dSeipin mutants and wild type . We found that in dSeipin mutant larvae the levels of total PA and most PA species are increased ( including PA32∶2 , PA34∶3 , PA36∶3 , PA36∶1 ) ( Figure 7A ) . To further confirm the genetic interaction results , we also compared PA levels in the salivary glands of CdsA1 and CdsA1;dSeipin double mutants and found that there was more PA in CdsA1;dSeipin double mutants than CdsA1 single mutants ( Figure 7B ) . These results clearly demonstrate a role for dSeipin in the metabolism of PA . In addition , the levels of total DAG and several DAG species , including 14∶0/18∶2 , 16∶1/18∶2 , 18∶ 0/18∶2 , 18∶1/18∶2 and 18∶2/18∶2 , are all significantly increased in mutant salivary glands ( Figure 7C ) . These results are consistent with the strong genetic interaction between dSeipin and DGAT ( Figure 5G , 5H and 5J ) . We also compared salivary gland TAG levels in different mutant backgrounds . In dSeipin mutants , the level of TAG is greatly reduced in the fat body , while the levels of several TAG species , including 46∶3 ( 18∶2 ) , 48∶4 ( 18∶2 ) , 50∶4 ( 18∶2 ) , 54∶4 ( 18∶2 ) , 54∶5 ( 18∶2 ) are significantly increased in mutant salivary glands ( Figure 7D ) . These results are consistent with the results obtained by Nile red staining ( Figure 1 ) . Similarly , we found that the TAG levels in dSeipin;48Y>DGAT and dSeipin;CdsA1 double mutants are higher than 48Y>DGAT or CdsA1 alone ( Figure S5 ) . Together , these results support the conclusions obtained from the genetic analysis . The above results indicate that dSeipin mutants have reduced lipid storage in the fat body but increased formation of lipid droplets in the salivary gland . Since dSeipin functions tissue autonomously , how could its absence cause opposite phenotypes in these two tissues ? It is possible that there are distinct functions of dSeipin in different tissues . Alternatively , dSeipin may have the same function , but different tissues might respond differently to the same alteration of lipid contents . We reasoned that if dSeipin has distinct functions in different tissues , it might have some structural differences to yeast Seipin , because yeast is a unicellular organism . We noticed that both dSeipin and hSeipin have an extended C-terminal region compared to yeast Seipin ( Figure 8A ) . Does the structure difference between dSeipin and yeast Seipin reflect the different functional requirement in various tissues in flies ? To test this , we made a transgene with a C-terminal truncation of dSeipin and examined its rescuing activity in the fat body and the salivary gland of dSeipin mutants . This transgene , when driven by ppl-Gal4 , fully rescued the reduced lipid storage phenotype in the fat body , but not the ectopic lipid droplet phenotype in the salivary gland ( Figure 8B and 8C ) . Although this result does not rule out the possibility that dSeipin functions similarly in different tissues , which respond differently to altered lipid levels , it more strongly suggests that dSeipin may have distinct functions in fat body and salivary gland . Moreover , it echoes previous conclusions that dSeipin functions tissue-autonomously . To test whether the function of Seipin is evolutionarily conserved , we expressed hSeipin in dSeipin mutants and examined its rescue effect . We found that hSeipin can functionally replace dSeipin in the fat body and the salivary gland . Lipid storage in the fat body is restored and ectopic lipid storage in the salivary gland is reversed in dSeipin mutants with ppl-Gal4-driven UAS-hSeipin ( Figure 8B and 8C ) . N88S and S90L , two point mutations of hSeipin , were previously found to be associated with Silver syndrome , a dominant motor neuron degenerative disease [37] . The S90L hSeipin mutation also rescued the fat body and salivary gland phenotypes of dSeipin mutants , supporting the previous finding that S90L is likely a gain-of-function mutation ( data not shown ) [37] . These results indicate that the function of Seipin is conserved between fly and human .
BSCL2 is a severe form of lipodystrophy which affects adipocyte development and results in ectopic lipid storage in non-adipose tissues . The exact function of BSCL2/Seipin and the causes of ectopic lipid storage are not known . Here we report analyses of the Drosophila dSeipin mutant . dSeipin mutants have reduced lipid storage in the fat body , which is the Drosophila adipose tissue , and ectopic lipid droplets in the salivary gland , a non-adipose tissue . It is worth noting that to the best of our knowledge the ectopic lipid droplet phenotype has not been reported previously in any invertebrate model organism . Consistent with previous findings that Seipin has a cell-autonomous function in the regulation of adipogenesis and adipocyte differentiation [13] , [14] , our results reveal a tissue-autonomous role of dSeipin in controlling lipid storage in adipocytes . Since the function of Seipin is conserved through evolution ( this study and [18] , [19] ) , mammalian Seipin may have a similar role in lipid storage in mature adipocytes . Moreover , although the gross morphology of dSeipin mutant fat bodies appears normal , the lipid storage defects may reflect impaired fat body differentiation . More importantly , our studies uncover an unexpected tissue-autonomous role of Seipin in preventing ectopic lipid storage in non-adipose tissues . Ectopic lipid storage is one of the main causes of pathological conditions in obesity and lipodystrophy [4] , [5] . Our in vivo studies demonstrated that defects within non-adipose tissues are the primary cause of ectopic lipid storage in dSeipin mutants . Is this tissue-autonomous mechanism specific to Seipin and lipodystrophy ? Many mouse models of lipodystrophy and obesity associated with ectopic lipid storage in liver and muscles have been reported . High serum free fatty acid levels are thought to be the primary cause of liver steatosis in several studies , implicating tissue-non-autonomous regulatory effects in ectopic lipid storage [38] , [39] . Interestingly , mice deficient in AGPAT2 , the BSCL1 lipodystrophy gene , were recently found to have normal or low levels of serum free fatty acids , but still developed robust liver steatosis [40] . Instead of being caused primarily by high serum free fatty acid levels , hepatic steatosis in AGPAT2 mice could be explained by a tissue-autonomous function of AGPAT2 . If this is true , both AGPAT2 and Seipin probably have tissue-autonomous functions in preventing ectopic lipid storage . Thus , the tissue-autonomous mechanism of ectopic lipid storage could be a general theme in lipodystrophy . It will be interesting to examine whether the ectopic lipid storage in previously reported mouse models of obesity and lipodystrophy are caused by tissue-autonomous or tissue-non-autonomous mechanisms . Within a cell , lipid storage could originate in two ways , increased lipogenesis and reduced lipolysis . We hypothesize that Seipin is involved in lipogenesis . The synergistic genetic interactions between dSeipin and lipogenic genes , in particular DGAT , strongly argue that Seipin participates in the lipogenic pathway . Ectopic lipid storage in salivary glands was observed in animals with overexpression of the lipogenic gene GPAT1 , but not in loss-of-function mutants of the lipolytic gene bmm , suggesting that increased lipogenesis but not reduced lipolysis causes ectopic lipid storage in vivo . Thus , Seipin mutants may have increased lipogenic activity in non-adipose tissues , which subsequently results in the formation of ectopic lipid droplets in midgut and salivary glands as shown in Figure 3A and 3B . Based on the lipidomic data and the genetic interactions between dSeipin and lipogenic genes , we propose that dSeipin participates in PA metabolism . Several lines of evidence support this hypothesis . Firstly , CdsA1 mutants , which have increased levels of PA34∶2 , exhibit the same ectopic lipid droplet phenotype as dSeipin mutants ( Figure 6I ) . Secondly , dSeipin synergizes with CdsA1 , which is a partial loss-of-function mutant of CdsA , in ectopic lipid droplet formation ( Figure 6J ) . Thirdly , overexpression of CdsA can fully suppress the ectopic lipid droplet phenotype of dSeipin mutants ( Figure 6L ) . Fourthly , dSeipin also synergizes with CdsA in the formation of large lipid droplets in Drosophila S2 cells ( Figure S4 ) . Lastly , AGPAT2-deficient mice have increased levels of PA , which may lead to ectopic lipid storage in a tissue-autonomous fashion [40] . Therefore , it is likely that three known lipodystrophy genes ( AGPAT2 , Lipin and Seipin ) are all involved in PA metabolism [11] , [12] , [41] . Although it has been suggested that hepatic TAG accumulation in AGPAT2-deficient mice is caused by a bypass pathway from LPA to monoacylglyceride ( MAG ) and subsequently to TAG , the contribution of elevated PA to excess TAG remains to be determined . Moreover , the exact cause of the increased levels of PA in AGPAT2-deficient mice is unclear . It could be due to elevated DAG kinase activity or increased expression of other AGPATs , such as AGPAT1 , 3 , and 8 [40] . The lipid profile of human Seipin mutant lymphoblastoid cell lines has been studied [42] . However , in that study the levels of PA weren't measured . Interestingly , it was found that the levels of unsaturated TAG species are decreased along with the increases of saturated TAG species . In our lipidomic analysis , the levels of unsaturated DAG and TAG species were significantly increased in salivary gland ( Figure 7 ) . These results suggest that different cells/tissues may response differently to Seipin mutation . Although the exact molecular function of dSeipin remains unclear , we propose that Seipin might act as an enzyme or a cofactor in regulating glycerolipid ( likely PA ) metabolism . PA occupies a specific branch point in the glycerolipid biosynthetic pathway . It can be converted to DAG by Lipin , or to CDP-DAG by CDS . DAG is the immediate precursor of TAG and PC , while CDP-DAG is the precursor of PI and PG . We propose that Seipin may influence lipogenesis by diverting PA from the lipogenic pathway in non-adipose tissues , such as the salivary gland . In the absence of Seipin the lipogenic pathway is more active , leading to lipid droplet formation . In addition , it is possible that PA could influence the formation of lipid droplets since the surface of lipid droplets is a monolayer of polar lipids . dSeipin mutants display opposite phenotypes in the fat body and salivary gland , but we still do not know why there is reduced lipid storage in the fat body of dSeipin mutants . It is possible that compared to the salivary gland , the fat body responds differently to altered levels of phospholipids . Alternatively , Seipin might have different roles in different tissues . These two possibilities are not mutually exclusive , although the specific rescue of the lipid storage phenotype in the fat body but not the salivary gland by C-terminal truncation of dSeipin favors the latter possibility . Identification of the specific function and the binding partner of the C-terminal region in the near future will shed more light on the exact function of Seipin . In summary , the Drosophila Seipin model has not only revealed a novel tissue-autonomous mechanism of ectopic lipid storage in lipodystrophy but has provided a new genetic tool to further identify the regulatory machinery controlling lipid storage . Additional studies combining yeast , Drosophila and mouse models will further advance our knowledge on lipodystrophy and benefit the development of therapeutic strategies to combat lipid storage diseases such as obesity .
Unless specified , Drosophila stocks were maintained in standard corn meal food with Angel dry yeast ( Angel Yeast CO . , LTD , Hubei , China ) . Canton-S was used as wild type and the transgenic line for generating dSeipin null mutants was treated as the control in some experiments where indicated . All stocks were obtained from the Bloomington Stock Center , the Harvard collection or the Vienna Drosophila RNAi center ( for all RNAi stocks ) except for ppl-Gal4 , bmm1 , UAS-bmm , and CdsA1 . All Gal4 lines were verified by crossing to UAS-GFP before use ( Table S1 ) . The effects of overexpression or RNAi of many lipogenic genes were also verified by qRT-PCR ( Figure S4 ) . To generated dSeipin null mutants , we followed the ends-out procedure developed by Golic et al [43] with minor modifications [44] . A dSeipin knockout allele was isolated and confirmed by PCR . The mutant was backcrossed three times to w1118 ( control ) to eliminate background mutations . Transgenic stocks were generated by standard methods . The coding region of Lsd-1 ( without the stop codon ) was amplified by RT-PCR and inserted into a T vector . The Lsd-1-mCherry fusion was created by ligating the Lsd-1 coding region ( EcoRI-KpnI ) in frame into a mCherry vector . The Lsd-1-mCherry fragment ( EcoRI and XbaI ) was shuttled to the transformation vector pUAST-attB to yield UAS-Lsd-1-mCherry . For UAS-dSeipin , full length cDNA was amplified from clone SD04409 and inserted into the pUAST-attB vector ( EcoRI and XhoI ) . UAS-dSeipin-YFP was generated by replacing the stop codon of the cDNA with a BglII site . For UAS-hSeipin , hSeipin cDNA was amplified from human SY5Y cells and inserted into the BglII and XhoI sites of the pUAST vector . The S90L mutation of hSeipin was generated through site-directed mutagenesis . All constructs requiring PCR amplification were confirmed by sequencing . All qRT–PCRs were performed on an ABI PRISM 7900HT real-time cycler ( Applied Biosystems ) using Power SYBR Green PCR Master Mix ( Applied Biosystems ) . Primer sequences are listed in Table S3 . For in situ hybridization , a 270bp dSeipin cDNA fragment was amplified and subcloned into pEasy-T3 vector with the following primers: 5′-agatctATGCCGGCCATATCGCACAC-3′and 5′-aagcttGCGCATCATGGCAGACCGAC-3′ . An anti-sense digoxygenin-labeled probe was made using a BglII-linearized template . Hybridization was detected by using anti-DIG alkaline phosphatase and the CBIP/NBT substrate ( Roche ) . For lipid droplet staining , larvae were dissected in PBS and fixed in 4% paraformaldehyde/PBS for 30 min at room temperature . Tissues were then rinsed twice with 1×PBS , incubated for 30 min in either a 1∶2500 dilution with PBS of 0 . 5mg/ml Nile red ( Sigma ) , or 0 . 06% Oil Red O ( Sigma ) , or a 1∶1000 dilution with PBS of 1mg/ml BODIPY 493/503 ( Invitrogen ) , and then rinsed twice with distilled water . Stained samples were mounted in 80% glycerol . For lysotracker staining , fed or starved larvae were dissected in 1∶1000 lysotracker ( Invitrogen ) and incubated for 5 min before mounting to a slide . All images were taken using a Nikon confocal scope or Zeiss fluorescent scope . The relative levels of lipid storage in fat body cells and salivary glands were quantified separately . Briefly , for lipid storage in fat body cells , the Nile red-positive areas of 30 fat body cells per genotype were measured by NIS-Elements BR 3 . 0 and then normalized to the whole cell area . The average lipid storage in wild type was set as 1 . For lipid storage in salivary glands , the Nile red positive areas of 15 salivary glands per genotype were measured by NIS-Elements BR 3 . 0 and normalized to the whole cell area . The average lipid storage in dSeipin mutants was set as 1 . Drosophila S2 cells were cultured in Schneider's Drosophila medium ( Invitrogen ) supplemented with 10% fetal bovine serum ( FBS ) at 25°C . dsRNA for RNAi treatment was produced by in vitro transcription of a PCR-generated DNA template containing the T7 sequence at both ends . The dsRNAs were generated using a MEGAscript T7 kit ( Ambion ) . Two different sets of primers were used for targeted genes , and the one with better RNAi efficiency was used for the experiments reported . The primer sequences for dSeipin were: forward , 5′-gaattaatacgactcactatagggagaCCATATCGCACACCCGAC-3′ , and reverse , 5′-gaattaatacgactcactatagggagaACTATGGCCGACAATACG-3′ . The primer sequences for CdsA were: forward , 5′-gaattaatacgactcactatagggagaTTTGGTTCGTGCTCTCACTG-3′ , and reverse , 5′-gaattaatacgactcactatagggagaCGTGAACAAATAGCTTGGCA-3′ . S2 cells were diluted to a final concentration of 5×106 cells/ml in Schneider's Drosophila medium without FBS in 6-well plates . 20 µg dsRNA was added to 1 ml of cell suspension and incubated for 45 minutes at 25°C . After the incubation , 3 ml complete medium was added and the cells were cultured for another 3 days . Cells were collected and split into two for total RNA extraction and Bodipy staining . For larvae starvation , wild type and mutant embryos were collected within a 4 hr period and raised at low density on standard fly food at 25°C . 60–65 hr after hatching , larvae were either fed with normal food or starved in PBS for 24 hr . Fed and starved larvae were then dissected and stained with Nile red or Oil Red O . Adult starvation tests were performed by transferring flies into vials ( 25 flies per vial ) with filter papers soaked with distilled water . Mortality rates were determined by regularly counting the number of dead flies . For each genotype , triplicate batches of 75 male flies each ( <36 hr of age ) were used . To determine total glyceride , ten male flies were homogenized in 100 µl PBST ( 0 . 05% Tween 20 ) , incubated at 70°C for 15 min and then centrifuged at 1 , 200 rpm for 5 min . The supernatants were incubated with Triglyceride analysis reagent ( Biosino Biotechnology ) at 37°C for 10 min before being analyzed with a Bio-RAD 550 microplate reader at 490 nm . Glyceride levels were normalized to protein levels using a Bradford assay . Hemolymph was collected from L3 larvae ( 20 from each group ) and diluted in 50 µl PBST ( 0 . 05% Tween 20 ) , heated at 70°C for 5 min , and centrifuged at 12 , 000 rpm for 5 min . Glyceride in the hemolymph supernatant was measured using TAG determination kits ( Sigma ) . Lipids from salivary glands of 50 larvae , fat bodies of 50 larvae or 10 whole larvae ( three sets of samples per genotype ) were extracted as previously described [36] . An Agilent high performance liquid chromatography ( HPLC ) system coupled with an Applied Biosystem Triple Quadrupole/Ion Trap mass spectrometer ( 3200Qtrap ) was used for quantification of individual phospholipids . Multiple reaction monitoring ( MRM ) transitions were set up for quantitative analysis of various polar lipids [19] , [45] . Normal phase HPLC was set up for separation of individual lipid classes . Levels of individual lipids were quantified using spiked internal standards including dimyristoyl dimyristoyl phosphatidic acid ( 28∶0-PA ) , which was obtained from Avanti Polar Lipids . Neutral lipids were analyzed using a sensitive HPLC/ESI/MRM method modified from a previous method [46] . TAG levels were calculated relative to the spiked d5-TAG 48∶0 internal standard ( CDN Isotopes Inc . ) , while DAGs were quantified using 4ME 16∶0 Diether DG ( Avanti ) as an internal standard . The results from three experiments were normalized and plotted in a heat map . | Obesity and lipodystrophy are medical conditions characterized by excess body fat or too little body fat , respectively . Interestingly , a common feature of both conditions is ectopic accumulation of lipids ( fat ) in cells where fat is not normally stored . This can cause tissue damage with health-threatening consequences . We are trying to understand how these two very different diseases lead to lipid storage in non-fat tissues . In this study , we used fruit flies ( Drosophila melanogaster ) with a mutation in the dSeipin gene as a lipodystrophy model to explore the mechanism of ectopic lipid storage . In dSeipin mutant flies , we found numerous lipid droplets in the salivary gland , a non-fat storage tissue , and reduced lipid storage in the fat body , an adipose tissue . Furthermore , we proved that dSeipin functions within salivary gland cells to prevent the formation of ectopic lipid droplets . We also found that dSeipin genetically interacts with other fat synthesis and metabolism genes in the formation of ectopic lipid droplets . The fruit fly dSeipin mutant provides an excellent model system for dissecting the mechanisms that regulate the storage of excess lipids . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"cell",
"biology/microbial",
"physiology",
"and",
"metabolism",
"diabetes",
"and",
"endocrinology/obesity",
"genetics",
"and",
"genomics/disease",
"models",
"genetics",
"and",
"genomics/genetics",
"of",
"disease"
] | 2011 | Tissue-Autonomous Function of Drosophila Seipin in Preventing Ectopic Lipid Droplet Formation |
A fundamental understanding of behavior requires predicting when and what an individual will choose . However , the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear . In the current study , we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions . We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period . Regarding when choices were made , we found bursts of rapidly occurring actions , separated by time-varying inactive periods , partially based on a circadian rhythm . Regarding what was chosen , we found sequential dynamics in affective choices characterized by two key features: ( a ) a highly biased choice distribution; and ( b ) preferential attachment , in which the animals were more likely to choose what they had previously chosen . To capture the temporal dynamics , we propose a dual-state model consisting of active and inactive states . We also introduce a satiation-attainment process for bursty activity , and a non-homogeneous Poisson process for longer inactivity between bursts . For the sequential dynamics , we propose a dual-control model consisting of goal-directed and habit systems , based on outcome valuation and choice history , respectively . This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems , leading to heavy tails in the distribution of behavior over time and choices .
Rather than regular , like a metronome , or homogenous ( i . e . , a constant overall rate of activity ) , timing of behavior and/or events in humans , non-human animals , and natural phenomena is often non-homogeneous , with periods or bursts of high activity separated by long inactive periods [1] , [2] . Examples in humans include e-mail [1] , [3]–[7] or mail communication [8] , library loans [3] , financial trading [9] , [10] , on-line movie watching [11] , internet browsing [3] , [12] , printing requests [13] , and mobile communication [14] , [15]; in non-human animals , locomotion [16]–[21] , and flying patterns [22]; and in natural phenomena , rainfall [23] , tsunamis [24] , and earthquakes [2] , [25] . A telltale diagnostic feature used to characterize non-homogeneous temporal processes is a heavy tail in the distribution of the inter-event intervals ( i . e . , the time interval between consecutive events ) [1] . A heavy tail reflects a larger number of longer inter-event intervals than occurs with homogeneous Poisson processes ( i . e . , those in which the events occur at an overall constant rate , but are independent of one another ) . Although a non-homogeneous process has been suggested as a universal feature of natural dynamical systems [2] , different specific underlying mechanisms can lead to a heavy-tailed distribution of the inter-event intervals [26] . For example , it has been suggested that the bursty nature of human interactions results from the combined effects of different periodicities at different timescales: e . g . , a circadian rhythm , as well as weekly , monthly , etc . cycles; and , in fact , bursty behavior can derive from a cascading non-homogeneous Poisson process model that combines multiple Poisson processes with different timescales [6] , [27] , [28] . At the same time , the bursty behavior of human interactions can also be induced by intrinsic correlations between actions [6] , [27]–[31] . Indeed , bursty behavior might also derive from a combination of such processes , which we explore in the current study . Here , we focus on foraging , a fundamental and frequent behavior for survival . Foraging mechanisms underlie the daily energy budget allocation across activities [32]–[34] . Unlike nature phenomena , feeding , and more generally , foraging behavior is influenced by both internal biological and external environmental factors: internal factors include preference , nutrition , memory , hunger and satiety; external factors include the daily light-dark cycle ( leading to a circadian rhythm ) , seasonal and social/societal effects [32] , [35] . Thus , the study of foraging behavior provides the opportunity to examine decision mechanisms that result from the interaction of important internal and external influences . Feeding behavior has been studied in large data sets of farm animals , pets , and captive wild animals , including cattle , pigs , chickens , ducks , turkeys , rats , and dolphins [33] , [35]–[39] . The temporal structure of feeding behavior consists of high frequency feeding events that are separated by relatively long non-feeding periods: i . e . , it is bursty [33] , [38] . In the current study , our first objective was to test the hypothesis that foraging timing is based on bursty behavior that is influenced by both the level of satiety ( internal ) and by the daily light- dark cycle ( external ) . Indeed , we found a heavy-tailed distribution of the inter-choice intervals ( ICI , the time interval between two choices ) , reflecting a non-homogenous process . Moreover , the ICI distribution exhibited bimodality , reflecting distinctive processes for short and longer timescales: bursty behavior for short ICIs and circadian rhythmic activity for longer ICIs . To explain this bimodality in foraging behavior , we propose a dual-state model consisting of active and inactive states , with correlated behavior producing bursty activity in the active state , and relatively uncorrelated behavior influenced by a circadian rhythm in the inactive state . Once activity timing is characterized , the decision dynamics of which option to select and whether to continue selecting it over repeated choices must be specified [40]–[43] . Although progress has been made on characterizing outcome-driven behavior as governed by the goal-directed system [44] , [45] , and stimulus-driven behavior as governed by the habit system [45]–[48] , it nonetheless remains difficult to predict an individual's preference and choice responses over a long period of time . For example , an individual's preference for different foods or music seems to fluctuate over time even when they have experienced the available options extensively and thus know all options well: e . g . , even if one's favorite food is a hamburger , it typically is not eaten every single day . Thus , the underlying mechanisms that lead to dynamically changing preference-based choice behavior remains unclear , especially with qualitatively different rewards in stable environments , in which an agent ‘knows’ the reward contingencies and thus does not require further learning . Therefore , the second objective of the current study was to help specify the mechanisms underlying seemingly unpredictable preference-based choices with ( a ) multiple qualitatively different options; and ( b ) repeated choices over an extended period in a stable environment that reflects real-world choice behavior . Here we extracted two distinctive features from an individual's dynamic choice sequence: ( 1 ) preference bias ( i . e . , the skew of the choice distribution based on the individual's rank order of choice options ) , and ( 2 ) choice persistence ( i . e . , the degree to which choices are repeated ) , which capture distinct underlying control processes that determine what to choose and whether to continue choosing it , respectively . We found individual differences in preferences that nonetheless could be characterized by choice option rank , reflecting a value-based process , as well as some persistent choice behavior , in which choices tended to be repeated , with an increasing likelihood of repeating a choice as a run of identical choices increased , reflecting a preferential-attachment process . We then developed a dual-control model incorporating a combination of goal-directed and habitual control to describe the dynamical patterns of the choice sequences .
We investigated the continuous choice behavior of 12 rats over the course of two weeks using a four-armed bandit task with four differently flavored pellets: chocolate , banana , coffee , and cinnamon . Each rat lived in an operant chamber for the entire two-week duration as a “closed economy” [49] with continuous access to water and the food pellets in the environment . Each trial was initiated by nose-poking in a lighted opening , after which four levers would extend from the opposite wall of the chamber ( Figure S1 ) . The rat then obtained one of the flavored pellets by pressing the corresponding lever . To examine when and what the animals chose , timing and choice sequences of lever-pressing activity for all rats were recorded for the entire experiment . With respect to when they chose , the animals actively foraged during the dark cycle and sporadically so during the light cycle as shown in Figure 1A . With respect to what they chose , we found dynamic changes in the animals' food choices , indicating that the rats did not commit themselves to a specific option but rather intermittently explored alternatives . To assess the degree of the animals' exploration or exploitation , we first computed entropy of choice sequences every hundred trials [50] , which is a measure of the uncertainty in choices , with zero being deterministic and solely exploitative and high entropy indicating a high degree of exploration ( Figure 1B ) . We found that the entropy of choice sequences fluctuated to some degree throughout the experimental period . Although entropy changes varied slightly across subjects , overall , there was no significant tendency of entropy to decrease at the group level , indicating that the animals maintained some level of exploring alternatives throughout the experiment rather than converging toward a particular option . Next , we compared the entropy of empirical choice sequences with randomly shuffled ones , which removes any dependency on past choices , to determine whether the degree of exploration or exploitation depended on previous choice history ( Figure 1B ) . We found that the levels of entropy in the empirical choice sequences were significantly lower than in randomly shuffled ones for all subjects ( paired t-test , p<0 . 001 ) . Thus , this result shows that previous choices influenced the current choice , consistent with other reports [40] , [42] , [51]–[54] . We next examined the amount of consumed pellets with respect to flavor , location , and rank . Rank was defined as the order of overall consumption of each food type for an individual , which would reflect the order of an individual's subjective values for the qualitatively different rewards . The percentages of mean choice for the four different locations – left ( LL ) , middle left ( ML ) , middle right ( MR ) and right ( RR ) – were not significantly different ( one-way ANOVA , F ( 3 , 44 ) = 0 . 781 , p = 0 . 511 ) ( Figure 1C ) , reflecting the counterbalancing of flavor and position across subjects , and demonstrating that there was no preferred location overall . In addition , to test whether there were differences in effort to reach each lever location from the initial nose poke position , we compared the response latencies between nose-poke and lever pressing for each location . The response latency medians across locations were not significantly different ( one-way ANOVA , F ( 3 , 44 ) = 0 . 009 , p = 0 . 998 ) , suggesting that the animals' response vigor for each location was similar [55] . The consumption rates for each flavor were significantly different ( one-way ANOVA , F ( 3 , 44 ) = 5 . 043 , p<0 . 01 ) : the chocolate flavor was statistically more consumed than the coffee flavor at the group level ( Dunnett-T3 post hoc test , p = 0 . 021 ) ( Figure 1D ) , although this was not the case for all subjects ( e . g . , Figure 1A ) ; nonetheless , all rats showed distinct individual preferences among the different flavors . Since the rats exhibited individual differences in preference , and since quality has no obvious natural corresponding number to represent its value ( especially when quality was essentially flavor ) , we analyzed choice behavior based on rank , which should be driven by an individual's subjective values of the options , and which provides a common scale to compare individuals . Comparing the percentages of mean choice for rank , there was a clear difference between food pellets of different ranks as shown in Figure 1E ( one-way ANOVA , F ( 3 , 44 ) = 74 . 897 , p<0 . 001; Dunnett-T3 post hoc test ) . Interestingly , choice rate appeared to decrease by nearly half as rank increased . To confirm this tendency , we transformed the percentage of food choice by rank to a log-linear scale . We found that the mean distribution of the choice percentage p as a function of rank r was well described by the log-linear distribution ( Figure 1E ) , where the slope of p versus log ( r ) was −70 . 7±4 . 95 ( mean ± standard error of the mean [s . e . m . ] , adj . R2 = 0 . 994 ) , indicating that preference was highly skewed toward the higher rank . Examining the timing characteristics of the choice behavior in more detail , we found periodic changes in food consumption . First , the animals consumed more pellets during the dark than the light cycle ( Figure 2A ) . To investigate the relationship between the foraging pattern and the daily light-dark cycle ( i . e . , a potential circadian rhythm effect ) , we measured the periodicity of the foraging pattern by calculating the time interval between peaks in the average autocorrelogram . The rats' foraging pattern period was approximately 24 hours , consistent with their circadian rhythm ( Figure 2B ) , indicating that it was one of the key factors that determined foraging timing in general . The remaining issue was how the specific timing of foraging was determined at a short timescale . We characterized the underlying action dynamics by analyzing the features of the inter-choice interval ( ICI ) distribution . We found that the majority of ICIs were short , but very long ICIs also sporadically occurred , indicating that there were bursts of activity separated by relatively long inactive periods ( Figure 2C ) . To measure this burstiness in the timing of foraging behavior , we used a burstiness index B , defined as , where and are the mean and the standard deviation of the ICI distribution , respectively [31] . B ranges between −1 and 1: B = 1 is the most bursty signal , B = 0 is neutral , and B = −1 is a completely periodic signal . We found that B of the foraging behavior was 0 . 794±0 . 008 ( mean ± s . e . m ) , indicating that the majority of activity was densely concentrated in short durations . Next , to characterize a memory effect , we calculated the correlation coefficient of consecutive inter-choice intervals , which is defined as , where is the number of ICIs measured from the timestamps , and m1 ( m2 ) and are the mean and standard deviations of the ICIs 's ( 's ) , respectively [31] . M ranges between −1 and 1: M is positive when the length of the current ICI is positively proportional to the length of the previous ICI; whereas , M is negative when the length of the current ICI is inversely proportional to the length of the previous ICI; M = 0 is neutral; and M = −1 is a completely periodic signal . We found that M of the foraging behavior was 0 . 046±0 . 006 ( mean ± s . e . m ) , indicating that the foraging activity had a relatively low correlation between consecutive ICIs . The bursty nature of the foraging behavior was reflected in the heavy-tailed ICI distributions . The cumulative distribution of ICIs , which is the probability of ICIs longer than a given ICI ( i . e . , the survival function ) , exhibited a heavy tail that was clearly seen in a log-log scale , representing a deviation from an exponential distribution resulting from a simple homogeneous Poisson process ( Figure 2D ) . This indicates that the time interval between spontaneous behaviors is not simply governed by a random process , but is modulated in a more sophisticated way by other processes at a longer timescale . In addition , heavy tails were also observed in the distributions of ICIs in both the light and dark cycles ( Figure 2D ) . Interestingly , the empirical ICI distribution exhibited bimodality ( Figure 2E ) . For short ICIs , the probability density function of the ICIs was highly left-skewed; whereas for longer ICIs , the probability density function did not appear to reflect the same left-skewed characteristic . The highly left-skewed component of the distribution for short ICIs was well fit by the power-law ( p = 0 . 68±0 . 09 for the fit to the power-law distribution—i . e . , the empirical and power-law distributions were not significantly different; see “Estimation of parameters in the inter-choice interval ( ICI ) distribution” in Material and Methods ) ( Figure 2E inset ) . The second component of the distribution for longer ICIs appeared to follow the Weibull distribution , exhibiting a stretched exponential decay; however , with combined light and dark cycles , the empirical and Weibull distributions were significantly different . When we decomposed the overall ICI distribution into the component light and dark cycles , however , the distributions of the short ICIs for both cycles followed the power-law distribution , and the distributions of the longer ICIs for both cycles followed the Weibull distribution ( Table 1 and Figure 2F ) . Thus , the cumulative bimodal ICI distributions for both the light and dark cycles could be described as the following:where is the lowest time boundary , is a time constant used to separate activities into independent bursts , µ is the power-law exponent , λ is a scale parameter , and γ is the shape parameter of the distribution . We calculated the value of τ0 as the local minimum of the bimodal distribution of ICIs , which separated the short and longer ICIs in the distributions . The estimated parameters of the bimodal ICI distributions are shown in Table 1 ( see “Estimation of parameters in the inter-choice interval ( ICI ) distribution” in Material and Methods for details ) . This bimodality in the ICI distributions suggests ( a ) different underlying processes at different timescales of ICIs , and ( b ) similar underlying processes in both the light and dark cycles leading to the power-law and Weibull distributions . We take up these implications in the discussion . When comparing the fitted parameters in the light and dark cycles , we found that the distributions for longer ICIs between the light and dark cycles exhibited different exponential decays reflected in the scale parameter λ ( light: [1 . 20±0 . 15] ×104 , dark: [2 . 74±0 . 2] ×103 , Sign test , p<0 . 001 ) , whereas the power-law distributions for the short ICIs in both cycles appeared to have similar slopes ( light: 2 . 21±0 . 07 , dark: 2 . 07±0 . 05 , Sign test , p = 0 . 146 ) ( Table 1 and Figure 2F ) . This finding comparing the light and dark cycles implies that the underlying mechanism governing longer ICIs was influenced by the circadian rhythm; whereas , the mechanism governing short ICIs may have been more weakly influenced by the circadian rhythm . We next analyzed the choice patterns to examine the sequential dynamics governing what is chosen over trials . First , we determined how long the rats continued to make the same choice . We defined a “run” as a series of consecutive identical choices . A trial-dependent change in a distribution of runs was then calculated , as shown in Figure 3A . The cumulative distribution of runs , defined as the probability of runs longer than a given length of run ( i . e . , the survival function ) , revealed a heavy tail in a log-log scale ( Figure 3B ) , indicating that the choice pattern consisted of a large number of short runs and a few extremely long runs . To test for a sequential dependency of previous choices , we compared the run distributions of the empirical sequences with those of randomly shuffled sequences of the same data for each rat . The randomly shuffled sequence has no dependency on previous choices yet maintains the same choice frequency as the empirical data . The cumulative run distribution of the empirical data was significantly different from that of the randomly shuffled choice sequences for all subjects ( Monte Carlo hypothesis testing , p<0 . 001 ) [6] . This result indicates that the choice sequences were highly influenced by the choice histories [40] , [42] , [52] , [54] . In addition , we examined whether there was an effect of choice history regardless of rank by comparing the run distribution of empirical data for each rank with that of randomly shuffled data ( Figure 3C ) . Although the lower ranking flavors had fewer long runs than the higher ranking ones , the run distribution of the empirical data for all ranks was significantly different from those of the randomly shuffled choice sequences for all subjects , with the exception of the fourth rank for two of the twelve subjects ( Monte Carlo hypothesis testing , p<0 . 001 ) [6] . The shared heavy-tailed feature of the run distribution for every rank suggests that the underlying processes determining whether a run would continue were relatively insensitive to reward outcome . Conducting a simple calculation with the cumulative distribution of runs , we obtained the hazard rate for ending a run as a function of the number of preceding choices in a run for each rank , i . e . , the conditional probability of ending a run at a given length of a run ( Figure 3D ) . We found that the hazard rate for ending a run decreased logarithmically and converged relatively quickly to approximately zero in all ranks . This indicates that a run was more likely to be terminated when the length of the preceding choices in a run was short; and the run was more likely to continue when the length of the preceding choices in a run was increased . In addition , the hazard rate converging to zero resulted in extremely long runs regardless of rank; indeed , there was no significant difference in the decreasing rate of the hazard rate between ranks ( one-way ANOVA , F ( 3 , 44 ) = 0 . 666 , p = 0 . 577 ) . Thus , in general , the rats were more likely to choose what they had chosen previously , irrespective of outcome , reflecting a status quo bias or preferential-attachment process that tends to continue a run until switching one's choice finally becomes more compelling .
A bimodal distribution has been suggested as a mixture of distinct distributions formed by different underlying processes [14] , [25] , [38] . We found that the empirical ICI distribution underlying the foraging behavior under free conditions exhibited bimodality with the power-law and Weibull distributions for short ICIs and longer ICIs , respectively . To characterize the bimodal temporal dynamics , we propose a dual-state model that can provide an integrative account of both the bursty and periodic features of the foraging behavior . The model consists of an active state and an inactive state , which executes correlated actions in bursts in the active state , and elicits intermittent uncorrelated actions in the inactive state ( Figure 4A ) . We consider an animal to be in an active state when the animal exhibits a high frequency of activity , with short ICIs that are less than a certain time period , and we assume that the events within the active state are correlated due to the influence of the motivational drive [2] . In our case , the motivational drive for feeding is to appease hunger ( i . e . , reach satiation ) . A known physiological mechanism underlying short-term regulation of feeding ( within a meal ) is that feeding is governed by a feedback mechanism from the delayed gastrointestinal aftereffects of eating [36]; the digestion of food inhibits eating , but the inhibitory effect is delayed . Here , we focus on the delay between the swallowing of food and the digestion of food , resulting in the delayed satiety signal as feedback . And this characteristic of feeding leads us to propose a satiation-attainment process , i . e . , an active waiting process based on feedback for upcoming satiation within each active state . In this process for the active state , we assume that whenever animals eat , they wait for the feedback signal by which they determine whether to eat more or stop . In other words , animals initiate eating and wait until they receive the satiety signal , which informs them that satiation is attained . If the satiety signal is lower than the satiation threshold , they would continue to eat and wait for the next feedback signal . Thus , the waiting time between eating and the feedback signal is important to determine time intervals between actions in an active state . Instead of a constant time delay of feedback , we assume that there is a non-linear relationship in the waiting time between eating and the feedback signal . A number of studies on human dynamics have suggested that the waiting time based on feedback in human communication patterns follows a power-law distribution [1] , [7] , [8] , [14] . Considering a similarity in the waiting process for feedback between feeding and human communication , we assume that the waiting time between eating and the feedback signal follows a power-law distribution; in active states , the probability density function of the time interval between choices is for where 1<µ<3 . In addition , an animal is considered to be in an inactive state when there is a period of inactivity longer than ; and thus the inactive state is defined as the time between the last event in a given active state and the first event in the next active state , which by definition , is longer than . We model timing in the inactive period with a non-homogeneous Poisson process with the inactivity rate , i . e . , the reciprocal of the mean inactive duration as a function of time . To capture the strong influence of the circadian rhythm on the longer ICIs , two temporal properties of the inactivity rate are further specified . First , the inactivity rate depends on time in a periodic manner , as expressed by the equation , where T is the period of the process . Since the animals' periodic activity is modulated by a circadian rhythm , we set the period T as 1 day . Second , the inactivity rate is proportional to the daily distribution of choice behavior in the inactive state , , where is the average rate in the inactive period , is the probability of beginning an active state during a particular hour of the day [6] , and b is the shape parameter . To quantify the transition between active and inactive states , we assume that a state transits from the active state to the inactive state with a probability ξ after each choice and remains in the active state with probability 1 – ξ . With the computational processes that determine when choices are made specified , we next delineate those that determine what choices are made . Here , we propose a simple heuristic model that accounts for two key sequential features of decision-making: ( 1 ) the heavy-tailed nature of the run distribution , reflecting choice persistence as habitual behavior , and ( 2 ) the biased rank distribution , reflecting goal-directed outcome valuation . First , to account for persistence in choice behavior , we assume an underlying preferential-attachment process , which has been proposed as the mechanism underlying heavy-tailed distributions [42] , [56] , [57] . In this process , the probability of continuing a run increases as a run proceeds ( thus , it also has been called the “rich get richer” process ) . We suggest that the same mechanism underlies choice behavior , in which the probability of choosing a particular option is proportional to the number of times the option was chosen previously . The process may underlie response persistence found in choice behavior in humans and nonhuman primates [40] , [42] , [58] , [59] . In addition , the preferential-attachment process occurs regardless of outcome type , reflecting its property of insensitivity to outcome , which is a defining feature of habitual behavior ( Figure 3D ) . Thus , this process may underlie the acquisition and maintenance of habits . We therefore more generally call this mechanism , habitual control . In the habit system , in addition to the preferential-attachment process , we apply a leaky integrator to the dynamic trial-by-trial model of habitual behavior , in which the integrated choice frequency over previous trials is discounted as a function of the distance passed from a given trial [52] , [57] , [60] , [61] . Thus , this integrator includes the effect of past choices [42] . Because the preferential-attachment process is insensitive to outcome , we assume that the discount rate is identical for all options regardless of rank . In habitual control , the action value of a particular option i at trial t , , is determined by the local choice history of that option with leakage: where is a weighting coefficient for choices occurring trials ago with an exponential decreasing profile , equal to , where is a free parameter for the decay constant , and is a binary vector denoting a chosen option i on trial t . The choice vector is 1 if option i was chosen on trial t and 0 if the option was not chosen on that trial . Second , for goal-directed control , we use a temporal difference ( TD ) reinforcement learning algorithm that updates the action-value on each trial according to its prediction error [62]–[66] . The TD learning algorithm provides a theoretical framework for instrumental reward learning in which actions must be chosen to optimize long-term rewards [63] , [67] . In addition , we incorporate a decay factor , which updates the chosen option and decays unchosen options [66] , [68] , [69] . Thus , at each trial t , the action value for the chosen option c and for the unchosen option u are updated according to:where and are learning rates and and are the reward prediction errors at given trial t for the chosen and unchosen options , respectively . The reward prediction errors , i . e . , the difference between the expected and received reward values , for the chosen and unchosen options are as follows: where is the reward value for the chosen option . We deductively estimated the reward value based on the mean choice rate across days , R: where is a parameter of sensitivity of behavior to differences in reward values among alternatives [70] . We refer to this outcome-driven process as “goal-directed . ” The goal-directed process plays an important role in determining the initial choice for a new run on the basis of value , which in turn generates a certain degree of bias toward a more valued option . Finally , for action selection , to capture the effects of both the habit and goal-directed systems on choice behavior , the goal-directed value and habit value are derived in parallel [71] . We then assume that the probability to choose an option i at trial t , , is determined according to a softmax choice function [63]:where the softmax inverse temperature parameters and represent the degree to which a choice is focused on the highest-valued option in goal-directed value and habit value , respectively . Note that , together , the combination of goal-directed and habit systems create two key features of sequential dynamics: a bias among choice options and a bursting property in which very long runs are interspersed among a majority of short runs .
Regarding when choices were made , we found bursts of rapidly occurring actions separated by time-varying inactive periods , partially based on a circadian rhythm . These characteristics of foraging behavior were reflected in a bimodal inter-choice interval ( ICI ) distribution comprised of a power-law for the short timescale ( i . e . , short ICIs ) and the Weibull distribution for the longer timescale ( i . e . , longer ICIs ) . Although the specific mechanisms of the bimodal inter-event times could vary across different systems [9] , [10] , [14] , [24] , [25] , [74] , [75] , a common dynamical feature of the underlying mechanisms appears to be the combination of distinct processes at different timescales [14] , [25] , [37] . To capture the temporal dynamics underlying foraging behavior , we propose a dual-state model consisting of active and inactive states for short and longer timescales based on a satiation-attainment process for bursty activity in the active states , and a non-homogeneous Poisson process for longer inactivity between bursts in the inactive states . For the short timescale , we found an inverse square power-law distribution for short ICIs with exponent . Interestingly , a recent study in human short message correspondence , which requires feedback between individuals , suggests that the waiting time of the bursty communication follows the power-law distribution with exponent . Analogously , a satiation-attainment process could govern the timing of feeding activity by waiting for satiation feedback . In fact , it is well known that short-term feeding is regulated by feedback from the delayed gastrointestinal aftereffects of eating and satiety signals: based on this feedback , meal termination is determined [36] , [76] . For the longer timescale , we found that longer ICIs follow the Weibull distribution in both the light and dark cycles . At the same time , the cumulative distributions of the longer ICIs in the light and dark cycles exhibited different decay rates . One possible account for this difference between the light and dark cycles is the effects of the circadian rhythm on the motivation for general activity [77] , as well as on specific activities such as sleep and feeding . A previous study on sleep-wake transitions suggested that long and periodic awake episodes in the sleep period are governed by the homeostatic sleep drive [78] . Thus , the long inactivity patterns might result from sleep-wake patterns . However , in contrast to the previous study , we found long inactivity patterns not only in the light cycles ( the sleep period in rats ) but also in the dark cycles . Thus , although it appears that sleep-wake patterns can contribute to generating longer inactivity patterns in the sleep period , it does not appear that the long inactivity patterns in the current study can be explained entirely by the homeostatic sleep drive . The longer ICIs are likely influenced by the homeostatic hunger drive . The Weibull distribution is commonly used to describe the time to a first event [79] , which in our case would be the time to the next foraging bout , i . e . , to the next burst . Consistent with the use of the Weibull distribution , a threshold mechanism can be implemented in controlling the timing between independent bursts [36] . Physiological regulatory mechanisms associated with satiety have been suggested to control the time interval between bouts in a wide range of animals: when the satiety signal reaches or rises above a certain threshold , animals stop eating; whereas , when the satiety signal falls below the threshold due to a long period of non-feeding , they initiate eating again [35] , [36] , [38] . In fact , a simple “bang-bang” control system has been proposed that describes such a straightforward mechanism that uses the comparison of a satiety signal to a threshold , with the first ‘bang’ occurring when below threshold , and the other once threshold is reached . Moreover , a change in the threshold level between night and day ( and potentially from hour-to-hour ) provides a possible time-varying mechanism for the time interval between meals [36] . Regarding what was chosen , we examined sequential dynamics underlying free choice patterns in a stable environment in which an animal could obtain the food items with certainty . Despite the certainty of reward delivery as well as a stable reward value , the rats exhibited rich choice dynamics rather than a monotonous pattern . In contrast to the popular notion that goal-directed behavior gives way to automatic habitual behavior in a stable environment [51] , we found that the entropy of the choice patterns remained relatively stable over the course of the experiment , suggesting that the animals maintained a balance between exploration and exploitation . This sustained balance suggests that the goal-directed process is indeed maintained , in order to maximize rewards even in stable , deterministic environments [80]–[82] . Instead of persisting with a particular option as a habit , maintaining the balance allows animals to monitor the environment for potential changes and to adapt more flexibly if and when changes occur . Such rich choice dynamics reveal that internal factors such as the value of available options and the previous choice history [42] , [55] , [83]–[85] play a critical role in generating choices . To extend beyond quantity-based decision-making , in this study we focused on the dynamics underlying choices based on individual preference with respect to qualitatively different rewards with different flavors . Because qualitatively different rewards have no obvious corresponding numerical value , we used rank as a means to measure their relative subjective value based on individual preference . Indeed , we found a highly biased rank distribution toward an individual's favorite option . This rank distribution reflects one of goal-directed behavior's key properties , that action selection is guided by the value of outcomes to the individual [44] , [45] , [86] , [87] . In our dual-control model , the subjective value of qualitatively different rewards was deductively estimated from each individual's choice behavior on the basis of the generalized matching law in which the choice rate matches the relative value of the options modulated by a sensitivity parameter [43] , [52] , [57] , [70] , [81] . When we tested the model with the empirical data , the reward value estimation resulted in small differences between the options . At the same time , the goal-directed control process successfully captured the highly biased rank distributions . This suggests that quality-based choice behavior can be modeled by a value-based process , and that a small difference in subjective values for quality can nonetheless generate large differences in choice behavior by an internal amplifying control process . To capture both the value-based and internal amplifying control processes , we modified the standard TD algorithm [63] to update the action value for the chosen option according to the outcome , and at the same time , to apply a decay to the unchosen options [66] , [68] , [69] , [88]–[90] . Thus , internal value representations for all available options are updated in this model . This process provided a superior fit to the empirical data . The addition of value updating of all options to the standard TD algorithm results in the action value of the chosen option increasing over trials and that of the unchosen options decreasing . The decay of action values for the unchosen options in turn results in a larger reward prediction error when the unchosen option is later chosen . Thus , the decay effect can lead to dynamic changes in choices due to variation in reward prediction errors over trials even in a stable environment . For habitual control , the dynamic choice patterns revealed two key characteristics of habitual behavior: repeated responses and insensitivity to outcome [44]–[46] . We found that the rats intermittently generated very long runs throughout the experiment , resulting in a heavy tail in the run distribution . Furthermore , the run distributions for all ranks exhibited this heavy-tailed property , indicating a general persistence or ‘stickiness’ to past choices regardless of outcome . This insensitivity is consistent with a recent study on monkeys showing heavy-tailed run distributions regardless of reward types ( water and apple juice ) [58] , as well as other studies showing that trial-by-trial choice dynamics are strongly influenced by past choices [40] , [42] , [54] , [57] . While a large number of studies that model goal-directed and habitual processes have recognized this effect of previous choices on current ones [40] , [54] , [59] , [91]–[94] , the detailed process underlying choice persistence has not been fully described . We have built upon this work by delineating the mechanism more explicitly .
Our empirical study shows that even in stable environments animals can exhibit rich temporal and sequential behavioral dynamics . In addition , our modeling work demonstrates how the interaction of different underlying processes can give rise to dynamic activity patterns . A dual-state model suggests that dynamic transitions between active and inactive states produce bursty and circadian rhythmic properties of temporal dynamics . A dual-control model suggests that goal-directed and habitual control processes cooperate , rather than compete , to generate sequential dynamics of choices that lead to a better option and increase the reliability of a performed action . Considering the ubiquity of decision-making in the lives of animals and in our everyday lives , temporal and sequential dynamics of spontaneous choice behavior raise the intriguing possibility that such dynamics derive from a harmonious collaboration of multiple underlying neural control systems – a collaboration that , when discordant , may lead to aberrant decisions such as binge eating or other forms of addictive behavior .
All procedures of animal care and experiment were performed according the KAIST guidelines for the care and use of laboratory animals and approved by the KAIST Institutional Animal Care and Use Committee . Twelve eight-week-old naïve male Sprague Dawley rats weighing 250–350 g were used in the study . The rats had all experienced a standard laboratory diet , and none had experience with the flavors used in the experiment . Each rat was individually housed in an operant chamber ( see Text S1 for details and Figure S1 ) and maintained on a 12-h light/dark cycle for two weeks . The animals had ad libitum access to water . Food was available according to the experimental task described below . The four types of flavored 45 mg pellets—chocolate , banana , coffee , and cinnamon—were made from the same meal substrate ( Bio-Serv , Frenchtown , NJ , USA ) and were consequently matched with regards to all macro- and micro-nutrients . The locations of the flavored pellets were counterbalanced across subjects . Trials were signaled by the illumination of the nose-poke light ( Med Associates , St Albans , VT ) inside the box . When the light was on , a nose-poke into the lighted opening resulted in the nose-poke light turning off and four retractable levers ( Med Associates , St Albans , VT ) extending on the opposite wall . A press of one of the four levers initiated ( a ) the delivery of a food pellet according to the flavor assigned to that lever as well as ( b ) the retraction of all levers . After a pellet was delivered , the nose-poke light was turned on again for the next trial . During the experiment , the spontaneous choices and corresponding response times were recorded ( see Text S1 for details ) . All experimental events were coordinated using MED-PC software ( Med Associates , St Albans , VT ) . We estimated the value of as the crossover point from the power-law to Weibull distribution , which would be represented as the local minimum value between these two distributions . Thus we calculated the value of for individual rats as the local minimum of the probability density function of ICIs in the range between 50 and 1000 seconds . For short ICIs , we estimated the power-law exponent based on maximum likelihood estimation and selected the minimum time boundary , which provides the minimum value of the Kolmogorov-Smirnov goodness-of-fit statistic D [95] . For longer ICIs , the scale and shape parameters and for the Weibull distribution were estimated by using a Matlab function , wblfit . m , on the basis of maximum likelihood estimation . The parameters of the dual-state model were estimated from the empirical data for individual rats . We assumed that the ICIs in the active states would be smaller than the periods of inactive states . For simulation , we set the time constant , the power-law exponent and the shape parameters for the light and dark cycles , and , as free parameters . Activities of empirical data were grouped into an active state when their ICIs were less than , and separated into independent active states if the ICI was larger than ; and thus the inactive state was defined as the time between the last event in a given active state and the first event in the next active state . Once the active and inactive states were determined , we estimated the average rate of the inactive period , i . e . , the reciprocal of the mean inactive duration , the probability of beginning an active state during a particular hour of the day , and the transition rate from the empirical data . Using these parameters , we generated simulated time series with the dual-state model . We estimated free parameters , , , , and , for each rat by using a least-area estimation [6] , which provides the best-estimated parameters that minimize the area test static between the cumulative ICI distributions of the empirical and simulated data in a log-log scale . To compare the fit of the dual-choice model with that of its nested models , i . e . the goal-directed or habit choice models alone , we used likelihood ratio tests and the Bayesian information criterion ( BIC ) [73] as follows: BIC = −2•LL + k•ln N where LL is the log-likehood of the model , k is the number of parameters of the model , and N is the number of trials . To examine how much better the models fit to empirical data compared to a random choice model , we calculated a pseudo-r2 statistic defined as ( R-L ) /R , where R is the log-likelihood of the random choice model and L is that of our models [88] . A higher value indicates a better model fit . | To understand spontaneous animal behavior , two key elements must be explained: when an action is made and what is chosen . Here , we conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period . With respect to when , we found bursts of rapidly occurring responses separated by long inactive periods . With respect to what , we found biased choice behavior toward the favorite items as well as repetitive behavior , reflecting goal-directed and habitual responding , respectively . We account for the when and what components with two distinct computational mechanisms , each composed of two processes: ( a ) active and inactive states for the temporal dynamics , and ( b ) goal-directed and habitual control for the sequential dynamics . This study provides behavioral and computational insights into the dynamical properties of decision-making that determine both when an animal will act and what the animal will choose . Our findings provide an integrated framework for describing the temporal and sequential structure of everyday choices among , for example , food , music , books , brands , web-browsing and social interaction . | [
"Abstract",
"Introduction",
"Results",
"Models",
"Discussion",
"Conclusions",
"Materials",
"and",
"Methods"
] | [
"behavioral",
"neuroscience",
"computational",
"neuroscience",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"neuroscience"
] | 2014 | Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions |
The regulation of Extracellular regulated kinase ( Erk ) activity is a key aspect of signalling by pathways activated by extracellular ligands acting through tyrosine kinase transmembrane receptors . In this process , participate proteins with kinase activity that phosphorylate and activate Erk , as well as different phosphatases that inactivate Erk by de-phosphorylation . The state of Erk phosphorylation affects not only its activity , but also its subcellular localization , defining the repertoire of Erk target proteins , and consequently , the cellular response to Erk . In this work , we characterise Tay bridge as a novel component of the EGFR/Erk signalling pathway . Tay bridge is a large nuclear protein with a domain of homology with human AUTS2 , and was previously identified due to the neuronal phenotypes displayed by loss-of-function mutations . We show that Tay bridge antagonizes EGFR signalling in the Drosophila melanogaster wing disc and other tissues , and that the protein interacts with both Erk and Mkp3 . We suggest that Tay bridge constitutes a novel element involved in the regulation of Erk activity , acting as a nuclear docking for Erk that retains this protein in an inactive form in the nucleus .
The Epidermal Growth Factor Receptor ( EGFR ) signalling pathway is a conserved module that plays multiple roles during development and tissue homeostasis in eukaryotic organisms [1]–[3] . The best-characterized functions of the pathway involve the EGFR downstream proteins Sos , Ras , Raf , Mek and Erk , the MAPK that is encoded by rolled in Drosophila melanogaster [4] . The activity of these core components is required in multiple developmental contexts , influencing cell proliferation , migration , apoptosis , epithelial integrity and cell fate acquisition [1] , [5] . A key node in the regulation of EGFR signalling occurs at the level of Erk phosphorylation and de-phosphorylation by Mek and dual-specificity phosphatases , respectively [6]–[8] . In general , upon activation by Mek , the Erk serine/threonine kinase is transported into the nucleus , where it can phosphorylate specific transcription factors , regulating their activity and consequently gene expression . Erk is de-phosphorylated and inactivated by dual-specificity phosphatases , which promote Erk accumulation in an inactive state in the cytoplasm [2] , [9] . The nucleus-cytoplasm compartmentalization of Erk is also regulated by several proteins acting as scaffolds , which influence the kinetics of Erk activation by favouring its association with upstream components , or that target Erk to different substrates by regulating its subcellular localization [10]–[11] . Thus , Kinase suppressor of Ras ( Ksr ) and MEK partner 1 ( MP-1 ) facilitate the phosphorylation of Erk by Mek [11]–[16] , whereas β-arrestin and Sef ( Similar Expression to FGF genes ) serve as scaffolds directing Erk activity toward different subcellular localizations and sets of target proteins [17]–[18] . In fact , because Erk lacks nuclear localization or export sequences , it appears that its subcellular compartmentalization is mostly determined by binding to scaffolds , anchors and substrates [8] , [10] , [19] . In the absence of active export , Erk tends to accumulate inside the nucleus , and it has been suggested that imported Erk binds to nuclear anchoring proteins that difficult its free diffusion to the cytoplasm [6] . The EGFR signalling system has been extensively characterised in Drosophila , an organism that has been instrumental to identify the intricacies of signalling regulation in vivo [1] , [20]–[22] . Furthermore , the exquisite sensitivity of several developmental processes to variations in levels of EGFR signalling has driven the search and identification of many components of the pathway through genetic screens , expression profiling and cell culture experiments [22]–[25] . The wing disc , the epithelial tissue that gives rise to the adult wing and part of the thorax , is particularly sensitive to changes in the levels of EGFR signalling [26]–[27] . The function of EGFR in this tissue is required for cell proliferation and viability [28] , for the specification of the wing disc and its territorial subdivision [26] , [29]–[32] , and also in cell fate choices affecting sensory organs and veins [33]–[34] . In this last process , the function of the pathway is needed to promote the formation of the veins , longitudinal stripes of cells that differentiate a cuticle thicker and more pigmented than the cuticle of inter-vein cells [35]–[36] . We conducted a gain-of-function screen aimed to identify genes regulating wing vein differentiation , expecting that some of these genes would encode novel components of the signalling pathways driving the formation of these structures [37] . In this screen , we identified a P-UAS insertion in the gene tay bridge ( tay ) that in combination with a vein-specific Gal4 driver causes the elimination of the longitudinal veins , a phenotype reminiscent of loss of EGFR activity in the developing veins [27] , [37] . Tay encodes a large protein of 2486 amino acids expressed predominantly in the central nervous system [38] . Mutant tay flies present a constriction in the protocerebral bridge , and display reduced walking speed , reduced sensitivity to the effects of alcohol and defective compensation of rotatory stimuli during walking [38]–[39] . The Carboxi-terminal part of Drosophila Tay presents homology with mammalian AUTS2 , a neuronal nuclear protein that is related to autism [40]–[41] , mental retardation [42] , [43] , Attention Deficit Hyperactivity Disorder [44] , and alcohol drinking behaviour [39] . Auts2 expression is maximal in maturating neurons and declines as these cells become mature , suggesting that its function is required for neuronal differentiation [41] , [45] . Here we report a genetic and developmental analysis of tay in the wing disc , and show that the function of Tay here is primarily related to the regulation of EGFR signalling . Thus , excess and loss of tay results in opposite phenotypes of loss- and extra veins , respectively , that are caused by changes in the levels of Erk activity . In addition , Tay level of expression modifies the phenotypic outcomes of altered EGFR signalling . We identify molecular interactions between Tay and Erk that might underline both the effects of Tay on Erk phosphorylation and the effects of Erk on Tay nuclear accumulation . All together , our results suggest that Tay is a novel component of the EGFR/Erk signalling pathway that regulates the nucleus/cytoplasm distribution of Erk .
EP-866 is a P-GS element inserted in the first intron of tay , and was selected in a gain-of-function screen designed to identify genes that , when over-expressed , affect the differentiation of the wing veins [37] . The combination of EP-866 with a variety of Gal4 lines reduces the size of the wing and causes the partial loss of longitudinal veins ( Fig . 1A–D; Fig . S1H–J ) . The most extreme phenotypes are observed in combinations of EP-866 with Gal4 drivers expressed in the entire wing blade and hinge ( nub-Gal4/EP-866; Fig . 1B ) . A weaker version of this phenotype is detected in combinations with a Gal4 driver expressed only in the central region of the wing blade ( salEPv-Gal4/EP-866; Fig . 1C ) . The reduction in wing size and loss of veins occurs in a compartment-specific manner , as they are also observed in combinations with the hh-Gal4 and ap-Gal4 drivers ( Fig . S1J and data not shown ) . In all cases , the drastic reduction in wing size is associated with a reduction of cell proliferation , and not to the induction of cell death . Thus , wing discs of combinations between EP-866 and Gal4 drivers show a very low number of mitotic cells and no activation of Caspase3 ( Fig . S1A–G′ ) . When the gene affected by the EP-866 insertion is over-expressed during pupal development , the size of the wing is normal , but the veins fail to differentiate ( Fig . 1D ) . EP-866/Gal4 combinations also display phenotypes in other adult structures , including fusion of tarsal joints in the legs ( dll-Gal4/EP-866; Fig . S1A , C ) , a significant reduction in the size of the eye ( ey-Gal4/EP-866; data not shown ) and loss of sensory organs in the thorax ( ap-Gal4/EP-866; Fig . S1B , D ) . The strength of the EP-866/Gal4 phenotype increases with the number of copies of both the Gal4 and the EP-866 insertion ( Fig . S1K–M ) . The most likely candidate to cause the over-expression phenotype of EP-866/Gal4 combinations is the gene tay ( Fig . 1E ) . Nonetheless , the genes CG15916 ( 5 Kb ) and shibire ( 7 Kb ) are close to the EP-866 insertion , and adjacent to tay is located CG9066 , which is oriented in the 3′ to 5′ direction of transcription regarding the UAS sequences of the P-GS insertion . We know that tay , CG15916 and shi are over-expressed when EP-866 is combined with the salEPv-Gal4 [37] . However , the phenotypes of wing size reduction and loss of veins observed in EP-866/salEPv-Gal4 and EP-866/shv-Gal4 flies are suppressed when we introduced a UAS-tay-RNAi construct in these combinations ( Fig . 1F–G , compare with 1C and D , respectively ) . In addition , the over-expression of Tay results in identical phenotypes of variable vein loss and wing size reduction ( see below ) , indicating that tay causes the over-expression phenotypes of EP-866/Gal4 combinations . tay encodes a protein of 2486 amino-acids which most remarkable characteristic is a 30% of identity in the 1764–2019 amino acid region with a 486–782 stretch of the 1295 amino acid long human protein AUTS2 ( Autism Susceptibility Candidate 2 ) ( see below ) . The expression of tay occurs ubiquitously in all imaginal discs ( Fig . 1I and data not shown ) , although we can also observe higher levels of expression in cells adjacent to the veins during pupal development ( Fig . 1J ) . Tay is also expressed at other developmental stages , and during embryonic development its mRNA and protein are detected prominently in the central nervous system ( Fig . S3E–G and data not shown ) . To visualize the accumulation of the Tay protein , we generated a specific polyclonal antibody ( Fig . S2B ) , and found that the protein is present in the nucleus of all imaginal discs and salivary gland cells ( Fig . 1K–N ) . The accumulation of Tay is very much reduced or lost in dorsal wing compartments expressing a tay RNA interference ( Fig . 1H–H′ ) . We also confirmed the specificity of this antibody by staining cells homozygous for a tay deficiency , where we found that the signal is completely lost ( Fig . S2C–C′ ) . The subcellular localization of the protein in wing discs over-expressing Tay is mostly nuclear , although some cytoplasmic staining is detected at higher level of over-expression ( Fig . 1O–O′ ) . These observations suggest that the adult phenotypes associated to Tay over-expression are caused by the accumulation of Tay at higher than normal levels in the nuclei of imaginal cells that normally express the gene . Interestingly , we also detected Tay in the cytoplasm of a subset of motoneurons in the central nervous system ( CM and JFdC , data not shown ) , indicating that the protein subcellular localization is regulated in a cell-type specific manner . To identify the normal requirement of Tay during wing development , we reduced the levels of tay mRNA by expressing its RNA interference ( tay-RNAi ) in different domains of the wing disc . When tay-RNAi is expressed in the wing blade ( 638-Gal4/UAS-tay-i ) the wings are reduced in size ( 32% smaller than wild type wings without changes in cellular size ) , display ectopic veins and show some defects in the most distal region of the wing margin ( Fig . 2B ) . These phenotypes are caused by the reduction of tay , because they are enhanced in a genetic background with only one copy of the gene ( Fig . 2C–D; 638-Gal4/+; Df ( 1 ) tay/UAS-tay-i ) . To generate stronger loss-of-function conditions , we made two small deficiencies by transposition ( EP-866Rev34 and EP-866Rev40; see Fig . S2A ) , and a deficiency that eliminates tay and the adjacent gene CG16952 ( Df ( 1 ) tay; Fig . S2A ) . These alleles are embryonic lethal in homozygous flies , and consequently they were analysed in mitotic recombination clones . The results obtained in Df ( 1 ) tay , EP-866rev40 and EP-866rev34 clones were identical , with cells deficient for tay forming clones that differentiate ectopic veins in inter-vein territories ( Fig . 2E–G and data not shown ) . Interestingly , only a fraction of the mutant cells in each clone differentiate as ectopic veins of normal thickness ( Fig . 2E–G ) . These phenotypes were very similar to those observed in wings expressing the tay-RNAi ( compare with Fig . 2A–D ) . The over-expression of tay in the wing imaginal disc prevents vein differentiation , macrochaetae formation and wing growth . Conversely , loss of tay function causes the formation of veins in inter-vein regions . These phenotypes are reminiscent to those caused by alterations in the levels of EGFR signalling , because loss of EGFR function impedes vein differentiation , and the increase in EGFR activity causes the formation of extra veins [27] , [46] . To study the possible interactions between Tay and EGFR signalling , we made genetic combinations in which tay gain or loss of expression conditions were introduced in genetic backgrounds with modified EGFR activity . We find that the reduction of tay expression enhances the extra-vein phenotype caused by increased EGFR signalling . Thus , knock-down of tay enhances vein differentiation in RasV12 ( Fig . 3A–C ) and ectopic rhomboid ( Fig . 3D–F ) backgrounds . These observations suggest that Tay function is necessary either to attenuate EGFR signalling or to reduce the response to particular levels of EGFR signalling . Compatible with these possibilities , Tay over-expression enhances the loss-of-vein phenotype caused by reduced activity of the pathway , for example in a situation when the expression of EGFR is reduced ( Fig . 3G–I ) . Interestingly , the reduction of tay expression does not modify the complete loss of vein phenotype caused by strong reductions in EGFR signalling ( Fig . 3J–L ) , indicating that Tay function is mostly required to modulate the levels of EGFR signalling once the pathway has been activated . To analyse whether changes in the expression of tay directly affect EGFR signalling , we monitored the levels of di-Phosphorylated Erk ( dP-Erk ) and the expression of the EGFR transcriptional targets Delta and argos in tay over-expression conditions . The accumulation of dP-Erk in wild type wing discs is maximal in the developing L3 and L4 longitudinal veins and in the marginal veins [34]; Fig . 4B ) . dP-Erk accumulation is strongly reduced in these territories when Tay is over-expressed in the wing blade ( Fig . 4F , compare with 4B ) . The expression of Delta ( Dl ) , which is regulated by EGFR signalling during imaginal development [47] , is maximal in the veins L3 , L4 and L5 and in the marginal veins in wild type wing discs ( Fig . 4C ) . Over-expression of tay in the central region of the wing blade causes a reduction of Dl expression in the veins L3 and L4 ( Fig . 4G , compare with 4C ) . The vein L5 is not affected , because it is located outside the domain of salEPv-Gal4 expression ( Fig . 4F–G ) . Therefore , this vein serves as an internal control in these experiments . We also observed changes in the transcription of argos , which expression is also regulated by the EGFR pathway and is maximal in the veins L3 , L4 and L5 and in the marginal veins in wild type wing discs [48]; Fig . 4D ) . Over-expression of tay reduces argos-LacZ expression ( Fig . 4H , compare with 4D ) . In all cases , the changes in Erk phosphorylation and in Dl/argos gene expression caused by Tay over-expression were consistently stronger than the loss of vein phenotype observed in the corresponding adult wings , as these wings still differentiate some stretches of the L3 and L4 veins ( Fig . 4E ) . We also checked the effects of loss of Tay in the accumulation of dP-Erk . For this experiment we expressed tay-RNAi in the dorsal compartment of the wing ( ap-Gal4/UAS-tay-i ) . In these discs the ventral compartment serves as an internal control . We observed that the reduction of tay expression increases dP-Erk accumulation in dorsal compartments compared with the ventral ones ( Fig . 4J–J′ ) . In addition , the expression of tay-RNAi in the entire wing blade ( 638-Gal4/UAS-tay-i ) causes ectopic argos-lacZ expression ( Fig . 4K , compare with 4D ) . Finally , we check whether excess of Tay can modulate dP-Erk accumulation under strong conditions of constitutive pathway activation . We find that Tay over-expression reduces the levels of dP-Erk induced by RasV12 in the central region of the wing disc ( Fig . 4L′ , M′ ) , and also the phenotype of ectopic veins caused by RasV12 ( Fig . 4L , M ) , suggesting that the negative effect of Tay on the activity of the EGFR pathway occurs downstream of Ras activation and affects the accumulation of dP-Erk . The effects of Tay loss and gain on dP-Erk accumulation were also detected in other imaginal discs , such as the eye disc ( not shown ) and the leg disc ( Fig . S3C–D′ ) , and also in embryos mutant for tay ( Fig . S3A–B′ ) , suggesting that Tay functions as a general modulator of Erk phosphorylation . The preferential nuclear localization of Tay and its effects on EGFR signalling and Erk phosphorylation prompted us to study the interactions between Tay and EGFR pathway components which subcellular localization shifts between the nucleus and the cytoplasm . We focussed this analysis on Erk and its specific phosphatase Mkp3 . These proteins can interact with each other in the cytoplasm , where Mkp3 retains ERK and prevents its phosphorylation , and also in the nucleus , where Mkp3 de-phosphorylates and inactivates Erk [8] , [49] . In addition , the phenotypes caused by the loss of Erk or Mkp3 are very similar to those cause by tay over-expression or loss of function , respectively . To study the genetic interactions between Tay and Erk we over-expressed wild type Erk or its mutant form sevenmaker ( Erksem ) , which bears a single amino acid substitution preventing Erk interactions with Mkp3 [50]–[51] . The use of Erksem allows the analysis of Erk over-expression conditions in the absence of its interaction with Mkp3 . The formation of ectopic veins caused by a reduction in Tay levels is only weakly increased when the normal form of Erk is over-expressed ( Fig . 5D–F ) . In contrast , loss of tay in a background of Erksem over-expression causes a strong increase in the differentiation of extra-vein tissue ( Fig . 5H ) , compared with loss of only tay ( Fig . 5D ) or with Erksem over-expression ( Fig . 5G ) . Interestingly , Tay over-expression reduces , but does not suppress , the ectopic veins caused by Erksem ( Fig . 5A–B ) . These results suggest that Erksem is much more effective when Tay levels are reduced , and , conversely , that Tay is less effective antagonizing Erk when this protein cannot interact with Mkp3 . In the case of Mkp3 , the loss of veins caused by its over-expression ( Fig . 5I ) is not modified by loss ( not shown ) or excess of tay ( Fig . 5J ) , confirming that Tay levels are not relevant upon a strong loss of Erk activation . In contrast , the formation of extra veins observed in tay loss-of-function conditions ( Fig . 5K , M ) depends on the gene dosage of Mkp3 , becoming stronger in Mkp3M76-R2b heterozygous flies ( Fig . 5N , compare with M ) or upon expression of Mkp3-RNAi ( Fig . 5L , compare with K ) . One possible explanation for these interactions is that Tay participates in the regulation of Erk inactivation , perhaps by promoting its de-phosphorylation . This possibility is compatible with the strong reduction of Erk phosphorylation caused by Tay over-expression , and implies that Tay over-expression phenotypes should be dependent on the presence and activity of Erk phosphatases such as Mkp3 . However , we notice that the phenotype of Tay over-expression is not modified in Mkp3 null mutant backgrounds ( Fig . 5O–Q ) . Thus , although we cannot exclude a role of Mkp3 in Tay function , this result indicates that the effects of Tay over-expression are not mediated exclusively by the activity of Mkp3 . Next , we wanted to visualize the activation of Erk in genetic backgrounds where the level of Erk and Tay expression is changed and the activity of the EGFR pathway is increased . To this end , we made tagged forms of Tay ( Tay-Flag ) , Erk ( Erk-HA ) and Erksem ( Erksem-HA ) and studied the accumulation of dP-Erk in wing discs of different genotypes . The expression of Erk-HA and Erksem-HA causes very weak ( Erk-HA; Fig . 5E ) or moderate ( Erksem-HA; Fig . 5G and 6I ) extra veins . In none of these over-expression backgrounds we were able to detect changes in the pattern or level of dP-Erk accumulation ( Fig . 6A and 6E ) . The reduction of Erk phosphorylation caused by Tay over-expression ( Fig . 4 ) is still observed when either Erk-HA ( Fig . 6B ) or Erksem-HA ( Fig . 6F ) is expressed in combination with Tay . The strong activation of the pathway caused by RasV12 is also observed in backgrounds of Erk-HA or Erksem-HA expression ( Fig . 6C and G , respectively ) . The introduction of Tay in these backgrounds causes a moderate reduction in dP-Erk accumulation ( Fig . 6D and H , compare with 6C and G ) , although the resulting phenotype of ectopic vein differentiation is not reduced ( Fig . 6K–L ) . From these observations we conclude that Tay is still effective in promoting the de-phosphorylation of Erk under conditions of Erk and Erksem over-expression , but less so in backgrounds of strong pathway activation . The subcellular localization of Mkp3 and Erk is dynamic , shifting between the nucleus and the cytoplasm [8] , [49] . We wanted to analyse whether Tay influences the accumulation of these proteins in wing imaginal cells in over-expression conditions . First , we confirmed that Mkp3-Myc is preferentially localised in the cytoplasm ( Fig . S4A–A′″ ) , and that both Erk-HA and Erksem-HA are detected in the nucleus and in the cytoplasm , with Erk-HA distributed at higher levels in the cytoplasm ( Fig . 7A and E , respectively and Fig . S4C–C″ and E–E′″ ) . The co-expression of Mkp3-Myc and Tay-Flag does not modify the preferential cytoplasmic ( Mkp3 ) or nuclear ( Tay ) accumulation of these proteins ( Fig . S4B–B′″ ) . The co-expression of Mkp3-Myc and Erk-HA results in a clear cytoplasmic retention of Erk-HA ( Fig . 7B , compare with A ) . In contrast , Mkp3-Myc does not modify the homogeneous nucleus-cytoplasm distribution of Erksem-HA ( Fig . 7F , compare with E ) . Neither the localization of Tay-Flag or Erk-HA changes when both are co-expressed in the same cells of the central region of the wing disc ( Fig . 7C and Fig . S4D–D′″ ) . In addition , the expression of RasV12 does not affect the localization of Erk-HA , which is still localised in the nucleus and cytoplasm ( Fig . 7D , compare with A , and Fig . S5A–A″ ) . In contrast , both Erksem-HA and Tay-Flag display a heterogeneous distribution when co-expressed ( Fig . 7G , J–J″ and Fig . S4F–F′″ ) . We took higher magnification pictures of sections taken from the most anterior region of the salEPv-Gal4 domain of expression , because in these cells the level of over-expression are lower and Tay retains its nuclear localization ( Fig . S7 ) . We observed that the nuclear level of Erksem-HA and Tay in each cell are not correlated ( r2 = 0 . 09; n = 60 ) . A similar heterogeneous distribution of ERKsem was observed in a RasV12 background ( Fig . 7H and Fig . S5C–C″ ) , and also when both Tay-Flag and Erksem-HA were co-expressed in a Rasv12 background ( Fig . S5D–D″ ) . We do not understand the molecular bases for these changes in Erksem and Tay accumulation in the presence of each other or upon strong pathway activation , but they might be related to a dynamic regulation of protein turnover when Tay and Erk are co-expressed at higher levels . To get a quantitative view of Erksem nuclear-cytoplasmic localization , we took serial sections of the wing disc , quantified the levels of Erksem in the cytoplasm ( apical in the epithelium; Fig . 7O ) and nucleus ( medial in the epithelium; Fig . 7O ) , and calculated the average cytoplasm/nucleus ratio of Erksem signal in different genetic backgrounds ( Fig . 7K–O ) . These measures show that Erksem is mostly localised apically in the cell ( cytoplasm ) , and that both the presence of Rasv12 ( Fig . 7K ) or Tay-Flag ( Fig . 7L ) strongly reduce the amount of cytoplasmic Erksem and weakly increase the level of nuclear Erksem ( Fig . 7N ) . In this manner , the expression of either RasV12 or Tay changes Erksem localization in a similar manner , but although both Tay and RasV12 reduce the cytoplasm/nucleus ratio of Erksem accumulation , Erk activation , as visualised by the presence of dP-Erk ( see Fig . 6 ) , only occurs in RasV12 conditions . We next considered the possibility that Tay might be directly interacting with Erk or Mkp3 in co-immunoprecipitation and pull-down experiments . Co-immunoprecipitation experiments were carried out from protein extracts obtained from embryos expressing combinations of Tay . FL-Flag , Mkp3-Myc , Erksem-HA and Erk-HA ( see Fig . S2D–E ) . Tay . FL-Flag was never detected in western blots , perhaps because the size of the protein prevents its transference to the membrane . However , when Tay-Flag is co-expressed with Mkp3-Myc or Erksem-HA , we detected co-immunoprecipitation when the IP was made using anti-Flag and the western blot revealed using anti-Myc ( Fig . 8A , line T+M from IP lanes ) or anti-HA ( Fig . 8B , line T+E from IP lanes ) . In protein extracts from embryos expressing only Mkp3-Myc or Erksem-HA and IP with anti-Flag , we never detected Myc or HA ( Fig . 8A–B , lines M and E , from IP lanes , respectively ) . The interaction between Tay and Erk and between Tay and Mkp3 might be direct , because they were also observed in pull-down experiments using in vitro translated Tay incubated with Erk-GST and Mkp3-GST fusion proteins ( Fig . 8C ) . To identify the region of Tay involved in these interactions , we made several truncated forms of the protein ( Fig . 8E and data not shown ) , and expressed them in the wing disc . We found that the 1292 amino acid N-terminal fragment of Tay ( Tay . 1 ) is located exclusively in the cytoplasm ( Fig . 8D′ ) , and its over-expression does not affect the differentiation of veins ( Fig . 8D′ ) . In contrast , the 1030 amino acid C-terminal fragment ( Tay . 2 ) is accumulated preferentially in the nucleus ( Fig . 8D″ ) , similar to the full-length Tay-Flag protein ( Fig . 8D ) . Interestingly , the expression of Tay . 2 consistently results in stronger phenotypes of vein loss and reduced wing size than those caused by the over-expression of the full-length protein ( Fig . 8D″ compare with D ) . This C-terminal fragment includes the domain of homology detected between Tay and human AUTS2 . The distribution of Erksem is not modified in the presence of the N-terminal portion of Tay ( data not shown ) . In contrast , Tay . 2 results in the same changes in the cytoplasm/nucleus ratio of Erksem accumulation as Tay . FL ( Fig . 7N–O ) . The C-terminal 1030 amino acid Tay fragment ( Tay . 2 ) contains all the information necessary to regulate the subcellular localization of the protein , and also all the domains necessary to reproduce the effects of the full-length protein ( see above ) . We repeated the immunoprecipitation experiments using this fragment , and found that Tay . 2 retains its interaction with Erksem ( Fig . 8G , line T+E , IP lanes ) , but loses its ability to interact with Mkp3 ( Fig . 8F , line T+M , IP lanes ) . The failure of Tay . 2 to interact with Mkp3 might increase the titration of ERK by Tay . 2 , explaining why Tay . 2 interferes with EGFR signalling more efficiently than the full-length protein . We also found that the levels of Tay accumulation in the nucleus are much higher than normal in cells over-expressing Erk or Erksem ( Fig . 8H–H′ and Fig . S7A–D ) . As Erk or Erksem over-expression do not change the expression of tay ( not show ) , these observations indicate that Erk increases the stability of Tay in the nucleus . This effect is independent of EGFR signalling , as neither RasV12 nor Mkp3 over-expression modified the accumulation of endogenous ( Fig . 8I–I′ ) or over-expressed Tay ( Fig . S6A–B″ ) . We conclude from these data that Tay can interact with Erk in the nucleus and that Erk protects Tay from degradation . Drosophila Tay and human AUTS2 are very different proteins in sequence and length , but they share a small 250 amino acid stretch with significant homology ( Fig . 9A ) . Our deletion analysis of Tay indicates that this region is included in the smaller fragment of Tay that we found has biological activity and nuclear localization ( C . M . and J . F . dC . , unpublished results ) . We wanted to check whether AUTS2 expressed in flies was able to reproduce some of the effects observed in Tay over-expression conditions . A Flag-tagged form of AUTS2 expressed in the wing disc is localised exclusively in the nuclei ( Fig . 9B–C ) , the same as Tay . Interestingly , the expression of AUTS2 in the wing leads to a phenotype of ectopic vein formation reminiscent to the consequence of Tay loss ( Fig . 9D ) . The extra veins that develop in AUTS2 over-expression conditions depend on EGFR signalling , because they are eliminated when the expression of Erk is reduced ( Fig . 9E–F ) . AUTS2 also enhances the formation of extra veins caused by the expression of Erksem ( Fig . 9H–I ) , and causes an increase in the levels of activated Erk ( ap-Gal4/UAS-hAUTS2-Flag; Fig . 9J–J′ ) . These data suggest that AUTS2 is able to interact with some , but not all , targets of Tay , and raise the possibility that AUTS2 normal function in humans is related to the regulation of the Erk signalling pathway , albeit in an opposite manner as Tay .
We have addressed the requirements and function of tay mostly in the wing disc , a convenient developmental system to analyse the contribution of signalling pathways to the regulation of organ size and pattern formation [56] . Tay was previously described as a protein that regulates locomotion and other neural aspects [38]–[39] . We have observed that changes in the level of EGFR signalling in the nervous system also cause locomotion defects ( Molnar and de Celis , in preparation ) , which is indicative of a role of Tay in the regulation of EGFR signalling also in the nervous system . In the context of wing development and vein differentiation , the loss of tay results in the differentiation of extra veins in inter-vein territories . This phenotype is very similar to those obtained in conditions of excess of EGFR signalling , suggesting that Tay negatively regulates the activity or the response to this pathway . In addition , loss of tay also causes a reduction in the size of the wing blade , a phenotype that is not expected in a situation of excess of EGFR/ERK activity . This last result suggests that Tay might also have functions independent of its role in the regulation of EGFR signalling . The consequences of gain of Tay expression mostly indicate that the role of Tay is related to the modulation of EGFR signalling . Thus , excess of Tay expression in different imaginal discs results in phenotypes that can be attributed to loss of EGFR signalling , such as loss of veins and bristles [33] , wing size reduction and failures in tarsal joint formation [57] and ommatidial differentiation ( data not shown ) . We further explore the relationships between Tay and EGFR signalling in genetic combinations in which the activity of the pathway is altered in backgrounds with modified levels of Tay expression . In all cases , we observed synergistic interactions between loss of tay and excess of EGFR , and between excess of tay and loss of EGFR activity . Furthermore , we notice that the extra veins differentiating in tay mutants require EGFR function , suggesting that Tay modulates EGFR signalling during vein formation . All together , the results of genetic combinations indicate that cells with lower levels of Tay become more sensitive to an increase in EGFR signalling , and that Tay over-expression prevents cells to acquire the level of EGFR signalling required for vein formation . The negative effect of Tay on EGFR signalling is more directly visualised by considering the effects of Tay in Erk phosphorylation and in the expression of the EGFR/Erk targets genes Dl and argos . Thus , Tay over-expression strongly suppresses Erk phosphorylation and prevents the expression of Dl and argos in the developing veins . Conversely , in loss of tay conditions we detect an increase in the levels of phosphorylated Erk , which is accompanied by a moderate ectopic expression of argos . The extra-vein phenotype of loss of tay is not as extreme as the massive vein differentiation that occurs upon strong and constitutive activation of the EGFR pathway . In fact , tay mutant wings differentiate a similar pattern of extra veins as moderate increases in EGFR signalling caused by , for example , mutations in the Mkp3 gene [58] . This suggest us that Tay primary function is to prevent increases in EGFR/Erk signalling in places where the pathway must be active but only at low levels . Thus , high levels of EGFR activity and dP-Erk accumulation are restricted to the presumptive veins in wild type third instar wing discs , but the pathway is also active at lower levels in the inter-veins , where it promotes cell proliferation and survival [28] . In tay or Mkp3 mutant backgrounds , a fraction of these cells initiates the vein differentiation program , escaping the negative feed-back loops that maintain low dP-Erk levels and entering the positive feed-back loops that normally operate in vein territories through the regulation of rhomboid expression [59] . In this model , Tay would participate in a mechanism that favours Erk de-phosphorylation and its nuclear retention in an inactive form . This mechanism of Tay action is compatible with the effects of its over-expression , which essentially cause a failure to accumulate dP-Erk in vein territories , and consequently a loss of vein differentiation . Signalling by Erk proteins in the nucleus is in part regulated by the rate of Erk nucleus/cytoplasm shuttling [60] . In the nucleus , signal termination involves Erk de-phosphorylation by nuclear phosphatases and also its sequestration away from cytoplasmic Erk kinases [61] . Because Erk does not contain nuclear localization nor export sequences , its subcellular localization relies on proteins acting as anchors [8] . We observed direct interactions between Tay and Erk and between Tay and Mkp3 , and these interactions were also detected in immunoprecipitation experiments from embryo protein extracts . These data suggests that Tay could form part of protein complexes including both Erk and Mkp3 in the nucleus . A direct interaction between Tay and Erk is also compatible with several observations regarding Tay stability and Erk subcellular localization . First , Erk and Erksem increase the accumulation of Tay in the nucleus , and do so independently of EGFR signalling , as neither RasV12 nor Mkp3 over-expression modified Tay accumulation . Second , Tay over-expression prevents the accumulation of dP-Erk , whereas loss of Tay has the converse effect . Finally , Tay over-expression modifies Erksem subcellular localization , increasing the nucleus/cytoplasm ratio of Erksem accumulation . In this regard , it is worth noting that the expression of RasV12 has the same effects on Erksem subcellular localization as the over-expression of Tay , as both Tay and RasV12 increase the nuclear/cytoplasm ratio of Erksem accumulation . We notice that the effects of Tay on Erk localization are only manifest when we used the Erksem form . Because we also see that Erksem is not retained in the cytoplasm by Mkp3 , we reason that Erksem , liberated of cytoplasmic anchorage by Mkp3 , is more sensitive to pathway activation and to the presence of other anchoring proteins , and that Tay might play this role in the nucleus . We also observed a direct interaction between Tay and Mkp3 . Mkp3 is a dual-specificity phosphatase that is predominantly localised in the cytoplasm , but it shuttles between the nucleus and cytoplasm and could play a role in translocating inactive Erk from the nucleus to the cytoplasm [8] . It is possible that Tay could promote the nuclear function of Mkp3 , but in addition , Tay should also act independently of Mkp3 to promote Erk inactivation and retention , because Tay is able to down-regulate Erk activity in Mkp3 mutant backgrounds . Most of the Tay interacting region with Erk is localised to the C-terminal part of Tay , a 1000 amino acid long region that includes the domain of homology between Tay and human AUTS2 . This fragment of Tay fails to interact with Mkp3 , and is even more efficient than the full-length protein in its effects on Erk subcellular localization and in its antagonism on Erk signalling . Intriguingly , AUTS2 expressed in the wing disc also interferes with EGFR signalling , but it does so in an opposite manner to Tay or to the Tay C-terminal domain . We cannot extract many conclusions from the consequences of AUTS2 expression in the wing disc , but speculate that this protein retains some of its interactions with Drosophila Erk that might protect this protein from inactivation by nuclear phosphatases . Similarly , the effects of AUTS2 on Drosophila EGFR signalling are compatible with a role for this protein in the regulation of Erk activity in humans , and that this effects might underline the effects of zebrafish , murine and human mutations in the onset of neurological disorders . From the analysis in the wing disc we conclude that Tay interacts with Erk in the nucleus , affecting its phosphorylation and promoting its nuclear retention . In this context , it is interesting to note that the free diffusion of human ERK2 is impeded within the nucleus , and that this limitation in mobility increases after ERK2 stimulation [6] . This has lead to postulate that ERK2 retention in the nucleus involves high-affinity interactions with unidentified low-mobility sites that are constitutively expressed [6] . We suggest that Tay could play such a role in vivo , acting as a nuclear anchor for Erk that facilitates its inactivation by nuclear phosphatases and its retention in an inactive state .
We used the Mkp3 allele Mkp3M76-R2b [58] , and the deficiencies EP-866rev34 , EP-866rev40 and Df ( 1 ) tay ( see below ) . We used the following Gal4 lines: shv3kpn-Gal4 [62] , 638-Gal4 , nub-Gal4 , salEPv-Gal4 [63] , ap-Gal4 , hh-Gal4 , bs-Gal4 , 1348-Gal4 , dll-Gal4 , eye-Gal4 and da-Gal4 [64] . We also used the UAS lines: UAS-RasV12 [65] , UAS-Rafact [66] , UAS-Erksem [67] , UAS-Erk-HA [55] , UAS-Erksem-HA [55] , UAS-rhomboid [47] , UAS-EGFR , UAS-EGFRDN [68] and UAS-GFP [69] and the P-GS lines EP-M76 and EP-866 [37] . We generated the following UAS lines: UAS-tay-i , UAS-tay . FL-Flag , UAS-tay . 1-Flag , UAS-tay . 2-Flag , UAS-hAUTS2-Flag , and UAS-Mkp3-Myc . We also used the RNA interference lines UAS-Mkp3-i ( 23911 ) , UAS-tay-i ( 29021 ) and UAS-rolled-i ( 35641 ) from the VDRC Stock Center , and the lines UAS-EGFR-i ( 10079R-2 ) and UAS-ras-i ( 9375R-1 ) from NIG-Fly . Df ( 1 ) tay: We used the insertions e03798 and d06351 [70] , which are separated by 15 Kb of DNA including tay and part of CG16952 . Flipase ( FLP ) -induced recombination was induced by a daily 1 h heat shock at 37°C to the progeny of e03798/d06351; hsFLP/+ females and FM7 males . Ten putative e03798-d06351/FM7 offspring females were individually crossed to FM7 males , and after 3 days , were used to extract genomic DNA to determinate by PCR the existence of FLP recombination . The position of the flanking insertions e03798 and d06351 and the extent of the tay deficiency are described in Suppl . Fig . S2A . EP-866rev40 and EP-866rev34: We used Δ2–3 as a source of transposase to mobilize the EP-866 P-GS element . Males carrying both EP-866 and Δ2–3 were crossed with N55e11/FM7c females . The offspring EP-866 males with white phenotype were selected to make individual stocks . A complementation test was done to analyse the behaviour of these new alleles . Fifty wild type ( control ) and homozygous EP-866rev40 and EP-866rev34 embryos were used to extract genomic DNA to identify by PCR the genomic region excised by the mobilization of the P-GS . We used the following primers: 5′GCCGTGGAAATGGACTCTG3′ and 5′TTGCTGCTGCTGGTGAAAT3′ . The size of the amplified fragments was 3629pb in wild type embryos , 2373pb in EP-866rev34 embryos and 932pb in EP-866rev40 embryos . The size of the generated deficiencies was confirmed by sequencing the PCR fragments sub-cloned in the pGEM-T-Easy vector ( Promega ) confirming an excision of 1276pb in EP-866rev34 and of 2717pb in EP-866rev40 . Homozygous Df ( 1 ) tay , EP-866rev40 and EP-866rev34 clones were induced in larvae of the following genotypes: Df ( 1 ) tay f36a FRT18A/FRT18A UbiGFP; hsFLP/+; EP-866rev40 f36a FRT18A/FRT18A UbiGFP; hsFLP/+ and EP-866rev34 f36a FRT18A/FRT18A UbiGFP; hsFLP/+ , respectively . Homozygous tay mutant cells were recognized in the adult wing by the cellular marker forked ( f ) and in the wing disc by the absence of GFP . We used the rabbit antibodies: anti-phospho-Histone3 , anti-activated Cas3 and anti-diphosphorylated ERK1&2 ( Cell Signalling ) . We also use the mouse monoclonal antibodies: anti-c-Myc 9E10 ( Santa Cruz Biotechnology ) , anti-HA 12CA5 ( Sigma ) , anti-FlagM2 ( Sigma ) , anti-βGal ( Promega ) , and anti-FasIII , anti-Dl and anti-Arm from the Hybridoma Bank at University of Iowa ( Iowa City , IA ) . Alexa Fluor secondary antibodies ( used at 1∶200 dilution ) were from Invitrogen . To stain the nuclei we used To-Pro and to stain F-actin we used Alexa Fluor Phalloidin , from Invitrogen . Imaginal wing discs were dissected , fixed , and stained as described in [72] . Confocal images were taken in a LSM510 confocal microscope ( Zeiss ) . In situ hybridization with the tay probe were carried out as described [72] . We used the cDNA LD22609 as template to synthesize the tay probe . The quantification of Erksem nuclear and cytoplasmic staining was carried out in Z-sections taken from 6 proximo-distal planes of 6 discs of each genotype along the length of the epithelium with the program ImageJ . The fusion proteins Mkp3-GST and Erk-GST and the GST protein ( negative control ) were expressed in E . coli BL21 ( DE3 ) , using the constructs pGEX2TK-DMkp3 and pGEX4T1-DErk [73] and the vector pGEX2T , respectively , and were purified using Glutathione Sepharose 4B ( GE Healthcare ) . The complete Tay protein was generated from the cDNA LD22609 using the TNT T7 Coupled Reticulocyte Lysate System ( Promega ) and radiolabeled with S35-Met . The pull-down assay was performed incubating over-night at 4°C the same amount of GST or GST fusion proteins bound to Glutathione Sepharose4B with in vitro translated Tay . After centrifugation and washes the proteins were resolved by 6% SDS/PAGE and the existence of pull-down proteins was analysed by autoradiography . The pulldown experiments were repeated five times with the same results . | Extracellular regulated kinases ( Erk ) mediate signalling by pathways activated by tyrosine kinase transmembrane receptors . The level of activated Erk depends on a highly regulated balance between cytoplasmic kinases and nuclear/cytoplasmic phosphatases , which determine the state of Erk phosphorylation . This affects Erk activity and its subcellular localization , defining the repertoire of Erk targets , and consequently , the cellular response to Erk . In this work , we use a genetic approach to characterise the gene tay bridge as a novel component of the EGFR/Erk signalling pathway . Tay bridge has a domain of homology with human AUTS2 , and was previously identified due to the neuronal phenotypes displayed by loss-of-function mutations . We show that Tay bridge antagonizes EGFR signalling in the Drosophila melanogaster wing disc and other tissues , and that the protein interacts with both Erk and Mkp3 . We suggest that Tay bridge constitutes a novel element involved in the regulation of Erk activity , acting as a nuclear docking for Erk that retains this protein in an inactive form in the nucleus . These results could provide important insights into the clinical consequences of AUTS2 mutations in humans , which are related to behavioural perturbations including autism , mental retardation , Attention Deficit Hyperactivity Disorder and alcohol drinking behaviour . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Tay Bridge Is a Negative Regulator of EGFR Signalling and Interacts with Erk and Mkp3 in the Drosophila melanogaster Wing |
During the last 50 years , the geographic range of the mosquito Aedes aegypti has increased dramatically , in parallel with a sharp increase in the disease burden from the viruses it transmits , including Zika , chikungunya , and dengue . There is a growing consensus that vector control is essential to prevent Aedes-borne diseases , even as effective vaccines become available . What remains unclear is how effective vector control is across broad operational scales because the data and the analytical tools necessary to isolate the effect of vector-oriented interventions have not been available . We developed a statistical framework to model Ae . aegypti abundance over space and time and applied it to explore the impact of citywide vector control conducted by the Ministry of Health ( MoH ) in Iquitos , Peru , over a 12-year period . Citywide interventions involved multiple rounds of intradomicile insecticide space spray over large portions of urban Iquitos ( up to 40% of all residences ) in response to dengue outbreaks . Our model captured significant levels of spatial , temporal , and spatio-temporal variation in Ae . aegypti abundance within and between years and across the city . We estimated the shape of the relationship between the coverage of neighborhood-level vector control and reductions in female Ae . aegypti abundance; i . e . , the dose-response curve . The dose-response curve , with its associated uncertainties , can be used to gauge the necessary spraying effort required to achieve a desired effect and is a critical tool currently absent from vector control programs . We found that with complete neighborhood coverage MoH intra-domicile space spray would decrease Ae . aegypti abundance on average by 67% in the treated neighborhood . Our framework can be directly translated to other interventions in other locations with geolocated mosquito abundance data . Results from our analysis can be used to inform future vector-control applications in Ae . aegypti endemic areas globally .
Aedes aegypti is the primary vector for multiple arboviruses [1] . Controlling these pathogens is of paramount importance , especially with recent invasions into the Americas of chikungunya and Zika , outbreaks of yellow fever reviving concerns of urban yellow fever epidemics , and the sub-optimal performance of the only licensed dengue vaccine [2] . Until better options arise , controlling transmission by reducing the vector population remains the primary tool available to combat the Aedes-borne disease burden . A hemisphere-wide eradication campaign in the Americas in the 1950s and 1960s demonstrated that control of this mosquito can be a viable option for controlling disease from the viruses they transmit [3] . In the vacuum left by the cessation of that eradication program , which was likely exacerbated by urbanization and globalization of trade and human travel [4] , Ae . aegypti recolonized significant portions of the Americas [5] . Current Aedes-transmitted disease control strategies lack the scale and support of the multi-country eradication effort . They are often developed and administered on a provincial or city-by-city basis and frequently ineffectively applied [6] . There is a notable lack of “best practices” to be followed [7] and an absence of rigorously collected , quantitative entomological data that can be used to inform vector control strategies [8] . Furthermore , resistance to various insecticides , particularly pyrethroids , further hinders the effectiveness of existing interventions [9] . Controlled laboratory experiments can assess the efficacy of an exact dosage of insecticide on a mosquito , but it is unclear how that translates to the real-world effectiveness of the application of that insecticide in , for example , a citywide space-spray campaign . Quantifying the effectiveness of real world control programs on mosquito populations is necessary to assess a program’s utility ( or lack thereof ) and to provide useful evidence for enhancing future intervention programs . There are three key factors that complicate the estimation of an intervention’s impact on Ae . aegypti population size . First , Ae . aegypti abundance is highly heterogeneous in space and time . Aedes aegypti population dynamics are strongly influenced by micro-climate [10 , 11] , they have a limited flight range ( ~100m [12] ) , and they exhibit fine-scale spatial clustering ( 30m in urban areas [13 , 14] ) . Small seasonal fluctuations in weather or available larval habitats can affect Ae . aegypti population dynamics from year to year , or even month to month [14] . Variation in observed populations , therefore , can be just as likely attributed to environmental fluctuations as a mosquito control program . Second , to calculate a control program’s effect size , it is necessary to estimate what would have happened without an intervention; i . e . , the counterfactual . In many locations , resources are limited and are directed towards control as opposed to surveillance . In the absence of substantial surveillance , it is extremely difficult to parse the impact of an intervention from natural variations in mosquito populations [6] . Third , accurately measuring Ae . aegypti abundance is difficult even under optimal circumstances [15] . Ae . aegypti is typically found in low abundances in and around human habitations , with sample sizes averaging sample of 1–5 adults per house [16] . Larval surveys do not capture variation or magnitude of adult abundance and miss cryptic habitats [17] . Most Ae . aegypti control programs do not have the personnel and procedures in place to estimate spatial and temporal variation in population abundance across cities , districts , or states , although there are some notable exceptions [18] . Large-scale , household-level measurements are required across multiple years using a consistent , reliable measurement method to characterize baseline spatio-temporal patterns in abundance , which is seldom done [19] . Overcoming measurement error represents a significant challenge , because Ae . aegypti is a low-abundance species , adults are difficult to collect , and there are no reliable entomological measures for risk of human arboviral infection and/or disease [20] . Iquitos , Peru , a city of approximately 370 , 000 and the largest urban center in the Peruvian Amazon , is endemic for Ae . aegypti and dengue viruses . Between 1999 and 2010 , 176 , 352 geolocated , household-level Ae . aegypti abundance surveys were conducted using hand-held mosquito aspirators ( Fig 1 ) , resulting in the capture of 48 , 015 female Ae . aegypti . Local Ministry of Health data was available on spatiotemporally explicit mosquito control efforts . The goals of the current work were two-fold . First , we developed a framework to model Ae . aegypti populations at a fine scale through space and time . Second , we used that framework to explore the impact of a city-wide vector control program by the Ministry of Health in Iquitos .
Summary statistics of mosquito data indicated a substantial effort to capture mosquitoes each month ( median of 1 , 257 visits / month , IQR = ( 976–1528 ) ) ; more than 50 individual female Ae . aegypti mosquitoes were captured during most months ( 136 / 146 months , median 224 per month , IQR = ( 119–426 ) , Fig 2A ) . The average number of captured female Ae . aegypti mosquitoes per visit varied monthly ( Fig 2B ) , with a considerable jump after switching from standard CDC backpack aspirators to a handheld “Prokopack” aspirator ( 0 . 20 female Ae . aegypti mosquitoes per visit on average before versus 0 . 59 female Ae . aegypti mosquitoes per visit on average afterwards ) [21]; the increased catch is indicated by the vertical dashed line in Fig 2 . The monthly coefficient of variation exhibited over-dispersion . Many months had a standard deviation greater than 1 . 5 times the mean ( Fig 2C ) . In space , there was similar heterogeneity in mosquito abundance ( Fig 3 ) . Across zones of the city , average adult female Ae . aegypti captured per visit varied from 0 . 24 to 0 . 71 , with , on average , more female Ae . aegypti mosquitoes captured in the North and the South of Iquitos than in the city center . Spatially , the coefficient of variation exhibited considerably larger variation than temporally , with values ranging from 1 . 2 to 17 . 1 , with the largest values in the Tupac Amaru region of western Iquitos . In this zone , while the average number of female Ae . aegypti mosquitoes caught per visit was 0 . 32 across 5 , 238 visits , the standard deviation was 5 . 43 . Over 100 mosquitoes were collected from a single home twice in this zone , and more than ten mosquitoes were collected from a single home 13 times . Even with the large number of visits to this zone , these extremely large collections ( and others ) indicated a high level of overdispersion . Out of the 176 , 348 collections , 4 , 432 occurred in a Ministry of Health Control ( MOH ) zone that received some level of space spraying within the previous three weeks . Adjusting for capture method , the average number of female mosquitoes collected in these visits was 0 . 12 versus 0 . 34 in non-sprayed homes . Were these collections within and without control areas to be independent draws from two theoretical populations of mosquito abundances , we would find the impact of space spraying to be extremely statistically significant and would estimate an average reduction of 64 . 4% . However , as exemplified by the spatial and temporal variation described above , each collection is not drawn from one of two populations ( with or without control ) , and an effect must be calculated by accounting for the spatial and temporal structure . Inclusion of the intervention term further improved fit compared to the full model ( ΔAIC = 144 ) . Inspection of the fitted shape-constrained additive model ( SCAM ) revealed that the smooth for control was linear ( edf = 1 ) . To simplify the model , we reverted to the GAM version of the model with the intervention term entering linearly . The negative binomial model has a log link , so terms that enter ‘linearly’ in the model relate to the response exponentially . The coefficient for the intervention term was found to be -1 . 1 ( standard error 0 . 09 ) . Fig 7 shows the predicted impact of various levels of intervention on a hypothetical home where , in the absence of control ( or “dose response curve” ) , 0 . 45 female Ae . aegypti mosquitoes were expected to be collected . If all the homes were sprayed across the zone ( i . e . , the intervention coverage was 1 ) , we would expect to see a 67% reduction to 0 . 15 mosquitoes . If half of the homes in a zone were sprayed , and thus the ‘intervention coverage’ was 0 . 5 , we would expect to see a reduction to 0 . 26 mosquitoes in that same hypothetical home; a 43% reduction . To assess both the robustness of the estimate of the impact of space spraying as well as begin to answer a critical question of “how much data was necessary to find such an effect” , we conducted a hold-out cross-validation experiment . We retained a fraction of the data randomly chosen , refit the final model , and tracked the estimated coefficient ( S13 Fig ) and standard error for the effect of space spraying ( S14 Fig ) . We then repeated this 100 times for each fraction of data retained , from 5% to 80% , by increments of 5% . As the final model run on all the data found no significant non-linearity in how the term entered the model , we again only considered the linear effect ( in log space ) . Not surprisingly , we found that as the amount of data that went into the model decreased , the standard error of the effect of space spraying increased ( S14 Fig ) . Also , it was not surprising that when one runs the model with only 5% of the data ( i . e . , 8817 collections ) the effect was sometimes found to not be statistically significantly different from no effect ( 34 out of 100 ) , nor that in rare cases when only 5% of the data was retained the effect was estimated to be positive ( 7 out of 100 ) . What was surprising was that when one increased the data retention only up to 10% ( i . e . , 176 , 348 collections ) , almost all hold-outs resulted in statistically significant effects ( 97 out of 100 ) and when one retained 15% of the data ( i . e . , 26 , 452 collections ) every holdout resulted in a statistically significant effect of space spraying . Moreover , more than 90% of holdouts for either the 10% or the 15% retain strategies showed a maximum effect of reducing mosquito abundance by at least half in the setting of perfect coverage ( as a reminder , we estimated an effect of up to a 67% reduction . Based on this analysis , it would seem that only a fraction of the mosquitoes would have had to have been collected to identify the effect of space spraying on mosquitoes . Furthermore , it demonstrates the robustness of this result .
Our aims were two-fold . First , to identify the dose-response curve between neighborhood-level spraying interventions and neighborhood Ae . aegypti population abundances . To achieve in the face of substantial spatio-temporal variation in mosquito population abundances and imperfectly sampled mosquito populations , we had to achieve our second goal , to develop a mosquito abundance-modeling framework . Our model had to account for both biotic and abiotic drivers of mosquito population dynamics and provide counter-factual estimates of what mosquito populations might have looked like had spraying not been conducted . By applying our model to Iquitos , Peru , we identified interpretable relationships between meteorological covariates and mosquito populations , as well as , site-specific spatial variation , with some regions of the city appearing to over- or under-produce mosquitoes consistently through the study period . When we then added an analysis of the existing space-spraying campaigns conducted by the Iquitos Ministry of Health , we found a diminishing return dose-response curve where , under optimal settings , we would expect to see a 67% reduction in female Ae . aegypti abundance from pre-intervention levels . This means that even a perfectly enacted Ministry of Health space-spraying campaign would not be adequate to reduce mosquito populations to zero . Univariate analyses conducted on each meteorological covariate revealed and confirmed relationships between key features of local weather and mosquito population dynamics . For temperature , precipitation , relative humidity , and daily temperature range , the effect of weather on mosquito abundance was not isolated to a particular lagged covariate . As expected , weather that directly influenced mosquito dynamics a few generations in the past indirectly influenced the number of mosquitoes collected on any given day . These results , and results from our full model analyses , highlight the importance of measuring daily weather when estimating mosquito abundance baselines . Our modeling approach continuously integrates data from the day of capture back through 30 days in the past , and we found significantly different influences of these weather variables at various lags . Factors that are rarely measured , such as maximum wind speed , were found to be strongly significant and should be considered for future entomological studies on Ae . aegypti abundance . Previous analysis also found that winds may influence the spatial patterns of dengue transmission in Iquitos [22] . It was not possible for us to assess the reason why high wind speeds was associated with higher capture rates in homes , but it has been observed that during high winds , mosquitoes may seek shelter [23] , and thus a positive relationship between wind speed and aspirator collection of Ae . aegypti may be reasonable . Windy conditions may increase the probability that mosquito sequester in indoor locations that are less effectively sampled than during more calm days . Our analysis of neighborhood spraying accommodated a variety of potential functional forms for the dose-response curve , yet the model-selection process yielded the simple , exponential decay as the approach with more support in explaining the data . Perhaps it is unsurprising that we found a diminishing return relationship between spray effort and mosquito reduction , and it may be that the true optimal effectiveness of the intervention we analyzed was a 67% reduction in adult mosquitoes . Importantly , this analysis ignored the likely super-linear increase in costs that would be associated with increasing effort . In the absence of an analysis that incorporates cost explicitly , it is difficult to recommend to a ministry of health that complete coverage is the only goal . Even a 50% coverage still achieved a reduction of mosquito abundance by 43% , and this would likely be considerably less costly to enact . Although our study is based on a large data set , analysis of over 175 , 000 individual home visits , we were not able to overcome all of the inherent limitations associated with mosquito abundance estimation . Aedes aegypti have cryptic landing and larval development sites [24 , 25] . Aspiration , even when conducted by a team of trained professionals , is an imperfect method to fully survey a house . Our Ae . aegypti abundance estimates for Iquitos underrepresent the true number of mosquitoes within the city . We similarly lacked spatially varying meteorological data . Weather at the local airport may not represent conditions in the houses where mosquitoes were collected . Microclimates that vary at the same scales as neighborhood blocks have been shown to influence Ae . aegypti ecology [26] and biting habits [27] . The metric we used to assess the impact of intervention for our analysis is an imperfect match to account for exactly which houses did and did not receive an intervention on which days . Although the data available in Iquitos was very detailed , even in this setting , more careful collection of what was done when and where would improve the ability to disentangle the effects of interventions from other factors . As efforts to control arbovirus transmission continue , cataloging detailed intervention activities should be a priority so that more of the work being done can be used to inform future control approaches . A significant limitation of our analyses was that it does not attempt to link mosquito abundance with virus transmission intensity . Mosquito abundance is only one of many spatio-temporally varying drivers of mosquito-borne pathogen transmission . Dengue virus transmission intensity in Iquitos varies from year to year [28] . An analysis of human dengue cases in Iquitos indicated that the incidence of people with symptomatic disease was negatively correlated to insecticide space spraying efforts only when the intervention was conducted early in the virus transmission season [29] . Our analyses indicate that interventions should have had an impact on mosquito populations independent of season . Due to a dearth of data , we could not assess a difference in chemical formulation of the space pray . Spatially , there is substantial evidence that human movement is a strong driver of the spatial patterns of DENV transmission [30 , 31] . It is unlikely , therefore , that targeting an intervention based solely on a model of mosquito abundance would capture all of the places contributing most to transmission . Although we expect that reducing mosquito populations will correlate with a reduction in transmission risk in many instances , our modeling framework is intended to serve as a part of a greater integrated modeling approach that attempts to optimally target interventions given adequate knowledge of the ecology of the mosquito vector , its human hosts , and the pathogen . Results from our analyses demonstrate that , even in the face of considerable unexplained variation , drivers of mosquito abundance can be identified and even a weak correlate for intervention effect size can identify a significant control impact . We provide a statistical framework that can be used to establish a mosquito abundance baseline in space and time . With a baseline , we are able to assess the impact of mosquito control on abundance by estimating the difference between the observed number of mosquitoes caught and the expected number caught if there was no intervention . Understanding the spatial scales of mosquito heterogeneity and mosquito control is essential for accurately accounting for variation in transmission risk during the design and execution of vaccine and vector control trials [32] . Likewise , an improved understanding of heterogeneity can inform the application of vector population reduction ( e . g . , genetically engineered mosquitoes [33] ) and population replacement ( e . g . , Wolbachia ) programs [34] . In the absence of reliable and accurate estimates of mosquito abundance , vector control programs will likely miscalculate , overestimate or underestimate , the required initial intensity and effect size of their intervention . The approaches described in our framework are intended to help address this gap by identifying site-specific drivers of mosquito abundance across different spatial and temporal scales in a diversity of ecological and epidemiological contexts .
Ethics Statements Entomological survey data were collected under protocols approved by Institutional Review Boards at the University of California at Davis , Tulane University , Emory University , San Diego State University , Liverpool School of Tropical Medicine , London School of Tropical Medicine and Hygiene , Peruvian National Institute of Health , Naval Medical Research Center , Bethesda , and U . S . Naval Medical Research Unit No . 6 . The latter included Peruvian representation , in compliance with all US Federal and Peruvian regulations governing the protection of human subjects . All protocols were reviewed and approved by the Loreto Regional Health Department , which oversees health research in Iquitos . Mosquito control data documented which zone of the city was covered , the number of homes within the zone , and the number of homes that received treatment . There was no information on exactly which homes within a zone were sprayed . Most control efforts consisted of three sequential applications of insecticide in the same zone over the course of one to two weeks . Across sequential applications , there was no indication of whether the same subset of homes received all , some , or none of the treatments . Consequently , in our analyses we were not able to incorporate ‘control’ at the household level . Instead , we applied vector control to entire zones of the city simultaneously . For each home in a treated zone , we defined the ‘intervention effort’ as the percent of the homes that were treated . Although homes that were not treated have imputed non-zero ‘intervention effort’ scores , this covariate had the desired properties such that if the score was equal to 0 , we were certain that all homes in a zone were not treated , and if it was equal to 1 , we were certain that all homes in the zone were treated . Results from recent neighborhood studies in Iquitos indicate that Ae . aegypti populations return to baseline within 3 weeks of an indoor insecticide space spraying application[42] . As a result , we not only imputed ‘intervention efforts’ to each home in a zone during the period of spraying , but we also extended the spray “effect” to 3 weeks after the end date of spraying . When a zone received multiple sequential interventions , the ‘intervention efforts’ did multiply . Rather , if two interventions in the same zone occurred within 3 weeks of each other , the ‘intervention effort’ for each round of spraying was calculated separately for each day and the maximum effort across rounds was retained ( S10 Fig ) . | Despite the growing threat of arboviruses , there is a dearth of ‘best practices’ for the primary vector control tools used in the field . In the absence of cluster randomized control trials , evidence on the utility ( or lack thereof ) of vector control interventions must be gleaned from ongoing control programs . Motivated by 12 years of household-level Ae . aegypti abundance surveys and neighborhood-level space-spray campaign data from Iquitos , Peru , we developed a new framework to model mosquito abundance . In spite of significant spatial and temporal heterogeneity , we identified a statistically significant and practically important impact of the local Ministry of Health space-spray campaign , specifically , a reduction of mosquito abundance of 67% when coverage was optimal . Our framework can be directly applied to other locations with geolocated mosquito abundance data and our findings can be used to both optimize resources within Iquitos as well as inform future vector-control interventions in Ae . aegypti endemic areas globally . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"atmospheric",
"science",
"animals",
"materials",
"science",
"surface",
"properties",
"infectious",
"disease",
"control",
"insect",
"vectors",
"surface",
"temperature",
"infectious",
"diseases",
"geography",
"agroc... | 2019 | Estimating the impact of city-wide Aedes aegypti population control: An observational study in Iquitos, Peru |
Prevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates , such as seasonality and transmission distance . Passive disease surveillance ( i . e . , case reports by public ) is a common method of monitoring emergence of wildlife diseases , but can be challenging to interpret due to spatial biases and limitations in data quantity and quality . We obtained passive rabies surveillance data from dead striped skunks ( Mephitis mephitis ) in an epizootic in northern Colorado , USA . We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling . We estimated the distance travelled per transmission event , direction of invasion , rate of spatial spread , and effects of infection density and season . We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic . Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread . Estimated mean transmission distances were 2 . 3 km ( 95% Highest Posterior Density ( HPD95 ) : 0 . 02 , 11 . 9; phylogeographic ) and 3 . 9 km ( 95% credible intervals ( CI95 ) : 1 . 4 , 11 . 3; occupancy ) . Estimated rates of spatial spread in km/year were: 29 . 8 ( HPD95: 20 . 8 , 39 . 8; phylogeographic , branch velocity , homogenous model ) , 22 . 6 ( HPD95: 15 . 3 , 29 . 7; phylogeographic , diffusion rate , homogenous model ) and 21 . 1 ( CI95: 16 . 7 , 25 . 5; occupancy ) . Initial colonization probability was twice as high in spring relative to fall . Skunk-to-skunk transmission was primarily local ( < 4 km ) suggesting that if interventions were needed , they could be applied at the wave front . Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions . Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases .
A central focus for disease ecologists and epidemiologists is to quantify processes that determine geographic spread of disease [1] . Surveillance systems , which rely on reporting by the public [2–5] , provide data that can improve understanding disease dynamics and planning interventions . However , passive surveillance data are challenging to interpret because the underlying sampling design is opportunistic . Raw patterns may depend on observation processes that produce a biased representation of disease occurrence . Interpretation of passive surveillance data from wildlife populations can be especially challenging because the underlying ecological processes , such as host population density , distribution , and demographic dynamics , are often unknown [e . g . , 6] . In these cases a phenomenological method that does not rely on explicit representation of often unavailable host ecological data , may be valuable for quantifying disease spread—especially when surveillance data are too sparse , or disease prevalence too low , to enable estimation of multiple unknown parameters representing non-linear processes from both the host and disease dynamics . Rabies virus ( RABV ) is a globally-distributed zoonotic pathogen that circulates naturally in a variety of carnivore and bat host species and has among the highest case fatality rate of known infectious diseases [7] . The principal burden of human and animal cases is associated with domestic dog populations [8] , but emergence in wild carnivores has been observed , especially in areas where domestic dog rabies has been managed or controlled [9 , 10] , or in areas with a long history of disease absence [11] . Risk of rabies transmission from wildlife is traditionally monitored through public health surveillance systems , which involve voluntary reports by the public of domestic animal or human exposures to potentially sick wildlife ( appearance of atypical behavior , and especially signs of neurologic illness ) , followed by diagnostic testing [7] . Animal movement can have significant consequences on geographic spread of RABV [12] , but the transmission distance during individual infections , an important component of geographic spreading , is poorly documented . Analysis of spatial public-health surveillance data have the potential to improve our understanding of the geographic spreading processes of RABVs , which will in turn help with planning prevention and response strategies , and prioritizing resources in space and time . In the United States of America ( USA ) , rabies infections of humans and animals became nationally reportable during 1938 [13] . There are several distinct enzootic lineages of RABVs circulating in bats and wild carnivores in the USA [14] , though most control and management efforts are focused on raccoon RABV [15] . The South Central Skunk ( SCSK ) variant of RABV was likely first detected in Texas as early as 1953 [16] , although typing methods which could detect and identify this particular variant were not reported until 1986 [17] . Recent studies have documented that this variant of RABV has been expanding in geographic distribution [18] and recently invaded novel areas in the USA , causing epizootics in the northern part of Colorado for the first time [19] . This well-documented invasion presents an opportunity to quantify spatial emergence dynamics of skunk RABVs , and assess the validity of estimating spatial epidemiological parameters from surveillance data reported by the public . Ecologists have developed occupancy models for quantifying species invasion processes [20] , which are essentially the same phenomena as the emergence and geographic spread of novel pathogens . Occupancy is the process of a target species or pathogen being present or absent across space . Dynamic occupancy processes additionally consider a time component . Occupancy frameworks can incorporate an ecological process ( es ) of geographic spread and observation error separately , allowing for clearer interpretation of the influence of factors driving each process . Recently , occupancy models have begun to be applied to disease systems for estimating pathogen prevalence [21–23] but are under-utilized for quantifying parameters that describe the spatial spread of disease . Using surveillance data from the SCSK epizootic in Colorado , we developed a dynamic patch-occupancy model to jointly estimate parameters describing the spatial spread of rabies while accounting for variable sampling effort . Considering surveillance data from dead skunks , we used the framework to: 1 ) quantify the rate of geographic spread and transmission distance per infection; 2 ) quantify effects of local infection density , seasonality and direction on the probability of geographic invasion; and 3 ) predict the occupancy probability and prevalence of RABV on the landscape through space and time . We also conducted a phylogeographic analysis of viral genetic data from a subset of the reported rabies positive skunks to validate the estimated rates of spatial spread and transmission distance that we estimated using the occupancy model . The occupancy framework we present is simple to implement and can be used for inferring rates and direction of geographic spread in a variety of emerging disease systems , providing basic insight for understanding disease emergence and practical insight for risk assessment and response planning .
The decision and implementation of euthanasia of animals was conducted by county authorities , before samples were received for the current study ( i . e . , we had no role in this ) . For the current study , the State of Colorado Department of Natural Resources issued annual Scientific Collection Licenses ( 14SALV2060 , 15SALV2060 ) in order for us to receive a subset of dead carcasses for testing . The study area included 8448 km2 ( 88 x 96 km; Fig 1 ) of Larimer , Boulder and Weld counties , Colorado , USA . Skunk samples were obtained by reports made by public to local health departments , which decided whether to submit a skunk for rabies testing to one of two diagnostic laboratories in the state . The decision and implementation of euthanasia of animals was conducted by county authorities , before samples were received for the current study . For the current study , the State of Colorado Department of Natural Resources issued annual Scientific Collection Licenses ( 14SALV2060 , 15SALV2060 ) in order for us to receive a subset of dead carcasses for testing . There was no vaccination campaign ( trap-vaccinate-release or oral rabies vaccination ) targeting wildlife during the epizootic , but there were alerts through local public media and leash laws in effect . Strange-acting or dead mammals found on the landscape with no reported human or domestic animal contact were not typically tested by the local health departments—especially later in the study period when the perceived risk of rabies infection was much lower and funding for testing had decreased . Because of the ongoing epizootic , these samples were referred for testing by United States Department of Agriculture , Animal and Plant Health Inspection Service , Wildlife Services , National Wildlife Research Center ( NWRC ) ; hereafter referred to as ‘enhanced surveillance’ . Enhanced surveillance accounted for 0% , 1% and 47% of surveillance data in 2012 , 2013 and 2014 respectively ( Table SM1 . 1 in S1 Text ) . Enhanced surveillance samples were identified through the public health surveillance system by the local health departments , but with carcass referral to NWRC for rabies testing . Carcasses were referred to NWRC when the perceived risk to public health was low ( i . e . , due to a lack of contact with humans or pets ) . Because the rabies epizootic was waning in 2014 , the high proportion of enhanced surveillance in 2014 relative to 2012 and 2013 did not contribute much extra information ( except for improving uncertainty levels ) using the occupancy model . Thus , typical surveillance systems could likely make the same type of inferences , but would show greater uncertainty as sampling decreased . An address matching-geocoding technique , using ArcGIS 10 . 3 ( Environmental Systems Research Institute , Redlands , CA , USA ) , was used to convert street addresses of the case reports to UTM ( Universal Transverse Mercator ) coordinates . Before 2012 , carnivore rabies had not been detected in the study area for several decades . From 2012–2014 there were 246 skunk reports and 379 non-skunk terrestrial animal reports tested for rabies ( Fig 1 , Table SM1 . 2 in S1 Text ) , with a total of 139 rabies-positive skunks ( raw prevalence ~ 57% , Table SM1 . 2 in S1 Text ) , and 21 rabies-positive cases from the 379 non-skunk terrestrial species tested ( 5 . 5% positive; Table SM1 . 2 in S1 Text ) including: raccoons ( 6 ) , bison ( 2 ) , foxes ( 6 ) cats ( 2 ) , horses ( 2 ) , cows ( 2 ) , coyotes ( 1 ) . All non-skunk terrestrial animals were excluded from the analysis because they comprised such a small proportion of the samples and because surveillance efforts may have differed in some of these hosts—especially raccoons which are higher density and more peridomestic , and were experiencing an ongoing epizootic of canine distemper virus . Human population data from 2013 [24] were organized at the grid-cell scale ( 8 x 8km ) to be included in the occupancy model as a potential factor contributing to reporting rate . We used a discrete-time , dynamic , patch-occupancy model to quantify the occurrence probability of skunk rabies in space and time . In a typical occupancy framework , there is the partially observable occupancy process and an observation process that is conditional on the occupancy process . For this dynamic model the occupancy process describes the probability of rabies being present by site ( grid cells ) and time ( months ) . The ecological process of rabies occurrence changed over grid cells and months due to the local component processes of colonization , local extinction , and persistence . Within the latent ecological process , we estimated important parameters describing the initial colonization dynamics ( i . e . , spatial invasion ) of skunk rabies , specifically: direction of invasion , distance to nearest infected grid cell , and density of infection in the local neighborhood . Conditional on the latent ecological process of occupancy , we modeled the observation process as the probability that an animal sampled has rabies , given that rabies is present ( prevalence given rabies occupancy ) . Within the observation process , we considered the effects of human population size on the proportion of skunks that were rabies positive . Thus , our approach allowed us to quantify parameters underlying the spatio-temporal dynamics of geographic spread while accounting for imperfect detection . For the occupancy model , we assumed the landscape was homogenous because our genetic model suggested that spread rates were homogenous across the landscape and we did not have enough degrees of freedom to incorporate more parameters in our occupancy model . We used a monthly time step to scale with the incubation period for rabies virus in skunks [25 , 26] . We divided the study area ( much of Larimer , Weld and Boulder Counties in Northern Colorado , USA ) into 8 x 8 km grid cells ( “patches” in occupancy modeling terms; Fig 1 ) . This spatial scale was much larger than a skunk home range size [27 , 28] suggesting that local transmission ( i . e . , due to skunk movements alone ) should be primarily within grid cells or nearest-neighbor grid cells . Also , at this spatial scale , there were multiple samples collected within grids ( Fig 1 ) at a given time step ( mean number of samples in grids with samples: 3 . 6 sampled skunks , range 1–19 sampled skunks ) , which is necessary for estimating grid-cell level prevalence and informing the observation process , but at finer spatial scales multiple samples per grid cell and time step were rare . Thus , because of the sparse sampling intensity in our study area , the 8 x 8 km grid cell size was the smallest size we could choose for approximating the skunk home range ( ~ 2 x 2 km; [25 , 26] ) and hence skunk-to-skunk transmission distances . Data on the presence of rabies in dead skunks ( sampling unit = 1 skunk ) were collected repeatedly in grid cells i = 1 , … , M during each month t = 1 , … , T ( spanning January 2012 through December 2014 ) . We modeled the true occupancy status zi , t conditioned on the previous time step zi , t-1 , as a latent Bernoulli variable defined by parameter Ψi , t ( occupancy probability ) , where zi , t = 0 indicates that grid cell i is not occupied ( no rabies in skunks ) in time step t , and zi , t = 1 indicates that grid cell i is occupied ( at least one skunk with rabies ) in time step t . We assumed the initial occupancy state for each grid cell ( zi , 1 ) was also a Bernoulli random variable described by occupancy probability Ψi , 1 , which had a Uniform prior distribution ( Ψi , 1~Unif ( 0 , 1 ) ) . We allowed occupancy probability in grid cell i at time t ( Ψi , t ) to be determined by three types of local dynamic processes: initial colonization ( γi , t ) , recolonization ( ζi , t ) and persistence ( ϕi , t ) , which were conditional on the latent occupancy status in the previous time step ( zi , t-1 ) . We used parameter Ai , t to distinguish an initial colonization event from subsequent colonization events . Although it is more typical to consider only occupancy and extinction dynamics in occupancy models , we further distinguished initial colonization from recolonization ( as in [29] ) because we were most interested in factors ( described below ) driving spatial invasion into new sites . We quantified the effects of multiple different factors ( direction , distance to nearest infected neighbor , neighborhood infection density ( Eqs 3–8 ) on initial colonization probability ( γi , t-1 ) , and treated the other processes as inherent contributors to occupancy probability ( Ψi , t ) without quantifying their potential drivers ( i . e . we used a global parameter for persistence- ϕit ~ Beta ( αϕ , βϕ ) and recolonization—ζit ~ Beta ( αζ , βζ ) . For γi , t-1 , an intercept term ( βo ) absorbed non-spatial effects ( Eq 3 ) . Direction ( north-south ( N ) or east-west ( E ) ) was modeled as increasing integers ( 1 representing the southernmost grids and 12 representing the northernmost grids for N; and 1 representing the westernmost grids and 11 representing the easternmost grids for E; Eq 4 , βk , where k = 1 , … , K for the number of initial colonization effects considered ) . East-west direction was represented similarly but using columns in the grid . We modeled an interaction between direction and a trend in time ( T ) by multiplying the direction covariate data by the time step ( Ni · T or Ei · T ) . We calculated distance to the nearest infected grid cell by taking the minimum distance for all pairwise distances between the centroid of target grid cell i* in time t and all other grid cell centroids ( i ) in time t-1 ( dii* ) . We estimated the relationship of initial colonization probability and distance using an exponential decay function which included a parameter describing the decay rate of initial colonization probability ( α ) with distance and a scaling parameter relative to maximum initial colonization probability ( βk , Eq 5 ) . We estimated the local neighborhood infection density for grid cell i* in time t using the occupancy prevalence in all immediate grid-cell neighbors ( queen’s neighbors; j = 1 , … , J; note j’s are a special subset of i—restricted to the closest neighbors of i* ) in time step t-1 and estimated its effect with parameter βk ( Eq 6 ) . We incorporated a seasonal effect as a factor where one level represented spring/summer ( Feb . –Aug . ) and the second level represented fall/winter ( Sept . –Jan . ) ( Eq 7 ) . We chose these time frames because the literature and surveillance data suggest peaks of rabies in spring and summer following arousal and mating activities , and subsequent peaks in fall and winter associated with dispersal and contact involving susceptible young of the year [16 , 25] . We modelled the infection density ( Eq 6 ) and distance effects ( Eq 5 ) separately because we were interested in the separate effects of distance versus infection intensity . Together these effects describe the spatial kernel for local transmission . Additionally , we modelled a weighted spatial kernel ( Eq 8 ) which accounted for the density of neighbors with infections but discounted the impact an infected cells at further distances from the grid of interest ( dii* represents the distance between grid cell ‘i' and the grid cell of interest ‘i*’ ) . logit ( γi , t-1 ) = β0 ( non spatial ) ( 3 ) β1N+β2T+β3N*T ( direction by time ) ( 4 ) β4e−αmin ( dii* ) ( transmission distance ) ( 5 ) β5 ( 1/J ) ∑Jj=1[zj , t−1] ( local neighborhood effect ) ( 6 ) β6S ( season – Feb . – Aug . vs Sept . – Jan . ) ( 7 ) β7∑zi , t−1=1[1/dii*] ( weighted spatial kernel ) ( 8 ) We considered these effects ( Eqs 4–8 ) separately and additively ( up to three effects including the Eq 3 ) in our model selection procedure ( described below ) . We did not present the fullest model because parameter estimates became inconsistent when more than three additional effects were in the model . Persistence and recolonization were important processes in the occupancy dynamics , thus we modeled them explicitly using Beta prior distributions with shape and scale parameters ( αϕ = 1 , βϕ = 1 , αζ = 1 , βζ = 1 ) . We did not include covariates that could potentially drive persistence and recolonization because we were only interested in the process of spatial spread ( i . e . , initial colonization ) . Prior distributions for all βk parameters were: βk ~ Norm ( 0 , 1 ) , except for the scaling parameter for the exponential decay model ( Eq 5 ) which was modeled as a Gamma ( 5 , 1 ) . For the observation layer of our model , we represented the number of skunks that were positive for rabies , y , in grid cell i at time t when rabies was present as the observed data using a binomial distribution where p was the estimated prevalence of rabies in dead skunks ( given occupancy ) and the number of trials ( Ri , t ) were the observed number of samples collected in grid cell i at time t . To account for variation in rabies detection due to variation in human population size , we examined prevalence as a linear function of human population size ( N ) . We only investigated this effect in the intercept-only model ( Eq 3 ) and the two-effect model we were most interested in ( Table SM2 . 1 in S1 Text for list of all models that were fit ) . The general model specification is given in section SM2 . 1 in S1 Text . To calculate the posterior distribution for the parameters of interest , we fit models using a Markov chain Monte Carlo ( MCMC ) algorithm with a Gibbs sampler including Metropolis-Hastings steps [30] custom written in program R [31] . Posterior estimates for the data are based on 50 , 000 iterations of the MCMC algorithm with the first 5 , 000 iterations discarded as burn-in . Convergence and mixing were assessed graphically . Example MCMC chains and posterior distributions are given in SM2 . 1 in S1 Text . Convergence of the best predictive model was additionally assessed using the Gelman-Rubin statistic [30] . The joint and conditional distributions are specified in SM2 . 3 in S1 Text . We evaluated the ability of our models to recover parameters by simulating data from known parameter values and estimating the parameter values using our fitting framework ( Model Validation Method in SM5 in S1 Text ) . We used a combination of Watanabe Akaike Information Criterion ( WAIC ) [32 , 33] , Area Under the receiver operator Curve ( AUC ) [34] , and leave-one-out cross validation ( looCV ) [31] to compare the importance of different effects on γ and p and assess goodness-of-fit ( presented in Table 1 , schematic of work flow shown in Fig . SM3 . 1 ) . WAIC is a model selection criterion based on the posterior predictive distribution , and was used to compare fits of models . WAIC was not used to compare models with human population modelled on prevalence ( p ) because the data were different and thus the WAIC values would not be comparable . AUC is a measure of how well variation is explained by the model—in our case , the ability to distinguish a presence from an absence—and was used to assess how well a particular model explained the data ( i . e . a measure of goodness of fit as in [35] ) . LooCV is a measure of the model’s ability to predict out of sample , and thus was used to compare predictive ability among models . When comparing predictive ability between models we used both AUC and looCV . We calculated WAIC and AUC ( Section SM4 in S1 Text ) for fits to the full data as well as the looCV predictions . WAIC is preferable to DIC ( Deviance Information Criterion—another Bayesian method for model selection ) for model selection using hierarchical models [32] because it considers the posterior predictive distribution explicitly and penalizes for complexity of model structure , not just the number of parameters . Similar to DIC , lower values of WAIC indicate a better model of the data . However , in contrast to DIC [36] , there is no standard quantitative difference between WAIC values from alternative models that indicates a significant difference between them ( i . e . , the only criterion is that lower is better ) . We presented two AUC scores: 1 ) AUC1 measured predictive ability of p using predicted y’s from posterior values of z and observed y’s , 2 ) AUC2 measured predictive ability of Ψ using the posterior values of z and the observed y’s transformed to binary data . For the out-of-sample predictions , we conducted looCV for each point in the data ( using all other points as the training data ) and presented means of AUC1 and AUC2 as measures of predictive ability . To further evaluate the predictive ability of our approach we predicted occupancy status over space and time using only parameters estimated from the “best predictive model” ( i . e . , considering out-of-sample statistics for AUC1 , AUC2 and looCV ) and the sample size data . First , we fit the best model to the full data and then predicted all yi , t from the posterior predictive distribution [30] . Each predicted yi , t depended on the predicted yi , t-1 , rather than the data , but the actual data for sample size ( Ri , t ) were used in the prediction in order to scale yi , t predictions appropriately . Using the model with the best out-of-sample looCV score , we predicted grid cell occupancy over time ( zi , t; rabies presence -1 , or absence -0 ) , and estimated the rate of southerly spread of the predictions using regression . First , we obtained the zi , t values from each MCMC iteration ( minus the burn in ) for grids across time . Each grid cell corresponded to a distance in kilometers from the southernmost border of the study area ( e . g . , 1 , … , 88 km; “North value” ) . For zi , t = 1 values , we ran a simple linear regression where the independent values were the months in time and the dependent values were the north values . The slope of this regression represents the monthly rate of southerly spread . We then converted this to an annual rate of southerly spread by multiplying the monthly rate by 12 . We calculated the annual variance using the delta method [37] . To verify spatial patterns indicated by our occupancy model , we sequenced the whole or partial glycoprotein ( G , 1575 base pairs [bp] ) and the non-coding region between the glycoprotein and polymerase genes ( GL , 560 bp ) for 53 viruses collected in Colorado between August 2012 and December 2014 ( Table SM1 . 3 in S1 Text ) . We added sequence data from 20 viruses collected through May 2015 ( 27 . 4% of the total genetic dataset ) to increase the precision of parameters of the molecular evolutionary models . We confirmed that these additional sequences comprised an extension of the same epizootic though preliminary phylogenetic analysis and the absence of changes in the inferred rate of spread in 2015 which could have biased our overall spread rate . Rabies virus RNA was extracted from brainstem tissue samples of rabies positive skunks using Trizol reagent following the manufacturer’s protocol . The conversion of RNA to cDNA ( RT ) and primary PCR amplification was accomplished using a Superscript III One-step RT-PCR system with Platinum Taq DNA Polymerase ( Invitrogen ) , targeting a 2 , 135bp region including the full glycoprotein gene with previously published primers [18 , 38] . PCR products were visualized by UV-light on a 2% agarose gel with ethidium bromide , and cleaned using ExoSap-IT ( Affymetrix ) following the manufacturer’s protocol . Sequencing reactions were performed using Big Dye Terminator v . 3 . 1 ( Applied Biosystems ) , with flanking and internal primers as previously described [18 , 38] . Sequencing products were cleaned using Sephadex G-50 columns ( GE Healthcare ) and run on a 3130 or 3500 analyzer ( Applied Biosystems ) . Forward and reverse sequences were aligned in Sequencher v . 5 . 2 . 4 ( Gene Codes Corporation ) , and ambiguities were resolved visually . Alignment of full or partial glycoprotein sequences was performed using BioEdit v . 7 . 2 . 0 . Continuous phylogeographic analysis was conducted in BEAST v . 1 . 8 . 4 [39] . Briefly , this method estimates the phylogenetic history connecting samples and conducts ancestral state reconstruction of the latitudes and longitudes of inferred nodes using time and location-annotated sequence data . Phylogenetic uncertainty is accommodated by summarizing estimates across the posterior distribution of trees from a Bayesian search . Analyses in TempEst ( software that analyzes correlations between the temporal and genetic distances between sequences ) showed evidence of a molecular clock signal in the coding ( G ) and non-coding ( GL ) regions of the RABV genome , but faster evolution in the G-L region compared to G [40] . Our BEAST analysis therefore modeled a single tree topology ( given that the samples represent the same underlying epidemic history ) using different molecular clock and substitution models to allow for differences in evolutionary rates in different parts of the RABV genome . Preliminary BEAST runs using the lognormal relaxed molecular clock indicated little variation in rates among branches , indicating the use of the strict molecular clock for both partitions , which was supported by equivocal differences in Bayes Factors ( BF ) between models assuming strict or relaxed molecular clocks ( BF = 1 . 5 in favor of strict clocks ) [41] . Final runs used customized substitution models for the following data partitions: G codon positions 1+2 = TPM1uf; G codon position 3 = TIM1+G , GL = TPM1uf+I , as suggested by AIC in jModeltest2 [42] . Among the 3 random walk phylogeographic models tested ( homogenous , Cauchy and gamma ) , marginal likelihoods estimated by the stepping stone method were highest in the homogenous model ( -3574 . 86 ) followed by the gamma ( -3577 . 27 ) and Cauchy ( -3594 . 05 ) models , suggesting relatively little variation in spread rates among branches or little power to detect such variation . To account for the possibility of among branch variation , we present the results of the gamma model , but note that parameter estimates were nearly identical in the similarly supported homogeneous rate model . All analyses used the Bayesian skyline model of demographic growth . MCMC chains were run for 100 million generations which generated effective samples sizes >200 after removal of burn-in . We used the Seraphim package of R to calculate viral diffusion rates from 500 randomly selected ( post burn-in ) trees from the posterior distribution of the BEAST analysis [43] . We estimated viral spread rates as ( i ) the “mean branch velocity” calculated as the average velocity across the branches of each tree , averaged across all trees and ( ii ) the weighted “diffusion rate” , obtained by summarizing distances and times spanning each tree and taking the average of that value across all trees ( i . e . , sum distances/sum time lengths over entire tree ) . To calculate the transmission distance per viral generation ( i . e . , per infected animal ) , we divided the distance traversed along each branch by the expected number of infections along that branch , assuming a generation time of 30 days , which corresponds to the incubation period of rabies virus in skunks [25 , 26] . However , the substitution rates that we estimated ( G: 4 . 1x10-4 substitutions per site per year ( 95% highest posterior density ( HPD ) = 2 . 3–6 . 05x10-4 ) ; GL: 8 . 7x10-4 [HPD95 = 4 . 1–13 . 4 x10-4] ) , imply that some transmission events would be expected to occur without detectable evolution in the partial genomes that we sequenced . This led to branch lengths that were less than the assumed generation time of RABV , which caused an upward bias in inferred transmission distances per infection . We corrected for this bias by forcing all branches to have a minimum generation time of 1 infection .
The probability of initial colonization ( γ ) was best explained by the minimum distance to infections , infection density in the local neighborhood , season , and spatial kernel ( see AUC2 of Models 1–7; Table 1 ) . Similarly , the best two-effect models included pairs of these effects ( distance + neighborhood , distance + season , kernel + season; Models 9 , 11 , 15; Table 1 ) . Accounting for direction ( i . e . , N∙T or E∙T ) provided more minor improvements to quantifying initial colonization probability ( AUC2 Table 1 ) . The inclusion of human population size on p ( rabies prevalence in occupied grid cells ) significantly improved predictive power of the model ( see AUC1 and looCV in Table 1 , Models 1b , 9b , 11b , 16b ) because it explained some of the prevalence variation due to differences in the number of samples reported by humans . We considered Model 11b to be the best predictive model because it had the lowest looCV , and we used this model for prediction . However , in order to study the effects of the most significant single predictors ( neighborhood , season and distance ) , we fit Model 16 to quantify the relative importance of these factors ( results presented in Fig 2 ) . We modeled the effect of distance to nearest infection using an exponential decay function . Using the estimated decay rate parameter , we calculated the distance at which initial colonization probability decayed to half its maximum ( referred to as “transmission distance” ) using the asymptotic limit as the minimum ( see section SM54 in S1 Text for calculation ) . We interpreted the transmission distance as a measure of skunk-to-skunk transmission distance . Transmission distance was 3 . 9 km ( 95% credible intervals ( CI95 ) : 1 . 4 , 11 . 3; Fig 2A ) . Similarly , using the genetic data independently , the mean distance per viral generation ( another proxy for skunk-to-skunk transmission distance ) was estimated to be 2 . 3 km ( 95% Highest Posterior Density ( HPD95 ) = 0 . 04–5 . 7; Fig 3C ) . Initial colonization probability increased exponentially with local neighborhood infection density , especially after 0 . 2 ( i . e . , > 1grid cell occupied; Fig 2B , Model 16 , Table 1 ) . Initial colonization probability was substantially higher during the spring/summer season relative to the fall/winter season ( Fig 2C , Model 16 ) . Of the 76% of variation in occupancy probability that was explained by the three-factor model ( AUC2 , Model 16 , Table 1 ) , 57% of initial colonization probability was explained by local effects ( distance or neighborhood ) , 41% was explained by season and 2% was explained by other factors ( which could include translocation or other unknown factors; Fig 2D ) . Although direction explained less variation in initial occupancy probability relative to other factors ( Table 1 , Models 3 and 4 versus Models 2 , 5 , 6 , and 7 ) , there appeared to be a significant increase in initial colonization probability in the southerly direction with time during the study period indicating southerly spread of the disease ( Fig SR1 . 1 in S1 Figures ) . However , east-west movement over time did not show significant visual trends ( Fig SR1 . 2 in S1 Figures ) . The “best” predictive model we examined ( Model 11b , Table 1 ) performed very well at predicting rabies cases in space and time using parameters trained on different data than the data being predicted ( Table 1 , out-of-sample prediction AUC1 & AUC2 ) . The model also performed very well at out-of-sample prediction using only the initial conditions , sample size data and parameters fit to the full data ( Fig SR2 . 1 in S1 Figures ) . Average occupancy probability across the study area was low over time ( Fig 4 , top row , mean: 0 . 034 , CI95: 0 . 006–0 . 24 ) but prevalence in sampled dead skunks in occupied grid cells was high ( Table 2 , mean: 0 . 91 , CI95: 0 . 86 , 0 . 95 ) . Occupancy probability was predicted to decrease substantially in the north and increase in the south during the time course of the study period ( Fig 4 , Figs SR3 . 1 & SR3 . 2 in S1 Figures ) . Also , earlier during the time course occupied patches were larger and more contiguous whereas later occupied patches became more fragmented and isolated ( Fig 4 ) . The overall rate of southerly spread estimated by the occupancy model was predicted to be 21 . 1 km/year ( CI95: 16 . 7 , 25 . 5 , Fig 5 ) . This was similar to the phylogeographic diffusion rate assuming either gamma 21 . 8 [HPD95: 14 . 9–29 . 0] or homogenous rate 22 . 6 [HPD95: 15 . 3–29 . 7] models . Mean branch velocities from the phylogeographic models were slightly higher but had overlapping HPD95s with the genetic and occupancy-based models ( gamma: 28 . 4 [HPD95: 19 . 6–39 . 8] or homogenous 29 . 8 [HPD95: 20 . 8–39 . 8] ) .
We used an occupancy modeling framework to understand and predict the spatial spread dynamics of an important zoonotic disease in a reservoir host species . The framework produced good out-of-sample predictions of spatial dynamics despite our relatively small dataset , suggesting it can be useful for predicting spread in other surveillance systems that rely on passive surveillance data . We converted occupancy probabilities into rates of directional spread , which is useful for planning interventions and allocating resources for disease management . The rates of spatial spread predicted by the occupancy model appeared similar to those predicted by phylogeographic analyses . Thus , our phenomenological approach for estimating rates of spatial spread may provide accurate prediction of spatial spread rates based on parameters estimated in the recent past for a particular region . While the phylogeographic method incorporates additional information regarding connectivity between cases ( i . e . , genetic sequence similarity ) , the occupancy model produces similar results with only the spatial and temporal information . The phylogeographic method has the advantage of not requiring negative samples , while the occupancy approach has the advantage of not requiring genetic data . Each may therefore be suited to address alternative data gaps typical to surveillance systems . Both the phylogeographic and occupancy approaches can be sensitive to sampling intensity and scale , such that interpretation of results should consider sampling design . Our approaches also present an alternative to detailed mechanistic models of disease transmission which may require more data than are available , such as knowledge of host population sizes and connectivity ( e . g . , [44] ) ( although we did not have host demographic data to explicitly test for differences between our phenomenological approach and a more mechanistic approach ) . The occupancy framework is flexible for testing the role of different contact heterogeneities . Any desired connectivity pattern could be incorporated by modifying the spatial terms using another description of spatio-temporal connectivity between rabies-positive cells . Similarly , if geographic barriers are important [45] , or if there are data on ecological processes of known importance such as host density , additional terms could be added to occupancy processes to quantify these effects and improve prediction . In addition , some surveillance systems ( e . g . , the National Rabies Management Program of the United States Department of Agriculture , Animal and Plant Health Inspection Service , Wildlife Services ) are comprised of multiple types of surveillance data ( e . g . , passive methods such as: public health reports , road kill reports , landowner reports of nuisance animals , or active methods such as: trapping ) , which each introduce their own sampling biases . The occupancy framework is flexible for incorporating multiple methods of surveillance simultaneously and , in the observation model , allowing for quantification of effects due to the type of surveillance data , which can in turn inform the design of surveillance . In situations where vaccination or other interventions are applied , effects of the intervention could be quantified by incorporating those data as a covariate in the latent ecological process . Thus , our framework could be used to identify the magnitude of intervention effects and facilitate planning of where additional interventions are needed . The directional rates we estimated by both the occupancy and phylogeographic models were twice as high as those estimated for SCSK RABV by [18] using a phylogeographic approach ( 10 km/year; range 4–12 km ) on data from south-central USA . One reason for this discrepancy could be that the analysis of [18] averaged rates over a long time period and large geographic area ( multiple US states ) , whereas our study covered a short time period within three counties in a single state . From a level IV ecoregion perspective , our study site included only 4 distinct ecoregions ( Rolling sand plains , Flat to rolling plains , Front range fans and Foothill shrublands ) , while that of [18] included 50–100 ecoregion types . Thus , the landscape within our study area was less heterogeneous than the landscape within the study of [18] , which could partly explain differences in rates of spatial spread of rabies . Also , estimates from longer time scales may have included re-emergence dynamics in addition to initial invasion dynamics ( reducing inferred rates of spatial spread ) . However , the rates we estimated are significantly lower than those found during the initial emergence of RABV in raccoons in the eastern USA ( ~38 km/year ) [43 , 46] , suggesting that skunk ecology , at least in south and western USA , leads to slower geographic spread of RABV . This is consistent with a previous study which used landscape resistance models and showed that skunks and raccoons used different movement corridors on the landscape [47] . Also , skunk rabies variants are 8 . 1 times less likely to be transmitted to foxes relative to raccoon rabies variants [14]–their lower propensity to spillover into longer-ranging hosts could contribute to slower spatial spreading . Our mean estimates of individual-level transmission distance ( ~ 3 . 9 km by the occupancy model and 2 . 3 km by the phylogeographic model ) were higher than the mean transmission distance estimated for dogs in the Serengeti , Tanzania ( 0 . 88 km ) [48] . This estimate , which was based on infection reports , suggest a much closer mean transmission distance than was estimated in study of canine rabies in South Africa which was conducted using genetic data [mean distance between the most probable linked cases = 14 . 9 km; [49] . The latter study suggests a significant influence of anthropogenic movement of dogs , which is unlikely for skunks , and no rabies-positive dogs were reported . Although there were some canine species ( 6 foxes and 1 coyote ) found to be positive for SCSK in our study area , these reports only comprised 5% of the rabies-positive reports suggesting that these longer-ranging hosts may only have made minor contributions to spatial spreading . Thus , our estimates of mean transmission distance likely reflect distances at which skunks move naturally during the contact and transmission process . Our approach required that multiple samples were collected in the same grid cell at the same time step in at least some of the grid cell/time steps ( here we had a total of 180 grid cells with > 1 sample in the same time step over a total of 132 grid cells x 36 time steps = 4752 ( 3 . 8% ) ) . Based on this requirement , we used a larger spatial scale ( 8 x 8 km ) than skunk home range size ( ~ 2 x 2 km , would have been preferable ) . Thus , our estimates of transmission distance were likely biased high . For example , the transmission distance estimated in the occupancy model ( 3 . 9 km ) was about half the width of a gird cell . As we used grid-cell centroids to calculate distances between cells , our approach may not have allowed for smaller resolution of transmission distance ( relative to the phylogenetic model ) even though the rate of spread on the landscape was slower in the occupancy model . Although local transmission explained the majority of the occupancy probability , seasonality effects were also substantial ( 41% of explained variation ) . We found initial colonization probability to be approximately twice as high in the spring/summer months relative to the fall winter months , which is consistent with other studies of rabies seasonal prevalence in skunks [16] . Some previous studies of spatial spread of RABV in raccoons showed that landscape barriers such as major rivers , highways or mountains influence the direction and speed of spread [45 , 50] , while other studies have not observed a strong effect of rivers and major roads [47 , 50] . In certain landscapes , the presence of suitable host habitat was observed to influence spread [51] and presence [52] of skunk RABV . Another study on spatial spread of skunk RABV in the mid-western USA observed differential impacts of river barriers depending on the skunk RABV variant [53] . Most of our study area was comprised of relatively flat terrain east of the Rocky Mountains . There were only two samples on the west side in the Rocky Mountains ( none above 2509 meters ) , and our study area did not include the other side of the Rocky mountains , thus our models were unable to test effects of high-altitude barriers explicitly . In addition , there were no major rivers ( e . g . , Colorado River ) in our study area , but there were two moderately sizes rivers ( Poudre River and Big Thompson River ) and one major road ( Interstate 25 ) . Although the rivers and roads could have been easily incorporated into our occupancy framework , we did not include them because: 1 ) the phylogenetic analysis suggested minimal heterogeneity in rates of spatial spread across the rivers and roads in our study area , and 2 ) our dataset was small relative to the number of parameters we wanted to estimate ( i . e . , we prioritized quantification of spatial parameters ) . Also , maps of the genetic data showed closely related rabies variants on both sides of the most major road ( Interstate 25 ) and rivers ( Poudre River , Big Thompson River ) throughout the study . Thus , although we did not explicitly test for effects of rivers and roads with our occupancy model , plots of the genetic data and the phylogenetic analyses suggested that the rivers and roads in our study area may not have presented major barriers that could reinforce controls against geographic spread [54] . If an intervention were deemed to be a necessary and/or cost-effective response , the primarily local transmission suggested that potential interventions could be applied as a relatively thin barrier ( 10–20 km ) at the wave front to curb invasion , which is less than half the ~40km width typically used during aerial vaccination operation against raccoon rabies in the USA ( Rich Chipman , National Rabies Management Program , personal communication ) . The strong seasonal difference in initial colonization probability suggests that interventions in the spring may be more effective than interventions in the fall . Our model predicted that geographic spread tended to be mostly local and north-south , resulting in a wave-like pattern of geographic invasion . The phylogeographic analyses suggested that the invading viruses were new to Northern Colorado , such that the skunk population in Northern Colorado had not seen this virus in the recent past and was likely to be highly susceptible to invasion . We hypothesize that the reason we found a stronger tendency for movement of rabies from north to south instead of east to west , may be due to the underlying distribution of the skunk population . In our study area ( called the Colorado Front Range ) , the human population forms a north-south belt , sandwiched by lower density regions to the west ( in the mountains ) and the east ( in the plains ) . Although the literature does not suggest a consistent relationship of increasing skunk density across the rural to urban gradient [55 , 56] , it is possible that skunk abundance was higher running north-south than east-west , which could partly explain our directional effects . Forecasting spatio-temporal disease spread in any system is challenging , but especially so for wildlife diseases due to an often poor understanding of host population abundance and ecology . Occupancy models present an alternative to approaches that rely on host demographic data , while quantifying rates and patterns of geographic spread based on underlying ecological processes . Although we accounted for observation error ( by modeling the effect of human population size on prevalence ) , we did not have detailed data informing the observation process . For example , the study area included many natural areas spanning large regions where there are no human residents , but potentially heavy public usage , year-round . Accounting for reporting bias through collection of system-specific appropriate metadata ( e . g . temporal usage patterns of the landscape by humans , geographic differences in the likelihood of human reporting ) could improve geographic spread predictions using occupancy modeling . Our study showed how quantifying spatial parameters governing geographic spread can provide important ecological insights for understanding spatial epidemiology of rabies , conducting risk assessment and planning interventions—contributing to the toolbox of approaches for guiding management of disease invasions [57] . | Rabies is a deadly zoonotic infection with a global distribution . In 2012 , an epizootic of skunk rabies established in northern Colorado , USA and spread rapidly through three counties . The epizootic was documented through reports of dead skunks by the public . We examined the reports to determine how rapidly rabies was moving and which factors could explain the patterns of spread . We compared these estimates of spatial movement of rabies to those obtained from analyzing rabies genetic sequences that we obtained from some of the dead skunks reported by the public . By both methods , we found the virus was moving south at a little over 20 km/year and that most transmission between skunks occurred at short distances ( < 4 km ) . Rabies was most likely to spread to new areas during the first half of the year , when skunk populations were producing new offspring . Our genetic model suggested that roads and rivers in the study landscape did not affect movement speed of rabies . We developed a framework that used the spatial data in the public reports to predict where and when skunk rabies would occur next . This framework could be used on public health surveillance data for other diseases or countries . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"epizootics",
"biogeography",
"animal",
"types",
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"animal",
"diseases",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"population",
"genetics",
"tropical",
"diseases",
"m... | 2017 | Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public |
Genetic and molecular studies have provided considerable insight into how various tissue progenitors are specified in early embryogenesis , but much less is known about how those progenitors create three-dimensional tissues and organs . The C . elegans intestine provides a simple system for studying how a single progenitor , the E blastomere , builds an epithelial tube of 20 cells . As the E descendants divide , they form a primordium that transitions between different shapes over time . We used cell contours , traced from confocal optical z-stacks , to build a 3D graphic reconstruction of intestine development . The reconstruction revealed several new aspects of morphogenesis that extend and clarify previous observations . The first 8 E descendants form a plane of four right cells and four left cells; the plane arises through oriented cell divisions and VANG-1/Van Gogh-dependent repositioning of any non-planar cells . LIN-12/Notch signaling affects the left cells in the E8 primordium , and initiates later asymmetry in cell packing . The next few stages involve cell repositioning and intercalation events that shuttle cells to their final positions , like shifting blocks in a Rubik’s cube . Repositioning involves breaking and replacing specific adhesive contacts , and some of these events involve EFN-4/Ephrin , MAB-20/semaphorin-2a , and SAX-3/Robo . Once cells in the primordium align along a common axis and in the correct order , cells at the anterior end rotate clockwise around the axis of the intestine . The anterior rotation appears to align segments of the developing lumen into a continuous structure , and requires the secreted ligand UNC-6/netrin , the receptor UNC-40/DCC , and an interacting protein called MADD-2 . Previous studies showed that rotation requires a second round of LIN-12/Notch signaling in cells on the right side of the primordium , and we show that MADD-2-GFP appears to be downregulated in those cells .
Epithelial tubes are fundamental components of most animal organs , where they have multiple functions that include the transport of liquids , gases or food [1 , 2] . The C . elegans digestive tract provides a simple genetic model system for studying epithelial cell polarization and tube morphogenesis [3–7] . The digestive tract consists of three linked epithelial tubes , the pharynx , valve , and intestine . Like other organs in C . elegans , these tubes are built from remarkably few cells; the pharynx contains 80 cells , the valve contains 6 cells , and the intestine contains 20 cells [8] . The various tubes have a circumference of no more than 9 cells , and as few as one , donut-shaped cell . C . elegans is able to form what are essentially micro-organs because it is able to control the positions and three-dimensional shapes of individual cells , creating differences between adjacent , or even sister , cells . This control is most obvious in the pharynx , which contains several different types of cells organized with distinct and reproducible symmetries . Pharyngeal morphogenesis involves an intermediate , cyst stage , where cells have completed division , developed apicobasal polarity , and become wedge shaped . The cyst transforms into a tube as cells move into their final , cell-type specific positions , by rotating clockwise or counterclockwise around the central axis [9] . Similar rotations occur in the development of the valve and intestine tubes , but the cues that guide the cell rotations are not known . C . elegans has several genes that function in directed cell movements and that are conserved in higher animals . For example , the anterior migration of muscle processes requires the Eph receptor VAB-1 and an Ephrin , EFN-1 [10 , 11] . The dorsal-ventral guidance of some neurons is thought to involve a ventral gradient of the ligand UNC-6/Netrin [12–14] . Movement toward UNC-6/Netrin can be mediated , in part , by the receptor UNC-40 , a homolog of DCC ( Deleted in Colorectal Cancer ) , and movement away from UNC-6/Netrin can be mediated by the receptor UNC-5 acting with UNC-40/DCC [15] . A reciprocal , dorsal gradient of SLT-1/Slit is thought to repel some axons through its receptor , SAX-3/Robo [16] . Some of these same genes have developmental roles in other types of cells , such as in the dorsal-ventral extensions of muscles [17 , 18] . In this report , we examine cell positioning during intestinal morphogenesis . The intestine in a newly hatched larva is a narrow , twisted tube [8] . The epithelial cells are organized into an anterior-posterior series of transverse rows , called intestine rings or int rings . The first int ring ( int1 ) contains four cells , and all other int rings contain two cells each ( Fig 1A and 1D ) . Cells in a given int ring arise from symmetrical cell lineages and have similar shapes , but can differ from cells in other rings . For example , the int5 cells associate specifically with the two primordial germ cells ( Fig 1A ) , and only cells in the int3-int9 rings express the PHO-1 acid phosphatase [19] . All 20 intestinal cells are derived from a single cell in the 8-cell embryo , called the E blastomere; the molecular specification of the E blastomere has been studied extensively and involves multiple pathways [5] . An isolated , cultured E blastomere divides to form a rounded cyst of polarized cells , but never forms a tube [6] . In the intact embryo , the E descendants initially form a primordium , and each stage of the primordium is named according to the number of E descendants; for example , E8 or E16 [6 , 8] . Apicobasal polarity is visible by the E16 stage , when the primordium contains two distinct layers of cells . Specific cells intercalate orthogonal to the central axis , such that all cells eventually align in two , longitudinal rows; these movements establish the basic form of the tube . Next , a subset of the anterior int rings rotate around the lumen clockwise , as viewed from the front of the animal [8 , 20 , 21] . The purpose of these rotations , collectively called intestinal twist , is not understood , but the rotations require the LIN-12/Notch signaling pathway [21 , 22] . LIN-12 signaling occurs twice in the intestinal primordium . Between the E4 and early E8 stages , the receptor LIN-12 is expressed in all intestinal cells . Ligand-expressing , non-intestinal cells contact the left side of the primordium and activate the expression of REF-1 , a bHLH transcription factor that is a direct target of LIN-12 [22 , 23] . At least one function of REF-1 is to downregulate LIN-12 in the left cells , apparently through the transcriptional corepressor UNC-37/Groucho . Thus , only right cells continue to express LIN-12 at the E16 stage , when a second LIN-12/Notch interaction triggers a new round of REF-1 expression [22] . Mutant analysis has shown that the second interaction is required for twist , suggesting that at least one function of the first interaction is to generate left-right asymmetry for the second interaction [21] . Here , we used confocal microscopy to visualize intestinal cell shapes during morphogenesis . We generated 3D reconstructions of the developing primordium , and used the reconstructed stages to assemble a movie of intestinal morphogenesis . The color-coded movie and 3D reconstructions facilitate visual tracking of individual cells , and provide several new insights into intestinal morphogenesis . We show that individual intestinal cells are positioned with a reproducible choreography that implies early anterior-posterior and left-right differentiation between cells . We show that LIN-12/Notch signaling is required for left-right asymmetry in intestinal cell contacts , hours before the known requirement for LIN-12/Notch in intestinal twist . Finally , we show that several genes with roles in axon guidance are important for the circumferential rotations of intestinal cells; we provide evidence that the rotations function to align the lumen , which forms piecemeal in the individual int rings .
The anterior end of the intestine is attached to the pharynx by a small , connecting tube called the valve ( Fig 1A ) . The most posterior ring of valve cells contains a dorsal cell called v3D ( valve ring 3 Dorsal ) and a ventral cell called v3V ( valve ring 3 Ventral ) . The intestine is flanked by a ventral nerve cord , four muscle groups ( quadrants ) , and by a single layer of epithelial ( hypodermal ) cells ( Fig 1B ) . Transverse views of the body show five hypodermal cells , although the dorsal “cell” is a syncytium created by the fusion of individual dorsal hypodermal cells . The description of cell movements during intestinal morphogenesis is complicated by conventional , anatomical names for cells that indicate only lineage origins or final positions [8]; different problems arise with an alternative system we proposed that numbers cells solely according to their initial positions [6] . Here , we use a hybrid nomenclature that labels cells according to their int ring number , and as either R ( Right ) or L ( Left ) depending on their positions in the E16 primordium . The E16 primordium is a convenient reference point , as it has a simple , bilateral symmetry , and contains most of the final 20 intestinal cells . For example , 2R is a right cell in the E16 primordium that later forms part of the int2 ring ( Fig 1A ) . This nomenclature emphasizes that each int ring begins as one R cell and one L cell . The R and L cells in the int1 ring undergo an additional , dorsal-ventral division to form a 4-cell ring . Cells in the int8 ring also undergo an additional division , but this division is anterior-posterior and creates a new , int9 ring . We observed infrequent , anterior-posterior divisions of 7R and/or 7L , consistent with previous results that intestines in newly hatched larvae occasionally have more than 20 cells [20] . We used confocal microscopy to visualize intestinal cell shapes and cell contacts in embryos and in live , newly hatched larvae ( Fig 1C and 1D ) . In most experiments , embryos/larvae expressed three fluorescent reporters for general membranes , intestine-specific membranes , and for pharyngeal nuclei; the pharyngeal nuclei are included for spatial reference ( Materials and Methods ) . Cell contours were traced and used for three-dimensional , graphic reconstructions of the developing primordium ( Fig 1E; S1 Video ) and of the larval intestine ( Fig 1F , S2 Video ) . Inspection of the color-coded reconstruction illustrates some of the cell rearrangements that occur between the E16 stage and hatching . For example , 2R is a ventral cell in the E16 primordium ( Fig 1E ) , but becomes a dorsal cell in the fully formed intestine ( Fig 1F ) . Similarly , 4L and 6L are adjacent , sister cells in the E16 primordium , but are separated by 5L in the larval intestine . In essence , intestinal morphogenesis creates a longitudinal row of R cells that twists around a longitudinal row of L cells ( Fig 1F ) . Schematic diagrams of the intestine typically depict int rings as smooth discs ( Fig 1A and [5 , 8] ) . However , cells within an int ring are offset , like bricks in a wall , and the actual int rings have jagged boundaries ( Fig 1C and 1F ) . The offset extends to the interior , apical surfaces of cells , and results in a complex , ladder-like appearance of adherens junctions ( Fig 1G and 1H ) . Rotating the 3D reconstruction reveals that the peripheral , basal surfaces of the intestinal cells are predominantly hexagonal ( S2 Video ) . To describe the pattern of cell packing , we score the diagonal contacts between the lateral surfaces of cells in two , adjacent int rings ( Fig 1I ) . If the R cell in a given ring , n , contacts the L cell in the anterior , n-1 ring , we term the contact RaL ( R to anterior L ) . If the L cell in the n ring contacts the R cell in the n-1 ring , we term the contact LaR ( L to anterior R ) . If all four cells meet at a common vertex , we term the pattern Cross . Because the intestine is a narrow tube , rather than a flat sheet , additional diagonal contacts are possible between the same four cells ( Fig 1J ) . For example , 6R makes diagonal contacts with two different faces of 5L; both of these are RaL contacts . Thus , the complete description of diagonal contacts between adjacent int rings can involve a combination of RaL , LaR , and Cross contacts . We found that cells in the int5-int8 rings are almost exclusively hexagonal in the larval intestine , and that nearly all diagonal contacts are RaL+ RaL ( Fig 1J; Table 1 ) . The int2-4 rings can contain non-hexagonal cells , and can have both LaR and RaL diagonal contacts ( Table 1 ) . For example , int2 always makes equal RaL and LaR contacts with the 4-cell int1 ring; 2R to 1LD ( RaL ) and 2L to 1RV ( LaR ) ( Fig 1A , Table 1 ) . We refer to the general predominance of RaL contacts in the larval intestine as RaL asymmetry . Our 3D reconstruction of the developing primordium was generated from a single embryo , imaged at 8-minute intervals between the E8 and E20 stages ( S1 Video ) . Additional , partial reconstructions were generated from other embryos , and these showed essentially no differences from the reference reconstruction except for variation in the pattern of int2 intercalation ( see below ) . Selected timepoints from the reconstruction that represent key developmental events are shown in Fig 2A–2C . For clarity in presentation , all developmental times are referenced to the reconstruction ( S1 Video ) by matching the first timepoint in an image sequence to the closest timepoint in the reconstruction; subsequent time intervals are as indicated ( see Materials and Methods ) . We present our findings on cell shapes and contacts within a general outline of intestinal morphogenesis , but only briefly review events that have been described in detail elsewhere [5 , 6] . The E8 primordium is a relatively flat , rectangular plane of four R cells and four L cells ( Fig 2A ) . In most embryos , the planar shape of the E8 primordium appears to result from the oriented divisions of the E blastomere and its descendants [6] . The E blastomere divides anterior-posterior , the E2 cells divide right-left ( Fig 3A ) , and the E4 cells divide anterior-posterior ( 63% , n = 60; Fig 3E ) . However , previous studies noted variability in the positions of some E4 cells in wild-type embryos [24] , and we found that the E4 primordium can be tetrahedral and non-planar , or planar but shaped like a T ( 27% , n = 60; Fig 3B ) . Nevertheless , each of these embryos develops a planar E8 primordium ( 100% , n = 60 ) . Thus , a mechanism must exist to reposition variant cells before or during the E8 stage . Previous studies showed that VANG-1 has a role in intestinal development; VANG-1 is the only C . elegans homolog of Strabismus/Van Gogh , a component of the planar cell polarity ( PCP ) pathway [25] . Those studies showed that 32% of vang-1 mutants have abnormal intestines , but the origin of the defects was unknown . We found that the E4 primordium in vang-1 mutant embryos exhibited variability in shape that was similar to the variability observed in wild-type embryos . Like the wild-type primordium , the vang-1 E4 primordium could be rectilinear and planar ( 41% , n = 85 ) , or have a variant shape ( 59%; Fig 3C and 3D ) . By contrast with wild-type embryos , the vang-1 mutant embryos failed to reposition the variant cells , resulting in an E8 primordium that remained misshapen and non-planar ( 39% , n = 85 embryos; Fig 3F ) . Nearly all of the vang-1 embryos with a misshapen E8 primordium developed an abnormal E16 primordium , with defects in int ring organization that were similar to defects reported previously ( Fig 3G ) . These results suggest that oriented cell divisions act in concert with VANG-1-mediated cell positioning to align the wild-type E8 cells into a plane . The planar E8 primordium becomes a bilayered , E16 primordium [6] . Previous studies that used differential interference contrast ( DIC ) microscopy to follow nuclei suggested that diagonal divisions of the E8 cells might create the layering [6] . However , we found that the E8 cells reposition before cell division ( Fig 3H and S1 Video ) . Repositioning begins when the second , transverse row of E8 cells ( 2/5L and 2/5R ) constrict their dorsal surfaces , shifting their cell bodies ventrally . At about the same time , flanking cells in the first and third rows extend thin , sheet-like processes that close together over the second row . After closing , the dorsal contact between the first and third rows increases and further displaces the second row cells ventrally . Thus , when the E8 cells divide to form the E16 primordium , the daughters of the second row cells are ventral to other cells ( Fig 3H ) . The primordium shifts dorsally during the E8 stage ( Fig 3H ) , suggesting that the repositioning of the second row cells could result from forces that restrict their dorsal movement . For example , the primordial germ cells attach to the ventral surface of the primordium , where they “hitchhike” into the body interior [26] . We used a laser microbeam to kill the parent of primordial germ cells ( the P4 blastomere ) , but observed normal , ventral repositioning of the second row of E8 cells in each of five operated embryos . Similarly , we observed normal repositioning of the second row cells after ablating the precursor of the hypodermal cells that lie above the E8 primordium ( the C blastomere , 8/8 operated embryos ) . These results suggest that the ventral repositioning of the E8 cells results from forces generated within the primordium . The ventral repositioning of the second row of E8 cells , and the subsequent E8 cell divisions , expand the anterior face of the E16 primordium such that it covers the entire posterior surface of the adjacent pharynx/valve primordium ( Figs 2A and 3H ) [9] . It is not known whether ventral repositioning is essential to create the final , tubular shape of the intestine , but the expanded face appears critical for the proper polarization of the pharynx and valve cells ( see Discussion ) . After the pharyngeal and valve cells polarize , the face of the intestinal primordium reduces in size as the int5 cells ( S3 Video ) , and subsequently the int2 cells ( S4 Video ) , intercalate and re-align in a common axis with other E16 cells . The int5 and int2 intercalations begin at about 273 minutes and 329 minutes , respectively ( Figs 4A and 5A ) . Because previous studies showed that intestinal cells develop apicobasal polarity by about 257 minutes [6] , both intercalations involve oriented movements by polarized cells . In essence , the intercalating cells complete their int rings by closing together around their nascent apical surfaces . The intercalation paths appear invariant , with the int5 cells extending between the int4 and int6 rings , and the int2 cells extending between the int1 and int3 rings . The intercalations are led by thin , lateral protrusions that are visible by confocal microscopy as an increase in local , membrane fluorescence ( Fig 4B; see also [9] ) . As the leading poles of the intercalating cells extend , the trailing poles become rounded and then flattened ( Figs 4B and 5A ) ; we presume the rounding reflects de-adhesion/contraction of the trailing pole . The intercalating int5 cells do not appear to simply force aside flanking cells . Instead , the neighboring cells undergo invariant changes in cell shape that likely facilitate int5 intercalation ( S3 Video ) . First , the dorsal surfaces of the right and left int3 cells constrict markedly ( Fig 4C and 4D ) ; this constriction appears to shift int3 cytoplasm ventrally , before most of the int5 cytoplasm shifts dorsally . After the int5 protrusions reach and close together at the roof , there is an apparent dorsal constriction of the int4 cells; this latter constriction appears to shift int4 cytoplasm ventrally , allowing the int5 cells to expand longitudinally onto the roof ( Fig 4C ) . The intercalation/closure of the int2 cells is considerably more variable than for the int5 cells; it can span 24 to over 48 minutes , and does not show a consistent coupling with changes in the shapes of flanking cells ( Figs 5A and S1; S4 Video ) . The int2 ring intercalates and closes using one of two general modes ( diagrammed in Fig 5B; examples of further variations within each mode are shown in S1 Fig ) . The first mode closely resembles int5 intercalation/closure , and occurs in about 27% of wild-type embryos ( n = 7/26 ) . Here , both of the int2 cells extend to , and close together at , the dorsal midline ( Fig 5C ) . In the second mode , the 2L cell extends dorsally for variable distances , but stops or stalls before reaching the dorsal midline; the stalling nearly always occurs when the adjacent 1L cell divides ( Fig 5D and 5E ) . 2L does not resume intercalation , and 2R closes the ring by rotating clockwise , past the dorsal midline , until it reaches 2L on the left side of the primordium ( Fig 5B , 5D and 5E ) . Although 2R intercalation can also stall when the adjacent 1R cell divides , 2R always resumes intercalation/rotation . The longitudinal expansion of the int2 ring onto the roof can occur considerably after closure , by contrast with the immediate expansion of the int5 ring . Instead , int2 expansion occurs gradually , when the intestine and the body of the embryo begin to elongate ( Fig 5E; S1 and S3 Videos ) . We wanted to determine how int rings develop the predominately RaL pattern of cell packing observed in the fully formed intestine ( Fig 1F ) . The first diagonal contacts occur in the E4 primordium . In embryos with a planar , rectilinear E4 primordium , the contacts are either LaR ( 79% ) or Cross ( 21% , n = 28 embryos; Fig 3A ) . Some diagonal contacts begin to transition to RaL during the E8 stage ( Fig 3E and Table 2 ) , and most become RaL within one hour of the E16 stage ( Fig 6A and 6C; S5 Video ) . We discovered that the transition to RaL contacts requires the LIN-12/Notch signaling pathway; nearly half of lin-12 ( n941 ) null mutant embryos examined at the E16 stage have multiple LaR or Cross contacts ( Fig 6B and 6C ) . Live imaging showed that the transition to RaL involves dynamic neighbor exchanges ( Fig 6D and 6F ) . For example , a LaR contact can transition to Cross , remain for several minutes , and then return to LaR or convert to RaL . Filamentous actin , as indicated by dMoesin-ABD localization , appears to increase transiently in Cross contacts ( Fig 6E ) , suggesting that the Cross configuration is created by localized cell constrictions . Most wild-type int rings maintain RaL contacts after the E16 stage , although contacts in the in2-4 rings are modified by rotation ( Fig 6F and see below ) . Intercalation of the int5 cells breaks a RaL contact from 6R to 4L ( Fig 4D at 297 minutes ) , but that contact is soon replaced by new RaL contacts from 5R to 4L , and from 6R to 5L ( Fig 4D at 313 minutes ) . Similarly , the RaL contact from 3R to 1L breaks during int2 intercalation ( S1 Fig ) . By contrast with wild-type embryos , the int rings in lin-12 ( n941 ) mutant embryos continue to switch between LaR and RaL contacts throughout the E16 stage ( Fig 6F ) . The above analysis scored diagonal contacts at a single optical plane near the roof of the primordium ( the basal surface ) . However , inspection of complete optical stacks showed that some rings with a RaL contact near the roof could also have an internal LaR contact ( S2A and S2B Fig ) . We used the 3D reconstruction to approximate the entire surfaces areas engaged in RaL or LaR contacts over time ( S2D and S2E Fig ) . These measurements show that the transition to RaL contact involves the entire lateral surfaces of the cells , and that the increase in RaL occurs by the relative loss of LaR contact , rather than by an increase in cell volume ( S2C Fig ) . Recent studies have demonstrated that polarized cell rearrangements in Drosophila epithelial cells involve Toll family receptors that are expressed in an overlapping , striped pattern [27] . Toll signaling pathways are best known for their conserved roles in innate immunity , and C . elegans contains homologs of multiple genes in the innate immunity pathway [28] . Interestingly , mutants lacking TOL-1 , the sole C . elegans Toll-like receptor , have severe developmental defects that are not observed for putative null alleles of the other innate immunity genes [28] . We found that the int rings in tol-1 ( nr2013 ) mutant embryos had predominately RaL contacts , but had a higher frequency of non-RaL contacts than are observed in wild-type embryos ( Fig 6C ) . Although these results suggest that TOL-1 contributes to intestinal patterning , the mutant embryos have extensive defects in non-intestinal tissues that might preclude normal intestinal development [28] . The int2-4 rings undergo a clockwise , circumferential rotation , as viewed from the anterior of the primordium ( Fig 2B and 2C; S6 Video ) . Views from the right side of the primordium ( Fig 7A ) show 2L , 3L , and 4L moving into the optical plane as the complementary R cells move away . The rotation begins at about 337 minutes , and shifts the R/L boundaries at about 0 . 1–0 . 2 microns/minute ( Fig 2D ) . Through rotation , the 2R , 3R , and 4R cells become approximately centered below the dorsal midline , and 2L , 3L , and 4L become centered above the ventral midline . The int5 ring does not undergo a comparable rotation , but 5R and 5L shift positions anterior-posterior while remaining in contact with the dorsal midline ( Fig 2C ) . R and L cell movements are tightly coupled within a rotating int ring: As the leading pole of one cell advances ( arrowheads in Fig 7A ) , the trailing pole of the complementary cell retreats with no apparent gaps between cells . By contrast , there is no obvious longitudinal coupling between the rotations of different int rings . For example , a rotating 3R can contact either 2L or 1L , depending on whether int2 has intercalated . Transverse views of rotating cells show that the trailing pole is rounded and that the leading pole extends a thin , basal protrusion over the complementary cell; the protrusion appears to fill with cytoplasm as the cell advances , and a new basal protrusion forms ( Figs 7B , S3A and S3B ) . Cell positions in the intestine of a newly hatched larva show that the posterior L and R cells must also rotate from their initial , bilaterally symmetrical positions ( Fig 1F ) . These events were not studied in detail here , as they occur after the embryo begins to move and roll within the eggshell . However , we found that the initial stages of int7 rotation could be imaged in some embryos , and that the rotation was always counterclockwise , opposite the rotation of the anterior int rings ( n = 8 embryos; Fig 7C ) . The embryos with int7 rotation did not show any rotation of the int8 or int9 cells , indicating that those rotations must occur later in development; the int8 and int9 cells are the last cells to be born in the primordium ( Fig 2A at 401 minutes ) , which might delay their rotations relative to other cells . We identified contacts between the intestine primordium and non-intestinal cells before and during rotation of the int2-int4 rings . The contacts are diagrammed in Fig 8A , and changes in the surrounding cells are described in detail in S3 Fig . Briefly , a region below the dorsal hypodermal cells that we term bz1 ( basal zone 1 ) is in direct contact with the primordium . Before int ring rotation , bz1 is a broad region that initially contacts R and L cells ( Fig 8B; 249–289 minutes ) . The dorsal hypodermal cell spreads circumferentially , and bz1 narrows such that it often contacts only , or predominantly , R cells ( Fig 8B; 305 minutes ) . As rotation begins , the 2R , 3R , and 4R cells continue clockwise past bz1 and under the left dorsal muscles ( Fig 8B; 321 minutes ) . By contrast , the int1 and int5 rings can rotate slightly counterclockwise at this stage , such that L cells that lost contact with bz1 regain contact ( Fig 8B; compare 305 minutes with 321 minutes ) . During the int ring rotations , the left and right muscle groups that flank the primordium split dorsal-ventral to form the four muscle quadrants ( Fig 8A ) . The dorsal hypodermis inserts between the dorsal and ventral muscle quadrants , and contacts the primordium at a new zone we term bz2 ( basal zone 2; Fig 8A at 361 minutes ) . 2R , 3R , and 4R rotate under bz2 , but stop before or at the boundary between bz2 and the left seam hypodermal cell ( Fig 8A and 8C; Table 3 ) . In the early E16 stage , 2L and 2R meet symmetrically at the ventral midline ( Fig 8A at 249 minutes ) . At around 289 minutes , however , 2L invariably shifts clockwise to replace , or displace , 2R at the midline; 2L shifts for variable distances over ventral neurons ( Fig 8D ) and behind the valve midline cell , v3V ( Fig 8E ) . We call this event the pre-rotation shift of 2L , as it can occur 30 or more minutes before any other evidence of rotation in the int2-4 rings . This asymmetric shift requires the LIN-12/Notch signaling pathway , as 2L and 2R remain symmetrically at or near the ventral midline in lin-12 ( n941 ) mutant embryos ( 10/10 embryos; Fig 8F ) . At the stage when the wild-type 2L shifts over ventral neurons , the int3 cells are separated from ventral neurons by the int2 cells ( Fig 8A at 289 minutes ) , and the int4 cells are separated from ventral neurons by the primordial germ cells ( Fig 7A at 337 minutes ) . The int3 and int4 cells begin to rotate clockwise soon after they first contact the ventral neurons . The int3 cells make contact when they spread ventrally , behind the int2 cells , and the int4 cells make contact when the primordial germ cells are partially engulfed by the int5 cells and/or displaced posteriorly by elongation of the intestine ( Fig 7A ) . The rotating 2L , 3L , and 4L cells continue clockwise rotation over ventral neurons and the left ventral muscle quadrant , stopping under the right ventral hypodermal cell or left seam hypodermal cell ( Figs 8A and S3D ) . We used a laser microbeam to ablate various blastomeres that are precursors of cells surrounding the primordium; the precursors were identified according to the embryonic lineage data [8] . We first ablated the precursor of the primordial germ cells , which initially separates the int4 cells from ventral neurons . In each of the operated embryos , 4L contacted ventral neurons prematurely and rotated prematurely; either at the same time as , or before , 2R and 3R ( 2/6 cases before 2R , 4/6 before 3R; Fig 9A ) . These results suggest that the primordial germ cells inhibit or delay int4 rotation . To examine whether v3V was required for the pre-rotation shift of 2L , we ablated a precursor of v3V . We found that 2L usually shifted over the ventral neurons ( 5/6 embryos; Fig 9B ) , although in one embryo both 2L and 2R remained at the ventral midline . Thus , contact with v3V is not essential for the pre-rotation shift of 2L . At later stages , the operated embryos showed an interesting correlation between int ring rotation and the position of the ablated blastomere . If the ablated blastomere was outside the body or was anterior of the primordium ( 4/6 embryos ) , the int2 and int3 rings contacted ventral neurons and rotated , as in normal embryos ( Fig 9C and 9D , respectively ) . However , if the ablated blastomere was lodged between the int rings and the ventral neurons , the int rings did not rotate ( 2/6 embryos; Fig 9E ) . We found that we could create a similar separation between the int rings and the ventral neurons by ablating precursors of anterior muscles that normally have no contact with the primordium; again , the int rings did not rotate ( 6/6 embryos , Fig 9F ) . These results support a hypothesis that int ring rotation involves contact with ventral neurons . To examine possible roles for dorsal cells , we ablated three precursors that together form the dorsal muscle quadrants flanking the int2-4 rings ( Fig 9G ) . Most of these embryos showed complete , or nearly complete , rotation of the int rings ( 5/6 embryos; the remaining embryo had severe body defects and could not be evaluated ) . Finally , we ablated precursors of the dorsal hypodermal syncytium that contacts the intestine primordium; these operated embryos showed little or no int ring rotation ( 7/7 embryos; Fig 9H ) . In addition , most of these embryos had a defect in int2 intercalation/closure , such that 2L and/or 2R failed to reach the dorsal midline ( 6/7 embryos; Fig 9H ) . We repeated these ablation experiments to examine earlier stages of the primordium . As predicted from the embryonic lineage , the ablations created an early , cell-free gap over most of the dorsal surface of the primordium ( Fig 9I ) . The early events of primordium development appeared normal: The int5 cells intercalated in all of the operated embryos , and the dorsal surface of the primordium developed normal RaL asymmetry ( 5/5 embryos , Fig 9I ) . On the ventral surface , 2L showed the normal , pre-rotation shift across ventral neurons and behind v3V ( 4/4 embryos , Fig 9J ) . However , the muscle groups failed to separate into quadrants in the operated embryos , and instead all muscles clustered ventrally ( Fig 9I ) . Thus , the intestine primordium in these operated embryos developed without the normal contacts to either the dorsal hypodermal cells or dorsal muscles . Because our results above show that dorsal muscles are not essential for rotation , we infer that rotation requires the dorsal hypodermis . In searching for genes involved in the various aspects of intestinal morphogenesis , we examined the C . elegans Ephrin pathway . C . elegans has a single Eph receptor ( VAB-1 ) , three conventional Ephrin ( EFN-1 , EFN-2 , and EFN-3 ) and a fourth , highly diverged Ephrin ( EFN-4 ) [29–31] . Mutants defective in any of these genes are viable , but have defects in body morphology [29–32] . We immunostained fixed embryos for adherens junctions , and scored the number of longitudinal segments as a proxy for cell number ( see Legends to Fig 10 and Table 4 ) . We found that intestine organization appeared normal in vab-1 mutants , in single mutants defective in any one of the ligands efn-1 , efn-2 , or efn-3 , and in triple mutants defective in all three of those ligands ( Fig 10 , Table 4 ) . By contrast , mutant embryos defective in the ligand efn-4 appeared to have abnormal numbers of intestinal cells adjacent to the int1 ring ( Fig 10 , Table 4 ) . Previous studies showed that the ligand EFN-4 has a role in epidermal morphogenesis that is at least partly independent of the Eph receptor VAB-1 , and that appears to involve MAB-20/semaphorin-2a [32 , 33] . Semaphorins are secreted and transmembrane proteins that act as short-range signals in a variety of roles , including axon guidance , cell migration , and tissue morphogenesis [34–38] . In addition to the secreted semaphorin MAB-20 , C . elegans encodes two transmembrane semaphorins called SMP-1 and SMP-2 [37 , 39] . We found that nearly all mab-20 mutant embryos , but no smp-1 or smp-2 embryos , have a defect in intestinal adherens junctions that is very similar to that of efn-4 embryos ( Fig 10 , Table 4 ) . Plexins are a major class of transmembrane receptors for semaphorins , and C . elegans encodes two plexins , PLX-1 and PLX-2 [40 , 41] . PLX-2 can bind to MAB-20 in biochemical studies , and PLX-1 appears to be expressed in intestinal cells [40 , 41] . However , the pattern of intestine adherens junctions appeared normal in plx-2 mutants , and in plx-1 plx-2 double mutants ( Table 4 ) , suggesting that the plexins are not essential receptors for MAB-20 in intestinal morphogenesis . Axon guidance along the midline of C . elegans can involve the receptor SAX-3/Robo and the secreted ligand SLT-1/Slit [16] . We found that about half of sax-3 mutant embryos have a defect in adherens junction patterning that appears identical to that of efn-4 and mab-20 mutant embryos , but that slt-1 mutants have no apparent defects ( Fig 10 , Table 4 ) . This phenotypic difference between sax-3 and slt-1 mutants is consistent with previous results showing that ( 1 ) sax-3 mutants have up to 80% embryonic/larval lethality , while most homozygous slt-1 mutant embryos are viable [16 , 42] , and that ( 2 ) SAX-3 can have both SLT-1-dependent and SLT-1–independent functions in axon migration [43] . We crossed fluorescent reporters for intestine-specific and/or general membranes into efn-4 , mab-20 , and sax-3 mutant strains to examine intestinal morphogenesis . We found that in each of the mutant embryos the abnormal pattern of adherens junctions resulted from a failure in int2 intercalation/closure . This defect resulted in a hybrid second ring with 3 cells ( 2L , 2R , and 3R; Fig 11A and 11C ) or occasionally 4 cells ( 2L , 2R , 3R , and 3L; Fig 11D ) . In normal development , 2R intercalation breaks R to R contacts from 3R to 1RD , and breaks diagonal RaL contacts from 3R to 1LD . In the sax-3 and mab-20 mutant embryos , 2R usually broke the R-R contact , but did not break the RaL contact . The efn-4 mutant embryos had a similar but more severe defect: 2R extended a lateral protrusion toward the dorsal midline , but the cell body either remained ventral , or retracted after shifting dorsally ( Fig 11E and 11F ) . Other aspects of intestinal morphogenesis appeared normal in the sax3 , mab-20 , and efn-4 mutant embryos ( see legend for Fig 11 ) . For example , all embryos showed the pre-rotation shift of 2L over ventral neurons , and most embryos developed RaL asymmetry ( Fig 11G ) . Both R and L cells in the int2-4 rings rotated , with the exception of 2R , which failed to cross the dorsal midline ( Fig 11A ) . However , the centering of the int1 R-L boundary ( Figs 8B and S3D ) appeared defective in some sax-3 mutant embryos ( Fig 11B ) . The efn-4 mutant embryos appeared to have additional , variable defects in maintaining some adhesive contacts between intestinal cells as follows . When wild-type int3 cells constrict at the beginning of int5 intercalation ( Fig 4D ) , the efn-4 mutant int3 cells could detach partially from each other and lose contact with the dorsal roof of the primordium ( 6/17 embryos; Fig 4E ) . Similarly , when the int4 cells constrict at the end of int5 intercalation , they could detach partially from each other ( 2/17 embryos ) . Although most efn-4 embryos eventually developed RaL asymmetry , several appeared defective in forming and/or maintaining RaL contacts ( Fig 6F ) . Previous studies showed that a mab-20::GFP reporter is expressed in all cells of the embryo beginning at about 240 minutes [39] . EFN-4-GFP is expressed in neural or epidermal precursors beginning at about 150 minutes , but intestinal expression has not been reported [32 , 40] . We crossed the latter reporter into a strain expressing a general membrane reporter , and found that EFN-4-GFP is detectable in intestinal cells prior to int5 intercalation ( Fig 11H ) . EFN-4-GFP is not detectable in intestinal cells at later stages , but is abundant in a subset of pharyngeal and valve cells anterior to the intestine ( Fig 11H ) . As expected for a secreted protein , EFN-4-GFP accumulates at high levels in all visible extracellular spaces within the embryo ( asterisk in Fig 11H ) . In an earlier report , we noticed what appeared to be only minor defects in the intestine primordium of mutants defective in axon guidance molecules such as UNC-6/netrin and its receptors UNC-40/DCC and UNC-5 [21] . As suitable fluorescent membrane reporters were not available , those early studies used DIC microscopy to infer intestinal cell rotation by nuclear positions . In the present study , we immunostained fixed unc-6 , unc-40 , and unc-5 mutant embryos for adherens junctions , and found that the mutants had the wild-type number of cells in the various intestinal rings ( Fig 10 and Table 4 ) . However , we were prompted to reconsider these genes after discovering that a mutation in MADD-2 ( Muscle Arm Development Defective-2 ) causes a penetrant defect in intestinal twist . MADD-2 is a multidomain protein with a RING finger , which functions as an E3 ubiquitin ligase; MADD-2 regulates signaling by UNC-40/DCC during C . elegans muscle development [44–46] . Briefly , we isolated a twist-defective mutant , zu475 , from a pilot screen for intestinal morphogenesis mutants . Mapping and genome sequencing showed that the twist defect is closely linked with two candidate mutations , including an S684L missense mutation in the madd-2 gene; the identical mutation had been described previously for the madd-2 ( tr162 ) allele [44] . We obtained a madd-2 ( tr162 ) mutant strain , and found that it has a twist defect apparently identical to that of zu475 [hereafter , madd-2 ( zu475 ) ] . We built unc-6 , unc-40 , madd-2 , and unc-5 strains expressing fluorescent membrane reporters to examine intestinal twist . unc-5 mutants embryos appeared to have normal intestinal twist . However , almost none of the unc-6 , unc-40 , or madd-2 mutant embryos showed normal rotation of all three of the int2-4 rings ( Fig 12A–12D; Table 5 ) . We examined rotation in transverse projections of comparably staged , wild-type and mutant embryos ( Fig 12E–12I ) . In wild-type embryogenesis , the dorsal midline valve cell , v3D , is born on the left side of the embryo , but rotates counterclockwise beneath bz1 ( Fig 12E ) [9] . v3D rotated normally in all unc-6 , unc-40 , and madd-2 mutant embryos ( n = 18–25 embryos each; Fig 12F–12H ) . However , v3D failed to rotate in most unc-5 mutants , and instead remained largely on the left side of the embryo ( n = 13/19 embryos; Fig 12I ) . The int1 ring appeared normal in all of the mutants , with both R and L cells contacting bz1 . The int2-4 rings rotated normally in the unc-5 mutants ( Fig 12I; Table 5 ) , but int ring rotation was defective in the unc-6 , unc-40 , and madd-2 mutants as follows . The leading poles of 3L and 4L remained at or near the ventral midline ( Fig 12F–12H ) . The leading poles of 3R and 4R usually remained at or near the dorsal midline ( Fig 12B ) , but could also be shifted a short distance clockwise or counterclockwise beneath the left or right muscle quadrants ( quantitated in Fig 8C and Table 3 ) . When an L cell crossed the dorsal midline counterclockwise , the complementary R cell sometimes crossed the dorsal midline clockwise ( Fig 12D ) . This resulted in a partial , dorsal interdigitation of R and L cells within an int ring , but did not result in mixing of cells between different int rings . Despite defects in rotation , many of the mutant rings had dorsal-ventral nuclei , similar to those of rotated , wild-type int rings ( see for example the int3 ring in Fig 12F ) . By contrast with the penetrant defects in int3 and int4 rotation , the int2 ring was fully rotated in 30–40% of the unc-6 , unc-40 , and madd-2 mutant embryos ( Fig 12C; Table 5 ) . Surprisingly , we found that the direction of int2 rotation was usually reversed; counterclockwise instead of clockwise . We found that most aspects of morphogenesis appeared normal in unc-5 , unc-6 , and madd-2 mutant embryos; for example , the embryos developed RaL asymmetry , int5 intercalated after int3 constriction , and the int2 cells intercalated ( n = 14–26 embryos each; Fig 12J and12K ) . The unc-5 mutants had the normal clockwise , pre-rotation shift of 2L over ventral neurons ( Fig 12L ) . However , this shift did not occur in unc-6 or madd-2 mutant embryos , or the direction was reversed with 2R spreading counterclockwise over the ventral neurons ( Fig 12M ) . As the int1 cells spread ventrally , prior to division ( see Fig 5C ) , 2R made an ectopic RaL contact with 1L and spread counterclockwise ( Fig 12N ) . This ectopic RaL contact appeared to shift the 2R cell body ventrally , such that the intercalating 2L cell reached the dorsal midline first ( Fig 12O at 329 minutes ) , and thereafter rotated across the midline ( Fig 12O at 337 minutes ) . These observations suggest that novel RaL contacts might contribute to the reversed , clockwise rotation of the int2 ring . We noticed morphological defects in the intestinal lumen of unc-6 , unc-40 , and madd-2 mutant embryos near hatching . The wild-type lumen appears to develop piecemeal in the individual int rings [6 , 47] , and as the lumen assembles and increases in width it closes to resemble a flattened or collapsed tube ( Figs 13A and S4 ) . In late embryogenesis , the closed lumen in the int2 and int3 rings is oriented horizontally , parallel to the boundary between the R and L cells ( Fig 13A ) . Surprisingly , we found that the closed lumen in the 4-cell int1 ring is also oriented horizontally , which is perpendicular to the boundary between the R and L cells ( Figs 13A and S4A ) . Before the dorsal-ventral division of the 2-cell int1 ring , the nascent apical membranes of 1R and 1L are oriented vertically , similar to other 2-cell int rings ( S4E Fig ) . After division , however , the apical membranes of the int1 daughter cells align in a horizontal plane ( S4E Fig ) . Thus , rotation of the int2-int4 rings appears to align their respective segments of the developing lumen with that of the shifted int1 lumen ( Fig 13B ) ; when the embryo moves in the eggshell , the lumen bends smoothly in the sagittal plane ( Fig 13C and 13D ) . The int1 lumen has the normal , horizontal orientation in unc-6 , unc-40 , and madd-2 mutant embryos , but is kinked either at the int1-int2 junction ( Fig 13E ) , or at the int2-int3 junction ( Fig 13F ) , depending on whether the int2 ring rotated . We conclude that at least one function of int ring rotation is to align the closed lumen between the anterior int rings , and that this alignment is defective in the mutant embryos . Previous studies showed that unc-6 is expressed in the right and left ventral hypodermal cells from about 280 to 340 minutes , before these cells extend to the ventral midline ( Fig 8A at 321 minutes ) [48] . However , the localization of secreted UNC-6 has not been determined . The receptor UNC-40 appears to be expressed in all embryonic cells until about 100 minutes , but disappears by about 400 minutes in most cells except ventral neurons [15] . Expression of MADD-2::GFP has not been reported in intestinal cells , but is observed in ventral hypodermal cells , myoblasts , and neurons [44 , 45] . We crossed a MADD-2::GFP reporter into a strain expressing the general membrane reporter , and found that MADD-2::GFP is expressed in intestinal cells in a complex and dynamic pattern ( Fig 14A and 14B ) . MADD-2::GFP is expressed at a low level in all intestinal cells before 297 minutes , but in older embryos expression is lost in the int1 ring and in most R cells . Interestingly , MADD-2::GFP is expressed asymmetrically in the L cells of the int2-int4 rings . The second LIN-12/Notch interaction in the intestine primordium is required for int ring rotation , activates expression of the repressor REF-1/bHLH in R cells , and requires the ligand APX-1/Delta [21 , 22] . We crossed MADD-2::GFP and the membrane reporter into a temperature sensitive apx-1 ( zu347ts ) background , then shifted the mutant embryos to restrictive temperature at the E8 stage . Each of 23 embryos lacked int ring rotation and showed symmetrical expression of MADD-2::GFP in R and L cells ( Fig 14C ) . These results suggest that asymmetric MADD-2 expression contributes to the different behaviors of R and L cells during rotation of the wild-type int rings .
The planar E8 primordium splits to become a two-layered E16 primordium . Because the E8 cells divide anterior-posterior , this split compacts the E16 primordium . In addition , the split expands the anterior face of the primordium such that it contacts the entire posterior surface of the pharynx/valve primordium; this contact appears critical for the proper polarization of the pharynx/valve cells [9 , 56] . We found that the split begins when two E8 cells shift ventrally; the daughters of those cells later intercalate to realign with the other intestinal cells . Our 3D analysis of the developing primordium shows that the split and subsequent cell intercalations each involve specific groups of anterior-posterior cells , and that cells behave differently within each group . For example , the split in the E8 primordium always begins by the dorsal constriction of the second , transverse row of cells . The int5 cells are always the first to intercalate at the E16 stage , and they follow an invariant path between the int4 and int6 cells . We described invariant changes in the shapes of the int3 and int4 cells during int5 intercalation , and propose that these changes facilitate intercalation by shifting mass away from the roof . These reciprocal movements allow intercalation to occur without changing the overall shape of the E16 primordium; this constancy is in marked contrast with the intercalating hypodermis , which expands like a sheet over underlying tissues [57] . The remarkable choreography of the intestinal cell movements suggests that unknown molecular differences subdivide the early primordium along the anterior-posterior axis . Key tasks in future studies will be to identify the anterior-posterior differences , and determine how those differences lead to cell-specific behaviors . The intestinal cells express signaling molecules such as EGL-15/FGF and CWN-2/Wnt , and EGL-15 appears to have a role in intestinal morphogenesis [25 , 58] . The E blastomere , and all of its descendants , expresses the GATA transcription factors END-1 and END-3 [59 , 60] , but very few transcription factors have been identified that are restricted to subsets of E descendants . These include the labial-like Hox gene ceh-13 and the Abd Hox gene nob-1 , which are expressed in posterior E descendants [61 , 62] . The transcription factor POP-1/TCF functions in combinatorial gene regulation , and is expressed asymmetrically between each pair of anterior-posterior sisters in the E8 primordium [21 , 63–65] . POP-1 appears to have a role in determining the normal anterior-posterior boundaries of intestinal twist [21]; however , genes that are regulated by POP-1 in primordium have not been identified . Despite the considerable repositioning of intestinal cells during morphogenesis , they do not intermingle with non-intestinal cells . Thus , the adhesive forces that maintain intestinal cell cohesion must be balanced with forces that move cells within the primordium . Our results suggest that the secreted ligands EFN-4/Ephrin and MAB-20/semaphorin-2a , and the receptor SAX-3/Robo , have roles in determining that balance . Previous studies showed that EFN-4 and MAB-20 function in the development of the C . elegans male tail , where specific subsets of neurons and structural cells group together to form structures called rays [39 , 66] . Adjacent rays can fuse inappropriately in efn-4 and mab-20 mutant males , suggesting that EFN-4 and MAB-20 contribute to ray-specific adhesion , possibly by preventing non-specific cell contacts [33 , 39 , 67] . Mutations in sax-3 do not appear to affect ray fusion , but result in defects in body morphogenesis [68] . In the intestinal primordium , mutations in efn-4 , mab-20 , and sax-3 result in similar defects in int2 intercalation/closure . Although plexins can function as receptors for semaphorins in other systems [69] , we found that mutants defective in the plexins PLX-1 and PLX-2 do not have intestinal phenotypes similar to mab-20 mutants; our results are consistent with previous studies suggesting that MAB-20 interacts with a unknown , non-plexin receptor in C . elegans [40] . The phenotypic similarity of mab-20 and sax-3 mutants is intriguing , and it will be important in future studies to investigate possible relationships between these proteins . Although the efn-4 , mab-20 , and sax-3 mutants are defective in the intercalation/closure of the int2 ring , they show no apparent defects in the intercalation/closure of the int5 ring . In both intercalations , the trailing poles of the cells round up , suggesting that de-adhesion/contraction at the trailing pole contributes to force generation at the leading pole , and both intercalations are led by lateral protrusions , which likely generate traction forces through adhesion to flanking cell surfaces . One possible difference is that int5 , but not int2 , intercalation appears to be facilitated by reciprocal movements of flanking cells . A second difference is that the int2 , but not int5 , cells intercalate while the flanking int1 cells are either beginning , or completing , cell division . We showed that the extension of 2R and 2L stalls or stops if the adjacent int1 cell divides , and studies in other systems have shown that dividing cells can transiently reduce adhesion to their neighbors [70 , 71] . Finally , int2 intercalation overlaps with elongation of the primordium , and longitudinal , pulling forces might exacerbate defects in lateral adhesion . Early studies on C . elegans development noted that the intestine was twisted with a reproducible handedness [20] . Any functional significance of the twist was not known , but twist has been speculated to affect the later growth of the gonad , which intertwines with the intestine [5] . We found that the intestinal lumen can be kinked in mutant embryos defect in int ring rotation , and propose that twist functions , at least in part , to align the developing lumen between consecutive int rings . The embryo develops within the fixed volume of the eggshell , and lumens in the intestine and other organs increase in width as closed or flattened structures that dilate just before or after hatching [6] . The closed lumen in the 4-cell int1 ring is oriented horizontally , possibly to link with the lumen of the 2-cell valve ring ( Figs 1A and S4A ) . The clockwise rotation of the int2-int4 rings shifts their apical membranes toward the horizontal ( S4E Fig ) , forming a smooth arc between the horizontal int1 lumen and the near vertical int5 lumen ( Fig 1A ) . The int5 lumen remains vertical because the int5 cells don’t rotate , possibly to allow each cell to associate with , and partially engulf , one of the two ventral primordial germ cells [26] . The lumen orientation returns to horizontal in the posterior int rings; we showed that the int7 ring rotates counterclockwise , and presume that the int8 and int9 rings undergo similar rotations later in embryogenesis . Because worm musculature dictates dorsal-ventral bending of the body , a predominately horizontal orientation might allow the lumen to flex like a diving board in response to body contraction ( Fig 13C ) , rather than a board set on edge . Int ring rotation is a remarkable example of coordinated cell movements , where each cell smoothly replaces , or displaces , the complementary cell . We showed that ventral neurons and dorsal hypodermal cells appear to contribute to intestinal morphogenesis , as removing these cells or blocking their contact with the intestine prevents or delays int ring rotation . Importantly , we did not observe ventral-specific , or dorsal-specific defects in any of our ablation experiments , consistent with the view that the movements or R and L cells are strictly coupled . UNC-6/netrin is expressed in left and right ventral hypodermal cells during rotation , but the localization of secreted UNC-6 is not known . Thus , ventral neurons might have a role in presenting or concentrating secreted UNC-6 , or contribute additional factors involved in rotation . Similar possibilities exist for the role of the dorsal hypodermal cells in int ring rotation . The UNC-6/netrin signaling pathway is best known for its role in axon guidance . Several neurons extend axons circumferentially during embryonic and postembryonic development in C . elegans . Ultrastructural reconstructions of the developing nervous system show that the circumferential growth of axons begins at about 480–500 minutes [72] , which is more than one hour after the int2-4 rings have completed rotation . The embryonic neurons have very small growth cones that are less than a micron in thickness and only a couple of microns in width [72] , sizes comparable to the basal protrusions from rotating intestinal cells . The growth cones travel over a basal lamina that is clearly visible by both electron microscopy and immunocytochemistry . By contrast , basal lamina components such as laminin and type IV collagen show little or no accumulation near the rotating int rings [56 , 73] . A rotating L cell develops a leading pole at the ventral midline , and an R cell develops a leading pole at the dorsal midline; this dorsal-ventral asymmetry between R and L cells provides a likely explanation for why rotation is clockwise . The asymmetry might result from an intrinsic chirality , as proposed for epithelial cells in the Drosophila hindgut [74] , or the leading poles could be induced by local , environmental cues; most L cells appear to lack a ventral leading pole in unc-6 mutants , suggesting that UNC-6/netrin might normally induce this pole . For example , UNC-6 can orient clustering of the receptor UNC-40/DCC , and UNC-40 can recruit F-actin effectors during anchor cell invasion in C . elegans [75] . Although UNC-40 appears to be expressed in all embryonic cells [15] , we showed that MADD-2::GFP is enriched in L cells; MADD-2 binds directly to UNC-40 , and appears to regulate UNC-40 activity [44 , 45] . Thus , MADD-2 activity in L cells might induce a ventral leading pole in response to UNC-6 . The late rotations of the wild-type , posterior int rings were not analyzed in the present study . However , at least the int7 ring undergoes a counterclockwise rotation , where the leading pole of 7L is dorsal , rather than ventral . MADD-2::GFP has a complex expression pattern in the posterior int rings , and it remains to be determined how or if the UNC-6/netrin pathway functions in the posterior cells . The apparent coupling between R and L cell movements in wild-type int rings raises the possibility that L cell-specific defects in unc-6 mutants could disrupt R cells indirectly . Some observations suggest that the 2L-4L cells develop an inappropriate dorsal leading pole in unc-6 , unc-40 , and madd-2 mutant embryos . First , L cells can partially rotate counterclockwise past the dorsal midline , with a dorsal morphology that is the mirror image of normal R cell morphology . Second , the dorsal tips of L cells can interdigitate with the tips of R cells , as though both tips attempt to lead . One interesting possibility is that unknown dorsal cues normally induce the leading poles of R cells , and can induce dorsal leading poles in both R and L cells in the absence of UNC-6/netrin signaling . If so , this might provide an explanation for why int2 , but not int3 or int4 , frequently undergoes a reversed , counterclockwise rotation in unc-6 , unc-40 , and madd-2 mutant embryos . In both wild-type and unc-6 mutant embryos , RaL asymmetry ensures that 3R and 4R occupy most of the dorsal midline before rotation . However , 2R must intercalate to reach the dorsal midline . In wild-type embryos , the pre-rotation shift of 2L moves it away from the dorsal midline , and 2R usually arrives at the dorsal midline before , or at about the same time , as 2L ( Fig 5B ) . The pre-rotation shift of 2L does not occur in the unc-6 mutant embryos , apparently allowing 2R to make an ectopic RaL contact with 1L that shifts 2R away from the dorsal midline ( Fig 12N ) . Thus , 2L can intercalate and occupy the dorsal midline before 2R ( Fig 12O ) . In conclusion , the intestine primordium provides a tractable genetic model for analyzing and modeling how cells assemble 3D structures . Important questions for future studies include how cell adhesion is established and modulated during intestinal cell movement , and the identification of factors that specify anterior-posterior patterning should provide useful tools to dissect and re-engineer cell behaviors in the primordium . Finally , the intestine is a relatively simple system for analyzing the molecular functions of known axon guidance genes , and for possibly identifying new ones .
General nematode culture was as described [78] . All animals were grown at 20–22°C , except for vang-1 mutants , which were grown at 25°C . Except for the JJ strains or noted otherwise , strains and alleles were obtained from the Caenorhabditis Genetics Center ( http://cbs . umn . edu/cgc/acknowledging-cgc ) . The “wild-type” strain used for imaging was JJ2360 , which is N2 Bristol containing reporters for intestine-specific membranes , general membranes , and for pharyngeal marginal cell nuclei . The general membrane reporter , ItIs44 , encodes mCherry linked to a pleckstrin homology domain , which binds to phosphoinositide lipids at the plasma membrane [79] . The intestine-specific membrane reporter , zuIs70 , encodes GFP linked to a CAAX sequence , which is targeted to the membrane by prenylation [80] . JJ2360 was built from the strains OD70 ( ltIs44 [pie-1::mCherry::PH ( PLC1-1 ) [81] , SM202 ( pax-1::HIS-GFP; rol-6 ) [82] , and JJ1609 ( zuIs70 ( end-1::GFP::CAAX; him-8 ( e1489 ) [80] ) . Unless noted otherwise below , constructions using JJ2360 transferred all three of the fluorescent reporters . Additional strains: JJ2323 [TH110 ( pie-1::mCherry::PAR-6 ) [83] ( gift from Tony Hyman ) and xnIs96 [hmr-1::HMR-1::GFP] [84] ( gift from Jeremy Nance ) ] , JJ2482 [efn-2 ( ev658 ) and JJ2369] , JJ2483 [vab-2 ( e96 ) and JJ2360] , JJ2484 [vab-1 ( dx31 ) and JJ2360] , JJ2486 [efn-4 ( bx80 ) and JJ2360] , JJ2492 [lin-12 ( n941 ) /unc-32 ( e189 ) and JJ2360] , JJ2502 [EFN-4-GFP built from CZ1566 ( lin-15B ( n765 ) juIs109 ) and OD70] , JJ2515 [vang-1 ( tm1422 ) and JJ2360 ) , nnIs[unc-119 ( + ) pie-1 promoter::gfp::Dm-moesin437-578[76] gift from Fabio Piano] , JJ2517[mab-20 ( ev574 ) and JJ2360] , JJ2521 [plx-1 ( ev724 ) ;plx-2 ( ev773 ) and JJ2360] , JJ2528 [madd-2 ( zu475 ) and JJ2360] , JJ2529 [unc-5 ( e53 ) and JJ2360 ) ] , JJ2530 [unc-40 ( e1430 ) and JJ2360] , JJ2531[tol-1 ( nr2013 ) and JJ2360 ) ] , JJ2532 [sax-3 ( ky123 ) and OD70 , SM202] , JJ2534 [unc-6 ( ev400 ) and JJ2360] , ] , JJ2536 [RP835 ( MADD-2-GFP ) gift from Peter Roy and OD70] , and JJ2537 [apx-1 ( zu347ts ) , RP835 ( MADD-2-GFP ) , and OD70] . Other mutant alleles examined are as follows: apx-1 ( zu347ts ) V , efn-1/vab-2 ( e96 ) IV , efn-2 ( ev658 ) IV , efn-2 ( ev658 ) IV; efn-3 ( ev696 ) X , efn-4 ( bx80 ) IV; him-5 ( e1490 ) V , lin-12 ( n941 ) , mab-20 ( ev574 ) , madd-2 ( tr162 ) V; tris25;rrf-3 ( pk1426 ) II ( gift from Peter Roy ) , madd-2 ( zu475 ) V , mab-20 ( ev574 ) ( gift from Joe Culotti ) , smp-1 ( ev715 ) I; jcIs1 IV; him-5 ( e1490 ) V , smp-2 ( ev709 ) I; jcIs1 IV; him-5 ( e1490 ) V , plx-2 ( ev773 ) ; him-5 ( e1490 ) V , plx-1 ( ev724 ) IV; plx-2 ( ev773 ) II , plx-1 ( nc37 ) IV; him-5 ( e1490 ) V , plx-2 ( ev773 ) II; him-5 ( e1490 ) V , sax-3 ( ky123 ) X , smp-1 ( ev715 ) I; jcIs1 IV; him-5 ( e1490 ) V , smp-2 ( ev709 ) I; jcIs1 IV; him-5 ( e1490 ) V , slt-1 ( eh15 ) X , unc-5 ( e53 ) IV , unc-6 ( ev400 ) X , unc-40 ( e1430 ) I , vab-1 ( dx31 ) II , vab-2 ( ju1 ) efn-2 ( ev658 ) IV; efn-3 ( ev696 ) X , tol-1 ( nr2013 ) I . Embryos selected for imaging were dissected from adult hermaphrodites and allowed to settle in dH20 onto a microscope dish ( Delta T dish , Bioptechs ) that was coated with polylysine ( Sigma ) . This technique avoids compression and the severe distortion of the embryo associated with conventional mounting methods between an agar pad and a coverslip [8] . Time-lapse movies were acquired with a Hamamatsu C9100-13 camera on a Nikon TE-2000 inverted microscope equipped with a Yokogawa CSU-10 spinning disk and operated with Volocity 5 . 3 . 3 software ( Improvision ) . For analysis , the first timepoint in an image sequence was matched to the closest timepoint in the reconstructed primordium ( S1 Video ) . Developmental events used for staging included the contraction of the int3 cells , intercalation of int5 and int2 , and the division of the int1 and int8 cells . Additional staging involved events described in S3D Fig . For the analysis of tissues surrounding the intestine ( Figs 8A and S3D ) , we used orthogonal projections of optical Z-stacks generated with ImageJ software [85 , 86] . Orthogonal , high-resolution projections with the mCherry reporter used for general membranes required a much higher laser intensity that for typical timelapse imaging . Hence , we generated a reference library of single timepoint , optical stacks through 104 embryos from about 249–377 minutes . These embryos were used to assess developmental variability over the time interval , and allowed us to use non-intestinal tissues to confirm developmental times in low resolution , timelapse imaging; for example , the intercalation , spreading , and fusion of hypodermal cells . The confocal optical stacks used for the U13 reference reconstruction shown in S1 Video were taken with a 60X water immersion objective ( Nikon ) , and consisted of 49 Z-slices at a step size of 0 . 5 microns . Additional U11 and U12 reconstruction were made from other embryos imaged for shorter time intervals with a 40X water immersion objective ( Nikon ) ; these reconstructions appeared identical to the U13 reconstruction . The events shown in the reconstruction are also supported by 44 recordings of live embryos covering all or part of the reconstructed interval ( S3 Video and S4 Video ) ; the main variation is in int2 intercalation/closure , as described in the text . Cell contours were traced using a Wacom bamboo pad and the TrakEM2 program in Fiji software [87] . The reconstruction of the intestine in a newly hatched larva used an optical stack taken with a 40X water immersion objective ( Nikon ) , and consisted of 49 Z-slices at 0 . 5 microns . Object files were colored and analyzed using Blender ( version 2 . 6 ) open source 3D graphics and animation software ( https://www . blender . org/ ) . We developed a “shadow” technique to estimate cell contact areas within the reconstructed primordium , based on the sophisticated light path tracking available with the Cycles Render option in Blender 2 . 6 ( S2 Fig ) . Our basic approach is to surround two objects of interest with light from all directions , at an intensity such that only direct contact between the objects can prevent light from reaching a given surface . Blender software was used to draw an empty 3D rectangular box that was slightly larger than the model of the primordium; this box was designated to function as a light-emitting source . The Blender camera was placed into the light box , along with two calibration cubes , A and B , which were each about the size of an E16 cell . All light rays from cube B to the camera were selected for exclusion in the rendered image . Thus , the rendered image includes only cube A and any shadow cast by cube B . The A and B cubes were then separated by 0 . 5 microns , the thickness of our optical sections , and the intensity of the light box was increased until B was unable to cast a shadow on A . Finally , the calibration cubes were removed , and the 3D primordium was placed into the light box . All cells in the primordium were selected to be invisible to the camera , other than a designated pair of R and L cells . The visibility and rendering attributes of the R cell were then set as above for the calibration cube B . After rendering , images were imported into Fiji [85 , 86] , and the areas of shadows were measured after thresholding . The madd-2 ( zu475 ) mutant was isolated in a pilot screen from the progeny of a pool of 100 F1 , JJ2360 adults following standard EMS ( EthylMethanesulfonate; Sigma ) mutagenesis [78] . The mutagenized animals expressed both the intestine-specific and general membrane reporters described above , and mutant embryos were scored by fluorescence microscopy and recovered from microscope slides . The mutant was mapped to LGV by conventional genetic techniques . Published guidelines were followed for WGS and SNP mapping [88] . Briefly , 25 F2 recombinant worms were selected from a cross between the linkage-mapped mutant ( Bristol background ) and CB4856 Hawaiian males; these were allowed to self-fertilize , and their F3 and F4 progeny were pooled together . DNA was prepared ( Truseq DNA kit ) , and samples sequenced using a Illumina HiSeq 2500 platform . Sequence analysis was with CloudMap’s Hawaiian Variant Mapping with the WGS Data tool as described [89] . This analysis identified two candidate genes , let-413 and madd-2 . Embryos shown in Fig 10 and described in Table 4 were fixed and stained as follows . Gravid adults were dissected on microscope slides in a small drop of M9 buffer and covered with a coverslip . The slide was frozen on dry ice , the coverslips removed , and the slide immersed in −20°C MeOH for 5 minutes , then rinsed in three changes of PBS for 5 minutes each . Slides were incubated overnight at 4°C with the antibody MH27 , which recognizes the adherens junction component AJM-1 ( Developmental Studies Hybridoma Bank ) . A 440-nm laser microbeam ( Photonics Instruments ) was used for all ablations . Embryos were mounted on agar pads and covered with a coverslip [8] . Immediately following ablation , the coverslip was removed to allow the embryos to develop without compression , and the slide was placed in a humidified chamber . After the appropriate time interval , the embryos were covered with a coverslip and analyzed . | This report uses the intestine of the nematode C . elegans as a model system to address how progenitor cells form a three-dimensional organ . The fully formed intestine is a cylindrical tube of only 20 epithelial cells , and all of these cells are descendants of a single cell , the E blastomere . The E descendants form a primordium that changes shape over time as different E descendants divide and move . Cells in the primordium must continually adhere to each other during these movements to maintain the integrity of the primordium . Here , we generated a 3D graphic reconstruction of the developing intestine in order to analyze these events . We found that the cell movements are highly reproducible , suggesting that they are programmed by asymmetric gene expression in the primordium . In particular , we found that the conserved receptor LIN-12/Notch appears to modulate left-right adhesion in the primordium , leading to the asymmetric packing of cells . One of the most remarkable events in intestinal morphogenesis is the circumferential rotation of a subset of cells . We found that rotation appears to have a role in aligning the developing lumen of the intestine , and involves a conserved , UNC-6/netrin signaling pathway that is best known for its roles in the guided growth of neurons . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"cell",
"division",
"analysis",
"caenorhabditis",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"neuroscience",
"animals",
"animal",
"models",
"developmental",
"biology",
"caenorhabditis",
"ele... | 2016 | Morphogenesis of the C. elegans Intestine Involves Axon Guidance Genes |
Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs . In pre-surgical planning , an array of data modalities , often including intra-cranial EEG , is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures . These regions are then resected with the hope that the individual is rendered seizure free as a consequence . However , post-operative seizure freedom is currently sub-optimal , suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks . Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection . A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery . Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures . Since this a computationally demanding problem , a first step for this aim is to facilitate tractability of this approach for large networks . To do this , we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional , canonical model that is quicker to simulate . We then use this simpler model to study the emergence of seizures in artificial networks with different topologies , and calculate which nodes should be removed to render the network seizure free . We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed , whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue . We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery , revealing rich-club structures within the obtained functional networks . We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed , in agreement with our theoretical predictions .
Epilepsy is a chronic neurological disorder that affects about 1% of people worldwide [1] . Antiepileptic drugs are the preferred treatment , but in around one third of cases , drugs do not stop seizures , and patients for whom this is the case are potential candidates for surgery [2] . Surgeons use an array of data modalities , including intra-cranial electroencephalogram ( iEEG ) , in an attempt to map regions of the brain thought to be crucial for the generation of seizures [3] . If these regions of the brain are amenable to surgery ( e . g . they do not overlie eloquent cortex ) , then they are removed with the hope that the individual is rendered seizure free as a consequence . However , long-term success rates from surgery may be as low as 15% , presumably in part due to failures of the assumptions used in the decision making process [4 , 5] . It is therefore crucial to advance our understanding of the mechanisms that generate seizures and the reasons why removing regions of brain tissue may or may not lead to seizure freedom . In this regard , seizures are increasingly recognised as arising in large-scale brain networks [6–9] . Emerging from such networks , both healthy and pathological dynamics are observed , for example through EEG , MEG or fMRI . These dynamics emerge due to the interplay between intrinsic properties of brain areas , structural connectivity , and modulating influences across multiple temporal and spatial scales [10–12] . This networks paradigm has led to imaging or electrographic data being used to inform network representations of the brain ( for example structural or functional brain networks ) , and graph theoretical measures are used to characterise the topology of these networks [13–19] . Studies analysing graph theoretical properties of networks have reported differences between functional and structural networks derived from healthy individuals versus people with epilepsy [20–26] . The emerging field of dynamics on networks is complementary to these traditional , “static” network analyses [27 , 28] , and moves beyond the study of the topology of networks . In this approach , mathematical models are used to link networks and the intrinsic properties of individual nodes to dynamic data [29] , which provides an avenue to understand the relationship between structure and function [30] . In particular , mathematical models that can recreate elements of pathological dynamics , for example the occurrence of seizures , have been used to understand the network mechanisms of disorders such as epilepsy [9 , 31–37] . Such approaches are also being used in translational applications , for example providing additional information to complement clinical interpretation , namely within the diagnosis of epilepsy [35 , 38] . Crucially , a dynamics on networks approach can be extended to study perturbations to networks . On one hand , lesions and traumatic brain injury can lead to the emergence of pathological brain activity , on the other hand , perturbations such as pharmacological treatment , single pulse electrical stimulation ( and other electrical stimulations ) , transcranial magnetic stimulation , thermocoagulation , among others , can transform brain dynamics from pathological to healthy states [36 , 39–41] , therefore revealing potential avenues for therapy . In the case of epilepsy surgery , we have demonstrated that a network model derived from iEEG data could provide relevant predictions for the outcome of epilepsy surgery [42] . Our findings have been recently replicated in an independent cohort of 16 people with pharmacoresistant epilepsy [43] offering further support to a dynamics on network approach . However , the ways in which networks with different topologies respond to perturbations is at present unknown . For example , in analogy to epilepsy surgery , it is unclear whether particular networks are amenable to a reduction in pathological dynamics upon removing nodes and if so which nodes would be best to target . Here , we use a dynamics on networks approach to study the generation of pathological activity in networks and how the removal of nodes can restore healthy dynamics . Our starting point is a neural mass model that has previously been shown to generate epileptiform rhythms in focal seizures [32 , 37 , 44] , and that we have successfully used to quantify and predict the outcome of epilepsy surgery [42] . It has been shown that the model , when placed close to a saddle-node on invariant circle ( SNIC ) bifurcation , can generate spontaneous , recurrent transitions to epileptiform dynamics ( both inter-ictal spikes as well as seizures ) when driven by noise [32 , 37 , 42] . In our framework , the neural mass model describes the dynamics of a single node within a wider network . The systematic exploration of node removal in brain networks is computationally demanding , and hence we seek a computationally efficient version of this model that preserves the quantification of the effect of removing nodes . We show that a modification of the theta-neuron model [45] is appropriate for this purpose since it is the canonical form of the bifurcation under consideration . This model is capable of generating spiking dynamics , which here represents seizure-like activity . The computational benefits of the theta-neuron model allow us to study the emergence of spiking dynamics in different types of networks and also to systematically quantify the effect of removing different nodes . Here , we study small-world , random , rich-club and scale-free and find that rich-club and scale-free networks more readily generate spiking dynamics , since they require a lower strength of coupling between connected nodes to do so . In terms of the contribution of nodes , we find that rich-club , random and scale-free networks possess a small number of nodes that drive spiking dynamics , whereas the propensity of generate spiking dynamics is more evenly distributed across nodes in small-world networks . Collectively , this suggests that patients whose brain networks display rich-club properties should be particularly amenable to current surgery paradigms . In order to test the relevance of these findings , we analyse data from patients who underwent surgery and for whom postoperative outcome is known . We demonstrate that functional networks inferred from iEEG during seizures display a rich-club connectivity structure and that the proportion of rich-club nodes removed correlates with the success of surgery .
This study was approved by the Internal Review Board of the Inselspital ( approval No . 159399 , dated 26th of November , 2013 ) . All patients gave written informed consent that imaging and EEG data may be used for research purposes . In order to model epilepsy surgery , we consider large-scale brain networks , where each network node is capable of generating epileptiform activity but will do so depending on the connectivity structure of the network . In this framework , a node putatively represents a portion of brain tissue potentially responsible for the emergence of seizure activity across the network . We assume that the dynamics of each node can be described by a neural mass model , such as the Wendling model [37 , 42] . The model depicts the dynamics of a macroscopic circuit in which a population of excitatory pyramidal neurons interacts with three populations of interneurons ( representing one excitatory and two inhibitory populations ) . The two inhibitory populations are classed as slow and fast , representing dendritic-projecting GABAA and somatic-projecting GABAA interneurons , respectively . The dynamics is described by the following 10 first-order ordinary differential equations ( ODEs ) : z˙1 ( t ) =z6 , z˙2 ( t ) =z7 , z˙3 ( t ) =z8 , z˙4 ( t ) =z9 , z˙5 ( t ) =z10 , z˙6 ( t ) =AaS{z2 ( t ) −z3 ( t ) −z4 ( t ) }−2az6 ( t ) −a2z1 ( t ) , z˙7 ( t ) =Aa ( p+C2S{C1z1 ( t ) } ) −2az7 ( t ) −a2z2 ( t ) , z˙8 ( t ) =BbC4S{C3z1 ( t ) }−2bz8 ( t ) −b2z3 ( t ) , z˙9 ( t ) =GgC7S{C5z1 ( t ) −z5 ( t ) }−2gz9 ( t ) −g2z4 ( t ) , z˙10 ( t ) =BbC6S{C3z1 ( t ) }−2bz10 ( t ) −b2z5 ( t ) , where z1-z5 are the output potentials in mV of the neuronal populations , namely z1 , z2 , z3 , and z4 are the outputs of the pyramidal cells , excitatory population , slow inhibitory population , and fast inhibitory population , respectively . z5 is the output of the slow inhibitory population that interacts with the fast inhibitory population . z6-z10 are auxiliary variables , S is a sigmoid function , S ( ν ) =2e01+er ( ν0−ν ) , and A , a , B , b , G , g , C1-C7 , p , e0 , r , and ν0 are parameters ( see Table 1 for their biophysical interpretation and values ) . The output of the model z2 ( t ) –z3 ( t ) –z4 ( t ) corresponds to the aggregated membrane potential of the excitatory cell population and its bifurcations have been extensively characterized [47] . In particular , a SNIC bifurcation has been identified as one mechanism for the generation of epileptiform rhythms observed in typical focal epilepsies [32] . This model and bifurcation were also previously employed to estimate brain network ictogenicity to predict the outcome of epilepsy surgery [42] . Therefore the parameters A , B and C were chosen so that the neural mass is in a steady state close to the SNIC bifurcation that gives rise to spiking dynamics which we consider a proxy for the patho-phenotype of the epileptic brain ( see the third figure , left panel , in [32] ) . p is an extrinsic input parameter that represents stimuli from other areas of the cortex . Although the neural mass model described above represents the dynamics of four interacting neuronal populations , at the scale we are interested in , it describes the dynamics of a single node in a wider network consisting of other interacting neural masses . Following previous studies [33 , 48] , we account for the coupling between neural masses ( nodes ) using the extrinsic input parameter p . We make the input of the j-th node both time and node dependent as follows , pj ( t ) =p0 ( j ) +ξ ( j ) ( t ) +1N∑i≠jλijaijS{z2 ( i ) ( t ) −z3 ( i ) ( t ) −z4 ( i ) ( t ) } . Here the index j denotes node j ( j = 1 , 2 , … , N , where N is the number of nodes ) . p0 ( j ) is used to control the distance to the SNIC bifurcation; ξ ( j ) ( t ) represents noisy inputs from other areas of the cortex outside of the network under consideration; λij is the coupling strength from node i to node j; and aij is the i , jth entry of the adjacency matrix ( the node receives the outputs of all his in-neighbours ) [33 , 48] . We consider Gaussian noise with mean p0 ( j ) and 〈ξ ( i ) ( t ) ξ ( j ) ( t′ ) 〉=σp2δi , jδ ( t−t′ ) , where σp2 is the variance . A node is in a resting state if pj ( t ) < pc , where pc is the critical point at which the SNIC bifurcation takes place . Since the Wendling Model ( WM ) becomes computationally expensive for studying large networks , we look for a parsimonious representation for spiking dynamics in brain networks . Taking into account that nodes of WM are operating in the vicinity of a SNIC bifurcation , we substitute networks of neural masses with networks in which each node is represented by the normal form of the SNIC , i . e . the theta-neuron model [45] . It is important to stress that although this model is traditionally used to describe the dynamics of a neuron , here we use it ( as effectively the canonical form of the SNIC bifurcation ) to represent the dynamics of a neural mass in an epileptic spiking regime . The canonical model ( CM ) is an alternative formulation of a quadratic integrate and fire neuron . It comprises the following ODE: θ˙j= ( 1−cosθj ) + ( 1+cosθj ) Ij ( t ) , where θj is the phase of node j , and Ij ( t ) is its input current . The SNIC bifurcation occurs at Ic = 0 . At Ij < Ic , the phase oscillator is resting , whereas at Ij > Ic it is oscillating . We define the coupling between the “canonical neural masses” analogous to the coupling defined within the WM , Ij ( t ) =I0 ( j ) +ξ ( j ) ( t ) +1N∑i≠jwijaij[1−cos ( θi−θi ( s ) ) ] , where Ij is the input current of node j , I0 ( j ) +ξ ( j ) ( t ) represents noisy inputs coming from other areas , wij is the coupling strength from node i to node j , and aij is the i , jth entry of the adjacency matrix . As in the WM , we consider Gaussian noise ( mean I0 ( j ) , and variance σI2 ) . We define the output of the in-neighbour i as 1−cos ( θi−θi ( s ) ) , where θi ( s ) is its steady state , so that if the node is resting its output is zero , and if it reaches θi ( s ) +π , its output is maximum . This uncoupled steady state θi ( s ) is obtained from setting θ˙i=0 , θi ( s ) =−Re{cos−1 ( 1+I0 ( i ) 1−I0 ( i ) ) } . We take the real part so that θi ( s ) =0 at I0 ( i ) >0 . At I0 ( i ) <0 , there are two fixed points: θi ( s ) is a stable fixed point , and −θi ( s ) is an unstable fixed point . A similar coupling in networks of theta-neurons was recently studied in [49] . Other authors have considered delta-like interactions [50] , or rapid rises in the synaptic gating variable [51] , which are a reasonable approximation for neurons , but inappropriate for neural masses . Note that the output of a neural mass is an average over the activity of a population of neurons , and so it displays properties of a low-pass filter [52] . For simplicity , we consider homogeneous nodes in both models , i . e . , all nodes in a network are at the same distance to the SNIC bifurcation ( p0 ( j ) =p0 and I0 ( j ) =I0 ) , and have the same coupling strength ( λij = λ and wij = w ) . This is a strong assumption that enables us to focus explicitly on the contribution of the network structure to the network ictogenicity . Thus , there are three free parameters in each model: ( p0 , σp , λ ) in WM , and ( I0 , σI , w ) in the CM . Since our aim is to consider whether the two network models display similar changes in dynamics upon the removal of nodes , it is important that these parameters are comparable between models . Taking into account that we require that the node dynamics switch between the resting state and the spiking dynamics , the three parameters are interdependent . For example , as parameter values of the nodes move closer to the SNIC the required noise variance to elicit spikes becomes smaller . Note , however , that the variance of the noise should not be too large as we wish to ensure that network interactions play a role in the emergent dynamics . Thus , we define σp*=σp/ ( pc−p0 ) and σI*=σI/ ( Ic−I0 ) to scale the effect of noise by the distance to the SNIC bifurcation so that the effect of the noise on the dynamics of both models is comparable . In order to establish a relation between the coupling strength and the noise , we also define λ* = 2e0cλ/ ( Nσp ) and w* = 2cw/ ( NσI ) , where c is the mean degree of the network . These relations compare the noise to the average maximum input that a node can receive , 2e0cλ/N and 2cw/N for WM and the CM , respectively . It provides a scale that compares noise perturbations to inputs received from in-neighbours . Note that , with respect to the input parameter , the dynamics of a node j change from resting to spiking in WM if pj ( t ) > pc , and likewise , in the CM a node j transitions to spiking if Ij ( t ) > Ic . Thus , in both models we have the following condition for a node j to be in the parameter region corresponding to a spiking regime at time t , x0 ( j ) +ξ ( j ) ( t ) +CN∑i≠jaijYi ( t ) >T , where x0 ( j ) +ξ ( j ) ( t ) is the noise , C the homogeneous coupling strength , Yi ( t ) the output of node i , and T the bifurcation point . If we assume that the network is in the resting state with an average node output of 〈Y〉 , then we can estimate the critical coupling Cc at which on average a certain node starts to spike , Cc=N ( T−[x0 ( j ) +〈ξ ( j ) ( t ) 〉] ) 〈Y〉kj ( i ) , where kj ( i ) is the in-degree of node j ( 〈ξ ( j ) ( t ) 〉 = 0 in the case of Gaussian noise ) . Therefore , for a given network of size N , the larger the in-degree , the smaller is Cc , meaning that nodes with higher in-degree are more likely to transition to spiking . This is valid in both models . Similarly , one can find the critical distance to the SNIC bifurcation , x0c ( j ) =T−C〈Y〉kj ( i ) N , which is smaller than T due to the inputs from the network ( 〈Y〉 > 0 ) . The adjacency matrix encodes the network structure on top of which the nodes interact . We consider random , scale-free , small-world and rich-club networks , both directed and undirected ( we discarded networks with disconnected components ) [53 , 54] . In order to quantify the “importance” of each node , we analyze the following traditional measures: degree , average neighbour degree , eigenvector centrality , betweenness centrality , closeness centrality , clustering coefficient , and local efficiency [55 , 56] . Additionally , we also consider eigencentrality based on Jaccard dissimilarity [57] and dynamical importance [58] . In the case of directed networks , we also consider in-degree , out-degree , as well as the sum and product of these measures . We focus our analysis upon two measurements that are relevant for our purposes of studying epileptic dynamics and surgery in silico , namely Brain Network Ictogenicity ( BNI ) [23 , 42 , 59] , and Node Ictogenicity ( NI ) [42] . BNI is a practical approach for quantifying the tendency of a network to generate spiking dynamics . It measures the average fraction of time spent in spiking dynamics by each node [23 , 42 , 59]: BNI=1N∑iTimespentinspikingdynamicsbynodeiTotaltime . Specifically , in the WM , first we extract the spikes generated by a node by applying a threshold to the average absolute amplitude of the model output over a sliding window of 0 . 05 s . Then , contiguous epochs of spiking dynamics are identified by evaluating the overlap of 1 s time windows centred in each spike . Finally , the time spent in spiking dynamics corresponds to the total time of these spiking epochs [42] . In the CM we use the same method , with similar time scales ( we use as conversion time scale the ratio of the full widths at half maximum of the spikes in each model ) . NI quantifies the contribution of each node to the ictogenicity of the network by measuring the relative difference in BNI upon removing node i from the network: NIi=BNIpre−BNIpostiBNIpre , where BNIpre corresponds to the BNI over the network prior to node resection and BNIposti is the BNI after the removal of node i . Note that NIi = 1 means that the removal of node i renders the network free of spiking dynamics , whereas NIi = 0 means that the resection of node i made no difference to the BNI . In practice , this quantity measures the success of a given surgery resection in silico , and it may have the potential to guide the search for an optimal surgical strategy . In general , this quantity may also be useful to quantify the result of temporary ablation , assuming that the ablation takes place in a much slower time scale than the network dynamics . In this paper we set BNIpre = 0 . 5 ( we have confirmed that the results are qualitatively the same for other reference values of BNIpre ) . To evaluate if the CM can be used as a proxy of the WM in this framework , we compare the NI ordering of the two models for a number of networks . Note that NI is essentially a vector with N entries quantifying the result of removing each node individually , being of particular interest the relative impact of each node removal compared to the others , rather than the absolute value of each one ( which is parameter dependent ) . We use a weighted Kendall's rank correlation measure [60 , 61] , which is defined as follows . Given two rankings ( NI ) of the same items ( nodes of the network ) , we calculate τ=P−QP+Q , where P is the number of items in the same order in the two rankings , and Q counts the number of items in reverse order . When τ = 1 the two rankings predict the same ordering , whereas τ = −1 means a reverse order of all items . Here we consider a weighted measure to take into account the relative values of NI: each NI comparison between two nodes i and j is weighted by the product of the distances in NI predicted by the two models , |NIWMi−NIWMj|×|NICMi−NICMj| , ( where NIWMi and NICMi are the NIs of node i calculated using WM and the CM , respectively ) . We assume that there are no ties . We focus on patients with pharmacoresistant epilepsy , since such patients are candidates for surgery . Data were collected from 16 patients ( 11 female , mean age 31 , and median post-surgical follow up 3 years ) who underwent pre-surgical monitoring at Inselspital Bern [42 , 62] . Following epilepsy surgery , six patients fell into Engel class I ( free of disabling seizures ) , five into Engel class II ( rare disabling seizures ) and five into Engel class IV ( no worthwhile improvement ) . All patients gave written informed consent that imaging and iEEG data may be used for research purposes . Other details about the data can be found elsewhere [42 , 62] . Before analysis , the signals were down-sampled to a sampling rate of 512 Hz and re-referenced against the median of all the channels free of permanent artefacts as judged by visual inspection by an experienced epileptologist ( K . S . ) . For each patient , two peri-ictal epochs were considered , which included three minutes before seizure onset , the seizure itself and three minutes after seizure termination ( seizure onset and offset were identified by visual inspection ( K . S . ) ) . Following the methods described in [62 , 63] , first we applied a band-pass filter between 0 . 5 and 120 Hz and a notch filter ( 48 to 52 Hz ) using a Butterworth filter . Each epoch was divided in a set of 8 seconds segments ( the segments were chosen 1 second apart from each other ) . For each segment we obtained 10 univariate iterated amplitude adjusted Fourier transform ( IAAFT ) surrogates independently . Next , the segments were divided in 10 subsegments of 1024 sampling points ( 2 seconds ) distributed with minimal overlap . Thus , we generated an ensemble of 10 subsegments for the original time series , and 100 subsegments for the surrogates ( 10 for each surrogate ) . To estimate the correlations between the time series of each iEEG channel , we used the Pearson's equal-time ( zero-lag ) cross-correlation coefficient ρ , and a non-parametric Mann-Whitney-Wilcoxon U-test was performed to assess the significance of different medians of ρ between the original time series ( ρo ) and the surrogates ( ρsurr ) . We further applied Bonferroni-Holm corrections to account for multiple comparisons . Finally , we obtained a surrogate-corrected correlation matrix using the heuristic formula [63 , 64] C=ρo−ρsurr1−ρsurrs , where s = 1 if the null hypothesis of the statistical test is rejected , or s = 0 otherwise . Using this method , we derived 102 ± 18 functional networks based on cross-correlation for each patient , depending on the duration of each seizure epoch . The organization of functionally derived networks into rich-clubs [65–67] was studied using a weighted rich-club parameter ϕ ( k ) [66] . The richness parameter is the degree k , and the procedure consists in finding groups ( clubs ) of nodes whose richness is larger than k . For a given degree k , we counted the number of connections E>k of the club , and summed their weights W>k . We then calculated the fraction of weights shared by the club out of the maximum edge weights that the club could have if they were linked by the strongest connections of the network , i . e . , ϕw ( k ) =W>k∑l=1E>kwlrank , where wlrank are the ranked weights of the network . This fraction is not enough to verify the existence of a rich-club , since even random networks can have an increasing function ϕw ( k ) as a result of chance alone ( nodes with higher degree are more likely to be connected ) . Therefore , ϕw ( k ) is normalized relative to ϕrand ( k ) obtained from a set of comparable random networks , ϕ ( k ) =ϕw ( k ) ϕrand ( k ) . Thus , a network exhibits rich-club organization if there is a range of degree k for which ϕ ( k ) > 1 [65 , 66] . We generated 100 random networks by applying a reshuffle procedure to the weights while keeping the topology of the original network intact , followed by a link and weight reshuffle procedure that preserves the original degree distribution [56 , 67] . ϕrand ( k ) was calculated as the average rich-club coefficient for each level of k . Finally , we evaluate the statistical significance of rich-club organization using a permutation test [67] , by testing whether ϕ ( k ) was statistically significantly larger than ϕrand ( k ) ( a one-sided p value was calculated as the percentage of the distribution of ϕrand ( k ) that exceeds ϕ ( k ) ) . We measured the rich-clubs of the average functional networks of the pre-seizure , seizure , post-seizure , and whole peri-ictal epochs for each patient separately .
We compared the dynamics of the CM to the WM in terms of the effect that model parameters have on BNI and the profile of NI for a suite of networks . Fig 1 demonstrates typical dynamics of each model applied to the same network ( a directed random network with N = 10 , and mean degree c = 1 . 6 ) . Both models display spiking dynamics , with a heterogeneous distribution of activity across nodes . For each model , nodes 2 , 5 , 7 , 9 and 10 show a greater extent of spiking dynamics than other nodes; thus the distribution of activity across the network is preserved in the canonical model . On the other hand , it is clear that the resting state is noisier in the CM . A predominant feature accounting for this is that the ratio of amplitude of the spiking trajectory to noise is larger in the WM . Moreover , one should also realize that whereas in the WM only positive inputs can move the system towards the SNIC bifurcation , in the CM both positive and negative inputs displace the phase , θ , from the resting state . Furthermore , the output of the resting state in the CM is zero , but non-zero in the WM . As described in the Methods , although we have identified three free parameters , the network dynamics are in fact affected by only two competing factors: the distance to the SNIC bifurcation and the coupling strength . The strength of noise required to elicit spikes is correlated with the distance to the SNIC bifurcation ( that is smaller noise variance is required to elicit spikes the closer the system is to the bifurcation ) . Thus , we can fix the noise variance and consider BNI as a function of the distance to the SNIC bifurcation and coupling strength . Fig 2 provides an evaluation of this function for an ensemble of 10 random networks with 15 nodes and demonstrates that the smaller the distance to the bifurcation , the easier it is to generate spiking dynamics , and consequently BNI is larger . In addition , BNI grows with increases in coupling strength . Fig 2 demonstrates that the shape of the BNI surface is similar for the two models , which provides evidence that the normal form of the SNIC is appropriate for the study of the propensity of a network to generate spiking dynamics . Similar results were obtained for both smaller and larger networks . Our results thus far indicate that despite some expected quantitative differences , network dynamics , and in particular the way that BNI changes with respect to system parameters , are qualitatively similar across the WM and the CM . However , our primary focus is to determine whether the CM would provide the same prediction for the effect on BNI of node removals ( i . e . NI ) . With the application of surgical resections in mind , we are predominantly interested in how comparable the ordering of NI is between the two models . In order to investigate this , we calculated the distribution of NI for a suite of random networks of size N = 15 , 30 and 50 and calculated the similarity in ordering of NI using Kendall's τ ( see Methods ) . Fig 3 shows that within models , the NI distribution is robust across different parameter sets for which BNIpre = 0 . 5 , which is our starting point for the calculation of NI ( see Methods ) and defines a line in the surface of Fig 2 . Across different choices of parameters within the WM , we find τ > 0 . 97 for all networks considered , indicating a strong preservation of the ordering of NI when different parameters are used . Within the CM , τ > 0 . 89 and thus there is slightly more variation across NI orderings for this model . Fig 3C and 3D show τ for comparisons of NI orderings between the two models . Fig 3C demonstrates that when model parameters are chosen randomly , the ordering of NI is preserved between the two models for small networks , but differences in predictions between the two models arise in larger networks ( for example with 50 nodes ) . However , Fig 4D demonstrates that a parameter set for each model can be found such that NI distributions are preserved across models in the larger networks studied ( 50 nodes , τ = 0 . 85 ± 0 . 09 ) . We note that as N increases , nodes become topologically similar in a random network , and therefore one can expect a homogeneous distribution of NI . However , we are primarily interested in networks for which nodes exist that should be resected to reduce the presence of spiking dynamics . We therefore study networks for which we might expect the distribution of NI to be heterogeneous ( as we will show below ) . A natural choice is a scale-free network characterized by a power law degree distribution P ( k ) ∼k−γ with a small exponent ( γ < 3 ) [54] . Fig 3 demonstrates that for scale-free networks arbitrary choices of parameters yield a strong similarity in ordering of NI ( τ = 0 . 87 ± 0 . 16 ) and that model parameters exist for which the ordering is essentially identical ( τ = 0 . 996 ± 0 . 003 ) . Fig 3E demonstrates the computational advantage gained by using the CM over the WM . We find that the ratio of computational time of the WM to the CM when estimating BNI is 4 . 6 for networks of size N = 15 , 4 . 9 for N = 30 , and 6 . 2 for N = 50 . Note that this gain does not correspond to the ratio of floating point operations needed by each model to simulate a time step because the time scales are different between these two models . Crucially , such gain will be very useful when applying this framework in the clinical setting , as it represents a speed-up in the computational time from days to hours . Having demonstrated similarity in the ordering of NI across the WM and CM , we proceed in the following sections to use the CM to study how NI varies across different types of network . We fix the number of nodes that we consider to be 64 , in line with a typical number of iEEG and depth electrodes used in pre-surgical planning applications [42 , 62 , 68] . Fig 4 shows how BNI varies as a function of the coupling strength under different choices of network topology . Scale-free networks are the most prone to transit to spiking dynamics since BNI becomes non-zero for smaller coupling strengths relative to the other topologies . The effect is more noticeable in networks with smaller exponent γ , which have a greater degree variance ( i . e . are more heterogeneous ) . However , the maximal value of BNI is less than one for scale-free networks , in particular in directed networks . Fig 4 demonstrates that rich-club networks exhibit a similar profile of increases in BNI with increases coupling strength features to scale-free networks , which is presumably a consequence of similarities in the degree distributions of these networks . Small-world and random networks have similar profiles , implying that the high clustering coefficient of small-world networks has little impact on a network’s ictogenicity ( BNI ) . Similar results were obtained for smaller and larger networks ( up to N = 128 ) , as well as for sparser ( c = 4 ) and denser networks ( c = 10 ) , and in the case of small-world networks for smaller rewiring probabilities ( p = 0 . 1 ) . Fig 4B shows that whilst the profile of random and small-world networks is similar for directed and undirected networks , the profile changes for rich-club and scale-free networks , most significantly for scale-free networks , whose BNI in directed networks has a very gradual increase with increasing coupling strength . This is likely due to the disparity between the in- and out-degree of nodes ( note that nodes can have a high out-degree but a low in-degree , meaning they can influence the network activity , but not be influenced by it , and vice versa ) . Fig 5 demonstrates the way that NI is distributed amongst nodes in networks with different topologies , and furthermore how NI correlates with graph theoretical properties of nodes . The first column of Fig 5 shows that random , scale-free and rich-club networks each have a skewed NI distribution , with a small subset of nodes having large NI , whereas small-world networks have a flat distribution , with small values of NI across nodes . Nodes in rich-clubs were found to have high NI . Similarly , we found several measures of node importance to correlate with NI , but in a topology-specific way . For example , whilst node degree correlates to a great extent with NI in scale-free networks , it does not for random networks , particularly when the mean degree is large . Eigenvector centrality and dynamical importance were found to be good predictors of NI ( R2 > 0 . 80 , see Fig 6 ) in all networks except those with small-world topology and random networks with large mean degree ( c = 10 ) . We found that small-world networks required all considered measures to achieve an adequate prediction of NI ( R2 > 0 . 85 for multiple regressions with all the considered node properties , for c = 4 and c = 6 ) . We note that directed networks typically did not contain nodes with NI > 0 . 5 and furthermore that NI did not correlate with graph theoretical measures in directed networks ( R2 < 0 . 3 , see S1 Fig ) . We tested the robustness of these results for other reference values of BNIpre ( BNIpre = 0 . 3 and BNIpre = 0 . 7 ) in all the networks ( c = 6 ) . We obtain similar results , although the networks become less sensible to perturbations for BNIpre = 0 . 7 . Depending upon the particular choice of network representation , resections from brain networks could include more than one node . We therefore sought to gain insight into how many nodes have to be removed from a network in order to render it incapable of generating spiking dynamics . In order to resect a minimum number of nodes while reducing BNI as much as possible , it seems sensible to target the highest NI nodes first . Fig 7 demonstrates how this strategy compares to random node removal ( we use the eigenvector centrality as a proxy of NI and therefore we target nodes with highest eigenvector centrality , but similar results were obtained targeting nodes with highest NI ) . Note that in the case of targeted node removal , when one node is removed , the whole network changes , and therefore the distribution of NI may change as well . Therefore , we recalculated the eigenvector centrality of each node of the new network after each node removal . The figure shows that a targeted node removal is much more effective than a random strategy in all topologies except small-world networks , in which the two strategies give similar outcomes . This is to be expected taking into account the highly homogenous distribution of NI in small-world networks ( see Fig 5 ) . Accordingly , the difference between the two strategies is particularly noticeable in scale-free networks , because of its heterogeneous NI distribution . Our results thus far demonstrate the distribution of NI throughout a network is dependent upon network structure . In particular , rich-club , or networks with highly connected hubs were found to contain nodes with high NI , even though all nodes were equivalently parameterized and therefore those nodes were not apparently pathological . In a practical setting those nodes would be natural targets for epilepsy surgery . We therefore sought to understand whether typically used clinical data would yield network representations of the brain with these properties . We thus quantified the presence of rich-club organization in functional networks derived from iEEG recordings from patients that were considered for epilepsy surgery . We considered peri-ictal recordings , and we found evidence of rich-club structure in the functional networks of pre-seizure , seizure and post-seizure epochs in each patient . Fig 8 shows rich-club functions for 3 representative functional networks from seizure epochs of 3 different patients . A rich-club coefficient larger than one over a range of degree k indicates the presence of rich-club organization [65 , 66] and we found this to be the case in all patients . Note that scale-free networks also display rich-club organization [65] , and so both types of network are identified by this type of analysis . Next we extended this analysis taking into account the location of resections relative to the placement of iEEG electrodes and the postoperative outcome [42 , 62] . Our model analysis led us to hypothesise that if the rich-club was partially or totally resected , the outcome for patients would likely be favourable , since we would expect nodes in the rich-club to have high NI . To test this , we estimated which nodes were members of the rich-club in each functional network ( over the pre-seizure , seizure , and post-seizure epochs and all combined ) as the collection of nodes belonging to the ‘richest’ club , i . e . , the nodes with degree larger than kr , where kr corresponds to the maximum of ϕ ( k ) ( see Methods ) . Fig 9 demonstrates that for networks derived from pre-seizure , post-seizure , or full peri-ictal epochs , the fraction of rich-club resected did not correlate with the outcome of surgery . However , in functional networks derived from the seizure epoch , there was a significant difference in fraction of rich-club resected between patients with good ( Engel classes I and II ) and poor ( Engel class IV ) post-operative outcome ( p = 0 . 038 , Kruskal-Wallis test ) . Patients with good postoperative outcome ( seizure free or almost seizure free ) had a significantly larger disruption to the rich-club than those with no postoperative improvement .
In this study we used a canonical form of the Wendling model to systematically explore the influence of network topology on the generation of spiking dynamics and the effect that removing nodes from a network has on its ability to generate such dynamics ( i . e . its ictogenicity ) . We demonstrated that networks with scale-free and rich-club topology are more ictogenic in the sense that they require a smaller coupling strength between connected nodes to lead to the onset of spiking dynamics . Furthermore , we showed that on the whole , the ictogenicity of nodes within an undirected network correlate with graph theoretical measures most notably degree , eigenvector centrality and dynamical importance [58] . This led us to hypothesise that disruption of rich-clubs in networks should lead to diminished ictogenicity . We tested this hypothesis by first demonstrating the presence of rich-clubs in functional networks derived from iEEG data of people with pharmacoresistant epilepsy , and further showing a significantly greater extent of disruption to rich-club structures in patients who had good postoperative outcome , compared to those with poor postoperative outcome . It has recently been suggested that there exists a local pathological hub near the epileptic focus responsible for spreading epileptic activity , which should be resected by surgery [69] . This hypothesis is supported by evidence demonstrating that betweenness centrality correlates with resected cortical regions in patients who had a favourable surgery outcome [70 , 71] . Our results are in agreement with this hypothesis . Indeed , the rich-club comprises a group of nodes with high betweenness centrality that can work as pathological hub spreading epileptic activity . Functional networks can be thought of as representing communication pathways in the brain that are active , or open , at a given moment . In the case of brain disorders such as epilepsy , we assume that there exist pathological pathways that support the emergence of seizures ( i . e . ictogenic networks ) . Since it is natural to assume that these networks are also expressed in electrographic data and can be characterized in terms of functional connectivity , we use such networks in our models . This is in contrast to modeling based on the structural connectivity of the brain [73] . Structural networks place a constraint on the rhythms of the brain that can emerge . However , network models of brain rhythms built on structural networks typically neglect biochemical processes taking place over multiple different time scales preventing us from knowing , at any given time , which connections are actually active . That is why we have chosen functional connectivity based modeling in this study . A potential limitation of this approach , however , is that the network studied depends on the choice of epoch and the method used to characterize functional connectivity . Interestingly , in our analysis a significant difference in fraction of the rich-club resected was only found for functional networks derived from epochs containing seizures . This is in line with our previous analysis in which the effect of resections could be predicted based on functional networks derived from seizure epochs [42] . Furthermore , our previous study utilised functional networks derived from a nonlinear channel association measure ( the surrogate corrected mutual information [62 , 63] ) , and we have therefore demonstrated that information relevant to the ictogenic network can be extracted from spatiotemporal seizure dynamics using both linear and nonlinear measures to infer the connectivity structure between nodes . Previous analyses of scalp EEG or MEG have demonstrated that information relevant to epilepsy is also present in background epochs ( i . e . those not containing seizures ) [22 , 23 , 35 , 38 , 59 , 72] . In particular , the predictive power of models for resective surgery has also been studied using inter-ictal ( e . g . away from seizure ) epochs [43] . Therefore future work should seek to ascertain whether information capable of guiding surgical strategies can be extracted from background data recorded using iEEG . Additionally , it is necessary to examine what neuroimaging modalities contain the most significant information to infer the ictogenic network . For our theoretical analysis of the impact of network structure on ictogenicity we quantitatively compared both the canonical model and the full neural mass ( Wendling model ) [37 , 46] . Our analysis demonstrated that the canonical model , which is essentially a normal form representation of the SNIC bifurcation present in the neural mass model , is a useful parsimonious model , particularly for the purpose of finding the distribution of node ictogenicity ( NI ) . In fact , we found that the distribution of NI across nodes of a network is almost independent from the particular choice of parameters for sufficiently small networks ( N < 30 ) . For larger networks ( N = 50 ) whose topology yields a non-uniform distribution of NI , the two models also return similar predictions of NI without the need for parameter calibration . This implies that NI depends predominantly on the presence of a bifurcation to spiking dynamics and network structure . We have further shown that the computational gain increases nonlinearly with increasing network size and it is therefore significant for networks such as those inferred from iEEG . The reduction of complexity of models to study fundamental mechanisms of epilepsy [9 , 74 , 75] or healthy brain dynamics [76 , 77] is becoming a well-accepted approach , and will be particularly important in computationally intensive applications , such as the study of perturbations to high-dimensional networks . Although here we have focused on noise-driven models close to a SNIC bifurcation to generate relevant dynamics , previous studies have suggested the use of other bifurcations , such as saddle-node and homoclinic bifurcations to model seizure onset and offset , respectively [75] . In particular , Jirsa et al . [75] used a data-driven approach to identify these bifurcations , under the assumption of a slowly changing control variable moving the model through parameter space . Further work is required to understand to what extent can data reveal which bifurcation underlies the observed dynamical transitions and how different bifurcation mechanisms can influence on the quantification of NI . Patient-specific assessment of the type of bifurcation that best describes the data may lead to further improvements of this modeling framework . We demonstrated that networks with high degree variance are more likely to seize for relatively smaller coupling strengths , whereas more homogeneous networks reach BNI = 1 within a more confined range of coupling strengths . This provides a potential explanation for observations of increased degree variance in functional networks derived from epilepsy patients , compared to healthy controls [23] . We also uncovered differences in the interplay between global and local spiking generating mechanisms in networks with different topologies: random and small-world networks display a switch-like mechanism for the emergence of spiking dynamics with respect to changes in global coupling , but a more gradual response to removal of nodes . Nodes in random or small-world networks have smaller degree variance , whereas nodes in scale-free or rich-club networks are more heterogeneous in their degree . Therefore , ictogenicity in the latter networks is likely to be concentrated within a few nodes , and thus larger connectivity strengths are required for spiking dynamics to be present in the whole network , which is required here for BNI to be large . These results are in agreement with findings that the critical coupling for the Kuramoto model decreases as the exponent of scale-free networks decreases [78 , 79] . It is important to note that we assumed all nodes equivalently excitable to focus on the contribution of the network structure to the emergence of spiking dynamics . Future work should consider the potential existence of pathological nodes with higher excitability that may drive the ictogenicity of the network and whose resection may be preferable . Our analysis suggests that if a brain network under consideration does not have rich-club organization , or if the rich-club were to overlap with eloquent cortex , a resection of a much greater number of nodes would be required . Note that in this context the brain network mapped from iEEG does not correspond to the whole brain , instead it corresponds to a clinically predetermined brain region under investigation as potential surgical targets . Interestingly , our results suggest that a considerable BNI reduction could be attained in most 64 node networks upon removal of 10 nodes at random in all topologies , which is comparable to the average epilepsy resection [62] . This is in agreement with findings that most patients undergoing surgery experience some reduction in seizures , even if they do not achieve seizure freedom [80] . In some cases , the rich-club may comprise non-adjacent nodes making it difficult to resect it through surgery . To tackle these cases , other techniques might be considered such as radiofrequency thermocoagulation [81] . Our approach may be improved by quantitatively assessing predictions for changes in seizures frequency , based on the baseline seizures rates of the individuals . Interestingly , our findings regarding undirected networks did not extend readily to directed networks . In particular , graph theoretical properties of nodes in directed networks did not correlate with NI , and the effect of node removals was found to be smaller than it would have in “similar” undirected networks . This has important implications for the choice of network representation of the brain used in studies of perturbations . Depending on the data modality under consideration , different approaches should be considered: for modalities that give rise to undirected networks our framework suggests to target nodes according to their eigenvector centrality , whereas if directed networks are derived it is necessary to assess NI using a model ( such as the CM ) . Ultimately , a representation of the brain will be deemed ( clinically ) useful in the context of our study if it is able to predict the outcome of perturbations . In the current study and our previous work [42] we have demonstrated that useful information is present in an undirected network representation of the brain . However , future work will ascertain whether approaches yielding a directed network may ultimately prove most beneficial . | Epilepsy is a chronic neurological disorder that affects about 1% of people worldwide . The administration of antiepileptic drugs is the preferable treatment , but in around one third of cases , drugs do not stop seizures , and these patients are potential candidates for surgery . Epilepsy surgery however is too often unsuccessful , with around one half of patients continuing to experience seizures . In this work we use mathematical models to study epilepsy surgery so to inform surgeons concerning the brain tissue that should be considered for surgery resection . We show that functional networks derived from data of epileptic patients considered for surgery present rich-club organization . For this kind of network structure , we propose an optimal surgery strategy that consists of disrupting the rich-club . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"neural",
"networks",
"neuroscience",
"surgical",
"and",
"invasive",
"medical",
"procedures",
"mathematics",
"scale-free",
"networks",
"algebra",
"network",
"analysis",
"directed",
"graphs",
"epilepsy",
"computer",
"and",
"inform... | 2017 | An optimal strategy for epilepsy surgery: Disruption of the rich-club? |
The patterning of the Arabidopsis root epidermis depends on a genetic regulatory network that operates both within and between cells . Genetic studies have identified a number of key components of this network , but a clear picture of the functional logic of the network is lacking . Here , we integrate existing genetic and biochemical data in a mathematical model that allows us to explore both the sufficiency of known network interactions and the extent to which additional assumptions about the model can account for wild-type and mutant data . Our model shows that an existing hypothesis concerning the autoregulation of WEREWOLF does not account fully for the expression patterns of components of the network . We confirm the lack of WEREWOLF autoregulation experimentally in transgenic plants . Rather , our modelling suggests that patterning depends on the movement of the CAPRICE and GLABRA3 transcriptional regulators between epidermal cells . Our combined modelling and experimental studies show that WEREWOLF autoregulation does not contribute to the initial patterning of epidermal cell fates in the Arabidopsis seedling root . In contrast to a patterning mechanism relying on local activation , we propose a mechanism based on lateral inhibition with feedback . The active intercellular movements of proteins that are central to our model underlie a mechanism for pattern formation in planar groups of cells that is centred on the mutual support of two cell fates rather than on local activation and lateral inhibition .
The cells of the Arabidopsis root epidermis emerge from the initial cells in the root meristem with the potential to adopt either of two cell fates—trichoblasts ( cells that can go on to differentiate as root hair cells ) or atrichoblasts ( that differentiate into non–hair-bearing epidermal cells ) . In the wild-type seedling , the two cell types are arranged in a stereotyped spatial pattern , with files of trichoblasts overlying two cortical cells ( the H position ) separated by files of atrichoblasts in contact with only one underlying cortical cell ( the N position ) ( Figure 1 ) [1 , 2] . This fixed pattern does not result from lineage restriction , but depends on a combination of positional information from the cortex and the operation of a genetic regulatory network within the epidermis [3–5] . At the core of this network lie protein complexes centred on the basic helix-loop-helix proteins GLABRA3 ( GL3 ) and ENHANCER OF GLABRA3 ( EGL3 ) and the WD40-repeat–containing protein TRANSPARENT TESTA GLABRA ( TTG ) . These proteins can bind to the MYB proteins WEREWOLF ( WER ) and CAPRICE ( CPC ) to form two protein complexes ( the WER- and CPC-complexes , respectively ) . Genetic and biochemical studies have highlighted a number of basic features of the epidermal interaction network . First , the WER-complex represses GL3/EGL3 transcription and enhances CPC transcription [6–8] . The CPC-complex is believed to lack transcriptional activity , but CPC has been reported to repress WER transcription [8] . Second , the CPC and GL3 proteins exhibit striking mobility , moving freely between epidermal cells [9–11] . Third , the SCRAMBLED ( SCM ) receptor-like kinase is believed to play a role in the interpretation of a cortical signal that biases pattern formation by repressing WER transcription in the H position [12 , 13] . These network features have been proposed to underlie a pattern-forming mechanism based on lateral inhibition [8] , but a detailed investigation of their sufficiency to account for experimental data has not been carried out . It has been suggested on theoretical grounds , however , that autoregulation of WER activity is necessary for epidermal pattern formation [14 , 15] , although experimental support for this proposal is lacking [14] . In this paper , we show by a combination of mathematical modelling and experimental studies that WER autoregulation does not play a significant role in the epidermal patterning network , and propose a mechanism for patterning that depends on the mutual support of the two epidermal cell fates .
We have developed a mathematical model representing the core epidermal interaction network , in order to investigate the regulatory logic of epidermal patterning . Our model seeks to capture all key interactions and protein movements identified in experimental studies ( Figure 2 ) . The nature of the regulation of WER transcription is central to our model . WER transcription is repressed by both SCM and CPC , but no specific activators of WER transcription have been identified . To address directly the open question of the necessity for WER autoregulation , we consider two alternative forms of WER regulation . In the first version , we assume local WER self-activation , implemented by the enhancement of WER transcription by WER-complex ( Figures 2A and 3A ) . In this scenario , CPC down-regulates WER indirectly via competition for TTG/GL3/EGL3 . In the second version , we do not include local WER self-activation , assuming instead that WER transcription is activated uniformly in all epidermal cells , with both CPC and SCM ( in the H-position cells ) repressing WER transcription directly ( Figures 2B and 3B ) . We refer to the genetic regulatory network containing the first version of WER regulation as the “local WER self-activation model , ” and the regulatory network containing the second version as the “mutual support model . ” In order to focus more clearly on the core logic of the epidermal patterning network , the model incorporates a number of simplifying assumptions . First , since the expression pattern of TTG within the epidermis is not known , we assume that it is expressed uniformly and that it plays only a permissive role in allowing the formation of WER and CPC protein complexes with GL3/EGL3 . On the basis of this assumption , we do not include an explicit TTG variable in our mathematical model . TTG is , however , present implicitly in all the cells of our model epidermis . Second , we do not include the CPC-complex explicitly in the model; rather , we represent the ability of CPC to compete with WER for binding to TTG/GL3/EGL3 [16] by a direct inhibition of WER-complex formation by CPC . The CPC-complex is implicitly present in all model cells that express both CPC and GL3/EGL3 . Third , GL3 and EGL3 , which act in a partially redundant manner [11] , are represented by a single network component . Similarly , the three MYB proteins CPC , TRIPTYCHON ( TRY ) , and ENHANCER OF TRY AND CPC1 ( ETC1 ) , which act in a partially redundant manner [17] , are also represented by a single network component ( denoted by CPC ) . In order to incorporate the observed intercellular movement of the CPC and GL3/EGL3 proteins , we have imposed a specific mechanism in our model: both CPC and GL3/EGL3 proteins are moved actively out of the cells in which they are produced ( translated ) . We adopt this active mechanism to reflect the observed accumulation of these proteins in the nuclei of cells neighbouring the cells in which they are produced . A GL3-YFP fusion protein , expressed under the GL3 promoter in a gl3 mutant background , accumulates in the nuclei of N-position cells , even though the corresponding mRNA is restricted to H-position cells [11] . Similarly , a HA-tagged CPC protein , expressed under the CPC promoter in a cpc mutant background , accumulates in the nuclei of H cells , even though its mRNA is restricted to N cells [10] . A CPC-GFP fusion protein can be observed in the nuclei of both cell types [9 , 10] . However , this protein is expressed at much higher levels than the endogenous CPC protein ( due perhaps to protein stabilisation ) and causes numerous cells in the N position to adopt the trichoblast fate [10] . These experimental results demonstrate that both CPC and GL3 proteins move away from their sites of production , but the mechanism by which they do this is not known . Given this uncertainty , we have incorporated in our model a simple movement scheme that captures the observed complementary patterns of protein production and accumulation . Possible molecular mechanisms underlying this scheme are discussed below . We simulate a ring of 16 epidermal cells ( which we refer to as the epi-net ) following its emergence from the meristem . This represents the stereotypical number of cells found in each epidermal ring in the apical region of the seedling root in which patterning takes place [1 , 2] . As the cells age ( and so move further away from the root apex ) , occasional anticlinal cell divisions can occur , increasing the number of cells in an epidermal ring [4 , 5] ( the cross-section in Figure 1 shows an example of an older epidermal ring in which this has occurred ) . However , since we are here modelling the earliest stages of patterning in the epidermis , we do not consider these later events explicitly . Each simulated cell ( referred to as a cell-net ) contains all the components of the Arabidopsis root hair patterning network shown in Figure 2 , and so in a simulated cell , any combination of components can be expressed , including the combinations specific to trichoblast or atrichoblast cells . Figure 3 shows the network state ( expression of network components and active interactions ) in cell-nets corresponding to epidermal cells that have adopted either a stable trichoblast or atrichoblast fate . The mechanistic differences between the local WER self-activation and mutual support models are clearly visible in Figure 3 . Since mechanistic details ( such as rate laws and the corresponding kinetic parameters ) of the epidermal interaction network are not known , a model based on differential equations would involve a large number of unknown parameters . Instead , we adopt a modelling framework that encodes the logical form of interactions . At a given time , the components of a cell-net are either expressed or not . Components that have only positive regulatory inputs ( WER , GL3/EGL3 , CPC , CPC , GL2 , and GL2—see Figure 2 ) are expressed if their direct positive regulators are expressed . For example , if WER ( mRNA ) is expressed in a cell-net , then WER ( protein ) will be expressed . GL3/EGL3 has one negative input ( the WER-complex ) and is expressed if its input is not . To specify similar logical rules for the expression of the two components ( WER and WER-complex ) whose production is regulated by a combination of positive and negative regulators would involve making arbitrary assumptions about the dominance of activators or repressors ( see Protocol S1 ) . To avoid this , and to allow scope for investigating the effects of any assumptions we make about dominance , we adopt a stochastic formalism in which these components each have a time-evolving probability of expression . The probability of a component being expressed corresponds to the average abundance of that component in the cell . In our formalism , the change in probability over time is determined by the expression of the component's direct regulators and the corresponding activation/inhibition “rates” ( which encode the relative strengths of the regulatory interactions ) . For example , the probability of the WER-complex being expressed is increased by a small amount if both GL3/EGL3 and WER are expressed , and decreased by a small amount if both GL3/EGL3 and CPC are expressed . The incorporation of stochasticity in our model not only increases the investigative scope , but also supplies a form of noise , which is an inherent feature of biological systems and is an integral part of cell differentiation . Furthermore , this stochasticity plays an important role in triggering fate assignment in our model of the scm mutant , which lacks positional cues from the cortical cells ( see Protocol S1 ) . However , the formalism that we adopt is not intended to provide a detailed representation of the stochastic nature of molecular dynamics in a cell . A detailed description of the modelling formalism and equations can be found in Materials and Methods . Our stochastic Boolean formalism provides a versatile setting in which to investigate the effects of the relative strengths of combinatorial regulators for a specified regulatory logic . However , the results that we obtain from the model are not dependent on the use of this specific formalism . In particular , the behaviour of the model epidermis can be produced using Boolean models with appropriately chosen deterministic logical functions . In this case , the stochasticity needed to trigger patterning in the scm mutant epidermis can be introduced by adopting an asynchronous update scheme ( see Protocol S1 ) . To assess the ability of the model networks to account for observed wild-type expression patterns , we simulated epi-nets in which all network components ( except SCM ) were initially expressed at the same level in all cells ( i . e . , all cell-nets are initially identical ) . To represent the positional bias received from the underlying cortex , SCM was set to be active only in cells located in the H position , resulting in a lower transcription rate of WER than in the N position . In an epi-net , H and N positions alternate: odd-numbered cell-nets are in the H position , while even-numbered cell-nets are in the N position ( see Figure 4 ) . With this imposed positional bias , both the local WER self-activation and mutual support models are capable of generating stable expression patterns that agree with the expression patterns observed in experimental data ( Figure 4 ) . In scm mutant plants , experimental data show that epidermal cells adopt well-defined fates , but in a pattern that is not strictly correlated with position relative to the cortex [12 , 13] . To assess whether the model networks can also account for this phenotype , we set SCM to be inactive in all cells . In these simulations , the only patterning cues come from the stochasticity inherent in our modelling approach ( we do not incorporate stochasticity in the initial conditions ) . Figure 5 shows a composite of the steady states resulting from 15 independent simulations in rings of cell-nets , aligned vertically to produce a virtual epidermis . However , it is important to note that such a picture does not represent the result of a full two-dimensional simulation , including aging and longitudinal signalling between cell rings . Both the local WER self-activation and mutual support models develop stable patterns in which each cell-net adopts a coherent state ( either trichoblast or atrichoblast ) . For both models , the patterns produced are qualitatively comparable to those observed in scm mutant roots [12 , 13] . The total removal of cortical bias in our simulations may not be entirely equivalent to the situation pertaining in scm mutant roots , as the phenotypes of existing scm alleles suggest that some cortical positional information persists in these cases [13] . However , our simulations show clearly that both forms of the epidermal patterning network are capable of spontaneous pattern formation , even in the absence of spatial bias . For both local WER self-activation and mutual support , the stochasticity in our modelling formalism acts to break symmetry allowing a spatially patterned state to emerge from a spatially uniform initial state . To simulate the effect of other mutations , we set the corresponding cell-net components to be inactive in all cell-nets . Simulations of a wer mutation ( unpublished data ) result in identical expression patterns for both models , in agreement with experimental data ( namely , the uniform expression of GL3/EGL3 ) [8 , 11 , 17] . We simulate WER overexpression by imposing uniform expression of both WER mRNA and WER protein throughout the epi-net . The epi-net steady states resulting from 15 independent simulations of the two versions of the epidermal patterning networks are shown in Figure 6 . The expression pattern of all network components other than WER mRNA and WER are as in the simulated scm mutant ( Figure 5 ) , with each cell-net adopting a coherent state corresponding to either a trichoblast or atrichoblast . This mirrors the expression patterns reported in [8 , 14] and reflects the fact that WER , when overexpressed uniformly , is no longer able to respond to an imposed cortical bias . Figure 7 shows the expression of WER mRNA and WER protein in a simulated cpc mutant . Although both the local WER self-activation and mutual support models generate expression patterns for most network components that are in line with experimental data [11] , they generate significantly different patterns of WER expression . In the local WER self-activation model ( Figure 2A ) , the activation of WER expression by the WER-complex results in a wild-type pattern of WER expression even in the absence of CPC ( Figure 7A , cf . Figure 4A ) . In contrast , the loss of CPC-mediated repression of WER in the mutual support model ( Figure 2B ) results in an increase in WER expression in the H positions , as it is only being repressed by SCM in the absence of CPC ( Figure 7B ) . This corresponds to the pattern of WER expression observed experimentally [8] . This result suggests that the mutual support model , which does not incorporate local WER self-activation , more accurately reflects events occurring during the patterning of the epidermis . Since the local WER self-activation model fails to reproduce the observed pattern of WER expression in the cpc mutant , we tested the ability of the WER-complex ( or WER ) to enhance WER expression by examining the expression of GFP driven by the WER promoter ( WERpro::GFP ) in a wer mutant background ( using a null mutant in which no functional WER protein is produced ) . We found GFP expression to be the same in wild type and the wer mutant , showing that WER transcription does not depend on the presence of functional WER protein ( Figure 8A and 8B ) . To test directly our alternative assumption that WER transcription is activated uniformly in all epidermal cells , we carefully examined WER promoter activity ( as visualised by WERpro::GFP ) in wild-type seedlings . Whereas WERpro::GFP is preferentially expressed in the N cell file in less apical cells of the meristem , it exhibits uniform activity between N and H cell positions in cells proximal to the initials ( Figure 8C ) . These results show that the initially uniform activity of the WER promoter throughout the epidermis resolves rapidly into a pattern matching that of WER transcription in wild-type roots even in the absence of WER protein . This strongly suggests that the establishment of patterned WER transcription—a key event in epidermal patterning—does not depend on local WER self-activation . Since the pattern of WER promoter activity in both wild-type and wer mutant roots corresponds to the wild-type pattern of cell fate in the epidermis , there is no obvious role for posttranscriptional regulation of WER activity ( since posttranscriptional regulation of WER can only occur in cells in which WER is transcribed ) . Taken together , our modelling and experimental results show that WER is initially activated uniformly in the epidermis , and suggest that its rapid repression in emerging trichoblasts is controlled by a combination of SCM-mediated positional information and CPC . To explore further the differences between the two model networks , we simulated mutants that are incapable of forming the WER-complex . Since GL3/EGL3 and TTG are required for complex formation , both the gl3 egl3 double mutant and the ttg mutant should lack WER-complex . In this scenario , the local WER self-activation and mutual support models predict different patterns of WER expression . In the local WER self-activation model , the failure of WER-complex formation results in a uniform loss of WER expression in the model epidermis ( Figure 9A ) . However , since WER expression does not depend on local self-activation in the mutual support model , WER is expressed in an essentially wild-type pattern in the model epidermis ( with an increased probability of expression in cells in the H position due to the lack of CPC-mediated repression ) ( Figure 9B ) . To test this prediction experimentally , we examined the expression of GFP driven by the WER promoter ( WERpro::GFP ) in these mutant backgrounds . As predicted by the mutual support model , GFP expression is essentially the same in the wild-type and mutant epidermis ( Figure 10 ) . This supports our finding that WER self-activation does not play a significant role in the early stages of epidermal patterning , and provides direct experimental validation of the predictions of the mutual support model of epidermal patterning .
Taken together , our modelling and experimental studies support a mechanism for spatial pattern formation in the Arabidopsis root epidermis that depends critically on the movement of mobile proteins between cells—a lateral inhibition with feedback ( LIF ) mechanism . Importantly , this mechanism does not depend on local WER self-activation , but relies instead on the repression of WER transcription in emerging trichoblasts by CPC protein . Previous theoretical discussions of epidermal patterning [14 , 15] have suggested that local WER self-activation is a necessary feature of the patterning network—a local activation and lateral inhibition ( LALI ) mechanism [18 , 19] . Although both the LALI and LIF mechanisms can generate similar stable patterns of cell fate , the logical structure of the underlying networks is quite different . LALI mechanisms depend on interlinked positive feedback ( short range ) and negative feedback ( long range ) whereas LIF depends on a single “double-negative” feedback loop , mediated by intercellular signalling , and does not depend on local self-activation . The logical structure of the LIF mechanism is analogous to the Delta-Notch signalling system in animal epithelia , in which proneural activity in one cell represses proneural activity in its neighbours through the transmembrane ligand Delta and its receptor Notch , ensuring directional signalling . Models of the Delta-Notch system exhibit spontaneous patterning that does not depend on any local self-activation [20 , 21] . In the LALI mechanism , the “activated” cell state ( atrichoblast ) inhibits its neighbours , which adopt an alternative fate ( trichoblast ) . In contrast , in the LIF mechanism , cells adopting one of the two epidermal fates are mutually supporting , producing factors required by cells adopting the alternative fate . Adoption of the atrichoblast fate ( high WER-complex ) requires GL3/EGL3 from neighbouring cells; adoption of the trichoblast fate ( low WER-complex ) requires CPC from neighbouring cells ( to prevent accumulation of WER-complex ) . In other words , a cell can only have high levels of WER-complex if a neighbouring cell has a low level of WER-complex and vice versa . This model therefore predicts that “runs” of three or more epidermal cells with similar levels of WER-complex should not occur . In the root apical meristem , where the early patterning of gene expression in the epidermis occurs , each ring of epidermal cells contains 16 cells , with alternating cells in H and N positions , as encoded in our model [22] . We therefore observe a strict alternating pattern in our wild-type simulations that incorporate a positional bias from the cortex . In the simulated scm mutant , which lacks cortical bias , we do not observe more than two cells of the same fate neighbouring each other . In growing roots , the number of cells in an epidermal ring tends to increase as cells move away from the root apical meristem , due to occasional anticlinal cell divisions [4 , 5] . This is shown clearly in Figure 1 , in which most H-position cells are separated by two N-position cells . In older epidermal rings , three or more adjacent cells are sometimes observed to have the same pattern of gene expression , which cannot be accounted for by our early patterning network in its current form . However , it is likely that once the basic pattern of expression of the core epidermal patterning components has been established , cell fate is stabilised by additional factors such as chromatin modification [23–25] . Such fate stabilisation mechanisms would allow cells to maintain their network state even when no longer supported by a neighbouring cell of the alternate fate . We have shown by model simulation that the LALI mechanism ( incorporating local WER self-activation ) fails to account fully for the previously reported phenotype of a cpc mutant root , and by experiment that a specific form of local self-activation ( WER-mediated up-regulation of WER transcription ) does not operate in the early patterning of the root epidermis . Our combined modelling and experimental results favour an alternative mechanism ( LIF ) in which the two emerging cell fates mutually support each other through the active exchange of the mobile proteins CPC and GL3 . The mutual support model predicts patterns of WER promoter activity in wer , gl3 egl3 , and ttg mutant roots that are similar to wild type . We have verified these predictions experimentally , providing validation for the model and further support for our proposed patterning mechanism . Importantly , the model based on the LALI mechanism does not account for these new observations . The mutual support model incorporates the active movement of the CPC and GL3 proteins from the cells in which they are produced to neighbouring cells . Such an active mechanism is suggested by the previously reported complementary patterns of production and accumulation of these proteins in the epidermis . We have adopted a modelling formalism based on binary states of expression ( “on” or “off” ) . In this formalism , the patterning of the model epidermis depends on this active mechanism of protein movement . However , the possibility remains that the observed complementary patterns of protein production and accumulation could result from simple diffusion of the proteins between cells , together with sequestration of the proteins into nuclear-localised protein complexes ( as occurs in the directed movement of the SHORTROOT protein in the root apical meristem [26] ) . Previous theoretical discussions of epidermal patterning have proposed that local self-activation is a necessary feature of a patterning mechanism [14 , 15] . This conclusion is based on the theory of two-component activator–inhibitor models in which movement is purely diffusive . To explore the validity of this conclusion for the root epidermal patterning network , we have analysed two different mathematical representations of the mutual support model . First , we have developed a logical state ( Boolean ) model in which CPC and GL3 protein movement depends on a movement parameter , allowing both active and passive ( diffusion-like ) movement to be represented . Analysis of this model shows that passive GL3 movement is sufficient to account for patterning , so long as CPC moves actively ( see Protocol S1 ) . Second , we have developed a reaction–diffusion analogue of our logical model in which both GL3 and CPC move between cells by simple diffusion alone ( see Protocol S1 ) . When reduced to an effective two-component model for GL3 and CPC ( by assuming that protein complex formation and WER dynamics reach equilibrium much faster than diffusive processes ) , we show that the model can take the form of a cross activator–inhibitor system , which is capable of spontaneous pattern formation via diffusion-driven instability [27] . This analysis shows that the mutual support mechanism we propose can generate pattern spontaneously by diffusive protein movement and protein complex formation , in the absence of any local self-activation reaction . Numerical simulation of both the full and reduced models confirms that the diffusive mechanism generates stable patterns with protein distributions that match those observed in the root epidermis ( see Protocol S1 ) . Our results serve to highlight the importance of a detailed investigation of the mechanisms of the intercellular movement of proteins such as CPC and GL3/EGL3 [28] . A number of simple mechanisms might underlie an effective directionality of protein movement away from producing cells . For example , the movement of proteins through plasmodesmata could be dependent on a chaperone protein that is produced only in cells producing the mobile protein . Alternatively , passage through plasmodesmata could depend on localisation of the protein in the endoplasmic reticulum , which would favour movement away from the cells in which the protein is translated . An intriguing parallel is provided by the movement of small metabolites through small intercellular pores ( microplasmodesmata ) in the filamentous cyanobactoria Anabaena . A recent study has shown that the permeability of pores ( and hence the mobility of metabolites ) mirrors the states of differentiation of the two cell types in this system [29] . In particular , as individual cells in the filament move towards a differentiated heterocyst fate , the permeability of pores between emerging heterocysts and neighbouring vegetative cells decreases compared to that between two vegetative cells . Thus , in this very different system , differential permeability of intercellular channels , dependent on cell fate , can establish spatially patterned protein distributions . The widespread occurrence of cell-to-cell trafficking of macromolecules in plant and animal tissues [30] suggests that mechanisms of the type we describe—centred on mobile proteins that can be sequestered in protein complexes—may play a role in a range of pattern-forming processes operating in planar groups of cells .
The WERpro::GFP construct was previously reported in [31] . Briefly , it included a 2 . 5-kb WER promoter fragment 5′ to the GFP coding sequence and a 1 . 1-kb 3′ WER fragment , and faithfully reported the WER transcription pattern . To examine the expression of WERpro::GFP in the wer , gl3 egl3 , and ttg mutant backgrounds , we used the published wer allele , wer-1 [31] , the gl3–1 egl3–1 line [11] , and the ttg1–13 mutant [2] . Plants homozygous for the WERpro::GFP insertion were crossed to plants homozygous for one of the mutant alleles . The resulting plants were self-pollinated , and F2 plants that were homozygous for the wer-1 , gl3–1 egl3–1 , or ttg1–12 mutations and the WERpro::GFP transgene were selected . These plants were in turn self-pollinated to produce a population of seed that were homozygous for the desired mutant allele and the WERpro::GFP transgene . For confocal microscopy imaging , 4- or 5-d-old roots were stained with 10 μg/ml propidium iodide and visualised on a Leica TC5 SP confocal microscope . Images were assembled using Adobe Photoshop . In our models , a ring of 16 epidermal cells ( the stereotypical number found in the apical region of the seedling root in which patterning takes place ) is represented by an epi-net comprising 16 identically composed cell-nets , indexed by the integer j = 1 , 2 , … , 16 . The set of components in each cell-net , together with their interactions , is shown schematically in Figure 2 . In the mathematical model , the state of mRNAs is represented by the corresponding gene name abbreviation ( for example , CPCtj represents the state of CPC mRNA at time t in cell-net j ) . The state of the corresponding protein carries an appended “p” ( for example , WERptj represents the state of WER protein at time t in cell-net j ) . The state of the WER-complex is denoted by WERc . In order to capture what we believe to be the essential logic of the epidermal patterning network , while keeping the number of distinct molecular species in the model to a minimum , a number of known network components have been left out of the model , or combined into a single model variable . Both GL3 and EGL3 are represented jointly by a single model element GL3 ( comprising variables for mRNA and protein ) . We justify this simplification by noting that all published data suggest that GL3 and EGL3 are regulated similarly and exhibit functional redundancy . Similarly , we represent the three single-repeat R3 MYB proteins CPC , TRIPTYCHON ( TRY ) , and ENHANCER OF TRY AND CPC1 ( ETC1 ) by a single model element CPC , since experimental evidence supports the idea that they act collectively and redundantly to specify the trichoblast fate [17] . Furthermore , in the absence of experimental data to the contrary , we assume that the WD-repeat protein TRANSPARENT TESTA GLABRA ( TTG ) , an essential component of the WER-complex , is expressed uniformly throughout the epidermis . This assumption renders the explicit representation of TTG in the models unnecessary , and our models do not contain TTG variables ( although the protein is implicitly assumed to be present in all cells ) . To investigate the patterning potential of the local WER self-activation and mutual support models ( Figure 2 ) , we use a discrete-time logical formalism . In this approach , the state of each network component is represented by a binary variable taking either value 1 ( component expressed ) or 0 ( not expressed ) . Time evolution of the network state is modelled by the synchronous update of the state of each network component at equally spaced time points ( t , t+1 , t+2 , … ) . For the network components whose state is regulated by only one other component type ( WERp , GL3 , GL3p , CPC , or CPCp ) , we adopt a conventional deterministic Boolean update formalism [32] . For the two components whose state is regulated by more than one input ( WER and WERc ) , we adopt a novel formalism based on the probability Ptj[X] that the state of component Xtj will be 1 at time t . Rather than specifying a deterministic function for the time evolution of the states of these components , we instead specify a deterministic rule for the time evolution of the probability of expression . This form of update allows us both to vary the relative strengths of the inputs and to incorporate stochasticity in the update process . Although this approach directly introduces stochasticity into the evolution equations of only two network variables , the stochasticity filters through to the other components . Thus , whereas all components could be represented probabilistically , this would necessitate the introduction of many more undetermined parameters without adding further functionality to the model . In a simulation of the network , the actual values ( 0 or 1 ) of WERtj and WERctj are determined stochastically at each time step according to the probability of expression , P tj[WER] and P tj[WERc] . The parameters in our probabilistic update functions ( see below ) allow us to explore the robustness of patterning to changes in the relative strengths of the inputs . Furthermore , the incorporation of stochasticity into the system is important , since stochasticity is an inherent feature of biological networks and is required in our models to initiate patterning in the simulated scm mutant . However , our approach does not attempt to mimic any specific form of stochasticity found in biological systems , and we have shown that the results obtained using our probabilistic formalism can be reproduced by using a deterministic Boolean model with stochasticity introduced in the form of asynchronous state update ( see Protocol S1 ) . Our probabilistic Boolean formalism provides a simple way of exploring the consequences of specific assumptions about the regulatory logic of the epidermal patterning network . However , the use of a logical ( on/off ) representation of the network state assumes that the regulatory interactions represented in the model ( e . g . , transcription and translation ) are essentially “all or nothing . ” Since our primary objective is to explore the differences between two alternative network structures , we believe that this assumption is appropriate . Other approaches to modelling regulatory networks , such as those based on differential equations , do not depend on such an assumption being made . However , these models require the specification of many more parameters than our model , to represent the details of specific interaction kinetics . Such models can provide more-realistic representations of the dynamical evolution of the state of the network . Given that there are currently no data , either from which appropriate parameters can be specified , or against which detailed network dynamics can be validated , we do not believe that these approaches currently have a significant advantage over our logical formalism . The local WER self-activation and mutual support models are defined in Equations ( 1 ) and ( 2 ) , respectively . The models are identical apart from the equation encoding the time-evolution of WER mRNA . The symbol ∨ represents the logical “inclusive OR” function ( i . e . , A∨B = 0 if and only if A = B = 0 ) . c0 , c1 , . . . c5 , are positive parameters that determine the relative strengths of the inputs in the probabilistic multi-input update functions for WER and WERc ( Figure 2 ) . The constant terms c1 and c5 represent either constitutive production or degradation , depending on their preceding signs . The regulatory inputs to WER and WERc specify the amount by which the probability of expression of these components changes during a single time step . This form of update rule is similar to the rate equations that form the basis of differential equation models ( in which the rate of change of a component is determined by the values of its direct regulators ) . Values within the brackets ⌊ ⌋ are forced to remain between 0 and 1 . A positional bias from the underlying cortex is incorporated in the models via the state of the SCRAMBLED ( SCM ) receptor-like kinase , which is taken to be 1 in cell nets occupying the H position and 0 in cell-nets occupying the N position . Activity of SCM results in a reduction in the rate of transcription of WER , determined by the parameter c0 . We assume the two positions to be arranged alternately , as is typically the case in the apical root epidermis ( anticlinal cell divisions in the epidermis , which can increase the spacing between H-position cells , typically occur further from the meristem , where the expression pattern of network components has already stabilised ) . The initial state of all components , bar SCM ( see above ) , is identical in all cell-nets , representing the fact that the final stable state of each cell-net is determined by its position relative to the underlying cortical cells rather than cell lineage . As the state of each cell-net evolves in time , the cell-nets adopt stable patterns of expression corresponding to either the trichoblast or atrichoblast cell fate ( Figure 4 ) . A detailed discussion of the dependence of the behaviour of the models on initial conditions and parameter values can be found in Protocol S1 . | The patterning of the Arabidopsis root epidermis depends on a genetic regulatory network that operates within and between cells . Genetic studies have identified a number of key components of this network , but the functional logic of the network has remained unclear . In this work , we integrate genetic and biochemical data in a mathematical model that we use to explore both the sufficiency of known network interactions and the extent to which additional assumptions about the model can account for wild-type and mutant data . Our model shows that an existing hypothesis concerning the autoregulation of the transcription factor WEREWOLF does not account fully for observed expression patterns , and we confirm the absence of autoregulation experimentally in transgenic plants . We propose an alternative mechanism centred on the movement of transcriptional regulators between epidermal cells , and present experimental support for this mechanism . These movements underlie a novel mechanism for pattern formation in planar groups of cells , centred on mutual support of two cell fates rather than local activation and lateral inhibition . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"plant",
"biology",
"computational",
"biology"
] | 2008 | A Mutual Support Mechanism through Intercellular Movement of CAPRICE and GLABRA3 Can Pattern the Arabidopsis Root Epidermis |
Cardiac electrical asynchrony occurs as a result of cardiac pacing or conduction disorders such as left bundle-branch block ( LBBB ) . Electrically asynchronous activation causes myocardial contraction heterogeneity that can be detrimental for cardiac function . Computational models provide a tool for understanding pathological consequences of dyssynchronous contraction . Simulations of mechanical dyssynchrony within the heart are typically performed using the finite element method , whose computational intensity may present an obstacle to clinical deployment of patient-specific models . We present an alternative based on the CircAdapt lumped-parameter model of the heart and circulatory system , called the MultiPatch module . Cardiac walls are subdivided into an arbitrary number of patches of homogeneous tissue . Tissue properties and activation time can differ between patches . All patches within a wall share a common wall tension and curvature . Consequently , spatial location within the wall is not required to calculate deformation in a patch . We test the hypothesis that activation time is more important than tissue location for determining mechanical deformation in asynchronous hearts . We perform simulations representing an experimental study of myocardial deformation induced by ventricular pacing , and a patient with LBBB and heart failure using endocardial recordings of electrical activation , wall volumes , and end-diastolic volumes . Direct comparison between simulated and experimental strain patterns shows both qualitative and quantitative agreement between model fibre strain and experimental circumferential strain in terms of shortening and rebound stretch during ejection . Local myofibre strain in the patient simulation shows qualitative agreement with circumferential strain patterns observed in the patient using tagged MRI . We conclude that the MultiPatch module produces realistic regional deformation patterns in the asynchronous heart and that activation time is more important than tissue location within a wall for determining myocardial deformation . The CircAdapt model is therefore capable of fast and realistic simulations of dyssynchronous myocardial deformation embedded within the closed-loop cardiovascular system .
The ventricles of the heart undergo rapid electrical activation under normal conditions through the cardiac conduction system , leading to a near-synchronous mechanical contraction of the ventricles [1] . Ventricular electrical asynchrony may be induced either by ventricular pacing or through disorders of the cardiac conduction system such as left bundle-branch block ( LBBB ) [2] . Electrically asynchronous ventricular activation results in reduced pump function , due to dis-coordinated contraction and relaxation of the inter-ventricular septum and left ventricular ( LV ) free wall [3 , 4] . Ventricular deformation recorded during tagged MRI [5] or echocardiography [6] may contain information on both the electrical activation pattern causing mechanical dyssynchrony , and the health of ventricular tissue [7] . However , confusion remains due to conflicting data over the usefulness of mechanical dyssynchrony as a marker for potential response to treatment of dyssynchronous heart failure with cardiac resynchronization therapy ( CRT ) [7–9] . Theoretical studies using computational models offer the ability to mechanistically link ventricular electrical activation and tissue condition to both mechanical deformation and haemodynamic changes . Computational models are beginning to provide insights into dyssynchronous heart failure and its treatment with CRT [7 , 10–17] . There is considerable interest in producing ‘patient-specific’ models with the ultimate goal of predicting response to treatment of dyssynchronous heart failure with CRT [18–21] . The Finite Element ( FE ) method is the standard method to simulate time-dependent cardiac mechanics with inhomogeneous mechanical behavior of the myocardium . Common outputs are spatial distributions of stress and strain , and changes in cardiac geometry during ventricular systole . Patient-specific modeling using the FE method requires a considerable amount of input data such as a cardiac geometry from the patient and spatial distribution of mechanical properties within the myocardium , including fibre orientation , stiffness , and contractility [22] . Many of these ventricular properties are difficult to obtain in a clinical setting , requiring the use of generalised data instead based on anatomical atlases [23] . Furthermore , simulations using the FE method require considerable computational resources preventing availability of calculated results during clinical measurement protocols . In an attempt to reduce the input information required , and to focus on clinically measurable output data , the CircAdapt model of the heart and circulation has been developed ( Fig 1A ) [24] . CircAdapt allows rapid simulation of cardiac pump function and cardiovascular system dynamics for both research and educational purposes ( www . circadapt . org ) . The model uses a highly simplified ventricular geometry where the cardiac walls are represented by thick-walled spherical shells consisting of myofibres . In the original CircAdapt model , mechanical properties and load were assumed to be evenly distributed within each wall . In the present study we introduce the MultiPatch module , allowing simulation of the effects of an uneven distribution of mechanical properties in the cardiac walls within the CircAdapt framework . Advantages of this approach over FE implementations include an extremely low computational effort , and a smooth haemodynamic incorporation of the heart within the entire circulation . Thus , real-time simulation of cardiac mechanics and haemodynamics is feasible using CircAdapt , widening the possibilities for patient-specific modeling in a clinical setting . The MultiPatch module subdivides a cardiac wall into a finite number of patches . Each patch has its own stress-strain relation for the constituent myofibers . Although cardiac geometry is known to be complicated , we postulated that a cardiac wall may be considered simply spherical to estimate mechanical load in the tissue . The hypothesis used is that each patch can be considered part of a spherical wall . Furthermore , in this wall , the two-dimensional tensor , representing wall tension , is considered isotropic . An important consequence of this hypothesis is that wall tension is homogeneous in each wall , according to Laplace’s law . The location of a patch within a wall is therefore not relevant for the calculation of stress and strain in the patch , since all patches in a wall behave as if in series and experience the same wall tension . Myofibre stress is not necessarily distributed homogeneously as the thickness of the patches may differ . For example , when simulating mechanical effects of asynchronous activation , the location in the wall determines timing of activation , which then determines fibre stress and strain in the patch . After performing the calculations fibre mechanics are known locally , given the activation time of the tissue . Consequently , computational effort is reduced tremendously relative to the FE method . In the present study , we quantitatively assessed the effectiveness of the MultiPatch module for simulating mechanical deformation during asynchronous ventricular activation . For that purpose , we simulated experiments in a well-known animal study into the effects of pacing-induced electrical dyssynchrony [4] . We directly compare strain , stress-strain loops , and haemodynamic response calculated by the model to experimental recordings to investigate the hypothesis underpinning the MultiPatch module , that activation time of ventricular tissue is more important than tissue location for determining myocardial deformation in the asynchronous heart . Furthermore , we show a proof-of-concept simulation of ventricular mechanical deformation in a patient with LBBB and heart failure . We use recordings of endocardial electrical activation , and the patient’s stroke volume and ejection fraction as inputs to our model , and compare the simulated deformation patterns of the left ventricular and septal walls to those recorded in the patient using tagged MRI .
The CircAdapt model , as shown schematically in Fig 1A , simulates the heart when incorporated in the whole circulation to provide realistic loading . The heart consists of five walls , i . e . , the left and right atrial walls , the left and right ventricular wall , and the interventricular septum . Cavity pressures are calculated from cavity volumes as follows . Cavity volumes determine wall area . Wall areas determine strain of the myofibres in the wall . A model of myofibre mechanics is used to calculate myofibre stress from myofibre strain . Myofibre stress determines wall tension in each cardiac wall . Using the TriSeg module [25] , the mechanical equilibrium between the three ventricular walls is used to calculate their shape when encapsulating the two ventricular cavities . Transmural pressure is calculated from wall tension and curvature for each wall using Laplace’s law . Cavity pressures are found by adding the transmural pressures to the intra-pericardial pressure surrounding the myocardial walls . In the basic CircAdapt model , wall area determines wall tension . In a cardiac wall , consisting of n different patches , the total area of the patches adds up to the given total wall area . The wall area is the area of the surface that divides the wall volume in half ( see Materials and Methods ) . The MultiPatch module is designed 1 ) to calculate wall tension; and 2 ) to determine the area assigned to the different patches . To solve for the unknown areas of n patches we use one equation , saying that the sum of all patch areas equals the given total wall area . Furthermore we use a set of n-1 equations , saying that wall tension in all patches is the same . Thus , in total , n equations are used to solve the area of n patches . From the area and the known constitutive equations of each patch , myofibre strains and stresses are calculated . For each patch , the resulting wall tension is calculated . Because of the applied set of equations wall tension is general to all patches . Thus , as in the regular wall , the MultiPatch module determines wall tension from wall area . The mechanical load of the myofibres in each patch is an important side result of these calculations . Details of the mathematical derivation are presented in the methods section .
Simulation of cardiovascular mechanics using the MultiPatch module requires a considerably reduced computational effort compared to FE implementations . Speed of the Matlab implementation can be assessed using CircAdapt files provided as an online supplement that perform all simulations in the present study ( . txt files ) . FE-based models of cardiac mechanics presently run significantly slower than real-time and generally require access to supercomputing facilities . The speed of simulation in CircAdapt allows assessment of the role of factors that require simulation of multiple full cardiac cycles , such as homeostatic control and tissue adaptation [24 , 28] . Real time simulation on regular desktop computers when implemented in C++ enables use of CircAdapt in a teaching environment [42] . A future application of the MultiPatch module is integration within the CircAdapt Simulator ( freely available through www . circadapt . org ) to allow education of clinical trainees in the haemodynamic consequences of myocardial scar and conduction disorders , and in the interpretation of regional deformation patterns in the failing heart . Our study focusses on the heart . However , the theory and methods could also be applied to computer models of other contractile organs such as the gastro-intestinal tract or uterus to allow faster simulation [43 , 44] . In the current study , we have established that the MultiPatch module in CircAdapt enables simulation of realistic deformation patterns in a patient-specific manner , based on a series of relatively simple parameter changes in the model ( Fig 5 ) and invasively recorded activation maps . Combined with the speed at which simulations can be performed in CircAdapt , this provides the opportunity for patient-specific simulations of mechanical dyssynchrony and haemodynamics that can be performed on a personal computer in a bedside context . We have focussed on the effects of asynchronous mechanical activation on deformation in this study , but the model is also capable of simulating regional wall hypocontractility and/or stiffening , consistent with acute or chronic regional myocardial infarction or fibrosis [30] . By simulating CRT combined with patient-specific myocardial properties obtained by fitting to observed myofibre strain , it may be possible to predict patient response to CRT . The ventricular geometry within the CircAdapt model is highly simplified . In particular , we assume that the curvature is the same at each point in a wall , which is not the case in the real heart . Mechanical effects of the atrioventricular rings on ventricular tissue are not included . Geometric simplifications in the CircAdapt model mean that circumferential , longitudinal , and fibre strain are considered to be equivalent , and so no apex-base axis is present . Our simplified approach prevents inclusion of transmural differences in electromechanical properties [45] , and simulation of mechanical shear . The MultiPatch module integrated within the CircAdapt model of the heart and circulation produces realistic simulations of local mechanical deformation , work distribution , and global haemodynamic pump function of the dyssynchronous heart . The success of the model in predicting fibre strain patterns suggests that once timing of activation of a region of tissue is known , the location of the tissue is not important for determining of myocardial deformation . We have demonstrated the potential of this model for use in patient-specific simulation of dyssynchronous heart failure , based on clinical recordings of the LV endocardial activation sequence . As a future perspective , comparing patient-specific simulations from the MultiPatch module against recorded myocardial deformation could be used to determine whether the tissue substrate underlying electro-mechanical dyssynchrony in a patient is amenable to CRT .
The patient included in the simulation part of the study was part of the CARTO-CRT trial and provided a written informed consent for the study . The CARTO-CRT trial protocol was approved by the CHU-Bordeaux ethics committee ( registered at clinicaltrial . gov: NCT01270646 ) . A deflectable-tip catheter was inserted into the left ventricle through a trans-septal route ( Navistar catheter , Biosense Webster , Diamond Bar , CA ) . Left ventricular endocardial electro-anatomical mapping was performed during sinus rhythm using Carto V3 ( Biosense Webster , Diamond Bar , CA ) . After the reconstruction of the left ventricular geometry , detailed activation mapping was conducted with a minimum of 100 equally distributed points . All points were reviewed manually to ensure the quality of the activation map . The location of the LV septum , the anterior , lateral and posterior wall , the mitral annulus and the apex were defined on the CARTO anatomical mesh . The AHA segmentation was then used to accurately localise each of the endocardial activation points . Mean activation times were then applied to the AHA segmentation as described in the online supplement , section ‘Aligning CARTO data with an AHA segmentation of the left ventricle’ . The MRI study was conducted on a 1 . 5 Tesla clinical device ( Magnetom Avanto , Siemens Medical Systems , Erlangen , Germany ) equipped with a 32-channel cardiac coil . Myocardial tissue tagging was performed using complementary spatial modulation of magnetization ( CSPAMM ) combined with steady state free precession cine imaging [46] . Images were acquired in three parallel short-axis planes at the basal , mid , and apical levels of the left ventricle . A multiple expiratory breath hold scheme was used to enable strain imaging at high temporal resolution ( 11 ms ) . Sequence parameters were: prospective triggering , repetition time 4 . 7 ms , echo time 2 . 3 ms , bandwidth = 369 Hz/pixel , flip angle = 20° , FoV 300 × 300 mm , matrix size 256 × 78 , slice thickness 6 mm , tag spacing 7 mm . CSPAMM images were processed using the software Osirix v3 . 9 . 4 ( Osirix Fondation , Geneva , Switzerland ) . Strain computation was performed with the Sine Wave Modeling method [47] using the inTag Osirix plugin ( CREATIS-INSA , Lyon , France ) . Circumferential strain curves were computed for each of the 16 left ventricular segments , according to the standard AHA segmentation , excluding the apical segment [27] . A summary of the notation used in this section is provided in Table 2 . Before describing the MultiPatch model , we recapitulate how wall tension is calculated in a uniform wall , as originally described for the TriSeg model [25] , simulating three ventricular walls , encapsulating two cavities . The volume of blood in the cavity Vc is used to derive the area Aw of the mid-wall surface on which wall tension is calculated as described by Lumens et al . [25] . The mid-wall surface is the surface dividing the wall volume in half in the radial direction , as shown in Fig 6 . Natural fibre strain εf is calculated from Aw assuming zero-strain reference for that area to be Aw , ref , the wall area when sarcomere length is 2μm: εf=12ln ( AwAw , Ref ) ( 1 ) Fibre stress σf was calculated as a function of strain εf , using a phenomenological model based on physiological experiments , as explained in the sarcomere module originally published by Lumens et al . [25] ( online supplement , section ‘The CircAdapt sarcomere contraction model’ ) . To calculate the wall tension Tw , considered to be concentrated on the mid-wall surface , Lumens et al . used the following relation derived from the principle of conservation of energy ( online supplement , section ‘Conservation of energy’ ) : Tw=σf ( εf ) Vw2Aw . ( 2 ) The symbol Vw refers to the wall volume . In the TriSeg model , the geometries of the three ventricular walls are iteratively calculated to satisfy mechanical equilibrium between the walls on their common intersection curve . As a result , wall areas Aw and wall curvatures Cm can change within the TriSeg iteration . Once the wall tension and curvature are known , the transmural pressure can be calculated using Laplace’s law , Ptrans=2TwCm , ( 3 ) where Cm is the mid-wall curvature , calculated from the geometry of either the chamber or the TriSeg module as described by Lumens et al . [25] . The wall tension in Eq 2 is the same as that in Laplace’s law ( Eq 3 ) , with units N/m , and is distinct from tensile force ( units N ) . In the MultiPatch module , a wall is subdivided into n patches , indexed j . Each patch is assigned properties including a reference area ApRef , j ( Fig 6 ) and tissue volume Vp , j . Tissue properties , and hence fibre Cauchy stress and strain , are allowed to vary between patches , and so Eq 2 no longer holds throughout the wall . To solve the related equilibrium equations , wall tension Tw is linearized about a working point corresponding to the wall area at zero wall tension ( A0 , w ) , i . e . wall area if the tissue would experience no external load; Tw ( Aw ) ≈dTwdAw ( Aw−A0 , w ) . ( 4 ) A0 , w and wall area stiffness dTw/dAw are calculated using the MultiPatch module so that Eq 4 can be used to calculate Tw . For each patch we use a linearized relation for the wall tension in one patch Tp , j as a function of patch mid-wall area Ap , j ( Fig 6 ) , equivalent to that for the wall in Eq 4: Tp , j ( Ap , j ) ≈dTp , jdAp , j ( Ap , j−Ap0 , j ) ( 5 ) The mid-wall surface of the patch is assumed to lie on a spherical surface , as in the TriSeg model . Since transmural pressure and curvature are common to all patches in a wall , by application of Laplace’s law it follows that the wall tension Tp , j must be the same for all patches in the wall , and equals Tw . If wall tension equals zero , the mid-wall area of the whole wall equals the sum of zero wall tension mid-wall areas of all patches , resulting in a value for A0 , w , to be substituted into Eq ( 4 ) for the whole wall: A0 , w=∑j=1nAp0 , j ( 6 ) We can differentiate Eq 6 with respect to wall tension . Taking the inverse of the resulting expression , we find that the total wall stiffness dTw/dAw in Eq ( 3 ) equals the inverse of the sum of inverse stiffness of all patches: dTwdAw=1/∑j=1n ( dTp , jdAp , j ) −1 ( 7 ) A0 , w and dTw/dAw as calculated in Eqs 6 and 7 are then used in Eq 4 to determine Tw for the current value of Aw in the TriSeg iterative scheme . A0 , w and dTw/dAw do not change between TriSeg iterations and so need only be calculated once per time step . Once the TriSeg solution is found , transmural pressure ptrans can be calculated by Laplace’s law ( Eq 4 ) using Tw and Cm . In this way , mechanical behaviour of the total wall is described in relation to the properties of its composing patches . After wall tension has been determined , the true areas Ap , j of the separate patches are calculated using Tw , dTp , j/dAp , j , and Ap0 , j in Eq ( 5 ) . The fibre stress σf , j and fibre natural strain εf , j are then calculated for each patch , using the patch equivalent of Eq ( 1 ) and the sarcomere module ( online supplement , section ‘The CircAdapt sarcomere contraction model’ ) . Different mechanical properties and activation times may be assigned to each patch and so can result in differences in fibre stress and strain between patches , despite having the same wall tension and curvature . The thickness of a patch can be calculated by dividing its constant volume Vp , j by its time-dependent area Ap , j . We now explain how Ap0 , j and dTp , j/ dAp , j are calculated for use in Eqs 6 and 7 . In the CircAdapt sarcomere module , sarcomere length Ls is described as follows , Ls , j=Lsi , j+Lse , j , ( 8 ) where Lse , j is the length of the series elastic element and Lsi , j is the intrinsic sarcomere length ( online supplement , section ‘The CircAdapt sarcomere contraction model’ ) . Lse , j is proportional to the active stress generated by the tissue . Assuming that area is proportional to the square of sarcomere length , Ls , j is related to patch area Ap , j by Ap , j= ( Ls , jLsRef , j ) 2ApRef , j . ( 9 ) ApRef , j is the reference area of patch j when the sarcomere length is LsRef , j ( i . e . 2μm , see Fig 6 ) . In order to calculate Ap0 , j , we use Eq 5 . Since Aw , and hence Ap , j , can change during the TriSeg iteration , Ls is unknown . We therefore use the intrinsic sarcomere length Lsi with Eq 9 to compute a first estimate of the patch area , Api , j . Api , j corresponds to the area of the patch without the influence of active stress . The first estimate of natural fibre strain in the patch is εfi , j=ln ( Lsi , jLsRef , j ) . ( 10 ) The strain εfi , j is used by the CircAdapt sarcomere module to calculate the fibre stress σfi , j and fibre stiffness dσfi , j / dεfi arising from Lsi , j . σfi , j is a component of the fibre passive stress . The stiffness dσfi , j / dεfi additionally depends on the contractile state of the tissue in the patch since the contractile and passive elements of the sarcomere module are arranged in parallel ( online supplement , Eq . 21 ) . Hence dσfi , j / dεfi is dependent on the timing of patch activation . To calculate the unloaded area Ap0 , we must subtract the effects of passive stress due to Lsi from Api , j . By the principle of conservation of energy ( Eq 2 ) , σfi , j and area Api , j must induce a part Tpi , j of the wall tension Tw felt by the patch , Tpi , j=σfi , jVp , j2Api , j , ( 11 ) where Vp , j is the wall volume assigned to patch j . The patch area stiffness dTpi , j /dApi , j is found by the chain rule: dTpi , jdApi , j=dTpi , jdεfi , j/dApi , jdεfi , j . ( 12 ) Substitution of Eqs ( 9 , 10 and 11 ) into Eq ( 12 ) , and taking the derivative gives: dTpi , jdApi , j= ( dσfi , jdεfi , j−2σfi , j ) Vp , j4Api , j2 . ( 13 ) We can now calculate the mechanically unloaded patch area Ap0 , j using Eq 5 by Ap0 , j=Api , j−Tpi , j/dTpi , jdApi , j . ( 14 ) A schematic showing calculations performed in the cavities , patches , and walls is given in Fig 7 . The CircAdapt model was used to obtain a simulation that represents the healthy human cardiovascular system . A resting cardiac output of 5 . 1 l/min and heart rate of 70bpm , and an exercise condition of three times cardiac output and doubled heart rate , were used to adapt tissue volumes and areas in the cardiac walls and large blood vessels as described previously [24 , 28] . Mean arterial pressure of 92 mmHg was maintained at both rest and exercise . The resulting baseline human simulation was used as the basis for all subsequent simulations . All parameters used in the reference simulation may be found in the PRef Matlab structure that is part of the code provided in the online supplement ( . txt files ) . Strain for comparison to MRI data is calculated by taking the model sarcomere length at a reference time point , Ls ( t0 ) , for each patch , then computing the strain in each patch Ej as Ej=Ls , jLs , j ( t0 ) −1 ( 15 ) Ej is non-dimensional . For the canine simulations , the reference time is the time of first ventricular activation . For the patient simulation , the reference time is 100ms before aortic valve opening . | Under normal conditions , the electrical activation of the heart is almost synchronous , leading to uniform contraction . Due to either pathology or electrical pacing , the heart can be activated asynchronously . The result is discoordinated contraction and a reduction in the ability to pump blood . There is considerable interest in using computer simulations to understand how asynchronous electrical activation affects cardiac deformation , and how pathologies of the cardiac conduction system can be treated by pacing the heart . We present the MultiPatch module for simulating the effects of asynchronous electrical activation on cardiac contraction in the relatively simple CircAdapt model of the heart and circulation . We quantitatively compare model simulations to deformation patterns recorded during an experimental study of pacing-induced electrical asynchrony . We then demonstrate a ‘patient-specific’ simulation of deformation in a patient with a conduction disorder called left bundle-branch block . We use timings from endocardial mapping of electrical activation in a patient as an input for the model , and compare the resulting simulated deformation patterns to tagged magnetic resonance imaging recordings from the same patient . The model qualitatively reproduces deformation as observed in the patient . We conclude that the MultiPatch module makes CircAdapt appropriate for simulation of dyssynchronous heart failure in patients . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Fast Simulation of Mechanical Heterogeneity in the Electrically Asynchronous Heart Using the MultiPatch Module |
Alpha-helical transmembrane proteins constitute roughly 30% of a typical genome and are involved in a wide variety of important biological processes including cell signalling , transport of membrane-impermeable molecules and cell recognition . Despite significant efforts to predict transmembrane protein topology , comparatively little attention has been directed toward developing a method to pack the helices together . Here , we present a novel approach to predict lipid exposure , residue contacts , helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins . Using molecular dynamics data , we have trained and cross-validated a support vector machine ( SVM ) classifier to predict per residue lipid exposure with 69% accuracy . This information is combined with additional features to train a second SVM to predict residue contacts which are then used to determine helix-helix interaction with up to 65% accuracy under stringent cross-validation on a non-redundant test set . Our method is also able to discriminate native from decoy helical packing arrangements with up to 70% accuracy . Finally , we employ a force-directed algorithm to construct the optimal helical packing arrangement which demonstrates success for proteins containing up to 13 transmembrane helices . This software is freely available as source code from http://bioinf . cs . ucl . ac . uk/memsat/mempack/ .
Alpha-helical transmembrane ( TM ) proteins constitute roughly 30% of the proteins encoded in a typical genome and are involved in a wide variety of important biological processes including cell signalling , transport of membrane-impermeable molecules and cell recognition . Many are also prime drug targets , and it has been estimated that more than half of all drugs currently on the market target membrane proteins [1] . Despite significant efforts to predict TM protein topology [2] , [3] , [4] , comparatively little attention has been directed toward developing a method to pack the helices together . Since the membrane-spanning region is predominantly composed of alpha-helices with a common alignment , this task should in principle be easier than predicting the fold of globular proteins as the longitudinal constraints of helix packing mostly reduces the solution space from three dimensions to two . However , topologies consisting of large numbers of TM helices as well as structural features including re-entrant , tilted and kinked helices render simple approaches that may work for regularly packed proteins unable to predict the diverse packing arrangements now present in structural databases . Early attempts to predict TM protein folds were based on sequence similarity to proteins with a known three-dimensional structure , using statistically derived environmental preference parameters combined with experimentally determined features [5] . Another method calculated amino acid substitution tables for residues in membrane proteins where the side chain was accessible to lipid . By comparing observed substitutions obtained from sequence alignments of TM regions , accessibility of residues to the lipid could be predicted . In combination with a Fourier transform method to detect alpha-helices , the buried and exposed faces could then be discriminated and the presence of charged residues used to construct a three-dimensional model [6] . Other methods also made use of exposed surface prediction to allocate helix positions , in combination with an existing framework for globular protein structure prediction involving the combinatorial enumeration of windings over a predefined architecture followed by the selection of preferred folds [7] . However , these methods were only suitable for 7 TM helix bundles such as rhodopsin and were unsuitable for other topologies . More recently , a modified version of the fragment-based protein tertiary structure prediction method FRAGFOLD [8] was modified to model TM proteins . FRAGFOLD is based on the assembly of super-secondary structural fragments using a simulated annealing algorithm in order to narrow the search of conformational space by pre-selecting fragments from a library of highly resolved protein structures . FILM [9] added a membrane potential to the FRAGFOLD energy terms which was derived from the statistical analysis of a data set of TM proteins with experimentally defined topologies . Results obtained by applying the method to small membrane proteins of known three-dimensional structure showed it could predict both helix topology and conformation at a reasonable accuracy level . Despite these good results , the combinatorial complexity of such ab initio protein folding methods means that it is unfeasible to use such approaches for large TM structures , many of which are longer than 150 residues . Modification of another globular protein ab initio modelling program , ROSETTA [10] , added an energy function that described membrane intra-protein interactions at atomic level and membrane protein/lipid interactions implicitly , while treating hydrogen bonds explicitly [11] . Results suggest that the model captures the essential physical properties that govern the solvation and stability of TM proteins , allowing the structures of small protein domains , up to 150 residues , to be predicted successfully to a resolution of less than 2 . 5 Å . A recent enhancement of the algorithm demonstrated that by constraining helix-helix packing arrangements at particular positions based on local sequence-structure correlations for each helix of the interface independently , TM proteins with more complex topologies could be modelled to within 4 Å of the native structure [12] . The prediction of helix-helix interactions , derived from residue contacts and topology , has only recently been investigated in TM proteins due to the relative paucity of TM protein crystal structures . In contrast , a number of globular protein contact predictors exist based on a variety of machine learning algorithms [13] , [14] , and contact prediction has also been used to assess globular protein models submitted to the Critical Assessment of Structure Prediction ( CASP ) experiment [15] . However , analysis has shown that such globular proteins contact predictors perform poorly when applied to TM proteins , most likely due to differences between TM and globular interaction motifs [16] . A number of studies have identified structural and sequence motifs recurring frequently during helix–helix interaction in TM proteins . One investigation analysed interacting helical pairs according to their three-dimensional similarity , allowing three quarters of pairs to be grouped into one of five tightly clustered motifs [17] . The largest of these consisted of an anti-parallel motif with left-handed packing angles , stabilised by the packing of small side chains every seven residues , while right-handed parallel and anti-parallel structures showed a similar tendency though spaced at four-residue intervals . Another study identified a specific aromatic pattern , aromatic-XX-aromatic , which was demonstrated to stabilise helix-helix interactions during assembly [18] , while others include the GXXXG motif found in glycophorin A [19] , heptad motifs of leucine residues [20] , and polar residues through formation of hydrogen bonds [21] . The discovery of these recurring motifs , and the likelihood that there are more as yet undiscovered , suggests predictability by a generalised pattern search strategy . Recently , two methods have been developed that attempt to predict residue contacts and helix-helix interaction . TMHcon [16] uses a neural network in combination with profile data , residue co-evolution information , predicted lipid exposure using the LIPS method [22] , and a number of TM protein specific features , such as residue position within the TM helix , in order to predict helix-helix interaction . TMhit [23] uses a two-level hierarchical approach in combination with a support vector machine ( SVM ) classifier . The first level discriminates between contacts and non-contacts on a per residue basis , before the second level determines the structure of the contact map from all possible pairs of predicted contact residues therefore avoiding the high computational cost incurred by the quadratic growth of residue pair prediction . Here , we present a novel method to predict lipid exposure , residue contacts , helix-helix interactions and finally the optimal helical packing arrangements of TM proteins . Using molecular dynamics data to label residues potentially exposed to lipid , we have trained and cross-validated a SVM classifier to predict per residue lipid exposure with 69% accuracy . This information is combined with PSI-BLAST profile data and a variety of sequence-based features to train an additional SVM to predict residue contacts . Combining these results with a priori topology information , we are able to predict helix-helix interaction with up to 65% accuracy under stringent cross-validation on a non-redundant test set of 74 protein chains . We then tested the ability of the method to discriminate native from decoy helical packing arrangement using a decoy set of 2811 structures . By comparing our predictions with the test set , we were able to identify the native packing arrangement with up to 70% accuracy . All these performance metrics represents significant improvements over existing methods . In order to visualise the global packing arrangement , we adopted a graph-based approach . By employing a force-directed algorithm , the method attempts to minimise edge crossing while maintaining uniform edge length , attributes common in native structures . Finally , a genetic algorithm is used to rotate helices in order to prevent residue contacts occurring across the longitudinal helix axis .
For any machine learning task , the use of a high quality data set for both training and validation purposes is essential . Our data set was based on a previously described crystal structure set [4] which contained data initially collected from MPTOPO [24] , OPM [25] , PDB_TM [26] and SWISS-PROT [27] before fragments , sequences containing chain breaks and non-native TM proteins such as venoms and colicins were removed . OPM was used to define TM helix boundaries , although where a visual inspection appeared to indicate incorrect placement of the membrane , PDB_TM helix boundary definitions were used instead . The data set was homology reduced at the 40% sequence identity level leaving 131 sequences , of which the 74 which contained at least two TM helices were used to predict residue contacts . For 53 of these multi-spanning sequences , and a further 24 single-spanning proteins , we were able to obtain molecular dynamics data from the Course Grained Database ( CGDB ) [28] which was used for lipid exposure prediction . We chose not to predict interactions between TM helices and re-entrant helices , found in many channels such as Aquaporin , as they are thought to be involved in channel gating and thus move into and out of the membrane region depending on physiological conditions . Including re-entrant helices would therefore be likely to introduce noise into the data set as contacts could be both positive and negative training examples . During TM protein crystallisation , detergents are used extensively for membrane solubilisation and then act as mimics of the lipid bilayer due to their self-assembly properties . As a result , crystallographic data rarely contains information regarding the positions of lipid molecules , therefore hindering the study , and prediction , of lipid exposed regions of TM protein . For investigating TM topology , a number of automated methods exist that attempt to position the protein within the membrane [25] , [26] . However , these methods are inappropriate for accurate studies of lipid exposure as they do not take into account the solvent-filled cavities and channels found in many TM proteins . To address this , we used the CGDB , a resource of coarse-grained simulation data , which contains analysis of lipid-protein interactions following 200 ns of molecular dynamics using GROMACS [29] to randomly surround TM proteins in dipalmitoylphosphatidylcholine lipids and solvent . A snapshot of each protein in its optimum position within the bilayer and residue statistics throughout the simulation are available . While difficult to validate , the approach has proved successful in reproducing the behaviour of equivalent atomistic simulations of model proteins , as well as allowing the insertion of various test peptides whose final configurations were in agreement with experimental data [30] . Additionally , channel-containing proteins such as aquaporin and potassium channels are solvent rather than lipid filled at the end of simulation . To train a SVM classifier , we used CGDB data to label residues that were lipid exposed . For the 77 proteins within out data set where CGDB data was available , each residue within the membrane was labelled as lipid exposed where the fraction of simulation time exposed to DPPC lipid was greater than 0 . 5 . PSI-BLAST [31] was used to generate position-specific scoring matrices for each of the 77 proteins in the data set using the UniRef 90 database . Two iterations were performed with a profile-inclusion E-value threshold of 0 . 001 . For each residue in a sequence , a sliding window approach was used with a window size of 7 , creating a feature vector of length 140 centred on the target residue . To determine this windows size , the data set was split randomly into two and the highest scoring window which ranked equally in each split was selected , therefore demonstrating consistency between data sets and reducing the risk of overfitting . Where the window extended beyond the protein termini , empty feature values were set to zero . All values for each feature position where then normalised by Z-score to enable faster SVM convergence . In training , the target sequence , along with any other sequences with an E-value less than 1e-4 , were excluded . We used SVM-Light [32] and a radial basis function kernel , in combination with a grid search of SVM parameters . Matthews Correlation Coefficient ( MCC ) was used to optimise these values as it has been shown to be a more robust measure than using recall or precision alone [33] . In order to make direct comparisons with other methods , we used three thresholds to consider a pair of residues to be in contact . Firstly , a maximal distance of 8 Å between their C-beta atoms ( C-alpha for glycine ) [13] , [14] ( contact definition 1 ) . Secondly , the distance between any two atoms from an interacting pair is less than the sum of their van der Waals radii plus a threshold of 0 . 6 Å [23] ( contact definition 2 ) . Thirdly , the minimal distance between side chain or backbone heavy atoms in an interacting pair is less than 5 . 5 Å [16] ( contact definition 3 ) . We defined TM helices as interacting if one residue from each helix was observed to be in contact . Using the three contact definitions , all residue pairs from different TM helices were labelled as contacting or non-contacting , resulting in a substantial bias of approximately 1∶50 . In order to balance training sets and reduce learning time , non-contacting examples were selected randomly in order to achieve approximately equal numbers of positive and negative examples , before fine adjustment of the SVM cost-factor parameter achieved a 1∶1 ratio . SVM input features were based largely on PSI-BLAST profile data , generated as described above . We used a sliding window of 7 residues , centred on each residue in the pair to produce a feature vector of length 280 . Again , this window size was determined by randomly splitting the data set . In addition to profile data , the raw SVM scores for predicted lipid exposure were added to the feature vector for each residue . We then added a number of sequence derived statistics . To define the sequence separation between the two residues , a binary vector was used corresponding to distances of 50 , 75 , 100 , 125 , 150 , 175 , 200 and greater than 200 residues . We also added a value which corresponded to the relative position of each residue within the two TM helices , generated by dividing the residue position in the TM helix by the helix length , and subtracting the value from one where the two residues were on adjacent TM helices or are separated by an even number . This value effectively represented a relative Z-coordinate for each residue , the rationale being that residues separated by a large degree on the Z-axis were unlikely to contact . We tried adding a number of additional values including the lengths of each TM helix , average lipid exposure scores for each TM helix , total number of TM helices , sequence length , and a number of residue co-evolution scores [34] , [35] . However , none of these values increased classification performance so were removed in the final model . Again , each feature position was normalised by Z-score , before the target sequence and any other sequences with an E-value less than 1e-4 were excluded from training sets . A radial basis function kernel was used and MCC was used to optimise SVM parameters . We then tested the ability of the method to discriminate native from decoy helical packing arrangement using the predicted helix-helix interactions . For each of the 74 multi-spanning proteins in our data set , decoys were generated using the REVCAS program [36] . Each chain was expanded into a larger set of structures by making it circular and introducing cyclically permuted breaks . The method involves a triple-point chain reconnection that avoids the restoration of native segments allowing the generation of a set of decoy structures . The method was successfully applied to the pore-forming colicin domain , an all alpha-helical structure that is typical of many TM proteins in that the amino and carboxy termini , which are joined when the structure is circularised , are at opposite ends of the protein , much like TM proteins whose termini are on opposite sides of the membrane [36] . By generating decoys in both forward and reverse directions , 24–48 decoys were generated for each protein resulting in a total set of 2811 structures . Decoys only contained C-alpha atoms , therefore the remaining backbone and side chain atoms were added and the structure was refined and energy minimised using the Jackal package [37] . Additionally , homology models of the native structures were constructed using MODELLER [38] . Native topologies were then used to define TM helix boundaries allowing observed helix-helix interactions to be extracted which were then compared to the helix-helix interactions predicted from sequence . Decoys and native structures were then scored by the number of interacting/non-interacting helices that matched the predictions and ranked accordingly . We accessed the frequency at which the native structure , or a model of the native structure , was ranked first . Once helix-helix interactions have been predicted , the helical packing arrangement is treated as an undirected graph where the helices form vertices and their interactions form edges . A force-directed algorithm is then applied which treats the graph as a virtual physical system . The system is simulated resulting in attractive and repulsive forces being applied to vertices , a process which is repeated iteratively until the system comes to an equilibrium state at which point the final graph layout is constructed . Using the Boost C++ programming library ( http://www . boost . org ) we employed a modified version of the Kamada-Kawai force-directed algorithm [39] which generates two-dimensional layouts for connected , undirected graphs . It accomplishes this by treating the graph as a dynamic spring system , where the strength of a spring between two vertices is inversely proportional to the square of the shortest distance between those two vertices , and attempting to minimise the energy within the system . In order to avoid producing a layout with only a local minima , the vertices are first arranged along the vertices of a regular n-sided polygon , where n is the number of TM helices , via a circular layout function . Given that the number of TM helices in a protein is expected to be less than 30 , energy minimisation occurs in a number of seconds on a modern computer , avoiding the high running time typically associated with force-directed algorithms and graphs containing a larger number of vertices . Resulting layouts demonstrate uniform edge length , uniform vertex distribution often showing symmetry , and minimisation of edge crossing – attributes that are common to the arrangement of TM helices and their interactions in native TM protein structures . In a number of cases , multiple helices share the same interactions resulting in numerous possible arrangements . In all cases where this occurs , a recursive function is used to score each arrangement according to the number of observed same-side loop crossovers . The score is determined by drawing a line ( loop ) between a pair of helices adjacent in sequence , before incrementing the helix position by two so that comparisons are between lines on the same side . Each line is compared to every other line on the same side and their intersection is established by determining the cross product . This is repeated for each side , before the total number of intersections per side is compared . Particularly when loops are short , it is unusual for loops to cross each other as this may result in side chain clashes . All arrangements are then returned , with those containing the least number of same-side loop crossovers scored highest . Finally , the constituent residues are superimposed on to their respective TM helices , before a genetic algorithm is used to rotate all helices around their respective Z-axes such that the sum of all predicted residue-residue contact distances is minimised , therefore preventing residues contacts occurring across the longitudinal helix axis . For each TM helix , a value in the range 0-359 is optimised to an accuracy of one degree .
We compared the per residue performance of our lipid exposure predictor to the LIPS method using all TM helix residues from our data set of 77 sequences . The data set contained 336 TM helices composed of 7016 residues of which 3687 were labelled as lipid exposed and 3329 were not , according to CGDB data . Optimal performance was achieved using a radial basis function kernel , a gamma value of 0 . 6 and a trade-off value of 1 . 5 . The LIPS method produces a per residue score generated by multiplying lipophilicity by positional entropy . The LIPS score that resulted in the optimal per residue performance was found to be 1 . 56 . Using leave-one-out cross-validation , our method achieved a MCC of 0 . 38 and accuracy of 69 . 3% , a significant improvement over the LIPS method which scored 0 . 23 and 61 . 7% respectively ( table 1 ) . Furthermore , the LIPS method is calculated using sequence profiles from 18 TM protein structures , the majority of which are included in the test set of 77 , therefore in the absence of cross-validation these results are likely to be an overestimate . However , as the LIPS method is based on an alternative definition of lipid exposure , we repeated the benchmarking of the two methods using the LIPS definition by labelling residues with a 1 . 9 Å probe . Under this definition both methods perform slightly worse although our method still outperforms LIPS , with an MCC value of 0 . 27 compared to 0 . 18 . This indicates that there is reasonably good correlation between the two definitions although the LIPS definition is slightly harder to predict , most likely because the 1 . 9 Å spherical probe is a poor approximation to the non-spherical nature of a membrane phospholipid , unlike , for example , a 1 . 4 Å spherical probe is to a water molecule . Residue pair contact prediction performance compared with two TM protein contact predictors ( TMHcon [16] and TMhit [23] ) and two globular protein contact predictors ( PROFcon [13] and SVMcon [14] ) using the data set of 74 sequences and three contact definitions is shown in table 2 . Existing methods all had the option of a L5 mode , where only the top L/5 positive results are returned where L is the sequence length , or for TM protein-specific methods , the total length of all TM helices . This generally has the effect of reducing the false positive rate though usually at the expense of increasing the false negative rate; however our method did not benefit from the use of this scoring method suggesting the SVM hyperplane is already optimally positioned . Performance at all three contact definitions was consistent , with a MCC value of approximately 0 . 28 although a slightly lower false positive rate using contact definition 2 . All three SVMs achieved optimal performance using radial basis function kernels with gamma and trade-off values of 24 and 1 respectively . Addition of the predicted lipid exposure scores to profile data in the SVM feature vector resulted in an improvement of approximately 0 . 05 MCC , while the additional sequence derived statistics contributed approximately 0 . 03 MCC . Although a combination of residue co-evolution scores did improve performance slightly compared with using profile data alone ( 0 . 02 MCC ) , this increment was lost when scores were added after predicted lipid exposure suggesting the two overlap in feature space . Compared to existing predictors , our method performed well with MCC scores substantially higher than both SVMcon and PROFcon ( contact definition 1 ) using either standard or L5 scoring schemes . SVMcon L5 was able to produce a lower false positive rate ( FPR ) but at the expense of a false negative rate ( FNR ) of 1 . 0 . Similarly , PROFcon produced a lower FNR of 0 . 41 but at the expense of a higher FPR of 0 . 46 , compared to 0 . 001 for our method . On this evidence , globular protein contact predictors appear to perform relatively poorly when applied to TM proteins . In comparison to TMhit , a recent SVM-based TM protein contact predictor , results were more comparable . While our method scores higher on all assessment metrics , the margin of improvement is narrower with a MCC of 0 . 28 compared to the TMhit value of 0 . 26 . This is not unexpected given that both methods use SVM classifiers , though more significantly there is a considerable overlap of 42 sequences in training sets . Given that we assessed our method using leave-one-out cross-validation whereas TMhit results were not cross-validated , TMhit results are likely to be overestimated therefore the actual margin of improvement may be larger . Compared to TMHcon , a recent neural network based approach , our method again performed well , with TMHcon results comparable to the globular protein contact predictors . We assessed performance of helix-helix interaction prediction requiring one residue from each helix to be in contact . Based on observed interactions there were comparable numbers of interacting and non-interacting helices for all contact definitions , with 668 and 733 respectively using contact definition 1 . Results using the data set of 74 sequences and three contact definitions is shown in table 3 . Our method achieved similar scores using contact definitions 1 and 2 , with a MCC of 0 . 29 and accuracies of 64 . 7% and 63 . 6% . Using contact definition 3 , results were slightly lower with a MCC of 0 . 37 and accuracy of 60 . 6% . The FNR was consistent across all definitions at approximately 0 . 84 . Compared to SVMcon and PROFcon , our method performed well with only PROFcon L5 approaching similar performance ( MCC 0 . 19 , accuracy 62 . 0% ) , suffering only from a higher FPR compared to our method . Other than PROFcon L5 which performed better than expected for a globular protein predictor , results were generally low with MCC values in the range 0 . 02–0 . 13 . The performance of TMhit surpasses that of our method with MCC 0 . 45 and accuracy 72 . 3% . However , as described above , the TMhit results were not cross-validated and are likely to be substantially overestimated given the overlap of 42 sequences in training sets . To give an estimate of the level of improvement this is likely to have resulted in , we scored our method in the absence of cross-validation for the 42 overlapping sequences and achieved scores of MCC 0 . 65 and accuracy 82 . 6% . We additionally compared the two methods using a smaller data set of 14 sequences for which both our method and TMhit results were fully cross-validated [23] . Requiring a single contacting pair of residues , our method achieved 66 . 3% accuracy compared to 39 . 1% for TMhit ( standard error ±5% ) . TMHcon achieved MCC 0 . 02 and accuracy of 52 . 3% , which reflected the relatively poor performance in residue contact prediction , caused largely by a high FPR of 0 . 37 . Using our decoy set , we were able to derive between 1 and 53 ( average 18 . 5 ) unique helical packing arrangements for 71 sequences in our data set . By combining these with unique helical packing arrangements derived from the native crystal structure and homology models of the native crystal structure , we assessed performance of our and existing methods at discriminating the native or native model arrangements from decoy arrangements . Each arrangement was scored according to the number of interacting/non-interacting helices that matched the prediction from sequence , with interacting/non-interacting helices scored equally . Accuracy was determined by counting the frequency at which the native or native model arrangement achieved the highest score . As discriminating 2 TM helix arrangements , where helices are either interacting or not , is somewhat trivial , table 4 shows results including and excluding 2 TM helix arrangements , where there are a total of 57 sequences with more than 1 unique packing arrangement . Consistent with prediction of helix-helix interactions , our method performed similarly using contact definitions 1 and 2 , although unexpectedly performed best using contact definition 3 ( 70 . 4% accuracy ) . Excluding 2 TM helix proteins , using all contact definitions , performance decreased slightly suggesting that , on average , discriminating 2 TM helix arrangements is slightly easier than for other topologies . SVMcon and PROFcon both performed best when evaluated using their L5 modes although both achieved accuracies over 10% lower than our method . TMhit achieved a slightly lower score than our method ( 66 . 2% ) though again in the absence of cross-validation . Excluding 2 TM helix proteins performance was almost 7% lower . TMHcon was not assessed using the complete set of 71 as it is unable to make predictions on 2 TM helix proteins , and performed below all other methods ( 40 . 4% accuracy ) on the set of 57 . Given that the generation of helical packing arrangements is based on the interconnection of vertices within a graph , accuracy is ultimately dependent on the detection of edges via prediction of helix-helix interactions . Out of the data set of 74 sequences , 17 ( 23% ) had all interactions successfully predicted although in 3 of these cases there were no observed interactions between helices . Predicted arrangements were then compared by visual inspection of a two-dimensional slice taken from the crystal structure approximately normal to the likely plane of the lipid bilayer , and assessed based on the overlap of helices from the predicted arrangement and the slice . Of these 17 cases , 9 arrangements produce overlaps for all TM helices and therefore can be considered as closely resembling the helix packing arrangement observed in the crystal structure . Among these 9 correct cases , three 7 TM helix proteins ( PDB: 1E12:A , 1XIO:A , 2F95:A ) produced helical packing arrangements that clearly resembled their respective crystal structures ( Figure 1 ) . Additionally , for each of these cases the correct arrangement was successfully determined from alternatives by scoring arrangements based on the number of same-side loop crossovers . Overall , this function successfully identified the correct arrangement in 4 out of 6 cases where multiple arrangements were generated when tested using observed helix-helix interaction information; in the remaining 3 cases , 2 had an equal number of crossovers for each of the alternative arrangements ( 2HYD:A , 1XFH:A ) – in these instances , the highest scoring arrangement was the one with the lowest total residue-residue contact distance resulting in one correct and one incorrect prediction , while in the remaining case the correct arrangement contained one more crossover than the incorrect arrangement ( 1XME:A ) . Other cases where all helix-helix interactions were successfully predicted and packing arrangements closely resembled crystal structures included the 5 TM helix ubiquinol oxidase ( 1FFT:C ) and 6 TM helix Aquaporin-4 ( 2D57:A ) . Below 4 TM helices , arrangements generally resembled crystal structures well although the task becomes more straightforward as the number of TM helices decreases . Where all helix-helix interactions were successfully predicted and packing arrangement resembled the crystal structure , application of a genetic algorithm to rotate helices around thei respective Z-axes usually resulted in helix orientations that aligned significantly better with native structures compared to arbitrary degrees of rotation ( Figure 2 ) . When helices were connected consecutively , for example where a 3 helix protein has interactions between helices 1–2 and 2–3 , the program was unable to determine the correct arrangement despite predicting all helix-helix interactions correctly . Under these circumstances , the algorithm defaults to a circular layout , which is frequently closest to the crystal structure as in the case of aquaporin ( 2D57:A ) where helices are arranged around a central pore . In a number of cases though , the correct arrangement is much closer to linear as in the case of Photosystem II ( 2AXT:A ) where there is significant interaction with additional chains in the complex . In such situations , the helix-helix interactions alone do not provide enough information to determine the correct arrangement . Where prediction of helix-helix interactions falls below 100% , packing arrangements generally fail to accurately resemble crystal structures . In some cases such as the ammonium transporter ( 2B2F:A ) , well connected sub-components of 3–5 TM helices were often correctly formed , but their arrangement in relation to each other was incorrect due to a number of missing helix-helix interaction . In three cases where there was substantial interconnection between TM helices , the arrangement does not succeed , most likely due to the algorithm encountering a local minima . It is also impossible to generate an arrangement from a disconnected graph , where all helix-helix interactions are incorrectly predicted , which occurs in 12 sequences ( 16 . 2% ) . A summary of results where all interactions were correctly predicted is shown in Table 5 . While the successful packing arrangements were achieved with topologies of less than 8 TM helices , we additionally tested the algorithm using observed data to validate its effectiveness at generating arrangements for topologies with large numbers of TM helices using observed helix-helix interaction data rather than predicted contacts . In a number of cases , complex packing arrangements were generated with up to 13 TM helices that clearly resembled the respective crystal structure . Examples include the 10 TM helix proton ATPase ( 1MHS ) , 12 TM helix multidrug transporter ( 2GFP:A ) and 13 TM helix cytochrome C oxidase ( 1XME:A ) shown in figure 3 , although in this case two helices that share the same helix-helix interactions are incorrectly replaced .
MEMPACK is available as source code from the URL below and is free for non-commercial use . All data sets are also available , and cross-validation SVM model files are available on request . The software has been tested on a Linux operating system . In order to compile and run , the gcc compiler , Perl interpreter , Boost C++ libraries and NCBI tools are required . http://bioinf . cs . ucl . ac . uk/memsat/mempack/ | Alpha-helical transmembrane proteins constitute a significant proportion of the proteins encoded in a typical genome and are involved in a wide variety of important biological processes . Many common diseases including diabetes , hypertension and epilepsy have been related to transmembrane protein dysfunction , therefore they represent one of the most important classes of protein for pharmaceutical intervention . However , due to the experimental difficulties of structure determination , this class of protein is severely under-represented in structural databases . Here , we present a novel approach that is able to predict lipid exposure , residue contacts , helix-helix interactions and finally the optimal helical packing arrangement of a transmembrane protein . Under stringent cross-validation , our approach demonstrates a significant improvement in prediction over existing software . This method can be used to gain insights into transmembrane protein folding and enhance the quality of ab initio modelling , while providing testable hypotheses for a variety of studies including protein design , mutagenesis and thermostability experiments . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"computational",
"biology/protein",
"structure",
"prediction"
] | 2010 | Predicting Transmembrane Helix Packing Arrangements using Residue Contacts and a Force-Directed Algorithm |
The recent use of Bacillus anthracis as a bioweapon has stimulated the search for novel antitoxins and vaccines that act rapidly and with minimal adverse effects . B . anthracis produces an AB-type toxin composed of the receptor-binding moiety protective antigen ( PA ) and the enzymatic moieties edema factor and lethal factor . PA is a key target for both antitoxin and vaccine development . We used the icosahedral insect virus Flock House virus as a platform to display 180 copies of the high affinity , PA-binding von Willebrand A domain of the ANTXR2 cellular receptor . The chimeric virus-like particles ( VLPs ) correctly displayed the receptor von Willebrand A domain on their surface and inhibited lethal toxin action in in vitro and in vivo models of anthrax intoxication . Moreover , VLPs complexed with PA elicited a potent toxin-neutralizing antibody response that protected rats from anthrax lethal toxin challenge after a single immunization without adjuvant . This recombinant VLP platform represents a novel and highly effective , dually-acting reagent for treatment and protection against anthrax .
Anthrax is caused by the spore-forming , Gram-positive bacterium Bacillus anthracis [1] . The disease is elicited when spores are inhaled , ingested , or transmitted through open wounds in the skin . Inhalational anthrax is the deadliest form of the disease , primarily because it is difficult to diagnose in a timely manner . Disease symptoms are initially nonspecific and systemic dissemination of anthrax toxin can occur prior to antibiotic treatment [2] . The deliberate release of B . anthracis spores in the US in 2001 , with the ensuing human fatalities and enormous cleanup costs , has underscored the need for better detection , treatment , and prevention of anthrax . The toxic effects of anthrax are predominantly due to an AB-type toxin made up of a single receptor-binding B subunit and two enzymatic A subunits [3] . The A subunits are edema factor ( EF , 89 kD ) , an adenylate cyclase that raises intracellular cyclic adenosine monophosphate levels [4] , and lethal factor ( LF , 90 kD ) , a zinc protease that cleaves mitogen-activated protein kinase kinases [5 , 6] . The receptor-binding B subunit is protective antigen ( PA ) , which is initially synthesized as an 83-kD precursor . Upon receptor binding , PA83 is cleaved by furin into a 63-kD product that forms heptamers that bind EF to form edema toxin ( EdTx ) and LF to form lethal toxin ( LeTx ) [3] . Two anthrax toxin receptors , widely distributed on human cells , have been identified: anthrax toxin receptor/tumor endothelial marker 8 ( ANTXR1 ) [7] and capillary morphogenesis gene 2 ( ANTXR2 ) [8] . Although both receptors bind PA through a 200–amino acid extracellular von Willebrand factor A ( VWA ) domain , the VWA domain of ANTXR2 has a 1 , 000-fold higher binding affinity for PA than the VWA domain of ANTXR1 . In addition , ANTXR2 has been shown to mediate intoxication in vivo [11] . Recently , the low-density lipoprotein receptor-related protein LRP6 was shown to function as a co-receptor for anthrax toxin internalization , although this finding is controversial [12 , 13] . The potential use of anthrax as a weapon of bioterrorism has prompted increased efforts to develop better antitoxins and vaccines . Protective immunity to B . anthracis infection is conferred by antibodies against PA , which is the primary component of anthrax-vaccine adsorbed ( AVA; Biothrax ) , the only currently licensed anthrax vaccine in the US . Although AVA is safe and effective , it is molecularly ill-defined , can cause adverse reactions , and is administered in a lengthy immunization schedule ( six doses over 18 months ) [14] . A second-generation vaccine based on recombinant PA adsorbed on aluminum hydroxide as adjuvant is currently in development . Preliminary data indicate that it is less potent than AVA , and it is likely that several immunizations will be required to confer protection in humans [15] . Thus , the development of a well-characterized vaccine that induces rapid immunity after a single injection remains an important goal . To develop a reagent that functions both as an anthrax antitoxin and as a molecular scaffold for an efficient anthrax vaccine , we took advantage of an icosahedral virus platform that permits polyvalent display of the extracellular VWA domain of ANTXR2 . This platform is based on Flock House virus ( FHV ) , a non-enveloped , icosahedral ( T = 3 ) insect virus of the family Nodaviridae [16] . The FHV capsid is composed of 180 subunits of a single type of coat protein , and the icosahedral solid shell encapsidates a bipartite , single-stranded RNA genome . The crystal structure of FHV particles shows that the coat protein contains several surface-exposed loops that can be targeted for insertion of foreign proteins and peptides [17] . Here , we report the synthesis and structural characterization of FHV-VWAANTXR2 chimeric particles and provide evidence for their efficacy as an anthrax toxin inhibitor in vitro and in vivo . In addition , we used the chimeric particles as a scaffold for the multivalent display of PA and show that this complex functions as a potent vaccine against LeTx .
The VWA domain of ANTXR2 forms a compact structure that adopts a Rossmann-like α/β-fold with a metal ion-dependent adhesion site motif that is involved in PA binding [9 , 18] . The N and C termini of this domain , residues C39 and C218 , respectively , are closely juxtaposed , thereby permitting , in principle , genetic insertion into a loop on a carrier protein . Modeling studies of the FHV coat protein subunit indicated that two surface-exposed loops at amino acid positions 206 and 264 would accommodate the 181 amino acid ANTXR2 VWA domain without disrupting coat protein assembly into virus-like particles ( VLPs ) ( Figure 1A ) . Based on these predictions , two chimeric proteins were generated . In FHV-VWAANTXR2 chimera 206 , the VWA domain and a C-terminal two–amino acid linker ( Ala-Glu ) replaced FHV coat protein residues 207–208 ( Figure 1B ) . In FHV-VWAANTXR2 chimera 264 , the VWA domain replaced FHV residues 265–267 . The chimeric proteins were expressed in Sf21 insect cells using recombinant baculovirus vectors . In this system , wild-type ( wt ) FHV coat protein forms VLPs whose high resolution structure is virtually indistinguishable from that of native virions ( unpublished data ) . However , VLPs contain random cellular RNA instead of the FHV genome and are therefore not infectious [19] . Putative chimeric VLPs were purified from the cells by sucrose gradient centrifugation and material sedimenting at a position similar to that observed for native virions harvested and analyzed by SDS-PAGE . As shown in Figure 1C , both samples contained a major protein and a slower migrating minor protein of the appropriate molecular weights ( ≈63 kD , the combined molecular weight of the 43-kD FHV coat protein and the 20-kD ANTXR2 VWA domain ) . Since the FHV coat protein undergoes a spontaneous cleavage reaction after assembly of particles ( Figure 1B ) [20] , the minor protein likely represented the unprocessed precursor protein , whereas the major protein represented the post-assembly cleavage product . Capsid proteins representing chimera 264 migrated more slowly through the gel than those representing chimera 206 , even though the amino acid composition of the two polypeptides was virtually identical . The reason for this differential behavior is not known but could reflect subtle differences in denaturation of the proteins under SDS-PAGE conditions . Electron microscopy of negatively stained samples confirmed the presence of VLPs in the gradient-purified material ( Figure 1D ) . Compared to the smooth exterior of native FHV virions , the surface of the chimeric particles was rough and distinct protrusions were visible . The appearance of the particles suggested that they were filled with RNA , as stain did not penetrate the interior . This conclusion was supported by the sedimentation rate of the VLPs , which was indistinguishable from that of wt FHV ( not shown ) . The soluble , monomeric ANTXR2 VWA domain ( sANTXR2 ) , expressed and purified from mammalian cells , was previously shown to effectively block entry of LeTx into susceptible cells by competing with cellular ANTXR2 for binding to PA [10] . PA has a very high binding affinity for sANTXR2 ( Kd = 170 pM ) and dissociates extremely slowly from this receptor decoy ( the half-life of the complex is approximately 17 h ) [21] . We used the same approach to test the inhibitory activity of FHV-VWAANTXR2 VLPs . Namely , the assay employed CHO-K1 cells and a modified form of LeTx , PA/LFN-DTA , in which the N-terminal portion of LF was fused to the catalytic portion of diphtheria toxin A-chain [22] . This recombinant toxin efficiently kills CHO-K1 cells within 48 h and uses the same PA-dependent entry mechanism as wt LF . The assay revealed that chimera 264 protected cells as efficiently as sANTXR2 , whereas a higher concentration of chimera 206 was required to achieve protection of the cells ( Figure 2 ) . The corresponding IC50 values for sANTXR2 and chimera 264 were 19 . 70 ± 0 . 87 nM and 18 . 50 ± 0 . 36 nM , respectively , while the IC50 was 32 . 71 ± 0 . 61 nM for chimera 206 . Thus , chimera 264 performed as well as the highly potent , monomeric sANTXR2 inhibitor in this assay . To confirm the ability of the particles to neutralize native LeTx , a macrophage-based toxin neutralization assay was performed with chimera 264 . The assay revealed that the particles protected RAW264 . 7 cells efficiently from a mixture of PA and wt LF , and the measured IC50 was 39 . 8 ± 2 . 2 nM . We next tested whether the chimeric particles were capable of protecting rats against LeTx challenge as was demonstrated previously for sANTXR2 [10] . In vivo experiments were only performed with chimera 264 because it had shown higher potency in the cell intoxication assay ( Figure 2 ) . As a positive control , sANTXR2 was used in parallel . Male Fisher 344 rats were inoculated intravenously with 5 minimal lethal doses ( MLDs ) of LeTx either in the presence or absence of chimera 264 or sANTXR2 as previously described [10] . As shown in Table 1 , both chimera 264 and sANTXR2 completely protected the animals when used at a molar ratio of 2:1 ( ANTXR2:PA ) . Moreover , the animals did not exhibit any symptoms of intoxication such as agitation , respiratory distress , or hypoxia . Injection vehicle ( PBS ) or wt FHV , used as a negative control , had no protective effect . While neither chimera 264 nor sANTXR2 were able to protect rats when used at a 10-fold lower concentration ( molar ratio of ANTXR2:PA = 0 . 2:1 ) , they each caused a delay in the time to death compared to the LeTx control ( Table 1 ) . The delay was notably longer for chimera 264 ( 89 min ) than sANTXR2 ( 77 min ) , and the difference was highly significant ( p = 0 . 0046 ) , suggesting increased therapeutic potency of the multivalent particles over monomeric sANTXR2 as an inhibitor of the anthrax toxin . It will be interesting to determine whether different pharmacokinetic profiles are observed in vivo for sANTXR2 and chimera 264 . Electron cryomicroscopy and image reconstruction of the FHV-VWAANTXR2 VLPs showed that , compared to wt FHV particles ( Figure S1C ) , both chimeric particles displayed additional density at higher radius ( Figure S1A and S1B ) , which is in agreement with the protrusions that were visible in negatively stained samples ( Figure 1D ) . To define the arrangement of the VWA domains on the surface of the chimeric particles , pseudoatomic models were generated by fitting the X-ray coordinates of the FHV coat protein subunit and the ANTXR2 VWA domain into the cryoEM density maps ( Figures 3A , 3B , S2A , and S2B ) . The models revealed that in chimera 206 the VWA domains were closely juxtaposed at the quasi 3-fold axes . Two of the three VWA domains in each asymmetric unit closely interacted with their 2-fold related counterparts , thereby creating an offset cluster of six domains . In contrast , the insertion site chosen for chimera 264 allowed for wider spacing and more even distribution of the individual VWA domains on the particle surface . To investigate the accessibility of PA to the VWA domains , PA83 was computationally docked onto the VWA domains of the pseudoatomic models of chimeras 264 and 206 using the X-ray structure of PA complexed with the ANTXR2 VWA domain as a guide [9 , 18] . It was evident that chimera 264 could accommodate significantly more PA molecules than chimera 206 given the wider spacing of the VWA domains on this particle ( Figures 3C , 3D , S3A , and S3B ) . Specifically , each subunit at the 5-fold axes and three of the six subunits around the quasi-6-fold axes could bind PA83 without steric interference , giving a total occupancy of 120 PA molecules per particle . In contrast , due to the close juxtaposition of the VWA domains on chimera 206 , a maximum occupancy of 60 PA molecules per particle was predicted . These predictions were in close agreement with results from biochemical analyses of complexes formed between the particles and PA83 under saturating conditions . Specifically , gel electrophoresis combined with densitometric analysis showed that chimera 206 could bind an average of 90 PA83 ligands , whereas chimera 264 bound an average of 130 PA83 ligands ( Figures S4A and 4B ) . Together , these results were consistent with the observation that a higher concentration of chimera 206 was required to protect cells from intoxication with PA/LFN-DTA ( Figure 2 ) . The observation that FHV-VWAANTXR2 chimera 264 functioned as a binding surface for multiple copies of PA suggested that a complex of the two components might constitute an effective antigen for induction of PA-specific antibodies . To test this , complexes were prepared by mixing chimera 264 with an excess of PA83 , and unbound PA83 was removed by ultracentrifugation . Electron microscopic analysis showed that PA83 formed thin protrusions emanating from the capsid surface ( not shown ) . For immunogenicity studies , rats ( four per group ) received two subcutaneous injections ( 0 and 3 wk ) of either 2 . 5 μg of PA83 , 5 . 4 μg of particle–PA complex ( molar equivalent of 2 . 5 μg of PA83 assuming an occupancy of 120 VWA domains ) , or 2 . 9 μg of chimera 264 . No adjuvants were employed in these experiments . The ELISA assay of pre- and post-inoculation sera showed that animals immunized with the complex had significantly higher levels of anti-PA antibody than animals receiving PA alone both at week 3 ( p = 0 . 0028 compared to PA alone ) and after boosting ( week 7; p = 0 . 0118 compared to PA alone ) ( Figure 4A ) . No significant antibody response against ANTXR2 VWA was detected in these animals , but a response against FHV protein was observed after the boost in rats that were immunized with the complex ( Figure 4B and 4C ) . Why animals immunized with chimera 264 alone did not mount a similar immune response against FHV protein is not clear . The presence of PA may somehow enhance the response to FHV or may influence the localization or interaction of FHV particles with antigen-presenting cells , causing a difference in the observed anti-FHV antibody response . To test whether the anti-PA antibodies were protective against anthrax toxin , the rats were challenged by intravenous inoculation with 10 MLDs of LeTx . All animals that had been immunized with the particle–PA complex survived , whereas all but one of the animals in the PA83 group died ( Figure 4D ) . Survival versus death correlated well with the level of anti-PA antibody detected in the sera . However , the animal in the PA-only group that survived LeTx challenge had a serologic response to PA that was well below that of a non-surviving animal in the same group ( Figure 4D ) . Based on the observation that animals immunized with the particle–PA complex showed a significant level of anti-PA antibody as early as 3 wk after the first immunization ( Figure 4A ) , we investigated whether animals could be protected against LeTx after a single injection . Rats ( five per group ) were immunized once with increased doses of the particle–PA complex ( 10 . 8 μg ) , PA83 ( 5 μg ) , or chimera 264 ( 5 . 8 μg ) as a control . After 3 wk , the animals were bled and challenged 1 wk later with 10 MLDs of LeTx . All rats that were immunized with the particle–PA complex survived , whereas all other animals died ( Figure 4F ) . Of those that died , one animal had a serologic antibody response to PA that was greater than that of two animals in the group of survivors ( Figure 4E and 4F ) , indicating that the magnitude of the antibody response is not a reliable predictor of protection . Taken together , our results show that multivalent display of PA on the FHV-VWAANTXR2 scaffold yields a significant advantage over monovalent , soluble PA as an immunogen for anthrax toxin .
In this study we have developed a novel reagent that combines the functions of anthrax antitoxin and vaccine in a single compound . It is based on multivalent display of the ANTXR2 VWA domain on the surface of the icosahedral insect nodavirus FHV . We demonstrate that the recombinant VLPs protect cultured cells and rats from anthrax intoxication as efficiently as the highly potent sANTXR2 receptor decoy and that they induce a potent immune response against LeTx when coated with PA . This immune response was neutralizing in vitro and protected animals against LeTx challenge following a single administration without adjuvant . The motivation for immunogenicity studies was based on the assumption that polyvalent display of PA would induce a more potent immune response than monomeric , recombinant PA , which is currently being developed as a second-generation anthrax vaccine [15 , 23] . Ordered arrays of antigens are known to permit particularly efficient cross-linking of B cell receptors , which in turn leads to faster and more robust B cell proliferation [24–26] . Given the exceptionally tight binding of PA to ANTXR2 under natural conditions ( Kd = 170 pM ) [21] , we reasoned that complexes formed between chimera 264 and PA would be sufficiently stable to serve as an immunogen in vivo . In support of this notion , results from in vitro cell intoxication experiments indicated that the complexes were stable for at least 40 h at 37 °C . Based upon a recent observation that naturally occurring PA neutralizing antibodies do not bind to the receptor-binding surface of PA [27] , we reasoned that PA immobilized on these particles should be able to elicit a protective immune response . Indeed , rats survived LeTx challenge 4 wk after a single injection of the VLP-PA complex , whereas animals injected with an equivalent amount of recombinant PA died . This result suggested rapid production of neutralizing antibodies in the absence of adjuvant , two key goals for the development of third-generation anthrax vaccines . No significant antibody response to ANTXR2 was observed , presumably because there are only two–amino acid differences between human ANTXR2 displayed on the particle and endogenous rat ANTXR2 [11] . An essential next step will be to characterize the neutralizing antibody response in individual animals after primary and secondary immunization . An important component of this analysis will be to determine the mechanism by which toxin neutralization occurs . For example , we noticed a slight difference in antibody response after primary and secondary immunization and a wide range of antibody titers between individual animals ( Figure 4 ) . It will be of key interest to establish whether these differences correlate with epitope specificity or are based on other immunologic parameters . In addition , it will be critical to confirm our findings in a B . anthracis spore challenge model , and studies to this end are currently underway . Because the chimeric particles are expressed from an mRNA that contains only the coding sequence of the modified FHV coat protein while all other FHV sequences are missing , the resulting VLPs are not infectious and thus cannot replicate in mammalian tissues [19] . Even native FHV particles are unable to initiate infection in mammals , as they do not carry the FHV receptor , and because FHV cannot replicate at temperatures above 31 °C [28] . We have also demonstrated previously that FHV VLPs expressed from baculovirus vectors in Sf21 cells do not contain baculoviral or cellular DNA [19] , thus ruling out potential integration of foreign DNA into mammalian genomes . Based on these properties , the chimeric particles can be expected to have a desirable safety profile for applications in animals and humans . The idea of combining the functions of anthrax vaccine and antitoxin in a single reagent has been explored previously . Aulinger et al . [29] demonstrated that a dominant-negative , inhibitory form of PA , DNI-PA , can elicit an antibody response that protects mice from LeTx challenge . DNI-PA forms mixed heptamers with wt PA and thereby acts as an antitoxin to block toxin translocation both in vitro and in vivo [30] . However , even after two injections in the presence of adjuvant there was only a weak antibody response to DNI-PA , and a third injection had to be performed to generate a sufficient antibody response to protect against LeTx challenge [29] . While the potency of the nanoparticles as a vaccine is most likely due to polyvalent display of PA , polyvalency is less of a factor in the function of the particles as an antitoxin given the extremely high affinity between PA and ANTXR2 . Moreover , since PA binds as a monomer to the particles , little , if any , polyvalent effect is to be expected . In fact , we detected no significant difference in IC50 when comparing nanoparticles with soluble ANTXR2 in cell intoxication assays . That polyvalency increases the affinity between a ligand and its target receptor is a well-established phenomenon [31] . Recently , Rai et al . [32] reported that “pattern matching” is an important parameter for polyvalency to reach its maximum potential . With this approach , they achieved similar IC50 values in cell intoxication assays for liposomes containing inhibitory peptides that block LF binding to the PA heptamer as we observed for our nanoparticles . However , the functionalized liposomes described in their study are without a vaccine application . In vivo potency of viral nanoparticles is also significantly determined by their pharmacokinetic parameters . Such parameters have recently been reported for viral nanoparticles derived from the plant virus cowpea mosaic virus [33] . It will be important to determine whether there are significant differences in the plasma clearance kinetics and biodistribution of soluble ANTXR2 versus ANTXR2-containing nanoparticles . The VWA domain of ANTXR2 was a particularly appealing candidate for insertion into a loop of the FHV coat protein because the N and C termini are only separated by 4 . 8 Å in the native structure [34] . In addition , this domain adopts a compact Rossmann-like α/β-fold that can evidently form independently within the context of a larger protein while not interfering with accurate folding of the carrier protein . This hypothesis was supported by the observation that the high-resolution structure of the VWA domain could be fitted easily into the cryoEM density maps . To our knowledge , hepatitis B virus is the only other virus for which icosahedral surface display of an entire protein in its biologically active conformation has been demonstrated . In that case , genetic insertion of the green fluorescent protein in a surface-exposed loop of the core protein resulted in efficient formation of fluorescent hepatitis B virus capsids [35] . In principle , it should be possible to expand the use of the FHV platform to display additional anthrax antigens either in the presence or absence of the ANTXR2 VWA domain . Specifically , direct insertion of peptides or entire domains derived from PA , LF , and EF may be feasible as long as the termini of the domains are in close enough proximity for insertion into the FHV coat protein loops . It is also conceivable that the two insertion sites at positions 206 and 264 could be used in combination to create particles with multiple functionalities . This could greatly enhance the protection afforded by the resulting particles . Numerous other strategies are being pursued to develop improved anthrax vaccines , including PA-expressing Salmonella [36] and B . subtilis [37] , adenovirus encoding PA domain 4 [38] , rabies virus encoding GP-PA fusion protein [39] , and bacteriophage T4 particles decorated with PA-hoc fusion proteins [40–42] . None of these , however , combine the function of vaccine and antitoxin . In those cases where immunized animals were challenged with LeTx or anthrax spores , only the adenovirus construct provided complete protection after a single immunization [38] . The strategy most comparable to that described in our study involves non-covalent surface display of intact proteins and protein complexes on bacteriophage T4 particles . The prolate lattice of the T4 capsid permits efficient surface presentation of anthrax toxin through in vitro addition of Hoc- and/or Soc protein fusions with PA , LF , or EF to hoc−soc− phage either separately or in combination [40 , 42] . Mice immunized with phage displaying PA , EF , and LF generated high levels of neutralizing antibodies [41] , but results from toxin or spore challenge experiments have not yet been reported . In summary , we have developed a reagent that serves a dual purpose in combating B . anthracis infection . It functions as a competitive inhibitor of anthrax toxin in vivo , suggesting that it could be useful as a therapeutic compound , particularly in combination with standard antibiotic therapy . In addition , when complexed with PA , it has significant advantages as an immunogen compared to monomeric PA and thus forms the basis for development of an improved anthrax vaccine .
DNA fragments encoding FHV coat protein-ANTXR2 VWA domain chimeras were generated by overlap extension PCR using Pfu polymerase [43] . Three DNA fragments containing the nucleotide sequence for the N-terminal portion of the coat protein , the ANTXR2 VWA domain ( GenBank accession number AY23345 , nts 115–657 , amino acids 38–218 ) , and the C-terminal portion of the coat protein , were initially generated . The template used for generating segments containing the FHV coat protein sequence was plasmid pBacPAK9RNA2δ [44] , which contains the full-length cDNA of FHV RNA2 in baculovirus transfer vector pBacPAK9 ( BD Biosciences ) . The template used for generating the segment containing the ANTXR2 VWA coding sequence was a derivative of plasmid PEGFP-N1 [8] . Following overlap extension PCR , the full-length product was digested with BamH1 and XbaI , gel-purified , and ligated into equally digested pBacPAK9 . The transfer vector was amplified in E . coli strain DH5α , purified , and sequenced to confirm the presence of the ANTXR2 VWA sequence and to ensure the absence of inadvertent mutations . Recombinant baculoviruses AcFHV-VWAANTXR2-264 and AcFHV-VWAANTXR2-206 were generated by transfecting Sf21 cells with a mixture of transfer vector and linearized Bsu36I-linearized BacPAK6 ( BD Biosciences ) baculovirus DNA as described previously [43] . Spodoptera frugiperda cells ( line IPLB-Sf21 ) were propagated and infected as described previously [19] . Trichoplusia ni cells were propagated and infected as described by Dong et al . [45] except that EX-CELL 401 medium was replaced with ESF921 ( Expression Systems ) . VLPs were purified from T . ni suspension cultures 5 to 6 days after infection . NP-40 substitute ( Fluka ) was added to the culture to a final concentration of 1% ( v/v ) followed by incubation on ice for 10–15 min . Cell debris was pelleted by centrifugation in a Beckman JA-14 rotor at 15 , 300g for 10 min at 4 °C . VLPs in the supernatant were precipitated by addition of NaCl to a final concentration of 0 . 2 M and polyethylene glycol 8000 ( Fluka ) to a final concentration of 8% ( w/v ) and stirring the mixture at 4 °C for 1 h . The precipitate was collected by centrifugation at 9 , 632g for 10 min at 4 °C in a JA-14 rotor and resuspended in 50 mM Hepes buffer ( pH 7 . 5 ) . Insoluble material was removed by centrifugation at 15 , 300g for 20 min at 4 °C . VLPs in the clarified supernatant were pelleted through a 4-ml 30% ( w/w ) sucrose cushion in 50 mM Hepes ( pH 7 . 5 ) by centrifugation in a Beckman 50 . 2 Ti rotor at 184 , 048g for 2 . 5 h at 11 °C . The pellet was resuspended in 50 mM Hepes buffer ( pH 7 . 5 ) and loaded onto a 10%–40% ( w/w ) sucrose gradient in the same buffer . The gradients were spun in a Beckman SW 28 rotor for 3 h at 103 , 745g . VLPs were collected from the gradient by inserting a needle below the VLP band and aspirating the material into a syringe . Alternatively , gradients were fractionated with continuous absorbance at 254 nm on an ISCO gradient fractionator at 0 . 75 ml/min and 0 . 5 min per fraction . Fractions containing VLPs were then dialyzed against 50 mM Hepes , ( pH 7 . 5 ) and concentrated to 1–5 mg/ml using a centrifugal concentrator with a 100 , 000 MW cut off ( Amicon , Millipore ) . The final protein concentration was determined by BCA assay ( Pierce Chemicals ) and purity was evaluated by densitometry ( FluorChem SP , Alpha Innotech ) after electrophoresis on a 10% Bis-Tris gel stained with Simply Blue ( Invitrogen ) . Samples of gradient-purified VLPs were negatively stained with 1% ( w/v ) uranyl acetate . A drop of each sample was adsorbed to a glow-discharged , collodion-covered copper grid and allowed to adsorb for 1–2 min . Excess solution was removed by blotting with filter paper . The grids were washed and blotted with filter paper three times by floating on droplets of 50 mM Hepes ( pH 7 . 5 ) . Each grid was then treated three times with a drop of 1% uranyl acetate solution and left in the third drop for 1–2 min prior to blotting and air drying . The samples were viewed in a Philips/FEI CM 100 transmission electron microscope at 100 kV . Frozen-hydrated samples were prepared using standard methods [46] . In brief , an aliquot of the sample was applied to a glow-discharged Quantifoil holey carbon-coated grid ( 2/4 Cu-Rh ) , blotted with filter paper , and rapidly plunged into liquid ethane . Low-dose electron micrographs of FHV-VWAANTXR2-264 VLPs were recorded onto Kodak SO163 film at a magnification of 45 , 000× on a Philips/FEI CM120 transmission electron microscope . For FHV-VWAANTXR2-206 VLPs , low-dose micrographs were recorded on a CCD camera at a magnification of 50 , 000× on a Philips/FEI Tecnai20 transmission electron microscope . The grids were maintained at −180 °C using a Gatan 626 cryo-stage . Micrographs with minimal astigmatism and drift , as assessed by visual inspection and optical diffraction , were digitized with a Zeiss microdensitometer ( Z/I Imaging ) , giving a step-size of 3 . 1 Å on the specimen . Images recorded on the CCD camera had a step-size of 2 . 26 Å . Particle images were extracted with the program X3D [47] and were processed by polar Fourier transform methods using the program PFT [48] . A previously calculated model of wt FHV [49] was used as the starting model . Initial refinement cycles were restricted to the radii spanning the FHV capsid and then relaxed to incorporate the extra domains . Using a Fourier shell correlation cut-off value of 0 . 5 , the FHV-VWAANTXR2-206 and FHV-VWAANTXR2-264 maps were refined to resolutions of 25 and 23 Å , respectively . The atomic coordinates of the FHV coat protein subunit and the VWA domain of ANTXR2 ( PDB ID: 1SHT ) were used to generate a pseudoatomic model of the FHV-VWAANTXR2-206/264 VLPs . Specifically , the models were created with the program O [50] by visually positioning the ANTXR2 VWA domains at the surface of the FHV structure and adjusting for overlap . The models were then further refined against the structure factor amplitudes derived from the cryoEM density using the program CNS [51] . Individual subunits and domains of the FHV-VWAANTXR2 chimera were allowed to move independently as rigid bodies and subjected to five rounds of 20 cycles of rigid body refinement . PA83 molecules were docked onto the resulting FHV-VWAANTXR2-206/264 models using the structure of PA63 complexed with the ANTXR2 VWA domain ( PDB ID: 1T6B ) as a guide [9] . Once all 180 ANTXR2 VWA domains on the FHV-VWAANTXR2 chimera were populated with PA83 molecules , a minimal number of PA molecules were selectively removed to relieve steric clashes with neighboring PA molecules . Recombinant PA83 ( List Biological Laboratories ) in 5 mM Hepes , 50 mM NaCl ( pH 7 . 5 ) was mixed with purified chimeras 206 and 264 in 50 mM Hepes ( pH 7 . 5 ) in a ratio of 180:1 ( equimolar amounts of PA83 and VWA domains ) . Following incubation for 20 min at room temprature , an aliquot from each of the samples was removed and stored at −20 °C pending analysis . The remainder of the samples was transferred to an ultracentrifuge tube and underlayed with a 30% ( w/w ) sucrose cushion in 50 mM Hepes ( pH 7 . 5 ) . Complexes of chimeras decorated with PA83 were pelleted by centrifugation at ∼200 , 000g for 45 min . The complexes were resuspended in 50 mM Hepes , mixed with SDS loading buffer , and heated at 95 °C for 10 min . Aliquots were electrophoresed through a 4%–12% Bis-Tris polyacrylamide gel , in parallel with the aliquots taken before pelleting . The gels were stained with Simply Blue ( Invitrogen ) . The amount of protein in each band was determined by densitometric analysis using FluorChem SP ( Alpha Innotech ) . Cell intoxication studies were performed in CHO-K1 cells as described previously [10] . Briefly , 5 × 103 cells in 100 μl of Hams-F12 nutrient mixture ( Gibco BRL ) supplemented with 10% fetal bovine serum were plated into wells of a 96-well microtitre plate a day prior to the assay . Varying amounts of FHV-VWAANTXR2 VLP or soluble ANTXR2 [8] were preincubated for 20 min in 100 μl of medium containing PA and LFN-DTA at a molar concentration of 10−8 and 10−10 , respectively . The mixture was added to the cells , which were incubated at 37 °C for approximately 40 h . The medium was then replaced with 50 μl of Celltiter-glo reagent ( Promega ) diluted 1:1 with PBS . Luciferase activity as a measure of cell viability was determined with a luminometer ( TopCount NXT , Perkin Elmer ) . Non-linear regression analysis was used to calculate IC50 values ( Prism , GraphPad Software ) . RAW264 . 7 cells ( 5 × 104 ) were plated in each well of a white 96-well tissue culture plate ( Corning Costar ) with 100 μl of Dulbecco's Modified Eagle Medium ( Gibco ) supplemented with 10% standard fetal bovine serum the day before the assay . Varying amounts of FHV-VWAANTXR2 VLP were preincubated for 20 min in 400 μl of medium containing PA and LF at a molar concentration of 10−8 and 10−9 , respectively . The mixture ( 100 μl ) was added to the cells in triplicate and incubated at 37 °C for approximately 5 h . Cell viability was determined as described for CHO-K1 cells . LeTx challenge experiments were performed in cannulated , male Fisher 344 rats ( 180–200 g , Harlan ) according to protocols approved by the Scripps Institutional Animal Care and Use Committee . LeTx for each rat was prepared by mixing 20 μg of PA and 8 μg of LF ( List Biological Laboratories ) . All rats were anesthetized with isofluorane and inoculated through a jugular vein cannula with 500 μl of LeTx ( control ) , or LeTx premixed with FHV-VWAANTXR2 VLPs or sANTXR2 ( test ) . Additional rat control groups were injected with either PBS or wt FHV . Rats recovered from anesthesia within 5 min after dosing and were monitored for symptoms of intoxication ( agitation , respiratory distress , hypoxia ) and death as determined by cessation of respiration . Data are presented as mean ± SD . Significance was reported using the Student's unpaired T-test ( Prism , GraphPad Software ) . p-Values <0 . 05 were considered statistically significant . Data were also analyzed using 2-way ANOVA followed by Tukey's post-comparison test to confirm significance . Purified chimera 264 VLPs in 50 mM Hepes ( pH 7 . 5 ) were mixed with a 4-fold molar excess of recombinant PA83 ( List Biological Laboratories ) in 5 mM Hepes , 50 mM NaCl ( pH 7 . 5 ) , and incubated at room temperature for 20 min with mild agitation . The sample was then transferred to an ultracentrifuge tube and underlayed with a 30% ( w/w ) sucrose cushion in 50 mM Hepes ( pH 7 . 5 ) . Complexes of chimera 264 decorated with PA83 were pelleted by centrifugation at 197 , 568g at 11 °C in an SW41Ti rotor for 1 . 5 h or an SW 55 Ti rotor for 45 min . The complexes were resuspended in 50 mM Hepes and analyzed by electrophoresis to confirm the presence of chimera 264 and PA83 proteins . Immunization studies were performed in male Harlan Sprague Dawley rats ( 180–200g , Harlan ) according to protocols approved by the Scripps Institutional Animal Care and Use Committee . For double dose immunization , rats were injected subcutaneously with 200 μl containing either 2 . 5 μg of PA83 ( List Biological Laboratories ) , 5 . 4 μg of FHV- VWAANTXR2-264-PA83 ( molar equivalent of 2 . 5 μg PA83 ) , or 2 . 9 μg FHV-VWAANTXR2-264 ( molar equivalent of particles complexed with PA83 ) all prepared in PBS . For single dose immunization , rats were injected subcutaneously with 200 μl containing either 5 μg of PA83 , 10 . 8 μg of FHV-VWAANTXR2-264-PA83 ( molar equivalent of 5 μg PA83 ) , or 5 . 8 μg of FHV-VWAANTXR2-264 ( molar equivalent of particles complexed with PA83 ) all in PBS . Rats were anesthetized with isofluorane before all procedures . Serum was prepared from blood ( 200 μl per rat ) collected from the retro-orbital plexus before immunization , 3 wk post primary immunization , and 4 wk post secondary immunization ( study 1 only ) . LeTx challenge experiments were performed 13 wk post initial immunization for the double dose immunization study and 4 wk post immunization for the single dose immunization study . LeTx for each rat was prepared by mixing 40 μg of PA and 8 μg of LF ( List Biological Laboratories ) in PBS . Rats were anesthetized with isofluorane and inoculated with 500 μl of LeTx or PBS ( control ) by intravenous tail vein injection . Rats recovered from anesthesia within 5 min after dosing and were monitored for symptoms of intoxication and death as described above . Serum samples were tested for antibody response to PA , ANTXR2 , and FHV by ELISA assays . Briefly , microtitre Immulon 2HB 96-well plates ( Dynex Technologies ) were coated with 100 μl of 10 μg/ml PA83 , sANTXR2 , or FHV in coating buffer ( 0 . 1 M NaHCO3 [pH 8 . 5] ) , blocked with 3% non-fat milk in TBS , and incubated with serum samples diluted 1:100 and 1:1 , 000 in 1% non-fat milk in TBS ( pH 7 . 0 ) with 0 . 05% Tween 20 for 1 h at room temperature . After washing , wells were incubated with biotin-SP-conjugated donkey anti-rat IgG ( Jackson ImmunoResearch ) diluted 1:20 , 000 for 1 h at room temperature . Plates were washed and incubated for 45 min with streptavidin-alkaline phosphatase conjugate ( GE Healthcare Bio-Sciences ) diluted 1:5 , 000 in TBS . After washing , plates were incubated with p-nitriphenyl phosphate ( Sigma-Aldrich ) at 37 °C for 20 min . 2 N NaOH was added to stop the reaction and the signal was quantified using an ELISA reader ( Molecular Devices ) at 405 nm .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) accession numbers for the proteins discussed in this paper are Flock House virus coat protein ( NC004144 ) and human capillary morphogenesis protein 2 ( CMG2 ) ( AY23345 ) . Protein Data Bank ( http://www . rcsb . org/pdb/ ) accession numbers are for the VWA domain of CMG2 ( 1SHT ) and B . anthracis protective antigen PA63 complexed with CMG2 ( 1T6B ) . The Virus Particle Explorer ( http://viperdb . scripps . edu/ ) was used for Flock House virus particle coordinates . | Anthrax is caused by the spore-forming , Gram-positive bacterium Bacillus anthracis . The toxic effects of B . anthracis are predominantly due to an AB-type toxin made up of the receptor-binding subunit protective antigen ( PA ) and two enzymatic subunits called lethal factor and edema factor . Protective immunity to B . anthracis infection is conferred by antibodies against PA , which is the primary component of the current anthrax vaccine . Although the vaccine is safe and effective , it requires multiple injections followed by annual boosters . The development of a well-characterized vaccine that induces immunity after a single injection is an important goal . We developed a reagent that combines the functions of an anthrax antitoxin and vaccine in a single compound . It is based on multivalent display of the anthrax toxin receptor , ANTXR2 , on the surface of an insect virus . We demonstrate that the recombinant virus-like particles protect rats from anthrax intoxication and that they induce a potent immune response against lethal toxin when coated with PA . This immune response protected animals against lethal toxin challenge after a single administration without adjuvant . The PA-coated particles have significant advantages as an immunogen compared to monomeric PA and form the basis for development of an improved anthrax vaccine . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] | [
"biotechnology",
"viruses",
"none",
"virology",
"molecular",
"biology",
"insects"
] | 2007 | A Viral Nanoparticle with Dual Function as an Anthrax Antitoxin and Vaccine |
Crimean-Congo hemorrhagic fever ( CCHF ) is the most medically important tick-borne viral disease of humans and tuberculosis is the leading cause of death worldwide by a bacterial pathogen . These two diseases overlap geographically , however , concurrent infection of CCHF virus ( CCHFV ) with mycobacterial infection has not been assessed nor has the ability of virus to persist and cause long-term sequela in a primate model . In this study , we compared the disease progression of two diverse strains of CCHFV in the recently described cynomolgus macaque model . All animals demonstrated signs of clinical illness , viremia , significant changes in clinical chemistry and hematology values , and serum cytokine profiles consistent with CCHF in humans . The European and Asian CCHFV strains caused very similar disease profiles in monkeys , which demonstrates that medical countermeasures can be evaluated in this animal model against multiple CCHFV strains . We identified evidence of CCHFV persistence in the testes of three male monkeys that survived infection . Furthermore , the histopathology unexpectedly revealed that six additional animals had evidence of a latent mycobacterial infection with granulomatous lesions . Interestingly , CCHFV persisted within the granulomas of two animals . This study is the first to demonstrate the persistence of CCHFV in the testes and within the granulomas of non-human primates with concurrent latent tuberculosis . Our results have important public health implications in overlapping endemic regions for these emerging pathogens .
Crimean-Congo hemorrhagic fever virus ( CCHFV ) is a tick-borne member of the Nairoviridae family in the RNA virus order Bunyavirales . Its extensive range includes Africa , the Balkan , the Middle East , Russia and western Asia , and this range is thought to be continuously expanding as a result of both climate change and ecological disruption [1 , 2] . CCHFV is maintained in nature through vertical and horizontal transmission cycles by several genera of ixodid ticks , which transmit the virus to a variety of wild and domestic animals that may be viremic without signs of illness . Human infections occur primarily by tick bites or exposure to blood or bodily fluids from infected animals or CCHF patients . Hyalomma ticks are the principal source of human viral infection and a subsequent disease state that can range in severity from a mild , non-specific febrile illness to a lethal infection characterized by hemorrhagic manifestations . Patient data and animal studies show that after an initial local replication , the virus spreads systemically and targets the liver and endothelium where it causes a massive dysregulation of the immune response sometimes culminating in hemorrhagic fever [3 , 4] . Recently , CCHFV was designated as one of ten high priority emerging infectious diseases by the World Health Organization ( WHO ) due to its epidemic emergence potential and lack of approved medical countermeasures [5] . CCHFV has a tripartite , negative-sense RNA genome comprising small ( S ) , medium ( M ) and large ( L ) segments . The S segment encodes the nucleocapsid protein ( N ) , the M segment encodes the glycoprotein open reading frame ( ORF ) that is cleaved into two structural glycoproteins ( GN and GC ) and nonstructural proteins , and the L segment encodes the RNA-dependent RNA polymerase ( RdRp; reviewed in [6] ) . CCHFV is the most genetically diverse arthropod-borne virus , divided into six clades , where the nucleotide sequence differences can range from 20% for the S segment , 22% for the L segment , and 31% for the M segment [7] . The overall impact of this genetic diversity on human pathogenesis is poorly understood . Infection by this virus can lead to severe disease in humans , with a global average case fatality rate ( CFR ) estimate of 13% [8] . However , the CCHF CFR shows a tremendous amount of variation geographically , with 5% reported in Turkey , but much higher CFRs reported in India ( 60% ) and Tajikistan ( 82% ) [8 , 9] . While some CFR variability may be the result of underdiagnosed cases of mild CCHF [7] , strain heterogeneity along with other factors , such as availability of advanced medical care , may also account for the broad CFR range . Until recently , there was no immunocompetent laboratory animal disease model available for the study of CCHF . A cynomolgus macaque severe disease model was recently established for CCHF using an intravenous ( IV ) or combined IV and subcutaneous ( SC ) exposure with a high dose ( >6 log10 PFU ) of the European CCHFV isolate , Kosova Hoti . Non-human primates ( NHPs ) became viremic and developed a severe and sometimes fatal disease [10] . Several features typical of human disease , including elevated liver enzymes , thrompocytopenia , leukopenia and fever [11 , 12] were present in infected NHPs . The development of the cynomolgus macaque model represents an important advancement in the field where an immunocompetent CCHF animal model is now available to study pathogenic disease mechanisms and evaluate medical countermeasures against CCHFV . The ability of CCHFV to persist and cause long-term sequela following infection in humans has not been well studied . Furthermore , CCHF in patients with latent or active tuberculosis has not been described despite the significant geographical overlap of these two important diseases . Tuberculosis threatens millions of lives world-wide and is the leading cause of death due to a bacterial pathogen . The WHO estimates that there were more than 10 million new active cases of tuberculosis and close to 1 . 3 million deaths in 2017 alone [13] . Geographically , the highest incidence of tuberculosis occurs in Asia and Africa . Only 5–10% of individuals infected with Mycobacterium tuberculosis ( M . tuberculosis ) develop active tuberculosis over their lifetime and the remaining 90–95% of infected individuals remain asymptomatic and are latently infected . Latent tuberculosis is the result of a complex set of interactions between mycobacterium and the host immune response , where the bacilli exist within granulomas and can subsequently reactivate to cause active disease [14] . Mycobacterium bovis ( M . bovis ) can also cause active or latent tuberculosis in humans , who can become infected after eating or drinking contaminated unpasteurized dairy products or by exposure during slaughter of infected animals . Tuberculosis caused by M . tuberculosis and M . bovis have similar symptoms , including fever , night sweats and weight loss [15] . Progressive granuloma formation is a hallmark of chronic mycobacterial infections . These organized structure of cells consists of a central area of necrotic debris surrounded by a layer of epithelioid macrophages , foamy macrophages , and multinucleate giant cell macrophages , further surrounded by lymphocytes , plasma cells and fibroblasts . The immune control of latent mycobacterial infections can be affected by co-infection with other pathogens . The most well-known example is the effect of the co-infection of M . tuberculosis and human immunodeficiency virus ( HIV ) , which has been described as a syndemic ( i . e . synergistic epidemic ) [16] . The NHP model of tuberculosis is the “gold-standard” for modeling tuberculosis and have been used to study HIV co-infection [17] . Here we compared the disease progression in cynomolgous macaques of the previously reported European strain of CCHFV ( Hoti ) to that caused by an Asian strain , which was isolated from a fatal case of CCHF that occurred in a U . S . military service member serving in Afghanistan [18] . Unexpectedly , during the course of our studies , we determined that some CCHFV infected NHPs had latent tuberculosis . This finding allowed us a unique opportunity to observe disease parameters of both pathogens in the same host . Because tuberculosis and CCHF occur in overlapping geographic regions , our results have important public health implications for these emerging pathogens .
Cynomolgus macaques were infected IV with a high dose of CCHFV strain Kosova Hoti ( 6 . 6 log10 PFU; hereafter referred to as Hoti; n = 6 ) or strain Afg09-2990 ( 6 . 2 log10 PFU; hereafter referred to as Afg09; n = 6 ) to directly compare the pathogenesis of the two isolates . The complete genomes for these virus strains have been described previously , where the Hoti strain was found to be phylogenetically related to the Europe/Turkey group [19] , and the Afg09 strain was related to Asian strains [20] . We confirmed the gene sequences of our virus stocks and compared strain Hoti and Afg09 , which were found to share nucleic acid and amino acid sequence homologies across their L/M/S segments of 87%/80%/87% and 97%/85%/97% , respectively ( S1 Fig ) . At the completion of the study , we learned that two NHPs infected with strain Hoti ( animal numbers 0184 , and 2038 ) and four NHPs infected with strain Afg09 ( animal numbers 1217 , 1338 , 2166 , 8248 ) had evidence of a latent mycobacterial infection with granulomas in the lung , liver , and/or lymph node ( discussed in more detail in a subsequent section ) . Animals in both groups became viremic , peaking on day 2 post-infection for NHPs exposed to the Afg09 strain ( 5 . 2 log10 PFU/mL ) or on day 3 post-infection for NHPs exposed to the Hoti strain ( 4 . 7 log10 PFU/mL; Fig 1A and S2A and S2B Fig ) . Overall there was no statistically significant difference ( p = 0 . 2778 ) in viremia between animals infected with either strain except on day 1 post-infection ( p = 0 . 0045 ) . While most NHPs exhibited transient weight loss subsequent to virus exposure ( S2C and S2D Fig ) , no animal met euthanasia criteria or otherwise succumbed to disease . Animals infected with both CCHFV strains lost a significant amount of weight compared to baseline values prior to virus exposure ( ANOVA; p<0 . 0001 ) , and had significant changes over time for both groups ( ANOVA; p = 0 . 0304 ) . However , the rate of weight change was similar between animals exposed to CCHFV strains Hoti and Afg09 ( p = 0 . 4331 ) . We observed clinical signs of disease as early as day 1 post-infection , and all animals experienced clinical illness by day 3 post-infection ( Fig 1B; S2G and S2H Fig ) , including anorexia , lymphadenopathy , fever , and some animals ( n = 2/12 ) developed a rash . One animal infected with the Afg09 strain developed a petechial rash that appeared in the axillary region on day 2 post-infection and completely resolved by day 5 post-infection ( S3A–S3D Fig ) . An animal infected with the Hoti strain developed a macular rash in the axillary region on day 3 post-infection that resolved by day 5 post-infection ( S3E and S3F Fig ) . Vaginal bleeding was observed in one animal infected with the Afg09 strain on days 1 and 3 post-infection and another animal infected with the Afg09 strain on day 6 post-infection . Vaginal bleeding occurred in one animal infected with the Hoti strain on days 5–7 post-infection and in another animal infected with Hoti on day 7 post-infection . However , distinction of these vaginal bleeding events from normal estrus is difficult to establish . The only other bleeding that was observed was epistaxis on day 7 post-infection in one animal infected with the Hoti strain . All animals developed a fever response ( defined as >3 standard deviations above baseline ) which was prominent from days 1 through 9 and had fully resolved by day 10 post-infection . The mean time to reach fever state was 1 . 09 days for animals infected with Afg09 strain and 1 . 03 days for animals infected with the Hoti strain . There was not a statistically significant difference in the mean time to fever state for animals infected with the Hoti vs . Afg09 strain . The fever-hours ( Fig 1C and 1D and S2E and S2F Fig ) is the sum of the significant temperature elevation values , which gives an indication of the intensity of the fever by measuring the area under the curve . Ten of the 12 NHPs had moderate fevers ( >100 fever-hours ) , and one NHP in each group had mild temperature changes ( <100 fever-hours; animals 5069 and 1207 ) . Hyperthermia was more prolonged in animals exposed to the Hoti strain with mean fever-hours of 161 . 4 compared to 123 . 6 for NHPs exposed to the Afg09 strain , but this was not statistically significant . The blood chemistry and complete blood count ( CBC ) values were determined on days -8 , 0–7 , 9 , 12 , 14 , 21 and 28 in NHPs exposed to the Hoti and Afg09 strains . Generally , all NHPs exhibited marked changes in multiple analytes as early as day 1 post-infection . Results from repeated measures ANOVA ( S1 and S2 Tables ) showed significant changes over time for all analytes except blood urea nitrogen ( BUN ) , basophils ( BASO ) , mean corpuscular volume ( MCV ) , and mean platelet volume ( MPV ) indicating that the CCHFV disease course affects chemistry and hematology function regardless of strain . Results from repeated measures ANOVA indicated some statistically significant differences in the hematology and chemistry results among animals infected with the Afg09 or Hoti strains , but not in the interaction between strain and time . The alanine aminotransferase ( ALT ) , aspartate aminotransferase ( AST ) , and alkaline phosphatase ( ALP ) levels increased during the first 7 days post-infection , which is an indication of hepatocellular damage ( Fig 2A and 2B and S4A Fig ) . The amylase ( AMY ) levels decreased during the first two days post-infection , suggesting virus was impacting kidney function ( S4B Fig ) . Other indicators of liver disease occurring in CCHFV infected animals included a significant decrease in total protein and albumin ( ALB ) levels ( S4C and S4D Fig ) . NHPs experienced thrombocytopenia , lymphopenia , and leukopenia ( Fig 2C and 2D and S4E Fig ) during the first 7 days post-infection . The neutrophils increased until day 3 post-infection followed by neutropenia ( S4F Fig ) . The eosinophils and monocytes decreased slightly followed by a significant increase that peaked on day 9 post-infection ( S4G and S4H Fig ) . Serum cytokine levels were evaluated via a multiplexed NHP cytokine detection kit on days 3 and 6 post-infection and were assessed relative to day 0 levels ( S5 Fig ) . We observed an increase in a number of cytokine markers including the interleukins ( IL ) -1RA , IL-6 , IL-10 , IL-15 , and IL-18 as well as monocyte chemo- attractive protein ( MCP ) -1 and interferon ( IFN ) -γ . For all the cytokines listed , we observed elevated levels of the markers at both days 3 and 6 post-infection , but with a general decrease in signal from day 3 to 6 . An exception to this for both strains was IL-10 and IFN- γ levels which remained constant or even increased from day 3 to 6 post-infection ( S5C and S5G Fig ) . Overall no statistically significant differences in cytokine levels was observed for animals infected with the Hoti vs . Afg09 strain . Host antibody response was measured by a combination of multiplexed immunoassays and neutralization assays using a virus-like particle ( VLP ) system . For detecting immunoglobulin reactivity against distinct viral antigens ( N or GN ) , we employed a novel bead-based assay which has an enhanced sensitivity profile relative to conventional ELISA [21] . Antigen-coupled beads were probed with NHP sera and then exposed to detector antibodies for either IgM or IgG . In observing the timing of host IgM response to infection by both CCHFV strains ( S6A Fig ) , we observed a rapid IgM response to CCHFV N , starting 3 days after infection , peaking 7 days post-infection and deteriorating rapidly thereafter . Anti-N IgM kinetics between the two strains were virtually identical . By contrast , host IgM seroconversion against recombinant GN antigen was much slower for both strains , reaching peak levels several days after N before starting to diminish . Unsurprisingly , host IgG response against both antigens was slower relative to IgM ( S6B Fig ) . Virus-neutralization response was evaluated using a VLP system with glycoproteins based on the IbAr 10200 CCHFV strain [22] . We chose IbAr 10200 VLPs as a neutral interrogator of broad neutralizing antibody response by the host against CCHFV glycoprotein complex ( GPC ) as it is from a clade distinct from both Hoti and Afg09 , but nonetheless shares 90% and 85% protein conservation with each of these isolates’ respectively . The timing of neutralization response was assessed in pooled NHP sera from each treatment group collected at different time-points . We observed the emergence of neutralizing antibodies in sera by day 9 post-infection for both groups , with broadly similar kinetics and endpoint titers between the Hoti and Afg09 infected groups ( S6B and S6C Fig ) . Endpoint neutralization titers ( day 28–30 post-infection ) were equivalent in both groups with no significant difference ( S6D Fig ) . All NHPs survived infection . On day 28–30 , necropsy was performed on each animal and a small piece of the following tissues were collected and evaluated for virus by plaque assay: lymph node ( axillary ) , liver , spleen , kidney , testis , epididymis , ovary , eye ( optic nerve/retina ) , eye fluid ( aqueous and vitreous humor ) , and brain . No viable virus was detected in any of these samples by plaque assay . All animals were clinically normal at the time of necropsy , and few animals had significant gross lesions . Animal 2166 had a focal 0 . 25 cm mass in the caudate lobe of the liver , and animal 1033 had a right testis that was approximately half the size of the left testis . There were no other significant gross lesions recorded at the time of necropsy . Significant histologic lesions suggestive of CCHFV infection were not observed in any tissue except the testes of three male monkeys . Animals 2255 , 7117 , and 1033 had unilateral inflammation in the testis ( orchitis ) . The lesions were characterized by lymphocytic and plasmacytic inflammation , spermatogonia/germ cell loss , luminal debris , seminiferous tubule atrophy and loss , and interstitial fibrosis . Lesions in the testis of animal 1033 were multifocal and mild; those in 2255 and 7117 were focally extensive , affecting around 25–70% of examined sections ( Fig 3A and 3B ) . Immunohistochemistry ( IHC ) and RNA in situ hybridization ( ISH ) performed on the testis were positive for CCHFV antigen/RNA in animal 1033 , and were negative in animals 2255 and 7117 ( Fig 3C and 3D; Fig 4 ) . CCHFV RNA/antigen was detected in the seminiferous tubules , which is the site of sperm production and an immune privileged area of the body . To determine the location of the viral antigen and RNA , we performed immunofluorescence ( IFA ) staining using an antibody against Sox9 , which is an established cell-specific marker for Sertoli cells . IFA demonstrated that CCHFV specifically infects Sertoli cells and not CD68+ macrophages/monocytes in the testis ( Fig 3E and 3F ) . Six animals had granulomas and/or granulomatous lesions in the lungs , tracheobronchial lymph node , and/or liver ( Fig 4; Fig 5A and 5B ) that were suspect for mycobacterial infection . A diagnosis of mycobacterial infection in lung and liver ( 2166 ) granulomas was confirmed in three animals ( 1217 , 1338 , 2166; Afg09 group ) using acid-fast histochemical staining and mycobacterium-specific IHC ( Fig 4; Fig 5C and 5D ) . IHC for mycobacteria was also performed on the testis of the three animals with orchitis ( 2255 , 7117 , 1033 ) , all with negative results . CCHFV IHC and ISH was performed on all granulomatous lesions . Positive IHC and ISH was observed in lung granulomas in animals 1338 and 2166 , as well as positive results in the liver granuloma in animal 2166 ( Fig 4; Fig 5E ) . IFA demonstrated that both mycobacteria and CCHFV were present in the necrotic area of the granuloma . Compared to adjacent normal liver tissue , there was an excessive number of CD3+ T cells and CD68+ macrophages/monocytes surrounding the granuloma ( Fig 5G and 5H ) . Interestingly , neither mycobacteria nor CCHFV infection was detected in surrounding CD68+ macrophages ( Fig 5I ) . We did not collect samples for sequencing from the animals in the current study , but sampling of granulomatous lesions from other NHPs housed in the same colony were found to be positive for M . bovis . The qualitative overview of the progression of CCHF as modeled in both virus exposure groups is depicted in Fig 6 and includes fever data , viremia , prominent changes in hematology and blood chemistry , and host markers associated with CCHF . Although we did not observe mortality , all animals demonstrated signs of clinical illness , viremia , significant changes in clinical chemistry and hematology values , serum cytokine profiles consistent with CCHF in humans , and seroconversion against CCHFV antigens . Overall , we found that the European and Asian CCHFV strains caused very similar disease profiles in cynomolgus macaques , and that the virus may persist in the testes and within lung and liver granulomas in animals concurrently infected with latent tuberculosis .
The development of a cynomolgus macaque disease model has provided a major advance for CCHFV research [10] . Our results are largely consistent with those from the earlier report and further demonstrate the utility of the model for another genetically diverse CCHFV strain . In contrast to the previous study , we demonstrated a febrile response in all animals that lasted on average between 5–7 days post-infection . The earlier study found that elevated temperatures were only observed on day 1 post-infection in 2 of 4 animals , which was likely due to measuring temperature changes rectally in anesthetized animals . The use of telemetric temperature monitoring in the current study clearly offers a more sensitive and accurate means to evaluate the febrile response , which is an important endpoint criteria for evaluating the effectiveness of medical countermeasures . While we achieved a clearly discernible disease state in all animals exposed to both CCHFV strains , none approached euthanasia criteria and all NHPs ( n = 12 ) survived challenge . In contrast , 75% ( 3/4 ) of the animals that were exposed IV with CCHFV strain Hoti in the earlier study met euthanasia criteria . The data indicate that more animals in that study experienced signs of severe disease such as body/facial edema and bleeding . Differences in scoring criteria between both studies may account for why no animals met euthanasia criteria in the current study . Other possible explanations could include variables in virus stock , dose , and genetic background of the NHPs . These differences should be further examined in an effort to refine and standardize the CCHF NHP model . The typical progression of CCHF in humans has been described in four distinct phases: incubation ( 3–7 days post-infection ) , prehemorrhagic ( 4–5 days ) , hemorrhagic ( 2–3 days ) , and convalescence ( 10–20 days post-onset of symptoms ) [12 , 23] . Unlike the earlier NHP study with strain Hoti , we observed almost no incubation period in either of our infection groups , as evidenced by rapid fever , viremia , and lymphopenia within one day of infection . It should be noted , however , that in both studies pathogenesis was considerably faster than CCHF in humans . The difference in the incubation period between humans and NHPs could be related to the route of exposure ( i . e . tick transmission/nosocomial vs . IV exposure ) and viral dose . The prehemorrhagic period in humans has been characterized by the onset of fever , headache , myalgia , and dizziness . Some cases have experienced diarrhea , nausea , vomiting , and hyperemia of the face , neck and chest ( reviewed in [24] ) . In the current NHP study , we observed fever and in some instances diarrhea and anorexia as evidence of the prehemorrhagic period with no major differences between the two strains . The serum cytokine monocyte chemo‐attractive protein ( MCP-1 ) , which is thought to be an activator of natural killer cells and important for effective innate immunity , was elevated in both groups of NHP [25 , 26] . Elevated serum MCP-1 was also reported in the earlier NHP study and has been described for human CCHF cases [10 , 27] . Other markers that were upregulated during the early active infection phase included IFN-γ , IL1-RA , IL-6 , IL-10 , IL-15 , and IL-18; all of which have been reported in cases of human disease [24 , 28] . Hallmarks of the hemorrhagic period in humans include the development of petechiae to large hematomas on the skin or mucous membranes . The nose , gastrointestinal system , uterus and urinary tract , and respiratory tract are common sites where bleeding has occurred [24 , 29] . Less commonly , bleeding has been described in the vagina , gums , and cerebrum [30] . Similarly , in our study , we observed signs of rash , vaginal bleeding , and epistaxis in a few cases with no major differences between the two strains . Humans have been described to enter the convalescence period 10–20 days after the onset of illness [24] , which is similar to what we observed in the NHP model . We did not observe significant differences in the kinetics of host IgM , IgG , or neutralizing responses between virus strains . In addition , we report here the first ever temporal resolution of host primate IgM response to CCHFV nucleocapsid antigen , a viral marker frequently used in diagnostic immunoassays ( ELISAs , etc . ) [31 , 32] . The rapid host IgM response we observed and its subsequent degradation were indistinguishable between these distantly-related viral strains , which reinforces the utility of this viral marker as a immunodiagnostic target of both acute , and early convalescent CCHFV infection [33] . The persistence of CCHFV in human survivors has not been described . Possible human sexual transmission of CCHFV has been reported in only a few cases [34 , 35] . One example described a possible case of the sexual transmission of CCHFV by a man in the convalescent phase of disease [34] . Another report suggested sexual transmission of CCHFV among spouses in the southern regions of Russia [35] . These spouses had sexual contact with the index cases at the end of the incubation period or during the early stage of a mild form of CCHF with no hemorrhagic symptoms in the first infected spouse . However , all of these cases only suggest probable sexual transmission of CCHFV as investigators were unable to isolate virus in the seminal fluid and could not completely rule out possible CCHFV transmission to the partner by a viremic animal or through a tick bite . More convincing evidence for sexual transmission of CCHFV came from a case report in which a male CCHF patient with epididymo-orchitis appears to have transmitted the virus to his partner . This observation suggested that the virus could replicate in the male genital tract [36] . Our study provides the first direct evidence that CCHFV can replicate in the male genital tract where we observed orchitis in 50% ( 3/6 ) of male NHPs infected with CCHFV . CCHFV viral RNA and antigen were detected by ISH/IHC in the testes of one of these animals suggesting that most of the animals ( 2/3 ) had already cleared CCHFV infection , but still had residual testicular damage by day 28 post-infection . Interestingly , histologic lesions were more extensive in the animals where no CCHFV RNA or antigen was detected , suggesting that the inflammatory response may have cleared the virus from the testes but left severe tissue damage . The single animal that was positive for CCHFV RNA/antigen had a testis that was approximately half the size of the other testis indicating testicular atrophy . This observation of gross testicular atrophy along with the histologic lesions indicating damage such as spermatogonia/germ cell loss and seminiferous tubule atrophy and loss suggest a deleterious effect on the overall reproductive function of the testes in these 3 NHPs . Furthermore , IFA demonstrated that CCHFV specifically infects Sertoli cells , which are essential for spermatogenesis . Sertoli cells have also been reported as a target of Zika virus ( ZIKV ) in immunodeficient mice and a cellular reservoir of persistent infection of Marburg virus [37] . Future studies with CCHFV in NHPs and human cases should attempt to isolate RNA and infectious virus from the semen . It is well documented that other viruses that cause hemorrhagic fever such as Ebola [38 , 39] , Marburg [40 , 41] , and Lassa [42] can be sexually transmitted in semen . Our results and the aforementioned case reports suggest that CCHFV is another virus that causes hemorrhagic fever that can be sexually transmitted . Collectively , these findings have important implications for nonvector-borne vertical transmission , as well as long-term potential reproductive deficiencies in CCHFV-infected males . It is also possible that CCHFV might persist in other immune-privileged sites similar to what has been described for Ebola virus ( EBOV ) and other re-emerging viruses such as ZIKV . However , we did not detect CCHFV in ocular tissues or the central nervous system . The only other location where we detected persistent CCHFV is within the granulomas of animals with concurrent mycobacterial infection . CCHFV RNA and antigen were detected within the necrotic region and not the CD68+ macrophages/monocytes , which surrounds the granuloma . It is possible that CCHFV infected circulating monocytes and/or infected tissue macrophages were recruited to the sites of inflammation to participate in the inflammatory response and became entrapped within the granulomas . Local factors influencing the immune response within the granulomas may have prevented systemic re-introduction of virus while preventing clearance of the virus within the granulomatous milieu . Concurrent infection of CCHFV with mycobacterial infection has never been reported . These two pathogens share a significant amount of geographical overlap and our results suggest that co-infection is possible , and is probably more likely as human case numbers increase . For example , it was not until the unprecedented EBOV outbreak occurred in late 2013 resulting in more than 28 , 000 human infections that we began to recognize the high frequency of viral RNA persistence in immune-privileged sites or fluids [43] . The ability for viral RNA to persist has important public health implications not only due to the need to ensure the full recovery of survivors but also to decrease the risk of outbreak re-ignition caused by viral spread from apparently healthy survivors to naïve individuals . The public health burden of persistent RNA virus infection is most well recognized with HIV . M . tuberculosis and HIV co-infection in the host potentiate one another by accelerating the deterioration of immunological functions . Although the NHPs in this study co-infected with CCHFV and M . bovis did not succumb from acute disease , it is unknown what long-term sequela this co-infection might cause . We also do not know what effect this co-infection has on acute CCHF . No mortality was observed in this current study as opposed to the previously described study by Haddock et al [10] . The lack of mortality in our study does not seem to be related to latent mycobacterial infections , as animals with latent tuberculosis did not respond differently to CCHFV infection compared to those without latent tuberculosis . Future efforts at understanding the interaction between mycobacterial and CCHFV infection in NHP models will likely be very difficult to execute due to husbandry concerns in BSL-4 facilities . However , this interaction could be further explored using existing murine models as well as human clinical studies in regions of the world where both diseases are endemic . In summary , we compared the disease progression of two diverse strains of CCHFV in the newly described cynomolgus macaque model . Both the Hoti and Afg09 strains recapitulated many of the clinical features seen in human disease and those initially reported in the strain Hoti NHP model [10] . While both Hoti and Afg09 demonstrated a broadly similar disease course , we observed a more persistent viremia , higher fever , and longer window of elevated clinical scores in the Hoti-infected NHPs relative to the Afg09 group . It will be of considerable interest for future studies to examine additional strains of CCHFV , and ascertain how variable their disease profiles are in animal models . Furthermore , this model can be used to understand mechanisms of viral RNA persistence and its effect on the development of long-term sequela , including its interactions with other pathogens such as mycobacteria . The utility of the cynomolgus macaque model of CCHF will advance the development and evaluation of medical countermeasures against this emerging infectious disease .
This work was supported by an approved USAMRIID Institutional Animal Care and Use Committee ( IACUC ) animal research 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 . All steps were taken to enrich the welfare and to avoid the suffering of the animals in accordance with the “Weatherall report for the use of nonhuman primates” recommendations . Animals were housed in adjoining individual primate cages allowing social interactions , under controlled conditions of humidity , temperature , and light ( 12-hour light/12-hour dark cycles ) . Food and water were available ad libitum . Animals were monitored and fed commercial monkey chow , treats and fruit twice daily by trained personnel . Environmental enrichment consisted of commercial toys . Highly trained personnel completed all procedures under the oversight of an attending veterinarian and all invasive clinical procedures were performed while animals were anesthetized . NHPs were humanely euthanized by administration of greater than or equal to 6mg/kg Telazol until a surgical plane of anesthesia was achieved , terminally bled intracardiacly ( IC ) , and administered 0 . 3–0 . 4ml/kg pentobarbital-based euthanasia solution ( Fatal-Plus ) IC . CCHFV strain Kosova Hoti ( Hoti ) was originally isolated from the blood of a female fatal case during the epidemic in Kosovo in 2001 [19] . CCHFV strain Hoti was kindly provided by Dr . Tatjana Avšič - Županc ( University of Ljubljana ) and Dr . Heinz Feldmann and had been passaged 7 times in Vero E6 cells and 3 times in SW13 cells prior to receipt at USAMRIID [10] . CCHFV strain Afg09-2990 was derived from a fatal human case in a U . S . soldier in Afghanistan in 2009 [18] . It was obtained from the Bernhard Nocht Institute ( Hamburg , Germany ) , where it had been passaged a total of 3 times in Vero E6 cells since isolation . Both strains were amplified by one passage in Huh7 cells ( human liver cell line ) at USAMRIID prior to use in the current study . Huh7 cells were used because CCHFV is hepatotropic and these cells produce less interferon than many immortalized cell lines . However , CCHFV does not plaque on Huh7 cells , so titers of both stocks were determined by plaque assay on SW13 cells ( described below ) and confirmed negative for mycoplasma and endotoxin . Twelve healthy adult cynomologous macaques , 4 to 9 years of age , ranging in body weight from 3 . 4 to 7 . 9 kg were obtained from World Wide Primates . None of these NHPs were exposed to infectious pathogens in previous studies , and all were determined to be naïve for previous CCHFV exposure based on both VLP neutralization assay as well as an anti-CCHFV N IgG ELISA [44] . Telemetry transmitters ( ITS T2J-1-Y , Konigsberg Instruments , Inc . , Pasadena , CA ) were surgically implanted into all animals to monitor body temperatures . Animals were allowed to recover 28 days prior to virus exposure . Prior to virus exposure ( day -5 ) and until 28 days after CCHFV infection , body temperatures were recorded continuously using the Notocord-hem Evolution software platform ( Version 4 . 3 . 0 . 47 , Notocord Inc . , Newark , NJ ) . Temperature values from the telemetry data files were extracted into MS Excel workbooks as 30-min averages . Additionally , 60 min temperature averages were provided to study personnel during the in-life portion of the study to support assessment of euthanasia criteria . For each animal , telemetry data collected for 5 days prior to challenge was used as baseline data to provide the average and SD for each 30-min daily time period of a 24-hr day . Telemetry data collected during the challenge study period was compared against the correspondent baseline values and used as study telemetry data . Significant temperature elevations during the study , represented by temperature data outside +3 standard deviations of baseline values , were used to compute fever duration ( number of hours of significant temperature elevation ) and fever-hours ( sum of the significant temperature elevations ) . For the study design , the twelve animals were randomly divided into two groups ( n = 6/group ) where six animals per virus strain received a target dose of 6 log10 PFU of CCHFV ( actual dose was 6 . 6 log10 PFU of strain Hoti and 6 . 2 log10 PFU of strain Afg09 ) diluted in 1 ml phosphate buffered saline ( PBS ) by IV exposure in the forearm . After CCHFV exposure , all animals were monitored for temperature changes by telemetry , weight loss , survival , and clinical signs , and blood samples were collected on days -8 , 0–7 , 9 , 12 , 14 , and once a week thereafter for virological , hematological , immunological , and chemical analyses . Individual clinical sign scores ranged on a scale of 0–3 and included monitoring of responsiveness , biscuit/fruit consumption , condition of stool , temperature change from baseline , presence/absence of a rash , bleeding , lymphadenopathy , and dehydration . A cumulative score of greater than or equal to 10 would have reached euthanasia criteria . All animals were humanely euthanized 28 days after challenge , and tissues were collected for determination of viral titer and histopathology . Full necropsies and histological examination were performed by a board-certified veterinary pathologist . The following tissues were collected during necropsy: axillary , inguinal , submandibular , mesenteric and tracheobronchial lymph node; submandibular salivary gland; haired skin; brachial plexus; sciatic nerve; skeletal muscle; bone marrow ( femur ) ; eyes; brain; pituitary gland; spleen; adrenal gland; kidney; liver; stomach; duodenum; pancreas; jejunum; ileum; cecum; colon; testis/ovary; prostate gland/uterus; urinary bladder; tongue; tonsil; trachea; esophagus; thyroid gland; lung; thymus; and heart . All collected tissues were immersion-fixed in 10% neutral buffered formalin for at least 30 days . The tissues were trimmed and processed according to standard protocol [45] . Histology sections were cut at 5 to 6 μm on a rotary microtome , mounted on glass slides , and stained with hemotoxylin and eosin . Slides prepared from select tissues were stained using the American Master Tech brand Acid-Fast Bacteria Stain Kit according to the manufacturer’s instructions contained in the product insert . Briefly , tissue was deparaffinized , rinsed with alcohol , rinsed with water , immersed in carbol fuchsin followed by immersion in 1% acid alcohol followed by immersion in light green counterstain . The slide was then dehydrated with three successive alcohol changes , cleared using xylene then coverslipped . For immunohistochemical analysis , red chromogen immunohistochemistry was performed using the UltraVision Quanto Detection System ( Thermo Scientific ) according to the manufacturer’s instructions . Briefly , after formalin-fixed paraffin embedded ( FFPE ) tissue sections were deparaffinized using xylene and a series of ethanol washes , the sections were heated in citrate buffer ( pH 6 . 0 ) for 15 min to reverse formaldehyde crosslinks . After rinses with PBS ( pH 7 . 4 ) , the sections were blocked with CAS-Block ( Life technology ) containing 5% normal goat serum overnight at 4°C . Then the sections were incubated with rabbit anti-CCHFV N protein ( IBT , 1:2500 ) , rabbit anti-mycobacterium antibody ( Abcam , ab20832 , 1:2500 ) , or rabbit anti-mycobacterium tuberculosis Ag85B antibody ( Abcam , ab43019 , 1:500 ) for 1 hr at room temperature . After rinses with PBS , the sections were incubated with alkaline phosphatase ( AP ) -conjugated polymer at room temperature for 30 min and then incubated with a Fast Red substrate solution for 12 min at room temperature . Sections were then stained with hematoxylin , air-dried , and mounted . To detect CCHFV genomic RNA , ISH was performed using the RNAscope 2 . 5 HD RED kit ( Advanced Cell Diagnostics ) according to the manufacturer’s instructions . Briefly , an ISH probe targeting the fragment 631–2702 of CCHFV genome with GenBank accession number HM452306 . 1 was designed and synthesized by Advanced Cell Diagnostics . Tissue sections were deparaffinized with xylene , underwent a series of ethanol washes and peroxidase blocking , and were then heated in kit-provided antigen retrieval buffer and then digested by kit-provided proteinase . Sections were exposed to ISH target probe pairs and incubated at 40°C in a hybridization oven for 2 h . After rinsing , ISH signal was amplified using kit-provided Pre-amplifier and Amplifier conjugated to alkaline phosphatase and incubated with a Fast Red substrate solution for 10 min at room temperature . Sections were then stained with hematoxylin , air-dried , and mounted . For immunofluorescence staining , slides were deparaffinized and treated with 0 . 1% Sudan Black B to reduce autofluorescence , and then tissues were heated in citrate buffer , pH 6 . 0 ( Sigma-Aldrich ) , for 15 min to reverse formaldehyde cross-links . After rinses with PBS , sections were blocked overnight with PBS containing 5% normal goat serum ( Sigma-Aldrich ) at 4°C . Sections were then incubated with the following primary antibodies for 2 hrs at room temperature: rabbit anti-CCHFV N protein antibody ( IBT , 1:2500 ) , guinea pig anti-mycobacterium antibody ( GeneTex , 1:500 ) , rabbit anti-mycobacterium antibody ( Abcam , ab20832 , 1:2500 ) , rabbit anti-CD3 antibody ( Dako Agilent Pathology Solutions , 1:200 ) , and/or mouse anti-human CD68 antibody ( Dako Agilent Pathology Solutions , 1:200 ) . After rinsing in PBS , sections were incubated with secondary goat IgG Alexa Fluor 488-conjugated anti-rabbit antibodies and with goat IgG Alexa Fluor 561 or 647-conjugated anti-mouse , anti-rabbit , or anti-guinea pig antibodies ( Life Technologies , Carlsbad , CA ) for 1 hr at room temperature . Sections were cover-slipped using VECTASHIELD antifade mounting medium with DAPI ( Vector Laboratories , Burlingame , CA , USA ) . Images were captured on an LSM 880 Confocal Microscope ( Zeiss ) and processed using open-source ImageJ software ( National Institutes of Health , Bethesda ) . Whole blood was added to EDTA tubes for CBC determinations using a Hemavet hematological analyzer ( Drew Scientific , Dallas , TX ) according to manufacturer’s instructions . Serum was isolated using a gel-based serum separator ( Sarstedt , Numbrecht , Germany ) and was stored at -80°C for subsequent analysis . Blood chemistries were performed on serum with a Piccolo chemistry analyzer ( Abaxis , Union City , CA ) utilizing General Chemistry 13 detection discs according to manufacturer’s instructions . Serum viremia was determined by plaque assay on confluent monolayers of SW13 cells in 6 well plates . Briefly , 10-fold serial dilutions of sera were made in media and incubated on cells for 1 hr at 37°C/5% CO2 prior to the addition of a 1:1 mixture of 1% Seakem agarose and 2X Basal Medium Eagle with Earle's Salts ( EBME ) solution containing 2X EBME , 10% heat inactivated fetal bovine serum ( FBS-HI ) and 2% L-Glutamine . After solidification of the overlay , cells were then incubated for 48 hrs at 37°C/5% CO2 prior to the addition PBS with 5% Neutral Red ( Gibco ) for 2 hrs before plaque counting . Sera collected on days 0 , 3 , and 6 of study were analyzed using a MilliPlex NHP 23-plex chemokine array ( Millipore ) . Briefly , NHP sera was diluted 1:2 in assay buffer and was incubated on detection bead set along with reference standards for 20 hrs at 4°C . The analysis plate was subsequently processed according to manufacturer’s instructions . The samples were analyzed using a MAGPIX Analyzer ( Luminex Inc . , Austin , TX , USA ) . CCHFV VLPs based on the IbAr 10200 strain were produced and purified as previously described with the following modifications [22 , 27 , 44] . Supernatants from transfected cells were harvested at 48 , 72 , and 96 hrs post-transfection . After clarification , pooled supernatant was clarified through a 0 . 45 μm filter . Supernatant was concentrated through an Amicon centrifugal concentrator unit with a 100 kDa cut-off ( Millipore ) . Concentrate was then diluted 1:10 with virus resuspension buffer ( VRB ) before being pelleted through 20% sucrose . Final viral pellets were resuspended in 1/1000 volume of VRB relative to starting volume . VLP titer was assessed based on 50% tissue culture infectious dose ( TCID50 ) assay as previously described [22] . For the neutralization assay , day 28 sera was diluted in half-log increments prior to being mixed with CCHF VLPs . Samples were incubated for 1 hr at 37°C prior to being added to SW13 target cells and incubated for an additional 24 hrs at 37°C/5% CO2 . Data collection and 80% CCHF VLP neutralization titers were determined as previously described [22] . Sera from a human convalescent patient was used as a positive control for the assay . This system has previously been used for the evaluation of DNA vaccines in mice as well as performance of potential therapeutic monoclonal antibodies , and compares favorably with conventional PRNT assays [44] . Recombinant CCHFV N , produced in a baculovirus expression system as previously described ( 13 ) , and GN ( Native Antigen , Inc . ) were conjugated to magnetic microspheres using the Luminex xMAP antibody coupling kit ( Luminex ) according to the manufacturer’s instructions . Both CCHFV antigens were based on the IbAr 10200 isolate . Briefly , 100 μL of Magplex microspheres ( 12 . 5 × 106 microspheres/mL ) were washed three times using a magnetic microcentrifuge tube holder and resuspended with 480 μL of activation buffer . Then , 10 μL of both sulfo-NHS and EDC solutions were added to the resuspended microspheres . The tube was covered with aluminum foil and placed on a benchtop rotating mixer for 20 min . After surface activation with EDC , the microspheres were washed three times with activation buffer prior to adding either NP or GN antigen at a final concentration of 4 μg antigen/1 × 106 microspheres . The tube was again covered with aluminum foil and placed on a benchtop rotating mixer for 2 hr . After this coupling step , the microspheres were washed three times with wash buffer and resuspended in 100μL of wash buffer for further use . NP and GN were coupled to Magplex microsphere regions #78 and #35 ( Luminex ) , respectively , in order to facilitate multiplexing experiments . Beads were stored at 4°C until further use . Antigen coupled beads were mixed at a 1:1 ratio and were diluted in PBS with 0 . 02% Tween-20 ( PBST ) to 5 × 104 microspheres/mL and added to the wells of a Costar polystyrene 96-well plate at 50 μL per well ( 2500 microspheres of each antigen bead set/well ) . The plate was placed on a Luminex plate magnet , covered with foil , and microspheres were allowed to collect for 60 sec . While still attached to the magnet , the buffer was removed from the plate by shaking . Then , 50 μL of 1:100 diluted serum was added to appropriate wells and the plate was covered and incubated with shaking for 1 hr at room temperature ( RT ) . The plate was washed three times with 100 μL of PBST using the plate magnet to retain the Magplex microspheres in the wells and then 50 μL of a 1:100 dilution of goat anti-human IgM phycoerythrin conjugate ( Invitrogen ) or goat anti-human IgG phycoerythrin conjugate ( Sigma ) in PBST was added to the wells . The plate was covered and incubated with shaking for 1 hr at RT . After incubation , the plate was washed three times and the Magplex microspheres were resuspended in 100 μL of PBST for analysis on the M . AGPIX instrument . Data was evaluated as signal to noise , with noise being the average median fluorescence intensity ( MFI ) of each bead set in response to naïve sera . CCHFV nucleic acid sequences were identified in GenBank ( access date 18 April 2018 ) . Only sequences containing at least 75% of the each segment’s length were analyzed using CLC Genomics Workbench v . 10 . 1 . 2 . Amino acid sequences for the RdRp , the glycoprotein complex ( GPC ) , and the nucleoprotein ( N ) were generated from these sequences . Both the nucleic acid and amino acid sequences were aligned , and the relatedness of the different CCHFV isolates are shown with phylogenetic trees generated using the following settings: Neighbor-Joining with Jukes-Cantor distance measurement and 1000 bootstrap replicates . SAS version 9 . 1 . 3 ( SAS Institute , Inc . , Cary , NC ) was used to determine differences in the telemetry data mean time to fever onset by t-test and the mean viremia , weights , blood chemistry , hematology , cytokines , and antibody response by repeated measures mixed-model ANOVA . The day after challenge was used as a repeated time effect and baseline values as a time-independent covariate . Baseline values used were an average of each animals Day -8 and Day 0 values for each parameter . | CCHF is an emerging tick-borne viral disease that is endemic across much of Africa and Asia , and parts of Europe where its range and exposure risk to human populations is expanding . Tuberculosis threatens millions of lives world-wide and is the leading cause of death due to a bacterial pathogen . Concurrent mycobacterial infection with other infectious diseases has been described , but not for CCHFV despite the geographic overlap of these two pathogens . During our study we unexpectedly determined that some of the animals had latent tuberculosis and that CCHFV can persist within the granulomas . Furthermore , our study provides the first direct evidence that CCHFV can replicate and persist in the male genital tract , which has important implications for human sexual transmission . The ability of viral RNA to persist in immune-privileged sites or fluids has been described with increasing frequency for other emerging infectious diseases and can cause a burden on public health . This provides the impetus to utilize the model described here to better understand the mechanisms of CCHFV persistence and its effect on the development of long-term sequela . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"cytokines",
"granulomas",
"crimean-congo",
"hemorrhagic",
"fever",
"immunology",
"tropical",
"diseases",
"ver... | 2019 | Persistent Crimean-Congo hemorrhagic fever virus infection in the testes and within granulomas of non-human primates with latent tuberculosis |
In spite of the worldwide impact of diabetes on human health , the mechanisms behind glucose toxicity remain elusive . Here we show that C . elegans mutants lacking paqr-2 , the worm homolog of the adiponectin receptors AdipoR1/2 , or its newly identified functional partner iglr-2 , are glucose intolerant and die in the presence of as little as 20 mM glucose . Using FRAP ( Fluorescence Recovery After Photobleaching ) on living worms , we found that cultivation in the presence of glucose causes a decrease in membrane fluidity in paqr-2 and iglr-2 mutants and that genetic suppressors of this sensitivity act to restore membrane fluidity by promoting fatty acid desaturation . The essential roles of paqr-2 and iglr-2 in the presence of glucose are completely independent from daf-2 and daf-16 , the C . elegans homologs of the insulin receptor and its downstream target FoxO , respectively . Using bimolecular fluorescence complementation , we also show that PAQR-2 and IGLR-2 interact on plasma membranes and thus may act together as a fluidity sensor that controls membrane lipid composition .
Plasma glucose levels are maintained within a narrow range , and even moderately elevated levels are harmful [1] . In spite of the worldwide impact of diabetes on human health , the mechanisms behind glucose toxicity are not fully understood . Oxidative stress [2] , advanced glycation end ( AGE ) products [3] , lipotoxicity [4] and glucose flux through the hexosamine pathway [5] have been proposed as possible mechanisms , though none has received universal acceptance , suggesting a complex phenomenon . Indeed , and quoting from a relatively recent review: "… even as several studies have been carried out in the last 40 years , there is no unifying theory explaining the mechanism of the deleterious effects of glucose toxicity" [6] . We reasoned that glucose toxicity could perhaps be better understood by discovering how protective mechanisms operate . To this end , we studied C . elegans mutants lacking PAQR-2 , a homolog of the human proteins AdipoR1 and AdipoR2 that have seven transmembrane domains , are unrelated to G- protein coupled receptors , and have anti-diabetic functions [7–9] . We previously showed that PAQR-2 , like its mammalian homologs , regulates fatty acid ( FA ) metabolism: the paqr-2 mutant shows an abnormal FA composition and is synthetic lethal with mutations in genes that promote FA turnover and desaturation , such as sbp-1 and nhr-49 ( C . elegans homologs of SREBP and HNF4/PPARα [10–12] ) . Furthermore , the paqr-2 mutant phenotypes can be suppressed genetically by mutations that promote FA desaturation [13 , 14] . Increasing the relative abundance of unsaturated FAs in biological membranes increases their fluidity [15–17] , and this is likely the primary function of PAQR-2 . Our previous studies suggest that PAQR-2 regulates membrane homeostasis and acts by promoting fatty acid desaturation in phospholipids during cold adaptation [13 , 14 , 18] . PAQR-2 therefore appears to act as a eukaryotic equivalent to DesK , a bacterial 5-transmembrane domain protein that senses membrane fluidity and activates a FA desaturase in response to excessive membrane rigidity [19–22] . The present study adds three main findings: 1 ) We identify IGLR-2 as an essential PAQR-2 partner for maintaining membrane homeostasis; 2 ) We show that supplying glucose to C . elegans causes a lethal decrease in membrane fluidity unless PAQR-2 and IGLR-2 are functional; and 3 ) We identify two ways to suppress the glucose toxicity in the paqr-2 or iglr-2 mutants , namely genetic suppression by mutations that cause increased FA desaturation , and chemical suppression with mild detergents . Previous studies have documented a reduced life span when C . elegans is cultivated in the presence of glucose , and implicated the insulin signalling pathway , e . g . DAF-2 and DAF-16 , and FA desaturation , regulated by SBP-1 and MDT-15 , as important mitigators of long-term glucose toxicity [23–25] . In contrast , the present study shows that PAQR-2 and IGLR-2 are essential to prevent acute glucose toxicity , and that they do not act via the insulin signalling pathway but rather by maintaining membrane homeostasis , at least in part via FA desaturation .
The C . elegans paqr-2 mutant has several distinct phenotypes [13 , 18]: it is cold sensitive ( Fig 1A ) , has a withered tail tip ( Fig 1B ) , and an excess of saturated fatty acids ( SFAs; Fig 1C and S1 and S2 Tables ) consistent with reduced expression of the Δ9 desaturase fat-7 ( Fig 1D and 1E ) . We also found that the paqr-2 mutant is sugar intolerant , being especially sensitive to glucose: as little as 20 mM glucose is sufficient to cause complete growth arrest and lethality in paqr-2 mutant larvae ( Fig 1F and 1G and S1A Fig ) . More specifically , almost all paqr-2 L1 larvae transferred to culture plates containing 20 mM glucose arrest as L1s and gradually die over the following few days . The effect of glucose is mostly reversible within the first 3 hours , but less so after longer exposures ( S1B Fig ) . Note that Lee et al . independently observed that paqr-2 mutants are sensitive to glucose , which they mention in the supplementary materials of a recent publication , though no quantification was then provided [25] . Importantly , the sensitivity is specific to D-glucose—L-glucose has no effect—which rules out direct chemical or biophysical mechanisms , such as glycation , and implicates a biological , enzymatic and metabolic response to glucose and other potent sugars ( Fig 1H ) . The glucose sensitivity of the paqr-2 mutant is a particularly thought-provoking phenotype because the homologs of PAQR-2 , i . e . AdipoR1 and AdipoR2 , are also metabolic regulators important for glucose tolerance in mammals [7 , 26]: conservation of this function in C . elegans therefore strengthens the case for using this organism as a model to elucidate the signalling pathway for these receptors . To identify novel components of the paqr-2 pathway that are important for glucose tolerance , we sought mutants that have phenotypes identical to that of paqr-2 , and which therefore may affect genes that act together with or downstream of PAQR-2 . A forward genetics screen of over 80 000 mutagenized haploid genomes yielded 5 mutants with phenotypes identical to that of paqr-2: three were alleles of the gene iglr-2 and two were novel alleles of paqr-2 itself ( Fig 2A and S2 Fig and Table 1 ) . The iglr-2 mutant alleles are remarkably similar to paqr-2 in all assays tested: they exhibit the same cold sensitivity ( Fig 1A and S1C Fig ) , tail tip defect ( Fig 1B ) , excess of saturated fatty acid ( Fig 1C and S1D Fig , S1 and S2 Tables ) , decreased fat-7 expression ( Fig 1D and S1E Fig ) , sugar intolerance ( Fig 1F and S1A and S1F Fig ) , as well as reduced brood size ( S1G Fig ) and slow growth rate ( S1H Fig ) . This striking similarity in phenotypes suggests that iglr-2 and paqr-2 act together as a complex or act in a simple direct sequence , one being downstream of the other . One prediction from these models is that the double mutant should exhibit the same phenotypes as the single mutants . This is indeed the case ( Fig 1A , 1C , 1D and 1F , S1A , S1G and S1H Fig; and S1 and S2 Tables ) . In summary , genetic evidence suggests that iglr-2 and paqr-2 act in a mutually dependent way during cold adaptation , tail tip morphogenesis , regulation of fatty acid composition , and glucose tolerance . IGLR-2 is predicted to consist of an intracellular C-terminal domain , a single transmembrane domain , and an extracellular part with an immunoglobulin ( Ig ) -like domain and several leucine rich repeats ( LRRs ) [27] ( Fig 2A ) . The sequence and domain structure of IGLR-2 is related to that of nearly forty mammalian LRIG-type membrane proteins with a range of expression patterns and functions [28] . Some , such as LRIG1 regulate the activity and stability of growth factor receptors [29] , while others , such as AMIGO , facilitate the clustering and activity of voltage-gated channels [30] . The three iglr-2 alleles that we isolated are likely loss-of-function ( lof ) alleles: et34 and et38 introduce premature STOP codons while et37 replaces a neutral glycine with the acidic amino acid aspartate within the transmembrane domain . An IGLR-2::GFP translational reporter containing the same iglr-2 gene and flanking sequence that efficiently rescues the mutant shows reproducible expression only on the plasma membranes of gonad sheath cells ( S3 Fig ) . This is similar to the previously described expression profile of PAQR-2 , which is also most readily observed in the gonad sheath cells , though low expression is also seen in some neurons of the head , ventral nerve cord and tail [13] . This suggests that PAQR-2 and IGLR-2 may have functions within the gonad sheath cells , a tissue that can regulate the metabolism and aging of other tissues [31 , 32] . We used Bimolecular Fluorescence Complementation analysis ( BiFC ) to test whether PAQR-2 and IGLR-2 actually interacts with each other . BiFC is a powerful method to visualize protein interactions in vivo that relies on fusing two separate portions of the Venus yellow fluorescent protein to each putative protein partner: physical interaction between the partners brings the complementary fragments of the fluorescent protein in close proximity , allowing its assembly and fluorescence [33 , 34] . BiFC shows that PAQR-2 and IGLR-2 interact on cell membranes ( Fig 2B ) . The BiFC interaction between PAQR-2 and IGLR-2 is specific: BiFC revealed no interaction between IGLR-2 and PAQR-1 , which we used as a control because of its structural similarity to PAQR-2 and which produced no fluorescence besides the endogenous autofluorescent gut granules present in transgenic worms containing empty vectors ( Fig 2B ) . We conclude that PAQR-2 and IGLR-2 can form a complex on plasma membranes , and that this likely explains the genetic evidence for mutual dependence . One hypothesis to explain the mutual dependency of PAQR-2 and IGLR-2 is that one requires the other for membrane localization or stability . This was tested by expressing the translational reporter for paqr-2 in the iglr-2 mutant , and vice versa . Expression of the pIGLR-2::GFP reporter is the same in wild-type and paqr-2 mutant worms ( S3A Fig ) . IGLR-2 therefore does not depend on PAQR-2 for its expression or localization to the gonad sheath cell membranes . In contrast , there is a dramatic reduction in the levels and frequency of expression of the pPAQR-2::GFP reporter in the iglr-2 mutant background ( S3A Fig ) . This indicates that IGLR-2 is important for PAQR-2 localization in gonad sheath cell membranes . IGLR-2 may therefore facilitate expression , processing , transport or stability of PAQR-2 . One of the novel paqr-2 mutant alleles , paqr-2 ( et36 ) is a point mutation within the cytoplasmic domain , 30 amino acids from the first transmembrane domain , a region proposed to regulate the membrane localization of the mammalian homologs AdipoR1 and AdipoR2 [35–37] . Using a GFP translational reporter and the BiFC assay , we found that paqr-2 ( et36 ) has the same membrane localization and interaction with IGLR-2 as the wild-type allele , suggesting that et36 does not interfere with either process ( S3B Fig ) . paqr-2 and iglr-2 are essential for C . elegans survival in the presence of glucose . To pinpoint the mechanisms behind this glucose sensitivity , we screened a collection of mutations that we previously identified as suppressors of the paqr-2 cold sensitivity and tail tip defect [18] . These suppressors fall into three classes , and each were tested for their ability to suppress the glucose sensitivity in single and double paqr-2 and iglr-2 mutants . The first class of suppressors are those with loss-of-function ( lof ) mutations in genes encoding enzymes of the fatty acid beta-oxidation pathway ( ech-7 ( et6 ) and hacd-1 ( et12 ) ) [38] . These had no effect on the glucose sensitivity of the paqr-2 mutant ( Fig 3A ) . Mutations in acdh-11 , which acts upstream of ech-7 and hacd-1 during FA beta-oxidation , were recently shown to also act as paqr-2 suppressors [39] . The acdh-11 ( gk753061 ) lof allele did not suppress the glucose sensitivity of the paqr-2 mutant ( S4B Fig ) , though it did rescue the cold adaptation and tail tip defect ( S4C Fig ) . hacd-1 ( et12 ) , chosen as a representative of this class , also had no effect on the glucose sensitivity of the iglr-2 single or paqr-2 iglr-2 double mutants ( Fig 3B ) , but could readily suppress the cold sensitivity of the paqr-2 and iglr-2 single or double mutants ( S4D Fig ) . We conclude that mutations in the FA beta-oxidation pathway are not suppressors of the glucose sensitivity in the paqr-2 or iglr-2 mutants , though they can suppress other phenotypes . The second class of mutations examined are lof alleles of genes in the phosphatidylcholine/ethanolamine biosynthesis pathway ( sams-1 ( ok2946 ) , pcyt-1 ( et9 ) and cept-1 ( et10 and 11 ) ) , which are thought to act by indirectly promoting sbp-1 activity [12] . Of these , only cept-1 and pcyt-1 partially suppressed the glucose sensitivity of the paqr-2 mutant ( Fig 3A and 3C and S4A Fig ) . cept-1 ( et10 ) , chosen as a representative of this class , also partially suppressed the glucose sensitivity of the iglr-2 single or paqr-2 iglr-2 double mutants ( Fig 3B ) , and could readily suppress the cold sensitivity of the paqr-2 and iglr-2 single or double mutants ( S4D Fig ) . We conclude that mutations in the phosphatidylcholine/ethanolamine biosynthesis pathway are partial suppressors of the glucose sensitivity in the paqr-2 or iglr-2 mutants , and strong suppressors of their cold adaptation defect . Finally , the third class of paqr-2 suppressors are gain-of-function ( gof ) mutations in transcriptional regulators of metabolism , notably acting as activators of Δ9 desaturases and causing a significant increase in the proportion of unsaturated fatty acids ( UFAs ) in membrane phospholipids and triglycerides ( nhr-49 ( et7 , et8 and et13 ) , mdt-15 ( et14 ) , and the sbp-1 overexpression transgene epEx141 ) [10 , 11 , 18 , 40–42] . These mutations and the transgene conferred increased resistance to the paqr-2 mutant ( Fig 3A and 3C and S4A Fig ) , allowing growth and reproduction in the presence of 20 mM glucose . nhr-49 ( et8 ) chosen as a representative of this class , also strongly suppressed the glucose sensitivity of the iglr-2 single or paqr-2 iglr-2 double mutants ( Fig 3B ) , and could readily suppress the cold sensitivity of the paqr-2 and iglr-2 single or double mutants ( S4D Fig ) . Interestingly , lof mutations in mdt-15 and sbp-1 also cause a glucose tolerance defect , though not as severe as in the paqr-2 or iglr-2 mutants ( Fig 3D and S4E Fig ) , which suggests that mdt-15 and/or sbp-1 are among the downstream targets of paqr-2 and iglr-2 in response to glucose exposure . We conclude from these genetic interaction studies of paqr-2 suppressors that regulation of fatty acid metabolism by PAQR-2 and IGLR-2 , and specifically upregulation of Δ9 desaturases that are key regulators of membrane composition and turnover [38 , 43] , is a particularly important aspect of glucose adaptation in C . elegans . The insulin signalling pathway is an important part of the nutrient/glucose response in most organisms , and therefore an obvious candidate for contributing to the glucose sensitivity observed in the paqr-2 and iglr-2 mutants . DAF-2 is the C . elegans homolog of the insulin receptor and an important regulator of metabolism: DAF-2 signalling promotes growth and reproduction when food is available , while lack of DAF-2 signalling during starvation allows activation of the forkhead transcription factor DAF-16 , which promotes “dauer” development hence fat storage , stress resistance and longevity [44 , 45] . Suppression of DAF-16 accounts for the reduced lifespan of wild type worms grown on 20 mM glucose [23] . Importantly , the daf-2 and daf-16 mutants showed no glucose sensitivity in our short term , acute toxicity assay , nor did they enhance the sensitivity of the paqr-2 iglr-2 double mutant ( Fig 3E ) . The roles of PAQR-2 and IGLR-2 during glucose exposure are therefore unrelated to insulin signalling and represent an entirely new pathway essential for survival in the presence of glucose . As noted earlier , the best suppressors of the glucose sensitivity in the paqr-2 and iglr-2 mutants are those that most directly activate expression of Δ9 desaturases , and hence should mediate the largest increase in membrane fluidity [46] . This is further supported by the observation that the paqr-2 and iglr-2 mutants have an excess of saturated fatty acids in their membranes , as noted earlier ( Fig 1C and S1 and S2 Tables ) . We previously hypothesized that the primary function of PAQR-2 during cold adaptation is to maintain membrane fluidity by upregulating Δ9 desaturases and thus increase the proportion of unsaturated phospholipids in membranes [18] . Could the same function explain glucose sensitivity in the paqr-2 and iglr-2 mutants ? In other words , could the availability of glucose promote the saturation of membrane lipids , hence membrane rigidity , to an extent that is incompatible with growth and survival unless compensatory desaturation , regulated by paqr-2 and iglr-2 , occurs ? To begin addressing this experimentally , we directly and quantitatively measured membrane fluidity in vivo using fluorescence recovery after photobleaching ( FRAP ) . In FRAP experiments , fluorescent molecules , such as GFP , are photobleached in a small area of the cell using a high-powered laser , and subsequent diffusion of non-bleached fluorescent molecules into the bleached area leads to recovery of fluorescence , which is recorded and quantified ( Fig 4A ) [47] . Using FRAP on transgenic worms expressing a prenylated GFP in intestinal membranes , we first confirmed that at the permissive temperature ( 20°C ) , the paqr-2 and iglr-2 mutants are indistinguishable from wild type , whereas at 15°C these mutant worms exhibit significantly reduced membrane fluidity ( S5A–S5C Fig ) . This provides strong experimental support for the hypothesis that paqr-2 and iglr-2 are regulators of membrane fluidity . Next , we examined membrane fluidity in the presence of glucose . Remarkably , the paqr-2 and iglr-2 mutant worms showed a clear decrease in membrane fluidity when cultivated in the presence of 20 mM glucose , which had no effect on wild-type worms ( Fig 4B–4D ) . Furthermore , the drop in fluidity was suppressed when the nhr-49 ( et8 ) gof allele was combined with the iglr-2 mutation; this is consistent with the nhr-49 ( et8 ) allele acting as an inducer of the Δ9 desaturases with the net effect being to normalize membrane fluidity ( Fig 4E ) . The increased membrane rigidity is likely due to changes in membrane composition: lipidomics profiling revealed a strong increase in the proportion of saturated FAs in phospholipids , especially among PE species , which are the most abundant phospholipids in C . elegans [48] , when the mutants are cultivated in the presence of glucose ( S5D Fig ) . Finally , and consistent with the hypothesis that glucose toxicity results from altered membrane fluidity , small amounts of a non-ionic detergent , which measurably increases membrane fluidity ( Fig 4F ) [49] , partially suppressed the glucose sensitivity of the paqr-2 and iglr-2 mutants ( Fig 4G ) . We showed earlier that PAQR-2 and IGLR-2 are most reproducibly expressed in the gonad sheath cells , where IGLR-2 is important for PAQR-2 membrane localization ( S3 Fig ) [13] . However , the FRAP results show that PAQR-2 and IGLR-2 regulate membrane fluidity in the intestine when cultivated in the cold or in the presence of glucose ( Fig 4B–4D and S5 Fig ) , and the lipidomics analysis of entire worms shows significant changes in the phospholipid composition of the mutant worms , especially when cultivated in the presence of glucose ( S5D Fig ) . Taken together , these results suggest that PAQR-2 and IGLR-2 may act cell non-autonomously to systemically regulate FA metabolism and membrane properties . We tested this hypothesis by performing a mosaic analysis . In C . elegans , transgenes are typically retained as multicopy extrachromosomal arrays that are not always segregated to both daughter cells during cell division , resulting in genetic mosaics [50] . Mosaic analysis has the merit that it makes no assumption about the specificity of putative tissue-specific promoters where weak expression in some tissues may go undetected . Similarly , it also does not rely on the visible expression pattern of a gene of interest based on reporter constructs that may also go undetected in weakly expressing tissues . For our study , we used the SUR-5GFP ( NLS ) reporter previously developed for this purpose [51] . This reporter is expressed in nearly all nuclei , as well as weakly in the cytoplasm of adult cells , being especially strong in intestine ( which can obscure expression in other cells ) . We scored iglr-2 mutant worms that grew from L1 to adults on 20 mM glucose while carrying an extrachromosomal array harboring SUR-5-GFP ( NLS ) together with a rescuing iglr-2 transgene . An initial survey of 150 such transgenic worms identified 6 that did not carry the transgene in intestinal cells ( Fig 4H and 4I ) . This demonstrates that IGLR-2 is not required in intestinal cells to permit resistance to glucose , but must instead have its essential function in some other tissue . Close inspection of 16 worms that grew into adults on 20 mM glucose while lacking intestinal expression revealed that iglr-2 is also definitely not required in the MS lineage , which produces the gonad sheath cells , nor in the C and D lineages , which produce mostly body wall muscles ( Table 2 ) . In contrast , descendants of ABa and ABp , and more specifically the hypodermis , were always positive for the transgene in glucose-tolerant worms . This strongly suggests that the hypodermis , or perhaps some neuron ( s ) of the AB lineage , is the essential site of iglr-2 activity for glucose tolerance . Note that this result does not exclude the possibility that PAQR-2 and IGLR-2 may also have important functions in the gonad sheath cells , though such functions would clearly not be essential for glucose tolerance .
We previously performed a paqr-2 suppressor screen that led to the isolation of 9 novel mutant alleles affecting 6 different genes that could suppress the cold sensitivity and tail tip defect in the paqr-2 mutant [18] . Among these , the most potent suppressors were three gof alleles in nhr-49 , and one gof allele of mdt-15 . Subsequent studies also revealed that a sbp-1 multi-copy transgene , which likely also acts as a gof allele , is also a potent paqr-2 suppressor . The common denominator that may functionally link nhr-49 , sbp-1 and mdt-15 with respect to membrane homeostasis is their effect on fatty acid metabolism , namely that all three activate the Δ9 desaturase genes in C . elegans . These same suppressors were also the most potent suppressors of the glucose sensitivity in the paqr-2 and iglr-2 single or double mutants , suggesting an essential requirement for desaturase activity when worms are cultivated in the presence of glucose . Lee et al . made a similar observation: they showed that addition of glucose to the culture plates causes increased saturated fat content in wild type C . elegans and that SBP-1 and MDT-15 protect against glucose toxicity on lifespan by promoting fatty acid desaturation [25] . Additionally , they implicated excess production of dihydroxyacetonephosphate , an intermediate metabolite in glycolysis , as a toxic molecule that causes short life span on a glucose-rich diet . It is important to note that Lee et al . studied the effects of rather high concentrations of glucose ( 2% , or 111 mM ) on the life span of wild-type or RNAi-treated worms , suggesting a more long-term toxicity in these conditions . In contrast , the paqr-2 or iglr-2 single or double mutants that were the focus of our study suffer much more rapidly from cultivation in the presence of smaller amounts of glucose , becoming developmentally arrested as L1s when cultivated in the presence of 20 mM glucose . This indicates a very direct and essential function , e . g . membrane homeostasis , for PAQR-2 and IGLR-2 upon cultivation in the presence of glucose , and that regulation of SBP-1 and MDT-15 may represent only a subset of the targets that are regulated by PAQR-2 and IGLR-2 . Our screen to isolate mutants phenotypically similar to paqr-2 yielded five alleles affecting only two genes , namely iglr-2 and paqr-2 . This suggests that it will be difficult to isolate more components of the paqr-2 pathway using this approach . IGLR-2 is related to the mammalian LRIG proteins , and is the first C . elegans member of that protein family to be characterized . In mammals , LRIG proteins vary in their expression patterns and functions [28] . Some , such as LRIG1 , 2 and 3 act as regulators of receptor tyrosine kinases , while others , such as the NLRRs , act as adhesion or signalling molecules [59 , 60] . Structurally , IGLR-2 is most similar to the mammalian AMIGO: both proteins have relatively few LRRs ( 5 and 7 , respectively ) , a single Ig domain and a relatively short cytoplasmic domain . Intriguingly , AMIGO acts as an anchor point around which several Kv2 . 1 channel complexes can cluster , which increases their activity [30] . IGLR-2 could play a similar role in facilitating PAQR-2 clustering and stability to promote its activity , with the additional refinement that the interaction may also be regulated by membrane fluidity . Dynamic regulation of membrane composition may be particularly important in an organism such as C . elegans where the majority of the membrane fatty acids are replaced within 24 hours [43] . While membrane turnover is slower in mammals [61–67] , it does occur constantly and , clearly , mechanisms must exist to monitor membrane properties and correspondingly adjust their composition for example in response to various diets . Virtually all cellular processes are in some fashion influenced by membranes , and regulating their composition is the primary mechanism to maintain optimal membrane properties [46 , 68–70] . Perhaps the best understood sensor and regulator of membrane properties is the bacterial DesKR two-component system , which includes the histidine kinase DesK , a membrane protein with five transmembrane domains and a cytoplasmic catalytic domain containing the dimerization , histidine phosphotransferase and ATP-binding domains [19–21] . DesK is a bifunctional enzyme: it acts as a phosphatase when unstimulated and as a kinase when stimulated by a reduction in membrane fluidity , thus regulating the activity of its cognate response regulator , DesR , itself a transcriptional regulator of Δ5-Des , a Δ5-desaturase gene . The activity of DesK is regulated by a conformational change: fluidity sensing involves a built-in structural instability near the N terminus of the first transmembrane domain that is buried in the lipid phase at low temperature but partially “buoy” to the aqueous phase at higher temperature with the thinning of the membrane , promoting the required conformational change [22] . Perhaps an aspect of PAQR-2 and/or IGLR-2 conformation is similarly regulated by membrane properties during cold adaptation or growth in the presence of glucose . Failure to correct membrane composition under these conditions , as in the paqr-2 or iglr-2 mutants , results in intolerable membrane rigidity . PAQR-2 and IGLR-2 likely act cell non-autonomously and systemically for glucose tolerance because: 1 ) Their common site of detectable expression is the gonad sheath cells , yet they regulate membrane fluidity in intestinal cells and phospholipid composition in whole worms; and 2 ) Mosaic analysis shows that IGLR-2 can prevent glucose toxicity when expressed in only a subset of cells , namely the hypodermal cells that are descendants of the ABa and ABp blast cells and are an important site of fat storage and metabolic regulation [71–74] . The expression levels of PAQR-2 and IGLR-2 are likely tightly controlled: unpublished efforts to drive paqr-2 expression from tissue-specific promoters have typically resulted in dead embryos , and it has also been quite difficult to generate transgenic animals carrying iglr-2 expressing transgenic arrays , except when using small amounts of plasmids in the microinjection mix . A low level of expression in hypodermal cells could therefore have gone undetected in our previous efforts to describe the expression pattern of these two genes using reporter constructs . Note that PAQR-2 and IGLR-2 may also have important functions in the gonad sheath cells , where they are both predominantly expressed and where PAQR-2 depends on IGLR-2 for efficient expression . Previous studies have shown that the gonad sheath cells can regulate metabolism in other tissues , including phospholipid composition [31 , 75] . It is therefore plausible that PAQR-2 and IGLR-2 act in these cells and/or the hypodermis to monitor and regulate membrane homeostasis systemically . Mouse mutants lacking either or both AdipoR1 and AdipoR2 are without obvious phenotypes when maintained under normal conditions but develop metabolic syndrome symptoms when challenged with high fat diets [76] . This is analogous to the C . elegans paqr-2 and iglr-2 mutants that exhibit no severe phenotype under normal conditions but exhibit severe defects when challenged with exogenous glucose . The AdipoR1 and AdipoR2 genes are widely expressed in mammals and it is likely that they regulate metabolism in many cells of the body , though their functions are best described in metabolically active tissues such as liver , skeletal muscle and adipose tissue [7] . It will be interesting to determine whether they too regulate membrane fluidity in mammalian cells . The relevance of our findings for the pathologies seen in diabetic patients clearly remains to be investigated . That elevated glucose may impair membrane properties also in humans is however supported by the increased rigidity of membranes in erythrocytes and other cell types associated with high blood glucose [77–80] , and reduced membrane fluidity is a proposed mechanism behind several of the cellular and vascular problems of diabetic patients [81] . It is also interesting to note that a decrease in membrane UFAs is a risk factor to develop overt diabetes [80] . In summary , we found that inclusion of glucose in the culture plates causes increased membrane rigidity in C . elegans lacking PAQR-2 or IGLR-2 , and that an important function of these proteins is to counter such an effect by promoting fatty acid desaturation .
The wild-type reference strain was the C . elegans Bristol variety strain , N2 . Unless otherwise stated , strains were obtained from the C . elegans Genetics Center ( CGC; MN , USA ) , and experiments were performed using the E . coli strain OP50 as food source , which was maintained by passaging either on NGM plates or liquid cultures in LB medium using standard protocols [82]; note that the effects of glucose reported here are even more pronounced when C . elegans is cultivated on fresh isolates of OP50 E . coli ( obtained from the C . elegans Genetics Center ) rather than multiply passaged OP50 . The paqr-2 ( tm3410 ) and iglr-2 ( et34 ) mutant alleles were used in most experiments and are simply referred to as the paqr-2 and iglr-2 mutants . The pPAQR-2::GFP construct [13] and the pfat-7::GFP ( rtIs30 ) carrying strain HA1842 ( a kind gift from Amy Walker ) [12] have been described elsewhere . acdh-11 ( gk753061 ) was a kind gift from Bob Horvitz [39] . Glucose plates were prepared by adding glucose ( 1M , sterile filtered ) into the cooled down NGM after autoclaving . For length measurement studies , synchronized L1s were plated onto glucose plates seeded with OP50 . Worms were mounted and photographed 72 h later and the length of 20–25 worms was measured using ImageJ [83] . Scoring of fertile adults on glucose plates was performed 96 h after plating the L1s ( n ≥ 100 ) . Two strategies were used to isolate novel mutants that are phenotypically similar to paqr-2 ( tm3410 ) among the F2 progeny of EMS-mutagenized worms . In the first strategy , we isolated mutants with a tail tip defect then searched among those for mutants that also had the cold sensitivity phenotype . Approximately 11 000 mutagenized haploid genomes were screened in this way , yielding 9 promising mutants ( alleles et34 , et35 , et37-et43 ) . In the second strategy , we isolated mutants with the cold sensitivity phenotype then searched among those for mutants that also had a tail tip defect . Approximately 70 000 mutagenized haploid genomes were screened in this way , yielding 1 mutant ( allele et36 ) . Each mutant was outcrossed 4–6 times prior to whole-genome sequencing , and a minimum of 10 times prior to careful phenotypic characterization . The genomes of the novel mutants outcrossed 4 or 6 times were sequenced to a depth of 25–40x and differences between the reference N2 genome and that of the mutants were identified as previously described [18] . For each novel mutant , one or two mutation clusters , i . e . small genomic areas containing several mutations , were identified , which is in accordance to previous reports . These candidate mutations were tested experimentally as described in the text . As a template to make pCE-IGLR2-VC155 a gene-cDNA hybrid retaining the first intron of iglr-2 was constructed in pUC19 using the following 5 fragments in a Gibson assembly: iglr-2 promoter ( primers: 5’- gacgttgtaaaacgacggccagtcctgcaggcaatgtccaaatccgaatccag-3’ and 5’- gctacgacgaaaaatacaaattttgcggccgctcgcatttattattgaattttttatg-3’ ) , beginning of iglr-2 gene until end of 2nd exon ( 5’-cataaaaaattcaataataaatgcgagcggccgcaaaatttgtatttttcgtcgtagc-3’ and 5’-cagttccattcagagattccaaatcaccatctcggagaattatatgttctagttgcggaaatg-3’ ) , iglr-2 cDNA from exon 3 to the last coding amino acid ( 5’- catttccgcaactagaacatataattctccgagatggtgatttggaatctctgaatggaactg-3’ and 5’- ttacttgtcatcgtcatccttgtaatccttgtcatcgtcatccttgtaatccttgtcatcgtcatccttgtaatctctcttttctggtggagaatctgg-3’ ) , iglr-2 3’UTR ( 5’-gagattacaaggatgacgatgacaaggattacaaggatgacgatgacaaggattacaaggatgacgatgacaagtaattctttaaatcattttgtttg-3’ and 5’-cacaggaaacagctatgaccatgattacgcccacgtggacaatgcttgaccgatcg-3’ ) and the pUC19 vector ( 5’-gctatcacagttccgatcggtcaagcattgtccacgtgggcgtaatcatggtcatagctgtttc-3’ and 5’- ctggattcggatttggacattgcctgcaggactggccgtcgttttacaacgtc-3’ ) . This plasmid was further used as template for amplification of the iglr-2 gene-cDNA hybrid ( 5’- agattacgctcgaaaatttgtatttttcgtcgtagctattct-3’ and 5’- cgccacctccgctcccgccacctcctctcttttctggtggagaatct-3’ ) that was cloned into the pCE-BiFC-VC155 vector[33] ( 5’-ggaggtggcgggagcggaggtggcgggagtgacaagcagaagaac-3’ and 5’- aattttcgagcgtaatctggaacatcgtatgggtacat-3’ ) using Gibson assembly ( NEB ) . As a template to make pCE-VN173-PAQR2 a gene-cDNA hybrid retaining the first intron of paqr-2 was constructed and assembled with pUC19 using 5 fragment Gibson assembly: paqr-2 promoter ( primers: 5’- gacgttgtaaaacgacggccagtcctgcagggtctagatggaatggcttgaggatctcgc-3’ and 5’- cttcagcgtagtctgggacgtcgtatgggtacgcggccgcctccattttgttaaagctgaattttag-3’ ) , beginning of paqr-2 gene until end of 2nd exon ( 5’- gcggccgcgtacccatacgacgtcccagactacgctgaagatgacgtggaatcggcaac-3’ and 5’- gataatattttctgcctctcggagcacatcagtaacactccgcaatgggctttgtagattactg-3’ ) , paqr-2 cDNA from exon 3 to the last coding amino acid ( 5’- cagtaatctacaaagcccattgcggagtgttactgatgtgctccgagaggcagaaaatattatc-3’ and 5’- tttaaaaataaaaaattggaaacaaatctacctcataaaccaacatccgccggtgtccagtc-3’ ) , paqr-2 3’UTR ( 5’- gactggacaccggcggatgttggtttatgaggtagatttgtttccaattttttatttttaaa-3’ and 5’- cacaggaaacagctatgaccatgattacgccctgaggagcaacaagtgaacaatgtgagagaac-3’ ) and the pUC19 vector ( 5’-gttctctcacattgttcacttgttgctcctcagggcgtaatcatggtcatagctgtttcctgtg-3’ and 5’-cctcaagccattccatctagaccctgcaggactggccgtcgttttacaacgtc-3’ ) . This plasmid was further used as template for amplification of the paqr-2 gene-cDNA hybrid ( 5’- ggtggcggaggttctggtggcggaggttctggtggcggaggttctgaggaagatgacgtggaatcggca-3’ and 5’- tcacttgtcatcgtcatccttgtaatccttgtcatcgtcatccttgtaatccttgtcatcgtcatccttgtaatctaaaccaacatccgccggtgt-3’ ) which was assembled with the VN173 fragment ( 5’- ggcccaccatggcatcaatggtgagcaagggcgagga-3’ and 5’- ctcagaacctccgccaccagaacctccgccaccagaacctccgccaccctcgatgttgtggcggat-3’ ) and the remaining pCE-BiFC-VN173 vector [33] ( 5’- gattacaaggatgacgatgacaaggattacaaggatgacgatgacaaggattacaaggatgacgatgacaagtgagcggccgcaggatcca-3’ and 5’-cattgatgccatggtgggcccgcgggtacaattgctagcca-3’ ) . The pCE-VN173-PAQR1 plasmid was constructed using Gibson assembly with 4 fragments: beginning of paqr-1 gene including 1st exon and intron ( 5’-ggtggcggaggttctggtggcggaggttctggtggcggaggttctaatccagatgaggtcaatcgag-3’ and 5’- gagtagaacacttcgattttgtcgccagtttttcttgcttccggatttttcacct-3’ ) , paqr-1 cDNA from exon 2 ( 5’- tcacttgtcatcgtcatccttgtaatccttgtcatcgtcatccttgtaatccttgtcatcgtcatccttgtaatctctaactggacattgttcgttcaga-3’ and 5’-aaaaactggcgacaaaatcgaagtgttctactcccgcaaaacaacggtcgt-3’ ) , VN173 ( 5’- ggcccaccatggcatcaatggtgagcaagggcgagga-3’ and 5’- ctcagaacctccgccaccagaacctccgccaccagaacctccgccaccctcgatgttgtggcggat-3’ ) and the remaining pCE-BiFC-VN173 vector[33] ( 5’- gattacaaggatgacgatgacaaggattacaaggatgacgatgacaaggattacaaggatgacgatgacaagtgagcggccgcaggatcca-3’ and 5’-cattgatgccatggtgggcccgcgggtacaattgctagcca-3’ ) . The different combinations of BiFC plasmids were injected into N2 worms at 15 ng/μl each , together with pRF4 ( rol-6 ) at 100 ng/μl as previously described [34] . Expression of the BiFC constructs were induced by heat shocks of 2 . 5 h and 1 . 5 h at 33°C , with 2 h recovery at 20°C in between . Scoring of fluorescence was preformed after 16 h of recovery at 20°C . The paqr-2 ( et36 ) constructs were made by modification of pPAQR-2::GFP and pCE-VN173-PAQR2 using the Q5 Site-Directed Mutagenesis Kit ( NEB ) with primers 5’-caaaataacgaatacctccgt-3’ and 5’-aagccattcgggtagagt-3’ . Samples were composed of synchronized L4 larvae ( one 9 cm diameter plate/sample ) grown on NGM or NGM containing 20 mM glucose overnight . Worms were washed 3 times with M9 , pelleted and stored at -80°C until analysis . For lipid extraction , the pellet was sonicated for 10 minutes in methanol and then extracted according to published methods [84] . Internal standards were added in the chloroform phase during the extraction . Lipid extracts were evaporated and reconstituted in chloroform:methanol [1:2] with 5 mM ammonium acetate . This solution was infused directly ( shotgun approach ) into a QTRAP 5500 mass spectrophotometer ( ABSciex , Toronto , Canada ) equipped with a Nanomate Triversa ( Advion Bioscience , Ithaca , NY ) as described previously [85] . Phospholipids were measured using multiple precursor ion scanning [86 , 87] . The data was evaluated using the LipidProfiler software [86] . FRAP experiments were carried out using a Zeiss LSM700inv laser scanning confocal microscope with a Plan-Apochromat 20X objective lens . The membranes of intestinal cells expressing the pGLO-1P::GFP-CAAX reporter [88] were photobleached over a circular region ( 7 pixels radius ) using 10 iterations of the 488 nm laser with 100% laser power transmission . Images were collected at a 12-bit intensity resolution over 512×512 pixels ( digital zoom 6X ) using a pixel dwell time of ∼1 μsec , and were all acquired under identical settings . The fluorescence recovery of the bleached region was calculated as follows . Firstly , all fluorescence values were adjusted to compensate for the slight and gradual bleaching caused by repetitive scanning and imaging . This was done by adjusting fluorescence values by the slope of the decreasing fluorescence in a reference non-photobleached region . In a next step , the lowest intensity value ( immediately after bleaching ) was identified and this value was subtracted from all intensities , thus setting the post-bleach fluorescence as zero . The average intensities of the five measurements that precede the bleaching were then determined , establishing a pre-bleach value; all intensities were normalized by dividing by that value . The average of the last five measurements ( assumed to approximate the plateau of recovery ) represent the maximum recovery and corresponds to the mobile fraction . The halftime of recovery is the time point where the fluorescence recovered to half of the maximum recovery . The data are expressed as means ± S . E . Experiments related to temperature ( 15–20°C ) were performed on L4 larvae grown overnight at two different temperatures . For the glucose experiments , L1s grown overnight with or without 20 mM glucose were used . For each strain , N>5 worms were immobilized using 100 mM levamisole prior to analysis . The plasmid pTG96 carrying the SUR-5GFP ( NLS ) [51] , a kind gift from Prof . Han , Boulder , Colorado , was co-injected into the gonad syncytium of wild-type worms at a concentration of 50 ng/μl together with 5 ng/μl of pIGLR-2 to establish a transgenic line . The extrachromosomal array was then crossed into the iglr-2 mutant background , and these transgenic worms were bleached and their eggs allowed to hatch overnight in M9 to produce synchronized L1s that were transferred to NGM plates containing 20 mM glucose . Worms that grew to into adults were scored 72 hours later . Generation of transgenic animals , self brood size assay , growth rate assay , 15°C growth assay , scoring of paqr-2 tail tip phenotype , RNAi feeding , and quantification of pfat-7::GFP expression were performed as previously described [18] . Error bars for worm length measurements show the standard error of the mean , and t-tests were used to identify significant differences between worm lengths . Error bars for the percentage of fertile adults and the frequency of the tail tip defect show the 95% confidence interval determined using Z-tests . | Using the nematode C . elegans as a model , we show that glucose decreases the fluidity of cellular membranes , which suggests a novel mechanism for the glucose toxicity observed in diabetics . We also show that the membrane proteins PAQR-2 and IGLR-2 are located together on cellular membranes and regulate fluidity in the presence of glucose . This is an important finding because PAQR-2 is a worm homolog of AdipoR1 and AdipoR2 , themselves important anti-diabetic proteins of which the cellular mechanism of action has been difficult to elucidate . Thus in one stroke our work may explain glucose toxicity , describes a cellular mechanism that counteracts it , helps clarify the anti-diabetic roles of AdipoR1/2 , and identifies IGLR-2 as a PAQR-2 co-receptor . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"invertebrates",
"carbohydrate",
"metabolism",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"gonads",
"chemical",
"compounds",
"pathology",
"and",
"laboratory",
"medicine",
"caenorhabditis",
"carbohydrates",
"glucose",
"metabolism",
"animals",
"organic... | 2016 | Caenorhabditis elegans PAQR-2 and IGLR-2 Protect against Glucose Toxicity by Modulating Membrane Lipid Composition |
Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious , partly due to uncertainty about emergency FMD vaccination . Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties . Here we calculate the expected value of resolving uncertainty about vaccine efficacy , time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK . If it were possible to resolve all uncertainty prior to the introduction of control , we could expect savings of £55 million in outbreak cost , 221 , 900 livestock culled and 4 . 3 days of outbreak duration . All vaccination strategies were found to be preferable to a culling only strategy . However , the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives . We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings , regardless of management objective . It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak , and this would enable decision makers to select the optimal control action via careful contingency planning .
During a new outbreak of an infectious disease , epidemiological models are generally utilised to inform policy decisions . However , such models are normally developed and parameterised using data from previous outbreaks . Whilst these models provide useful information , each novel crisis is likely to unfold in a unique way dependent on the factors particular to that epidemic . In the United Kingdom during 2001 there was a major epidemic of foot-and-mouth disease ( FMD ) , a highly infectious disease of cloven-hoofed animals caused by infection with the virus Aphthae epizooticae . The most relevant information about the 2001 FMD outbreak came from analysis of the dynamics of that particular outbreak as it occurred [1–3] . Between outbreaks , research can be focused on minimising future outbreak uncertainty . Value of information ( VOI ) analysis is a method that allows a decision maker to place a value on reducing the level of uncertainty , by measuring how much the expected outcomes from the decision could be improved if uncertainty could be reduced [4] . This allows for the identification of uncertainties that are important to management . Research to resolve those important uncertainties can then be prioritised . Value of information methods were initially developed in economic and process control settings in the 1960s , but have since been applied in health risk management [4] , natural resource management [5] and other fields . The application of value of information methods in infectious disease management has only recently been explored [6–8] . The importation of FMD into a previously disease-free nation has the potential to incur large economic losses owing to the export bans of products from FMD-susceptible animals . Therefore , control measures aim to balance achieving disease-free status as rapidly as possible ( which can be reinstated no sooner than 3 months after culling of the last infected animal ) with minimising livestock losses through culling [9] . The methods used to tackle the UK 2001 FMD outbreak included culling of all livestock on infected premises ( IPs ) ( those with confirmed cases of FMD ) as well as those farms thought to be at high risk of being infected , classified as dangerous contacts ( DCs ) . Proximity culling was also implemented , including culling of livestock on contiguous premises ( CPs ) ( those sharing a border with an IP ) and ring culling in certain parts of the country [10] . Around 7 million animals were slaughtered to try to prevent the spread of infection . The agricultural industry and related rural and tourism industries were affected with an estimated total cost to the UK economy of £8 billion [10] . Routine FMD vaccination is not permitted under EU legislation . Emergency vaccination may be used during an outbreak to control the spread of disease or to protect certain livestock but its use is contentious . Also , any previously FMD-free country introducing vaccination during an outbreak will be subject to a change in their OIE ( World Organisation for Animal Health ) FMD-free status and this can have serious repercussions for their export markets [11] . Owing to this , and to the fact that the limited resources available at the time were thought to be insufficient to have a significant effect , use of emergency vaccination was discussed during the 2001 epidemic but never implemented . Since 2001 , modelling work on the FMD outbreak has estimated that , had ring vaccination been implemented alongside culling of IPs and DCs , there would have been a decrease in the duration of the outbreak and the number of farms infected [12–14] . The choice of whether to implement FMD vaccination or not is hampered by uncertainty [10] . There will always be unresolvable uncertainty due to the stochasticity that results in differences between outbreaks of the same disease . However , epistemic uncertainty [15] , which encompasses scientific uncertainty about the structure of a model due to incomplete knowledge , can be reduced through research [16] . Resolving the important uncertainties leads to higher expected achievement of management objectives . Short-term learning , via adaptive management ( AM ) , may reduce epistemic uncertainty and lead to long-term improvements in management [5] . Previous work on FMD suggests that a temporally-static approach to management would result in severe strategies , such as culling of IPs , DCs and CPs , being optimal in high density farming regions [6] . However , an approach that uses adaptive management may allow for less severe culling strategies to be introduced initially , under certain conditions . Once uncertainty regarding disease spread had been resolved , additional culling would only be performed if necessary . Simulations of this adaptive strategy were found to result in a significant overall saving in average outbreak cost [6] . It is therefore crucial to quantify the VOI during the early stages of a disease outbreak in order to inform policy makers regarding how much they should invest in “learning” about how a disease is spreading and the resources available for control so that appropriate interventions can be chosen that will minimise the overall cost of the outbreak . This research considers the impact of resolving uncertainty surrounding emergency vaccination prior to an FMD epidemic in the UK . We investigated two different types of uncertainty associated with vaccination: 1 ) uncertainty surrounding emergency vaccine deployment in relation to the number of herds and total head of cattle for which there would be the capacity to vaccinate each day in the midst of an FMD outbreak; and 2 ) uncertainty concerning the efficacy of FMD vaccine as , despite some work on the effectiveness of vaccination in endemic countries [11 , 17] , there is still significant uncertainty regarding the time delay from vaccination to immunity and the efficacy of the vaccine at the herd level during an FMD outbreak [18] . Additionally , it is important to have a clearly defined management objective , as this may have an effect upon the choice of control strategy whilst also allowing for stakeholders and policymakers to be presented with performance information that is most relevant to them . In this paper , we establish the optimal vaccination strategy in the event of uncertainty regarding vaccine capacity and efficacy , whilst considering three alternative management objectives: minimising outbreak duration , minimising total head of livestock culled and minimising epidemic cost ( see methods section for a description of the cost function ) . We determine the optimal vaccination strategies in the presence of these uncertainties and explore the expected performance improvement of resolving these uncertainties prior to deployment .
Our model results indicated that an IPDC control strategy would result in an average of 7 . 96 million head of livestock culled ( 95% prediction interval 5 . 93–10 . 26 million head ) at a cost of £2 . 01 billion ( 95% prediction interval £1 . 55—£2 . 52 billion ) and a mean outbreak duration of 343 days ( 95% prediction interval 229–540 days ) . All vaccination strategies were found to perform better than IPDC culling alone under all combinations of vaccine assumptions and using any of the outcome measures ( Figs 1 and 2 , S1 Fig ) . Vaccination rings of 3km were found to result in the largest number of animals culled and outbreak cost regardless of the vaccination assumptions , unless vaccination capacity was only 20 , 000 doses per day . In that case , vaccination at 15km resulted in the largest epidemics ( Figs 1 and 2 ) . The optimal vaccination strategy was highly dependent upon the daily vaccination capacity for all outcome measures—as the number of doses increased , there was a preference for larger vaccination rings . However , there appears to be little dependence upon either the vaccine efficacy or the time delay to immunity . When daily vaccine capacity was high and vaccine efficacy was low , larger rings were preferred to minimize the number of livestock culled ( Fig 1 ) , whilst higher vaccine efficacies generally resulted in smaller rings being optimal for the same outcome measure . Results for the number of livestock culled ( Fig 1 ) and the outbreak cost ( Fig 2 ) follow similar trends because livestock culled was a function of the cost calculation . The converse was true if the outcome measure of interest was minimising outbreak duration , with large rings being optimal when vaccine efficacy and daily capacity were high ( S1 Fig ) . These results highlight the necessity to clearly define the objective of management when determining the control policy that should be implemented [18] . With equal probability weightings for each of the vaccination assumptions ( Table 1 ) , the control strategy yielding the worst expected performance in terms of the number of livestock culled and outbreak cost was 3km ring vaccination ( 5 . 18 million livestock culled or £1 , 167 million ) and that with the best expected performance was 7km ring vaccination ( 4 . 04 million livestock culled or £891 million average cost ) . Under equal probability weightings the EVPI was 221 , 900 head or £55 million ( 5 . 8% and 6 . 6% of the expected value in the face of uncertainty respectively ) . If outbreak duration is the measure of interest , then 10km vaccination is preferred , though the EVPI was only 4 . 3 days ( Table 1 ) . The results indicate that the optimal vaccination strategy is highly dependent upon the daily vaccination capacity . With this in mind , we calculated the EVPXI for all of the model parameters under each management objective ( S1–S9 Tables ) . Resolving uncertainty regarding the time delay to immunity resulted in no benefit in determining the optimal control policy ( S3 , S6 and S9 Tables ) , whilst resolving uncertainty in the vaccine efficacy resulted in modest gains ( 10 . 8% of the EVPI for outbreak duration ( S1 Table ) and 7 . 3% for livestock culled ( S4 Table ) ) . However , if one were able to resolve the uncertainty regarding the number of animals that could be vaccinated per day , this can result in significant benefits: 88 . 7% of the EVPI can be recovered when considering outbreak duration ( S2 Table ) , 89 . 7% for livestock culled ( S5 Table ) and 96 . 6% of the EVPI for epidemic cost ( S8 Table ) . This indicates that , prior to a new outbreak of FMD , it is crucial to determine the capacity for administering vaccination , as this can have a significant influence upon the ability to determine the vaccination radius that should be implemented around all infected farms . Finally , we investigated the predictions of the optimal vaccination radius as two of the three vaccination assumptions were fixed at their intermediate values , whilst the weights on the assumptions for the remaining parameter were varied ( Fig 3 ) . When vaccine efficacy was set to 70% and capacity was set to 35000 animals per day , 15km and 7km vaccination was optimal to minimise epidemic duration and cost respectively , regardless of the weighting on the three time delay assumptions ( Fig 3 , left column , top and bottom panels ) . However , if we were interested in minimising the number of livestock culled , 7km vaccination was optimal unless the weight of belief on a 2 day delay was high , in which case 10km vaccination was optimal ( Fig 3 , left column , middle panel ) . A similar result was found when varying the weights on vaccine efficacy , with time delay fixed at 4 days and capacity at 35000 animals per day . Vaccination at 15km and 7km was again optimal for minimising outbreak duration and cost respectively ( Fig 3 , middle column , top and bottom panels ) . However , 7km vaccination was optimal for minimising the number of livestock culled , unless the weighting on 50% efficacy was high . In that case , 10km vaccination was again optimal ( Fig 3 , middle column middle panel ) . An alternative to fixing two of the three vaccination criteria to their intermediate value is averaging ( calculating the mean ) across the range of values for these criteria . The results of this analysis are shown in S2 Fig . We find qualitatively similar results in this case , indicating that resolving uncertainty in the number of doses that can be administered per day is key to determining the optimal vaccination strategy . We saw dramatically different results when we varied the weights on the daily vaccination capacity ( fixing time delay to 4 days and efficacy to 70% ) . In this case , large vaccination radii were found to be optimal when the weighting on the largest capacity , 50000 animals per day , was high , for all management objectives ( Fig 3 , right column ) . As the weight of belief on the lowest capacity increased , smaller vaccination radii become optimal . As more doses are available per day to carry out vaccination , it is possible to vaccinate in a larger area around each IP , thus creating a larger zone within which the susceptibility of the population is reduced . As the capacity decreases , the time taken to vaccinate farms in large rings will increase and this will also increase the risk of the virus escaping . This result adds support to the EVPXI results outlined above , that it is crucial to resolve uncertainty regarding vaccination capacity in order to determine the optimal control policy .
In the event of outbreaks of foot-and-mouth disease , vaccination is usually considered as part of a set of control strategies to reduce the impact of the epidemic . However , the adoption of vaccination as an active control measure is limited due to significant uncertainty regarding the effectiveness of vaccination in the field [19] and the resources available to carry out such a vaccination campaign . In the UK 2001 outbreak , the use of vaccination was contentious [10] and ultimately emergency FMD vaccination was not implemented . Despite these uncertainties , vaccination remains part of the UK FMD contingency plan and would be considered for future outbreaks . In this paper , we have quantified the costs associated with uncertainty regarding three key factors: time delay to immunity after vaccination , the efficacy of the vaccine and the number of animals that can be vaccinated per day . Our results show that if uncertainty could be resolved a priori , this would result in an expected decrease of 4 . 3 days of outbreak duration , 221 , 900 livestock culled and £55 million based on the 2001 FMD outbreak in the UK . These simulations also show that all simulated vaccination strategies are worth considering in the event of a future outbreak of FMD in the UK as vaccination is expected to reduce the duration of an outbreak , the number of livestock culled and therefore the outbreak cost in comparison to IPDC culling alone . This is in agreement with previous work [12 , 13] . Using expected value of partial perfect information ( EVPXI ) analysis we established that there are minimal potential savings to be made through reducing uncertainty in the efficacy of FMD vaccination or the delay between vaccination and conferral of immunity . However , there are larger potential savings to be made by resolving the uncertainty surrounding the daily vaccination capacity within the UK during an FMD epidemic . A clear understanding of daily capacity would also allow policy makers to make more informed decisions regarding the size of the vaccination ring that should be implemented . If there is confidence that vaccination capacity is low ( 20 , 000 doses per day ) , smaller vaccination rings of 5 or 7km are preferential . In contrast , if there is confidence that vaccination capacity is high ( 50 , 000 doses per day ) then there are the resources to rapidly vaccinate larger areas and 10 or 15km vaccination rings become the better strategy for minimising epidemic impact . By resolving the uncertainty surrounding vaccination capacity , we calculate that the majority of the EVPI ( >85% ) could be returned regardless of the management objective of interest . Such a result is relevant to outbreak control , because resolving the biological uncertainty surrounding FMD vaccination is likely to be expensive as it would involve extensive vaccine testing in livestock . Even with such research , there would likely still be unresolved uncertainty as vaccines may vary in efficacy dependent on factors such as the serotype of FMD that has caused the outbreak and the brand of vaccine that is being used . Such a result is also useful prior to an outbreak . It is relatively straightforward to resolve the uncertainty surrounding vaccine capacity within the UK through outbreak planning . Furthermore , our results show that higher capacity is generally better for all objectives . Contingency planning prior to an outbreak allows policy makers an opportunity to prepare and ensure sufficient capacity . In a real world situation , there may be the resources to vaccinate a known number of livestock daily but this may not be achievable depending on the spatial deployment of vaccination teams in comparison to the dynamics of the outbreak . For example , there may be a large difference between planned capacity and realised capacity if the outbreak is dispersed rather than localised . The model used to run these simulations did not take this partial controllability issue into account . Alternative FMD simulation models such as AusSpread [20] take local resource limitations into account and including this within the Warwick model would give greater confidence in the accuracy of those results on a local level . It would also allow for other resource-limited factors to be considered , such as disposal capacity of culled carcasses . These simulations focused only on the uncertainty surrounding FMD vaccination as a control strategy whereas , in reality , there are many different uncertainties in an outbreak situation [6] . To conduct an EVPI analysis requires placing a belief weighting on each of these uncertainties , which may not be simple to do in practice . This could be improved if there was more knowledge about the different uncertainties that were considered . For example , knowing more about the range and belief weightings of potential vaccination capacities that could be available in a future FMD epidemic would allow for more accurate outbreak planning . The use of the cost function in this paper is a simplification of the real economic costs associated with an outbreak . Our main aim in including a cost function was to be able to represent the relative costs of culling compared with vaccination . As compensation costs for culling of livestock ( cattle in particular ) are generally much higher than costs associated with vaccination , strategies that include significant levels of vaccination may actually be more economical than strategies that involve culling of livestock alone . The vaccination cost estimation was based only on calculations of emergency vaccination cost for herds in the US [21] as there is a dearth of published data in this area . The culling costs were taken from within the compensation range reported from the 2001 outbreak as detailed extensively in the Lessons to be Learned Inquiry [10] . Both were only designed to be representative values and these would need to be updated in a real outbreak scenario . There are also many other economic costs related to an FMD outbreak , such as those arising from export bans and losses to tourism . A more comprehensive cost function could be developed to take into account the wider costs of an FMD epidemic , in particular the economic cost associated with longer export bans as soon as livestock are vaccinated against FMD and the more local impact on businesses in FMD affected areas throughout the duration of the outbreak . However , regardless of the measure used to determine management success , the same conclusion is reached , that resolving uncertainty regarding vaccine capacity is critical in determining the optimal control policy . In conclusion , these results indicate that emergency vaccination is an important control action to consider during an FMD outbreak situation despite the uncertainty surrounding vaccine behaviour . We show that the level of vaccine efficacy and the time delay to immunity has relatively little importance on the EVPI and the optimal control strategy . Therefore , whilst better information regarding efficacy and time delay will provide more accurate predictions of the number of farms and animals infected , more knowledge in these areas is not vital in order for policy makers and stakeholders to make decisions about the use of vaccination as a control policy . Reliable information on vaccination capacity should be obtained as soon as possible during an outbreak or , better yet , enhanced through contingency planning prior to an outbreak . This approach can also be employed to address similar issues for emergency vaccination campaigns for other diseases .
The Warwick FMD model was used to simulate several control measures under a range of scenarios across different levels of uncertainty surrounding vaccination assumptions [1] . This stochastic , fully spatial , premises-based model was developed at the University of Cambridge during the 2001 UK FMD epidemic . Since 2003 it has continued to be developed at the University of Warwick and has been widely used for investigating culling and vaccination strategies during outbreak scenarios [1 , 13 , 22–24] . See S1 Appendix for further detail on the Warwick FMD model . Vaccination does not confer immediate nor complete immunity . In an outbreak scenario , vaccine effectiveness and delay between vaccination and immunity may have an important effect on how useful an emergency vaccination response will be . Equally , during an epidemic , resource limitation may restrict how many doses of vaccination can be delivered daily . The Warwick model was adapted to investigate each of these possibilities . Previous work suggests that vaccine efficacy during an FMD outbreak can range from 60–85% depending upon the serotype of the virus and the vaccine used [17 , 25] . In this paper , in order to capture this uncertainty , we considered the possibility that vaccination confers 90% , 70% or 50% immunity . It was assumed that on vaccinated farms the proportion of cattle for which the vaccine was effective became completely immune and the remaining proportion stayed totally susceptible and were capable of infection by , and transmission of , the virus . In other words , for a model with 90% vaccine efficacy , vaccinated farms were assumed to have the same susceptibility and transmissibility as an unvaccinated farm with 10% of the number of cattle . There is also uncertainty about the time delay between vaccination and the conferral of immunity . Previous work shows that levels of virus neutralising antibodies rise rapidly between 2 and 6 days after vaccination [26] . Therefore , we considered the possibility of a 2 , 4 or 6 day delay between vaccination and the conferral of immunity . We assumed that during the delay time the vaccinated animals would be completely susceptible to FMD ( and also fully capable of transmission ) , although this is a somewhat conservative estimate as immunity should build up over this time . After the delay period , the protected cattle were assumed to be completely immune and unable to transmit the virus . The European Union FMD vaccine bank holds substantial supplies of vaccine , although in the midst of an outbreak it may not be possible for all identified animals to be vaccinated each day owing to resource limitations [27] . As Defra's ( Department for Environment Food and Rural Affairs ) daily expected capacity of emergency vaccination in the aftermath of the 2001 FMD outbreak was thought to be around 35 000 doses per day [13] , we considered the possibility of emergency vaccination capacity of 20 000 , 35 000 and 50 000 doses per day . All combinations of these three parameters ( vaccine efficacy , delay and capacity ) were considered , giving 27 sets of assumptions regarding vaccination in total . We ran simulations using the FMD model to determine the effectiveness of ring vaccination for a range of ring sizes , in order to determine the optimal vaccination radius that should be introduced in the presence of uncertainty . In the event of ring vaccination being implemented , all farms within a given radius of an IP would be vaccinated , whilst IP and DC culling would also be carried out . Vaccination rings of 3km , 5km , 7km , 10km and 15km were considered . Vaccination in the model takes place firstly in the order in which IPs are reported and then from the outside of each ring moving in towards the centre . Alternative prioritisations for ring vaccination have been investigated elsewhere [13] . For each possible scenario , as well as a control scenario of IP and DC culling only ( IPDC ) , 2000 simulations were conducted using the same state of the outbreak as that on the date that movement restrictions were introduced during the 2001 UK epidemic ( 23rd February 2001 ) . In the event of an outbreak of infectious disease , policy makers will make a control decision based upon a set of management objectives . Dependent upon the outbreak scenario , these objectives may range from minimising the duration of the epidemic , the total number of individuals infected or the total economic cost of an outbreak . With this in mind , we have considered three different management objectives , with the caveat that true objectives in the event of an outbreak may be more complex than those investigated here . The first objective we considered was to minimise the duration of the epidemic ( in days ) . This is likely to lead to severe culling strategies being preferred in order to quickly ‘stamp out’ the epidemic but this also causes large livestock losses . Therefore , our second objective of interest was minimising total livestock culled . In this scenario , policy makers may be more likely to favour mass vaccination strategies . However , costs are often an important factor when deciding on control strategies so for the final management objective we considered minimising outbreak cost , focusing on the costs of culling and vaccinating livestock . We used a previously developed cost function designed to measure the cost of culling livestock [5] and included a term for the cost of vaccination . The estimate of the cost of the control measures was calculated using: C=1000Mculled , cattle+100Mculled , sheep+20Mvaccinated Here , C is the cost in pounds sterling , Mculled , cattle is the total number of cattle culled , Mculled , sheep is the total number of sheep culled and Mvaccinated is the total number of cattle vaccinated in the simulation models . From the 2001 Lessons to be Learned Inquiry [10] , compensation costs for culled cattle ranged from £150 to £1100 , whilst compensation costs for sheep ranged from £32 to £150 . In line with previous work [5] , we estimated the average compensation costs to be £1000 per culled cow and £100 per culled sheep , which represents an intermediate value in the compensation cost range from 2001 . Should vaccination be implemented in the UK , it is likely to be only targeted at cattle owing to the high values associated with cattle herds [13] . We therefore base our vaccination costs on an estimate of the cost of emergency FMD vaccination of small herds of cattle in the United States [21] , with an average estimated cost of £20 per vaccinated animal . Whilst we accept that the actual cost associated with livestock epidemics is more complex than that stated here , our aim in this paper is not to determine the actual ‘best’ vaccination policy to implement for a livestock disease outbreak , but to understand the impact of uncertainty in control actions upon a model’s ability to provide policy recommendations . If all parameter combinations generate model results that are in agreement about the optimal action , then the decision can be made without further analysis . When there is disagreement under varying assumptions , the expected value of perfect information ( EVPI ) can calculate the theoretical maximum achievable benefit of resolving uncertainty . The EVPI is the difference between the expected value with uncertainty ( the action with the best weighted average over all parameter combinations ) and the expected value without uncertainty ( the weighted average of the optimum outcome over all parameter combinations ) , and is calculated as: EVPI=Es[minaV ( a , s ) ]−minaEs[V ( a , s ) ] where a is the control action taken , s is the parameter combination , V ( a , s ) is the value of action a under parameter combination s , and min is the minimum over all potential actions for the chosen value function of interest ( livestock culled , cost or outbreak duration ) [5 , 6] . The reader should note , especially when comparing to other texts , that here all values are expected to be minimized and hence the EVPI will be a negative value ( if the operator was a maximum , EVPI would be a positive value ) . EVPI analyses were conducted using either outbreak duration , livestock culled or cost as the management objective of interest , and assuming equal belief weightings for each outcome . The expected value of partial perfect information ( EVPXI ) can be used to identify how much each individual parameter contributes to the overall decision problem [5] . It is calculated as: EVPXI ( si ) =Esi[minaEsic[V ( a , si , sic ) ]]−minaEsi , sic[V ( a , si , sic ) ] where si is a subset of parameter combinations and sic is its complement [5 , 6] . We calculated EVPXI for each of the three parameters in turn whilst there remained uncertainty surrounding the other two parameters . This allowed us to identify the expected value of completely resolving uncertainty for that particular parameter . The optimal control strategy was also calculated across a range of different belief weightings for each of the three management objectives . We considered each parameter independently , by changing that parameter but keeping the other two fixed at the middle value of the three under consideration ( e . g . for vaccine efficacy we consider 50%/70%/90% whilst delay remains at 4 days and capacity is fixed at 35 , 000 doses per day ) . These middle values were chosen as they were judged to be closest to the true values based on the existing literature [13 , 17 , 25 , 26] . We also calculated the average optimal control strategy by changing one parameter and taking the average results of all the model simulations ( e . g . for vaccine efficacy we calculated the mean of all the results for varying time delay to immunity and vaccine capacity as the weight of belief regarding vaccine efficacy varies ) . | In the UK during 2001 there was an outbreak of foot-and-mouth disease ( FMD ) which cost the economy an estimated £8 billion and led to the culling of approximately 7 million livestock . The main methods used to control the epidemic were movement bans and culling of infected and high-risk livestock . FMD vaccines were available but not used because of concerns about their effectiveness and how their use would affect the UK’s disease-free status . Using the Warwick FMD model , we ran simulations of FMD outbreaks in the UK including ring vaccination as a method of outbreak control with varying levels of vaccine efficacy , time delay between vaccination and conferral of immunity , and vaccination capacity . We applied value of information analysis to these results and found that the most important factor in determining the optimal vaccination strategy was knowledge of the vaccination capacity . In contrast , vaccine efficacy and delay between vaccination and immunity were relatively unimportant from a decision making perspective . This work could inform contingency planning that would lead to cost savings in the event of a future FMD outbreak and could also be applied to other infectious diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"livestock",
"medicine",
"and",
"health",
"sciences",
"animal",
"diseases",
"foot",
"and",
"mouth",
"disease",
"ruminants",
"immunology",
"vertebrates",
"animals",
"mammals",
"vaccines",
"preventive",
"medicine",
"infectious",
"disease",
"control",
"vaccination",
"and",... | 2017 | Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns |
Japanese encephalitis ( JE ) is major emerging neurologic disease caused by JE virus . To date , the impact of TLR molecules on JE progression has not been addressed . Here , we determined whether each TLR modulates JE , using several TLR-deficient mouse strains ( TLR2 , TLR3 , TLR4 , TLR7 , TLR9 ) . Surprisingly , among the tested TLR-deficient mice there were contrasting results in TLR3−/− and TLR4−/− mice , i . e . TLR3−/− mice were highly susceptible to JE , whereas TLR4−/− mice showed enhanced resistance to JE . TLR3 ablation induced severe CNS inflammation characterized by early infiltration of inflammatory CD11b+Ly-6Chigh monocytes along with profoundly increased viral burden , proinflammatory cytokine/chemokine expression as well as BBB permeability . In contrast , TLR4−/− mice showed mild CNS inflammation manifested by reduced viral burden , leukocyte infiltration and proinflammatory cytokine expression . Interestingly , TLR4 ablation provided potent in vivo systemic type I IFN innate response , as well as ex vivo type I IFN production associated with strong induction of antiviral PRRs ( RIG-I , MDA5 ) , transcription factors ( IRF-3 , IRF-7 ) , and IFN-dependent ( PKR , Oas1 , Mx ) and independent ISGs ( ISG49 , ISG54 , ISG56 ) by alternative activation of IRF3 and NF-κB in myeloid-derived DCs and macrophages , as compared to TLR3−/− myeloid-derived cells which were more permissive to viral replication through impaired type I IFN innate response . TLR4 ablation also appeared to mount an enhanced type I IFN innate and humoral , CD4+ and CD8+ T cell responses , which were mediated by altered immune cell populations ( increased number of plasmacytoid DCs and NK cells , reduced CD11b+Ly-6Chigh monocytes ) and CD4+Foxp3+ Treg number in lymphoid tissue . Thus , potent type I IFN innate and adaptive immune responses in the absence of TLR4 were closely coupled with reduced JE lethality . Collectively , these results suggest that a balanced triggering of TLR signal array by viral components during JE progression could be responsible for determining disease outcome through regulating negative and positive factors .
Due to rapid changes in climate and demography , vector-transmitted arboviral diseases pose an increasing threat to global health and welfare [1]–[3] . Among the most severe arboviral infections known to affect the human race are those caused by members of the Flavivirus genus of the Flaviviridae . As such , flaviviruses , including Japanese encephalitis ( JE ) , West Nile ( WN ) , dengue , and tick-borne encephalitis virus ( TBEV ) , are major emerging human pathogens , affecting millions of individuals worldwide . In addition , neurological disease frequently occurs upon infection with emerging flaviviruses , such as JEV , WNV , and TBEV [1]–[3] . Among neurotrophic flaviviruses , JEV is the most prevalent cause of viral encephalitis in the world , with approximately 67 , 900 cases reported annually [4] . Of these cases , about 25–30% are fatal and 50% result in permanent neuropsychiatric sequelae [4] , for which JE is considered to be more fatal than West Nile encephalitis resulting in a fatality of 3–5% ( 1 , 100 death/29 , 000 symptomatic infection ) [5] . Indeed , more than 60% of the world's population inhabit JE endemic areas which include eastern and southern Asia , and the virus is currently spreading to previously unaffected regions , such as Indonesia , Pakistan , and the northern area of Australia [6] , [7] . Considerable progress in understanding the kinetics and mechanisms of JEV dissemination and pathogenesis has been made in murine models [1]–[3] . However , the molecular pathogenesis of JE still remains elusive . After peripheral amplification of the virus in dendritic cells ( DC ) and macrophages as primary target cells , the virus gains entry into the CNS through blood-brain barrier ( BBB ) . While JEV infects and kills neurons directly [8] , viral replication within microglia/glia and infiltrated monocytes leads to indirect neuronal killing via the secretion of pro-inflammatory cytokines ( such as IL-6 and TNF-α ) and soluble mediators which cause neuronal death [9] . Thus , it is believed that uncontrolled over-activation of microglia/glia and infiltrated monocytes during JE progression is one of the key factors in indirect neuronal cell death [9] . JEV-specific T cells and virus-neutralizing IgM and IgG are considered in part to play a role in the clearance of virus from peripheral lymphoid tissues , as well as from the CNS [7] . However , innate immune responses are considered to play a more crucial role in the early control of JEV infection due to delayed establishment of adaptive immunity , and may also be responsible for generating pathological levels of inflammation . Type I IFN gene expression and signaling are essential components of innate immune programs and control various viral infections , and thus may be potentially required for host control of JEV infection [10]–[13] . Studies in genetically deficient models suggest that type I IFN production after WNV infection is triggered by recognition of viral pathogenic-associated molecular patterns ( PAMPs ) through cytoplasmic helicases RIG-I and MDA5 as host PRRs [14]–[16] , and thus the ablation of these molecules , their downstream signaling molecules ( IPS-1 ) , or transcriptional activators ( IRF-3 and IRF-7 ) results in a greatly enhanced susceptibility to WNV infection [14]–[16] . However , type I IFN innate responses have also evolved through the recognition of membrane-bound cell-surface or intracellular Toll-like receptors ( TLRs ) [17] , [18] . While RIG-I and MDA5 helicases recognize single- and double-stranded RNA in the cytosol and signal through IPS-1 , TLRs on the cell surface or within endosomes recognize single- and double-stranded RNA and viral components , and subsequently transmit intracellular signals through adaptor molecules MyD88 and/or TRIF . The role of TLR signal pathway through MyD88 and/or TRIF in restricting flaviviral infection , as well as in modulating immune responses , remains less clear because of conflicting and intricate data [19]–[21] . Moreover , the impact of each TLR signal pathway on JE progression has not been addressed to date . We therefore became interested in identifying the key TLR molecule ( s ) which regulate JEV-induced neurological disease . TLRs function as intermediates by interacting with products of viral replication , and transmitting signals to a cascade of adaptors and kinases that ultimately lead to the activation of transcription of cytokines and type I IFN genes . TLR3 recruits the adaptor molecule TRIF to induce type I IFN gene via interactions with TRAF3 , TBK1 , and IKKε , which , in turn , activate the latent transcription factors IRF-3 and IRF-7 , whereas other TLRs associating with the adaptor protein MyD88 form a complex with TRAF6 , IRAK1 , and IRAK4 to activate kinases that regulate IRF-5 and IRF-7 . Notably , TLR4 signal pathway uses both adaptor molecules MyD88 and TRIF to initiate the production of cytokine and type I IFN proteins . In viral infection , four TLRs , including TLR3 , TLR7 , TLR8 and TLR9 , seem to play critical roles in the recognition of viral nucleic acid components , and TLR2 and TLR4 were shown to detect viral components such as envelope glycoproteins [22]–[26] . We have previously shown that JEV can modulate innate immune responses and subsequent adaptive responses in MyD88-dependent and independent pathways [27] , which indicate that JEV may be recognized by certain TLR signal pathways , thereby affecting the outcome of JEV-induced neurological diseases . Therefore , we aimed to determine whether each TLR signal pathway modulated neurological disease caused by JEV infection , using several TLR-deficient mice ( TLR2 , TLR3 , TLR4 , TLR7 , TLR9 ) . Surprisingly , among the tested TLR-deficient mouse strains we found a contrasting result in TLR3−/− and TLR4−/− mice , i . e . TLR3−/− mice were highly susceptible to JE , whereas TLR4−/− mice showed markedly enhanced resistance to JE . Subsequently , we investigated the pathologic feature , type I IFN innate and adaptive immunity of TLR3−/− and TLR4−/− mice during JE progression . TLR3−/− mice displayed severe neuroinflammatory reactions as well as enhanced BBB permeability by failure of the early control of viral replication , whereas TLR4−/− mice elicited the effective regulation of viral replication and subsequent inflammatory reaction by inducing potent type I IFN innate immune responses against JEV . Notably , our data revealed that TLR4 ablation provided potent type I IFN innate responses through enhanced induction of antiviral ISG genes by alternative activation of IRF-3 and NF-κB in myeloid-derived DCs and macrophages . Also , TLR4−/− mice showed an alteration of plasmacytoid DC subpopulation and CD4+Foxp3+ regulatory T cells , which were closely associated with enhanced type I IFN innate immune and JEV-specific CD4+ and CD8+ T cell responses . These results suggest that the balanced triggering of TLR array during JE progression plays a pivotal role in predicting the outcome of neurological disease .
It is believed that intracellular signaling through TLRs regulates host responses against various bacterial and viral infections . However , to date , the impact of TLR signaling on JEV-induced neuroinflammatory diseases and how this response is propagated and regulates in vivo innate and adaptive immunity have not been defined . To this end , we assessed the impact of each TLR molecule on JE , by evaluating the susceptibility of TLR2−/− , TLR3−/− , TLR4−/− , TLR7−/− , and TLR9−/− mice to JEV infection ( 1 . 4×107 pfu ) ( Figure S1 ) . The ablation of TLR2 , TLR7 , and TLR9 molecules did not significantly affect the progression of encephalitis caused by JEV . However , somewhat surprisingly , TLR3- and TLR4- triggered molecular signaling pathways were observed to induce a completely contrasting regulation of JE . While all TLR3−/− mice succumbed to neuroinflammatory diseases caused by JEV infection ( p = 0 . 0153 ) , TLR4−/− mice showed enhanced resistance to JE , compared to wild-type mice ( p = 0 . 0819 ) . This contrasting regulation of JE by TLR3 and TLR4 molecules was more apparent ( p = 0 . 0314 for TLR3 and p = 0 . 0342 for TLR4 ) , when we evaluated the susceptibility of TLR3−/− and TLR4−/− mice to neuroinflammatory diseases after infection with a higher dose of JEV ( 2 . 8×107 pfu ) ( Figure 1A ) . Also , the ablation of both TLR3 and TLR4 molecules induced a highly increased susceptibility to encephalitis caused by JEV infection ( p = 0 . 0122 ) . Likewise , TLR3−/− mice infected with JEV showed more rapid signs of neurological disorder starting from 3 days pi , whereas TLR4−/− mice showed delayed signs of neurological disorder with a lower frequency of occurrence , compared to wild-type mice ( Figure 1B ) . To further examine the contrasting roles of TLR3 and TLR4 molecules , we assessed viral burden within lymphoid and the CNS tissues ( Figure 1C ) . TLR3−/− mice were found to exhibit 100–1 , 000-fold elevated viral load in spleen , brain , and spinal cord , but TLR4−/− mice retained significantly lower viral loads with 10–100-fold decreased levels in the spleen , brain , and spinal cord , compared to those of wild-type mice . In addition , since two genetic backgrounds of mouse strains used for TLR3−/− and TLR4−/− mice could complicate the comparison of susceptibility to JE , we directly compared the susceptibility to JE between TLR3−/− mice and wild-type mice , using TLR3−/− mice derived from the same genetic background ( H-2b ) as TLR4−/− mice . As expected , all TLR3−/− ( H-2b ) mice succumbed to JE after infection with two different doses of JEV ( 1 . 4×107 and 2 . 8×107 pfu ) , while wild-type ( H-2b ) mice showed similar 50% and 70% mortality to wild-type mice of mouse strain ( H-2d ) used for TLR3−/− mice , respectively ( Figure S2A and B ) . This indicates that the genetic background of mouse strains used in this study did not affect the progression of neuroinflammation caused by JEV infection . Also , TLR3−/− ( H-2b ) mice showed faster neurological disorder and severely reduced body weight by JEV infection . Supportively , TLR3−/− ( H-2b ) mice retained higher viral burden within lymphoid and the CNS tissues ( Figure S2C ) . Collectively , these results clearly indicate that triggering signal pathways through TLR3 and TL4 molecules differentially affect the outcome of neuroinflammatory disease caused by JEV infection and in vivo viral replication . To further characterize the CNS inflammation caused by JEV infection , we assessed the infiltration of CD11b+Ly-6Chigh cells into the CNS , as it has been demonstrated that CD11b+Ly-6Chigh cells have properties of inflammatory monocytes [28] . Our results revealed that nearly identical percentage of CD11b+Gr-1high neutrophil was retained in the brain of TLR3−/− and wild-type mice 3 days following JEV infection , whereas a markedly higher frequency of infiltrated CD11b+Ly-6Chigh monocytes in TLR3−/− mice was observed with 10–20-fold increased levels 3 days after JEV infection , as compared to wild-type mice ( Figure 2A ) . However , there were no significant changes in the proportion of CD11b+Gr-1high neurophils and CD11b+Ly-6Chigh inflammatory monocytes infiltrated in the brain of TLR4−/− mice , following JEV infection . Also , the absolute number of inflammatory CD11b+Ly-6Chigh monocytes infiltrated in the brain of TLR3−/− mice increased 100–200-fold , whereas TLR4−/− mice showed no significant changes in the absolute number of infiltrated monocytes or neutrophils following JEV infection ( Figure 2B ) . To further determine whether the activation of infiltrated CD11b+Ly-6Chigh monocytes could be affected by the ablation of TLR3 and TLR4 molecules , we characterized the phenotypes of infiltrated CD11b+Ly-6Chigh monocytes . However , we found that there were no significant changes in phenotypic levels ( CD40 , CD80 , CD86 , MHC I , MHC II , F4/80 ) of brain infiltrated CD11b+Ly-6Chigh monocytes between TLR3−/− and TLR4−/− mice ( data not shown ) . It has been shown that microglia cells contribute to the pathogenesis of encephalitis caused by some neurotrophic viruses such as WNV [28] , [29] . Thus , triple-color staining ( CD11c/CD11b/CD45 ) was used to distinguish the resting and activated microglia . Based on the CNS myeloid cell classification of Ford et al . [30] , equivalent percentages and absolute numbers of resting microglia ( CD11bintCD45intCD11c− ) were observed in brains of TLR3−/− and TLR4−/− mice following JEV infection . However , the frequency and absolute number of activated microglia ( CD11bhighCD45highCD11c− ) were increased 4–5-fold in TLR3−/− mice ( Figure 2C and D ) . To confirm the effect of TLR3 and TLR4 molecules on patterns of leukocyte accumulation within the CNS , histological and confocal examinations were performed . Histological examination revealed that increased BBB permeability in JEV-infected TLR3−/− mice was associated with perivascular cuffing , while JEV infection of TLR4−/− mice elicited reduced numbers of infiltrating foci ( Figure 2E ) . Similarly , significantly higher numbers of infiltrated CD11b+ cells were detected in TLR3−/− mice by confocal microscopy , and a small subset of CD11b+ myeloid cells co-stained positive with JEV antigen ( Figure 2F ) . Taken together , these results demonstrate that TLR3-induced signal pathway is essential for the control of neuroinflammation caused by JEV infection , while TLR4 molecules may be dispensable to provide resistance to fatal encephalitis . In terms of severe neuroinflammation in TLR3−/− mice , the expression levels of cytokines and chemokines within the CNS can be required for further explain encephalitis , because encephalitis caused by neurotrophic viruses is indirectly derived from CNS degeneration caused by robust immunological responses , such as the uncontrolled secretion of cytokines and chemokines , and resultant activation of microglia and astrocytes [7]–[9] . Therefore , we examined the expression of cytokines and chemokines in inflammatory tissues . We found that JEV infection of TLR3−/− mice induced a highly enhanced expression of IL-6 and TNF-α in the CNS , including brain and spinal cord , whereas moderate changes in the expression of pro-inflammatory cytokines were observed in TLR4−/− mice ( Figure 3A and B ) . Also , the expression levels of chemokines including CCL2 , CCL3 , CCL4 , CCL5 , and CXCL10 , which are involved in the migration of leukocytes into the CNS , was increased 10–1 , 000-fold in the brain and spinal cord of TLR3−/− mice ( Figure 3C and D ) . To further characterize how TLR3 and TLR4 molecules modulate the inflammatory reaction to JEV infection , we measured the levels of systemic IL-6 in serum of JEV-infected mice at 4 and 6 days pi . A trend towards more rapid induction and increased levels of IL-6 were observed in serum of TLR3−/− mice compared to those of the wild-type mice ( Figure 3E ) . However , no detectable differences in serum TNF-α levels were observed in TLR3−/− or wild-type mice , since all samples clustered near the limit of detection by ELISA . Also , it was note worthy that TLR4 ablation induced no significant induction of systemic IL-6 and TNF-α . These results demonstrate that in the absence of TLR3 , but not TLR4 molecules , greater pro-inflammatory cytokine and chemokine responses are induced during JE progression . Since BBB integrity is known to be damaged by neurotrophic virus-induced inflammation , such as WNV infection [20] , [21] , we assessed whether the ablation of TLR3 and TLR4 molecules could modulate BBB permeability and , possibly , allow for the earlier entry of virus and leukocytes within the CNS . Changes in BBB integrity over time following JEV infection , as revealed by extravasated Evans blue dye , showed that JEV infection of TLR3−/− mice gave rise to increased BBB permeability 3 days pi ( Figure 4A ) . In contrast , TLR4−/− mice showed no significant change in BBB permeability following JEV infection . Supportively , the ablation of TLR3 molecules was found to induce increased BBB permeability by JEV infection , when the amount of extravasated Evans blue dye within the brain was measured by photometric analysis ( Figure 4B ) . Notably , TLR3−/− mice apparently retained increased amounts of extravasated Evans blue dye in the brain 3 days pi , compared to those of wild-type mice . This demonstrates that the ablation of TLR3 , but not TLR4 molecule , is able to regulate BBB integrity following JEV infection . TLR3−/− and TLR4−/− mice showed distinct viral burdens in the CNS , which were closely associated with lethality to JE . This phenotype could be due to differential dissemination from the periphery and/or an independent antiviral effect in the CNS . To test this , wild-type , TLR3−/− , and TLR4−/− mice were inoculated with 103 pfu of JEV directly into the cerebral cortex via the intracranial ( IC ) route , and viral burdens in sub-tissues of brain ( cortex , olfactory bulb , hippocampus , brain stem , cerebellum , and spinal cord ) were monitored ( Figure 5A–F ) . Wild-type as well as TLR3−/− and TLR4−/− mice showed rapid and complete mortality following IC infection of JEV , and there was no significant difference in the average survival time between wild-type and KO mice following IC infection of JEV ( data not shown ) . Interestingly , TLR3−/− and TLR4−/− mice showed slightly lower levels of median viral burden in several sub-tissues of the brain . These data suggest that TLR3 and TLR4 molecules had no regulatory function on viral dissemination within the CNS following introduction , but appeared to have a subtle role in regulating viral replication in the CNS . In addition , we examined the expression of pro-inflammatory cytokine ( IL-6 and TNF-α ) , chemokine ( CCL2 ) , and type I IFN ( IFN-α and IFN-β ) . The expression of such cytokines in sub-tissues of brain following IC infection of JEV was consistently the same between wild-type and KO mice ( Figure 5G ) . The accumulation of CD11b+Ly-6Chigh leukocytes in the brain was slightly , but not significantly , higher in TLR3−/− mice following IC infection of JEV , as compared to wild-type mice , and TLR4−/− mice showed no significant change in leukocyte accumulation by IC infection of JEV ( Figure 5H ) . Collectively , these results imply that TLR3 and TLR4 molecules have different roles in controlling the dissemination of JEV from the periphery into the CNS , rather than a regulatory role ( s ) on viral dissemination within the CNS after CNS invasion . It has been demonstrated that TLR3 molecules , in concert with RIG-I , MDA5 , and TLR7 , recognize viral RNA and induce type I IFNs through activation of adaptor molecule TRIF and subsequent transcription regulators IRF-3 and IRF-7 . Also , triggering signal pathway by TLR4 molecule can activate IRF-3 , IRF-5 , and IRF-7 through adaptor molecules TRIF and MyD88 , thereby inducing the production of type I IFNs ( IFN-α and β ) [31]–[33] . Therefore , since TLR3 and TLR4 molecules contribute to the generation of a normal IFN response through activation of IRF-3 , IRF-5 , and IRF-7 after infection with neurotrophic virus [14] , [15] , we tested whether the ablation of TLR3 and TLR4 molecules affected type I innate responses in JEV infection . Our data revealed that the expressions of IFN-α and β mRNA were increased in inflammatory and lymphoid tissues of TLR3−/− mice with the levels peaked at 4 days pi , compared to those of wild-type mice ( Figure 6A and B ) . In contrast , TLR4−/− mice showed no significant increase of IFN-α or β mRNA expression in inflammatory or lymphoid tissues after JEV infection , compared to wild-type mice . Thus , the expression of type I IFN mRNA was not blunted in lymphoid and inflammatory tissues of TLR3−/− mice following JEV infection , which indicates that alternate signal pathways via innate immune receptors , such as TLR7 , RIG-I , and MDA5 , can contribute to type I IFN responses in the absence of the TLR3 molecule . Similarly , TLR3−/− mice showed delayed but slightly increased production of systemic IFN-β with peak levels attained at 48 h pi , compared to those of wild-type mice ( Figure 6C ) . However , paradoxically and surprisingly , TLR4 ablation induced more rapid and markedly increased production of systemic IFN-β in serum , as compared to production rates in TLR3−/− or wild-type mice . This result indicates that a deficiency of TLR4 molecules can modify the systemic production of type I IFNs . Importantly , it is worthy to note that this markedly enhanced production of systemic type I IFN-β protein in TLR4−/− mice might contribute to the early control of viral replication in the periphery , thereby ultimately preventing viral dissemination into the CNS . Myeloid cells , including tissue and lymphoid DCs and macrophages , are primary target cells of JEV infection and function to regulate the spread of virus to distant tissues such as the CNS [7] . Also , diverse cell populations can differentially utilize PRRs to induce innate immune responses upon viral infection . Therefore , these subtle functions may affect viral dissemination in the body and subsequent viral diseases . In addition , since our data showed that TLR4 ablation provided rapid and increased production of systemic IFN-β , we assessed whether TLR3 and TLR4 molecules affect JEV replication and type I IFN responses in myeloid-derived cells as primary target cells , in order to further define the differential roles of TLR3 and TLR4 molecules in controlling the progression of JE . Bone marrow-derived DCs ( BMDC ) and macrophages ( BMDM ) of TLR3−/− and TLR4−/− mice were infected with JEV and used to evaluate viral replication and the induction of pro-inflammatory cytokines and type I IFNs . TLR3−/− BMDC sustained significantly higher JEV replication throughout the examination period compared to those of wild-type BMDC infected with JEV , whereas TLR4−/− BMDC and BMDM showed delayed JEV replication at 24 and 48 h pi ( Figure 7A ) . Also , a rapid and increased induction of IL-6 mRNA in TLR3−/− BMDC and BMDM was observed , while TNF-α expression was increased 2-fold in JEV-infected TLR4−/− BMDC and BMDM ( Figure 7B and C ) , implying that the ablation of each TLR molecule could cause to trigger differential signal pathways to compensate for the production of pro-inflammatory cytokines . Surprising data was obtained from type I IFN innate responses of TLR4−/− BMDC and BMDM after JEV infection . TLR4−/− BMDC and BMDM induced rapid expressions of type I IFNs ( IFN-α and β ) mRNA with 10–100-fold increase in response to JEV infection , compared to wild-type BMDC and BMDM ( Figure 7D and E ) . In contrast , IFN-α and β expression by TLR3−/− BMDC and BMDM was virtually identical to those of wild-type BMDC and BMDM following JEV infection , except at an early time point ( 24 h pi ) , where levels were notably lower in TLR3−/− BMDC . In support , TLR4−/− BMDC and BMDM showed rapid secretion of IFN-β protein with 5–10-fold increase in response to JEV infection , while TLR3−/− BMDC and BMDM showed slightly higher or identical levels of secreted IFN-β protein , compared to wild-type BMDC and BMDM ( Figure 7F ) . Importantly , levels of IFN-β secretion in TLR3−/− BMDC and BMDM were much lower than those of TLR4−/− BMDC and BMDM . Conceivably , it is possible that potent type I IFN innate responses in TLR4−/− myeloid-derived cells provides rapid and increased production of in vivo systemic type I IFNs , thereby contributing to the early control of viral replication in the absence of the TLR4 molecule . Collectively , these results indicate that TLR4 molecules are dispensable to induce rapid and increased response of type I IFN innate immunity in myeloid-derived cells upon JEV infection , and that a deficiency of TLR3 molecules does not virtually compromise type I IFN production in BMDC and BMDM after JEV infection . Since myeloid-derived DCs and macrophages of TLR4-ablated mice showed highly enhanced production and expression of antiviral type I IFNs upon JEV infection , we measured the induction levels of antiviral ISG genes to define this finding in greater detail . We specifically focused on PRRs ( RIG-I [DDX1] , MDA5 [IFITH1] ) , their transcription factors ( IRF3 , IRF5 , IRF7 ) , and IFNAR transcription factor ( STAT1 ) as well as IFN-independent ( ISG49 [IFIT3] , ISG54 [IFIT2] , ISG56 [IFIT1] , CXCL10 ) and dependent genes ( PKR , Mx1 , Oas1 , Oasl-1 ) . Our results revealed that TLR3−/− BMDC and BMDM showed differential responses of antiviral ISG expression upon JEV infection ( Figure 8A and B ) . TLR3−/− BMDC showed less induction of PRR genes ( RIG-I and MDA5 ) and their transcription factors ( IRF-3 and IRF-7 ) , but member of genes ( ISG49 , ISG54 , ISG56 , CXCL10 ) that are induced in IFNAR−/− cells ( i . e . , are IFN-independent ) [34] , [35] were expressed in TLR3−/− BMDC with slightly higher levels , compared to those of wild-type BMDC . This result was consistent with the fact that TLR3−/− BMDC showed slightly higher or identical secretion of IFN-β compared to wild-type BMDC ( Figure 7F ) , because IFN-independent ISG genes can also be induced through ISRE binding of ISGF3 complex initiated by type I IFN receptor [15] . In contrast , TLR3−/− BMDM showed less induction of IFN-independent ISG genes ( ISG49 , ISG54 , ISG56 , CXCL10 ) as well as IFN-dependent ISG genes ( PKR , Mx1 , Mx2 ) and members of the 2′-5′-oligoadenylate synthetase family ( Oas1 , Oasl-1 ) , compared to wild-type BMDM . This result implies that macrophages could be more compromised in the inductiveness of type I IFN innate responses than DCs , if the TLR3 molecule was ablated . The prominent induction of antiviral ISG genes was observed in TLR4−/− BMDC and BMDM after JEV infection ( Figure 8A and B ) . TLR4−/− BMDC showed enhanced expression of PRR genes ( MDA-5 ) and its transcription factors ( IRF-3 , IRF-5 , IRF-7 ) , and IFN-dependent genes ( PKR , Oasl-1 ) , as well as IFN-independent genes ( ISG49 , ISG 54 , ISG 56 , CXCL10 ) . Also , TLR4−/− BMDM showed much more apparently and highly induced expression of antiviral ISG genes after JEV infection compared to those of wild-type BMDM and other cells , because TLR4−/− BMDM induced the expression of all tested ISG genes ( PRRs , transcription factors , IFN-dependent and independent genes ) with higher levels than other cells . Notably , TLR4−/− BMDM showed markedly enhanced induction of both IFN-dependent ( PKR , Oas1 , Oasl-1 , Mx1 , Mx2 ) and independent genes ( ISG49 , ISG54 , ISG56 , CXCL10 ) , compared to TLR3−/− BMDM that showed less induction of such genes . Therefore , these results support that a deficiency of TLR4 molecule provides more efficient type I IFN innate immune responses in DCs and macrophages following JEV infection . To further define the induction of antiviral IFN-independent and dependent ISG genes in JEV-infected TLR3−/− and TLR4−/− DCs and macrophages , the activation state of associated transcription factors was examined by western blot . In line with antiviral ISG induction data , TLR3−/− BMDC displayed decreased expression of IRF-3 and IRF-7 at 6–48 h and 48 h pi , respectively , and phosphorylated form of IRF-3 was not detected in both TLR3−/− and TLR4−/− BMDC ( Figure 8C ) , which supports that enhanced induction of antiviral IFN-independent ISG genes ( ISG49 , ISG54 , ISG 56 , CXCL10 ) in TLR3−/− and TLR4−/− BMDC may be caused by stimulation of IFNAR signal through increased IFN-β secretion [15] . Since slightly delayed phosphorylation of STAT1 , an IFNAR transcription factor , was observed in TLR3−/− and TLR4−/− BMDC , other pathways to activate NF-κB were also considered to contribute to enhanced induction of IFN-independent ISG genes . Interestingly , this notion can be explained by the result that faster degradation of IκBα was detected in TLR4−/− BMDC . IκBα proteins are phosphorylated via IκB kinase ( IKK ) activated by signal transducers , and are subsequently degraded after release of NF-κB [36] . Therefore , these results suggest that TLR4−/− BMDC could have evolved as yet unknown pathway ( s ) to activate NF-κB upon JEV infection , thereby inducing enhanced expression of type I IFNs and ISG genes . In addition , somewhat interestingly , transiently phosphorylated form of IRF-3 was strongly detected in TLR4−/− BMDM , but not TLR3−/− BMDM , as early as 6 and 12 h pi ( Figure 8D ) . Also , TLR4−/− BMDM showed prolonged and strong phosphorylation of STAT1 after JEV infection , compared to wild-type BMDM . Therefore , it was considered that activation of IRF3 and STAT1 in TLR4−/− BMDM derived potent type I IFN production as well as the induction of broad antiviral IFN-independent and IFN-dependent ISG genes . Neurons may be the main target cell of JEV infection in the CNS , and their death is a key factor in pathogenesis and neurological sequelae [8] . To examine whether TLR3 and TLR4 molecules can regulate JEV replication in neurons , primary cortical neurons generated from wild-type as well as TLR3−/− and TLR4−/− mice were infected with JEV , and virus yield , type I IFN responses and ISG expression were evaluated . It was likely that wild-type neurons were more permissive to JEV infection than DCs or macrophages , because infection of neurons with 10-fold less virus ( MOI 0 . 1 versus 1 . 0 ) produced over ∼105 viral RNA within 24 h ( Figure 7A and Figure 9A ) . In the absence of TLR3 molecule , JEV replicated faster , resulting in a 1 . 5–2 . 0-fold increase in infectious virus production between 24 h and 48 h pi , as compared to infected wild-type neurons . The ablation of TLR4 molecule showed earlier replication of JEV at 24 h pi , but the levels of virus were similar in both wild-type and TLR4−/− neurons at 48 h pi ( Figure 9A ) . Biphasic type I IFN mRNA induction was observed , with slightly higher levels at 24 h pi but much lower at 48 h pi in TLR3−/− neurons , compared to wild-type neurons ( Figure 9B ) . In contrast , TLR4−/− neurons showed transient induction of IFN-β at 24 h pi , after which IFN-β mRNA levels were comparable in both wild-type and TLR4−/− neurons . The secretion of IFN-β protein in culture media was markedly lower in TLR3−/− neurons at 48 h pi , while TLR4−/− neurons showed increased expression and production of IFN-β at both 24 h and 48 h pi , as compared to those of wild-type neurons ( Figure 9B ) . Also , it seemed that the expression of antiviral ISGs in TLR3−/− neurons followed type I IFN responses; hence , ISG49 and ISG56 showed transient increases at 24 h pi but much lower expression at 48 h pi ( Figure 9C ) . Also , a higher expression of RIG-I and MDA-5 , a cytosolic PRRs of viral RNA , was observed in TLR3−/− neurons , but their transcription factor IRF-3 was shown with decreased expression levels , as compared to wild-type neurons . It was thought that this caused the reduction in IFN-β production in TLR3−/− neurons at 48 h pi . TLR4−/− neurons showed transiently higher expression of ISG54 and MDA5 at 24 h pi , but the decreased levels of RIG-I and IRF-7 expression was observed at 48 h pi . Collectively , these results suggest that TLR3 may have an independent and subordinate role in triggering type I IFN innate responses in cortical neurons , because type I IFN responses and ISGs expression were much decreased at a later time point ( 48 h pi ) . Also , TLR4−/− cortical neurons appeared to induce less potent type I IFN innate immune responses than TLR4−/− DCs and macrophages , which indicates that specific types of cells differentially trigger innate immune responses following JEV infection . TLR signal pathway through MyD88 and/or TRIF adaptor molecules is required in some cases for antigen-specific antibody responses [37] , [38] , which may contribute to the control of JEV dissemination and replication in the brain . Our data revealed that TLR3 ablation showed slightly , but not significantly , increased level of IgM and IgG , while TLR4−/− mice showed significantly increased levels of JEV-specific IgM and IgG , compared to wild-type mice ( Figure S3A ) . Also , JEV infection showed marginally increased numbers of CD4+ , CD8+ T , and CD19+ B cells with activated phenotypes , as corroborated by the expression of surface markers , such as CD69 , CD44 , and CD80; however TLR3 and TLR4 molecules did not show apparently regulatory functions in T and B lymphocytes ( Table S1 ) . Since effector antigen-specific CD4+ and CD8+ T cell responses are also required for the control and clearance of JEV in the CNS as well as in peripheral tissues [7] , we evaluated whether the ablation of TLR3 and TLR4 molecules altered JEV antigen-specific CD4+ and CD8+ T cell responses . A deficiency of TLR3 molecules resulted in a similar percentage and absolute number of CD4+ and CD8+ T cells expressing IFN-γ and TNF-α , whereas TLR4−/− mice showed an increased percentage and absolute number of IFN-γ and TNF-α-producing CD4+ and CD8+ T cells ( Figure S3B–E ) . Along with potent type I IFN innate responses , these data indicate that TLR4 ablation could provide enhanced antigen-specific responses , thereby contributing in part to the control of virus replication and dissemination . Therefore , to further characterize the immunological parameters associated with potent type I IFN innate and adaptive immune responses in JEV-infected TLR4−/− mice , we analyzed the immune cellular components related to type I IFN innate and adaptive immune responses . TLR3−/− and TLR4−/− mice were challenged with JEV , and spleens were harvested at 3 and 5 days pi . At the early phase of infection , analysis of the spleen provides an insight into how TLR3 and TLR4 molecules modulate innate immune and inflammatory responses immediately after infection , because JEV was administered intraperitoneally . Analysis of lymphoid CD8α+ and myeloid CD11b+ DC subsets revealed that JEV-infected TLR3−/− and TLR4−/− mice exhibited similar increases in both DC subsets , compared to those of infected wild-type mice ( Figure 10A ) . However , somewhat surprisingly , the ablation of TLR4 molecule resulted in a highly increased number of CD11cintPDCA-1high plasmacytoid DC ( pDC ) subset , which is known as a potent cellular component to produce type I IFNs in response to viral infection [39] . Thus , it was considered that highly increased pDC number might contribute in part to enhanced production of systemic IFN-β in TLR4−/− mice . TLR4−/− mice also showed a decreased frequency of inflammatory CD11c−CD11b+Ly-6Chigh monocytes and no significant changes in the absolute number , whereas a significant increased number , but not frequency , of inflammatory monocytes was observed in TLR3−/− mice , compared to that in wild-type mice ( Figure 10B and C ) . This implies that TLR4−/− mice exhibit a mild inflammatory reaction in the spleen . In addition , a deficiency of TLR4 molecule provided an increased number of NK cells at 5 days pi , but TLR3 molecule had no modulatory function on NK cell number ( Figure 10D ) . Moreover , since CD4+CD25+Foxp3+ Treg cells contribute to the dampening of innate and adaptive immune responses during acute viral infection [40] , we addressed the frequency and number of CD4+CD25+Foxp3+ Treg cells in the spleen . We found that the frequency and absolute number of CD4+CD25+Foxp3+ Treg cells were increased 1 . 5–2-fold in response to JEV infection in wild-type mice ( Figure 10E and F ) . TLR3−/− mice showed identical increase of CD4+CD25+Foxp3+ Treg cells to wild-type mice , while in TLR4−/− mice a reduced frequency and absolute number of CD4+CD25+Foxp3+ Treg cells was observed , which indicates that TLR4 molecule could be involved in the increase of CD4+CD25+Foxp3+ Treg cell numbers following JEV infection . Collectively , these results suggest that increased number of CD11cintPDCA-1high pDC subpopulation and reduced CD4+CD25+Foxp3+ Treg cells are closely associated with enhanced type I IFN innate immunity and JEV-specific CD4+ and CD8+ T cell responses in TLR4−/− mice .
Although recognition of ssRNA virus , such as flavivirus , via cytosolic helicase RIG-I and MDA5 may be dominant to induce type I IFN innate responses [14]–[16] , the role of TLRs as first-front line of innate immune receptors in the extracellular space , including the cell membrane and endosome , remains still undefined in flaviviral infections , due to conflicting and intricate data . Furthermore , despite the pathological importance of JE as a major cause of acute encephalitis , the role of TLR signal pathways in JE progression has not been fully explored to date . Here , we observed strikingly contrasting regulation of JE via TLR3 and TLR4 signal pathways; TLR3 ablation elicited highly enhanced susceptibility to JE , whereas TLR4 ablation provided significantly enhanced resistance to JE rather than inducing increased susceptibility . In the present study , interesting clues to such contrasting regulation of JE by TLR3 and TLR4 molecules were derived from the differential induction of type I IFN innate responses in TLR3−/− and TLR4−/− mice . Notably , TLR4 ablation induced potent type I IFN innate responses through enhanced induction of antiviral ISG genes by alternative activation of IRF-3 and NF-κB in DCs and macrophages . Additionally , altered CD11cintPDCA-1high pDC and CD4+CD25+Foxp3+ Treg number in TLR4−/− mice appeared to contribute in part to enhanced type I IFN innate as well as JEV-specific T cell responses . Collectively , potent type I IFN innate and adaptive immune responses generated in peripheral lymphoid tissues after JEV infection were closely coupled with a reduced JE lethality in TLR4−/− mice . These findings imply that the balanced triggering of TLR signal array by viral components during JE progression could be responsible for determining the outcome of disease through negative and positive regulatory factors . There are several conflicting reports on the role of TLR3 signaling pathway in neurological diseases caused by viral infection [41] , [42] . A deficiency of TLR3 in humans predisposes to a genetic risk factor for herpes simplex virus encephalitis [43] and influenza A virus-induced encephalopathy [44] , but TLR3−/− mice infected with influenza [45] , punta toro [46] , and vaccinia viruses [47] showed improved survival and decreased production of inflammatory cytokines . Strikingly conflicting role of TLR3 signal pathway was derived from an infection model with WNV [20] , [21] . While TLR3 ablation protected mice from WNV lethal infection by decreased systemic TNF-α and IL-6 production and BBB permeability [20] , there is a report demonstrating that TLR3 molecules are essential in protecting from WNV infection [21] . Our results favor the latter report . TLR3−/− BMDC , but not to BMDM , showed defective type I IFN innate responses at an early time ( 24 h pi ) , which may allow early viral replication . This result is in contrast to that of WNV infection , where TLR3 molecule did not modulate WNV replication and IFN induction in primary myeloid cells [21] . Although TLR3−/− BMDC is more permissive to JEV replication , JEV-infected TLR3−/− BMDC elicited similar levels of type I IFN responses to wild-type BMDC with delayed kinetics , and TLR3−/− mice also showed no blunted type I IFN responses in lymphoid and local tissues . This suggests that enhanced tissue tropism and rapid viral entry into the CNS is not affected by locally induced type I IFN responses . Type I IFN responses of TLR3−/− mice were considered not to be attenuated since cytosolic RIG-I and MDA5 molecules are intact . However , TLR3 molecule appeared to play more important role in inducing type I IFN responses of neuron cells than BMDC and BMDM , because TLR3−/− neuron cells showed a markedly reduced expression and production of type I IFN and ISGs at a late time ( 48 h pi ) , thereby promoting viral replication . This implies that TLR3 molecule had differential modulatory functions on type I IFN innate responses and JEV replication in a cell-type restricted manner . However , considering that TLR3−/− and TLR4−/− mice showed no difference in CNS replication of JEV following IC infection , subtle changes of CNS system , such as innate responses of microglia and astrocyte , appear to modulate the in vivo spread of directly inoculated JEV in the CNS . Increased BBB permeability by systemic TNF-α and IL-6 appears to promote an earlier entry of virus into the CNS . In contrast to WNV infection , where TLR3−/− mice showed no change in BBB permeability [21] , TLR3−/− mice , but not TLR4−/− mice , elicited increased BBB permeability associated with a huge production of systemic IL-6 . Also , this result was in contrast with a previous study in which TLR3−/− mice showed reduced cytokine ( e . g . , TNF-α and IL-6 ) responses , BBB permeability , neuroinvasion , and mortality following infection with mammalian cell-passaged WNV [20] . Nonetheless , our results showed some similarities with previous reports using WNV , such as increased viral burden in peripheral tissues . Although the impact of TLR3 molecule on BBB permeability is likely to differ , depending on the context and details of the model , virus-culture conditions , and the viral strain being tested , the failure of early viral clearance in the periphery of TLR3−/− mice may ultimately cause enhanced inflammatory reactions , thereby increasing BBB permeability and viral load in the CNS . One intriguing result in this study was that TLR7−/− mice showed no change in susceptibility to JE , since TLR7 molecule can recognize ssRNA of JEV . This result was in contrast with the report that the TLR7 molecule is involved in modulating the progression of WNV encephalitis via an IL-23-dependent accumulation of leukocytes in the CNS [48] . Although systemic levels of proinflammatory cytokines and type I IFNs were higher in TLR7−/− than in wild-type mice [48] , it is expected that splenic pDC or circulating pDC from TLR3−/− mice may also have contributed to the type I IFN responses , because TLR7 signal pathway was intact in TLR3−/− mice . In addition , we previously found that TLR2 molecule had modulatory function in cross-presentation of OVA protein using JEV-infected TLR2−/− mice , suggesting that JEV infection may be also be recognized by TLR2 molecule [49] . However , in this study , TLR2 signal pathway had no impact on the progression of JE . One trivial explanation of this result is that TLR2 signal pathway was not involved in inducing pathologic disease by JEV infection , no matter what OVA cross-presentation is regulated by JEV infection in a TLR2-dependent manner . The most intriguing result in this study was that TLR4−/− mice showed markedly enhanced resistance to JE . To date , the role of TLR4 signal pathway in inducing innate and adaptive immune response against JEV and other flaviviruses has not been defined . Our results demonstrate that TLR4 ablation strongly induces in vivo systemic type I IFN innate responses , as well as type I IFN expression and production from myeloid-derived cells upon JEV infection . This presumably promotes early clearance of virus . In spite of the existence of TLR4 prototype ligand , LPS , a growing number of reports suggest that TLR4 molecule is biologically relevant , and is responsive to viral proteins , including those of Ebola virus [23] , hepatitis C virus [24] , and respiratory syncytial virus [25] , leading to the induction of proinflammatory cytokines . We are not sure whether the induction of potent type I IFN innate responses in the absence of TLR4 signal pathway was mediated directly by enhanced signal transduction of other PRRs , such as TLR3 , RIG-I , and MDA5 , and/or indirectly by soluble factors produced from host cells by viral infection , i . e . DAMPs . However , our results provide one explanation as to how TLR4−/− myeloid-derived cells induce potent type I IFN innate responses , i . e . enhanced activation of NF-κB through unknown pathway ( s ) in DCs , and transient activation of IRF3 at 6–12 h pi and prolonged activation of STAT1 in macrophages . The expression of antiviral ISG genes in myeloid-derived cells after JEV infection was induced by both direct ( by IRF-3 ) and indirect ( by IFN-β production and IFNAR signaling ) pathways . Considering that only small faction ( 10–20% ) of myeloid-derived cells is infected by JEV [49] , uninfected myeloid-derived cells are thought to substantially contribute to antiviral ISG induction through stimulation of IFNAR signal after binding with secreted IFN-β proteins . This notion was supported by two results , i . e . 1 ) induction of IFN-dependent genes ( PKR , Mx1 , Oas1 ) in TLR3−/− BMDC , TLR4−/− BMDC and BMDM with increased secretion of IFN-β after JEV infection , and 2 ) no detection of phosphorylated IRF-3 except in TLR4−/− BMDM . Also , transient activation of IRF3 and prolonged activation of STAT1 explains strong induction of both IFN-independent ISG ( ISG49 , ISG54 , ISG56 , CXCL10 ) and dependent genes ( PKR , Oas1 , Mx1 , Mx2 ) in TLR4−/− macrophages . Although NF-κB activation in DCs and IRF-3 and STAT1 activation in macrophages after JEV infection support potent type I IFN innate responses in the absence of TLR4 molecule , how these signal molecules are activated remains still undefined . Therefore , future studies will be required to delineate the mechanistic and functional intermediates that link and regulate NF-κB , IRF-3 and STAT1 signal pathway in the absence of TLR4 molecule . In addition , our results is strengthened by a recent report that TLR4−/− or TLR4 antagonist-treated mice are highly refractory to influenza-induced lethality , due to blocking inflammation by host-derived , oxidized phospholipid that potently stimulates TLR4 [50] , [51] . One similarity with our data is that mice treated with TLR4 antagonist , Eritoran , or TLR4−/− mice had reduced lung pathology to infection with influenza virus , which is characterized by the reduction of viral burden and proinflammatory cytokine expression . However , it is not certain whether Eritoran-treated or TLR4−/− mice displayed rapid and enhanced type I IFN innate responses after infection with influenza virus . Thus , it is worthwhile identifying whether blocking TLR4 signal pathway by antagonists such as Eritoran , affects JE progression through the induction of potent type I IFN innate responses . This study will provide valuable insights into developing therapeutic strategies to viral encephalitis caused by neurotrophic virus such as JEV and WNV . Analogously , in the absence of TLR4 molecule , the enhanced expansion of CD11b+Ly-6Chigh “inflammatory monocytes” was not observed in comparison with TLR3−/− mice , which was suggestive that in TLR4−/− mice mild inflammatory responses were elicited in the spleen . This monocyte subset migrates to the site of infection , secretes pro-inflammatory cytokines , and thereby exacerbates immunopathologic diseases [28] . Thus , the aberrant recruitment and expansion of these CD11b+Ly-6Chigh inflammatory monocytes may also contribute to JE immunopathogenesis in TLR3−/− mice . The production and response of type I IFN is considered to be a major linkage point between innate and adaptive immunity , because IFN-α/β sustains B cell activation and differentiation [52] , [53] , expands antigen-specific CD8+ T cells [54] , CD4+ T cells [55] , and activation of NK cells [56] . Therefore , another intriguing finding of this study was the global alteration of immune responses in TLR4−/− mice . This suggests that TLR4 molecule is largely dispensable for the efficient link between innate and adaptive immunity in JEV infection . Infection of TLR4−/− mice with JEV exhibited the expansion of pDC and NK cells , and enhanced JEV-specific CD4+ and CD8+ T cell responses , which are involved in viral clearance at early and late phases of infection , respectively . Also , it is likely that increased number of pDCs contributed in part to the potent induction of type I IFN innate responses in TLR4−/− mice . In addition , TLR4−/− mice showed limited expansion of CD4+CD25+Foxp3+ Tregs , which have been known to suppress innate and effector T cells , thus preventing or controlling reactivity to self-antigen and pathogens , and thereby blunting severe inflammation and maintaining antigen-specific T cell homeostasis [40] . The role of CD4+CD25+Foxp3+ Tregs in acute viral diseases is still debatable [57] , [58] . Recent work implicates CD4+Foxp3+ Tregs in the control of WNV pathogenesis , wherein peripheral expansion of Treg was associated with mild inflammation , but reduced Treg levels were associated with WNV encephalitis [57] . However , while CD4+Foxp3+ Tregs that were adoptively transferred 2 days prior to JEV infection made the recipients vulnerable to JE , CD4+Foxp3+ Tregs that were adoptively transferred 2 days after infection provided resistance to JE ( unpublished personal data ) . This suggests that CD4+Foxp3+ Tregs elicit dual-phased roles during the progression of JEV-induced neurological disorders . More importantly , Treg induction during a viral infection is considered to be a detrimental response that promotes virus persistence without benefits to the host [59] , [60] . One trivial explanation of CD4+Foxp3+ Treg role is that initially low number of CD4+Foxp3+ Tregs in TLR4−/− mice may promote the expansion of effector CD4+ and CD8+ T cells specific for JEV antigen as well as innate immune responses , thereby inducing enhanced anti-viral response and virus-specific CTL to promote early viral clearance . JE pathogenesis in the murine model may be altered by the route of peripheral administration , virus-propagation condition , and viral strains [7] , [20] , [21] . It is also possible that the genetic background of mice affects the immunopathogenesis of JE . However , we found that two backgrounds of mouse strains used for TLR3−/− and TLR4−/− mice showed comparable mortality and similar clinical signs after JEV infection , which indicates that JE pathogenesis is unaffected by genetic background of mouse strains used in this study . Although JEV infected via i . p . route does not directly reflect natural infection mediated by intradermal or intramuscular route after biting of mosquitoes , JEV infected via i . p . route displays entirely similar pathogenesis to natural infection , due to peripheral amplification in the spleen . Also , since mice infected i . p . with JEV usually exhibited neurological disorder at 4–5 days pi , rapid innate immune responses are more critical to control JE progression than adaptive T cell responses , which take time to develop . Indeed , the role of T cells in flavivirus encephalitis is less clear . This is , in part , due to variation of virus strain , the infection dose , the route of administration , mouse strain and age of the mice . Therefore , considering that the character of CD4+ and CD8+ T cells specific for JEV is also governed by innate immune responses initiated by recognition of PRRs , triggering of each PRR by direct viral components and/or host factors derived from infection could affect innate immune responses to shape adaptive immune responses , thereby influencing JE pathogenesis . A better understanding of the mechanisms that govern the induction of protective immunity plays a critical role in developing novel therapeutic strategies against JE .
C57BL/6 ( H-2b ) and BALB/c ( H-2d ) mice ( 4–6 weeks old ) were purchased from Samtako ( O-San , Korea ) . TLR2 ( H-2b ) , TLR3 ( H-2d and H-2b ) , TLR4 ( H-2b ) , TLR7 ( H-2d ) , and TLR9 ( H-2b ) -deficient mice have been described elsewhere [33] . TLR3/4−/− mice that are deficient in both TLR3 and TLR4 molecules were generated by backcrossing with TLR3 and TLR4-deficient mice . All mice were genotyped and bred in the animal facilities of Chonbuk National University . All experimental procedures were pre-approved and adhered to the guidelines set by the Institutional Animal Care and Use Committees ( IACUC ) , Chonbuk National University ( Permission code 2013-0028 ) , which is fully accredited by the Korea Association for Laboratory Animal Sciences ( KALAS ) . JEV Beijing-1 strain was obtained from Green Cross Research Institute ( Suwon , Korea ) and propagated in the mosquito cell line ( C6/36 ) using DMEM supplemented with 2% FBS , penicillin ( 100 U/ml ) , and streptomycin ( 100 U/ml ) . The C6/36 cultures were infected with JEV Beijing-1 at a multiplicity of infection ( MOI ) of 0 . 1 , and were incubated in a humidified CO2 incubator for 1 h at 28°C . After absorption , the inoculum was removed , and 7 ml of a maintenance medium containing 2% FBS was added . Approximately 6–7 days pi , cultures of the host cells showing an 80–90% cytopathic effect were harvested . The virus stocks were titrated by conventional plaque assay or focus-forming assay , and were stored in aliquots at −80°C until use . The mAbs used for the flow cytometric analysis and other experiments were obtained from eBioscience ( San Diego , CA ) or BD Biosciences ( San Diego , CA ) which include: fluorescein isothiocyanate ( FITC ) conjugate-anti-CD3ε ( 145-2C11 ) , CD4 ( RM4-5 ) , CD8 ( 53-6 . 7 ) , CD44 ( IM7 ) , CD62L ( MEL-14 ) , CD69 ( H1 . 2F3 ) , Ly-6G ( 1A8 ) , anti-rabbit IgG , phycoerythrin ( PE ) conjugate-anti-mouse-CD11b ( M1/70 ) , Foxp3 ( FJK-16s ) , IFN-γ ( XMG1 . 2 ) , goat anti-mouse IgG , peridinin chlorophyll protein complex ( PerCP ) conjugate-anti-Ly-6C ( HK1 . 4 ) , PE-Cyanine dye ( Cy7 ) -anti-mouse NK1 . 1 ( PK136 ) , allophycocyanin ( APC ) conjugate-anti-mouse-CD25 ( PC62 . 5 ) , Ly-6G ( Gr-1 ) , TNF-α ( MP6-XT22 ) . The peptides of the defined I-Ab-restricted epitopes JEV NS1132–145 ( TFVVDGPETKECPD ) , H-2Db-restricted epitope JEV NS4B215–223 ( SAVWNSTTA ) [61] , and H-2d-restricted epitope JEV E60–68 ( CYHASVTDI ) [62] were chemically synthesized at Peptron Inc . ( Daejeon , Korea ) . Poly ( I:C ) was purchased from Sigma-Aldrich ( St . Louis , MO ) . JEV-specific primers for the detection of viral RNA ( JEV10 , 564–10 , 583 forward , 5′-CCC TCA GAA CCG TCT CGG AA-3′ and JEV10 , 862–10 , 886 reverse , 5′-CTA TTC CCA GGT GTC AAT ATG CTG T-3′ ) [27] and primers specific for cytokines , type I IFNs ( IFN-α/β ) , and ISGs ( Table S2 ) were synthesized at Bioneer Corp . ( Daejeon , Korea ) and used for PCR amplification of target genes . Viral burden , cytokine ( IL-1β , IL-6 , TNF-α , IFN-α , and IFN-β ) and chemokine ( CCL2 , CCL3 , CCL4 , CCL5 , and CXCL10 ) expression in inflammatory and lymphoid tissues were determined by conducting quantitative SYBR Green-based real-time RT-PCR ( real-time qRT-PCR ) . Mice were infected intraperitoneally ( i . p . ) with JEV ( 1 . 4 × 107 PFU ) and tissues including brain , spinal cord , and spleen were harvested at 2 , 3 , 4 , and 6 days pi following extensive cardiac perfusion with Hanks balanced salt solution ( HBSS ) . Total RNAs extracted from tissues using easyBLUE ( iNtRON , INC . , Daejeon , Korea ) were employed for real-time qRT-PCR using a CFX96 Real-Time PCR Detection system ( Bio-Rad Laboratories , Hercules , CA ) . Following reverse transcription of total RNAs with High-Capacity cDNA Reverse Transcription Kits ( Applied Biosystems , Foster , CA ) , the reaction mixture contained 2 µl of template cDNA , 10 µl of 2× SYBR Primix Ex Taq , and 200 nM primers at a final volume of 20 µl . The reactions were denatured at 95°C for 30 s , and then subjected to 45 cycles of 95°C for 5 s , and 60°C for 20 s . After the reaction cycle was completed the temperature was increased from 65°C to 95°C at a rate of 0 . 2°C/15 s , and the fluorescence was measured every 5 s to construct a melting curve . A control sample that contained no template DNA was run with each assay , and all determinations were performed at least in duplicate to ensure reproducibility . The authenticity of the amplified product was determined by melting curve analysis . The relative ratio of viral RNA in the infected samples to uninfected samples was determined . All data were analyzed using the Bio-Rad CFX Manager , version 2 . 1 analysis software ( Bio-Rad Laboratories ) . Mice infected with JEV were perfused with 30 ml of HBSS on day 3 pi via cardiac puncture of the left ventricle . Brains were then harvested , and homogenized by gently pressing them through 100-mesh tissue sieve , after which they were digested with 25 µg/ml of collagenase type IV ( Worthington Biochem , Freehold , NJ ) , 0 . 1 µg/ml trypsin inhibitor Nα-p-tosyl-L-lysine chloromethyl ketone , 10 µg/ml DNase I ( Amresco , Solon , OH ) , and 10 mM HEPE in HBSS for 1 h at 37°C with shaking . Cells were separated by using Optiprep density gradient ( 18/10/5% ) centrifugation at 400 ×g for 30 min ( Axis-Shield , Oslo , Norway ) , after which cells were collected from 18% to 10% interface and washed twice with PBS . Cells were then counted and stained for CD11b , Gr-1 , Ly6G , Ly6C , CD3 , CD4 , CD8 , and NK1 . 1 with directly conjugated antibodies ( eBioscience ) for 30 min at 4°C . Finally , the cells were fixed with 10% formaldehyde . Data collection and analysis were performed with a FACSCalibur flow cytometer ( Becton Dickson Medical Systems , Sharon , MA ) and FlowJo ( Tree Star , San Carlos , CA ) software . Brain derived from mock and JEV-infected mice were embedded in paraffin and 10-µm sections were prepared and stained with hematoxylin and eosin ( H&E ) . Sections were analyzed using a Nikon Eclipse E600 microscope ( Nikon , Tokyo , Japan ) . For confocal microscopy of brain tissue , brains were collected and frozen in optimum cutting temperature ( OCT ) compound ( Sakura Finetechnical Co . , Tokyo , Japan ) following vigorous perfusion with HBSS . 6–7 µm thick sections were then cut , air-dried , and fixed in cold solution ( 1∶1 mixture of acetone and methanol ) for 15 min at −20°C . Non-specific binding was blocked with 10% normal goat serum , and samples were then permeabilized with 0 . 1% Triton X-100 . Staining was performed by incubating sections overnight in moist chamber at 4°C with anti-JEV and biotin conjugated anti-mouse CD11b ( BD Biosciences , San Diego , CA ) antibody . Primary antibodies were detected with secondary FITC-conjugated streptavidin and PE-conjugated goat anti-mouse Ab . Nuclei were counterstained with DAPI ( 4′6-diamidino-2-phenylindole ) ( Sigma-Aldrich ) . Finally , the fluorescence was observed by confocal laser scanning microscope ( CalZeiss , Zena , Germany ) . Blood–brain barrier ( BBB ) permeability was determined by visualizing and quantifying extravasated Evans blue dye into the brain , as described earlier with some modification [20] , [21] . Briefly , JEV-infected mice were injected i . p . with 800 µl of 1% ( w/v ) Evans Blue dye ( Sigma-Aldrich ) 2 and 3 days pi , and perfused via intracardiac puncture with HBSS 1 h later . Brains were subsequently removed , weighed , and stored at −80°C following visualization with a high resolution digital camera . For Evans blue quantification , brain tissues were homogenized in 1 ml of PBS , and 1 ml of 100% trichloroacetic acid ( TCA ) ( Sigma-Aldrich ) was then added to the homogenate to precipitate proteins . The mixture was then vigorously shaken to precipitate the proteins for 2 min and cooled for 30 min at 4°C . After centrifugation ( 30 min at 4 , 000 ×g ) , the absorbance of the supernatant was measured at 620 nm using a spectrophotometer . The content of Evans blue was valued as micrograms of dye per gram of brain tissue by using a standard curve . BMDC and BMDM infected with JEV were lysed in RIPA buffer ( 10 mM Tris , 150 mM NaCl , 0 . 02% sodium azide , 1% sodium deoxycholate , 1% Triton X-100 , 0 . 1% SDS , pH 7 . 4 ) supplemented with protease inhibitors ( iNtRON Biotech , Daejeon , Korea ) . Samples ( 15 µg ) were resolved by electrophoresis on 10 to 12 . 5% SDS-polyacrylamide gels . After proteins were transferred to PVDF Immobilon-P Transfer Membrane ( Millipore , Billerica , MA ) , blots were blocked with 5% non-fat dried milk or 3% BSA overnight at 4°C , and probed with the following panel of primary antibodies: rabbit anti-IRF-3 , phospho-IRF-3 ( Ser396 ) , STAT1 , phospho-STAT1 ( Tyr701 ) , mouse anti-IκBα ( Amino-terminal Antigen ) antibodies ( Cell Signaling , Danvers , MA ) , and rabbit anti-IRF-7 , phospho-IRF-7 ( Ser471+Ser472 ) antibodies ( Bioss Inc , Woburn , MA ) . Western blots were incubated with peroxidase-conjugated secondary antibodies ( SouthernBiotech , Birmingham , AL ) and visualized with WEST-ZOL Plus Immunoblotting detection reagents ( iNtRON Biotech ) using chemi-documentation system ( Fusion Fx7 , Vilber Lourmat , Cedex1 , France ) . JEV-specific IgM and IgG levels in sera of infected mice were determined by conventional ELISA using JEV E glycoprotein antigen ( Abcam , Cambridge , UK ) . JEV-specific CD4+ and CD8+ T cell responses were determined by intracellular IFN-γ and TNF-α staining in response to antigen stimulation . Mice were infected i . p . with 2 . 8 ×106 PFU of JEV and were sacrificed at day 7 pi and splenocytes were prepared . The erythrocytes were depleted by treating single-cell suspensions with ammonium chloride-containing Tris buffer ( NH4Cl-Tris ) for 5 min at 37°C . The splenocytes were cultured in 96-well culture plates ( 5×105 cells/well ) in the presence of synthetic peptide epitopes ( NS1132–145 and NS4B215–225 or E60–68 ) for 12 h and 6 h in order to observe CD4+ and CD8+ T cell responses , respectively . CD4+ responses of BALB/c genetic background ( H-2d ) strain groups were evaluated by stimulation with UV-irradiated virus for 12 h at 37°C . Monensin at the concentration of 2 µM was added to antigen-stimulated cells 6 h before harvest . The cells were washed twice with PBS , and surface stained for FITC-anti-CD4 or CD8 antibodies for 30 min at 4°C , and then washed twice with PBS containing monensin . After fixation , the cells were washed twice with permeabilization buffer ( eBioscience ) and then stained with PE-anti-IFN-γ , or APC-anti-TNF-α in permeabilization buffer for 30 min at room temperature . Finally , the cells were washed twice with PBS and fixed using fixation buffer . Sample analysis was performed with FACS Calibur flow cytometer ( Becton Dickson Medical Systems , Sharon , MA ) and FlowJo ( Tree Star , San Carlos , CA ) software . Splenocytes from infected mice were isolated and digested with collagenase ( Roche ) and type I DNase in serum-free RPMI media at 37°C for 40 min with mechanical disruption . Cells were washed twice with RPMI media containing 10% FBS before FACS staining . Myeloid-derived and immune cells were stained antibodies specific for CD11c , CD11b , B220 , CD3 , CD4 , CD8α , NK1 . 1 , DX5 , Gr-1 , Ly-6C , PDCA-1 , CD25 , and intracellular Foxp3 . Finally , the cells were fixed with 10% formaldehyde , and analyzed with FACS Calibur flow cytometer and FlowJo software . All data were expressed as the average ± standard deviation , and statistically significant differences between groups were analyzed by unpaired two-tailed Student's t-test for in vitro experiments and immune cell analysis or ANOVA and post-hoc test for multiple comparisons of the mean . The statistical significance of viral burden and in vivo cytokine gene expression were evaluated by Mann-Whitney test or unpaired two-tailed Student's t-test . Kaplan-Meier survival curves were analyzed by the log-rank test . A p-value ≤0 . 05 was considered significant . All data were analyzed using Prism software ( GraphPadPrism4 , San Diego , CA ) . | Japanese encephalitis ( JE ) is major emerging encephalitis , and more than 60% of global population inhabits JE endemic areas . The etiological virus is currently spreading to previously unaffected regions due to rapid changes in climate and demography . However , the impact of TLR molecules on JE progression has not been addressed to date . We found that the distinct outcomes of JE progression occurred in TLR3 and TLR4-dependent manner , i . e . TLR3−/− mice were highly susceptible , whereas TLR4−/− mice showed enhanced resistance to JE . TLR3 ablation induced severe CNS inflammation manifested by early CD11b+Ly-6Chigh monocyte infiltration , high expression of proinflammatory cytokines , as well as increased BBB permeability . In contrast , TLR4 ablation provided potent type I IFN innate response in infected mice , as well as in myeloid-derived cells closely associated with strong induction of antiviral ISG genes , and also resulted in enhanced humoral , CD4+ , and CD8+ T cell responses along with altered plasmacytoid DC and CD4+Foxp3+ Treg number . Thus , potent type I IFN innate and adaptive immune responses in the absence of TLR4 were coupled with reduced JE lethality . Our studies provide an insight into the role of each TLR molecule on the modulation of JE , as well as its mechanism of neuroinflammation control during JE progression . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"activation",
"immunology",
"tropical",
"diseases",
"encephalitis",
"infectious",
"disease",
"immunology",
"global",
"health",
"neglected",
"tropical",
"diseases",
"japanese",
"encephalitis",... | 2014 | Distinct Dictation of Japanese Encephalitis Virus-Induced Neuroinflammation and Lethality via Triggering TLR3 and TLR4 Signal Pathways |
EDS1 , PAD4 , and SAG101 are common regulators of plant immunity against many pathogens . EDS1 interacts with both PAD4 and SAG101 but direct interaction between PAD4 and SAG101 has not been detected , leading to the suggestion that the EDS1-PAD4 and EDS1-SAG101 complexes are distinct . We show that EDS1 , PAD4 , and SAG101 are present in a single complex in planta . While this complex is preferentially nuclear localized , it can be redirected to the cytoplasm in the presence of an extranuclear form of EDS1 . PAD4 and SAG101 can in turn , regulate the subcellular localization of EDS1 . We also show that the Arabidopsis genome encodes two functionally redundant isoforms of EDS1 , either of which can form ternary complexes with PAD4 and SAG101 . Simultaneous mutations in both EDS1 isoforms are essential to abrogate resistance ( R ) protein-mediated defense against turnip crinkle virus ( TCV ) as well as avrRps4 expressing Pseudomonas syringae . Interestingly , unlike its function as a PAD4 substitute in bacterial resistance , SAG101 is required for R-mediated resistance to TCV , thus implicating a role for the ternary complex in this defense response . However , only EDS1 is required for HRT-mediated HR to TCV , while only PAD4 is required for SA-dependent induction of HRT . Together , these results suggest that EDS1 , PAD4 and SAG101 also perform independent functions in HRT-mediated resistance .
One of the most studied plant defense mechanisms involves the deployment of resistance ( R ) proteins , which primarily provides protection against specific races of pathogens carrying corresponding avirulence ( Avr ) genes ( “gene-for-gene” interactions [1] ) . R gene-mediated or race-specific immunity is induced when a strain-specific Avr protein from the pathogen associates directly/indirectly with a cognate plant R protein [2]–[4] . Induction of R-mediated responses is often accompanied by the formation of a hypersensitive response ( HR ) at the site of pathogen entry [5] . Although HR is considered one of the first visible manifestations of pathogen-induced host defense , whether it is the cause or consequence of resistance signaling remains unclear . Concurrent with R-mediated response , defense reactions are triggered in both local and distant parts of the plant . These include a local and systemic increase in the endogenous salicylic acid ( SA ) levels and the upregulation of a large set of defense genes , including those encoding pathogenesis-related ( PR ) proteins [6]–[7] . The SA signal transduction pathway plays a key role in plant defense signaling [8] . Arabidopsis mutants that are impaired in SA responsiveness , such as npr1 ( nonexpressor of PR [9]–[10] ) , or are defective in pathogen-induced SA accumulation , such as eds1 ( enhanced disease susceptibility 1 [11] ) , eds5 [12] , sid2 ( isochorishmate synthase [13] ) and pad4 ( phytoalexin deficient 4 [14] ) , exhibit enhanced susceptibility to pathogen infection and show impaired PR gene expression . The EDS1 , EDS5 , PAD4 , NPR1 proteins and the SA synthesizing enzyme SID2 participate in both basal and R protein-mediated defense responses [9]–[14] . EDS1 interacts with PAD4 and SAG ( senescence associated gene ) 101 and the combined activities of EDS1 and PAD4 proteins are required for HR formation and the restriction of pathogen growth [15]–[17] . EDS1 is thought to form two distinct complexes with PAD4 and SAG101 since direct interaction between PAD4 and SAG101 has not been detected . EDS1 and PAD4 are present in the nucleus and cytoplasm , whereas SAG101 preferentially localizes to the nucleus . A recent study suggested that both the nuclear and cytosolic fractions of EDS1 are required to complement eds1-conferred enhanced susceptibility [18] . SAG101 is thought to be functionally redundant with PAD4 , because a mutation in SAG101 alone does not confer bacterial susceptibility . However , sag101 pad4 double mutant plants do exhibit significantly enhanced susceptibility in comparison to pad4 single mutants [17] , [19] . EDS1 , PAD4 , and SAG101 are structurally related to lipase/esterase-like proteins although lipase-like biochemical activities have not been demonstrated for EDS1 or PAD4 [11] , [16] , [17] . EDS1 was thought to participate in the resistance signaling mediated by toll-interleukin-nucleotide binding site-leucine rich repeat ( TIR-NBS-LRR ) category of R proteins [20] . However , recent results have shown that EDS1 and SA function redundantly in R-mediated signaling and this masks the requirement for EDS1 [21] . Thus , the requirement for EDS1 by R proteins previously thought to be independent of EDS1 , became evident only in plants lacking the capacity to synthesize pathogen-responsive SA [21] . This includes RPS2 , RPP8 , and HRT , which encode coiled coil ( CC ) -NBS-LRR type R proteins and confer resistance to bacterial , oomycete and viral pathogens , respectively . The R protein HRT confers resistance to turnip crinkle virus ( TCV ) and requires EDS1 and SA for resistance signaling; mutations in either EDS1 or SA synthesizing enzyme SID2 are sufficient to compromise resistance to TCV [22] . However , EDS1 and SA fulfill redundant functions in HR mediated by HRT; HR to TCV is only compromised in plants lacking EDS1 as well as SA [21] . Besides EDS1 and SA , HRT-mediated resistance also requires PAD4 and EDS5 , a recessive locus rrt ( regulates resistance to TCV ) , and the blue-light photoreceptors [22]-[24] . Although SA appears to function downstream of the HRT-derived recognition of TCV , it cannot confer resistance in the absence of HRT [22] , [23] , [25] , [26] . Exogenous SA confers resistance in HRT background by upregulating expression of HRT [22]–[25] . Interestingly , the requirement for rrt in resistance can be overcome by increasing the levels of HRT via exogenous application of SA or by transgenic overexpression of HRT [22]–[25] , [27] . HRT-mediated signaling is activated in the presence of TCV coat protein ( CP ) [27]–[29] . However , direct interactions between HRT and CP have not been detected . Here , we examined the roles of EDS1 , PAD4 and SAG101 in HRT-mediated signaling . We find that EDS1 , but not PAD4 or SAG101 , is required for CP-triggered HR in HRT expressing plants . This correlates with direct interactions between EDS1 and HRT . We also show that SAG101 , which forms a ternary complex with EDS1 and PAD4 , is an essential component of HRT-mediated signaling . Not only does SAG101 interact with PAD4 in the presence of EDS1 , but it also induces the nuclear localization of EDS1 . Conversely , the subcellular localization of the SAG101-EDS1-PAD4 ternary complex is driven by the location of EDS1 . These results suggest that the inability of extranuclear EDS1 to complement eds1-1 phenotypes might be due to the altered localization of PAD4 and SAG101 . Our studies also show that the Arabidopsis genome encodes two functionally redundant EDS1 isoforms , both of which can function in the R-mediated response to TCV or Pseudomonas syringae .
Genetics analysis of F2 plants derived from crosses between resistant ecotype Di-17 and susceptible plants eds1-1 ( Ws ecotype ) or eds1-2 ( Ler ecotype ) mutants showed that all HRT eds1 plants were susceptible to TCV ( Table S1 , [22] , [24] ) . In comparison , ∼25% of F2 progeny ( homo/heterozygous for HRT , but homozygous for rrt ) were able to resist TCV infection in control crosses between Di-17 and Col-0/Ws/Ler [Table S1 , 22] . Surprisingly , F2 progeny derived from a Di-17 x eds1-22 ( At3 g48090; Col-0 ecotype , [21] ) cross showed normal segregation of resistant plants; 25% of HRT plants showed resistance ( Table S1 ) . We investigated this further and realized that previous reports had indicated the presence of two EDS1 isoforms in the Arabidopsis ecotype Col-0 [17] , [30] , only one of which ( encoded by At3 g48090 , redesignated EDS1-90 ) has been functionally characterized [16] , [17] . The other isoform ( encoded by At3 g48080 , designated EDS1-80 ) exhibits ∼85% amino acid ( aa ) identity with EDS1-90 ( Figure S1A ) and its transcript is induced by SA and TCV , similar to EDS1-90 ( Figures S1B , S1C ) . Similar to Col-0 , both Di-17 and Ler plants expressed both EDS1-80 and EDS1-90 but the EDS1-80 gene in Ler and Di-17 contained a 28 bp deletion in the second exon ( Figures S1D , S1E ) . This deletion would result in the expression of a truncated EDS1-80 protein comprising of only the first 162 aa instead of the 629 aa long full-length protein . Thus , Ler-eds1-2 plants would essentially be defective in both EDS1 isoforms . Similarly , RT-PCR analysis showed that Ws and Ws-eds1-1 genotypes express EDS1-90 , but not EDS1-80 ( Figure S1F ) , suggesting that similar to Ler-eds1-2 , Ws-eds1-1 plants are also compromised in the activities of both isoforms . These results also suggested that the presence of a functional EDS1-90 isoform in Di-17 was sufficient for HRT-mediated resistance to TCV . To reconfirm this we isolated a T-DNA knockout ( KO ) mutant in EDS1-80 in the Col-0 background ( designated eds1-80; Figure S2A ) , crossed this KO line with Di-17 and analyzed segregation of resistance in the F2 plants ( Table S1 ) . Similar to Di-17 x Col-0 cross , plants of the HRT eds1-80 genotypes segregated normally for resistance; ∼25% plants were resistant to TCV ( Table S1 , Figures S2B , S2C ) . Genetic analysis based on EDS1-90 and EDS1-80 KO mutants suggested that either of the EDS1 isoforms can mediate HRT-mediated resistance to TCV . We tested this further by evaluating the response of another R gene RPS4 , which mediates resistance to Pseudomonas syringae expressing AvrRps4 ( Figure S3 ) and is known to require EDS1 . Unlike wild-type Ws plants , inoculation of avrRps4 bacteria induced prominent chlorosis and cell death in Ws-eds1-1 plants . The Col-0-eds1-80 and Col-0-eds1-90 plants on the other hand showed a similar response as wild-type Col-0 plants ( Figures S3A , S3B ) . Similarly , pathogen inoculation induced SA and PR-1 levels in wild-type and eds1-80 or eds1-90 plants , but not in eds1-1 ( Figures S3C , S3D ) . The eds1-80 or eds1-90 plants supported similar levels of bacterial growth as wild-type plants , which were ∼40-fold lower than that of the eds1-1 plants ( Figure S3E ) . Together , these results show that single mutations in EDS1-80 or EDS1-90 in Col-0 background were insufficient to compromise RPS4-mediated resistance against avrRps4 bacteria . To determine if EDS1-80 and EDS1-90 encoded functional proteins , corresponding to their orthologs in Ws and Ler ecotypes , we tested their ability to complement eds1-1 phenotypes . The EDS1-80 and EDS1-90 isoforms were expressed under the 35S promoter in the eds1-1 background ( Figures S4A , S4B ) and the T2 plants obtained from four independent lines expressing low or high EDS1 transcripts were analyzed for resistance to avrRps4 bacteria . Typical chlorosis and cell death phenotypes associated with avrRps4 infection on eds1-1 plants were not evident in plants expressing EDS1-80 or EDS1-90 , regardless of their transcript levels ( Figures 1A , S4C ) . Concurrently , these plants showed wt-like levels of ion-leakage ( Figure 1B ) , PR-1 expression ( Figure 1C ) , and SA levels ( Figure 1D ) in response to avrRps4 inoculation . The eds1-1 plants expressing EDS1-80 or EDS1-90 also supported wt-like growth of avrRps4 bacteria ( Figure 1E ) . Together , these results suggest that both EDS1-80 and EDS1-90 encode functional proteins and expression of either gene complements the enhanced disease susceptibility phenotype in eds1-1 plants . Together , these results suggest that the two EDS1 isoforms likely function redundantly and that simultaneous mutations in both EDS1 isoforms are required to compromise HRT-mediated resistance to TCV . To determine if EDS1 was required for the activation of HRT-mediated signaling we developed a transient system based on reconstitution of HR in Nicotiana benthamiana . This was essential since EDS1 and SA act redundantly to regulate HRT-mediated signaling in Arabidopsis [21] , thereby rendering it difficult to test the function of EDS1 alone . This assay was facilitated by the fact that co-infiltration of HRT and its cognate avirulence effector CP induced a delayed and weak HR in N . benthamiana . Thus , any factor participating in the activation of HRT-mediated signaling should promote HR formation . Interestingly , co-infiltration of EDS1-80 or EDS1-90 together with HRT and CP promoted HR formation ( Figure 2A ) , suggesting that both EDS1 isoforms likely facilitate the recognition of CP . This was further confirmed by assaying ion-leakage ( Figure 2B ) . Unlike EDS1 , co-infiltration of the eds1-1 mutant protein , SAG101 or PAD4 did not induce a strong HR in the presence of HRT and CP ( Figures 2A , 2B ) . HRT and CP were expressed at comparable levels in the presence or absence of SAG101 and PAD4 , suggesting that lack of HR in the HRT+CP+SAG101/PAD4 plants was not due to insufficient levels of HRT and/or CP ( Figure 2C ) . Likewise , expression levels of SAG101 and PAD4 were similar to EDS1 . Notably , as in Arabidopsis [17] , eds1-1 protein was unstable and accumulated to very low levels in N . benthamiana ( Figure 2C ) . To determine if EDS1 promoted HRT+CP-dependent HR via interactions with HRT and/or CP , we carried out bimolecular fluorescence complementation ( BiFC ) assays ( Figure 2D ) . As expected , HRT associated with its interacting partner CRT1 [21] , [31] , but no interaction was detected between either EDS1 isoforms and HRT or CP ( Figure 2D ) . However , co-immunoprecipitation ( IP ) assays showed that EDS1 interacted with HRT , but not CP ( Figures 2E , 2F ) . Neither HRT nor EDS1 interacted with GST ( data not shown ) . Consistent with their ability to promote HR formation , both EDS1 isoforms associated with HRT in co-IP assays . Interaction between EDS1 and HRT was further verified by expressing these proteins under their native promoters in N . benthamiana plants ( Figure S5A ) as well as in Arabidopsis protoplasts ( Figure S5B ) . Notably , EDS1 accumulated to similar levels when expressed under the 35S or its native promoter ( Figure S5C ) . In comparison , HRT accumulated to higher levels when expressed under its native promoters , compared to 35S ( Figure S5D ) . These results argue that interaction between HRT and EDS1 was not due to overexpression of these proteins . Together these results suggest that HRT associates with EDS1 , albeit indirectly , and this likely facilitates the CP-triggered induction of HR in the presence of HRT . In view of the functional redundancy between EDS1-80 and 90 and their association with HRT , it was important to determine if EDS1-80 was capable of forming a complex with PAD4 and SAG101 . Indeed , similar to EDS1-90 , EDS1-80 interacted with both PAD4 and SAG101 but not GST; the EDS1-80-PAD4 interaction was detected in both the periphery and nucleus of plant cells ( Figure 3A ) . In comparison , the EDS1-80-SAG101 complex was primarily seen in the nucleus ( Figure 3A ) . Co-IP assays further confirmed results obtained in the BiFC ( Figures 3B , 3C ) . Since EDS1 and PAD4 are well known to regulate pathogen-induced accumulation of SA [11] , [14] , we next tested if SA altered the EDS1-80-PAD4 or EDS1-80-SAG101 interactions . No obvious differences in the intensity or site of interactions were noticed ( data not shown ) , suggesting that increased SA might not alter these interactions . Unlike EDS1-90 , the EDS1-80 isoform did not interact with itself or with EDS1-90 in BiFC assays ( Figures 3A , S6A ) . In contrast , IP assays detected EDS1-80 interaction with itself and EDS1-90 ( Figures 3D , 3E ) . This suggests that the homo and heterodimerization of EDS1-80 and EDS1-90 was likely indirect . We next tested whether the presence of PAD4 or SAG101 affected the formation of the EDS1-80-90 heterodimer . EDS1-80 and EDS1-90 were co-expressed with PAD4 or SAG101 , and immunoprecipitates were assayed for the EDS1-90 , PAD4 or SAG101 proteins . Interestingly , EDS1-80 preferentially bound PAD4 in the presence of EDS1-90 ( Figures 4A , S6B ) . EDS1-80 also showed slightly more affinity for SAG101 over EDS1-90 ( Figure 4B ) . We next compared the relative affinities of EDS1-90 for PAD4 and SAG101 . Similar levels of EDS1-PAD4 complex were detected in the absence or presence of SAG101 ( Figure 4C ) . Similarly , levels of the EDS1-SAG101 complex did not alter significantly in the presence or absence of PAD4 ( Figure 4D ) . We next assayed interaction of SAG101 and PAD4 with the lipase ( LP; N-t 351 aa ) and EDS1-PAD4-like ( EP; C-t 351–623 ) domains of EDS1 . Both SAG101 and PAD4 interacted with the LP , but not the EP , domain of EDS1 ( Figure S6C ) . Together , these results suggest that SAG101 and PAD4 likely interact with different residues within the LP domain of EDS1 and therefore do not compete for binding with EDS1 . We noticed in our BiFC assays that the EDS1-SAG101 interaction occurred primarily in the nucleus ( Figure 3A ) , even though EDS1-80 or 90 were present in both the cytosol and the nucleus ( Figure 5A ) . We tested whether SAG101 influenced the subcellular localization of EDS1 and/or PAD4 . Coexpression of EDS1-GFP with PAD4-RFP did not alter the localization of either protein; EDS1-80 , EDS1-90 and PAD4 localized to the nucleus and periphery of the cell , similar to when expressed individually ( Figures 5A , 5B , S7A ) . Similarly , coexpression of PAD4-GFP and SAG101-RFP did not alter the localization of either protein ( Figure 5B ) . However , co-expression of EDS1-GFP with SAG101-RFP or SAG101-MYC altered the localization of EDS1 , but not SAG101; in the presence of SAG101 , EDS1 was preferentially detected in the nucleus ( Figures 5B , S7A , S7B ) . This SAG101 triggered nuclear localization of EDS1-80 and EDS1-90 was not due to increased expression of EDS1 in the presence of SAG101 ( Figure 5C ) . This result is in agreement with the previous report where co-localization of EDS1 and SAG101 was tested in wild-type Arabidopsis [17] . Interestingly , coexpression of PAD4-MYC or PAD4-Cerulean together with EDS1-GFP and SAG101-RFP retained some portion of EDS1 in the cytosol ( Figures 5D , S7C ) . Notably , nuclear-cytoplasmic localization of EDS1 was only observed when PAD4 was coexpressed with EDS1 and SAG101 . EDS1 remained preferentially in the nucleus when PAD4 was expressed 24 or 48 h after EDS1 and SAG101 ( data not shown ) . This suggested that , rather than relocalizing EDS1 , PAD4 merely retained it in the cytosol . This further suggested that EDS1 might be retained inside the nucleus in the presence of SAG101 , although the nuclear localization of EDS1 was not dependent on SAG101 ( data not shown ) . Unlike PAD4 , SAG101 accumulated to higher levels when expressed under 35S , compared to its native promoter ( Figures S7D , S7E ) . Thus , it was possible that the nuclear relocalization of EDS1 was dependent on the levels of SAG101 . Indeed , when expressed under its native promoter , SAG101 was unable to relocalize all the cytosolic EDS1 into the nucleus ( Figure 5E ) . These results , together with the observation that EDS1 and PAD4 levels change during pathogen infection [16] , suggest that relative levels of EDS1 , PAD4 and SAG101 might regulate distribution and/or localization of these proteins in response to pathogen stimulus . To further confirm that the SAG101 triggered nuclear relocalization of EDS1 was a specific phenotype , we tagged EDS1 with a nuclear export signal ( NES ) , or its mutant derivative ( nes ) [18] . As expected , both EDS1-80-NES and EDS1-90-NES were preferentially detected outside the nucleus while EDS1-80-nes and EDS1-90-nes localized like wild-type EDS1 ( Figure 6A ) . Coexpression experiments showed that only EDS1-nes , but not EDS1-NES , relocalized to the nucleus in the presence of SAG101 ( Figure 6B ) . Coexpression with PAD4 did not alter the localization of either form of EDS1 . However , nuclear localization of PAD4 was affected by the presence of EDS1-NES , but not EDS1-nes ( Figure 6B ) . Similar levels of EDS1-80-NES/nes protein in plants infiltrated with SAG101-RFP or PAD4-RFP suggested that localization of EDS1 was not associated with levels of protein expression ( Figure 6C ) . Furthermore , EDS1-80-NES or EDS1-90-NES failed to induce the nuclear exclusions of RFP-tagged EDS1-90 ( Figures 6B , S7D ) , suggesting that EDS1-NES-dependent extranuclear localization of SAG101 and PAD4 was a specific phenotype . We next evaluated the interaction of PAD4 and SAG101 with EDS1-NES or EDS1-nes . As expected , EDS1-nes behaved similar to EDS1 ( Figures 3A , 6D , S8 ) . Notably , although EDS1-NES associated with both PAD4 and SAG101 , these interactions occurred preferentially outside the nucleus ( Figures 6D , S8 ) . This was particularly evident in the case of SAG101 , since the EDS1-SAG101 complex is normally located inside the nucleus . Together , these results suggest that the selective retention of EDS1 in a subcellular compartment can drive the localization of the EDS1-PAD4 and EDS1-SAG101 complexes . The fact that EDS1 can induce the cytosolic relocalization of SAG101 further suggests that , the previously reported inability of EDS1-NES to fully complement eds1-1 phenotypes might be due to the altered/mis-localization of PAD4 and/or SAG101 , rather than the absence of EDS1 in the nucleus [18] . To determine if altered localization of EDS1-NES affected its ability to promote HRT-CP triggered cell death phenotype , we monitored visual phenotypes and ion-leakage in N . benthamiana plants infiltrated with HRT+CP+EDS1-NES . No significant difference was noticed in HRT+CP-mediated cell death phenotype induced in the presence of EDS1 , EDS1-NES or EDS1-nes ( Figures 7A , 7B ) . This further correlated with positive interaction seen between EDS1-NES and HRT proteins ( Figure 7C ) . Together , these results suggested that the extranuclear retention of EDS1 , and by extension that of PAD4 and SAG101 , do not suppress HRT+CP-mediated HR . The ability of EDS1 , PAD4 , SAG101 proteins to relocalize their interacting partners suggested that these proteins might be present in a ternary complex . However , consistent with earlier observations [17] , we were unable to detect interactions between SAG101 and PAD4 in BiFC or co-IP assays ( Figures 8A , 8B ) . We considered the possibility that factors present only during induced defense might be required for the PAD4-SAG101 association , if any . Since both EDS1 and PAD4 are known to regulate SA levels and because exogenous SA can induce reducing conditions required for relocating proteins [32] , we tested binding between SAG101 and PAD4 in plants pretreated with SA . No SAG101-PAD4 interaction was detected in SA pretreated plants ( Figure 8A ) . Another possibility was that SAG101-PAD4 interacted via a third protein , possibly EDS1 , since both SAG101 and PAD4 interacted with EDS1 . We tested the SAG101-PAD4 interaction in the presence of EDS1-80 or EDS1-90 ( Figure 8A ) . Indeed , SAG101 and PAD4 associated with each other in the presence of either EDS1 isoform . This was further confirmed using co-IP assays ( Figure 8C ) . Increased nuclear fluorescence in the BiFC assays suggested that a majority of the SAG101-EDS1-PAD4 complex was present in the nucleus . Pretreatment with SA did not alter formation or localization of the SAG101-EDS1-PAD4 complex . Interestingly , full length EDS1 was required for SAG101-EDS1-PAD4 complex formation , even though EDS1-LP domain alone was sufficient for interaction with SAG101 or PAD4 ( Figure 8A ) . We next assayed the interaction between SAG101 and PAD4 in the presence of EDS1-NES and EDS1-nes , which were expressed as MYC tagged proteins ( Figure S9 ) . Surprisingly , in the presence of EDS1-NES , the SAG101-PAD4 interaction was preferentially detected outside the nucleus ( Figure 8A ) , suggesting that the subcellular location of EDS1 might drive the localization of the SAG101-EDS1-PAD4 complex . The fact that SAG101 forms a ternary complex with EDS1 and PAD4 , and that it can drive the nuclear localization of EDS1 is inconsistent with a proposed redundant role for SAG101 in plant defense [17] , [19] . We therefore investigated the requirement for SAG101 in HRT-mediated signaling . We crossed Di-17 plants with sag101 and analyzed HR and resistance in the F2 population . Approximately 75% of the plants showed HR to TCV , regardless of their genotype at the SAG101 locus ( Figure 9A ) . HR phenotype correlated with increased expression of PR-1 gene in both HRT SAG101 and HRT sag101 plants ( Figure 9B ) . However , all the HRT sag101 plants showed susceptibility to TCV and allowed increased accumulation of TCV in the distal tissues ( Figures 9C , 9D , Table S1 ) . The susceptible phenotype correlated with a significant reduction in SA and SAG accumulation in TCV inoculated HRT sag101 plants ( Figure 9E ) . Together , these results suggested that SAG101 is required for HRT-mediated resistance . To determine if the sag101 mutation compromised resistance to TCV by affecting the accumulation of HRT , we mobilized the HRT-FLAG transgene into HRT sag101 plants and analyzed HRT-FLAG levels . The HRT sag101 plants contained wt-like levels of HRT protein , before and after TCV inoculation ( Figure 9F ) . In addition , as in Di-17 plants , all the HRT protein was present in the membranous fraction of extracts from HRT sag101 plants ( Figure 9G ) . These results indicate that the inability of HRT sag101 plants to induce pathogen-responsive SA accumulation was not due to the altered levels or localization of the R protein . Notably , pretreatment with SA or its analog BTH restored resistance in HRT sag101 plants ( Figure 9H ) . SA pretreatment also restored resistance in HRT eds1 , but not in HRT pad4 plants ( Figure 9H ) . The resistant and susceptible phenotypes in HRT sag101/HRT eds1 and HRT pad4 plants , respectively , correlated with HRT transcript levels; BTH treatment increased HRT transcript levels in HRT sag101 and HRT eds1 , but not in HRT pad4 plants ( Figure 9I ) . Thus , PAD4 , but not EDS1 or SAG101 , is required for the SA-mediated induction of HRT . Together , these results suggest that SAG101 does fulfill an independent function in HRT-mediated signaling .
Two functionally redundant isoforms of EDS1 in the Arabidopsis genome participate in resistance signaling such that the presence of either isoform is sufficient to mediate R-derived defense against microbial pathogens . Consistent with the co-operative roles of PAD4 and SAG101 in EDS1 function , either EDS1 isoforms can interact with PAD4 and SAG101 , as well as form ternary complexes with these proteins . While SAG101 can drive the nuclear localization of EDS1 , the presence of PAD4 can disrupt this to retain some EDS1 in the cytosol . This raises the possibility that the relative levels of SAG101 and PAD4 may drive the subcellular localization of EDS1 . Conversely , EDS1 can also drive the localization of SAG101 . For example , even though the majority of the EDS1-SAG101 or the SAG101-EDS1-PAD4 complexes are present in the nucleus , preferential retention of EDS1 in the cytosol via the addition of a nuclear export signal relocates SAG101 and the ternary complex to the cytosol . Furthermore , these data suggest that dynamic changes in the levels of EDS1/PAD4/SAG101 could drive the subcellular localization of the binary/ternary complexes to regulate defense signaling . These results also offer a possible mechanistic explanation for the inability of EDS1-NES to fully complement eds1 phenotypes [18] . A significantly large recovery of the EDS1-PAD4 and EDS-SAG101 complexes versus the SAG101-EDS1-PAD4 complex in co-IP studies suggests that EDS1 might be primarily present in a complex with PAD4 and SAG101 . However , at this point we cannot discount the possibility that SAG101-EDS1-PAD4 complex is inherently unstable in cell free extracts . Indeed , earlier studies were unsuccessful in isolating SAG101-EDS1-PAD4 complex from cell free extracts [17] , [33] . Notably , both PAD4 and SAG101 interact with the LP ( 1–350 aa in EDS1-90 and 1–357 aa in EDS1-80 ) domain of EDS1-80 , which could potentially lead to steric hindrances resulting in protein instability . Intriguingly , our interaction studies with EDS1-LP and EP domains are not consistent with a previous report [16] , which showed that the EP domain ( 351–623 aa ) of EDS1-90 can self-interact , however the full-length EDS1-90 protein is required for interaction with PAD4 . We find that the LP , but not the EP domains of both EDS1 isoforms , are required for interactions with self as well with PAD4 and SAG101 . One possibility for these discrepancies is that our studies were carried out in planta , which likely better mimic the native environment than those in the Feys et al [17] study . Nonetheless , detection of the SAG101-EDS1-PAD4 complex required the full length EDS1 protein . Unlike EDS1 and PAD4 , which are well known to be essential for R protein-mediated defense to several pathogens [11] , [16] , [17] , [20] , [28] , [29] , SAG101 has been assigned a redundant role [17] , [19] . This is because mutations in SAG101 do not compromise RPM1 , RPS4 , or RPP2-mediated resistance to the respective bacterial or oomycete pathogens , but does enhance susceptibility in pad4 plants [17] . In comparison to single mutants , simultaneous mutations in SAG101 and PAD4 also confer increased susceptibility to non-host pathogens [19] , further supporting the functional redundancy between SAG101 and PAD4 . We find that at least in the case of HRT-mediated signaling , SAG101 is as important as EDS1 and PAD4 , since the absence of SAG101 alone can compromise HRT-mediated resistance to TCV . A PAD4-independent role for SAG101 is further supported by the fact that unlike PAD4 , SAG101 is not required for the SA-mediated induction of HRT . Interestingly , similar to the eds1-1 and pad4-1 mutations [22] , the sag101-1 mutation also reduced TCV-induced SA levels . Thus , similar to EDS1 and PAD4 , SAG101 also regulates TCV-induced SA accumulation and might function in a feedback loop with SA . The sag101 plants also show a nominal reduction in the levels of EDS1-90 and PAD4 proteins [17] , which could contribute towards susceptibility to TCV in these plants . However , this is unlikely because sag101 plants are unaltered in RPS4-mediated resistance , which like HRT , is dependent on EDS1 and PAD4 . In this regard , it is interesting to note that while loss of both EDS1 isoforms is thought to destabilize PAD4 and SAG101 [17] and thereby pathogen resistance , lack of a single isoform does not compromise resistance to TCV or avrRps4 bacteria . Clearly , the levels of EDS1 , PAD4 , SAG101 essential for initiating signaling response needs further clarification . It is possible that these proteins initiate normal signaling even at very low levels . This is not unusual as HRT cry2 and HRT phot2 plants show normal HR even though they contain significantly lower levels of HRT compared to wild type plants [24] . An important aspect that has not been addressed thus far is whether EDS1 , PAD4 , and SAG101 function as individual proteins or in a complex . Clearly , at least PAD4 fulfills a unique function in HRT-mediated signaling on its own; SA-mediated increase in HRT requires PAD4 , but not EDS1 or SAG101 . Similarly , only EDS1 facilitated HRT+CP-mediated HR to TCV . The EDS1-80-EDS-90 complex also does not appear to be essential for HRT- or RPS4-mediated signaling , since these continue to function in the eds1-80 or eds1-90 mutants . Interaction between EDS1 and HRT and the fact that EDS1 forms a complex with PAD4 and SAG101 suggests that these proteins might be part of a multi-protein complex and thus regulate signaling by modulating the activity of HRT . The absence of EDS1-HRT interaction in the BiFC assay suggests that EDS1 may associate indirectly with HRT . Interestingly , EDS1 does not dissociate from HRT in the presence of CP , suggesting that CP-triggered activation of HRT does not involve the release of EDS1 . Whether CP-triggered activation of HRT utilizes the EDS1 , PAD4 , and SAG101 proteins individually or as complexes needs further clarification . However , the requirements for all three proteins do support the notion that the SAG101-EDS1-PAD4 ternary complex might be important . It will indeed be important to establish the biochemical activities of EDS1 , PAD4 and SAG101 proteins in order to accurately access the importance of the ternary complex in R protein-mediated signaling .
Plants were grown in MTPS 144 Conviron ( Winnipeg , MB , Canada ) walk-in-chambers at 22°C , 65% relative humidity and 14 hour photoperiod . The photon flux density of the day period was 106 . 9 µmoles m−2 s−1 and was measured using a digital light meter ( Phytotronic Inc , Earth city , MO ) . All crosses were performed by emasculating the flowers of the recipient genotype and pollinatng with the pollen from the donor . F2 plants showing the wt genotype at the mutant locus were used as controls in all experiments . The wt and mutant alleles were identified by PCR , CAPS , or dCAPS analysis [21]–[26] . The EDS1 KO mutant in At3 g48080 and At3 g48090 were isolated by screening SALK_019545 and SALK_071051 insertion lines , respectively . This EDS1 KO in At3 g48090 was previously designated eds1-22 and redesignated here as eds1-90 . The homozygous insertion lines were verified by sequencing PCR products obtained with primers specific for the T-DNA left border in combination with an EDS1-specific primer ( Table S2 ) . The full length cDNA corresponding to EDS1-80 and EDS1-90 genes were PCR amplified using linkered primers and cloned downstream of 35S promotor in pRTL2 . GUS . For Arabidopsis transformation , the fragment containing the promotor , cDNA and terminator was removed from pRTL2-EDS1 vectors and cloned into binary vector pCambia or pBAR . These clones in binary vectors were moved into Agrobacterium tumefaciens strain MP90 by electroporation and were used to transform Arabidopsis via the floral dip method . Selection of transformants was carried out on medium containing hygromycin or soil sprayed with the herbicide BASTA . Small-scale extraction of RNA from one or two leaves was performed with the TRIzol reagent ( Invitrogen , CA ) , following the manufacturer's instructions . Northern blot analysis and synthesis of random-primed probes for PR-1 and PR-2 were carried out as described previously [26] . RNA quality and concentration were determined by gel electrophoresis and determination of A260 . Reverse transcription ( RT ) and first strand cDNA synthesis were carried out using Superscript II ( Invitrogen , CA ) . Two-to-three independent RNA preparations were used for RT-PCR and each of these were analyzed at least twice by RT-PCR . The RT-PCR was carried out for 35 cycles in order to determine absolute levels of transcripts . The number of amplification cycles was reduced to 21–25 in order to evaluate and quantify differences among transcript levels before they reached saturation . The amplified products were quantified using ImageQuant TL image analysis software ( GE , USA ) . Gene-specific primers used for RT-PCR analysis are described in Table S2 . The leaves were vacuum-infiltrated with trypan-blue stain prepared in 10 mL acidic phenol , 10 mL glycerol , and 20 mL sterile water with 10 mg of trypan blue . The samples were placed in a heated water bath ( 90°C ) for 2 min and incubated at room temperature for 2–12 h . The samples were destained using chloral hydrate ( 25 g/10 mL sterile water; Sigma ) , mounted on slides and observed for cell death with a compound microscope . The samples were photographed using an AxioCam camera ( Zeiss , Germany ) and images were analyzed using Openlab 3 . 5 . 2 ( Improvision ) software . The bacterial strain DC3000 derivatives containing pVSP61 ( empty vector ) , or avrRps4 were grown overnight in King's B medium containing rifampicin and kanamycin ( Sigma , MO ) . The bacterial cells were harvested , washed and suspended in10 mM MgCl2 . The cells were diluted to a final density of 105 CFU/mL ( A600 ) and used for infiltration . The bacterial suspension was injected into the abaxial surface of the leaf using a needle-less syringae . Three leaf discs from the inoculated leaves were collected at 0 and 3 or 6 dpi . The leaf discs were homogenized in 10 mM MgCl2 , diluted 103 or 104 fold and plated on King's B medium . Transcripts synthesized in vitro from a cloned cDNA of TCV using T7 RNA polymerase were used for viral infections . For inoculations , the viral transcript was suspended at a concentration of 0 . 05 µg/ µL in inoculation buffer , and the inoculation was performed as described earlier [31] . After viral inoculations , the plants were transferred to a Conviron MTR30 reach-in chamber maintained at 22°C , 65% relative humidity and 14 hour photoperiod . HR was determined visually three-to-four days post-inoculation ( dpi ) . Resistance and susceptibility was scored at 14 to 21 dpi and confirmed by northern gel blot analysis . Susceptible plants showed stunted growth , crinkling of leaves and drooping of the bolt . A protocol adapted from Dellagi et al . [34] was used for conductivity measurements . Briefly , 5 leaf discs per plant ( 7 mm ) were removed with a cork borer , floated in distilled water for 50 min , and subsequently transferred to tubes containing 5 ml of distilled water . Conductivity of the solution was determined with an NIST traceable digital Conductivity Meter ( Fisher Scientific ) at the indicated time points . Standard deviation was calculated from four replicate measurements per genotype per experiment . SA and SAG quantifications were carried out from ∼300 mg of leaf tissue as described before [23] . SA or BTH treatments were carried out by spraying or subirrigating 3-week-old plants with 500 µM SA or 100 µM BTH , respectively . Proteins were extracted in buffer containing 50 mM Tris-HCl , pH7 . 5 , 10% glycerol , 150 mM NaCl , 10 mM MgCl2 , 5 mM EDTA , 5 mM DTT , and 1 X protease inhibitor cocktail ( Sigma-Aldrich , St . Louis , MO ) . Protein concentration was measured by the Bio-RAD protein assay ( Bio-Rad , CA ) . For Ponceau-S staining , PVDF membranes were incubated in Ponceau-S solution ( 40% methanol ( v/v ) , 15% acetic acid ( v/v ) , 0 . 25% Ponceau-S ) . The membranes were destained using deionized water . For soluble versus microsomal fractionations , proteins were extracted in buffer containing 50 mM Tris-MES , pH 8 . 0 , 0 . 5 M sucrose , 1 mM MgCl2 , 10 mM EDTA , 10 mM EGTA , 10 mM ascorbic acid , 5 mM DTT and 1X protease inhibitor cocktail ( Sigma-Aldrich , St . Louis , MO ) . Total protein extract was centrifuged at 10 , 000 g followed by a second centrifugation at 125 , 000 g . The microsomal fraction was suspended in a buffer containing 5 mM potassium phosphate pH 7 . 8 , 2 mM DTT and 1 X protease inhibitor cocktail . Proteins ( 30–50 µg ) were fractionated on a 7–10% SDS-PAGE gel and subjected to immunoblot analysis using α-CP , α-MYC , α-FLAG ( Sigma-Aldrich , St . Louis , MO ) or α-GFP antibody . Immunoblots were developed using ECL detection kit ( Roche ) or alkaline-phosphatase-based color detection . Coimmunoprecipitations were carried out as described earlier [24] . Protoplasts were isolated from three-week-old Arabidopsis Col-0 plants as described earlier [35] . For protoplast transfection , ∼104 protoplasts were incubated at room temperature with 20 µg of plasmid DNA and an equal volume of solution containing 40% PEG 4000 , 0 . 1 M CaCl2 and 0 . 2 M mannitol . After 5 min , 3 ml of wash solution containing 154 mM NaCl , 125 mM CaCl2 , 5 mM KCl , 5 mM glucose , 2 mM MES ( pH 5 . 7 ) was added slowly to the protoplast and the protoplasts were pelleted by centrifugation at 100 x g for 1 min . The protoplasts were washed twice and finally suspended in 1 ml of wash solution . The protoplasts were incubated in a round bottom glass vial for 12 h prior to protein extraction . For confocal imaging , samples were scanned on an Olympus FV1000 microscope ( Olympus America , Melvile , NY ) . GFP ( YFP ) , CFP ( Cerulean ) and RFP were excited using 488 , 440 , and 543 nm laser lines , respectively . Constructs were made using pSITE [36] or pEarlyGate binary vectors using Gateway technology and introduced in A . tumefaciens strain LBA4404 or MP90 for agroinfiltration into N . benthamiana or Arabidopsis , respectively . Agrobacterium strains carrying various constructs were infiltrated into wild-type or transgenic N . benthamiana plants expressing CFP-tagged nuclear protein H2B or Arabidopsis plants . 48 h later , water-mounted sections of leaf tissue were examined by confocal microscopy using a water immersion PLAPO60XWLSM 2 ( NA 1 . 0 ) objective on a FV1000 point-scanning/point-detection laser scanning confocal 3 microscope ( Olympus ) equipped with lasers spanning the spectral range of 405–633 nm . RFP , CFP and GFP overlay images ( 40X magnification ) were acquired at a scan rate of 10 ms/pixel . Images were acquired sequentially when multiple fluors were used . Olympus FLUOVIEW 1 . 5 was used to control the microscope , image acquisition and the export of TIFF files . | Plant immunity to pathogens requires several proteins , including EDS1 , PAD4 , SAG101 , and these are thought to act downstream of resistance protein-mediated signaling . EDS1 interacts with both PAD4 and SAG101 but no interaction has been detected between SAG101 and PAD4 . We show that SAG101 interacts with PAD4 via EDS1 and that the SAG101-EDS1-PAD4 ternary complex is present in the nucleus . EDS1 , which is present in the cytoplasm and nucleus , is detected preferentially in the nucleus in the presence of SAG101 . The presence of PAD4 restores the cytoplasmic localization of EDS1 . Conversely , the SAG101-EDS1-PAD4 ternary complex , which is detected primarily in the nucleus , is redirected to cytoplasm in the presence of an extranuclear form of EDS1 . These results show that protein localization changes in relation to the subcellular localization and/or relative levels of their interacting partners . We further show that Arabidopsis plants encode two functional isoforms of EDS1 . Both isoforms interact with self and each other , as well as form ternary complexes . SAG101 , which is thought to serve as a substitute for PAD4 , functions independently in defense signaling against turnip crinkle virus . Our results suggest that EDS1 , PAD4 , SAG101 function independently as well as in a ternary complex to mediate plant defense signaling . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"plant",
"science",
"plant",
"biology",
"plant",
"cell",
"biology",
"plant",
"pathology",
"biology",
"plant",
"physiology"
] | 2011 | SAG101 Forms a Ternary Complex with EDS1 and PAD4 and Is Required for Resistance Signaling against Turnip Crinkle Virus |
We propose a minimal mathematical model for the physical basis of membrane protein patterning in the immunological synapse ( IS ) , which encompass membrane mechanics , protein binding kinetics and motion , and fluid flow in the synaptic cleft . Our theory leads to simple predictions for the spatial and temporal scales of protein cluster formation , growth and arrest as a function of membrane stiffness , rigidity and kinetics of the adhesive proteins , and the fluid flow in the synaptic cleft . Numerical simulations complement these scaling laws by quantifying the nucleation , growth and stabilization of proteins domains on the size of the cell . Direct comparison with experiment shows that passive elastohydrodynamics and kinetics of protein binding in the synaptic cleft can describe the short-time formation and organization of protein clusters , without evoking any active processes in the cytoskeleton . Despite the apparent complexity of the process , our analysis shows that just two dimensionless parameters characterize the spatial and temporal evolution of the protein pattern: a ratio of membrane elasticity to protein stiffness , and the ratio of a hydrodynamic time scale for fluid flow relative to the protein binding rate . A simple phase diagram encompasses the variety of patterns that can arise .
Recognition of self or non-self is essential for an effective and functional adaptive immune response . The main players in this process are immune cells ( T-lymphocyte cells ( T-cells ) [1–3] , B-cells , natural killer ( NK ) cells [4] and phagocytes [5 , 6] that are constantly on the move scanning surfaces for antigenic peptides on Antigen Presenting Cells ( APC ) . Receptors on the membrane of the immune cells are responsible for sensing and translating information from the extracellular matrix into the cell . Upon antigen recognition the immune cell orchestrates a spatio-temporal organization of its membrane bound proteins into the Immunological Synapse ( IS ) [7] . Intercellular signaling in a functional IS relates to the formation of large protein domains [2 , 3] , whereas their formation and the cluster-to-cluster interaction plays an important role in determining the overall cell signaling mechanism . [8] . In the widely studied T-cells , the compartmentalization of membrane-bound protein patterns into different protein domains on the cellular scale leads to the formation of Supra Molecular Activation Clusters ( SMACs ) [2 , 3] . In particular , T-Cell Receptors ( TCR ) form bonds with the peptide Molecular HistoComplex ( pMHC ) on the APC , while Leukocyte-Function-Associated antigen-1 ( LFA ) -integrin on the T-cell bind with Intercellular Adhesion Molecules ( ICAM ) [3] . Soon ( O ( 1 s ) ) [9] after membrane-to-membrane contact sub micron protein clusters are formed that start to translocate ( O ( 1 min ) ) [3] . This is followed by long range transport and a concomitant coarsening to form large-scale protein domains at longer times ( O ( 40 min ) ) [3 , 13] . Observations of the T-cell IS show a central accumulation of TCR-pMHC , surrounded by a donut-shaped preferential protein domain of LFA-ICAM [2 , 11] where protein clusters nucleate and act as signaling entities [12–14] . Understanding the biophysical basis for protein patterning by deciphering the quantitative rules for their formation and motion [14] is a first step in characterizing recognition and communication in the immune system . A particularly interesting question in this regard is the role of passive physicochemical processes relative to active motor-driven processes in generating these patterns [15] . Recent experiments suggest that early on during the process , active processes may not be important , and it is only later that the protein pattern in the T-cell membrane is subject to cytoskeletally generated centripetal transport [16–23] . The question of characterizing the mechanics of the IS patterns has led to a range of mathematical models that take one of two forms: those that treat the system as a collection of discrete units [25–29] or as a continuum [30 , 32 , 33] . While these models are capable of explaining the spatial patterning seen in the IS , they all neglect the fluid flow in the synaptic cleft and thus rely on ad-hoc assumptions for the characteristic time scales over which the patterns form , and use approaches based on gradient descent [30 , 32–34] or stochastic variations of energy minimization of the membrane coupled to protein kinetics [25–28] . Here , we provide a description of the passive responses in the IS which includes the mechanical forces due to stretching and bending of the cell membrane which are driven by protein attachment and fluid flow , which itself causes flow of the trans-membrane proteins . This requires that we integrate cell membrane bending and tension , viscous flow in the synaptic cleft and protein attachment-detachment kinetics , and allows us to capture the essential spatiotemporal protein dynamics ( nucleation , translation and coalescence of protein clusters ) during the formation of SMACs . Furthermore , we show that our description of the passive dynamics in the IS implies that the slow dynamics of fluid flow can limit the rate of protein patterning , without evoking any active cytoskeletal processes .
In Fig 1 we illustrate the interaction between a T-cell and an antigen seeded bilayer , which mimics the most commonly used experimental setup [3 , 20 , 21 , 23] , and describes the components in the mathematical model described below . Once the T-cell is close to the bilayer ( Fig 1 ) the membrane-bound receptors form adhesive bonds with their ligand counterparts in the bilayer , which pull the membranes together and squeezing the fluid out of the cleft . When these two types of receptors form bonds with ligands , they get compressed or stretched . We assume that their spring stiffnesses κ i = l 1 l i κ are inversely proportional to the protein length li that may vary among different protein types [31] . The subscript i = 1 corresponds to the TCR-pMHC complex and i = 2 corresponds to the LFA-ICAM complex . Ci = Ci ( x , y , t ) is the number of attached proteins per surface area ( associated with at the total equilibrium receptor density C0 ) , their deformation creates a local pressure ∼ κi Ci ( x , y , t ) ( li − h ) . This pressure deforms the cell membrane , approximated here as a bilayer with a bending stiffness B m = E b 3 12 ( 1 − ν 2 ) , with E the Young’s modulus , b the membrane thickness and ν the Poisson ratio ( see S1 Text ) , and a mechanical response quantified by p ( x , y , t ) = B m ∇ 4 h + κ C 1 ( h - l 1 ) + κ 2 l 1 l 2 C 2 ( h - l 2 ) ( 1 ) where p = p ( x , y , t ) is the pressure along the membrane , and h = h ( x , y , t ) is the height of the fluid-filled synaptic cleft . We focus here on the limit when membrane bending dominates , but we show in the S3 Fig that the influence of membrane tension smooths some of the small scale pattern features . Active cytoskeletal forces would appear as additional source terms in p , but has been neglected below as we focus on the passive dynamics . Any membrane deformation initiates fluid motion and give rise to hydrodynamic forces in the synaptic cleft , which consequently affects the membrane dynamics . In typical experiments , the synaptic pattern has a lateral size L comparable to the cell size ( ≈ 10 μm ) , while the cleft has a height comparable to size of the longest protein bond ( l2 = 45 nm ) . Thus the aspect ratio of the IS is small l2/L ≪ 1 . When combined with the fact that at these small length scales , the flow in the synaptic cleft is viscously dominated , we may use lubrication theory [36] to simplify the equations governing fluid flow . Under the assumption of local Poiseuille flow [36] we can derive a single non-linear scalar partial differential equation for the thin film height h ( x , y , t ) [37] similar to that seen in other elastohydrodynamic phenomena [38–40] ∂ h ∂ t = ∇ · ( h 3 12 μ ∇ p ) , i . e . ∂ h ∂ t = ∇ · ( h 3 12 μ ∇ ( B m ∇ 4 h + κ C 1 ( h - l 1 ) + κ l 1 l 2 C 2 ( h - l 2 ) ) ) , ( 2 ) where Eq 2 follows by using Eq 1 for pressure ( p ) , where μ is the fluid viscosity . We note that the presence of proteins and other polymers in the membrane gap can lead to the formation of a porous structure that impedes flow and leads to a Darcy-like regime rather than a Poiseuille-like regime . In this situation , we expect the flow to be determined by the relation u ≈ − K p μ ∇ p , where Kp is the effective hydraulic permeability in the gap; when Kp = h2 we recover the Poiseuille relationship used in Eq 2 . Here , we limit ourselves to the Poiseuille-form , noting that many qualitative features of our results will carry over to the Darcy regime as well . Here , we have also neglected the effects of fluid permeation across the membrane in the absence of experimental evidence for this . Finally we have neglected thermal fluctuations of the membrane since these will be strongly damped by enthalpic protein binding . We only follow the dynamics of the membrane-bound proteins that can bind and unbind from their complementary ligands , which is equivalent to stating that the number of these proteins involved in the binding kinetics is large compared to the free proteins in the cytoplasm . In the membrane we assume that the total number of membrane-bound proteins per unit area is constant and given by Ci , 0 , where i = 1 corresponds to TCR and i = 2 corresponds to LFA . Of these , the number density of bound receptors is denoted by Ci ( x , y , t ) . Their dynamics can be described mathematically by a reaction-convection-diffusion equation , which accounts for their diffusion and transport in addition to the binding and detachment , and in dimensional form reads ∂ C i ∂ t = h l i μ ∇ P · ∇ C i + ∇ · ( D i ∇ C i + k b T D i μ ( C i ∇ h ( h - l i ) ) ) + ( C i , 0 - C i ) K o n ( l i ) - C i K o f f ( l i ) . ( 3 ) The first term on the right side is an advective term due to the fluid flow in the synaptic cleft driven by local pressure gradients associated with membrane deformation . The second term is a membrane protein flux due to molecular diffusion Di∇Ci , where the diffusion coefficient Di = ( l1/li ) D is assumed to be inversely proportional to the protein length following the Stokes-Einstein equation . Alternatively , the membrane diffusivity can be influenced by the membrane anchors , but our results are fairly insensitive to the molecular diffusion term ( see SI ) and we ignore them here . The third term on the right side is a drift in response to membrane deformation at a rate k B T D i μ ∇ ( C i ∇ h ( h − l i ) [30 , 34] , where KB T is the thermal energy . The last two terms correspond to receptor binding at a rate ( Ci , 0 − Ci ) Kon ( li ) and unbinding at a rate Ci Koff ( li ) . The kinetic rates Kon ( li ) and Koff ( li ) are described in terms of the mean first passage time over an energy barrier [41 , 42] , with a distribution centered around the natural protein length ( li ) and being a function of li/l2 − h , given by Bond formation : K o n ( l i ) = 1 τ k exp ( - ( l i l 2 - h l 2 σ o n l i l 2 ) 2 ) Bond depletion : K o f f ( l i ) = 1 3 τ k exp ( - ( l i l 2 - h l 2 σ o f f l i l 2 ) 2 ) , ( 4 ) where τk is the kinetic time . To favor protein binding for h ∼ li , we assume that proteins lose their bonds three times slower ( 3τk ) [27] than the rate at which they form . Alternatively , if we assume that the off-rate increases with spring tension , so that proteins would unbind as h ≪ li and h ≫ li and in its simplest form given by a constant off-rate ( σoff = ∞ ) in Eq 4 that produce similar results ( see SI ) . Although the exact form of these rates are not known , experiments show that the the different protein pairs form non-overlapping patterns [2 , 3 , 20] , which we mimic via the choice of the width of the kinetic distributions σon = 0 . 2 and σoff = 0 . 6 [35] . Narrowing the distributions generates wider protein free areas that separate TCR-pMHC and LFA-ICAM rich regions . In contrast , increasing the distribution widths make the different protein species overlap , which is unrealistic . All together , our model focuses on protein transport due to physicochemical processes driven by protein binding , fluid flow and membrane deformation and neglects the role of active cytoskeleton dynamics in the cell e . g . polarized release of T-cell-receptor-enriched microvesicles [24] , endocytosis and exocytosis of proteins [5] . The material properties of the cell , the fluid and the proteins that are relevant to the IS and needed as input into Eqs 1–4 are summarized in Table 1 as reported in previous work in the literature . It is natural to scale the horizontal length scales using the cell size , i . e . [x , y] ∼ L , the height of the synaptic cleft using the typical protein length i . e . h ∼ l2 , the pressure by the local receptor force/area , i . e . p ∼ C0 κl2 ≡ p0 , and time by a viscous time , i . e . τ μ = μ C 0 κ l 2 . In Eqs 1–4 , the use of the scaled variables p ( x , y , t ) = p* ( x , y , t ) p0 = p* ( x , y , t ) C0 κl2 , h ( x , y , t ) = h ( x , y , t ) *l2 , x = x*L , y = y*L , t = t*τμ , Ci ( x , y , t ) = Ci ( x , y , t ) *C0 yields six non-dimensional numbers that govern the dynamics of protein patterning , as shown in Table 2: B = B m κ C 0 L 4 is the ratio of pressure generated by membrane bending and the protein spring pressure , l1/l2 is the ratio between the natural length of the proteins which is approximately 1/3 , l2/L is the aspect ratio of the membrane gap , P e = L 2 C 0 κ l 2 D μ is the ratio between advection and diffusion , M = D μ k b T C 0 l 2 is the ratio between protein diffusion and protein sliding mobility , τ = τ μ τ k = μ τ k C 0 κ l 2 is the ratio between the local hydrodynamic time τμ and the kinetic time τk ( Table 1 ) . As we will show , our results are insensitive to variations in Pe , M and initial conditions ( see SI ) , and only the dimensionless numbers B and τ control the qualitative aspects of our phase space of patterns . The variations in two important dimensionless numbers B and τ can be used to capture the potential variations in the membrane properties and/or the protein biochemistry across different experiments . In particular , the membrane properties depends on its composition , where the presence of inclusions e . g . cholesterol , peptides , proteins , can alter its stiffness . τ depends on the fluid in the synaptic cleft and the biochemistry of protein binding . In particular , if τ > 1 bonds form rapidly relative to the time for fluid flow in the cleft which is then rate limiting , and conversely when τ < 1 , fluid flow is fast relative to bond formation which is then rate limiting . Two characteristic lengths are observed in the IS , the micro-cluster scale lc and the large domain scale L . From Eq 1 we derive a scaling law for the cluster size , by balancing the spring pressure and bending pressure B m l 2 / l c 4 ≈ C 0 l 2 that leads to l c ≈ ( B m C 0 κ ) 1 4 . ( 5 ) For the simulated Bm ( SI ) l c ≈ ( B m C 0 κ 1 ) 1 4 = 70 − 200 n m i . e . in dimensionless units l c * = B 1 4 ≈ 0 . 02 − 0 . 06 for B ∈ [10−9 , 10−7] , qualitatively consistent with experimental observations [20 , 21] . Protein patterning at the micro-cluster ( lc ) size occurs on short time scales ( τc ) , while patterning at the cell scale ( L ) occurs on long time scales ( τL ) . Fluid continuity and force balance embodied in Eq 2 yields a short time scale τc corresponding to drainage on the micro-cluster scale lc , given by τ c = 12 ( l c l 2 ) 2 τ μ = 12 ( B m C 0 κ l 2 4 ) 1 2 μ C 0 κ l 2 . ( 6 ) Substituting in parameter values yields τc ≈ 0 . 1–1 s i . e . in dimensionless time units τ c * = 12 ( l c l 2 ) 2 ≈ 24 − 240 ( see SI ) . Fluid drainage on the cellular scale L yields a long time scale given by τ L = 12 ( L l 2 ) 2 τ μ = 12 ( L l 2 ) 2 μ C 0 κ l 2 . ( 7 ) Substituting parameter values yields τL ≈ 40 min i . e . in dimensionless units τ L * ≈ 5 × 10 4 . To solve the nonlinear system of Eqs 1–4 , we numerically discretize these with a finite element method ( see S1 Text ) in two-dimensions , which gives the membrane topography in three-dimensions . For consistency with experimental observations , the simulations are performed in a circular domain that capture the central region of the cell-to-cell contact , which is assumed not to be influenced by the motion of the cell leading edge . At the edge of the IS the membrane is assumed to be torque free with no bending moment ( ∇2 h = 0 ) and at a constant pressure ( p = 0 ) , which allows fluid flux through the boundary . The membrane is pinned at the edge ( h = 0 . 5l2 ) and the equilibrium number of proteins per membrane area at that given height ( C1 = C2 = 0 . 01C0 ) see [35] and S1 Text and S1 Fig for details . The membrane is initialized with six small Gaussian shaped bumps of different widths ( ≈ 0 . 1L ) and amplitude ( ( 0 . 075–0 . 1 ) l2 ) . Additional information about the numerical method [48] , [49] , parameter sensitivity and alternative boundary conditions are in the SI .
Within the phase space described by B and τ , we start by considering a cell that has a stiffness that scales with the thermal energy Bm ≈ kB T and binding rates that are similar to those reported in experiments [3] ≈ 10−4 Ms , with an association constant ≈ 0 . 1M−1 giving τk ≈ 10−5 s . We note that the hydrodynamic time scale is larger τμ ≈ 3 × 10−3 s than τk suggesting that the IS dynamics is rate limited by the fluid flow i . e . τ ≫ 1 , which we verify below . In Fig 2 we show the time evolution of the IS for these parameters ( B = 2 × 10−9 , τ = 15 ) and note that the qualitative behavior of our model is consistent with the observed asymmetric IS dynamics [3 , 11 , 14 , 20 , 43] ( see S1 Movie ) , and recapitulates the protein aggregation of dense non-overlapping regions of TCR-pMHC and LFA-ICAM , which vary with time . At short times dispersed micron-sized protein clusters nucleate on the membrane , with a characteristic cluster size ≈ 1μm ( containing ≈ 160 proteins ) . These protein clusters are transported by the centripetal fluid flow generated by membrane deformation . At long times , we see the appearance of larger spatial protein domains , with a “donut-shaped” LFA-ICAM structure ( peripheral SMAC ) surrounding a dense central domain of TCR-pMHC ( central SMAC ) ( Fig 2 ) . This similarity is particularly striking since we did not evoke any active processes . We note that our results are also in concordance with recent experiments on latrunculin treated cells [13] , wherein disrupting the actin cytoskeleton does not change the early-stage patterns . To illustrate how these transport processes are correlated with domain coarsening , we show the pressure and velocity fields in Fig 3a . At short times ( t < 4 min ) the nucleation and coalescence of protein domains at a length scale ≈ lc generates a local flow field , while at long times ( t > 12 min ) the flow occurs over a global length scale ≈ L wherein the centripetal flow moves the clusters to the center of the domain and coarsens the protein pattern . In Fig 3b we directly compare the dynamics of the TCR clusters in the simulation with experiments [3] . With increasing time , the number of attached TCR rapidly increases upon first contact as micro clusters nucleate . A distinct peak in the number of attached TCR is observed around t ≈ 5 min in Fig 3b , followed by a decay in the number of attached receptors over longer times . The agreement with experiments for t < 20 min is striking since no active processes are evoked and suggests that the slow dynamics of fluid drainage in the synaptic cleft limits the rate of protein patterning during the early stages of IS dynamics . At longer times ( t > 20 min ) the results of the simulation and experiments deviate from each other , indicating an important role for active processes to stabilize the dynamical synapse . Over this period ( ≈ 60 min ) , a distinctive feature in experiment [3] is the appearance of a stable dense circular region of TCR-pMHC surrounded by a “donut-shaped” ring of LFA-ICAM . Compared to the TCR , the attached LFA concentration increases monotonically in time ( Fig 3c ) and around t ≈ 60 min saturates the nearly flat membrane . While a similar time evolution is also observed in experiments [3] , the choice of scaling makes a direct comparison challenging . This is because one also sees the appearance of a stable circular region of TCR-pMHC surrounded by a donut shaped ring of LFA-ICAM . Moving beyond direct comparison with experiments , we turn to a qualitative phase-space of protein patterning characterized by τ , B , Pe , M , initial conditions and boundary conditions . Our simulations show that the pattern dynamics are insensitive to variations in Pe , M and the initial conditions ( SI ) , leaving the scaled membrane stiffness ( B ) and the ratio of time scales ( τ ) as the main players responsible for variations in the patterns . In Fig 4 , we show this in terms of a phase diagram of pattern possibilities illustrated by snapshots of the protein distributions at t = 40 min , a stage corresponding to a mature IS [2 , 3 , 10 , 20] . Two distinct protein patterns may be identified corresponding to either large diffuse domains or a dispersed micro cluster phase . We can further categorize the latter into two distinct regimes . For τ < 0 . 1 the membrane proteins fail to form an IS and their dynamics are primarily dominated by diffusive fluxes and the results are insensitive to B . For τ > 0 . 3 islands of non-overlapping micro-scale protein clusters form different shapes on the membrane . For 0 . 3 ≤ τ ≤ 3 long-lived LFA clusters form at the center and at the edge of the membrane . In this regime , kinetic processes and diffusive fluxes make comparable contributions . By further decreasing the kinetic rate ( τ > 3 ) the protein dynamics become hydrodynamically limited with a sharper protein interface . In this regime , a large central domain of TCR with a few internalized LFA micro-clusters form on the membrane , which is surrounded by LFA . We emphasize that at very long times the equilibrium state corresponds to a nearly flat membrane adhesively bound by either TCR or LFA to the bilayer . However , a change in boundary condition that replaces the constant pressure along the edge with a vanishing fluid flux , i . e . ∇p ⋅ n = 0 where n is normal vector at the boundary , leads to an arrested inhomogeneous protein pattern ( see S4 Fig and S2 Movie ) . Our calculations of the protein patterns show that the formation of IS-like domains only occurs in the hydrodynamically limited regime for τ > 0 . 3 . In this regime , protein clusters nucleate at short-time t ≈ 1 min forming a patchy pattern , with a characteristic cluster size that scales as l c ≈ ( B m C 0 κ ) ( Eq 5 ) . These micro scale protein clusters move centripetally by the self-generated fluid flow since membrane deformation by protein binding generates flow , which assists sorting and formation of protein domains . Cluster translocation leads to the formation of large protein domains at long times t ≈ 30 min with the characteristic “donut-shaped” LFA domain that surrounds a central domain dens in TCR ( see Fig 4 ) , similar in structure to what is often referred to as a peripheral-SMAC and a central-SMAC in experiments [3 , 11 , 14 , 20 , 43] .
To get at an accurate description of the spatiotemporal dynamics of protein patterning in the IS we have formulated and solved a minimal mathematical model that account for membrane mechanics , protein binding kinetics and hydrodynamics , while setting the stage for the quantification of passive and active mechanisms in the IS . Our scaling laws for the size of protein clusters , as well as short and long time protein patterning dynamics are corroborated by simulations without ad-hoc physical assumptions . In particular we show that slow dynamics of fluid drainage in the synaptic cleft can account for the time scales of protein patterning . Direct comparison of our computations with experiments [3] suggests that at early times passive dynamics suffices to describe the formation and organization of trans-membrane receptors , and suggests a natural time scale for when active processes come into play . Our passive model of the immune-cell synaptic cleft is a simplification , where we have neglected the mechanisms by which receptor binding generates signaling that triggers internal activity e . g . actomyosin contractility , endo-/exo-cytosis , release of TCR through microvesicles , local recruitment of integrins etc . Since all these effects can influence the patterning dynamics , to challenge our passive physicochemical theory and to help identify the key biophysical process underlying the formation of the IS , we now turn to some experimentally testable predictions . First , a characteristic spatial scale for membrane deformation is predicted by l c = ( B m C 0 κ ) 1 4 , where Bm is bending stiffness , C0 protein number density and κ protein stiffness . Since lc is fairly parameter insensitive , modifying cell membrane rigidity ( e . g . using wheat germ agglutinin ( WGA ) [44] ) , the protein number density ( corralling [46] ) or protein stiffness ( linker length [45] ) would only produce moderate changes in cluster size . Second , two time scales control the dynamics . At short time protein clusters nucleate τ c ≈ ( l c L ) 2 μ C 0 κ = ( l c l 2 ) 2 τ μ and at long time and length scales large protein domains form τ L ≈ ( L l 2 ) 2 τ μ = ( L l 2 ) 2 μ C 0 κ l 2 . In contrast to the prediction for lc , both τc and τL are sensitive to changes in protein number density ( C0 ) , protein ( κ ) and membrane stiffness ( Bm ) , which can be experimentally changed by corralling , linker-length and WGA and will change these three parameters . Thus , our theory predicts that the time scales for the IS can be changed without much variation in the spatial features . Third , our numerical simulations predict the formation of IS-like protein domains for τμ < τk , identifying protein kinetics as a critical component in IS formation . This can be tested by changing the adhesion molecules to vary the kinetic time τk , while τμ can be modified by changing the protein number density ( corralling ) or protein stiffness ( linker length ) . Fourth , the effective boundary condition at the periphery of the synaptic cleft is found to be a key component in the longevity of the pattern . Simulations allowing fluid flux through the edge of the IS show that the SMACs become unstable at long times . The formation of a tyrosine phosphatase network at the synapse periphery generates additional resistance to fluid drainage and may limit the rate of mass flux . Thus , the proteins at the boundary of the IS may regulate its stability and the disruption of this protein network should affect its longevity . Fifth , the fluid motion in the membrane gap has hitherto not been quantified . Such experiments may be feasible with quantum dot tracking techniques [47] and may shed new light on the fluid pathway during the patterning . Fluid can either become trapped in thesynaptic cleft , internalized by the cell or escape along its edge . Sixth , we predict nucleation , translation and sorting of protein clusters in the absence of active processes . Recent observations by [15] of non-immune cells show protein patterning and makes an experimental platform ideal to challenge our spatiotemporal predictions . Our mathematical model is a minimal and general theoretical skeleton for a description of cell-to-cell interaction , and may be useful more broadly to understand aspects of cell adhesion , communication and motility . | The cellular basis for the adaptive immune response during antigen recognition relies on a specialized protein interface known as the immunological synapse ( IS ) . Understanding the biophysical basis for protein patterning by deciphering the quantitative rules for their formation and motion is an important aspect of characterizing immune cell recognition and thence the rules for immune system activation . We propose a minimal mathematical model for the physical basis of dynamic membrane protein patterning in the IS , which encompass membrane mechanics , protein binding kinetics and motion , and fluid flow in the synaptic cleft . In particular we quantify the nucleation , growth and stabilization of proteins domains . We describe a phase diagram of possible protein patterns by two dimensionless parameters; a ratio of membrane elasticity to protein stiffness , and the ratio of a hydrodynamic time scale for fluid flow relative to the protein binding rate . Direct comparison with experiment suggests that passive processes i . e . viscous fluid flow , elastic membrane bending and protein binding kinetics , can describe the short-time formation and transport of protein clusters , while active cytoskeletal processes enable long-time stabilization of the IS . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Elastohydrodynamics and Kinetics of Protein Patterning in the Immunological Synapse |
Maternal antibodies inhibit seroconversion and the generation of measles virus ( MeV ) -specific antibodies ( both neutralizing and non-neutralizing antibodies ) after vaccination whereas T cell responses are usually unaffected . The lack of seroconversion leaves individuals susceptible to vaccine-preventable infections . Inhibition of antibody secretion is due to the inhibition of B cells through a cross-link of the B cell receptor with the inhibitory FcγIIB receptor ( CD32 ) by maternal antibody/vaccine complexes . Here , we demonstrate that a combination of TLR-3 and TLR-9 agonists induces synergistically higher levels of type I interferon in vitro and in vivo than either agonist alone . The synergistic action of TLR-3 and TLR-9 agonists is based on a feedback loop through the interferon receptor . Finally , we have identified CD21 as a potential receptor for interferon α on B cells which contributes to interferon α-mediated activation of B cells in the presence of maternal antibodies . The combination leads to complete restoration of B cell and antibody responses after immunization in the presence of inhibitory MeV-specific IgG . The strong stimulatory action of type I interferon is due to the fact that type I interferon uses not only the interferon receptor but also CD21 as a functional receptor for B cell activation .
A fundamental unresolved issue in vaccinology is the inhibition of vaccination against infectious diseases of humans [1] , [2] , [3] , [4] , [5] , [6] , [7] and animals [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] by maternal antibodies . Studies in patients as well as experiments in animal models testing adjuvants and vaccine vectors have shown that maternal antibodies do not inhibit T cell responses [18] , [19] , [20] , [14] . However , if protection ( at least partially ) depends on the B cell response and production of neutralizing antibodies ( as it does for measles virus and many other pathogens ) , vaccination regularly fails . Worldwide , close to 200 , 000 children die of measles virus every year . During their first year of life , children are protected by neutralizing maternal antibodies against MeV infection . Over time , these antibody titers wane and eventually do not protect against wildtype virus infection ( for review [21] ) . However , even these low non-protective antibody titers inhibit the generation of MeV-specific antibodies ( both neutralizing and non-neutralizing antibodies ) but not the development of a MeV-specific T cell response [18] . As neutralizing antibodies but not T cells protect against infection [19] , [22] , [23] , these children are susceptible to MeV infection . We have used the cotton rat ( Sigmodon hispidus ) model of measles vaccination to analyze the inhibitory mechanism of maternal antibodies because the cotton rat is the only rodent in which measles virus after intranasal inoculation replicates in the respiratory tract and lymphoid organs [24] . In this animal model , we have been able to demonstrate that both natural maternal MeV-specific IgG antibodies , as well as passively transferred human and mouse MeV-specific IgG are able to inhibit the generation of MeV-specific antibodies ( both neutralizing and non-neutralizing antibodies ) after immunization [19] , [25] , [26] . B cell inhibition is due to cross-linking of the B cell receptors ( BCR ) and Fcγ receptors IIB ( FcγRIIB ) by a complex formed by maternal IgG and the MeV vaccine [26] . This inhibitory effect can be partially overcome by activation of B cells through cross-linking BCR and complement receptor 2 ( CR-2/CD21 ) with a complex of MeV vaccine , MeV-specific IgM and complement protein C3d [26] . Two viral vector systems ( vesicular stomatitis virus ( VSV ) and Newcastle Disease virus ( NDV ) ) which express measles virus hemagglutinin ( H ) can induce H specific neutralizing antibodies after vaccination in the presence of inhibitory MeV-specific IgG . In contrast to measles virus , both VSV [27] as well as NDV induce type I interferon [28] . For NDV we have shown that its ability to induce neutralizing antibodies correlates with its ability to induce type I interferon in cotton rat plasmacytoid dendritic cells , and in cotton rat lung tissue [28] . In vitro , neutralization of IFN α abrogates stimulation of B cell responses by NDV . Viruses induce type I interferon through viral nucleic acids which are recognized by TLR-3 ( single-stranded RNA ) , TLR-7 ( double-stranded RNA ) and TLR-9 ( DNA ) . We used a combination of TLR-3 and TLR-9 agonists in order to mimic infection with RNA and DNA viruses , to increase the induction of type I interferon and fully restore the B cell response after vaccination in the presence of inhibitory MeV-specific IgG . TLR-3 signals through the TRIF/IRF-3 pathway and mainly induces IFNβ whereas TLR-9 signals through the MyD88/IRF-7 pathway and mainly induces IFNα . It was hypothesized that the combination of these agonists would result in superior induction of type I interferon because of synergistic effects mediated through the interferon receptor feedback loop . We were able to demonstrate higher induction of type I interferon , and a complete restoration of the B cell response after immunization with MeV and a TLR-3/TLR-9 agonist combination in the presence of inhibitory MeV-specific IgG . In addition , it was shown that IFNα uses CD21 as a functional receptor ( in addition to IFN receptor ) to drive B cell responses , and this dual receptor usage likely explains its strong stimulatory potential for the B cell inhibited by maternal antibodies .
We have shown previously that two viral vector systems which express measles virus hemagglutinin can induce H specific neutralizing antibodies after vaccination in the presence of inhibitory MeV-specific IgG . NDV is able to induce type I interferon in cotton rat plasmacytoid dendritic cells , and in cotton rat lung tissue . For VSV , we tested its ability to induce type I interferon in cotton rat plasmacytoid dendritic cells and found levels of 128 U/106 pDC ( moi of 1 ) . These results led us to try to improve the stimulation of the B cell response by using TLR-3 and TLR-9 agonists as inducers of type I interferon . We tested pI:C as TLR-3 agonist and human ODN type A , C and P as TLR-9 agonists on plasmacytoid dendritic cells individually and in combination . The combination of pI:C and human ODN 2216 ( type A ) proved to induce the highest levels of type I interferon in plasmacytoid dendritic cells ( Figure 1A ) . In lung tissue , the combination of pI:C and ODN 2216 was also able to induce higher amounts of type I interferon then either agonist alone ( Figure 1B ) . It is worth noting that the combination of pI:C and ODN 2216 induced higher levels of type I interferon than either NDV ( 256 U/ml [28] ) or VSV ( 128 U/mL ) in plasmacytoid dendritic cells , or NDV in cotton rat lung tissue ( 250 U/ml [28] ) . The second cytokine to influence the development of human and mouse plasma cells is IL-6 . Similar to these species , cotton rat IL-6 stimulates B cells in an ELISPOT assay ( data not shown ) . However , pI:C and ODN 2216 did not induce higher levels of IL-6 in broncho-alveolar lavage cells than measles virus alone ( Figure 1B ) . In order to test whether the action of the TLR agonist combination acts through the IFN receptor ( IFNR ) feedback loop , we blocked the signaling chain of the IFNR with antibody ( Figure 2A and B ) . This inhibited the type I interferon response in an antibody concentration dependent manner . In a reverse experiment , we demonstrated that even minute amounts of interferon alpha in combination with either pI:C or ODN 2216 were able to significantly improve the induction of type I interferon induced by either pI:C or ODN 2216 alone , again emphasizing the necessity for the IFNR feedback loop ( Figure 2C ) . In order to test the ability of the pI:C/ODN2216 combination to stimulate B cell and antibody responses , the TLR agonists were used in an ELISPOT assay . Both pI:C and ODN 2216 stimulated higher B cell responses from the bone marrow of MeV-immune cotton rats but the combination was superior to either agonist alone ( Figure 3A ) . Stimulation of B cells by TLR agonists could be reduced with neutralizing antibody against type I interferon as well as IL-6 . This is consistent with the role of sequential action of type I interferon and IL-6 for B cells to transition from activated B cells to plasmablasts ( type I interferon ) , and then from plasmablasts to antibody secreting plasma cell ( IL-6 ) [29] . When cotton rats are immunized with MeV either intranasally or subcutaneously , the generation of neutralizing antibodies is suppressed by inhibitory MeV-specific IgG . If MeV was supplemented with either pI:C , ODN 2216 , or a combination of both , the induction of neutralizing antibodies was detected ( Figure 3B ) . After i . n . immunization ( Figure 3B , upper panel ) both agonists alone and the combination induced comparable levels of neutralizing antibody levels . However , these titers were lower than after immunization with MeV in the absence of inhibitory antibody . After s . c . immunization ( Figure 3B , lower panel ) , either agonist alone induced neutralizing antibodies , but the combination of pI:C/ODN 2216 induced higher antibody levels than either agonist alone , and antibody levels were comparable to MeV immunization in the absence of inhibitory MeV-specific IgG . After immunization in the presence of maternal antibodies , the generation of neutralizing antibodies is inhibited . This could be due to a lack of B cell generation , or a lack of antibody secretion by B cells . In order to determine B cell development after immunization with MeV , cotton rats were immunized i . n . , and mediastinal lymph nodes ( MDLN; which drain the lung ) , spleen and bone marrow were tested for the presence of MeV-specific B cells on day 5 , 8 , 12 and in weekly intervals between week 3 and 10 by ELISPOT ( Figure 4 ) . Antibody levels in serum were determined by neutralization assay . After MeV immunization in the presence of MeV-specific IgG , no neutralizing antibodies were produced ( NT below the threshold of 10 ) , and the level of total MeV-specific antibodies was low ( maximal average 1∶500 by endpoint titration ) . Immunization in the absence of MeV-specific IgG led to measurable neutralizing antibodies from day 8 and to increased total MeV-specific antibody titers ( maximal average 1∶5000 by endpoint titration ) . In MDLN from animals immunized in the absence of MeV-specific IgG , numbers of MeV-specific B cells peaked at day 12 and declined towards day 21 . B cell numbers in spleen and bone marrow increased steadily over time and remained stable for at least ten weeks . After immunization in the presence of MeV-specific antibodies , B cells in MDLN proliferated with a delay of three weeks at low levels . MeV-specific B cells accumulated in bone marrow slower and to lower numbers than in animals immunized in the absence of MeV-specific IgG . The interesting result was the accumulation of relatively high numbers of B cells in the spleen without generation of neutralizing antibodies . These data indicate that the early activation and proliferation of B cells in MDLN is necessary for subsequent population of spleen and bone marrow , and is blunted in animals immunized in the presence of MeV-specific IgG . The lack of stimulation in the early phase of the B cell response correlates with low long term immune responses ( no neutralizing antibodies and low B cell numbers in bone marrow ) . In order to test the stimulatory effect of the pI:C/ODN 2216 combination on MeV immunization , one set of cotton rats was immunized i . n . and B cell numbers in lymph nodes , spleen and bone marrow were measured on day 12 and day 35 after immunization ( Figure 5A ) . A second set of cotton rats was immunized s . c . and B cell numbers in spleen and bone marrow were measured on day 12 and day 35 after immunization ( Figure 5B ) . In addition , sera were tested for neutralizing antibodies at day 35 . After i . n . immunization with MeV alone , the early activation of B cells in MDLN , spleen and bone marrow was confirmed at day 12 . At day 35 , B cell numbers in MDLN were low , high in bone marrow and higher in spleen , and neutralizing antibodies could be measured . When i . n . immunization with MeV was compared to immunization with MeV plus the combination of pI:C/ODN 2216 , a slight increase in B cell numbers was found on day 12 and 35 , but no increase in neutralizing antibody titers at day 35 . After immunization with MeV in the presence of inhibitory MeV-specific IgG , no neutralizing antibodies were induced and B cell numbers were as low as determined before ( see Figure 4 ) after i . n . immunization . After immunization with MeV and the combination of pI:C/ODN 2216 B cell numbers were restored to levels as in animals immunized with MeV in the absence of inhibitory IgG , and neutralizing antibodies were detected on day 35 . Similarly , s . c . immunization with MeV led to early activation of B cells in spleen and bone marrow at day 12 , with increased numbers in spleen and bone marrow , and neutralizing antibodies at day 35 . After s . c . immunization with MeV and pI:C/ODN 2216 , B cell numbers were comparable to MeV immunization alone but the titer of neutralizing antibodies was increased on day 35 . After s . c . immunization with MeV in the presence of inhibitory MeV-specific IgG , no neutralizing antibodies were induced , and B cell numbers in spleen and bone marrow were similarly low as after i . n . immunization with MeV in the presence of inhibitory MeV-specific IgG . After immunization with MeV and pI:C/ODN 2216 , B cell numbers in spleen and bone marrow were reduced at day 12 compared to immunization with MeV in the absence of inhibitory IgG . However , at day 35 B cell numbers and neutralizing antibodies were comparable to immunization with MeV alone . In summary , these data demonstrate that MeV with the combination of pI:C/ODN 2216 restores the B cell and antibody response in the presence of inhibitory antibodies after both i . n . and s . c . immunization . In this and other studies , interferon α proved to be a stronger stimulator of the B cell response than other cytokines including IL-6 . A potential explanation might be provided by recent data which demonstrate that interferon α binds to CD21 with the same avidity as its natural ligand , C3d , which also stimulates B cell responses [30] . In consequence , IFN α is able to stimulate IFN-dependent genes through CD21 [31] . However , whether binding of IFN α to CD21 is also able to stimulate B cell responses has not been investigated . In order to determine whether the combined use of CD21 and IFN-R as receptors might be beneficial for B cell stimulation , we stimulated B cells with the natural ligand for CD21 , C3d , and the natural ligand for IFN-R , IFN α , and a combination of both in an ELISPOT assay . C3d as well as IFN α were able to increase the B cell response in this assay system ( Figure 6A ) . When a constant amount of C3d was combined with increasing amounts of IFN α , the stimulation was always stronger than if either one was used alone . However , with saturating amounts of IFN α , the synergistic effect decreased . These data demonstrate that the engagement of both CD21 and IFN-R stimulates B cells in a synergistic fashion . To find out whether B cells could also be stimulated by binding of IFN α to CD21 , we stimulated B cells from MeV-immune cotton rats and mice with MeV and IFN α , and subsequently blocked CD21 , IFN-R , or both by antibody ( Figure 6C ) . Antibodies against both molecules led to a reduction in B cell stimulation through IFN α , indicating that CD21 can function as receptor for IFN α . To investigate whether CD21 alone could stimulate B cell responses in an IFN α dependent manner , we immunized mice with a deletion in the IFN-R gene with MeV . B cells from these mice could be stimulated with IFN α in vitro ( Figure 6D ) . This effect was abolished by blockage of CD21 with antibody . These data demonstrate that CD21 can act as a receptor for IFN α and stimulate B cells , and it might explain the strong stimulatory effect IFN α has on B cells .
The inhibition of seroconversion after immunization in the presence of maternal antibodies is a well-documented phenomenon . The lack of generation of antibody suggests an impairment of B cell activation and development , although this was never proven . Our data demonstrate clearly that the inhibition in the generation of antibody correlates with the inhibition of B cell development , and particularly with early activation of B cells in draining lymph nodes . We have shown previously that the inhibitory action of maternal IgG is mediated through a complex of MeV vaccine/IgG cross-linking the B cell receptor ( BCR ) with the inhibitory receptor CD32 [26] . This inhibition could be partially overcome by MeV-specific IgM which crosslinks complement receptor 2/CD21 with BCR through a complex of MeV vaccine/IgM/complement protein C3d . It could also be partially overcome with viral vector systems which provided MeV hemagglutinin as antigen and induced type I interferon which is known to act through IFN-R [28] . This is consistent with data demonstrating that the induction of type I interferon supports B cell differentiation [29] and antibody secretion from B cells [32] [33] [34] [35] . Based on our data , the strong stimulatory activity of type I interferon on B cell responses seems to be due to the dual use of both CD21 and interferon receptor as functional receptors on B cells . The critical role of CD21 in the B cell response has been shown in Cr2−/− mice that are deficient in CR2 ( = CD21 ) . Cr2−/− mice lack CD21 on both B cells and follicular dendritic cells [36] . They demonstrate substantial defects in antigen-specific , T cell-dependent and T cell-independent humoral immune responses [37] , [38] , [39] . In addition , defects in B cell memory [40] , [41] and the development of the natural antibody repertoire [42] , [43] are found in Cr2−/− mice . The natural ligand of CD21 is the complement protein C3d , and the binding of C3d to CD21 stimulates B cell responses . Interferon alpha contains a peptide sequence similar to one in C3d which is located at the C3d-CR2 binding site [30] and interferon alpha binds to CD21 with an affinity comparable to the natural ligand C3d . In vitro blockage of CD21 by antibody on human peripheral blood B cells diminishes the expression of interferon inducible genes [31] . In an ELISPOT assay , antibody secretion from B cells by type I interferon was clearly reduced when CD21 was blocked both on cotton rat and mouse B cells . Importantly , B cells from mice with a gene deletion in the interferon receptor respond to IFN α and this response can be blocked by antibodies against CD21 . These data clearly demonstrate that CD21 is a functional receptor for IFN α on B cells . It seems likely that activation of the B cell through CD21 and IFN-R by type I interferon counteracts the inhibitory signal induced by CD32 through MeV-specific IgG and leads to a net stimulatory signal . Previously , we have shown that the provision of type I interferon by immunization with NDV-H was able to partially restore the neutralizing antibody response in the presence of maternal antibodies . NDV interacts with TLR-7 and the RIG-I pathway which leads to the induction of type I interferon in vivo [44] , [45] . TLRs use either the MyD88 ( TLR-2 , 4 , 5 , 7 , 8 , 9 ) or TRIF dependent signaling pathways ( TLR-3 , 4 ) ( reviewed in [46] ) . It has been suggested that the expression of different TLR ligands by pathogens might enhance immune responses by signaling via both adapter pathways [47] . Here , we aimed to achieve the highest level of type I interferon by activating TLR-3 and TLR-9 through their agonists . We were able to show that a TLR-3 agonist ( poly I:C ) in combination with a TLR-9 agonist ( ODN 2216 ) induced synergistically higher levels of type I interferon than either ligand alone . A combination of TLR agonists does not necessarily have a synergistic effect . Ghosh et al compared cytokine responses of all the possible combinations of known TLR ligands in human PBMCs [48] . TLR-9 agonist ODN 2216 produced high levels of interferon alpha but type I interferon induction through TLR-9 was downregulated in combination with TLR-7 or TLR-8 agonists . In contrast , TLR-3 induced significant amounts of type I interferon when used in a combination with agonists to TLR-2 , 5 , 7/8 [48] which support our data that co-stimulation of TLRs which are TRIF dependent ( TLR-3 ) and MyD88 dependent ( TLR-9 ) has a synergistic effect on type I interferon induction . One possible application of our data would be the direct inoculation of IFN α with MeV vaccine in order to stimulate a better B cell and antibody response . In mice , the inoculation of a high dose of IFN α and influenza vaccine induced good T cell and B cell responses [49] , but in humans this approach was not successful [50] . A possible reason is the difference in the effect of IFN α on the immune system of mice and humans . In humans , IFN α drives TH1 development and acts through STAT-4 which is not the case in mice [51] . The conclusion from these studies was that the mouse is not an informative animal model to study adjuvants which are targeted for use in the human respiratory system [50] . In cotton rats , inoculation of MeV vaccine and cotton rat IFN α intranasally did also not result in increased B cell responses ( data not shown ) . The failure to stimulate the B cell response might be related to technical problems . Although doses comparable to the amounts found in lung lavage after TLR agonist application were used , it is possible that higher doses of IFN α are needed , or that pegylated interferon which is more stable in vivo is required . Alternatively , the cotton rat might be an animal model better suited to test intranasal adjuvants for humans [52] , [53] , [54] . This notion might be supported by the fact that in cotton rats a TLR-9 agonist optimized for human cells was most effective ( ODN2216 , data not shown ) , whereas in mice TLR-9 agonists optimized for mouse cells have to be used . In summary , we have demonstrated that a combination of TLR-3 and TLR-9 agonists induces higher levels of type I interferon than either agonist alone . Subsequently , IFN α utilizes both IFN-R and CD21 as functional receptors for B cell stimulation and leads to restoration of B cell responses after immunization in the presence of inhibitory MeV-specific IgG .
Animal use protocols for the experiments reported in this manuscript have been approved by the Institutional Animal Care and Use Committee of The Ohio State University in accordance with the Animal Welfare Act of the United States of America . Inbred cotton rats ( Sigmodon hispidus ) were purchased from Harlan Laboratories , Inc . For immunization experiments in the presence of inhibitory antibody , MeV-specific IgG ( NT of 100 ) was injected intraperitoneally ( i . p . ) into cotton rats to replace natural maternal IgG . One day later , animals were immunized with 105 pfu of MeV ( Schwarz strain ) intranasally or subcutaneously with TLR agonists as indicated in each experiment . We have chosen human MeV-specific antibodies to standardize our experimental approach and because of experimental advantages: 1 . the level of natural maternal antibodies in pups varies ( presumably because of the suckling hierarchy ) , human antibodies metabolize faster ( an experimental advantage ) and can be distinguished by ELISA from cotton rat antibodies . The Schwarz vaccine strain of measles virus was grown and titrated on Vero cells as described [28] . Human MEV-specific polyvalent IgG ( Carimmune ) with a neutralization titer ( NT ) of 640 was purchased from ZLB Behring . Neutralizing goat antibodies against cotton rat interferon alpha and IL-6 were purchased from R&D systems . Rabbit IgG specific for human/mouse/rat CD21 was purchased from Santa Cruz Biotechnology . Goat antibodies specific for human interferon receptor 1 and chicken antibodies specific for human interferon receptor 2 with neutralizing capacity were purchased from Thermo Scientific . Spleen cells were stimulated with 10 µg/ml LPS ( Sigma ) or 2 . 5 µg/ml Concanavalin A for 72 hours and lymphoblasts purified by centrifugation over a ficoll gradient . Subsequently , cells were stained with antibodies specific for CD21 , or interferon receptor 1 or 2 . The respective secondary antibody labeled with FITC was pre-absorbed with cotton rat serum for 1 hour on ice . Subsequently cells were analyzed by flow cytometry ( Facscan , Becton Dickenson ) . CpG ODN 2216 ( 5′-ggGGGACGATCGTCgggggg-3′ ( bases shown in capital letters are phosphodiester , and in lower case phosphorothioate ) , type P ODN ( CpG ODN 21798 [55]; 5′-tCGtCGaCGatCGgcgCGcgccg-3′ ) and type C ODN ( CpG ODN M362 , 5′-tcgtcgtcgttcgaacgacgttgat-3′ ) were purchased from Invivogen . Poly ( cytidylic-inosinic ) acid potassium salt ( poly I:C ) was purchased from Sigma . Bone marrow derived dendritic cells were generated as described [28] . Bone marrow cells were cultured for 7 days in Advanced RPMI/5% FCS supplemented with 100 ng mouse Flt-3 ligand ( R&D Systems ) . Every 2 days , fresh medium supplemented with Flt-3 ligand was added . On day 8 , plasmacytoid dendritic cells were cultured with ODN 2216 , poly I:C or measles virus , and 1 day later , the supernatants were harvested and analyzed by bioassay for the presence of type I interferon . Samples from three wells were pooled and treated with 0 . 1M hydrochloric acid to inactivate IFN-γ for 2 hours and the acid was neutralized with sodium bicarbonate . Cotton rat osteosarcoma cells CCRT were incubated with serially diluted samples ( in duplicate ) or recombinant cotton rat IFN-α ( R&D Systems ) as standard for 24 hours . Subsequently , CCRT cells were infected with 103 pfu of recombinant vesicular stomatitis virus expressing green fluorescent protein ( rVSV-GFP ) . After 48 hours of incubation at 37°C , plates were evaluated for the presence or absence of green fluorescence on an Olympus 1 IX51 fluorescent microscope . IFN-α/β concentrations in samples were expressed as units with 1 unit being the equivalent of 1 pg of IFN-α . The threshold of detection was 16 units . Total RNA from cotton rat lung tissue was extracted using Qiagen RNeasy kit ( Qiagen ) and treated with DNase ( Ambion ) . To generate cDNA , AffinityScript multiple temperature reverse transcriptase ( Agilent technologies ) and random primers ( Promega ) were used . Quantitative polymerase chain reaction was performed using a LightCycler RNA amplification kit with SYBR green I ( Roche ) . The primer sequence for IL-6 was 5′-TTGGCACACTTAGGCACAGC-3′ ( forward ) and 5′-CAAAAGGACTGGCCGAGGAC-3′ ( reverse ) , and the plasmid pCR-script Amp SK ( + ) -Cotton rat IL-6 [56] was used as standard . Data were analyzed using LightCycler software version 3 . Quantification was based upon fit point analysis with arithmetic baseline adjustment . Melting peak and melting curve analyses were performed using the polynomial calculation method . Cotton rat serum samples were two-fold serially diluted and incubated with 50 pfu MeV strain NSE for one hour at 37°C in a 96-well . One hour post-incubation 104 Vero cells were added per well . Five days post-infection cytopathic effect ( CPE ) was determined microscopically . The titer was defined as the reciprocal of the last protective serum dilution , as calculated from duplicate measurements . For the B cell ELISPOT assay , mediastinal lymph node cells , bone marrow cells or spleens from measles virus immune cotton rats four to eight weeks after s . c . immunization with 105 pfu of MeV ( Schwarz strain ) were used . 96 well plates ( Millipore ) were coated with gradient-purified , UV-inactivated MeV antigen in sodium carbonate buffer ( pH 9 . 6 ) overnight at 4°C . Serially diluted lymphoid cells were plated and cultured at 37°C in an incubator . After overnight incubation , plates were washed with PBS/0 . 05% Tween 20 . Plates were incubated with rabbit anti-cotton rat IgG ( Virion Systems ) and subsequently with goat anti-rabbit alkaline-phosphatase conjugated IgG ( Zymed ) in PBS/10% cotton rat serum . For development of spots , plates were washed three times with PBS and TMB-HK ( high kinetic 3 , 3′ , 5 , 5′-tetramethylbenzidine , Moss , Inc . ) substrate was added . Plates were incubated at room temperature for 5 minutes and spots were counted using an ELISPOT plate reader ( CTL ) . Multiple comparison analysis was done by one-way analysis of variance ( ANOVA ) using Graph Pad InStat 3 for Windows ( GraphPad Software ) . The number of asterisks denotes the level of statistical significance ( *p<0 . 05; **p<0 . 01; ***p<0 . 001 ) . | Maternal antibodies provide protection against infection with pathogens early in life but also interfere with vaccination . This interference is caused by a vaccine/maternal antibody complex which links the B cell receptor to the inhibitory CD32 molecule . Here , we show that this cross-link results in impaired B cell activation and proliferation which is correlated with diminished antibody responses . We also found that induction of large amounts of type I interferon restores the neutralizing antibody response in the presence of maternal antibodies . The best induction of type I interferon was accomplished by a combination of known activators of interferon secretion ( a combination of TLR-3 and TLR-9 agonists ) . The strong stimulation by interferon is due to the previously unappreciated role of CD21 as functional receptor for interferon alpha . Our findings demonstrate that the dual receptor usage of type I interferon receptor and CD21 is crucial for B cell activation in the presence of maternal antibodies . This study suggests that measles vaccine , and potentially other vaccines , may induce optimal antibody responses when they are reconstituted with TLR-3 and TLR-9 agonists and thus these agonists may have great potential for clinical use . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"neonatology",
"animal",
"types",
"public",
"health",
"and",
"epidemiology",
"respiratory",
"infections",
"pulmonology",
"pediatrics",
"lower",
"respiratory",
"tract",
"infections",
"veterinary",
"science",
"immunizations",
"infectious",
"diseases",
"pediatrics"... | 2013 | Synergistic Induction of Interferon α through TLR-3 and TLR-9 Agonists Identifies CD21 as Interferon α Receptor for the B Cell Response |
Predicting protein function from structure remains an active area of interest , particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation . Although global structure comparison methods can be used to transfer functional annotations , the relationship between fold and function is complex , particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core . The majority of prediction algorithms employ local templates built on known or predicted functional residues . Here , we present a novel method ( FLORA ) that automatically generates structural motifs associated with different functional sub-families ( FSGs ) within functionally diverse domain superfamilies . Templates are created purely on the basis of their specificity for a given FSG , and the method makes no prior prediction of functional sites , nor assumes specific physico-chemical properties of residues . FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms ( a 2–3 fold increase in coverage at low error rates ) popular structure comparison methods and a leading function prediction method . We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes ( α , β , αβ ) and demonstrate the functional relevance of the motifs it identifies . We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative . Overall , we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues .
The prediction of protein function from structure has become of increasing interest as a significant proportion [1] of structures solved by the structural genomics initiatives ( SGI ) lack functional annotation ( for a review see [2] ) . Furthermore , structure-based approaches are of particular interest for predicting binding sites and/or catalytic sites for the purposes of protein engineering and pharmaceutical development ( for reviews see [2] , [3] ) . Many current methods focus on encoding a “template” of functional residues and then aligning this template to whole structures . The problems with taking this approach are deciding what qualifies as a functional residue ( e . g . one directly involved in catalysis or ligand binding ) and creating biologically-accurate templates for the ever increasing number of available protein structures being deposited in the PDB [4] . Resources such as the Catalytic Site Atlas [5] are carefully curated by hand and restricted to residues directly involved in catalysis , whereas MSDSite [6] and PDBSite [7] , [8] generate templates based on active site residues defined in the PDB file by the authors . Although these resources are undoubtedly extremely valuable , it is questionable whether sufficient coverage of the PDB can be maintained when manual intervention is required . To address the problem of generating templates for all protein structures , there are a number of methods that aim to do this automatically . For example , the reverse template method [1] ( available as part of the PROFUNC suite [9] ) decomposes a query structure into tri-peptide fragments ( putative catalytic triads ) , which are then matched against a non-redundant set of PDB structures using the search algorithm JESS [1] . Hits are evaluated according to the sequence similarity of the local environment of the template . The GASP method [10] uses a genetic algorithm to construct templates based on their ability to discriminate between different protein families against a background of representatives from the SCOP database [11] . Similarly , DRESPAT [12] implements a graph theoretical approach to discover structural patterns associated with a given family of proteins to locate ligand binding motifs ( the PINTS method [13] uses a related approach ) . MultiProt [14] can provide template of structures through a multiple structure alignment . A recent extension of the Evolutionary Trace method for binding site prediction was used to create structural templates based on predicted functional residues [15] . SiteEngines [16] produces templates by matching the geometry and physico-chemical properties of residues in binding site clefts . As well as atom or residue-level templates , other non-template-based approaches seek to compare the electrostatic properties of binding sites ( ef-Site , [17] , SURF's UP [18] ) or surface accessible clefts which often co-locate with active sites ( pvSOAR ( CASTp ) [19] ) . One inherent complexity of using PDB structures to transfer annotations between enzymes is the binding state in which the protein is crystallised — for example , structures crystallised with non-cognate ligands , substrate analogs , transition states or apo-enzymes [20] . As a consequence , precise geometric matching in the active site region can be problematic due to the conformational changes that occur on ligand binding . To address this issue , the methods mentioned above use a variety of approaches such as graph matching or geometric hashing with various tolerance levels . The SOIPPA method [21] , [22] takes the alternative approach of using a “geometric potential” to characterise the shape formed by a given set of Cα atoms , to account for both local and global relationships between residues across the protein structure . In a recent ligand-binding site comparison analysis , SOIPPA was able to detect distant similarities between very different protein folds binding a range of adenine-containing ligands [21] . Despite the many template methods present in the literature , very few are publicly available to the general user . Hence , the first step in assigning function by structure is often to use global structure comparison methods ( e . g . CE [23] , DALI [24] , CATHEDRAL [25] , MAMMOTH [26] , FatCat [27] , MSDFold [28] ) , which can detect distant evolutionary relationships even where sequence similarity is weak . These methods have been specifically applied to function prediction ( ProKnow [29] , Annolite [30] ) to assign confidence values when inheriting GO terms between related structures . However , detecting very distant relatives remains a challenge as structure comparison methods generally give an absolute measure ( or score ) of structural distance , such as RMSD , and applying a cut-off at which one can deduce that two proteins perform related functions results in many missed relationships . Analyses of CATH [31] , [32] have shown that although function and structure are well conserved in the majority of superfamilies , there are a significant number of highly diverse superfamilies where this is not the case [31] . Moreover , the latter superfamilies are disproportionately represented in both the PDB and in the genomes and tend to exhibit a wide range of core biological functions across a large range of species [33] . An analysis by Reeves et al . [31] showed that relatives within these superfamilies tend to share a common evolutionary core , but this core is embellished with different insertions of secondary structure elements that often correlate with changes in function . However , although structural embellishments might change some facet of function ( e . g . ligand specificity , protein-protein interactions ) , others have found that relatives can still retain other aspects in common ( e . g . catalytic mechanism , such as kinase activity ) [34] , [35] . Therefore , calculating a global measure of structural similarity or distance ( e . g . RMSD ) between two proteins can be less informative than focussing on the structural motifs relevant to a given aspect of function . The FLORA algorithm presented here was designed to derive structural templates for functional sub-groups ( FSGs ) within diverse CATH superfamilies . FLORA first performs global structure alignment across the superfamily to recognise the distinctive structural patterns associated with each FSG and builds templates based on these patterns . New functional homologues are then detected by using the global structural alignments to relatives in each FSG again , but only scoring the similarity over positions identified by the FLORA motif . This approach performs very well in discriminating between different enzymatic functions , compared to global methods and another motif-based approach . Although we benchmark here on enzyme superfamilies , the method is applicable to superfamilies containing non-enzymatic relatives . To test FLORA , we have automatically generated a large data set of domains from 29 diverse superfamilies ( containing multiple FSGs ) . Our data set allows us to look at the variation of FLORA results between superfamilies and to stress the importance of using a large test data set for benchmarking methods . We have benchmarked FLORA against CE [23] , CATHEDRAL [25] and Reverse Templates ( RT ) [1] to give an indication of how it performs in comparison to other standard methods of function prediction . We also present some examples of structural motifs identified by FLORA and explain their functional relevance . Finally , we use FLORA to make novel predictions of function for proteins solved by the Protein Structure Initiative ( PSI ) .
In order to benchmark FLORA as a protein function prediction method , it was important to generate a relatively large and unbiased data set . We focussed on functionally diverse superfamilies ( ≥3 functions at the third E . C . [36] level ) in the CATH database , where global fold similarity and evidence of homology is not necessarily indicative of a functional similarity . An overview of the protocol is shown in Figure 1 . All protein chains from PDB structures classified in CATH v3 . 1 were annotated with an E . C . number using PDBSprotEC [37] , which maps PDB chains to corresponding entries in the SwissProt database [38] . E . C . annotations were then transferred from the whole chain level to each constituent domain in a chain . Assigning functional annotation to individual domains is not a straight-forward process , as other domains in the chain ( or indeed , residues from other chains in the protein ) may be required for the enzyme to be catalytically active . This problem is dealt with more extensively in the PROCOGNATE resource [39] . However , we were only interested in finding domains that were “associated” with proteins of a given enzymatic function , as FLORA was designed to consider all residues for inclusion in a template and not just those in the active site . To simplify the benchmark data set , all domains from enzymes assigned more than one E . C . ( i . e . multifunctional enzymes ) were removed . This exclusion criterion removed less than 8% of enzymatic chains in the PDB . In addition , any domains with an incomplete E . C . number ( e . g . 2 . 7 . - . - ) were also excluded . All annotated domains in CATH were clustered at 60% sequence identity and a representative taken from each cluster ( S60Rep ) . This threshold was applied as 60% has been found to be an appropriate sequence cut-off for functional similarity [40] , [41] . Discovering homologous domains sharing more than 60% sequence identity is trivial using BLAST [42] and other sequence-base methods and we wished to generate a benchmark data set that contained more challenging cases . S60Reps were then grouped within the superfamily if they shared at least the first three E . C . numbers; to create what we will subsequently refer to as a functional sub-group ( FSG ) . A CATH superfamily was then included in the data set if it contained at least 3 FSGs , where each enzyme family contained at least 4 S60Reps . These criteria were chosen to create a sufficiently diverse data set , which could be effectively assessed using leave-one-out benchmarking . The final domain data set ( Dataset S1 ) comprised: 82 FSGs from 29 different CATH superfamilies ( a total of 911 S60Reps domains ) , covering all 3 major protein classes ( α , beta and mixed α-beta ) . Although the data set comprises ∼2% of the total number of superfamilies in CATH , these superfamilies account for ∼48% of domain sequences from functionally diverse superfamilies in Uniprot . Furthermore , they represent a set of domains where global fold similarity does not necessarily correlate with functional similarity . An outline of the FLORAMake algorithm is shown in Figure 2 . The aim was to select a set of conserved vectors from a given domain in a given FSG which when compared against relatives of different functions/FSGs would produce a low score and similarly a high score to relatives with the same function . As FLORA is essentially a pattern discovery method , it was vital to assess its performance in an unbiased fashion . We took a standard leave-one-out ( or jack-knifing ) approach as is often used to test machine learning methods . For each superfamily , one test domain was removed , while training on the remaining domains . The test domain was then scored against all the resulting templates . The aim of this process to was accurately reproduce a situation where a novel domain is classified into a CATH superfamily and then needs to be assigned to a functional group . The performance of FLORA , CATHEDRAL [25] , CE [23] and Reverse Template ( RT ) [1] were analysed by plotting sensitivity ( i . e . tp/ ( tp+fn ) ) versus precision ( tp/ ( tp+fp ) ) . We compared the performance on individual superfamilies by calculating AUC value ( area under ROC curve ) . In order to examine where residues identified by FLORA overlapped with known functional residues , we compared the location of FLORA positions to those in the Catalytic Site Atlas [5] ( v2 . 2 . 9 ) . For each functional sub-group ( FSG ) , we selected the domain that had the highest mean global structural similarity ( measured by CATHEDRAL ) to all other members of the FSG as a representative . All residues , from each relative within an FSG , identified by FLORA and CSA annotations were then mapped onto this representative using the CATHEDRAL structural alignment . Consequently , for each FSG we had a representative structure where all residues were annotated as FLORA positions , catalytic residues , or neither . The CSA provided annotations for 61 out of 82 FSGs ( 74% ) . We then calculated the average distance between the FLORA residues to the catalytic residues and the average distance between non-FLORA and the catalytic residues . FLORA produces a set of inter-residue vectors for each domain in a given FSG that are considered to be specific to its enzymatic function , in the context of its evolutionary superfamily . In order to visualise where these vectors lay , we took each set of domain templates for a given enzyme family and mapped them onto the most representative structure — i . e . the structure with the greatest cumulative global structural similarity to all other domains in the family . A given residue was then coloured if it was involved in the top 30% of FLORA template vectors . Residues that are conserved across the whole superfamily ( in 75% of relatives ) were also identified and those which overlapped with FLORA residues were coloured gold . Despite targeting proteins with no significant sequence similarity to existing structures in the PDB , Protein Structure Initiative ( PSI ) structures can often be classified into one of the large , diverse superfamilies in CATH by structure comparison methods once their structure has been solved . However , these superfamilies contain a significant number of relatives with different functions and therefore to be able to further assign these proteins to a specific functional sub-group is of great use for guiding future functional studies . We took all PSI structures solved up to January 2008 that had been newly classified in v3 . 2 of the CATH database and selected the 276 domains which fell into one the superfamilies in our data set . These 276 were further clustered at 60% sequence identity to produce a non-redundant test set of 104 domains , which was then scanned against the FLORA templates for each FSG in order to predict their function . To exclude hits that could have been fairly confidently assigned using global structure comparison , we removed any structures that matched a CATH domain in v3 . 1 library with a SIMAX score<1 . 5 [25] .
To fairly benchmark any function prediction algorithm , it is important to compare against current methods . Unfortunately , the vast majority of function prediction methods are not publicly available , however here we compare against CE as this method has been used as a benchmark for other structure-based function prediction methods ( e . g . [10] , [21] ) . We also compare the performance of FLORA against a more sensitive structure comparison method ( CATHEDRAL [25] ) and a leading function prediction method ( RT [1] ) . Initially , we investigated to what extent global structure comparison could be used to reliably assign function . The graph of sensitivity versus precision ( Figure 3 ) shows the ability of CE and CATHEDRAL to discriminate between domains in the same enzyme family across our entire data set . It can be seen that at high precision ( ∼90% ) , CATHEDRAL outperforms CE , although the sensitivity is still very low ( 18% ) . We suspect that the superior performance of CATHEDRAL over CE is due to the fact that it is able to generate improved alignments of homologous structures by aligning more equivalent residues ( as shown in [25] ) . The performance of both methods shown here is fairly poor for correctly classifying domains into FSGs , but it is obviously important to note that neither of the methods was designed to detect functional relationships . FLORAMake and FLORAScan were applied to the domain data set and the performance was assessed using a leave-one-out approach ( described in the Methods section ) . It can be seen from Figure 3 that even at high precision , FLORA significantly outperforms CATHEDRAL , CE and RT — e . g . 90% precision , CATHEDRAL detects only 15% of true functional homologues , versus 27% for RT and 61% for FLORA . These results show that the FLORA algorithm significantly outperforms global structure comparison . This can be explained by the fact that although FLORA uses the same alignments as CATHEDRAL , it only scores those positions which have been identified as functionally-relevant ( i . e . captured by the FLORA template ) within a given FSG . Furthermore , FLORA uses data from multiple structures and is able to accurately discover functionally-relevant structural motifs and discover more than twice the number of functional homologues at 90% precision than RT . This suggests that where the data are available , exploiting multiple structures with similar functions can improve the sensitivity of function prediction methods . However , where these is not available , methods such as RT [1] can be very valuable . FLORA was benchmarked on 29 functionally diverse enzyme superfamilies and the performance quoted thus far refers to an average calculated over the entire data set . Figure 4 shows the performance per superfamily ( as measured by the Area Under ROC Curve ( AUC ) ) for FLORA and CATHEDRAL . It can be seen that where FLORA is able to perfectly discriminate between domains in different functional sub-groups ( i . e . AUC = 1 . 0 ) , CATHEDRAL is also able to do so as functionally-similar domains must share high global structural similarity . However , for all but one ( CATH code: 3 . 30 . 830 . 10 ) of the superfamilies in the data set , FLORA out-performs CATHEDRAL . Superfamily 3 . 30 . 830 . 10 comprises two FSGs ( aminopeptidases and carboxypeptidases ) , which contain domains that are part of larger multi-domain complexes . For example , the protein chain 1hr6A actually contains two homologous yet non-identical domains ( <30 sequence identity ) , both of which are members of this superfamily — i . e . a domain duplication has produced the multi-domain architecture 3 . 30 . 830 . 10::3 . 30 . 830 . 10 . As a consequence , it is more biologically meaningful to align this superfamily at the chain level , which indeed improves the performance of FLORA ( AUC increases from 0 . 32 to 0 . 88 , see next section and Figure 5 ) . Although there is only one example of this case in our data set , it will be important to account for domain duplications when building templates in the future . For example , we encountered similar problems in a superfamily of periplasmic binding domains ( CATH 3 . 40 . 190 . 10 ) , where a domain duplication creates a receptor of two halves involved in the transportation of small ligands ( unpublished data ) . At this point , it can be seen that simply focussing at the domain level FLORA is able to very effectively improve the recognition of structures in the same FSG . This is interesting given that the majority of structure-based function prediction methods tend to use the whole protein chain . A possible explanation of the power of FLORA could be that the domains in our data set form a core part of the enzymatically active region of the whole protein . Alternatively , it could be that the selected vectors for each template also contain residues that interact with other enzymatic domains within the chain , and it is these interaction sites that FLORA is detecting . To see whether any improvement could be achieved by using the whole protein chain , we used CATHEDRAL to re-align the corresponding PDB chain for each of the domains in the data set and performed an identical benchmark as before . Figure 5 shows that the performance increase of using whole chains over using the component domains is minimal . This suggests that there is enough of a structural signal at the domain level and adding vectors from other domains in the protein chain does not seem to be advantageous . It also means that FLORA could be used to transfer functional annotation between relatives with different multi-domain architectures , therefore expanding the scope of the method . The benchmarking analysis presented above shows that FLORA is indeed able to correctly discriminate between homologous domains from different FSGs better than global structure comparison , despite using global alignments to determine residue correspondence . This suggests that although a global alignment may not be perfect , especially between very distant relatives , it still aligns enough residues that are important for maintaining different functions . To examine where these function-specific residue lay , we chose a representative structure for each enzyme family and visualised the conserved FLORA residues ( see Methods section ) . We have analysed these motifs further in domains from the HUP superfamily ( CATH 3 . 40 . 50 . 620 [46] ) , which is the subject of particular attention within our group . HUP domains are very diverse in terms of sequence , structure and function , and are involved in various essential biological processes ( e . g . protein translation ) . In addition , several proteins with HUP domains have attracted attention due to their medical importance ( e . g . [47] ) . Domains in this superfamily adopt a Rossmann-like fold with a central parallel β-sheet surrounded on both sides by α-helices . The main active site is always located in the C-terminal half of the central β-sheet and is generally involved in nucleotide-binding . HUP domains in the FLORA dataset divide into 3 major FSGs when clustered using the first three digits of the E . C . numbers . In the following section , we consider one representative member of each of these FSGs to describe motifs identified by FLORA . The first FSG consists of the catalytic domain of class I aminoacyl-tRNA synthetases ( EC 6 . 1 . 1 . - ) . These enzymes are essential for protein translation as they catalyse the ligation of amino-acids to their cognate tRNAs in a two-step mechanism that involves ATP . The HUP domains of aminoacyl-tRNA synthetases are found in many different multi-domain contexts in CATH , which appear to partially depend on the amino-acid substrate ( data not shown ) . In representatives from this group , ( S . cerevisiae arginyl-tRNA synthetase , PDB: 1f7u ) , FLORA identifies two major motifs , one of which is located in the amino-acid and ATP binding site , whereas the other covers residues in loops that bind the tRNA ( Figure 6A ) . The next FSG in the HUP superfamily is a group of metabolic enzymes called nucleotidyltransferases ( EC 2 . 7 . 7 . - ) , which transfer nucleotidyl groups from nucleotide tri-phosphates to other compounds . The nucleotidyltransferase we have analysed further ( Th . Thermophilus pantetheine phosphate adenylyltransferase PDB: 1od6 ) , is a relatively small protein and consists of a homo-hexamer of single HUP domain subunits . FLORA identifies two motifs in this domain , one of which locates in the main active site in the C-terminal half of the central β-sheet , whereas the other maps to the inter-subunit interface ( Figure 6B ) . Finally , the third FSG consists exclusively of argininosuccinate synthases ( EC 6 . 3 . 4 . 5 ) , which catalyse the ATP-dependent synthesis of argininosuccinate from citrulline and aspartate . These enzymes are homo-tetramers in which each subunit is comprised of a nucleotide-binding HUP domain and an additional domain involved in multimerisation and catalysis . Three motifs are identified by FLORA in E . coli argininosuccinate synthase: one is located in the nucleotide-binding site ( C-terminal half of the central β-sheet ) , another consists of residues at the interface with other subunits of the tetramer , whereas the third motif is comprised of residues from N-terminal α-helices that are not involved in any identified interactions to our knowledge ( Figure 6C ) . The location of these α-helices on the outward surface of the tetramer cannot exclude the possibility that these FLORA residues might be involved in interactions that have yet to be described in the literature . Analyses of residues identified by FLORA in these domains and others in this superfamily ( data not shown ) suggest that FLORA is generally able to target motifs known to be involved in different aspects of molecular function , like binding interfaces or catalytic sites . This behaviour is somewhat expected from FLORA , which was specifically designed to detect such function-related signatures in homologous domains . By mapping catalytic residues from the CSA onto each FSG representative ( see Methods ) , we found that in 59% of cases the FLORA residues were closer to the functional site than other residues in the domain . This is interesting as it means that in a significant number of FSGs , FLORA is identifying other positions in the protein , for example those involved in interaction sites as demonstrated by the examples discussed above . In the particular case of the HUP superfamily mentioned above , it is noteworthy that in each FSG , FLORA not only identifies functional regions which are unique to the FSG ( e . g . the tRNA binding site in aminoacyl-tRNA synthetases ) , but also residues in the main nucleotide-binding active site which is shared by HUP domains from all FSGs at the C-terminal half of the central β-sheet . Although this would require further investigation , it suggests that FLORA is able to detect relatively small differences in residue positions and orientations between similar active sites in different FSGs . Examining similar representatives from the Class I aldolase superfamily ( 3 . 20 . 20 . 70 ) reveals that FLORA template residues ( Text S1 ) tend to cluster around the active site of the enzymes ( data on active site residues from the Catalytic Site Atlas [5] ) , which suggest that it is where the majority of structural features characteristic of each FSG occur . Our analysis thus far has shown that FLORA is able to substantially improve on the performance of global structure comparison for reliably assigning domains to functional sub-groups . We therefore sought to use it to make novel predictions for structural genomics targets from the PSI . As a data set , we took structures that had been assigned to superfamilies in the latest version of CATH ( v3 . 2 ) and scanned these against the FLORA templates . Using the benchmark curve from the leave-one-out benchmark , we took a score cut-off corresponding to a precision of 95% ( Z-score>3 . 4 ) to ensure high confidence in our assignments . All hits above this cut-off were collated , rather than simply taking the top hit so that we could account for bi-functional enzymes and observe any conflicting predictions ( i . e . those structures which hit more than one FSG template ) . A complete table of results is shown in Text S1 . 104 domains from our v3 . 2 PSI set correspond to 94 PDB structures . Of these 94 , we were able to make predictions for 66 ( 70 . 4% ) with FLORA . To assess the added value of using FLORA over global structure comparison , we took out any PSI structures that matched a domain in CATH with a SIMAX score<1 . 5 ( see Methods ) . This left us with 51/66 ( 78% ) predictions that could not be easily assigned with CATHEDRAL . This supports the earlier benchmark of FLORA , which shows that scoring structural similarity over all FSG-specific residues can dramatically increase the number of functional homologues we are able to detect . Figure 7 shows the structure of 2pbl ( a putative thiol esterase from the Joint Center For Structural Genomics ) superposed against its best hit 1epx . A closer superposition of the active site shows conservation of the surrounding secondary structures and even the positions of the catalytic residues . FLORA finds significant hits to all members of the FSG ( E . C . 3 . 1 . 1- , Carboxylic ester hydrolases ) in superfamily 3 . 40 . 50 . 1820 , despite none of the domains superposing with an RMSD less than 4 , indicating that 2pbl is a distant relative of other superfamily members . The other FSG in the superfamily corresponds to E . C . 3 . 4 . 16 . - , which is a group of Serine-type carboxypeptidases to which FLORA assigns no significant hits . FLORA predicts 2pbl to be a carboxylic ester hydrolase , as opposed to a Thiolester hydrolase ( E . C . 3 . 1 . 2 ) as suggested by the authors . However , given that there are no examples of thiolesterases currently in the superfamily it is possible that they are in fact closely related to the carboxylic ester hydrolases . Biochemically , this function is certainly closer than the peptidase function of FSG ( EC 3 . 4 . 16 . - ) . FLORA predicted NESG structure 2bdt with the E . C . number 2 . 7 . 1 . - , which is a group including enzymes such as fructose 1- , 6 bisphosphate . When this structure was published , it was assigned as a putative gluconate kinase but currently has no official E . C . annotation . PDB 1vm8 from the JESG consortium was functionally characterised when the structure was solved as UDP-n-acetylglucosamine pyrophosphatase and given the E . C . number E . C . 2 . 7 . 7 . 23 . Again , FLORA correctly predicts the E . C . number as 2 . 7 . 7 . - , despite low global structural similarity to any domains in the template data set . 1ylo is a hypothetical protein solved by the MCSG consortium in 2005 . FLORA predicted the E . C . number 3 . 4 . 11 . - , which comprises a group of amino-acid specific peptidases , with significant hits ( Z-score>4 ) to three domain templates in our data set . A BLAST search indeed reveals significant hits ( >99% sequence identity ) to annotated amino peptidases , as the protein has now been functionally characterised since its structure was solved . Again , these trivial hits were not in the data set we used , which demonstrates the power of FLORA to find functional homologues even after significant evolutionary divergence .
FLORA is a novel algorithm which exploits patterns of structural conservation to derive templates for different functional sub-groups ( FSGs ) within diverse domain superfamilies . Unlike many other methods which focus on generating templates based on known or predicted functional residues [1] , [10] , [15] , FLORA considers all residues to provide a more discriminating functional fingerprint . We have shown it is able to use these templates effectively to discriminate between domains with different functions better than global structure comparison ( CATHEDRAL ) , CE and RT . By generating a superfamily-specific Z-score , we found that the performance of FLORA increases significantly . This suggests that the degree of structural variation that confers a change in function is specific to each superfamily and the absolute structural similarity must be compared to a background distribution . Therefore , as has also been identified at the sequence level [40] , [41] , function prediction methods should account for the divergence of the superfamily , rather than adopt one similarity measure that applies to all superfamilies . However , we acknowledge that a representative distribution can only be obtained in sufficiently populated superfamilies . Another important novelty in our approach was to create a large data set comprising 29 superfamilies ( which is made publically available ) . Although FLORA performed well across the majority of superfamilies , this was not universally true , which suggests that function prediction methods should be benchmarked across as diverse a data set as possible . We have also shown that CATHEDRAL outperforms CE , probably due to producing superior alignments outside of the conserved structural core . Although global structure comparison is not always able to reliably find distant functional relatives , we feel it is appropriate for benchmarking new methods to give a guide of the value they add to structure-based function prediction . As detailed in the methods , FLORA calculates vectors based on the geometry of Cβ side chain atoms . However , a re-implementation using just Cα co-ordinates produces almost identical performance on the data set ( data not shown ) . This is encouraging as it increases applicability of our method to theoretical and homology-based models . One of the major ways in which FLORA differs to other methods is by focussing on the domain , rather than at the whole chain or protein complex level . Simply because a domain is present in a given enzyme does not necessarily mean it contributes to or confers catalytic activity . Indeed it might be responsible for protein-protein interactions or other aspects of function , such as locating the protein in a given part of the cell . We have shown that except in the case where there has been a domain duplication ( superfamily 3 . 30 . 830 . 10 ) , deriving structural motifs at the domain level performs as well as aligning whole multi-domain chains . Our hypothesis is that where FLORA does not locate conserved positions around the active site , it is able to find parts of the domain that interact with other catalytic domains . We intend to undertake more detailed analysis of other CATH superfamilies to confirm this . FLORA makes no assumptions about the physico-chemical ( e . g . solvent accessibility or polarity ) or sequence conservation properties of residues in the templates it derives , only that they show high structural conservation within a given functional sub-group . As a consequence , we observed residues both around the enzymatic active sites and in other locations in the protein . In two of the example superfamilies presented here , we have shown that FLORA template vectors co-locate around the active site . This is possibly due to structural changes in the protein that allow for different relatives to bind different ligands . However , this trend is not observed across the whole data set , where only 59% of FLORA template vectors are on average closer to the active site than other residues in the protein . This suggests that it is not only the enzymatic site that is important for discriminating between different FSGs , but other locations in the structure related to domain-domain or protein-protein interfaces . The substantial improvement in performance of FLORA over global structure comparison has allowed us to assign 70% of structural genomics targets , assigned to superfamilies in our data set to functional sub-groups , in this case predicting the type of catalytic reaction they perform . Of our FLORA predictions , 78% could not have been reliably made by standard structure comparison techniques , as we were able to transfer annotation from far more distant relatives ( RMSD>4 Å ) . Although some of the predictions we made are supported by experimental work that occurred after the structure was solved , the accuracy of the rest remains for future functional characterisation work . Taken in the context of our previous analysis of functional divergence across large domain superfamilies in the CATH database [31] , we have shown that it is indeed possible to derive structural templates that can be used to characterise these different functional sub-groups , without explicitly focussing on known or predicted catalytic residues . Both CATHEDRAL and FLORA exploit the same algorithm to align structures , but the performance increase observed by FLORA is due to the fact that it identifies those positions which are distinctive to a function group and only scores the structural similarity over these positions , whereas CATHEDRAL calculates a global score . Although we have benchmarked here using CATH enzyme superfamilies , FLORA can be applied to any other functional or superfamily classification ( both enzyme and non-enzyme ) where there are sufficient structural data . We are currently implementing FLORA as a web service for the structural biology community . | Understanding how the three-dimensional ( 3D ) molecular structure of proteins influences their function can provide insights into the workings of biological systems . Structural Genomics Initiatives have been set up to investigate these structures on a large scale and make the data available to the wider biological research community . However , in a significant number of cases , there is little known about the functions of the structures that are solved . To address this , computational methods can be used as a predictive tool to guide future experimental investigations . One such approach is to exploit global structural comparison to assign the protein in question to an evolutionary family , which has already been functionally characterised . However , this is problematic in some large evolutionary families , which contain a number of different functional sub-families . We have developed a new method ( FLORA ) which is able to calculate 3D “motifs” which are specific to each of these sub-families . Any new protein structure can then be compared against these motifs to make a more accurate prediction of its function . Our paper shows that FLORA substantially outperforms other standard approaches for predicting function from structure . We use our method to make confident functional predictions for a set of proteins solved by the structural genomics projects , which could not have been assigned reliably by global structure comparison . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"computational",
"biology"
] | 2009 | FLORA: A Novel Method to Predict Protein Function from Structure in Diverse Superfamilies |
Leptospirosis is one of the most important neglected tropical bacterial diseases worldwide . However , there is limited information on the genetic diversity and host selectivity of pathogenic Leptospira in wild small mammal populations . Jiangxi Province , located in southern China , is a region highly endemic for leptospirosis . In this study , among a total of 3 , 531 trapped rodents dominated by Apodemus agrarius ( 59 . 7% ) , 330 Leptospira strains were successfully isolated from six different sites in Jiangxi between 2002 and 2015 . Adding 71 local strains from humans , various kinds of livestock and wild animals in Jiangxi , a total of 401 epidemic strains were characterized using 16S rRNA gene senquencing , multilocus sequence typing ( MLST ) and the microscopic agglutination test ( MAT ) . Among them , the most prevalent serogroup was Icterohaemorrhagiae ( 61 . 10% ) , followed by Javanica ( 19 . 20% ) and Australis ( 9 . 73% ) ; the remaining five serogroups , Canicola , Autumnalis , Grippotyphosa , Hebdomadis and Pomona , accounted for 9 . 97% . Species identification revealed that 325 were L . interrogans and 76 were L . borgpetersenii . Moreover , L . interrogans was the only pathogenic species in Fuliang and Shanggao and was predominant in Shangrao ( 95 . 0% ) ; L . borgpetersenii was the most common in the remaining three sites . Twenty-one sequence types ( STs ) were identified . Similarly , ST1 and serogroup Icterohaemorrhagiae were most prevalent in Shangrao ( 86 . 0% and 86 . 4% ) and Fuliang ( 90 . 4% and 90 . 4% ) , ST143 and serogroup Javanica in Shangyou ( 88 . 5% and 90 . 4% ) and Longnan ( 73 . 1% and 73 . 1% ) , and ST105 and serogroup Australis in Shanggao ( 46 . 3% and 56 . 1% ) . Serogroup Icterohaemorhagiae primarily linked to A . agrarius ( 86 . 9% ) , serogroup Canicola to dogs ( 83 . 3% ) . There were significant differences in the distribution of leptospiral species/serogroups/STs prevalence across host species/collected locations among the 394 animal-associated strains ( Fisher’s exact test , p<0 . 001 ) . Our study demonstrated high genetic diversity of pathogenic Leptospira strains from wild small animals in Jiangxi from 2002 to 2015 . A . agrarius was the most abundantly trapped animal reservoir , and serogroup Icterohaemorrhagiae and ST1 were the most dominant in Jiangxi . Significant geographic variation and host diversity in the distribution of dominant species , STs and serogroups were observed . Moreover , rat-to-human transmission might play a crucial role in the circulation of Leptospirosis in Jiangxi . Details of the serological and molecular characteristics circulating in this region will be essential in implementing prevention and intervention measures to reduce the risk of disease transmission in China . However , phylogenetic analysis of more Leptospira isolates should explore the impact of ecological change on leptospirosis transmission dynamics and investigate how such new knowledge might better impact environmental monitoring for disease control and prevention at a public health level .
Leptospirosis , primarily caused by pathogenic spirochaetes of the genus Leptospira , is one of the most widespread and significant zoonotic diseases; it annually causes 1 . 0 million estimated cases of severe human leptospirosis and 58 , 900 estimated deaths , as well as great veterinary economic losses worldwide [1 , 2] . The genus Leptospira contains at least 22 species and more than 300 serovars based on agglutinating lipopolysaccharide ( LPS ) antigens [3] , with 76 serovars and 18 serogroups being reported in China [4] . L . interrogans , L . borgpetersenii and L . kirschneri are the main pathogenic species of leptospirosis in humans and animals worldwide [5 , 6] . The clinical symptoms of human leptospirosis range from asymptomatic or mild infection to severe manifestations causing multi-organ dysfunction and even death . In addition to human hosts , pathogenic Leptospira also infect a wide range of animals , including domestic mammals ( livestock ) and wild animals , especially rodents , which are considered the main reservoir for Leptospira infections in humans [7] . Humans can be infected through direct contact with infected animals , or indirect contact with water and soil contaminated by the urine of infected animals [8] . Therefore , long-term active surveillance and investigations into the carriage status of animal reservoirs and epidemiological characteristics of animal-associated causative agents and infected individuals will contribute to understanding animal-to-human transmission , field epidemiology , outbreak investigation and source tracking for leptospirosis . Since 1955 , Leptospirosis has been classified as a nationally notifiable disease in China . During 1955–2010 , ten large outbreaks of leptospirosis with incidences rates of more than 10 cases per 100 , 000 have been previously reported [9] . The Chinese National Notifiable Infectious Disease Surveillance System was established in 2005 , in which 25 monitoring sites throughout the whole country were selected to continually survey human cases or animal reservoirs of leptospirosis . The incidence of leptospirosis in the recent decade decreased to 0 . 1 cases per 100 , 000 compared to 1 . 4 cases per 100 , 000 in the 1990s [9] . Although the incidence of leptospirosis has significantly decreased , small-scale local outbreaks and high prevalence rates were still reported recently in some epidemic regions of China [10] . Jiangxi Province is located in southern China . Historically , this province has been a significant endemic region for leptospirosis . Here , a large number of rivers and lakes , a moist subtropical monsoon climate , abundant rainfall and forest coverage , a wide variety of wild animals , and rice cultivation provide a favorable environment for the broad spread and prevalence of Leptospira , as well as a large population of reservoir mammals . Compared to the annual average incidence in China ( average 0 . 0692 cases per 100 , 000 inhabitants between 2002 and 2015 ) , the annual average human incidence of leptospirosis in Jiangxi was as high as 0 . 1764 cases per 100 , 000 inhabitants during the period of 2002–2015 , showing that Jiangxi Province was a significant epidemic area of leptospirosis ( data available from the annual infectious disease reports of the National Notifiable Infectious Disease Surveillance System in China ) . Furthermore , the annual average incidence rates in some regions , such as Ganzhou , Yichun , Shangrao and Yingtan city , are markedly higher than those of other regions in Jiangxi Province , while Nanchang and Jingdezhen have relatively lower incidences rates . To date , no detailed long-term studies have focused on the epidemiological characteristics and genetic diversity , including the predominant serogroups or genotypes in wild animals of Jiangxi Province . To investigate potential reservoir populations and the genetic diversity of the strains , a large-scale dataset composed of 401 epidemiological strains from multiple sources in Jiangxi Province was characterized using 16S rRNA gene sequencing and MLST typing . These isolates were primarily obtained from a wide range of wild small animal reservoirs and leptospirosis patients in Jiangxi Province through the National Notifiable Infection Diseases Surveillance System of Leptospirosis over a period of 14 years . To the best of our knowledge , this retrospective study represents the longest and largest field epidemiological investigation on the etiological characteristics and genetic diversity of pathogenic Leptospira among dogs , livestock ( pigs and cattle ) and other small wild animals ( A . agrarius , R . rattoides , R . norvegicus , Rattus flavipectus , common skunks , Rana nigromaculata and so on ) and human populations in Jiangxi Province . The detailed serological and molecular characteristics circulating in this region may provide new insights into the epidemiology and guidelines for the control of leptospirosis in China .
This study and the research protocol were reviewed and approved by the Ethical Committee ( Institutional Review Board , IRB ) of National Institute for Communicable Disease Control and Prevention , Chinese Centre for Disease control and Prevention ( License number: ICDC-2015361 ) . All patients gave written informed consent for participation in this study with their identifiable information , and the legal guardians of young children ( less than 12 years of age ) provided informed consent on their behalf; in accordance with the Declaration of Helsinki and IRB approval . No livestock were euthanized . Moreover , permission to sample dead livestock of suspected leptospirosis was provided by the owners of these animals . The trapping , handling and euthanasia of wild rodents , R . nigromaculata and skunks in this study were carried out following the procedures and protocols approved by the Ethical Committee of the National Institute for Communicable Disease Control and Prevention , Chinese Centre for Disease Control and Prevention ( License number: ICDC-2015361 ) . In our study , six monitoring sites located in three ( designated as Habitat A , B , and C ) of five epidemic habitats previously reported in Jiangxi Province based on the incidence of leptospirosis , as well as geographical latitude , longitude , altitude and geomorphic conditions involved in the National Notifiable Infectious Disease Surveillance System [11] ( Fig 1 ) . These monitoring sites in Jiangxi represent different ecosystems containing large wild animal populations as well as a biodiversity index . Habitat A , with the lowest incidence rate , includes Xinjian city ( average 0 . 0015 cases per 100 , 000 between 2002 and 2014 ) and lies in the northern lower Poyang Lake area of Jiangxi Province . The topography is generally 15–26 meters above sea level . Habitat B has a higher incidence rate; this area includes Fuliang ( average 0 . 0329 cases per 100 , 000 during 2002–2014 ) , and Shangrao ( average 0 . 2781 cases per 100 , 000 during 2002–2014 ) , Shanggao ( average 0 . 3162 cases per 100 , 000 between 2002 and 2014 ) and lies in the northeastern hilly plain area ( 100–300 meters above sea level ) . Habitat C has the highest incidence rate and encompasses Shangyou and Longnan ( average 0 . 2994 cases per 100 , 000 between 2002 and 2014 ) with 80 . 6% of the land covered by forests containing abundant wild animal resources . Habitat C is located in the southwestern South Jiangxi mountain region ( 1000–1600 meters above sea level ) . Hence , the diverse topography and ecological conditions of Jiangxi Province made it suitable for investigating the genetic diversity of Leptospira from wild small animals in these different ecosystems . The six trapping sites in Jiangxi were located as follows: Xinjian ( 28 . 69 N; 115 . 82 E ) , Fuliang ( 29 . 4 N; 117 . 2 E ) , Shangrao ( 28 . 5 N; 117 . 9 E ) , Shanggao ( 28 . 2 N; 114 . 9 E ) , Longnan ( 24 . 9 N; 114 . 8 E ) and Shangyou ( 25 . 8 N; 114 . 5 E ) . Within each locality , rodent trapping was conducted over an area of approximately 10 kilometers squared . Field rodents were trapped using the Trap-night method from 2007 to 2015 in humid rice field environments known to contain large rodent populations within the framework of the CERoPath project ( www . ceropath . org ) [12] . The traps were loaded with peanut butter bait in the evening and collected early morning . For each site , 10 trapping lines , consisting of 10 locally hand-made wire traps ( approximately 40×12×12 cm ) every five meters , were placed during a period of five days and four nights [12] . Field rodents were trapped twice in the wet season among April to June and August to October every year at the same place ( using a Global Positioning System receiver ) . The trapped field rodents and small mammals were identified by genus , species , and gender based on phenotypic characteristics ( ears , body , tail , fur color , sex ) [13] . The rodent density was calculated using the formula: ( Number of rodents trapped each year / Number of total traps successfully placed for each year * 100 ) . In addition , another 64 strains isolated from dogs , livestock ( pigs and cattle ) and other wild small animals ( A . agrarius , R . rattoides , R . norvegicus , R . flavipectus , skunk and R . nigromaculata ) , serving as potential reservoir animals of leptospirosis in Jiangxi Province; these samples were collected from the same six regions through the same surveillance system between 2002 and 2015 ( S1 Table ) . A total of 7 strains isolated from clinical cases of leptospirosis were collected from urine samples in the local hospitals in Shanggao ( S1 Table ) . Kidney samples from cattle , pigs and dogs were directly collected from the owners of these dead animals in these monitoring sites . Approximately 1 g of fresh kidney samples from the animals or 100–200 μl of whole blood from animals suspected of leptospirosis were cultured in 10 ml of liquid Ellinghausen-McCullough-Johnson-Harris ( EMJH ) medium ( Difco Laboratories , USA ) with 5-fluorouracil ( Merck , Germany ) at 28°C and observed weekly by dark-field microscopy for the presence of Leptospira for up to 3 months . Samples with no growth of Leptospira after 3 months were considered negative [14] . Species identification was performed using 16S rRNA gene sequencing as previously described [15] . A total of 20 accessible Leptospira species reference sequences representing pathogenic , intermediate and non-pathogenic Leptospira species were obtained from the GenBank database . Leptonema illini NCTC 11301T and Turneriella parva NCTC 11395T were set as the outgroup ( S2 Table ) [15 , 16] . The sequences of all the 401 Leptospira strains isolates from Jiangxi and the 20 representative sequences were compared using Clustal W . A neighbor-joining tree was constructed using Mega software version 5 . 10 with a bootstrap value of 1 , 000 . Serogroup identification of these leptospiral strains was conducted by MAT against 15 Chinese standard serogroup-specific rabbit antisera from the National Institutes of Food and Drug Control , China , representing the most prevalent pathogenic Leptospira serogroup in China . The serogroup scoring the highest MAT titer of the test strain agglutinating 50% of live leptospiral against a given serogroup-specific rabbit antisera was defined as the presumptive corresponding serogroup . MLST was performed using seven housekeeping genes ( glmU , pntA , sucA , tpiA , pfkB , mreA and caiB ) as previously described [17] . The PubMLST Leptospira database ( http://pubmlst . org/leptospira/ ) was used for nucleotide analysis . Minimum spanning trees ( MST ) were applied to determine the relationships among STs through BioNumerics software version 5 . 10 ( Applied Maths , Kortrijk , Belgium ) . Clonal complexes ( CCs ) were defined with clustered STs differing by one or two loci and named on the basis of the putative founder ST or the ST associated with the largest number of single-locus variants . Singletons are defined as the STs differing by at least three alleles from other STs . Phylogenetic analysis was performed using the unweighted pair group method with average linkages provided in BioNumerics software version 5 . 10 . The effects of the prevalent serogroups , STs and species on host-species , collected years and locations were investigated . Fisher’s exact test was used to compare the differences in the distribution of leptospiral prevalence across species , serogroups and STs between small animal species and collected locations among the animal-associated strains . The p-value was computed by Monte Carlo simulation . The statistical Kruskal-Wallis chi-squared test was used to investigate whether there were significant differences in the distributions of leptospiral prevalence across species , serogroups and STs among collected years among the animal-associated strains . All statistical analyses were performed using R software ( R version 3 . 5 . 1 , https://www . r-project . org/ ) [18] , considering a significance level of 0 . 05 .
A total of 45 , 144 traps were placed and 3531 field rodents belonging to 9 different species were successfully captured between 2007 and 2015 . Species identification of trapped rodents and the number of rodents with positive renal cultures is presented in Table 1 . Those species represented most of the small mammal diversity in Jiangxi Province . The density of field rodents was between 4 . 76–12 . 56% in Jiangxi Province . The most abundantly trapped species was A . agrarius ( 59 . 7% , 2107/3531 ) , followed by R . rattoides ( 23 . 0% ) . A total of 330 strains from 5 different species of field rodent were isolated ( Table 1 ) . In addition , 34 strains isolated from small wild animals including 10 A . agrarius , 10 R . rattoides , 5 R . norvegicus , 5 R . flavipectus , 2 skunks and 2 R . nigromaculatas; 30 isolated from livestock including 5 cattle , 1 pig and 24 dogs; and 7 isolated from humans were also collected in the same six sites between 2002–2015 . In this study , a total of 401 non-epidemiologically related leptospiral strains collected between 2002 and 2015 in Jiangxi were used ( S1 Table ) . Among these 394 animal-associated strains , A . agrarius was the main abundantly trapped animal reservoir , accounting for 59 . 4% ( 234/394 ) of carriers identified , followed by R . rattoides ( 17 . 3% ) . Using 16S rRNA gene sequencing , two pathogenic species: L . interrogans and L . borgpetersenii , were identified among the 401 isolates ( S1 Fig ) . Fisher’s exact test revealed highly significant differences in the distribution of leptospiral species prevalence across host species/collected locations among the 394 animal-associated strains ( p<0 . 001 for all comparisons ) ( S3 and S4 Tables ) . L . interrogans was the predominant species ( 81 . 1% ) , widely represented in all six regions ( Fig 1 ) and identified from humans and a wide range of animals ( i . e . , 10 host species , S3 Table ) , while L . borgpetersenii was only identified from 4 host species ( S3 Table ) . Furthermore , there were some special geographic differences between the circulating pathogenic Leptospira species . L . interrogans was the only species in the two cities of Fuliang and Shanggao and was the predominant species in Shangrao ( 133/140 ) , while L . borgpetersenii was the dominant species in the remaining three cities of Shangyou ( 46/52 ) , Longnan ( 19/26 ) and Xinjian ( 4/6 ) ( S4 Table ) . The Kruskal-Wallis chi-squared test showed a significant difference in the distribution of leptospiral species prevalence across collected years among the 394 animal-associated strains ( χ2 = 60 . 9 , df = 9 , P < 0 . 001 ) ( S5 Table ) . L . interrogans was present in every year from 2005 to 2015 , but L . borgpetersenii was not present in 2012 . A total of eight serogroups were identified among 401 isolates . Serogroup Icterohaemorrhagiae , as the most frequent serogroup , accounted for 61 . 1% , followed by Javanica ( 19 . 20% ) and Australis ( 9 . 73% ) . The remaining five serogroups , Canicola , Autumnalis , Grippotyphosa , Hebdomadis and Pomona , accounted for only 10 . 0% ( S1 Table ) . Fisher’s exact test revealed highly significant differences in the distribution of leptospiral prevalence across collected locations among the 394 animal-associated strains ( P < 0 . 001 ) ( S6 Table ) . Leptospiral serogroup diversity was higher in Shangrao , Shanggao and Fuliang than in Shangyou , Longnan and Xinjian ( Fig 1 ) . In addition , there were some significant regional variations in the distribution of the dominant serogroups . Icterohaemorrhagiae was the most common serogroup in Shangrao ( 121/140 ) and Fuliang ( 123/136 ) and Australis in Shanggao ( 23/41 ) , while serogroup Javanica was dominant in the remaining three cities of Shangyou ( 47/52 ) , Longnan ( 19/26 ) and Xinjian ( 4/6 ) ( Fig 1 ) . Fisher’s exact test revealed highly significant differences in the distribution of leptospiral prevalence across hosts species among these 394 animal-associated strains ( P < 0 . 001 ) ( S7 Table ) . A couple of serogroups were primarily associated with one or multiple host species . For example , Icterohaemorhagiae was preferentially restricted in A . agrarius ( 86 . 9% ) , Canicola in dogs ( 83 . 3% ) , and Javanica in R . rattoides ( 40 . 3% ) and R . norvegicus ( 33 . 8% ) . With the exception of 3 isolates in 2002 and 2 isolates in 2005 , there were 31–54 isolates per year between 2006 and 2015 in the 401 isolates . The diversity of serogroups was relatively lower in recent years . An average of 5–7 different serogroups were found per year before 2011 . After 2011 , only 2 to 4 serogroups were isolated annually . The Kruskal-Wallis chi-squared test showed a significant difference in the proportional prevalence of leptospiral serogroups in different years among the 394 animal-associated strains ( χ2 = 66 . 1 , df = 9 , P < 0 . 001 ) ( S8 Table ) . The most prevalent serogroups , Icterohaemorhagiae , Javanica and Australis , were present every year from 2006 to 2015 except 2005 , 2012 and 2014 . Serogroup Canicola was present in 2006–2007 , 2009 and 2011 . In this study , a total of 21 different STs were obtained from 401 pathogenic Leptospira isolates in Jiangxi Province ( S1 Table ) . The most prevalent ST was ST1 ( 235/401 ) , followed by ST143 ( 72/401 ) , ST105 ( 24/401 ) , ST37 ( 15/401 ) and ST17 ( 10/401 ) . The remaining 45 isolates belonged to 16 different STs ( S1 Table ) . Additionally , among the 21 STs , only ST209 and ST143 were identified as L . borgpetersenii; the other STs were identified as L . interrogans . Minimum spanning tree analysis revealed 3 singletons ( CC1 , CC17 and CC216 ) and seven main CCs ( CC143 , CC105 , CC37 , CC107 , CC214 , CC106 and CC224 ) ( Fig 2 and S1 Table ) . The most abundant CC was the Singleton CC1 , with 235 ST1 isolates . The second most abundant , CC143 , contained 76 isolates and was subdivided into two STs ( ST143 and ST209 ) , followed by CC105 ( 28 isolates in 3 STs ) and CC37 ( 19 isolates in 4 STs ) . There was no coexistence of different species within a CC . As expected , only CC143 was classified as L . borgpetersenii , whereas the remaining six CCs and 3 singletons were classified as L . interrogans , showing no coexistence of different species within the same CC ( Fig 2 ) . Fisher’s exact test revealed highly significant differences in the distribution of leptospiral STs prevalence across collection locations of the 394 animal-associated strains ( P < 0 . 001 ) ( S9 Table ) . Based on the minimum spanning trees color-coded by monitoring sites , significant geographic variations in the distribution of dominant STs were also found: ST1 was the most prevalent ST in Shangrao ( 112/140 ) and Fuliang ( 123/136 ) ; ST143 was the most prevalent in Shangyou ( 46/52 ) and Longnan ( 19/26 ) , but was also present in Shangrao; ST105 was the most prevalent in Shanggao ( 19/41 ) ; and ST209 was the most prevalent in Xinjian ( 4/6 ) ( S2 Fig ) . The four most predominant genotypes , ST1 , ST143 , ST105 and ST37 , were temporally ( between 2002 and 2014 ) and geographically diverse ( 2 or 3 cities ) . Fisher’s exact test revealed highly significant differences in the distribution of leptospiral ST prevalence across host species among the 394 animal-associated strains ( P < 0 . 001 ) ( S10 Table ) . From the Minimum spanning trees color-coded by animal host , significant differences in the distribution of leptospiral ST prevalence across host species were also found: ST1 and ST17 dominated in A . agrarius ( 86 . 9% and 80 . 0% , respectively ) , ST37 in dogs ( 86 . 7% ) , ST143 in R . rattoides ( 43 . 1% ) and ST105 in R . rattoides ( 33 . 3% ) ( S3 Fig ) . Interestingly , ST105 strains were isolated from diverse sources including humans and a wide range of different animal hosts ( A . agrarius , dogs , cattle , Mus musculus , R . rattoides , R . norvegicus and R . nigromaculata ) , indicating potential animal-to-human transmission . From the minimum spanning trees color-coded by serogroup , each different serogroup corresponded to a special ST , except ST37 ( S4 Fig ) . ST37 was related to serogroups of Canicola and Hebdomadis . The Kruskal-Wallis chi-squared test showed a significant difference in the proportion of leptospiral ST prevalence in different years among the 394 animal-associated strains ( χ2 = 84 . 7 , df = 9 , P < 0 . 001 ) ( S11 Table ) . ST1 , ST143 and ST105 , as the prevalent STs , were present in nearly every year during the 2005–2015 period , with the exception of 2005 and 2014 . ST17 was present in 2006–2009 and 2012 . ST37 was present in 2006–2007 , 2009 and 2011 . Among the 401 isolates , the diversity of STs isolated annually was relatively low later in the survey period . Only two STs , ST107 and ST143 , were found in 2005 . An average of 5–14 different STs were found yearly between 2006 and 2011 , but only 3 or 4 different STs were found each year between 2012 and 2015 . The UPGMA dendrogram of the 401 isolates showed a relatively similar clustering patterns as determined using MST analysis ( Figs 2 and 3 ) . Seven main clades ( CC143 , CC105 , CC37 , CC107 , CC214 , CC106 and CC224 ) were generated and the remaining isolates were dispersed among three unrelated singletons ( CC1 , CC17 and CC216 ) ( Fig 3 ) . The majority of our isolates ( 58 . 6% , 235/401 ) belonged to the singleton CC1 , followed by 76 isolates belonged to ST143 and ST209 of CC143 . The UPGMA dendrogram of STs showed that some isolates from humans and animals clustered closely together ( Fig 3 ) , such as ST17 ( humans , A . agrarius and R . norvegicus ) , ST106 ( humans , A . agrarius and skunk ) , ST105 ( humans , A . agrarius , dogs , cattle , M . musculus musculus , R . rattoides , R . norvegicus and R . nigromaculata ) , ST140 ( humans and pigs ) and ST37 ( humans and dogs ) , For example , one clinical ST17 strain from 2006 and 9 rat-associated ST17 strains ( A . agrarius and R . norvegicus ) collected later clustered together . Another clinical ST106 strain also closely clustered with 3 ST106 strains from 1 A . agrarius and 2 common skunks in Shanggao . Moreover , 13 ST37 strains from dogs in 2006 , 2007 , 2009 , 2011 and 2 clinical strains in Shanggao distributed into one cluster ( Fig 3 ) , indicating multiple animal-to-human transmission patterns of L . interrogans .
To our knowledge , this is the first large-scale study investigating distribution and abundance of pathogenic Leptospira strains isolated from small animal populations and their Leptospira carriage rates in Jiangxi Province from 2002 to 2015 . Moreover , we provided the first description of circulating Leptospira serogroups , species and genotypes in humans and potential small animal reservoirs and/or carriers of leptospirosis in Jiangxi . Based on serological and microbiological methods in our study , we revealed that the proportion of infectious leptospiral prevalence in this study varied significantly across serogroups , species and STs , which was generally related to geography and the host species . The application of an epidemiological approach that includes ecological and evolutionary investigations can help provide insights into potential disease factors that may influence the morbidity rates of leptospirosis . Host associations and biogeography are two important factors that may have direct effects on the pathogen transmission patterns of leptospirosis around the world . To our knowledge , this is the first study that takes into account the relationship between Leptospira prevalence proportion and the geographical location , collection time , and host species over a period of 14 years in Jiangxi Province , China . The statistical tests showed significant differences in the distribution of leptospiral prevalence across different host species , collection years and locations ( S3–S11 Tables ) . Among the 401 Jiangxi isolates , serological typing revealed that the three predominant serogroups , Icterohaemorrhagiae , Javanica and Australis , were responsible for leptospirosis in Jiangxi Province from 2002 to 2014 . Host-specific associations by serogroup existed at some degree in this study . Some serogroups were primarily associated with one host species; for example , Rattus spp . was the main carriers of Icterohaemorrhagiae , while dogs were the main carriers of Canicola . This was consistent with leptospiral serogroup-host associations that have been generally observed worldwide . For example , Rattus spp . are known carriers of Icterohaemorrhagiae [6 , 19] . Differences in geographic distribution of serovars between ecological zones on Tutuila revealed that the three dominant serovars had different host species that live in different environments , which supports the hypothesis that environmental factors play an important role in the transmission dynamics of serovars [20] . This study suggests high diversity of pathogenic Leptospira , as the widespread and prevalent species , L . interrogan , and L . borgpetersenii , have been reported in humans and potentially identified in small animal reservoirs in China . This observation is consistent with previous genotyping investigations indicating that L . interrogans , L . borgpetersenii and L . kirschneri are the most abundant species circulating worldwide [21] . One previous study demonstrated that L . interrogans and L . kirschneri were identified using 16S rRNA gene sequencing and MLSA ( multilocus sequence analysis ) among 51 strains isolated from a variety of sources and geographical areas in France [22] . L . interrogans was found in several outbreaks in Brazil , Cambodia , Lao PDR and Thailand [21 , 23–25] . In our present study , a significant difference in the proportions of leptospiral species prevalence across different host species/collection locations was found . L . interrogans was the most prevalent species , identified in all seven humans and 80 . 7% of animals screened in Jiangxi . As the most prevalent species in Jiangxi , L . interrogans was widely isolated from A . agrarius , R . rattus , R . norvegicus , R . flavipectus , dogs , cattle , R . nigromaculata , M . musculus musculus , pigs , skunks and humans . Among these hosts , A . agrarius was the most abundantly trapped host , whereas , L . borgpetersenii was isolated from A . agrarius , R . rattus , R . norvegicus , and R . flavipectus . This is not consistent with a previous report revealing host-species association in pathogenic Leptospira species in other countries [26] . In contrast with previous surveys where carriage rates ranged from 11% to 80 . 3% when based on culture isolation , the carriage rate in rats detected in this investigation ( 9 . 35% ) was lower [27–29] . Our results give the first direct confirmation that A . agrarius infected with the same prevalent serogroups of Icterohaemorrhagiae was recognized as the major potential animal reservoir of L . interrogans , the same most prevalent species identified from human leptospirosis patients in China . This observation is consistent with the fact that L . interrogans and L . borgpetersenii are commonly associated with rodents worldwide [25 , 30 , 31] . Leptospiral diversity and prevalence can be affected by a number of environmental factors . The distribution of L . interrogans and L . borgpetersenii associated with special geographic regions in Jiangxi was also confirmed in this study . L . interrogans and L . borgpetersenii , as the two most prevalent Leptospira species , may have different epidemiological transmission patterns . It has been reported that L . interrogans infection in rodents is restricted to humid habitats , while L . borgpetersenii infection occurs in both humid and dry climates [25 , 32] . Leptospiral diversity may be due to the difference of special geographic regions . In this study , Shangrao , Fuliang and Shanggao ( Habitat B ) were in the northeastern hilly plain area and have relatively humid habitats characteristic of Jiangxi , while Shangyou and Longnan ( Habitat C ) are located in the southwestern Jiangxi mountain region and have relatively dry habitats; Xinjian ( Habitat A ) is located in the northern lower Poyang Lake flat area . The differences in climate , geomorphology and altitude among these areas may influence leptospiral clade diversity . How host-pathogen interactions , ecosystems and geographical factors influence the community ecology of a pathogen is unclear . Among wild animals , rodents are the primary prevalence maintenance hosts for Leptospira spp . and may transfer infection to livestock , small wild animals and humans [33] . In our study , the prevalent serogroups/STs of the strains isolated from patients and possible animal reservoirs display a high similarity in Jiangxi Province ( Fig 3 ) , indicating the close transmission relationship of these Leptospira . For example , CC37 , as one of the main clone complexes , was common in pigs , dogs , cattle , and R . rattoides , as well as humans , in Shanggao , Shangrao and Fuliang between 2002 and 2011 . It was shown that dogs , pigs and cattle , as well as rodents , are also important reservoirs for the transmission of Leptospira to humans . It was reported that most of the leptospirosis cases in China occurred from July to December , with a peak in September [34] . This is the period of rice planting and harvest in Southern China . Farmers can be infected through direct contact with infected domestic animals ( pigs , dogs and cattle ) or wild animals ( rodents , the common skunks , R . nigromaculata and so on ) or through indirect contact with water and soil contaminated by the urine of infected animals . The results in our study may assist in efforts to track the potential transmission source of leptospirosis outbreaks and to establish a better control program against leptospirosis in different epidemic regions . Further studies are needed to determine whether the prevalence of leptospirosis in Jiangxi Province is similar to that in other countries worldwide . Five of the most prevalent STs , ST1 , ST143 , ST105 , ST37 and ST17 , were identified as longterm and ubiquitous virulent strains throughout Jiangxi Province . Compared to the predominant ST1 widely distributed between host clades ( A . agrarius , R . norvegicus , R . rattoides and R . flavipectus ) and geographic locations ( Shangrao , and Fuliang ) during 2006–2015 , ST37 was mainly distributed among dogs and humans in Fuliang , Shanggao and Shangrao in 2002 , 2006–2007 , 2009 and 2011 . Similarly , ST17 was distributed between A . agrarius , R . norvegicus and humans in Jiangxi in 2006–2009 and 2012 , whereas ST105 was distributed between wider spectrum of host clades ( A . agrarius , dogs , cattle , M . musculus , R . nigromaculata , R . norvegicus and R . rattoides ) during 2006–2013 and 2015 . ST143 , as the most predominant genotype ST in Jiangxi , was also reported in Malaysia [35] . The different isolation locations may be the key factor in the diversity of circulating Leptospira spp . Shangyou and Longnan ( Habitat C ) were located in the south Jiangxi mountain region , with elevations ranging from 1000–1600 meters above sea level . The differences in climate , geomorphology and altitude among these locations may influence leptospiral clade diversity . In our study , variation of the serogroups or STs reflected the features of Leptospira in Jiangxi . Thaipadungpanit et al . reported that ST34 was the most frequent genotype in 101 L . interrogans strains in Thailand in 2007 [21] . Caimi et al . demonstrated that ST37 was the main genotype in 18 isolates in Argentina [24] . ST17 was identified in 90 strains of serogroup Icterohaemorrhagiae in Sao Paulo [36] . Five common STs , ST37 , ST17 , ST 199 , ST110 , and ST146 , were reported to have a longterm and ubiquitous distribution in Russia [37] . ST37 , ST118 and ST119 were isolated from dogs in Japan [38] . ST110 , ST50 , ST143 and ST242 were reported in small mammals in Malaysia [39] . Therefore , these prevalent STs ( ST17 , ST37 and ST143 ) reported in China are also the same prevalent STs in the rest of the world . At the same time , the genetic diversity of Leptospira in China is generally different from that observed in other countries , suggesting a high degree of diversity of circulating Leptospira spp worldwide . ST17 and ST37 were found to be the most globally prevalent strains of pathogenic Leptospira circulating in a wide geographic region that includes China . These specific dominant epidemic strains , such as ST17 and ST37 , may have selective advantages in the environment or in possible animal reservoirs that have allowed them to survive and become unusually geographically widespread . However , some STs appear to be concentrated in specific geographic regions: ST1 , as the most prevalent ST , has only been reported in Chinese strains; ST145 , the most prevalent ST in India , is not distributed worldwide [40] . L . interrogans , L . kirschneri ST117 and L . kirschneri ST110 were present in small mammals at all three sites surveyed in Germany [41] . Generally , there is a complex population structure and biased distribution of genotypes of Leptospira isolates worldwide . The findings of this study highlight the importance of understanding the epidemiology and ecology of Leptospira worldwide . Here , our focus on pathogenic Leptospira using serogroup identification , 16S rRNA gene sequencing and MLST analysis for phylogenetic analysis has led to a better understanding of diversity of Leptospira . MLST provides evidence that the diversity of STs is very high in China . The results may be useful in developing a strategy and guidelines for the prevention and control of leptospirosis in China . While , phylogenetic analysis of more globally Leptospira isolates is necessary , we nonetheless believe that our present study provides a blueprint for further phylogenetic studies . | Leptospirosis , caused by pathogenic Leptospira spp , is one of the most widespread zoonoses . In recent years , human leptospirosis with occasionally fatal infections has been frequently reported in Jiangxi Province , a highly endemic region located in the south of China . However , there is a lack of information on circulating Leptospira strains in this province . To identify the etiological characteristics , 401 Leptospira from Jiangxi were characterized using serological and molecular typing methods . Serological typing revealed that 61 . 10% of the isolates belonged to serogroup Icterohaemorrhagiae . Two species , L . interrogans and L . borgpetersenii , were identified using 16S rRNA gene sequencing . A . agrarius may be the main carrier of leptospirosis in this endemic region . Furthermore , the diversity of leptospiral isolates was demonstrated using MLST analysis . ST1 , as the most prevalent ST of pathogenic leptospires , was widely dispersed in China . Significant geographic variation and host diversity in the distribution of dominant species , serogroups and STs were found in Jiangxi . This study is the first to demonstrate the distribution of Leptospira in domestic and wildlife animals in Jiangxi . This retrospective study represents the longest and largest field epidemiological investigation on the etiological characteristics and genetic diversity of pathogenic Leptospira among large wild animal reservoirs and human populations in Jiangxi . A better understanding of the circulating etiological agents and epidemiology of leptospirosis will provide a good starting point for efforts to control and prevent this disease . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"livestock",
"medicine",
"and",
"health",
"sciences",
"leptospira",
"pathology",
"and",
"laboratory",
"medicine",
"infectious",
"disease",
"epidemiology",
"pathogens",
"tropical",
"diseases",
"microbiology",
"vertebrates",
"animals",
"mammals",
"dogs",
"bacterial",
"disea... | 2019 | Genetic characteristics of pathogenic Leptospira in wild small animals and livestock in Jiangxi Province, China, 2002–2015 |
Many genes that are required at specific points in the cell cycle exhibit cell cycle–dependent expression . In the early-diverging model eukaryote and important human pathogen Trypanosoma brucei , regulation of gene expression in the cell cycle and other processes is almost entirely post-transcriptional . Here , we show that the T . brucei RNA-binding protein PUF9 stabilizes certain transcripts during S-phase . Target transcripts of PUF9—LIGKA , PNT1 and PNT2—were identified by affinity purification with TAP-tagged PUF9 . RNAi against PUF9 caused an accumulation of cells in G2/M phase and unexpectedly destabilized the PUF9 target mRNAs , despite the fact that most known Puf-domain proteins promote degradation of their target mRNAs . The levels of the PUF9-regulated transcripts were cell cycle dependent , peaking in mid- to late- S-phase , and this effect was abolished when PUF9 was targeted by RNAi . The sequence UUGUACC was over-represented in the 3′ UTRs of PUF9 targets; a point mutation in this motif abolished PUF9-dependent stabilization of a reporter transcript carrying the PNT1 3′ UTR . LIGKA is involved in replication of the kinetoplast , and here we show that PNT1 is also kinetoplast-associated and its over-expression causes kinetoplast-related defects , while PNT2 is localized to the nucleus in G1 phase and redistributes to the mitotic spindle during mitosis . PUF9 targets may constitute a post-transcriptional regulon , encoding proteins involved in temporally coordinated replicative processes in early G2 phase .
The eukaryotic cell cycle is an ordered program of coordinated processes that mediate the replication of key cellular structures and their subsequent distribution to daughter cells . The proteins involved are often expressed at specific points during the cell cycle , for example dihydrofolate reductase ( DHFR ) [1] and histones [2] , which are required before and after DNA synthesis , respectively , and whose regulation has been intensively studied . In most eukaryotes , such regulation might be expected to occur through transcriptional regulation . However , post-transcriptional regulation of mRNA , particularly in terms of differential message stability , is also important in ensuring that mRNAs do not remain for long after their usefulness has expired . For example , differential RNA stability of both DHFR [3] , [4] and histone [5] mRNAs during the cell cycle plays an important part in the cell-cycle dependent expression of their protein products . Post-transcriptional regulation is especially important for the kinetoplastids , an early diverging branch of model unicellular eukaryotes that includes many important human pathogens . This is due to an unusual feature of kinetoplastids: their mature mRNA transcripts are produced through cleavage and processing of large polycistronic RNAs , which are synthesized via unidirectional , RNA pol II transcription across large stretches of chromatin [6] . This unusual mechanism of mRNA generation precludes individual regulation of gene expression at the level of transcription . Instead , regulation of gene expression generally occurs via differential mRNA decay or other post-transcriptional mechanisms [7] . Despite this , kinetoplastids are capable of complex developmental regulation . For example , the model kinetoplastid parasite Trypanosoma brucei differentiates into two distinct cell types in the mammalian host and at least five in the Tsetse fly vector [8] , and the different types have distinct gene expression profiles [9] . Kinetoplastids therefore constitute a class of eukaryotes that are able to proliferate , differentiate and adapt by means of entirely post-transcriptional gene regulatory networks , raising interesting questions about the nature of the pathways involved . Several genes are known to be strongly differentially expressed during the kinetoplastid cell cycle . One group of co-regulated genes includes DHFR , topoisomerase 2 ( TOP2 ) and replication protein-A ( RPA1 ) , whose transcript levels peak during S-phase due to the presence of a cycling element in the 5′ UTR with a consensus sequence ( C/A ) AUAGAA ( G/A ) [10] . This element can be functional in the 3′ UTR [11] or even in the pre-mRNA only [12] , indicating that regulation can occur prior to mRNA maturation; and it also seems to be conserved in other kinetoplastids [13] . The DHFR , TOP2 and RPA1 transcripts , while originating from different parts of the genome , appear to belong to a post-transcriptional regulon [14] since they are co-regulated via common cis-regulatory motifs , and possess related biological functions . Another interesting feature of the kinetoplastids is that organelles such as the mitochondrion , Golgi apparatus and flagellum exist as single-copy structures whose replicative cycles are tightly linked to that of the cell ( reviewed in [15] ) . Hence , the expression of proteins involved in duplication of these organelles may also be regulated in concert with the cell cycle . This is illustrated by the replication of the kinetoplast , which is a distinctive structure housing the mitochondrial DNA ( kDNA ) , a network of concatenated open-circular DNA minicircles and maxicircles and associated proteins . The replication of the kinetoplast is coordinated with the cell cycle and involves several dedicated kDNA-processing proteins , some of which are members of the post-transcriptional regulon mentioned above . Kinetoplast DNA ligase alpha ( LIGKA ) is involved in kDNA replication and is regulated during the cell cycle in Crithidia fasciculata [16] and T . brucei [17] , however its transcript levels peak some time after those of the DHFR co-regulated group . Whether LIGKA is unique or a member of a broader group of similarly regulated transcripts is unknown , as is the nature of the RNA elements responsible for its regulation in cis . Differential expression may also occur via mechanisms operating at the levels of protein translation , localization and/or stability , since the kDNA ligase proteins are unstable and differentially localized during the cell cycle [16] . Indeed , regulation at both the mRNA and protein levels could synergize to ensure that certain key players in DNA replication , organelle replication , and cell division are tightly regulated . This would especially apply to proteins whose ectopic expression at other points in the cell cycle could short-circuit the program of organellar and cellular duplication . Since kinetoplastid protozoa rely on RNA-binding proteins , rather than transcription factors , to regulate gene expression , their genomes might be expected to contain a disproportionately large number of genes coding for proteins with RNA-binding domains . This is certainly true for proteins possessing a Puf ( Pumilio/Fem-3 ) RNA-binding domain , of which we found at least 10 encoded in the T . brucei genome [18] . The structure of the Puf domain consists of multiple copies of a tri-helical Puf repeat . Each tri-helical repeat binds one nucleotide via three key amino acid residues that cooperatively determine the base preference for that repeat [19] , [20] . The various Puf proteins found in yeast bind a significant proportion of all mRNAs and those mRNAs bound by the same Puf protein tend to encode proteins that function in similar locations and processes [21] . Thus , Puf proteins have found functions regulating several large post-transcriptional regulons in a single-celled eukaryote . Here we describe a Puf protein of T . brucei , PUF9 , which is conserved within kinetoplastids and possesses 6 copies of the Puf tri-helical repeat . Our results indicate that in mid-to-late S-phase , PUF9 neutralizes a specific destabilizing sequence motif present on its target mRNAs , thus stabilizing them . Consistent with their temporal expression profiles , some of the proteins encoded by PUF9 target mRNAs appear to play roles in maturation and segregation of the daughter kinetoplasts after division , a role supported by the protein localization and over-expression phenotype of an uncharacterized PUF9 target , PNT1 , as well as the previously reported characteristics of another target , LIGKA . A third PUF9 target transcript , PNT2 , encodes a nuclear protein that relocates to the mitotic spindle midzone during nuclear division . Hence , PUF9 could function in the temporal coordination of nuclear and kinetoplast replication .
All T . brucei cells used were derived from the Lister 427 line . To obtain stably transformed clonal lines , 1-2×107 cells were transfected by electroporation with ∼10 µg linearized DNA at 1 . 5 kV followed by cloning by limiting dilution in medium containing the appropriate selective drug . For tet-inducible expression constructs , expression was induced by including 100 ng/ml tetracycline in the culture medium . Plasmids created for transformation of T . brucei cells are summarized in Table 1 . Primers used to generate PUF9 fragments by PCR for cloning were as described [18] . The PNT1 ORF was amplified using the following primers: GATAAGCTTATGTTGTCCCGAGCCCCA / GATGGATCCGCCGTTCTCACTGCTCACG . The PNT2 ORF was amplified using: GATAAGCTTATGCAGTGGAAGAAAGATGACT / GATGGATCCGAAATGCAGAGGTAAACTTTCG . The PNT1 3′ intergenic region was amplified using: GATCGGATCCGCATAGATGGAGAGAGTTATACG / GATCACTAGTCTCCACCTTTGTCACTATCCTG . Point mutations were introduced into the PNT1 3′ UTR sequence in CAT-reporter plasmid pHD1876 by site-directed ligase-independent mutagenesis ( SLIM ) [22] in a multiplex PCR reaction using a plasmid containing the wild-type UTR as template and the forward primers: GTAATGTAACATTATACCATTTGTGTTGTTGTTTAG and ACCATTTGTGTTGTTGTTTAG and reverse primers: TAATGTTACATTACAACACCCGCTGCAGAATTTTTGTG and ACCCGCTGCAGAATTTTTGTG ( mutated residues underlined ) . The products of this reaction were heat-denatured , re-annealed and transformed into E . coli . Plasmids from the resulting transformants were isolated and sequenced to check for side-mutations . Due to a strain-specific G→T SNP in the Lister427 gDNA initially used as template , the G residue 9 nt downstream from the point mutation is actually a T in the wild-type PNT1 3′ UTR but was mutated back to a G in this point-mutant construct , because the SLIM primers were designed from the published genomic sequence from the TREU 927 strain . The TAP ( Tandem Affinity Purification ) tag used here possesses Protein A and Calmodulin Binding Protein domains . The PUF9 ORF was cloned into plasmid pHD918 , generating a construct encoding PUF9 linked to the TAP tag at the C-terminus via a peptide linker that contains a TEV protease cleave site . This was expressed from the PARP promoter , under the control of the Tet-repressor , in bloodstream form ( BS ) cells by induction with 100 ng/ml tetracycline for 24 hr . RNA co-purification was performed as described [23] . Approximately 3×109 cells were induced to express the fusion protein by the addition of tetracycline for 12–24 hr prior to harvesting . Cells were washed in cold PBS , crosslinked on ice by UV irradiation at 400 mJ/cm2 in a Stratalinker , then snap frozen . Cell pellets were broken in 6 ml breakage buffer ( 10 mM Tris-HCl pH 7 . 8 , 10 mM NaCl , 0 . 1% IGEPAL CA 630 ( Sigma; identical to the previously used detergent Nonidet P-40 ) , 4 mM Vanadyl Ribonucleoside complexes ( VRCs , Sigma ) , 4 U/ml RNAseIn ( Promega ) , 1× Complete Inhibitor without EDTA ( Roche ) ) by passing through a 21-gauge syringe 15 times at 4°C . Insoluble material was removed by ultracentrifugation ( 100 , 000 ×g , 45 min at 4°C ) and the salt concentration of the supernatant was adjusted to 150 mM . 200 µl of IgG sepharose bead suspension ( Fastflow – GE Healthcare ) was washed in IPP150 and rotated with the lysate for 2 hours at 4°C . IPP150 contained 10 mM Tris-HCl , pH 7 . 8 , 150 mM NaCl , 0 . 1% IGEPAL CA 630 . The flow-though was collected , and beads washed three times in 10 ml of IPP150 and once in 10 ml of TEV cleavage buffer ( IPP150 with 0 . 5 mM EDTA , 1 mM DTT , 2 mM VRCs , 4 U/ml RNAseIn ( Promega ) ) . The TAP tag was then cleaved by adding 1 ml of TEV cleavage buffer and 100 units of TEV protease ( Invitrogen ) , and rotating the beads for two hours at 16°C followed by collecting the eluate . RNA was isolated from the eluate using the QIAgen RNAeasy kit or Trizol LS according to the manufacture's instructions . The entire procedure was scaled down if less RNA was required , e . g . for RT-PCR . Aliquots , equivalent to 4×106 cells , were taken at various points in the procedure for analysis by western blot . Calmodulin selection was not used for RNA isolation due to the requirement to add calcium to the buffer during binding . Genomic T . brucei microarrays were generated containing 24 , 567 random shotgun clones from T . brucei brucei strain TREU927/4 genomic DNA [9] . Test and control samples of RNA were reverse-transcribed using SuperscriptII ( Invitrogen ) according to the manufacturer's instructions in the presence of either Cy5-dCTP or Cy3-dCTP and cDNA purified using the QIAquick PCR purification kit ( QIAGEN ) , ethanol-precipitated and resuspended in 5 µl TE . Cy3- and Cy5- labelled cDNAs were mixed , denatured at 95°C for 5 min and snap-chilled , then added to 60 µl of hybridization buffer ( 50% formamide , 3× SSC , 1% SDS , 5× Denhardt's reagent and 5% dextran sulphate ) . This was added to the slide , a coverslip affixed and incubated at 62°C overnight in a humidified chamber . Slides were washed at RT for 10 min in 2× SSC , 0 . 2% SDS , 10 min in 2× SSC , and 10 min in 0 . 2× SSC , dipped in isopropanol and dried . Microarrays were scanned with ScanArray 5000 ( Packard BioScience , Dreieich , Germany ) and analyses of resulting images were performed using GenePix software ( Axon Instruments , Union City , USA ) . The software package MCHIPS [24] was used for data quality assessment and normalization . Clones corresponding to positive hits were sequenced from one end and mapped onto the published T . brucei genome . For RNA detection by Northern blot , RNA was size-separated by overnight agarose-gel electrophoresis on a 3 . 5% formaldehyde gel , transferred onto a nylon membrane by capillary transfer and fixed by UV irradiation as described [25] . The membrane was prehybridized in a hybridization bottle in 5× SSC , 0 . 5% SDS with salmon sperm DNA ( 200 µg/ml ) and 1× Denhardt's solution for 2 hours at 65°C . Probe was generated by PCR in the presence of [32P]-labelled dCTP followed by purification using the QIAGEN nucleotide removal kit according to the manufacturer's instructions . Probe was added to the prehybridization solution and the bottle rotated at 67°C overnight . After rinsing the membrane in 1× SSC/0 . 5% SDS , probe was washed out with two 20 minute washes in 0 . 2× SSC/0 . 5% SDS at 67°C and the membrane exposed on a phosphorimaging screen for 2–48 hours . The screen was read on a recently calibrated Fugifilm FLA-3000 reader . Signal density from bands were quantified in Image Quant v3 . 45 and background signal density from a nearby region within the same lane of the gel was quantified and subtracted from the value for the band . Blot picture intensities were adjusted such that the darkest pixel was set to zero intensity , and 5% of the lightest pixels were clipped to 100% intensity . TAP-copurified RNA , or RNA from the flow-through ( derived from the equivalent of ∼2×108 or 4×107 cells respectively ) was reverse-transcribed using a cocktail of gene-specific primers and Superscript III reverse transcriptase ( Invitrogen ) in a 20 µl reaction . This was used as template in a semi-quantitative PCR reaction to detect control and test genes ( 1 µl cDNA per 50 µl reaction ) . Samples ( 7 µl ) were removed after 28 , 32 and 36 cycles and analysed on an agarose gel . All primer pairs were designed using Primer3 [26] and had similar melting temperatures ( ∼60°C ) and product lengths ( 250–400 nt ) . We adapted the cell-starvation protocol previously described [27] , which produces semi-synchronous cultures arrested predominantly at the G1 phase by starvation . Cells were seeded at 1×106/ml , and two days later the starved culture ( ∼2–4×107 cells/ml ) was diluted in auto-conditioned MEM-pros medium [28] to induce resumption of the cell cycle . Aliquots of ∼106 cells were taken at regular intervals over the next 9–17 hours for flow cytometry . Cells were collected from these samples by centrifugation , resuspended in 100 µl of PBS , fixed by dropwise addition of 1 ml of 70% ethanol , 30% PBS while gently vortexing , and stored at 4°C . Cells were collected by centrifugation ( 2000 ×g , 10 min ) , resuspended in 500 µl of PBS with 10 µg/ml RNase A and 30 µg/ml propidium iodide , incubated at 37°C for 30 minutes and analysed by FACSSCAN . The proportion of cells in each phase of the cell cycle was estimated using the Watson algorithm [29] as implemented in the Flowjo software package . Approximately 106 cells were collected by centrifugation and resuspended in 50 µl of PBS . Cells were fixed in 4% paraformaldehyde ( or 4% formaldehyde/5% acetic acid if staining for mitotic spindles ) in PBS for 20 min , washed twice in PBS and allowed to settle onto polylysine-coated slides . Cells were permeabilized in 0 . 1% Triton-X in PBS , washed twice and blocked in 1% BSA or gelatine for 1 hour . Primary antibody was added at the recommended dilution , incubated for 1 hour and the slides washed 3 times in PBS before addition of fluor-conjugated secondary antibody in the dark for 1 hour . Slides were washed , stained with DAPI for 10 minutes , and washed twice more before drying the slide . A drop of Vectorshield was added , the coverslip was affixed , and cells were viewed on a Leica DM fluorescence microscope . Where Mitotracker staining was used , Mitotracker CMXros ( Invitrogen ) was added to the cell culture medium at 250 nM for 30 min , after which the cells were pelleted and resuspended in fresh media . After incubating for 15 minutes , cells were fixed and stained using the protocol described above . The 3′-UTR sequences of T . brucei genes were taken from the published genomic sequence of the TREU 927 strain , using start and end positions predicted previously [30] or , where the prediction was absent , taking the entire intergenic region to a maximum of 5 kb downstream of the stop codon . The sequences of interest were compared to 1000 randomly chosen 3′ UTRs as a background sample using Trawler [31] . Homologous genes in Trypanosoma congolense were also identified by TBLASTN searches and the 3′-UTR sequences predicted from the downstream intergenic regions by similar means . Synthesis and maturation of mRNA were simultaneously inhibited by addition to the growth medium of 10 µg/mL actinomycin D and 2 . 5 µg/ml sinefungin . Sinefungin was added 5 minutes prior to actinomycin D [32] . Cells were collected at the indicated time points and RNA isolated by Trizol extraction . RNA levels were estimated by Northern blotting using [32P]-labelled probes , and quantitated by phosphorimaging . The stable SRP RNA was used as a loading control .
The Puf domain protein PUF9 ( Tb927 . 1 . 2600 ) is conserved among Trypanosoma and Leishmania species . It contains 6 Puf repeats , as well as extended N- and C- terminal domains lacking any homology to characterized proteins ( Figure 1A ) . We decided to investigate the biological function of PUF9 through RNAi and by co-purification of target RNAs . The phenotype of bloodstream-form ( BS ) cells in which PUF9 was depleted by tet-inducible RNAi , or over-expressed using a tet-inducible VSG promoter , was examined . A Northern blot confirmed PUF9 mRNA knockdown after 24 hours of induction ( Figure 1B ) . Although over-expression of PUF9 did not cause any noticeable phenotype , PUF9 RNAi reduced overall cell growth over the six days of RNAi induction ( Figure 1C ) . We used freshly thawed clonal cell lines , which may account for the growth phenotype not seen previously [18] . Flow cytometry revealed an accumulation of cells with 2C DNA content ( G2/M cells ) in PUF9 RNAi cells relative to uninduced cells ( Figure 1D ) , and there were also more polyploid ( >2C ) cells . Examination of trypanosomes by fluorescence microscopy after staining for DNA can also be used to score them for cell cycle stage: cells with a single nucleus and kinetoplast ( 1N1K ) are in G1 or S phase , cells with two kinetoplasts and one nucleus ( 1N2K ) are in G2 , and cells with two kinetoplasts and two nuclei ( 2N2K ) are mitotic or post-mitotic . The proportion of 1N1K cells was lower in PUF9 RNAi cells ( Figure 1D ) . In addition , the PUF9 RNAi cells often possessed more than two flagellae , nuclei , or kinetoplasts ( Figure 1E ) . Extra nuclei or kinetoplasts were seen after 24 hours of PUF9 RNAi ( Figure 1D ) , suggesting a possible defect in control of organelle copy number . However , there was no obvious difference in the occurrence of annucleate “zoid” cells . Similar experiments in insect-form procyclic ( PC ) cells yielded no obvious phenotype ( not shown ) . In order to find out which mRNAs were targeted by PUF9 , we expressed the protein with a C-terminal TAP tag in BS cells to allow affinity purification . Western blotting indicated that the tagged protein was stable in BS cell lysate and it was found in the cytoplasm of BS cells by immunofluorescence staining ( Figure 1F ) . Protein-RNA complexes from BS lysates were selected on an IgG column , and eluted by cleaving the tag with TEV protease . Cells expressing the TAP tag alone served as a control . RNA that co-precipitated with PUF9::TAP or the TAP-tag alone was reverse transcribed with fluorescently labelled nucleotides and the cDNA hybridized to a microarray of shotgun genomic clones . Spots showing more than 2-fold higher intensity for the PUF9 channel were flagged and the corresponding genomic clones end-sequenced . Results from two biological replicates are summarized in Table 2 . Several genomic loci were identified from multiple overlapping DNA clones , indicating that a gene of interest was present in the overlapping regions . Genes were considered as candidates for interactions with PUF9 if more than one spot was flagged that contained sequences overlapping that gene , or the same spot containing the gene was flagged from both biological replicates . rRNA and PUF9 itself were also identified in one replicate . While intriguing , it is possible that these hits may represent artefacts caused by the over-expression of the tagged PUF9 , which is integrated into an RRNA locus , leading to higher background levels of these two RNAs . The number of transcripts that were identified was surprisingly small in comparison to similar experiments in yeast where hundreds of transcripts were coprecipitated with PUF proteins [21]; nonetheless , the fact that sequencing of several different flagged spots resulted in them repeatedly being mapped back to the same few transcripts indicates that significant coverage of high-affinity PUF9 targets was attained . Since some microarray hits spanned several adjacent genes , another independent TAP-purification was performed and the co-purifying RNA was analysed for enrichment of mRNAs by semi-quantitative RT-PCR using primers specific for individual gene ORFs . Four candidate sequences were amplified more strongly from the PUF9-copurified RNA than the TAP-only copurified RNA , while amplification of the abundant TUBA transcript was approximately equal between the samples ( Figure 2 ) . This was not due to differences in transcript abundance in the lysates since RT-PCR on RNA isolated from flow-through fractions showed no detectable enrichment . The confirmed PUF9-associated transcripts are LIGKA ( kDNA ligase α/Tb927 . 7 . 610 ) , a histone H4 variant ( H4V/Tb927 . 2 . 2670 , possibly involved in transcription termination [33] ) , and two uncharacterized kinetoplastid-specific genes that we have named Puf Nine Target 1 ( PNT1/Tb11 . 02 . 4400 ) , and Puf Nine Target 2 ( PNT2/Tb11 . 01 . 6470 ) . Tb11 . 02 . 6460 , an adjacent gene to PNT2 , was also tested ( not shown ) because we could not delineate which of the two transcripts was responsible for the hits around this genomic locus using the microarray data alone ( Table 2 ) . However , only PNT2 mRNA was found to be a genuine target of PUF9 after analysis by RT-PCR . The PUF9 transcript itself could not be amplified from cDNA from the PUF9-copurified RNA . However , this transcript has an exceptionally long 3′ UTR that might be vulnerable to degradation during the procedure . The effect of PUF9 on its target transcripts was examined by Northern blotting of RNA from the PUF9 over-expressing and PUF9 RNAi BS cells . Probing the Northern blots for the four target genes showed that RNAs from three of them were clearly more abundant when PUF9 was over-expressed and less abundant when PUF9 was depleted ( Figure 3A and Figure S1 ) , while the remaining target , H4V , was only slightly affected . Because H4V transcript is only weakly regulated by PUF9 , it was excluded from further analysis here . However , its association with PUF9 could still indicate regulation at a different level , e . g . translation , that was not tested in this work . Tb11 . 01 . 6460 , which is adjacent to PNT2 , showed no dependence on PUF9 ( not shown ) , consistent with the PUF9-mediated upregulation operating on individual mature transcripts rather than genomic loci or polycistronic precursor RNA . To find out whether mRNA half-lives were influenced by PUF9 , we inhibited mRNA synthesis using actinomycin D and sinefungin [32] , and followed the abundance of the PUF9 target mRNA LIGKA . Actinomycin D binds to DNA and inhibits RNA transcription elongation , while sinefungin inhibits methylation of the spliced leader RNA leading to a blockage in mRNA maturation [34] . LIGKA mRNA half-life was approximately 30 minutes in normal cells , but was reduced four-fold when PUF9 was depleted by RNAi , while the half-life of the actin control transcript remained unchanged ( Figure 3B ) . These data support a role for PUF9 in stabilizing its target mRNAs . The effects on target transcript abundance and half-life were not due to slower growth or the previously observed increase in the proportion of cells in G2 phase , because they also occurred when PUF9 was targeted by RNAi in PC cells , which exhibited wild-type growth and flow cytometry profiles ( see below ) . The PUF9 target gene LIGKA has a homologue in C . fasciculata ( kinetoplast DNA ligase α ) , for which the mRNA was previously shown to vary in abundance with the cell cycle [16] . This , together with the PUF9 RNAi phenotype that hinted at a defect in cell cycle progression , led us to investigate whether PUF9 plays a role in cell-cycle-coupled differential expression of genes . PC cells are amenable to synchronization by starvation [27] and by hydroxyurea treatment [35] while BS cells have also recently been synchronized by hydroxyurea [36] . Although no hydroxyurea-generated artifacts have been observed during synchronization of T . brucei , drug-mediated synchronization has been observed to cause uncoupling of DNA replication status from cyclin levels in other systems [37] , therefore we opted to follow the T . brucei starvation-induced synchronization protocol [27] . Starved PC trypanosomes accumulate in the G1 phase of the cell cycle ( Figure 4 ) . Upon release from starvation , about 70% of cells begin to progress through the cell cycle after a ∼4 hour lag ( Figure 4 ) . Analysis by Northern blot showed that transcript levels of housekeeping genes such as TUBA increased rapidly for the first hour , then increased at a steady rate throughout the assay , relative to structural RNAs such as SRP ( data not shown ) , therefore TUBA was used as a “baseline” mRNA transcript to normalize for loading and the global effects of starvation upon mRNA levels . DHFR and HISH4 transcripts , which are known to be regulated during the eukaryotic cell cycle , peaked in early- and mid- S-phase respectively , consistent with good synchronization of the recovering cell culture ( Figure 4 ) . The timing of their transcript maxima fits with the fact that DHFR protein is needed prior to DNA synthesis while HISH4 is required during or immediately after synthesis . The three PUF9 target genes LIGKA , PNT1 and PNT2 were also regulated during the cell cycle , peaking in mid- to late- S-phase . The C . fasciculata homologue of LIGKA also cycles out-of-phase with the DHFR transcript in that organism [16] . To determine whether PUF9 plays a role in the S-phase-specific upregulation of its target genes , PUF9 was targeted by RNAi in procyclics during synchronization . Northern blotting showed that RNAi against PUF9 effectively repressed the PUF9 transcript and also lowered levels of PUF9 target transcripts as with BS cells ( Figure S2 ) ; although as previously noted , PUF9 RNAi had no effect on PC growth , perhaps due to residual expression that is sufficient for function in PCs , or stage-specific differences between the cell-cycle checkpoint mechanisms . Flow cytometry also showed that there was no significant difference between synchronization efficiencies of induced and uninduced PUF9 RNAi PC cells , and this is supported by the nearly identical cyclical expression of the cell-cycle regulated HISH4 transcript between induced and uninduced cells ( Figure 5A , triangles in 5B ) . However , probing for the PNT1 and PNT2 transcripts revealed that they no longer oscillated over the cell cycle when RNAi against PUF9 was induced ( Figure 5A , circles in 5B ) , indicating that PUF9 is required for the peak in their transcript levels that occurs in S-phase in the control cells . Interestingly , the PUF9 transcript itself also showed moderate cell cycle-coupled regulation , peaking at a similar time to its target mRNAs ( Figure 4 bottom panel ) . However , it seems doubtful that this relatively moderate regulation at the mRNA level could fully account for the larger changes in expression of the target mRNAs , so it is likely that other regulatory mechanisms exist . Attempts to generate PUF9 antisera failed; therefore we tagged one PUF9 allele in situ with an N-terminal V5 epitope . However , the protein showed approximately the same degree of variation through the cell cycle as was previously observed for the mRNA levels , and no difference was seen in the electrophoretic mobility by western blot ( not shown ) . It should be noted that N-terminal in situ tagging replaces the 5′ UTR , so 5′ UTR-dependent translational regulation would not be detected . We also attempted to co-purify interacting protein partners of PUF9 by tandem affinity purification of the TAP-tagged PUF9 protein , but mass-spectrometry of affinity-purified protein bands only revealed degradation products of PUF9 itself . Thus , the mechanism whereby PUF9 specifically stabilizes its target transcripts in late S-phase remains to be elucidated . PUF9 targets appear to be regulated independently to most previously characterized cell-cycle-regulated transcripts ( e . g . DHFR , TOP2 , RPA1 ) , which peak at a different time and possess known sequence motifs that act as cell-cycle regulatory elements ( CCREs ) [10] , [11] , [12] , [13] , [16] . To identify the RNA determinants responsible for PUF9-mediated cell-cycle-coupled transcript regulation , a combination of bioinformatics and experimental approaches was used . The 3′ UTRs of the three known mRNAs regulated by PUF9 were analyzed using Trawler , a program that identifies over-represented motifs in sets of sequences relative to a background set of genes [31] . This strategy is useful for identifying putative recognition sequences for Puf proteins because they tend to recognize primary RNA sequences rather than secondary RNA structures [38] . The most over-represented motif instance identified by Trawler was “UUGUACCW” , found 7 times in the 3 sequences . The best cluster within this family is summarized in a position weight matrix ( Figure 6A ) . This motif is a promising candidate PUF9-recognition motif as it contains the Puf protein consensus core binding sequence “UGUA” [39] . In addition , the predicted key nucleotide-binding residues of the 4th , 5th and 6th Puf repeats in the T . brucei PUF9 protein are homologous , respectively , to “U” , “G” and “U”-binding repeats of characterized Puf proteins [40] , indicating that at least the minimal conserved “UGU” trinucleotide forms part of the PUF9 recognition motif . A search of the preliminary T . congolense genome ( the closest relative of T . brucei for which a largely complete genomic sequence is available ) shows that it possesses clear homologues to all three T . brucei PUF9 targets . Despite being sufficiently distant from T . brucei to have no detectable similarity in most of the 3′ UTRs , the three PUF9 target homologues possessed several copies of the candidate motif in their 3′ UTRs . To test experimentally whether the 3′ UTRs of PUF9 target genes contain CCREs , the 3′ intergenic region of PNT1 was cloned downstream of a CAT reporter gene and the reporter was expressed in procyclic cells . This 3′ UTR was chosen because its three “UGUA” motifs are clustered within a 20 nt region , the last one of which is contained within “UUGUACC” ( the motif we identified as being over-represented in PUF9 targets ) . Cell synchronization experiments showed that the 3′ UTR could indeed confer cell cycle-coupled regulation upon the reporter transcript ( Figure 6B , 6C ) , similar to the behavior of the native PNT1 transcript , although the magnitude of regulation was somewhat reduced and the peak in transcript level is delayed ( see below ) . This is possibly due to increased stability conferred by the CAT ORF or the exogenous 5′ UTR . Alternatively , the 3′ UTR of PNT1 may contain only one component of a set of dispersed functional elements located over the entire transcript that cooperatively lead to efficient cell cycle-driven regulation . To locate this element , we further examined the PNT1 3′ UTR . All the “UGUA” motifs are clustered around ∼425 nt downstream of the stop codon , and initial mapping experiments showed that the region between +324 nt and +680 nt was critical for cycling of the CAT::PNT1-3′ UTR reporter transcripts ( Figure 6B ) . We then mutated the central , conserved “G” of the last motif to an “A” and found that this also abolished its ability to confer cell-cycle regulation to the CAT reporter transcript ( Figure 6B , 6C ) . Thus , this motif seems to act as a CCRE component . Despite this , preliminary expression-profiling experiments on synchronized cells ( data not shown ) showed no evidence for cell-cycle regulation in several hundred other transcripts containing at least one copy of this motif in the predicted 3′-UTR region , indicating that , although necessary , it is not sufficient to mediate cell-cycle regulation . Hence , the secondary structural context of the motif or the presence of other cooperating elements may heavily influence its effectiveness . When the 3′-UTR reporter constructs were transformed into PUF9 RNAi PC or BS cells , the abundance of the reporter mRNA bearing the wild-type 3′ UTR was dependent upon the expression of PUF9 ( Figure 6D ) . Subsequently , Western blotting indicated that CAT protein levels were also reduced in PUF9 knockdown cells , but as this seemed to be roughly proportional to the drop in mRNA levels ( Figure S3 ) , it is still uncertain whether PUF9 appreciably modulates translation . More elaborate kinetic studies and reporter transcripts also bearing the 5′ UTR of PNT1 would be required to thoroughly investigate potential translational regulation by PUF9 . Significantly , the same point mutation that abolished cell-cycle response also abolished the dependence of the transcript levels on PUF9 ( Figure 6D ) . This may indicate that the CCRE is potentially a direct binding site for PUF9 , and that the point mutation abolishes PUF9 binding . However , if PUF9 were the only player in regulation then the mutant transcript should be constitutively unstable , which is not the case: steady-state levels of the mutant reporter ( normalized against the SRP RNA ) were closer to those of the wild-type in the presence of PUF9 . The proteins encoded by PUF9 target transcripts might be expected to function in similar processes since they are co-regulated in the cell cycle . To determine if this is the case , PNT1 and PNT2 were expressed as C-terminally myc-tagged proteins in PC cells . LIGKA is already known to be associated with the kDNA [17] . Remarkably , PNT1::myc was also found either forming a doublet closely flanking the kDNA or overlapping it ( Figure 7A ) . We cannot rule out that the apparently overlapping signals are actually doublets orientated in-line with the kDNA as seen from above . Interestingly , when PNT1::myc was over-expressed , very small and faintly staining additional kinetoplasts appeared in 33% of cells after 8 hours , relative to 5% in uninduced cells . These were similar in size and localization to the “ancillary kinetoplasts” observed at low frequency in some other kinetoplastids [41] . They stained for PNT1::myc ( red arrowheads , Figure 7A and 7B ) , and unlike normal kinetoplasts , were often mislocated anterior to the nucleus . These extra structures could represent degenerating kinetoplasts retained from a sister cell during division , fragmented kinetoplasts , or semi-synthesized kinetoplasts formed by aborted , late re-replication . A minor proportion of cells ( ∼1-3% , only marginally higher than in uninduced cells ) lacked a normal kinetoplast , possessing only ancillary kinetoplasts or no kinetoplast at all . Total myc signal was dramatically increased in these cells and was localized throughout the mitochondrion ( Figure 7B ) . Consistent with this , SignalP 2 . 0 HMM predicts a potential peptidase cleavage site 25 amino acids from the N-terminus , hinting at the presence of an N-terminal signal peptide . PNT2::myc was expressed in PC cells using the same system described above . In clonal transfected cell lines , PNT2::myc appeared to localize to the mitotic spindle during mitosis and by the 2N2K stage it was seen in an elongated structure mid-way between the two nuclei ( Figure 7C ) . Co-staining cells with the KMX1 antibody , which reveals mitotic spindles , confirmed that this structure was the mitotic spindle midzone ( Figure 7D ) . The slightly granular appearance of cells here is an artifact of acetic acid fixation . No obvious over-expression phenotype was seen for PNT2::myc . However , PNT-2::myc was only expressed at low levels as seen by western blots of multiple transfected clones ( not shown ) , which could preclude generation of an over-expression phenotype . We cannot rule out the presence of PNT2::myc in the cytoplasm since there was some diffuse cytoplasmic signal , but this was similar to the background signal seen in untransfected controls .
The increased multiplicity of protein function in eukaryotes has been proposed to be due partially to the replacement of inflexible prokaryotic polycistronic regulatory operons with gene-specific combinations of cis-acting regulatory elements [14] . The kinetoplastids may at first appear to challenge this paradigm of individualized gene regulation in eukaryotes , since they transcribe virtually all genes in large polycistrons that are even larger than operons of prokaryotic species . However , the kinetoplastid genomes are approximately two-fold enriched in genes encoding Pumilio domain and CCCH zinc finger proteins , relative to unicellular organisms that show transcriptional control , suggesting that these RNA-binding proteins have stepped in to replace transcription factors in regulating gene expression . This idea is supported by the current study where a small group of mRNAs that are likely to function in coordinated biological processes is shown to be under the control of a common upstream regulator , PUF9 . The temporal expression and localization of the proteins encoded by the PUF9 target transcripts indicates that they function in certain organelles ( the nucleus , mitotic spindle and kinetoplast ) at a specific time in the cell cycle ( late S-phase/G2 phase ) . In kinetoplastid cells , the copy numbers of kinetoplasts and other major organelles and cellular structures are stringently maintained at one per G1 phase cell , and their replication is coordinated with each other and coupled to that of the cell . We hypothesize that PUF9 switches on the expression of target genes in late S-phase of the cell cycle to ensure simultaneous performance of their respective functions in organelle replication or division . Three lines of evidence support a role for PUF9 in co-coordinating cell-cycle governed replicative processes: firstly , the extra nuclei and kinetoplasts seen in BS cells when PUF9 is knocked down indicate that organelle replication is de-coupled from cell division . Secondly , PUF9 drives the upregulation of its target transcripts specifically in mid- to late- S-phase . Thirdly , bypassing PUF9 regulation by directly interfering with the expression of the downstream LIGKA or PNT1 proteins results in aberrant kinetoplast DNA content [17] or copy-number ( shown here ) . The parallels between two PUF9 targets , LIGKA ( characterized previously [16] , [17] ) and PNT1 , are particularly striking . Both encode proteins localized to the kinetoplast and both probably function in kinetoplast replication as indicated by their over-expression or knockdown phenotypes . The kinetoplast DNA is unique in nature in that it is a disc-shaped network of open circular DNA molecules , concatenated together with a “chain mail” topology ( reviewed in [42] ) . It also has a unique mechanism of replication: individual minicircles disassociate from the network , migrate to the posterior kinetoflagellar zone where they are replicated , and then migrate back to one of the two “antipodal” sites which flank the disk at its perimeter , and where topoisomerase-mediated minicircle reattachment to the kinetoplast occurs . It has been hypothesized that LIGKA seals the final nick in replicated kDNA minicircles , re-licensing them for replication [16] . If so , this process would require some temporal and spatial regulation to prevent re-licensing in the vicinity of active minicircle replication machinery in the same cell cycle . Similarly , the fact that PNT1 over-expression results in an observable defect suggests that a tight reign on its protein levels must also be maintained to avoid negative consequences for the cell . We speculate that these shared requirements for tightly controlled expression may explain the involvement of PUF9 in regulating these two genes . Interestingly , the doublet that PNT1 sometimes forms , flanking the kinetoplast disc , is similar to that seen for proteins belonging to the antipodal sites where newly replicated minicircles are reattached to the kinetoplast . PNT2::myc displayed an interesting cell-cycle dependent dynamic localization , being present in the nucleus in pre-mitotic cells but localized to the spindle midzone in post-mitotic cells . Because its mRNA levels peak at around mid- to late- S-phase , the protein levels of endogenous PNT2 might be expected to peak shortly afterwards , probably co-inciding with this relocalization during mitosis . Similar localization patterns have been reported for certain other proteins such as TbNOP86 , a nucleolar protein that localizes to the spindle during cytokinesis and whose RNAi phenotype resembles that of PUF9 , i . e . an increase in G2 phase and polyploid cells [43] . Some chromosomal passenger proteins such as TbCPC1 and TbCPC2 also display similar localization during mitosis , although they additionally relocalize to a dot on the cleavage furrow during cytokinesis , which we could not detect for PNT2::myc . These proteins are involved in spindle function and cytokinesis as their suppression in PCs leads to mitotic spindle abnormalities and accumulation of G2-phase cells [44] . Given its similar localization and temporal expression , we speculate that PNT2 may have a similar function related to the timing of mitotic spindle assembly or disassembly . In addition to being regulated at the mRNA level , LIGKA , PNT1 and PNT2 are probably regulated at the protein level . LIGKA protein is known to be relatively unstable [16] , while levels of tagged PNT1 in the PNT1::myc over-expressing cells seem to be self-limiting , in light of the fact that many cells displaying the associated phenotype no longer expressed detectable protein at the time of fixation ( e . g . the top cell in Figure 7A , which possesses an anterior kinetoplast fragment but does not express PNT1::myc ) . Further , the massive increase in PNT1::myc protein levels in the mitochondrion of cells lacking a normal kinetoplast may mean that the kinetoplast not only sequesters PNT1 but also suppresses PNT1 expression at the protein level , although here we have only demonstrated an associative , rather than causal , relationship between kinetoplast loss and increased PNT1 protein . Additionally , the ancillary kinetoplasts we observed , while capable of sequestering PNT1 , were not by themselves associated with repressed PNT1 protein levels . In general , rapid protein degradation for cyclically regulated proteins should allow a sharper peak in protein levels to occur at the proper time . Puf proteins in other eukaryotes generally act to destabilize their target transcripts . However , PUF9 was shown to stabilize its target transcripts , and similarly , to stabilize a reporter transcript carrying the 3′ UTR of PNT1 . The presence of the UUGUACC motif ( common to all PUF9 target 3′ UTRs ) is essential for regulation by PUF9 . The fact that mutations in this motif appear to stabilize the transcript , rather than destabilizing it , suggests that the motif recruits a destabilizing factor that is then inhibited by PUF9 . Probable candidates for this destabilizing factor include the other Puf-domain proteins , since as mentioned , they generally act to destabilize targets , and are quite likely to bind the same motif as PUF9 because Puf proteins usually bind a conserved “UGUA”-containing motif [39] . This would potentiate a simple binding-site competition model of regulation whereby PUF9 and the destabilizing factor compete for binding at the same RNA motif . Other more complex mechanisms are also plausible , and it is worth noting that the 3′ UTR of PNT-1 , while capable of conferring cell-cycle regulation onto a reporter transcript , did not confer exactly the same expression as the native transcript , which implicates the 5′ UTR and ORF , and perhaps even pre-mRNA sequences , in fine-tuning post-transcriptional regulation of PNT1 . The means by which PUF9 seems to be most active in late S-phase is of particular interest if PUF9 is to be placed within a cell-cycle regulatory network . Higher expression of PUF9 protein during S-phase may occur , and indeed there was an indication that PUF9 transcript levels oscillate with the cell cycle . However , the magnitude of PUF9 transcript upregulation in S-phase seemed to be insufficient to fully explain that of its downstream targets . While regulation may occur at the translational level , a suitable antibody against PUF9 could not be raised to test this . Post-translational modifications such as phosphorylation , ubiquitination etc . may also play a role , although a V5-tagged PUF9 protein showed no changes in electrophoretic mobility ( which might indicate post-translational modification ) over the cell cycle , and a recent analysis of the T . brucei phosphoproteome did not identify PUF9 among the set of phosphorylated proteins [45] . However , all these possible modes of regulation might not act only on PUF9 but rather on factors cooperating with PUF9 in regulating transcriptional stability , for instance the factor hypothesized above to act contrary to PUF9 to destabilize the target transcripts . The characterization of PUF9 target transcripts in T . brucei appears to be revealing a post-transcriptional regulon of genes involved in coordinating the replication of subcellular structures in the cell cycle . This is regulated independently to the set of cell-cycle-regulated transcripts investigated by several other groups [10] , [11] , [12] , [13] containing DHFR , LIGKB , TOP2 and RPA1 , because the timing of peak expression of the PUF9 targets is significantly later in the cell cycle ( as was first observed when the expression of LIGKB was compared to that of LIGKA in C . fasciculata [16] ) . The three PUF9 targets encode kinetoplast- or spindle- associated proteins , are co-regulated in the cell cycle , and perturbations in expression of at least two of them ( PNT1 and LIGKA [17] ) cause defects in kinetoplast replication . The main function of PUF9 is probably to ensure simultaneous function of its target genes during late S-phase in order to temporally coordinate certain processes in organellar and cellular replication . In particular , the putative roles for PUF9 target proteins in kinetoplast replication and the mitotic spindle implicates PUF9 in synchronizing kinetoplast maturation and mitotic spindle function . The further investigation of the upstream regulatory network and downstream effectors will lead to insights into how trypanosomes coordinate the replication of their organelles with that of the entire cell , and how they regulate gene expression in general without transcriptional control . | The unicellular protozoan Trypanosoma brucei is the causative agent of African sleeping sickness , responsible for over 100 , 000 deaths annually , and is related to other important pathogens ( e . g . Leishmania major and Trypanosoma cruzi ) . Unusually , these organisms do not regulate their genes by changing the rate at which they are copied into RNA , but by changing the rate of RNA destruction or the rate of translation into protein . We identified an RNA-binding protein , PUF9 , responsible for the accumulation of several RNA molecules at a specific time point in the cell division cycle , just after DNA replication . Correspondingly , the proteins encoded by these RNAs appear to function in the division of various cellular structures at this time point or shortly afterwards . Two of them facilitate replication of the kinetoplast ( an organelle containing the mitochondrial DNA ) while another was found in the mitotic spindle . Their temporal co-expression may stem from another unusual feature of trypanosomes: only one copy of the kinetoplast ( and several other organelles ) are present per cell , their replication being coordinated with cell division . Indeed , PUF9 may be important in the control of organelle copy-number because suppression of PUF9 resulted in cells with too many kinetoplasts , flagella , or nuclei . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"molecular",
"biology/rna-protein",
"interactions",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"cell",
"biology/cell",
"growth",
"and",
"division",
"molecular",
"biology/mrna",
"stability",
"microbiology/parasitology",
"cell",
"biology/gene",
"expression"
] | 2009 | Trypanosoma brucei PUF9 Regulates mRNAs for Proteins Involved in Replicative Processes over the Cell Cycle |
The relationship between Apolipoprotein E ( ApoE ) and the aggregation processes of the amyloid β ( Aβ ) peptide has been shown to be crucial for Alzheimer's disease ( AD ) . The presence of the ApoE4 isoform is considered to be a contributing risk factor for AD . However , the detailed molecular properties of ApoE4 interacting with the Aβ peptide are unknown , although various mechanisms have been proposed to explain the physiological and pathological role of this relationship . Here , computer simulations have been used to investigate the process of Aβ interaction with the N-terminal domain of the human ApoE isoforms ( ApoE2 , ApoE3 and ApoE4 ) . Molecular docking combined with molecular dynamics simulations have been undertaken to determine the Aβ peptide binding sites and the relative stability of binding to each of the ApoE isoforms . Our results show that from the several ApoE isoforms investigated , only ApoE4 presents a misfolded intermediate when bound to Aβ . Moreover , the initial α-helix used as the Aβ peptide model structure also becomes unstructured due to the interaction with ApoE4 . These structural changes appear to be related to a rearrangement of the salt bridge network in ApoE4 , for which we propose a model . It seems plausible that ApoE4 in its partially unfolded state is incapable of performing the clearance of Aβ , thereby promoting amyloid forming processes . Hence , the proposed model can be used to identify potential drug binding sites in the ApoE4-Aβ complex , where the interaction between the two molecules can be inhibited .
Alzheimer's disease ( AD ) is one of the most common neurodegenerative diseases at the present time . The disease is characterized by the formation of neurofibrillary tangles and plaques in the brain , leading to neuronal dysfunction , neuronal loss and finally death . The main component of the plaques is the amyloid-β peptide ( Aβ ) , a 39–43 amino acids long hydrophobic peptide generated by the cleavage of the amyloid precursor , which accumulates in the form of soluble and non-soluble aggregates . The connection between Apolipoprotein E ( ApoE ) and AD is well established [1] , [2] . Structurally , ApoE is a 299 residues protein with an N-terminal domain involved in binding to heparin , low density lipoprotein receptors ( LDLR ) and LDLR-related proteins [3] , [4] . The C-terminal domain has been related to heparin and lipid binding [5] , [6] . Three main isoforms have been described for human ApoE , i . e . ApoE2 , ApoE3 and ApoE4 . The standard variant is ApoE3 , while ApoE2 is defective for receptor binding , causing APOE ε2/ε2 homozygotic individuals to have a higher predisposition to diseases related to high amounts of cholesterol and triglycerides [3] , [7] . For ApoE4 , the receptor binding affinity remains unaffected , but APOE ε4/ε4 homozygotic individuals have higher risk for coronary heart disease and a significantly greater risk for developing AD . [1] , [8] Around 80% of all AD cases are related to the genetic variance at the ApoE locus [9] , [10] . The only difference between the ApoE isoforms is found in residues 112 and 158 , where Cys112/Cys158 corresponds to ApoE2 , Cys112/Arg158 to ApoE3 , and Arg112/Arg158 to ApoE4 . The presence of cysteines at these positions confers oligomerization properties to ApoE . Indeed , ApoE2 and ApoE3 are able to form disulfide-linked homo- and hetero-oligomers due to the presence of “respectively” two and one Cys residue . ApoE4 lacks the possibility of strong disulfide linking; however , it is unclear whether weaker interactions could promote the oligomerization of ApoE4 . The Cys/Arg substitution in ApoE4 also has molecular impact in terms of intra-protein polar contacts: the orientation of Arg61 is different in ApoE4 compared to ApoE3; the orientation of Arg61 towards the C-terminal domain ( See Figure 1A ) facilitates a salt bridge between Arg61 and Glu255 . The electrostatic interaction between Arg61 and Glu255 promotes an N- and C-domain interaction that packs the structure tighter , which seems crucial for the interaction of ApoE4 with triglyceride-rich lipoproteins . The interaction between Arg61 and Glu255 is absent in ApoE3 leading to a more open structure and a preferential binding of phospholipid-rich high-density lipoproteins [11] , [12] . Chemical and thermal denaturation experiments have shown that the most unstable structure belongs to ApoE4 , which displays a partially unfolded intermediate ( molten globule ) containing some β structure that may be related to the fact that ApoE4 enhances the deposition of Aβ [13] , [14] . Although different mechanisms have been proposed to explain the physiological and pathological relationship between ApoE and the Aβ peptide , the details of the interaction between ApoE and Aβ at a molecular level are unknown . Such detailed knowledge is however important for the understanding of the pathological mechanisms of AD , and may also help to identify potential therapeutic target sites where the interaction between ApoE4 and Aβ can be blocked . In the present study we are using molecular docking simulations based on global minimum energy to investigate the interaction process of Aβ with the N-terminal domain of the different ApoE isoforms in order to determine potential Aβ peptide binding sites in ApoE . In the next step , molecular dynamics ( MD ) calculations are undertaken to explore the conformational dynamics of ApoE under Aβ interaction and evaluate the stability of each of the ApoE-Aβ complexes . From the analysis and the statistics of the electrostatic interactions of the three ApoE isoforms , we present a model explaining the role of the Aβ-ApoE interaction and its relevance for AD .
Molecular dockings followed by MD simulations were used to study the interaction of Aβ with the different isoforms of ApoE . In order to study the Aβ peptide binding site on the N-terminal domains of the three ApoE truncated isoforms we used the Aβ ( 1–40 ) peptide as ligand , employing an SDS-induced α-helix solution structure previously determined by NMR spectroscopy [15] . Indeed , such helical fold in the Aβ monomeric state ( non-aggregated ) has been shown to be the most populated one in highly hydrophobic environments [16] . On the other hand , the structures of the three ApoE truncated isoforms were taken from lipid-free structure determinations by X-ray crystallography [11] , [17] , [18] , which correspond only to the N-terminal domain ( 144 residues including the LDLR domanin of ApoE ) . Water molecules in the pdb files were removed prior to docking and energy minimizations were carried out to refine the structures . All 3D models of the ApoE-Aβ complexes were found to be quite different . Although the Aβ ( 1–40 ) peptide assembles between the first and fourth ApoE helix for all ApoE isoforms , the orientation of the peptide was found to depend on the ApoE variant ( Figure 1B; see Figure S1 for comparison of the 10 lowest energy solutions for each isoform ) . For ApoE2 and ApoE4 , the C-terminus of the peptide faces the N-terminus of the protein , though the assembly is different . For ApoE3 , the peptide is turned around , and the N-terminus of the peptide faces the N-terminus of the protein . Early studies indicated that ApoE interaction with Aβ fibrils is partially dependent on ionic interactions [19] . Thus , the single change of Cys158 in ApoE2 to Arg158 in ApoE3 changes the distribution of ionic residues influencing the assembly of Aβ ( 1–40 ) , while the double change of Cys112 and Cys158 to Arg112 and Arg158 in ApoE4 distributes the ionic residues in an ApoE2-like way . A 10 ns classical MD simulation including explicit water of the three ApoE isoforms together with the Aβ peptide was carried out on each of the lowest energy ApoE-Aβ models obtained by docking calculations as well as on each isolated species . Figure 1C shows the root-mean-square deviation ( RMSD ) of the MD simulation for the three ApoE isoforms in the presence and absence of the peptide . In their unbound form , no conformational transitions were detected for the ApoE isoforms , in agreement with previous results [20] . However , in presence of the peptide , different behaviors were observed between the isoforms . Despite the existence of interaction , no conformational transitions were detected for the ApoE2-Aβ or the ApoE3-Aβ complexes . However , the ApoE4-Aβ complex showed a large conformational transition indicated by a significant RMSD change of about 10 Å in the 10 ns timescale ( Figure 1C ) . In Figure 1D , four snapshots of the 10 ns MD simulation for the ApoE4-Aβ complex are presented . Focusing on ApoE4 , during the first 0 . 3 ns , the third helix of ApoE4 started to unfold and a loop appeared between residues 112 and 92 which affected the whole third helix . This structural disturbance was caused by the onset of new electrostatic interactions rising from the interaction with the peptide . For the Aβ peptide , the first conformational change appeared in the Glu22-Asp23 region . At 1ns the second helix of ApoE4 showed a conformational change . In the snapshots of 5 ns , the first and fourth helices of ApoE4 were still stable , but at 10 ns a large conformational change had occurred , coinciding with a fully extended Aβ ( 1–40 ) peptide . At 10 ns , the hydrophobic groups inside the ApoE4 helices had become exposed to the solvent . The interruption of the stable salt bridge network by external electrostatic interactions ( coming from the peptide ) was thus transmitted from the dense helix region to the whole protein , causing a severe loss of α-helical structure . Further investigation on the conformational change induced in ApoE4 by the complexation with Aβ was carried out through the analysis of the distances between charged residues . For this analysis , direct salt bridges have been assumed to be around 4 . 3 Å , whereas indirect or water-mediated salt bridges have been assumed to have a distance between 4 . 3 and 7 . 0 Å as reported by Dzubiella et al . [21] . In the most stable ApoE4-Aβ complex , the peptide interacted with helices I and IV of ApoE4 . The Aβ residues responsible for these interactions were the negatively charged Asp1 and Asp23 , which interacted with positively charged arginines in ApoE4 ( Arg38 in helix I and Arg142 in helix IV respectively ) . The direct salt bridge between AβAsp23 and ApoE4Arg38 was very strong ( Figure 2A ) , while the salt bridge between AβAsp1 and ApoE4Arg142 did not exist during most of the MD simulation , and only became more plausible at the end of the MD simulation ( the distance for an indirect salt bridge being reached after circa 8 ns , Figure 2A ) . Focusing on helices I and II of the N-terminal domain of ApoE4 , the distance between Arg38 and Asp35 changed during the 10 ns time window ( see Figure 2B ) . A transition occurred from 10 to 2 . 5 Å in the 2 ns time window , which then went back to 10 Å ( indicating the breaking of the Arg38-Asp35 salt bridge ) , and became stable at 7 ns . For comparison , the same distance is shown for the MD simulation of ApoE4 alone , where no change at all can be seen , as the distance was within the 4 . 3 and 7 . 0 Å range during the whole 10 ns ( Figure 2B ) . The salt bridge between Asp35 and Arg32 was stable below 4 . 4 Å before 2 ns ( Figure 2C ) . For ApoE4 in the absence of Aβ , the distance remained constant around the 7 . 0 Å threshold , making it difficult to determine the existence of an indirect salt bridge . For the ApoE4-Aβ complex , the direct salt bridge involving Arg32 and Glu66 ( in helices I and II , respectively ) was affected and showed a maximal fluctuation from 2 . 5 to 7 . 5 Å and then back to 2 . 5 Å in the 10 ns time window ( Figure 2D ) . In the ApoE4 alone MD , this Arg32-Glu66 pair did not show any propensity to interact ( the distance was over 7 . 0 Å during the whole 10 ns ) . For helices II and III of the N-terminal domain of ApoE4 , the transitions of the Arg61-Glu66 , Arg61-Glu109 and Glu109-Arg112 salt bridges were monitored in the ApoE4-Aβ complex ( see Figure 3 ) . At 5 ns the distance between Glu66 and Arg61 from helix II dropped from about 10 to 3 Å , becoming stable and forming a direct salt bridge ( see Figure 3A ) . However , for ApoE4 alone , this salt bridge was never formed . For the ApoE4-Aβ complex in the 5 ns interval , the direct salt bridge between Arg61 and Glu109 ( helix III ) broke down ( the distance increased from about 3 to 12 . 5 Å , Figure 3B ) . In ApoE4 alone the distance for this pair was out of range during most of the MD simulation . However , the distance between Glu109 and Arg112 ( both in helix III ) remained relatively stable and below the salt bridge distance threshold ( Figure 3C ) . In the ApoE4-Aβ complex , the Glu109-Arg112 salt bridge was direct ( below 4 . 3 Å ) , whereas for ApoE4 alone , the salt bridge was more indirect or water mediated . The MD results for the Arg112-Asp110 pair ( Figure 3D ) were similar to those for the Arg112-Glu109 pair . In the complex , the distance for the electrostatic pair indicated a direct salt bridge , whereas for ApoE4 alone , this distance was closer to an indirect salt bridge ( if any ) . Electrostatic interactions between helix III and helix IV were more complex and insensitive to the interaction with the peptide , and the bridge network involving helices III and IV remained stable during the simulation ( data not shown ) . In the ApoE4-Aβ complex , the interaction between Arg112 with Asp110 and Glu109 in helix III is connected to helix IV via the Asp110-Arg147 and Asp107-Arg151 ion pairs ( see Figure 4 ) . Also Asp107 in helix III and Asp151 in helix IV interacted with Arg147 . Another inter-helical ion pair network existed between Arg103 , Glu96 and Arg92 in helix III and Arg150 , Arg153 , Arg154 and Arg158 in helix IV ( see Figure 4 ) . Arg158 acted as a bridge for extending the electrostatic interaction between Glu96 and Arg92 .
Our computational approach assumes a direct interaction between ApoE and Aβ . Although the docking was plausible for ApoE2 and ApoE3 , the interactions did not generate any conformational transition in the 10 ns time window while for ApoE4 , the interaction promoted unfolding of the ApoE4 , as shown by the MD simulations . This result is compatible with earlier thermal and chemical denaturation studies using circular dichroism and scanning calorimetry , which have indicated stability differences ( ApoE4<ApoE3<ApoE2 ) among the three isofoms ( experiments were carried out on the 22 KDa truncated protein , corresponding to the N-terminal domain ) [13] , [14] . The present results also agree with the existence of a partially unfolded intermediate for ApoE4 [22] . However , a direct comparison of the present results with the previous experimental results is not possible . The MD results for the ApoE isoforms alone do not indicate any of the trends shown experimentally , probably because of the time scale ( nanoseconds vs . seconds/minutes ) . But in the case of ApoE4 , it is likely the Aβ peptide behaves as an unfolding catalyzer . Thus , effects on the stability of ApoE2 and ApoE3 exerted by Aβ peptide at longer time scale cannot be discarded . The proposed ApoE4-Aβ complex forms between helices I and IV of ApoE4 ( proposed model in Figures 1B and 4 ) . As seen from the docking procedure , the complex formation does not directly affect the salt bridges involving Arg61 , but the cascade of events generated by the interaction leads to the stabilization and destabilization of the Arg61-Glu66 and Arg61-Glu109 salt bridges , respectively . Arg112 in ApoE4 causes the side chain of Arg61 to extend away from the four–helix bundle which will allow electrostatic interaction with Asp65 , Glu66 and Glu59 ( see Figure 4 ) . In ApoE2 and ApoE3 , Arg61 shows a different orientation ( due to Cys122 , see Figure 1A ) , hindering the interaction with the charged residues from helix III . The fluctuation of the salt bridges in helices I and II could be explained by the interruption of the Arg38-Asp25 salt bridge in ApoE4 . This effect is most likely induced by Asp23 of Aβ , which will affect the neighboring salt bridge between Asp35 and Arg32 . Another affected interaction would be the inter-helix salt bridge between Arg32 ( helix I ) and Glu66 ( helix II ) . The MD simulations show that this initial chain of events induced by the presence of the Aβ peptide and occurring in helices I and II of ApoE4 ( but not ApoE3 ) would soon be transmitted to helix III stabilizing the Arg61-Glu66 and breaking the Arg61-Glu109 salt bridges in this N-terminal domain , and probably affecting also the Arg61-Glu255 salt bridge in the full protein form . Disruption of this domain interaction by the ApoE4 R61T mutation has been shown to reduce Aβ production [23] . In the same study , an ApoE4 docking site involving residues 109 , 112 and 61 , was defined as a binding site for blocking agents capable to disrupt the domain interaction leading to a decrease in Aβ production [23] . The other contact point comprising AβAsp1 and ApoE4Arg142 appears less relevant for the destabilization of the salt bridge network; however , Arg142 is within the heparin and receptor binding region ( localized around residues 141–150 of ApoE ) . This direct interaction may shield the ApoE4 binding region , affecting the cell membrane recognition of ApoE4 interacting with Aβ . As shown by in vitro studies , both ApoE3 and ApoE4 interact with Aβ and form SDS stable complexes . ApoE-Aβ complexes have been isolated from AD brain extracts and shown to be stable and as tightly packed as Aβ fibrils [24] , [25] . Our results indicate the possibility that both ApoE3 and ApoE4 bind to the peptide with different orientations . Assuming the protective role of ApoE3 compared to the detrimental role of ApoE4 in AD ( for an extensive review see Huang et al . [26] ) , we can speculate the following: the binding of the peptide with ApoE3 does not affect the stability of the protein nor the complex , leading to the peptide clearance . On the other hand , the lower stability of ApoE4 is even more emphasized by the interaction with Aβ: the interaction triggers the partial unfolding of ApoE4 into a misfolded intermediate which we suggest is incapable of performing the clearance of Aβ , and leading to pathogenic effects such as the promotion of amyloid forming processes . In our results , mostly the N-terminus of the peptide is involved in the ApoE4-Aβ complex formation ( residues 1 and 23 ) . Previous studies with Aβ peptide have shown that electrostatic interactions are the main cause for the formation of larger oligomers and that the C-terminus region is important for the formation of such oligomers [27] . Discrete MD simulations have shown that the Gly37-Gly38 turn plays an important role in the formation of Aβ ( 1–42 ) pentamers [28] . Thus , we can speculate that the non-involvement of the C-terminus in the complex formation could favor the interaction of free Aβ C-termini , thus provoking the aggregation of the ApoE4-Aβ complexes . This Aβ effect could probably be overcome by the usage of agents ( such as GIND-25 and GIND-105 ) [23] binding to the Arg61/Glu109/Arg112 ApoE4 binding site , which would stabilize the protein by disrupting the Arg61-Glu255 salt bridge , generating an ApoE3-like variant . In the same way , Aβ and ApoE derived peptides have also been used as blocking therapeutic agents of both the protein and the peptide [29] , [30] . We propose that the interaction of Aβ with ApoE4 induces a partially unfolded intermediate by the frustration of the existent network of salt bridges . The four-helix bundle of ApoE4 opens up and the hydrophobic core becomes exposed due to the ApoE4-Aβ complex formation , presumably rendering the protein incapable of performing Aβ clearance . The interaction with Aβ affects the proposed binding site formed by Arg61/Glu109/Arg112 in ApoE4 , a binding site that has been shown to be relevant for substances capable of reducing the Aβ production . The model here presented has implications for therapeutic drug design for AD , as it defines on a molecular level the ApoE-Aβ complex as a potential drug target .
Crystal structures of the three ApoE truncated isoforms ( containing only the N-terminal domain ) were downloaded from the PDB database ( ApoE2 , E3 , E4 , respective ID's: 1LE2 , 1LPE and 1LE4 ) , together with the Aβ peptide solution structure , determined by NMR in 10% SDS/Water ( ID:1BA4 ) and used as the docking model . Crystallographic waters were removed and the structures were fully solvated before energy minimization . Energy minimization was performed for the macromolecules using the GROMACS3 . 3 . 2 software with GROMOS96 as the force field [31] . The RMSD between the initial and the energy minimized structures was lower than 0 . 01 Å for the ApoE isoforms . For the Aβ peptide , due to the flexibility of the N-terminus , the RMSD was 4 . 7 Å ( RMSD of 0 . 8 Å for the α-helix Aβ residues 13 to 40 ) . The structures obtained after energy minimization were used in PatchDock ( http://bioinfo3d . cs . tau . ac . il/ ) , where candidate solutions were generated by rigid-body docking methods [32] , [33] . PatchDock determined the best starting candidate solutions based on shape complementarity of soft molecular surfaces . The Clustering RMSD was 4 . 0 Å for analysis and the complex type was set to default . The PatchDock algorithm divides the Connolly dot surface representation of the molecules into concave , convex and flat patches . Then , complementary patches are matched in order to generate candidate transformations [32] , [33] . Each candidate transformation is further evaluated by a scoring function that considers both geometric fit and atomic desolvation energy . The 1000 best docked candidate transforms from PatchDock , based on global energy , aVdW , rVdW , atomic contact energy , and insideness measurements , were then used in FireDock ( http://bioinfo3d . cs . tau . ac . il/ ) [34] . FireDock optimized , refined and rescored the 10 top candidate solutions by restricting the flexibility to the side-chains of the interacting surface and allowing small rigid-body movements . For this study , we selected the first best candidate solution from FireDock for the ApoE2- , ApoE3- , and ApoE4-Aβ complex . Energy minimization , equilibration and molecular dynamics simulations were carried out at neutral pH using the GROMACS3 . 3 . 2 software with GROMOS96 as the force field [31] . The complexes of each of the three ApoE isoforms with Aβ peptide from the above-mentioned docking were used as the starting points for the simulations . Bond lengths were constrained using the LINCS algorithm and the SETTLE algorithm was used for hydrogen bonding of water . First , macromolecules from the docking model were solvated in a cubic box of 8Å cutoff with TIP3P water . Each complex was minimized with 2000 steps using the steepest descent algorithm in order to relieve bad interactions between ApoE and Aβ peptide . The system was equilibrated by first running 10 ps of position-restrained molecular dynamics; then the temperature of the system was gradually increased to 300 K . Berendsen's temperature coupling method ( time constant of 0 . 1 ps ) was used in an unrestrained simulation . Water molecules were equilibrated in the presence of the protein complex for 10 ps before running an unrestrained molecular dynamics simulation for 10 ns . For unrestrained molecular dynamics simulation , the temperature coupling and pressure coupling were conducted in the NpT ensemble by using a Berendsen thermostat of 300 K and 0 . 1 ps relaxation time . The pressure was 0 . 5 bar with 0 . 000045 compressibility and 1ps relaxation time , respectively . The simulations with 300 K were applied by 173529 seeds . Isotropic pressure coupling and Berendsen's temperature coupling were then used during a 10 ns molecular dynamics simulation . In addition , two MD simulations were run involving the three ApoE isoforms alone , following the above-mentioned process . All molecular representations in this study were generated using Chimera v1 . 4 ( http://www . cgl . ucsf . edu/chimera/ ) [35] . The g_rms and g_dist of GROMACS3 . 3 . 2 were used to analyze the MD results . | Unraveling the molecular details of the interaction between apolipoprotein E and the amyloid β peptide will yield insights into the relationship between Alzheimer's disease and lipid transport and metabolism . The isoform E4 of apolipoprotein E has been shown to be closely related to Alzheimer's disease . We have therefore used a computational approach to depict a detailed interaction map for this peptide-lipoprotein interaction . The simulation shows that the specific formation of the lipoprotein isoform E4 and the peptide complex affects the structure of the lipoprotein and the peptide . We suggest that this is related to some of the pathogenic effects in Alzheimer's disease . Our results provide a molecular model to work with for the design of potential therapeutic agents capable of modulating this interaction . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neurological",
"disorders/alzheimer",
"disease",
"computational",
"biology/molecular",
"dynamics"
] | 2010 | In Silico Analysis of the Apolipoprotein E and the Amyloid β Peptide Interaction: Misfolding Induced by Frustration of the Salt Bridge Network |
A critical step in the assembly of the neural circuits that control tetrapod locomotion is the specification of the lateral motor column ( LMC ) , a diverse motor neuron population targeting limb musculature . Hox6 paralog group genes have been implicated as key determinants of LMC fate at forelimb levels of the spinal cord , through their ability to promote expression of the LMC-restricted genes Foxp1 and Raldh2 and to suppress thoracic fates through exclusion of Hoxc9 . The specific roles and mechanisms of Hox6 gene function in LMC neurons , however , are not known . We show that Hox6 genes are critical for diverse facets of LMC identity and define motifs required for their in vivo specificities . Although Hox6 genes are necessary for generating the appropriate number of LMC neurons , they are not absolutely required for the induction of forelimb LMC molecular determinants . In the absence of Hox6 activity , LMC identity appears to be preserved through a diverse array of Hox5–Hox8 paralogs , which are sufficient to reprogram thoracic motor neurons to an LMC fate . In contrast to the apparently permissive Hox inputs to early LMC gene programs , individual Hox genes , such as Hoxc6 , have specific roles in promoting motor neuron pool diversity within the LMC . Dissection of motifs required for Hox in vivo specificities reveals that either cross-repressive interactions or cooperativity with Pbx cofactors are sufficient to induce LMC identity , with the N-terminus capable of promoting columnar , but not pool , identity when transferred to a heterologous homeodomain . These results indicate that Hox proteins orchestrate diverse aspects of cell fate specification through both the convergent regulation of gene programs regulated by many paralogs and also more restricted actions encoded through specificity determinants in the N-terminus .
The neural circuits that govern locomotor behaviors rely on the establishment of orderly sets of connections between motor neurons ( MNs ) and their peripheral and central synaptic targets . A critical and early step in the emergence of locomotor circuitry is the selection of specific muscle targets by a diverse array of MN subtypes . Three organizational features of MNs emerge during embryonic development that contributes to the specificity of their connections with target cells . First , MNs that project axons to common peripheral targets are organized into columns longitudinally arrayed along the rostrocaudal axis of the spinal cord [1] , [2] . For example , MNs that project into the limb are contained within the lateral motor columns ( LMCs ) , which are generated specifically at brachial and lumbar levels of the spinal cord . LMC neurons subsequently segregate into medial and lateral divisions , a program that dictates whether motor axons project into dorsal or ventral compartments of the limb mesenchyme [3] , [4] . Finally , cells within each division further segregate into MN pools , each pool a cluster of stereotypically positioned MNs that innervates one of the ∼100 muscles in the limbs [2] , [5]–[7] . MNs must therefore acquire a sufficient level of subtype diversity to ensure the appropriate muscle connectivity required for the emergence of coordinate locomotor behavior . Within the developing spinal cord , Hox proteins exert central roles in the specification of MN columnar and pool subtypes [8] , [9] . Nearly half of the 39 Hox genes are expressed by MNs , with subsets of related paralogs functioning at distinct levels of the MN differentiation pathway [10] . Three paralog groups , Hox6 , Hox9 , and Hox10 genes have been implicated in the early columnar organization of MNs and contribute to the specificity of their initial projections into the periphery [11]–[13] . The actions of a much larger group of ∼20 Hox genes contribute to the specification of MN pools , in part , through the induction of intermediate transcription factors [10] , [14]–[16] . During these programs of MN diversification , Hox proteins mediate both the selective activation of downstream targets and the exclusion of other determinants through mutual cross repression , two distinct activities that appear to be intrinsic to Hox proteins [10] , [11] . Despite significant progress towards defining roles for Hox proteins in MNs , the mechanisms by which they control diverse features of MN subtype identity are largely unknown . Studies in Drosophila indicate two key mechanisms through which Hox proteins regulate target genes [17] . The first involves the selection of DNA target sites . Hox proteins typically display low affinity for DNA in vitro , with high fidelity binding requiring cooperative interactions with the TALE-domain containing homeodomain factors extradenticle and homothorax ( Pbx and Meis proteins in vertebrates ) [18] . While TALE-domain protein interactions increase the affinity and selectivity of Hox proteins for DNA , they have only a subtle influence on the specificity of site selection in vitro , particularly amongst Hox proteins expressed in more caudal regions of the embryo [19]–[21] . Recent evidence , however , suggests that in vivo specificity can be achieved by sequences N-terminal to the homeodomain , which mediate contacts with the minor groove at target sites [22] , [23] . Once bound to a target gene , the activities of Hox/Pbx complexes can be further modulated through the actions of ancillary transcription factors that typically bind in proximity to Hox targets [24] , [25] . In this mode of action , a Hox protein may not depend as much on DNA site selection for specificity , but rather on how it interacts with factors it engages at a target sequence . Some insights into the mechanisms by which Hox proteins regulate target genes in MNs have emerged through analysis of mice mutant for a single thoracically expressed Hox gene , Hoxc9 . Hoxc9 is required for the appearance of thoracic-level MN columnar subtypes including preganglionic column ( PGC ) and hypaxial motor column ( HMC ) neurons [26] . A critical aspect of Hoxc9 function is to establish the boundary between thoracic and forelimb-level MN populations through cross repression , as in the absence of Hoxc9 all brachial Hox genes are derepressed at thoracic levels , and MNs acquire an LMC fate . This broad repressive activity appears to be mediated by direct interactions of Hoxc9 with multiple sites in the HoxA and HoxC loci . Genome-wide analysis of Hoxc9 binding revealed a consensus binding motif which matches a high affinity Hox/Pbx site ( TGATTTAT ) identified by several groups through in vitro site selection [19] , [20] . This sequence engages a wide range of Hox paralogs , raising the issue of how the in vivo specificities of Hox proteins in MNs are achieved if they are not dependent on the recognition of specific genomic target sites . The problem of Hox specificity in MNs is particularly relevant at limb levels of the spinal cord , where individual neurons express multiple Hox proteins at the time of their differentiation [10] , [11] , [27] . In this context Hox proteins appear to contribute to both gene programs common to all LMC neurons as well as more restricted actions necessary for diversification of LMC neurons into MN pools . At limb levels of the spinal cord the actions of Hox6 and Hox10 genes have been implicated in the initiation of the LMC program at brachial and lumbar levels respectively , through activation of the gene encoding the transcription factor FoxP1 [28] . FoxP1 is subsequently required for the expression of the gene encoding the retinoic acid synthesizing enzyme Raldh2 [28] , [29] . This MN-derived source of retinoids is necessary for the Lim homeodomain protein-mediated segregation of the LMC into medial and lateral divisions [30] , [31] . Thus the deployment of the LMC program at forelimb and hindlimb levels is mediated by two distinct sets of Hox paralogs that activate a common set of downstream pathways required for MN columnar and divisional specification . While Hox proteins seem to be critical for LMC specification , it is less clear how they contribute to MN pool diversity . At brachial levels the LMC is broadly divided into rostral and caudal domains by expression of Hox5 genes ( Hoxa5 and Hoxc5 ) and Hoxc8 , respectively; and the actions of these Hox genes are necessary for delineating the rostrocaudal position of MN pools [10] . Within a given segment a repression-based network of Hox4–Hox8 proteins are thought to promote the intrasegmental diversity of MNs , by defining specific molecular codes for each pool subtype . For example , misexpression studies in chick have provided evidence that Hoxc6 is selectively required for the intrasegmental differentiation of pools within the caudal ( Hoxc8+ ) half of the LMC [10] . Thus the same Hox6 paralog group that determines the early columnar identity of forelimb-innervating MNs contains members that promote motor pool fates . In this study we sought to address several unresolved issues concerning the function and specificity of Hox6 genes during MN columnar and pool specification programs . First , what are the specific contributions of the three murine Hox6 genes to MN fate specification ? Second , to what extent are the diverse activities of a Hox protein unique , or are they shared amongst gene paralogs within a cluster ? Third , are there motifs intrinsic to Hox proteins that subfunctionalize in vivo specificities ? To address these questions we analyzed mice in which all Hox6 genes are mutated , as well as employed an in vivo approach to dissect functional domains required for Hox specificity in MNs . We find that although LMC specification is retained in mice lacking Hox6 genes , Hoxc6 has a specific role in promoting MN pool identity and appropriate patterns of limb connectivity . The preservation of LMC fate in Hox6 mutants appears to be mediated by a diverse group of Hox5–Hox8 genes expressed at brachial levels . Dissection of a single Hox protein reveals in vivo specificity relies on motifs that ensure deployment of programs common to all LMC neurons , as well as distinct modules that contribute to MN pool identity .
Studies in chick have implicated Hox6 genes in the specification of LMC neurons at brachial levels of the spinal cord . Two Hox6 genes , Hoxa6 and Hoxc6 , are selectively expressed by brachial MNs in chick , and can convert HMC and PGC neurons to an LMC fate when misexpressed at thoracic levels [11] . Whether Hox6 activities are absolutely required for LMC specification in mice is not known . To begin to answer this question we first analyzed the expression of Hox6 paralogs ( Hoxa6 , Hoxb6 , and Hoxc6 ) at brachial levels near the time of LMC differentiation at embryonic day ( e ) 11 . 5 . Hoxa6 and Hoxc6 are expressed throughout the brachial LMC , while Hoxb6 is expressed by MN progenitors ( Figure 1A , 1B , 1G , 1I ) . Hox6 genes also displayed temporally dynamic patterns; after e11 . 5 Hoxa6 expression was only weakly detected in the spinal cord , while Hoxc6 was attenuated in subsets of LMC neurons by e12 . 5 , and downregulated in most LMC neurons by e13 . 5 ( Figure 1A , 1B and data not shown ) . To assess the function of Hox6 genes in LMC neurons we generated and analyzed mice containing various combinations of Hox6 mutant alleles [32] , [33] . Single and combined mutation of Hoxa6 and Hoxc6 had no effect on general features of MN identity , as assessed by the presence of the early MN determinants Hb9 , Islet1/2 and Lhx3 ( Figure 2E , 2F; Figure S1A ) . In addition Hoxa6 was not upregulated in Hoxc6 mutants , nor was Hoxb6 upregulated in MNs of Hoxa6/c6 mutants , and the normal patterns of brachially expressed HoxA and HoxC proteins were maintained ( Figure S1B , S1C and data not shown ) . Moreover , the thoracic Hoxc9 gene was not noticeably derepressed at brachial levels in Hox6 mutants ( Figure S2 ) , likely due to compensation by other Hox paralog groups ( see below ) . Hox6 genes are therefore not required for the generation of MNs as a class or in maintaining Hox expression patterns . We next determined the profile of LMC determinants in Hoxa6/c6 mutants . We assessed the expression of Foxp1 and Raldh2 , two genes that are induced downstream of Hox proteins [28] . In Hoxa6−/−Hoxc6+/+ , Hoxa6+/−Hoxc6+/− , and Hoxa6−/− Hoxc6+/− embryos the number of LMC neurons was similar to wildtype embryos , while both Hoxc6−/− and Hoxa6−/−Hoxc6−/− mutants displayed significant LMC losses ( Figure 2A–2D , 2I–2J; Figure S3A ) . To quantify the reduction of LMC neurons in Hoxc6−/− and Hoxa6−/−Hoxc6−/− embryos , we performed serial sectioning on e12 . 5 embryos and determined the total number of FoxP1+ LMC neurons averaged from n>3 mutants and control littermates . This analysis revealed a 28% loss of LMC neurons in Hoxc6 mutants , and a 37% loss in Hoxa6/Hoxc6 double mutants ( Figure 2I–2J ) . The loss of LMC neurons was particularly prominent in the rostral half of the LMC ( Hoxa5/c5+ region ) , where we observed a 41% decrease in FoxP1+ MNs in Hoxc6 mutants and a 56% decrease in Hoxa6/c6 double mutants ( Figure 2A , 2C ) . In addition mutation of Hoxa6/c6 had a more severe impact on Raldh2 expression , with a near complete absence of expression in the rostral brachial spinal cord , possibly due to an attenuation in FoxP1 expression levels in the remaining MNs ( Figure S3B ) . Similar defects in LMC specification were observed at e10 . 5 and e11 . 5 , indicating they are present at the time of LMC generation ( Figure S3B–S3D ) . Hoxa6 and Hoxc6 are therefore necessary for the appearance of the normal number of LMC neurons . We next determined the fate of the LMC neurons that are lost in Hoxa6/c6 mutant mice . Analysis of Foxp1 mutants suggests that in the absence of a Hox-programmed LMC identity , MNs remain in the “default” fate of the hypaxial motor column ( HMC ) subtype , a motor neuron column normally present at thoracic levels [28] , [29] . Consistent with this idea , we find an increase in the number of MNs with an HMC character , defined by high levels of Hb9 and Isl1 coexpression ( Figure 2E–2H , Figure S1A ) . In contrast the number of Lhx3+ Hb9+ MMC neurons , a Hox-independent columnar subtype present at all levels of the spinal cord , was unchanged ( Figure 2E–2H ) . These observations demonstrate that in the absence of Hoxa6/Hoxc6 , MNs that fail to acquire an LMC fate revert to an HMC-like identity . Because expression of Hoxb6 in MN progenitors could account for the maintenance of LMC identity in Hoxa6/Hoxc6 mutants we also analyzed mice in which all three murine Hox6 alleles are deleted . We found that in Hoxa6/Hoxb6/Hoxc6 triple mutants FoxP1+/Raldh2+ MNs were present , and LMC numbers were grossly similar to Hoxa6/Hoxc6 double mutants ( Figure S4 ) . As in Hoxa6/Hoxc6 mutants , the LMC loss was most prevalent at more rostral brachial levels , while caudal brachial LMC MNs were less affected ( Figure S4 ) . Thus Hox6 genes are necessary for appropriate LMC numbers , but are not absolutely required for the activation of LMC molecular determinants in brachial spinal cord . The perseverance of LMC identity in Hox6 mutants raises the question of whether other Hox paralogs might contribute to their specification . To address this question we began by analyzing the expression patterns of additional Hox genes at brachial levels . In chick spinal cord several genes belonging to the Hox4–Hox8 paralog groups are expressed by brachial LMC neurons . We therefore determined the expression patterns of Hox4–Hox8 genes in mouse at e11 . 5 with reference to the brachial LMC . This analysis revealed patterns of Hox4–Hox8 paralog expression that were similar to patterns in chick [10] , with HoxA and HoxC cluster genes ( Hoxa5 , Hoxa6 , Hoxa7 , Hoxc4 , Hoxc5 , Hoxc6 , Hoxc8 ) the most prominently expressed by brachial MNs ( Figure 1A–1K ) . These observations indicate that Hox patterns are largely conserved between mouse and chick , with multiple Hox genes expressed by brachial LMC neurons at the time of their differentiation . To determine the influence of brachially expressed Hox4–Hox8 genes on MN differentiation we used chick neural tube electroporation to assess the effects of misexpression at thoracic levels . Since Hox overexpression could lead to neomorphic effects , we optimized plasmid concentrations in electroporations to be qualitatively similar to levels found in the endogenous brachial domain in chick ( Figure S5A , S5B ) . We compared the effects of thoracic Hox misexpression to that of Hoxa6 and Hoxc6 , which have been shown to induce LMC identity at thoracic levels ( Figure 3A ) [11] , [28] . Consistent with previous observations , Hoxc6 and Hoxa6 were similar in their capacity to induce expression of markers of LMC identity including high levels of FoxP1 and Raldh2 , and to cell autonomously abolish expression of thoracic determinants of PGC fate , including Hoxc9 and phospho ( p ) Smad1/5/8 ( Figure 3D , 3E ) . We next tested the effects of thoracic misexpression of representative genes from the Hox4 , Hox5 , Hox7 , and Hox8 paralog groups . We observed that thoracic misexpression of either Hoxa5 , Hoxa7 or Hoxc8 could redirect thoracic MN fate towards a brachial LMC identity as assessed by induction of Foxp1 and Raldh2 and loss of pSmad expression ( Figure 3C , 3F , 3G ) . In contrast , although Hoxc4 is expressed by most LMC neurons , thoracic misexpression of Hoxc4 failed to induce high FoxP1 or Raldh2 ( Figure 3B ) . These observations indicate that many , but not all , brachially expressed Hox genes can promote an LMC character at thoracic levels . We additionally found that the capacity of different Hox proteins to specify aspects of LMC identity varies depending on the Hox protein . While 64% of thoracic MNs expressing Hox6 proteins acquired an LMC identity , Hoxa5 only induced LMC fate in 41% of electroporated MNs ( Figure S5C ) . Hox proteins thus differ in the extent to which they can generate LMC neurons . The reduced potential of Hoxa5 to promote LMC fate likely accounts for the more pronounced decline in LMC numbers at rostral brachial levels in Hox6 mutants , as this region lacks the more potent LMC inducers , Hoxa7 and Hoxc8 . In addition Hoxc8 , Hoxa7 , and Hoxa5 were less effective than Hox6 proteins in extinguishing Hoxc9 expression , and many of these Hox-induced LMC motor neurons continued to express Hoxc9 ( Figure 3C , 3F , 3G ) . Together these observations indicate that multiple Hox proteins can specify features common to all LMC neurons , while individual Hox proteins diverge with respect to LMC promoting efficacies and cross-repressive activities . While multiple Hox genes appear to converge in regulating early programs of brachial LMC differentiation , it is possible that they have distinct roles during motor neuron pool diversification within the LMC . Within the Hox6 paralog group , Hoxc6 has been implicated in the specification of MN pools innervating specific muscles in the chick embryo , independent of its function in promoting LMC identity [10] . We therefore determined whether Hoxc6 has an obligate role in MN pool differentiation . Within the caudal half of the brachial LMC , some MN pools can be defined by expression of the Ets protein Pea3 as well as the POU domain protein Scip [26] , [34] , [35] . In the caudal ( Hoxc8+ ) half of the LMC , Hoxc6 has been argued to promote the specification of the Pea3+ pool and restrict expression of Scip [10] . We therefore assessed the specification of these pools in Hox6 mutants . While the number of FoxP1+ LMC neurons generated at caudal brachial levels was not significantly reduced in Hoxa6/c6 mutants , we observed a significant defect in motor neuron pool differentiation . In both Hoxc6 and Hoxa6/c6 double mutants the number of Pea3+ MNs was markedly reduced , while the Scip+ pool was relatively spared ( Figure 4A–4D , Figure S6E ) . Because LMC neuron numbers are reduced overall in Hoxc6 mutants we quantified the number of Pea3+ and Scip+ MNs as a percentage of the total number of LMC neurons generated . Within the rostrocaudal limits of the pool , Pea3 MNs account for 30% of all LMC neurons; and in Hoxc6 mutants this number was reduced to 12% ( Figure 4B ) . Scip+ MNs account for 35% of LMC neurons within its limits , and in Hoxc6 mutants , this number was 32% ( Figure 4D ) . Thus Hoxc6 is selectively required for the normal appearance of the Pea3 pool , independent of its role in LMC specification , but only has a minor contribution to Scip+ LMC neurons . In mice , Pea3 is expressed by MNs that target the cutaneous maximus ( CM ) muscle , whereas Scip+ MN pools project along the median and ulnar nerve [26] , [35] . To further assess the impact of loss of Hoxc6 on MN development , we bred Hox6 mutant mice to a line in which all MNs are labeled with GFP ( Hb9::GFP mice ) [36] , [37] and analyzed motor axon projections in the limb . In Pea3 mutants , motor axons project to the CM but fail to branch and arborize the muscle [35] . Consistent with a loss of the Pea3+ MN pool there was a drastic reduction in the arborization of the CM in Hoxc6 mutants ( Figure 5A–5D , Figure S6A–S6D ) . In addition distal branches of the musculocutaneous nerve were poorly formed ( Figure 5A–5D , Figure S6A–S6D ) , suggesting that Hoxc6 may have roles in the specification of additional pools that cannot yet be defined by unique molecular markers . Interestingly , projections along the ulnar nerve were not reduced , but instead displayed supernumerary branches at the distal end , which were atypically directed towards the paw at e12 . 5 and e13 . 5 ( Figure 5B , 5D; Figure S6B , S6D ) . We considered the possibility that the ulnar nerve might receive innervation from LMC neurons that have lost Pea3 . To test this we injected Rhodamine ( RhD ) dextran tracers into the ulnar nerve and assessed the molecular identity of retrogradely labeled MNs . In control animals all RhD-labeled MNs expressed Scip , while in Hoxc6 mutants many RhD+ Scip− neurons were observed ( Figure 5E , Figure S7A–S7C ) . Many of these RhD+ Scip− MNs were located more rostral to the Scip pool , occupying a position where Pea3+ MNs would normally reside ( Figure S7 ) . Thus Hoxc6 is required for the specification of Pea3+ CM MNs , and in the absence of this program many motor axons appear to acquire the projection characteristics of ulnar MNs . Collectively , our findings suggest that while multiple Hox genes share a common function in promoting LMC fates they diverge with respect to MN pool specification . To understand the basis for the differential activities of Hox proteins in MNs we searched for intrinsic domains that contribute to their MN-specific activities in vivo . We decided to focus on Hoxc6 for this analysis as it is initially expressed by the majority of brachial LMC neurons , its activities are required for normal LMC generation , the specification of the Pea3+ pool , and it can extinguish Hoxc9 through its repressive functions . We first asked whether the capacity of Hoxc6 to promote the identity of the Pea3 pool reflects a specific activity of this particular Hox gene . Consistent with a restricted role in promoting Pea3+ MN fates , we find that Hoxc6 can induce Pea3 in a subset of the ectopic FoxP1+ LMC neurons generated after thoracic misexpression ( Figure 6A , 6B ) . In contrast , thoracic expression of Hoxa5 , Hoxa6 , Hoxa7 , and Hoxc8 failed to induce expression of Pea3 within the ectopic LMC population ( Figure 6C–6F and data not shown ) . Thoracic Hoxc6 expression is therefore sufficient to promote both columnar and pool fates at thoracic levels , and its Pea3 pool promoting activity appears to be unique to Hoxc6 . Next we defined regions that contribute to the ability of Hoxc6 to induce columnar and pool fates . We first tested the activities of chimeras between the N-terminus of Hoxc6 ( containing all amino acids up to the homeodomain ) and the homeodomains of heterologous Hox proteins . Fusion of the Hoxc6 N-terminus to the homeodomain ( HD ) of the “LMC-neutral” Hox protein , Hoxc4 ( Figure 3B ) , activated high levels of Foxp1 at thoracic levels ( Figure 6G , 6H ) . Thus the LMC inducing actions of Hoxc6 can be transferred to a Hox protein that normally cannot induce LMC fates . However , this chimera failed to induce expression of the pool marker Pea3 , and many MNs continued to express Hoxc9 ( Figure 6I , 6J ) . Similarly , fusion of the Hoxc6 N-terminus to the HD of Hoxc8 induced an LMC fate , but failed to promote Pea3 expression ( Figure 6K , 6L ) . These observations indicate that Hox proteins rely on the activities of both the N-terminal and HD sequences , with the N-terminal region sufficient for LMC induction and the N-terminal+HD controlling aspects of MN pool specification . Thus while early LMC programs are initiated through activities presumably common to many Hox proteins , pool-restricted actions require a coherent N-terminal and HD region ( Figure 6M ) . Hox proteins are known to contain peptide motifs that confer activation and repression of target genes independent of the homeodomain [38]–[40] , although how these activities contribute to MN columnar and pool identities are unclear . Based on our analysis of Hox chimeras we next asked whether the actions of Hoxc6 in LMC specification are mediated through modules in the N-terminus . Specifically we sought to define whether there are specific domains that determine how Hoxc6 promotes LMC fate , the specification of the Pea3 pool , and represses Hoxc9 . To further define regions in Hoxc6 that contribute to its in vivo specificities we generated and characterized a series of HA-tagged deletion constructs and point mutations in Hoxc6 . To discriminate between activities that influence DNA binding from those that affect target gene regulation , we tested the capacity of mutant derivatives to bind Hox recognition elements . To accomplish this we first needed to identify cognate sequences that are bound by Hoxc6 in vivo . Because Foxp1 is regulated by Hox proteins we searched for potential Hox sites within the Foxp1 locus . In silico analysis using the Vista enhancer browser [41] suggested a potential Hox-dependent enhancer upstream of Foxp1 transcription start site . This enhancer ( Foxp1/hs1149 ) is highly conserved amongst vertebrates and drives high levels of expression at limb levels of the spinal cord , and lower levels thoracically ( Figure 7A ) . To test whether this element is regulated by Hox genes in vivo we bred a Foxp1/hs1149::LacZ line to Hoxc9 mutants , in which all brachial Hox genes are derepressed at thoracic levels . This analysis revealed ectopic expression of hs1149::LacZ at thoracic levels ( Figure 7A ) . These results identify a Hox-regulated element that targets the normal rostrocaudal domain where Foxp1 expression is highest . We next used gel mobility shift assays to determine if Hox proteins could bind the hs1149 element . Since Pbx cofactors are generally necessary for high affinity Hox binding , we performed binding assays in the presence of Pbx3 , a Pbx protein expressed by MNs . Scanning of the ∼1 kb hs1149 enhancer identified a single potential binding site ( TGAATTATCA ) , which generally conforms to the Hox/Pbx consensus [21] . We observed that Hoxc6 and Pbx3 cooperatively bound to this site in vitro ( Figure 7B ) . To test whether Hox proteins bind this element in vivo we performed chromatin immunoprecipitation ( ChIP ) assays on chromatin prepared from e13 . 5 brachial and thoracic-level spinal cord using a Hoxc6 antibody . ChIP analysis revealed specific binding by Hoxc6 at brachial levels , but not at thoracic levels ( Figure 7C ) , indicating the hs1149 element is bound by Hoxc6 in vivo . Using gel mobility shift assays we then tested the ability of Hoxc6 mutant derivatives to bind the hs1149 element , as well as an optimized Hox consensus binding site ( Fkh250con* ) [22] , [42] . We first tested the binding properties of mutant derivatives that would in principle preserve DNA recognition ( i . e . retain the homeodomain [HD] and Pbx interaction motif [YPWM] ) but might influence Hoxc6 activities at target sites . We tested the binding of two large N-terminal deletions , Hoxc6NΔ91 and Hoxc6NΔ111 , finding that both deletions cooperatively bound with Pbx3 to Foxp1/hs1149 and Fkh250con* , although with slightly reduced affinity ( Figure 7D ) . In contrast a mutation of the conserved Pbx interaction motif ( YPWM->AAAM ) failed to display cooperative binding of Hoxc6 and Pbx3 to Foxp1/hs1149 or Fkh250con* ( Figure 7D ) . To test the in vivo activities of Hoxc6 mutant derivatives , we assessed their ability to influence MN differentiation at thoracic levels , using chick electroporation . To ensure that epitope tagged proteins were stable and that expression levels were similar amongst mutant derivatives , we monitored nuclear HA localization in electroporated cells and expressed mutant proteins at levels that were qualitatively similar to a HA-tagged wildtype Hoxc6 construct ( Figure 8B ) . Expression of the large N-terminal deletion ( Hoxc6NΔ111 ) at thoracic levels was inefficient in promoting Foxp1 , Raldh2 , Pea3 , and repressing Hoxc9 expression ( Figure 8F ) , indicating that the N-terminus is essential for Hoxc6 actions , independent of its ability to bind DNA . We next tested the activities of additional deletion constructs within the N-terminus . Deletion of the first 13 amino acids , containing the highly conserved “SYF” motif present in the N-terminus of many Hox proteins , did not affect the ability of Hoxc6 to induce Foxp1 , Raldh2 , Pea3 or repress Hoxc9 at thoracic levels ( Figure 8C ) . Further deletion of the N-terminal 64 amino acids ( Hoxc6NΔ64 ) abrogated the capacity of Hoxc6 to repress Hoxc9 , as neurons co-expressed both the deletion mutant and Hoxc9 ( Figure 8D ) . Notably , the Hoxc6NΔ64 induced Foxp1 and Raldh2 expression , although this derivative was unable to generate Pea3+ MNs ( Figure 8D ) . Hoxc6 can thus program LMC fate , even in the presence of a Hox protein that normally promotes thoracic fates . Further analysis revealed a requirement for amino acids 105–111 to activate LMC genes , as deletions up to amino acid 104 continued to induce high Foxp1 and Raldh2 expression ( Figure 8E , 8F ) . These results indicate that the Hoxc6 N-terminus contains modular domains that are essential for diverse aspects of Hoxc6 function . Because Hox proteins often require cooperative interactions with Pbx proteins to bind DNA , we also examined the consequences of mutating the YPWM motif ( Hoxc6IM: YPWM->AAAM ) . Surprisingly expression of Hoxc6IM did not alter the capacity of Hoxc6 to repress Hoxc9 or to induce high Foxp1 levels and Raldh2 at thoracic levels ( Figure 9B ) . Because Hoxc6IM retains the capacity to repress Hoxc9 , and deletion of Hoxc9 in mice leads to the derepression of brachial Hox genes at thoracic levels [26] , we considered the possibility that expression of Hoxc6IM causes ectopic Hox4–Hox8 gene expression , and that these in turn activate the LMC program . However , we did not observe ectopic brachial Hox gene expression after expression of Hoxc6IM at thoracic levels ( data not shown ) , possibly because Hoxc9-mediated repression is established prior to the time Hoxc6IM is expressed . These observations suggest that either Pbx interactions are dispensable for LMC specification , or that the LMC neurons produced under these conditions reflect the actions of Hox proteins resident to the thoracic spinal cord whose functions are unmasked through suppression of Hoxc9 . To test this idea we generated a Hoxc6 derivative in which both the Hoxc9 repression domain ( NΔ91 ) and YPWM motif were mutated , but retained the intrinsic activation domain ( aa 105–111 ) . While Hoxc6 NΔ91 cooperatively bound with Pbx3 to the hs1149 element and Fkh250con* and activated LMC genes ( Figure 9A and Figure 7D ) , the combined NΔ91/IM mutant failed to bind or reprogram thoracic MNs to an LMC fate ( Figure 9C and Figure 7D ) . Thus only through removal of both the Hoxc9 repression domain and Pbx interaction motif is the ability of Hoxc6 to promote LMC fate lost . These results suggest that Hoxc6 can promote LMC identity through two distinct mechanisms 1 ) Pbx-independent restriction of Hoxc9 and 2 ) Pbx-dependent interactions with Foxp1 , activities that are separable ( Figure 9D ) . Together these data indicate Hoxc6 contains specific motifs required for LMC induction , cross-repressive interactions , and pool specification , with LMC induction mediated by redundant activities of Hox cross repression and cooperative interactions with Pbx proteins .
While experiments in chick have implicated Hox6 gene function in the specification of brachial LMC neurons , we find in mice lacking all Hox6 activity that LMC neurons are still generated , although significantly reduced in numbers . In the absence of Hox6 genes the forelimb LMC program appears to be maintained by a large set of Hox5–Hox8 genes , which likely act by promoting high levels of Foxp1 expression in MNs . This idea is supported by the observations that multiple Hox paralogs are expressed by brachial MNs , and that Hox5–Hox8 proteins can convert PGC neurons to an LMC identity when expressed at thoracic levels . Thus a diverse group of Hox proteins promote columnar identities , through regulating a common set of genes capable of interpreting Hox inputs from several paralog groups . Similar redundant activities amongst diverse Hox paralogs have been demonstrated during haematopoiesis [43] , suggesting that in certain cellular contexts key target effectors may permissively engage any Hox protein present . The non-selective Hox inputs to LMC-restricted genes may further explain the ability of a dominant repressor form of Hoxc6 to inhibit LMC specification [11] , by superseding the compensatory actions of Hox5–Hox8 proteins . Why then do so many Hox genes contribute to LMC identity ? The simplest explanation is that it represents an extreme example of functional redundancy , ensuring that spurious Hox mutations do not lead to devastating consequences for the organism . Alternatively , it may reflect a strategy employed to allow individual Hox proteins to exert distinct functions in MN pool specification , with redundant Hox factors serving the role of maintaining LMC identity over the course of MN differentiation . The Hox protein most closely aligned with the brachial LMC , Hoxc6 , displays a dynamic expression pattern during LMC differentiation , with many MNs attenuating Hoxc6 expression shortly after columnar identity is established . This pattern may allow for MN pool subtypes that might otherwise be inhibited by the presence of Hoxc6 to retain an LMC fate , while simultaneously acquiring a unique pool identity . The logic of the limb-level Hox network in MNs contrasts with the columnar program at thoracic levels of the spinal cord , which is largely mediated through the actions of a single Hox gene , Hoxc9 [26] . In addition to maintaining low levels of FoxP1 in PGC neurons , an essential function of Hoxc9 is to restrict expression of Hox4–Hox8 genes to brachial levels . Thus while brachial MNs express many Hox factors that appear to act on common LMC targets , thoracic MNs express relatively few Hox genes , one of which operates to restrict expression of multiple Hox genes , in an apparently selective manner . The distinct transcriptional strategies employed at these two levels of the spinal cord are therefore uniquely geared towards either confining or expanding the diversity of MN subtypes , in accordance with the relative complexity of the target tissues they innervate . Although individual Hox genes are largely dispensable for early programs of LMC differentiation , they are critical in the specification of motor neuron pools . While Hoxc6 mutants display no significant LMC losses in caudal brachial regions , the MN pool defined by Pea3 is markedly reduced in size . Presumably the LMC is preserved in this region due to compensatory actions of Hoxa7 and Hoxc8 , which maintain high FoxP1 levels but are apparently insufficient to promote the normal pattern of Pea3 expression . This differential effect on columnar and pool specification likely reflects distinct Hox specificities in the regulation of target effectors , with LMC determinants like Foxp1 and Raldh2 integrating multiple Hox inputs , and pool specific genes relying on specific Hox gene activities . This mechanism mirrors the “semi-paralog specific” and “paralog-specific” activities of Hox proteins observed in Drosophila [17] . Our data indicate that these distinct modes of Hox-dependent gene regulation operate when neurons are confronted by multiple Hox proteins within the same cell . Previous studies in chick indicate that the specification of the MN pools defined by Pea3 and Scip expression involve the combinatorial interactions between multiple Hox proteins and their cofactors , including Hoxc4 , Hoxc6 , Hoxc8 and Meis1 [10] . It is somewhat surprising that expression of Hoxc6 is sufficient to direct the specification of the Pea3 pool independent of these factors . This finding however appears to reflect how the network operates within the LMC . A primary function of Hoxc8 and Hoxc4 is to exclude Meis1 from a subset of MNs , with Hoxc6 acting to promote Pea3 expression within the context of a Meis1− LMC neuron [10] . Thoracic MNs contain an endogenous subpopulation that lacks Meis1 ( J . Dasen unpublished observation ) , allowing Hoxc6 to promote a Pea3+ pool identity . Thus while individual Hox proteins can promote pool fates , these activities are normally constrained by the actions of other Hox proteins in the network , and can only operate in the context of a MN that has been programmed to an LMC identity . While regions outside the homeodomain that contribute to the specificities of Hox proteins in Drosophila have been well documented [44]–[47] , few studies have defined functional motifs in the vertebrate nervous system . Our analysis of regulatory domains within the Hoxc6 protein provides insight into the mechanisms through which Hox proteins contribute to MN columnar and pool identities . We find that Hoxc6 contains N-terminal motifs that are critical for deployment of LMC programs as well as cross-repressive functions , independent of its core DNA binding domain . Surprisingly either of these activities is sufficient to promote LMC identity . Removal of the Hoxc9 repression domain does not affect Hoxc6's capacity to generate LMC neurons at thoracic levels , even in the presence of an inhibitory Hox program . The underlying mechanism for this phenomenon likely reflects how Hox proteins regulate LMC-targets , such as Foxp1 . Foxp1 is induced at low levels by Hoxc9 in thoracic MNs , and thus appears to be equally capable of receiving inputs from both limb and thoracic-level Hox proteins . Forced expression of LMC Hox determinants ( Hox5–Hox8 paralogs ) presumably acts by overriding the dampening influence of Hoxc9 , perhaps by competing for binding sites . This idea is supported by the observation that Hox5–Hox8 proteins can induce aspects of LMC differentiation without suppressing Hoxc9 . During normal development this process is likely played out in two distinct contexts: at the border between brachial and thoracic spinal cord , where many LMC MNs express low levels of Hoxc9 [26] , and within the LMC where FoxP1 is maintained by pools of MNs which have lost Hoxc6 expression . Competitive and compensatory interactions between Hox proteins at genomic targets therefore may be more generally utilized in MNs both as means to generate diversity and to allow for certain gene programs to be maintained in the face of dynamic transcription factor profiles . Our findings indicate that suppression of Hoxc9 is a condition sufficient to promote LMC fates even in the absence of a functional LMC Hox protein . When the Pbx interaction motif is deleted from Hoxc6 , Hoxc9 is still repressed and LMC neurons are generated , although Hoxc6 presumably will no longer have a direct impact on activating the LMC program . One plausible explanation for this result is that the suppression of Hoxc9 unmasks the activities of Hox proteins that are endogenous to the thoracic spinal cord that have the capacity to activate high levels of Foxp1 ( Figure 9D ) . While the identity of these Hox proteins is unclear , they may include more broadly expressed Hox genes ( e . g . Hoxd4 [48] ) or the activities of “brachial” Hox genes , such as Hoxa7 and Hoxc8 , which are expressed at low levels in chick thoracic spinal cord [10] . Thus we favor a model in which in the absence of Pbx interactions , the repressive influence of Hoxc6 on Hoxc9 is retained , and LMC neurons are generated through a Pbx-dependent mechanism that utilizes Hox proteins resident to thoracic levels of the spinal cord ( Figure 9 ) . This idea is consistent with studies in flies indicating that Hox proteins repress targets in the absence of Pbx interactions where they bind as monomers [49] . In addition our observations may also account for the finding that when similar Pbx-interaction mutations are generated in the lumbar LMC-determinant Hoxd10 , LMC fates are induced when expressed at thoracic levels [50] . Collectively these studies add to emerging evidence that the specific actions of Hox proteins are defined through peptide modules that shape both the selection of DNA targets , and gate the activities of Hox proteins once bound to a site [17] , [38] , [39] , [44] , [51] . While the precise mechanisms that determine how Hox proteins deploy their restricted actions in MNs are unresolved , they likely rely on gene-specific interactions of Hox proteins with other transcription factors or cosignaling partners [17] . Our studies indicate that the subfunctionalization of Hoxc6 , and Hox proteins in general , into motifs that confer the activation and repression at multiple gene targets can expand the repertoire of Hox function even within a single cell , allowing them to execute their multifaceted roles during cellular differentiation .
The Hox6 mutant strains [33] , and the Hb9::GFP line [36] , have been described previously . Electroporation was performed on stage 12 to 16 chick embryos as described [11] . Results for each experiment are representative of at least three electroporated embryos from three or more independent experiments in which the electroporation efficiency in MNs was >60% . The amount of pCAGGs plasmid DNA in each electroporation was titrated to achieve levels of expression qualitatively similar to endogenous expression levels , typically in the range of 100–300 ng/µl , using pBKS as carrier DNA ( 1 . 8–2 µg/µl ) . Electroporations of mutant and chimera Hox derivatives were optimized to ensure qualitatively similar levels of expression to endogenous Hox levels by comparing HA-wildtype Hoxc6 to non-tagged Hoxc6 , and comparing HA-tagged Hoxc6 to HA-tagged mutant derivatives . Stability of expression of these constructs was determined by the presence of nuclear HA staining . Chromatin immunoprecipitation was performed as previously described [26] . Briefly , brachial and thoracic spinal cords were dissected from e13 . 5 mouse embryos . Tissues were homogenized in 1 . 1% formaldehyde , chromatin was extracted and fragmented to 500–1000 bp by sonication and chromatin extracts were subjected to immunoprecipitation with either specific antibodies or species-matched IgGs . Antibodies used were goat anti-mouse Hoxc6 ( Santa Cruz ) or rabbit ant-mouse Hoxc6 ( Abcam ) . Genomic regions were amplified using Sybr Green PCR Master Mix ( Applied Biosystems ) and detected with Mx 3005P real-time PCR apparatus ( Stratagene ) . Fold enrichment was calculated over IgG using the ΔΔCt method: fold enrichment = 2− ( ΔΔCt ) , where ΔΔCt = ( CtIP−CtInput ) − ( CtIgG−CtInput ) . pET-14b plasmids carrying His-tagged constructs were used to transform BL21 pLys bacterial strain and protein expression was induced by 0 . 5 mM IPTG at room temperature overnight . Bacteria were lysed under native conditions in lysis buffer ( 50 mM Tris pH 7 . 5 and 100 mM NaCl ) followed by sonication . The supernatant was incubated with Ni-NTA agarose beads at 4°C for 1 . 5 hours and washed three times in buffer containing 50 mM Tris pH 7 . 5 , 300 mM NaCl , 20 mM imidazole and 0 . 5% Igepal CA-630 . Recombinant proteins were eluted in 50 mM Tris pH 7 . 5 , 300 mM NaCl , 250 mM imidazole and dialyzed overnight in 50 mM Tris-HCl pH 8 and 150 mM NaCl . Oligonucleotides containing putative Hox binding sites were annealed to an IRDye-800 labeled linker ( IRD800-AGCTGTGGGACGAGG ) . Double stranded probes were synthesized using Klenow DNA polymerase . Binding between recombinant proteins and DNA probes was performed in binding buffer containing 50 mM Tris-HCl pH 7 . 5 , 250 mM NaCl , 5 mM MgCl2 , 20% Glycerol , 2 . 5 mM DTT , 2 . 5 mM EDTA pH 8 , 250 ng/µL poly dIdC and 0 . 1% BSA for 20 minutes at room temperature . For each binding assay equivalent molar amounts ( 0 . 5–2 pmol/reaction ) of recombinant protein were used . Binding reactions were resolved on a non-denaturing acrylamide gel and the IRDye-800 was detected using the Odyssey system ( Li-Cor ) . In situ hybridization and immunohistochemistry were performed on 16 µm cryostat sections as described [52] . Whole-mount antibody staining was performed as described [14] and GFP-labeled motor axons were visualized in projections of confocal Z-stacks ( 500–1000 µm ) . Antibodies against Hox proteins , LIM HD proteins , and other proteins were generated as described [10] , [27] , [28] , [52] . Retrograde labeling of MNs was performed as described [28] . Procedures involving animals abide by the NYUMC policy on the care and use of laboratory animals . Experiments involving animals are not conducted unless approved by the Institutional Animal Care and Use Committee ( IACUC ) . We do not work with a species or procedure , including euthanasia , with which I and those members of my research staff involved in this project are not experienced , without first seeking the advice and instruction of a veterinarian from the Division of Laboratory Animal Resources , consult the Division of Laboratory Animal Resources as circumstances require . To the best of my knowledge , the research does not unnecessarily duplicate previous research with respect to the use of laboratory animals . We comply with all requests for data as may be required by governmental and institutional guidelines . We seek the approval of the Institutional Animal Care and Use Committee on all procedures which involve laboratory animals . | Coordinated motor behaviors—as complex as playing a musical instrument or as simple as walking—rely on the ability of motor neurons within the spinal cord to navigate towards and establish specific connections with muscles in the limbs . The establishment of connections between motor neurons and limb muscles is mediated through the actions of genes encoding Hox proteins , a large family of transcription factors conserved amongst all metazoans . However , the specific requirements for Hox genes in motor neuron specification and patterns of muscle connectivity are poorly understood . We have found that members of the Hox6 gene paralog group ( Hoxa6 , Hoxc6 , and Hoxb6 ) contribute to diverse aspects of motor neuron subtype differentiation . Hox6 gene activity is required during two critical phases of motor neuron development: first as motor axons select a trajectory toward the forelimb and second as they choose specific muscles to innervate . At the molecular level , these two functions are encoded by distinct peptide domains within Hox proteins . This work indicates that Hox proteins execute their critical functions in motor neurons through intrinsic modules that confer distinct specificities and that these activities are central in the genetic network required for motor neuron differentiation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neurogenesis",
"neuroscience",
"cell",
"differentiation",
"gene",
"function",
"developmental",
"biology",
"organism",
"development",
"pattern",
"formation",
"developmental",
"neuroscience",
"gene",
"expression",
"biology",
"axon",
"guidance",
"genetics",
"genetics",
"and",... | 2013 | Genetic and Functional Modularity of Hox Activities in the Specification of Limb-Innervating Motor Neurons |
The LFA-1 integrin plays a pivotal role in sustained leukocyte adhesion to the endothelial surface , which is a precondition for leukocyte recruitment into inflammation sites . Strong correlative evidence implicates LFA-1 clustering as being essential for sustained adhesion , and it may also facilitate rebinding events with its ligand ICAM-1 . We cannot challenge those hypotheses directly because it is infeasible to measure either process during leukocyte adhesion following rolling . The alternative approach undertaken was to challenge the hypothesized mechanisms by experimenting on validated , working counterparts: simulations in which diffusible , LFA1 objects on the surfaces of quasi-autonomous leukocytes interact with simulated , diffusible , ICAM1 objects on endothelial surfaces during simulated adhesion following rolling . We used object-oriented , agent-based methods to build and execute multi-level , multi-attribute analogues of leukocytes and endothelial surfaces . Validation was achieved across different experimental conditions , in vitro , ex vivo , and in vivo , at both the individual cell and population levels . Because those mechanisms exhibit all of the characteristics of biological mechanisms , they can stand as a concrete , working theory about detailed events occurring at the leukocyte–surface interface during leukocyte rolling and adhesion experiments . We challenged mechanistic hypotheses by conducting experiments in which the consequences of multiple mechanistic events were tracked . We quantified rebinding events between individual components under different conditions , and the role of LFA1 clustering in sustaining leukocyte–surface adhesion and in improving adhesion efficiency . Early during simulations ICAM1 rebinding ( to LFA1 ) but not LFA1 rebinding ( to ICAM1 ) was enhanced by clustering . Later , clustering caused both types of rebinding events to increase . We discovered that clustering was not necessary to achieve adhesion as long as LFA1 and ICAM1 object densities were above a critical level . Importantly , at low densities LFA1 clustering enabled improved efficiency: adhesion exhibited measurable , cell level positive cooperativity .
Rolling , activation , and adhesion are critical events in the inflammatory response , as they are required for the proper recruitment of leukocytes to sites of tissue damage . The selectin family of receptors and their respective carbohydrate ligands largely mediate the initial interactions between leukocytes and endothelial cells . Their high frequency rates of association and dissociation allow for transient interactions that slow the speed of travel , enabling leukocytes to roll along the activated endothelial surface and integrate inflammatory signals , such as immobilized chemokines [18] . The transition from rolling to adhesion is exclusively mediated by integrin receptors . They can exist in multiple conformational states each having different ligand binding properties . Low- , intermediate- , and high-affinity states have been identified . Natively , these integrins exist in non-adhesive , low-affinity states to prevent leukocytes from sticking non-specifically to blood vessel surfaces . Upon detection of immobilized chemokines , such as CXCL1 , intracellular signaling events can trigger integrin conformational changes to high affinity states that enable a leukocyte to adhere firmly [19] . Post-adhesion events involving integrins are thought to help strengthen the attachment to the endothelium [13] , [20] . Earlier studies , in addition to those cited in the Introduction , have provided evidence that LFA-1 clustering is mediated by PI3K and that the process is important for leukocyte adhesion . Constantin et al . showed that chemokines triggered a rapid increase in LFA-1 affinity on lymphocytes . Using immunofluorescently labeled LFA-1 with confocal microscopy , they observed that chemokines also stimulated LFA-1 movement into clusters and large polar patches [4] . Inhibiting PI3K activity blocked LFA-1 mobility but had no effect on LFA-1 affinity change . In separate experiments , PI3K inhibitors prevented lymphocytes from adhering to low densities of immobilized ICAM-1 substrate . At high ICAM-1 densities , inhibiting PI3K had no effect on lymphocyte adhesion . Whether this same phenomena exists in neutrophils remains to be determined .
The simulation system we have constructed is designed to be an experimentally useful analogue of the wet-lab experimental systems used to study leukocyte rolling and adhesion ( Figure 1 ) . Wet-lab experimental systems provide data that can be used to generate mechanistic hypotheses . We used the synthetic modeling method , an example of what has been referred to as executable biology [21] , [22] , as a means to instantiate those mechanistic hypotheses so that they can be evaluated and tested . Object-oriented , agent-based software components were designed , instantiated , verified , plugged together , and then operated in ways that can map concretely to mechanisms and processes believed responsible for leukocyte rolling and adhesion . An agent is a quasi-autonomous object capable of scheduling its own actions in much the same way as we imagine cells and some of their components ( such as a mitochondrion or modular subsystem ) doing . See [12] for details of this approach , the basic framework of the in silico white blood cell ( ISWBC ) , and an iterative refinement protocol for successively developing and validating simulation results . To distinguish the previous version from the current one , we refer to the former as ISWBC1 ( in silico white blood cell ) and the latter as ISWBC2 . Below we summarize details and the new ISWBC2 capabilities . The greater the similarity between the measured behaviors , or phenotype , of an ISWBC2 and corresponding wet-lab attributes of interest , the more useful it will become as a research tool and as an expression of the coalesced , relevant leukocyte knowledge . The expectation has been that increasing phenotype similarity will require , and can be achieved in part through , similarities in design plan and in generative mechanisms . We follow an iterative refinement protocol to systematically and sequentially extend the overlap of the model and referent system phenotypes . This iterative refinement protocol allows us to concatenate in a dynamic fashion new knowledge about the referent systems into the synthetic model , without having to reengineer the whole system , and without having to compromise already validated features and behaviors [12] , [23]–[25] . This approach has its grounding in the use of modeling and simulation as a research topic [26] . It is as follows: It is important to note that validity is distinguishable from verity . For this technical context , we define validity as the degree to which an assertion ( such as an ISWBC2 ) can be trusted as true enough or true to within some tolerance ( e . g . , the similarities discussed herein ) . In contrast , verity targets ontological truth , regardless of any arguments or belief . One cannot verify a representation such as an ISWBC2 against reality because of the ontological wall . Therefore , we define ( http://biosystems . ucsf . edu/research_dictionary . html ) verification as the process of determining where two statements agree and disagree , as in comparing a model to its corresponding simulation . One can only validate a representation against reality . Model validation can be achieved at several levels [27] . For example , one level of validation is through the qualitative reproduction of observed behaviors . Another level can be determined through parameter variability and sensitivity analysis . Changes to the values of input and internal parameters should affect model behaviors in a fashion similar to when analogous changes are made to the real system . Additionally , a level of validation can be achieved through the reliable prediction of new data . The most appropriate type and level of validation will depend on the intended model usage . ISWBCs were designed specifically to explain the diverse observations of leukocytes as they interact with endothelial surfaces , which is in contrast to inductive mathematical models that are typically intended for precise prediction [11] . We previously reported progress validating against our initial set of targeted attributes [12] . The ISWBC1 successfully represented the dynamics of individual leukocytes rolling separately on P-selectin and VCAM-1 , along with the transition from rolling to adhesion on P-selectin and VCAM-1 in the presence of GRO-α chemokine . Additionally , the individual in silico and in vitro behavioral similarities translated successfully to population-level measures ( Table 1A ) . Herein we extend the model by also targeting the phenotypic attributes of LFA-1 and ICAM-1 receptor mobility and clustering ( Table 1B ) . Doing so allowed us to observe their hypothesized role in initiating adhesion ex vivo and sustaining adhesion in vivo . We used RePast 3 as our modeling and simulation framework . It is a java-based software toolkit developed at the University of Chicago for creating and exercising agent-based models ( http://repast . sourceforge . net/repast_3/index . html ) . The libraries provided were used to create , run , display , and collect data . The ISWBC2 can be downloaded at [28] . To avoid confusion and clearly distinguish model components , features , measurements , and events from their ex vivo or in vivo counterparts , we use small caps when referring to those of analogues . The biological aspects of the referent experimental systems and their ISWBC2 counterparts are listed in Table 2 . The surface with which leukocytes interact is discretized into independent units of function called surface units . The leukocyte's membrane is similarly discretized into matching units of function called membrane units . For simplicity , the surface and membrane are both implemented as 2D toroidal lattices . With current parameter values , one surface unit maps to approximately 1 µm2 and when rolling or adhered , one membrane unit maps to the same amount of surface area on the cell membrane . Contained within each unit are receptor objects , each one mapping to receptors found on the surface or leukocyte membrane . An eight × ten unit region shared between the surface and the membrane identifies the contact zone . It determines which surface and membrane units ( and which of their receptors ) can interact . In ISWBC1s [12] , the receptors found at the tips of microvilli ( PSGL-1 , VLA-4 , and CXCR-2 ) were represented by the receptor objects psgl1 , vla4 , and cxcr2 . Each one mapped to several binding molecules of the same type that may be found within a discrete area within the referent system . For example , a psgl1 receptor mapped to several PSGL-1 adhesion molecules . The approximate number to which that object maps is determined by its parameter , TotalNumber . In ISWBC2 , we have increased granularity ( spatial resolution ) by adding receptor objects lfa1 and icam1 and placing them within a higher granularity 2D hexagonal grid ( lfa1grid and icam1grid ) within some membrane units and surface units , respectively , as illustrated in Figures 1B , C . lfa1 maps to the integrin molecule LFA-1 that is found on leukocyte membranes between microvilli [29] , [30] , while icam1 maps to its ligand-receptor ICAM-1 . Distinct from the other receptor objects , each lfa1 and icam1 object maps to single molecules . Each high-resolution hexagonal grid space maps to approximately 100 nm2 of leukocyte membrane or surface . ISWBC2 experiments are analogous to those performed in vivo or using an ex vivo flow chamber system . While on the surface , leukocytes use their receptors and the decisional processes sketched in Text S1 to interact and form bonds with receptors on the surface . Those interactions are recorded and measured . The ISWBC2 consists of components having three levels of spatial resolution illustrated in Figure 1: Leukocyte-level , Membrane/Surface Unit-level , and lfa1 grid/Icam1 grid-level . High-level behaviors are dependent upon the collective operation of objects and agents contained within each of the lower levels . For example , the behavior of membrane and surface units arise from the receptor objects contained within each . Similarly , the behavior at the leukocyte-level is dependent upon the collective events that occur within the underlying membrane/surface units . Conversely , events at the highest level impose constraints upon allowed lower level behaviors . For example , the positioning and movement of the leukocyte on the surface dictate which membrane and surface units are overlapping and can interact . Local activation of an integrin within a membrane unit occurs when a chemokine receptor detects a chemokine in an overlapping surface unit . When an activation signal is detected , local lfa1 receptors change from low to high affinity state . They achieve that by changing the parameter values Pon ( bond formation rates ) , bond b0 , b1 , and bLimit ( bond dissociation properties ) , as described in Text S1 . Relationships between the parameter bondforce and probability of bond dissociation for each of the four ligand pairs included in the ISWBC2 are given in Text S1 along with corresponding in vitro measurements . The diffusive properties of lfa1 , specified by the parameter LFA1MoveNum , are also dependent on state . Lfa1 lateral mobility parameters were specified as described Text S1 such that they have diffusive properties similar to those observed in vitro . Values are provided in Text S1 . LFA1MoveNum determines the number of attempts that an lfa1 object will take to move to a neighboring space within the lfa1grid during a single simulation cycle . During lateral movement , Lfa1 has an equal probability of moving into any of its six neighboring spaces , but it cannot move into an already occupied space . When simulating the endothelial cell surface , icam1 can similarly diffuse on the icam1grid with a rate determined by the parameter ICAM1MoveNum . As a simple representation of some LFA-1 trafficking events ( removal from endocytosis , deactivation , extraction from the membrane ) [31] , [32] , unbound lfa1 is removed from the membrane during each simulation cycle with a probability of LFA1RemovalRate . The number and location of bonds at the surface and membrane unit level combined with the decisional processes sketched in Text S1 determine Leukocyte behavior . If there are bonds between adhesion molecules within the rear column of the contact zone , the leukocyte pauses , or remains stationary , until the next simulation cycle . If there are no bonds within the rear column of the contact zone , the leukocyte , influenced by the simulated shear force , performs a forward rolling movement . Rolling is the result of a sequence of forward ratchet events . The process involves removing a column from the rear of the membrane's rectangular contact zone while a new one is placed at the front of the zone above the surface . At the beginning of each simulation experiment , Boolean parameters ( Table 3 ) determine whether Lfa1 clustering ( LFA1Clustering ) , preformed icam1 clustering in endothelial surface units ( ICAM1Pre-Clustered ) , or icam1 tetramer formation ( ICAM1Tetramer ) is allowed to occur . When mean ISWBC2 results were within the range of mean ± SD of wet-lab results , the two sets of data were declared experimentally indistinguishable . A lfa1 cluster on the lfa1Grid is specified by the parameter LFA1ClusterDiameter . It determines the length and width of the region that LFA1 objects must stay within while diffusing on the lfa1grid . Lfa1 clusters are formed by randomly choosing non-overlapping regions on the lfa1grid and then filling each with all Lfa1 within that membrane unit ( Figure 2B ) . The number of clusters per lfa1grid is specified by the parameter NumLFA1Clusters . During a simulation , clusters with unbound lfa1 randomly move to new and unoccupied locations within the lfa1grid , a process that maps to molecular diffusion within a membrane region . Different configurations of ICAM-1 have been reported in the literature . One study suggested that the native state of ICAM-1 is monomeric , because a monomer contains the complete binding site for LFA-1 [33] . Other studies provided evidence that dimeric ICAM-1 is the predominant form expressed by cytokine-activated endothelium [34] , [35] . One such study showed that a dramatic conformational change of dimeric ICAM-1 occurs upon binding to LFA-1 such that they form one-dimensional chains of W-tetramers and higher order oligomers [36] . However , more recent studies by Barreiro et al . observed that ICAM-1 is clustered with other adhesion molecules into preformed tetraspanin-enriched microdomains on the surface of activated endothelial cells . They provided convincing evidence that these preformed microdomains might act as specialized endothelial adhesive platforms for leukocytes during adhesion and extravasation [37] . Evidence from immuno-electron microscopy of fixed endothelial cells suggested that these organized microdomains might be smaller than 100 nm in diameter [38] . Scanning electron microscope images of activated endothelial cells stained with anti–ICAM-1 antibodies followed by 40-nm gold immunolabeling showed a cluster size of 2 . 4±0 . 1 ( mean ± SE ) particles per nanoclusters . In our simulations of in vivo experiments , we implemented the latter case where ICAM-1 exists preclustered ( ICAM1Pre-Clustered = true ) . However , we also implemented the three other hypothesized ICAM-1 spatial configurations in order to observe their relative effects on leukocyte adhesion: ( 1 ) native monomeric ICAM1 ( Figure 2C ) , ( 2 ) native dimeric ICAM1 ( Figure 2D ) , and ( 3 ) in Figure 2F , dimeric ICAM-1 forming linear W-tetramers upon ligand binding ( ICAM1Tetramer = true ) . In the simulations , when ICAM1Tetramer = true , a bound icam1 forms a linear tetramer structure with a nearby unbound dimeric icam1 object ( Figure 2F ) . To keep things simple in simulations where ICAM1Pre-Clustered = true , preformed ICAM-1 nanoclusters were represented using three icam1 molecules held together ( Figure 2E ) . Upon achieving leukocyte behaviors that matched those from ex vivo and in vivo experiments , robustness to changes in a variety of key parameters were measured . Small ( 5–10% ) and large ( 50% ) parameter value changes were made to Pon ( high affinity lfa1-icam1 ) ( bond formation probabilities for lfa1-icam1 ) , RearForce , and LFA1RemovalRate , while other factors were held constant . For each , ISWBC2 behaviors were recorded . During a simulation , each lfa1 and icam1 object kept track of its bond event and position history . When needed , that information was transferred to a separate file . Each membrane unit and surface unit also kept track of the number of bond events that involved receptor objects contained within , including lfa1 and icam1 rebinding events .
Many iterative refinement cycles were performed in order to get from ISWBC1 to ISWBC2 . While no code changes were made to the leukocyte receptors psgl1 and vla4 or to the substrates pselectin and vcam1 , software changes were made to manage the simulation output . Therefore , we deemed it necessary to test the ISWBC2's ability to reproduce some of the essential targeted phenotypic attributes from Table 1 that were previously used to validate the ISWBC1 . We repeated simulations of in vitro parallel plate flow chamber experiments that observed neutrophils rolling on various densities of P-selectin ( 9 and 25 sites/µm2 ) and under varying wall shear rates ( 0 . 5 , 1 . 0 , and 2 . 0 dyn/cm2 ) , exactly as executed previously [12] . Using analogous in silico experimental conditions , the ISWBC2 leukocytes produced behaviors ( not shown ) that were indistinguishable from wet-lab results and from simulation results previously reported . Leukocytes exhibited the characteristic jerky stop-and-go movement with highly fluctuating rolling velocities . Average rolling velocities were calculated and were still within the ranges reported in the literature . Higher average leukocyte rolling velocities were observed at higher simulated shear rates . At higher pselectin substrate densities , leukocytes rolled with smaller average rolling velocities . Lastly , comparison of pause time distributions revealed no apparent difference between the ISWBC1 and ISWBC2 . From these studies , we concluded that the new code introduced to transform ISWBC1 into ISWBC2 did not affect the model's ability to reproduce previously targeted phenotypic attributes . To gain insight into the role of PI3Kγ on leukocyte rolling and adhesion under flow conditions , Smith et al . compared the behaviors of leukocytes from PI3Kγ KO and WT mice ex vivo using blood-perfused micro-flow chambers coated with the endothelial cell substrate molecules P-selectin , ICAM-1 , and CXCL1 [13] . Nine one-minute recordings of a field of view were taken of the center of each chamber under each experimental condition . Population-level measures of rolling and adhesion were obtained by averaging the number of rolling and adherent cells for each condition . Arrested cells were defined as those that were adherent for at least 30 s . They observed a reduced ability of leukocytes from PI3Kγ KO mice to adhere to the coated surfaces in comparison to leukocytes from WT mice . We simulated analogous experimental conditions to determine if the addition of a simple lfa1 clustering mechanism and its inhibition would enable the in silico system to mimic that adhesion defect . We used the same combination of substrate analogues . We explored the consequences of changing LFA1Clustering in isolation of other variables during each experiment . We chose parameter values based on reported literature values or searched empirically for parameter sets that would provide acceptable matches for all eight experimental conditions . Listed parameters found in the literature were from experiments specifically using murine neutrophils . The leukocyte parameters from Table 4 and the environment parameters from Table 5 ( part II ) are such a set; they produced the results in Figure 3 . The data are averages from 20 sets of experiments containing 30 leukocytes each , with the duration of each run being 600 simulation cycles ( maps to 1 minute ) . The number of rolling and adhering leukocytes for each batch were counted and averaged . Leukocytes that remained stationary on the surface for at least 300 simulation cycles ( about 30 seconds ) were classified adherent . Figure 3 shows that for all ligand combinations and genotypic variations simulated , both the leukocyte rolling and adhesion data matched that from ex vivo experiments: the in silico and wet-lab results were indistinguishable experimentally . We varied the values of ICAM1Density ( density of icam1 ) to determine their impact on simulation outcomes . The results in Figure 4 show that at ICAM1Density values≥60 , disabling clustering ( by changing LFA1ClusteringAllowed from true to false ) did not change adhesion . However , at ICAM1Density values≤50 , disabling clustering reduced adhesion . The greatest differences were observed at the lower ICAM1Density values , indicating that with clustering there was cooperative binding between surfaces . We repeated this set of experiments twice ( varying ICAM1Density values ) , but LFA1GridDensity ( the fraction of all membrane units that contain lfa1grids ) values were changed first to 0 . 1 and then to 0 . 4 . Similar cooperative binding effects were observed at both LFA1GridDensity values , but at different ICAM1Density values and with differing magnitudes ( Text S1 ) . The experimental observations above map well to results of wet-lab experimental studies done by Constantin and co-workers . They demonstrated that PI3K inhibition blocked lymphocyte adhesion at low densities of ICAM-1 . At high ICAM-1 densities , lymphocytes were able to overcome the requirement for PI3K for efficient adhesion [4] . Results of sensitivity analysis experiments for changes in LFA1RemovalRate , Pon ( high affinity lfa1-icam1 ) , and RearForce are graphed in Figure 5 . The simulations were repeated using small ( 5–10% ) and large variations ( >50% ) in each of these parameters separately , while holding all other parameter values constant . Figure 5 shows the ratio of ISWBC2-to-wet-lab results for rolling and adhesion data from both WT and KO mice for four parameter variations selected to show the trends observed . Small variations in all three parameter values resulted in minimal changes in rolling and adhesion both with and without clustering . The results still matched referent data reasonably well . As was expected , large increases in the RearForce ( Figure 5A ) and LFA1RemovalRate ( Figure 5B ) , or a large decrease in Pon values ( Figure 5C ) resulted in a significant decrease in adhesion both with and without clustering . Those results failed to match referent data . Results of these and other robustness explorations ( not shown ) demonstrated that there are many parameter vectors close to the ones in Table 4 and Table 5 that produce ISWBC2s that validate . Smith et al . observed individual leukocytes in vivo after injection of CXCL1 into the carotid artery of WT and KO mice to determine its role in adhesion . Events in post-capillary venules were recorded using intravital microscopy . Individual cells were tracked in each vessel starting one minute before and ending one minute after CXCL1 injection . After CXCL1 injection , leukocytes rapidly adhered to the vessel wall and remained attached over time in WT mice . However , in KO mice , leukocytes did not attach or attached transiently . We simulated similar conditions and observed whether disabling lfa1 clustering would allow the ISWBC2 system to reproduce the observed defect in sustained adhesion . We chose parameter values based on corresponding literature values . When none were available , we searched empirically . Lfa1 clustering was enabled when simulating conditions in WT mice . It was disabled when simulating conditions in KO mice . Figure 6A shows that when lfa1 clustering was enabled , individual leukocytes initiated adhesion within seconds of exposure to cxcl1 and were able to sustain adhesion for the duration of the simulation . Leukocyte population level measurements were also similar to those observed in vivo ( Figure 6B ) . When lfa1 clustering was disabled to simulate conditions in KO mice , leukocytes exhibited the same transient adhesion observed in vivo ( Figure 6C ) . In the presence of cxcl1 chemokine , leukocytes rolled for a brief period and then initiated adhesion to the surface for a brief interval before again initiating rolling . Figure 6D shows that the similarity in individual leukocyte behaviors translated successfully to population level measurements . In PI3Kγ KO mice during the above-described experiments , a small portion of leukocytes was still able to sustain adhesion . That observation may implicate an additional mechanism . Using ISWBC2 , some leukocytes initiated and maintained adhesion for long intervals . However , none sustained adhesion for the entire duration of the simulation . Additionally , in WT mice , some leukocytes were adherent prior to chemokine injection . The intercept values for the wet-lab data in Figure 6B show that , at the time of injection , adherent leukocytes were already present . That observation may indicate some leukocyte pre-activation . The ISWBC2 system does not include any pre-activation effects . Consequently , we do not observe any adhering leukocytes at the start of a simulation . Nonetheless , the increases in the number of adherent leukocytes after chemokine addition were similar to referent observations . It has been suggested that integrin clustering may facilitate rebinding events thus enhancing leukocyte adhesion [5] , [6] . Rebinding effects are those in which an ICAM-1 that is displaced from one LFA-1 will rapidly bind to a neighboring LFA-1 integrin if it is in sufficiently close proximity . We counted the cumulative number of lfa1 rebinding events ( Figure 7A ) and icam1 rebinding events ( Figure 7B ) at 50 simulation cycle intervals ( every 5 seconds ) for each leukocyte . We defined an lfa1 ( or icam1 ) rebinding event as a bond formation event by an lfa1 ( or icam1 ) that had already participated in a bond formation event during a previous simulation cycle . Averages were taken of 30 leukocytes from the simulations of the in vivo experiments for each experimental condition ( LFA1Clustering enabled or disabled ) . All simulation data for the enabled LFA1Clustering experimental condition was generated by leukocytes that sustained adhesion ( rolling followed by at least 30 simulation cycles of arrest until the end of the simulation ) , while all simulation data for the disabled LFA1Clustering condition was generated by leukocytes that exhibited initial and transient adhesion ( at least 300 simulation cycles of arrest; average amount of time leukocytes remained stationary was 39 . 8±7 . 2 seconds ) . The average time that leukocytes detached following transient adhesion was 43 . 9±7 . 1 seconds for the disabled LFA1Clustering condition . At simulation times prior to 43 . 9 seconds , there were no significant differences in lfa1 rebinding events for enabled and disabled LFA1Clustering condition ( Figure 7A ) . A significant difference was observed after 43 . 9 seconds , as expected . leukocytes with LFA1Clustering disabled began to roll again after 43 . 9 seconds allowing different lfa1 objects on the membrane to form new interactions with icam1 . Those events caused the number of lfa1 rebinding events to plateau . In contrast , leukocytes with LFA1Clustering enabled continued to sustain adhesion enabling the same lfa1 and icam1 objects to interact , as evidenced by the rapidly increasing numbers of lfa1 rebinding events until the end of the simulation . A different situation was observed with icam1 rebinding . Figure 7B shows a significant difference in icam1 rebinding events when lfa1 clustering is enabled and disabled . As early as 25 seconds , the number of icam1 rebinding events was significantly larger for the lfa1 clustering enabled condition than for the disabled condition . The difference increased throughout the duration of the simulation . These results indicated that when lfa1 was clustered , more lfa1 objects were rebinding to the same icam1 objects than when lfa1 was randomly distributed and non-clustered . ICAM-1 has been reported to arrange itself on endothelial cell membrane surfaces in at least four forms: ( 1 ) monomeric , ( 2 ) dimeric , ( 3 ) dimeric that forms linear tetramers upon ligand binding , and ( 4 ) preclustered into tetraspanin enriched microdomains . We implemented each of these spatial configurations in order to compare their effect , in combination with lfa1 clustering , on leukocyte's ability to sustain adhesion . We observed the consequences over a range of LFA1GridDensity and ICAM1Density values . For each icam1 configuration , LFA1Clustering parameter setting , LFA1GridDensity value , and ICAM1Density value , we performed 450 leukocyte simulations within lfa1 and icam1 density ranges that showed the greatest influence of lfa1 clustering . We then calculated the percentage of leukocytes that were able to initiate and sustain adhesion for the duration of the simulation . There was no difference in sustained adhesion between simulations when lfa1 clustering was either enabled or disabled when icam1 existed in a monomeric configuration ( not shown ) . That result was expected because the mechanism is initiated only after a multimeric bond is formed between multiple lfa1 objects and multiple icam1 objects . If icam1 is monomeric , the chance of the lfa1 clustering mechanism being initiated is very small . In contrast , the results in Figure 8 show that , for all multimeric configurations tested , significant differences in sustained adhesion were observed when lfa1 clustering was either enabled or disabled . Whether icam1 existed as a dimer , a dimer that formed into linear tetramers upon ligand-binding , or preclustered produced only slight differences in the percentage of leukocytes that were able to sustain adhesion .
We constructed and validated models of leukocyte rolling , activation , and adhesion . We then experimented on them to test the plausibility of mechanistic hypotheses of how molecular components may interact to cause leukocyte behaviors at the cell and population level . We began with significant leukocyte rolling and adhesion data from ex vivo flow chamber and in vivo mouse cremaster muscle experiments , in which mice lacking functional PI3Kγ exhibited defects in adhesion and sustained adhesion in comparison to leukocytes from WT mice . Smith et al . hypothesized that the adhesion defects were a result of an inability of LFA-1 to redistribute and cluster on the leukocyte membrane in the PI3Kγ KO mice [13] . To challenge that hypothesis , software objects were constructed and assembled according to the mechanistic design in Figure 1 using the operating logic in Text S1 . The resulting ISWBC2 system was iteratively refined until validation was achieved across multiple experimental conditions and attributes . LFA1 objects were designed to cluster upon leukocyte activation and post-ligand binding to multimeric icam1 . During execution , leukocytes exhibited behaviors indistinguishable from leukocytes observed in both the ex vivo ( Figures 3 and 5 ) and in vivo ( Figure 6 ) experiments using WT mice . More importantly , inhibiting this mechanism allowed leukocyte behaviors to mimic the adhesion defects observed both ex vivo and in vivo in KO mice . Thus , ISWBC2 simulations provide a tested theory about the mechanistic events that may be occurring in both WT and KO mice . At higher lfa1 and icam1 densities , enabling lfa1 clustering did not improve adhesion . However , at low densities , enabling clustering led to cooperativity at the level of the leukocyte-surface zone of contact , and that increased adhesion . Analysis of rebinding events ( Figure 7 ) showed that at later but not earlier times , enabling lfa1 clustering allowed for an increase in lfa1 rebinding events in comparison to when lfa1 did not cluster . Clustering enabled increased icam1 rebinding events at early times . Given the multi-attribute validation evidence , it is reasonable to claim that ISWBC2 mechanisms have mouse counterparts . Though still relatively simple , we have demonstrated that ISWBC2s can achieve a substantial list of targeted attributes under a variety of experimental conditions ( Table 1A , B ) . Traditional inductive , equation-based models typically focus on just one or a few different experimental conditions . This observation motivates commentary about the differences between traditional , inductive , equation based models and synthetic , relationally grounded analogues like ISWBC2s . The issues are discussed in detail in [11] . Grounding is defined as the units , dimensions , and/or objects to which a variable or model constituent refers [11] . In models grounded to metric spaces , parameters serve mostly to shift model behavior within a smooth region of the output metric space . In relationally grounded models , like ISWBC2s , in addition to that function , parameters also serve to shift model behavior discontinuously ( even abruptly ) into an entirely different region of behavior space: they change the analogue's dynamic phenotype . In metrically grounded models , the character of the model is bounded , whereas with relational grounding , model character can change completely with a change in parameters . In the former case , parameters describe one , particular ( though abstract ) model type . In the latter case , parameters describe families of different yet related models . The ISWBC2s functioning in various experimental conditions are examples of the latter . Relational grounding enables flexible , adaptable analogues , but requires a separate analogue-to-referent mapping model . The process of discovering a normal ISWBC2 that eventually achieved sustained adhesion typical of WT mouse counterparts , and the process of subsequently discovering modifications that eventually showed defective adhesion indistinguishable from that observed in PI3Kγ KO mice , was the same . It followed the iterative refinement protocol . Each ISWBC2 structure and parameterization was a hypothesis: upon execution , as a consequence of combined micro-mechanisms , measures of leukocyte rolling and adhesion will mimic referent data . Execution and measurement provides data that either support or falsify the hypothesis . Early during iterative refinement , all mechanistic hypotheses were falsified: they failed to achieve the prespecified target attributes . Why one was falsified was often somewhat surprising , reflecting uncertainties about the actual referents' underlying mechanisms . At times it reflected incorrect ideas about how micromechanisms influence ISWBC2 behaviors . The many cycles of iterative refinement that followed required and exercised abductive reasoning , which is essential to achieving new scientific insight [39] , [40] . A failure of an early ISWBC2 to achieve one or more prespecified attributes taught us something about those ISWBC2s and improved insight into the referent systems . Failure forced us to think more deeply about plausible mechanistic details , and that in turn forced us to think differently about leukocytes and the process of rolling , activation , and adhesion . Previous studies by Constantin et al . provided evidence of PI3K mediated LFA-1 clustering in immobilized lymphocytes treated with chemokines [4] . Chemokines triggered a rapid increase in LFA-1 affinity and stimulated LFA-1 movement into clusters and large polar patches . Inhibition of PI3K activity blocked LFA-1 mobility but not LFA-1 affinity changes , and that prevented lymphocytes from adhering to low densities of immobilized ICAM-1 . At high densities of immobilized ICAM-1 , inhibiting PI3K activity had no effect on lymphocyte adhesion . Because the cell type was different , those observations were not among those originally targeted ( Table 1 ) . Nevertheless , the validated ISWBC2 that gave the results in Figure 4 correctly predicted those results . Whether this same mechanism is operative and influential in murine neutrophils remains to be determined . However , it is noteworthy that our simulation results are consistent . In ISWBC2 experiments , lfa clustering was important at low icam1 densities , but no differences in adhesion were observed at high icam1 densities ( Figure 4 ) . Lum et al . used sophisticated in vitro methods to study murine neutrophils after stimulation with IL-8 chemokine [3] . They correlated the dynamics of adhesion with the increased expression of high affinity LFA-1 and membrane redistribution . Using fluorescence microscopy , they observed redistribution of high affinity LFA-1 into small punctate submicron clusters and large polar caps several µm2 in area within 30 s of IL-8 stimulation . Within 2 min of chemokine stimulation , the polar caps dissipated into numerous smaller clusters . By 10 min , practically all clusters had dispersed and the number of active LFA-1 was observed to have dropped by ∼50% . Inhibition of PI3K activity by treatment with wortmannin did not affect the expression of high affinity LFA-1 , but significantly inhibited the amount of LFA-1 clustering and formation of polar caps . Treatment with wortmannin after IL-8 stimulation also significantly decreased the amount of adhesion to fluorescent microbeads coated with ICAM-1 in a flow cytometric based assay . They also used a parallel plate flow chamber coated with an ICAM-1 monolayer to observe the strength and stability of neutrophil adhesion over time . Interestingly , the transience of LFA-1 cluster formation and number of active LFA-1 on the membrane correlated with a reversibility of firm adhesion observed in the flow chamber . We did not target any of the preceding results . One can question whether the observed behaviors from such in vitro murine neutrophils studies are relevant to those of native cells in the circulation under physiologic conditions [41] , such as those used in the ex vivo and in vivo experiments that we targeted . It is recognized that procedures for isolating neutrophils to be studied in vitro can be inefficient and time-consuming . Previous reports have shown that neutrophils become unintentionally modified or activated because of the large number of steps required during isolation [42]–[44] . For example , in vitro isolated and stained neutrophils do not show normal rolling behavior when injected back in mice [38] . Use of the auto-perfused ex vivo flow chamber system allows one to bypass these cell isolation procedures . Bailey et al . constructed a multi-cell , tissue-level , agent-oriented analogue of human adipose-derived stromal cell trafficking through a microvasculature structure within skeletal muscle following acute ischemia [45] . A goal was to identify potential bottlenecks that may limit the efficiency of administered therapeutic cells being recruited into the site of ischemic injury after intravenous injection . They used confocal microscopy images to manually construct an image of the morphology of a characteristic microvascular network . Endothelial cells lining the vessel surface , tissue resident macrophages , circulating monocytes , and therapeutic stem cells were individual agents . The agent-oriented model was coupled with a network blood flow analysis program that calculated blood pressure , flow velocities , and shear stresses throughout the microvascular network . Each endothelial cell , monocyte , and counterpart to a human , adipose-derived , stromal cell ( hasc ) could be either positive or negative in expression of each of several adhesion molecules . Similarly , each endothelial cell , monocyte , and tissue resident macrophage could be either positive or negative for secretion of each of the chemokines and cytokines . Whether a circulating monocyte or hasc rolled or adhered depended on whether they experienced a specified combination of adhesion molecule states and chemokine secretion states from a nearby endothelial cell , and whether they experienced a wall shear stress below a certain threshold level . If the cell adhered for more than a specified number of simulation cycles , it transmigrated into the tissue space . They observed that introduction of an additional adhesion molecule , with properties similar to PSGL-1 , enabled the model to more closely mimic in vivo experimental results . They showed that small fractions of hASC's are able to roll on P-selectin even though they do not express PSGL-1 . They proposed that the additional adhesion molecule might map to the cellular adhesion molecule CD24 . This new knowledge gained reinforces the merit of investigating the complex mechanisms mediating leukocyte adhesion using a synthetic modeling and simulation approach . While ISWBC2s were constructed using similar methods and components , there are notable differences . The Bailey et al . model focused on leukocyte trafficking events at the tissue level . They explicitly represent in silico counterparts of differing blood pressures , flow velocities , and shear stresses throughout a microvascular network . Leukocytes rolling and adhering on a substrate coated surface within an ISWBC2 system is a small aspect of leukocyte trafficking through microvascular tissue . ISWBC2s focus more on the molecular and cellular level interactions , and have concrete counterparts to the molecular interactions between leukocyte and endothelial cell adhesion molecules . The discrete-time Adhesive Dynamics ( AD ) simulations by Hammer and co-workers are the most developed models of leukocyte rolling and adhesion to date [46]–[49] . In their models , leukocytes are idealized as solid spheres with extensible cylindrical protrusions , to represent microvilli , with receptors located at the tips . Using a Monte Carlo algorithm for the determination of receptor-ligand interactions , they have successfully produced a jerky stop-and-go pattern similar to that observed for rolling leukocytes . Their simulations have also allowed them to explore the molecular properties of adhesion molecules , such as reaction rates and bond elasticity , and how these properties may relate to macroscopic behavior such as rolling and adhesion [47] , [48] . At each time step in the Adhesive Dynamics simulation , positions of bonds on the spherical particle are tracked enabling the authors to calculate the forces that each bond experiences . The net force and torque acting on the cell from bonds , fluid shear , steric repulsion , and gravity are calculated assuming the cell is a solid sphere . The position of the cell is then determined for each time step from the net force and torque on the cell using a hydrodynamic mobility function for a sphere near a plane wall in a viscous fluid . In a recent version of their model , they have simulated the transition from rolling to adhesion upon detection of the IL-8 chemokine [49] . To represent the local G-protein intracellular signaling events , they use a 1D lattice of 1000 units on which a small set of intracellular signaling molecules can diffuse and interact . The bottom end of the lattice represents the microvilli tip . In their model , detection of IL-8 by CXCR1 on a microvilli tip initiates the dissociation of G-protein into two subunits , α and βγ , which can then diffuse along the 1D lattice . Effector molecules become activated when bound to the βγ subunit . In turn , the activated effector molecule can then diffuse and bind to the intracellular portion of LFA-1 at the bottom end of the lattice , converting it into a high affinity integrin . They observed a progressive activation of the integrins as cells rolled and interacted with chemokines , leading to a deceleration of the leukocyte before firm adhesion . The slowing of the leukocyte in their model was on a timescale similar to leukocytes from in vitro experiments . In addition , they were able to observe a chemokine density-dependent effect on adhesion time similar to that observed in vitro . The ISWBC2 differs in many aspects from this recent version of the AD model , which are briefly discussed below . A more detailed comparison of the ISWBCs with the AD models and with other models of leukocyte motility and adhesion can be found in the Appraisal of Model Specifications section within Text S1 and in [12] . In the above AD model , LFA-1 is found at the tips of microvilli . Studies have shown that LFA-1 exists on the cell body surface hidden between the leukocyte microvilli [29] , [30] . Therefore , in our ISWBC2 we have specified simply that lfa1 objects are located on sparse regions on the membrane surface . The fraction of membrane units containing lfa1grids and lfa1 objects was determined by the parameter LFA1GridDensity . The most recent version of the AD simulations also included a more explicit representation of the signaling events initiated upon chemokine detection . With the ISWBC2 , it was not our objective to model the signaling network in detail , and therefore it was not listed as a currently targeted phenotypic attribute in Table 1 . Our current goal was to test the hypothesized molecular mechanisms at the cell interface of the leukocyte and endothelial substrate surface . However , we have shown previously that synthetic models like ISWBCs can be easily refined to become increasingly realistic in terms of both components and behaviors [12] . As needed , any of the abstract , low-resolution components can be replaced with more realistic , higher resolution composite objects composed of components that map to more detailed biological counterparts . The current ISWBC2 represents the hypothesized mechanisms and processes at a level of detail and resolution that is just sufficient to simulate the currently targeted attributes listed in Table 1 . The ISWBC2 system represents significant progress towards a larger goal of discovering and validating concrete plausible mechanistic details of cell-cell interactions , in the face of considerable uncertainty , by building computational devices comprised of instantiated mechanisms . ISWBC2s are capable of mimicking only a portion ( Table 1 ) of a long list of targeted phenotypic attributes . However , we have shown here and previously that our iterative refinement method provides a means to concatenate in a dynamic fashion new knowledge of leukocyte rolling and adhesion into our ISWBC systems as it becomes available , without having to reengineer the whole system , and without having to compromise already validated features and behaviors [12] . The expectation is that ISWBCs can be iteratively refined to become increasingly realistic in terms of components , mechanisms , and behaviors . The greater the similarity , the more useful the analogue will become as an observable expression of coalesced , relevant leukocyte knowledge . Future targeted attributes should include those associated with abnormal disease-associated leukocyte adhesion , and predicting the consequences of interventions . | To gain access to sites of inflammation , leukocytes must first adhere to the blood vessel wall using integrin molecules . It has been hypothesized that integrin clustering is essential for sustaining adhesion prior to transmigration into the inflamed tissue . We cannot challenge such hypotheses directly because it is infeasible to measure molecular level events during the leukocyte adhesion process . At best correlative relationships have been made . The alternative approach undertaken was to experimentally challenge the hypothesized mechanisms in silico . We used object-oriented , software engineering methods to build and execute multi-level , multi-attribute analogues of leukocytes and binding surfaces . The simulated leukocytes contained diffusible objects ( representing integrins ) on their surface that were allowed to interact with binding partners on simulated endothelial surfaces . Validation was achieved across different experimental conditions , in vitro , ex vivo , and in vivo , at both the individual cell and population levels . Consequently , the finalized virtual mechanisms stand as a concrete , working theory about detailed events occurring at the leukocyte-surface interface during adhesion . We challenged mechanistic hypotheses by conducting experiments in which the consequences of multiple mechanistic events were tracked . We discovered that integrin clustering was not necessary to achieve adhesion as long as integrin and binding partner object densities were above a critical level . Importantly , at low densities integrin clustering enabled adhesion that exhibited measurable , cell level positive cooperativity . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"computer",
"science/applications",
"computational",
"biology/systems",
"biology",
"immunology/leukocyte",
"activation"
] | 2010 | Identifying the Rules of Engagement Enabling Leukocyte Rolling, Activation, and Adhesion |
Neuraminidase ( NA ) mutations conferring resistance to NA inhibitors were believed to compromise influenza virus fitness . Unexpectedly , an oseltamivir-resistant A/Brisbane/59/2007 ( Bris07 ) -like H1N1 H275Y NA variant emerged in 2007 and completely replaced the wild-type ( WT ) strain in 2008–2009 . The NA of such variant contained additional NA changes ( R222Q , V234M and D344N ) that potentially counteracted the detrimental effect of the H275Y mutation on viral fitness . Here , we rescued a recombinant Bris07-like WT virus and 4 NA mutants/revertants ( H275Y , H275Y/Q222R , H275Y/M234V and H275Y/N344D ) and characterized them in vitro and in ferrets . A fluorometric-based NA assay was used to determine Vmax and Km values . Replicative capacities were evaluated by yield assays in ST6Gal1-MDCK cells . Recombinant NA proteins were expressed in 293T cells and surface NA activity was determined . Infectivity and contact transmission experiments were evaluated for the WT , H275Y and H275Y/Q222R recombinants in ferrets . The H275Y mutation did not significantly alter Km and Vmax values compared to WT . The H275Y/N344D mutant had a reduced affinity ( Km of 50 vs 12 µM ) whereas the H275Y/M234V mutant had a reduced activity ( 22 vs 28 U/sec ) . In contrast , the H275Y/Q222R mutant showed a significant decrease of both affinity ( 40 µM ) and activity ( 7 U/sec ) . The WT , H275Y , H275Y/M234V and H275Y/N344D recombinants had comparable replicative capacities contrasting with H275Y/Q222R mutant whose viral titers were significantly reduced . All studied mutations reduced the cell surface NA activity compared to WT with the maximum reduction being obtained for the H275Y/Q222R mutant . Comparable infectivity and transmissibility were seen between the WT and the H275Y mutant in ferrets whereas the H275Y/Q222R mutant was associated with significantly lower lung viral titers . In conclusion , the Q222R reversion mutation compromised Bris07-like H1N1 virus in vitro and in vivo . Thus , the R222Q NA mutation present in the WT virus may have facilitated the emergence of NAI-resistant Bris07 variants .
Influenza viruses are respiratory pathogens associated with significant public health consequences . Each year , influenza epidemics can be responsible for significant morbidity in the general population and excess mortality in elderly patients and individuals with chronic underlying conditions . Influenza A viruses of the H1N1 subtype have been associated with seasonal influenza epidemics for many decades and , in presence of immunological pressure , such viruses continue to evolve through genetic variability which is mainly confined to virus segments encoding surface glycoproteins i . e . , the hemagglutinin ( HA ) and neuraminidase ( NA ) [1] . Consequently , viral strains to be used in annual influenza vaccines should be regularly updated to ensure optimal protection . Besides vaccines , neuraminidase inhibitors ( NAI ) including inhaled zanamivir , oral oseltamivir and intravenous peramivir provide an important additional measure for the control of influenza infections [2] . These antivirals target the active center of the influenza NA molecule , which is constituted by 8 functional ( R-118 , D-151 , R-152 , R-224 , E-276 , R-292 , R-371 , and Y-406; N2 numbering ) and 11 framework ( E-119 , R-156 , W-178 , S-179 , D-198 , I-222 , E-227 , H-274 , E-277 , N-294 , and E-425; N2 numbering ) residues that are largely conserved among influenza A and B viruses [3] . However , the emergence of NAI-resistant viruses , as a result of drug use or due to circulation of natural variants , may compromise the clinical utility of this class of anti-influenza agents . The H275Y ( H274Y in N2 numbering ) NA mutation conferring resistance to oseltamivir and peramivir has been detected with increasing frequency in seasonal A/H1N1 viruses since 2007 to the extent that almost all characterized A/Brisbane/59/2007-like ( Bris07 ) ( H1N1 ) influenza strains that circulated worldwide during the 2008–09 season were H275Y variants [4] , [5] . Interestingly , this drug-resistant strain seemed to have emerged independently of NAI use [6] , [7] . The rapid dissemination of the H275Y Bris07 variants in the absence of antiviral pressure suggests that the H275Y NA mutation may not compromise viral fitness and transmissibility in this recent H1N1 viral background . This contrasts with previous studies that analyzed the role of the H275Y mutation using older ( A/Texas/36/91 [8] and A/New Caledonia/99/01 [9] ) drug-selected H1N1 variants . Recent reports by our group and others have confirmed the differential impact of the H275Y mutation on viral fitness and enzymatic properties in the context of old and recent influenza H1N1 isolates [10] , [11] . In an attempt to provide a molecular explanation for this observation , previous authors suggested that secondary NA mutations such as D344N that emerged in H1N1 variants isolated after the 2006–07 season were associated with higher NA activity and affinity and could have facilitated the emergence of the H275Y mutation [11] , [12] . Such drug-resistant mutants may have a better HA-NA balance than the susceptible viruses and indeed completely replaced them in a short period of time . In addition , Bloom and colleagues recently described two other secondary NA mutations at codons 222 and 234 that may have counteracted the compromising impact of the H275Y mutation [13] . In that study , the V234M and R222Q mutations were shown to restore the viral fitness of an A/New Caledonia/20/99 H1N1 variant containing the H275Y mutation [13] . To further investigate which secondary NA mutations may have facilitated the introduction of the H275Y mutation in contemporarily seasonal H1N1 viruses and allowed their dissemination , we developed a reverse genetics system using a clinical Bris07 ( H1N1 ) isolate as genetic background and evaluated the impact of the H275Y oseltamivir resistance mutation as well as several potential compensatory NA mutations on enzyme activity , viral fitness and transmissibility .
In the present study , five recombinant Bris07 influenza viruses were generated i . e . , the WT virus ( containing the putative permissive mutations ) that briefly circulated during the 2007–08 season , the single H275Y oseltamivir-resistant variant and three double mutants containing the H275Y mutation as well as reversion of potential permissive mutations ( H275Y/Q222R , H275Y/M234V and H275Y/N344D ) . NA enzymatic properties using equivalent titers of recombinants were first analyzed with determination of relative NA enzymatic activity ( Vmax values ) , which reflects the total NA activity per virion , and Km values , which reflect the affinity for the substrate . As shown in Table 1 , the single H275Y mutation had no significant impact on NA affinity and activity compared to the WT virus in the context of the Bris07 background . By contrast , the double H275Y/Q222R mutation was associated with a significant reduction of both NA affinity ( Km of 40 . 31 vs 11 . 95 µM , P<0 . 001 ) and relative NA activity ( 7 . 01 vs 28 . 19 U/sec , P<0 . 001 ) compared to the WT ( Table 1 and Fig . 1 ) . The H275Y/M234V mutant had a Km value comparable to that of the WT , whereas its relative NA activity was significantly reduced ( Vmax of 21 . 89 vs 28 . 19 U/sec , P<0 . 05 ) . The H275Y/N344D mutant showed a significantly reduced affinity ( Km of 50 . 77 vs 11 . 95 µM , P<0 . 001 ) with no change in NA activity compared to the WT . When comparing the double mutants to the single H275Y mutant , the Km values were significantly increased for the H275Y/Q222R and H275Y/N344D mutants ( P<0 . 001 ) whereas only the double H275Y/Q222R mutant had a significantly lower relative NA activity ( P<0 . 001 ) . Using recombinant NA proteins expressed in 293T cells , we further investigated the impact of NA mutations on the amount of NA activity at the cell surface . As shown in Fig . 2 , all studied mutations were associated with a significant reduction of total surface NA activity compared to the WT with relative total surface activities of 66% ( P<0 . 01 ) , 9 . 72% ( P<0 . 001 ) , 32 . 07% ( P<0 . 001 ) and 54 . 89% ( P<0 . 01 ) for the H275Y , H275Y/Q222R , H275Y/M234V and H275Y/N344D mutant proteins , respectively . When compared to the single H275Y mutant , H275Y/Q222R ( P<0 . 001 ) , H275Y/M234V ( P<0 . 001 ) and H275Y/N344D ( P<0 . 05 ) double mutants also had significantly reduced surface NA activities . The differences observed in total surface NA activity between the different recombinant NA proteins may be due to a decreased number of NA molecules that reached the cell surface or to less activity per enzyme . We next determined the phenotype of resistance to NAIs for the 5 recombinant viruses . As expected , the presence of the H275Y mutation was associated with resistance to oseltamivir ( mean fold increase of 2627 in IC50 values ) and peramivir ( mean fold increase of 998 ) with no impact on zanamivir susceptibility ( Table 2 ) . Interestingly , comparison of the levels of resistance for the double recombinant mutants versus the single H275Y mutant revealed a significant reduction in the level of resistance to peramivir for the double H275Y/Q222R mutant ( IC50 of 35 . 25 nM vs 59 . 85 nM , P<0 . 01 ) . A similar trend was observed for oseltamivir ( IC50 of 651 . 86 nM vs 1024 . 54 nM ) although , in this case , the difference between IC50 values was not statistically significant . Viral fitness of recombinant A/Brisbane/59/2007-like viruses was assessed in vitro using ST6Gal1-MDCK cells . The double H275Y/Q222R mutant produced viral plaques with a significantly reduced area compared to the recombinant WT ( 0 . 13 mm2 vs 0 . 53 mm2 , P<0 . 001 ) whereas the remaining recombinants generated plaques of comparable sizes ( Table 1 ) . Of note , the reduction in plaque size for the H275Y/Q222R mutant was also significant compared to that of the single H275Y mutant ( P<0 . 001 ) . In replication kinetics experiments , the peak viral titers for all recombinants were obtained at 36 h post-infection ( PI ) with viral titers ranging from 5 . 6×106 PFU/ml ( H275Y/Q222R ) to 5 . 3×107 PFU/ml ( WT ) ( Fig . 3 ) . The WT , the single ( H275Y ) and the double ( H275Y/N344D ) mutants had comparable viral titers at all time points . By contrast , and in accordance with plaque size data , the double H275Y/Q222R mutant was associated with a significant reduction in viral titers at 36 h ( P<0 . 001 ) and 48 h ( P<0 . 05 ) PI compared to the WT ( Fig . 3 ) . There was also a significant reduction in the viral titer obtained at 36 h PI for the double H275Y/M234V mutant compared to the WT ( P<0 . 001 ) . When compared to the single ( H275Y ) mutant , viral titers of the double H275Y/Q222R and H275Y/M234V mutants were significantly lower at 36 h ( P<0 . 001 ) . Intranasal inoculation of ferrets with the WT and two mutant ( H275Y and H275Y/Q222R ) Bris07 recombinant viruses resulted in a febrile response that peaked on day 2 PI ( Fig . 4A ) . The area under the curve ( AUC ) of temperatures between days 0 and 6 PI was similar for the 3 groups of ferrets i . e . 6 . 81±1 . 19 for the WT virus , 5 . 99±1 . 9 for the H275Y/Q222R mutant and 7 . 26±0 . 55 for the H275Y mutant . There was no significant difference in body weight between the three groups of animals at any time points ( data not shown ) . As shown in Fig . 5A , mean viral titers in nasal wash samples collected on day 2 PI from ferrets infected with the recombinant WT and the single H275Y mutant were comparable ( 4×105±2 . 9×104 PFU/ml for the WT and 2 . 6×105±8 . 7×104 PFU/ml for the H275Y mutant ) whereas the H275Y/Q222R mutant had a reduced mean viral titer ( 4 . 6×104±4 . 2×103 PFU/ml; P<0 . 05 vs WT ) . Similarly , mean viral titers in nasal wash samples of ferrets infected with the H275Y/Q222R were significantly lower than those of the H275Y mutant ( P<0 . 05 ) and WT virus ( P<0 . 01 ) on day 4 PI ( 3 . 4×103±1 . 7×103 , 1 . 1×104±6 . 7×103 and 1 . 5×104±9 . 6×102 PFU/ml , respectively ) . On the other hand , the three recombinants were associated with comparable mean viral titers on day 6 PI ( 2×102±4 . 6×101 PFU/ml for the WT , 1 . 1×102±5 . 8×101 PFU/ml for the H275Y/Q222R and 1 . 3×102±8 . 1×10PFU/ml for the H275Y ) . All contact ferrets seroconverted for A/Brisbane/59/2007 when tested 14 days after contact , with geometrical mean hemagglutination inhibition ( HAI ) titers of 160±33 , 145±119 and 95±55 for the WT , H275Y and H275Y/Q222R recombinant viruses , respectively . A febrile response could be observed on days 4 and 5 in the WT and the H275Y groups , respectively , but not in the H275Y/Q222R group ( Fig . 4B ) . The AUC of temperatures between days 2 and 6 PI was similar between groups of ferrets infected with the recombinant WT ( 5 . 29±0 . 34 ) and its H275Y variant ( 4 . 54±0 . 19 ) whereas the AUC of the H275Y/Q222R group was significantly lower than that of the WT group ( 4 . 09±0 . 96; P<0 . 05 ) . Viral titers in nasal wash samples collected on days 2 , 4 and 6 PI are shown in Fig . 5B . Only the WT virus was detected on day 2 PI . Mean viral titers were comparable for the H275Y mutant and the WT virus on days 4 and 6 PI . In contrast , the H275Y/Q222R mutant was associated with significantly lower mean viral titers compared to WT on both day 4 ( 2 . 7×102±1 . 2×102 vs 1 . 2×104±3 . 5×103 PFU/ml , P<0 . 01 ) and day 6 PI ( 3 . 8×103±2 . 1×103 vs 1 . 2×104±2 . 5×103 PFU/ml , P<0 . 01 ) .
In this study , we used recombinant viruses derived from a clinical WT Bris07 strain to demonstrate using both in vitro and ferret experiments that the R222Q NA mutation was the main but possibly not the only permissive mutation that allowed the widespread dissemination of the oseltamivir-resistant H275Y mutant during the 2007–09 influenza seasons . Although such mutant seems to have disappeared since the emergence of the pandemic H1N1 virus in April 2009 , understanding the mechanisms leading to the transmission of this unique virus is of great importance and could have an impact on the future use of NAIs . The influenza NA protein plays a major role during the viral replication cycle . Its sialidase activity promotes virion release by removing sialic residues from viral glycoproteins and infected cells [14] . The NA enzyme also mediates virus penetration in the mucin layer of the respiratory tract , facilitating virus spread [15] . Importantly , the catalytic site of the NA enzyme has been shown to be conserved in all influenza A subtypes and influenza B viruses [3] . Therefore , the influenza NA protein has been considered as a suitable target for designing anti-influenza agents for both prophylactic and therapeutic purposes . Besides its functional role , the NA protein is a major structural surface glycoprotein that is exposed to the host immune pressure [14] . The NA gene , like the HA one , is therefore subject to more genetic variations than the rest of the influenza genome . Consequently , some amino acid ( a . a . ) changes , part of antigenic sites of the NA protein , may significantly contribute to the emergence of drifted variants , whereas certain substitutions located in or near the catalytic site may also affect the NA enzyme properties . For instance , Hensley and colleagues have recently identified NA mutations conferring resistance to zanamivir in variants of an influenza A/Puerto Rico/8/1934 H1N1 virus that was subjected to anti-HA monoclonal antibodies pressure [16] . In this study , we focused on a . a . changes that occurred in the NA protein during the evolution of recent seasonal influenza H1N1 viruses and that may have been involved in the development and dissemination of resistance to NAIs . These changes included the well-known framework H275Y mutation , responsible for the resistance phenotype to oseltamivir and peramivir , as well as other substitutions ( V234M , R222Q and D344N ) that may have contributed to the emergence and dissemination of resistance by acting as permissive/compensatory mutations . Phylogenetic analyses previously demonstrated that the V234M mutation was already present in oseltamivir-susceptible A/Solomon Islands/3/2006 ( SI06 ) viruses [13] . In another report , NA enzyme properties of SI06 viruses were found to be similar to those of older oseltamivir-susceptible strains such as A/New Caledonia/99/2001 in terms of relative NA activity ( Vmax ) and affinity ( Km ) [11] . By contrast , the appearance of the R222Q and D344N mutations in H1N1 viruses isolated after 2007 was associated with a significant increase in NA affinity ( decreased Km values ) in both 275H and 275Y strains [11] . In accordance with these observations , we demonstrated a sharp impact for the Q222R and N344D reversion mutations on Km values using our Bris07 recombinants ( Table 1 ) . Besides its effect on NA affinity , the Q222R reversion mutation was also associated with a significant decrease in relative NA activity ( Table 1 and Fig . 1 ) and total NA activity that was expressed on the cell surface ( Fig . 2 ) , in line with previously-reported results in another viral background [13] . As a result , the H275Y/Q222R mutant virus was significantly compromised in vitro based on plaque size and replication kinetics patterns . Such decreased viral replication of the H275Y/Q222R mutant was also evident in vivo , resulting in lower viral titers in nasal wash samples and an absence of febrile response in contact ferrets . However , the H275Y/Q222R mutant was transmitted to all naïve ferrets by direct contact meaning that the combination of several permissive NA mutations and/or mutations elsewhere in the viral genome may be necessary to recapitulate the epidemiological observations showing increased transmission of the oseltamivir-resistant Bris07 virus . Also , it should be noted that naïve ( non-immune ) ferrets may not completely capture the fitness of Bris07 in humans with pre-existing immunity . Alternatively , the Q222R mutation could affect airborne transmission which has not been evaluated in our study . Of note , possibly due to the lower affinity of Q222R for MUNANA , less NAIs were required for competitive inhibition of the H275Y/Q222R mutant compared to the H275Y mutant . Residue 222 is located in the vicinity of the catalytic site of the N1 enzyme based on 3-D structure analysis [17] . Thus , substitution of a charged ( R ) by an uncharged ( Q ) a . a . at codon 222 may be the main change that dramatically altered the NA enzyme properties of recent seasonal H1N1 viruses . Of interest , only one NA substitution ( R194G ) was sufficient to restore the viral fitness of an influenza A/WSN/33 ( H1N1 ) virus containing the compromising H275Y NA mutation [13] . In addition to the R222Q mutation , a permissive role was also suggested for V234M and D344N substitutions [11] , [13] . Interestingly , in a recent report on the evolution of influenza NA genes , positive epistasis ( i . e . combination of mutations that are substantially more beneficial than single mutations alone ) was detected in pairs of codons within the NA gene of the N1 subtype including 275−222 , 275−234 , and 275−344 [18] . In our study , although the M234V and N344D reversions were associated with decreased relative NA activity and affinity , respectively ( Table 1 and Fig . 1 ) , none of these mutations significantly altered the viral fitness in vitro . Nevertheless , a possible synergy between these mutations and Q222R cannot be completely excluded . Our study revealed that the H275Y NA mutation was not deleterious to fitness in the Bris07 genetic context in contrast to older H1N1 strains . However , this mutant did not have a replicative advantage compared to the WT as suggested by epidemiological studies . Indeed , the recombinant WT virus and its H275Y variant demonstrated similar replication kinetics during in vitro experiments . In addition , these recombinants had comparable infectivity and contact transmissibility in ferrets . Thus , the presence of the permissive mutations ( R222Q , V234M and D344N ) in the NA protein of our WT strain was apparently not sufficient to alter the viral fitness to the level that a compensatory change , such as the H275Y mutation , would be necessary . Therefore , we believe that changes in the NA gene alone may not provide a complete explanation for the emergence and spread of the oseltamivir-resistant H275Y Bris07 variant . Other changes in the genome might have been involved in this event . For instance , Yang and colleagues recently demonstrated that the dominant H275Y variant that emerged in Taiwan in 2007–2008 was a result of intra-subtypic reassortments between HA , NA , PB2 and PA genes from one clade ( clade 2B ) and the remaining 4 genes from another one ( clade 1 ) [19] . Furthermore , the H275Y NA substitution and other changes in NA , HA , PB1 and PB2 proteins occurred in that background [19] . Thus , it would be also interesting to assess the effect of HA and particularly polymerase mutations that differed between WT and H275Y mutant clinical Bris07 isolates on replicative capacities and transmissibility . Despite the fact that the secondary mutations described here were not investigated individually but in conjunction with H275Y , our study provides a comprehensive analysis of relevant permissive NA mutations in the contemporarily seasonal H1N1 background . This included in vitro characterization , assessment of viral fitness and contact transmission in ferrets as well as NA enzyme properties of recombinant mutants . In particular , our investigation clearly demonstrated the positive impact of one specific NA substitution ( i . e . R222Q ) in conjunction with the oseltamivir resistance H275Y mutation on enzymatic properties and viral fitness of the Bris07 H1N1 strain . Noteworthy , our results suggest that total NA activity was more likely predictive of in vitro and in vivo viral fitness than the enzyme affinity ( Km ) parameter . Whether the Q222R mutation is also deleterious in the absence of H275Y was not investigated here; however , in a previous work , influenza A/Paris/497/2007 ( 222Q/275H ) and A/Solomon Islands/3/2006 ( 222R/275H ) seasonal H1N1 isolates grew to comparable titers in in vitro kinetics experiments [11] . Although clinical 2009 pandemic H1N1 variants containing such permissive mutations have not been reported , a computational approach had recently led to the identification of R257K and T289M as potential secondary mutations in that context [20] . Thus , monitoring for resistance in influenza viruses should take into consideration not only NA resistance-mutations themselves but also permissive/secondary ones as the latter may significantly affect the clinical and epidemiological impacts of seasonal or pandemic influenza viruses .
All procedures were approved by the Institutional Animal Care Committee at Laval University according to the guidelines of the Canadian Council on Animal Care . Reverse transcription-PCR using universal influenza primers [21] was used to amplify the eight genomic segments of an oseltamivir-susceptible A/Quebec/15230/08 ( H1N1 ) isolate whose HA and NA genes shared respectively 99 . 53% and 99 . 71% nucleotide identity with those of the influenza A/Brisbane/59/2007 vaccine strain [10] . All segments were cloned into the pJET plasmid ( Fermentas , Burlington , ON , Canada ) and sequenced . Sequence analysis confirmed the presence of histidine ( H ) , glutamine ( Q ) , methionine ( M ) and asparagine ( N ) residues at residues 275 , 222 , 234 and 344 ( N1 numbering ) , respectively , of the NA protein . The PB1 , PB2 and PA segments were sub-cloned into pLLBG whereas the HA , NA , NP , M1/M2 and NS1/NS2 segments were sub-cloned into pLLBA bidirectional expression/translation vectors as described [22] . The pLLBA plasmid containing the NA gene was used for the introduction of the H275Y mutation using appropriate primers and the QuikChangeTM Site-Directed Mutagenesis kit ( Stratagene , La Jolla , CA ) . The resulting pLLB-NA275Y mutant plasmid was then used for reverting potential compensatory mutations ( Q222R , M234V or N344D ) as described above . All recombinant plasmids were sequenced to confirm the absence of undesired mutations . The eight bidirectional plasmids were cotransfected into 293T human embryonic kidney cells using the LipofectamineTM 2000 reagent ( Invitrogen , Carlsbad , CA ) as previously described [23] . Supernatants were collected 72 h post-transfection and used to inoculate ST6Gal1-MDCK cells kindly provided by Dr . Y . Kawaoka , University of Wisconsin , Madison , WI ) . The recombinant wild-type ( WT ) and H275Y , H275Y/Q222R , H275Y/M234V and H275Y/N344D mutant viruses were subsequently sequenced and titrated by standard plaque assays in ST6Gal1-MDCK cells . A fluorometric based assay using MUNANA ( Methylumbelliferyl-N-acetylneuraminic acid ) ( Sigma , St-Louis , MO ) as substrate was performed to determine total NA enzymatic activity per infectious virus [24] . Briefly , recombinant viruses were standardized to an equivalent dose of 106 plaque forming-units ( PFU ) /ml and incubated at 37°C in 50-µl reactions with different concentrations of MUNANA . The final concentration of the substrate ranged from 0 to 3000 µM . Fluorescence was monitored every 90 s for 53 min ( 35 measures ) . The Michaelis-Menten constant ( Km ) and the relative NA activity ( Vmax ) were calculated with the Prism software ( GraphPad , version 5 ) , by fitting the data to the Michaelis-Menten equation using nonlinear regression [25] . Recombinant NA plasmids and pCAGGS-PA , -PB1 , -PB2 and -NP plasmids were used to co-transfect 293T cells in order to express recombinant NA enzymes [26] . Twenty-four hours after transfection , the cells were briefly treated with trypsin-EDTA and neutralized by the addition of serum followed by centrifugation at 3000 RPM for 5 min . After washing twice with PBS , the cells were resuspended in a non-lysing buffer ( 15 mM MOPS , 145 mM sodium chloride , 2 . 7 mM potassium chloride and 4 mM calcium chloride , adjusted to pH 7 . 4 ) and used in an NA assay using the MUNANA substrate [13] . The drug resistance phenotype was determined by NA inhibition assays using the MUNANA substrate as previously described [26] , with minor modifications . Briefly , recombinant viruses were standardized to a NA activity ten-fold higher than that of the background and then incubated with serial three-fold dilutions of the drugs ( final concentrations ranging from 0 to 1800 nM ) , including oseltamivir carboxylate ( Hoffmann-La Roche , Basel , Switzerland ) , zanamivir ( GlaxoSmithKline , Stevenage , UK ) and peramivir ( BioCryst , Birmingham , AL ) . The 50% inhibitory concentration ( IC50 ) was determined from the dose-response curve . Replicative capacities of the recombinant viruses were evaluated by infecting ST6Gal1-MDCK cells with a multiplicity of infection ( MOI ) of 0 . 001 plaque-forming units ( PFUs ) /cell . Supernatants were collected every 12 h until 60 h PI and titrated by plaque assays . The mean viral plaque area of recombinant viruses was determined from a minimum of 16 plaques obtained after 60 h of incubation under agarose overlay using the ImageJ software ( version 1 . 41 ) , developed by Wayne Rasband of the National Institutes of Health as previously described [25] . Groups of 4 seronegative ( 900–1500 g ) male ferrets ( Triple F Farms , Sayre , PA ) were lightly anesthetised by isoflurane and received an intranasal instillation of 1 . 25×105 PFUs of the recombinant Bris07-like WT , H275Y or H275Y/Q222R variants . Temperature of ferrets was measured by rectal thermometers every day until day 10 PI . Ferrets were weighed daily and nasal wash samples were collected from animals on days 2 , 4 and 6 PI . Virus titers from nasal wash samples were determined by plaque assays using ST6Gal1-MDCK cells . Serum samples were collected from each ferret before intranasal infection and on day 14 PI to evaluate specific antibody levels against the seasonal Bris07 strain using standard HAI assays . To evaluate contact-transmissibility , inoculated-contact animal pairs were established by placing a naïve ferret into each cage 24 h after inoculation of the index ferret [27] . Contact animals were monitored for clinical signs and nasal wash and serum samples were collected as described above for determination of viral titers and serological status , respectively . NA kinetic parameters ( Km and Vmax values ) , NAI IC50 values and viral titers in vitro and in nasal washes of ferrets were compared by one-way ANOVA analysis of variance , with the Tukey's multiple comparison post test . The amount of NA activity on the cell surface and plaque sizes of the recombinants were compared to those of the WT virus and/or the H275Y mutant by the use of unpaired two-tailed t tests . | The H275Y neuraminidase ( NA ) mutation conferring resistance to oseltamivir was shown to impair old influenza H1N1 strains both in vitro and in vivo . By contrast , an oseltamivir-resistant A/Brisbane/59/2007 ( Bris07 ) -like H1N1 H275Y NA variant emerged in 2007 and completely replaced the wild-type ( WT ) strain in 2008–2009 . This discrepancy could be attributed to permissive NA mutations ( R222Q , V234M and D344N ) that were identified in most Bris07-like oseltamivir-resistant variants . To verify this hypothesis , we developed a reverse genetics system for a sensitive Bris07-like isolate ( 275H ) whose NA protein contains the 3 permissive mutations ( 222Q , 234M , 344N ) . Using mutagenesis , we first introduced the H275Y then reverted codons at positions 222 , 234 and 344 . The resulting 5 recombinants ( WT , H275Y , H275Y/Q222R , H275Y/M234V and H275Y/N344D ) were compared with regard to NA enzyme properties , replicative capacities in vitro as well as infectivity and contact-transmissibility in ferrets . Among the studied permissive mutations , Q222R was associated with a significant reduction of both affinity and activity of the NA enzyme resulting in a virus with a reduced replicative capacity in vitro and decreased replication in lungs of ferrets . Thus , the R222Q mutation may have been the major permissive NA change that facilitated the emergence and spread of NAI-resistant Bris07 variants . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"biology"
] | 2011 | Role of Permissive Neuraminidase Mutations in Influenza A/Brisbane/59/2007-like (H1N1) Viruses |
The gene expression of plus-strand RNA viruses with a polycistronic genome depends on translation and replication of the genomic mRNA , as well as synthesis of subgenomic ( sg ) mRNAs . Arteriviruses and coronaviruses , distantly related members of the nidovirus order , employ a unique mechanism of discontinuous minus-strand RNA synthesis to generate subgenome-length templates for the synthesis of a nested set of sg mRNAs . Non-structural protein 1 ( nsp1 ) of the arterivirus equine arteritis virus ( EAV ) , a multifunctional regulator of viral RNA synthesis and virion biogenesis , was previously implicated in controlling the balance between genome replication and sg mRNA synthesis . Here , we employed reverse and forward genetics to gain insight into the multiple regulatory roles of nsp1 . Our analysis revealed that the relative abundance of viral mRNAs is tightly controlled by an intricate network of interactions involving all nsp1 subdomains . Distinct nsp1 mutations affected the quantitative balance among viral mRNA species , and our data implicate nsp1 in controlling the accumulation of full-length and subgenome-length minus-strand templates for viral mRNA synthesis . The moderate differential changes in viral mRNA abundance of nsp1 mutants resulted in similarly altered viral protein levels , but progeny virus yields were greatly reduced . Pseudorevertant analysis provided compelling genetic evidence that balanced EAV mRNA accumulation is critical for efficient virus production . This first report on protein-mediated , mRNA-specific control of nidovirus RNA synthesis reveals the existence of an integral control mechanism to fine-tune replication , sg mRNA synthesis , and virus production , and establishes a major role for nsp1 in coordinating the arterivirus replicative cycle .
Plus-strand RNA ( +RNA ) viruses are ubiquitous pathogens of plants , animals , and humans . The translation of their messenger-sense RNA genome yields the core viral enzymes that always include an RNA-dependent RNA polymerase ( RdRp ) and assemble into a cytoplasmic machinery for viral RNA synthesis . Many +RNA virus groups employ polycistronic genomes and different mechanisms to express genes located downstream of the 5′-proximal open reading frame ( ORF ) . One of these mechanisms involves the synthesis of subgenomic ( sg ) mRNAs ( referred to as “transcription” in this paper ) . Although the sg mRNAs of +RNA viruses are invariably 3′-coterminal with the viral genome , diverse +RNA viruses have evolved different mechanisms for their production [1] . The order Nidovirales comprises several clades of distantly related enveloped +RNA viruses , including the arteri- and coronavirus families , which infect a wide variety of hosts , ranging from invertebrates to humans . Human coronaviruses are associated with respiratory disease ( including severe acute respiratory syndrome ( SARS ) , reviewed in [2] ) and arteriviruses like porcine reproductive and respiratory syndrome virus ( PRRSV ) are important veterinary pathogens . Members of the nidovirus order are characterized by their exceptional genetic complexity , and the group includes the virus families with the largest RNA genomes described to date ( 25–32 kb ) . Nidoviruses share important traits in their genome organization and gene expression mechanisms , and their key replicative enzymes are presumed to be evolutionarily related ( for a review , see [3] ) . Their polycistronic genomes are 5′-capped , 3′-polyadenylated , and the two 5′-most open reading frames ( ORFs ) – ORF1a and ORF1b , encode the viral replicase subunits segregated in two large replicase polyproteins , pp1a and pp1ab , the expression of the latter controlled by a −1 ribosomal frameshift ( Fig . 1A ) . Autoproteolytic processing of these precursors generates between 13 and 16 non-structural proteins ( nsps ) that direct viral RNA synthesis . Besides genome replication , arteri- and coronavirus RdRp-containing complexes also mediate the synthesis of a distinctive nested set of sg mRNAs that are both 5′- and 3′-coterminal with the viral genome and hence consist of sequences that are noncontiguous in the genomic RNA ( Fig . 1B ) . Despite recent advances in the structural and functional characterization of individual replicase subunits , the molecular details of nidovirus replication and gene expression remain poorly understood . Studies with nidovirus model systems such as equine arteritis virus ( EAV ) , the arterivirus prototype , have provided some critical insights about viral replicase functions and the regulation of RNA synthesis in infected cells . EAV replicase pp1a and pp1ab are co- and post-translationally cleaved into 13 nsps by viral proteases residing in nsp1 , nsp2 , and nsp4 . The seven viral structural proteins , which are all dispensable for replication and transcription [4] , are encoded in a set of overlapping ORFs located in the 3′-proximal quarter of the genome ( Fig . 1A ) . In the six sg mRNAs used to express these ORFs , a common “leader” sequence representing the 5′-proximal 206 nucleotides of the genome is linked to different “body” segments that are co-linear with the 3′-proximal part of the genome ( Fig . 1B ) . According to the widely supported model proposed by Sawicki and Sawicki [5] ( Fig . 1C ) , the structure of the arterivirus and coronavirus sg mRNAs derives from a discontinuous step during minus-strand RNA synthesis , which is guided by specific RNA signals and resembles copy-choice RNA recombination [6]–[8] . Conserved transcription-regulating sequences ( TRS; core sequence 5′ UCAACU 3′ in EAV ) precede each structural protein ORF ( body TRSs ) . The same sequence motif is also present at the 3′-end of the genomic leader sequence ( leader TRS ) . Minus-strand RNA synthesis , initiated at the 3′-end of the viral genome , is presumably attenuated at one of the body TRS regions ( Fig . 1C; reviewed in [9] , [10] ) . Subsequently , the nascent minus strand , carrying the body TRS complement at its 3′end , is translocated to the 5′-proximal region of the genomic template . During this step , the genomic leader TRS serves as a base-pairing target for the 3′ end of the nascent minus strand , a role that is facilitated , in the case of EAV , by its presence in the loop of an RNA hairpin [11] . When minus strand synthesis resumes , nascent strands are extended with the complement of the genomic leader sequence , yielding a nested set of subgenome-length minus-strand templates that are used for the subsequent synthesis of the various sg mRNAs . If attenuation does not occur , minus-strand RNA synthesis proceeds to yield a full-length complement of the genome , the intermediate required for its replication . Clearly , the protein and RNA factors that determine whether a nidovirus RdRp complex operates in continuous or discontinuous mode , i . e . produces a full-length or a subgenome-length minus strand , must be critical for the coordination of the nidovirus replicative cycle . As in other nidoviruses , the EAV genomic RNA ( RNA1 ) and sg mRNAs ( RNA2–RNA7 ) accumulate in specific molar ratios ( see Fig . 1B ) that are essentially constant until the peak of viral RNA synthesis is reached [12] . The relative abundance of the transcripts is presumably dictated by the “attenuation rate” at each of the successive body TRSs encountered during minus-strand synthesis , which is primarily determined by the base-pairing potential between the leader TRS and the body TRS complement in the nascent minus strand . Also the sequence context of body TRS motifs and their proximity to the genomic 3′end , which is reflected in the “gradient” of sg RNA sizes , can influence the accumulation of viral RNA species . The importance of TRS-driven RNA-RNA interactions and the potential for a regulatory role of higher order RNA structures was outlined above ( reviewed in [9] , [10] ) . At the protein level , however , only a single nidovirus protein specifically involved in transcription was identified thus far: EAV nsp1 was found to be essential for sg mRNA production , while being dispensable for genome replication [13] , [14] . Remarkably , nsp1 is also the first protein expressed during infection: it is co-translationally released from the nascent replicase polyproteins by a papain-like cysteine proteinase activity ( PCPβ ) in its C-terminal domain ( Fig . 2 ) . Comparative sequence analysis identified two additional conserved domains: a second , proteolytically silent PCP domain that is functional in other arteriviruses ( PCPα; [15] ) , and an N-terminal zinc finger ( ZF ) domain [13] , [16] that is critical for transcription and efficient production of infectious progeny [13] , [14] . Since the accumulation of all sg mRNAs was blocked in the absence of nsp1 , the protein was proposed to control a switch between replication and transcription [13] . We have now explored the key regulatory roles of nsp1 in the EAV replicative cycle in unprecedented detail . Our results indicate that in addition to the ZF region , both PCP subdomains of nsp1 are essential for transcription , and suggest an additional role of PCPα in virus production . We also established that nsp1 modulates viral RNA accumulation in an mRNA-specific manner , and thus maintains the balance among the seven viral mRNAs , including the genome . Our data suggests that nsp1 does so by controlling the levels of the full-length and subgenome-length minus-strand templates required for viral mRNA synthesis . The results we obtained from detailed characterization of nsp1 mutants and pseudorevertants provided compelling evidence for a close link between the regulation of individual nidovirus mRNA levels and the efficient production of infectious progeny .
Previous studies of the role of nsp1 in the EAV replicative cycle focused on the conserved amino acids presumed to be essential either for zinc binding by the ZF domain or for the catalytic activity of the PCPβ autoprotease [14] . Mutations that blocked the release of nsp1 from the replicase polyproteins were lethal , likely due to their interference with downstream polyprotein processing steps that are essential for genome replication [17]–[19] . By contrast , replacements of putative zinc-coordinating residues either selectively abolished transcription of all viral sg mRNAs or interfered with virus production without affecting viral mRNA accumulation . In an attempt to expand our repertoire of viable nsp1 mutants , we now used two approaches: i ) alanine scanning mutagenesis of non-conserved clusters of polar residues found throughout the nsp1 sequence , and ii ) a Cys↔His interchange at the positions of residues Cys-25 and His-27 , which have both been implicated in zinc coordination [13] , [16] . The first approach is less likely to perturb the protein's overall stability , since clusters of charged residues are usually found on the protein surface , where they may mediate interactions with other biomolecules via electrostatic interactions or hydrogen bond formation [20]–[22] . We reasoned that the second approach might preserve zinc coordination but could nevertheless have a subtle effect on zinc binding that might be translated in a measurable effect on one or more of nsp1's functions . Moreover , if these substitutions would compromise virus replication , isolation of revertant viruses encoding compensatory second-site mutations might reveal potential regulatory protein-protein or protein-RNA interactions . Table 1 lists the nsp1 mutants characterized in this study . In all three subdomains of nsp1 , clusters of two or three charged amino acids within a five- to seven-amino acid stretch were substituted with Ala . Constructs with Ala replacements in the ZF domain were designated Z ( 1 to 3 ) , while those with replacements in the PCPα and PCPβ domains were designated A ( 1 to 4 ) and B ( 1 and 2 ) , respectively ( see Table 1 and Fig . 2 ) . In addition , we swapped the Cys-25 and His-27 residues to generate the ZCH mutant . Full-length RNA transcribed from EAV cDNA clones encoding these nsp1 mutations was transfected into BHK-21 cells . Analysis of nsp1 mutant phenotypes was performed during the peak of viral RNA synthesis and before the bulk of infectious progeny was produced by the wild-type ( wt ) control ( 11 h post-transfection; referred to as first-cycle analysis ) . Previous studies of intracellular viral RNA levels had been hampered by the considerable variability in transfection efficiencies of synthetic EAV full-length RNAs . For comparison between mutants , these studies used the genomic RNA as an internal standard for each sample to calculate relative ratios of viral mRNA accumulation levels [6] , [14] . Using an improved electroporation protocol ( for details , see Materials and Methods ) , we now achieved very consistent and relatively high RNA transfection efficiencies ( between 45% and 55% of positive cells at 11 h post-transfection with replication-competent synthetic EAV RNAs; data not shown ) . This allowed for the comparison of the absolute levels of mRNA accumulation and the detailed first-cycle analysis of EAV nsp1 mutants at a time point at which differences in virus production or ( pseudo ) reversion would not influence the assessment of their phenotype . The ZF domain of nsp1 is essential for sg mRNA production [13] , [14] , but the question of whether the PCPα and PCPβ domains also contribute to the protein's function in transcription has not been previously addressed , partly due to the nonviable phenotype of PCPβ mutants in which the nsp1/2 cleavage was impaired [14] . Consequently , we first analyzed the impact of clustered charged-to-alanine replacements in nsp1 on viral mRNA accumulation . Cells transfected with nsp1 mutants were harvested 11 h after transfection , intracellular RNA was isolated and resolved in denaturing gels , and viral mRNAs were detected by hybridization to a probe complementary to the 3′-end of the genome and thus recognizing all viral mRNAs . Substitutions in the ZF domain ( mutant Z1 ) , as well as in the region connecting the ZF and PCPα domains ( Z3 ) , the PCPα domain itself ( A2 ) and , notably , also the PCPβ domain ( B2 ) rendered viral sg mRNAs undetectable . In addition , all four mutants displayed a noticeable increase in genomic RNA levels ( Fig . 3A ) . By contrast , accumulation of all viral mRNAs was blocked in the B1 mutant ( Table 1 and data not shown ) , possibly due to the proximity of the charged cluster to the active site Cys of PCPβ ( Fig . 2 ) . These results demonstrate that all subdomains of nsp1 , including PCPβ , are important for transcriptional control . This novel role of the PCPβ domain seems to be genetically separable from its autoproteolytic activity . We previously reported that certain substitutions of proposed nsp1 zinc-coordinating residues considerably reduced the yield of infectious progeny without noticeably affecting viral RNA accumulation [14] . This phenotype was also observed in this study for mutants Z2 ( ZF domain ) and A3 ( PCPα domain ) , in which clusters of alanine substitutions were introduced . These had no apparent effect on viral mRNA levels ( Fig . 3B ) , while progeny virus titers were reduced by 10- and 200-fold , respectively ( Fig . 3C ) , in supernatants harvested 24 h after transfection , well beyond the time point of maximum virus production by the wt control ( data not shown ) . Accordingly , plaques of the Z2 mutant were somewhat smaller than those of the wt virus , and those of the A3 mutant were minute ( Fig . 3C ) . Titers and plaque phenotypes remained essentially unchanged at 48 h post-transfection , arguing against a delay in virus production . Sequence analysis of the A3 progeny revealed reversion of the E113A mutation to the wt sequence at later time points ( data not shown ) . These observations imply that both the ZF and the PCPα domains of EAV nsp1 are involved in a step of the viral replicative cycle that is downstream of transcription and is critical for the efficient production of infectious virus particles . Two mutant phenotypes were previously described upon examination of the role of EAV nsp1 in transcription: one in which sg mRNA accumulation was selectively abolished , and another in which the levels of all sg mRNAs were uniformly reduced relative to that of the genomic RNA [13] , [14] . In this study , the ZCH , A1 , and A4 mutants displayed a third phenotype , demonstrating that replacements in nsp1 can affect EAV RNA levels in an mRNA-specific manner . The swapping of two proposed zinc-coordinating residues in the ZCH mutant resulted in the upregulation of a subset of sg mRNAs . In comparison to the wt control , the accumulation levels of RNA3 , 4 , 5 , 6 and 7 were increased , while those of the viral genome and RNA2 remained largely unchanged ( Fig . 4 , A and B ) . The increase in mRNA levels was not uniform , being more pronounced for RNA5 and RNA6 ( 4 . 5-fold and 3-fold , respectively ) than for RNAs 3 , 4 , and 7 ( ∼2-fold ) . Also , the substitution of two positively charged residues in the PCPα domain of the A1 mutant ( see Fig . 2 ) resulted in reduced accumulation levels of RNA5 and RNA6 ( 3- to 4-fold ) , and RNA7 ( ∼30% ) , but not of RNAs 2 to 4 ( Fig . 4 , A and B ) . In contrast , genomic RNA accumulation was dramatically enhanced in the A1 mutant ( Fig . 4 , A and B ) . This aspect of the mutant phenotype had been previously described in mutants that did not produce any sg mRNAs [14] , in which it was attributed to the increased availability of key factors for viral replication . This explanation does not seem likely for the A1 mutant , however , in which accumulation of only two of the six viral sg mRNAs was reduced ( Fig . 4 ) . Furthermore , another PCPα mutant - A4 ( see Fig . 2 ) , displayed 4-fold higher levels of genome accumulation without any significant decrease in sg mRNA production ( Fig . 4 , A and B ) . Since EAV mRNAs accumulate to different levels in molar ratios that are maintained through most of the replicative cycle ( see Fig . 1B ) , we will refer to these levels in the wt control as “balanced” . By contrast , the ZCH , A1 , and A4 mutants exerted differential effects on viral mRNA abundance , resulting in “imbalanced” viral mRNA accumulation profiles . The unusual phenotypes of these mutants implicated nsp1 in the mRNA-specific modulation of viral RNA levels , and prompted us to investigate their molecular basis in greater detail . According to the current consensus in the field , minus-strand RNA synthesis in arteri- and coronaviruses can operate in either continuous or discontinuous mode , generating genome- or subgenome-length templates respectively [5] , [9] , [10] . The relative abundance of the corresponding minus-strand template presumably determines the level to which each of the viral mRNAs accumulates [23] , [24] . We therefore sought to determine whether the differential effects of nsp1 mutations on the accumulation of EAV mRNA species were accompanied by changes in the levels of the corresponding minus-strand templates . To this end , we developed an RNase protection assay for the detection and quantification of EAV minus-strand RNA species . We employed a two-step protocol in which total RNA extracted from cells transfected with nsp1 mutants was first denatured and self-annealed . Due to the large excess of plus strands present in RNA samples extracted from EAV-infected cells [25] , all minus strands are expected to be present in duplexes after this annealing step , facilitating their subsequent reliable quantification . The remaining single-stranded RNA was then removed by RNase T1 digestion . Following inactivation of the enzyme , we added an excess of 32P-labeled transcripts of positive polarity , which were derived either from a region unique to the EAV genome , or from the leader-body junction regions of RNA6 and RNA7 . The samples were then subjected to a second round of RNA denaturation , hybridization , and digestion with RNase A and T1 , after which the protected fragments were analyzed by electrophoresis . The minus-strand templates of the most abundant viral mRNAs – RNA1 , 6 , and 7 , were selected for quantitative analysis . Accumulation levels of genome-length [ ( − ) RNA1] and subgenome-length minus strands corresponding to RNA6 and RNA7 [ ( − ) RNA6 and ( − ) RNA7] were quantified in total intracellular RNA extracted at 11 h post-transfection with the ZCH , A1 , and A4 mutants , and a wt control . Subgenomic minus- and plus-strand levels were similarly affected by the nsp1 mutations in a sg RNA-specific manner ( Fig . 5 ) . Genomic minus-strand accumulation was increased in the A1 and A4 mutants , albeit to a somewhat lesser extent as compared to the increase in genomic plus-strands ( Fig . 4 ) . These results clearly implicate nsp1 in a regulatory step ( or steps ) that controls minus-strand RNA accumulation , and ultimately determines the levels to which both genome- and subgenome-length mRNA species accumulate in EAV-infected cells . The relative abundance of nidovirus mRNAs likely serves to regulate the relative concentration of their respective translation products during infection . It was therefore important to determine whether viral protein levels indeed mirrored the specific changes in viral mRNA levels caused by mutations in nsp1 . To this end , we examined the intracellular accumulation of the EAV replicase subunit nsp3 , and the structural proteins M and N , in cell lysates harvested 11 h after transfection ( Fig . 6A ) . When compared to the wt control , nsp3 was more abundant in cells transfected with the A1 and A4 mutants , in line with the increased genome levels observed for these mutants at the same time point ( Fig . 4 ) . The intracellular levels of the M and N proteins were also in general agreement with the abundance of their corresponding mRNA templates ( RNA6 and RNA7 , respectively ) . Interestingly , even the modest reduction in RNA7 levels detected for the A1 mutant ( ∼30% , Fig . 4B ) was reflected in a decrease in N protein levels ( Fig . 6A ) . The close correlation between mRNA and corresponding protein levels argues against the possibility that the engineered nsp1 mutations might have caused a defect in viral mRNA translation . Overall , these data establish that even modest changes in viral mRNA accumulation are directly translated into altered viral protein levels during EAV infection . We next examined whether the production of infectious virus particles was disturbed in the three nsp1 mutants with imbalanced mRNA accumulation profiles . EAV assembly involves the coordinated interplay of the N protein and six envelope proteins , all of which are required for virus infectivity [4] , [26] . A critical step in assembly is the heterodimerization of the major viral envelope proteins , GP5 and M [27] , [28] , and both the GP5 and M protein levels , as well as the levels of their mRNA templates , RNA5 and RNA6 , were strongly affected in the ZCH and A1 mutants ( Fig . 4B and 6A ) . Furthermore , other documented interactions , such as the oligomerization of the minor envelope proteins - GP2b , GP3 and GP4 [29] , could be readily affected by the altered relative abundances of viral structural proteins . The decreased ratio of N protein and genomic RNA in the A1 and A4 mutants ( Fig . 4B and Fig . 6A ) could also adversely affect the assembly of infectious virions . Analysis of the infectious progeny virus titers in culture supernatants harvested 24 h after transfection with the ZCH , A1 , and A4 mutants indeed revealed a dramatic loss of infectivity ( Fig . 6B ) . In comparison to wt , the infectious progeny yield of the ZCH and A4 mutants were reduced by approximately 4 logs and that of the A1 mutant by ∼5 logs . We subsequently purified virions by sedimentation through a sucrose cushion in order to quantify their genomic RNA content by reverse transcription-quantitative PCR . Virions from medium harvested at 24 h after transfection with the ZCH , A1 , and A4 mutants were compared with the progeny produced by the wt control . Consistent with the reduction in infectious progeny titers , these results showed a decrease in the total number of genome-containing virus particles secreted from cells transfected with each of the three mutants ( Table 2 ) . Interestingly , when the relative specific infectivity of each mutant virion preparation was assessed by relating the genomic RNA content to the plaque-forming units ( pfu ) , a marked decrease in the pfu per unit of genomic RNA ratio of the ZCH , A1 , and A4 virion preparations was revealed ( Table 2 ) . The ZCH , A1 , and A4 mutations thus seem to affect both the total number of secreted virions , as well as their specific infectivity . Also , even at this relatively early time point post transfection , the three mutants exhibited heterogeneous plaque morphology , indicative of rapid reversion ( Fig . 6B ) . These results , together with the RNA and protein analyses outlined above ( Fig . 4B and 6A ) , demonstrate that even moderate changes in the accumulation of EAV mRNAs and proteins can be associated with a dramatic decrease in the yield of infectious progeny . Thus , a previously unnoticed link seems to exist between the fine-tuning of the relative abundance of EAV mRNAs ( and , consequently , viral protein levels ) and the efficiency of virion biogenesis . To gain more insight into the molecular basis of the nsp1 mutant phenotypes described above , we attempted to isolate revertant viruses encoding compensatory second-site mutations . The mutants in which sg mRNA accumulation was completely blocked , however , proved to be extremely stable . We were repeatedly unable to detect infectious particles in supernatants of transfected cells , even after prolonged incubation ( up to 70 h ) at 39 . 5°C or at a reduced temperature of 35°C , or after using these supernatants to infect fresh BHK-21 cells ( data not shown ) . By contrast , the rapid appearance of large plaque variants among the prevailing small plaques produced by the ZCH , A1 , and A4 mutants ( see Fig . 6B ) suggested genetic heterogeneity . Large plaque clones of these three mutants were isolated and propagated in fresh cells , and the EAV nsp1 gene was amplified by RT-PCR . Sequence analysis of the PCR products confirmed the presence of the originally engineered mutant codons , and identified additional mutations in the nsp1-coding sequence in the majority of the plaques analyzed ( data not shown ) . A pseudorevertant of ZCH had acquired a substitution in the vicinity of the original mutations ( Ala-29 to Asp ) . Interestingly , Ala-29 was also mutated in five independent A1 pseudorevertants , where it had been replaced with Lys due to two nucleotide substitutions ( Table 3 ) . In addition , four clones of the A4 offspring contained a Thr-196 to Lys substitution , and Gly-47 to Ala and Glu-112 to Lys replacements were found in one clone each . To ascertain these second-site substitutions conferred a replicative advantage , they were introduced into their respective parental ( mutant ) full-length cDNA clones , yielding a set of viruses collectively referred to as “nsp1 pseudorevertants” . Cell culture supernatants harvested 24 h after transfection with RNA from nsp1 pseudorevertants indeed contained between 2 and ∼4 logs more infectious progeny than those of the original mutants ( Table 3 ) , confirming the compensatory nature of the second-site mutations . Both virus titer and plaque size of the three viruses carrying a second-site mutation in the A4 mutant background were similar to those of the wt control ( Table 3; data not shown ) . Replacement of Ala-29 with Asp or Lys in the ZCH and A4 backgrounds , respectively , increased virus titers by 2 to ∼4 logs , with plaque sizes being intermediate between those of the parental mutant and the wt control ( Table 3; data not shown ) . The relative specific infectivities of virion preparations derived from all nsp1 pseudorevertants were also considerably higher in comparison to those of the parental mutants ( data not shown ) . Notably , the pseudorevertants showed a partial or complete restoration of the mRNA-specific defects that we had observed for the original mutants ( Fig . 7 ) . Introduction of the Ala-29 to Asp substitution in the ZCH mutant was accompanied by a considerable reduction of the otherwise abnormally high accumulation of RNAs 3 to 7 , though RNA5 and RNA6 were still present at ∼150% of the normal level ( Fig . 7B ) . Likewise , the Ala-29 to Lys replacement moderated the effect of the A4 substitutions on the accumulation levels of RNA1 , RNA5 , and RNA6; those of RNA1 and RNA2 were somewhat reduced relative to wt . Introduction of G47A , E112K , or T196K in the A4 background in each case suppressed the increased ratio of genomic to sg mRNA that was characteristic for the parental mutant virus ( Fig . 7D ) . Thus , both the pronounced mRNA-specific accumulation defects , as well as the associated drop in virus production observed for the ZCH , A1 , and A4 mutants ( Fig . 4 ) were considerably alleviated by second-site mutations in nsp1 . The location of both the original and the second-site mutations in nsp1 revealed multiple genetic interactions between all nsp1 subdomains that are important for the protein's role in regulating the relative abundance of EAV mRNAs . In addition , the increased virus production by the pseudorevertants was invariably correlated with a distinct shift towards an mRNA accumulation profile that was ( more ) similar to wt ( Fig . 7 , B and D ) . These observations provide compelling evidence that efficient virus production depends on maintaining “balanced” viral mRNA levels in EAV-infected cells .
Previous reports have suggested that the multiple roles of nsp1 during EAV infection can ( in part ) be functionally separated , with the ZF domain being essential for transcription and efficient virus production , and the PCPβ protease cleaving the nsp1/2 site , irrespective of the ZF integrity [14] . This study extended and refined the above concept showing , first , that the functional repertoire of nsp1 also includes the differential control of mRNA accumulation , and , second , that some functions may be based on the interplay between two or even all three of the protein's subdomains . The phenotypes observed upon replacing charged amino acid clusters with alanine established the importance of all nsp1 subdomains for sg mRNA accumulation ( Fig . 3A ) . The involvement of PCPβ in transcription is seemingly unrelated to its autoproteolytic function , whose inactivation is detrimental to genome replication [14] . The co-translational , cis-cleavage of the nsp1/2 site in the nascent replicase polyproteins [30] is probably the sole processing step mediated by PCPβ , which becomes rapidly available to exercise any trans-acting , non-proteolytic activities it may have . Such secondary non-proteolytic functions have previously been reported for several +RNA viral autoproteinase domains , such as those of hepatitis C virus NS2 [31] and beet yellow virus L-Pro [32] . The previously uncharacterized PCPα domain , a PCPβ paralog that has lost its proteolytic capacity in EAV [15] , appears to cooperate with the ZF in transcription and virion biogenesis ( Fig . 3 ) . Likewise , it works in concert with both flanking nsp1 subdomains in controlling the relative abundance of viral mRNAs ( Fig . 4 , Table 3 , and Fig . 7 ) . The Lys and Arg residues replaced with alanine in the A1 and A4 mutants , and the Lys that had evolved in their pseudorevertants are all basic residues . They are found in different nsp1 subdomains , but could well be spatially juxtaposed to provide a positively charged side chain to a functional region that is involved in interactions essential for one or more of the protein's activities . The Gly-47 to Ala reversion , which maps to the junction region between the ZF and PCPα , might serve to reposition these subdomains relative to each other . A similar readjustment of the protein's tertiary structure might account for the compensatory effect exerted by the Ala-29 to Asp reversion on the ZCH mutant . Despite the fact that its orthologs in other arteriviruses have retained their proteolytic activity , it is tempting to speculate that the incorporation of the PCPá domain in the arterivirus nsp1 region can be primarily attributed to the non-proteolytic functions outlined above . On the whole , there seems to be considerable cooperation between nsp1 subdomains . This interplay appears crucial for coupling the different processes that nsp1 controls and may be based on interactions that are either intra- or intermolecular , in view of the protein's ability to form homo-oligomers [33] . Notably , a recent paper describing the crystal structure of PRRSV nsp1α , an arterivirus ortholog of the EAV ZF and PCPα domains , reported that the protein exists in equilibrium between monomers and dimers in solution . Residues from both subdomains also contribute both to nsp1α dimerization and the formation of a hydrophilic groove at the dimer surface [16] . Characterization of the three nsp1 mutants with imbalanced viral mRNA accumulation profiles showed that the disruption of the balance was due to a reduction of the levels of certain viral mRNA species only for the A1 mutant ( see Fig . 4B ) . By contrast , specific upregulation of most mRNAs was observed for both the ZCH and A4 mutants and the associated dramatic defects in virus production were surprising , also in view of the apparently undisturbed translation of viral mRNAs ( Fig . 6A ) . In an attempt to quantify the relationship between virus yield and viral mRNA accumulation , we used the data sets of Fig . 4B , 7B , and 7D to calculate the mean relative mRNA accumulation for the ZCH , A1 , and A4 mutants , as well as their pseudorevertants . Plotting these values versus the corresponding infectivity titers ( derived from Fig . 6B and Table 3 ) revealed efficient production of infectious progeny for viruses with mRNA accumulation that was close to that of the wt control or modestly decreased ( Fig . 8A ) . In contrast , severely reduced virus yields were observed when viral mRNA accumulation was increased , as seen for the ZCH , A1 , and A4 mutants . This somewhat counterintuitive observation was rationalized by assessing the importance of the quantitative balance among EAV mRNA species for infectious virus production . In order to establish this relationship , for each mRNA species the absolute deviation of its relative accumulation from the mean of the complete nested set of mRNAs ( Fig . 8A ) was calculated . From these seven values , the mean ( absolute ) deviation was calculated for each mutant or pseudorevertant , and these values were also plotted against virus titers ( Fig . 8B ) . By definition , the mean deviation is equal to zero for the wt virus , whose mRNA levels we refer to as “balanced” . All nsp1 mutants and their pseudorevertants have mean deviations larger than zero and these values reflect the magnitude of imbalance of their mRNA accumulation profiles . Remarkably , the plot revealed that the data nicely fit ( R2 = 0 . 95 ) a negative exponential regression between infectious virus yield and mRNA imbalance , which is depicted as a negative linear regression in the semi-logarithmic plot of Fig . 8B . This strong relationship underlines that the magnitude of imbalance between different mRNA species , rather than the accumulation levels of mRNA species per se , is a chief factor affecting progeny yield . It should be noted that several factors may have affected our analysis of this relationship to a certain extent . For example , only a relatively small variety of nsp1 mutants and pseudorevertants was analyzed and although some pseudorevertants were recovered repeatedly , they were plotted only once . Also , the rapid emergence of pseudorevertants likely contributed to the virus titers measured for the ZCH , A1 , and A4 mutants a 24 h post transfection , which would thus be overestimated in our analysis . Therefore , an extension of this study with new mutants and pseudorevertants may further refine the relationship illustrated by Fig . 8B . It would also be interesting to evaluate how virus yields are affected by a general imbalance between replication and transcription ( A4 mutant ) versus sg mRNA-specific changes ( ZCH mutant ) , although the latter seem to have an added negative effect ( A1 mutant ) . The molecular basis of perturbed infectious particle production in the mutants is probably complex , although it is likely related to the altered relative abundances of viral structural proteins resulting from the respective changes in the levels of their mRNA templates ( Fig . 4B and 6A ) . The observation that increased production of infectious progeny by the nsp1 pseudorevertants invariably correlated with restoration of balanced viral mRNA accumulation ( Table 3 and Fig . 7 ) lends further support to this hypothesis . Differential changes in structural protein levels might adversely affect their ability to form complexes that drive particle assembly , or alter the stoichiometry of these complexes when incorporated in virions . The latter option is consistent with the observed decrease in relative specific infectivity of virion preparations from the ZCH , A1 , and A4 mutants ( Table 2 ) . Detailed information on the architecture of EAV virions and the molecular interactions among EAV structural proteins that drive virus assembly is unfortunately lacking . Nevertheless , our data clearly establish a previously unknown link between balanced EAV mRNA accumulation and efficient virus production . We thus conclude that nsp1 critically promotes virion biogenesis by modulating viral mRNA accumulation , as well as acting at an additional , currently unknown step of the EAV replicative cycle , downstream of viral RNA synthesis ( Fig . 3; [14] ) The onset of +RNA gene expression is marked by translation of the viral genome . In subsequent stages of infection , this molecule is utilized also as the template for replication and , in some cases , transcription , as well as packaging into progeny virus particles . Virtually nothing is known about the temporal coordination of these distinct processes in the nidovirus replicative cycle . Non-structural protein expression is extensively regulated at the translational and post-translational level , by ribosomal frame-shifting and concerted autoproteolytic processing of replicase polyproteins , which together control the production of the core viral replicative enzymes [18] , [19] , [34] , [35] . By contrast , regulation of structural protein expression is presumed to occur mainly at the transcriptional level , although the exact significance and control of the relative abundance of the various viral mRNAs in infected cells had not been examined for any nidovirus . Prior studies [13] , [14] , together with our present findings , clearly implicate EAV nsp1 in controlling the balance between replication and transcription ( Fig . 3 and 4 ) . Mutations in nsp1 were previously shown to selectively block or equally reduce the accumulation of all sg mRNA species . The pronounced upregulation of genomic RNA levels in nsp1 mutants with a complete block in sg mRNA production was also described before [14] and suggested to result from redirecting a limited pool of RNA-synthesizing complexes , normally engaged in both replication and transcription , to the exclusive amplification of the viral genome . Some of the mutant phenotypes in this report , however , are poorly compatible with the above scenario . Genome RNA levels were increased 4–6 fold in the A1 and A4 mutants , for which sg mRNAs synthesis was clearly detectable and even enhanced ( Fig . 4 ) . In addition , the ZCH and A1 mutations differentially modulated the accumulation levels of a specific subset of sg mRNAs and their subgenome-length minus-strand templates ( Fig . 4 and 5 ) . This effect was accompanied by unchanged genome RNA levels in the ZCH mutant . Taken together , these data imply that nsp1 does not only allow the viral RdRp complex to engage in discontinuous minus-strand synthesis , but also enables it to differentiate between the various body TRS motifs it encounters while traversing the genomic template . This occurs by a currently unknown mechanism that is different from the previously described “polar attenuation” caused by the relative position of body TRSs in the array of successive attenuation signals that need to be “overcome” by the minus-strand RNA-synthesizing complex [36] . The changes in viral RNA accumulation observed for the A1 and A4 mutants could result from partial loss of recruitment of nsp1 to the RdRp complex , or its compromised ability to recognize RNA signals that direct discontinuous RNA synthesis . Viral RNA synthesis would then be shifted towards replication , but the increased availability of template and/or viral enzymes that this causes ( Fig . 4B and 6A ) might account for the relatively high levels of sg mRNA accumulation in these two mutants . This , in turn , would imply that the availability of nsp1 is important for its function in transcription . Indeed , the intracellular distribution of nsp1 is distinct from that of the other EAV nsps – a large fraction of the protein is present in the host cell nucleus [33] , while only ∼25% of the cytoplasmic nsp1 fraction co-sediments with the membrane-bound viral RNA-synthesizing complexes upon their isolation from infected cells [37] . Immunofluorescence analysis did not reveal a significant change in intracellular nsp1 distribution for any of the mutants we described here ( data not shown ) , but more rigorous biochemical studies are needed to ascertain the recruitment of nsp1 to RdRp complexes is completely unchanged . Nsp1 mutations clearly influenced minus-strand RNA accumulation ( Fig . 5 ) , although we cannot formally exclude that the protein controls minus-strand RNA stability rather than synthesis . Unfortunately , analysis of the kinetics of minus-strand accumulation in the ZCH and A1 mutants was hampered by the rapid emergence of pseudorevertants ( Fig . 6B; Table 3 ) and the low abundance of these molecules at earlier time points post-transfection , which precluded their accurate quantitation ( data not shown ) . Resolving this issue thus remains a formidable technical challenge . The recently described protocols for isolation of active viral RNA-synthesizing complexes from EAV-infected cells [37] , as well as in vitro activity assays using a recombinant form of the EAV RdRp [38] might provide better platforms for future research on the mechanistic aspects of nsp1 function . Nsp1 remains the only known arterivirus protein specifically implicated in the regulation of transcription [13] , [39] and , to date , a functional counterpart has not been identified in coronaviruses or other nidoviruses . Species-specific changes in viral mRNA abundance were observed upon inactivation of the nsp14 exoribonuclease of human coronavirus 229E [40] . However , the major effect of this mutation was a reduction of the accumulation of all viral mRNAs by more than 100-fold . In view of the ( indirect ) dependence of transcription on genome replication and translation , the phenotype of the nsp14 mutants should be interpreted with caution . Nevertheless , a potential specific role of the coronavirus exonuclease in sg mRNA transcription deserves further investigation , although its analysis may be complicated by the multifunctionality of this protein , which was implicated in improving the fidelity of viral RNA synthesis [41] , [42] and also includes an N7-methyltransferase domain [43] . Elegant studies of the tombusvirus replicase have shown that genome replication and sg mRNA synthesis can be effectively uncoupled by mutations in the C-terminus of the viral RdRp , which could only be achieved after separation of the protein-coding sequence from overlapping regulatory RNA sequences [44] . This example once again underlines the theoretical and technical challenges encountered while dissecting the complex mechanisms coordinating +RNA virus replication and transcription .
Baby hamster kidney cells ( BHK-21; ATCC CCL10 ) were used for all experiments . The cells were maintained at 37°C in BHK-21 medium ( Glasgow MEM; Invitrogen ) supplemented with 5% fetal calf serum ( FCS ) , 10% tryptose phosphate broth , 100 U/ml of penicillin , 100 µg/ml of streptomycin and 10 mM HEPES , pH = 7 . 4 . Upon transfection or infection with wt or mutant EAV , BHK-21 cells were incubated at 39 . 5°C , since this elevated temperature shortens the replication time of the virus substantially without any adverse side effects [45] . The substitutions in the nsp1-coding sequence listed in Table 1 and Table 3 were engineered using appropriate shuttle vectors and standard site-directed mutagenesis PCR [46] . Sequence analysis of the cloned fragments was used to verify the introduction of the appropriate nucleotide substitutions and exclude the presence of undesired mutations . The mutations in the nsp1-coding sequence were then transferred to pEAV211 or pEAN551 , both derivatives of EAV full-length cDNA clone pEAV030 [45] containing some engineered restriction sites . Viruses derived from either pEAV211 or pEAN551 were previously shown to display a wt phenotype [17] , [47] . The virus derived from the pEAV211 construct was used as a wt control in all experiments . In vitro RNA transcription from XhoI-linearized wt or mutant EAV full-length cDNA clones was performed using the mMESSAGE mMACHINE T7 Kit ( Ambion ) . Seven µg of in vitro-synthesized EAV RNA were electroporated into 3 . 5×106 BHK-21 cells using the Amaxa Cell Line Nucleofector Kit T and the program T-020 of the Amaxa Nucleofector ( Lonza ) according to the manufacturer's instructions . Cells were resuspended in BHK-21 medium and subsequently seeded on coverslips for immunofluorescence analysis or in 6-well clusters for analysis of intracellular protein and RNA levels , as well as virus production . For EAV infection , subconfluent monolayers of BHK-21 cells were inoculated with transfected cell culture supernatant diluted in PBS-DEAE/2% FCS . Following incubation at 39 . 5°C for 1 h , the inoculum was removed , DMEM/2% FCS was added , and the cells were incubated at 39 . 5°C for 16–18 h . For virus titration , BHK-21 cells seeded in 6-well clusters were infected with serial 10-fold dilutions of supernatants harvested from transfected cells and then incubated under semi-solid overlays consisting of DMEM supplemented with 50 mM HEPES , pH = 7 . 4 , 2% FCS and 1 . 2% Avicel ( FMC BioPolymer ) at 39 . 5°C for 72 h . The overlays were aspirated , cells were fixed with 8% formaldehyde in PBS , and stained with crystal violet . For plaque purification , a solid overlay of DMEM containing 50 mM HEPES , pH = 7 . 4 , 2% FCS and 1% agarose was used . Immunofluorescence analysis of EAV-transfected cells was performed as described previously [48] . Briefly , cells were analyzed at 11 hour post-transfection by dual labeling with a rabbit antiserum recognizing EAV nsp3 [49] and an anti-N mouse monoclonal antibody ( 3E2; [50] ) . These proteins are expressed from RNA1 and RNA7 , respectively . Nuclei were visualized for cell counting by staining with 1 µg/ml Hoechst 33258 ( Sigma-Aldrich ) . Transfection efficiencies were determined at 11 h post-transfection by counting cells with the Scion Image software ( Scion Corporation ) and calculating the percentage of cells positive for EAV nsp3 . Analysis of viral RNA accumulation was carried out before completion of the first replication cycle . Cells transfected with wt or mutant EAV derivatives were lysed at 11 h post-transfection , and total intracellular RNA was isolated by acid phenol extraction as previously described [25] . Viral mRNAs were detected by resolving total RNA in denaturing agarose-formaldehyde gels , and equal sample loading was confirmed by ethidium bromide staining of ribosomal RNA . The gels were subsequently dried and hybridized to a 32P-labeled probe ( E154 , 5′-TTGGTTCCTGGGTGGCTAATAACTACTT-3′ ) complementary to the 3′ end of the genome that recognizes both genome and all sg mRNAs [25] . The gels were exposed to phosphorimager screens , which were subsequently scanned using a Typhoon Variable Mode Imager ( GE Healthcare ) . Image analysis and quantification of band intensities were performed with the ImageQuant TL software ( GE Healthcare ) . A two-cycle ribonuclease ( RNase ) protection assay , adapted from [51] and [24] , was used for the detection of EAV minus-strand RNAs . Total intracellular RNA isolated at 11 h post-transfection was dissolved in 10 µl of Hybridization Buffer III ( RPA III Kit; Ambion ) , denatured for 3 min at 95°C and incubated for 16 h at 55°C . Samples were then digested with 5 U of RNase T1 ( Ambion ) per µg total RNA in 10 mM Tris pH = 7 . 5 , 300 mM NaCl , 5 mM EDTA for 60 min at ambient temperature . Following proteinase K treatment and phenol∶chlorophorm extraction , 0 . 5 µg of yeast RNA per µg total RNA was added as a carrier . After ethanol precipitation , equal amounts of a radiolabelled probe ( see below ) were added to each sample and , following sample denaturation at 85°C for 5 min , hybridization was carried out at 55°C for 16 h . RNase digestion of unhybridized RNA was performed using the RPA III Kit according to the manufacturer's protocol . Protected fragments were resolved in 5% polyacrylamide/8M urea gels , which were dried and exposed to phosphorimager screens . Image analysis and quantification were performed as described above . To generate probes for minus-strand detection , cDNA fragments derived from the EAV genome ( nucleotides ( nt ) 3687–4013 ) , RNA6 ( nt 68–206 from the leader sequence and nt 11870–12057 from the body sequence ) and RNA7 ( nt 68–206 and nt 12252–12429 ) were inserted downstream of the T7 promoter in pcDNA3 . 1 using standard cloning procedures . Radiolabelled RNA transcripts were generated by in vitro transcription in the presence of [α-32P]CTP ( Perkin Elmer ) using MAXIscript T7 Kit ( Ambion ) and purified from 5% polyacrylamide/8M urea gels by elution for 3h at 37°C in 0 . 5 M NH4OAc , 0 . 2% SDS , 1mM EDTA . The transcript generated for detection of genomic minus strands –pRNA1 , was 356 nt long and contained 327 nt of ( − ) RNA1-specific sequence . The transcript and EAV-specific sequence length were 382 nt and 327 nt , respectively , for the probe detecting ( − ) RNA6 ( pRNA6 ) , and 372 nt and 319 nt for the probe specific for ( − ) RNA7 ( pRNA7 ) . Detection of RNA1 ( − ) was performed using sample RNA corresponding to approximately 1 . 25×104 cells and 20 fmol of radiolabelled probe . Levels of ( − ) RNA6 and ( − ) RNA7 were determined in samples corresponding to 4×104 and 2 . 5×104 cells , respectively , using 5 fmol radiolabelled probe . These conditions ensured that the values obtained were in the linear range of the assays ( data not shown ) . Cells transfected with wt or mutant EAV derivatives were lysed at 11 h post-transfection as described previously [52] . The protein concentration in the lysates was determined using the Bio-Rad protein assay reagent . Equal amounts of total protein were subjected to SDS-PAGE and transferred to Hybond-P PVDF membrane ( GE Healthcare ) by semidry blotting . After blocking with 5% non-fat milk in PBS containing 0 . 5% Tween-20 , the membranes were incubated with the following antibodies: anti-EAV nsp3 ( see above ) , rabbit anti-M [52] , anti-N ( see above ) , or an anti-β-actin mouse monoclonal antibody ( AC-74 , Sigma ) , all diluted in PBS containing 5% non-fat milk , 0 . 5% bovine serum albumin and 0 . 5% Tween-20 . HRP-conjugated secondary antibodies ( DAKO ) and an ECL-Plus kit ( GE Healthcare ) were used for detection . In order to determine relative specific infectivity , supernatants from BHK-21 cells transfected with wt or mutant EAV derivatives were harvested 24 h after transfection and clarified by low-speed centrifugation . Virions from 1 ml of clarified supernatant were purified by pelleting through a 0 . 4 ml cushion of 20% sucrose in 20 mM Tris pH = 7 . 5 , 100 mM NaCl , 1 mM EDTA at 55 , 000 rpm for 45 min at 4°C using a TLS-55 rotor in a Beckman tabletop ultracentrifuge . Virion RNA was isolated from pellet fractions by acid phenol extraction as described above . Complementary DNAs were synthesized with Thermoscript Reverse Transcriptase ( Invitrogen ) using a primer complementary to a region in ORF1a of the EAV genome ( EAV418as , 5′-AGCCGCACCTTCACATTG-3′ ) . Quantitative PCR ( qPCR ) was performed essentially as previously described [53] , [54] . Briefly , a cDNA aliquot was amplified with EAV-specific oligonucleotides EAV418as and EAV417s ( 5′ CATCTCTTGCTTTGCTCCTTAG-3′ ) using HotStar Taq Polymerase ( Qiagen ) and SYBR Green I ( Molecular Probes ) in an iCycler machine ( Bio-Rad ) . The data obtained were analyzed with iCycler software , and the specificity of the reaction was confirmed by the melting curve of the amplified products . To generate a standard curve , serial ten-fold dilutions of the virion RNA sample derived from cells transfected with the wt EAV construct were reverse-transcribed and amplified by qPCR in parallel . The resulting standard curve had an R2 = 0 . 99 and a 6-log linear range for the EAV ORF1a amplicon ( data not shown ) . The relative genomic RNA contents of virions produced by EAV mutants were calculated by comparing their threshold cycle ( Ct ) values against the standard curve and the resulting values were normalized to the wt genomic RNA content , which was set at 1 . The relative specific infectivity of each EAV mutant was then determined by dividing the respective mutant∶wt pfu ratio by the mutant∶wt relative genomic RNA content . | Plus-strand RNA viruses , a major group of plant and animal pathogens , employ a variety of gene expression strategies . In some groups , the genome is translated into a single polyprotein precursor comprising all viral proteins , while the expression of genomes containing multiple open reading frames commonly depends on the production of additional , subgenomic mRNAs . These serve to translate the open reading frames that are inaccessible to host cell ribosomes engaged in genome translation . Arteriviruses and coronaviruses secure the expression of their structural protein genes by generating an extensive nested set of subgenomic mRNAs , which are copied from a set of complementary minus-strand templates . The production of these subgenome-length minus strands involves a unique mechanism of discontinuous RNA synthesis that essentially competes with the production of the full-length minus strand , the template for genome replication . We describe here that arterivirus non-structural protein 1 ( nsp1 ) modulates the accumulation of minus-strand RNAs to control the relative abundance of both genome-length and subgenomic mRNAs , thereby ensuring efficient production of new virus particles . We found that specific nsp1 mutants with imbalanced mRNA levels and low virus production rapidly acquire additional nsp1 mutations that rescue these defects . Thus , a single arterivirus protein plays a decisive role in the integral control of replication , sg mRNA synthesis , and virus production . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/viral",
"replication",
"and",
"gene",
"regulation"
] | 2010 | Arterivirus Nsp1 Modulates the Accumulation of Minus-Strand Templates to Control the Relative Abundance of Viral mRNAs |
The actin depolymerizing factors ( ADFs ) play important roles in several cellular processes that require cytoskeletal rearrangements , such as cell migration , but little is known about the in vivo functions of ADFs in developmental events like branching morphogenesis . While the molecular control of ureteric bud ( UB ) branching during kidney development has been extensively studied , the detailed cellular events underlying this process remain poorly understood . To gain insight into the role of actin cytoskeletal dynamics during renal branching morphogenesis , we studied the functional requirements for the closely related ADFs cofilin1 ( Cfl1 ) and destrin ( Dstn ) during mouse development . Either deletion of Cfl1 in UB epithelium or an inactivating mutation in Dstn has no effect on renal morphogenesis , but simultaneous lack of both genes arrests branching morphogenesis at an early stage , revealing considerable functional overlap between cofilin1 and destrin . Lack of Cfl1 and Dstn in the UB causes accumulation of filamentous actin , disruption of normal epithelial organization , and defects in cell migration . Animals with less severe combinations of mutant Cfl1 and Dstn alleles , which retain one wild-type Cfl1 or Dstn allele , display abnormalities including ureter duplication , renal hypoplasia , and abnormal kidney shape . The results indicate that ADF activity , provided by either cofilin1 or destrin , is essential in UB epithelial cells for normal growth and branching .
Depolymerization and severing of actin filaments produces new actin monomers and new free ends that facilitate dynamic changes in the actin cytoskeleton . These events are essential for several cellular processes including cell survival , shaping , cytokinesis , migration and chemotaxis [1] . For example , during migration and chemotaxis , cell protrusions are formed as a result of localized actin polymerization in the leading edge of a motile cell [2] . In dividing cells , actin depolymerization plays an important role in chromosome congression , cleavage plane orientation and furrow formation [3] . Three genes encode actin depolymerization factors ( ADFs ) in mammals: Cofilin1 ( Cfl1 , non-muscle Cofilin , n-Cofilin ) , Cofilin2 ( Cfl2 , muscle Cofilin ) and Destrin ( Dstn , also called ADF or Corn1 ) . Despite the vast amount of in vitro data on the functions of ADFs , remarkably little is known about their in vivo roles . ADF genes share overlapping expression patterns in many cell types , but the phenotypes of mouse mutants in either Cfl1 or Dstn suggest that they have somewhat distinct in vivo functions . Mice lacking Cfl1 are embryonic lethal at E11 . 5–12 . 5 and display defects in neural tube closure and neural crest cell migration [4] , even though Dstn is highly expressed in the cranial neuroectoderm of these mutant embryos; thus , in this situation , Dstn seemed unable to compensate for the absence of Cfl1 . No in vivo data on Cfl2 function are yet available . Dstn−/− homozygotes are viable but have corneal defects leading to eventual blindness in adult mice , whereas two alleles with inactivating point mutations ( Dstncorn1 and Dstncorn1-2J ) cause the same phenotype , indicating that they behave as null alleles [5] , [6] . While Dstn−/− brains have a normal gross morphology , conditional deletion of Cfl1 in neuronal cells causes excessive differentiation , changes in cell proliferation , and migration defects , resulting in a lissencephaly phenotype [5] . While Cfl1 seems to be essential for the normal development of the neural tube and neuronal differentiation , the requirement for specific ADFs in other basic morphogenetic processes has not been addressed . Several organs including mammary and salivary gland , lung and kidney develop largely from an epithelial outgrowth that subsequently branches in an organ-specific manner . Kidney development , which serves as an excellent model to study epithelial branching , begins by the formation of the ureteric bud ( UB ) ( at E10 . 5 ) , which first arises as a thickening of the Wolffian ( or nephric ) duct . Subsequently , the UB elongates , invades the adjacent metanephric mesenchyme ( MM ) and begins to branch in a repeated and characteristic pattern , a process that continues until early postnatal life , and gives rise to the tree-like collecting duct system . The UB branching rate and pattern are tightly regulated by signals from the surrounding MM , while at the same time , the UB tips induce nephron progenitors in the MM to undergo mesenchyme-to-epithelium transformation , leading to formation of different nephron segments [7]–[9] . We became interested in potential role of ADF genes in renal epithelial branching for several reasons . First , evagination of the UB from the WD and its subsequent growth and branching require a number of cellular processes that involve the actin cytoskeleton , such as cell migration and proliferation [10] , [11] . In vitro inhibition of ROCK , an upstream regulator of Cofilin1 activity , has controversial effects on renal development as it can either increase or decrease kidney size [12] , [13] . Second , it was reported that Cfl1 gene expression is upregulated by the expression of activated forms of the Ret receptor tyrosine kinase in NIH3T3 cells [14] . Ret , which is expressed by UB cells , is the receptor for the secreted protein GDNF , which is produced by the MM . Ret and GDNF ( as well as the GDNF co-receptor Gfrα1 ) play a critical role in UB branching morphogenesis , and their absence leads to renal agenesis in mice and humans [15]–[17] . Here we show that mice lacking Cfl1 in the ureteric epithelium , or those with an inactivating mutation in Dstn , develop mostly normal kidneys . However , simultaneous inactivation of both genes in UB epithelium causes a severe and early block in UB branching , indicating considerable functional overlap between cofilin1 and destrin in UB cells . Double mutant UB epithelial cells accumulate excess filamentous actin ( F-actin ) , resulting in irregular epithelial organization and a defect in cell migration . The results show that actin depolymerization by either Cfl1 or Dstn is required for normal growth and branching of the UB epithelium during renal branching morphogenesis .
Cfl1 and Dstn are both expressed in most or all cells of the developing kidney , while Cfl2 is apparently not expressed in kidney [18] . In order to investigate the requirement for ADF activity in ureteric bud morphogenesis , we studied the effects Dstn and Cfl1 mutations during renal development , initially using conventional loss-of function alleles . For Dstn , we used the Dstncorn1-2J mutant allele , which is phenotypically similar to the knockout allele [5] , [6] and we therefore refer to Dstncorn1-2J heterozygotes and homozygotes as Dstn+/− and Dstn−/− . We observed no renal or ureteric abnormalities in Cfl1+/− or Dstn+/− heterozygotes ( Figure 1A and 1B , and Figure 2A , 2C , 2E , 2G ) , or in Dstn+/−;Cfl1+/− compound heterozygotes ( Table 1 , column 3 ) . Twenty-three percent of Dstn−/− embryos displayed a duplicated ureter , sometimes resulting in a duplex kidney ( Table 1 , column 1; Figure 1C , Figure S1A , S1B ) , but renal development appeared otherwise normal ( data not shown ) . Surprisingly , none of the twenty-one Cfl1+/−;Dstn−/− embryos we examined displayed ureter duplications ( Table 1 , column 4 ) , but instead 38% had kidneys that were abnormally shaped , elongated and uneven on the surface ( Figure 1D , 1F ) , while an additional 29% had mildly hypoplastic kidneys ( approximately 25% reduced in size , e . g . , Figure 1H ) . Cultures of E12 . 5 Cfl1+/−;Dstn−/− renal explants sometimes showed a slight delay in branching , resulting in reduced UB tip numbers , but no obvious branching pattern abnormalities that might explain the abnormal kidney shapes were observed ( Figure S1C , S1D , S1E , S1F ) . It is not clear why removing one Cfl1 allele would apparently rescue the ureteric duplications seen in some Dstn−/− mutants , but this may be a consequence of the mixed genetic background . Overall , however , the spectrum of ureteric and mild renal abnormalities observed in some Dstn−/− and Cfl1+/−;Dstn−/− kidneys suggests a role for actin depolymerization during renal branching morphogenesis . Since Cfl1 and Dstn are expressed in most or all cell types in the kidney , the abnormal renal morphogenesis in some Cfl1+/−; Dstn−/− compound mutants could be due to either a direct effect in ureteric bud cells , or an indirect effect of faulty induction by the MM or stroma . To address this issue , and also to circumvent the early lethality in Cfl1−/− mice [4] we used a conditional knockout strategy to delete one or both Cfl1 alleles in the Wolffian duct and ureteric bud lineage . Mice carrying a floxed Cfl1 allele , Cfl1F [5] were crossed with Hoxb7/CreGFP , a transgenic line expressing Cre recombinase together with GFP in the Wolffian duct ( from ∼E9 . 5 ) and UB [19] . Deletion of one or both Cfl1 alleles in a Dstn+/+ background ( Hoxb7/CreGFP;Cfl1F/F or Hoxb7/CreGFP; Cfl1F/+ ) had no apparent effect on ureter or kidney development ( data not shown ) . This suggested that Cfl1 is not required in the WD/UB lineage when Dstn is present at wild-type levels . However , deleting both Cfl1 alleles in the UB in a Dstn+/− background ( Table 1 , column 8 ) , or deleting one Cfl1 allele in the UB in a Dstn−/− background ( Table 1 , column 6 ) , caused occasional ureter duplications , and the same occasional renal hypoplasia or abnormal kidney shapes as seen in Cfl1+/−;Dstn−/− mice ( Table 1 , column 4 ) . While the low frequency of animals with each of these specific genotypes precluded a quantitative comparison , the combined results indicate that normal cofilin1 levels in the UB are important , when Dstn is either reduced or absent , for normal renal and ureteric morphogenesis . When both Cfl1 alleles were deleted in the UB , in the Dstn−/− background ( Hoxb7/CreGFP; Cfl1F/F; Dstn−/− ) kidney development failed almost completely ( Figure 2A and 2B ) . Histological analysis revealed the presence of ureter and different nephron segments ( glomeruli , proximal and distal tubules ) but very little collecting duct epithelium ( Figure 2D ) , and no renal pelvis or proper cortex-medulla compartmentalization . Hoxb7/CreGFP; Cfl1F/−; Dstn−/− kidneys ( with one null and one floxed Cfl1 allele ) appeared identical to Cfl1F/F; Dstn−/− ( data not shown ) , and both of these genotypes will hereafter be called “double mutant” . As double mutant kidneys contained virtually no UB derivatives at E18 ( Figure 2D ) , we wanted to study the development of the UB at earlier stages . We used the GFP encoded by the Hoxb7/CreGFP transgene to visualize the UB in control and double mutant kidneys . The ureteric bud normally grows out from Wolffian duct at E10 . 5 , by E11 . 5 it has branched once to form the “T-bud” shaped kidney , and by E12 . 5 it has branched several more times ( Figure 2E ) . In all double mutant embryos examined at E11 . 5 ( N = 6 ) or E12 . 5 ( N = 8 ) , the two UBs had successfully grown out from the Wolffian ducts and elongated , but none of them had branched within the kidney ( Figure 2F ) . When cultured for 24 hours , the control kidneys continued to branch ( Figure 2G ) , while the double mutant failed to branch ( Figure 2H ) . Thus , deletion of Cfl1 using Hoxb7/CreGFP , in the absence of Dstn , did not prevent UB outgrowth , but completely blocked subsequent branching . The finding that the UB always formed in Cfl1;Dstn double mutant kidneys , but failed to branch , raised the possibility that the activities of cofilin1 and destrin are required for UB branching , but not for Wolffian duct growth or initial UB formation . Alternatively , there might be a delay in the elimination of cofilin1 by Hoxb7/CreGFP , such that there is still sufficient cofilin1 at the time of UB outgrowth ( E10 . 5 ) but not when branching initiates ( E11 . 5 ) . To study the timing and efficiency of cofilin1 elimination we stained mutant kidneys with anti-cofilin1 antibody . While the UB epithelium was clearly devoid of cofilin1 at E11 . 5 ( data not shown ) and E12 . 5 ( Figure 3C–3D' ) , confirming the activity of Hoxb7/CreGFP , we found that cofilin1 protein levels at E10 . 5 were normal in the forming ureteric bud ( Figure 3A–3B' ) . Thus , the ability of the UB to grow out in double mutants , but not to branch subsequently , is most likely due to residual cofilin1 expression at E10 . 5 , which is eliminated by E11 . 5 . A similar delay in cofilin1 protein elimination was observed when the floxed Cfl1 allele was deleted in the brain using nestinCre , a result that was attributed to the long half-life of the protein [5] . GDNF/Ret signaling is one of the most important pathways that promotes primary UB formation and subsequent branching [16] . To test if the UB branching defect in Cfl1;Dstn double mutants is due to insufficient GDNF/Ret signaling ( as occurs in many other mutants with defective UB branching ) [8] , [15] , we cultured double mutant kidneys with or without exogenous GDNF . When cultured without added GDNF , control kidneys with early T-shaped UBs at E11 . 5 developed several secondary branches over the next 48 h in culture ( Figure 4A , 4B , 4I ) , while those cultured with GDNF showed a swelling of the UB tip and ectopic budding from Wolffian duct ( Figure 4C , 4D , 4J ) , the typical response [20] . The Cfl1;Dstn double mutant UBs had not branched normally when dissected at E11 . 5 ( Figure 4E ) and when cultured for 48 hrs without added GDNF they elongated slightly but did not branch ( Figure 4F , 4K ) . Added GDNF had no effect on UB morphogenesis in Cfl1;Dstn double mutant kidneys cultured from E11 . 5 , and it was also unable to induce ectopic ureteric bud formation from the double mutant Wolffian duct ( Figure 4G , 4H , 4L ) . However , all kidneys lacking two or three out of the four Cfl1;Dstn alleles responded like wild-type kidneys to GDNF treatment ( data not shown ) . The finding that exogenous GDNF was unable to rescue UB branching in Cfl1;Dstn double mutant kidneys had at least two potential explanations . First we explored the possibility that lack of expression of the GDNF receptor Ret impairs the ability of ureteric epithelium to respond to GDNF signals . However , we found that Ret was expressed in a normal pattern ( i . e . , at the UB tip ) and normal level in E11 . 5 double mutant kidneys ( Figure S2A , S2B ) , but was somewhat reduced at a slightly later stage ( Figure S2C , S2D ) . Like Ret , the Gdnf/Ret target gene Wnt11 [21] was expressed normally in the E11 . 5 UB tip ( Figure S2E , S2F ) . Thus , a lack of Ret expression or Ret signaling in the UB seemed not to be the cause of the failure to branch . We next explored an alternative explanation , that defective branching was primarily due to cytoskeletal changes caused by the lack of ADF activity . Dynamic actin cytoskeleton rearrangements are involved in several cellular processes , such as apoptosis , proliferation and migration [1] . We took the advantage of the Hoxb7/myrVenus transgenic line , which expresses myristylated-Venus fluorescent protein at the cell membrane [22] , to visualize epithelial cell shape and organization in the ureteric buds of Cfl1;Dstn double mutant mice . Confocal scanning of the UB epithelium at E12 . 5 revealed a variety of cell shapes in control kidneys , but the epithelium was well organized and cell outlines smooth and distinct ( Figure 5A and 5B ) . In contrast , UB epithelial cells in Cfl1;Dstn mutant kidneys were disorganized , irregular in size and shape , and contained abnormal membranous ( i . e . , Venus-positive ) bodies ( Figure 5C and 5D ) . Thus , lack of both cofilin1 and destrin in the UB disrupts normal epithelial cell shape and organization . In vivo , mutations in ADF genes have variable consequences on F-actin; they can cause accumulation of actin filaments [5] , [6] , [23] or a shortage of actin filaments [4] , [23] . The latter suggests involvement of ADFs in actin nucleation , and is supported by in vitro studies [24] , [25] . To understand how the absence of cofilin1 and destrin in UB epithelium affects the actin cytoskeleton , we stained sections of control and double mutant kidneys with phalloidin to visualize F-actin at E11 . 5 ( data not shown ) and E12 . 5 ( Figure 5E–5H ) . As reported previously [12] , the strongest phalloidin staining in control kidneys was observed at apical membranes of UB epithelial cells , but maximal projections of confocal images also revealed some actin filaments at the basolateral membranes ( Figure 5E–5F ) . In accordance with the normal cofilin1 levels in UB epithelium of Cfl1;Dstn double mutant kidneys at E10 . 5 ( Figure 3A–3B' ) , phalloidin staining was indistinguishable in double mutant and control kidneys at this stage ( Figure S3 ) . However , the epithelium of double mutant kidneys was full of phalloidin-positive inclusions at E11 . 5 ( data not shown ) and E12 . 5 ( Figure 5G–5H ) . Accumulation of F-actin was strongest in the apical membranes but also obvious on the basolateral sides of mutant epithelial cells . These data suggest that cofilin1 and destrin are not required for actin nucleation in the UB epithelium but pivotal in its depolymerization and turnover . Accumulation of actin filaments within the cells can impair their proliferation and migration [1] . No differences in the mitotic indices ( % of phosphohistoneH3+ cells ) of Cfl1;Dstn double mutant ( 1 . 6%±0 . 5 , n = 4 ) and control ( 1 . 6%±0 . 3 , n = 4 ) UB epithelium were detected at E11 . 5 ( data not shown ) suggesting that the primary cause for the branching defect in mutant mice is not a defect in cell proliferation . While the double mutant UBs did not branch , their continued elongation after E11 . 5 presumably reflects this continuing cell proliferation . Primary cell cultures derived from the UB [26] , allowed us to apply a scratch assay to non-immortalized primary cell cultures obtained from UBs of different Cfl1;Dstn genotypes . Briefly , individual UBs at E11 . 5 or E12 . 5 were separated from the surrounding mesenchyme and plated in fibronectin-coated wells , where the UB cells attached to the bottom and formed monolayers within the next 48 h . These primary epithelial cells survived for approximately two weeks without immortalization ( for details , see Materials and Methods ) . The UB cells of all genotypes including double mutants remained quiescent as judged by lack of the proliferative marker Ki67 ( Figure S4A and data not shown ) . The cells in such cultures were positive for the UB epithelial marker pan-cytokeratin , confirming their origin from the UB ( Figure S4B ) . No differences in the capacity to adhere or form monolayers were observed between control cells ( e . g . , Dstn−/− ) and those from Cfl1;Dstn double mutant UBs ( Figure S4C , S4D ) . Migration of epithelial cells was studied by introducing a scratch in the confluent cell monolayers at 48 h after plating the UB . Cultures were photographed after 3 h , 8 h and 24 h . The control cells migrated to completely fill in the gap by 24 h ( n = 12 , Figure 6A and 6B ) , while Cfl1;Dstn double mutant cells showed some movement but always failed to close the gap ( n = 5 , Figure 6C and 6D ) . Similarly to what we observed in the UB epithelium of E11 . 5 and E12 . 5 double mutant kidneys in vivo , the double mutant cells in culture were heterogeneous in size and morphology ( Figure 6C and 6D ) and huge F-actin accumulation was evident by phalloidin staining ( data not shown ) , as it is in the intact UB ( Figure 5 ) . As Cfl1;Dstn double mutant kidneys are delayed in their growth at E11 . 5 ( Figure 4 ) , we were concerned that , in addition to the abnormal cell morphology , the reduced number of cells in each UB might influence their ability to migrate in the scratch assay . To avoid this potential problem , we also measured the migration of primary UB cells isolated from Hoxb7/CreGFP;Cfl1F/F;Dstn+/− kidneys , which exhibit approximately similar numbers of UB branches as control kidneys at E12 . 5 ( data not shown ) . The morphology of primary UB cells derived from Hoxb7/CreGFP;Cfl1F/F;Dstn+/− kidneys ( Figure 7D–7F ) was much better than that of double mutant cells ( Figure 6C and 6D ) , and most of the time they had filled the gap by 24 h after the scratch was made ( data not shown ) . However , their migration rate was slower than that of the wild-type cells: at 8 hr post-scratch , while wild-type cells had filled 61% of the gap , the mutant cells had filled only 31% of the gap ( p<0 . 009 ) ( Figure 7A–7G ) . Thus , even cells retaining one Dstn allele , in the absence of Cfl1 , have a migration deficit . Altogether , these data suggest that loss of cofilin1 and Destrin in Cfl1;Dstn double mutants causes actin accumulation and defects in epithelial organization and cell migration , resulting in a failure of branching morphogenesis . Cfl1 was identified as one of the potential Ret-induced genes in Ret-expressing NIH3T3 cells [14] and we were therefore interested in determining if Cfl1 could be induced by Ret signaling in the ureteric epithelium . Both gain- and loss-of-function strategies were used to analyze Cfl1 mRNA and protein regulation by GDNF/Ret signaling . Cfl1 is expressed in ureteric epithelium and metanephric mesenchyme of E11 . 5 kidneys cultured for 24 h ( Figure 8A–8A' ) . While GDNF-soaked beads induced ectopic ureteric budding and local swelling of the UB tip , confirming the functionality of the protein , no change in Cfl1 mRNA expression was observed ( Figure 8B–8B' ) . Two different Ret mutant mouse lines were used to study the effect of either reduced Ret signaling ( Ret-hypomorphic mice ) or lack of Ret signaling ( Ret−/− mice ) on cofilin1 protein levels . The Ret-hypomorphic mutant Rettm2 ( RET ) Vpa has reduced UB branching [27] while UB formation fails in most of the Ret−/− kidneys [28] . Cofilin1 was present at normal levels in the early UB of E10 . 5 Ret−/− mutants ( Figure 8C , 8D ) as well as in E15 Ret-hypomorphic kidneys ( data not shown ) . Furthermore , the distribution of F-actin appeared normal in both types of Ret-mutant kidneys ( Figure 8E , 8F and data not shown ) . Therefore , although the failure of UB outgrowth in Ret−/− kidneys is phenotypically similar to the phenotype of Cfl1;Dstn double mutant UBs , this is apparently not due to reduced expression of Cfl1 in the Ret mutant .
We examined the requirement for cofilin1- and destrin-mediated cytoskeletal functions during UB branching . While animals retaining at least one wild type Cfl1 or Dstn allele exhibited either normal kidneys or low a frequency of renal/ureteric defects , double homozygotes lacking any cofilin1 or destrin in the UB epithelium had a severe branching defect at an early phase of kidney development . Characterization of cellular defects in Cfl1;Dstn double mutant animals revealed a huge accumulation of F-actin in UB cells , and a disorganized epithelium , at the same stage when the block in branching occurred . We found that primary UB epithelial cells isolated from Cfl1;Dstn double mutants were impaired in their ability to migrate , suggesting that the block in UB growth and branching in vivo is due , at least in part , to disruption in normal cell motility . One important issue that our study addressed was the extent of functional overlap between cofilin1 and destrin , in vivo . The biochemical properties of these two proteins are highly similar , but significant functional differences have been observed in vitro: for example , destrin is more active in actin-depolymerization , while cofilin1 is a more potent nucleator of actinADP assembly [29] . These differences , as well as differences in expression patterns , led to the suggestion that the ADFs evolved to fulfill specific requirements for actin filament dynamics in different cell types [18] . During development , the ability of Cfl1 and Dstn to substitute for each other depends on two factors: their overlap in expression and their overlap in function . Cfl1 is more widely expressed than Dstn in the embryo [18] , so the ability of Dstn−/− mice to develop normally ( except for corneal defects ) could be due to the ability of cofilin1 to replace the absent destrin in many cell types . In Cfl1−/− mutants , absence of cofilin1 throughout the embryo results in embryonic lethality at E11 . 5 , with specific defects in the neural tube and neural crest cells , even though Dstn is strongly upregulated in the Cfl1−/− embryo [4] . This suggested a specific function for cofilin1 . Similarly , the conditional knockout of Cfl1 in the brain ( where Dstn is coexpressed ) resulted in F-actin accumulation and defects in cell migration and cell cycle progression , indicating that cofilin1 performs roles that cannot be assumed by destrin [5] . In this study , a direct comparison of mice lacking Cfl1 , Dstn or both genes in the UB allowed a clear test of redundancy . The lack of any cellular or developmental defects , or abnormal F-actin accumulation , in the kidneys of Dstn−/− or Hoxb7/CreGFP;Cfl1F/F mice suggests that either cofilin1 or destrin is sufficient for normal cellular functions . Furthermore , when both genes were deleted in the UB cells , there was a complete block to UB growth and branching . This clearly indicates that only in the absence of Cfl1 is Dstn required , while only in the absence of Dstn is Cfl1 required – in other words , there is high degree of functional overlap , at least in this cell lineage . In embryos in which both Cfl1 alleles were deleted in the Wolffian duct/ureteric bud lineage , in a Dstn−/− background , UB growth was arrested shortly after outgrowth from the Wolffian duct , but before further branching . The timing of this developmental block was apparently a consequence of the slow turnover of Cofilin1 protein following deletion of the floxed gene: although the Hoxb7 promoter is active in the Wolffian duct at least as early as E9 . 5 [22] , [30] , and the Cfl1F alleles were presumably deleted in most or all cells by E10 . 5 , cofilin1 protein was still present at normal levels in the UB at E10 . 5 . Cofilin1 was not absent , nor did excess F-actin accumulate , until ∼E11 . 5 , the approximate stage at which UB branching ceased . Thus , there is no reason to believe that ADF activity has a specific role in UB branching: it likely has a more general role in UB epithelial morphogenesis . ADFs are important in various cellular processes involving the actin cytoskeleton [1] , [31] , [32] . We found that simultaneous lack of Cfl1;Dstn in UB epithelium does not impair the cells' ability to proliferate at normal rates . Therefore , the block in UB branching is not simply due to lack of cell division . Recently it has been demonstrated that cell movements in the Wolffian duct epithelium are important for primary UB outgrowth [10] , and it is likely that similar cell movements continue to play a role during later UB growth and branching events . Therefore , the defects in motility of Cfl1;Dstn double mutant UB cells , which we observed in a scratch assay using primary UB cell cultures , provides one plausible explanation for the failure of UB growth . In addition to the migratory defects observed in cell cultures , the double mutant UB epithelium in vivo displayed unusual heterogeneity in cell size and shape , which is possibly a result of the abnormal accumulation of F-actin preventing the normal re-shaping of epithelial cells during branch formation . Mice with intermediate numbers of mutant alleles ( e . g . , with one remaining wild type Dstn or Cfl1 allele ) usually developed normal kidneys and ureters , revealing that a single Dstn or Cfl1 gene is usually sufficient , in the UB . But a fraction of these mice displayed visible defects , including double ureter , mild renal hypoplasia or irregular kidney shape . This effect of reduced gene dosage suggests that the level of total ADF expression ( cofilin1 + destrin ) in UB cells is important , which is consistent with the model , based on biochemical studies , that the various activities of ADFs ( severing , stabilizing or nucleating actin filaments ) are concentration-dependent [1] . UB cells with only one wild type Dstn allele and no Cfl1 ( Hoxb7/CreGFP; Cfl1F/F; Dstn+/− ) also showed a migration defect in the scratch assay , although less severe than the double-null cells; it is possible that this cellular defect contributes to the phenotypic defects observed in some mice of these genotypes . Ureter duplications , which occur when the Wolffian duct gives rise to two UBs instead of one , were also seen in a fraction of Dstn−/− mice . Ureter duplication can result from excess GDNF/Ret signaling , ectopic GDNF expression , or reduced BMP4 , an inhibitor of UB outgrowth [15] , but the cause of this defect in ADF-deficient mice is not clear . It has also been observed in embryos lacking the chemokine SDF-1/CXCL12 or its receptor CXCR4 ( F . C . and B . Lu , unpublished data ) , which play an important role in cell migration , and are also known to activate ADF proteins [33] . We therefore asked if Cxcr4 and Dstn would genetically interact in the control of ureteric bud formation , by examining Dstn−/−;Cxcr4−/− mutant embryos . These embryos displayed no increase in double UB formation compared to Dstn−/− alone , failing to support a synergistic role of CXCR4 signaling and ADF activity in this process . Renal hypoplasia can result from reduced UB growth and branching; while we did not observe a consistent reduction in early UB branching in cultured E11 . 5 kidneys from embryos of the intermediate genotypes that sometimes cause renal hypoplasia , this may be due to the low penetrance of the defect . The irregular kidney shape sometimes observed in mice of several of the intermediate genotypes is an unusual phenotype , which has not been previously described , to our knowledge . It is likely that this also results from an abnormality in UB growth or elongation , as the collecting duct system is thought to be the main determinant of kidney shape [34]; however , no particular abnormality in early UB morphogenesis was observed in these kidneys , suggesting that the abnormal renal shape arises at a later stage of organogenesis , or else is not revealed in cultured kidneys , which flatten and lose their three dimensional shapes . One of the most important pathways regulating UB branching is GDNF/Ret signaling , which was previously shown to induce Cfl1 expression in NIH3T3 cells [14] . The finding that Cfl1 expression is not regulated by GDNF/Ret signaling in developing kidney was therefore to some extent a surprise , but it is supported by a recent GDNF target screen performed in our laboratory [35] , in which no changes in Cfl1 expression were detected in the UB . We cannot exclude the possibility that activation of Ret signaling would affect ADF protein activity , which is regulated by its phosphorylation/dephosphorylation status [1] , but even if this was the case , Ret signaling apparently does not play major role in regulation of actin cytoskeleton dynamics in these cells , as the actin cytoskeleton appeared normal in Ret−/− epithelium . In summary , our results demonstrate that actin depolymerization by the cooperative function of cofilin1 and destrin is essential for normal UB branching , which is blocked due to F-actin accumulation in UB epithelial cells of double mutant Cfl1;Dstn mice . As renal abnormalities are common in newborns , this finding may help to understand the origins of certain congenital malformations in humans .
All work on animals was conducted under PHS guidelines and approved by the relevant Institutional Animal Care and Use Committees . Cfl1 null and floxed alleles as well Ret−/− and Ret hypomorphic ( Rettm2 ( RET ) Vpa ) mice and their genotyping by PCR have been described [4]–[6] , [27] , [28] . Dstncorn1-2J [6] mice were genotyped by amplifying the region of genomic DNA where the single mutation occurs with the primers 5′ TCC ACT GCA GCT GTC TTCAGACA 3′ and 5′ ATG ACA AAC CAA TGG ATC CCC AC 3′ , then digesting with BanI , whose recognition site is mutated , resulting in a Pro106Ser substitution , in Dstncorn1-2J mice . All mice were on mixed genetic backgrounds ( including strains C57BL6/J , FVB/N and 129/SvEv ) except for Ret+/− mice , which were inbred 129/SvEv . E11 . 5 or E12 . 5 kidneys were isolated and cultured on Transwell filters ( Fisher ) in DMEM with 10% fetal calf serum , 1% Glutamax and 1% penicillin/streptomycin at 37°C and 5% CO2 for the indicated times . For GDNF beads , Affigel blue beads ( 100–200 mesh , Bio-Rad ) were washed with PBS/0 . 1% BSA before incubating with 50 ng/µl recombinant GDNF ( R&D ) for 30 min at 37°C . Control beads were prepared similarly but incubated in 1% BSA . For organ cultures with GDNF in the culture medium , the concentration was 100 ng/ml . Whole mount immunofluorescence staining with anti-Pax2 ( 1∶200 , Zymed ) and anti-pan-cytokeratin ( 1∶200 , Sigma ) antibodies ( AB ) was performed as previously described [36] . PFA-fixed 10 µm frozen sections were stained with anti-Calbindin AB ( 1∶200 , Santa Cruz ) , phosphohistone-H3 ( 1∶100 , Cell Signaling Technology ) and phalloidin ( 1∶40 , Molecular Probes ) . Antigen retrieval by 1 mg/ml pepsin ( Sigma ) digestion ( 10 min , 37°C ) was performed for the samples stained with Cofilin1 AB [4] . For quantification of mitotic indexes , total epithelial cells and phosphohistone-H3+ epithelial cells were counted in sections through four E11 . 5 Dstn+/− and four Hoxb7/CreGFP; Cfl1F/F; Dstn−/− kidneys . For each specimen , the UB cells were counted in 15–19 serial sections . The percentage of pH3+ epithelial cells in mutant and control samples were compared using Student's t-test ( two-tailed , equal variance ) . Samples for whole mount in situ hybridization were dipped in ice-cold methanol , and fixed in 4% PFA overnight . Hybridization with digoxigenin-labeled Cfl1 [4] and Ret [37] riboprobes was performed according to Wilkinson [38] . After an enzymatic treatment with collagenase ( 4 µg/µl , Gibco ) ureteric buds were dissected free of metanephric mesenchyme and placed on fibronectin coated wells ( BD Biosciences ) containing DMEM supplemented with 10% FBS , 1% Glutamax , 1% penicillin/streptomycin , 5 ng/ml GDNF , 25 µg/ml FGF2 and 50 µg/ml HGF . Cultures were allowed to settle and form single cell layers for 48 h , before introducing the scratch , using standard 10 µl plastic pipette tips . For immunofluorescence staining , single cell layers were fixed in 4% PFA for 10 min , washed with PBS , and incubated with anti-pan-cytokeratin ( 1∶200 , Sigma ) and anti-Ki67 ( 1∶100 , Abcam ) antibodies or with phalloidin ( 1∶40 , Molecular Probes ) . To quantify the cell migration , the width of the gap formed by the scratch was measured immediately after the scratch , and 3 h and 8 h later . The proportion of gap filled was calculated by dividing the width at each time by the initial gap width . All measurements were done using Image J program . | Development of the ureter and collecting ducts of the kidney requires extensive growth and branching of an epithelial tube , the ureteric bud . While many genes that control this process are known , the cellular events that underlie renal morphogenesis remain poorly understood . Many cellular changes that might contribute to ureteric bud morphogenesis , such as migration and changes in shape , involve the actin cytoskeleton . Actin depolymerizing factors ( ADFs ) are important for changes in the organization of the cytoskeleton in cultured cells , but the roles of the ADF genes in vivo remain to be fully elucidated . Here , we examine the importance of the ADFs cofilin1 and destrin in ureteric bud branching and find that lack of both genes arrests this process at an early stage , while lesser reductions in ADF gene dosage cause more subtle defects in kidney development . This finding may help us to understand the origins of certain congenital malformations in humans . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/animal",
"genetics",
"developmental",
"biology/morphogenesis",
"and",
"cell",
"biology",
"cell",
"biology/morphogenesis",
"and",
"cell",
"biology",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"genetics",
"and",
"genomics/gene",
"... | 2010 | Actin Depolymerizing Factors Cofilin1 and Destrin Are Required for Ureteric Bud Branching Morphogenesis |
Legumes have an intrinsic capacity to accommodate both symbiotic and endophytic bacteria within root nodules . For the symbionts , a complex genetic mechanism that allows mutual recognition and plant infection has emerged from genetic studies under axenic conditions . In contrast , little is known about the mechanisms controlling the endophytic infection . Here we investigate the contribution of both the host and the symbiotic microbe to endophyte infection and development of mixed colonised nodules in Lotus japonicus . We found that infection threads initiated by Mesorhizobium loti , the natural symbiont of Lotus , can selectively guide endophytic bacteria towards nodule primordia , where competent strains multiply and colonise the nodule together with the nitrogen-fixing symbiotic partner . Further co-inoculation studies with the competent coloniser , Rhizobium mesosinicum strain KAW12 , show that endophytic nodule infection depends on functional and efficient M . loti-driven Nod factor signalling . KAW12 exopolysaccharide ( EPS ) enabled endophyte nodule infection whilst compatible M . loti EPS restricted it . Analysis of plant mutants that control different stages of the symbiotic infection showed that both symbiont and endophyte accommodation within nodules is under host genetic control . This demonstrates that when legume plants are exposed to complex communities they selectively regulate access and accommodation of bacteria occupying this specialized environmental niche , the root nodule .
Plants are the major manufacturers of carbohydrates in ecosystems , and their roots develop in soil environments rich in heterotrophic microorganisms that require carbon for their growth . To adapt to this habitat , plants have evolved sophisticated surveillance systems for monitoring microbial presence , or invasion and corresponding response strategies [1–4] . As a consequence , only a limited range of microbes , endophytes and symbionts have the ability to colonise internal plant tissues with minimal or no host damage [5 , 6] . The legume-symbiotic rhizobia interaction is a well-studied example of a very selective and clearly defined host/non-host plant-microbe association . Rhizobial-produced lipochitin oligosaccharide ( Nod factors ) are recognised by receptors in the host that subsequently trigger cell dedifferentiation , organogenesis and infection of root nodules [7–9] . In most legumes the infection starts at the stage of bacterial entrapment within curled root hairs . This is followed by initiation and elongation of infection threads ( ITs ) , which are plant-derived tubular structures that guide the microbe through the plant’s epidermal and cortical cell layers towards the nodule primordia , in which the bacteria are endocytosed in organelle-like symbiosomes where they develop into bacteroids and fix nitrogen . Characterization of plant mutants impaired at different stages of the symbiotic process has identified genes required to establish and regulate this microbial infection . In Lotus japonicus , SymRK , Nup133 , Nup85 , Nena , Castor and Pollux act upstream of the nuclear calcium spiking induced by the symbionts and are required for nodule organogenesis [10–14] . A calcium-calmodulin dependent kinase , CCaMK , subsequently interprets these calcium oscillations and interacts with CYCLOPS to coordinate infection with organogenesis [15–17] . Activation of cytokinin signalling via the LHK1 receptor leads to cell division , and downstream transcriptional activators Nsp1 , Nsp2 and Nin , control both the infection and the organogenesis [18–21] . In Lotus , which has spherical determinate nodules with a transient meristem , genes involved in actin rearrangement or nucleation ( Nap1 , Pir1 , ArpC1 ) , a putative ubiquitin E3 ligase ( Cerberus ) and a pectate lyase ( Npl1 ) are required for IT initiation and progression towards primordia , a process which also appears to be controlled by two genes , Alb1 and Crinkle , whose products await identification [22–27] . Later in the developmental process several genes , for example Sst1 , encoding a sulphate transporter , are required for bacterial persistence inside the plant cell [28] , and Medicago truncatula , which develops indeterminate nodules with a persistent meristem , produces nodule-specific cysteine-rich ( NCR ) peptides to control the irreversible terminal differentiation of bacteria [29] . From the bacterial side , Nod factors are the main signals recognised by the host , but lipopolysaccharides ( LPS ) , exopolysaccharides ( EPS ) and cyclic beta-glucans are also critical for infection and bacterial release inside the plant cells [30–32] . In addition , an array of species-specific bacterial effectors orchestrates another level of the specificity identified in the legume-rhizobia symbiosis [33–35] . Given that the final outcome of this highly controlled host-microbe interaction is the bacterial fixation of atmospheric nitrogen in exchange for plant-produced carbohydrates , it is surprising , that , as far as it is currently known , the host selects its symbiont on the basis of bacterial features that are not correlated with their capacity to fix nitrogen [36–39] . In accordance with this notion , inventories of bacterial species retrieved from nodules of legumes growing in a variety of environmental conditions and soils revealed a bacterial community composed of both symbionts and endophytes [40–46] . The presence of poor or even nonsymbionts within nodules of economically important legumes may thus negatively affect the efficiency of their symbiotic nitrogen fixation , and hence plant growth [47 , 48] . However , to date there has been no evaluation of the role of endophytic bacteria in pioneer legumes grown in poor soils where fully compatible , highly efficient nitrogen fixing symbionts are either low in titre , or which might need to evolve into more effective symbionts [49] . Interestingly , recent experimental evolution studies have revealed that in the presence of the legume host as a selective environment , a more rapid evolution of symbiont compatibility takes place in a bacterial community [50] . Nevertheless , the co-habitation of diverse bacteria inside nodules raises questions with respect to endophyte recognition by the plant , their infection path ( s ) , and the mechanisms employed by the host-symbiont-endophyte interacting partners leading to access and accommodation of endophytes . Currently there is limited information regarding the entry mode of endophytic bacteria and the role of the legume host , or the proficient symbiont , in the process of nodule colonisation by endophytes . Here we report that in Lotus the colonisation of nodules by endophytic bacteria follows a selective process with at least three steps , that endophyte nodule occupancy is host-controlled , and that exopolysaccharides represent key bacterial features for chronic infection of nodules .
In order to test the ability of endophytic bacteria to colonise and multiply inside Lotus nodules we chose to: i ) investigate endophytic bacteria that were previously found inside plant roots , as endophytes or presumptive endophytes , and ii ) monitor their ability to colonise nodules by visualising their presence inside primordia induced by the M . loti symbiont . In our tests we included Herbaspirillum frisingense GSF30 , Herbaspirillum sp . B501 endophytic bacteria from Miscanthus and rice ( Oryza sativa ) , respectively [51 , 52] , Rhizobium giardinii sp . 129E isolated from Arabidopsis roots [53] , and Burkholderia sp . KAW25 ( KAW25 ) , R . mesosinicum KAW12 ( KAW12 ) isolated from Lotus roots ( see Material and Methods ) . None of these bacterial strains induced nodule formation when applied individually to Lotus roots . Fluorescently labelled endophytes and M . loti were mixed in a 1:1 inoculum , which was applied to Lotus seedlings . After nodule development , whole nodules , or hand sections were inspected microscopically for the presence of the two bacterial strains ( Table 1 ) . We found that , with the exception of H . frisingense , the other four strains were present inside the nodules or the cortical ITs induced by M . loti ( Figs 1A and S1 ) , but endophyte amplification and effective colonisation of the nodule interior was observed only for Burkholderia KAW25 and Rhizobium KAW12 ( Figs 1B and S1C ) . These results show that in Lotus , the infection threads induced by M . loti can be inhabited by an endophyte ( Fig 1A and 1C ) , and that particular bacteria have the capacity to employ this route for access into the nodules in which they multiply . We observed that even when both symbiotic and endophytic bacteria were able to infect the nodule , the well-adapted symbiont , M . loti , occupied most of the nodule interior , while KAW12 or KAW25 remained within small , distinct sectors ( Figs 1B and S1 ) . Interestingly , the host response to the endophytic infection by KAW12 and KAW25 was different . Nodule sectors containing KAW25 bacteria were found to show signs of necrosis ( S1C Fig ) , whilst no similar response was detected in the nodules containing KAW12 ( Fig 1B ) . This indicates that infection and multiplication of endophytic bacteria within Lotus nodules is based on host-microbe compatibility . Among the five different bacteria included in our study , KAW12 presented the highest level of nodule infection . One third of tested plants ( 29 . 8% ) contained at least one KAW12-infected nodule ( Table 1 ) , and 20 out of the 243 analysed nodules ( 8 . 2% ) were co-infected by KAW12 , demonstrating the ability of this endophyte to colonise Lotus nodules . These results based on analysis of a limited , but diverse set of endophytes show their differential capacity for nodule infection in the presence of M . loti , and that a sequential selection process shapes the community of bacterial inhabitants inside the nodules , i . e . i ) access and/or persistence inside the IT , ii ) within the nodule , iii ) multiplication within the nodule without causing damage to the host . KAW12 was identified as a root-inhabiting bacterium in Lotus plants grown in Japanese forest soil ( see Material and Methods ) , that causes no obvious effect ( positive or negative ) on its host ( S3 Fig ) . A comparison of its 16S rRNA sequence against known bacteria revealed a close relationship to nodulating Rhizobium species ( S2A Fig ) . In spite of this similarity to symbiotic bacteria , Nod and Nif gene clusters , including key symbiotic genes such as nodC , which is required for Nod-factor synthesis , and nifH , which encodes the Fe subunit of nitrogenase , were not found in the KAW12 genome ( S2C Fig ) . In order to determine the type of infection that this bacterium , which neither produces Nod factors nor fixes atmospheric nitrogen , establishes with Lotus , the KAW12 derivative constitutively expressing DsRED was used for detailed analyses . Confirming the absence of symbiotic genes , KAW12 alone , or co-inoculated with M . loti nodC was unable to induce root hair curling or microcolony formation and was unable to nodulate Lotus ( S3 Fig ) . A nitrogen-starved phenotype was observed when KAW12-inoculated plants were grown under low nitrogen conditions ( 1 mM KNO3 ) compared to plants inoculated with the effective symbiont M . loti ( Fig 1D and 1E ) . However , careful inspection of KAW12-inoculated tissue revealed that KAW12 colonised the intercellular spaces of Lotus roots ( Figs 1F and S3A ) . These results illustrate that KAW12 is a nonsymbiotic Rhizobium with endophytic features and a capacity for infecting symbiotic nodules . Research into the binary interaction between legumes and nitrogen fixing rhizobia has revealed that a number of molecular components produced by the bacteria are required and/or contribute to a successful symbiotic association . Nod factor , EPS , LPS , cyclic beta-glucans and Type-III Secretion System ( T3SS ) effectors have been shown to be major modulators of the host response [31] . The increased capacity of KAW12 to infect Lotus nodules , together with its apparent acceptance by the Lotus host , provided us with a unique opportunity to study the interplay between the legume host and various bacterial partners during mixed infections , and to identify molecular and genetic components contributing to nodule infection by endophytic bacteria . In order to investigate if KAW12 has the capacity to launch an active infection once the symbiotic Nod factor signalling has been initiated in Lotus roots , we used two different symbiotic bacteria as co-inoculating partners i . e . Azorhizobium caulinodans ORS571 , and a M . loti nodZ mutant . Azorhizobium caulinodans ORS571 , a symbiont of Sesbania rostrata [54] induced root hair curling , microcolony formation , and a large number of nodule primordia ( 17 per plant ) , but approximately 99% of them remained uninfected ( Table 1 ) . Furthermore , ITs penetrating the primordia were not observed , indicating that the infection pathway induced by A . caulinodans Nod factors is only partly effective . This restricted symbiotic development was used as a background for assaying the contributions from KAW12 during infection . At 6 weeks after inoculation with A . caulinodans and KAW12 only 3 of 26 plants had nodules colonised by KAW12 , and the overall frequency of colonisation was also very limited ( 4 out of 231 primordia ) ( Table 1 and S4 Fig ) . We then tested the ability of KAW12 to colonise the nodules induced by the M . loti nodZ mutant . This mutant strain produces Nod factors lacking the acetylated-fucosyl decoration , and as a consequence the induction of primordia and the infection process are delayed and less effective [55] . Inspection of plants inoculated with the M . loti nodZ and KAW12 , showed that only 2 . 4% of the induced nodules were infected by the endophyte . This is more than 3-fold fewer than in the co-inoculation with the M . loti wild-type ( 8 . 2% ) . The frequency of plants containing at least one KAW12-colonised nodule was also reduced; 12% compared to 29 . 8% in the wild-type M . loti co-inoculation ( Fig 1G and Table 1 ) . This lower frequency of bacterial infection in the absence of a fully functional Nod factor signalling indicates that signalling components possessed by KAW12 cannot complement nor bypass an ineffective Nod factor-dependent infection pathway . In addition to Nod factor-induced signalling , host perception of compatible bacterial polysaccharides , such as EPS , is also important for symbiont recognition and efficient nodule infection [37 , 56] . For example , in Lotus , perception of incompatible EPS produced by M . loti R7A exoU mutant severely impairs IT initiation and elongation is reduced , and consequently infected nodules are rare [32] ( Table 1 and Fig 2A and 2B and S5A ) . Considering that Nod factor signalling is functional in the Lotus-exoU interaction [32] , we investigated the ability of KAW12 to colonise nodules in the presence of incompatible symbiotic EPS signalling . Analysis of plants co-inoculated with exoU and KAW12 revealed that KAW12 had the ability to colonise the primordia and the ITs initiated by exoU bacteria ( Table 1 and Fig 2A and 2C and Fig 2D ) . Infection threads colonised by KAW12 reached the base of the root hair where they expanded into an infection pocket , and from there , bacterial infection progressed into the underlying nodule primordium ( Fig 2E ) . This indicates that the KAW12 endophyte has the capacity to rescue , and progress the arrested infection process induced by the exoU . Nodules infected by the exoU , KAW12 , or by both bacteria , could be observed based on fluorescence marker screening ( Fig 2A–2C ) . Unexpectedly , the majority of plants ( 98% ) had at least one nodule containing KAW12 , and overall 33% of primordia ( 1169 of 3588 nodules ) were infected by KAW12 , suggesting that molecular features of KAW12 may substitute for the lack of compatible M . loti EPS ( Table 1 ) . The increased frequency of KAW12-colonised nodules ( 33% compared to 8 . 2% in M . loti wild-type co-inoculation ) also indicates that KAW12 infection is competitively restricted by the fully compatible EPS produced by wild-type M . loti . The ability of KAW12 to overcome the arrested infection of the exoU suggested that KAW12 EPS might act as an important factor for its nodule colonisation ability . We tested this hypothesis by investigating the capacity of EPS-defective KAW12 to colonise the exoU-induced nodules . An EPS mutant of KAW12 was isolated from a random mutagenesis screen utilising the transposon mTn5-GNm [57] . The gene disrupted in this mutant encodes for a protein that shows high similarity ( 70% ) to PssN from R . leguminosarum ( S6A Fig ) , which is involved in polymerisation and export of EPS [58–60] . In contrast to the wild-type KAW12 , the eps mutant displayed a non-mucoid colony growth phenotype , a typical characteristic of EPS deficiency ( S6B Fig ) . In planta analyses of the colonisation phenotype revealed that this mutant , despite its presence inside root hair ITs when co-inoculated with the exoU ( S6C Fig ) , was unable to infect and multiply within nodules , while exoU maintained its low infection ability ( Table 1 ) . In the reciprocal experiment , we found that co-inoculation of the KAW12 eps mutant with M . loti wild type enabled access of endophytes inside nodules , albeit to a very low frequency compared to EPS proficient KAW12 wild type bacteria ( S6D Fig and Table 1 ) . These results show that EPS is an important molecular feature of KAW12 allowing it to colonise the symbiont-induced primordium , and that co-infecting bacteria may complement each other for the lack of compatible EPS . These co-inoculation studies pinpoint the critical role of EPS during nodule infection by symbiotic and endophytic bacteria , and have revealed that compatible EPS provides the wild-type symbiont with a clear advantage over the endophyte during mixed nodule infection . The nodule is a unique root organ where the intracellular accommodation and multiplication of compatible symbionts is permitted . Many of the nodules infected by KAW12 were abundantly colonised by endophytic bacteria in comparison to their sparse infection of the root intercellular spaces ( Fig 1B and 1F ) . In spite of this increased nodule colonisation no signs of hypersensitive reactions or necrosis were observed in KAW12-colonised nodules ( Fig 2A ) . This apparent acceptance of KAW12 endophytic bacteria by the host might be due to their distinct colonisation pattern within nodules that involves inter- and/or intracellular accommodation . To investigate this we studied the infection pattern of KAW12 in more detail using light and transmission electron microscopy ( TEM ) applied to selected nodules ( Material and Methods ) . We observed that KAW12 multiplied extensively in the central zone of the nodules where bulbous structures accommodating numerous bacteria were observed between and within the plant cells ( Figs 2F–2I and S5B ) . These disorganised structures differed in size and shape from the fully colonised nodule cells containing the exoU symbiont ( Fig 3A and 3C ) and from the finely defined ITs induced and occupied by symbiotic rhizobia ( Figs 1C and 3B and 3D and S5C–S5H ) . Similar to the ITs induced by symbiotic bacteria , the KAW12-containing structures were encapsulated within cell wall material , as illustrated by the presence of a homogalacturonan epitope which is present in the plant cell wall and which was detected by the monoclonal antibody JIM5 ( Figs 3E–3G and S5E and S5F ) . Glycoproteins , usually present in the IT matrix containing symbiotic bacteria and detected by the MAC236 antibody [61] , were rarely observed in the matrix of KAW12-containing lagoons , but instead were found in the surrounding plant cells ( Figs 3H and 3I and S5G and S5H ) . Localised cell wall degradation was observed leading to singular or multiple bacterial entrapments in the plant cell ( Figs 3F and S5E–S5H ) . No membrane-like structure was observed around the internalised KAW12 indicating that symbiosomes were not formed . The infected plant cells contained KAW12 bacteria that were clustered together and surrounded by a white , undefined matrix ( S5C–S5H Fig ) . The infected plant cells appeared to be viable , based on their apparently normal internal structure ( S5F Fig ) , however , collapsed plant cells with massive intracellular infection of un-clustered KAW12 bacteria were also observed . These results show that KAW12 is able to multiply within the nodules both intra- and inter-cellularly , and to a higher extent than that observed in the root tissue , indicating that nodules offer a competent biological niche for microbial accommodation . The results obtained from co-inoculation of Lotus wild-type plants showed that KAW12 has the ability to colonise ITs and nodules induced by M . loti and to progress the infection initiated by the M . loti exoU toward nodule primordia . In order to determine if plant genes required for infection by symbionts would also be necessary for the progression of the KAW12 infection , a panel of plant mutants impaired at different stages during symbiotic infection were analysed for their ability to sustain KAW12 colonisation . Mutation of a non-essential plant gene was assumed to result in a KAW12 infection frequency of nodule primordia similar to that of wild-type plants ( i . e . 33% ) . First , we analysed the Cyclops , Cerberus , Nap1 and ArpC1 genes involved in the signalling pathway controlling IT initiation and elongation . After the co-inoculation of mutants impaired in these Lotus genes by exoU and KAW12 only a negligible KAW12 infection of primordia was detected ( Table 2 ) , revealing that these plant symbiotic genes are essential for KAW12 infection of symbiotic nodules . These results confirm the dependency of KAW12 infection on the root hair IT initiation that is host-symbiont controlled . We then analysed the involvement of Npl1 , Alb1 and Crinkle , controlling symbiotic infection at the stage of IT passage through the epidermal/cortical barrier . After co-inoculation KAW12 was found impaired in infection of nodules induced by exoU on npl1 and alb1 mutants , but not on crinkle mutants where the infection frequency was similar to Lotus Gifu wild-type plants ( Table 2 ) . This indicates that the Npl1 and Alb1 genes , together with Cyclops , Cerberus , Nap1 and ArpC1 are required for both M . loti and KAW12 nodule infection via ITs . On the other hand , the mutation present in crinkle , which limits M . loti wild-type infection [23] , does not affect KAW12 colonisation . This observation is interesting , since alb1 and crinkle have been reported to have similar mutant phenotypes in the presence of wild-type M . loti , and , therefore , have been suggested to be impaired at corresponding stages of infection [26] . Identification of the Alb1 and Crinkle genes would likely help to explain the observed differences . Finally , we investigated the role of the symbiotic gene Sst1 , involved in the later stages of the Lotus-M . loti symbiosis . We observed that the sst1 mutation had a limited effect on KAW12 colonisation , indicating that this gene is not required for KAW12 multiplication inside nodules ( Table 2 ) . Taken together , our analyses revealed that the legume host controls the access into nodules for both symbionts and endophytes when ITs are used as entry route , and that selective mechanisms may exist to control the accommodation of compatible symbionts and/or endophytes .
Land plants develop their root systems in a microbe-rich soil environment and have sophisticated mechanisms for microbial surveillance . In addition to the selection pressure imposed from the plant host , differences in the physiology of microbes and their ability to establish various microbe-microbe interactions , contribute to the composition of microbial communities in the soil , rhizosphere and in planta [4 , 62–65] . There is a large diversity and wealth of diazotrophs in the soil , but it has become clear in the last decades that legumes select the infecting root nodule symbionts on the basis of molecular signatures , such as Nod factors , EPS , and LPS , that are unrelated to their symbiotic function performed within nodules [31] . As a consequence of this indirect selection mechanism , legumes that grow in natural habitats end up hosting a varied bacterial community inside nodules [45] . Experimental data support this suggestion; efficient nitrogen fixing bacteria , but also poor nitrogen fixers and endophytes have been shown to co-exist as part of the nodule bacterial community [42] . Likewise , laboratory studies using defined mixed symbiotic inocula and field studies monitoring the symbionts within nodules have revealed that mutants or poor nitrogen-fixing symbionts can infect and colonise nodules together with compatible strains [32 , 66 , 67] . Previous reports presented theoretical models or experimental evidence for the various mechanisms employed by the host to sanction the non/poor symbionts after establishment within nodules [68–70] . Our study focuses on the early stages of nodule infection by the endophytes in order to identify which molecular signatures and genetic components favour/allow an endophytic nodule infection . Using co-inoculation experiments with a panel of endophytic bacteria together with the efficient symbiont M . loti , we show that complex host-microbe and microbe-microbe interactions can be captured and studied in Lotus plants grown under controlled conditions . Additional information may be gained from similar studies in legumes where rhizobial infection doesn’t follow the well-characterised root hair infection pathway . Using fluorescently labelled bacteria we monitored microbial infection patterns , and found that in the presence of M . loti the infection and accommodation of compatible endophytes within Lotus nodules is regulated in at least three steps ( Fig 1H ) . Four of the tested endophytes were able to colonise cortical ITs induced by M . loti while only two infected and multiplied inside the nodules . Finally , R . mesosinicum KAW12 persisted inside nodules without inducing necrosis . Since KAW12 lacks the crucial genetic basis for establishing a nitrogen-fixing symbiosis a tempting explanation for this competence could reside in the endophytic features that enable KAW12 to colonise the intercellular spaces of Lotus roots in the absence of M . loti . Rhizobial species are frequently found as endophytes in a wide range of plant species [53 , 71–75] , indicating either an improved fitness compared to other soil bacteria or a better communication with the plant host . Nevertheless , the fact that the two Rhizobium species included in our study differ in their ability to colonise the nodules demonstrates that specific bacterial determinants contribute to their acceptance by the host . Co-inoculation studies revealed that even if the endophyte had the competence to infect and multiply within nodules it was the M . loti symbiont which occupied most of the nodule interior , demonstrating its adapted ability to compete with other bacteria and to efficiently communicate with the host during infection ( Fig 1B ) . The ability of KAW12 to co-infect the nodules in the presence of M . loti provided the opportunity to study the role/contribution of the symbiont and the host to endophyte infection . Rhizobium KAW12 utilises M . loti-induced ITs as a route for access into the nodules ( Fig 1C ) and this infection pattern prompted us to investigate the role of the Nod factor signalling induced by the M . loti symbiont for the endophyte infection . Symbiotic rhizobia produce Nod factors continuously during root and nodule infection , and previous studies have revealed that fully compatible Nod factor signalling is important for the initiation and fast progression of ITs towards nodule primordia to ensure rapid infection and symbiotic development [54 , 76 , 77] . Our results from the co-inoculation experiments of Lotus with KAW12 and symbionts , such as A . caulinodans ORS571 or the M . loti nodZ mutant that produces less-compatible Nod factors , revealed a lower rate of KAW12 infection when compared to its co-inoculation with wild-type M . loti R7A . Based on these results we conclude that fully compatible Nod factor signalling is important for nodule infection by KAW12 , as it allows rapid access of the endophyte into the nodule primordium . We then investigated the role of bacterial exopolysaccharides , and demonstrated that during nodule infection compatible EPS provides the symbiotic bacteria with an advantage over the co-infecting endophyte . Both the frequency and the nodule volume presenting endophytic infection increased after co-inoculation with KAW12 and the M . loti exoU ( Table 1 and Fig 2 ) . Furthermore , KAW12 had the ability to rescue the exoU-containing aborted ITs within the root hairs and thus to progress the infection towards the nodule primordia in a manner similar to a nodA mutant of M . loti defective for Nod factor production [32] . Using a KAW12 eps mutant we show that this ability to bypass the requirement for compatible symbiotic EPS is dependent on KAW12 EPS . This is consistent with results showing EPS to be crucial for legume colonisation by symbiotic nitrogen-fixing bacteria , where both a protective activity against host defence responses and a positive signalling role have been proposed [32 , 37 , 56] . Interestingly , genes involved in EPS biosynthesis or export were found as targets of selection among several Sinorhizobium medicae and S . meliloti strains that share host plants [78] . Our results demonstrate that EPS represent a key molecular feature during nodule infection by both symbiotic and endophytic bacteria , and opens up the possibility that nodule infection by KAW12 is facilitated by perceptions of endophytic , yet compatible , EPS by the host . Additional indications for a compatible host-endophyte interaction came from the analyses aimed to determine the role of the legume host for KAW12 infection . We found that the ability of KAW12 to progress the exoU-arrested ITs and to infect nodule primordia is dependent on early symbiotic genes . Lotus mutants impaired in the early events required for IT initiation and elongation ( Cyclops , Cerberus , Nap1 , ArpC1 , Npl1 , Alb1 ) were also defective for the endophyte infection ( Table 2 ) . We conclude that these host symbiotic genes gate the access of both symbiotic and endophytic microbes . On the other hand , genes like Crinkle that are required for M . loti infection , and Sst1 which supports the symbiotic function of M . loti within nodules , did not seem to be required for KAW12 colonisation , indicating specific mechanisms operating inside host nodules to control the persistence of both symbiotic and endophytic bacteria . Maintaining populations of highly competitive symbionts in the soil , and ensuring predominant occupancy by effective nitrogen fixing bacteria represents a major challenge that limits legume cultivation [79–82] . So far , most progress was obtained from the application of selected bioinoculants that are edaphically adapted [82 , 83] . However , conventional application of superior nitrogen-fixing rhizobia did not prove to be a consistent solution for this challenge [84–86] . On the other hand , when the legume-breeding programme was performed in the presence of elite-selected rhizobia , and in conditions that favoured biological nitrogen fixation , a significant and consistent increase in plant yields was obtained as a result of selection for improved host-symbiont compatibility [87] . These practical results , together with those provided by the biodiversity studies corroborate with the results we present here and provide an explanation i . e . ; the legume nodule is a unique environmental niche with an adapted program for accommodation of host-selected compatible soil microbes , and layers of compatibility determine access and colonisation efficiencies , symbiotic or not . Our study shows that genetic resources available for the model legumes , in combination with co-inoculation strategies provide a reliable framework for identifying the genetic mechanisms operating behind this compatibility at the plant root interface , thus allowing developments to further address this challenge in a targeted manner .
Plant genotypes and bacterial strains used in this work are listed in S1 Table and S2 Table . Forest soil ( 0-4cm ) was sampled from the Botanical Garden of Tohoku University ( 12–2 Kawauchi Aoba-ku Sendai Miyagi , Japan ) on December 2006 . Sterilized seeds of the L . japonicus cCaMK ( Ljsym72 ) mutant were incubated with the soil in Magenta containers for 3 months . Plants were grown in 16h/light and 8h/dark conditions at 25°C . Whole plants were surface sterilized using 0 . 5% bleach and homogenized with 10 ml sterilized water . 500μl of the homogenated samples were inoculated onto sterilized seeds of the same mutant in Magenta containers with sterilized vermiculite supplemented with B&D medium , and were incubated for two months . Whole plants were sterilized and homogenized . These homogenized samples were plated onto TY medium , and KAW12 together with KAW25 were isolated among the bacteria growing on the plates . The 16S rRNA from KAW12 and KAW25 has been PCR-amplified , sequenced and analysed for similarity to other bacterial sequences present in the NCBI database ( S2A and S2B Fig ) . According to the results of the 16SrRNA-gene sequences , the Burkholderia sp . KAW25 belongs to the plant-associated branch of the genus Burkholderia , [88] while the Rhizobium sp . KAW12 is within the Rhizobium mesosinicum species . The KAW12 eps1 mutant was isolated from a random mutagenesis screen utilising the transposon mTn5-GNm [57] . The transposon insertions site was identified by arbitrary PCR and sequencing . The KAW12 eps1 was found to harbour an insertion in a gene encoding a polysaccharide export protein with 70% amino acid identity to PssN of R . leguminosarum bv . trifolii ( S6A Fig ) . The mutant strain distinguished from wild-type KAW12 by displaying nonmucoid colony growth on YMB and G/RDM media . Plants were grown under nitrogen-limited conditions ( 1mM KNO3 ) and analysed for infection after 5 to 6 weeks . For the co-inoculation experiments a 1:1 mixture of bacteria ( OD600- 0 . 02 ) was used . Screening for colonised nodules was performed by whole plant inspection on a Leica M165FC stereomicroscope in bright field and using filters for GFP and DsRED . Selected nodules were fine-sectioned ( 50 μm ) using a Leica VT1000S vibratome , and analysed for internal colonisation with a Zeiss LSM510 Meta microscope . Semithin nodule sections were analysed by light microscopy . Ultrathin nodule sections were analysed by transmission electron microscopy ( TEM ) as previously described ( Madsen et al . , 2010 ) . Commercially available DsRED and GFP antibodies were used to identify the bacteria on TEM sections ( Fig 3A to 3D ) via immunogold labelling [89] . | Plants have evolved elaborated mechanisms to monitor microbial presence and to control their infection , therefore only particular microbes , so called “endophytes , ” are able to colonise the internal tissues with minimal or no host damage . The legume root nodule is a unique environmental niche induced by symbiotic bacteria , but where multiple species , symbiotic and endophytic co-exist . Genetic studies of the binary interaction legume-symbiont led to the discovery of key components evolved in the two partners allowing mutual recognition and nodule infection . In contrast , there is limited knowledge about the endophytic nodule infection , the role of the legume host , or the symbiont in the process of nodule colonisation by endophytes . Here we focus on the early stages of nodule infection in order to identify which molecular signatures and genetic components favour/allow endophyte accommodation , and multiple species co-existence inside nodules . We found that colonisation of Lotus japonicus nodules by endophytic bacteria is a selective process , that endophyte nodule occupancy is host-controlled , and that exopolysaccharides are key bacterial features for chronic infection of nodules . Our strategy based on model legume genetics and co-inoculation can thus be used for identifying mechanisms operating behind host-microbes compatibility in environments where multiple species co-exist . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] | [] | 2015 | A Legume Genetic Framework Controls Infection of Nodules by Symbiotic and Endophytic Bacteria |
A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest . RosettaAntibodyDesign ( RAbD ) samples the diverse sequence , structure , and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications . The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs ( North et al . , J . Mol . Biol . , 406:228–256 , 2011 ) . It then performs sequence design according to amino acid sequence profiles of each cluster , and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints . Starting from an existing experimental or computationally modeled antigen-antibody structure , RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length , conformation , and sequence . We rigorously benchmarked RAbD on a set of 60 diverse antibody–antigen complexes , using two design strategies—optimizing total Rosetta energy and optimizing interface energy alone . We utilized two novel metrics for measuring success in computational protein design . The design risk ratio ( DRR ) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure . Ratios greater than 1 . 0 indicate that the design process is picking out the native more frequently than expected from their sampled rate . We achieved DRRs for the non-H3 CDRs of between 2 . 4 and 4 . 0 . The antigen risk ratio ( ARR ) is the ratio of frequencies of the native amino acid types , CDR lengths , and clusters in the output decoys for simulations performed in the presence and absence of the antigen . For CDRs , we achieved cluster ARRs as high as 2 . 5 for L1 and 1 . 5 for H2 . For sequence design simulations without CDR grafting , the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen , for an ARR of 1 . 5 . For the non-contacting residues , the ARR was 1 . 08 . This shows that the sequence profiles are able to maintain the amino acid types of these conserved , buried sites , while recovery of the exposed , contacting residues requires the presence of the antigen-antibody interface . We tested RAbD experimentally on both a lambda and kappa antibody–antigen complex , successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters .
Antibodies are a key component of the adaptive immune system and form the basis of its ability to detect and respond to foreign pathogens through binding of molecular epitopes . Antibodies are increasingly a focus of biomedical research for drug and vaccine development in addition to their numerous applications in biotechnology by private companies , government , and academia [1–7] . Experimentally , antibodies may be discovered and optimized through in vitro phage and yeast display [8 , 9] , screening with large antibody libraries [10–13] and/or affinity maturation through error-prone PCR [14–16] . They may also be derived in vivo through a combination of animal immunization and antibody screening through ELISA or Western blots , and humanization of the animal antibody [17–19] . Although these methods have been successfully applied to create new antibodies , they can take many months to complete and can be prohibitively expensive . In addition , for many targets , these methods may not produce antibodies with desirable properties , because the antigen is difficult to target [20–22] or because the antibody is required to bind to a specific epitope for various functional reasons such as the neutralization of a target pathogen [23] , initialization of a downstream signaling cascade [24] , or the blocking of a binding protein from being able to engage the site [25] . We believe that computational design methods developed specifically for antibodies can be used in tandem with state-of-the-art experimental methods to save time , money , and increase our ability to design or enhance antibodies to many different targets . Various computational methods including rational , structure-based design , protein design algorithms , and antibody-specific modeling techniques can aid in the design of antibodies [26–28] . General protein design methods have been applied to affinity maturation [29–31] , improving stability [32–34] , humanization [35 , 36] , and the design of phage/yeast display libraries [37–40] , while three software programs have been developed specifically for antibody computational design . Maranas and colleagues have developed the OptCDR [41] and OptMAVEn [42] methods , which sample and combine elements of antibody structure in an effort to assemble antibodies to bind to novel epitopes . OptCDR samples from clusters of the six CDRs in the presence of a fixed antigen position . This is followed by placement of side chains according to sequence preferences within each cluster , a rotamer search from a backbone-dependent rotamer library [43] , and a CHARMM-based energy function . The method has not been experimentally tested . OptMAVEn divides antibody structures in a manner inspired by V ( D ) J recombination: antibody heavy- and light-chain V regions , CDR3s , and post-CDR3 segments from the MAPS database [44] . OptMAVEn has been tested experimentally and was used to design antibodies against a very hydrophobic heptamer peptide antigen with a repetitive sequence ( FYPYPYA ) , starting from the structure of an existing antibody bound to a dodecamer peptide containing this sequence ( PDB 4HOH [45]; only the heptamer has coordinates and was used in the design process ) [46] . Lapidoth et al . have presented AbDesign [47] that follows a similar methodology to OptMAVEn , breaking up antibodies into V regions and CDR3 by analogy to V ( D ) J recombination . They clustered V region structures purely by length of the CDR1 and CDR2 segments in VH and VL , grouping sequences from distantly related germlines and different CDR conformations into clusters from which sequence profiles were derived . As implemented in the Rosetta Software Suite , AbDesign combinatorially builds antibodies and performs sequence design from position-specific scoring matrices of aligned antibody sequences of their length-based clusters of the V regions and CDR3 regions . Because CDR1 and CDR2 are grafted together , AbDesign has limited flexibility in terms of setting which CDRs to design and what CDR lengths or conformation combinations to sample . The computational benchmarking of AbDesign consisted of reporting the Cα RMSD from native for each CDR of the top design for each of 9 antibodies . Only the CDRs with the most common canonical conformations were reproduced; those with less common conformations were poorly predicted , making it difficult to evaluate the statistical significance of their results . AbDesign was used recently to create lead antibodies against insulin and mycobacterial acyl-carrier protein , which were then synthesized and tested for binding [48] . Three weak binders were then subjected to random mutagenesis in a yeast-display library screen followed by manually chosen mutations , which resulted in antibodies with affinity in the 50–100 nM range . Two residues in the epitope of each of the two ACP-binding antibodies were mutated to test the designs . Only one of these four reduced binding significantly ( by 75%; two others reduced binding by 10% and 20% ) . One mutation from valine to glutamic acid actually increased binding . Two residues outside the epitope of each of the two ACP designs were also mutated; three of these mutations increased binding of their respective antibodies 2 to 5 fold and one of them surprisingly abrogated binding . The equivocal computational benchmarking and experimental results for AbDesign suggest that further development of antibody computational design is warranted . Taking advantage of the influx of new structures of antibodies in the PDB , we presented a new clustering of all CDR structures in the Protein Data Bank ( PDB ) in 2011 , updating the Chothia classification developed in the 1980s and 1990s [49–52] . Our clustering was performed with a dihedral angle metric and an affinity-propagation clustering algorithm , and was presented with a systematic nomenclature [53] , which is now in common use . From this classification , we developed the PyIgClassify database [54] , which is updated monthly and contains CDR sequences and cluster identifications for all antibodies in the PDB . PyIgClassify includes identification of species and IMGT germline V regions [55] , and is provided as a relational database for use in antibody structure prediction and design . We hypothesized that the clusters in PyIgClassify could form the core of a knowledge-based approach to antibody design . In this paper , we test this hypothesis through a computational benchmark and experimental validation on two separate antibodies . Using the data from PyIgClassify , our main approach to design is to graft CDRs from populated clusters onto the antibody and to sample the sequence and structure space of that CDR according to the observed variation in sequence and structure of that cluster in the database . Our goal was to create a flexible , generalized antibody design framework and program that can be applied to numerous types of antibody design projects from affinity maturation to de novo design . To create a reliable antibody design framework from our structural bioinformatics efforts , we leveraged the Rosetta Software Suite [56] , a collaborative research project across many independent labs around the world . Rosetta has been developed and used for a variety of modeling and design tasks , such as loop modeling [57 , 58] , protein–protein docking [45 , 59] , structure refinement [60–63] , de novo protein design [64] , enzyme design [65–67] , and interface design [68–70] . Rosetta provides frameworks for sampling and optimizing the conformations of the backbone and side chains of a protein–protein complex while simultaneously changing the sequence at specified positions in the interface ( in this case , primarily in the CDRs ) in order to optimize the total energy of the system . Alternatively , the program can optimize the interface energy , which is the difference between the energy of the relaxed complex and the sum of the energies of the separated components after relaxation . The program and methodology we have developed is called RosettaAntibodyDesign or RAbD . In this paper , we describe RAbD and both experimental testing and extensive computational benchmarking . To develop RAbD: ( 1 ) we created a database of CDR structures annotated according to our CDR cluster nomenclature and added this database to Rosetta; ( 2 ) implemented user-controlled sampling of CDR structures from this database for antibody design; ( 3 ) developed new grafting methods using the cyclic coordinate descent algorithm [71] in Rosetta; ( 4 ) implemented an algorithm that utilizes sequence profiles for our CDR clusters for sampling amino acid changes during antibody design and exploits existing structure optimization and Monte Carlo design strategies in Rosetta; and ( 5 ) added antibody-specific analysis tools to Rosetta to provide data that can be used in selecting antibody designs for synthesis and testing . The RAbD Framework consists of around 50 new Rosetta classes and over 20 , 000 lines of code , all of which is used by the RAbD program and available in RosettaScripts . A common method for computational benchmarking of protein design methods is the use of the concept of sequence recovery [72] . Sequence recovery tests how the sequences in the final design models match the native sequence , calculated as percent sequence identity for all or the subset of the designable residues . Rosetta’s sequence recovery tends to be in the 35–40% range for full design of monomeric proteins [73] , since many surface positions are tolerant to amino acid substitution , and the benchmark protocols do not include functional interactions with other proteins , nucleic acids , or ligands . However , since our antibody design protocol includes potential changes in the overall structure of the CDRs by sampling different CDR lengths , clusters , and sequences , the standard sequence recovery metric is inadequate for testing computational antibody design . We have therefore expanded the concept of sequence recovery to include recovery of structural features of the designed antibodies . Although antibodies in the PDB are not likely to be the highest affinity possible to a given epitope , they bind strongly enough for crystallography . Thus , maximizing the recovery of CDR lengths , clusters , and sequences is a reasonable strategy to optimize sampling and scoring strategies for antibody design . We have developed novel recovery metrics and a way of assessing the statistical significance of these metrics , which may be used in any protein design scenario . To do this , we borrow a concept from statistical epidemiology , the Risk Ratio . The Risk Ratio ( RR ) is defined as the ratio of two frequencies ( or proportions ) : the frequency of event X in situation A ( e . g . , disease progression while taking a drug ) and the frequency of event X in situation B ( e . g . , disease progression with no drug treatment ) . The Risk Ratio is similar to the odds ratio , which is simply the ratio of the odds of X to not-X in situation A and the odds of X to not-X in situation B . However , the interpretation of the odds ratio is often misleading and used incorrectly to inflate a sense of benefit or risk [74] . For antibody design , we have defined two design metrics–the design risk ratio and the antigen risk ratio . We define the design risk ratio as the ratio of the frequency of native CDR clusters , lengths , or residue identities in the top scoring designs divided by the frequency of the same native features sampled during the design trajectory . In this way , we can account for any uneven sampling of the native structure and sequence during the design process . The antigen risk ratio is the ratio of the frequency of the native CDR length , cluster , or residue identities achieved in the top scoring decoys in independent antigen-present and antigen-absent simulations . This metric accounts for any bias Rosetta may have for the native CDRs even in the absence of the antigen , possibly because of favorable framework–CDR or CDR–CDR interactions . In this paper , we utilize a benchmark of 60 κ and λ antigen-antibody complexes that we chose to be as diverse in CDR lengths and clusters as possible . We show that RAbD is able to achieve risk ratios greater than 1 . 0 for each CDR , and we show statistical significance of these results with 95% confidence intervals . To enable repeatable analysis and comparison of native , modeled , and designed antibody structures output by RAbD , we developed a set of antibody-specific FeatureReporters and Feature R Scripts within the Rosetta Feature Reporter framework [73 , 75 , 76] . These have enabled comparison of antibody design strategies and benchmarks , were used in the design of the antibodies in this paper , and have aided in the general optimization of the antibody design framework . Finally , we show results where RAbD and the feature analysis reporters were used to experimentally improve binding affinity of antibodies from two different antibody–antigen complexes .
We created a general framework and application for antibody design within the Rosetta software suite written in C++ . This highly customizable framework enables the tailored design of antibody CDRs , frameworks , and antigens using highly expanded core components of the RosettaAntibody framework [77–79] and our PyIgClassify clustering of antibody CDRs [53 , 54] as its base . As with other Rosetta design protocols , RosettaAntibodyDesign depends on a “Monte Carlo plus minimization” ( MCM ) procedure [80] . This means that at each stage of the simulation , a change in sequence and/or structure is sampled randomly , followed by energy minimization within the Rosetta energy function . If the resulting minimized structure ( a “decoy” ) has lower energy than the previous decoy in the protocol , then the new structure is accepted . If the energy of the new design is higher than the previous decoy , the new design is accepted with probability exp ( −ΔE/RT ) where ΔE is the change in energy . This energy can be either the total energy or the calculated interface energy , which is the energy of the complex minus the energies of the separated antigen and antibody after side-chain repacking [81] , or a weighted combination of both . The RAbD algorithm samples the diverse sequence , structure , and binding space of an antibody-antigen complex ( Fig 1; Fig A in S1 Supporting Information ) . The protocol begins with the three-dimensional structure of an antibody–antigen complex . This structure may be an experimental structure of an existing antibody in complex with its antigen or a predicted structure of an existing antibody docked computationally to its antigen . As a prelude to de novo design , the best scoring results of low-resolution docking of a large number of unrelated antibodies to a desired epitope on a target antigen structure may be used . It should be noted that design on predicted structures is generally less reliable than design on high-resolution crystal structures due to possible inaccuracies in the model . The RosettaAntibodyDesign protocol is driven by a set of command-line options and an optional set of design instructions provided as an input file for increased control . Details and example command lines and instruction files are provided in the Supplemental Methods section . RAbD enables the grafting of CDRs from diverse clusters of different lengths within the PyIgClassify database , sampling from the sequence and structural variation within each cluster . Broadly , the RAbD protocol consists of alternating outer and inner Monte Carlo cycles . Each outer cycle ( of Nouter cycles ) ( Fig 1A ) consists of randomly choosing a CDR ( L1 , L2 , etc . ) from those CDRs set to design , randomly choosing a cluster and then a structure from that cluster from the database according to the input instructions . The CDR is then grafted onto the antibody framework in place of the existing CDR ( GraftDesign ) . The program then performs Ninner rounds of the inner cycle ( Fig 1B ) , consisting of sequence design ( SeqDesign ) and local structure optimization . Sequence design is performed by Rosetta’s side-chain repacking algorithm: a residue is chosen randomly and the energy of each of its rotamers is evaluated ( both internal energy and interaction with the environment ) ; if the residue is set to be designed , then the rotamers of multiple residue types are tested; the side chain is then placed in the rotamer ( and residue type ) with lowest energy . This is repeated for residues in the grafted CDR as well as residues in neighboring CDRs and the framework ( where only the native residue types are used ) . Once this design is completed , local structure optimization is performed with Rosetta’s standard local energy minimization routines . Amino acid changes are typically sampled from profiles derived for each CDR cluster in PyIgClassify . Conservative amino acid substitutions ( according to the BLOSUM62 substitution matrix ) may be performed when too few sequences are available to produce a profile ( e . g . , for H3 ) . Each inner cycle structurally optimizes the backbone and repacks side chains of the CDR and its neighbors in order to optimize interactions of the CDR with the antigen and other CDRs ( Fig 2 ) . Backbone dihedral angle constraints derived from the cluster data are applied to limit deleterious structural perturbations . After each inner cycle is completed , the new sequence and structure are accepted according to the Metropolis Monte Carlo criterion . After Ninner rounds of the inner cycle , the program returns to the outer cycle , at which point the energy of the resulting design is compared to the previous design in the outer cycle . The new design is accepted or rejected according to the Monte Carlo criterion . After Nouter cycles ( default of 25 ) , the lowest energy design observed during the run is output by the program as the final design . In practice , the whole procedure is performed in parallel on a cluster to produce 100s or 1000s of output structures ( decoys ) . This ensemble of designs is then analyzed to choose specific sequences for experimental testing , typically based on both total energy and interface energy , which are reported in the decoys , or the needs of the specific project . Decoy discrimination , analysis , and selection are critical to the experimental success of the final designs . RAbD can be tailored for a variety of design projects and design strategies . This is accomplished through the use of a set of command-line options and an optional CDR Instruction File . The CDR Instruction File ( Fig 3 ) uses a simple syntax and enables control over what lengths , clusters , germlines , and organism of each CDR will be sampled ( the CDRSet ) and which structural optimizations are used to minimize the score of each design . Each instruction can be set for all of the CDRs using a specific keyword , or they can be set individually . For example , in a redesign project , we may want to design an antibody with a particular CDR that is longer than the existing CDR in order to create new contacts with the antigen that are not present in the starting structure . Alternatively , we may simply want to optimize the sequence of a particular CDR or set of CDRs using the cluster profiles from PyIgClassify . These examples can be accomplished easily through the CDR Instruction File , and this flexibility has been used to design the antibodies described below . A core component of the RAbD protocol is an SQLITE3 antibody design database that houses all structures , CDR-clustering information , species , germline , and sequence profile data used for design . The database benchmarked in this paper comes from the August 2017 release of PyIgClassify , but up-to-date versions that reflect the current Protein Data Bank ( PDB ) can also be obtained from the PyIgClassify website ( http://dunbrack2 . fccc . edu/PyIgClassify ) . If RAbD uses non-redundant databases without outliers ( the default ) , defined as CDRs greater than 40° or 1 . 5 Å RMSD from one of our cluster centroids ( not applied to H3 ) , this database comprises 657 L1 sequences , 471 L2 sequences , 681 L3 sequences , 805 H1 sequences , 930 H2 sequences , and 985 H3 sequences . In order to improve framework-CDR compatibility in the final designs , λ and κ type antibodies are designed by limiting the resulting CDRSet to only those CDRs derived from the same light chain type as the antibody undergoing design ( Fig 2 ) . To develop metrics for recovery of CDR lengths and clusters , we must account for the fact that CDR lengths and clusters are not evenly distributed in nature , the PDB , or in the PyIgClassify database and are not necessarily sampled evenly during RAbD’s Monte-Carlo trajectories . The probability of choosing the native cluster and length during sampling directly influences the statistical significance of the final recovery of the native length and cluster . To account for this phenomenon , we borrow a concept from statistical epidemiology , the Risk Ratio . The Risk Ratio ( RR ) is defined as the ratio of two frequencies: the frequency of event X in situation A ( e . g . , disease progression while taking a drug ) and the frequency of event X in situation B ( e . g . , disease progression with no drug treatment ) . The Risk Ratio is similar to the odds ratio , which is simply the ratio of the odds of X to not-X in situation A and the odds of X to not-X in situation B . However , the interpretation of the odds ratio is often misleading and used incorrectly to inflate a sense of benefit or risk [74] . In standard protein design scenarios , we may define the risk ratio as the frequency of the native structure ( or sequence ) in the top scoring designs divided by the frequency of the native structure ( or sequence ) sampled during the protocol . If we perform design simulations on an existing high-affinity antibody–antigen complex , it is reasonable to suppose that a successful protocol will recover the native CDR lengths , conformations , and sequences of a high-affinity antibody more often than they are sampled . We therefore define the design risk ratio ( DRR ) for CDR lengths and clusters as the frequency of the native length or cluster in the top scoring designs ( the top decoys , one from each run of the program ) divided by the frequency that the native length or cluster was sampled during the design simulations . Because Rosetta might prefer some CDR conformations and lengths because they are lower energy , even in the absence of antigen , we also define , the antigen risk ratio ( ARR ) , which is the frequency of the native CDR length or cluster in the top scoring designs in the presence of the antigen divided by the frequency of the native in the top scoring designs from independent simulations performed in the absence of the antigen . It is straightforward to calculate confidence intervals for the design and antigen risk ratios so that statistical significance of the results can be assessed ( see Methods ) . We tested two types of design methods: ‘opt-E’ , which uses the Metropolis Monte Carlo criterion to optimize Total Rosetta Energy of the antibody-antigen complex , and ‘opt-dG’ , which optimizes the calculated interface energy . The interface energy is equal to the Total Rosetta Energy of the complex minus the Total Rosetta Energy of the separated antigen and antibody , after side-chain repacking . For the opt-E method , we calculate both the DRR and ARR values . Since opt-dG includes a step of separating the antigen and antibody , an antigen-free simulation is not relevant to the calculation , and we therefore only calculate the DRR for the opt-dG designs . All 5 non-H3 CDRs were graft-designed , while all CDR sequences , including H3 , were sequence-designed either preferentially using derived CDR cluster profiles or conservative design where cluster sequence data were sparse . All 5 non-H3 CDRs began each simulation with randomly inserted CDRs from the antibody design database . Prior to design calculations , the structure of each antigen-antibody complex was minimized into the Rosetta energy function with tight coordinate constraints on both backbone and side-chain regions [62] ( see Methods for protocol ) . We used an up-to-date version of the antibody design database derived from the PDB as of August 2017 . It contains 3 , 974 CDRs , while our original clustering in North et al . contained 1 , 346 CDRs ( http://dunbrack2 . fccc . edu/pyigclassify ) . A diverse set of 46 κ and 14 λ antibody–antigen complexes were used for the computational benchmarks ( Table 1; more details on the benchmark antibodies are provided in Table A in S1 Supporting Information ) . This set of antibody–antigen complexes includes a diverse set of CDR lengths and clusters , with many of the clusters commonly found in the PDB . The benchmark complexes were selected to satisfy several criteria: ( 1 ) resolution ≤ 2 . 5 Å; ( 2 ) buried surface area in the antigen-antibody complex > 700 Å2; ( 3 ) CDR1 and CDR2 within 40° of one of our cluster centroids; ( 4 ) contacts with CDRs in both the light chain and the heavy chain variable domains; ( 5 ) non-redundancy–antibodies which bind the same antigen were only selected if they bound to completely different sites on the antigen; 6 ) benchmark antibodies were prioritized so as to comprise as diverse a set of CDR lengths and clusters given the distribution of lengths and clusters present in the PDB . The benchmark contains 22 length classes and 35 clusters over the 5 non-H3 CDRs and lengths of H3 from 6 to 24 residues . We define the “%Sampled” as the rate at which the native length or cluster is sampled during the design trajectories . The 5 non-H3 CDRs are very different in terms of the diversity of lengths and clusters that are observed in the PDB [53] , with L2 and H1 having more than 90% of CDRs in the PDB with a single length and conformation ( clusters L2-8-1 and H1-13-1 respectively ) , while L1 , L3 , and H2 are more diverse in both length and conformation . We ran design simulations for the antibodies in the benchmark set by sampling the clusters of each CDR evenly ( regardless of length ) of all clusters represented in the database by 5 or more unique sequences from the same antibody gene ( heavy , λ , or κ antibody CDRs ) . Thus , the %Sampled of native CDR lengths ( Fig 4A ) is only 23% for the most length-diverse CDR , L1; followed by L3 ( 50% ) and H2 and H1 ( both 66% ) , and L2 ( 92% ) , which is the least diverse ( a few λ L2 CDRs are length 12 ) . The %Sampled of native CDR clusters is only 10–14% for L1 , L3 , H1 , and H2 and 34% for L2 ( Fig 4B ) . For each of the 60 antibodies , we ran 100 design trajectories , each with 100 outer design cycles ( Fig 1A ) for each experiment ( representing a total of 10 , 000 full design cycles for each antibody ) and analyzed the lengths and clusters of the final decoy from each of the 100 Monte Carlo simulations . The %Recovered is then the number of final decoys out of 100 runs that contain the native length or cluster ( Fig 4A ) for any given CDR . The %Recovered of length generally runs parallel with the %Sampled with the least length-diverse CDRs ( L2 and H1 ) having higher length recovery than the others . The highest cluster recovery is for L2 . The Design Risk Ratio ( DRR ) is then defined by Eq 1: DRR=%Recovered%Sampled ( 1 ) where %Recovered and %Sampled are calculated over the 100 output decoys for all 60 antibodies ( 6000 total ) . A DRR greater than 1 indicates that the length or cluster was present in the output decoys more frequently that it was sampled during the trajectories . The DRRs for the length of CDRs are highest for L1 and H3 with values of 2 . 5 and 1 . 5 respectively . We do not expect high DRRs for L2 , H2 , and H1 since their length diversity in the PDB is very limited in the first place . The DRRs for the clusters are much higher . For the opt-E protocol , the cluster risk ratios are above 2 . 4 for all 5 non-H3 CDRs , and over 3 . 5 for L3 and H1 . The results demonstrate the utility of the DRR in accounting for the different levels of diversity in length and cluster across the five CDRs in which GraftDesign was enabled . This result may come from both more favorable interactions and higher shape complementarity with the antigen–antibody interface using the native cluster ( s ) , as well as local CDR–CDR interactions , which help to enrich certain lengths and clusters together . There is a possibility that Rosetta scores some CDR structures more favorably than others because of internal interactions within the CDR or interactions with other CDRs or the framework . Some rare clusters may be high in energy or even artifacts of highly engineered antibodies or errors in structure determination . To investigate this , we performed the opt-E protocol without the antigen present in the simulations . We calculated an antigen risk ratio ( ARR ) from Eq 2 as the ratio of the frequency of the native length or cluster in the final decoys from the antigen-present simulations and the frequency of the native in the final decoys from the antigen-absent simulations: ARR=%Recovered ( withantigen ) %Recovered ( withoutantigen ) ( 2 ) where %Recovered ( with antigen ) and %Recovered ( without antigen ) are calculated from 100 design decoys of 60 antibodies ( 6000 structures each ) . The recovery values ( Fig 5A ) in the presence of antigen are all higher than the recovery values in the absence of antigen , with the exception of L3 where they are approximately equal . This is reflected in the ARR results ( Fig 5B ) antigen risk ratios demonstrate that the native lengths and clusters are enriched particularly for the L1 and H2 CDRs in the presence of the antigen . For the other CDRs , the values are a little over 1 . 0 , indicating that Rosetta prefers some of the more common clusters in the PDB , even in the absence of antigen . For the light-chain CDRs , we sampled only from lengths and clusters that contained at least 5 examples of structures from the same light-chain type , either λ or κ . Especially for L3 , this choice strongly restricts the number of applicable lengths and clusters , and thus the antigen risk ratios for L3 , like H1 and L2 , are lower that one might expect otherwise . In addition to optimizing the total energy , design simulations in RAbD can alternatively optimize the interface energy , which for our purposes is defined as the total Rosetta energy of the antibody-antigen complex minus the energy of the separated antibody and antigen after repacking and minimizing the energy of side-chain conformations of the interface residues . The %Recovered is greater than the %Sampled ( Fig 6A ) for the lengths and clusters of all 5 CDRs , which is reflected in the DRR values . The cluster DRRs are greater than 1 . 5 for all 5 CDRs , while the length DRRs are only significantly above 1 . 0 for L1 which is the most length-diverse CDR for both κ or λ antibodies . To further investigate the effect of the antigen’s presence during the design phase , we performed sequence design on one CDR at a time ( 6 per target , including H3 ) starting from the native sequence and structure with and without the antigen present using the opt-E protocol described above ( the opt-dG protocol does not make sense in the absence of the antigen and there is no straightforward way to calculate the sampling rate of amino acid types during the simulations ) . In each of these twelve simulations ( 6 CDRs with and without antigen ) , we produced 100 models for analysis . Fig 7 shows the sequence recovery and antigen risk ratios separately for residues in contact with the antigen in the starting structures and those not directly in contact with the antigen in the starting complexes . The risk ratios were calculated from Eq 3: ARR ( CDR ) =∑PDBid∑i∈CDRsPDBid , i ( antigenpresent ) ∑PDBid∑i∈CDRsPDBid , i ( antigenabsent ) ( 3 ) where sPDBid , i is the fraction of 100 decoys that have the native residue at position i of the given CDR in each PDBid . For the non-contacting residues in all of the CDRs , most of which are part of the CDR anchors or buried in the hydrophobic core of the variable domains , the sequence recovery rate is 73% during simulations in the presence of the antigen and 67% during simulations in the absence of the antigen . This is an overall antigen risk ratio of 1 . 084 . The resulting ARR values are near 1 . 0 for all six CDRs ( Fig 7A ) . Many of these residues are strongly conserved in the PyIgClassify profiles , and their recovery with and without the antigen present is expected . By sharp contrast , residues in contact with the antigen have a lower recovery of only 48% in the absence of the antigen but a much higher recovery rate of 72% in the presence of the antigen . This is an overall antigen risk ratio for the antigen-contacting residues of 1 . 50 ( 95%CI = [1 . 489 , 1 . 514] ) . The contact residues ARRs range from 1 . 2 to 1 . 9 for the six CDRs ( Fig 7B ) . Since H3 contributes many residues that contributed to binding free energy , it is gratifying that the H3 risk ratio is above 1 . 5 and that H3 has the highest sequence recovery rate with the antigen ( Fig 7A ) . We investigated the physical properties of the designed antibody-antigen complexes resulting from the opt-E and opt-dG benchmarks . As expected , the opt-dG simulations result in lower interface energies than the opt-E simulations and nearly the same as the native antigen-antibody complexes ( Fig B in S1 Supporting Information ) . The total energies of the opt-E and opt-dG simulations are relatively similar to each other and somewhat higher than the natives ( Fig C in S1 Supporting Information ) . The shape complementarities and surface areas of the designed antibody-antigen complexes are also very close to the native structures , with the opt-dG showing a slight improvement over the opt-E simulations ( Fig D and Fig E in S1 Supporting Information ) . Although computational benchmarking can be extremely useful in optimizing a protocol and its parameters for protein design , the true measure of new protein design methodologies is to test computationally derived sequences experimentally by expressing and purifying the proteins and testing them for desired functionality , including binding affinity and biophysical properties of the designed molecules . We tested a common scenario for which RAbD was intended–improving the affinity of an existing antigen–antibody complex by grafting new CDRs in place of one or more of the native CDRs . To this end , we tested the ability of the RAbD program to create viable antibody designs that improve binding affinity in two antibody–antigen complexes: an HIV-neutralizing antibody known as CH103 ( PDB: 4JAN ) [82] that binds to the CD4 binding site of HIV gp120 , and an antibody that binds to the enzyme hyaluronidase , which is the main allergen in bee venom ( PDB: 2J88 ) [83] . These antibodies are not dominated by interactions of H3 with the antigen and use common canonical clusters for the CDRs at the binding interface . Using this knowledge and the general knowledge of CDR length and cluster variability , we designed both L1 and L3 together , or H2 in the CH103 antibody . For the 2J88 antibody , we designed either L1 and the light chain DE loop , or H2 . The DE loop is a short loop between strands D and E of the variable domain β sheet ( residues 82–89 in AHo numbering ) . The ability of RAbD to treat both the heavy- and light-chain DE loops as CDRs , which are typically considered framework regions , was added later in program development after the elucidation of the role of the loop in both antigen binding and stabilization , especially in regard to intra-CDR contacts with L1 [84] . In light of this , with the L1 design of the 2J88 antibody , we enabled sequence design ( but not graft design ) of this light chain DE loop , which we call L4 . The CDRs selected for design were set to undergo graft- and sequence-sampling with the relax protocol , allowing for new lengths and clusters in the final antibody design , while the framework residues and antigen residues held their starting amino acid identities . The crystal structures 2J88 and 4JAN were first minimized into the Rosetta energy function before being used as starting points for the designs . An instruction file was given for each CDR design strategy ( L1 + L4 , H2 , and L1 + L3 ) with different algorithms and selection methods used to choose the final designs . Details of these settings , instruction files , and design and selection strategies can be found in Methods . For 2J88 , 30 designs were chosen , expressed , and purified with no detectable aggregation . These 30 were chosen with no visual or manual inspection , and based purely on a ranking of physical characteristics of each decoy determined by the AntibodyFeature reporters for each design strategy and selection characteristic ( Methods ) . Of these 30 , 20 showed some degree of binding affinity through an acceptable kinetic sensorgram signal of at least μM binding consisting of L1 loops of length 11 , 15 , and 17 residues ( wild-type: 11 ) in addition to a single H2 design of 12 residues ( wild-type: 9 ) ( Fig 8A , Table B in S1 Supporting Information ) . Three of these designs had improved binding affinity over the wild-type ( WT ) , which binds at 9 . 2 nM ( Fig 8B ) , with the best design exhibiting a 12-fold improvement over wild-type with a KD of 770 pM , as determined by Surface Plasmon Resonance ( SPR ) on a ProteOn XPR ( Fig 8C ) . This design , designated as L14_7 , had a different L1 cluster ( L1-11-2 ) than the wild-type ( L1-11-1 ) , with six amino acid differences in the L1 sequence , and a single amino acid difference in the L4 loop ( Fig 8D and 8E ) , which makes important contacts with the new L1 loop in the design model . Of the L1/L4 design group where docking was enabled , L14_7 had the lowest computational ΔG after filtering out the worst 90% of the designs by total Rosetta energy . The other two designs with better affinity than the native , L1_10 ( Fig F in S1 Supporting Information ) and L1_5 ( Fig G in S1 Supporting Information ) contained the same cluster as the native , but with 4 amino acid changes in L1 out of 11 positions . Kinetic studies of these designs and WT were done on both a Biacore 4000 and an XPR for a total of 3 replicates . Binding affinity was improved against WT for each of these designs in each replicate . Thermostability ( Tm ) of WT and these designs were similar , as measured by Differential Scanning Calorimetry ( DSC ) . While both the WT and designs had two Tm peaks , the major peak of the WT 2J88 antibody was measured at 75 . 0 °C , while the designs L14_7 , L1_10 , and L1_5 had similar Tm values of 71 . 9 , 71 . 5 , and 72 . 1 °C respectively ( Fig H in S1 Supporting Information ) . Mutational analysis was done to delineate hotspot residues at the antibody-antigen interface . Rosetta was used to guide this manual analysis through the use of the PyRosetta Toolkit [85] and FoldIt Standalone [86] GUIs . Residue 7 ( Lys ) of the L1-11 loop of the L14_7 design was selected for mutation due to its proximity to the antigen ( making hydrogen bonds to the antigen in the design model–Fig 8D ) and favorable Rosetta energy . This position was mutated back to its sequence-aligned WT residue ( K38Y ) . Binding affinity worsened by approximately 3–4 fold as determined by SPR on a Biacore 4000 ( Fig I in S1 Supporting Information ) . The reverse experiment was done on the WT antibody for position 38 ( Y38K ) and the mutant exhibited improved binding ( 14 . 2 nM to 4 . 3 nM ) as expected ( Fig J in S1 Supporting Information ) . Finally , as a proof-of-concept , we improved one of the weaker-binding designs ( L1_4 ) , which harbors a very long L1-17-1 loop by 2 . 5x , through a single S->V mutation at position 36 in the L1 loop ( S37V , 655 nM -> 261 nM ) ( Fig K in S1 Supporting Information ) . We chose 27 design variants of the antibody CH103 to express and test for binding to HIV gp120 by using the AntibodyFeature reporters to rank and select prospective decoys . Of the 27 designs , 21 designs could be purified and tested for binding affinity against a panel of gp120 from different strains of HIV . Of these , 7 designs could bind one or more of the gp120 strains as determined through SPR on a Biacore 4000 ( Fig 9A and Table C in S1 Supporting Information ) . One of these antibodies , H2-6 , improved binding affinity to most of the gp120s tested , with a 54-fold improvement to Core-Bal ( 91 nM to 1 . 7 nM ) and a 40-fold improvement to HXB2 ( 52 nM to 1 . 32 nM ) ( Fig 9B and 9C , Fig L in S1 Supporting Information ) . H2-6 had the least number of buried unsatisfied hydrogen bonds in the interface in the H2 design group ( Methods ) . This antibody design had a longer H2 loop ( cluster H2-10-6 ) than the native ( cluster H2-9-1 ) , came from an unrelated mouse antibody [87] in the antibody design database , and is significantly different than the WT CDR ( Fig 9D and 9E ) . We performed mutational analysis on the CH103 designs and WT antibodies . Based on the sequence alignment of the H2 loops from the H2-6 design and the WT antibody ( Fig 9E ) and structural observation using the Rosetta GUIs , we mutated two hypothesized hotspot residues within the H2 loop at positions 3 and 8 of the designed length-10 CDR in the H2-6 design ( AHo numbering 59 and 67 respectively ) to the aligned WT residue ( Y->F and Y->E respectively ) . Binding affinity was measured for Core HXB2 and Core Bal using SPR on a ProteON XPR . Notably , the position 67 mutant decreased binding significantly ( 1 . 7 nM to 30 . 9 nM for HXB2; 8 . 8 nM to 212 nM for Core Bal ) , while the position 59 mutants had a smaller effect ( 1 . 7 nM to 2 . 2 nM for HXB2; 8 . 8 nM to 12 . 8 nM for Core Bal ) ( Fig M in S1 Supporting Information ) . The reverse experiment was also done , where the H2-6 residues at the same positions were placed into the WT antibody . This reverse experiment confirmed position 67 as a hotspot residue ( Fig N in S1 Supporting Information ) ; Kd for HXB2 improved 60-fold from 138 nM to 2 . 3 nM , and Kd for Core Bal improved 93 fold from 1 . 1 μM to 11 . 7 nM . To investigate the role of glycans in CH103 binding , we created glycan knockouts in both the native antibody ( Fig O in S1 Supporting Information ) and the ZM176 strain gp120 ( Fig P in S1 Supporting Information ) . Native antibodies do not usually have N-linked glycosylation sites near the paratope and in all cases except AC10 , the glycan-knockout antibodies did not affect binding affinity significantly . However , multiple antibody designs were sensitive to the 463 and/or 386 glycans of gp120 , which are in structural proximity to the antibody binding site . A single antibody design that included L1 and L3 CDRs design together was able to bind , but only with the potential 386 glycan knocked out . Meanwhile , two designs bound significantly better to ZM176 when the 386 glycan was knocked out . These glycan knockout studies show the importance of glycan considerations for some antigens and for antibody-design in general . The results shown here demonstrate that RAbD can be used to successfully improve the binding affinity of antibodies , and that those designs can have different CDR lengths and clusters from the starting antibody .
The knowledge-based RosettaAntibodyDesign framework and application was developed to enable reliable , customizable structure-based antibody design for a wide variety of design goals and strategies based on a comprehensive clustering of antibody CDR structures [53] . To test the ability of RAbD to produce native-like antibody designs before it was used experimentally , we performed rigorous computational benchmarking using novel recovery metrics , the design risk ratio ( DRR ) and the antigen risk ratio ( ARR ) , which provided needed statistical significance for recovery metrics over random sampling . The results showed that RAbD was able to enrich for native lengths and clusters–even with the large structural diversity of our underlying antibody design database and flat sampling over CDR clusters , while recovering native-like physical characteristics of the interface and antibody . We applied RAbD to two different antigen-antibody systems where the ability to tailor the program to a specific need and the use of our knowledge-based approach to both antibody design and selection led to successful experimental designs that improve binding affinity significantly using different CDR lengths and/or clusters . While RAbD is highly tailorable , there are only a few choices that must be made for any particular antibody design project . First , after examining the initial structure of the antigen-antibody complex , the user must choose which CDRs to design and whether these CDRs should be subject to graft-design or only sequence-design . It may be the case that one CDR does not contact the antigen at all in the starting structure , and a user may choose to subject only that CDR to graft-design . The other CDRs may or may not require sequence design as well . In other cases , more drastic changes in the starting antibody may be desirable . For example in ab initio design to a new epitope or in redesigning an existing antibody for a homologue of its antigen , the user may choose to perform graft design on multiple CDRs . The user should also decide whether to optimize interface energy ( opt-dG ) , total energy ( opt-E ) during the Monte Carlo design simulations , or a weighted combination of both , . If the existing antibody has low affinity , then interface dG may be the more relevant choice; however , if the existing antibody has low stability but reasonable affinity , then total energy may be more suitable . Second , the user may select different optimization protocols for the inner loop of RAbD . This includes whether to perform docking refinements or not and whether to use more computationally intensive relax algorithms . For design against a native antigen present in the starting structure , we recommend not using the additional docking step , since local optimization will usually be sufficient for this purpose . It will usually be better to generate more decoy designs rather than expending CPU time on docking . However , if the antigen is not the same as the one for the starting antibody , either because it is an ab initio design or because it is homologous to the starting antigen , then we recommend including the docking step in the inner loop . Third , the user can determine the number of inner and outer loop steps and the number of individual design runs to perform . The default values for the inner and outer loops steps are reasonable and usually do not need to be altered . The number of design runs should be at least 1 , 000 and may be as high as 10 , 000 , depending on CPU availability for the final production run ( 100 was used for benchmarking purposes ) . Finally , significant user input is needed in deciding how many and which antibodies to synthesize and test experimentally . Our rates of success–the number of successfully improved binders out of the number of antibodies expressed and tested in binding studies , were 3 in 30 for the bee venom antibodies and 1 in 27 for the HIV gp120 antibody redesign , which successfully bound better to gp120 from several strains of HIV . These are comparable to applications in other systems in the literature [70 , 88 , 89] . Our experience and that of others [90] acts as a guide for employing computational design techniques in real-world applications of computational interface design . We recommend that users consider both the total energy of the antibody-antigen complex as well as the interface ΔG of the complex ( regardless of which is used to govern the Monte Carlo decision steps ) . These values are reported in every output decoy file and the associated score file . In our experimental tests , we selected designs to synthesize that had low values of both total energy and interface dG . Other important features may include shape complementarity of the antibody and antigen and the number of hydrogen bonds and unsaturated hydrogen bonds within the interface . The choice of criterion should be based on the stability and affinity of the starting antibody and the goals of the design project . RAbD is most similar to methods previously developed by two other research groups: OptCDR [41] and OptMAVEn [42] , developed by Maranas et al . , and AbDesign developed by Fleishman et al . [47] These authors also present computational benchmarking of their methods , and our benchmarking procedures , metrics , and results can be compared with theirs . We believe our benchmarks are better suited to testing computational antibody design methods than the work of previous authors , and that the risk ratios we have used provided needed context and statistical significance missing from earlier studies . For OptCDR , Pantazas and Maranas constructed 100 decoys for 254 antibodies with CDRs borrowed from other antibody structures and used a simple scoring system that penalized steric conflicts of CDR backbone atoms with any atom of the antigen , favored interactions with a flat score between the sum of van der Waals radii and 8 Å between CDR backbone atoms and atoms of the antigen , and a zero score for longer distances . The native CDR coordinates had better scores than the constructed decoys on average . This is not surprising since almost all of the decoys would have at least one non-native CDR length or cluster , and the antibody as a whole would score worse than the exact native structure , which would have zero clash score and a favorable contact profile . They did not evaluate whether decoys with similar CDR lengths or clusters as the native scored well , as we have done with the length and cluster design risk ratios . In a second computational experiment , they tested a set of 95 experimentally characterized mutants of a single antibody ( anti-VLA1 , PDB: 1MHP ) , 12% of which had improved affinity experimentally [91] . They claim 78% binary total accuracy ( Q2 ) on this set , and a 50% positive predictive value ( PPV ) , which is the percentage of their positive predictions that are true positives ( improved affinities ) . It is impossible to discern a consistent and complete set of evaluative measures typically used in binary prediction methods ( TPR , TNR , PPV , and NPV , balanced accuracy , etc . ) [92] from these limited pieces of data . Finally , they performed a sequence design test and found that the native sequence scored better than all decoy sequences for two thirds of 38 test cases , although this does not indicate that the method could sample and find these native sequences from scratch , which is the typical sequence recovery metric in protein design . They did not provide any measures of statistical significance of these results , as we have done . By contrast , we measured sequence recovery of residues in contact with the antigen and achieved a 72% recovery in the presence of the antigen and 48% in simulations without the antigen , which is an ARR of 1 . 50±0 . 01 ( 95% confidence interval ) . OptMAVEn [42] is based on the MAPS database developed by the same authors . For the heavy chain , κ light chain , and λ light chain , MAPS contains separate PDB files for the structures of V regions ( through the beginning of CDR3 ) , CDR3 segments , and post-CDR3 segments ( “J regions” ) . If we count unique sequences: for the variable regions , there are 60 heavy , 34 κ and 21 λ segments in MAPS; for CDR3 , there are 413 heavy , 199 κ and 39 λ sequences; and for the J regions , there are 3 heavy , 4 κ , and 6 λ sequences . RAbD uses an updated and updateable database of 754 non-redundant sequences per CDR ( on average over 6 CDRs ) to graft CDRs in any combination onto any starting framework , rather than spending CPU on designing the whole antibody variable domains , which may have already been optimized for stability or other properties . RAbD can mix CDR1s and CDR2s from different sources , rather than restricting them to a given V region from the PDB , as OptMAVEn does . Generally , this is a positive feature , since it allows RAbD to sample sequences and structures that are not likely to be present in an animal immune system or in an antibody display library . RAbD also has the ability to keep CDR sampling within a particular germline , including that of the starting antibody . In their computational testing of OptMAVEn , Li et al . demonstrated that their grid search over antigen positions and orientations is able to sample ( but not rank or score ) structures relatively similar to the native structure for 120 antigen-antibody complexes ( antigen protein lengths of 4 to 148 amino acids ) [42] . This is useful to know but does not represent a recovery metric . They utilized OptMAVEn to produce designs for the same benchmark set and were able to produce designed antibodies with lower calculated interaction energies than the native for 42% of the cases , but this does not show that such antibodies would in fact bind better than the native , nor does it show that the native CDR lengths or conformations or sequences were obtained more frequently than one would expect , as our DRR does . For two antibodies , the authors were able to show that they could recover 20% ( HIV VRC01 ) and 35% ( Influenza CH65 ) of mutations from low-affinity antibodies to high-affinity , matured antibodies . For their AbDesign method , Lapidoth et al . clustered the V regions of antibodies ( up to the beginning of CDR3 of each variable domain ) purely by the combination of lengths present at CDR1 and CDR2 [47] . They clustered CDR3 for each domain by length and RMSD . The input data consisted of 788 heavy-chain domains and 785 κ light-chain domains ( no λ chains were included ) , broadly clustered into 5 κ V-regions , 2 κ L3 conformations , 9 heavy-chain V regions , and 50 H3 conformations . By contrast , RAbD uses the 72 CDR clusters of North et al . to group the non-H3 CDRs and contains 985 unique H3 sequences . Like OptMAVEn , AbDesign combines fragments that comprise the entire variable domains , rather than concentrating CPU on the design of the CDRs that contact the antigen . Thus it is not suitable for many design projects , which usually involve changing the sequences of one or more CDRs rather than a wholesale design of a new antibody , including the frameworks . AbDesign was computationally tested on only 9 antibodies [47] . The authors compared features such as shape complementarity , buried surface areas , and interaction scores with the native antibodies . The average shape complementarity of their decoys was approximately 0 . 61 , while that of the natives was 0 . 68 . Our opt-dG decoys reached an average of 0 . 68 in shape complementary scores while the native structures in our benchmark averaged 0 . 70 ( Fig D in S1 Supporting Information ) . AbDesign’s 9 designed antibodies achieved an average of -26 . 1 REU in binding energy , while our 60 designed antibodies averaged -42 REU in the opt-dG simulations ( Fig B in S1 Supporting Information ) . In terms of recovery of the native structure , they calculated Cα RMSDs to native for each of the CDRs of the top scoring design for each of the 9 antibodies . For all of the non-H3 CDRs and 6 of the H3s , the designs had CDR lengths that matched the native antibodies . For 36 of the 45 non-H3 CDRs ( 80% ) , the Cα RMSDs were better than 1 . 0 Å . It is difficult to assess the significance of these results , because the source database for sampling in AbDesign must be dominated by the same CDR lengths and clusters found in the native antibodies in the benchmark . To investigate this , we searched PyIgClassify for the 9 antibodies in this benchmark for their clusters according to our nomenclature . Since H3 does not cluster well beyond length 8 , we report only the lengths of H3 . The representation of clusters in their benchmark is as follows , including the number out of 9 antibodies in their benchmark set: L1-11-1 ( 4/9 ) ; L1-11-2 ( 3/9 ) ; L1-12-1 ( 1/9 ) ; L1-16-1 ( 1/9 ) ; L2-8-1 ( 9/9 ) ; L3-9-cis7-1 ( 7/9 ) ; L3-9-cis7-2 ( 1/9 ) ; L3-9-1 ( 1/9 ) ; H1-13-1 ( 8/9 ) ; H1-13-3 ( 1/9 ) ; H2-10-1 ( 7/9 ) ; H2-10-3 ( 1/9 ) ; H2-9-1 ( 1/9 ) ; H3-9 ( 1/9 ) ; H3-10 ( 4/9 ) ; H3-11 ( 2/9 ) ; H3-12 ( 2/9 ) . L1-11-1 and L1-11-2 are very similar to each other ( <0 . 5 Å RMSD ) . As it turns out , the 4 non-H3 CDRs with the largest RMSDs to native ( >1 . 8 Å ) are those with less common clusters or lengths: L1-12-1 ( 1IQD , 1 . 85 Å RMSD ) , L3-9-1 ( 1IQD , 2 . 12 Å RMSD ) , H1-13-3 ( 2CMR , 2 . 04 Å RMSD ) , and H2-10-3 ( 1P2C , 1 . 90 Å RMSD ) . For comparison , the very common H1-13-1 cluster is about 1 . 6 Å from H1-13-3 , and the common H2-10-1 is 0 . 7 Å away from H2-10-3 . This indicates that AbDesign is dominated by its sampling database in a way that makes the benchmarking data difficult to interpret . The design risk ratio we developed in this work solves this problem by demonstrating the increase in recovery over the sampling rate of any particular conformation in the database . Similarly , the antigen risk ratio demonstrates that the sampling and scoring is able to choose native-like CDRs when the antigen is present in the simulations more frequently than when it is absent , indicating that the design process is choosing CDR structures and sequences likely to bind the antigen . Finally , Lapidoth et al . achieved a sequence identity of 32% for residues in the binding site of the antibodies in their benchmark , compared to RAbD’s values of 72% in our opt-E benchmark ( and a risk ratio of 1 . 50 over simulations in the absence of the antigen ) . RAbD and AbDesign have a number of similarities and several important differences . They both utilize structural clusters of fragments of antibody structure and their associated sequence profiles to build new antibodies during a design simulation . They both utilize Rosetta’s docking and side-chain repacking routines to optimize the structure of the antigen-antibody complex during design . The clustering of antibody structures and PSSM derivation differ substantially between the two methods . AbDesign breaks up each domain of antibodies into two segments–the V region up to the beginning of CDR3 and a segment containing CDR3 and the rest of the variable domain up to its C-terminus . AbDesign clusters its V regions only by the combination of sequence lengths of CDR1 and CDR2 . Thus it samples the entire V domain and merges sequence data from different canonical structures of CDR1 and CDR2 . This can lead to problems because many CDR clusters have required residue types , often glycine or proline , at certain positions in order to form the correct loop conformation . The strategy of replacing the entire heavy and light-chain variable domains with different fragments means that AbDesign is not suitable for optimizing existing antibodies , which is a very common task in antibody engineering and therapeutic development projects . Instead , as intended , it is more suitable for ab initio design of antibodies , which is a very challenging task . Conversely , RAbD treats each CDR separately and samples structures from canonical clusters and their individual sequence profiles as defined in our PyIgClassify database , which is updated on a monthly basis . This allows mixing and matching of CDR1 and CDR2 , while our PSSMs are more closely defined by the structural requirements of each canonical conformation . The CDRs are grafted onto the antibody framework provided by the user , which may have already been optimized for specific properties , rather than redesigning the entire variable domains , as AbDesign does . RAbD and AbDesign are implemented in quite different ways . AbDesign depends on a series of Rosetta scripts , which are xml files that control Rosetta functions . It depends on a mover called splice , which is not documented . It has only been benchmarked on the score12 scoring function of Rosetta , which has not been the default scoring function since 2013 . Finally , AbDesign is difficult to customize for specific problems in antibody design , such as sampling defined lengths of a given CDR or sampling from within a particular germline or CDR cluster . RAbD is a full-fledged Rosetta application , a command-line program that runs the simulation according to command line options and rules provided in an optional CDR Instruction File . The run can be setup as simple as: antibody_designer . macosclangrelease -s 2r0l_1 . pdb -graft_design_cdrs L1 -seq_design_cdrs L1 L2 L3 -light_chain kappa -nstruct 100 The Instruction File makes RAbD highly tailorable . One or more CDRs can be designed or not designed and sampling of CDR structures for grafting can be restricted by length , cluster , species , germline , etc . All of RAbD’s dependencies are available in the public release of Rosetta . RAbD is also very well documented so that new users can quickly set up their design runs . RAbD has been benchmarked on the current Rosetta energy function , REF2015 [93] , which utilizes our smoothed backbone-dependent rotamer library for protein side chains [94] , our smoothed Ramachandran probability densities , and cubic splines for all ϕ , ψ-dependent scoring functions , as described by Leaver-Fay et al . [73] . These scoring functions are important for locally minimizing the Rosetta scoring function by altering backbone and side-chain dihedral angles . The older scoring function used by AbDesign contained very rough surfaces and linear spline estimates for the Ramachandran terms that resulted in poor structure optimization . A major challenge moving forward , especially in regard to true de novo design , is the difficulty in effective sampling and design of the H3 loop , owing to its extreme variability and lack of clustering . To aid in this , H3-specific design strategies in the program can include up-weighting H3 graft sampling from other CDRs , restricting the use of H3 loops in the CDRSet to kinked-only ( See Methods ) [95 , 96] , and reducing the search space to only short loop lengths that cluster well; however , much more work is needed to benchmark and test H3-specific design . Recently , we have shown that H3-like loops can be found in non-antibody proteins [95] . These loop structures could be used to supplement existing H3 structures in our antibody database as additional templates for H3 GraftDesign . The specific design of antibody H3 loops will be a major challenge in the next phase of antibody design methodology development , but using the RAbD framework and new methods for antibody design benchmarking outlined here should aid in this challenge . These promising computational and experimental results show that RAbD is able to design antibodies with similar features to the native antibodies and antibodies with improved affinity . It can be used for a variety of antibody design tasks through the use of its highly customizable interface . RAbD represents a generalized framework and program for antibody design and makes many antibody design projects feasible that are either difficult or prohibitive using historical , traditional means , making computational antibody design a tangible reality .
All antibody designs were expressed as IgGs in 293F cells using the pFUSE vectors ( pFUSEss-CHIg-hG1 ( human heavy ) , pFUSEss-CLIg-hL2 ( human lambda ) , and pFUSEss-CLIg-hk ( human kappa ) ) . Bee Hyaluronidase and gp120 antigens were expressed in 293F cells using pHLsec vectors . Opti-Mem media and FreeStyle293 Expression media were first warmed to 37 °C . 293F cells were checked for viability at 95% and at a concentration greater than or equal to 2 . 4x106 cells/ml . 6 mls of OptiMem were mixed with 125 μg of heavy chain DNA , and 125 μg of light chain DNA in one 15 ml conical tube , and 250 μg of fectin in the other . After a five minute incubation , the DNA tube was poured into the fectin tube and was left to incubate for 21 minutes . The 293F cells were then diluted to 1 . 2x106 cells/ml , added to a 500 ml shaker flask , and the fectin/DNA mixture was added to the flask . The 500 ml flask was incubated in a 37 °C , 8 . 0% CO2 , 80% humidity shaking incubator for four days . The supernatant was harvested on the fifth day using 500 ml centrifuge tubes and spinning for 20 minutes at 4000 rpm . The supernatant was then filtered in a 500 ml filter unit . Antibodies were purified by first using 1 ml of GE rProtein A Sepharose Fast Flow resin in a chromatography column , and then washing with 10 ml dH2O and 10 ml PBS . The antibody supernatant was poured onto the column and then washed with 10 ml of PDB , followed by 10 ml of 0 . 5 M NaCl in PDB , and then another 10 ml of PBS after all supernatant had passed through the column . The antibody was then eluted with 6 ml of Thermo Scientific IgG Elution Buffer into a 50 ml conical tube of 0 . 5 ml , 1M Tris-HCl . The eluted antibody was then placed into a Slide-A-Lyzer cassette and dialyzed against PBS with three changes . After dialysis , the antibody solution was filtered using a 0 . 22 micron syringe filer and the OD was checked to obtain the final concentration of antibody . Kinetics and affinity of antibody-antigen interactions were determined on a Biacore 4000 ( GE Healthcare ) using Series S Sensor Chip CM5 ( BR-1005-30 , GE Healthcare ) and 1x HBS-EP+ pH 7 . 4 running buffer ( 20x stock from Teknova , Cat . No H8022 ) supplemented with BSA at 1 mg/ml . We followed Human Antibody Capture Kit instructions ( Cat . No BR-1008-39 from GE Healthcare ) to prepare chip surface for ligands capture . In a typical experiment about 9000 RU of capture antibody was amine-coupled in appropriate flow cells of CM5 Chip . 3M Magnesium Chloride was used as a regeneration solution with 180 seconds contact time and injected once per each cycle . Raw sensorgrams were analyzed using Evaluation software ( GE Healthcare ) , double referencing , Equilibrium or Kinetic with Langmuir model or both where applicable . Analyte concentrations were measured on NanoDrop 2000c Spectrophotometer using Absorption signal at 280 nm . Antibody-Antigen binding kinetics were confirmed on a ProteOn XPR36 ( Bio-Rad ) using GLC Sensor Chip ( Bio-Rad ) and 1x HBS-EP+ pH 7 . 4 running buffer ( 20x stock from Teknova , Cat . No H8022 ) supplemented with BSA at 1mg/ml . We followed Human Antibody Capture Kit instructions ( Cat . No BR-1008-39 from GE ) to prepare chip surface for ligand capture . In a typical experiment , about 6000 RU of capture antibody was amine-coupled in all 6 flow cells of GLC Chip . 3M Magnesium Chloride was our regeneration solution with 180 seconds contact time and injected four times per each cycle . Raw sensorgrams were analyzed using ProteOn Manager software ( Bio-Rad ) , interspot and column double referencing , Equilibrium or Kinetic with Langmuir model or both where applicable . Analyte concentrations were measured on NanoDrop 2000c Spectrophotometer using Absorption signal at 280 nm . Differential scanning calorimetry ( DSC ) experiments were performed on a MicroCal VP-Capillary differential scanning calorimeter ( Malvern Instruments ) . The HEPES buffered saline ( HBS ) buffer was used for baseline scans and the protein samples were diluted into HBS buffer to adjust to 0 . 6 mg/ml . The system was set to equilibrate at 20 °C for 15 min and then heat up until a temperature of 125 °C was reached at a scan rate of 90 °C/h . Buffer correction , normalization , and baseline subtraction were applied during data analysis using Origin 7 . 0 software . The non-two-state model was used for data fitting . The RAbD protocol consists of repeated execution of an outer loop and an inner loop ( Fig 1 ) . Most Rosetta protocols utilize Monte Carlo + minimization algorithms to optimize sequence and structure effectively . By allowing occasional increases in energy , we enable structures to overcome energy barriers to escape local energy wells in order to drive the energy down further . In order to traverse the energy landscape more effectively , the Monte Carlo criterion is applied during the design simulation for both the outer and inner loops of the algorithm . The general RosettaAntibodyDesign protocol consists of 4 major tasks: The entire procedure may be repeated many times ( 1 , 000–10 , 000 ) so that an ensemble of designs is produced from which some number of the top-ranking sequences may be chosen for synthesis and testing . The outer and inner loops of RAbD can be tailored for a variety of design projects and design strategies through the optional CDR Instruction File , an abundant set of command-line options , and object-oriented code design , which enables RosettaScript-able [97] framework components . Each of the five basic steps is described below in turn . Further details are provided in the Supplemental Methods . The CDRSet instructions tell the program which CDR lengths , clusters , and specific structures to include or exclude from the antibody design database for graft-based design . By default , every CDR length is enabled . The light chain type ( κ or λ ) be specified on the command-line in order to limit the CDRSet to those that originate from that gene , which is aimed at increasing stability of the final antibody . No light chain is specified for camelid antibody design . There are three simple algorithms that control how the CDR structure is chosen from the database during the GraftDesign stage . The default is to choose a CDR cluster from the list of available clusters and then choose a structure from that cluster ( even_cluster_mc ) . One can also choose a structure from all available structures , which samples according to the prevalence of that length and cluster in the database ( gen_mc ) . Or the outer loop can choose a length randomly and then a cluster given that length , and finally a structure from that cluster ( even_length_cluster_mc ) . We recommend even_cluster_mc for most purposes . Finally , when designing a single CDR , the deterministic_graft algorithm can be used to graft every structure available . The lengths , clusters , and particular structures that are sampled and grafted can be controlled through the CDR Instruction File . RosettaAntibodyDesign uses an SQLITE3 database to house all antibody and CDR data needed for the program , including full structural coordinates of CDRs , CDR length , cluster , species , and germline identifications , as well as CDR cluster sequence profile data . The publicly available release of Rosetta includes a smaller database ( about 30 MBytes ) that includes only the structures in the data analyzed by North et al in 2011 . As with other large databases utilized by Rosetta [98] , the current database is too large to be distributed with Rosetta by default . It can be obtained from PyIgClassify [54] , which is typically updated every month and reflects data from the current PDB . All computational benchmarking in this paper utilized a recent version of the database ( August 2017 ) . The experimental tested designs utilized a version from November 2016 . The up-to-date database consists of only non-redundant CDR data at a 2 . 8 Å resolution and 0 . 3 R factor cutoff . CDR cluster outliers are then removed as described in the Supplemental Methods . In order to cull for non-redundancy in the remaining CDR loops , the CDR is selected in the order of: highest resolution → lowest R factor → lowest normalized distance to the cluster centroid . These databases are used in all aspects of the antibody design algorithm , including the GraftDesign step , which uses the raw coordinates in the database and the SeqDesign step , which uses an additional table for CDR cluster profiles ( residue probabilities at each position ) created from the non-redundant sequence data . The CDR Instruction File helps enable additional culling during the GraftDesign step , controlling which lengths , clusters , species , germlines , and structures , are used or left out . The majority of H3 structures contain a “kink” at the C-terminus involving a Cα-Cα-Cα-Cα dihedral around 0° for the last three residues of H3 and the conserved tryptophan residue immediately following H3 . More than 80% of H3 structures contain this kink , whose function in part is to break the β-sheet and allow the H3 CDR to form diverse non-β structures [95] . H3-specific control is available , such as limiting the H3 CDRSet to kinked-only structures . The kink option is useful if H3 is being sequence-designed , as some mutations in kinked H3s may make the H3 adopt an extended β-strand-like conformation and vice-versa . In addition , by default , we disable sequence design of the H3 stem region , which is known to influence the H3-kink [95 , 96] . The span of framework residues with AHo numbering 82–89 comprise what is commonly referred to as the “DE loop” ( Chothia residues 66–71 in the light chain and 71–78 in the heavy chain ) . These variable residues form a loop physically in contact with CDR1 and are occasionally observed making contacts with antigen [53 , 84] . In RosettaAntibodyDesign , we denote the DE loop region as L4 and H4 for the light and heavy chains respectively . The typical κ L4 is different in sequence and conformation than the typical λ L4 , so that λ L4s should be used with λ L1 CDRs and frameworks [84] and κ L4s should be used with κ L1 CDRs and frameworks . Both loops can be considered CDRs in the application and can be specified just as any other CDR except for the GraftDesign stage . However , since the conformation of L4 and H4 is largely conserved among κ , λ , and heavy chain variable domains , and these loops do not usually contact the antigen , we typically do not set them to graft-design and in most cases do not set them to sequence-design either . In order to sample whole structures of CDR conformations from our design database , a way to graft them onto a given antibody was needed that was quick and accurate enough to minimally perturb the CDR region without leaving breaks in the structure or non-realistic peptide bond lengths and angles . Our grafting algorithm ( CCDEndsGraftMover ) first superimposes three residues on either end of the CDR to be grafted onto the template framework . Those residues are then deleted and the Cyclic Coordinate Descent ( CCD ) algorithm of Canutescu and Dunbrack [71] is used to attach the CDR to the framework using two residues of the framework and the first residue of the CDR ( on both sides of the CDR ) , while all other residues are held fixed . Each closure attempt first perturbs the backbone ϕ and ψ of these residues and the energy of these residues is minimized after the attempted closure . A graft is considered closed if the peptide bond C-N distance is less than 1 . 5 Å and both the Cα-C-N and C-N-Cα angles are less then 15 degrees away from the ideal min and max values determined by Berkholz et al . [99] ( 114 . 5° , 119 . 5° and 120° , 126° respectively ) . If the graft is not closed after a specific number of cycles , we use the grafting algorithm from the older Anchored Design Protocol [81] followed by a minimization of the CDR and connecting residues with tight dihedral constraints on all residues . This protocol is generally much slower and can result in larger perturbations to the overall structure of the CDR loop relative to the framework , but can close most grafts due to the mobility of the entire insert region . When both terminal ends are closed during the protocol in either algorithm , we continue the design protocol . Using this combined grafting algorithm , most CDR grafts can be completed in less than a second and we accomplish 100% of CDR graft closure while minimally perturbing the internal CDR structure , if at all . This algorithm is now also used for grafting within the main antibody application of RosettaAntibody [79] , fixing many loop closure imperfections of the original application . The SeqDesign options control which strategy to use when doing sequence design ( primary strategy ) , and which strategy to use if the primary strategy cannot be used for that CDR ( fallback strategy ) , such as conservative design or no design . In Rosetta , the optimization of side-chain conformations is referred to as packing ( or repacking ) . Packing in Rosetta consists of traversing the set of residues to be optimized randomly until no residues are left in the pool and selecting the best rotamer of all rotamers defined for the given residue [64] . Design is accomplished in the packing algorithm by sampling all rotamers ( using the 2010 Dunbrack Rotamer Library [94] ) of a specified set of residue types allowed at a given position . In general , we use a probability distribution of residue types for each CDR position , embedded in the antibody design database . In the RAbD framework , it is how we sample from the profile of a given CDR cluster type . Each time packing is applied , a residue type for each position is chosen based on distributions from the antibody design database . If this residue is different from the starting residue , it is added to the design types . This process can be performed repeatedly to increase the sampling according to the distributions . This methodology helps to maintain the residue profile of a given CDR cluster . This is in use in the Antibody Design framework by default if enough statistics for that CDR cluster are present . Alternatively , we use a set of conservative mutations as design types for each specified position . The conservative mutations for each residue type are composed of the substitutions for each residue which score ≥ 0 in one of the BLOSUM matrices [100] . All BLOSUM matrices can be used for conservative design , and are specified through the use of a command-line option . The numbers of the matrix ( such as BLOSUM62 ) indicate the sequence similarity cutoffs used to derive the BLOSUM matrices , with higher numbers being a more conservative set of mutations . By default , the conservative mutations from the BLOSUM62 matrix are used to strike a balance between variability and conservation . This methodology is the default fallback sequence design strategy but can be used generally instead of profile-based sequence design . By default we disable sequence design for prolines , cysteine residues involved in disulfide bonds , and the H3 kink-determining stem region ( the first 2 and last 3 residues of H3 ) [95 , 96] in order to limit large , unproductive perturbations of the CDR loops from disruptive sequence changes . Users may further disallow amino acid types for all positions through a command-line option , for specific CDRs through the CDR instruction file , and for specific positions through the use of the Rosetta resfile format . The resfile can also be used to disable specific positions from design or packing altogether . Within the protocol , both antibody-antigen interface residues and neighbor residues ( Fig 2 ) that are computed for side-chain packing and design are updated on-the-fly before each packing/design step . This allows the algorithm to continually adapt to the changing environment and is accomplished through Rosetta’s graph-based neighbor detection algorithms . Following sequence design via repacking ( Step 3 ) , the conformations of the grafted CDR , its neighboring CDRs , and nearby framework residues are optimized . The type of minimization and which CDRs are minimized as neighbors to other CDRs during the protocol can be specified through the MinProtocol section of the Instruction File . Many minimization types are implemented . The default is the standard lbfgs_armijo_nonmonotone minimizer in Rosetta with a tolerance of 0 . 001 REU . But other options include the backrub motion protocol [63 , 101] , and the Relax algorithm , which includes alternating cycles of reducing and then ramping up the repulsive van der Waals energy term , and at each step performing side-chain repacking and local dihedral angle space energy minimization . In order to achieve flexible-backbone design and environmental adaptation of the packing/design algorithm as described above , we updated the FastRelax algorithm [60] to enable sequence design during backbone and side-chain optimization . These changes to FastRelax , which we call RelaxedDesign , were used in the optimization step of Jacobs et al . to general success [102] . This is an optional alternative for the minimization step One further option is in the inner loop is integrated sampling of the antibody-antigen orientation during design uses the underlying framework and docking algorithms of RosettaDock [45 , 103–106] . A ‘dock cycle’ consists of a low-resolution docking step , side-chain repacking of the interface residues ( defined as residues of the antibody or antigen that are within the set interface distance of each other ( 8 Å default ) , minimization of the rigid body orientation ( the ‘jump’ in Rosetta parlance ) between the antigen and the antibody , and a shortened high-resolution dock consisting of 3 outer cycles and 10 inner cycles as opposed to 4 and 45 , which is used for a full RosettaDock high-resolution run . During high-resolution docking , the current interface side chains are optimized , while any CDRs or specific residues set to design are designed . In this way , sequence design and antibody-antigen orientation optimization are coupled in the same vein as sequence design is accomplished during CDR structural optimization . Once a structure exits the inner loop after Ninner cycles ( default 1 ) , the structure is then passed back to the outer loop where the Monte Carlo criterion is applied , comparing the structure that entered the inner loop and the structure that exited the inner loop . The outer loop Metropolis Criterion can either be applied on the Total Energy ( opt-E ) or the Interface Energy ( opt-dG ) or some weighted combination of the two . Once Nouter cycles have been completed ( Steps 1–5 , default 25 ) , the output design is the structure with the lowest energy observed during the simulation . In order to reduce artifacts , all benchmarking complexes and starting complexes for antibody design were first minimized into the Rosetta energy function using the Pareto-optimal protocol of Nivon et al . [62] , which relaxes [60] the starting structure using restraints on the backbone and side-chain atoms to strike a balance between deviation from the starting structure and minimization of the energy . This Pareto-optimal method produces models with all-atom Root Mean Square Deviations ( RMSD ) to the starting structures at a mean of 0 . 176 Å [62] . For all starting structures , the lowest-energy model of ten decoys was used as the starting structure . An example command to run this protocol and the flags are given in the Supplemental Methods . All antibodies were renumbered into the AHo numbering scheme [107] using PyIgClassify . The CDRClusterFeature , InterfaceFeature , and AntibodyFeature reporters ( Table D , Table E and Table F in S1 Supporting Information ) were used to determine CDR length and cluster information and physical characteristics of the decoys for benchmarking and design selection . In general , analysis was done using the Feature Reporters to analyze decoys and create databases with physical data , and the public , open-source Jade repository ( https://github . com/SchiefLab/Jade ) was used for benchmarking calculations and selections . Antibody-protein complexes used for benchmarking consist of 46 κ and 14 λ structures . These complexes were chosen with the following criteria: 1 ) resolution ≤ 2 . 5 Å; 2 ) interface surface area of ≥ 700 Å2; 3 ) CDR1 and CDR2 within 40° of one of the cluster centroids in PyIgClassify ( most L3 CDRs were also within 40° , Table A in S1 Supporting Information ) ; 4 ) contacts with the antigen from both the heavy and light-chain CDRs with a preference for contacts of all 6 CDRs; 5 ) non-redundant such that no two antibodies in the benchmark contacted the same antigen with overlapping epitopes; 6 ) a diversity of CDR lengths and clusters . For each input antibody , we ran a total of 100 simulations , and the best decoy observed during each design run was output . The benchmarking was run on a compute cluster in parallel via MPI . The RunRosettaMPIBenchmarks . py script of the Jade github repository was used to help launch and configure benchmarks on the cluster ( https://github . com/SchiefLab/Jade ) . The number of outer cycles for each parallel run was set to 100 , so each input antibody underwent 10 , 000 total design cycles for each experimental group . All 5 non-H3 CDRs were allowed to undergo GraftDesign , while all 6 CDRs went through SequenceDesign . The starting CDR for each non-H3 CDR was removed at the start of the program and a random CDR from the CDRSet was grafted onto the starting antibody through the option–random_start . To calculate the risk ratios over the entire benchmark , we calculated the percent recovered and the percent sampled over the 100 decoys for the 60 antibodies in the benchmark . Thus , the recovery frequency was the number of native clusters observed in the 6000 decoys of the benchmark for each CDR divided by 6000 . Similarly , the sampled frequency was calculated as the number of native cluster CDRs grafted divided by the total number of grafts for a CDR during the 6000 simulations ( for each CDR , ( 100 outer loops ) x ( 1/6 CDRs ) x 100 simulations x 60 antibodies = 100 , 000 ) . For the antigen risk ratios , the frequencies of recovered CDR lengths and clusters were calculated for the final decoys from the antigen-present and antigen-free simulations . The confidence intervals are calculated as described by Gertsman [108] . If RR = pRecovered/pSampled then: CI=exp ( lnRR±1 . 96 ( 1−pRecovered ) NRecoveredpRecovered+ ( 1−pSampled ) NSampledpSampled ) Similarly , for the antigen risk ratio , if RR = pantigen/pnoantigen , where p represents the frequency of the native cluster , length , or residue type in the antigen or no-antigen simulations , then CI=exp ( lnRR±1 . 96 ( 1−pantigen ) Nantigenpantigen+ ( 1−pnoantigen ) Nnoantigenpnoantigen ) The starting antibody complex , was obtained from the Protein Data Bank with ID 2J88 [109] , renumbered using PyIgClassify , and minimized into the Rosetta energy function as described above . To begin design , we used a multiple-strategy approach including with and without docking and the explicit use of only CDR clusters which have cluster profiles ( more than 10 non-redundant members in the database ) , as well as differential CDR design for both graft-based and sequence-based design ( H2 vs . L1 ) . When designing the L1 loop , we also included a strategy in which we allowed L4 to undergo sequence design , for a total of 6 antibody design strategies . The WT L1 or H2 CDR was removed and a random CDR from the database was grafted in order to start design with the non-native CDR , as well as start with a potentially higher-energy structure . All CDR structures from the WT 2J88 antibody were left out of the CDRSet . For the strategies in which docking was used , automatic epitope SiteConstraints were enabled to constrain the antibody paratope to the starting epitope . A total of 1000 top decoys were output as separate Monte Carlo trajectories in parallel for each design strategy using a compute cluster via MPI , with 100 outer cycles for each parallel run , for a total of 100 , 000 design rounds per strategy . The RunRosettaMPI Bio-Jade python application was used to aid the cluster run ( https://bio-jade . readthedocs . io/en/latest/ ) . The command to run the application , flags , and CDR Instructions are given in the Supplemental Methods . Decoys were analyzed by the Rosetta Feature reporter framework in the exact same manner as the benchmarking . The feature databases were then used in the RAbD Jade Antibody Design GUI in order to sort them for selection ( Fig Q in S1 Supporting Information ) . For both relaxed and unrelaxed sets of decoys and each antibody design strategy , we sorted the models according to their computed interface energy ( dG ) after culling to only the top 10% of the models by total energy ( dG_top_p_total ) , or by the lowest density of unsaturated hydrogen bonds per interface area ( delta_unsats_per_1000_dSASA ) [90] for a total of 24 sorted groups ( 6 design strategies x 2 decoy discrimination methods ( relaxed/unrelaxed ) x 2 sorting methods ) . For the three design strategies where docking was enabled and sorted by unsaturated hydrogen bond density ( 3 design strategies x 2 decoy discrimination methods x 1 sorting strategy ) , the best two models had antibodies that were too far from the native binding site , even with the use of epitope SiteConstraints . This could be due to not using the constraint energy as a filter in this case , only to guide the design–i . e . , not in the sorting of the total energy . Due to this , these 6 groups were left out for a total of 18 groups . The best two models from the sorted , unrelaxed groups ( 9 groups , 18 designs ) and the best model of the sorted , relaxed groups were expressed ( 9 groups , 9 designs ) ( i . e . , chosen with no human intervention ) . Three other models of the sorted relaxed groups for L1 design were added to the expression group , as these were the second-best scoring models of the L1 relaxed groups , for a total of 30 antibodies selected for expression . Sequences were obtained from the decoys and processed for inclusion into the expression vector sequences using the get_seq application of Jade . The starting antibody complex was obtained from the Protein Data Bank with ID 4JAN [82] , renumbered using PyIgClassify , and minimized into the Rosetta energy function as described above . A total of four antibody design strategies were used where either H2 or L1+L3 were designed and the CDRSet included only clusters with enough data to use profiles . 250 top decoys were output for parallel Monte Carlo trajectories in parallel for each antibody design strategy with the outer cycle rounds set to 200 , for a total of 50 , 000 design cycles per design strategy . Commands , flags , and CDR Instructions are given in the Supplemental Methods . Decoys were analyzed with both the RosettaFeature reporters and physical data and sorted as described for 2J88 . In addition to sorting by the top dG of the top 10% of total energy ( dG_top_Ptotal ) and density of unsaturated hydrogen bonds per interface area ( delta_unsats_per_1000_dSASA ) , we sorted by the Lawrence and Colman Shape Complementarity score [110] ( sc_value ) through the Jade RAbD GUI . Sorts were done for both relaxed and nonrelaxed decoys to aid in decoy discrimination . The sorts were done for individual antibody design strategies and all combined for a total of 28 sorted groups . Jade was used to output PyMol sessions of each group . The top 10 designs from each group were visually analyzed in PyMol and 27 designs were selected based on physical characteristics such as good shape complementarity , hydrogen bonding , interface , and total energies , as well as cluster and sequence redundancy in the designs . Generally , the top design selected from each sort was expressed , unless it was redundant or the structure held some abnormality , such as bad shape complementarity . Sequences were obtained from the decoys and processed for inclusion into the expression vector sequences using the get_seq application of Jade . RosettaAntibodyDesign is distributed with the Rosetta Software Suite ( www . rosettacommons . org ) and is included with Rosetta versions starting at 3 . 8 . All RosettaAntibodyDesign framework classes are available for scripting within the RosettaScripts framework [97] , including the main application . The public Rosetta distribution includes a database of the original North-Lehmann-Dunbrack clustering data [53] . Up-to-date antibody design databases for use with RosettaAntibodyDesign can be obtained from PyIgClassify ( http://dunbrack2 . fccc . edu/pyigclassify ) . Documentation on the use of RosettaAntibodyDesign , including instructions for installing an up-to-date PyIgClassify database , can be found with the RosettaCommons documentation: https://www . rosettacommons . org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign . Bio-Jade is an open-source python package , with scripts and modules created specifically for RAbD ( https://bio-jade . readthedocs . io/en/latest/ ) . | Antibodies are proteins produced by the immune system to attack infections and cancer and are also used as drugs to treat cancer and autoimmune diseases . The mechanism that has evolved to produce them is able to make 10s of millions of different antibodies , each with a different surface used to bind the foreign or mutated molecule . We have developed a method to design antibodies computationally , based on the 1000s of experimentally determined three-dimensional structures of antibodies available . The method works by treating pieces of these structures as a collection of parts that can be combined in new ways to make better antibodies . Our method has been implemented in the protein modeling program Rosetta , and is called RosettaAntibodyDesign ( RAbD ) . We tested RAbD both computationally and experimentally . The experimental test shows that we can improve existing antibodies by 10 to 50 fold , paving the way for design of entirely new antibodies in the future . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"biotechnology",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"enzyme-linked",
"immunoassays",
"engineering",
"and",
"technology",
"synthetic",
"biology",
"immunology",
"synthetic",
"bioengineering",
"human",
"factors",
"engineering",
"mathematics",
"sta... | 2018 | RosettaAntibodyDesign (RAbD): A general framework for computational antibody design |
Neural systems are organized in a modular way , serving multiple functionalities . This multiplicity requires that both positive ( e . g . excitatory , phase-coherent ) and negative ( e . g . inhibitory , phase-opposing ) interactions take place across brain modules . Unfortunately , most methods to detect modules from time series either neglect or convert to positive , any measured negative correlation . This may leave a significant part of the sign-dependent functional structure undetected . Here we present a novel method , based on random matrix theory , for the identification of sign-dependent modules in the brain . Our method filters out both local ( unit-specific ) noise and global ( system-wide ) dependencies that typically obfuscate the presence of such structure . The method is guaranteed to identify an optimally contrasted functional ‘signature’ , i . e . a partition into modules that are positively correlated internally and negatively correlated across . The method is purely data-driven , does not use any arbitrary threshold or network projection , and outputs only statistically significant structure . In measurements of neuronal gene expression in the biological clock of mice , the method systematically uncovers two otherwise undetectable , negatively correlated modules whose relative size and mutual interaction strength are found to depend on photoperiod .
Understanding how billions of neurons collectively self-organise into a functionally ordered brain able to coordinate a variety of neural , cognitive and bodily processes is probably the most fundamental quest in neuroscience . Over the last decades , evidence has accumulated that the functional organisation of the brain is modular and hierarchical [1] . This means that the brain appears to be partitioned into mesoscopic ‘functional modules’ where each module is composed of neurons with a relatively similar dynamical activity , while different modules are comparatively less related to each other . Each such module may also contain submodules hierarchically nested within it . Reliably identifying functional modules is a nontrivial task because of their irreducibility to contiguous anatomical regions defined a priori and/or to local neighbourhoods in the underlying structural network of neuron-to-neuron anatomical connections [2] . Indeed , while on the one hand functional modules partly reflect the local brain anatomy , on the other hand major deviations between functional and structural networks are observed . One key example is the distinctive ‘long-range’ left-right splitting of some functional modules: often , a single module is found to be composed of two or more spatially non-contiguous populations of neurons , located in possibly distant ( sometimes symmetric , sometimes asymmetric [3] ) regions in the left-right direction [4 , 5] . As an opposite example , an anatomically well-defined brain region can be functionally heterogeneous [6 , 7] and sometimes even display anti-correlation between the activity of some of its parts [8 , 9] . These examples indicate the lack of a one-to-one correspondence between structural and functional modules , showing that it is in general impossible to infer the latter purely from spatial information . Indeed , it is expected that the mapping between functional and structural networks is many-to-one , thus allowing a diversity of functions to arise from a common neuronal anatomy [2] . On top of this , both structural and functional brain networks are characterized by plasticity , i . e . possibility of temporal rearrangements , but at typically different spatial and temporal scales . Precisely because they cannot be reduced to ‘spatially obvious’ brain regions , functional modules must entail an emergent , non-structural level of neural organisation which can only be investigated via the explicit analysis of time series of activity of individual neurons or , at a more coarse-grained level , regions of interest ( ROIs ) . More specifically , recordings of multiple time series are normally used to construct an association ( e . g . cross-correlation , mutual information , etc . ) matrix capturing the mutual relations between pairs of ROIs ( see Fig 1 ) . Next , the matrix can be analysed in different ways to detect the presence of functional dependencies or structure in the system . Importantly , these dependencies can be positive ( + ) or negative ( − ) , leading to measured correlation or anti-correlation . For instance , synaptic interactions between neurons will influence their mutual phases and lead to different states of synchronization in a brain circuit . The degree of synchronization ( + ) versus desynchronization ( − ) is important for neural function and a disturbance in this balance can contribute to neurological disorders . In the paradigmatic example of the central mammalian clock situated in the suprachiasmatic nucleus ( SCN ) of the hypothalamus , the state of synchronization of neurons can influence responses of the circadian system to light and is actually used to encode seasonal changes in day length . It has been suggested that inhibitory ( − ) as well as excitatory ( + ) neuronal interactions will contribute to the phase differences observed under different photoperiods [10 , 11] . The balance between excitatory and inhibitory activity ( E/I balance ) , which is a hallmark of healthy network performance , can actually change with photoperiod [12] . The motivation for the present paper is the expectation that , in the brain and in possibly many other biological networks as well , the presence of both positive and negative interactions should have a significant impact on how the modular functional organization is both mathematically defined and empirically identified . For instance , even within a functionally homogeneous region there may be negatively correlated substructures arising from the need to create and/or modulate the internal mutual phase relationships . Similarly , across two functionally distinct modules there may be a need for dependencies of both negative and positive sign , depending on whether the two functions should inhibit or enhance each other . Consequently , we stress that a proper definition of functional modules should take the sign of the defining correlations into serious account and tools should be devised to reliably identify such sign-dependent structure from time series data . This is crucial in order to map how function is distributed across the modular brain landscape and to properly constrain models of the underlying neural dynamics . In this paper , we argue that the available approaches to the theoretical definition and empirical detection of functional modules treat negative dependencies in essentially unsatisfactory ways . On one hand , most techniques either entirely dismiss negative values [13] , or turn them into positive ones [14] , thereby using no information about the sign of the dependency . On the other hand , the few methods that do take negative correlations into account use ( null ) models that treat all pairwise correlation coefficients as statistically independent entities , thus violating important structural properties of correlation matrices . Other popular approaches like Principal Component Analysis ( PCA ) or Independent Component Analysis ( ICA ) look for independent , rather than anticorrelated , components , thus serving a different purpose . Moreover , most of these approaches fail to provide a stastistical validation of the modules identified , and are therefore prone to misidentification due to the presence of both ROI-specific noise and brain-wide common trends obfuscating the underlying mesoscopic modular patterns . Here , we propose a novel method that targets specifically the positive and negative interactions in brain data and filters the underlying noise and common trends using an appropriate null model based on Random Matrix Theory ( RMT ) . Our approach generalizes a recent community detection method tailored for correlation matrices [15 , 16] , originally formulated for financial time series that have an inherently random and non-periodic pattern , and extends it to the case where arbitrarily structured temporal trends are allowed . We also pay specific attention to the fact that noise and global trends have a previously overlooked coupled effect on the spectrum of correlations , and we rigorously correct for this coupling . Technically , the method makes use of a modified Wishart ensemble of random correlation matrices constructed using precisely the same common trend and expected noise level as the empirical time series , but under the null hypothesis that no modular organization is present . This ensemble serves as a natural , reliable and more appropriate null model for correlation matrices arising in brain research . A comparison between empirical and null correlation matrices reveals the functional modules present in the data and by construction absent in the model . The resulting method is threshold-free and does not require the arbitrary projection onto a network ( see Fig 1 ) . Moreover , in contrast with most of the current approaches , it is designed to yield an optimally sign-contrasted structure , where positive interactions are clustered inside the modules and negative values are expelled across modules . We call the resulting optimized structure the functional signature of the system . This structure is composed of functional modules whose overall internal correlation is guaranteed to be positive and whose overall mutual correlation is guaranteed to be negative . The method only outputs statistically significant structure , if present . We should stress that in any stage of the process there are no presumptions about the output of the method ( such as a predefined number or size of modules ) and the results are completely and non-parametrically driven by the data themselves . If needed , the method can be used iteratively to detect sub-modules hierarchically nested within modules . Besides formulating the method , we apply it to the analysis of the aforementioned SCN , which is responsible for regulating the circadian rhythms of physiology and behaviour in mammals . We chose the SCN of mice because of its relatively small size ( ca 20 , 000 neurons ) and high degree of functional plasticity . Single SCN neurons are capable of generating circadian rhythms in , amongst others , gene expression and electrical activity . The phase differences between the cells can vary with changes in the environment , such as different photoperiods or prolonged light exposure , or with an attenuation of the degree of coupling between the neurons as seen in aging or disease . This makes the SCN an optimal case study for a dynamic network of neurons with different internal oscillations , mechanistically coupled to E/I processes . We show how our method can be used to reliably search the SCN for sign-dependent functional modules reflecting the phase ordering of oscillating cell populations , based on both strength and sign of their coupling interactions . We use samples taken from mice that were subjected to different photoperiods . The method identifies two otherwise undetectable clusters of functionally connected SCN neurons that have a strong resemblance to a known core/shell distinction [17] and that have never been found before without the use of prior knowledge . Importantly , we are able to detect physiological differences present in different photoperiods in the functional signature of the two clusters . We find that the sizes of the two modules change with photoperiod as the result of a majority of neurons remaining in the same module irrespective of photoperiod , and a minority alternating between the two modules at their interface . This finding highlights a possible population of alternating neurons responbile for the functional plasticity required for adjustment to photoperiod and circadian modulation .
Our approach aims at overcoming various limitations of the existing methods . It is therefore convenient to briefly mention these limitations in order to gradually introduce some of the defining elements of our method . First , we want to avoid the use of thresholds on the entries of the correlation matrix . Indeed , most approaches identify functional modules via the introduction of a threshold used to project the original correlation matrix into a network ( see Fig 1 ) [18 , 19] . On this network , various graph-theoretic quantities can be measured to identify modules in terms of e . g . connected components [20] , rich clubs [21] , k-cores [22] or communities [23] . The well known limitations of this approach are the uncontrolled information loss induced by discarding some of the observations , the complete arbitrariness of the choice of the threshold value , and the resulting unavoidable threshold-dependence of the output [24] . Moreover , since thresholds are introduced to project the original matrix into a sparse network , and since the number of negative entries in such matrix is usually smaller than that of positive ones , this procedure essentially imposes a positive threshold , thereby completely disregarding all the negative correlations . Second , we want to avoid turning the negative correlations into positive ones . Based on the ( correct ) consideration that negative correlations indicate functional dependency ( rather than no dependency ) , many approaches aim at exploiting both positive and negative values as cohesive interactions in the definition of functional modules . To this end , they take e . g . the absolute value or the square of the original correlations . However in this way the negative correlations are treated just like the positive ones , making it impossible for the output modules to encode any information about the original sign of the functional dependencies . We instead believe that the sign should be retained and used as a repulsive interaction in the definition of modules , with the understanding that the latter should not be interpreted as functionally independent of each other , but rather as dependent sub-modules in mutual anticorrelation , possibly nested within larger modules that may eventually be functionally unrelated . Third , we want to avoid the ‘merging bias’ that affects even the few remaining methods that do preserve the sign of correlations in the definition of modules [14 , 25 , 26] . These methods are adaptations of the so-called ‘modularity maximization’ techniques introduced in the literature about community detection in networks and targeted at finding groups of nodes that are more densely connected internally , and less densely connected across , than expected under a random null model [23] . The main null models for networks have statistically independent links , i . e . a link can be placed between any two nodes without affecting the probability of placing links elsewhere in the network . The methods that generalize these null models to correlation matrices extend them in the direction of allowing links with both positive and negative weight , but unfortunately retain the assumption of independent matrix entries [14 , 25 , 26] . While justified for networks , this assumption becomes incorrect for correlation matrices , whose entries are subject to basic ‘metric’ properties that make them depend on each other [15] . For instance , negative triangular relationships of the type Ci , j < 0 , Cj , k < 0 , Ck , i < 0 are in general very rare in empirical correlation matrices ( and become impossible if Ci , j = Cj , k = Ck , i = −1 ) , while they are much more likely in a null model with independent entries . This effectively creates the systematic bias of erroneously interpreting the absence or scarcity of such negative triangles in the data as strong statistical evidence for the nodes i , j and k being ‘attracted’ to each other . As a net result , the three nodes are likely to be merged in the same module ( hence the merging bias ) , although their mutual anticorrelation represents statistical evidence that they should in fact belong to three separate modules . Fourth , we want to avoid misidentification due to the presence of common trends across all ROIs in the sample . Indeed , depending on the spatial and temporal resolution of the data , experimental time series may contain a multitude of periodic or systematic trends at different frequencies ( e . g . heartbeat , breathing , circadian rhythms ) that impart an overall positive correlation to all or several ROIs , without actually representing any real functional relatedness among the ROIs themselves . One of the side effects of such ‘global mode’ is a reduction of the detectability of the underlying modular structure . Certain techniques aim at solving this problem by preliminary subtraction of the measured average trend from each time series separately ( thus effectively removing the global mode ) , and then calculating the resulting correlation matrix . Along these lines , methods like Bazzi et al [27] proposed a null model in which the elements are correlated at some baseline level , where the amplitude of this level is determined by a tunable parameter . These procedures have been criticized because they tend to generate both positive and negative correlations by construction , with no guarantee that the corresponding signs represent a true signature of functional modularity , e . g . even if the original time series were all independent and their increments relative to the average trend were merely due to chance or noise . Fifth , and connected to the point above , we want to accurately characterize the level of noise in the data . This point is connected to many of the points above . For instance , being able to separate noise from information would allow us to avoid the use of arbitrary thresholds , discriminate between true and random modularity , and arrive at a safer definition of modules based on trends relative to the global one , thus enhancing the detectability of functional substructure . We are now ready to introduce our method which is designed in order to avoid the limitations described above . Given an empirical correlation matrix constructed from multiple time series of neuronal activity , our method looks for functional modules upon removing the joint effects of noise in the data and of common temporal trends , as both features may obfuscate the empirical identification of possible underlying substructure . For this task the method first introduces a null model that serves as a random benchmark , thus accurately highlighting the non-random modular patterns in the empirical correlation matrix . This improved null model , based on random matrix theory , takes into account cell to cell variability and does not require the unrealistic assumption that the time series are stationary . Therefore we can allow for any temporal modulation ( see section Materials and methods ) , both in individual time series and in their resulting common trend . This is very important , given the strongly time-dependent nature of functional brain data in general , and of our time-modulated oscillating signals in particular . So , even if the calculation and interpretation of correlation matrices usually assumes stationarity , here we can statistically treat correlation matrices calculated from nonstationary data as well . The first step is an exact calculation of the combined , undesired effects of noise and common trends on the density of eigenvalues ρ ( λ ) of a theoretical cross-correlation matrix . This step corresponds to the definition of a null model for a correlation matrix without modular patterns , but with a noise level calibrated to the observed one and with a global trend that exactly follows the one in the empirical time series . The output of this first step is illustrated in Fig 2A . The density of eigenvalues , which is calculated exactly in the null model [see section Materials and methods] , features one largest eigenvalue λmax due to the global trend , plus a “random bulk” extending between a minimum ( λ− ) and a maximum ( λ+ ) eigenvalue . The second step is a filtering of the original correlation matrix via the identification of the empirical eigenvalues that deviate , in a statistically significant manner , from the ones predicted by the module-free null model . In practice , this reduces to the selection of the empirical eigenvalues that are found in the range ( λ+ , λmax ) . A crucial result in this study , overlooked in previous analyses [15] , is a precise calculation of λ+ showing that the higher λmax , the lower λ+ . The fact that the values of λmax and λ+ depend on each other is a proof that noise and global trends jointly affect the features of the expected eigenvalue density of the correlation matrix . Our calculation of λ+ allows us to recover statistically significant features of the empirical correlation matrix that would otherwise be incorrectly classified as noise . Looking again at Fig 2 , we indeed see the presence of eigenvalues in the empirical spectrum ( red ) that deviate from our adjusted null model ( green ) and include eigenvalues that would be incorrectly classified as noisy if λ+ were not corrected for λmax ( blue ) . This step is completed by the selection of the eigencomponent of the correlation matrix associated with the deviating eigenvalues . The resulting , cleaned component of the original matrix contains statistically significant , noise- and trend-filtered information about the presence of functional modules . Once the original correlation matrix has been filtered by the null model , only the statistically significant dependencies are guaranteed to remain in the matrix . At this point our aim is the identification of functional modules that are positively correlated internally and negatively correlated externally . This can be transformed into an optimization problem . We employ community-detection techniques that take the filtered correlation matrix as input and return the optimized partition of the system into functional modules . The optimized partition will tend to place the positive dependencies ( correlation ) inside the clusters while expelling the negative dependencies ( anti-correlation ) across the clusters . We should stress that , by construction , the emergent functional structure will be detectable only if it is statistically significant . Moreover , the number of detected clusters is not defined a priori , and is found automatically by the method itself . It should be noted that , while the use of information contained in the eigenvectors of the largest eigenvalues is common to other methods ( such as Principal Component Analysis and it generalization , aka Independent Component Analysis [28 , 29] ) as well , our approach distinguishes itself from these approaches in various respects . First , those methods look for the independent components in which the orginal signal can be optimally decomposed , while our aim is to pinpoint the anticorrelated groups of units . Second , our iterative optimization procedure reformulated for correlation matrices guarantees that the final output is maximally contrasted in terms of the signs of the detected modules . Finally , the other approaches focus on the strongest eigenvalues but do not implement a null model , tailored to capture both local noise and global trends , to assess which of the eigenvalues are informative and which are noisy . Indeed , in ICA the desired number of components has to be specified by the user , whereas in our method the optimal number of modules is given as output by the algorithm . By using an appropriate null model that takes into account the presence of strong global trends , our method avoids the misidentification due to common trends and merging bias of other methods described above . To illustrate this , in Fig 3 we show a synthetic sample with 300 oscillating signals divided into 3 main groups , in each of which 100 signals are randomly assigned different phases around a ‘master signal’ ( top ) . We also consider the same exact system with strong ( periodic ) global trend , which obscure the positive and negative correlations ( bottom ) . We can clearly see that due to the differences in phase between the groups , the relations between different groups become negative ( anti-correlation ) in sample one ( top ) , however , in the system with the global trend all of the correlations are shifted to positive values ( bottom ) . We then process the correlation matrix with the ( independent-entries ) method proposed in [14] and with our method . While our method is able to cluster the 3 groups perfectly in both cases , the general modularity method clusters the modules correctly only in simple case where no global trends are present . The brain region we apply our method to is the suprachiasmatic nucleus ( SCN ) , located in the hypothalamus in the brain , and recognized as the site of the central circadian clock in mammals . This clock is important for the regulation of our daily and seasonal rhythms . It has been shown that the neuronal network organization of the SCN changes in different photoperiods [30] , however , the mechanisms behind these changes are still elusive . Furthermore , only a subset of neurons within the SCN network are directly responsive to light [31] , which poses the question how encoding for seasonally changing day length is achieved in the SCN network . The SCN is a prototypical example of a brain structure for which resolving functional organization is challenging for the reasons outlined above: it consists of about 20000 neurons that are spatially close ( total size of 1 mm3—so , structurally speaking , these neurons form a single densely connected cluster , whose only anatomical substructure is a left-right split into two lobes ) while at the same time displaying a high variability in terms of the signals of the constituent neurons . Currently , brain networks are most often derived from data acquisition techniques that do not have the possibility to perform recordings at the single cell level . Techniques such as ( functional ) Magnetic Resonance Imaging ( ( f ) MRI ) , Electroencephalography ( EEG ) or Magnetoencephalography ( MEG ) use brain regions as nodes in the network and statistical associations of regional/sensor temporal activity as edges . We investigate the SCN network at the micro-scale where nodes are single cells and edges are functional connections between the cells . We use single-neuron data on gene expression of a clock gene period2 in the SCN . The data were sampled every hour for at least three days by means of a bioluminescence reporter PER2::LUC . We first perform a standard analysis based on the mainstream method [see Fig 1] for detecting communities via functional networks . This is a useful reference as a comparison with our own method . In Fig 4 we present the community structure , resolved by the standard method , for different thresholds . In blue are the nodes that belong to the large cluster , while in gray are isolated nodes ( communities that only contain one node ) . In the right panel , we plot the fraction of nodes in the largest connected component S = L C C N in blue , and the fraction of communities detected M = C o m m u n i t i e s N in red . It is evident that applying different thresholds essentially detaches isolated nodes from the large cluster , and there is no optimal value for the threshold . Therefore , the standard method can only observe a “radial gradient” of connectivity , and there is no sense of multiple communities of neurons , which is one of the signatures of functional as opposed to structural connectivity . This poor performance of the method is a known limitation when applied to very dense networks . Our method detects mostly two communities which coincide with the core and shell distinction within the SCN [17] . The core of the SCN receives light input and adjusts quickly to changing light schemes , while the shell of the SCN lags behind [32] . Mostly the core-shell distinction of the SCN is interpreted as a distinction between the ventrolateral and the dorsomedial part of the SCN , which is predominantly based on anatomical data [33] . In this study the two clusters that were found were more dorsolaterally and ventromedially located , and while it is based on functional data this may differ from known anatomical distinctions . Furthermore , the SCN is much more heterogeneous when looked at cellular phenotype or gene expression [6 , 34] . The anatomical loci do not necessarily delineate the phenotypical SCN regions very precisely , which implies that functionally , the core-shell distinction is less clearly defined and may differ from the described anatomical division ( see also [17] ) . Next , we perform the analysis using the method presented in Rubinov& Sporns ( 2011 ) [14] , which uses a modified modularity matrix to incorporate signed matrices . Since the method does not require a threshold parameter and using all the data entries in the correlation matrix to resolve the community structure , we anticipate a better performance than the standard threshold procedure . In Fig 5 we plot the community structure of four different samples ( A , B , C , D ) as resolved by the two methods . The panels represents the partitions detected , where each community is marked with a different colour . Strikingly , the signed Leuven method community structure is very comparable to the clear core periphery structure that is detected with the random matrix approach . However , it also detects with high consistency three communities , when the third community is changing in size and location for each sample . The differences in the structures might result from the improve ability of the random matrix approach to filter common trends , as seen in Fig 3 . Nevertheless , the presence of the general core periphery pattern is reinforced by both of the methods . Regional analysis of the SCN using functional time series has been performed by other groups . Evans and co-workers used a similar approach to identify single-cell-like regions of interest , but did not use clustering algorithms and chose the regions by hand [35] . Silver and co-workers also used regions of interest , called superpixels , but these were not necessarily identified as single-cells . Based on these superpixels they used threshold methods to identify regional differences in the SCN [36 , 37] . Abel and co-workers applied a threshold method based on mutual information on single-cell-like regions of interest [38] . These approaches encounter similar problems as described in this paper when using the threshold method: they only find one cluster ( in the core , or ventral part ) and many non-clustered cell-like ROIs ( in the shell or dorsal part ) . Our results presented here are in line with the regional division of the SCN proposed in these studies , but we were able to identify both the core and shell clusters . To visualize the general community structure we plot the bioluminescence image of one SCN sample with the resolved average partition average partition over all the samples ( Fig 6A and 6B ) . We also plot the average signal of each community to observe the optimized anti-correlation pattern ( Fig 6C and 6D ) , which corresponds to the functional signature of the SCN . Furthermore , our approach is able to identify the two clusters in different experimental conditions , ranging from summer conditions ( long days , short nights: LD 16h:8h ) to winter conditions ( short days , long nights: LD 8h:16h ) . On the contrary , Evans and co-workers identified changes occuring in the organization of the SCN , where the two regions similar to our clusters were found , only for very long day conditions ( LD 20h:4h ) [39] . As a next step we analyzed the values in the functional signatures and compared those between different photoperiods that the animals have been subjected to . With this step we reveal the dynamics within the population of neurons in the clusters and between the clusters . As the cluster-partition is based on the functional signature , we will now investigate the values within and between the clusters , exploring the inner and outer level of correlation . This extra information links physiological properties of the SCN to the functional signature found in the data . We measure the average residual correlation within each cluster detected by our method and we plot the community distribution of the measured values ( Fig 7A and 7B ) . We then identify the cumulative probabilty of the values in the clusters and we see that in short photoperiods the average values are much higher than in long photoperiods ( Fig 7C ) . This means that the correlation within the clusters is significantly higher in short photoperiods than in long photoperiods . When we examine the values between the clusters , we see that the average value is lower in short versus long photoperiod , meaning that the clusters are less correlated in short phtoperiods ( Fig 7D ) . These results connect directly to previous results in physiological properties as described in [7] and is supported in other papers [30 , 40] . Thus , we show that the hidden functional representation reveals the phase ordering of oscillating cell populations caused by physiological properties of the SCN .
Our method reveals hidden functional dependencies that are obfuscated by the presence of a global mode in the neuronal gene expression , which imparts an overall positive correlation . This problem becomes particularly evident when searching for functional structure in neuronal systems where the global signal is very strong , making the identification of functional modules very challenging . Our method is able to deal with the effects of noise and common global trends in the original data in a robust manner . In fact , we have shown that the effects of noise and those of the global signal are coupled , as their signatures in the spectrum of the correlation matrix depend on each other . We found a distinctive left-right functional symmetry with core-shell features in the SCN . This structure reveals non-contiguous regions that display strongly synchronized activity , despite being at a relatively large distance from each other , similar to [4] . Remarkably , here we detect this functional symmetry on a micro-scale level where nodes are single cells . In this respect , it is important to notice that while the traditional threshold method applied to the SCN resolves only a radial gradient of functional connectivity that closely mirrors the anatomical proximity of neurons without singling out any modularity or boundary , our method systematically reveals two sharp modules , a ventral core and a dorsal periphery . These modules feature distinct signatures of functional ( as opposed to structural ) connectivity , namely left-right symmetry , spatial non-contiguity , and almost perfect dynamical anti-correlation once the global SCN-wide signal is filtered out . The left and right shell regions of the SCN , despite being spatially disconnected into two non-contiguous regions , are functionally joined into a single module . These symmetrical structures in the SCN raise important questions with respect to the mechanism in the system , and can possibly be explored in the future . The ability to exploit all the information from the correlation matrix , i . e . both the negative and the positive dependencies ( correlation and anti-correlation ) , in order to detect the functional modules is very powerful . The strength of our method is to detect communal phase differences in neuronal networks by analysing time series data without using any presumptions or threshold definitions . Phase differences and phase adjustments in neuronal networks are an key feature for physiological function and can be used to define the functional state of a network in health and disease . Our method allows the identification of synchronized clusters of cells . Synchronization within a neuronal network was suggested to play a major role in the occurrence of epilepsy [41 , 42] , Parkinsons disease [43 , 44] and schizophrenia [45 , 46] . It is noteworthy that the clusters determined with our methods are not influenced by the functional change in E-I balance occurring in different photoperiods . This is advantegous since our analysis will also detect functional clusters within neuronal networks with altered E/I balance often found in neurological disease ( e . g . epilepsy , RETT , FragileX , autism ) and in the aging brain . The results presented here show that our method offers great potential for detecting hidden functional synchronization and desynchronization in brain networks and are not limited to gene expression rhythms . Time series from other modalities , such as electrical action potential recordings , EEG recordings and fMRI recordings can also be interpreted through this new method . As such , the method may offer diagnostic or pre-diagnostic applications in medical health care .
The experiments were performed in accordance to the Dutch law on Animal welfare and approved by the Dutch government ( DEC 11010 ) . Experimental methods and results are treated in our accompanying paper [7] . Briefly , male homozygous PER2::LUCIFERASE knock-in mice [47] were bred in the animal facility of the Leiden University Medical Center ( LUMC ) . The animals were entrained to different photoperiods , being either summer days with 16 hours of light and 8 hours of darkness ( LD 16h:8h ) or to winter days with 8 hours of light and 16 hours of darkness ( LD 8h:16h ) . The mice were entrained for at least 28 days to their respective photoperiod . Animals were sacrificed within two hours before lights off , since dissection during that period is found to least affect the SCN rhythm [48 , 49] . Organotypic cultures of the SCN were prepared as described previously [7] . In brief , mice were truncated and the brain immediately dissected and placed in ice cold , low Ca2+ and high Mg2+ artificial cerebrospinal fluid ( ACSF ) , containing ( in mM ) : NaCl ( 116 . 4 ) , KCl ( 5 . 4 ) , NaH2PO4 ( 1 . 0 ) , MgSO4 ( 0 . 8 ) , CaCl2 ( 1 . 0 ) , MgCl2 ( 4 . 0 ) , NaHCO3 ( 23 . 8 ) , D-glucose ( 16 . 7 ) and 5 mg/L gentamicin ( Sigma Aldrich ) saturated with 95% O2—5% CO2 ( pH 7 . 4 ) . From each animal , the hypothalamus containing the SCN was cut in 200 μm thick slices , using a VT 1000S vibrating microtome ( Leica ) . From two consecutive coronal slices ( the SCN was isolated and placed on a Millicell membrane insert ( PICMORG50 , Millipore ) . Membrane inserts were placed in a 35 mm dish , which contained 1 . 2 mL of Dulbecco’s Modified Eagles Medium ( D7777 , Sigma-Aldrich ) supplemented with 10 mM HEPES-buffer ( Sigma-Aldrich ) , 2% B-27 ( Gibco ) , 5 U/ml penicillin and 5 μg/ml streptomycin ( 0 . 1% penicillin-streptomycin , Sigma-Aldrich ) and 0 . 2 mM D-luciferine—sodium salt ( Promega ) , adjusted to pH 7 . 2 with NaOH . The dish was sealed with vacuum grease ( details ) and a 40 mm coverslip . The dish containing the cultured SCN tissue were immediately transferred to a light tight and temperature controlled chamber , kept at 37 °C ( Life Imaging Services , Reinach , Switzerland ) . The chamber was equipped with an upright microscope ( BX51WIF , Olympus ) with a long-working distance objective ( HN10X/22 , Olympus ) and a cooled CCD camera ( ORCA –UU-BT-1024 , Hamamatsu ) . The bioluminescence images were obtained from two SCN cultures per experiment , with an exposure times of 29 min , resulting in one image per hour . Stage and focus position , as well as image acquisition was controlled by Image Pro Plus software ( MediaCybernetics , Warrendale PA USA; StagePro plug-in , Objective Imaging , Cambridge , UK ) , driving a motorized stage ( XY-shifting table 240 , Luigs & Neumann Ratingen , Germany ) and a focus control ( MA-42Z , Märzhäuser , Wetzlar , Germany ) both connected to an OASIS-4i Four Axis Controller . A MATLAB-based ( Mathworks , Natick , MA ) custom-made program was used to analyze the images . An automated detection procedure identified cell-like regions of interest ( ROIs ) consisting of groups of pixels with luminescence intensity above the noise level . The time series from the cell-like ROIs were smoothed and the data was resampled to one data point per minute to reduce noise and increase the efficiency for subsequent analyses [50] . The cell-like ROIs were evaluated on consistency of location throughout the recording , and the smoothed signals on sustained PER2::LUC signal and circadian rhythmicity . All single cells had a minimum of three consecutive cycles , where the average peak interval was in the circadian range ( 20-28h ) . Both raw data as well as smoothed data were tested using the mathematical method described in this paper , which did not yield significantly different results . We describe the redefined modularity for correlation matrices [15] . Let us consider a system with N cells . One can introduce a number of partitions of the N cells into non-overlapping sets . The different partitions will be represented by an N-dimensional vector σ → where the i-th component σi denotes the set in which cell i is placed by that particular partition . Now , we introduce the modularity measure Q ( σ → ) which indicates the quality of a particular choice of partition σ → measured by a high degree of inter-community connectivity and a low degree of intra-community connectivity . So-called modularity optimization algorithms look for the specific partition that maximizes the value of Q ( σ → ) , the objective function . The latter is defined as Q ( σ → ) = 1 C n o r m ∑ i , j [ C i j - ⟨ C i j ⟩ ] δ ( σ i , σ j ) ( 1 ) where 〈Cij〉 is a null model that needs to identify the random properties of empirical correlation matrices . In this approach , the empirical correlation matrix is first decomposed and then reconstructed using only the eigenvalues ( and eigenvectors ) that are not reproduced by the random null model . Once compared with the observed spectrum of the empirical correlation matrix , the model will identify the non-random eigenvalues ( by elimination ) . The non-random eigenvalues will be later used to generate the new filtered matrix . Here we proceed to the exact calculation of the null model , that will be used as a random benchmark in the modularity . The aim is to calculate the the density of eigenvalues ρ ( λ ) of a theoretical cross-correlation matrix , however , here we look at a special case of a random system with common trends . As a crucial difference with respect to a similar method introduced in [15] , we do not require the unrealistic assumption that the time series are stationary . Therefore we can allow for any temporal modulation ( see Fig 8 ) , both in individual time series and in their resulting common trend [see section Materials and methods] . This is very important , given the strongly time-dependent nature of functional brain data in general , and of our time-modulated oscillating signals in particular . So , even if the calculation and interpretation of correlation matrices usually assumes stationarity , here we can statistically treat correlation matrices calculated from nonstationary data as well . A second and related improvement takes into account the effects of common ( nonstationary ) trends for a system with N cells , and in particular the largest eigenvalue λmax . We realize that the effects of noise are inseparably coupled to those of the global trend [51] , as the presence of the latter modifies and left-shifts the density of eigenvalues that we would otherwise observe in presence of noise only . So we do not simply superimpose the two effects as in [15]; on the contrary , we calculate the modification of the random bulk exactly , given the system’s empirical λmax . In particular , we calculate the shifted value of an original Wishart matrix [15] to find λ ± = ( 1 - λ m a x N ) ( 1 ± 1 Q ) 2 ( 2 ) where Q = T/N is the ratio between the number of time steps in the data T and the number of cells N . Fig 2 shows both the modified and unmodified spectral densities . It also shows that taking the left-shift of the random bulk into account is very important , as it unveils informative empirical eigenvalues that would otherwise be classified as consistent with the random spectrum and hence discarded . We employ a popular community-detection technique that take the filtered correlation matrix as input and return the best partition of the system into functional modules [23 , 52] . In this set-up the algorithm is clustering positive dependencies within the clusters and expelling negative dependencies outside . The optimized partition , which maximizes the modularity Eq 1 , is considered the binary signature of the system . We should stress that while standard community-detection methods are based on null models that are justified only for networks , but not for time series , our method builds on the appropriate null model described above and calculated exactly in the first step . The use of a correct null model allows for a recursive analysis of specific time series in the data , i . e . analyze different hierarchical levels of the community structure . Here , unlike in the analysis of a network topology , further analyzing the communities for the detection of sub-clusters is not acting on missing information ( ignoring inter-clusters links ) . We take the original time series of each cluster and construct a new correlation matrix , this matrix will then be filtered and analyzed with the same approach . In Fig 9 we present an hierarchical community structure of an SCN sample as resolved by our method . In this study we only explore the first partition , since the data contains a limited number of cells , which makes the next partitions unreliable . However , this feature marks a great potential for future data sets and studies . | In recent years an increasing number of studies demonstrate that functional organization of the brain has a vital importance in the manifestation of diseases and aging processes . This functional structure is composed of modules sharing similar dynamics , in order to serve multiple functionalities . Here we present a novel method , based on random matrix theory , for the identification of functional modules in the brain . Our approach overcomes known inherit methodological limitations of current methods , breaking the resolution limits and resolves a cell to cell functional networks . Moreover , the results represent a great potential for detecting hidden functional synchronization and de-synchronization in brain networks , which play a major role in the occurrence of epilepsy , Parkinson’s disease , and schizophrenia . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"ecology",
"and",
"environmental",
"sciences",
"neural",
"networks",
"community",
"structure",
"light",
"mathematical",
"models",
"neuroscience",
"electromagnetic",
"radiation",
"mathematics",
"algebra",
"computational",
"neuroscience",
"research",
"and",
"analysis",
"metho... | 2019 | Uncovering functional signature in neural systems via random matrix theory |
In enterobacteria , the Rcs system ( Regulator of capsule synthesis ) monitors envelope integrity and induces a stress response when damages occur in the outer membrane or in the peptidoglycan layer . Built around a two-component system , Rcs controls gene expression via a cascade of phosphoryl transfer reactions . Being particularly complex , Rcs also involves the outer membrane lipoprotein RcsF and the inner membrane essential protein IgaA ( Intracellular growth attenuator ) . RcsF and IgaA , which are located upstream of the phosphorelay , are required for normal Rcs functioning . Here , we establish the stress-dependent formation of a complex between RcsF and the periplasmic domain of IgaA as the molecular signal triggering Rcs . Moreover , molecular dissection of IgaA reveals that its negative regulatory role on Rcs is mostly carried by its first N-terminal cytoplasmic domain . Altogether , our results support a model in which IgaA regulates Rcs activation by playing a direct role in the transfer of signals from the cell envelope to the cytoplasm . This remarkable feature further distinguishes Rcs from other envelope stress response systems .
Gram-negative bacteria are surrounded by the cell envelope , a multi-layered structure composed of an outer membrane ( OM ) and an inner membrane ( IM ) . These two membranes delimit the periplasm , a viscous and oxidizing compartment enclosing the cell wall , a thin peptidoglycan ( PG ) layer . The cell envelope is required for growth and survival , maintaining cell shape and providing osmotic protection to cells [1] . Being at the interface with the environment , the envelope is also a permeability barrier protecting bacteria from environmental stress and antibacterial compounds [2] . Proteins playing a role in the assembly and maintenance of the cell envelope are therefore attractive targets for antibiotic development . Given the functional and structural importance of the envelope , it is a matter of life and death for bacteria to detect breach in envelope integrity and to respond in a fast and adequate manner . Bacteria have therefore evolved sophisticated systems that allow them to monitor envelope integrity and to elicit cellular responses when perturbations occur [3] . In Escherichia coli and enterobacteria , the Rcs system ( Fig 1A ) detects a variety of envelope perturbations , the most prominent being OM and PG damage [4 , 5] . In response , Rcs modulates the expression of dozens of genes , including those involved in the biosynthesis of colanic acid , an exopolysaccharide that accumulates on the cell surface to form a protective capsule [5 , 6] . In addition to its role in capsule formation , Rcs is also required for normal biofilm development and regulates virulence-associated structures [5 , 7] . Like several envelope stress sensing systems , Rcs is built around a two-component system . In classical two-component systems , an IM-localized histidine kinase autophosphorylates on a histidine residue in response to a specific signal . The phosphoryl group is then transferred to an aspartate present in a cytoplasmic response regulator , which then binds to target promoters on the chromosome to control gene transcription [5] . In Rcs , the phosphorylation cascade is however more complex . Indeed , following autophosphorylation of the sensor histidine kinase RcsC , the phosphoryl group is first transferred to an aspartate residue present in a receiver domain on the same protein . It is then being handed over to a histidine residue present in the IM protein RcsD , before being finally transferred to an aspartate present in the receiver domain of the response regulator RcsB ( Fig 1A ) . Thus , in the Rcs system , signal transduction involves a multi-step phosphorelay [8–10] . A second unusual feature of Rcs is that proteins that do not directly participate in the phosphorylation cascade modulate the activity of the system . It is the case of RcsF , an OM lipoprotein that is at least partially exposed on the cell surface [11–14] . Surface exposure of RcsF is mediated by the β-barrel assembly machinery via the assembly of complexes between RcsF and abundant β-barrels [11 , 12] . Interestingly , RcsF is required for sensing most Rcs-inducing cues , including OM alterations by cationic antimicrobial peptides [15] or weakening of the PG sacculus by mecillinam , a β-lactam antibiotic inhibiting the essential transpeptidase PBP2 [4] . A second auxiliary protein that is important for Rcs function is YrfF ( Fig 1B ) , a poorly abundant IM protein that down-regulates Rcs via a still unknown mechanism . YrfF has been mainly studied in Salmonella , where it was found to be implicated in pathogenicity and antimicrobial resistance [16–19] . Because yrfF inhibits growth of Salmonella inside fibroblasts , it was renamed to IgaA ( for Intracellular growth attenuator ) [18] . We will adopt this nomenclature here for the E . coli gene . Interestingly , igaA is the only gene encoding an Rcs component that is essential [20] , indicating that excessive Rcs activation is toxic for cells . Notably , igaA null alleles are only viable when combined with deletions of rcsB , rcsC and rcsD , but not rcsF , which implies that IgaA lies upstream of the components of the phosphorelay and downstream of RcsF in the signaling cascade [11 , 20 , 21] . We previously reported that , when the periplasmic domain of IgaA is expressed as a soluble protein , it forms complexes with RcsF which can be pulled-down after cross-linking [11] . The interaction between these two proteins was also confirmed in vitro [11] and by using bimolecular fluorescence complementation [22] . These and other results led us to propose a model in which OM or PG-related stress prevents newly synthesized RcsF from interacting with the β-barrel assembly machinery , which results into new RcsF molecules being exposed to the periplasm , where they bind to IgaA . Following interaction with RcsF , IgaA would then relieve the inhibition that it exerts on the phosphorylation cascade , turning on Rcs [11] . However , direct evidence for the stress-dependent formation of the RcsF-IgaA complex is still missing . Furthermore , nothing is known on how IgaA interacts with the downstream Rcs components and regulates the phosphorelay . Here , we clearly established the functional relevance of the RcsF-IgaA interaction by obtaining direct evidence for the stress-induced formation of the RcsF-IgaA complex . In addition , by testing the ability of a series of IgaA constructs corresponding to different domains of the protein to complement an igaA depletion strain , we functionally dissected IgaA to gain insights into its mechanism of action . We found that while the C-terminal , periplasmic domain of IgaA serves as the primary receiver of the signal transmitted by RcsF , it is not required for Rcs inhibition . By contrast , substantial Rcs inhibition was observed when the first cytoplasmic domain of IgaA was expressed as a soluble protein , revealing the important role of this domain in Rcs regulation . Full Rcs repression required , however , co-expression of the N-terminal and C-terminal portions of IgaA . Altogether , our results establish IgaA as a multimodal platform capable of integrating signals on both sides of the IM .
The functional importance of the RcsF-IgaA interaction and its role in turning on Rcs under stress remained unclear . To close this gap , we engineered a molecular system to monitor the formation of the RcsF-IgaA complex in vivo and determine its levels under different conditions . Because IgaA is a relatively low abundant protein , being present at ~200 copies per cell [23] , we expressed it from a low-copy plasmid to increase its expression levels and facilitate detection . A triple flag tag ( denoted as fl ) was fused to the C-terminus of the protein ( IgaA-fl ) for purification and detection purposes . The fact that ΔigaA cells expressing IgaA-fl are viable ( S1 Fig ) indicated that the fusion protein , which correctly localizes to the IM ( S2A Fig ) , is functional . We first decided to determine whether RcsF interacts with IgaA in non-stressed cells , as suggested by the fact that basal Rcs activity was measured in cells grown under normal conditions ( S3 Fig ) . To that end , IgaA-fl was expressed in ΔrcsBΔigaA cells and the water soluble cross linker 3 , 3'-dithiobis ( sulfosuccinimidylpropionate ) ( DTSSP ) was added . We used cells deleted for rcsB to prevent Rcs induction , which could otherwise modify cellular permeability to the crosslinker ( for instance via capsule production ) and influence results . After immunoprecipitation with beads conjugated with the anti-flag antibody , a band of ∼100 kDa , the size expected for the IgaA-fl ( 82 kDa ) -RcsF ( 14 kDa ) complex , was detected by immunoblotting with an anti-RcsF antibody in the DTSSP-treated sample ( lane 2 , Fig 2A ) . This band was not observed in cells lacking rcsF ( lane 4 , Fig 2A ) , indicating that it most likely corresponded to the IgaA-RcsF complex . To provide further experimental support for this identification , the in vivo cross-linking experiment was repeated using cells expressing a truncated variant of IgaA ( from S324 to E711 , here referred as IgaA324-711-fl ) corresponding to the periplasmic domain of the protein anchored to the IM ( S2B Fig ) via transmembrane ( TM ) segments IV and V ( Fig 1C ) . The expression of IgaA324-711-fl was induced by addition of IPTG in ΔrcsBΔigaA and ΔrcsBΔigaAΔrcsF cells . After pull-down , a band of ∼60 kDa corresponding to the size expected for a complex between IgaA324-711-fl ( 45 kDa ) and RcsF ( 14 kDa ) was detected both by the anti-RcsF and the anti-Flag antibodies ( lane 1 , Fig 2B ) . This band was observed in ΔrcsBΔigaA cells treated with DTSSP , but not in ΔrcsBΔigaAΔrcsF ( lane 3 , Fig 2B ) . Thus , our data clearly established that RcsF and IgaA interact in vivo , even in the absence of stress . They also indicated that the C-terminal periplasmic domain of IgaA is sufficient to mediate the interaction . We next asked whether exposure to stress would increase the levels of the RcsF-IgaA complex , as expected if this interaction serves as the molecular signal triggering Rcs . To that purpose , we monitored complex formation in cells treated with mecillinam , a β-lactam antibiotic that inhibits the essential transpeptidase PBP2 [24 , 25] and activates the Rcs system in an RcsF-dependent manner [4] . Remarkably , the 100 kDa-band corresponding to RcsF-IgaA substantially increased following mecillinam treatment ( lane 3 , Fig 2A ) . A similar increase was observed for the ~60 kDa-band in cells expressing the truncated protein IgaA324-711-fl ( lane 2 , Fig 2B ) . Thus , these results demonstrate for the first time the increased formation of the RcsF-IgaA complex in response to Rcs-inducing stress , providing crucial experimental support to the model that the RcsF-IgaA interaction controls Rcs activation . According to topology models , IgaA is inserted in the IM via 5 TM segments ( Fig 1B ) . These segments determine two N-terminal cytoplasmic domains of 21 and 11 kDa , respectively , separated by a short periplasmic connector consisting of three amino acids residues , and a C-terminal periplasmic domain of 295 residues . Thus , the N-terminal part of IgaA comprised between TMI and TMIV appears to be mostly exposed to the cytoplasm , while the C-terminal portion is mainly periplasmic ( Fig 1B ) . Nothing is known on how IgaA modulates Rcs activity . Because both RcsC and RcsD possess a large periplasmic domain ( Fig 1A ) , it is possible that IgaA inhibits Rcs by interacting with one or both proteins in the periplasm . In this case , conformational changes in the periplasmic domain of IgaA upon formation of a complex with RcsF would , in turn , alleviate the inhibition on RcsC and/or RcsD , turning on Rcs . Alternatively , RcsF binding in the periplasm may trigger conformational rearrangements in the cytoplasmic part of IgaA that would then be sensed by RcsC and/or RcsD in this compartment . In this second scenario , IgaA inhibits Rcs via its cytoplasmic domain . These two models are not mutually exclusive , and it is possible that both the periplasmic and cytoplasmic portions of IgaA contribute to Rcs regulation . To obtain insights into the mechanism used by IgaA to regulate Rcs , we decided to molecularly dissect this protein to investigate the different roles of its periplasmic and cytoplasmic portions . However , before proceeding with further experiments , we first generated an igaA depletion strain because of the essentiality of igaA [20] . To that purpose , an L-arabinose-inducible copy of igaA on a medium copy-number vector was transformed into a wild-type strain . This strain also carried an rprA::lacZ fusion on the chromosome to monitor Rcs activity [25] . Then , the chromosomal copy of igaA was deleted by P1 transduction of the igaA::kan allele in the presence of L-arabinose . Under permissive conditions , this strain was viable and Rcs activity was comparable to that measured in wild-type cells carrying the chromosomal copy of igaA ( S4 Fig ) . After growing overnight under permissive conditions , cells were subjected to an initial depletion step by growing for ~8 generations in the presence of D-fucose , a non-metabolizable analog of L-arabinose which can be used to lower the expression levels from PBAD [25] . In this case , IgaA became undetectable ( S5 Fig; IgaA was detected by taking advantage of a penta His-tag present at the C-terminus ) . However , no growth defect was observed , consistent with the fact that IgaA efficiently represses Rcs even when expressed at low levels ( Fig 3A ) . Only after the cells were sub-cultured in non-permissive conditions ( in presence of D-fucose ) , the growth of the igaA depletion strain became severely affected ( Fig 3A ) . As expected , decreased growth correlated with Rcs induction ( Fig 3B ) . In parallel experiments , cells in which IgaA had been initially depleted were serially diluted and spotted on LB-agar plates . Corroborating the results above , these cells could not grow on plates supplemented with L-fucose ( non-permissive conditions ) when they had a functional Rcs system , whereas ΔrcsB cells grew normally , thus confirming the essential role played by IgaA in inhibiting the Rcs system ( Fig 3C ) . To gain insights into the mechanism of action of IgaA , we generated a series of IgaA variants corresponding to different topological regions of this protein . We tested their ability , when expressed from an IPTG-dependent promoter carried on a plasmid , to control Rcs and complement the growth defect of the depletion strain under non-permissive conditions . IgaA324-711-fl , corresponding to the C-terminal periplasmic domain anchored to the IM via TMIV and TMV as explained above ( Fig 1C ) , was tested first . However , as shown in Fig 4 , it failed to rescue cell survival and had almost no repressing effect on Rcs . We next tested IgaAMalF-fl , an IgaA variant in which the C-terminal periplasmic domain is replaced by the periplasmic domain of MalF , the maltose transport system permease ( Fig 1C ) . Thus , this variant ( here referred as IgaAMalF-fl ) lacks the periplasmic domain while keeping the cytoplasmic and membrane parts of IgaA intact . Remarkably , IgaAMalF-fl was able to fully rescue the growth of the depletion strain under non-permissive conditions ( Fig 4A ) and repressed Rcs activation to a level similar to that observed when wild-type IgaA was expressed ( Fig 4B ) . Consistent with this , cells expressing IgaAMalF-fl remained viable after igaA deletion , only exhibiting a mild growth defect ( Table 1 ) . Thus , altogether , these data indicated that the inhibitory activity exerted by IgaA on the Rcs system does not depend on the periplasmic domain but rather on the cytoplasmic and membranous regions . To further zoom in on the portion of IgaA responsible for the inhibitory activity of this protein and directly investigate the importance of the cytoplasmic region , we then tested the impact of expressing the two cytosolic domains ( IgaAcyt1-cyt2-fl , in which the two domains are joined by a disordered linker ) on the growth of the depletion strain under non-permissive conditions . Expression of IgaAcyt1-cyt2-fl could partially rescue igaA lethality ( Fig 4A ) and repress Rcs ( Fig 4B ) . Remarkably , similar results were obtained when the first N- terminal cytosolic domain ( IgaAcyt1-fl ) was expressed alone . Thus , the cytoplasmic portion of IgaA in general and the first cytosolic domain in particular appear to contribute to a large extent to the inhibitory activity of this protein . Although significant , the impact of expressing IgaAcyt1-cyt2-fl or IgaAcyt1-fl on Rcs repression was , however , only partial ( Fig 4B ) . Consistent with this , expression of these two IgaA variants did not allow igaA to be deleted from the chromosome ( Table 1 ) . This led us to investigate the importance of anchoring the cytoplasmic domains of IgaA to the membrane . To that purpose , we generated IgaA1-370-fl , a variant corresponding to the N-terminal portion of the protein comprised between TMI and TMIV . In this variant , both cytoplasmic domains are anchored to the IM . However , expression of IgaA1-370-fl did not substantially improve survival of the depletion strain compared to IgaAcyt1-cyt2-fl or IgaAcyt1-fl ( Fig 4A ) . It also did not have a significant impact on Rcs repression compared to the cytosolic domains alone ( Fig 4B ) and did not allow the igaA::kan allele to be transduced ( Table 1 ) . Thus , anchoring the cytoplasmic domains to the membrane does not significantly increase their ability to repress Rcs . This led us to investigate whether full IgaA inhibitory activity could be recovered by co-expressing its N- and C-terminal portions . Excitingly , we found that co-expression of IgaA1-370-fl and IgaA324-711-His allowed deletion of the chromosomal copy of igaA ( Table 1 ) and fully repressed Rcs ( Fig 5 ) . Thus , although the N-terminal domain of IgaA is crucially important for tuning down Rcs , complete inhibition can only be achieved when the C-terminal domain is co-expressed ( see Discussion ) . This reconstituted IgaA could not , however , respond to cues that induce Rcs in an RcsF-dependent manner ( Fig 5 ) .
Because of the crucial importance of the envelope for growth and survival , bacteria invest a great deal in sensing perturbations that occur in that compartment . In E . coli , several stress-sensing systems cooperate in monitoring envelope integrity . Investigating how bacterial cells sense and respond to envelope stress will reveal how they efficiently integrate different types of stress signals and rapidly convey the information from the envelope to the cytoplasm , where cellular behavior is controlled . It will also contribute to elucidating how bacteria coordinate signal sensing with envelope growth and assembly . Rcs , which senses defects in OM and PG integrity , is a particularly intricate envelope surveillance system . It is probably because of this complexity that , despite decades of research on Rcs , our understanding of its functioning remains incomplete . For instance , although major insights into how the lipoprotein RcsF detects stress in the outer part of the envelope were recently reported [11 , 12 , 26] , we still only partially understand the sensing mechanism . In addition , a couple of genetic perturbations have been shown to activate Rcs independently of RcsF , but the mechanism of action is completely unknown [5 , 27] . Here , we investigated the IM protein IgaA in order to understand how this auxiliary Rcs component receives stress signals and controls the phosphorelay . Other proteins that regulate two-component systems have been described , such as E . coli CpxP , a periplasmic protein that controls the activity of the Cpx system [28 , 29] . However , in comparison to most two-component system fine-tuners , IgaA is particularly intriguing for at least two reasons . First , IgaA is essential [20] . Second , whereas two-component systems regulators are usually small proteins located either in the periplasm or in the cytoplasm [29 , 30] , IgaA is a polytopic membrane protein with large cytoplasmic and periplasmic domains [16 , 31] , suggesting that it regulates signal sensing and transduction by integrating molecular information in both cellular compartments . Thus , the unusual features of IgaA point to a unique and particularly complex mode of action . We and other previously reported the ability of RcsF to interact with the periplasmic domain of IgaA when this latter was expressed as a soluble protein [11 , 22] . However , the physiological importance of this interaction remained unclear . Here , we clearly demonstrated that RcsF interacts with the full-length , IM-anchored IgaA and that the interaction increases in response to stress . These data both establish the functional relevance of the IgaA-RcsF interaction and provide strong experimental support to the model that RcsF induces Rcs by interacting with IgaA . They also confirm the role of the periplasmic domain of IgaA in being the primary receiver of the signal sensed and transmitted by RcsF . Finally , they are consistent with our recent finding that preventing the RcsF-IgaA interaction by increasing the periplasmic size does not allow Rcs system activation in response to stress [32] . Interestingly , results from ribosomal profiling experiments [23] indicated that RcsF ( ~3 , 100 copies/cell ) is in large excess over IgaA ( ~200 copies/cell ) , thus predicting that if less than 10% of the pool of RcsF molecules interact with IgaA , full Rcs activation will be observed . Remarkably , this is exactly what we previously showed by using a version of RcsF that remains soluble in the periplasm and is therefore not sequestered away from IgaA by its β-barrel partners in the OM: Rcs was fully activated when this soluble version of RcsF was expressed at ~10% of the of the wild-type RcsF levels [11] . This further highlight and confirms the pivotal role played by the IgaA-RcsF interaction in controlling Rcs activity . We also observed that the IgaA-RcsF interaction occurs even in non-stressed cells , in which basal Rcs activity is measured . Under normal conditions , RcsF is occluded from IgaA by its OM partners [11] . We therefore think that the IgaA-RcsF complex that is detected in the absence of stress ( and that is responsible for basal Rcs activity ) involves the small fraction of the pool of RcsF molecules that is found in the IM when E . coli membranes are fractionated on sucrose density gradients [11] . This fraction is likely constituted by newly synthesized RcsF molecules waiting to be extracted from the IM by the LolCDE complex and transferred to the chaperone LolA for transport to the OM [11] . Thus , if the activity of the Lol system is impaired following envelope stress , RcsF will likely accumulate in the IM , turning on Rcs . Interestingly , Rcs induction leads to higher lolA expression [33] , which might help to overcome the damage . It is therefore possible that one of the roles of the Rcs system is to monitor lipoprotein trafficking across the cell envelope , as previously suggested [11] . While stress is first sensed by RcsF in the outer part of the envelope , the reactions of the phosphorelay occur in the cytoplasm . With domains located in both cellular compartments , IgaA appears therefore to be well equipped to play a direct role in transducing stress signals across the IM . Supporting this idea , we showed that while the C-terminal periplasmic domain of IgaA serves as the primary signal receiver , the N-terminal cytoplasmic domains , and the first cytoplasmic domain in particular , play an important role in inhibiting Rcs . It is therefore possible that formation of the RcsF-IgaA interaction in the periplasm triggers conformational rearrangements in the cytosolic part of IgaA that , as a result , impact the inhibitory function of this protein on the phosphorelay ( S6 Fig ) . The fact that co-expressing the N-terminal and C-terminal parts of IgaA , while restoring full Rcs inhibition , fails to reconstitute a protein able to trigger Rcs under stress supports the idea that IgaA is involved in signal transduction across the IM and indicates that the transfer of information across the membrane requires a full-length , intact polypeptide . Interestingly , it was recently found that the redox state of cysteine residues located in the periplasmic domain of S . enterica IgaA was altered by a mutation in the cytosolic domain of this protein , thus further highlighting the functional connection between the two parts of IgaA [31] . Although nothing is known on how IgaAcyt1 down-regulates Rcs , it is most likely by interacting with one or more of the downstream components of the phosphorelay . In the absence of stress , IgaAcyt1 could , for instance , interact with RcsC to alter the phosphatase/kinase balance or perturb the phosphotransfer reaction between RcsC and RcsD or RcsD and RcsB . Interestingly , IgaAcyt1 exhibits significant structural similarity to the OB fold ( oligonucleotide/oligosaccharide binding motif ) , a fold often found in domains involved in protein-protein interaction and nucleotide binding [34] . Future work is required to understand in detail how IgaAcyt1 inhibits Rcs . Our results also show that , although important , the cytoplasmic part of IgaA is not sufficient for full Rcs repression , which , indeed , requires co-expression of the C-terminal portion . It is interesting that a more potent repression was observed in cells expressing IgaAMalF but not in those expressing IgaA1-370 ( Table 1 and Fig 4B ) . Indeed , the only segment of the IgaA sequence that is present in IgaAMalF but absent in IgaA1-370 is the C-terminally located TMV . Thus , this result suggests that TMV may also play a role in down-regulating Rcs . Future work is also required to investigate how RcsC and RcsD are interconnected to the other Rcs components . Indeed , although the role of these two IM proteins in the phosphorelay is well established , nothing is known on how they are regulated . We also do not know to what extent they participate in signal sensing . As discussed above , our results suggest that IgaAcyt1 could interact with the cytoplasmic domain of RcsC and/or RcsD . In addition , both RcsC and RcsD display large periplasmic domains whose function remains elusive . A recent report suggests that the periplasmic domain of RcsC interacts with RcsF , but the functional role of this potential interaction remains to be shown [22] . It is possible that , by interacting with the periplasmic domain of RcsC ( and perhaps also RcsD ) , RcsF influences how these proteins are inhibited by IgaA . Alternatively , an RcsF-RcsC interaction might also contribute to fine-tuning the activity of the Rcs system . It is also possible that additional proteins further modulate Rcs signaling , such as YfgM , a single pass IM protein , that was suggested to work as an anti-RcsB factor , but whose mechanism of action remains unknown [35] . Dissecting the interplay between different Rcs components and understanding how they cooperate in integrating stress signals will likely prove to be a complex and challenging task .
The bacterial strains used in this study are all derivatives of E . coli MG1655 carrying a chromosomal rprA::lacZ fusion at the lambda attachment site ( DH300 ) [36] . All derivatives used are listed in S1 Table . Bacterial cells were cultured using LB-Miller at 37°C containing ( whenever necessary ) the following concentration of antibiotics: chloramphenicol ( 25 μg/μl ) , spectinomycin ( 100 μg/ml ) and kanamycin ( 50 μg/μl ) . When two antibiotics were combined , half of the mentioned concentrations were used . Except for igaA::kan mutations , all null alleles were generated from the corresponding single deletion mutants in the Keio collection [37] and transferred to the wild-type DH300 strain using P1 phage transduction . All generated mutants were checked by PCR . For excision of the kanamycin resistance cassette , we used the pCP20 plasmid [38] . Plasmids used in this study are all derived from pNH401 ( pBAD33-based ) or pSC232 ( pAM238-based ) and are listed in S2 Table . For cloning purposes , standard molecular biology techniques were followed , using KOD polymerase ( Novagen ) , restriction enzymes ( New England Biolabs ) and XL-1 blue as cloning strain . Chromosomal DNA from MG1655 was used as a template DNA . The sequences of the primers used for cloning and checking gene replacement are available upon request . β-galactosidase assays were performed according to the modified Miller assay as described previously [32 , 39] . We first generated an igaA::kan mutant in the rcsB mutant of the Keio collection ( in which igaA is dispensable; the kanamycin cassette had previously been excised ) . To that purpose , the ΔrscB mutant , harboring pKD46 , was transformed with a PCR product corresponding to the kanamycin cassette flanked by 50 bp igaA up- and downstream its genomic locus [38] , generating , after recombination , strain SEN549 . The igaA::kan allele from strain SEN549 was then P1 transduced into DH300 cells harboring pNH586 ( pBAD33 with IgaA-His ) in the presence of 0 . 2% L-arabinose . This strain was renamed NH594 . In order to deplete IgaA-His , an initial igaA depletion was performed by growing NH594 overnight in presence of the corresponding antibiotic and 0 . 2% L-arabinose . The cells were then washed three times with arabinose- free medium and diluted 1/1000 in LB-Miller broth containing 0 . 2% D-fucose until an OD600 of 0 . 8–1 , yielding igaA-depleted cells . The cells were then washed thoroughly with LB , serially diluted and spotted on LB-Miller-agar plates supplemented either with 0 . 2% L-arabinose or 0 . 2% D-fucose . Alternatively , the igaA-depleted cells were inoculated in fresh LB-Miller supplemented with either 0 . 2% L-arabinose or 0 . 2% D-fucose and the growth was monitored by measuring the optical density ( OD ) at 600 nm every hour . At the indicated intervals , aliquots were saved to monitor Rcs system activation by β-galactosidase assay and protein expression levels by western blotting . The same protocols were followed to assess the ability of the different IgaA variants to complement igaA depletion in strains expressing pSC232 , pSC238 , pNH441 , pNH561 , pNH692 , pNH714 or pNH636 in NH594 . In this case , 0 . 2% glucose was added to repress both arabinose and IPTG-inducible promoters in the initial depletion , while 100 μM IPTG was added to induce expression of the abovementioned plasmids . Growth curves ( without prior igaA depletion ) were constructed by growing the corresponding strains overnight in presence of 0 . 2% L-arabinose and 100 μM IPTG ( if required ) , then diluting 1/1000 in fresh media . The growth was monitored by measuring OD at 600 nm . In vivo DTSSP crosslinking was performed as previously described [11] with some modifications . Briefly , strains expressing pSC238 or pNH441 were grown in presence of 100 μM IPTG until late log phase . Whenever needed , mecillinam at a final concentration of 0 . 3 μg/ml was added when the cells reached an OD600 of 0 . 2 and incubated for one hour . The cells were then washed with PBS , pH 7 and treated with 200 μM of DTSSP ( Covachem ) for one hour at 30°C . Following quenching with 100 mM glycine , the cells were TCA precipitated and dissolved in 5X non-reducing Laemmli buffer at 60°C before dilution with TBS buffer containing 0 . 2% n-Dodecyl-β-D-Maltoside ( DDM ) and incubated overnight with Flag-conjugated beads ( Sigma ) . After three washing steps , the proteins were eluted with 100 mM glycine , pH 2 , containing 0 . 2% DDM and then subjected to western blot analysis . Aliquots from growing cultures were TCA precipitated and solubilized by heating at 60°C with 1X non-reducing Laemmli buffer . Eluted samples after immunoprecipitation were prepared similarly after measuring protein concentration but without TCA precipitation . The samples were loaded on precast NuPAGE Bis-Tris gels ( Thermo ) . Transfer was performed using standard semi-dry transfer method on nitrocellulose membrane ( Thermo ) and the membranes were blocked using 5% non-fat dry milk . Primary antibodies were used at the following dilutions: anti-RcsF ( 1:2 , 000 ) , anti-flag ( Sigma 1:3 , 000 ) , anti-PtsI ( 1:30 , 000 ) anti-His ( Qiagen , 1:8 , 000 ) . Horse-radish peroxidase-conjugated secondary antibody was used at a concentration of 1:10 , 000 or 1:20 , 000 and the membranes were developed using ECL ( Thermo ) or ECL-Prime ( GE healthcare ) , respectively . Chemiluminescence signal was detected on Fuji X-ray films . Curves and bar charts represent an average of at least three biological replicates and were prepared using Prism 6 ( Graph-Pad Software , Inc . ) . Statistical analysis was performed using the same software . Statistical significance was calculated based on two- way ANOVA tests for all experiments except for Fig 4B where one-way ANOVA was used . | A thorough understanding of the mechanisms that allow bacteria to thrive in various environments is crucial to the development of new antibiotics , an urgent endeavor to combat antimicrobial resistance . A landmark feature of Gram-negative bacteria is the presence of a multi-layered envelope . Because this structure is essential , its integrity is constantly monitored to detect and respond to potential breaches in a fast and adequate manner . Here , we describe how IgaA , an essential protein present in the cytoplasmic membrane of enterobacteria , participates in the transfer of stress signals from the envelope to the cytoplasm . We provide evidence that IgaA works in concert with RcsF , a lipoprotein that is posted as a sentinel in the outermost envelope layer , to detect envelope stress: under stress conditions , RcsF forms a complex with the C-terminal , periplasmic domain of IgaA . As a result , cells turn on the Rcs response . We also discovered that the N-terminal , cytoplasmic domain of IgaA plays an important role in inhibiting Rcs in the absence of stress . Together , these findings reveal that distinct IgaA domains coordinate stress sensing and Rcs activation across the cytoplasmic membrane . They enhance our understanding of Rcs regulation and open new avenues for the development of new antibacterials . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"antimicrobials",
"periplasm",
"medicine",
"and",
"health",
"sciences",
"drugs",
"microbiology",
"membrane",
"proteins",
"regulator",
"genes",
"immunoprecipitation",
"antibiotics",
"outer",
"membrane",
"proteins",
"gene",
"types",
"pharmacology",
"cellular",
"structures",
... | 2018 | Distinct domains of Escherichia coli IgaA connect envelope stress sensing and down-regulation of the Rcs phosphorelay across subcellular compartments |
Early stages of Human Immunodeficiency Virus-1 ( HIV-1 ) infection are associated with local recruitment and activation of important effectors of innate immunity , i . e . natural killer ( NK ) cells and dendritic cells ( DCs ) . Immature DCs ( iDCs ) capture HIV-1 through specific receptors and can disseminate the infection to lymphoid tissues following their migration , which is associated to a maturation process . This process is dependent on NK cells , whose role is to keep in check the quality and the quantity of DCs undergoing maturation . If DC maturation is inappropriate , NK cells will kill them ( “editing process” ) at sites of tissue inflammation , thus optimizing the adaptive immunity . In the context of a viral infection , NK-dependent killing of infected-DCs is a crucial event required for early elimination of infected target cells . Here , we report that NK-mediated editing of iDCs is impaired if DCs are infected with HIV-1 . We first addressed the question of the mechanisms involved in iDC editing , and we show that cognate NK-iDC interaction triggers apoptosis via the TNF-related apoptosis-inducing ligand ( TRAIL ) -Death Receptor 4 ( DR4 ) pathway and not via the perforin pathway . Nevertheless , once infected with HIV-1 , DCHIV become resistant to NK-induced TRAIL-mediated apoptosis . This resistance occurs despite normal amounts of TRAIL released by NK cells and comparable DR4 expression on DCHIV . The escape of DCHIV from NK killing is due to the upregulation of two anti-apoptotic molecules , the cellular-Flice like inhibitory protein ( c-FLIP ) and the cellular inhibitor of apoptosis 2 ( c-IAP2 ) , induced by NK-DCHIV cognate interaction . High-mobility group box 1 ( HMGB1 ) , an alarmin and a key mediator of NK-DC cross-talk , was found to play a pivotal role in NK-dependent upregulation of c-FLIP and c-IAP2 in DCHIV . Finally , we demonstrate that restoration of DCHIV susceptibility to NK-induced TRAIL killing can be obtained either by silencing c-FLIP and c-IAP2 by specific siRNA , or by inhibiting HMGB1 with blocking antibodies or glycyrrhizin , arguing for a key role of HMGB1 in TRAIL resistance and DCHIV survival . These findings provide evidence for a new strategy developed by HIV to escape immune attack , they challenge the question of the involvement of HMGB1 in the establishment of viral reservoirs in DCs , and they identify potential therapeutic targets to eliminate infected DCs .
Dendritic cells ( DCs ) are crucial for the generation and the regulation of adaptive immunity . Immature DCs ( iDCs ) sample the environment via pattern recognition receptors such as Toll-like receptors ( TLRs ) , and they undergo a maturation process characterized by increased expression of HLA class I proteins and surface molecules ( CCR7 , CD80 , CD86 , HLA class II ) , and secretion of proinflammatory cytokines and chemokines . Resulting mature DCs migrate to secondary lymphoid tissues , where they prime an antigen-specific T cell response [1] . Recently , the fate of DCs has been found to be extremely dependent on NK cells [2] . After being recruited into inflamed tissues , NK cells can interact with iDCs , resulting in their activation that , in turn , induce DC maturation or killing , depending on their respective density [3] [4] [5] . Thus , one of the major roles of NK cells is to keep in check the quality and the quantity of DCs undergoing maturation [6] . DC maturation is dependent on TNF-α produced by activated NK cells [4] , but it also involves the alarmin high-mobility group box 1 protein ( HMGB1 ) released at the synaptic cleft in response to IL-18 produced by DCs [7] . HMGB1 is a highly mobile nuclear protein that functions to stabilize nucleosome formation , and acts as a transcription-factor-like protein that regulates the expression of several genes [8] . HMGB1 is either secreted actively from inflammatory cells ( activated macrophages and NK cells in response to inflammatory stimuli ) , or passively released from necrotic cells to signal tissue injury . It triggers a cascade of inflammatory responses through its binding to receptor for advanced glycation end products ( RAGE ) , TLR2 , or TLR4 expressed on monocytes , macrophages and NK cells [9]–[11] . As an alarmin , HMGB1 has mobilizing and activating effects for host defense [12] , it facilitates the trafficking of inflammatory leukocytes , and it is critical for DCs to mature , reach the lymph nodes and promote polarization of antigen-specific T cells towards a Th1 phenotype [13]–[16] . The cognate NK-DC interaction in inflamed tissues can also result in acquisition of NK cytotoxicity against iDCs , which may represent a mechanism of DC selection required for the control of downstream adaptive immune response [6] . This editing process is dependent upon the engagement of NKp30 [17] [18] and DNAM-1 [19] by ligands expressed on iDC , and the down-regulation on iDC of HLA-E , the ligand for CD94/NKG2A inhibitory receptor on NK cells [20] . While NK-dependent killing of allogeneic DCs in a murine model of skin graft rejection was reported to involve the perforin pathway [21] , the cytotoxic pathway involved in the killing of syngeneic iDCs has not been identified yet . In the context of a viral infection , NK-dependent killing of infected-DCs is a crucial event required for early elimination of infected target cells . Indeed , in murine CMV infection , infected DCs are capable of activating NK cell cytotoxicity in vitro and also able to enhance NK-cell dependent virus clearance in vivo [22] . HIV has evolved ways to exploit DCs , allowing evasion of antiviral immunity . Recent reports suggest that NK-DC interactions are altered in HIV-1-infection . A defect in NK cell lysis of immature monocyte-derived DCs generated from HIV-1-infected individuals has been reported [23] . In viremic patients , in vitro interactions between a CD56neg/CD16pos subset of NK cells and autologous DCs were found markedly impaired , evidenced by abnormalities in the process of reciprocal NK-DC activation and maturation , as well as a defect in NK-cell elimination of iDCs [24] . However , the mechanisms involved in this escape from NK cytotoxicity have not yet been elucidated . In this study , we first identified the molecular pathway implicated in the editing process of non-infected iDCs by NK cells and found that it did not involve the perforin pathway but rather the TRAIL/DR4-dependent death receptor pathway . We then addressed the question of the mechanisms involved in the resistance of HIV-1-infected DCs ( DCHIV ) to NK-dependent killing , and found that it was linked to the dramatic upregulation of c-IAP2 and c-FLIP in DCHIV , induced by the cognate interaction with NK cells , and leading to the resistance of DCs to TRAIL-dependent apoptosis . Furthermore , HMGB1 , a key mediator of NK-DC cross-talk [7] , [16] , was found to play a pivotal role in the process by upregulating c-IAP2 and c-FLIP in infected DCs . At present , these data provide evidence for a new strategy developed by HIV-1 to escape immune attack through the induction in DCs of a potent anti-apoptotic mechanism leading to their escape from innate cytotoxicity . They also challenge the question of the involvement of HMGB1 in the establishment of viral reservoirs in DCs , and the possible destruction of these reservoirs by c-IAP2 and c-FLIP antagonists .
To investigate the impact of NK cells on the fate of DCs , iDCs were generated from monocytes sorted from healthy donors and cocultured with autologous purified NK cells ( all sorted NK cells expressed CD56 ) . NK cells were kept either unstimulated ( rNK ) or were activated ( aNK ) with a combination of PHA and IL-2 . The fate of iDCs in 24h NK-DC cocultures was studied by flow cytometry after exclusion from the analysis of CD56+ cells ( NK cells ) . 24 h coculture of aNK cells with autologous iDCs induced either the survival or apoptosis of iDCs , depending on aNK∶DC ratio , consistent with previous reports [4] . Indeed , at low aNK-DC ratios ( 1∶5 ) , this interaction induced DC maturation and cytokine production , as previously reported [4] , [16] , while higher NK∶DC ratios ( 5∶1 ) induced DC killing by autologous NK cells [4] ( Fig . 1A ) . FSC/7-AAD dot plots distinguish living ( 7-AADneg FSChigh ) from apoptotic ( 7-AADpos FSClow ) DCs and apoptotic bodies ( 7-AADneg FSClow ) [25] . Reduced DCs survival combined with the accumulation of apoptotic bodies was detected at high aNK∶DC ratio ( 5∶1 ) , while normal iDC survival and no accumulation of apoptotic bodies was observed at aNK∶DC ratio of 1∶5 , as compared with iDCs cultured alone ( Fig . 1A ) . Under the same conditions , rNK cells had no impact on the fate of iDCs and did not kill them ( Fig . 1A ) . In order to confirm that apoptotic cells and bodies were generated from iDCs , two additional approaches were used . In the first one , the level of apoptosis in gated CD56+ aNK cells was compared when cultured alone or in the presence of iDCs . Figure S1 ( panel A ) shows that there was no induction of apoptosis in aNK cells after their cross-talk with iDCs ( ratio 5∶1 ) , and no increase of apoptotic bodies either ( not shown ) . In the second approach , aNK cells were stained with CFSE prior to their coculture with iDCs , and apoptosis was analyzed in Carboxyfluorescein succinimidyl ester ( CFSE ) neg cells , e . g . DCs . Figure S1 ( panel B ) confirms that , at 5∶1 ratio , aNK cells kill iDCs , as shown by the increased percentages of apoptotic cells ( 7-AADpos FSClow ) and apoptotic bodies ( 7-AADneg FSClow ) in CFSEneg cells in aNK∶DC cocultures compared to iDCs cultured alone . Live video microscopy pictures in Fig . 1B show the kinetics of events following the rapid contact of one NK cell with one DC , leading to an NK cell kiss of death , inducing immediate plasma membrane blebbling in the DC , a classical feature of apoptotic death ( see the video http://www . bioimageanalysis . org/plospathogens2010_suppldata ) . Death triggering of iDCs by aNK cells is a very rapid process , detected a few seconds after cell contact at the single cell level ( Fig . 1B ) , and reaching a plateau after 1 hour in bulk cultures , as measured by 7-AAD staining in 24 h kinetics experiments ( Fig . 1C ) . Since the editing process of iDCs by NK cells is supposed to keep in check the quality of DCs undergoing maturation [6] , we assumed that DCs surviving the interaction with aNK cells were mature . Fig . 1D shows that survival of DCs in aNK∶iDC cocultures was associated with their maturation , as demonstrated by the coexpression of the maturation markers CD86 and HLA-DR [16] in all DCs . Similar characteristics were observed in control mature DC0 , induced by 48 h stimulation with LPS ( Fig . 1D ) . The expression of two other maturation markers , DC-SIGN and CD83 was investigated ( Fig . 1E ) . Surviving iDCs in aNK∶DC cocultures showed increased expression of CD83 and decreased expression of DC-SIGN as compared to DC cultures alone , confirming their mature stage . Classically , NK cells kill their targets through the perforin/granzyme pathway . To determine its possible involvement in NK-dependent killing of iDCs , intracellular perforin staining was compared in rNK , aNK , and aNK-iDC cocultures . Perforin was found mostly expressed in CD56dim NK cells and activation of NK cells did not induce marked changes in perforin expression . Following coculture with iDCs , aNK cells did not express more perforin ( Fig . 2A ) , suggesting that this cytotoxic pathway was not used to kill iDCs . This was confirmed by the lack of CD107a externalization when aNK cells were cocultured with iDCs , while NK stimulation with PMA/ionomycin induced CD107a expression ( Fig . 2B ) . These observations were made under conditions whereby aNK cells were able to kill iDCs , from 1 to 24 hours of coculture . Furthermore , the addition of an inhibitor of perforin-mediated lysis , Concanamycin A ( CMA ) , at concentrations ranging from 50 to 500 nM , was unable to block NK-dependent killing of iDCs ( Fig . 2C ) . Altogether , these data are not in favor of the involvement of the perforin pathway in the NK-dependent editing process of iDCs . TNF-related apoptosis-inducing ligand ( TRAIL ) is active in two forms , either expressed at the cell membrane ( mTRAIL ) , or as a soluble form secreted in the cell environment ( sTRAIL ) . mTRAIL expression on CD56+ aNK cells was found restricted to CD56bright NK cells ( Fig . 3A ) . However , following their contact with iDCs , both CD56bright and CD56dim NK cells expressed mTRAIL suggesting that killing of iDCs was not restricted to CD56bright cells ( data not shown ) . sTRAIL was mainly secreted by aNK cells , including in cocultures of aNK∶iDC , under conditions whereby iDCs were killed by aNK cells ( NK∶DC ratio of 5∶1 ) ( Fig . 3B ) . iDCs did not express mTRAIL , whether cultured alone or in the presence of NK cells ( data not shown ) , and accordingly they did not release sTRAIL in culture supernatant ( Fig . 3B ) . iDCs were susceptible to rhsTRAIL , which in turn induced DR4 expression on these cells ( Fig . 3C ) . 24h kinetic experiments of DC-killing by aNK cells showed the progressive increase of apoptotic DCs within DR4+DCs , arguing for the involvement of DR4 receptor in the death of iDCs ( Fig . 3D ) . DR5 expression was not induced on iDCs by aNK cells , ruling out its involvement in the apoptosis process ( Fig . 3E ) , neither was Fas expressed on iDCs under the same conditions ( Fig . 3E ) . The involvement of the TRAIL/DR4 pathway in aNK-dependent killing of iDCs was confirmed by the blocking effect of anti-TRAIL antibodies ( 1µg/ml ) that resulted in a significant decrease in DR4+ apoptotic DCs ( Fig . 3F ) . Moreover , anti-DR4 antibodies abrogated aNK-induced apoptosis of iDCs , while anti-Fas antibodies had no effect ( Fig . 3G ) . Collectively , these results indicate that the pathway used by aNK cells to kill iDCs involves the death ligand TRAIL , whose release by NK cells induces DR4 receptor on iDCs , followed by their killing . To address the question of the impact of HIV-1 on DC's susceptibility to NK killing , iDCs were infected with 1ng of p24/ml of R5-HIV-1BaL , 24 h prior to coculture with aNK cells . HIV-1-infected iDCs ( DCHIV ) were found resistant to aNK-induced cytotoxicity , the proportions of living DCs ( FSChigh 7-AADlow ) being unchanged after cognate interaction with aNK cells ( Fig . 4A ) . It was also evidenced by the lack of caspase-3 activation in DCHIV under the same culture conditions ( Fig . 4B ) . Surviving DCs exhibited a mature phenotype , as shown by the co-expression of CD86 and HLA-DR on their cell surface ( Fig . 4A ) . DCHIV resistance to NK killing was dependent on HIV-1 replication in DCs . Indeed , adding AZT at the initiation of iDCs infection ( 1ng/ml of R5-HIV-1BaL ) , at the concentration of 1mM that completely inhibited HIV-1 replication in these cells ( as shown by the lack of detection of p24 in culture supernatant ) , preserved the editing process ( e . g . susceptibility of DCHIV to NK killing ) ( Fig . 4C ) . Thus , productive HIV-1 infection of DCs was required for their acquired resistance to NK-mediated cytotoxicity ( Fig . 4C ) . Resistance of DCHIV to NK cell killing was independent of DR4 expression , the frequency of DR4+ cells being similar in infected vs . uninfected DCs ( Fig . 4D , left panel ) , and the amount of sTRAIL released by aNK cells was comparable in NK∶DC vs NK∶DCHIV cocultures ( Fig . 4D , middle panel ) . Moreover , DCHIV were found to be as susceptible as uninfected iDCs to recombinant sTRAIL ( Fig . 4D , right panel ) . Thus , DCHIV resistance to NK cytotoxicity was not associated with defective TRAIL release by NK cells or altered DR4 expression by DCHIV . In order to identify key molecules involved in the resistance of DCHIV to NK killing , gene array experiments were performed and differential gene expression was compared between aNK∶DCHIV vs aNK∶iDC . The expression profiles shown in Fig . 4E indicate a decrease in the expression of some caspase genes ( Caspase-2 , -6 , -7 and -9 ) , or death-ligand genes ( e . g . TNF or FasL ) in cocultures of aNK-iDCHIV , . In parallel , a dramatic upregulation of two anti-apoptotic genes , c-IAP2 ( 26 . 1 fold increase ) and c-FLIP ( 15 fold ) was detected in aNK∶DCHIV as compared to aNK∶iDC cocultures ( Fig . 4E ) . The expression of Bcl-2 and Mcl-1 was unchanged , whether tested in gene array experiments , or by flow cytometry in DCHIV in the presence or absence of NK cells ( data not shown ) . The strong impact of aNK cells on c-IAP2 upregulation in DCHIV during NK-DC cross-talk is shown in Fig . 5A Intracellular flow cytometry analysis shows that c-IAP2 is expressed in the majority of surviving DCs at low levels , whether infected or not by HIV-1 . Cognate interaction of DCHIV with aNK cells induced a strong up-regulation of c-IAP2 in the great majority of DCHIV ( CD56neg ) , whereas it had no effect on uninfected iDCs under the same conditions ( 85% vs 8% of c-IAP2bright cells ) ( Fig . 5A ) . HIV-1 infection by itself only slightly increased c-IAP2 expression in iDCs ( 11% in DCHIV vs 5% in iDCs of c-IAP2bright cells ) . Thus , resistance of DCHIV to NK killing is associated with the upregulation of c-IAP2 in these cells . c-IAP2 up-regulation in DCHIV following their cognate interaction with aNK cells was confirmed by confocal microscopy ( Fig . 5B ) . DC survival was strongly dependent upon c-IAP2 expression . Indeed , the knock-down of c-IAP2 with specific siRNA induced apoptosis of both uninfected iDCs and DCHIV , in a dose-dependent manner ( Fig . 5C ) . The expression of another anti-apoptotic molecule was identified by microarray analysis as being upregulated in DCHIV following their contact with aNK cells , i . e . c-FLIP ( Fig . 4D ) . This was confirmed by intracellular flow cytometry analysis , showing that c-FLIP expression was slightly increased in DCHIV vs iDCs ( 13% vs 5% ) , whereas a strong induction of c-FLIP was observed following their interaction with aNK cells ( 86% of c-FLIPhigh in DCHIV vs 21% in iDC ) ( Fig . 5D ) . The key role of c-FLIP on DC resistance to NK killing was confirmed using a specific inhibitor Bisindolylmalmeimide III ( Bis III ) . Blocking c-FLIP activity with Bis III added at 25µM in NK-DCHIV cocultures significantly restored the susceptibility of HIV-1-infected DCs to NK cytotoxicity , whereas it preserved the survival of iDC or DCHIV cultured alone ( Fig . 5E ) . The involvement of c-FLIP in DC survival was confirmed with specific siRNAs . Indeed , the knock-out of c-FLIP induced apoptosis of iDCs , in a dose-dependent manner ( Fig . 5F ) . Strikingly , higher siRNA concentrations were needed to induce apoptosis in DCHIV as compared to iDCs ( Fig . 5F ) . Overall these silencing experiments show the essential role of c-FLIP and c-IAP2 on the survival of primary DCs , which is consistent with the mechanism proposed for DC resistance to NK killing . We recently reported that activated NK cells were able to induce the maturation of HIV-1-infected DCs , at NK-DC ratio of 1∶5 , but it resulted in a dramatic increase in viral replication and proviral DNA expression in DCs [16] . This process was mainly triggered by HMGB1 , released by both cell types , as a consequence of NK-DC cross-talk [7] . Fig . 6A confirms that both uninfected and HIV-1-infected iDC spontaneously released high levels of HMGB1 during a 24h culture . Their interaction with aNK cells at a 5∶1 ratio resulted in an enhancement of HMGB1 production in culture supernatant due to the additive effect of HMGB1 released by NK cells , as we previously showed [16] . Analysis of NK-DC conjugates by confocal microscopy confirmed the expression of HMGB1 both by NK cells and DCs , and whatever the infected status of DCs ( Fig . 6B ) [16] . The possible involvement of HMGB1 in the induction of the resistance state of DCHIV to NK killing was tested in NK-DC co-cultures in the presence of either Glycyrrhizin , known to interact specifically with soluble HMGB1 [26] , or anti-HMGB1 neutralizing antibodies . Both inhibitors , added at the initiation of the 24h aNK-iDCHIV cocultures , restored the susceptibility of HIV-1-infected DCs to NK cytotoxicity , leading to a significant decrease in DCHIV survival ( Fig . 6C ) . These inhibitors had no impact on DCHIV survival in the absence of NK cells . Simultaneous flow cytometry analysis of c-IAP2 expression revealed that anti-HMGB1 antibodies abrogated c-IAP2 up-regulation in DCHIV ( Fig . 6D lower panel ) and , as a corollary , rHMGB1 was found to upregulate c-IAP2 in iDCs ( Fig . 6D upper panel ) . Similar observations were made for c-FLIP . Anti-HMGB1 antibodies induced a strong decrease in c-FLIP expression in DCHIV cocultured with aNK cells ( Fig . 6E ) , while rHMGB1 increased c-FLIP expression in iDC when cocultured with aNK cells ( Fig . 6F ) . Altogether , these observations argue for a pivotal role of HMGB1 in the acquired resistance of HIV-1-infected DCs to NK cytotoxicity , preventing TRAIL-induced apoptosis by upregulating two potent inhibitors , c-IAP2 and c-FLIP . In a previous study , we reported that NK∶DCHIV cognate interaction under conditions of DC maturation ( ratio 1∶5 ) resulted in a strong increase in HIV-1 viral replication in DCs ( detected at day 3 after infection ) , that was mediated by HMGB1 [16] . We confirmed these observations in the present study , showing that NK-DCHIV cross-talk under conditions of protection of DCHIV from TRAIL-induced apoptosis stimulates HIV-1 replication in DCHIV , measured either as p24 release in culture supernatant of following intracellular detection of p24 in DCs ( data not shown ) . Thus , DC escape from NK cytotoxicity contributes to viral dissemination and persistence .
In opposition to the immune sentinel function of DCs to capture and present processed antigens from pathogens , HIV-1 hijacks DCs to promote viral dissemination . In a recent study , we reported that the cross-talk between NK cells and HIV-1-infected DCs , under conditions of DC maturation , resulted in a dramatic increase in viral replication and proviral DNA in DCs , and this process was mainly triggered by HMGB1 , a DC maturation factor produced in NK-DC cocultures [16] . In the present study , we show for the first time that NK-DC cross-talk , under conditions of killing of iDC by NK cells , results in a strong resistance of HIV-1 infected DCs to NK cytotoxicity , preventing elimination of infected DCs and thus contributing to viral dissemination . HMGB1 was found to be a key factor in DC resistance to NK killing , inducing the upregulation of c-FLIP and c-IAP2 in infected DCs , thus preventing TRAIL-dependent NK-mediated cytotoxicity . Therefore , cognate interactions between NK cells and DCs , required for regulation of innate immunity [6] , and found in vivo to be integral to the activation of effective antiviral immunity [22] , become detrimental to the host in the context of HIV infection . NK cells have the capacity to spontaneously kill tumor cell lines , in contrast they do not generally kill non transformed autologous cells . NK cells were recently shown to play a relevant role in the process of DC maturation , either by direct DC stimulation or through killing those DCs that did not properly acquire a mature phenotype ( ‘DC editing’ ) [27] [6] [28] . Killing of autologous immature DCs was reported to involve NK-p30 [17] and DNAM-1 [19] and to be confined to NK-cells expressing CD94-NKG2A inhibitory receptor [20] . While the NK receptors involved in NK-mediated DC killing have been , at least in part , identified , the molecular pathway whereby NK cells kill DCs is less understood . In the present study , we identified the cytotoxicity pathway used by human NK cells to kill autologous iDCs and found that the TRAIL/DR4 pathway was used rather than the perforin pathway . TRAIL is a member of the TNF ligand family that signals apoptosis via the death domain–containing receptors TRAIL-R1 ( DR4 ) and TRAIL-R2 ( DR5 ) . It is primarily expressed as a type II membrane protein ( mTRAIL ) and is also secreted in a soluble form ( sTRAIL ) only by activated T and NK cells . In the present study , we found mTRAIL expression confined to CD56bright cells , whereas perforin expression was restricted to CD56dim cells and independent of NK-cell activation state ( in agreement with previous reports [29] ) . Activation of NK cells triggered TRAIL release , and cognate interaction with iDCs induced DR4 expression on a fraction of them , which became susceptible to TRAIL-mediated apoptosis . iDCs by themselves were not able to produce TRAIL . Preventing TRAIL-DR4 interaction with specific blocking antibodies inhibited NK-mediated iDC killing , while the perforin-based cytotoxicity inhibitor concanamycin A had no effect . These data are consistent with a previous study showing that in vivo elimination of iDCs by murine NK cells is TRAIL-dependent [30] . Surface-bound TRAIL is one of the effector mechanisms of NK cells in suppression of tumor cell growth in vivo [31] , and it selectively kills virus-infected cells while leaving uninfected cells intact [32] . We report herein for the first time that TRAIL is the major effector mechanism of iDC editing by human NK cells . The contribution of NK-DC crosstalk to the control of viral infections is poorly documented . Andoniou et al . demonstrated , in an in vivo murine model of MCMV infection , that virus-infected DCs are capable of enhancing NK-cell cytotoxicity and NK cell-dependent clearance of the virus [22] , arguing for a protective role of innate cell cross-talk . Nevertheless , HIV-1 seems to exploit this cross-talk to its own advantage . Indeed , abnormalities were reported in the in vitro interactions between NK cells and autologous DCs from viremic HIV-1-infected patients , including an impairment in NK-cell-mediated elimination of iDCs [24] . It has been attributed to defective NK-cell mediated killing associated to impaired expression and function of NKp30 and TRAIL by patients' NK cells [24] . In the present study , we addressed whether infected DCs had any role in the impairment of NK cells to kill them . We report for the first time that HIV-1 infection protects DCs from NK cell cytotoxicity through a mechanism involving the cognate interaction between aNK cells and DCHIV , which rescues DCHIV from TRAIL-mediated killing by upregulating two potent inhibitors of apoptosis , c-FLIP and c-IAP2 . Apoptosis is a tightly regulated cell process where intracellular proteins c-FLIP [33] and c-IAPs [34] , [35] are major players , blocking the death receptor signaling pathway by preventing caspase-8 activation [36] at the death-inducing signaling complex ( DISC ) [37] , and inhibiting the effector capsase-3 , respectively . Thus , while c-IAP2 and c-FLIP were expressed by all iDCs , whether infected or not , cognate interaction between aNK cells and iDCs upregulated the expression of both inhibitors of apoptosis in HIV-1-infected DCs only , making these cells resistant to NK killing . From these observations we concluded that c-IAP2bright and c-FLIPbright DCs acquired a state of resistance to TRAIL-mediated NK cytotoxicity . We also discovered that DCHIV rescuing process was mediated by the alarmin HMGB1 , essential for the promotion of DC maturation and elicitation of an immune response [13] , [38] . HMGB1 , detected in the nucleus of sorted primary NK cells [16] and translocated to the cytoplasm following their activation [16] , was also expressed in DCs , whether infected by HIV-1 or not . HMGB1 was also released by both cells in culture supernatants , at similar levels in iDCs or DCHIV and in aNK-iDC or aNK-DCHIV cocultures . In a recent study , it was proposed that HMGB1 secretion by NK cells is dependent upon IL-18 released by iDC at the synaptic cleft , inducing DC maturation and protection from lysis [7] . However , the mechanism involved in DC protection from NK lysis was unknown , and our study is the first to report a strong inducer effect of HMGB1 on c-FLIP and c-IAP2 expression as a survival mechanism . Our observations are compatible with a recent study reporting in human colon carcinomas a correlation between increased HMGB1 levels and enhanced amounts of c-IAP2 [39] . Since DCs are located in the mucosa [1] and thought to be among the first cells that encounter HIV-1 during sexual transmission [40] and disseminate the virus in lymphoid organs , it was important to determine the consequences of aNK-iDCs interaction on viral replication and persistence . Our previous study [16] and this one strongly argue for a deleterious role of NK cells , through HMGB1 release , on HIV-1 control . Indeed , under conditions of iDC maturation ( NK∶DC ratio of 1∶5 ) [16] and iDC killing ( NK∶DC ratio 5∶1 ) ( not shown ) , aNK-DCHIV interaction led in a few hours to a significant increase in the frequency of infected DCs and in the production of viral particles [16] . Furthermore , under conditions of DC killing , aNK-DCHIV interaction led to the resistance of infected DCs to NK cytotoxicity . The pivotal role of HMGB1 in that context is puzzling . It was reported to activate replication of latent HIV-1 in infected monocytic cell lines [41] but also in primary infected DCs [16] , but in some cases HMGB1 was found to inhibit viral production by macrophages , either by increasing the release of CCR5-interacting chemokines ( RANTES , MIP-1α and MIP-1ß ) [41] or by another mechanism to be determined [42] . Nevertheless , our report reveals that HMGB1 is essential for the survival of infected DCs , promoting the up-regulation of potent suppressors of apoptosis , c-FLIP and c-IAP2 . Interestingly , the resistance of infected DCs to NK killing required productive HIV-1 infection , as the addition of AZT at the time of DC infection preserved their susceptibility to NK killing . Since specific inhibitors of HMGB1 also allowed productively infected DCs to be killed by NK cells , these observations indicate a conjugated effect of both HMGB1 and HIV on the acquired resistance of DCHIV to TRAIL-induced apoptosis . Inhibition of the apoptotic response to TRAIL in many cancer cells involves NF-κB activation that upregulates , among anti-apoptotic molecules , c-FLIP and c-IAP2 [43] [44] . In our study , the involvement of NF-κB in TRAIL-induced resistance of DCHIV is suggested by the recent demonstration of the ability of HMGB1 to increase c-IAP2 expression levels in carcinoma cells by enhancing NF-κB activity [39] , and the requirement for HIV to promote its replication and prevent host-cell apoptosis by activating NF-κB [45] . This hypothesis is currently under investigation . Thus , from our previous data [16] , coupled with the results presented here , we conclude that certain bidirectional NK-DC interactions , that normally occur after an inflammatory insult , are altered after infection of iDCs with HIV-1 , resulting in ( i ) the abnormal maturation of DCHIV that are impaired in their ability to secrete IL-12 and IL-18 and to prime Th1 cells [16] , ( ii ) increased expression of HIV-RNA and HIV-DNA in mature DCHIV [16] , ( iii ) deficient killing of infected DCs by NK cells due to HMGB1-dependent upregulation of the anti-apoptotic molecules c-FLIP and c-IAP2 in DCHIV ( the present study ) . The pivotal role of HMGB1 in NK-DCHIV interaction was highlighted by the observation that it was responsible for promoting the protection of DCHIV from NK-dependent cytotoxicity , and also involved in the triggering of HIV-1 replication in DCHIV [16] . The role of HMGB1 in HIV disease is currently unknown , though plasma levels were reported to be elevated in HIV-1-infected patients compared to healthy donors , with the highest concentrations found in patients with clinical complications [46] . Moreover , circulating HMGB1 levels were found correlated with HIV-RNA plasma viral load ( M-L Gougeon et al . unpublished data ) . Altogether these observations provide evidence for the crucial role of NK-DC cross-talk in promoting viral dissemination and maintaining viability of long-term reservoirs in DC population , they challenge the question of the in vivo involvement of HMGB1 in the triggering of viral replication and persistence of these reservoirs , and they suggest that c-FLIP or c-IAP2 molecules may represent therapeutic targets for the destruction of infected DCs .
Peripheral Blood Mononuclear Cells ( PBMCs ) were separated from the blood of healthy adult donors on a Ficoll-Hypaque density gradient . Blood was obtained through the EFS ( Etablissement Français du Sang ) in the setting of EFS-Institut Pasteur Convention . A written informed consent was obtained for each donor to use the cells for clinical research according to French laws . Our study was approved by IRB , external ( EFS Board ) as required by French law and internal ( Biomedical Research Committee Board , Institut Pasteur ) as required by Institut Pasteur . We isolated CD14+ monocytes from PBMCs by positive selection using CD14-specific immunomagnetic beads ( Miltenyi Biotech ) . To generate iDCs , purified CD14+ monocytes ( purity >95% ) were cultured for 5 days ( 106 cells/ml ) in RPMI 1640 medium supplemented with 2mM glutamine , 10% FCS , penicillin ( 100 U/ml ) and streptomycin ( 100 µg/ml ) , in the presence of 10 ng/ml of recombinant human ( rhu ) GM-CSF and 10 ng/ml rhuIL- 4 ( Peprotech ) , as described [47] . Culture medium was replaced every 2 days . CD56+ NK cells were isolated by negative selection from PBMCs using the «EasySep NK depletion Kit» ( StemCell Technologies ) . NK cell fraction ( CD3−CD56+ ) was more than 95% pure , as assessed by flow cytometry ( FACScalibur , BD ) using FITC-conjugated anti-CD3 and APC-conjugated anti-CD56 antibodies . Contamination with myeloid cells , assessed with FITC-conjugated anti-CD14 antibodies , was consistently less than 1% . Purified NK cells were cultured at 106cells/ml for 48 hours either in the presence of suboptimal concentration of IL-2 ( 100 ng/ml ) ( Peprotech ) to maintain their viability ( referred as rNK ) , or were activated by a combination of PHA ( 10 µg/ml ) ( Sigma ) and rhuIL-2 ( 10 µg/ml ) ( referred as aNK cells ) , before launching NK-DC coculture experiments . In experiments analyzing CD107a expression , NK cells were stimulated for 24 h with PMA ( 50 ng/ml ) ( Sigma ) and ionomycin ( 300 ng/ml ) ( Sigma ) . Virus stock was prepared by amplification of R5-HIV-1BaL on Monocytes-derivMDM . Viral stock was then clarified by centrifugation prior to determination of HIV-1 p24 concentration . iDCs were plated in 96-well culture plates at 200 , 000 cells/well and incubated for 24 hours at 37°C in a 5% CO2 atmosphere with HIV-1 at 1 ng p24/ml . Cells were harvested , washed three times with media containing 10% FCS and , when indicated , rNK or aNK cells were added at a NK∶DC ratio of 5∶1 . NK-DC cocultures lasted 24h , unless otherwise indicated , before analysis of DCs phenotype , viability , or quantification of viral production . In some experiments , HIV-1 infected iDCs were incubated alone or with aNK cells , in the presence of rhu-HMGB1 ( 1 µg/ml ) ( R&D Systems ) , blocking anti-HMGB1 Abs ( 10 µg/ml ) ( Abcam ) , Glycyrrhizin ( 10 µg/ml ) ( Sigma-Aldrich ) or Bisindolylmalmeimide III ( 25µM ) ( Alexis Biochemicals ) . HIV-1 concentration in culture supernatant was determined with the p24 ELISA kit ( Ingen ) . The frequency of HIV-1-infected cells was determined by flow cytometry to detect intracellular p24 molecule . Cells were surface stained with antibodies specific for CD40 ( BD ) to target DC and intracellular stained with p24-specific antibodies ( Beckman Coulter ) . Stained cells were immediately acquired on a FACScalibur and analyzed with FlowJo software . Surface staining of DCs was performed with anti-CD40 , -HLA-DR , -CD83 , -CD86 , -HLA-E or -DC-SIGN ( BD Biosciences ) , -DR4 or -DR5 ( e-Bioscience ) mAbs conjugated to FITC , PE or APC . Surface staining of NK cells was performed with anti-mTRAIL ( e-Bioscience ) , -CD56 or -CD107a ( BD ) mAbs conjugated to PE or APC . Cells were stained for 30 minutes at 4°C , washed twice in PBS/BSA/NaN3 ( 0 . 5% BSA , 0 . 01% NaN3 ) , fixed with 1% PFA , acquired on a FACScalibur and analyzed with FlowJo software . Populations of interest were selected by gating according to FSC/SSC parameters , then targeting DCs as CD56− cells and NK cells as CD56+ cells . For intracellular staining , cells were fixed with 4% PFA , permeabilized using 0 . 5% BSA , 0 . 01% NaN3 , 0 . 5% Saponin buffer , stained for 20 minutes at room temperature with FITC-labeled anti-perforin mAbs ( BD ) , PE-conjugated anti-p24 mAbs ( Beckman Coulter ) , PE-conjugated anti-active caspase-3 Abs ( BD ) , unconjugated rabbit anti-human c-IAP2 polyclonal antibody ( clone H-85 , Santa Cruz Biotechnology ) or c-FLIP antibodies ( Santa Cruz biotechnology ) . For indirect staining , donkey anti-rabbit PE-conjugated secondary antibodies ( Abcam ) were used . Samples were acquired on a FACSCalibur and analyzed with FlowJo software . Cells were stained with anti-CD40 mAbs ( BD ) , anti-HMGB1 ( Abcam ) or anti-c-IAP2 unconjugated rabbit polyclonal antibodies ( clone H-85 , Santa Cruz Biotechnology ) . Goat anti-rabbit Cy5-conjugated secondary antibodies ( Abcam ) were used . Cells were washed in PBS , fixed on poly-L-lysine coated slides ( Kindler , Freiburg , Germany ) , and mounted in an anti-fade DAPI Fluoromount-G ( Southern Biotech ) . Slides were observed using a Zeiss Axiovert 200M Perkin-Elmer Spinning Disk equipped with a Hamamatsu ORCA II ER camera . rNK or aNK cells were cocultured during 24h with uninfected or HIV-infected iDCs at different NK∶DC ratios: 1∶5 ( 2×105 NK+106 DCs/ml ) , 1∶1 ( 2×105 NK+2×105 DC/ml ) and 5∶1 ( 1×106 NK+2×105 DC ) . In some experiments , mature DCs ( DC0 ) were used as controls . DC maturation was induced by 48h stimulation of iDCs ( 106/ml ) with 10 µg/ml LPS ( E . Coli serotype 026-B6 , Sigma-Aldrich ) . DC survival was determined with the 7-AAD assay , as described previously [25] . Briefly , cultured cells were stained with 20 µg/mL nuclear dye 7-amino-actinomycin D ( 7-AAD; Sigma-Aldrich ) for 30 minutes at 4°C , and co-stained with CD56-specific antibody ( BD ) . Surviving DCs were identified as CD56− 7-AAD− FSChigh cells . When phenotypic characterization of DCs was performed in NK-DC cocultures , NK cells were always excluded from the FACS analysis through their staining with CD56-specific antibodies . In some experiments , iDCs were incubated in the presence of rhuTRAIL ( 1–1000 ng/ml ) , ( Alexis Biochemicals ) or neutralizing antibodies i . e . anti-TRAIL ( 1 µg/ml ) , anti-DR4 ( 250 ng/ml ) ( R&D ) or anti-Fas ligand ( 250 ng/ml ) . Concanamycin A ( Alexis Biochemicals ) was used at 100 nM for blocking perforin-mediated cytotoxicity in cocultures . Cell free culture supernatants were tested for soluble TRAIL and HMGB1 levels using ELISA kit from Diaclone , and Shino-test ELISA kit ( IBL ) , respectively . siRNAs against c-IAP2 , c-FLIP and control FITC-labeled siRNA were purchased from SantaCruz technologies . iDCs or HIV-infected DCs were seeded at 3×104 cells/ml in 96-well plates . Gene-specific siRNAs and the control siRNA ( 0 . 1–50nM ) were added to media containing Polymag magnetofection beads ( OZ Biosciences ) and incubated 20 minutes at room temperature . Cells were transfected either with Polymag beads alone , control FITC-labeled siRNA , c-IAP2 siRNA or with c-FLIP siRNA in a strong magnetic field ( Magnetic plate , OZ Biosciences ) for 20 minutes at 37°C , 5% CO2 . iDCs were kept in culture in complete medium for 24h before measuring cell death . Total RNA was isolated from cocultures of aNK∶iDC or aNK∶DCHIV , and microarray experiments were performed by Miltenyi Biotech . Cells were centrifuged at 300g and resuspended in 1 ml Lysis/Binding Buffer . Lysate was sheared through a 21G needle and cleared using a LysateClear Column . Fifty microliter Oligo ( dT ) MicroBeads were added to the lysate and mRNA fixed and purified on a micro column . mRNA was transcribed to labeled cDNA using the thermo MACS Separator ( Miltenyi Biotech ) in a 60 min , 42°C in-column incubation with cDNA labeling mix and 1 µl Cy3-dCTP or Cy5-dCTP ( 1 mM , GE Healthcare ) . Labeled cDNA/mRNA hybrids were washed and RNase H digested in a 5 min , 42°C in-column incubation . cDNA was eluted with 50 µl cDNA Elution Buffer . PIQOR Microarray Immunology , human , sense ( Miltenyi Biotech ) hybridization was performed according to the manufacturer's instructions using an automized hybridization machine ( a-Hyb , Miltenyi Biotech ) . Microarrays were blow-dried and scanned with the ScanArray Lite ( GSI Lumonics ) and the Agilent DNA-Microarray Scanner . Signal processing and quantification was conducted with ImaGene software version 5 . 0 ( BioDiscovery ) . Local background was subtracted from the signal to obtain the net signal intensity and the ratio of Cy5/Cy3 . Subsequently , the mean of the ratios of four corresponding spots representing the same cDNA was computed . Cluster analysis of pro-apoptotic and anti-apoptotic gene expression was performed by Miltenyi Bioinformatic services . Pearson correlation coefficient analysis was performed with the unfiltered ratio 2bkg dataset logarithmized to the basis of two . One and two dimensional average linkage hierarchical clustering using Euclidean distance as well as statistical analysis of microarrays ( SAM ) 4 was performed with TIGR MeV version TM4 setting the percentage cutoff filter to 30% . aNK cells were added to iDCs at aNK∶iDC ratio of 5∶1 . Cells were centrifuged and the pellet incubated for 15 minutes at 37°C in a 5% CO2 atmosphere to favor cell contacts . The pellet was then reconstituted in complete culture medium and cells were seeded at a concentration of 250 000 cells/ ml in a 35 mm microdish ( Ibidi , France ) . The culture was observed in bright field using an inverted microscope Zeiss Axiovert 200M . Cells were maintained in a 37°C , 5% CO2 chamber while videos were recorded . Statistical analyses were made with the non-parametric Mann-Whitney test . The P value of significant differences is reported . Plotted data represent mean ± standard deviation ( s . d . ) . | Dendritic cells ( DCs ) , the professional antigen presenting cells , are critical for host immunity by inducing specific immune responses against a broad variety of pathogens . Human Immunodeficiency Virus-1 ( HIV-1 ) has evolved ways to exploit DCs , thereby facilitating viral dissemination and allowing evasion of antiviral immunity . In particular , infected DCs may function as cellular reservoirs for HIV-1 , thus contributing to viral persistence in lymphoid tissues . The mechanisms involved in the constitution of HIV reservoirs in DCs are poorly understood . In this study , we reveal that DCs infected with HIV-1 ( DCHIV ) become resistant to killing by natural killer ( NK ) cells , early effectors of innate immunity involved in the destruction of virus infected cells or cancer cells . This protection of DCHIV from NK cytotoxicity is induced through a cross-talk between NK cells and DCHIV , which induces the upregulation in DCHIV of two inhibitors of cell death , i . e . cellular-Flice like inhibitory protein ( c-FLIP ) and cellular inhibitor of apoptosis 2 ( c-IAP2 ) . The molecule responsible for the induction of these inhibitors is High-mobility group box 1 ( HMGB1 ) , an alarmin involved in the functional maturation of DCs . Blocking HMGB1 restores DCHIV susceptibility to NK cell killing , arguing for a key role of HMGB1 in the persistence of DCHIV . These findings provide evidence of the crucial role of NK-DC cross-talk in promoting viral persistence , and they identify potential therapeutic targets to eliminate infected DCs . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/persistence",
"and",
"latency",
"virology/immunodeficiency",
"viruses",
"immunology/innate",
"immunity",
"infectious",
"diseases/viral",
"infections",
"virology/immune",
"evasion",
"immunology/immunity",
"to",
"infections"
] | 2010 | Escape of HIV-1-Infected Dendritic Cells from TRAIL-Mediated NK Cell Cytotoxicity during NK-DC Cross-Talk—A Pivotal Role of HMGB1 |
The new world arenavirus Junín virus ( JUNV ) is the causative agent of Argentine hemorrhagic fever , a lethal human infectious disease . Adult laboratory mice are generally resistant to peripheral infection by JUNV . The mechanism underlying the mouse resistance to JUNV infection is largely unknown . We have reported that interferon receptor knockout mice succumb to JUNV infection , indicating the critical role of interferon in restricting JUNV infection in mice . Here we report that the pathogenic and vaccine strains of JUNV were highly sensitive to interferon in murine primary cells . Treatment with low concentrations of interferon abrogated viral NP protein expression in murine cells . The replication of both JUNVs was enhanced in IRF3/IRF7 deficient cells . In addition , the vaccine strain of JUNV displayed impaired growth in primary murine cells . Our data suggested a direct and potent role of host interferon response in restricting JUNV replication in mice . The defect in viral growth for vaccine JUNV might also partially explain its attenuation in mice .
Arenaviruses are enveloped RNA viruses with bi-segmented , negative-sense genomic RNA [1] . Based on the antigenicity , phylogeny , and geographical distribution , they are divided into the Old World ( Lassa-Lymphocytic choriomeningitis complex ) arenaviruses and the New World ( Tacaribe complex ) arenaviruses . Recent studies have identified several snake-borne arenaviruses that are highly divergent from known arenaviruses [2]–[4] . The lymphocytic choriomeningitis virus ( LCMV ) from the Old World ( OW ) arenaviruses is the prototype arenavirus . The New World ( NW ) arenaviruses are further classified into clades A , B , and C NW arenaviruses . Arenaviruses often chronically infect their natural rodent hosts [1] . Infection in humans is mostly acute and occurs probably through mucosal exposure to aerosols or by direct contact of abraded skin with infectious materials . The Arenaviridae family includes several important human pathogens [1] , [5] , [6] . The OW Lassa virus ( LASV ) is the causative agent of Lassa fever , a major public health concern in western Africa [7] . Several clade B NW arenaviruses , including Junín virus ( JUNV ) , Machupo virus ( MACV ) , Guanarito virus ( GTOV ) , Sabia virus ( SABV ) and Chapare virus ( CHAV ) , cause human hemorrhagic fever diseases in South America [1] , [5]–[8] . JUNV is the causative agent of Argentine hemorrhagic fever [1] , a highly infectious human disease with 15–30% case fatality [8]–[12] , meanwhile JUNV could induce lethal , transient or persistent infection in its natural rodent host , Calomys musculinus [1] , [13] . Field and laboratory studies have demonstrated that JUNV infection in its primary natural rodent host is largely through horizontal transmission by close contact and that rodents undergoing persistent infection could shed a large amount of virus in saliva and urine [12] , [14] . JUNV is classified as a select agent by the Centers for Diseases Control and Prevention in the United States . Research work utilizing infectious JUNV requires a high-containment biosafety level 4 facility in the USA . A live attenuated vaccine Candid #1 was developed as a collaborative effort by the US Army Medical Research Institute of Infectious Diseases ( USAMRIID ) and the Argentine Ministry of Health and Social Action [15] , [16] . The original human pathogenic XJ strain had been serially passaged in guinea pigs and mouse brains . The resulting XJ44 strain was attenuated for guinea pigs and humans but not for young mice infected intracranially . After additional serial passages in FRhL-2 cells , the virus eventually became attenuated in young mice and was selected as the final Candid #1 vaccine stock . Adult laboratory mice ( older than 21-days ) are generally resistant to peripheral infection by any JUNV [6] , [12] , [13] . Suckling mice are vulnerable to lethal virus challenge intracranially and mainly develop neurotropic and immunopathological diseases distinct from symptoms observed in patients and in experimentally infected nonhuman primates and guinea pigs [12] . The mechanism underlying the mouse resistance to JUNV infection is largely unknown . Previously , we have reported that mice lacking type I and type II interferon ( IFN ) receptors succumbed to lethal JUNV infection , which provides a novel model that recapitulates some symptoms found in AHF patients [17] . This result demonstrated the critical role of IFN pathway in restricting JUNV infection in mice and prompted us to further explore whether IFN could directly inhibit JUNV replication in murine cells . In this study , we provide evidence showing that both pathogenic and vaccine strains of JUNV were highly sensitive to interferon treatment in murine primary cells . The multiplication of JUNVs was enhanced in IRF3/IRF7 deficient cells . In addition , the vaccine strain JUNV displayed impaired growth in murine primary cells , which could partially explain the attenuation of virus .
The pathogenic strain Romero JUNV was obtained from Dr . Thomas G . Ksiazek ( Centers for Disease Control and Prevention , Atlanta , GA ) . The vaccine strain Candid#1 JUNV and Vesicular stomatitis virus were provided by Dr . Robert Tesh ( The World Reference Center for Emerging Viruses and Arboviruses ( WRCEVA ) , University of Texas Medical Branch , Galveston , TX ) . Virus stocks were propagated on Vero cells ( American Tissue Culture Collection , Manassas , VA ) , followed by filtration through filters ( 0 . 45 µm pore size ) to remove cell debris and by purification with Ultra 100 K Filters Devices ( Ultralcel 100 K , molecular weight cutoff 100 , 000 , Amicon , Millipore ) . Human lung epithelial A549 cells were obtained from ATCC . Primary mouse embryonic fibroblast cells derived from wild type C57BL/6 mice and IRF3/7 knockout mice were provided by Dr . Michael Diamond ( Washington University ) . All work with the pathogenic Romero strain JUNV was performed in the University of Texas Medical Branch BSL-4 facilities ( the Galveston National Laboratory ) in accordance with institutional health and safety guidelines and federal regulations as described previously [18] . Vero and A549 cells were seeded into 96-well plates for 24 h and treated with human IFN-α-2b ( Intron A , Schering Corporation , NJ ) , IFN-β1a ( PBL , NJ ) or IFN-γ ( Sigma-Aldrich , MO ) at 125 , 250 , 500 and 1000 U/ml for 16 h . MEF cells were treated with mouse IFN-β ( PBL ) as indicated . Cells were then infected with VSV , Candid#1 JUNV or Romero JUNV at an MOI of 0 . 1 PFU/cell . IFNs were supplemented after virus infection . For Romero and Candid#1 JUNV infection , supernatants were collected at 3 days post infection and assayed for virus production by plaque assay . For VSV infection , supernatants were collected at 16 hr p . i . . Data represent the mean of three experiments ±SEM . Cells were seeded into 12-well plates for 24 h and then treated with various concentrations of human IFN-β1a or mouse IFN-β ( PBL ) as indicated in each experiment . Cells were infected with Candid#1 JUNV at an MOI of 3 PFU/cell . IFNs were supplemented after virus infection . Protein lysates were prepared in 2x Laemmli sample buffer at 1 and 2 days p . i . from MEF cells and A549 cells , or from Vero cells at 2 days p . i . . Protein samples were resolved on 4–20% SDS-PAGE gel and transferred to PVDF membranes using Mini Trans-Blot Electrophoretic Transfer Cell apparatus ( Bio-Rad , CA ) . Membranes were incubated with primary antibodies overnight at 4°C and then with appropriate secondary antibodies for 1 h at room temperature . Proteins were visualized with ECL Western Blotting Detection Reagents ( GE , NJ ) according to the manufacturer's instruction . Viral NP protein was detected with a monoclonal mouse anti-JUNV NP antibody ( AG12 , BEI ) . Equal loading of samples was confirmed by immunoblotting of the same membranes with an antibody to β-actin ( sc-1616 , Santa Cruz ) . Secondary antibodies HRP-conjugated Goat anti-mouse IgG ( 115-035-146 , Jackson Immunology ) and HRP-conjugated donkey anti-goat IgG ( sc-2020 , Santa Cruz ) were used . Wild-type MEF cells and IRF3/7 knockout MEF cells were infected by Romero and Candid#1 viruses at MOI of 0 . 1 or 0 . 001 . Supernatants from infected cells were harvested daily and subjected to plaque assay as described previously [18] . Statistical analysis of virus growth kinetics was performed by two way ANOVA test .
To understand if IFN has direct impact on JUNV infection , we characterized the effects of IFN treatment on JUNV multiplication in primary murine embryonic fibroblast cells ( MEF ) derived from C57BL/6 mice , Vero cells or human lung epithelial A549 cells . MEF cells were treated with mouse IFN-β at 1 , 10 , 50 or 100 U/ml for 16 hrs before and after virus infection , meanwhile Vero cells and A549 cells were treated with human IFN-α , IFN-β or IFN-γ for 16 hrs before and after infection at 125 , 250 , 500 or 1000 U/ml ( Fig 1 ) . Cells were then infected with the pathogenic Romero strain or the vaccine strain Candid#1 JUNV at a multiplicity of infection ( MOI ) of 0 . 1 PFU/cell . At 72 hr post infection ( p . i . ) virus titers in tissue culture supernatants were determined by plaque assay . We also included the IFN-sensitive VSV as a control . The result showed that IFN-β mediated a potent antiviral effect against both Romero and Candid#1 JUNV infection in MEF cells ( Fig 1A ) . Notably a low dose of murine IFN-β treatment ( 1 U/ml ) resulted in drastic decrease in virus titers by over 3-log for Candid#1 and by 2 . 7-log for Romero JUNV . Meanwhile , the titer of VSV was reduced by 4-log at 1 U/ml ( Fig 1A ) . The multiplication of Candid#1 was completely abolished in MEF cells treated with 1 U/ml IFN-β , while the multiplication of Romero virus was abolished at 50 U/ml ( Fig 1A ) , demonstrating the high sensitivity of JUNV to IFN-mediated antiviral effect in murine cells . In comparison , the virus titers of some IFN-sensitive LCMV strains are decreased by approximately 2-log when treated with 100 U/ml murine IFN-α/β in murine cells [19] . In IFN-α/β gene defective Vero cells , the titers of JUNV were reduced by less than 1-log when treated with a high concentration of human IFN-α , β or γ ( 1000 U/ml ) ( Fig 1B ) , which was consistent with our previous studies [20] . In comparison , the titer of VSV was remarkably reduced by close to 5-log in the presence of 125 U/ml human IFN-β , indicating the relative insensitivity of both JUNV strains to IFN in Vero cells . In human A549 cells , treatment with 500 U/ml human IFN-β suppressed virus growth by 2-log and 3-log for Candid#1 strain and Romero strain , respectively , while treatment with 125 U/ml human IFN-β reduced virus titer by more than 6-log for VSV ( Fig 1C ) . This result showed that both strains of JUNV were relatively more sensitive to IFN in A549 cells than in Vero cells , but less susceptible to IFN than the IFN-sensitive VSV . It seems that the pathogenic Romero virus replicated more efficiently than the vaccine strain Candid#1 virus did in wild-type MEF cells ( Fig 1A ) . We characterized JUNV growth in primary MEF cells at an MOI of 0 . 1 and found that the Romero virus indeed multiplied significantly greater than the Candid#1 virus ( Fig 2A , P<0 . 001 , two way ANOVA test ) . The peak titer of Romero virus ( 3 . 6×105 PFU/ml ) was about 240-fold of that of Candid#1 virus ( 1 . 5×103 PFU/ml ) at 4 d . p . i . ( Fig 2A ) . JUNV growth was further examined in IRF3/IRF7 double knockout ( KO ) MEF cells ( MOI = 0 . 1 ) where the IFN response is largely abrogated [21] . While both strains showed enhanced growth in IRF3/7 KO MEF cells ( Fig 2B ) , the Romero virus replicated more efficiently than the vaccine Candid#1 virus ( P<0 . 001 , two way ANOVA test ) . The peak titer of Romero virus ( 6 . 2×106 PFU/ml ) was 47-fold of that of Candid#1 virus ( 1 . 3×105 PFU/ml ) at 4 d . p . i . . The difference was largely comparable to that in wild-type MEF ( Fig 2A ) , implying the impaired growth of Candid#1 virus was less likely associated with host IFN response but more likely due to its intrinsic growth deficiency in primary MEF cells . We further examined JUNV growth in MEF cells at lower MOI ( MOI 0 . 001 ) , a condition more mimicking virus infection in vivo ( Fig 2C ) . As expected , Romero virus grew at lower peak titers in both cell lines than it did at MOI of 0 . 1 . We again observed more productive multiplication for Romero virus in IRF3/7 KO MEF cells ( 2 . 5×105 PFU/ml , 5 d . p . i . ) than in wt MEF ( 2 . 3×104 PFU/ml , 5 d . p . i . ) , supporting the role of IFN pathway in suppressing JUNV infection in murine cells . However , the growth of Candid#1 virus was below the detection level in wt MEF or IRF3/7 KO MEF cells at MOI of 0 . 001 , demonstrating the impaired Candid#1 virus growth in primary murine cells as compared with the Romero virus regardless of the integrity of the IFN pathway . Next , we studied the effect of IFN on viral replication to understand the mechanism of IFN-induced antivirus activity . MEF cells were pretreated with murine IFN-β at 1 , 5 , 10 and 50 U/ml followed by infection with Candid#1 JUNV at an MOI of 3 . Our data clearly showed that treatment with 1 U/ml of IFN-β almost abolished viral NP protein expression at days 1 and 2 p . i . ( Fig 3A ) , consistent with the virus titration results ( Fig 1A ) . NP protein is one of the early viral gene products expressed during virus infection [1] . Our result suggested that IFN probably targeted early steps of virus infection , such as virus entry , disassembly or early stages of viral RNA replication/transcription in mouse cells . In A549 cells , NP protein expression was inhibited by 90% in the presence of 500 U/ml human IFN-β ( Fig 3B ) , which was consistent with the virus titration data ( Fig 1C ) . In Vero cells , the synthesis of NP protein was moderately suppressed ( Fig 3C ) after 1000 U/ml human IFN-β treatment , in agreement with the relative resistance of JUNV to IFN treatment in this cell line ( Fig 1B ) .
Overall our studies revealed the high sensitivity of JUNV to IFN as well as the attenuated growth of Candid#1 virus in primary murine cells . Treatment with 1 U/ml of IFN-β resulted in a drastic decrease in JUNV titers ( over 3-log reduction for Candid#1 and 2 . 7-log for Romero ) , similar to the inhibitory effect of IFN on the IFN-sensitive VSV ( Fig 1A ) . Expression of NP protein was abolished in the presence of exogenous 1 U/ml IFN-β for Candid#1 JUNV in murine cells at MOI 3 . This result also suggested that the early stage of the virus replication was efficiently suppressed by IFN in murine cells . Known examples of IFN-induced gene products that could inhibit virus replication at early life cycle include: 1 ) MxA , which inhibits viral nucleocapsid shuttling and primary transcription of influenza virus , HCV and VSV [22]; 2 ) IFITMs , which inhibit the entry of influenza A virus , SARS coronavirus and West Nile virus [23] [24] and 3 ) IFIT1/2/3/5 complex , which blocks the replication of certain strains of WNV [25] and Venezuelan equine encephalitis virus [26] by targeting viral RNA either lacking 2′O-methylated cap structure or containing tri-phosphate group at the 5′-end . Future studies are required to identify which steps of virus replication are blocked by IFN treatment , as such study will help us better understand the JUNV-host interaction and will also facilitate the design of antiviral strategies . Interestingly for some IFN-sensitive LCMV strains ( the WE and Armstrong strains ) , treatment with a higher dose of murine IFN-α/β ( 100 u/ml ) leads to about 2-log reduction in virus titer in murine cells [19] . The capacity of LCMV strains to establish persistent infection in adult immunocompetent mice has also been correlated with their relative resistance to IFN-α/β and IFN-γ [19] . For Lassa virus , treatment with high concentration of human IFN-α ( 1000 U/ml ) leads to approximately 2-log reduction in virus titer in human cells [27] . Because of the difference in experimental conditions , it is difficult to directly compare the IFN sensitivity of JUNV in MEF cells with those results in aforementioned studies for LCMV and LASV . However , JUNV is apparently highly sensitive to IFN in murine cells , which could at least partially explain the mouse resistance to JUNV as discussed below . Since both human pathogenic and vaccine strains of JUNV are highly susceptible to IFN in murine cells as identified in this study , no correlation could be established between the pathogenicity of JUNV in humans and the IFN sensitivity in murine cells . While relatively resistant to IFN in Vero cells , JUNVs were more susceptible to IFN in A549 cells . Based on the two-step positive-feedback loop model for IFN production [28] , IFN-β and IFN-α4 are produced upon virus infection at the first step and secreted to induce IRF7 expression . At the next stage , activated IRF7 further stimulates the expression of other IFNs and allows the cells to mount a full scale antiviral response . Accordingly , it is possible that exogenous IFN-β could induce the synthesis of various subtypes of endogenous IFN in IFN-production competent A549 cells , resulting in a robust and sustained antiviral response . In Vero cells , due to its defect in IFN-β and IFN-α genes [29] , the IFN-mediated antiviral response could be less potent as in A549 cells . The high sensitivity of JUNVs to IFN in murine cells might explain in part the requirement of intact IFN pathway for adult mice to be resistant to JUNV . Macrophages are known as one of the initial targets of JUNV infection in vivo [8] , [30] , [31] . Mouse macrophages are found to produce IFN and other cytokines in response to infection by Candid#1 virus and presumably by pathogenic JUNV as a result of host recognition of viral glycoprotein protein in a TLR-2-dependent manner [32] . Considering the high IFN sensitivity of JUNV in murine cells as identified herein , it is possible that productive viral infection might be suppressed directly by IFN-induced antiviral gene products in macrophages or other cells . Moreover , induced IFN and cytokines could also activate different immune cells to promote JUNV clearance in vivo . These host barriers could be detrimental to JUNV dissemination in mice at the initial stage of viral infection , which might eventually render adult mice resistant to JUNV . In the absence of functional IFN pathway , the Romero JUNV is able to establish successful virus infection and become pathogenic in mice [17] . The role of IFN response in JUNV pathogenesis is still not well understood . High levels of IFN-α have been detected in serum samples from Argentine hemorrhagic fever ( AHF ) patients and have been associated with severe and lethal disease outcomes [9] . IFN has been linked to some of the clinical symptoms including thrombocytopenia [33] . In natural rodent host , the role of IFN in JUNV pathogenesis and persistent infection remains unclear , largely due to lack of laboratory inbred animals [13] . Although the mouse is not the natural host for JUNV , our results with murine cells provide some insights into the basis for Candid#1 virus attenuation . Candid#1-specific mutations leading to virus attenuation in humans and guinea pigs are not established . Its ancestor XJ#44 strain , which was established by 44 passages of the human pathogenic XJ strain in mouse brain [34] , is attenuated for humans and guinea pigs but still virulent for suckling mice when introduced intracranially . The vaccine Candid#1 strain was established after additional passages of the XJ#44 strain in FRhL-2 cells . Attenuation of Candid#1 has been well characterized with a 14-day-old mouse model in a recent genetic study [35] , in which the viral GPC glycoprotein has been found as the main determinant of JUNV virulence in mice . Among a total of six amino acid changes in Candid#1 virus sequence as compared with the XJ44 strain [35] , [36] , a single F427I substitution in the transmembrane region of GPC is sufficient for JUNV attenuation in suckling mice [35] . However , the mechanism of attenuation or the possible effect of accumulated mutations on virus replication in murine systems has not been established . We demonstrated the impaired growth of Candid#1 virus in primary murine cells , which was more evident at an MOI of 0 . 001 . This defect might relate to the increased dependency of Candid#1 glycoprotein on human transferrin receptor for virus entry [36] , the compromised efficiency of Candid#1 NP and L proteins in supporting viral RNA transcription/replication [37] or other mechanisms remained to be identified in future studies . A systemic characterization of the effect of Candid#1-specific mutations on virus replication in murine systems is warranted by utilizing the JUNV reverse genetic systems . The impaired virus growth in murine cells for Candid#1 strain is biologically relevant to its attenuation , as it could at least in part explain the inability of Candid#1 virus to cause disseminated infection in mice lacking functional IFN system ( our unpublished observation ) . | The new world arenavirus Junín virus ( JUNV ) is the causative agent of a lethal human infectious disease , Argentine hemorrhagic fever . Laboratory mice are used as models to study many viral diseases . However , adult laboratory mice are generally resistant to JUNV infection . Interferons are early immune regulatory molecules that induce potent anti-viral status in host cells and activate host immune cells to counteract virus infection . The activity of interferons relies on their cell surface receptors . We have previously reported that mutant mice with defect in interferon receptors succumbed to challenge with JUNV , highlighting the critical role of interferon in restricting JUNV infection in mice . Here we further study the basis of mouse resistance to JUNV infection and report that the replication of both pathogenic JUNV and its vaccine strains are highly sensitive to type I IFN treatment in mouse cells . However , both strains replicate efficiently in Africa green monkey-derived Vero cells and human cells when treated with high doses of interferon . Additionally , the vaccine strain replicates less efficiently in mouse cells compared with the pathogenic strain , which might partially explain its attenuation in mice . Our new findings help better understand the JUNV-host interaction . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"animal",
"models",
"of",
"infection",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"viral",
"vaccines",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"host-pathogen",
"interactions",
"virology",
"neglected",
"trop... | 2014 | Potent Inhibition of Junín Virus Infection by Interferon in Murine Cells |
The bloodsucking hemipteran Rhodnius prolixus is a vector of Chagas' disease , which affects 7–8 million people today in Latin America . In contrast to other hematophagous insects , the triatomine gut is compartmentalized into three segments that perform different functions during blood digestion . Here we report analysis of transcriptomes for each of the segments using pyrosequencing technology . Comparison of transcript frequency in digestive libraries with a whole-body library was used to evaluate expression levels . All classes of digestive enzymes were highly expressed , with a predominance of cysteine and aspartic proteinases , the latter showing a significant expansion through gene duplication . Although no protein digestion is known to occur in the anterior midgut ( AM ) , protease transcripts were found , suggesting secretion as pro-enzymes , being possibly activated in the posterior midgut ( PM ) . As expected , genes related to cytoskeleton , protein synthesis apparatus , protein traffic , and secretion were abundantly transcribed . Despite the absence of a chitinous peritrophic membrane in hemipterans - which have instead a lipidic perimicrovillar membrane lining over midgut epithelia - several gut-specific peritrophin transcripts were found , suggesting that these proteins perform functions other than being a structural component of the peritrophic membrane . Among immunity-related transcripts , while lysozymes and lectins were the most highly expressed , several genes belonging to the Toll pathway - found at low levels in the gut of most insects - were identified , contrasting with a low abundance of transcripts from IMD and STAT pathways . Analysis of transcripts related to lipid metabolism indicates that lipids play multiple roles , being a major energy source , a substrate for perimicrovillar membrane formation , and a source for hydrocarbons possibly to produce the wax layer of the hindgut . Transcripts related to amino acid metabolism showed an unanticipated priority for degradation of tyrosine , phenylalanine , and tryptophan . Analysis of transcripts related to signaling pathways suggested a role for MAP kinases , GTPases , and LKBP1/AMP kinases related to control of cell shape and polarity , possibly in connection with regulation of cell survival , response of pathogens and nutrients . Together , our findings present a new view of the triatomine digestive apparatus and will help us understand trypanosome interaction and allow insights into hemipteran metabolic adaptations to a blood-based diet .
Triatomine bugs belong to the family Reduviidae within the order Hemiptera ( infra-order: Heteroptera ) , all instars of which feed exclusively on blood [1] , [2] . Several species are vectors of Chagas' disease in the Americas , a chronic and debilitating disease , often fatal , which infects 7–8 million people in Latin America today [3] . Among the 140 triatomine species in five tribes [4] , Rhodnius prolixus—a vector in Central and South America—became a model insect for insect physiology and biochemistry thanks to its use by Dr . Vincent Wigglesworth in the 1930s and onward [5] . Despite being a bloodfeeder , due to its taxonomic position , R . prolixus data are useful for researchers working with heteropteran agricultural pests [1] . Recently , its genome was targeted for sequencing , and included in this effort was the sequencing of several organ-specific cDNA libraries using pyrosequencing technology , which are described here . The gut of triatomines differs from other hematophagous insects for which genomic data are available ( mainly Diptera ) because it is divided into three distinct segments ( anterior midgut , AM; posterior midgut , PM and rectum , RE ) that perform different functions during digestion of the blood meal and make this insect highly adapted for a blood meal . For example , a 30-mg R . prolixus Vth instar nymph can take 10 times its own weight in blood in fifteen minutes , the blood being stored in the bug's AM . Within seconds of initiating the meal , diuretic hormones and serotonin are released into the hemolymph triggering salt and water transport from the meal to the hemolymph , and into the Malpighian tubules and finally into the RE , thus concentrating the meal and reducing the bug's weight [5] , [6] . Indeed , the bug's meal is reduced to its half by this urination within a few hours [5] . R . prolixus evolved from ancestors that on adapting to plant sap sucking lost their digestive serine proteinases and associated peritrophic membrane . This is a chitin-protein anatomical structure that may be synthesized by the whole or part of the midgut ( type I ) or by a ring of cells at the entrance of the midgut ( type II ) . The peritrophic membrane envelops the food bolus in the midgut of most insects , leading to compartmentalization of the digestive process [7] , [8] . Instead , the midgut cell microvilli in Hemiptera are ensheathed by a phospholipid membrane , the perimicrovillar membrane ( PMM ) [7] , [9] , which extends toward the midgut lumen with dead ends and , when collapsing , forms sheath packs [10]–[12] . PMMs were isolated from both R . prolixus [12] and Dysdercus peruvianus [13] midguts , leading to the identification of α-glucosidase as their biochemical enzyme marker . The presumed role of PMM was to absorb nutrients ( mainly free amino acids ) from the dilute sap ingested by the hemipteran and thysanopteran ancestors . On adapting to a diet rich in proteins , the heteropteran hemipteran ( like R . prolixus and D . peruvianus ) used lysosome-derived enzymes for digestion and the PMM as a substitute for the peritrophic membrane in the compartmentalization of digestion [7] , [9] , [12] . The AM additionally harbors an endosymbiont , Rhodococcus rhodnii , which is essential for the bugs' development and fertility [14]–[18] . The digestive tract is also where Trypanosoma cruzi , the protozoan agent of Chagas' disease , develops [19] . No proteolytic digestion occurs in the AM , where hemoglobin remains red in color for over a week after feeding , but where various endoglycosidases have been described [20] . Digestion of complex lipids , as triacylglycerol , is negligible in AM and takes place in the PM [21] . The AM slowly releases its contents into the PM over a period of ∼20 days , when the Vth instar nymph molts to an adult [5] . While most insects have trypsin-like enzymes , and an alkaline gut pH , for digesting proteins , Hemiptera have lysosomal-like cathepsins which are secreted into an acidic gut [22] . There are a negligible [23] and a major [24] cysteine proteinase that accounts for 85% of the total proteinase activity . This activity was initially interpreted as a cathepsin B but later was shown to include a cathepsin L-like proteinase [24] , [25] . A cathepsin D-like proteinase accounts for the remaining midgut proteinase activity [24] . Amino and carboxypeptidases produce amino acids from the endopeptidase products [24] , [26] . Toxic amounts of oxygen radical-producing heme are a byproduct of hemoglobin digestion , but these are stacked in the gut as a non-oxidizing form similar to the malarial pigment hemozoin . The stacking process in R . prolixus is dependent on the presence of PMM [27] , [28] . The RE , like the mammalian bladder , possesses a transitional epithelium that can stretch to accommodate the feces and urine [5] , [29] . It is from the rectal discharges that T . cruzi is released onto the mammalian host . The epithelia of the three gut segments are surrounded by smooth muscle [5] . As part of the R . prolixus genome sequencing effort several tissues in different post-feeding states and from different developmental stages were used to construct cDNA libraries which were submitted to pyrosequencing , including a whole body library ( WB , 862 , 980 reads ) and gut segment libraries from AM ( 156 , 780 reads ) , PM ( 145 , 986 reads ) and RE ( 170 , 565 reads ) . Other tissues were also investigated , including fat body ( FB , 177 , 944 reads ) , Malpighian tubule ( MT , 186 , 149 reads ) , ovary ( OV , 111 , 190 ) , and testes ( TE , 140 , 156 reads ) . These reads were assembled together into contigs , allowing identification of transcripts which are significantly overexpressed in particular tissues , thus allowing an insight on digestive organs' specific transcripts in R . prolixus . Additionally , over 2 , 900 coding sequences ( CDS ) were obtained , most ( ∼2 , 300 ) of them full length ( Met to stop codon ) , which should help train the gene-finder programs for this organism and help characterize specifically transcribed genes in the R . prolixus digestive tract .
All animal care and experimental protocols were conducted following the guidelines of the institutional care and use committee ( Committee for Evaluation of Animal Use for Research from the Federal University of Rio de Janeiro , CAUAP-UFRJ ) and the NIH Guide for the Care and Use of Laboratory Animals ( ISBN 0-309-05377-3 ) . The protocols were approved by CAUAP-UFRJ under registry #IBQM001 . Technicians dedicated to the animal facility at the Institute of Medical Biochemistry ( UFRJ ) carried out all aspects related to rabbit husbandry under strict guidelines to insure careful and consistent handling of the animals . Insects used for transcriptome were R . prolixus from a colony kept at UFRJ ( Rio de Janeiro ) , fed with rabbit blood , and raised at 28°C and 70% relative humidity . Adult females ( five from each condition ) receiving their second blood meal after the imaginal molt were dissected before feeding , twelve hours , twenty-four hours , two days , and five days after blood meal . A group of males ( blood fed , five days after blood meal ) was dissected to obtain testes . Organs ( AM , PM , RE , FB , OV , MT , and TE ) were dissected , homogenized in TriZol reagent ( Invitrogen , San Diego , CA , USA ) , and processed as described below . To obtain a whole body ( WB ) library , nymphs and adults in several stages of feeding plus eggs were collected and extracted with TriZol , as follows: Eggs were collected at the day of oviposition and at days 2 , 5 and 7 of development . First instars were collected at fasting ( 2 weeks after emergence ) and at 2 , 5 and 7 days after feeding ( DAF ) ; second and third instars were collected at fasting and at 2 , 5 , 7 and 9 DAF . Fourth instars were collected at fasting and at 2 , 5 , 7 , 9 and 12 DAF . Fifth instars were collected at fasting and at 2 , 5 , 7 , 9 , 12 , 14 , 17 and 19 DAF . Adult males and females were collected at fasting and at 2 , 5 , 7 , 9 and 12 DAF . All these 45 RNA preparations were pooled and used to obtain WB cDNA as described below . Organs were homogenized in TriZol reagent , and total RNA was isolated , followed by mRNA purification using the Micro-Fast track 2 . 0 kit from Invitrogen ( San Diego , CA , USA ) according to manufacturer's instructions . Libraries were constructed using the Smart cDNA Library Construction kit from Clontech ( Palo Alto , CA , USA ) and normalized using the Trimmer cDNA Normalization kit from Evrogen ( Moscow , Russia ) . The libraries were sequenced on a 454 genome sequencer FLX Titanium machine ( Roche 454 Life Sciences , Branford , CT , USA ) . A detailed description of our bioinformatic pipeline can be found in our previous publication [30] . Pyrosequencing reads were extracted from vector and primer sequences by running VecScreen . The resulting assemblies plus the clean pyrosequenced data were joined by an iterative BLAST and cap3 assembler [30] . This assembler tracks all reads used for each contig , allowing deconvolution of the number of reads used from each library for tissue expression comparisons using a χ2 test . To compare gene expression between libraries , paired comparisons of their number of reads hitting each contig were calculated by X2 tests to detect significant differences between samples when the minimum expected value was larger than 5 and P<0 . 05 . A 2-fold change ( up or down ) was considered of interest when statistically significant . Normalized fold ratios of the library reads were computed by adjusting the numerator by a factor based on the ratio of the total number of reads in each library , and adding one to the denominator to avoid division by zero . Notice that due to library normalization , the actually reported ratios are smaller than in reality . This assembled contigs can be browsed on Supporting Information S1 which is a hyperlinked excel file . Coding sequences were extracted using an automated pipeline based on similarities to known proteins or by obtaining CDS from the larger open reading frame of the contigs containing a signal peptide . A non-redundant set of the coding and their protein sequences were mapped into a hyperlinked Excel spreadsheet , which is presented as Supporting Information S2 . Signal peptide , transmembrane domains , furin cleavage sites , and mucin-type glycosylation were determined with software from the Center for Biological Sequence Analysis ( Technical University of Denmark , Lyngby , Denmark ) . To assign coding sequences as being of bacterial , viral , or invertebrate origins , the top blastp scores of the deduced proteins against each database were compared . If the ratio between the top two scores was larger than 1 . 25 and the e value of the blastp against pathogen or vertebrate was smaller than 1e-15 , then the CDS was assigned to the top-scoring organism group . This automatic analysis was followed up by manual verification . Functional classification of the contigs and proteins was done using a program written by JMCR that takes in consideration a vocabulary of 280 words that are scanned against matches to the KOG , GO , CDD , SwissProt and NR databases , and assigned to 29 functional categories , as explained in [30] . The algorithm also takes in consideration the position of the word in the match description . Sequence alignments were done with the ClustalX software package [31] . Phylogenetic analysis and statistical neighbor-joining bootstrap tests of the phylogenies were done with the Mega5 package [32] . Raw sequences were deposited on the Sequence Read Archive ( SRA ) from the NCBI under bioproject accession PRJNA191820 . The individual run files received accession numbers SRR206936 , SRR206937 , SRR206938 , SRR206946 , SRR206947 , SRR206948 , SRR206952 , SRR206983 , and SRR206984 . A total of 2 , 475 coding sequences and their translations were submitted to the Transcriptome Shotgun Assembly ( TSA ) project deposited at DDBJ/EMBL/GenBank under the accessions GAHY01000001-2475 .
The 1 , 951 , 750 reads were assembled into 317 , 104 contigs and singletons , 66 , 010 of which had a length above 250 nt . These contigs are found in Supporting Information S1 . Only this larger set was used in this work , which included a total of 1 , 641 , 334 reads , or 84% of the total . The assembly had 27 , 751 contigs larger than 499 nt , 8 , 324 contigs with lengths above 999 nt , and 972 above 1999 nt . Because the assembly algorithm included tracking of the reads , the number of reads resulting from each tissue could be accounted in the final contig , allowing for statistical tests of significant departure from expected values , namely χ2 tests . The nature of the RNA could be estimated by BLAST [35] comparisons to different databases , as indicated in the Methods section . We accordingly identified transcripts that were significantly more expressed in the whole digestive tract when compared to the WB library ( Table 1 ) , those more expressed in the AM when compared to the PM ( Table 2 ) , those more expressed in the PM when compared to the AM ( Table 3 ) , and those more expressed in the RE when compared to the combined AM+PM set ( Table 4 ) . Analysis was concentrated on contigs that were overexpressed in the digestive system with a P value<0 . 05; however , contigs related to selected specific aspects of midgut metabolism were also included in the analysis even when found at lower gut expression . We also made an effort to obtain coding sequences for all contigs that were significantly more expressed in the gut as well as for transcripts that presented >90% coverage with their best protein matches from the NR database , provided in Supporting Information S2 , containing 2 , 570 CDS . The following sections highlight the gut-overexpressed transcripts but also include other CDS of related families for comparison . These are located in the several worksheets of Supporting Information S2 following the worksheet named RP-CDS . We will make frequent reference to the number of “reads” from the pyrosequencing runs , each read being one sequence unit that was used to assemble the contigs that are the subject of analysis . In the remainder of this paper , when mentioning a contig represented in Supporting Information S1 , this will be indicated by Asb-### where ### is the contig number shown in column A . When reference is made to a CDS from Supporting Information S2 , this will be indicated by RP-### where ### refers to the CDS number shown also in column A . An exploratory proteomic analysis of Rhodnius' gut compartments was performed . The samples analyzed were prepared from insects fed on blood . The tissues were harvested on the fourth day after blood feeding . Regardless of this one point harvesting , about 10% of the proteins deduced from conceptual translation of the assembled 454 reads had their existence confirmed by this proteomic approach . Additional figure S1 shows the SDS-PAGE fractionation of membrane and soluble protein extracts obtained as described in methods from the tree compartments of Rhodnius' digestive tract . This figure exhibits the numbering of each fraction that was in gel digested and subsequently analyzed by mass spectrometry . The assignment of the ions produced by mass spectrometry to the deduced proteins was first done by the use of Mascot ( www . matrixscience . com ) and subsequently converted to Prosite block format as described in methods . This data-containing file was used to search matches in a formatted database of the deduced proteins , using the Seedtop program . The result of the Seedtop search was inserted into the hyperlinked spreadsheet ( Supporting Information S3 ) to produce a hyperlinked text file with details of the match . Supporting Information S3 exhibits in columns CH to DE of the first worksheet the information that was considered as a confirmation of protein existence . The gel fraction number with larger coverage was assigned only when two or more ions were detected . The total number of fragments , including same ion when detected in more than one band , and the coverage in total amino acid residues without duplication is presented . To summarize these findings , Supporting Information S3 was created . This spreadsheet contains a subset of worksheet named CDS from Supporting Information S2 and is also hyperlinked to the information on the ions that corroborate the deduced proteins' existence . Additional table S1 is a table containing the functional classification of the deduced proteins confirmed through this proteomic approach . These proteins cover almost all classes that figures in tables 1–4 . The rows in the spreadsheet presented as Supporting Information S3 were ordered alphabetically through column DG where this functional classification is presented . It is important to notice that eight proteins classified as unknown conserved were confirmed by this approach . This classification means that similar proteins have been found before in other species but no function has been assigned to them . The following sections are a guide to explore the several worksheets of Supporting Information S2 having the same names as the following headings: The polyprotein for a picornavirus similar to the honey bee slow paralysis virus is found expressed in the WB , AM , and RE ( Asb-4202 ) . This viral sequence was not found in the genome scaffolds , suggesting it may not be part of the insect genome . The DNA helicase of a virus similar to Cotesia vestalis bracovirus was also found in ( Asb-64576 ) ; other transcripts matching Cotesia virus were also found . Several phage proteins were also identified , and these could derive from bacterial transcripts . For example , Asb-15041 is 70% identical to a phage from a Wolbachia endosymbiont , but this is mapped to R . prolixus genome in contig 5802 and could represent a horizontal transfer . Also , 80 transcripts best-matched bacterial proteins ( presented in worksheet “Bacteria Virus TE” in Supporting Information S1 ) , many of which appear to be mapped to the genomic contig 17820 ( assembly version 3 . 0 ) including several sequences best matching Wolbachia endosymbionts . These could be interpreted as contaminant microorganisms present in both the colonies used to make the transcriptome reported here and the colonies that were used to sequence the genome . As these colonies have been kept in captivity for decades and were obtained independently from very distant places , this would make this Wolbachia a strong symbiont candidate . If these genomic contigs do not represent artifacts of genome assembly , this could represent an insertion of Wolbachia genetic material common to both Rhodnius strains , as has been reported for several insect species , where segments as large as the entire genome of a bacteria are found inserted into the genome of the arthropod [234] . Abundant transcripts coding for TEs , on the other hand , are found incorporated into the genome , as expected . In particular class I TE sequences of the families Gypsy , Bell , Line , and Copia are abundant . The class II ( cut and paste ) transposons are also particularly abundant , with expressed sequence tags coding for full-length transposases of a Mariner element ( Asb-69103 , RP-85192 ) , suggesting active transposition . PIF/Harbinger elements are also transcribed ( Asb-6109 ) . One-zinc-dependent metalloprotease was detected ( RP-9242 ) , which may be involved in cleaving growth factors or extracellular matrix components . Transcript Asb-10133 codes for a small protein of 80 amino acids , highly expressed in the Rec and homologous to the bladder cancer-associated protein ( BLCAP/Bc10 ) downregulated during invasive cancer growth in bladder [235] . The function of this protein is unknown , but its expression is characteristic of stratified epithelia , also found in Rhodnius hindgut . Transcript Asb-820 codes for a pantheteinase overexpressed in all three segments ( 1025 reads from gut libraries and 197 from WB ) . Enzymes belonging to this gene family are involved in vitamin recycling , both hydrolyzing biotinyl-peptides , generating free biotin , and transferring biotin to acceptor proteins . These proteins could in this way make biotin from the diet available to allow the insect to synthesize its own biotin-dependent enzymes , such as carboxylases . Currently , the R . prolixus genome has been sequenced with a 9× coverage . Transcripts reported here helped to obtain the predicted gene set that is available at vector base homepage ( https://www . vectorbase . org/organisms/rhodnius-prolixus ) and were also used to support the manual annotation effort . The transcriptome described here represents a significant increase in the amount of information on Rhodnius genome , with 2 , 475 near full-length coding sequences being deposited to GenBank . Several transcripts corresponding to functions that were expected— such as digestive enzymes and transporters—appeared in large numbers , and some findings have added new data that can help to understand aspects of the digestive physiology of this insect and its interaction with intestinal microbiota and trypanosomatids , as well as generate new working hypotheses for future research . The differential expression data here reported is based in a single sample comparison and further results using microarray or RNAseq data are required for their validation . | The bloodsucking bug Rhodnius prolixus is a vector of Chagas' disease , which affects 7–8 million people in Latin America . In contrast to other insects , the digestive tract of Rhodnius has three segments that perform different functions during blood digestion . Here we report analysis of transcriptomes for each of these segments using pyrosequencing technology amounting to several million sequences . Comparison of transcript frequency in digestive libraries with a whole-body library was used to evaluate expression levels , leading to the discovery of several families of enzymes associated with the digestion of proteins , carbohydrates , and lipids , as well as proteins involved in immunity , signal transduction , amino-acid metabolism , and detoxification . Together , our findings present a new view of the triatomine digestive apparatus and will help us understand the mechanism of blood digestion by Rhodnius and its interaction with the agent of Chagas' disease , Trypanosoma cruzi , a parasite that grows within the insect's digestive system . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] | [
"sequence",
"analysis",
"medicine",
"infectious",
"diseases",
"genomics",
"functional",
"genomics",
"chagas",
"disease",
"entomology",
"neglected",
"tropical",
"diseases",
"digestive",
"regulation",
"computational",
"biology",
"digestive",
"functions",
"biology",
"anatomy",... | 2014 | An Insight into the Transcriptome of the Digestive Tract of the Bloodsucking Bug, Rhodnius prolixus |
Reconstituted antiviral defense pathway in surrogate host yeast is used as an intracellular probe to further our understanding of virus-host interactions and the role of co-opted host factors in formation of membrane-bound viral replicase complexes in protection of the viral RNA against ribonucleases . The inhibitory effect of the RNA interference ( RNAi ) machinery of S . castellii , which only consists of the two-component DCR1 and AGO1 genes , was measured against tomato bushy stunt virus ( TBSV ) in wild type and mutant yeasts . We show that deletion of the co-opted ESCRT-I ( endosomal sorting complexes required for transport I ) or ESCRT-III factors makes TBSV replication more sensitive to the RNAi machinery in yeast . Moreover , the lack of these pro-viral cellular factors in cell-free extracts ( CFEs ) used for in vitro assembly of the TBSV replicase results in destruction of dsRNA replication intermediate by a ribonuclease at the 60 min time point when the CFE from wt yeast has provided protection for dsRNA . In addition , we demonstrate that co-opted oxysterol-binding proteins and membrane contact sites , which are involved in enrichment of sterols within the tombusvirus replication compartment , are required for protection of viral dsRNA . We also show that phosphatidylethanolamine level influences the formation of RNAi-resistant replication compartment . In the absence of peroxisomes in pex3Δ yeast , TBSV subverts the ER membranes , which provide as good protection for TBSV dsRNA against RNAi or ribonucleases as the peroxisomal membranes in wt yeast . Altogether , these results demonstrate that co-opted protein factors and usurped lipids are exploited by tombusviruses to build protective subcellular environment against the RNAi machinery and possibly other cellular ribonucleases .
One of the hallmark features of positive-strand ( + ) RNA viruses , including tomato bushy stunt virus ( TBSV ) , is to assemble numerous membrane-bound viral replicase complexes ( VRCs ) that leads to replication of the viral genomic RNA inside the infected cells . These viruses co-opt subcellular membranes and alter lipid metabolism in addition to usurping host proteins to form replication compartment or organelle . For several viruses , including TBSV , the extensive replication compartments contain many membranous vesicle-like structures , also called spherules , which are 50–100 nm invaginations with a narrow opening towards the cytosol . Other viruses have membranous , protrusion-type structures with single- or double-membrane structures formed via major membrane rearrangements [1–5] . Regardless of the structure of these replication organelles , it has been proposed that these elaborate membranous structures serve as platforms to assemble VRCs and to concentrate viral and host components for more efficient viral RNA synthesis . In addition , VRCs might also hide the viral RNAs from recognition by the antiviral surveillance system and protect against degradation by cytosolic ribonucleases . In case of plant and insect viruses , the viral RNA-triggered adaptive innate immune response , called RNAi or RNA silencing response limits viruses to replicate and spread in infected tissues [6–11] . RNAi also contributes to viral RNA recombination and defective viral RNA production [12 , 13] . The viral double-stranded ( ds ) RNA replication intermediate or long structured portions of ssRNAs are recognized by the core components of RNAi , which consist of the Dicer-like ( DCL ) ribonuclease and Argonaute ( AGO ) -like proteins with RNA slicing activities [7 , 14–17] . The Dicer-like nucleases process these RNAs into 21–24 nt dsRNAs , called small-interfering siRNAs , via their RNase III activities . Then , the siRNAs are incorporated into the RNA-induced silencing complex ( RISC ) that contains the AGO proteins . Then , the RISC recognizes the target ssRNAs , followed by slicing and destruction of the target RNA by the RNase H-like activity of AGO [7 , 8 , 14] . The core RNAi components and pathways are conserved from some fungi ( Neurospora and others ) and plants to invertebrates and mammals [7 , 10 , 18 , 19] . TBSV is a well-characterized plant ( + ) RNA virus with one 4 . 8 kilobase genomic ( g ) RNA , which codes for only five proteins , two of which are replication proteins , namely p33 and p92pol . The p92pol RNA-dependent RNA polymerase ( RdRp ) is expressed from the gRNA via a translational readthrough mechanism of the p33 stop codon [20–22] . The pre-readthrough protein p33 is an RNA chaperone , and a membrane-associated RNA-binding protein that functions as a master regulator of TBSV replication . Accordingly , p33 plays a role in every step of replication , including in viral RNA recruitment , VRC assembly and RNA synthesis [23–26] . Through its interactions with other p33 and p92 molecules , cellular lipids and 50–100 host proteins , p33 is involved in the formation of the replication compartment [24 , 27–30] . The tombusvirus VRC contains several host proteins [30–32] , including heat shock protein 70 ( Hsp70 ) , glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) , eukaryotic elongation factor 1A ( eEF1A ) , eEF1Bγ , DEAD-box RNA helicases , and the ESCRT ( endosomal sorting complexes required for transport ) family of host proteins [32–39] . These proteins are required for VRC assembly or affect viral RNA synthesis [27 , 34 , 37 , 40–42] . The TBSV replication process also depends on phospholipids , mainly phosphatidylethanolamine ( PE ) , and sterols , which are actively enriched within the viral replication compartment [43–47] . It is assumed that ( + ) RNA viruses avoid the powerful RNAi response in plants by forming VRCs and replication compartments hidden away from RNAi and also via expression of suppressors of RNAi , which are part of counter defense strategies against RNAi [9 , 48 , 49] . Accordingly , the dsRNA replication intermediate formed during TBSV replication [50] , is part of the membrane-bound VRCs . A long-standing unanswered question is that which co-opted host components are involved in VRC assembly that protect ( + ) RNA viruses during replication from the potent RNAi response . The protection provided by VRCs seems significant , because plant ( + ) RNA viruses could replicate and accumulate even in the absence of RNAi suppressors [51 , 52] . Moreover , the Dicer-like enzymes could not completely degrade the viral dsRNA in infected cells , indicating that the viral dsRNA enjoys significant protection during replication . To answer this fundamental question on the putative role of the co-opted host factors in protecting the viral dsRNA during replication , we took advantage of yeast ( Saccharomyces cerevisiae ) , which lacks the RNAi machinery , as a surrogate host for TBSV . Co-expression of a TBSV replicon ( rep ) RNA with p33 and p92pol replication proteins leads to robust TBSV replication in yeast [53 , 54] , helping the dissection of the roles of subverted host factors in virus replication and virus–host interactions [27 , 28 , 55] . We have used the reconstituted RNAi machinery from S . castellii , which consists of the two-component DCR1 and AGO1 genes [56] , as a simple , easily tractable system to study the effect of RNAi on TBSV in yeast . Based on this surrogate host system , we show evidence that deletion of a selected group of host factors , namely ESCRT proteins , and alteration of lipid levels , including PE and sterols , greatly affect TBSV accumulation when RNAi activity is induced . Based on our results , we propose that the co-opted host factors are critical for TBSV to assemble membranous VRCs that protect against RNAi activity .
Many co-opted host components are involved in tombusvirus VRC assembly [27 , 28 , 55] and they likely protect TBSV RNA from nucleases and antiviral responses during replication . To test this model , we adapted the simple RNAi machinery from S . castellii [56] as an intracellular probe to measure if a given co-opted host factor contributes to the protection of the viral RNA . Briefly , constitutive co-expression of S . castellii DCR1 and AGO1 from TEF1 promoter led to complete inhibition of TBSV repRNA accumulation in wt yeast ( S1A Fig ) , whereas separate expression of DCR1 and AGO1 did not interfere with TBSV repRNA accumulation , suggesting that co-expression of the two components is required for the RNAi machinery . A more suitable strategy for this research was based on the inducible co-expression of S . castellii DCR1 and AGO1 from GAL1 promoter from plasmids , which could be suppressed by the addition of glucose and induced by the addition of galactose to the culture media ( Fig 1A , lanes 5–6 ) . In this system , after the induction of the expression of both DCR1 and AGO1 , the induced RNAi pathway moderately inhibited viral RNA accumulation in wt yeast ( Fig 1A , lanes 1–6 ) , likely due to the protection provided by the membranous VRCs . The accumulation of the expected 23 bp vsiRNA demonstrates the operation of the RNAi machinery in this yeast strains expressing either DCR1 alone or co-expressing DCR1 and AGO1 ( S1B and S1C Fig ) . TBSV recruits the cellular ESCRT machinery to deform membranes and build spherules containing VRCs within the replication compartment [57 , 58] . First , the Vps23p ESCRT-I or Bro1p ESCRT accessory protein are recruited via direct binding to p33 replication proteins , followed by recruitment of the ESCRT-III proteins [38 , 58 , 59] . Then , Vps4p AAA ATPase is bound by p33 , which likely stabilizes the neck structure of the spherule , to prevent scission and closure of the neck [57] . To test if cellular ESCRT factors required for TBSV-induced spherule formation are important for protecting the viral RNAs from RNAi-based degradation , we launched TBSV replication in vps23Δbro1Δ yeast [58] . Since the co-opted ESCRT factors affect the absolute level of TBSV replication in yeast [38 , 58 , 59] , we calculated the extent of reduction in TBSV RNA protection level in the presence of the RNAi machinery based on the repRNA accumulation in the corresponding yeast strain control , not expressing the RNAi machinery ( 100% ) . We found that TBSV repRNA accumulation was inhibited by the RNAi machinery by almost three-times more efficiently in vps23Δbro1Δ yeast when compared with wt yeast expressing the full-set of the ESCRT components ( Fig 1A , lanes 11–12 versus 5–6 ) . Single expression of DCR1 had only small effect on TBSV RNA accumulation in both yeast strains ( Fig 1A ) . The expressions of both DCR1 and AGO1 were comparable in vps23Δbro1Δ and wt yeasts ( Fig 1A ) . Moreover , the expression of the tombusvirus p33 and p92pol replication proteins in vps23Δbro1Δ or wt yeasts was not affected by the co-expression of DCR1 and AGO1 ( Fig 1A , bottom images ) , suggesting that enhanced susceptibility of tombusviral RNA in vps23Δbro1Δ yeast was likely due to the increased antiviral effect of the reconstituted S . castellii RNAi machinery on the viral RNAs . Detection of vsiRNA ( + ) abundance revealed reduced level in vps23Δbro1Δ yeast in comparison with the wt yeast expressing DCR1 ( Fig 1B ) . However , the reduction in vsiRNA ( + ) abundance is likely due to the reduced target viral RNA level in vps23Δbro1Δ yeast , which supports only ~20% TBSV repRNA level in comparison with the wt yeast ( lacking the RNAi machinery ) [58] . Indeed , comparison of vsiRNA ( - ) level ( generated by DCR1 from dsRNA replication intermediate ) revealed that vps23Δbro1Δ yeast generated almost as much vsiRNA ( - ) as the wt yeast did ( Fig 1D ) , whereas the repRNA ( - ) level ( representing the dsRNA replication intermediate of the repRNA ) in vps23Δbro1Δ yeast was half of the level of repRNA ( - ) detected in wt yeast ( Fig 1C ) . To examine if the membranous VRCs in vps23Δbro1Δ yeast indeed provide less protection to the viral RNAs , we used cell-free extract ( CFE ) -based assay in the presence or absence of ribonucleases ( the single-stranded ssRNA-specific RNase A and the dsRNA-specific RNase III ) . The TBSV dsRNA was ~2-fold more sensitive to RNases when CFE was prepared from vps23Δbro1Δ yeast in comparison with the CFE from wt yeast ( Fig 1E ) . The TBSV ssRNAs , which are continuously released from VRCs as replication goes on , were fully degraded in both CFE assays . In a second assay to test the level of protection provided by VRCs , we performed in vitro replicase assembly with purified recombinant viral proteins and ( + ) repRNA transcripts as schematically shown in Fig 1F , followed by viral RNA replication in the presence of micrococcal nuclease ( MNase ) to destroy the unprotected viral RNAs . The MNase was added at different time points ( as shown ) for 20 min and then it was inactivated by EGTA , followed by TBSV repRNA replication on the protected TBSV repRNAs up to 3 hours ( Fig 1F ) . When CFE was prepared from wt yeast , then the VRC partially protected the viral dsRNA [produced by minus-strand synthesis on the ( + ) RNA template] after 40 min , whereas the protection of viral dsRNA was high after 60 min of incubation ( Fig 1G , lanes 11 and 12 versus 9 ) . In contrast , the in vitro assembled VRC based on CFE prepared from vps23Δbro1Δ yeast did not provide any detectable level of protection after 60 min of incubation . We also tested the VRCs assembled in CFEs prepared from vps23Δ or bro1Δ yeasts . These MNase protection experiments revealed poor dsRNA protection in CFEs from vps23Δ or bro1Δ yeasts at both 40 and 60 min time points ( Fig 1G ) . However , the protection of TBSV dsRNA in CFEs from vps23Δ or bro1Δ yeasts were more significant at the 60 min time point than the lack of dsRNA protection provided in CFE prepared from vps23Δbro1Δ yeast ( compare lanes 4 and 8 with 16 , Fig 1G ) . These data suggest that due to the partially overlapping roles of Vps23p and Bro1p in supporting the formation of VRCs [58] , the CFEs prepared from single deletion yeast strains provided better dsRNA protection in vitro than the CFE from double-deletion yeast strain . Altogether , the results from two separate in vitro replication assays with CFEs prepared from vps23Δbro1Δ yeast showed that the TBSV dsRNA is not well protected from ribonucleases even after the VRC assembly step , thus indicating that the dsRNA inside the VRCs in the absence of Vps23p and Bro1p is continuously exposed to the RNAi machinery or ribonucleases , likely due to incomplete VRC assembly . Formation of complete vesicle-like structures induced by TBSV in yeast and plants also requires ESCRT-III factors [58] . In the absence of ESCRT-III factors , crescent-like membrane invaginations are formed in yeast replicating TBSV repRNA [58] . To test if Snf7p and Vps20p ESCRT-III factors are important for protecting the viral dsRNA from RNAi-based degradation , we launched TBSV replication either in snf7Δ or vps20Δ yeasts . We found that TBSV repRNA accumulation was reduced by ~2-fold more in both snf7Δ and vps20Δ yeasts when AGO1 and DCR1 were co-expressed in comparison with the wt yeast ( Fig 2A ) . Similarly , the dsRNA replication intermediate was protected from MNase treatment by 2-to-4-fold less effectively by the in vitro assembled TBSV replicases prepared with CFEs from snf7Δ and vps20Δ yeasts than by the CFE from WT yeast ( Fig 2B ) . The CFE-based replication assay using the double-deletion strain ( snf7Δvps20Δ yeast ) revealed that the level of dsRNA protection was comparable to that provided by the CFE from single deletion strain ( snf7Δ yeast ) against MNase treatment ( Fig 2B ) , suggesting that the ESCRT-III factors do not have complementary roles in protecting the viral RNA from RNAi activities . Altogether , the results from the in vitro replication assay with CFE prepared from single deletion or snf7Δvps20Δ yeasts indicated that the dsRNA inside the VRCs in the absence of Snf7p or Vps20p ESCRT-III factors is continuously exposed to the RNAi machinery or ribonucleases , likely due to incomplete VRC assembly . Formation of tombusvirus VRCs is greatly affected by sterols in vitro , in yeast and plants [45 , 47] . The co-opted sterols , which are enriched within the replication compartment [45] , likely enhance the stability of vesicle-like structures and facilitate tighter packing of phospholipids in the membranes used by TBSV for VRC assembly . These features of sterols might also contribute to the protection of viral dsRNA replication intermediate provided by the membranous VRCs against cellular nucleases . This theory was first tested using a yeast strain deficient in ergosterol ( yeast version of cholesterol ) biosynthesis due to deletion of C-24 sterol reductase ( erg4Δ yeast ) [47 , 60] . Induction of the RNAi machinery in erg4Δ yeast inhibited TBSV repRNA accumulation by ~2-fold more efficiently than in wt yeast ( Fig 3A and 3B ) . Detection of vsiRNA ( + ) abundance revealed reduced level in erg4Δ yeast in comparison with the wt yeast expressing DCR1 ( Fig 3C ) . However , the reduction in vsiRNA ( + ) abundance is likely due to the reduced target viral RNA level in erg4Δ yeast , which supports only ~20% TBSV repRNA level in comparison with the wt yeast ( lacking the RNAi machinery ) [60] . Therefore , we also measured vsiRNA ( - ) level ( generated by DCR1 from dsRNA replication intermediate ) , which showed only slight reduction in erg4Δ yeast in comparison with vsiRNA ( - ) level in wt yeast ( Fig 3E ) . However , the repRNA ( - ) level ( representing the dsRNA replication intermediate of the repRNA ) in erg4Δ yeast was only ~20% of the level of repRNA ( - ) detected in wt yeast ( Fig 3D ) , which is comparable to the reduction in repRNA ( + ) level in erg4Δ versus wt yeasts [60] . Thus , the vsiRNA ( - ) was generated from 5-fold less dsRNA templates by DCR1 in erg4Δ yeast than in wt yeast , suggesting that DCR1 could produce vsiRNA ( - ) in ~3-fold higher ratio in erg4Δ yeast than in wt yeast . Moreover , the CFE-based TBSV replication assay showed poor protection of TBSV dsRNA against the dsRNA-specific RNase III within VRCs assembled in erg4Δ yeast in comparison with wt yeast ( Fig 3F ) . This suggests that the dsRNA within the VRCs formed in sterol-depleted cells is continuously exposed to RNAi/ribonucleases . To further confirm the critical role of the co-opted sterols in the formation of more ribonuclease resistant VRCs , we also studied the effect of co-opted cellular proteins involved in membrane-contact site ( MCS ) formation [45] . The tombusvirus-induced MCSs are formed between the ER and peroxisome membranes , when the two organellar membranes are juxtaposed , with the help of co-opted ER-resident VAP proteins ( Scs2 in yeast , see below ) and the oxysterol-binding proteins ( OSBP-related protein or ORP ) . The tombusvirus-induced MCSs help the enrichment of sterols within the replication compartment . First , we targeted four members of the oxysterol-binding proteins , namely Osh3 , 5 , 6 and 7 , which are recruited by TBSV via interaction with p33 replication proteins to the site of replication . These ORP/Osh proteins are involved in the formation of MCSs and enrichment of sterols within the replication compartment [45] . Induction of AGO1 and DCR1 in osh3 , 5 , 6 , 7Δ yeast inhibited TBSV repRNA accumulation by ~2-fold more efficiently than in wt yeast ( Fig 4A and 4B ) . In addition , the CFE-based in vitro replication assay revealed that the VRCs assembled in osh3 , 5 , 6 , 7Δ yeast could not protect efficiently the TBSV dsRNA intermediate against RNase III ( Fig 4C ) . Second , we tested the ER-resident Scs2p VAP , which is also recruited by TBSV via interaction with p33 replication proteins to help the formation of MCSs and enrichment of sterols within the replication compartment [45 , 61] . Induction of AGO1 and DCR1 in scs2Δ yeast led to ~3-fold decreased TBSV repRNA accumulation than in the absence of RNAi , while the RNAi was less efficient in the parental yeast strain carrying wt copy of SCS2 ( S2 Fig ) . The VRCs assembled in scs2Δ yeast provided negligible level of protection against RNase III in the in vitro replication assay ( Fig 3F and S2C Fig ) . Overall , reduction of sterol biosynthesis ( in erg4Δ yeast ) or inhibition of virus-induced MCS formation ( in osh3 , 5 , 6 , 7Δ or scs2Δ yeasts ) that hinders the local sterol enrichment at replication sites greatly inhibited the assembly of RNase-resistant VRCs in yeast or in vitro . Thus , co-opted sterols play important roles in formation of RNAi-resistant replication compartment . Tombusvirus replication greatly depends on phospholipid levels , especially on phosphatidylethanolamine ( PE ) , which is highly enriched within the replication compartment [43] . PE is required for TBSV replication in an artificial vesicle- ( liposome ) -based in vitro assay [43] and it enhances the activation of the p92 RdRp in vitro [62] . Moreover , the PE level is increased during TBSV replication in yeast and plant cells and high PE level in yeast via modulation of phospholipid biogenesis genes also leads to enhanced TBSV replication [43 , 44] . To test the role of PE-rich membranes in the protection of the viral dsRNA from RNAi , we induced the RNAi machinery in cho2Δ yeast , which is partially defective in converting PE to PC , thus leading to elevated PE level and enhanced TBSV replication [43] . Since the VRCs formed in wt yeast could provide good level of protection against RNAi under our standard conditions ( Figs 1–4 ) , we applied constitutive expression of the RNAi machinery ( AGO1 and DCR1 ) that could enhance the effectiveness of RNAi in these experiments . This is achieved by culturing yeast in media supplemented with galactose all the time ( resulting in constitutive expression of both DCR1 and AGO1 ) . Under these conditions , the VRCs assembled in wt yeast provided less protection ( Fig 5A , lanes 4–6 ) . On the contrary , the level of TBSV dsRNA protection was ~5-fold higher in cho2Δ yeast expressing AGO1 and DCR1 ( Fig 5B , lanes 4–6 ) , suggesting that increased PE levels provide more protective subcellular environment for VRC assembly . To further test if the viral RNA is less accessible in cho2Δ yeast to the RNAi machinery than in wt yeast , we utilized an in vitro slicing assay based on purification of FLAG-tagged AGO1 from cho2Δ and wt yeasts replicating TBSV repRNA and co-expressing DCR1 and FLAG-AGO1 . In these yeasts , AGO1 is expected to be loaded with vsiRNA , which then could activate AGO1 endonuclease activity specifically against TBSV RNA target in vitro ( Fig 5C ) . By using comparable amounts of purified FLAG-AGO1 preloaded with vsiRNA , we tested the slicing activity on 32P-labeled TBSV RNA in vitro . The purified FLAG-AGO1 from wt yeast replicating the TBSV repRNA showed slicing activity against 32P-labeled TBSV gRNA ( Fig 5D , lane 1 ) , whereas the purified FLAG-AGO1 from wt yeast replicating the unrelated Flock House virus RNA1 did not have a slicing activity against 32P-labeled TBSV gRNA ( Fig 5D , lane 3 ) , thus confirming the presence of bona fide RNAi machinery in wt yeast co-expressing AGO1 and DCR1 . On the contrary , the purified FLAG-AGO1 from cho2Δ yeast replicating TBSV repRNA showed negligible slicing activity against 32P-labeled TBSV gRNA ( Fig 5D , lane 4 ) , suggesting that either DCR1 or AGO1 had a limited access to the TBSV replication compartment . To define how increased PE level provides better protection against ribonucleases , we performed CFE-based replication/protection assay using purified recombinant p33/p92 for de novo assembly of VRCs in CFEs prepared from cho2Δ versus wt yeasts ( Fig 5E ) . This assay can test the speed of VRC assembly in vitro , based on measurement of the level of dsRNA protection provided by VRCs against MNase , which was added at various time points ( Fig 5E ) . When CFE was prepared from wt yeast , then the in vitro assembled VRCs partially protected the viral dsRNA after 45 min , and ~60% after 60 min of incubation ( Fig 5E , lanes 2 and 3 versus 1 ) . On the other hand , CFE prepared from cho2Δ yeast showed some dsRNA protection as early as 30 min and high level of protection by 45 min and complete protection by 60 min of incubation prior to MNase treatment ( Fig 5E , lanes 7–9 versus 6 ) . Thus , we suggest that accelerated VRC assembly due to high PE level in cho2Δ yeast might decrease the time available for ribonucleases to associate with VRCs during their assembly process . The overall phospholipid content in yeast could be increased by deleting OPI1 , which is a repressor of expression of many phospholipid synthesis genes [63] . The higher level of phospholipids favors TBSV repRNA accumulation by providing easy access to membranes for VRC assembly [46 , 64] . TBSV repRNA accumulation reached ~2-fold higher level in opi1Δ yeast than in WT yeast expressing AGO1 and DCR1 constitutively ( Fig 6B , lanes 4–6 ) , suggesting that increased phospholipid levels facilitate VRC assembly in a more protective subcellular environment . The CFE prepared from opi1Δ yeast supported VRC assembly that provided high level of dsRNA protection by 45 min and complete protection by 60 min of incubation prior to MNase treatment ( Fig 6C and 6D , lanes 6–7 versus 10 ) . Whereas , CFE prepared from wt yeast supported the in vitro VRC assembly at a slower pace as the viral dsRNA was only partially protected after 45 min , and at a ~60% level after 60 min of incubation ( Fig 6D , lanes 1 and 2 versus 5 ) . These data suggest that the VRC assembly was enhanced due to increased level of phospholipids in opi1Δ yeast , possibly leading to reduced time available for ribonucleases to associate with VRCs during their assembly process . On the contrary , decreasing cellular phospholipid levels via deletion of INO2 transcription factor required for expression of many phospholipid synthesis genes [63] , resulted in ~3-fold more reduction in repRNA level when AGO1 and DCR1 were expressed in ino2Δ yeast in comparison with wt yeast ( supplement S3 Fig ) . In addition , the CFE preparation obtained from ino2Δ yeast provided poor dsRNA protection in vitro against RNase III ( S3 Fig , panel C , lanes 7–8 versus 5–6 ) . Thus , these data indicate that inhibition of phospholipid synthesis likely reduces the formation of RNAi-resistant replication compartment . One of the remarkable features of TBSV replication is that it can switch to the ER membranes when the peroxisomes are absent in yeast due to deletion of either PEX3 or PEX19 peroxisome membrane biogenesis genes [65] . To test if the VRCs formed by usurping ER membranes are RNAi insensitive , we co-expressed AGO1 and DCR1 in pex3Δ and wt yeasts , respectively , replicating TBSV repRNA . The accumulation of repRNA was comparable in these yeasts ( Fig 7A ) . In addition , the CFE preparation obtained from pex3Δ yeast provided comparable level of dsRNA protection against RNase III to the CFE prepared from wt yeast ( Fig 7B , lanes 3 versus 7 ) . In the second assay to test the level of dsRNA protection provided by VRCs formed using the ER membranes , we performed in vitro replication using CFEs prepared from wt and pex3Δ yeast with pre-expressed viral proteins ( Fig 7C ) . These CFEs were programmed with ( + ) repRNA transcripts , followed by viral RNA replication in the presence of MNase to destroy the unprotected viral RNAs . The MNase was added at different time points ( as shown ) for 15 min and then the MNase was inactivated by EGTA , followed by TBSV repRNA replication based on the protected TBSV repRNAs up to 3 hours ( Fig 7C ) . The level of protection of the viral dsRNA provided by the VRCs was comparable after 60 min of incubation in wt and pex3Δ CFEs ( Fig 7C , lanes 5 versus 10 ) . Also , the kinetics of VRC assembly , based on the level of protection of the viral dsRNA provided by the VRCs , was comparable at four different time points in wt and pex3Δ CFEs ( Fig 7C ) . To further confirm these findings , we performed a third assay , in which the CFEs prepared from wt and pex3Δ yeasts ( lacking viral components ) were programmed with purified recombinant p33 and p92 replication proteins and ( + ) repRNA transcripts as depicted in Fig 7D . The level of protection of the viral dsRNA provided by the VRCs against MNase treatment was comparable in all four time points tested in wt and pex3Δ CFEs ( Fig 7D ) . These results indicate that the subverted ER membranes in pex3Δ could provide as good protection for TBSV dsRNA against RNAi or ribonucleases as the peroxisomal membranes in wt yeast .
In this paper , we have used a reconstituted antiviral defense pathway in a model host system to further our understanding of virus-host interactions in general , and specifically the role of the membranous VRC in protection of the viral dsRNA replication intermediates against ribonucleases . This is based on the RNAi machinery of S . castellii , which only consists of the two-component DCR1 and AGO1 genes [56] . This simple RNAi machinery is known to be effective against the yeast L-A dsRNA virus and its satellite RNA in yeast [66] , and TBSV ( this work ) . Similar to the RNAi machinery of higher eukaryotes [9] , the reconstituted RNAi machinery of S . castellii also requires both DCR1 and AGO1 proteins to be effective against TBSV . DCR1 produces 23 bp vsiRNA [66] , as we also observed in case of TBSV . We found that this reconstituted antiviral defense pathway in surrogate host yeast is useful as an intracellular probe to further our understanding of virus-host interactions and the role of co-opted host factors in formation of membrane-bound viral replicase complexes in protection of the viral RNA against ribonucleases . We have obtained evidence that a group of pro-viral cellular factors involved in VRC formation is essential for the generation of the protective membranous subcellular environment for TBSV in cells . Previous works with TBSV have shown that tombusviruses co-opt several host factors to build replication compartment required for replication . The replication compartment consists of mostly peroxisomal membranes and includes many virus-induced spherules , which harbor the VRCs [67] . Spherule formation requires the viral replication proteins , the viral ( + ) RNA and co-opted ESCRT proteins [57 , 58] that are involved in bending the boundary membranes of peroxisomes or ER membranes towards the organellar lumen [67] . While spherule formation is inhibited when Vps23 ESCRT-I component is missing in yeast , incomplete spherule-like structures with wide openings are formed when ESCRT-III or Vps4 AAA ATPase are deleted in yeast [57 , 58] . One major proposed function of spherule formation is the protection of the viral dsRNA replication intermediate formed during TBSV replication ( Fig 8A ) [50] . How much extent is the viral dsRNA , which is present within the membrane-bound VRC , accessible to DCR1 and the RNAi machinery ? We found that in comparison with wt yeast expressing the full set of ESCRT factors , deletions of Vps23 ESCRT-I and Bro1 accessory ESCRT protein , which play partial overlapping roles in TBSV replication [58] , have rendered the viral dsRNA replication intermediate highly sensitive to the RNAi machinery in yeast and to nucleases in vitro . Similarly , deletions of SNF7 or VPS20 ESCRT-III factors made TBSV replication more sensitive to AGO1 and DCR1 expression in yeast . Moreover , the lack of Snf7p or Vps20p in combination with Snf7p in CFEs used for assembly of the TBSV replicase in vitro resulted in destruction of dsRNA replication intermediate by MNase at the 60 min time point when the CFE from wt yeast has provided good protection for dsRNA ( Fig 2 ) . Altogether , these results unambiguously demonstrate that co-opted protein factors , namely the ESCRT factors , are exploited by tombusviruses not only for pro-viral functions to facilitate replication [38 , 57 , 58 , 68] , but the ESCRT factors are also recruited to build protective subcellular environment against the RNAi machinery and other ribonucleases . Without the co-opted ESCRT factors , the tombusvirus VRCs seem to have permanent assembly deficiency , rendering the dsRNAs harbored within VRCs continuously exposed to the RNAi machinery , other host ribonucleases , and possibly cellular dsRNA sensors ( Fig 8B ) . The TBSV-induced spherules harboring VRCs are relatively stable membranous structures that likely synthesize viral progeny RNAs in cells for several hours , which occurs even in vitro when membranous VRCs are isolated [69] . However , the ESCRT proteins are known to act rapidly and temporarily in membrane budding events [70 , 71] . Therefore , TBSV likely involves additional cellular components to stabilize the vesicle-like structures in infected cells . Co-opted lipids are likely candidates for this function due to their known involvement in shaping membrane characteristics [72–74] . Accordingly , we have tested the role of sterols and phospholipids , which are critical for TBSV replication [43–47] , in providing protection of viral RNA against RNAi . Indeed , deletion of ORPs or Scs2p VAP , which are known to affect MCS formation and co-opted by tombusviruses to enrich sterols at replication sites [45] , resulted in reduced protection of the viral RNAs against RNAi in yeast or ribonucleases in vitro . Similarly , deletion of ERG4 involved in ergosterol synthesis ( the major cholesterol-like lipid in yeast ) also sensitized TBSV against RNAi . Based on these data , we suggest that sterols likely facilitate the formation of more stable and durable spherules/VRCs . Similar to the co-opted ESCRT factors , enrichment of sterols in the replication compartment , and likely within individual spherules , seems to be required to assemble tombusvirus VRCs that are not continuously exposed to the RNAi machinery ( Fig 8C ) . Interestingly , sterols are also thought to make the plasma membrane less permeable and wider [73] . In addition to sterols , we find PE and phospholipid levels also have a critical role in the formation of RNAi-insensitive replication compartment . Accordingly , the high PE level in cho2Δ yeast made the dsRNA replication intermediate less sensitive to RNAi in yeast and ribonuclease in vitro , suggesting more rapid and efficient VRC assembly when PE is abundant in membranes . The subverted PE molecules , due to their conical molecular structures , might facilitate the formation and stability of spherule structures by introducing negative curvature into lipid bilayers [73] . This model is further supported by an in vitro slicing assay , which demonstrated that purified FLAG-AGO1 from wt yeast showed slicing activity against TBSV RNA , whereas the purified FLAG-AGO1 from cho2Δ yeast showed negligible slicing activity , suggesting that DCR1 might have a limited access to TBSV dsRNA intermediates in cho2Δ yeast with high PE level . Based on in vitro observations , we suggest that nucleases , including DCR1 and AGO1 , can likely enter the VRC during the assembly process that makes the dsRNA sensitive to RNAi . Thus , rapid VRC assembly due to high PE level in cho2Δ yeast or high level of phospholipids in opi1Δ yeast decreases the time available for ribonucleases to associate with VRCs during their assembly process , thus leading to the enhanced protection of TBSV against RNAi in cho2Δ and opi1Δ yeasts . While in wt yeast the TBSV-induced local PE enrichment within the replication compartment takes up more time than the already PE-rich membranes in cho2Δ yeast . Thus the speed or efficiency of VRC assembly could be a key factor affecting the chance for the RNAi machinery to interact with viral RNAs harbored within VRCs . The slower pace of VRC assembly process ( Fig 8D ) observed in wt versus cho2Δ or opi1Δ yeast CFEs ( which could facilitate the entry of DCR1 and AGO1 into the forming VRCs ) might also explain that continuous expression of DCR1 and AGO1 more efficiently inhibited TBSV repRNA accumulation in wt yeast than suppression of DCR1 and AGO1 expression during pre-growth ( i . e . , prior to induction of viral replication ) in wt yeast ( compare Figs 1 and 2A versus Figs 5 and 6A and S1A ) . The somewhat variable level of reduction in repRNA accumulation by the RNAi machinery in wt yeast background is likely due to differences among yeast colonies in their abilities to induce the expression of AGO1 and DCR1 from the pre-repressed GAL1 promoter after the addition of galactose to the culture media . The development of the reconstituted RNAi as a cellular probe also allowed us to demonstrate that the tombusvirus VRCs act as protective structures when assembled in the ER membranes ( in the absence of peroxisomal membranes ) similar to those assembled in the peroxisomal membranes . This observation indicates that TBSV could usurp the ER membrane and efficiently co-opt pro-viral host proteins and enrich lipids in this new subcellular location , giving high flexibility for this virus without sacrificing the protective quality and functionality of VRCs formed . Overall , the reconstituted RNAi machinery of S . castellii in yeast , which supports the replication of TBSV RNA in membranous compartments , is useful intracellular probe to study the direct interaction between the RNAi machinery and the viral replicase complex , and the roles of subverted host factors in protecting the viral dsRNA replication intermediate from RNAi-based degradation . The interpretation of data in this system is simplified since S . cerevisiae does not code for an RNA-dependent RNA polymerase , which is important component of the RNAi machinery in plants and animals by producing dsRNA templates for amplifying the silencing signals [7 , 9] . Nevertheless , this work has demonstrated the role of co-opted cellular proteins and lipids in generation of membranous subcellular environment protected from RNAi to support TBSV replication . Other ( + ) RNA viruses also co-opt cellular proteins , including ESCRT factors , and subvert lipids for generation of membranous replication organelles [1 , 5 , 75–80] , thus , it seems highly likely that our findings will also be applicable for wide-range of viruses . Summary: By using the reconstituted RNAi in yeast , we have developed a simple intracellular probe to characterize membranous VRCs and viral replication compartments formed with the help of co-opted host factors in cells replicating TBSV . Moreover , we have compared the cellular data with in vitro replication results to gain deeper insights into the level of protection provided by the membranous VRCs against ribonucleases . These approaches have helped us uncover that the RNAi machinery and ribonucleases could harm viruses the most efficiently when one of two aspects of VRC assembly goes wrong . First , when the VRC assembly is permanently hindered by a missing co-opted host factor ( such as the ESCRT proteins ) or in the absence of local sterol enrichment in the replication compartment , then the dsRNA within the VRC is continuously exposed to RNAi or ribonucleases . Second , when the VRC assembly is slow due to a limiting host factor ( such as PE accessibility ) , which possibly allows the components of RNAi machinery or ribonucleases to enter the VRCs prior to the completion of VRC assembly . This deficiency in excluding ribonucleases from VRCs due to slow assembly then leads to lower level of viral RNA accumulation .
Yeast ( Saccharomyces cerevisiae ) strain BY4741 ( MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) and the YKO library were obtained from Open Biosystems ( Huntsville , AL , USA ) . Double deletion yeast strains ΔBro1::kanMX4 , ΔVps23::hphNT1 and ΔVps20::kanMX4 , ΔSnf7::hphNT1 were described previously [58] . SEY6210 ( MATa ura3-52 his3Δ200 lys2-801 leu2-3 , trp1Δ901 suc2Δ9 ) , and JRY6266-his3 ( SEY6210 osh3Δ::LYS2 osh5Δ::LEU2 osh6Δ::LEU2 osh7Δ::kan-MX4 ) were provided by Dr . Christopher T . Beh ( Simon Fraser University ) [81] . Yeast expression plasmids pESC-Ura-Gal10-HisDcr1 and pESC-Ura-Gal1-HisAgo1-Gal10-HisDcr1 were obtained as follows: First , DCR1 and AGO1 genes from S . castellii were PCR-amplified using primers #6015 ( CCAGGAATTCATGGGTCATCATCATCATCATCATATGAATAGAGAAAAAAGCGCCGATC ) , #6016 ( CCAGACTAGTTCACAGATTGTTGCAATGCCTC ) and pRS315-Dcr1 [56] as a template for DCR1 and primers #6013 ( CCAGGTCGACATGGGTCATCATCATCATCATCATATGTCATCCAATTCGGAGGAG ) , #6014 ( CCAGAAGCTTTCATATGTAGTACATGATGTCAGTG ) and pRS314-Ago1 [56] as a template for AGO1 . The obtained PCR product of DCR1 was digested with EcoRI/SpeI and inserted into pESC-Ura plasmid ( Stratagene ) , which was digested with EcoRI and SpeI to generate pESC-Ura-Gal10-HisDcr1 . The PCR product of AGO1 was digested with SalI/HindIII and inserted into pESC-Ura-Gal10-HisDcr1 , which was digested with SalI and HindIII . To study the effect of DCR1 and AGO1 co-expression on TBSV replication in yeast strains with ESCRT gene deletions , we co-transformed yeast strains BY4741 ( parental , control ) , vps20Δ snf7Δ , vps20Δ and bro1Δvps23Δ with three plasmids: pGBK-HIS-Cup-Flag33-Gal-DI-72 , pGAD-Cup-Flag92 and one of the following: pESC-Ura ( used as a control ) , pESC-Ura-Gal10-HisDcr1 or pESC-Ura-Gal1-HisAgo1-Gal10-HisDcr1 . Transformed yeast cells were selected on SC-ULH- plates and pre-grown in 1 ml SC-ULH- media supplemented with 2% glucose and 100 μM BCS for 24 h at 29°C . Yeast cells were then centrifuged at 2 , 000 rpm for 3 min , washed with SC-ULH- media supplemented with 2% galactose and resuspended in 2 ml SC-ULH- media with 2% galactose and 100 μM BCS followed by culturing at 29°C for 24 h to express AGO1 and DCR1 prior to initiation of replication . Then , yeast cells were centrifuged at 2 , 000 rpm for 3 min , washed with SC-ULH- media supplemented with 2% galactose and resuspended in 3 ml SC-ULH- media with 2% galactose and 50 μM CuSO4 , followed by culturing yeast cells at 23°C for 16 h ( Fig 2 ) or 24 h ( Fig 1 ) , and then processed for total RNA and protein extractions . Northern blotting and Western blotting were done as previously published [53] . Yeast strains BY4741 , ino2Δ , scs2Δ , and erg4Δ were transformed with plasmids pGAD-CUP1-His-p92 ( Leu2 selection ) , pGBK-CUP1-His-p33-ADH1-DI72 ( His3 selection ) , and pESC-Ura-Gal1-HisAgo1-Gal10-HisDcr1 ( Ura3 selection ) , pESC-Ura-Gal10-HisDcr1 or pESC empty . Transformed yeast were pre-grown at 23°C for 24 h in SC-ULH- media supplemented with 2% glucose and 100 μM BCS . After cell harvest and a washing step , the yeasts were grown at 23°C for 16 h ( for 8 h in case of pex3Δ yeast ) , in SC-ULH- media supplemented with 2% galactose and 100 μM BCS . Then , yeast cells were centrifuged at 2 , 000 rpm for 3 min , washed with SC-ULH- media supplemented with 2% galactose and resuspended in SC-ULH- media with 2% galactose and 50 μM CuSO4 , followed by culturing yeast cells at 23°C for 16 or 24 h , followed by RNA and protein analysis as described [53] . BY4741 , cho2Δ and opi1Δ yeasts were transformed with plasmids pGAD-CUP1-His-p92 ( Leu2 selection ) , pGBK-CUP1-His-p33-Gal1-DI72 ( His3 selection ) , and pESC-Ura-Gal1-HisAgo1-Gal10-HisDcr1 ( Ura3 selection ) , pESC-Ura-Gal10-HisDcr1 or pESC-empty . Transformed yeasts were pre-grown at 23°C for 24 h in SC-ULH- media supplemented with 2% galactose and 100 μM BCS . Then , yeast cells were centrifuged at 2 , 000 rpm for 3 min , washed with SC-ULH- media supplemented with 2% galactose and resuspended in SC-ULH- media with 2% galactose and 50 μM CuSO4 , followed by culturing yeast cells at 23°C for 16 h , followed by RNA and protein analysis as described [53] . SEY6210 and JRY6266-his3 yeast strains were transformed with plasmids pGAD-CUP1-His-p92 ( Trp1 selection ) , pGBK-CUP1-His-p33-Gal1-DI72 ( His3 selection ) , and pESC-Ura-Gal1-HisAgo1-Gal10-HisDcr1 ( Ura3 selection ) , pESC-Ura-Gal10-HisDcr1 or pESC-empty . Transformed yeasts were pre-grown at 29°C overnight in SC-UTH- media supplemented with 2% galactose and 100 μM BCS . After cell harvest and a washing step , the yeasts were grown at 29°C for 16 h in SC-UTH- media with 2% galactose and 50 μM CuSO4 , followed by RNA and protein analysis as described [53] . To support in vitro TBSV replication , cell-free extracts ( CFE ) were prepared from untransformed BY4741 , snf7Δ , vps20Δ , snf7Δvps20Δ , bro1Δ , vps23Δ and bro1Δvps23Δ yeast strains as described earlier [42] , whereas CFEs were obtained from cho2Δ , opi1Δ , and BY4741 ( control ) as described in [43] . Reaction mixture for the in vitro TBSV replication contained 2 μl of CFE , 0 . 15 μg ( + ) DI-72 RNA , 400 ng affinity-purified MBP-p33 , 400 ng affinity-purified MBP-p92pol in 20 μl total volume . The reactions were performed for 3 h at 25°C . To support in vitro TBSV replication , CFEs were prepared from BY4741 , ino2Δ , scs2Δ , erg4Δ , pex3Δ , SEY6210 and JRY6266 yeast strains , which were transformed with pESC-CUP1-p92 ( Ura3 selection ) and pGBK-CUP1-p33 ( His3 selection ) . The CFE-based reaction mixtures were programmed with 0 . 5 μg DI-72 ( + ) RNA transcripts as described [42 , 69] . The CFE-based replication mixtures were incubated at 25°C for 3 h . Treatments of the RNA products from the CFE-based TBSV replication reactions ( for Fig 1B ) with RNases were done as follows: After 1 h incubation at 25°C , the TBSV replication products were treated with both ssRNA-specific RNase A ( VWR ) and dsRNA-specific ribonuclease RNase III ( NEB ) . After incubation at 37°C for either 15 or 20 min , the RNA samples were extracted with phenol-chloroform and precipitated . Treatments of in vitro assembled VRCs in CFEs with the micrococcal nuclease ( Amersham ) were performed as follows: MNase ( final concentration of 0 . 1 or 0 . 05 U/μl in the presence of 1 mM CaCl2 ) was added at different time points to CFE mixtures as shown in Figures . The reaction mixtures were incubated for 15 or 20 min at room temperature and , then , 2 . 5 mM EGTA was added to the samples to inactivate the MNase . The CFE reactions were further incubated for a total of 3 h ( counted from the start of the replication assay ) at 25°C before the products were extracted with phenol-chloroform and precipitated . The obtained 32P-labeled RNA products were separated by electrophoresis in 5% semi-denaturing polyacrylamide gel containing 8 M urea with 0 . 5x Tris-borate-EDTA buffer [50 , 58] . The CFE-based TBSV replication assay #2 was performed in the presence of 0 . 5 U RNase III ( NEB ) during the entire incubation ( 3 hours at 25°C ) . Then , the RNA samples were extracted with phenol-chloroform and precipitated . The obtained 32P-labeled RNA products without heat treatment were analyzed in 5% acrylamide/ 8M Urea gels to detect dsRNA level [50] . | Positive-strand RNA viruses build membranous replication compartment to support their replication in the infected hosts . One of the proposed functions of the usurped subcellular membranes is to protect the viral RNA from recognition and destruction by various cellular RNA sensors and ribonucleases . To answer this fundamental question on the putative role of co-opted host factors and membranes in protecting the viral double-stranded RNA replication intermediate during replication , the authors took advantage of yeast ( Saccharomyces cerevisiae ) , which lacks the conserved RNAi machinery , as a surrogate host for TBSV . The reconstituted RNAi machinery from S . castellii in S . cerevisiae was used as an intracellular probe to study the effect of various co-opted cellular proteins and lipids on the formation of RNAi-insensitive replication compartment . Overall , the authors demonstrate the interaction between the RNAi machinery and the viral replicase complex , and the essential roles of usurped host factors in protecting the viral dsRNA replication intermediate from RNAi-based degradation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"rna",
"interference",
"nucleases",
"enzymes",
"gene",
"regulation",
"microbiology",
"dna-binding",
"proteins",
"enzymology",
"fungi",
"model",
"organisms",
"experimental",
"organism",
"systems",
"epigenetics",
"research",
"and",
"analysis",
"methods",
"saccharomyces",
"s... | 2017 | The role of co-opted ESCRT proteins and lipid factors in protection of tombusviral double-stranded RNA replication intermediate against reconstituted RNAi in yeast |
Many virtual screening methods have been developed for identifying single-target inhibitors based on the strategy of “one–disease , one–target , one–drug” . The hit rates of these methods are often low because they cannot capture the features that play key roles in the biological functions of the target protein . Furthermore , single-target inhibitors are often susceptible to drug resistance and are ineffective for complex diseases such as cancers . Therefore , a new strategy is required for enriching the hit rate and identifying multitarget inhibitors . To address these issues , we propose the pathway-based screening strategy ( called PathSiMMap ) to derive binding mechanisms for increasing the hit rate and discovering multitarget inhibitors using site-moiety maps . This strategy simultaneously screens multiple target proteins in the same pathway; these proteins bind intermediates with common substructures . These proteins possess similar conserved binding environments ( pathway anchors ) when the product of one protein is the substrate of the next protein in the pathway despite their low sequence identity and structure similarity . We successfully discovered two multitarget inhibitors with IC50 of <10 µM for shikimate dehydrogenase and shikimate kinase in the shikimate pathway of Helicobacter pylori . Furthermore , we found two selective inhibitors ( IC50 of <10 µM ) for shikimate dehydrogenase using the specific anchors derived by our method . Our experimental results reveal that this strategy can enhance the hit rates and the pathway anchors are highly conserved and important for biological functions . We believe that our strategy provides a great value for elucidating protein binding mechanisms and discovering multitarget inhibitors .
The concept of “one–disease , one–target , one–drug” has dominated drug development strategy for decades [1] , [2] . Based on this strategy , many virtual screening methods have been developed and applied successfully for identifying specific inhibitors of a single target [3]–[5] . However , the hit rates of these screening methods are often low because they generally cannot identify the key features from a single protein for understanding biological functions or determining inhibitor activities . In addition , single-target inhibitors often lose their potency owing to a single residue mutation in the target binding sites , resulting in drug resistance . For instance , some influenza strains containing a single residue mutation are resistant to the drug oseltamivir [6] . Another well-known example is human immunodeficiency virus type 1 , which has a high mutation rate and rapidly develops resistance to drugs [7] . Furthermore , the single-target inhibitors are often therapeutically inefficient for complex diseases that were caused by multiple targets ( such as cancers ) . Therefore , an emerging strategy for drug discovery is to enrich the hit rate and identify multitarget inhibitors , decreasing the probability of drug resistance and enhancing therapeutic efficiency by inhibiting multiple targets . Some proteins share similarities in physicochemical properties and shapes of their localized binding sites despite low sequence or low overall structural similarities . For example , the proteins in a metabolic pathway contain conserved binding environments where the product of one enzyme is the substrate of the next enzyme in a series of catalytic reactions . Using this property , it is possible to design a multitarget inhibitor to simultaneously inhibit multiple proteins in a disease pathway to increase the therapeutic effectiveness against the disease . Recently , the concept of polypharmacology has been proposed for drug design , which deals with drugs that bind multiple target proteins [8]–[10] . However , designing these inhibitors is a challenging task because the proteins in a pathway often lack structural and sequence homology [11] , [12] . Therefore , a new strategy for extracting conserved binding environments from these proteins is needed for the discovery of multitarget inhibitors . To address these issues , we propose a new strategy , called pathway-based screening by using pathway site-moiety maps ( PathSiMMaps ) . The strategy is an extension of our previous studies , which described a site-moiety map of a protein and core site-moiety maps of orthologous proteins [13] , [14] . The main concept of this strategy is the simultaneous screening of multiple target proteins in the same metabolic pathway that interact with compounds sharing similar common substructures . Our previous studies showed that a site-moiety map can identify the moiety preferences and the physico-chemical properties of a binding site for elucidating binding mechanisms [13] , [14] . A site-moiety map contains several anchors , and an anchor has three essential elements: ( 1 ) conserved interacting residues of a binding pocket ( i . e . , a part of the binding site ) ; ( 2 ) moiety preference of the binding pocket; and ( 3 ) the type of interaction between the moieties and the binding pocket . Here we have extensively enhanced and modified our previous works to develop PathSiMMaps of multiple proteins with low sequence and structural similarities using an anchor-based alignment method . PathSiMMaps represents the conserved binding environments ( i . e . , conserved anchors , called pathway anchors ) of multiple proteins in a pathway . Pathway anchors often play key roles in the series of catalytic reactions , and therefore can be used for the discovery of multitarget inhibitors and to increase the hit rate . The major enhancements in PathSiMMaps developed in the present study compared with the core site-moiety maps developed in our previous study are as follows . The PathSiMMaps are designed to identify multitarget inhibitors for multiple proteins lacking structural similarity and sequence homology . In contrast , the core site-moiety maps were designed for orthologous proteins , which often have the same functions and similar structures in their binding sites . An anchor-based alignment method was developed to identify pathway anchors without relying on sequence or structure alignments . The PathSiMMaps are also designed to find specific anchors and selective inhibitors for a specific protein . Furthermore , we developed a PathSiMMap-based scoring method to enrich the hit rate . We have applied the pathway-based screening strategy to identify pathway anchors and multitarget inhibitors of shikimate dehydrogenase ( SDH ) and shikimate kinase ( SK ) in the shikimate pathway of Helicobacter pylori ( H . pylori ) , which causes peptic ulcer disease [15] , [16] . The shikimate pathway consisting of seven proteins is an attractive target pathway for drug development because it is absent in humans [17] . SDH and SK are among the seven proteins in the pathway . We first identified four pathway anchors of SDH and SK despite their low sequence identity ( 8 . 3% ) and structure similarity ( RMSD is 4 . 8 Å ) . Based on these pathway anchors , two multitarget inhibitors were successfully discovered for SDH and SK with IC50 of <10 µM . Experimental results show that pathway anchors and their residues are highly conserved and play important roles for studying biological functions and designing multiple-target inhibitors . Our screening strategy significantly enriches the hit rate based on multiple targets . These experimental results reveal that the pathway-based screening strategy can find pathway anchors and multitarget inhibitors of structurally dissimilar proteins . We believe that our strategy is useful for studying protein-inhibitor binding mechanisms and discovering multitarget inhibitors for human complex diseases .
The concept of the pathway-based screening strategy is to simultaneously screen multiple proteins in a pathway and extract conserved binding environments of these proteins to discover multitarget inhibitors ( Fig . 1 ) . The screening strategy relies on the following criteria: ( 1 ) the proteins are in the same pathway; ( 2 ) the proteins catalyze similar ligands with common substructures; and ( 3 ) the site-moiety maps of these proteins share comparable pathway anchors . The strategy can work efficiently when proteins in the same pathway perform a series of catalytic reactions to yield a product compound . The intermediates of these proteins often share common substructures and their binding sites may share similar physicochemical properties and shapes . Seven proteins in the shikimate pathway catalyze several metabolites with similar substructures to synthesize chorismate [18] ( Figs . 1A , 1B , and S1 ) . The similarities of the substrates , products and cofactors were represented by MACCS-Tanimoto values obtained from OpenBabel ( http://openbabel . org/wiki/Main_Page ) ( Figs . S1B and S1C ) . The similarity matrix showed that the substrates/products generally share similarities ( average Tanimoto value: 0 . 61 ) . After 3-Dehydroquinate synthase converts DAHP to DHQ by NAD+ , the downstream substrates/products ( 3-dehydroshikimate , shikimate , shikimate-3-phosphate , EPSP , and chorismate ) of the protein have relatively similarities ( Tanimoto value: 0 . 75 ) because these substrates/products share similar scaffolds ( blue part in Fig . S1A ) . The cofactors in this pathway also have similar scaffolds ( Fig . S1C ) . In this study , we selected two proteins as the test screening targets: SDH and SK . These proteins are the fourth and fifth enzymes , respectively , in the shikimate pathway . SDH converts 3-dehydroshikimate into shikimate using NADPH as a cofactor [18] ( Fig . 1B ) . Then , SK converts shikimate into shikimate 3-phosphate by another cofactor , ATP [19] . The major scaffolds ( blue part ) of 3-dehydroshikimate , shikimate , and shikimate 3-phosphate are the same ( blue part in Fig . 1B ) , which implies that SDH and SK have conserved binding environments for recognizing this common scaffold . We used the anchors of site-moiety maps to describe the binding environments of protein binding sites . The anchors have three interaction types: electrostatic ( E ) , hydrogen-bonding ( H ) , and van der Waals ( V ) interactions . First , we docked 302 , 909 compounds collected from public compound databases to binding sites of SDH and SK using our in-house docking tool , GEMDOCK [20] . Our previous studies revealed that GEMDOCK has similar performance to other docking methods such as DOCK [21] , FlexX [22] , and GOLD [20] , [23] , [24] . Furthermore , we have successfully used GEMDOCK to identify novel inhibitors and binding sites for several targets [25]–[27] . Subsequently , the site-moiety maps of SDH and SK were established by statistical analysis of the top 6 , 000 docked compounds ( approximately 2% of the 302 , 909 compounds ) ( Fig . 1C ) . We then developed an anchor-based alignment method to find pathway anchors that are conserved in SDH and SK , for constructing the PathSiMMaps ( Fig . 1D ) . The pathway anchors of the PathSiMMaps reflect conserved interactions between binding pockets with specific physico-chemical properties and their preferred functional groups , all of which are essential for pathway functions ( Fig . 1E ) . Finally , the compounds that simultaneously matched pathway anchors of multiple targets were selected for the bioassay ( Fig . 1F ) . The site-moiety map of SDH consisted of five H anchors ( H1 , H2 , H3 , H4 , and H5 ) and four V anchors ( V1 , V2 , V3 , and V4 ) ( Figs . 1C and S2 ) . For each anchor , several residues comprising a binding pocket with specific physicochemical properties , moiety compositions , and interaction type were identified from the top-ranked compounds . The H1 anchor ( Fig . S2C ) , consisting of three residues ( T65 , K69 , and D105 ) , prefers polar moieties such as carbonyl , amide , and nitro groups . The hydroxyl moiety of shikimate participates in the dehydrogenase reaction ( Fig . 1B ) and forms hydrogen bonds with the three residues of the H1 anchor ( Fig . S2B ) . The two residues ( K69 and D105 ) of this anchor are highly conserved and are responsible for transferring a hydride ion between NADPH and shikimate in SDH of Thermus thermophilus [28] . Three residues ( H15 , T65 , and Y210 ) , constituting the H2 anchor , form hydrogen bonds with amide , carbonyl , sulfonate , amide , and carboxylic acid groups of the top-ranked compounds . The H4 ( S129 , A179 , and T180 ) and H5 ( K69 and S129 ) anchors interact with NADPH and are composed of two polar binding pockets ( Fig . S2B ) . The major interacting moieties of the H4 anchor are carboxylic acid amide , ketone , ether , and hydrazine derivatives . The H5 anchor favors carboxylic acid amide , ketone , sulfonate , and carboxylic acid groups . The H3 anchor , which is spatially distant from the shikimate and NADPH binding sites , consists of three residues ( T180 , D207 , and L208 ) , revealing an additional binding pocket for designing inhibitors . This binding pocket often forms interactions with nitro , ether , sulfonate , and ketone moieties . Ring moieties are the major moiety types of the V1 , V2 , V3 , and V4 anchors of SDH . Among the 6000 top-ranked compounds , the aromatic moieties of 1 , 879; 886; 745; and 1 , 454 compounds form van der Waals contacts with the residues of the V1 , V2 , V3 , and V4 anchors , respectively . The number of compounds ( 3 , 509 of 6 , 000 compounds ) interacting with the V1 anchor , formed by three hydrophobic residues ( L66 , L208 , and A209 ) , is higher than those for the other V anchors . Aromatic ring , phenol , alkene , and oxohetarene moieties are the major compositions of the V1 anchor . The ribose of the cofactor NADPH is located in the V1 anchor ( Fig . S2B ) , suggesting the importance of the V1 anchor for maintaining the function of the protein . The V2 anchor constituted by three residues ( L208 , Y210 , and Q237 ) forms van der Waals interactions with 1 , 933 docked compounds by bulky moieties such as aromatic and heterocyclic moieties . Furthermore , this anchor occupies the position of the pyridine ring of NADPH ( Fig . S2B ) . The V4 and the V3 anchors are situated in the groove and close to the entrance of the NADPH binding site , respectively . The residues ( L66 , G127 , and G128 ) of the V4 anchors often interact with aromatic ring , phenol , alkene , and oxohetarene moieties . The three residues ( L184 , A209 , and Y210 ) comprise the V3 anchor , and their preferred moieties are aromatic ring , alkene , phenol , and oxohetarene moieties . These nine anchors ( five H and four V anchors ) describe binding environments that can be used to design SDH inhibitors that block the binding of shikimate or NADPH . We have previously described the site-moiety map of SK [14] . Three anchors ( E1 , V2 , and H3 ) are located at the shikimate binding site ( Fig . S3 ) . The E1 anchor pocket consists of two positively charged residues ( R57 and R132 ) which are essential for shikimate binding [29] . The anchor prefers negatively charged moieties such as carboxyl , sulfonate , and phosphate groups . The V2 anchor residues ( D33 , F48 , G80 , and G81 ) form van der Waals interactions with the ring of shikimate ( Fig . S3B ) . This pocket often interacts with aromatic rings , carboxylic acid amidine , oxohetarene , and alkene moieties . The polar pocket of the H3 anchor consists of three residues ( K14 , D33 , and G80 ) which often form hydrogen-bonding interactions with polar moieties ( carboxylic acid amide , ketone , sulfonate , and ether ) of the docked compounds . The H1 , H2 , and V1 anchors are situated at the ATP site . The H1 ( G11 , S12 , G13 , K14 , and S15 ) and H2 ( S15 , D31 , and D33 ) anchors are involved in the Walker A motif ( K14 and S15 ) and a DT/SD motif ( D31 and D33 ) , respectively , and bind the phosphate groups of ATP [29] . The two anchors favor similar polar moieties , such as carboxylic acid amide , ketone , and sulfonate . The V1 anchor ( M10 , G11 , S12 , G13 , K14 , and S15 ) is situated between the H1 anchor and the H2 anchors , and its frequently interacting moieties are aromatic groups , oxohetarene , phenols , heterocyclic groups , and alkenes . SDH and SK have four pathway anchors identified by the anchor-based alignment method despite their low sequence and structure similarity ( Figs . 2 and 3 ) . The pathway hydrogen-bonding anchor 1 ( PH1 ) was derived from alignment of the H1 anchor of SDH and the E1 anchor of SK . The interaction type of the PH1 anchor was assigned as the hydrogen-bonding type because the preferred moieties of the E1 anchor are able to participate in hydrogen bonding . The pathway hydrogen-bonding anchor 2 ( PH2 ) was derived from the alignment of the H4 anchor of SDH and the H3 anchor of SK . The pathway van der Waals anchor 1 ( PV1 ) was derived from the alignment of the V4 anchor of SDH and the V1 anchor of SK . The pathway van der Waals anchor ( PV2 ) was derived from alignment of the H5 anchor of SDH and the spatially close V2 anchor of SK . The PH1 anchor consists of residues T65 , K69 , and D105 for SDH and R57 and R132 for SK ( Fig . 2 ) . The PH1 anchor prefers polar moieties such as nitro and carboxylic acid groups and is involved in the dehydrogenase reaction for SDH and the binding of shikimate [29] ( Figs . 2B and 3 ) . Interestingly , the shikimates of SDH and SK consistently occupy the location of the PH1 anchor . This result indicates that the PH1 anchor is essential for catalysis and substrate binding of these two proteins in the shikimate pathway . For SDH , the residues ( S129 , A179 , and T180 ) of the PH2 anchor interact with NADPH; similarly , the SK residues ( K14 , D33 , and G80 ) of PH2 are involved in the Walker A motif and DT/SD motif , both of which are involved in shikimate and ATP binding to SK [19] ( Fig . 3 ) . This suggests that the PH2 anchor is involved in shikimate binding and the binding of cofactors such as NADPH of SDH and ATP of SK . The interaction residues of the PV1 anchor ( L66 , G127 , and G128 in SDH; M10 , G11 , S12 , G13 , K14 , and S15 in SK ) constitute a binding pocket that frequently yields van der Waals interactions with compound moieties ( Fig . 2B ) . The major moieties of the PV1 anchor are aromatic ring ( 40% ) , alkene ( 18% ) , and phenol ( 8% ) . The high preference of the aromatic ring may derive from the long side chains of the residues ( L66 in SDH; M10 in SK ) , which can form stable van der Waals interactions with the aromatic rings of the compounds . For the PV1 anchor of SDH , the anchor residues ( L66 , G127 , and G128 ) interact with the phosphate group of NADPH through van der Waals interactions . In addition , the residue L66 yields van der Waals interactions with the adenosine ribose of NADPH , which may stabilize NADPH binding . Similarly , the anchor residues ( M10 , G11 , S12 , G13 , K14 , and S15 ) of the SK PV1 anchor surround the phosphate groups of ATP and provide van der Waals interactions with ATP . These observations showed that the PV1 anchor plays an important role in interacting and transferring the phosphate groups of ATP ( SK ) and NADPH ( SDH ) during catalytic reactions , despite the different functions of SDH and SK ( Fig . 3 ) . For the PV2 anchor , the side chains of its interaction residues ( K69 and S129 in SDH; D33 , F48 , G80 , and G81 in SK ) provide van der Waals contacts with alkene ( 22% ) , aromatic ring ( 17% ) , enamine ( 7% ) , and heterocyclic moieties ( 5% ) ( Fig . 2B ) . The aromatic ring composition of the PV2 anchor is lower than that of the PV1 anchor , which may have resulted from the less compact binding environment of the PV2 anchor comprising a relatively small residue number . For SDH , the van der Waals interactions are formed between the residues ( K69 and S129 ) of the PV2 anchor and the pyridine ring of NADPH ( Fig . 3 ) . Moreover , the residue K69 is a catalytic residue for the dehydrogenase reaction based on the SDH structure of Thermus thermophilus [28] . The SK PV2 anchor is located at the shikimate binding site , and its residues ( D33 and F48 ) make van der Waals interactions with the cyclohexene group of shikimate . D33A or F48A mutations result in a loss of SK activity [14] , revealing the anchor is essential for the shikimate binding . Although SDH and SK have different residue compositions in their PV2 anchors , these residues interact with similar ring moieties ( e . g . , cyclohexene of shikimate and the pyridine ring of NADPH ) during their catalytic processes . We evaluated the pathway anchors by site-directed mutagenesis . A site-directed mutagenesis study on SDH of Escherichia coli showed that it lost substrate-binding activity when the residues were mutated at positions 67 , 92 , and 107 ( T65 , J69 , and D105 , respectively in SDH of H . pylori ) [30] . Our previous study also showed that mutations in the pathway anchor residues ( M10 , S12 , S15 , D33 , F48 , R57 , and R132 in SK ) reduced the activity of shikimate kinase [14] , [31] . These results suggest that the pathway anchors are essential for catalytic reactions and that the mutations on the pathway anchor resides often decrease enzyme activities of SDH and SK ( Figs . 3C and 3D ) . Three multitarget inhibitors that simultaneously inhibit SDH and SK were identified based on the PathSiMMap scores . Two inhibitors NSC45174 and NSC45611 , match the four pathway anchors in both targets ( Fig . 4 ) and their IC50 values were consistently <10 µM . The inhibitor RH00037 lacks a polar moiety near the PH1 anchor , resulting in poor IC50 values ( 24 . 8 µM for SDH and 23 . 8 µM for SK ) ( Fig . 4A ) . The sulfonate group of NSC45174 and the carboxyl group of NSC45611 form hydrogen bonds with the residues of the PH1 anchor in the same way as the hydroxyl group of shikimate in SDH and the carboxyl groups of shikimate in SK . The elimination of polar moieties in RH00037 causes an approximately 10-fold reduction in inhibitory ability , revealing the importance of the PH1 anchor for multitarget inhibitor design . Although the urea moiety of NSC45174 is different from the azo moieties of NSC45611 and RH00037 , these moieties consistently form hydrogen-bonding interactions with the pocket of the PH2 anchor ( Fig . 4 ) . NSC45174 uses naphthalene , whereas NSC45611 and RH00037 use aromatic moieties to make van der Waals contacts with the residues of the PV1 anchor . Similarly , NSC45174 , NSC45611 , and RH00037 use naphthalene , aromatic ring , and 9H-xanthene to make van der Waals contacts with the residues of the PV2 anchor , respectively . These ring moieties can consistently engage in van der Waals interactions with residues of PV1 and PV2 despite their differing moieties . In these inhibitors , the presence of different moieties with similar physico-chemical properties reveals the advantages of the PathSiMMaps for identifying diverse multitarget inhibitors and providing an opportunity for lead optimization . We further carried out experiments to compare three dose-response curves ( Fig . S4 ) : ( 1 ) shikimate dehydrogenase ( SDH ) activity; ( 2 ) shikimate kinase ( SK ) activity; and ( 3 ) dual enzyme ( SDH and SK ) activity . The dual enzyme assay is based on the determination of the release of ADP from the substrate 3-dehydroshikimate in the presence of two enzymes . For the inhibitors ( NSC45611 and NSC45174 ) that simultaneously blocked SDH and SK , it was interesting that the dual enzyme curve had the median effect . At inhibitor concentrations greater than the IC90 value , it was intriguing that the dual enzyme curves swiftly approached approximately 0 , revealing the greater combined inhibitory effect . In contrast , there were nearly identical profiles for the SK-specific inhibitor ( NSC162535 [14] ) . The proteins share similarities in physicochemical properties and shapes of their localized binding sites , despite low sequence or low overall structural similarities . This provides an opportunity to design multitarget inhibitors or results in unexpected side effects . For complex diseases such as cancer , diabetes , and cardiovascular diseases , the inhibition of multiple proteins is necessary for efficient therapy . Current therapeutic strategies use drug combination for these diseases , which frequently results in unwanted side effects . Our studies reveal that the anchor-based alignment method can be applied to measure binding environment similarities between proteins instead of relying on sequence or structure alignments . We further examined the pathway anchors with respect to residue conservation ( Fig . 5 ) . The residues of SDH and SK were classified into four groups: pathway anchor residues , anchor residues , binding site residues , and other residues according to the following rules . The residues of the pathway anchors were classified as pathway anchor residues . The residues that formed anchors but were not pathway anchor residues were classified as anchor residues . The residues of the defined binding sites that were neither pathway anchor nor anchor residues were classified as binding site residues . The remaining residues were classified as other residues . Each residue position was assigned an evolutionary conservation score according to the Consurf server [32] . For a query protein , the Consurf server provided a multiple sequence alignment of its homologous sequences for measuring the conservation degree of each residue position . The conservation degree was divided into nine grades . Residues with the highest conservation score , 9 , represented the highly conserved positions , which often play important roles for maintaining protein functions/structure during the evolutionary process . The statistical results revealed that the pathway anchor residues are the most conserved among the four groups ( Fig . 5A ) . The conservation score of 9 was observed for 81% of pathway anchor residues , 63% of anchor residues , 30% of binding site residues , and 5% of other residues . When we calculated an average conservation score for each anchor and pathway anchor , the pathway anchors proved to be more conserved than the anchors ( Fig . 5B ) . For example , the conservation score for the PH1 anchor is 9 , and the conservation score for each of its residues ( T69 , K69 , and D105 in SDH; R57 and R132 in SK ) is 9 . The high conservation of the pathway anchors implies that they have been essential for a series of catalytic reactions during evolution owing to their importance for interacting with shikimate . This is based on structure complex observations ( Fig . 3 ) . One of the advantages of the pathway-based screening strategy is to design multiple-target inhibitors that occupy the pathway anchors for reducing the probability of drug resistance . For multitarget inhibitors , the probability of simultaneously arising resistant mutations is exponentially lower than that of any single mutation . In contrast , the conventional strategy for developing drugs is easily susceptible to resistant mutations using a “one-disease , one-target , one-drug” strategy . The conventional strategy is ineffective against diseases with high mutation rates , such as influenza virus , cancers , and human immunodeficiency virus type 1 [7] , [33] , [34] . Therefore , the pathway-based screening strategy is useful for designing multitarget inhibitors for such diseases . The alignment of the site-moiety maps of SDH and SK revealed a specific site for SDH despite many similarities shared by the two targets ( Fig . 6A ) . The specific site consists of the H3 , V1 , and V3 anchors , which are not involved in the NADPH and shikimate binding sites . The specific site provided an opportunity to discover selective inhibitors for SDH . We evaluated this concept using two selective inhibitors ( NRB03174 and HTS02873 ) that occupied three-specific anchors with high PathSiMMap scores ( Fig . 6B ) . NRB03174 and HTS02873 inhibited SDH with IC50 values 9 . 7 µM and 4 . 9 µM , respectively , whereas they demonstrated no inhibitory effect at 100 µM for SK ( Fig . 6B ) . NRB03174 interacts with the residues of the V1 and V3 anchors using the bromobenzene moiety ( Fig . 6C ) ; similarly , HTS02873 makes van der Waals contacts with the residues of the V1 and V3 anchors using the anisole moiety ( Fig . 6D ) . Although no hydrogen-bonding interactions were observed in the specific anchors of SDH for the NRB03174/HTS02873 molecules , these two inhibitors formed hydrogen-bonding interactions with the anchor residues of the pathway anchors . For example , NRB03174 yielded hydrogen bonds with the anchor residues ( L66 , K69 , S129 , and A179 ) , and HTS02873 made hydrogen-bonding interactions with the residues ( S129 , and A179 ) . Designing selective inhibitors for disease-specific proteins can prevent unexpected side effects that are major obstacles in clinical trials and often result in treatment failure . For example , more than 100 p38 MAP kinase inhibitors that were designed for treating inflammatory or cardiovascular diseases were suspended because of their serious side effects [35] . The above results suggested that specific anchors and the pathway anchors can be used to design selective inhibitors and multitarget inhibitors , respectively . Thus the concept of the pathway-based screening strategy can be further extended to design multitarget inhibitors of disease-specific proteins . By combining specific anchors and the pathway anchors of multiple disease-related proteins , it is possible to design multitarget inhibitors that bind disease-specific but not non-specific proteins . Such multitarget inhibitors can enhance therapeutic potency and minimize side effects . The accuracy of the PathSiMMap was assessed using the hit rate and compared with site-moiety map and energy-based methods . The energy-based method used here was the piecewise linear potential ( PLP ) of GEMDOCK [20] . GEMDOCK is comparable to some docking methods ( e . g . , DOCK , FlexX , and GOLD ) on the 100 protein-ligand complexes and has similar accuracy to some energy-based scoring functions in the prediction of binding affinities [20] , [24] . During the docking process , GEMDOCK first assigned formal charge and atom type ( i . e . , donor , acceptor , both , or nonpolar ) to atoms of compounds and proteins . Then , the GEMDOCK PLP measures intermolecular potential energy between proteins and docked compounds . The intermolecular potential energy includes electrostatic , van der Waals , and hydrogen-bonding interactions . The compounds can be ranked based on their intermolecular potential energy . The hit rate is defined as Ah/Th ( % ) , where Ah is the number of active compounds among the Th highest-ranking compounds . For SDH , the active compounds used for verification were the three multitarget inhibitors and the two specific inhibitors ( Ah = 5 ) . For SK , the active compounds used for verification were the seven SK inhibitors [14] ( Fig . S5 ) , and three multitarget inhibitors ( Ah = 10 ) . The hit rate of the PathSiMMap was considerably better than that of other methods used for identifying inhibitors of SDH and SK ( Fig . 7 , Tables S1 and S2 ) . Our pathway-based screening strategy can be used to enhance the hit rate because the pathway anchors are often highly conserved and important for biological functions ( Figs . 3 and 5 ) . This suggests that the pathway anchors often play important roles for ligand binding . Thus , the compounds that match the pathway anchors are often potential inhibitors of the target proteins . For example , for SDH , the ranks of NSC45174 were 3810 by the energy-based method , 177 by the site-moiety map , and 13 by PathSiMMap . We selected 20 compounds ( Tables S3 and S4 ) for bioassay based on their PathSiMMap scores , drug-like properties , availabilities , and domain knowledge . We performed the compound-anchor profile analysis to find why NSC45174 and NSC45611 were more potency than other top-ranked compounds ( Fig . S6 ) . This profile analysis showed that NSC45174 and NSC45611 simultaneously matched the four pathway anchors of SDH and SK ( Fig . S6A ) and inhibited them with IC50 values ≦10 µM . In contrast , most of the inactive compounds matched 2–3 pathway anchors of SDH and SK . For example , KM02359 has no polar moieties to yield hydrogen-bonding interactions with the PH1 anchor residues of SDH and SK ( Figs . S6A and S6C ) . CD01870 lacks a polar moiety in the PH1 anchor and is unable to form hydrogen bonds with the anchor residues of SDH and SK ( Figs . S6A and S6D ) . We next analyzed the compound–residue interaction profiles to find why some compounds that matched the four pathway anchors were inactive for both SDH and SK ( Fig . S6B ) . These profiles showed that NSC45174 , NSC45611 , and RH00037 maintained the conserved interactions ( i . e . , those commonly found with >50% of inhibitors ) with the anchor residues of SDH and SK ( e . g . , K69 , D105 , G127 , A179 , L208 , and S129 in SDH; M10 , G11 , S12 , G13 , K14 , S15 , D33 , R57 , G80 , and R132 in SK ) . These conserved interactions of the pathway anchors may have accounted for the potency of NSC45174 and NSC45611 . These profiles indicated that some compounds ( e . g . HTS05470 ) with high PathSiMMap scores lacked several of the conserved interactions , which may have resulted in their inactivity . For example , HTS05470 lost the conserved hydrogen-bonding interactions with these residues ( A179 and L208 in SDH; K14 and S15 in SK ) ( Figs . S6B and S6E ) . According to both compound-anchor profiles and compound–residue interaction profiles , these results showed that the compound often inhibits proteins when it highly matches the pathway anchors and keeps conserved interactions . In addition , we applied the pathway-based screening strategy for additional four pathways ( Figs . S7 , S8 , S9 , S10 , and Text S1 ) . The pathway-based screening strategy to discover multitarget inhibitors relies on the following criteria: ( 1 ) the proteins catalyze ligands with common substructures , and ( 2 ) these proteins share conserved binding environments and comparable anchors in their site-moiety maps . We selected the other five proteins in the shikimate pathway of Helicobacter pylori to examine whether they share conserved binding environments ( i . e . pathway anchors ) with SDH and SK ( Fig . S11 ) . These proteins include DAHP synthase , 3-dehydroquinate synthase ( 3CLH ) , 3-dehydroquinate dehydratase ( 1J2Y ) , EPSP synthase , and chorismate synthase ( 1UM0 ) . Because structures of DAHP synthase and EPSP synthase are unavailable , we obtained their structures using an in-house homology-modeling server [36] . First , the site-moiety maps of these five proteins were established . The anchor-based alignment method was then applied to identify the pathway anchors of these seven proteins . Among these proteins , 3-dehydroquinate synthase , SDH , SK , and EPSP synthase share the four pathway anchors ( Fig . S11 ) . The former three proteins have similar substrates ( DAHP , 3-dehydro shikimate , and shikimate ) and cofactors ( NAD+ , NADPH , and ATP ) ( Fig . S1 ) . Conversely , the PEP , the cofactor of EPSP synthase , is much smaller than NAD+ , NADPH , or ATP . These four pathway anchors located across substrate and cofactor sites often play key roles in catalytic reactions and ligand bindings for 3-dehydroquinate synthase , SDH , SK , and EPSP synthase ( Figs . 3 and S12 ) . 3-dehydroquinate synthase converts DAHP into DHQ with the cofactor NAD+ ( Fig . S1 ) . The PH1 anchor of 3-dehydroquinate synthase is situated at the DAHP site ( Fig . S12 ) , while the PH2 , PV1 , and PV2 sit at the NAD+ site . Three polar residues ( D126 , K210 , and R224 ) comprise the PH1 anchor . The carboxyl moiety of DAHP forms hydrogen-bonding interactions with the PH1 anchor residues ( K210 and R224 ) , involving in the catalytic reaction [37] . The nicotinamide moiety of NAD+ interacts with the PH2 anchor residue ( D99 ) and the PV2 anchor residues ( D126 , K132 , and K210 ) by hydrogen-bonding and van der Waals interactions , respectively . Two residues ( G95 and L122 ) constitute the PV1 anchor and make van der Waals interactions with the tetrahydrofuran-3 , 4-diol moiety of NAD+ . EPSP synthase catalyzes the conversion of shikimate-3-phosphate into EPSP with PEP ( Fig . S1 ) . The PH1 anchor of EPSP synthase consists of three residues ( A154 , S155 , and K329 ) . A hydrogen bonding network is formed between the anchor residues ( S155 and K329 ) and the phosphate moiety of shikimate-3-phosphate . Three polar residues comprise ( K11 , T83 , and D302 ) the PH2 anchor , and these residues yield hydrogen bonds with the phosphate moiety of PEP and the hydroxyl moiety of shikimate-3-phosphate . The PV1 anchor consists of three residues with long side chains , including K11 , D302 , and E330 . The acrylic acid moiety of PEP is situated at this anchor , and makes van der Waals interactions with these residues . The cyclohexene moiety of shikimate-3-phosphate is sandwiched between the PV2 anchor residues ( Q157 , R182 , and I301 ) and forms stacking interactions with them . These observations show the importance of these pathway anchors for performing biological functions of these proteins . In addition , although these four proteins have different functions , their pathway anchor residues have similar physicochemical properties for interacting their substrates and cofactors . For example , the PH1 anchor residues of 3-dehydroquinate synthase , SDH , SK , and EPSP synthase are polar and consistently form hydrogen bonding interactions with carboxyl , ketone , carboxyl , and phosphate moieties of their substrates , respectively . We then docked the multitarget inhibitors of SDH and SK into 3-dehydroquinate synthase and EPSP synthase to examine whether these inhibitors match the pathway anchors of these two proteins . The docked poses show that NSC45174 matches the four pathway anchors in 3-dehydroquinate synthase , while NSC45611 and RH00037 match three pathway anchors ( Fig . S13 ) . The docked pose of NSC45174 in 3-dehydroquinate synthase is similar to those in SDH and SK . For example , the sulfonate moiety of NSC45174 is located at the PH1 anchor of these three proteins and consistently forms hydrogen bonds with the PH1 anchor residues ( Figs . 4B , 4E , and S13A ) . Similarly , the naphthalene moiety of NSC45174 consistently sits at the PV2 anchor , and makes van der Waals interactions with the anchor residues . In contrast , these three compound match 2–3 pathway anchors in EPSP synthase . For instance , the sulfonate moiety of NSC45174 is located at the PV1 anchor and thereby is unable to form hydrogen-bonding interactions with the PH2 anchor residues ( Fig . S13D ) . Next , we carried out experiments to determine IC50 values of the three compounds for 3-dehydroquinate synthase . NSC45174 inhibited 3-dehydroquinate synthase with an IC50 value 7 . 1 µM , while NSC45611 and RH00037 showed no inhibitions ( Figs . S13G and S13I ) . NSC45174 is a novel multitarget inhibitor that simultaneously inhibited three proteins ( SDH , SK , and 3-dehydroquinate synthase ) of the shikimate pathway . These results reveal that the pathway-based screening strategy can identify multitarget inhibitors in a pathway .
Apo-form structures of SDH and SK were selected for virtual screening because the use of closed-form structures induced by bound ligands may limit the diversity of identified inhibitors . For defining binding sites , the apo-form structures of SDH ( 3PHG ) and SK ( 1ZUH [19] ) were aligned to their respective closed-form structures SDH ( 3PHI ) and SK ( 1ZUI [19] ) , using a structural alignment tool [38] . The bound ligands ( shikimate and NADPH for SDH and shikimate and phosphate for SK ) were used to determine the binding sites of SDH and SK . The binding sites of these structures were defined by residues situated ≤8 Å from the bound ligands . We selected compounds from two public databases , Maybridge and National Cancer Institute , to generate the PathSiMMaps and discover multitarget inhibitors because of their rapid availability and low cost . Compounds with molecular weight <200 or >650 daltons were not selected . The total number of compounds selected for screening was 302 , 909 . The 302 , 909 compounds were docked into the binding sites of SDH and SK using an in-house docking tool , GEMDOCK [20] ( Fig . S14A ) to establish the site-moiety maps of target proteins . Subsequently , the top 2% compounds ( approximately 6 , 000 ) ranked by docking energy were selected to establish site-moiety maps . We inferred site-moiety maps to recognize interaction preferences between binding pockets and moieties using the top-ranked 2% compounds . First , protein-compound interaction profiles were generated based on the PLP calculated by GEMDOCK ( Fig . S14B ) . The profiles described the interactions ( i . e . , E , H , and V interactions ) between the compounds and the protein residues . Each profile can be represented by a matrix with size P×C , where P and C are the numbers of docked compounds and interacting residues of a protein . For the E and H profiles , the entry was set to 1 ( green regions in Fig . S14B ) if the compound forms electrostatic or hydrogen-bonding interactions with the residues such as T65 , K69 , and D105 in the anchor H1; otherwise , the entry was set to 0 ( black regions ) . For the V profile , the entry was set to 1 if the V energy was less than −4 kcal/mol . The consensus interacting residues ( e . g . , T65 , K69 , and D105 ) of the profiles recognized according to Z scores often play key roles in biological functions . For each profile , the Z score ( Zi ) of the protein residue i was computed by , where fi is the observed interaction frequency between compounds and residue i , and μ and σ are the mean and the standard deviation of interaction frequency derived from 1 , 000 randomly shuffled profiles . We considered the residue i to be a consensus interacting residue if its Z score was greater than 1 . 645 , a common threshold used in statistics ( corresponding to a 95% confidence level ) . Then spatially neighboring interacting residues and their interactive moieties with statistical significance were assigned as an anchor ( Fig . S14C ) . Finally , the site-moiety map of each target was constructed ( Fig . S14D ) . Pathway anchors , which are conserved anchors among the target proteins , represent key features including consensus interactions between the compounds and the binding pockets in a pathway ( Figs . 2 and 3 ) . Identifying pathway anchors using a structural alignment tool is a challenging task because of low sequence identity ( 8 . 3% ) and structure similarity ( RMSD is 4 . 8 Å ) between SDH and SK [38] . To address this task , we developed an anchor-based alignment method according to spatial arrangements , the interaction-type similarity , and the volume similarity of the aligned anchors ( Fig . S15 ) . Each aligned anchor pair x between SDH and SK site-moiety maps is assigned an anchor alignment score ( AAS ( x ) ) , which is defined aswhere i is interaction-type similarity , V is anchor-volume similarity , and d is the distance between the aligned anchors . i is set to 1 if the aligned anchors have the same interaction type or to 0 . 5 when an E anchor is aligned to an H anchor because negatively/positively charged moieties of the E anchor are able to form hydrogen bonds as well as polar moieties of the H anchor; otherwise i is set to 0 . V is defined as , where Vmax and Vmin are the respective volumes of the larger and the smaller anchor . Then the alignment was achieved by maximizing the similarity score ( S ) between the site-moiety maps of SDH and SK . The similarity score is defined as , where n is the number of the aligned anchors . The alignment of the two site-moiety maps was achieved by seeking the highest similarity score using exhaustively superimposing the anchors . The aligned anchors were considered to be the pathway anchors , and the center of the pathway anchor was defined as the geometric center of the two aligned anchors . These pathway anchors consisted of the PathSiMMaps of SDH and SK ( Fig . S15C ) . Compounds matching the pathway anchors were considered potential inhibitors for the shikimate pathway . For compound j at a binding site , the PathSiMMap score ( PS ) , a measure of the inhibition capability , was calculated aswhere PASp ( j ) is the pathway anchor score of compound j in the pathway anchor p; ASa ( j ) is the anchor score of compound j in anchor a; P and A are the numbers of the pathway anchors and anchors , respectively . Here PASp ( j ) is set to 1 if the compound j matches the pathway anchor p and otherwise to 0 . Similarly , ASa ( j ) is set to 1 if the compound j matches the anchor a . For example , P is 4 , and A is 9 and 6 for SDH and SK , respectively . The screening compounds were ranked based on their PathSiMMap scores for SDH and SK . Then , the compounds were re-ranked by consensus rankings of SDH and SK PathSiMMap rankings for selecting potential multitarget inhibitors . Finally , the top-ranked compounds that were commercially available were selected for bioassay . In addition , for SDH , the top-ranked compounds derived from the specific anchor were selected for bioassay . These compounds were considered to be selective inhibitors for SDH . The SK activity was measured by coupling the release of ADP from the SK-catalyzed reaction to the oxidation of NADH using pyruvate kinase ( EC 2 . 7 . 1 . 40 ) and lactate dehydrogenase ( EC 1 . 1 . 1 . 27 ) as coupling enzymes [31] . SDH activity was determined by monitoring the formation of NADPH . The initial rate of the reaction was measured by the increase in absorbance at A340 ( ε = 6 , 200 M−1 cm−1 ) in the present of shikimate . The assay was performed at 25°C in a mixture of 100 mM Tris-HCl buffer , pH 8 . 0 . Both SK and SDH were used in a final enzyme concentration of 100 nM . For determination of IC50 for each inhibitor , the assay was initiated by the addition of shikimate ( 1 . 6 mM ) after incubation in a buffer containing cofactor ( 2 mM ATP for SK or 2 mM NADP for SDH ) , enzyme , and inhibitor ( dissolved in 5% dimethyl sulfoxide ) . All assays were conducted in a 96-well microplate and analyzed with a spectrophotometer ( FLUOstar OPTIMA , BMG LABTECH ) . A dose-response curve was fitted using the non-linear regression function of GraphPad Prism® . We performed ADP assay providing a direct method to analyze SDH-SK dual enzyme activity . The assay was initiated by addition of the 3-dehydroshikimate ( 2 mM ) after incubating in a reaction mixture containing 2 . 5 mM ATP , 0 . 5 mM NADPH , 100 nM SDH , 100 nM SK enzyme , 50 mM KCl , 5 mM MgCl2 and different inhibitors . The reaction was carried out at 25°C in a mixture of 100 mM Tris-HCl buffer , pH 7 . 5 and terminated at 100°C for 5 mins in the reaction time of initial rate . The amount of ADP was measured by using ADP Colorimetric Assay Kit II ( BioVision ) according to the manufacturer's instruction . We also performed 3-dehydroquinate synthase inhibition assay . The reaction was comprised of 3-deoxy-D-arabinoheptulosonate 7-phosphate ( 1 mM ) and NAD+ ( 0 . 5 mM ) . The amount of NADH was measured by using NAD+/NADH Quantification Kit ( BioVision ) . All assays were conducted in a 96-well microplate and analyzed with a spectrophotometer ( FLUOstar OPTIMA , BMG LABTECH ) . The dose-response curve was fitted using the non-linear regression function of GraphPad Prism . The IC90 values were computed from the IC50 and Hill slop . | Many drug development strategies focus on designing inhibitors for single targets . These inhibitors often lose potency owing to mutations in the protein binding sites and are ineffective for complex diseases . Multitarget inhibitors can decrease probability of drug resistance and enhance the therapeutic efficiency; however , identifying them is still a challenge because targets often have low sequence and structure similarities in their binding sites . Here we propose a pathway-based screening strategy that simultaneously screens proteins in a metabolic pathway for discovering multitarget inhibitors . Because these proteins interact with similar metabolites and modify them step-by-step , the proteins share similarities in binding sites . We developed pathway site-moiety maps that present the conserved binding environments of the proteins without relying on the sequence or structure alignment . Compounds that bind these conserved binding environments are often multitarget inhibitors . We applied this strategy to the shikimate pathway of Helicobacter pylori , and discovered two multitarget inhibitors ( IC50<10 µM ) for shikimate dehydrogenase and shikimate kinase . In addition , we found two selective inhibitors based on specific binding environments for shikimate dehydrogenase . Thus the pathway-based screening strategy is useful for identifying multitarget inhibitors and elucidating protein-ligand binding mechanisms and has the potential to be applied to human diseases . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"biotechnology",
"biochemistry",
"proteins",
"small",
"molecules",
"biology",
"computational",
"biology",
"drug",
"discovery"
] | 2013 | Pathway-based Screening Strategy for Multitarget Inhibitors of Diverse Proteins in Metabolic Pathways |
Diverse enteropathogen exposures associate with childhood malnutrition . To elucidate mechanistic pathways whereby enteric microbes interact during malnutrition , we used protein deficiency in mice to develop a new model of co-enteropathogen enteropathy . Focusing on common enteropathogens in malnourished children , Giardia lamblia and enteroaggregative Escherichia coli ( EAEC ) , we provide new insights into intersecting pathogen-specific mechanisms that enhance malnutrition . We show for the first time that during protein malnutrition , the intestinal microbiota permits persistent Giardia colonization and simultaneously contributes to growth impairment . Despite signals of intestinal injury , such as IL1α , Giardia-infected mice lack pro-inflammatory intestinal responses , similar to endemic pediatric Giardia infections . Rather , Giardia perturbs microbial host co-metabolites of proteolysis during growth impairment , whereas host nicotinamide utilization adaptations that correspond with growth recovery increase . EAEC promotes intestinal inflammation and markers of myeloid cell activation . During co-infection , intestinal inflammatory signaling and cellular recruitment responses to EAEC are preserved together with a Giardia-mediated diminishment in myeloid cell activation . Conversely , EAEC extinguishes markers of host energy expenditure regulatory responses to Giardia , as host metabolic adaptations appear exhausted . Integrating immunologic and metabolic profiles during co-pathogen infection and malnutrition , we develop a working mechanistic model of how cumulative diet-induced and pathogen-triggered microbial perturbations result in an increasingly wasted host .
Childhood malnutrition and its resultant host developmental , metabolic , and immunologic sequelae continue to affect 156 million children less than five years of age worldwide [1] . Impaired child growth attainment is epidemiologically associated with 1 ) alterations in resident intestinal microbiota ( dysbiosis ) [1 , 2]; 2 ) increased susceptibility to multiple concurrent and recurrent enteric pathogens [3 , 4]; 3 ) intestinal dysfunction together with markers of increased intestinal myeloid [5] and T-cell activation ( termed Environmental Enteropathy ( EE ) ) [3 , 6]; and 4 ) perturbations in gut microbial-host co-metabolism [7] . Emerging data from the Malnutrition and Enteric Diseases ( Mal-ED ) multisite international study has revealed that cumulative pathogen exposures confer a high associated risk for poor growth [8] . These exposures are diverse , with prokaryotic pathogens enteroaggregative Escherichia coli and the oftentimes persistent protozoan Giardia lamblia among the most commonly detected [9 , 10] . Elucidating mechanistic pathways by which these diverse microbial triggers interact to potentiate the malnourished condition could improve restorative interventions for malnourished children . Indeed , data from randomized control therapeutic trials expose knowledge gaps in our biological understanding of microbial drivers of malnutrition [11–15] . Inconsistent or only partial benefits are achieved from interventions that target a single component of this pathogenesis such as nutrient supplementation alone [11] or in combination with broad-spectrum antibacterials [12 , 13] , anti-parasitic drugs [14] , or anti-inflammatory agents [15] . Furthermore , field studies of endemic pediatric Giardia have associated Giardia with decreased risk of severe diarrhea and inflammatory biomarkers of EE , yet increased risk for growth impairment , suggesting that for some pathogens , novel pathways may contribute to impaired child development [9] . Using murine models of malnutrition that result in diet-dependent changes in the microbiota [16] we have published that challenge with the human EAEC isolate ( strain 042 ) [17] or G . lamblia ( assemblage B , strain H3 , cysts ) [18] is sufficient to impair growth and disrupt mucosal architecture , but with unique intestinal pathologies . We have not previously investigated whether and how these individual pathogens interact with the resident microbiota or with one another , in part due to the use of antimicrobial mediated microbiota depletion to support human pathogen colonization in mice [17–21] . Also , although xenotransplantation of feces from discordant healthy and malnourished children into gnotobiotic murine recipients demonstrate the functional ability of human-derived microbial communities to selectively recapitulate phenotypes of undernutrition , dysbiosis , [1 , 2 , 22] , and disrupted metabolism [1 , 23] , these studies are only beginning to examine the influence of enteropathogenic bacteria accompanying the dysbiosis [22 , 23] and have yet to uncouple these effects from direct or residual influences of intestinal eukaryotes also present in donor feces [1] . Thus , while existing murine models provide insight into individual nutritional and microbial triggers that influence gut function , metabolism , and host growth , none to date have intentionally examined the integrated effects of dysbiosis with sequential and diverse pathogen exposures common in malnourished children . To address how gut microbial adaptations to undernutrition combine with cumulative enteropathogen burdens to influence host growth , mucosal immune responses , and metabolism , we developed a new integrated model of protein-malnutrition induced microbial disruption and multi-pathogen enteropathy . G . lamblia and EAEC , pathogens commonly detected in malnourished children , were selected as pathogens of interest . In addition to identifying new pathogen-specific pathways that contribute to malnutrition , we demonstrate co-modulation of mucosal immune and metabolic responses that converge to worsen host growth . Furthermore , gut microbial-mediated proteolysis was amplified in the increasingly wasted host along with exhaustion of co-metabolic adaptations in energy regulatory and compensatory metabolic pathways .
Murine intestinal microbiota can differentially prevent prolonged Giardia lamblia colonization , even in T and B-cell deficient hosts . Thus , we and other investigators have used continuous antibiotics ( ampicillin , vancomycin , neomycin ) ( Abx ) in drinking water to enhance G . lamblia infection [18–21] . Using this Abx cocktail we previously published that G . lamblia ( Assemblage B , strain H3 cysts ) challenge results in detectable shedding at 104−105/gram feces by qPCR of the 18S small ribosomal subunit through the first 5–7 days post-challenge ( early infection ) . But unlike clearance following G . lamblia ( Assemblage A , strain WB trophozoites ) challenge , G . lamblia H3 shedding increases by ~ 2 logs after day 9 and remains consistent through 4–6 weeks together with small intestinal trophozoite colonization ( persistent infection ) [18] . To test the hypothesis that the disrupted intestinal 16S community during protein malnutrition [16] would functionally impair microbiota-mediated colonization resistance [24] , we eliminated Abx from the model . Weaned mice were fed either a protein deficient ( 2% protein ) diet ( PD ) or an isocaloric but protein sufficient ( 20% protein ) control diet ( CD ) for 15 days prior to challenge with 106 G . lamblia H3 ( Assemblage B ) cysts ( Fig 1A ) . We previously published that this duration of acclimation on diet is sufficient to establish discrepant 16S rRNA genetic profiles [16] . Small intestinal tissues harvested at 5 days ( early ) and 28 days ( persistent ) post-infection ( dpi ) demonstrated higher abundance of Giardia on day 5 in mice fed PD and only mice fed PD remained infected through 28 days by qPCR ( Fig 1B ) . Histopathology confirmed the presence of mucosal-associated Giardia trophozoites in H3 cyst-challenged mice fed PD ( Fig 1C ) . In separate experiments , we confirmed that parasites persisted in mice fed CD and challenged with 106 G . lamblia H3 cysts if concurrently treated with Abx . Giardia was detected in the duodenum of abx-treated mice fed CD at 35 dpi , and regardless of diet , Giardia was detected in stools through 42 dpi ( S1 Fig ) . Finally , consistent with the greater infectious potential of the partially stomach-acid resistant parasite cyst stage compared with the excysted trophozoite stage , we confirmed that regardless of Abx , only H3 cysts and not axenized H3 trophozoites were sufficient to achieve consistent Giardia colonization by both light microscopy and qPCR ( S1 Fig ) in this model . Similar to what we observed previously in abx-treated mice [18] , protein deficiency combined with Giardia to impair host growth ( P<0 . 05 PD-Giardia vs uninfected CD-fed control in Fig 1D ) . Giardia infection in mice fed a PD diet also had greater duodenal bacterial abundance , measured by both universal 16S rRNA qPCR/gram tissue and V3-V4 specific amplicon product , than infected mice fed CD ( P<0 . 05 ) ( Fig 1E and 1F ) . Consistent with several features of microbial alterations in both malnourished children [25] and protein-deficient diet fed mice [26] , the duodenal 16S rRNA composition in mice fed PD demonstrated an increased Firmicutes:Bacteroidetes ratio ( S1 Fig ) that was mainly driven by an increase in the abundance of Clostridiales ( Fig 1G ) . Giardia tended to enhance this skew towards Firmicutes together with a reduction in Bacteroidetes from 12%—7% ( Fig 1H ) . Thus , rather than excluding Giardia , the small intestinal microbiota in mice fed the protein deficient diet permitted persistent Giardia infection whereas abx were necessary for prolonged parasite detection in mice fed the CD diet . To examine whether PD in this model had interfered with protective mucosal responses against Giardia we performed flow cytometry on upper small intestinal lamina propria in the same mice . Regardless of infection , mice fed PD demonstrated a skew toward increased myeloid cells ( CD11b+ ) with reciprocal reductions in T-cells ( CD3ε+ ) among CD45+ cells analyzed compared with mice fed the CD-diet ( Fig 1I ) . In addition , mice fed PD demonstrated a reduction in B-cell frequency ( B220+ ) at 5 dpi compared with infected mice fed CD ( P<0 . 05 ) ( Fig 1I ) . Concurrently , we analyzed mucosal production of key cytokines that promote Giardia clearance , such as IL-6 and IL-17A [27 , 28] , compared with those that resembled the profile of prolonged infections in children ( increased IL-4 ) [28] ( Fig 1J ) . Corresponding to persistent infection , mice fed PD demonstrated a trend toward decreased IL-6 , IL-17A , and IL12p40 , with a significant increase in IL-4 ( P<0 . 05 for IL-4 ) , irrespective of Giardia infection . Having established that Giardia incorporates into a disrupted intestinal microbiota in mice fed the PD diet , we next investigated the role of resident microbiota as determinants of growth impairment during Giardia infection in PD diet fed mice ( Fig 2A ) . Continuous exposure to the antibacterials that have no anti-giardial activity ( Abx ) prevented Giardia-induced growth impairment ( Fig 2B ) even despite an early increase in Giardia fecal shedding ( Fig 2C ) and a similar intestinal Giardia burden through 14 dpi ( Fig 2D ) . The Abx exposure resulted in a fecal dominance of Lactococcus ( >98% ) regardless of infection ( Fig 1S ) . In non-Abx treated mice , there were no significant differences in fecal 16S rRNA composition between infected and non-infected mice ( Fig 2E ) , although Enterobacteriaceae tended to be over-represented in Giardia infected mice ( Fig 2F ) . Targeted qPCR to determine absolute abundance of predominant taxa ( Firmicutes and Bacteroidetes ) as well as Enterobacteriaceae identified reductions in both Firmicutes and Bacteroidetes to below the limit of detection in the duodenum and 3–4 log decreases in the feces during Abx treatment regardless of infection ( Fig 2G ) . In non-Abx treated Giardia-infected mice there was a 1 . 5 log increase in Firmicutes ( P<0 . 05 ) in the duodenum ( Fig 2G ) . In addition , consistent with findings that increased numbers of E . coli in small intestinal aspirates recovered from patients with giardiasis correlate with greater symptom severity [29] , increased fecal Enterobacteriaceae abundance at 15 dpi in non-Abx treated mice was a better predictor of poor growth in individual Giardia-infected mice than Giardia burden in either stool or duodenum ( Fig 2H ) . To test whether alterations in intestinal microbiota and mucosal immune responses during persistent Giardia infection would enhance or diminish growth impairment during enteropathogen co-infection , we next developed a sequential co-infection model using one of the most common pathogen isolated in malnourished children , EAEC [10] . For these experiments we used EAEC042 that elicits acute myeloid cell inflammation during other nutrient deficient states [17] . First , we established that challenge with 109 EAEC042 in mice fed the PD diet but not mice fed CD diet led to rapid weight loss ( 7% body weight compared with uninfected PD-fed controls ( P<0 . 001 3 dpi ) ( Fig 3A ) and mucosal inflammation that persisted through 14 days post-EAEC challenge ( Fig 3B ) . Next , we acclimated mice on either the PD or CD diet for 15 days prior to Giardia exposure and then sequentially challenged with EAEC042 during the persistent phase ( 14 dpi ) of Giardia infection ( Fig 3C ) . The two pathogens combined to enhance weight loss in mice fed the PD diet ( ~100% greater loss of initial weight , P<0 . 05 in co-infected mice compared with uninfected PD-fed controls ) ( Fig 3D ) . In mice fed CD and co-infected with both pathogens , no weight loss was observed ( Fig 3D ) . Giardia did not influence EAEC042 stool shedding which was 2 logs greater in mice fed the PD diet as determined by qPCR of the EAEC-specific aap target ( Fig 3E ) . Giardia , however , did alter inflammatory markers of environmental enteropathy when present alone and during EAEC co-infection in protein deficient fed mice . Myeloperoxidase ( MPO ) a product of activated neutrophils , was variably detected in the mice fed protein deficient . Fecal MPO tended to be elevated in response to either pathogen alone , but paradoxically decreased to levels similar to uninfected controls in co-infected mice ( Fig 3F ) . Calprotectin ( Cp ) , another marker of myeloid cell activation , was elevated only in EAEC042 mono-infected animals , but was decreased in any Giardia infected group ( Fig 3F ) . Lipocalin-2 ( LCN ) , a marker of either neutrophil or epithelial cell activation was elevated only in Giardia-infected mice regardless of EAEC co-infection . Immune responses in the mucosal compartment were also altered in persistent Giardia infected mice later challenged with EAEC . EAEC infection led to significant increases in myeloid lineage ( CD11b+ cells ) in the ileum at 17 dpi Giardia challenge and 3 dpi EAEC challenge ( Fig 3G ) . The increased proportion of lymphocytes ( both CD3ε+ and B220+ cells ) at 28 dpi Giardia challenge and 14 dpi EAEC challenge ( Fig 3H ) was similar in either EAEC mono-infected or co-infected mice . In contrast , total LPLs , particularly lymphocytes ( CD3ε+ and B220+ cells ) were decreased during persistent ( 17 dpi ) Giardia infection compared with uninfected controls ( S2 Fig ) . Using a broad-based luminex 32-plex panel we performed an unbiased analysis of cytokine and chemokine responses on all protein deficient diet fed mice at 17 dpi Giardia challenge and 3 dpi EAEC challenge . We detected 28 of 32 targets in at least 2 mice in each group that are shown as fold change relative to uninfected controls ( Fig 3I ) . Both pathogens modulated the cytokine/chemokine response alone and during co-infection . In all conditions , IL1α , a pro-inflammatory alarmin that is released by enterocytes during intestinal injury [30] , was elevated in all groups and reached significance in Giardia infected mice ( ~30-fold ) and robustly increased in co-infection ( ~80-fold ) . IL-9 was significantly elevated in Giardia mono-infected and by ~20-fold in co-infected mice , together with a tendency towards greater IL-4 and IL-13 . Each group demonstrated a decrease in IFNγ , that was significant in EAEC mono-infected mice . CCL5 was elevated ( ~2 fold ) in EAEC infected and co-infected mice . Consistent with the early expansion of myeloid cells in EAEC infected mice , CXCL8 ( IL-8/KC ) was also elevated in EAEC and co-infected mice ( ~1 . 6 fold ) . CCL11 ( eotaxin ) was uniquely elevated ( ~6-fold ) only in co-infected mice . This change corresponded to a trend toward increased eosinophils ( CD45+SiglecF+ ) among myeloid cells in co-infected compared with EAEC mono-infected mice ( 52% vs 39% , ns ) at later timepoints ( Fig 3H ) . Changes in select cytokines and chemokines in Giardia mono-infected mice from early ( 5 dpi ) to persistent ( 17 dpi ) timepoints compared with uninfected controls revealed alterations in mucosal immune responses during persistent Giardia infection . Giardia lead to progressive increases in IL-1α ( P<0 . 05 ) and IL-2 ( P<0 . 05 ) as well as IL-4 and IL-13 , but IFNγ progressively decreased ( P<0 . 05 ) in persistently infected mice ( S2 Fig ) . To determine whether either pathogen alone or the pathogens in combination altered gut microbial host metabolism , we performed 16S rRNA sequencing in feces simultaneously with urinary metabolic profiling ( metabonomics ) using 1H nuclear magnetic resonance ( NMR ) spectroscopy in mice fed the PD diet ( Fig 4 ) . In this experiment , weaned mice were highly susceptible to weight loss following 106 G . lamblia H3 cyst challenge , that was further potentiated following EAEC co-infection six days later ( Fig 4A ) . Focusing first on 16S rRNA sequencing , in Giardia mono-infected mice , phyla-level changes at day 7 and day 13 showed consistent relative increases in Firmicutes and reductions in Verrucomicrobia ( Akkermansia mucinophila ) in Giardia mono-infected mice ( Fig 4B ) . Anaerobes such as Clostridiales members ( day 7 and day 13 after G . lamblia challenge ) and Turicibacter ( day 7 after G . lamblia challenge ) as well as Enterococcus sp . ( day 13 after Giardia challenge ) accounted for the Firmicutes expansion in Giardia mono-infected mice ( S3 Fig ) . Either Giardia or EAEC mono-infected mice had a reduction in Bifidobacterium pseudolongum . In EAEC mono-infected or co-infected mice Enterobacteriaceae were increased relative to uninfected controls or Giardia mono-infection ( S3 Fig ) . Phyla-level 16S rRNA composition in co-infected mice otherwise more closely resembled EAEC mono-infection . Orthogonal projection to latent structures-discriminant analysis ( OPLS-DA ) coefficient plots identified a range of urinary metabolic perturbations induced by Giardia infection on both day 7 and day 13 ( Fig 4C ) ( Q2Y = 0 . 40; P = 0 . 02 vs uninfected PBS controls ) many of which were also elevated in co-infected compared with EAEC mono-infected mice ( Q2Y = 0 . 82; p = 0 . 001 ) ( Fig 4D ) . We observed no significant difference in the OPLS-DA metabolic profiles of Giardia infected mice between day 7 or 13 days post-challenge ( Q2Y = 0 . 33 R2X = 0 . 24 , P = 0 . 12 ) . Significantly altered metabolites are summarized in a heat map in Fig 4E along with their correlation to class membership . Focusing first on metabolites unique to Giardia infection ( Fig 4E ) , consistent with Giardia trophozoite reliance upon on host-derived lipids for membrane synthesis and optimal growth ( ie . lecithin , gylcocholic and taurocholic bile ) [31] , Giardia-infected mice demonstrated increased excretion of bile acid constituents , phosphatidylcholine ( PC ) coupled with choline breakdown metabolites methylamine ( MA ) and dimethylamine ( DMA ) and the taurine metabolite isethionate . These indicators of bile acid deconjugation and lipid breakdown were present on both day 7 and day 13 post-Giardia challenge ( Fig 4E ) . Increases in MA and DMA occurred independent of a concurrent increase in the microbial-dependent precursor trimethylamine ( TMA ) or its hepatic oxidized metabolite TMAO , a biochemical pattern resembling that observed with Kwashiorkor-type malnutrition [1] , and were thus suggestive of increased choline availability in the small intestine rather than downstream gut microbial-dependent choline breakdown . Alanine , a by-product of Giardia glucose fermentation , was elevated at day 13 post-Giardia challenge in mono-infected mice , while pipecolic acid , one of the most abundant amino acid byproducts of Giardia metabolism in vitro [32] was identified in Giardia-infected mice at both timepoints , regardless of co-infection . Giardia also enhanced gut microbial-host co-metabolites of aromatic amino acids including tyrosine ( 4-cresol glucuronide ( 4-CG ) and 4-cresyl sulfate ( 4-CS ) and 4-hydroxyphenylacetyl ( 4-HPA ) sulfate ) , tryptophan ( 3-indoxyl sulfate ( 3-IS ) and indole-3-acetylglycine ( IAG ) ) , and phenylalanine ( phenylacetylglycine ( PAG ) ) . Increases in urinary β-oxidation metabolites , accumulation of the early tricarboxylic acid cycle intermediate cis-aconitate , and changes in muscle metabolites creatine and creatinine indicated altered host energy utilization in Giardia-infected mice . In addition , methylated nicotinamide derivatives capable of regulating energy expenditure ( N-methylnicotinamide ( NMND ) and nicotinamide-N-oxide ( NAO ) ) [23] were increased in Giardia infected mice . Consistent with the finding that increased urinary NMND predicts catch-up growth in undernourished children [7] , persistently Giardia-infected mice fed the protein deficient diet developed ‘overshoot’ growth gains compared with uninfected age and diet-matched controls upon re-nourishment ( switched from the PD to the CD diet on 42 dpi ) ( S3 Fig ) . The Giardia-induced changes in gut microbial host co-metabolites of proteolysis either persisted ( 4-HPA sulfate , IAG ) or overlapped ( PAG , 4-CG , 4-CS ) with those seen during EAEC infection alone , and these metabolites were even further magnified in co-infected mice ( Fig 4E ) . However , EAEC-mediated increases in TMA and TMAO ( Fig 4E ) , indicative of microbial-dependent choline breakdown , were reversed in Giardia co-infected mice ( Fig 4E ) , and resembled the metabolic perturbation in choline metabolism of Giardia infection alone ( Fig 4E ) . Similarly , elevated taurine excretion in Giardia- infected mice persisted through co-infection ( Fig 4E ) . Whereas either infection increased lipid oxidation evident in increased β-oxidation breakdown products ( hexanoylglycine , butyrylglycine , and isovalarylglycine ) along with the β-oxidation pathway precursor acetyl-carnitine , metabolism during co-infection shifted away from β-oxidation as indicated by a decrease in acetyl-carnitine and downstream β-oxidation metabolites . Concurrently , co-infection led to an inversion of creatine:creatinine ratios , suggesting altered muscle metabolism compared with either infection alone ( Fig 4E ) . Finally , host energy expenditure adaptations via the nicotinamide pathway ( NMND and NAO ) during Giardia infection alone ( Fig 4E ) were extinguished following EAEC co-infection ( Fig 4E ) .
Multiple and diverse pathogen exposures are hypothesized to cause intestinal dysfunction , also termed Environmental Enteropathy ( EE ) , in malnourished children . In the present study , we modeled co-infection with two of the most commonly isolated pathogens in malnourished children , Giardia lamblia and enteroaggregative Escherichia coli ( EAEC ) . We used protein deficiency in weaned mice to investigate how microbial-specific pathways intersect to impair host growth , mucosal inflammation , and metabolism during malnutrition . Our integrated nutritional , microbial , immunological , and metabolic observations add insight into how changes in resident microbiota combine with cumulative enteropathogen exposures to interfere with host growth and metabolic adaptations to protein malnutrition . For the first time we demonstrate that a resident microbiota that is permissive to enteropathogen colonization also simultaneously promotes growth impairment during persistent Giardia infection . Although Giardia was insufficient to induce intestinal inflammation characteristic of EE-like changes , despite evidence of mucosal injury ( IL1α ) , the parasite had a profound effect on gut microbial-host co-metabolism . EAEC , on the other hand , promoted robust expansion of lamina propria cells coupled with secretion of myeloid ( CXCL8 ( IL-8 ) ) and lymphoid ( CCL5 ) chemokines . Together , these pathogens synergistically increased signals of intestinal injury , IL1α , and CCL11 . EAEC-dependent increases in myeloid cells were preserved in co-infected mice; however , persistent Giardia infection resulted in diminished myeloid cell specific activation markers ( Cp and to a lesser degree MPO ) consistent with parasite-mediated alterations in host immune pro-inflammatory responses . Strikingly , these non-invasive co-pathogens resulted in an increasingly proteolytic microbiota that dominated the co-metabolic profile ( specifically leading to increased tryptophan , tyrosine , and phenylalanine co-metabolites ) , despite relatively restricted changes in the 16S rRNA composition . Simultaneously , host metabolic adaptation to protein deficiency progressively declined , eventually resulting in the loss of host-mediated nicotinamide-pathway energy regulation , and disrupting lipid oxidation up-regulation and muscle metabolism in the increasingly malnourished host . A working model of these specific pathogen-mediated microbial , immunologic , and metabolic alterations are shown in Fig 5 . Our findings support that an ability to better compete for restricted resources in the intestinal environment is one mechanism whereby enteropathogens may more successfully infect malnourished hosts . For example , the protein deficient diet contains 0 . 34% rather than the 3 . 4% arginine contained in the 20% protein sufficient control diet [16] . In weaned mice , this protein deficient diet recapitulates several dysbiotic features described in malnourished children: altered maturation of the fecal intestinal microbiota [16 , 33] , increased susceptibility to Giardia and EAEC , and increased microbial-mediated tryptophan breakdown [7 , 16] . In the present study we also observed an altered Firmicutes:Bacteroidetes ratio in the protein deficient diet-fed mice [25] , that was modestly increased at early timepoints after Giardia infection . In contrast arginine-supplementation has been shown to increase the abundance of Bacteroidetes relative to Firmicutes in the small intestine [34] . Since Giardia is a microaerophilic protozoan that can utilize either glucose or arginine for growth and replication , we speculate that a diet-dependent decrease in Bacteroidetes reduced bacterial competition for arginine . A limitation of 1H-NMR profiling is an inability to directly detect arginine metabolites ( ornithine , citrulline ) , and thus we could not determine whether Giardia infection was sufficient to further magnify host arginine deficiency . However , consistent with Giardia use of arginine in order to evade host immune defenses through arginine-deiminase ( ADI ) [35] , the continued decline in IFNγ in mice infected with Giardia could be a result of the actions of Giardia ADI to skew dendritic cell TLR-responses away from pro-inflammatory cytokines [36] . Furthermore , a reduction in B-cells as seen in Giardia infected mice , is similar to other models of arginine deficiency [37] . Also , unlike arginine-mediated increases in Bacteroidetes that can enhance TLR-dependent mucosal immune responses [38] similar to some specific Lactobacilli that facilitate Giardia clearance in mice [34] , the protein deficient diet led to decreases in pro-inflammatory cytokines associated with Giardia [27 , 28 , 39 , 40] or EAEC [41] clearance: namely IL-6 , IL-17A , and IFNγ . Rather , reciprocal increases in IL-4 , a correlate of prolonged duration of Giardia shedding in children [18 , 42] were seen . Interestingly , like the arrested maturation of the microbiota during protein deficient conditions [16] , this relative shift toward a predominately Th2-type cytokine mileu also resembles that of the neonatal period [43] . This Th2-type cytokine shift can also be differentially induced via upregulation of thymic stromal lymphopoietin ( TSLP ) in response to Firmicutes-rich altered Schaedler flora [44] . These collective findings suggest that the isolated protein deficiency in this model establishes a threshold nutrient deficiency that is sufficient to disrupt microbiota-mediated pathogen exclusion , and adds insights into why postnatal Giardia acquisition ( up to 6 months of age ) may be such a vulnerable period for longitudinal growth impairment [7 , 45] . Despite variation among reports , many epidemiologic studies in malnourished children reveal that early and persistent Giardia associates with impaired growth attainment [9 , 45] despite inversely decreased stool markers of EE-like inflammation myeloperoxidase ( MPO ) [7] and the T-cell activation marker neopterin [9 , 46] . Also , there is an apparent decreased risk for acute diarrhea and diminished markers of systemic inflammation in children infected with Giardia [47] that may be abolished following multi-nutrient supplementation [48] . It was critical , therefore , to examine how Giardia interacted alone and during co-infection . Previous findings have shown that bacteria cultivated from jejunal aspirates of patients with symptomatic giardiasis elicit more inflammation in germ free mice than axenized Giardia trophozoites [49] , and that Giardia increased bacterial mucosal translocation , even after parasite clearance in some animal models [50 , 51] . This led us to hypothesize that bacteria may similarly influence growth outcomes during giardiasis . Using continuous antibiotic exposures , we show for the first time that these interactions are crucial for host growth attainment . However , unlike the same antibiotic cocktail that led to reduced CD8+T-cell activation and consequently , decreased host-mediated immunopathogenesis in another Giardia model [52] , we did not see significant inflammation in the mucosa of protein deficient diet fed infected mice . Therefore , the primary driver of growth impairment in this model appears to be a Giardia-mediated disruption in microbial-host metabolism . These data support that one mechanism of Giardia-mediated growth faltering is through an altered intestinal ecology [53] . For example , pipecolic acid , a byproduct of lysine degradation that is significantly increased in Giardia spent media [32] , was uniquely detected only in Giardia-infected or co-infected mice , and could represent a pathway whereby intestinal parasites limit luminal availability of essential amino acids in the undernourished host [16] . Our findings of increased phosphatidylcholine ( PC ) , choline , and taurine/isothionate in Giardia infected mice , may also indicate disrupted lipid metabolism through the parasite’s consumption of bile salts ( independent of known expression of bile-salt hydrolases ) and acquisition/turnover of exogenous lipids in the small intestine via phospholipid-transporting transmembrane proteins ( such as flipases ) [32] , as well as choline kinases and phosphatidylcholine synthases [32] . These perturbations in bile acid and/or lipid homoestasis could have implications for growth in malnourished children [31 , 54] . Finally , urinary alanine , a unique byproduct of Giardia glucose fermentation under low-oxygen tension [55] , was elevated together with relative increases in fecal Clostridiales , suggesting an increased anaerobic environment in the Giardia infected mice on a protein deficient diet . Other microbial-dependent urinary metabolites that are altered during Giardia infection are not known to be direct products of Giardia metabolism: such as MA , DMA , and TMA as well as metabolites of aromatic amino acid breakdown ( ie . PAG ( phenylalanine ) , 4-CS/4-CG ( tyrosine ) , and 3-IS/3-IAG ( tryptophan ) ) . Also , since EAEC alone also fueled microbial-dependent proteolysis of aromatic amino acids with the exception of 4-HPA sulfate , a breakdown product of tyramine that may be another unique metabolite of Giardia [32] , we suspect these markers of amino acid catabolism indicate products of bacterial metabolism . Since decreases in the dietary constituents sucrose and tartrate in either Giardia or EAEC infected mice suggested reduced exogenous protein intake , this metabolic shift could have resulted from microbial degradation of host derived proteins , potentially released from injured or sloughted epithelial cells or leakage across disrupted tight-junctions . Furthermore , since these same proteolytic metabolites have been identified in undernourished children [7] , and were present together with an uncoupling of TCA intermediates in Kwashiorkor-associated dysbiosis [1] our findings raise the need to elucidate the role of Giardia and EAEC in metabolic-based studies in human infections . Also , follow-up integrated proteomic and metabolomics analyses across various Giardia and EAEC strains could greatly expand the presently limited systems biology databases of these and other enteropathogens [16 , 56] . Our data raise important considerations for host mucosal immune consequences of multi-enteropathogen infections in malnourished children . The lack of intestinal inflammation seen in these protein deficient diet fed mice during persistent Giardia infection is reminiscent of the majority of intestinal biopsies in children with Giardia infection [9] . This is in contrast to the persistent inflammation seen in symptomatic chronic giardiasis in adult humans [39 , 57] as we previously reported in Abx-treated otherwise healthy nourished chronically infected mice [18] . Rather , our findings that Giardia potentiated signals of intestinal injury ( mucosal IL1α and CCL11 and luminal LCN-2 ) , but dampened markers of myeloid activation ( MPO and Calprotectin ) during EAEC co-infection suggests the Giardia-mediated mucosal immune modulation may have led to inappropriate and deleterious responses to bacterial co-infection . Notably , these findings in a persistent G . lamblia assemblage B infection model demonstrate a potentially different mechanism of Giardia-induced immune modulation compared to prior reports of G . lamblia assemblage A-dependent cathepsin-B mediated cleavage of IL-8 that led to reduced myeloid cell chemotactic responses described by Cotton , et al [21] . Rather , prior Giardia infection resulted in greater IL-4 and IL-13 and increased IL-9 , a potential marker of mast cell activity [58] . The elevated CCL11 but reduced calprotectin together with an increase in Th2-type cytokines , could be consistent with the presence of intermediate-type macrophages [59] that were recently found to expand following Giardia challenge in mice [60] . Additional studies inclusive of more stringent flow cytometry characterization are needed to investigate the mechanisms driving this altered response . Future mechanistic studies , for example , are planned to determine whether this phenotype is the result of potential taurine-dependent inhibition of NFκB signaling in host macrophages [61] . Integrating metabolic data with altered mucosal immune responses , we identify putative microbial-mediated mechanisms driving the severity of malnutrition with cumulative pathogen exposures ( Fig 5 ) . Tryptophan , for example is required for protein synthesis and optimal host growth , but its fate is highly influenced by intestinal microbial metabolism as well as competing host tissue-compartmentalized metabolic and immune stressors . During cytokine-mediated chronic inflammation , tryptophan is metabolized in the kynurenine pathway via upregulation of indoleamine 2 , 3 dioxygenase ( IDO ) that promotes local T-cell proliferation , switching from Th17 to T- regulatory phenotypes [62] and IL-22 mediated homeostasis [63] . Alternatively , tryptophan is an important route of nicotinamide synthesis via hepatic tryptophan 2 , 3 dioxygenase ( TDO ) . This production of nicotinamide increases available nicotinamide adenine dinucleotide ( NAD+ ) , an essential co-factor for tricarboxylic acid cycle ( TCA ) metabolism and preservation of oxidative phosphorylation [64] . Low serum tryptophan levels as well as increases in tryptophan degradation via IDO in the kynurenine pathway have been documented in malnourished children [3 , 65] , whereas urinary excretion of N-methly-nicotinamide ( NMND ) predicts catch-up growth in undernourished children [7] . In this regard , in uninfected mice fed a protein deficient diet , increased carbohydrate metabolism through the TCA cycle occurred together with increased excretion of NMND [16] , suggesting increased methylation of the NAD ( H ) generated during the TCA cycle to NMND via the irreversible actions of nicotinamide N-methyltransferase ( NMNT ) . Although NMND excretion was also increased during Giardia infection , the TCA cycle activity was not . Instead , during Giardia infection , there was an accumulation of cis-aconitate , a precursor to the irreversible first NAD-requiring enzyme isocitrate dehydrogenase ( IDH ) in the TCA cycle . This finding is compatible with an overall NAD+ pool deficit as a potential consequence of hypermethylation of available nicotinamide via NMNT , a potential feature of catabolic states [66] . The ability of NMNT to regulate energy expenditure [7] may ultimately be an advantageous adaptation during malnutrition . As in undernourished children [67] , Giardia-infected mice in this model corresponded with increased ‘catch-up’ growth potential after refeeding . During EAEC infection , however , inflammatory cells likely competed for tryptophan via cytokine-mediated upregulation of IDO in the intestinal mucosa . As a result , not only was TCA activity decreased , but increased NMND was also lost during co-infection . In addition to the enhanced EAEC-mediated intestinal inflammation , when combined with Giardia infection , there was further evidence of microbial-mediated tryptophan breakdown . The consequences of these two intersecting pathways may have ultimately led to an insurmountable exogenous tryptophan deficit , that together with increased IL1α [68] , further fueled muscle catabolism ( increased creatine excretion ) and exhausted cellular aerobic respiration including the loss of compensatory increases in lipid metabolism ( hexanoylglycine , butyrylglycine , and isovalarylglycine and decreased acetyl-carnitine ) . Although we used carefully age , sex , diet , and vendor-source matched controls to address potential biological variability when studying outcomes dependent upon intestinal microbiota , one inherent limitation of these studies performed in a specific pathogen free ( SPF ) environment is the inability to directly determine which microbe-microbe interactions were most consequential for pathogenesis . We speculate , therefore , that the correlation between Enterobacteriaceae burden and growth impairment during giardiasis and potentially enhanced EAEC virulence may be mediated through a shared Giardia-dependent metabolic perturbation . Recently , cultivation of a laboratory strain E . coli in Giardia-spent media led to conversion from a commensal to a pathogenic phenotype in a nematode infection model [69] as well as decreased taurine-receptor gene expression in the E . coli grown in Giardia-spent media [69] . Our in vivo data that Giardia leads to increased taurine excretion , even during EAEC co-infection , may similarly indicate decreased bacterial taurine metabolism . More rigorous co-association studies in gnotobiotic conditions are needed to differentiate Giardia-mediated interactions with select resident or pathogenic Enterobacteriaceae . Also , contrasting deleterious microbial interactions with microbes associated with health , such as Bifidobacterium pseudolongum [25] or Akkermansia muciniphila [22] that were variably reduced across groups in this model , are needed to unravel how these intriguing intestinal ecological interactions influence malnutrition . In conclusion , this murine model of protein malnutrition and microbial disruptions using common enteropathogen challenges in malnourished children provides important insights into microbial interactions and metabolic mechanisms that contribute to undernutrition . These collective integrated studies raise important considerations for ongoing longitudinal studies of childhood malnutrition that rely upon immune and metabolic biomarkers as surrogates for small intestinal pathology . As in this model , correlating specific enteropathogen exposures with these metabolic perturbations and immune biomarkers may help to define appropriate ( physiological ) from maladaptive ( pathological ) responses , and identify critical windows of optimal and potentially individualized interventions . For example , children who excrete NMND despite enteropathogen infection , like Giardia-infected mice in this model , may be primed to ‘catch-up’ and thus respond to targeted nutrient therapy , whereas the lack of NMND excretion during Giardia infection may signify a need to identify and treat a co-existing enteropathogen such as EAEC or another trigger of intestinal inflammation . Similarly , although fecal MPO may indicate environmental enteropathy predictive of poor growth , an inappropriately suppressed MPO in the setting of an immune-modulating co-infection may be an alarm for ensuing host immune and metabolic exhaustion and morbidity . Indeed , translating integrated findings in experimental models such as those presented in this study may not only help to identify novel interventions that disrupt a vicious cycle of microbial-mediated enteric failure , but may better leverage combinations of existing interventions to restore microbial-host mutualism and promote mucosal restitution .
This study included the use of mice . This study was conducted in strict accordance with recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the International Animal Care and Use Committee at the University of Virginia ( Animal Care and Use Committee Protocol number: 3315 ) . Tissue procurement was performed following anesthesia ( ketamine hydrochloride and xylazine ) and cervical dislocation , and all efforts were made to minimize suffering . All experiments were performed using weaned male C57Bl/6 mice received from Jackson Laboratories at 3 weeks of age . Mice were initiated on either a protein deficient diet ( PD; 2% protein , Harlan Laboratories or Research Diets ) or an isocaloric control diet ( CD; 20% protein , Harlan Laboratories or Research Diets ) within three days of arrival ( 24 days of life ) . For all experiments mice were randomized into weight-matched groups and continued on experimental diets throughout the duration of the experiment . Mice receiving continuous antimicrobials received ampicillin ( 1 mg/mL , Fischer ) , vancomycin ( 1 mg/mL , Novaplus ) , and neomycin ( 1 . 4 mg/mL , Durvet ) in drinking water changed ad libitum or every five days . Serial weights were obtained every 1–7 days from arrival through the termination of the experiment . Stools were collected every other day following infection [18] . Gerbil-passaged purified G . lamblia H3 ( Assemblage B ) cysts were purchased from Waterborne , Inc . ( New Orleans , LA ) . Cysts were washed and diluted in PBS and used within 48 hours of arrival . Each infected mouse received an inoculum of 104−106 cysts . G . lamblia H3 trophozoites were also obtained from Waterborne , Inc and maintained in modified TYI-S-33 media prior to inoculum preparation ( 107/mouse ) as previously described [18] . The EAEC strain 042 was originally obtained from James Nataro at the University of Virginia . For each experiment , a separate inoculum of 109/mouse was grown from a glycerol stocked maintained at -80°C and prepared in DMEM high glucose medium as previously described [17] . All pathogen preparations were maintained on ice until administered via oral gavage using 22-gauged feeding needles in 100 μL volumes . Uninfected controls were similarly gavaged with either 100 μL of PBS ( for Giardia ) or DMEM high glucose ( for EAEC ) control . At the time of euthanasia , 4-cm segments of small intestine were removed beginning 0 . 5 cm from the pyloric sphincter , and placed into 4-mL of chilled PBS on ice for 30 minutes . Trophozoites were identified using an inverted microscope and counted on a hemacytomer with a limit of detection of 104 trophozoites/mL . DNA from stool and/or intestinal tissue was extracted from thawed samples using the QIAmp DNA stool Kit ( Qiagen ) as previously described [18] . For detection of 16S rRNA genes , modifications to enrich detection including additional steps of homogenization in bead tubes ( UltraClean fecal DNA bead tubes , Mo Bio Laboratories ) in 400/360μL of ASL/ATL buffer using a Mini-Beadbeater for 1 minute , 30/40 μL of Proteinase K for stool/tissue homogenates , and incubation of tissues at 56°C for two hours [70] . See S1 Table for a list of gene targets used in this study . For all qPCR studies , a standard curve serial dilution for the respective target was run in replicate on all plates for validation purposes . A run was considered valid only if the BioRad CFX Detection System detected an efficiency of 90–110% and a correlation r2 > 0 . 98 . Both experimental ( i . e . infected animal fecal DNA ) and controls ( i . e . uninfected animal fecal DNA ) were run on the plate . A non-template control was universally included on every plate to control for non-specific amplifications . Quantification of G . lamblia and EAEC infections and bacterial group targets ( see bleow ) were performed in a BioRad CFX Detection System by interpoloating Ct values of each run with a standard curve of known amounts of each respected pathogen DNA and transformed into number of organisms per milligram of sample [17 , 18] . Development of these assays included spiking known quantities of pathogen into uninfected mouse stool as well as serial dilutions demonstrating no significant evidence of inhibition in the assay [17 , 18] . The master mix solution and conditions for specific target detection of G . lamblia 18S small ribosomal subunit [forward primer , 5’- GACGGCTCAGGACAACGGTT-3’ ( Operon ) , reverse primer , 5’- TTGCCAGCGGTGTCCG-3’ ( Operon ) , and probe FAM-5’- CCCGCGGCGGTCCCTGCTAG-3’-BHQ ( IDT ) ) ] and the EAEC aap gene ( forward 5’-CTTGGGTATCAGCCTGAATG-3’ and reverse 5’-AACCCATTCGGTTAGAGCAC-3’ primers ) for EAEC detection are described elsewhere [17 , 18] . Amplification for G . lamblia consisted of 3 minutes at 95°C , followed by 39 cycles of 15 seconds at 95°C , and 60 seconds at 58°C . Amplification for EAEC consisted of 3min at 95°C , followed by 40 cycles of 10 seconds at 95°C and 30 seconds at 61 . 5°C , followed by 40 cycles of 10 seconds , starting at 65°C with 0 . 5°C increments for melt curve . For both pathogens , a standard curve ranging from either 102−107 G . lamblia cysts ( determined by hemacytometer ) [limit of detection 103/1000 mg stool or tissue as previously described [18] or 101−108 EAEC CFU/mL ( determined by OD600 and confirmed by plate counts ) , limit of detection 101/1000 mg stool or tissue as previously described [17] was included on every PCR run . For both pathogens , sample Ct values in the range of 37–40 and/or non-specific melt curves ( EAEC ) were considered non-specific and excluded from the analysis . The SYBR Green master mix solution and conditions for detection of Firmicutes , Bacteroidetes , and Enterobacteriacea are described elsewhere [70] . The Bact934 forward primer ( 5’- GGARCATGTGGTTTAATTCGATGAT -3’ ) and Bact106 reverse primer ( 5’- AGCTGACGACAACCATGCAG—3’ ) were used for Bacteroidetes detection; Firm350 forward primer ( 5’—GGCAGCAGTRGGGAATCTTC—3’ ) and Firm814 reverse primer ( 5’—ACACYTAGYACTCATCGTT—3’ ) were used for Firmicutes detection; and Uni515 forward primer ( 5’—GTGCCAGCMGCCGCGGTAA—3’ ) and Ent826 reverse primer ( 5’–GCCTCAAGGGCACAACCTCCAAG—3’ ) for Enterobacteriaceae detection . Amplification conditions consisted of 5 minutes at 95°C , then 40 cycles of 10 seconds at 95°C and 59°C for 30 seconds . Melt curve analysis was carried out in 0 . 5-degree increments for 5 seconds starting at 65°C and ending with 95°C . The Ct values on each run were compared to standards of known concentrations of bacterial DNA on the same plate as previously described [71] . For universal 16S detection , we used forward primer ( 5’ GTGSTGCAYGGYTGTCGTCA -3’ ) and reverse primer ( 5’ ACGTCRTCCMCACCTTCCTC -3’ ) [72] . and an Enterococcus faecalis with 4x16S copies/genome as a standard . For universal 16S detection ( total bacteria ) , SYBR Green mastermix ( Biolegend ) was used with 10 ng of template DNA . Amplification conditions consisted of 2 minutes at 95°C , then 45 cycles of 15 seconds at 95°C and 50°C for 30 seconds and 72°C for 45 seconds . Melt curve analysis was carried out in 0 . 5-degree increments for 5 seconds starting at 65°C and ending with 95°C . Any Ct values between 37–40 and/or melt curves that did not align with the respective melt curve of the known bacterial DNA standards were considered non-specific amplification and excluded from analysis . The V3-V4 region of the 16S rRNA gene was amplified according to manufacturer specifications ( Ilumina Mi-Seq ) from 5 ng of purified genomic DNA . Index primers ( Nexterea XT Index 1 and 2 ) were used to label individual samples prior to library quantification . A pico-green assay was used to quantify individual sample libraries prior to normalization , pooling , and sequencing using Ilumina Mi-Seq . 16S libraries were pooled and sequenced using Ilumina MiSeq at the Microbiome Core at UNC or the Genomics Core Facility at UVA . Reads were assigned to samples using Illumina BaseSpace demultiplexing . From these reads , bacterial presence and relative abundance were quantified using the QIIME package , version 1 . 9 . 1 [73] . Fastq-join was called via QIIME to join paired-end reads with a minimum of 6 base pair overlap and 8 percent maximum difference [74] . Barcodes were extracted from paired reads , then reads were quality-filtered using split_libraries . py from QIIME with default parameters . Chimeric sequences were detected and removed using reference-based and de novo chimera identification with USEARCH61 [75] and the GreenGenes 16S rRNA database [75] . Identification of operational taxonomic units ( OTUs ) was performed by referencing the GreenGenes database with UCLUST ( 97% sequence identity cutoff ) and de novo otu-picking with QIIME . The RDP classifier was used to assign taxonomy to identified OTUs . The weighted UniFrac distance [76] between each sample was calculated and principal coordinates analysis ( PCoA ) was performed on the resulting distance matrix . PCoA results were visualized with EMPeror [77] . To prepare OTU data for relative abundance comparisons , samples with fewer than 311 reads were excluded and OTUs were filtered by two criteria: being present in at least two samples and having 0 . 5% relative abundance in at least one sample . Using the remaining OTUs and their relative abundances , the DESeq2 package [78] was used , as implemented in QIIME , to determine differentially abundant OTUs . OTUs with a multiple-testing corrected p value <0 . 05 were considered differentially abundant . For duodenal tissues there was a high degree of unassigned taxa . We therefore restricted the V3-V4 pipeline analysis to only those OTUs with at least 10 , 000 reads ( “high-abundance” ) . This restriction resulted in 541 , 910 retained high quality reads in the duodenum ( mean 15 , 483 reads/sample ) . 50–98% of otherwise unassigned taxa were successfully eliminated from these groups . Metagenomic visualizations were generated using KRONA open source software ( https://github . com/marbl/Krona/wiki ) ) after uploading OTU data files transferred to Krona ExcelTemplates ( https://github . com/marbl/Krona/wiki/ExcelTemplate ) ) [79] . Urine samples were on individual mice at each timepoint indicated . Sample collections occurred regularly at 2 pm on each collection data . Urines were placed immediately on ice , and stored at -80°C before shipping on dry ice to JMP and JS . Urines were then analyzed individually by 1H nuclear magnetic resonance ( NMR ) spectroscopy . Each sample was prepared by combining 30 μl of urine with 30 μl of phosphate buffer ( pH 7 . 4; 100% D2O ) containing 1 mM of the internal standard , 3-trimethylsilyl-1-[2 , 2 , 3 , 3-2H4] propionate ( TSP ) . Samples were mixed by vortex and spun ( 10 , 000 g ) for 10 minutes before transfer to a 1 . 7 mm NMR tube . Spectroscopic analysis was carried out on a 700 MHz Bruker NMR spectrometer equipped with a cryo-probe . Standard one-dimensional 1H NMR spectra of the urine samples were acquired with water peak suppression using a standard pulse sequence . For each sample , 8 dummy scans were followed by 128 scans and collected in 64K data points . A recycle delay of 2 s , a mixing time of 10 μs and an acquisition time of 3 . 8 s was used . The spectral width was set at 20 ppm . Chemical shifts in the spectra were referenced to the TSP singlet at δ 0 . 0 . Spectra were manually phased and corrected for baseline distortions . 1H NMR spectra ( δ 0 . 2–10 . 0 ) were digitized into consecutive integrated spectral regions ( ~20 , 000 ) of equal width ( 0 . 00055 ppm ) . The regions between δ 4 . 50–5 . 00 were removed in order to minimize the effect of baseline effects caused by imperfect water suppression . Each spectrum was then normalized to unit area . Multivariate modeling was performed in Matlab using in-house scripts . This included principal components analysis ( PCA ) using pareto scaling and orthogonal projection to latent structures-discriminant analysis ( OPLS-DA ) constructed using unit variance scaling . OPLS-DA models were constructed to assist model interpretation . Here , 1H NMR spectroscopic profiles were used as the descriptor matrix and class membership ( e . g . Giardia , EAEC , uninfected ) was used as the response variable . The predictive performance ( Q2Y ) of the model was calculated using a 7-fold cross validation approach and model validity was established by permutation testing ( 1000 permutations ) [16] . Metabolites associated with a series of pair-wise OPLS-DA models were identified by the correlation coefficient ( R ) with the class membership and summarized in a heat map . Flow cytometry of lamina propria cells was performed according to our previously published protocols [18] . For isolation of cells from ileum segments , suspensions of small intestinal lamina propria cells were prepared from 4 cm segments of distal small intestine beginning 1 cm from the ileocecal valve . After segments were PBS-flushed and cleaned of gross debris and mucus , they were incubated at 37°C in HBSS buffer containing 50mM EDTA and 1mM DTT for 30 minutes in a shaking incubator at 250 rpm in order to remove epithelial-layer cells . The digested tissue was passed through a 100-μm filter and the filtrate centrifuged as previously described [17] . For lamina propria cell isolations , the tissue pieces were minced and suspended in 10 ml RPMI media with 4% FBS containing 1 . 2 mg/ml collagenase Type IV , 1 . 0 mg/ml dispase , and 25–40 U/ml DNase I enzyme solution for 30 minutes at 37°C in a shaking incubator and strained through a 40-μm filter . The resulting pellets were resuspended in 1% BSA-PBS buffer . Fluorophore-conjugated purified mAbs used in flow cytometry were purchased from BD Biosciences ( CD4-PE-Cy7 , CD3ε-BV421 , CD45-V500 , and CD11b-APC-Cy7 ( clone M1/70 ) ) and Biolegend ( B220 [CD45R]-PerCP ) , and cell surface staining was performed according to the manufacturer’s instructions . All samples were acquired on a CyAn ADP LX analyzer ( BD Biosciences and Cytek Development ) . The leukocyte population was gated based on forward/side scatter , the threshold was set at 50 FSC and single cells isolated via pulse width . All gates were applied universally to all samples within each batch . Data is represented as number positive per normalized total events ( i . e . , 500 , 000 ) or frequency within a specified gate . This methodology was used to elucidate the proportional leukocyte changes in equal amounts of tissue [80] . Cell analysis was performed using FlowJo version 9 . 3 . 3 software ( Tree Star ) . For mucosal cytokine and chemokine responses , 0 . 5–1 . 0 cm of ileum were immediately placed in liquid nitrogen at the time of euthanasia and stored at -80°C until use . Protein was collected from ileum lysates , which were made using a lysis buffer containing 50 mM HEPES , 1% Triton X-100 , and Halt protease inhibitor on ice and homogenized in Zirconia beads ( Biospec ) using a Mini-Beadbeater ( Biospec ) for 60 seconds . Clarified supernatants were stored at -80°C . Multiplex protein quantification was performed using Luminex 100 IS System at the University of Virginia Biomolecular Core facility . Markers of inflammation were measured in the cecal contents or stool using methods described previously [5 , 16] . Briefly , stool or cecal contents were collected when cecal and other intestinal tissue was harvested and stored at -20°C until measurement . At the time of analysis specimens were allowed to thaw at room temperature and were diluted 7-fold in buffer with protease inhibitors ( RIPA , radioimmunoprecipitation assay buffer ) . The samples were vortexed , centrifuged and the supernatants used to measure the biomarkers . The fecal myeloperoxidase ( MPO ) and lipocalin-2 concentrations were measured using commercially available kits that employed polyclonal antibody-based enzyme-linked immunosorbent assay ( ELISA ) methods , from R&D systems ( Minneapolis , USA ) according the manufacturer’s instructions . Cecal calprotectin was quantified by ELISA ( Hycult Biotech ) according to manufacturer's instructions using 1:1000 dilution of cecal contents based on optimization using pooled cecal samples . Measured protein levels were normalized to total lysate protein for respective specimens as determined by bicinchoninic assay ( BCA ) ( Thermoscientific ) at 562nm absorbance ( Biotek ELISA plate reader ) after 30 minutes incubation of sample with reagent at room temperature . Total protein of each sample was assayed using the BCA Protein Assay Kit from Pierce ( Pittsburgh , PA ) . The absorption was measured using an Epoch plate reader , Bio-tek Instruments , Inc . ( USA ) . Units were expressed as pg/mg of total protein . Data analyses were performed with GraphPad Prism 6 . 0 and 7 . 0 software . All statistical analyses were done from raw data with the use of One-way and Two-way analysis of variance with Dunn’s or Bonferroni post hoc analysis from multi-group comparisons . For comparisons between only two groups , Student t tests were used for parametrically distributed data and Mann-Whitney tests for non-parametrically distributed data where applicable . Differences were considered significant at P<0 . 05 . Data are represented as means ± standard errors of the mean . | Malnourished children are exposed to multiple sequential , and oftentimes , persistent enteropathogens . Intestinal microbial disruption and inflammation are known to contribute to the pathogenesis of malnutrition , but how co-pathogens interact with each other , with the resident microbiota , or with the host to alter these pathways is unknown . Using a new model of enteric co-infection with Giardia lamblia and enteroaggregative Escherichia coli in mice fed a protein deficient diet , we identify host growth and intestinal immune responses that are differentially mediated by pathogen-microbe interactions , including parasite-mediated changes in intestinal microbial host co-metabolism , and altered immune responses during co-infection . Our data model how early life cumulative enteropathogen exposures progressively disrupt intestinal immunity and host metabolism during crucial developmental periods . Furthermore , studies in this co-infection model reveal new insights into environmental and microbial determinants of pathogenicity for presently common , but poorly understood enteropathogens like Giardia lamblia , that may not conform to existing paradigms of microbial pathogenesis based on single pathogen-designed models . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"protein",
"metabolism",
"pathology",
"and",
"laboratory",
"medicine",
"giardia",
"diet",
"parasitic",
"protozoans",
"protozoans",
"nutrition",
"malnutrition",
"digestive",
"system",
"giardia",
"lamblia",
"infectious",
"diseases",
... | 2017 | Cross-modulation of pathogen-specific pathways enhances malnutrition during enteric co-infection with Giardia lamblia and enteroaggregative Escherichia coli |
Relapsing fever spirochetes are global yet neglected pathogens causing recurrent febrile episodes , chills , nausea , vomiting , and pregnancy complications . Given these nonspecific clinical manifestations , improving diagnostic assays for relapsing fever spirochetes will allow for identification of endemic foci and expedite proper treatment . Previously , an antigen designated the Borrelia immunogenic protein A ( BipA ) was identified in the North American species Borrelia hermsii . Thus far , BipA appears unique to relapsing fever spirochetes . The antigen remains unidentified outside of these pathogens , while interspecies amino acid identity for BipA in relapsing fever spirochetes is only 24–36% . The current study investigated the immunogenicity of BipA in Borrelia turicatae , a species distributed in the southern United States and Latin America . bipA was amplified from six isolates of Borrelia turicatae , and sequence analysis demonstrated that the gene is conserved among isolates . A tick transmission system was developed for B . turicatae in mice and a canine , two likely vertebrate hosts , which enabled the evaluation of serological responses against recombinant BipA ( rBipA ) . These studies indicated that BipA is antigenic in both animal systems after infection by tick bite , yet serum antibodies failed to bind to B . hermsii rBipA at a detectable level . Moreover , mice continued to generate an antibody response against BipA one year after the initial infection , further demonstrating the protein's potential toward identifying endemic foci for B . turicatae . These initial studies support the hypothesis that BipA is a spirochete antigen unique to a relapsing fever Borrelia species , and could be used to improve efforts for identifying B . turicatae endemic regions .
Three causative agents of tick-borne relapsing fever borreliosis in the New World are Borrelia hermsii , Borrelia parkeri , and Borrelia turicatae , with B . hermsii being the most epidemiologically and ecologically characterized species [1] . While B . hermsii is distributed in high elevation coniferous forests and maintained in enzootic cycles with rodents as the primary reservoir , less is known regarding the other two species . Moreover , few epidemiological studies have been performed and little molecular data exists for B . turicatae and its arthropod vectors Ornithodoros turicata . There are endemic foci for B . turicatae in Texas and Florida , where clinical isolates have been obtained from sick dogs [2] , [3] , which suggests a role for wild canids in the maintenance of the spirochetes in nature . Dr . Oscar Felsenfeld also reported the distribution of O . turicata into Mexico , Central , and South America [4] , yet given the absence of Latin American isolates for B . turicatae the identification of endemic foci is unclear . A limitation in defining the distribution of B . turicatae has been the absence of diagnostic antigens specific for the species . Previously , the Borrelia immunogenic protein A ( BipA ) of B . hermsii was demonstrated to discriminate infections caused by Lyme and relapsing fever borreliosis . Outside of relapsing fever spirochete spp . a homologue of BipA has not been cataloged in the GenBank database [5] . With 36% amino acid identity between B . hermsii and B . turicatae BipA [5] , it is also unclear if the B . turicatae homologue induces a host antibody response during infection . This study investigated the sequence similarity of BipA between B . turicatae isolates , and we developed a tick transmission system for the spirochetes to determine the antigenicity of recombinant BipA ( rBipA ) during rodent and canine infections . Collectively , these results suggests that BipA can be used as a diagnostic antigen for B . turicatae .
All animal studies were in accordance with the Mississippi State University Institutional Animal Care and Use Committee ( IACUC protocol #'s 11-091 and 12-067 ) . Animal husbandry was provided by veterinary staff and technicians within the Association for Assessment and Accreditation of Laboratory Animal Care and the National Institutes of Health Office of Laboratory Animal Welfare assured program at Mississippi State University . All work was performed in adherence to the United States Public Health Service Policy on Humane Care and Use of Laboratory Animals and the Guide for the Care and Use of Laboratory Animals . B . turicatae was cultivated in mBSK medium containing 12% rabbit serum [6] , [7] . Amplification and sequencing primers for bipA were designed using the 91E135 isolate of B . turicatae ( Table 1 ) . Additional samples were water ( negative control ) , 95PE-570 , 95PE-1807 , TCB-1 , TCB-2 , and FCB-1 isolates [2] . Polymerase chain reaction ( PCR ) was performed as previously described using the GoTaq Flexi DNA Polymerase ( Promega Corporation , Madison , WI , USA ) . Amplicons were electrophoresed on a 1% agarose gel to visualize the DNA fragment and processed through the QIAquick PCR Purification kit ( Qiagen , Germantown , MD , USA ) , and sequencing performed at Biodesign Institute ( Arizona State University , Phoenix , AZ , USA ) . Nucleotide sequences were analyzed with the Vector NTI software ( Life Technologies , Carlsbad , CA , USA ) , and deposited to GenBank under accession numbers KC845527-KC845531 . The ticks used in the study originated from 12 uninfected adults initially maintained at the Rocky Mountain Laboratories . A cohort of second nymphal stage O . turicata was infected by first needle inoculating a group of 3 three Swiss Webster mice with B . turicatae 91E135 [2] , and uninfected ticks were allowed to engorge on the animals . After molting , vector colonization was confirmed by dissecting the midgut and salivary glands from five ticks and performing immunofluorescent assays ( IFA ) as previously described [8] . Chicken serum generated by Cocalico Biologicals Inc . against B . turicatae recombinant flagellin ( rFlaB ) was used to detect spirochetes and the secondary antibody was goat anti-chicken IgY Alexa Fluor 568 ( Life Technologies , Grand Island , NY , USA ) . Prior to the transmission studies , pre-infection serum samples were collected from all animals . Cohorts of five infected ticks per animal were allowed to feed to repletion . The animals used in the study were 10 outbred Swiss Webster mice ( Harlan Laboratories Inc . , Tampa , FL , USA ) and a one-year-old Bluetick hound ( Marshall Bioresources , North Rose , NY , USA ) . For 16 consecutive days , animals were monitored for clinical symptoms and a drop of blood was collected from the mice by tail nick and from the canine's cephalic vein to visualize spirochetes . The day following the first and second febrile episode , 3 ml of blood were collected from the canine for a complete blood count and serum chemistry profile . Of the mice used in the initial transmission study , four were maintained for one year to assess long-term serological responses generated against BipA . Prior to serum collection for immunoblotting , 2 . 5 µl of blood was collected for 10 consecutive days and placed into 47 . 5 µl of SideStep Lysis and Stabilization Buffer ( Agilent , Santa Clara , CA , USA ) . Quantitative PCR ( qPCR ) was performed as previously described [9] to determine the presence of circulating spirochetes . The probe ( flaB probe ) and primer set ( flaB F and flaB R ) used for qPCR were designed for B . turicatae flagellin ( Table 1 ) . Two groups of three mice were also needle inoculated intraperitoneally with 1×105 TCB-1 or FCB-1 spirochetes . Infection was confirmed by dark field microscopy and serum samples were collected one month after inoculation to determine reactivity to rBipA expressed from the 91E135 isolate . To express bipA from B . turicatae 91E135 as a 75 kDa thioredoxin and histidine tagged fusion protein in Escherichia coli , the gene was amplified as previously described [5] using bipA F Topo and bipA R Topo primers ( Table 1 ) . The amplicon was cloned into the pET 102/Directional TOPO expression vector following the manufacturer's instructions ( Life Technologies ) . Top10 E . coli were transformed , plasmid DNA isolated , and sequence analysis using bipA F1 , bipA F2 , bipA R1 , bipA R2 primers ( Table 1 ) was performed as previously described [5] to determine if an error had been introduced during amplification . To produce recombinant protein , BL21 E . coli were transformed with the bipA expression vector following the manufacturer's instructions , and induction was performed with 1 mM IPTG . rBipA was purified using the Ni-MAC Purification system ( Novagen , Durmstadt , Germany ) . Immunoblotting was performed to evaluate the immunogenicity of rBipA during B . turicatae infections . Protein lysates from 1×108 spirochetes , 1 µg of B . hermsii rBipA , and 1 µg B . turicatae rBipA were electrophoresed and transferred to polyvinylidene fluoride ( PVDF ) membranes using TGX gels , the Mini-PROTEAN Tera cell , and the Mini Trans Blot system ( BioRad , Hercules , CA , USA ) . Pre- and post-infection serum samples were evaluated by immunoblotting at a 1∶500 dilution and the secondary molecule used was Rec-protein G-HRP ( Life Technologies ) at a 1∶4 , 000 dilution . Immunoblots were also probed with the Anti-polyHistidine Peroxidase monoclonal antibody ( Sigma-Aldrich , St . Louis , MO , USA ) at a 1∶4 , 000 dilution . Titers against rBipA were determined by immunoblotting with serum dilutions ranging from 1∶500 to 1∶125 , 000 . Linear regression analysis was performed by calculating the density of rBipA protein bands from immunoblots probed with serum samples diluted from 1∶800 to 1∶12 , 800 . Serum samples included Anti-polyHistidine Peroxidase monoclonal antibody ( Sigma-Aldrich , St . Louis , MO , USA ) and serum samples from mice and the canine infected by tick bite . ImageJ , http://imagej . nih . gov/ij ( National Institutes of Health , Bethesda , Maryland , USA ) , was used to analyze digitally scanned immunoblots and the density of each protein band was calculated . The R software package , www . r-project . org , was used to calculate equations of regression , R2 values , and significance . One year after tick bite , mice were euthanized by isoflurane inhalation followed by cervical dislocation and tissues were placed in 10% neutral buffered formalin for fixation . Sections of brain and synovial joints from the front and rear leg were processed and embedded in paraffin using standard histologic techniques . Paraffin embedded tissues were cut in 5 µm sections , deparaffinized , adhered to glass slides , and stained with hematoxylin and eosin ( H&E ) , or silver stained using standard histologic techniques . The sections were examined by light microscopy for inflammatory and degenerative lesions in the cerebrum , cerebellum , brainstem , and synovial joints by a board certified veterinary pathologist at Mississippi State University .
BipA was originally identified by an immunoproteomic antigen discovery approach [5] , [10] . The degree dissimilarity between B . hermsii and B . turicatae homologues prompted our investigation to compare sequences between B . turicatae isolates , and to evaluate the protein's immunogenicity in two mammals likely to be naturally exposed to the spirochetes . PCR amplification of bipA from three canine ( FCB-1 , TCB-1 , and TCB-2 ) and three tick ( 91E135 , 95PE-570 , and 95PE-1807 ) isolates produced a product of the expected molecular mass , with TCB-1 producing a slightly larger amplicon ( Figure 1 ) . Sequence analysis identified an additional 135 nucleotides encoding 45 amino acids for TCB-1 ( Figure 2 ) . While there was an overall 89% amino acid identity of BipA between isolates , the sequences flanking the 45 additional amino acids of TCB-1 were 97% identical . A similar observation was reported for B . hermsii BipA [5] . The protein from genomic group II ( GG II ) isolates of B . hermsii contained five regions of 3–24 amino acid insertions when compared to genomic group I ( GG I ) isolates . Furthermore , the amino and carboxy terminus of BipA between GGI and GGII isolates shared the highest degree of conservation . Currently , the only known isolates of B . turicatae originate from argasid soft ticks and sick dogs [2] . Furthermore , the mammalian hosts for most species of relapsing fever spirochetes include rodents and insectivores [11] . This knowledge directed us to evaluate the antigenicity of B . turicatae rBipA after tick bite using canine and murine animal models . To establish an infected tick colony , uninfected O . turicata engorged on a Swiss Webster mouse needle-inoculated with B . turicatae 91E135 . Spirochete colonization was confirmed by performing IFA on the midgut and salivary glands after the ticks molted ( data not shown ) . The remaining infected ticks fed to repletion on Swiss Webster mice and a Bluetick hound , and within four and eight days after tick bite spirochetes were visualized in murine and canine blood , respectively ( Figure 3 A and B ) . While the mice remained active when B . turicatae were visualized in the blood , the canine became febrile , lethargic , and following a given spirochetemic episode , acutely thrombocytopenic ( Table 2 ) . B . turicatae repopulated the blood from both groups of animals within four days after the initial spirochetemia , after which bacteria were undetectable by microscopy . Producing rBipA using the same expression vector as B . hermsii bipA [5] indicated that infected animals generated an immunological response that recognized B . turicatae rBipA , yet antibody binding against the recombinant B . hermsii homologue was undetectable ( Figure 4 A and B ) . Probing the immunoblots with an anti-polyhistidine monoclonal antibody confirmed that similar protein loads of B . turicatae and B . hermsii rBipA were electrophoresed , while pre-infection serum samples failed to produce a detectable antibody response against B . turicatae protein lysates or rBipA ( Figure 4 C–E ) . Canine and murine IgG titers using serum samples collected 8 weeks after tick bite ranged from 1∶12 , 800 to1∶28 , 800 . Also , regression analysis indicated significant differences ( P≤0 . 05 ) in slopes and correlation coefficients ( R2 ) when immune serum samples were probed against B . turicatae and B . hermsii rBipA ( Figure 5 A–C ) . These results indicate different affinity characteristics against rBipA from a given species when animals were infected with B . turicatae . With BipA from TCB-1 and FCB-1 being the most divergent to the 91E135 homologue , we evaluated serological responses against rBipA from animals infected with TCB-1 and FCB-1 . Inoculating mice with each isolate determined that antibodies generated against TCB-1 and FCB-1 BipA were cross reactive against rBipA from B . turicatae 91E135 ( Figure 6 A–D ) . Similarly , in B . hermsii there was sufficient amino acid conservation between BipA from B . hermsii GGI and GGII isolates that mice infected with GG II isolates produced a detectable serological response to rBipA that was expressed from a GG I isolate [5] . Collectively , these results suggest that BipA may be a unique antigen for the given species of relapsing fever spirochete causing infection . Previous studies by Cadavid et al . reported that BALB/c and SCID mice needle inoculated with the Ozona isolate of B . turicatae developed long-term infections of the brain and joints [12] . Given the persistent nature of the spirochetes within rodents , IgG responses in mice were evaluated one year after transmission by tick bite . Prior to serological analyses , qPCR performed on murine blood samples collected for 10 consecutive days indicated that the mice were no longer spirochetemic ( data not shown ) . Immunoblotting demonstrated that three of four mice continued to generate an IgG response against rBipA one year after the initial exposure , while one animal produced a weakly detectable response ( Figure 7 A–D ) . Interestingly , B . turicatae is no longer detected within the blood of Swiss Webster mice after approximately 14 days after tick bite ( data not shown ) , and with a serum half-life of 20–30 days for IgG , these results suggest a persistent infection and antigen exposure to the host immune response . Central nervous system ( CNS ) infections by relapsing fever spirochetes vary between species and genetic variants . Borrelia duttonii can reemerge in the blood from the brain after a period of quiescence [13] . Serotype A of B . turicatae Ozona were neurotropic in mice , while animals infected with serotype B spirochetes colonize the joints and heart [12] , [14] , [15] . Interestingly , CNS infection caused by B . duttonii and serotype A of B . turicatae Ozona failed to produce noticeable tissue damage [12] , [13] . In our study , postmortem necropsies of mice one year after infection by tick bite did not identify inflammatory or degenerative changes within the cerebrum , cerebellum , brain stem , or diarthrodial joints of the fore or hind limbs ( data not shown ) . Spirochetes were also undetectable in tissue sections , and it was unclear if the animals were still infected at the time of euthanasia . However , the persistent antibody responses generated against rBipA can be targeted to increase the likelihood of determining if an animal has been exposed to the spirochetes . With results suggesting that BipA may be a species-specific antigen , additional studies should evaluate homologues from less characterized yet closely related species to B . turicatae . For example multilocus sequencing indicated that Borrelia johnsonii , a novel species of relapsing fever spirochete that colonizes Carios kelleyi [16] , was closely related to B . turicatae and Borrelia parkeri . As additional sequence information is obtained from B . johnsonii and B . parkeri and animal models developed , the diagnostic potential of BipA can be further evaluated as an antigen unique to a given species of relapsing fever spirochete . The maintenance and ecology of B . turicatae in the southern United States and Latin America is poorly understood , and given the nonspecific clinical symptoms , the disease is likely under reported . Historically , mapping endemic foci has been associated with capturing ticks at sites where human infection occurred and evaluating the arthropods for spirochete colonization , or by obtaining clinical isolates from sick dogs [2] , [3] , [17] . Pathogen surveillance based on identifying infected ticks can be difficult because O . turicata are nest- , den- , and cave-dwelling with a 5–60 minute bloodmeal [3] , [11] , [18] , and the ticks are rarely identified on the host . We are also unaware of serological surveys for B . turicatae probably due to the degree of antibody cross-reactivity that occurs during spirochete infections [5] , [19] , [20] . Given the characterization of BipA , serological analyses to identify endemic foci for B . turicatae where rodents and wild canids are monitored as sentinels are possible . | Undiagnosed febrile illnesses continue to afflict those in resource poor countries . Relapsing fever spirochetes are one such pathogen causing a significant health burden , yet the pathogenesis , ecology , and distribution of B . turicatae is understudied . To address these shortcomings , we analyzed the amino acid sequence of the Borrelia immunogenic protein A ( BipA ) in isolates of B . turicatae . Mice and a canine were also infected by tick bite and transmission and serological responses were evaluated in these two likely mammalian hosts . B . turicatae was visualized within the blood of both animals and antibody responses generated against recombinant BipA indicated that the antigen that may be unique to infections caused by B . turicatae . Moreover , mice continued to generate antibodies a year after tick bite , suggesting a persistent infection . Our results indicate that the immune responses generated against BipA could identify additional vertebrate hosts , define endemic foci for B . turicatae , and increase the awareness of the disease to improve healthcare . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] | [] | 2013 | Sequence Analysis and Serological Responses against Borrelia turicatae BipA, a Putative Species-Specific Antigen |
The immune response in the skin of dogs infected with Leishmania infantum is poorly understood , and limited studies have described the immunopathological profile with regard to distinct levels of tissue parasitism and the clinical progression of canine visceral leishmaniasis ( CVL ) . A detailed analysis of inflammatory cells ( neutrophils , eosinophils , mast cells , lymphocytes , and macrophages ) as well as the expression of chemokines ( CCL2 , CCL4 , CCL5 , CCL13 , CCL17 , CCL21 , CCL24 , and CXCL8 ) was carried out in dermis skin samples from 35 dogs that were naturally infected with L . infantum . The analysis was based on real-time polymerase chain reaction ( PCR ) in the context of skin parasitism and the clinical status of CVL . We demonstrated increased inflammatory infiltrate composed mainly of mononuclear cells in the skin of animals with severe forms of CVL and high parasite density . Analysis of the inflammatory cell profile of the skin revealed an increase in the number of macrophages and reductions in lymphocytes , eosinophils , and mast cells that correlated with clinical progression of the disease . Additionally , enhanced parasite density was correlated with an increase in macrophages and decreases in eosinophils and mast cells . The chemokine mRNA expression demonstrated that enhanced parasite density was positively correlated with the expression of CCL2 , CCL4 , CCL5 , CCL21 , and CXCL8 . In contrast , there was a negative correlation between parasite density and CCL24 expression . These findings represent an advance in the knowledge about skin inflammatory infiltrates in CVL and the systemic consequences . Additionally , the findings may contribute to the design of new and more efficient prophylactic tools and immunological therapies against CVL .
Visceral leishmaniasis ( VL ) , caused by Leishmania ( Leishmania ) infantum [syn . Leishmania ( Leishmania ) chagasi] , is endemic in over 88 countries in Europe and Latin America and is transmitted by the bite of the female sand fly ( phlebotomine ) [1] . The skin is considered a key reservoir compartment for amastigotes in both asymptomatic and symptomatic Leishmania-infected dogs , and the important role of dogs in VL transmission in urban areas is supported by the high parasite loads found in the skin of infected animals and their shared habitat with humans [2]–[4] . Previous investigations have revealed that symptomatic Leishmania-infected dogs exhibit an intense diffuse dermal inflammatory infiltrate and a high parasitic burden in comparison with their asymptomatic counterparts [4] . On this basis it was proposed that the immunopathological changes in the skin and the levels of cutaneous parasitism are directly related to the clinical severity of the disease . Several previous studies correlated immunopathological aspects of canine visceral leishmaniasis ( CVL ) with tissue parasite load and/or the clinical status of the disease [4]–[17] . The typical histopathological finding in tissues is a granulomatous inflammatory reaction associated with the presence of Leishmania amastigotes within macrophages [18] . In the skin of Leishmania infantum–infected dogs , the histopathological alterations consist of variable degrees of focal or diffuse inflammatory infiltrate in the dermis and variable numbers of plasma cells , macrophages , lymphocytes , and isolated neutrophils [19] , [20] . Furthermore , it has recently been demonstrated that parasite density in the skin , bone marrow , and spleen compartments increases according to the severity of the clinical manifestation of CVL [6] , [14] , [16] . Calabrese et al . [21] evaluated histopathological aspects of the skin in naturally infected dogs and showed that low parasite load is associated with an intense inflammatory reaction driven mainly by mast cells , indicating that these cells exert a role in innate immunity and in the resistance against canine Leishmania infection . Recently , different aspects of the immune response in Leishmania-infected dogs have been studied , particularly the profile of cytokines in distinct compartments [5] , [9] , [12] , [17] , [22]–[24] . However , the role of chemokines in disease progression or parasite burdens of the visceralising species represents an important approach for understanding immunopathology in CVL . Chemokines are chemotactic factors that coordinate recruitment of leukocytes that are involved in homeostasis as well as innate and adaptive immune responses . In the context of experimental or natural infection in CVL , an up-regulation of the chemokines in the spleen has been described , although only CXCL10 and CCL5 were markedly elevated in oligosymptomatic dogs [24] . In addition , the augmented levels of chemokines suggested an accumulation of infiltrating monocytes attracted by CCL3 and CCL2 . CD4+Th1 and CD8+ cells also accumulated and may have been recruited by CXCL10 , with further expression induced through IFN-γ secretion [24] . Considering the importance of chemokines on the pattern of CVL and the lack of studies on this topic , understanding the chemokine profile during ongoing L . infantum infection in dogs is a prerequisite for identifying the mechanisms for resistance or susceptibility in this experimental model . In the present study , the immunopathology of CVL was investigated by performing detailed analyses of the RNA expression of different chemokines ( CCL2 , CCL4 , CCL5 , CCL13 , CCL17 , CCL21 , CCL24 , and CXCL8 ) and the occurrence of inflammatory cells ( neutrophils , eosinophils , mast cells , lymphocytes , and macrophages ) . We focused on selected chemokines in order to characterize their role in recruiting particular cell types to the inflammatory infiltrate in skin from L . infantum–infected dogs . Thus , we found that the chemokines CCL2 , CCL4 , CCL5 , and CCL21 attract macrophages; CCL5 and CCL4 attract inflammatory lymphocytes , particularly Th1-type cells; CCL24 attracts eosinophils; and CXCL8 attracts neutrophils , monocytes , and lymphocytes . The chemokine CCL17 helps to establish the inflammatory infiltrate , a characteristic feature of various inflammatory skin conditions , by attracting CCR4-bearing cells , which are especially polarized to Th2-type cells and regulatory T cells [25] . This study was carried out using the skin from 35 dogs that were naturally infected with L . infantum .
The study was approved by the Committees of Ethics in Animal Experimentation of the Universidade Federal de Ouro Preto ( protocol no . 083/2007 ) and of the Universidade Federal de Minas Gerais ( protocol no . 020/2007 ) and the City Council of Belo Horizonte ( protocol no . 001/2008 ) . All procedures in this study were according to the guidelines set by the Brazilian Animal Experimental Collage ( COBEA ) , Federal Law number 11794 . The study population comprised 51 adult dogs ( aged between 2 and 6 years ) of both sexes that had been captured by the Center of Zoonosis Control in Belo Horizonte ( Minas Gerais , Brazil ) , a region with a high prevalence of CVL and human VL . The animals were maintained under quarantine at the kennels of the Instituto de Ciências Biológicas ( Universidade Federal de Minas Gerais ) prior to tissue collection for 40 days and treated for intestinal helminthic infections ( Endal Plus; Schering-Plough Coopers , São Paulo , SP , Brazil ) . We treated kennels with pyrethroid insecticide monthly during the quarantine and throughout the experiments . Experimental animals were categorized on the basis of serological results from an indirect immunofluorescence assay test , the “gold standard” immunological test in Brazil for the diagnosis of CVL . Sixteen dogs with negative immunofluorescence assay test results from serum samples diluted 1∶40 and negative results for Leishmania in tissue smears ( bone marrow , ear skin , spleen , liver , and popliteal lymph node ) were considered to be non-infected and were used as the control group ( CD , n = 16 ) . Thirty-five animals with positive immunofluorescence assay titers ≥1∶40 were considered CVL positive and comprised the infected animal groups . The infected animal groups were subdivided on the basis of the presence or absence of signs of infection according to Mancianti et al . [25] as follows: an asymptomatic group ( AD , n = 10 ) , in which indicative signs of the disease were absent; an oligosymptomatic group ( OD , n = 10 ) , in which a maximum of three clinical signs of the disease were present , including opaque bristles , localized alopecia , and/or moderate weight loss; and a symptomatic group ( SD , n = 15 ) , in which characteristic clinical signs of the disease were present , including cutaneous lesions , onycogryphosis , opaque bristles , severe loss of weight , apathy , and keratoconjunctivitis . Animals were euthanized with sodium thiopental ( Abbott Laboratories , Abbott Park , IL , USA; 30 mg/kg body weight ) and samples of skin tissue were collected from ear areas without lesions . A skin fragment from each group was used for tissue imprints on microscopic slides coded for blinded analysis . The samples were fixed in methanol , stained with Giemsa , and examined under an optical microscope . Leishmania amastigote stages were counted and parasite densities were expressed as Leishman Donovan Units ( LDU ) , corresponding to the number of Leishmania amastigotes per 1000 nucleated cells per skin imprint as described by Stauber [27] , with some modifications according to Reis et al . [7] , [8] . Parasite densities were categorized statistically into tertiles as absent ( LDU = 0; CD group , n = 16 ) , low ( LDU = 1–9; LP group , n = 12 ) , medium ( LDU = 10–130; MP group , n = 11 ) , and high ( LDU = 131–7246; HP group , n = 12 ) . A second fragment of ear skin was stored at −80°C until required for RNA analysis . Total RNA was extracted by homogenizing approximately 20 mg of skin tissue with 1 mL of TRIzol reagent ( Invitrogen Brasil , São Paulo , SP , Brazil ) in a rotor stator . The lysate was incubated at room temperature for 10 min , mixed with chloroform ( 200 µL ) by tube inversion , and centrifuged at 12 , 000× g for 10 min at 4°C . The aqueous phase was collected , and RNA extraction was done by using the SV Total RNA Isolation System ( Promega , Madison , WI , USA ) according to the manufacturer's recommendations , which included a DNase treatment step . The efficiency of DNAse treatment was evaluated by PCR amplification of the cDNA reaction mix without the addition of the ThermoScript enzyme . Finally , each quantitative PCR ( q-PCR ) run was performed with two internal controls assessing both potential genomic DNA contamination ( no reverse transcriptase added ) and purity of the reagents used ( no cDNA added ) . Strand cDNAs were synthesized from 1 . 0 µg of total RNA using the ThermoScript RT-PCR System ( Invitrogen Brasil ) with oligo-dT primers according to the manufacturer's instructions . Primers were designed with the aid of Gene Runner version 3 . 05 ( Hasting Software Inc . 2004 ) using specific canine sequences obtained from GenBank ( http://www . ncbi . nlm . nih . gov/genbank/ ) . The sequences of the primers are listed in Table 1 . The primers were synthesized by Eurogentec ( Southampton , UK ) and reconstituted in nuclease-free water . q-PCR was performed on an ABI Prism 7000 DNA Sequence Detection System using SYBR Green PCR Master Mix ( PE Applied Biosystems , Foster City , CA , USA ) , with 100 mM of each primer and cDNA diluted to 1∶5 . The samples were incubated at 95°C for 10 min and then submitted to 40 cycles of 95°C for 15 s and 60°C for 1 min , during which time fluorescence data were collected . The efficiency of each pair of primers was evaluated by serial dilution of cDNA according to the protocol developed by PE Applied Biosystems . In order to evaluate gene expression of the chemokines CCL2 , CCL4 , CCL5 , CCL13 , CCL17 , CCL21 , CCL24 , and CXCL8 , three replicate analyses were performed , and the amount of target RNA was normalized with respect to the endogenous control ( housekeeping ) gene GAPDH . Data were expressed according to the 2−ΔΔCt method using the mean value of the ΔCt of the control group as the calibrator [26] . After normalization , the expression levels of chemokines in the infected groups were considered up-regulated or down-regulated compared to expression levels in the control group . PCR products were cloned with pGEM-T Easy Vector ( Promega ) and sequenced to check specificity by using an ABI 3100 Automated Sequencer ( PE Applied Biosystems ) and a Dye Terminator Kit . Table 2 presents a summary of the different chemokines and their biological effects during Leishmania infection in dogs , mice , and humans . These data illustrate how recruitment of specific cells might influence the pathogenesis of Leishmania infection . For standard histological examination ( morphometric analysis and leukocyte differential counting ) sections were coded and stained with hematoxylin and eosin and subsequently underwent blinded analysis under an optical microscope ( model CH3RF100 , Olympus Optical Co . , Tokyo , Japan ) . The inflammatory cells ( neutrophils , eosinophils , macrophages , mast cells , and lymphocytes ) that were recruited to the dermis were counted , and the results are expressed in percentages . Cell types in the cellular infiltrate in the dermis were quantified by using 20 random images ( total area = 1 . 5×106 µm2 ) that adequately represented a slide . Thus , the density and predominance of cells in the inflammatory infiltrate and their distribution within the skin layers were assessed and registered quantitatively . The images displayed in the 40× objective were digitized through a Leica DM5000B microscope with a coupled camera using the program Leica Application Suite ( version 2 . 4 . 0 R1 , Leica Microsystems Ltd . , Heerbrugg , Switzerland ) . For the analysis of images , Leica QWin V3 ( Leica Microsystems Ltd . ) was used to count all cell nuclei , excluding the pilose follicles , skin annexes , and epidermal cells . Statistical analyses were performed using the GraphPad Prism software package version 5 . 0 ( GraphPad Software , San Diego , CA , USA ) . Normality of the data was established using the Kolmogorov-Smirnoff test . The Kruskal-Wallis test was used for comparative studies between groups , followed by Dunn's test for multiple comparisons . Spearman's rank correlation was also computed in order to investigate relationships between the expression of chemokine mRNAs with clinical forms and skin parasite density as well as cell counts . In all cases , differences were considered significant when the probabilities of equality ( p values ) were ≤0 . 05 .
In order to investigate the relationship between clinical forms of CVL and skin parasite density as well as cellular infiltrate , correlation analyses were conducted with these parameters in L . infantum–infected animals ( n = 35 ) ( Fig . 1 ) . The main histopathological findings are shown in photomicrographs ( Fig . 1A ) . Histopathological examination of the skin showed no histological changes within the CD group ( Fig . 1A , panels 1 and 2 ) . In the LP and AD groups ( Fig . 1A , panels 3 and 4 ) , there was a mild inflammatory infiltrate , composed mainly of mononuclear cells , while in the OD and MP groups , this infiltration was mild to moderate , as shown in Fig . 1A ( panels 5 and 6 ) . In panels 7 and 8 of Fig . 1A , which represent sections of ear skin in the SD group , an intense cellular infiltrate composed mainly of mononuclear cells was observed . The intensity and predominance of cells in the inflammatory infiltrate and their distribution within the skin layers were assessed ( Fig . 1B , 1C ) . Our results demonstrated a positive correlation between cellular infiltrate and clinical status ( r = 0 . 5400 , p = 0 . 0004 ) ( Fig . 1B ) and skin parasite density ( r = 0 . 7352 , p<0 . 0001 ) ( Fig . 1C ) . Significant increases in the inflammatory infiltrate in the skin samples were observed in the AD and SD groups as compared with CD animals ( Fig . 1B ) . The HP group had a significant increase in inflammatory infiltrate compared with the CD and LP groups ( Fig . 1C ) . Moreover , the inflammatory infiltrate within the MP group was significantly increased as compared with the CD group ( Fig . 1C ) . The results also indicated positive correlation among clinical evolution of CVL and the increase of parasite density in the skin ( r = 0 . 4409 , p = 0 . 0080 ) ( Fig . 1D ) . Additionally , an increase in parasite density ( p<0 . 05 ) was detected in the skin of dogs showing the maximum clinical score ( SD ) when compared with the AD group ( Fig . 1D ) . The study of skin tissue cellularity included an assessment of the percentage of cell types ( neutrophils , eosinophils , mast cells , lymphocytes , and macrophages ) present in the inflammatory infiltrate in the skin of dogs that were naturally infected by L . infantum and categorized by clinical status and dogs that were uninfected ( Fig . 2A ) . In this context , we observed a reduction in the percentage of eosinophils in the SD group compared with the CD group ( p<0 . 05 ) , and a negative correlation between this cell population ( r = −0 . 4760 , p = 0 . 0059 ) and clinical status . Additionally , there was a decrease ( p<0 . 05 ) in the percentage of mast cells in the OD and SD groups when compared with the CD group . Similarly , a negative correlation was observed in the percentage of mast cells ( r = −0 . 6018 , p = 0 . 0002 ) compared with the clinical form of CVL . For lymphocytes , we observed an increased ( p<0 . 05 ) percentage in the AD group in comparison with the SD group and control dogs . Furthermore , we also observed an increase ( p<0 . 05 ) in the OD group as compared with the SD group . The analysis of correlation between lymphocyte counts and clinical status showed a negative correlation between the increase of lymphocytes versus the clinical outcome in CVL ( r = −0 . 6283 , p<0 . 0001 ) ( Fig . 2A ) . Significant increases ( p<0 . 05 ) were observed in the OD and SD groups in the population of macrophages in comparison to the CD group , and a positive correlation was observed ( r = 0 . 5553 , p<0 . 0010 ) between the percentage of macrophages and degree of disease . An assessment of cellular infiltrate in the skin of dogs naturally infected by L . infantum and uninfected dogs was performed by categorizing them according to skin parasite density ( Fig . 2B ) . Although neutrophil and lymphocyte subsets did not have significant changes , a shift in the cell profiles related to the innate immune response was observed . The percentage of eosinophils decreased ( p<0 . 05 ) in the MP and HP groups when compared with the CD group . Associated with these observations , a negative correlation between the percentage of eosinophils and skin parasite density was found ( r = −0 . 3885 , p = 0 . 0255 ) . The percentage of mast cells was lower in the LP , MP , and HP groups when compared with the CD group ( p<0 . 05 ) . Accordingly , a significant increase ( p<0 . 05 ) in the percentage of macrophages in the MP and HP groups in comparison with the CD group was found . Furthermore , a positive correlation between the percentage of macrophages and skin parasite density ( r = 0 . 4163 , p = 0 . 0198 ) was observed . Enhanced parasite density was positively correlated with higher expression of chemokines CCL2 , CCL4 , CCL5 , CCL21 , and CXCL8 and lesser expression of CCL24 in the skin of dogs naturally infected by L . infantum . The involvement of chemokines in recruiting cells to the skin and developing a protective response against Leishmania infection was evaluated according to skin parasitism . These results are described in Figure 3 . In this study , we also performed correlation analysis between the levels of chemokine expression and the clinical status , but significant differences did not exist between the groups ( data not shown ) . The mRNA expression of CCL2 was increased ( 5 . 8-fold; p<0 . 05 ) in the HP group as compared with the LP group . Furthermore , the correlation analysis showed that CCL2 was positively associated with an increase of parasite load in the skin of these animals ( r = 0 . 5329 , p = 0 . 0010 ) . CCL4 was up-regulated in all groups in relation to the CD group and highly expressed in the HP group in comparison to the LP and MP groups ( 3 . 5-fold and 2 . 8-fold , respectively; p<0 . 05 ) . Additionally , a positive correlation ( r = 0 . 5774 , p = 0 . 0003 ) with an increase in skin parasite density was detected . Similarly , CCL5 expression indicated a significant up-regulation occurred in all infected groups when compared to CD , and increased levels were observed in the HP group in comparison with the LP and MP groups ( 2 . 1-fold and 1 . 7-fold , respectively; p<0 . 05 ) . Moreover , a positive correlation could be established between the expression of CCL5 and skin parasite density ( r = 0 . 5480 , p = 0 . 0014 ) . CCL13 and CCL17 were down-regulated in all groups in comparison to the CD group; however , significant differences were not found between experimental groups . For the chemokine CCL21 , we observed increased expression in all groups compared with the CD group , and high levels were found in the HP group compared with the LP and MP groups ( 1 . 4-fold and 1 . 3-fold , respectively; p<0 . 05 ) . In addition , a positive correlation was observed between CCL21 expression and parasite density ( r = 0 . 6000 , p = 0 . 0004 ) . The expression of CCL24 was up-regulated in the LP and MP groups and down-regulated in the HP group compared to the CD group . In addition , higher CCL24 expression was observed in the MP group when compared with the HP group ( 0 . 9-fold; p<0 . 05 ) . Furthermore , a negative correlation was observed between CCL24 expression and skin parasite density ( r = −0 . 3368 , p = 0 . 0479 ) . With regard to CXCL8 , we observed an increase in the target transcript levels in the LP and HP groups and down-regulation in the MP group compared with CD . In addition , CXCL8 expression was significantly higher in the HP group compared with the LP and MP groups ( 6 . 9-fold and 8 . 3-fold , respectively; p<0 . 05 ) . Positive correlation was observed between CXCL8 expression and skin parasite density ( r = 0 . 4180 , p = 0 . 0155 ) . In order to better identify the association between inflammatory cells present in skin and chemokine levels , we performed additional correlation analyses between distinct cell types and cutaneous chemokine expression ( Fig . 4 ) . Interestingly , our results indicated that macrophages were the cell type that was most likely to be recruited by chemokines CCL2 ( r = 0 . 3514; p = 0 . 0486 ) , CCL4 ( r = 0 . 3600; p = 0 . 0396 ) , CCL5 ( r = 0 . 3485; p = 0 . 0469 ) , and CCL21 ( r = 0 . 3440; p = 0 . 0499 ) ( Fig . 4 ) . Negative correlation was observed between CCL21 levels and neutrophils ( r = −0 . 3562; p = 0 . 0419 ) .
The analysis of chemokine expression in lymphoid compartments is crucial for assessing central regulation and pathophysiological processes , including traffic homeostasis , inflammation , and hematopoiesis [27] , [28] . In this context , few studies have investigated the levels of chemokines in ongoing CVL . In one of these studies , Strauss-Ayali et al . [24] evaluated the expression of CCL2 , CCL4 , CCL5 , and CXCL10 in the spleen of dogs naturally or experimentally infected by L . infantum and found an increase of CCL2 and CCL5 in the experimentally infected dogs . Herein , increased levels of CCL2 , CCL4 , and CCL5 in dogs with high parasitism were observed , and these chemokines were positively correlated with parasite density . These results indicate a preferential migration of macrophages into the skin , suggesting a host strategy to control parasitism during ongoing CVL . It has been proposed that in leishmaniasis , chemokines CCL2 , CCL4 , and CCL5 generally play a role not only as chemotactic factors but also as co-activators of macrophages and consequently have a part in the elimination of parasites [29]–[32] . After stimulation with CCL2 , human macrophages experimentally infected with L . infantum produced levels of nitric oxide that were similar to those obtained by stimulation with IFN-γ , which increased the ability of these cells to eliminate the parasite [33] . In addition , CCL2 and CCL3 may induce leishmanicidal ability in vitro in human macrophages infected by L . infantum and can control the growth and multiplication of intracellular L . donovani via regulatory mechanisms mediated by nitric oxide [34] . In the present work , we demonstrated an increase in the percentage of macrophages in dogs with clinical signs ( OD and SD ) or with moderate to high parasitism ( MP and HP ) . There was also a positive correlation between the percentage of macrophages and expression of CCL2 , CCL4 , and CCL5 . Previous data published by our group demonstrated a decrease in absolute values of circulating monocytes as a hallmark found in the symptomatic group and in the group with the higher parasite load [6] . These data may suggest the recruitment of monocytes to other tissues during active CVL , where they might play an important role in immunological connections throughout antigen presentation and parasite clearance . However , the presence of macrophages in the skin infiltrates does not guarantee their ongoing function since histological analysis of skin during CVL described in this and other studies showed an intense cell infiltrate composed of mononuclear cells in animals with high parasitism that were clinically symptomatic [19] . The finding that expression of macrophage chemoattractants was associated with parasite burden contradicts previous in vitro data demonstrating that these chemokines have a macrophage-activating protective effect . This would suggest that the chemokines are recruiting immature or unresponsive macrophages . Moreover , the levels of CXCL8 observed in HP animals , despite inducing macrophage recruitment , seemed to favor the persistence of the parasite in the skin compartment . In addition , high levels of macrophages in the skin of dogs with active CVL ( OD and SD ) and in dogs with MP and HP density were demonstrated and highlighted the inability of these cells to control parasitism . Our study represents the first investigation on the involvement of CCL21 in CVL . We also observed increased levels of CCL21 in animals with high parasitism , independent of the positive correlation between the chemokine and cutaneous parasitism . It has been reported that CCL21 is an important chemokine involved in recruiting antigen-presenting cells ( APCs ) to lymphoid organs [35] . In murine infection by L . donovani , Ato et al . [36] demonstrated that CCL21 is important in the marginal zone of the spleen for maintaining the structure of its cellular composition and capturing blood antigens during Leishmania infection . Moreover , mice deficient for the gene encoding CCL21 have greater susceptibility to infection when exposed to L . donovani due to the loss of dendritic cell migration [37] . In this context , we hypothesize that increased skin parasitism has the potential to stimulate the expression of CCL21 , resulting in the recruitment of APCs in the skin from the lymphoid organs . However , it is possible that either these cells , like macrophages , do not present a functional profile favoring a Th1-immune response that would be effective against Leishmania infection . Alternately , the increase of CCL21 may lead to retention of APCs in the skin and reduce their migration to the regional lymph node where antigens would be presented to T cells . Several studies have reported the involvement of mast cells in regulating immunity against various Leishmania species [38]–[40] . In the present study , a decrease of this population was observed in the skin of animals presenting severe clinical forms of the disease ( OD and SD group ) and in all groups categorized according to parasite density ( LP , MP , and HP ) when compared with the control group . This finding could be related to this cell type being involved in attempts to contain the intense skin parasite density , as described in several studies that evaluated a murine model [41]–[43] . Calabrese et al . [21] described an intense inflammatory skin reaction formed mainly by mast cells , indicating that these cells might exert a role in innate immunity against L . infantum infection . Our data regarding mast cells conflict with this possibility , however . The discrepancy might be explained by L . infantum infection causing a diverse range of clinical and histopathological manifestations . Variations in host resistance may help to explain the variations found in the skin parasite load in dogs . Moreover , when dogs from different regions are compared , additional factors must be considered , such as variations in weather conditions ( e . g . , Leishmania infection seems to occur chiefly in dry seasons ) . In the present study , decreases in the eosinophil population and CCL24 expression were observed that were related to the clinical progression and skin parasite density . CCL24 is a specific agonist for CCR3 , attracting and activating eosinophils in parasitic diseases [44] . Some authors have described a microbicidal capability of eosinophils against L . donovani and L . major parasites [45] , [46] and suggested this cell type could play an important role in protection against Leishmania infection [47] . Moreover , Amusategui et al . [48] reported that eosinophil counts were higher in dogs that presented cutaneous signs , and they suggested that this finding was associated with allergenic responses . More studies are necessary to determine the role of eosinophils in the cutaneous immune response in CVL . The participation of neutrophils in addressing infection by parasites of the genus Leishmania has been studied in recent years to understand the mechanisms related to the innate immune response [49]–[51] . We observed that higher CXCL8 levels existed in dogs presenting high cutaneous parasitism . This chemokine induces neutrophil chemotaxis , and the initial influx of neutrophils seems to be beneficial for the survival of Leishmania in the infected tissue [50] . Interestingly , it has been reported that the parasite itself produces a protein with chemoattractant properties , called Leishmania chemotactic factor , which promotes the migration of neutrophils to the site of infection [49] , thereby boosting the phagocytosis of the parasite . Peters et al . [51] evaluated the events that occur in the skin during the initial phase of the transmission of L . major by sand flies and observed that a decrease in neutrophils at the infection site is associated with the inability of parasites to establish infection . This hypothesis is strongly supported by a recently published study from our group that showed a mixed cytokine profile during active CVL with predominantly higher cutaneous levels of interleukin ( IL ) -10 and transforming growth factor β1 apart from lower expression of IL-12 . These findings might represent a key condition that allows persistence of parasite replication in the skin [17] . Herein , our data highlight the skin as an important organ in CVL and suggest that increased levels of CCL2 , CCL4 , CCL5 , and CCL21 are associated with the immunopathogenesis of CVL . Our data also suggest that the expression of these cytokines in skin could be used as biomarkers for disease progression in dogs naturally infected by L . infantum . Our findings represent an advance in the knowledge of the involvement of skin inflammatory infiltrates in CVL and the systemic consequences and may contribute to developing a rational strategy for the design of new and more efficient prophylactic tools and immunological therapies against CVL . | Several previous studies correlated immunopathological aspects of canine visceral leishmaniasis ( CVL ) with tissue parasite load and/or the clinical status of the disease . Recently , different aspects of the immune response in Leishmania-infected dogs have been studied , particularly the profile of cytokines in distinct compartments . However , the role of chemokines in disease progression or parasite burdens of the visceralising species represents an important approach for understanding immunopathology in CVL . We found an increase in inflammatory infiltrate , which was mainly composed of mononuclear cells , in the skin of animals presenting severe forms of CVL and high parasite density . Our data also demonstrated that enhanced parasite density is positively correlated with the expression of CCL2 , CCL4 , CCL5 , CCL21 , and CXCL8 . In contrast , there was a negative correlation between parasite density and CCL24 expression . These findings represent an advance in the knowledge of the involvement of skin inflammatory infiltrates in CVL and the systemic consequences and may contribute to developing a rational strategy for the design of new and more efficient prophylactic tools and immunological therapies against CVL . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"veterinary",
"pathology",
"veterinary",
"immunology",
"veterinary",
"diseases",
"pathology",
"zoonotic",
"diseases",
"leishmaniasis",
"immunology",
"veterinary",
"science",
"veterinary",
"medicine"
] | 2012 | Higher Expression of CCL2, CCL4, CCL5, CCL21, and CXCL8 Chemokines in the Skin Associated with Parasite Density in Canine Visceral Leishmaniasis |
Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology . Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however , the Boolean formalism is restricted to characterizing protein species as either fully active or inactive . To advance beyond this limitation , we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements . Our new approach , termed constrained fuzzy logic ( cFL ) , converts a prior knowledge network ( obtained from literature or interactome databases ) into a computable model that describes graded values of protein activation across multiple pathways . We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: ( a ) generating experimentally testable biological hypotheses concerning pathway crosstalk , ( b ) establishing capability for quantitative prediction of protein activity , and ( c ) prediction and understanding of the cytokine release phenotypic response . Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data . This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone .
Signaling networks regulate cell phenotypic responses to stimuli present in the extracellular environment [1] . High throughput “interactome” data provide critical information on the composition of these networks [2] , [3] , [4] , but understanding their operation as signal processing systems is strongly advanced by direct interface with dedicated experimental data representing measured responses of biochemical species in the network ( proteins , mRNA , miRNA , etc . ) to stimulation by environmental cues in the presence or absence of perturbation [5] , [6] , [7] , [8] . Immediate early responses are dominated by protein post-translational modifications ( we focus here on phosphorylation ) , assembly of multi-protein complexes , and changes in protein stability and localization . Such responses are typically highly context dependent , varying with cell type and biological environment . A critical question for the field is how large scale measurements of these responses can be combined with a signed , directed protein signaling network ( PSN ) to better understand the operation of complex biochemical systems [9] . PSNs are typically deduced by manual or automated annotation of the literature ( e . g . [10] ) or directly from high-throughput experimental data ( e . g . [11] , [12] , [13] ) using a variety of computational techniques . PSNs are represented as node-edge graphs [14] , and although they provide high-level insight into the composition and topology of regulatory networks [15] , [16] , [17] , [18] , [19] , [20] , as currently constituted PSNs are not readily ‘computable’ in that they cannot be used to calculate activation states of the key proteins in a pathway given a set of input cues , nor can quantitative relationships between pathways be determined . This restricts the utility of PSNs for explicit prediction of responses and makes it difficult to compare network representations to functional experimental data . A chief motivation of our current work is to determine how information encoded in a PSN can be made computable and compared to experimental data from a specific cell type , resulting in a context-specific network model . Logic-based models ( e . g . [21] , [22] , [23] , [24] , [25] , [26]; reviewed in [27] , [28] ) offer one means for converting interaction maps into computable models . We have previously used Boolean logic ( BL ) to convert a literature-derived signed , directed PSN ( comprising for this purpose a ‘prior knowledge network’ [PKN] ) into a computable model that could be compared to experimental data consisting largely of the phospho-states of signal transduction proteins in the presence of different ligands and drugs [29] . This approach allowed us to determine which links in the PKN were supported by the data , and generated models that were useful in making predictions about network topology [29] and drug targets [30] . However , Boolean logic has a significant limitation , since real biochemical interactions rarely have simple on-off characteristics assumed by Boolean logic . Thus , we require a means to encode graded responses and typical sigmoidal biological relationships in a logic-based framework . One way to accomplish this is to apply traditional fuzzy logic [FL] , as demonstrated previously in modeling continuous input-output relationships to encode a complex signaling network [31] , [32] . In the realm of control theory , FL modeling is an established technique for predicting the outputs of complex industrial processes when the influences of inputs cannot be characterized precisely [33] , [34] , [35] . A central feature of FL is that it accounts for graded values of process states using a virtually unlimited repertoire of relationships between model species or components . However , for past application to biochemical signaling networks , the flexibility of conventional FL modeling necessitated that the network topology be fixed prior to either manual [31] or computational [32] parameter fitting , rendering a formal training of network topology to experimental data infeasible . In this paper we develop and employ a new approach to fuzzy logic modeling of biological networks that we term ‘constrained fuzzy logic’ [cFL] for descriptive purposes . A key feature of cFL modeling is that it limits the repertoire of relationships between model species , enabling the formal training of a PKN to experimental data and resulting in a quantitative network model . To maximize broad dissemination across the computational biology community , we implement cFL in an exisiting software tool CellNetOptimizer v2 . 0 ( CellNOpt ) , significantly extended to accommodate the further requirements of cFL while maintaining the BL analytic approach ( freely available at http://www . ebi . ac . uk/saezrodriguez/software . html ) . We demonstrate the value of the CellNOpt-cFL method by elucidating new information from a recently published experimental dataset describing phospho-protein signaling in HepG2 cells exposed to a set of inflammatory cytokines [36] . We show that a cFL model can be trained against a dataset and then validated by successful a priori prediction of test data absent from the training data . We also establish the benefits of cFL relative to BL in three key areas: ( a ) generation of new biological understanding; ( b ) quantitative prediction of signaling nodes; and ( c ) modeling quantitative relationships between signaling and cytokine release nodes . Particular examples of validated biological predictions include: ( i ) TGFα-induced partial activation of the JNK pathway and ( ii ) IL6-induced partial activation of multiple unexpected downstream species via the MEK pathway . Our work demonstrates the technical feasibility of cFL in modeling real biological data and generating new biological insights concerning the operation of canonical signaling networks in specific cellular contexts .
Fuzzy logic is a highly flexible methodology to transform linguistic observations into quantitative specification of how the output of a gate depends on the values of the inputs [33] , [37] , [38] , [39] . For example , in the simplest , ‘Sugeno’ form of fuzzy logic , one specifies the following: ‘membership functions’ designating a variable number of discrete categories ( “low , medium , high' , etc . ) as well as what quantitative value of a particular input belongs either wholly or partially to these categories; ‘rules’ designating the logical relationships between the gate inputs and outputs; AND and OR ‘methods’ designating the mathematical execution of each logical relationship; ‘weights’ designating the credence given any rule; and ‘defuzzification’ designating a scheme for determining a final output value from the evaluation of multiple rules [40] . This flexibility is important in industrial process control [41] , which aims to use uncertain and subjective linguistic terms to predict how a controller should modulate a process variable to achieve the desired output . However , our goal is to train models on quantitative biological data that are inevitably incomplete in the sense that ( i ) measurements are not obtained under all possible conditions and ( ii ) available data are not sufficient to constrain both the topology and quantitative parameters of the underlying networks . Accordingly , we sought to develop a fuzzy logic system that minimizes the number of parameters to avoid over-fitting and simplifies the logic structure to facilitate model interpretability . Because we aim to represent relationships among proteins in enzymatic cascades , mathematical relationships should be biologically relevant . We therefore use a simple Sugeno fuzzy logic gate with a defined form ( see Text S1 ) based on transfer functions ( mathematical functions describing the relationship between input and output node values ) that approximate the Hill functions of classical enzymology . Our ‘constrained’ fuzzy logic ( cFL ) framework uses a simplified fuzzy logic gate that is best described by the mathematical representation in Figure 1 . The value of an output node of a one-input positive interaction is evaluated using a transfer function . In this paper ‘input-output’ refers to the nodes of a specific cFL logic gate , where ‘nodes’ are molecular species . We use the terms ‘model inputs’ and ‘model outputs’ to denote the overall relationship between model inputs such as ligand stimulation of cells and the collective output of the network ( protein modifications or phenotypic states in our application ) . The transfer function underlying cFL gates is a normalized Hill function with two parameters: ( 1 ) the Hill coefficient , n , which determines the sharpness of the sigmoidal transition between high and low output node values and ( 2 ) the sensitivity parameter , k , which determines the midpoint of the function ( corresponding to the EC50 value in a dose-response curve , Figure 1a ) . A negative interaction is represented similarly , except that the transfer function is subtracted from one , effectively inverting it ( Figure 1b ) . Varying these parameters allows us to create a range of input-output transfer functions including linear , sigmoidal and step-like ( Figure 1a ) . Moreover , this transfer function is biologically relevant: protein-protein interactions and enzymatic reactions can be described by Hill function formulations to a good approximation [42] , . In some cases , use of a normalized function is too restrictive for practical application . For example , if model inputs are purely binary ( values of either zero or one ) , the output of a normalized function would also be zero or one , making it impossible for a cFL gate to achieve intermediate states of activation . Accordingly , our cFL method allows for alternative transfer functions . For example , although the method is not limited to binary model inputs , the ligand inputs of our current work are binary ( either present or not ) . If we used normalized transfer functions to relate these model inputs to downstream outputs , all model species would also be either zero or one . Thus , for these transfer functions , we used a constant multiplied by the binary ligand input value ( see Materials & Methods ) . If more than one input node influences an output node , this relationship is categorized as either an “AND” or “OR” interaction . An AND gate is used when both input nodes must be active to activate the output node , whereas an OR gate is used when either input node must be active . Mathematically , we represent AND behavior by evaluating each input-output transfer function and selecting the minimal possible output node value ( i . e . , applying the “min” operator , Figure 1c ) whereas we select the maximal value ( “max” operator; Figure 1d ) to evaluate an OR gate . Finally , if both AND and OR gates are used to relate input nodes to an output node , our formalism evaluates all AND gates prior to OR gates . This order of operations corresponds to the disjunctive normal or sum of products form [45] . The process of training a cFL network ( CellNOpt-cFL ) has two starting requirements . The first is a prior knowledge network ( ‘PKN’; Figure 2 , box A ) . A PKN depicts interactions among the nodes as a signed , directed graph ( such as a PSN ) and can be obtained directly from the literature . Alternatively , a large number of commercial ( e . g . , Ingenuity Systems: www . ingenuity . com; GeneGo: www . genego . com ) or academic ( e . g . , Pathway Commons: www . pathwaycommons . org , reviewed in [46] ) pathway databases as well as integrative tools ( e . g . [47] , [48] ) can be utilized to construct a PKN . The second requirement is a dataset describing experimental measurements characterizing node activities following stimulation of and/or perturbations in upstream nodes ( ligand and inhibitor treatment in our example; Figure 2 , box B ) . CellNOpt-cFL is then used to systematically and quantitatively compare the hypothesized PKN to the experimental dataset . In practice , available experimental data is usually insufficient to fully constrain both the parameters and topology of the cFL models , and CellNOpt-cFL recovers many models that describe the data equally well . Due to this typical absence of firm structural and parametric identifiability [29] , [49] , [50] , we examine families of models that fit the data equally well rather than attempting to identify a single global best fit . Specifically , we examine interactions in the PKN that were either retained or consistently removed by training . We also use individual models to predict input-output characteristics . This treatment allows us to calculate both an average prediction as well as a standard deviation , which we show below can be useful for discrediting inaccurate predictions . Our method comprises three main stages ( Figure 2 ) : first , structure processing converts a PKN into a cFL model; second , model training trains the model to experimental data; and third , model reduction and refinement simplifies trained models . To illustrate CellNOpt-cFL , we examine a simple toy problem of training a PKN of the phospho-protein signaling network response to TGFα and TNFα ( Figure 2a . i ) to in silico data of activation of several downstream kinases in response to these ligands in the presence or absence of PI3K or MEK inhibition ( Figure 2a . ii ) . In the first step , we streamline the network to contain only measured and perturbed nodes as well as any other nodes necessary to preserve logical consistency between those that were measured or perturbed ( [29]; Figure 2 , Step 1 ) , resulting in a compressed PKN ( Figure 2 box C ) . In our example , many nodes that were in the original PKN were neither measured nor perturbed experimentally . Because these nodes could be removed without causing logical inconsistencies , they were not explicitly included in the compressed network ( Figure 2b ) . In the second step , we expand the network into the multiple logical relationships ( combinations of AND and OR gates ) that can relate output nodes to their input nodes ( Figure 2 , Step 2 ) . For example , our toy PKN was expanded to include all possible two-input AND gates governing the response of nodes with more than one possible input node ( Figure 2c ) . In the third step , we train the cFL models to the data ( Figure 2 , Step 3 ) . We start by limiting the possible parameter combinations to a subset of discrete parameter values that specify seven allowed transfer functions as well as the possibility that the input does not affect the output node ( i . e . the cFL gate is not present ) . A discrete genetic algorithm determines transfer functions and a network topology that fit the data well by minimizing the mean squared error ( MSE , defined in Materials & Methods ) with respect to the experimental data . Due to the stochastic nature of genetic algorithms , multiple optimization runs return models with slightly different topologies and transfer function parameters that result in a range of MSEs . Models with an MSE significantly higher than the best models are simply eliminated from further consideration . Models with similar MSEs but different topology and parameters result from the insufficiency of the data to constrain the model such that each model fits the data well albeit with slightly different features . We consider each individual in this group as a viable model , and all are included for subsequent analysis . Thus , after multiple independent optimization runs using the discrete genetic algorithm to train the expanded PKN against the data , a family of models with transfer functions chosen from a discrete number of possibilities is obtained . For each of these models , we generate unprocessed models ( Figure 2 , box F ) by removing all cFL gates that are logically redundant with other cFL gates ( e . g . , in the gate “ ( B AND C ) OR B activate D” , the AND gate is logically redundant with the “B activates D” gate ) . These gates are removed because they increase model complexity by using multiple logic gates to encode a relationship that can be specified by a simpler gate . In our toy example , a family of twenty unprocessed models was obtained by training the expanded map ( Figure 2c ) to in silico data ( Figure 2a . ii . ) using the discrete genetic algorithm . The unprocessed models from different optimization runs had similar topologies with the exception of the gate describing the relationship of MEK to its input nodes: TGFα and Akt ( Figure 2d , brown and green dashed gates ) . Sixteen of the unprocessed models described the activation of MEK as depending only on TGFα ( brown , dashed gate ) whereas four described activation using the AND NOT gate ( green , dashed gate ) . In the model reduction and refinement stage ( Steps 4–6 ) , we determine which gates can be removed altogether as well as AND gates that can be replaced with one-input cFL gates without significantly affecting the MSE . We implemented the non-exhaustive heuristic search procedure described below on each unprocessed model and illustrate its application to our toy example ( Figure 3 ) . In the fourth step , we remove or replace all gates for which the alteration does not increase the MSE of the unprocessed model over some threshold , which we term the ‘reduction threshold’ . We use a range of reduction thresholds such that each unprocessed model results in several models , one for each reduction threshold used . Following this step , the resultant models are considered reduced models . In the fifth step , we fix the model topology to that obtained during Step 4 and treat the transfer function parameters in each reduced model ( Figure 2 , Step 5 ) as continuous parameters rather than the discrete set of transfer function parameters required for use of the discrete genetic algorithm . We use a Sequential Quadratic Programming method ( Text S1 ) to refine the model parameters and further improve the fit of the models to the experimental data . The resulting models are termed reduced-refined models , which have a range of MSEs depending on the reduction threshold used ( Figure 3a ) . In the sixth and final step , we specify a reduced-refined model to represent each unprocessed model ( Figure 2 , Step 6 ) . For each unprocessed model , we choose the reduced-refined model that has the fewest number of fitted transfer function parameters without increasing the MSE above a defined ‘selection threshold . ’ The selection threshold is chosen by comparing the average number of parameters in the family of models to the average MSE of the models ( Figure 3b ) . The net result is a set of reduced-refined-filtered models ( hereafter referred to as filtered models , Figure 2 , Box G ) . In our toy example , the filtered models have identical topology and in no case does Akt inhibit MEK activation ( Figure 2e ) . This topology is , in fact , the topology from which the in silico data was derived . The ability of cFL to fit intermediate values made it possible to recover the correct model topology , whereas BL did not identify the correct model , and a gate linking TGFα to PI3K was consistently missing ( Figure 2e , dashed arrow ) . Specifically , BL was unable to return the correct topology because nodes downstream of PI3K ( Akt and JNK ) were partially activated ( 0 . 32 and 0 . 19 , respectively ) under conditions of TGFα stimulation , and a BL model that included the TGFα to PI3K gate had a higher error ( MSE = 0 . 56 ) than a model that omitted the interaction ( MSE = 0 . 07 ) . In contrast , the improved ability of cFL to model graded activities made it possible to recover the true network topology . While the expansion step ( Figure 2 , step 2 ) captures the many possible combinations of AND and OR logic relationships between nodes , it also increases the complexity of the network , resulting in an increase in the size of the optimization problem . Depending on the biological network of interest , some or most of these AND gates might not be biologically relevant . For example , it is unlikely that six receptors must be active in order to activate another species , as would be the case for a six-input AND gate ( instead , it is more likely to be a OR gate ) . A profusion of AND gates also makes the resultant networks difficult to interpret because most AND gates are in only a few models whereas the majority of models contain single-input and OR gates . Thus , the AND gates can effectively appear as system “noise” , interfering with visual assessment as well as computational analysis of the model topologies . Because of these potential complications , the expansion step can be limited to include only AND gates with a few inputs , depending on the complexity one would like to capture with the trained network models . In the current paper , we have limited the search in the discrete genetic algorithm to a set of seven transfer functions . Use of more or fewer transfer functions is possible , but we found that seven transfer functions allowed us to represent a variety of input-output relationships without unduly increasing problem complexity to the point that the discrete genetic algorithm no longer consistently returned models that fit the data well ( see Materials & Methods ) . To test the ability of cFL modeling to analyze real biological data , we modeled a set of measurements describing the response of the HepG2 hepatocellular carcinoma cell line to various pro-survival , pro-death , or inflammatory cytokines in the presence or absence of specific small molecule kinase inhibitors . This dataset was used to construct a recent BL model [29] . Here we ran an independent analysis using the cFL approach and compare the results to the BL previously reported . The dataset comprises measurement of phosphorylation states as markers of activation of 15 intracellular proteins before and 30 minutes after stimulation by one of six cytokines in the presence or absence of seven specific small molecule kinase inhibitors ( Figure 4a , Figure S1 ) . The measurements were normalized to continuous values between zero and one using a routine implemented in the MATLAB toolbox DataRail [51] , as previously described ( [29] , see Text S1 ) . The HepG2 dataset was trained to several related PKNs which are enumerated in Table 1 and Figure S2 . These PKNs were derived , with various extensions , from the Ingenuity Systems database ( www . ingenuity . com ) with manual addition of literature data about IRS1 that was obviously missing [29] . The first PKN , termed PKN0 was identical the one used previously for BL modeling [29] . In the course of our analysis , we found it necessary to search the literature for interactions missing in PKN0 but supported by the data , resulting in several PKNs ( Table 1 ) . Furthermore , we limited the manner in which the PKNs were expanded in two ways: ( 1 ) expansion into all possible two-input AND gates or ( 2 ) expansion into a two-input AND gate only when one input was inhibitory . In the second case , the expansion of inhibitory gates was necessary because , in logic terms , an inhibitory gate indicates that the output node is active when the input node is not active . In biological networks , this is true if the output node is constitutively active , which was not observed in the normalized HepG2 data . Thus , in order to accurately model the inhibitory effect , it had to occur in conjunction with activation by some other input node , which is captured by an AND gate . If a PKN was processed with both types of expansion , we include a superscript to differentiate between the two cases – i . e . , PKN1a for the expansion of all gates and PKN1i for the expansion of only the inhibitory case . PKN0 was expanded to include all possible two-input AND gates and trained to the HepG2 dataset with CellNOpt-cFL ( Figure S2 ) . The 90 unprocessed cFL models obtained after training showed that PKN0 exhibited a poor fit to IL1α-induced protein phosphorylation ( Figure S3 ) , a result we had also observed with BL analysis [29] , confirming that the poor fit of BL was due to errors in the topology of PKN0 and not the inability of Boolean logic to fit intermediate values . An inspection of systematic model/data disparity ( Figure S3 ) immediately indicated that the models did not fit IL1α-induced phosphorylation of IRS1 , MEK and several species known to be modulated by the MEK pathway . In PKN0 , no paths between IL1α and MEK or IRS1 were present . Based on careful reading of the literature , we added two links to PKN0: a TRAF6 → MEK link [52] , and an ERK → IRS1 link [53] . These links had been inferred by the BL framework [29] and were supported by further literature evidence . To add a link that provided a path between IL1α and MEK in the absence of BL inference results , for simplicity one should first consider links from species that IL1α is already known to activate . In this case , TRAF6 is the most upstream species which experimental evidence suggests can activate MEK [52] . In the case of IRS1 signal activation , the specific phosphorylation site measured should be considered . Our data included measurements of phospho-S636/639 , and S636 is a known phosphorylation site of ERK2 [53] . A novel finding from CellNOpt-cFL analysis of the HepG2 data was that IL6 treatment led to phosphorylation of several downstream proteins . Similarly to the links just considered , PKN0 included no paths between IL6 stimulation and these downstream proteins , resulting in an inability to fit this pattern of phosphorylation . Importantly , however , BL analysis would not have recognized this partial activation due to its inability to fit intermediate values ( as illustrated in our earlier toy example ) . Because IL6 was observed to partially activate Akt in the data and known mechanisms exist for this activation [54] , we added a prospective IL6R → PI3K link to the PKN , thus providing an extended PKN ( PKN1 ) that we use below for subsequent CellNOpt-cFL analysis . PKN1 was expanded to include all possible two-input AND gates ( PKN1a ) for a total of 170 discrete parameters corresponding to 105 logic gates . The resultant network was trained to the HepG2 data . Reduction of the PKN1a–derived models indicated that almost all AND gates could be removed or replaced by single-input gates . Since the AND gates appeared to add unnecessary complexity to the cFL models , we also expanded PKN1 to only include AND gates if an input node was inhibitory ( PKN1i; Table 1 ) , resulting in only 60 discrete parameters corresponding to 56 logic gates . We then compared the PKN1a- and PKN1i-derived cFL models . The comparison of these two PKN-derived model families revealed a clear tradeoff between model fit and complexity . The more complex PKN1a-derived models were able to fit the data slightly better than the PKN1i-derived models ( average unprocessed model MSE of 0 . 032±0 . 002 compared to 0 . 035±0 . 002 , p<0 . 001 ) . However , the more complex PKN1a-derived models contained many more parameters than the PKN1i-derived models both before and after optimization ( 170 compared to 60 discrete parameters before optimization and an average of 72 . 8±4 . 9 compared to 66 . 6±3 . 9 continuous parameters after optimization ( p<0 . 001 ) ; Figure S4 ) . The simpler PKN1i-derived models used fewer initial and final parameters to arrive at a fit to the data only 9% worse than PKN1a-derived models . Since the 9% deviation is in the range of error in the normalized data ( error estimated to be 10% by comparing similar stimulation conditions ) , we focused subsequent analysis on the simpler PKN1i-derived models . For completeness , we include the results of PKN1a-derived models as supplemental information ( Figure S5 ) . To determine the statistical significance of our results , we compared the family of 243 unprocessed models with unprocessed models obtained from either training PKN1i to randomized data or training a randomized PKN1i to the data ( Table S1 ) . Data was randomized by pairwise exchange of all data values while network topologies were randomized either by generation of an entirely random topology or by random pairwise exchange of gate inputs , gate outputs , or nodes' inputs [29] . When compared to the results of all types of randomization , models trained to the real data and PKN1 were highly significant ( P-value <0 . 001 , Table S1 ) , indicating that the family of trained cFL models fit the data better than expected by random chance . To probe the dependence of the CellNOpt-cFL training process on the quality of the PKN used , we randomly added links to or removed links from the PKN and trained the resultant PKN to the data . As expected , the models derived from PKNs with links randomly removed had a poorer fit to data than those derived from the complete PKN1i ( Figure 4b , solid line ) . Conversely , when links were randomly added to the PKN , cFL-CellNOpt effectively removed the links ( Figure S6 ) , resulting in models with similar goodness of fit as models derived from PKN1i ( Figure 4b , dashed line ) . We thus conclude that an incomplete PKN degrades the ability of CellNOpt-cFL to fit the data whereas models derived from a PKN with extraneous links retain this ability . As an initial investigation of model predictive capacity and a check for over-fitting , we performed a ten-fold cross-validation by randomly dividing the HepG2 data into ten subsets and , for each subset , reserving one as a test set while training with the remaining nine data subsets . The similar fits of the training and test data provided evidence that the family of models obtained from this procedure were predictive , and the difference in test and training MSEs did not depend on selection threshold , a measure of model size , suggesting that the models were not over-fit ( Figure 4c ) . Analysis of this cross-validation result combined with a plot of average filtered model size and fit ( MSE ) as a function of selection threshold ( Figure 4d ) suggested that a selection threshold in the range 1×10−3 – 1×10−2 would result in a family of models that contain slightly fewer number of parameters than lower thresholds ( Figure 4d , dashed line ) while retaining the ability to fit the data well ( Figure 4d , solid line ) . We used a threshold of 5 . 0×10−3 for the remainder of our analysis unless otherwise noted . Finally , we obtain a family of 243 filtered models for further analysis ( Figure 5 ) . By taking note of which cFL gates are removed during the CellNOpt-cFL training and reduction processes , one can generate hypotheses regarding these gates . Table 2 summarizes a set of biological hypotheses readily suggested by our cFL model topologies . Analysis of error between the family of cFL models and experimental data ( Figure S7 ) highlighted consistent error in TGFα-induced partial activation of c-Jun . Both PKN0 and PKN1 allowed for TGFα-induced activation of c-Jun by the JNK pathway via crosstalk from Ras or PI3K to MAP3K1 . In the BL methodology , this crosstalk was removed due to the inability to fit partial activation , and no BL model allowed for activation of c-Jun after TGFα stimulation . However , we found that a subset of cFL models accounted for this c-Jun partial activation by including crosstalk between Ras or PI3K and MAP3K1 . These models also partially activated JNK after TGFα stimulation , a feature that was inconsistent with the training data ( Figure S8 ) . Thus , these models predict that JNK was actually phosphorylated under conditions of TGFα stimulation , but our measurements did not detect it . To test this prediction directly , we undertook de novo measurement of JNK and c-Jun phosphorylation following stimulation with different doses of TGFα ( Figure 6a ) . These new data show that JNK does indeed become phosphorylated upon stimulation of HepG2 cells with TGFα . Thus , the cFL models containing crosstalk from Ras or PI3K to MAP3K1 were the correct models . Combined with Table 2 , this analysis highlighted the partial activation of the JNK pathway after TGFα stimulation as a singular instance of crosstalk from a pro-growth ligand to an inflammatory pathway . In support of the significance of our finding here , we note that TGFα-induced JNK activation has been shown to be important for hepatic regeneration [55] and stimulation of DNA synthesis [56] in primary rat hepatocytes . As previously mentioned , PKN0 was unable to fit IL6-induced protein phosphorylation ( a feature of the data unappreciated by the BL methodology ) . Because Akt was observed to be partially phosphorylated under these conditions and we found literature evidence for a prospective IL6R → PI3K link , we added the link to PKN1 . However , the media-only condition also induced partial phosphorylation of Akt . Discovery of the partial activation of Akt in the media-only control led us to consider that perhaps the IL6-induced phosphorylation of Akt was simply an assay artifact . Thus , we inserted an Assay → PI3K link into the PKN . This “Assay” node represents cell stress arising from changing environmental conditions during the assay ( media change , etc . ) ; it is postulated to activate PI3K because only Akt is consistently active in the untreated control . Having accounted for the potential that IL6-induced partial phosphorylation of Akt was an artifact , we undertook a series of computational experiments to determine the mechanism of IL6-induced phosphorylation of downstream proteins . Upon exposure to IL6 , SHP2 has been reported to bind to gp130 , a subunit of the IL6 receptor complex . SHP2 is then phosphorylated in a JAK1-dependent manner . This phosphorylation can lead to PI3K/Akt pathway activation through interactions with Gab-1 or IRS1 or Ras/MEK/ERK pathway activation through Grb2 or Gab1 [54] . Thus , our computational experiments were designed to infer which pathway ( PI3K/Akt or Ras/MEK/ERK ) was mediating the IL6-induced protein phosphorylation . Four families of 150 filtered models were examined , all of which were obtained after training a new PKN to the normalized HepG2 dataset ( Table 3 , PKN2A – PKN2D ) . The inability of PKN2A-derived cFL models with only the Assay → PI3K link to fit well the IL6-induced protein phosphorylation data suggested that some other link was necessary to fit this data . In our trained networks , the IL6R → PI3K link was present in only a fraction of the relevant trained models ( PKN2B and PKN2C ) , but the IL6R → Ras link was present in more than 90% of relevant trained models ( PKN2C and PKN2D ) . Additionally , models with IL6R → Ras links were better able to fit the IL6-induced protein phosphorylation . Consequently , our cFL results supported the hypothesis that IL6R activates downstream proteins through the Ras/Raf pathway . This hypothesis is supported by an independent dataset [29] , where the IL6-induced protein phosphorylation response was more robust than in the training data ( Figures S1 and S9 ) . Inhibition of MEK either alone or in combination with other inhibitors resulted in ablation of downstream protein activation whereas inhibition of PI3K did not ( Figure 6b ) . Thus , we infer that IL6-induced protein phosphorylation was not an assay artifact and was instead mediated by the Ras/Raf pathway . CFL relates nodes in a network with transfer functions that describe quantitative input-output relationships between protein species represented as network nodes . To investigate the ability of the cFL models to predict these transfer functions , we simulated the PKN1i-derived , filtered cFL models to determine the activation state of a specified node under many theoretical combinations of its input nodes . We then plotted the model predictions of quantitative input-output relationships . As one instance , Figure 7 shows the predicted average and standard deviation of the quantitative values of CREB phosphorylation as a function of the activation of upstream nodes , p38 and MEK1/2 . The resulting plots indicated that we were able to predict the activation response of CREB to the entire range of p38 and MEK1/2 although training set measurements were limited to a few values of these nodes ( Figure 7 , black circles ) . We tested this prediction using a set of data with combinations of ligands and inhibitors not present in the training data ( [29] , Figure S9 ) . Roughly 20% of the test conditions were also present in the training data set , allowing us to control for differences between both data sets . When we compared this dataset to the predicted transfer functions , we observed that most of the data fell within one standard deviation of the predicted value ( Figure 7 , green diamonds ) with exception of overestimation under conditions of TGFα stimulation . This overestimation is expected , as a comparison of common conditions between the training and test dataset indicated that the normalized experimental values of CREB in the validation dataset were 38±4% lower than that in the training set . This result demonstrates the ability of the trained cFL models to predict the quantitative relationship between nodes in the network . We also found that the family of cFL models was able to fit the phospho-protein signaling response in the validation dataset well , which we demonstrate as supplementary information ( Figure S9 ) . We performed a series of nineteen cross-validation experiments to further investigate the ability of our methodology to predict the signaling response under conditions that were not represented in the training data . For each experiment , we used training data from which we had removed the phosphorylation data of a specific protein signal , s , under a single ligand stimulation condition and all inhibitor treatments . Nineteen signal/stimulation combinations were chosen to be test sets according to two criteria: ( 1 ) s is at least partially activated under the stimulation condition of interest and ( 2 ) s is at least partially activated under some other stimulation condition ( Table S2 ) . These criteria ensured that the remaining training data contained some information regarding the activation of s but it did not contain information regarding the activation of s under the stimulation condition of interest . This procedure is a more stringent test for predictive capability than a random cross-validation procedure because training sets from which random data is removed might retain other data with the same information as the removed data ( e . g . , based on the network topology , Akt phosphorylation in the absence of MEK inhibition is the same as Akt phosphorylation with MEK inhibition , so removing only one of these data points is not a stringent test of predictive capacity ) . We examined the ability of models trained on reduced training sets ( n>45 for each case ) to predict phosphorylation of the test protein signals . Because we used each individual in the family of models to predict the test signal , we could determine if the models were constrained in their predictions by examining the coefficient of variance ( CV; standard deviation divided by mean ) of the prediction . If the CV was high , the models were not constrained to a specific prediction ( i . e . the prediction was imprecise ) , and the average prediction should be discounted . Thus , for these cross-validation results , we compared the precision ( CV ) and accuracy ( MSE ) of the models' predictions , where precise and accurate predictions exhibited both a low CV and low MSE ( Figure 8a ) . We found that the families of models trained on these reduced training sets were able to precisely predict phosphorylation of the test protein signals in twelve of the nineteen cases ( Figure 8b and c , green field ) . In six of the test sets , the models did not agree , although their average prediction was reasonably accurate ( Figure 8b and c , yellow field ) . We observed no test sets for which the training sets agreed about an inaccurate prediction ( Figure 8b , orange field ) . In one case ( prediction of Iκb signaling under TNFα stimulation ) , the predicted phosphorylation state was highly inaccurate ( MSE >0 . 20 ) . However , this prediction was also very imprecise ( CV >0 . 25 ) , indicating that the average prediction was unreliable ( Figure 8b , blue field ) . Thus , by taking the precision of the models' predictions into account , we were able to discredit an inaccurate prediction . This result underscores the importance of considering consensus among the family of models rather than examining the results of only one cFL model . The ability to quantitatively model protein signal activation with cFL offers the prospect of predicting phenotypic response upon exposure to stimuli and inhibitors . To investigate the ability of cFL to model phenotypic data , we turned to data describing cytokine release three hours after stimulation under the same conditions as the phosphorylation data [36] . As a first approach , we linked the output of our family of cFL models to a partial least squares regression model [6] obtained by regressing normalized data of release of five cytokines ( IL1β , IL4 , G-CSF , IFNγ , and SDF1α ) to the normalized protein phosphorylation measurements ( see Text S1 ) . The cFL models linked to a PLSR model were able to model phenotypic response with an accuracy of R2 = 0 . 79 , near that of the PLSR model ( R2 = 0 . 81; see Figures S10 ) . However , we found that the correlation indicated by regression coefficients did not lead to easily interpretable insights about phenotype because proteins in the same pathway were also highly correlated with each other . To obtain a more interpretable model , we utilized a second approach where we included nodes specifying cytokine release in the PKN and linked them to a few protein signaling nodes . These nodes were chosen based on principle component analysis: if protein signals in a pathway clustered together in principle component space , the signal most downstream in the pathway was linked to cytokine release . Based on this analysis , the following protein signaling nodes were linked to each cytokine release node: MEK1/2 , CREB , GSK3 , c-Jun , Hsp27 , Iκb , and STAT3 ( Table 1 , PKN3 ) . We then trained a family of cFL models to the normalized dataset comprised of cytokine release at three hours and protein signaling at thirty minutes . The resultant models were able to fit the cytokine release data reasonably well ( R2 = 0 . 78 for the average predicted by a subset of best-fitting models , Figure S11 ) . Furthermore , the low frequency of several gates in the resultant family of cFL models ( Figure S12 , Table S3 ) indicated that , although the promoters of several of the modeled cytokines contained binding sites of transcription factors are known to be modulated by the MEK1/2 , GSK3 , and CREB pathways ( Table S4 ) , activation of these nodes did not predict cytokine release . Thus , we altered our previous PKN by removing the links between these protein signaling and cytokine release nodes and trained it to the data . The resultant family of cFL models ( Figure 9 ) indicated that STAT3 activation explained cytokine release after IL6 stimulation and other signals ( Iκb , c-Jun , and Hsp27 ) explained cytokine release three hours after TNFα or IL1α stimulation .
In this paper , we have described cFL for formal training of a prior knowledge network obtained from a protein signaling network map to experimental data and demonstrated that the ability of cFL to fit intermediate activities was crucial for understanding key features of a biological network . We validated two important biological insights concerning network operation in the HepG2 cells under inflammatory cytokine and growth factor treatment: ( i ) identification of c-Jun as a downstream locus of crosstalk between growth factor and inflammatory cytokine treatments and ( ii ) the Ras/Raf/MEK pathway as an avenue for activation of key downstream proteins following exposure of cells to IL6 . Both of these insights were dependent on the ability of our cFL models to fit partial protein activation and were thus not appreciated by BL modeling . We note that the ability of cFL to model intermediate activity data comes at the cost of increased model complexity . This complexity calls into question the identifiability of a cFL model ( i . e . ability of the CellNOpt-cFL training process to train both parameters and topology given limited data ) . To address this concern , we considered families of models where each individual model predicted signaling states and the resulting predictions had an average and standard deviation . The standard deviation provided a metric for discrediting predictions for which the models were not constrained . With regard to topology , we considered how often a gate was present in the trained cFL models . This allowed us to determine hypothesized links ( those present in the PKN ) that were either inconsistent with the data ( cFL gates removed from unprocessed models ) or only marginally important for fitting the data ( cFL gates removed from filtered models ) . Thus , the consideration of consensus and variation in an ensemble of models allowed us to account for the non-identifiability of any individual model . We also illustrated the use of CellNOpt-cFL to ( i ) predict quantitative phenotypic response data with the same quality as a regression-based approach and ( ii ) increase the biological understanding of a phenotypic response by generating hypotheses regarding protein signaling pathways that led to cytokine release . Transcriptional and/or non-transcriptional mechanisms could underlie the biological link between the signaling network activation and cytokine release profiles . We investigated predicted and known transcription factor binding sites in the promoters of relevant genes ( Table S4 ) , finding that several transcription factors hypothesized by CellNOpt-cFL to drive cytokine release ( STAT3 and NFκB ) could , in concert with IRF1 , potentially lead to the production and secretion of the observed cytokines . Our subsequent test of this notion by qRT-PCR measurement , however , yielded a negative result; expression of the HepG2-secreted proteins were not significantly up-regulated by IL6 stimulation ( data not shown ) . Thus , it appears more likely that non-transcriptional mechanisms , such as exocytosis of secretory vesicles [57] , [58] or proteolytic cleavage of pro-forms at the cell plasma membrane [59] , [60] , was responsible for the cytokine release observations . The persistent development and application of CellNOpt-cFL and complementary methods ( [6] , [7] , [36] and Melas , et al . , submitted ) should continue to deepen our understanding of how signaling networks inform phenotypic responses . We have shown that CellNOpt-cFL is useful for systematically and quantitatively comparing experimental datasets to a PKN that summarizes decades of dedicated biochemical studies . However , our aim in this work is not to argue for exclusive use of cFL modeling instead of BL or other modeling approaches , but rather to delineate key advantages of cFL modeling for addressing data with intermediate activity values . Training with CellNOpt-cFL is a more difficult optimization problem that is not efficiently solved for networks much larger than those in this work . The BL optimization problem scales as 2w , where w is the number of gates in the processed PKN , whereas the CellNOpt-cFL optimization problem scales as ( 1+a ) h , where a is the number of transfer functions in the set chosen by the genetic algorithm ( ( 1+a ) ≥2; ( 1+a ) = 8 as formulated here ) and h is the number of possible input-output transfer functions in the network ( h≥w ) . Additionally , as was the case with the reformulation of the BL optimization problem with Integer Linear Programming [30] , we acknowledge that there may be more efficient , rigorous ways to solve the optimization problem presented by CellNOpt-cFL . When training a prior knowledge network to data , we often encountered the need to add links to the prior knowledge network in order to fully describe the data . In this study , this was done manually simply by searching the literature . In the absence of such information , one should automate the process of testing many candidate links . A simple heuristic procedure such as the one we employed for the BL methodology based on mismatches between the best-fit models and data is one option [29] . Alternatively , more complex reverse engineering techniques could be used . The additional complexity of cFL modeling poses significant complications for the implementation of a simple heuristic or reverse engineering technique , but future efforts should investigate best practices for the automation of this process . An additional prospective application of CellNOpt-cFL is to use a trained cFL model to inform the construction of a model with a different mathematical formalism . One intriguing possibility is that the CellNOpt-cFL methodology might be used to determine topologies to translate into a system of ordinary differential equations ( ODEs ) with methods such as that presented in [61] . The precise relationship between cFL and ODE parameters is unclear , but the ease of translating from one formalism to the other might be facilitated through the use of continuous AND and OR operators rather than the Min/Max operators utilized in this study . As a first step , we have retrained one of our main results ( that presented in Figure 5 ) using the product of possible outputs to evaluate AND gates and the sum of possible outputs to evaluate OR gates . The models resulting from this procedure ( Figure S13 ) were similar to those obtained previously ( Figures 4c , 5 ) , demonstrating the flexibility of this approach to accommodate different AND and OR operators as well as transfer function forms . Such flexibility should aid future attempts to translate CellNOpt-cFL results into other mathematical formalisms . Finally , the dataset used here was gathered for training a BL model . This dataset was explicitly designed to maximally stimulate or inhibit pathways through the application of saturating doses of ligand and drugs . However , cells in vivo face a much more subtle and interesting situation in which ligands are present in combination , often at very different levels . Because cFL can model the graded activation of cell signaling pathways , we suspect that CellNOpt-cFL should prove particularly useful with signaling data collected under more physiological conditions . Our laboratories are currently pursuing experimental studies in this direction .
Model compression and expansion was performed with CellNOpt as previously described [29] . The discrete genetic algorithm in the CellNOpt BL variant was adapted so that discrete variables specified a transfer function rather than the gate type . Because our datasets ( toy example and HepG2 ) only contained saturating concentrations of ligand stimulation , the normalized values of ligand model inputs were one or zero . In this instance , using normalized Hill functions to model interactions downstream of these zero or one inputs would result in all downstream nodes also reaching levels of zero or one ( a Boolean simulation ) . To circumvent this issue , we represented interactions linking a ligand input to a downstream component with linear transfer functions with a y-intercept of zero and possible values of slope of 0 . 2 , 0 . 3 , 0 . 4 , 0 . 5 , 0 . 6 , 0 . 7 , and 0 . 8 as well as the absence of the interaction . All other interactions were modeled with the normalized Hill function described in Figure 1 where the following transfer functions were possible: gate not active , approximately linear transfer function ( n = 1 . 01 , k = 68 . 5098 chosen for computation efficiency and numerical stability ) , or sigmoidal transfer function ( n = 3 ) with an EC50 of 0 . 2 , 0 . 3 , 0 . 4 , 0 . 5 , 0 . 6 , or 0 . 7 ( Figure S14 ) . These transfer functions were chosen because the models resulting from the training represented many different topologies while still fitting the data well . We found that including a subset of three to five of the aforementioned transfer functions would have also accomplished these goals , but including ten transfer functions resulted in a larger fraction of models that did not fit the data well . This necessitated the addition of a step to choose a subset of well-fitted models from the family of trained models , and this subset did not significantly differ from the family of models obtained with fewer possible transfer functions . Given that more transfer functions allowed us to more accurately represent parameter space , this result implied that the genetic algorithm was converging to poorly-fit local minima because the search space was too large . We therefore concluded that usage of seven transfer functions balanced coverage of search space and ability to identify well-fitting models . Sensitivity is calculated as ( 1 – EC50 ) for cFL gates modeled with normalized Hill functions and 0 . 5*slope for cFL gates modeled with weighted linear transfer functions . Mean squared error was calculated with the following formulawhere N is the total number of data points , Nsig is the number of protein signals measured , Nstim is the number of cytokine or growth factor stimulations , Ninhib is the number of inhibition conditions used , and xpredi , j , k and xobsi , j , k are the predicted and observed level of the ith protein signal under the jth stimulation and kth inhibition condition , respectively . In some cases , only the MSE of a subset of the data points is calculated for more specific error analysis . In these instances , the previous formula holds , but signal and/or stimulation conditions are constant and indicated with subscripts ( e . g . MSEIL6 is the MSE of all signal measurements under all inhibition conditions and IL6 stimulation ) . Protein phosphorylation and cytokine release were measured as described in [36] . Briefly , cells were incubated with small molecule inhibitor before exposure to ligand . Luminex bead-based bioassays were used to determine protein phosphorylation in cell lysate collected immediately before and 30 minutes after ligand exposure . Three hours after ligand exposure , supernatant was collected and Luminex bead-based bioassay used to measure the amount of cytokine that had been secreted . | Over the past few years , many methods have been developed to construct large-scale networks from the literature or databases of genetic and physical interactions . With the advent of high-throughput biochemical methods , it is also possible to measure the states and activities of many proteins in these biochemical networks under different conditions of cellular stimulation and perturbation . Here we use constrained fuzzy logic to systematically compare interaction networks to experimental data . This systematic comparison elucidates interactions that were theoretically possible but not actually operating in the biological system of interest , as well as data that was not described by interactions in the prior knowledge network , pointing to a need to increase our knowledge in specific parts of the network . Furthermore , the result of this comparison is a trained , quantitative model that can be used to make a priori quantitative predictions about how the cellular protein network will respond in conditions not initially tested . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"computational",
"biology/systems",
"biology",
"computational",
"biology/signaling",
"networks"
] | 2011 | Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli |
Metal hyperaccumulation , in which plants store exceptional concentrations of metals in their shoots , is an unusual trait whose evolutionary and ecological significance has prompted extensive debate . Hyperaccumulator plants are usually found on metalliferous soils , and it has been proposed that hyperaccumulation provides a defense against herbivores and pathogens , an idea termed the ‘elemental defense’ hypothesis . We have investigated this hypothesis using the crucifer Thlaspi caerulescens , a hyperaccumulator of zinc , nickel , and cadmium , and the bacterial pathogen Pseudomonas syringae pv . maculicola ( Psm ) . Using leaf inoculation assays , we have shown that hyperaccumulation of any of the three metals inhibits growth of Psm in planta . Metal concentrations in the bulk leaf and in the apoplast , through which the pathogen invades the leaf , were shown to be sufficient to account for the defensive effect by comparison with in vitro dose–response curves . Further , mutants of Psm with increased and decreased zinc tolerance created by transposon insertion had either enhanced or reduced ability , respectively , to grow in high-zinc plants , indicating that the metal affects the pathogen directly . Finally , we have shown that bacteria naturally colonizing T . caerulescens leaves at the site of a former lead–zinc mine have high zinc tolerance compared with bacteria isolated from non-accumulating plants , suggesting local adaptation to high metal . These results demonstrate that the disease resistance observed in metal-exposed T . caerulescens can be attributed to a direct effect of metal hyperaccumulation , which may thus be functionally analogous to the resistance conferred by antimicrobial metabolites in non-accumulating plants .
Metal hyperaccumulation is described as the accumulation of exceptionally high concentrations of metallic elements in the aerial parts of a plant [1] , [2] . The phenomenon has evolved in around 450 plant species , distributed across several families [2] , [3] , [4] , and most hyperaccumulator species are endemic to metalliferous soils , either natural or anthropogenic in origin [5] . This unusual characteristic has attracted considerable interest , and a number of hypotheses have been proposed to explain the evolution of the hyperaccumulation phenotype [6] , [7] . The possibility that accumulated metal provides a defense against herbivores or pathogens , termed the ‘elemental defense hypothesis’ , has received most attention and support [7] , [8] , [9] . A number of studies have reported findings consistent with a defensive effect of hyperaccumulated metals against herbivores [10] , [11] , [12] , but the interpretation of these results remains controversial , as other studies have failed to support the defense hypothesis . For instance , Noret et al . [13] , [14] were unable to find a defensive effect attributable to metal hyperaccumulation in the field , despite that reported in laboratory trials . There is also some evidence that the importance of metal-based defense is dependent upon the mode of herbivory [15] . In the case of defense against pathogens , considerably fewer tests have been carried out [e . g . [16]] and only one study [17] has explicitly tested the defense hypothesis with regard to bacterial pathogens . So far , although some evidence has been provided that plants exposed to high metal concentrations have reduced susceptibility to various pathogens , no study has demonstrated that the metal itself was directly responsible for this effect . It is therefore not possible , at present , to state that metal hyperaccumulation trait evolved as an antimicrobial defense . Here , we test the possibility of a direct elemental defense against bacterial pathogens in the crucifer Thlaspi caerulescens , a hyperaccumulator plant characteristically associated with metalliferous soils [18] . In its natural habitats , this species can hyperaccumulate three different metals: zinc , nickel , and cadmium [19] , [20] , and it has been widely used in studies of the hyperaccumulation trait [21] , [22] , [23] , [24] , [25] . We have tested the elemental defense hypothesis via a three-pronged approach by means of studies in vitro , in planta , and in the field . First , we have monitored the growth of Pseudomonas syringae pv . maculicola M4 , a model pathogen of Arabidopsis thaliana [26] , in T . caerulescens plants treated with different metal concentrations . This pathogen grows in the apoplastic spaces between plant cells , so we have investigated metal concentrations in the apoplastic phase specifically to determine whether they are likely to be inhibitory to pathogen growth . A number of studies attempting to determine the location of hyperaccumulated metals within the leaf have found the majority to be accumulated in the epidermal vacuoles [27] , [28] , [29] , but apoplastic metal concentrations have not been examined in previous studies . Further , we have assessed the growth of Psm mutants with altered zinc sensitivity in T . caerulescens plants grown on different zinc regimes . This has allowed us to test whether zinc tolerance is important for bacterial growth in planta , as expected if metals are directly involved in plant defense . Finally , we have tested the zinc tolerance of endophytic bacteria found in the leaves of T . caerulescens growing on a metal-rich soil at the site of a former lead–zinc mine to determine whether there is evidence that this form of defense may be effective under natural field conditions .
When Thlaspi caerulescens plants were grown in the glasshouse , it was observed that occasional outbreaks of powdery mildew affected only those plants growing on low metal treatments ( Figure 1A and B ) . This observation prompted us to consider the importance of metals in defending T . caerulescens against pathogens . As powdery mildews are obligate biotrophs and therefore unculturable , we sought a more tractable model system for further study of this phenomenon . Bacterial plant pathogens are suitable for such investigations because they are easily cultured in vitro and are relatively straightforward subjects for genetic manipulations such as mutagenesis . Of sixteen plant pathogenic bacteria tested , eight were found to cause necrotic symptoms in T . caerulescens within 2 to 4 days after inoculation [see Supporting Information Table S1] . Pseudomonas syringae pv . maculicola M4 ( Psm ) , a rifampicin-resistant derivative of Psm 4326 [26] , caused necrosis more rapidly and to a greater extent than any other tested strain . To confirm that Psm is pathogenic on T . caerulescens , we assessed the ability of Psm , Psm ES4326 ( a streptomycin resistant derivative of Psm 4326 ) [30] and two type III secretion system ( T3SS ) mutants of ES4326 ( gift of K . Schreiber and D . Desveaux ) to grow in T . caerulescens . Over 5 days post-inoculation , Psm M4 and Psm ES4326 both multiplied two to three logs in the leaves of T . caerulescens plants grown on minimal ( 0 . 04 µM ) zinc . However , neither T3SS mutant was able to grow in T . caerulescens at any of the zinc concentrations tested ( Figure 2 ) . Both T3SS mutants grew similarly to wild-type bacteria in vitro ( Figure S1 ) . This confirms that Psm is pathogenic towards T . caerulescens and shows that the ability of Psm to grow in T . caerulescens is T3SS-dependent . On inoculation of Psm into T . caerulescens plants treated with progressively higher concentrations of zinc , both symptom development and Psm growth were significantly reduced as the concentration of the zinc treatment increased ( Figure 1C and D; Figure 2 ) . T . caerulescens is able to accumulate three metals , zinc , nickel and cadmium , in its shoots . To determine whether nickel and cadmium also conferred increased resistance to infection , bacterial growth was quantified in leaves of T . caerulescens plants grown on nutrient solution supplemented with different concentrations of zinc , nickel , or cadmium . For all three metals , increasing metal concentration resulted in reduced bacterial growth . Zinc treatments at concentrations ≥30 µM , and nickel and cadmium treatments at concentrations ≥10 µM , caused significant inhibition of bacterial growth at 2 and 5 days post-inoculation ( Figure 3 ) . Accumulation of any of these metals therefore inhibits bacterial growth in planta and defends T . caerulescens against disease . Studies of plants that are not normally exposed to high concentrations of metals have shown that metal treatment can induce a range of stress responses , including up-regulation of genes associated with local plant defense responses and systemic acquired resistance ( SAR ) such as PR-1 [31] . In order to determine whether some of the protection conferred by high zinc was a consequence of the effect of high zinc concentrations on defense-associated gene expression , we compared the expression of PR-1 in leaves of T . caerulescens plants grown on 0 . 04 and 300 µM zinc by quantitative real-time PCR . Normalized PR-1 expression in the two metal treatments was similar to PR-1 expression in healthy A . thaliana leaves ( relative expression levels in 0 . 04 µM , 1 . 423±0 . 299; 300 µM Zn , 1 . 942±0 . 520; untreated A . thaliana , 2 . 102±1 . 435 ) , and there was no significant increase in expression in response to increased zinc ( Figure S2 ) . This indicates that the inhibitory effect of increasing metal treatments on Psm growth is not due to up-regulation of SAR . Psm infects the apoplastic phase between plant cells . To test whether metal accumulation reduces the suitability of this environment for bacterial growth , Psm was cultivated in vitro in apoplastic fluid extracted from plants treated with supplementary metals . Apoplast extracts from zinc- and nickel-treated plants supported significantly less bacterial growth than those from plants grown without supplementary metal ( Figure 4A and B ) . Apoplast extracts from plants treated with higher cadmium concentrations also inhibited bacterial growth for up to 18 hours , although this inhibition was eventually released ( Figure 4C ) . As such , it is clear that metal hyperaccumulation by T . caerulescens makes the apoplast a more hostile environment for the growth of Psm . To determine whether hyperaccumulated metal could be responsible for the observed reductions in bacterial growth in leaves , the ability of Psm to tolerate each metal was tested in a range of synthetic media and in extracted apoplast ( Figure S3 ) and compared with the metal concentrations of apoplast extracts and whole-leaf tissue samples ( Figure 5; Table 1 ) . For all metals tested , concentrations in whole-leaf tissue of T . caerulescens plants grown at the two highest treatments were sufficient ( i . e . higher than the IC50 values for the respective metals ) to explain the observed reduction in bacterial growth in planta . Apoplastic zinc and nickel concentrations from the highest treatments were also sufficient to explain bacterial growth reduction . Apoplastic cadmium concentrations were considerably lower , reaching around one-third of the IC50 concentration for Psm in vitro in higher treatments , perhaps explaining the ability of Psm eventually to overcome inhibition by cadmium . The importance of metal tolerance for bacteria growing in metal-hyperaccumulating T . caerulescens was assessed by screening a transposon mutant library of Psm to identify mutants with increased or decreased zinc tolerance relative to wild-type Psm . The performance of four representative mutants in planta was then compared to that of the wild-type strain under four zinc regimes . Two mutants with increased zinc tolerance ( 9A6 and 9A3 ) and two with reduced zinc tolerance ( 10C1 and 7C11 ) were used ( Figure 6 ) . These four mutants grew similarly to wild-type Psm in A . thaliana ( Figure S4 ) and were able to cause similar symptoms to wild-type Psm in A . thaliana and pak choi , Brassica rapa ssp . chinensis ( data not shown ) . Mutant 7C11 showed slightly reduced growth relative to wild-type Psm during late log phase and stationary phase in in vitro growth assays in KB broth ( Figure S5 ) , but the in vitro growth kinetics of the other three mutants were not significantly different from wild-type Psm . The disrupted genes were sequenced using an inverse PCR method and their predicted functions are described in Table 2 . Interestingly , mutant 7C11 was found to contain an insertion in a TonB-dependent siderophore receptor ( PSPTO_2152 ) , which suggests that the slight growth defect observed in KB broth for this strain may be linked to impaired iron acquisition . The other mutation giving rise to reduced zinc tolerance , in mutant 10C1 , was located in an NAD-dependent DNA ligase gene ( PSPTO_0382 ) ; this does not have an obvious role in metal transport or metal tolerance , but is located close to two operons predicted to encode a heavy-metal-sensing two component regulatory system and components of a cobalt-zinc-cadmium ( Czc ) cation efflux system ( PSPTO_0375 – PSPTO_0379 ) in the genome of P . syringae pv . tomato DC3000 . The two mutations giving rise to increased zinc tolerance were located in pslF ( PSPTO_3533; 9A3 ) , a gene within the psl operon , involved in exopolysaccharide synthesis and biofilm formation in Pseudomonas aeruginosa [32] , and in a proline iminopeptidase ( pip ) gene ( PSPTO_5164; 9A6 ) . The pip gene of Xanthomonas campestris pv . campestris has been shown to be induced during plant colonisation and to be essential for pathogenesis on cabbage [33] . However , our results indicate that the pip gene of Psm is not required for pathogenesis in A . thaliana . Pip belongs to a family of metalloproteases and its enzymatic activity may be dependent on a metal cofactor such as zinc or cobalt . In the genome of P . syringae pv . tomato DC3000 , pip is located upstream of , and in a putative operon with , a predicted D-Tyr-tRNAtyr deacylase , which may have a role in protecting cells against D-tyrosine toxicity . In E . coli , zinc and D-tyrosine have been shown to have opposite effects on the phosphatase activity of the aromatic amino acid biosynthesis regulator TyrR , which is stimulated by zinc and suppressed by L-tyrosine and D-tyrosine [34] . The ability of the four Psm mutants to grow in T . caerulescens plants cultivated on 0 . 04 , 10 , 30 , or 300 µM zinc was found to vary according to the zinc tolerance of the inoculated strain ( Figure 7 ) . Thus , while the wild-type showed significant growth reduction with plant zinc treatments of 30 µM or higher , the two mutants with increased zinc tolerance , 9A6 and 9A3 , were capable of growth in plants treated with 300 µM zinc . The two mutants with decreased zinc tolerance , 7C11 and 10C1 , were unable to grow even in plants treated with 10 µM zinc , at which concentration the wild-type grew well . Because increased zinc tolerance could be correlated with reduced zinc uptake , we tested the ability of all four mutants to grow at low zinc concentrations . Wild-type Psm grew equally well in M9 minimal medium and in M9 supplemented with 1 to 10 µM Zn . Of the four mutants , only 9A6 had an increased zinc requirement relative to wild-type bacteria , showing optimal growth at concentrations ranging from 1 to 5 µM zinc . To test further the hypothesis that high metal tolerance is a prerequisite for bacterial growth in T . caerulescens , endophytic bacteria were collected from leaves of a natural population of T . caerulescens plants at Hafna Mine , a former lead–zinc mine in North Wales , UK [35] , and their zinc tolerance assessed . The mean zinc content of the leaves of these plants was 16 . 2±1 . 39 g Zn per kg leaf dry mass ( ± s . e . m . , n = 23 leaves ) , slightly greater ( p<0 . 05 ) than that of plants treated with the highest concentration of zinc ( 300 µM ) used in the laboratory experiment ( 13 . 7±0 . 71 g Zn per kg dry mass ( ± s . e . m . , n = 18 leaves ) ) . The average IC50 for zinc of these naturally occurring endophytes was 9 . 4 mM ( n = 86 ) in KB medium . In contrast , a set of plant pathogenic bacteria isolated from non-hyperaccumulating plants were found to have a significantly ( p<0 . 001 ) lower mean IC50 for zinc of 5 . 4 mM in KB ( n = 7; Figure 8 ) . When the zinc IC50 values for individual strains isolated from the Hafna mine plants were compared to the zinc IC50 of Psm , the most zinc tolerant of the plant pathogens used in this study , 65% were found to have a significantly higher zinc tolerance ( p≤0 . 05 ) , while only a single strain out of 85 had a significantly lower zinc tolerance . Therefore , in the field , as in the laboratory , only metal-tolerant bacteria can colonize T . caerulescens .
In this work , we have developed a model system to study the elemental defense hypothesis for plant metal hyperaccumulation using the bacterial pathogen Pseudomonas syringae pv . maculicola M4 . We have shown that Psm displays T3SS-dependent pathogenesis in Thlaspi caerulescens plants grown in low metal concentrations and that metal hyperaccumulation by T . caerulescens at higher metal concentrations provides an effective defense against Psm . Further , we have demonstrated that zinc tolerance is essential for bacterial colonization of zinc-hyperaccumulating plants . Thus , our work is consistent with the hypothesis that hyperaccumulation benefits plants by increasing their resistance to pathogens . Although studies have found that zinc , nickel and cadmium are mainly stored in the leaf cell vacuoles of Thlaspi species , particularly in epidermal cells [27] , [28] , [36] , [37] , [38] , metals are transported into the leaf through the extracellular spaces of the apoplast by means of the transpiration stream . By measuring metal concentrations in the apoplastic fluid , we were able to provide an approximation of the conditions experienced by invading pathogens in the leaves of T . caerulescens . Comparison of the metal concentrations in planta with the IC50 values measured for Psm in vitro in extracted apoplastic fluid allowed us to demonstrate that direct inhibition of bacterial growth by hyperaccumulated zinc or nickel is a realistic possibility . T . caerulescens , when grown on at least 10 µM Zn or 30 µM Ni , accumulated sufficient metal in apoplastic fluid to provide an elemental defense against Psm . This result is particularly striking considering that , when grown at 10 µM Zn , T . caerulescens accumulated only an average of 0 . 3 g Zn per kg dry mass , while the threshold for designation as a zinc hyperaccumulating plant is 10 000 mg per kg [1] . This indicates that T . caerulescens could be protected against pathogens by zinc accumulation even when growing on relatively low-zinc soils . However , although T . caerulescens accumulates sufficiently high metal concentrations to account for its metal-dependent resistance to Psm , we cannot rule out the possibility that additional metal-dependent factors act in conjunction with metals to limit the growth of Psm in T . caerulescens . Accumulation of metals requires mechanisms by which the plant may tolerate elevated intracellular metal concentrations , which have not been fully elucidated , but which have been shown to involve redox-related compounds such as glutathione [39] , enzymes such as superoxide dismutase [40] , and metal-binding ligands such as organic acids and amino acids [27] , [41] , as well as proteins ( e . g . metallothioneins [42] ) . Such changes may affect the quality of the plant environment for growth of Psm or zinc tolerance in Psm , without having any expressly defensive function . One case in which additional factors seem likely to contribute to metal-dependent defenses is in plants treated with cadmium . Cadmium was able to defend T . caerulescens against Psm in planta when plants were treated with ≥10 µM cadmium . Bacterial growth was also inhibited for around 18 hours in apoplastic fluid extracted from these plants . After this time , inhibition was released , possibly as a result of changes in bacterial gene expression or in the composition of the apoplast during this time in vitro , which may include alterations in cadmium bioavailability . However , the cadmium concentrations detected in apoplast extracts from cadmium-treated plants were lower than those required for inhibition of bacterial growth in apoplast extracts from plants grown in the absence of cadmium , so it is possible that cadmium concentrations are correlated with another defensive factor which may be unstable ( such as ROS ) or volatile . To investigate further the possibility of a true elemental defense , we tested the importance of zinc tolerance for bacterial growth in planta in zinc-treated T . caerulescens . The results obtained using mutants of Psm provide the clearest evidence to date of a direct role for zinc as an elemental defense against pathogens in T . caerulescens . Mutants with reduced zinc tolerance were unable to multiply in plants grown at 10 µM zinc , in leaves of which their wild-type counterpart was successful; conversely , mutants with increased zinc tolerance grew well in leaves of plants grown at 30 µM or even 300 µM zinc , in which the wild-type could not survive . This clear link between bacterial zinc tolerance and ability to colonize plants hyperaccumulating zinc provides strong support for the concept of an elemental defense by zinc in T . caerulescens . Finally , we have compared the zinc tolerance of strains used in this work with that of bacteria isolated from the leaves of T . caerulescens plants growing under natural conditions in a zinc-polluted field site . We have shown that bacteria naturally colonizing these plants in the field had a range of zinc tolerances much higher than those of plant pathogenic strains isolated from non-accumulating crop plants , providing evidence for local adaptation of these endophytes to their environment [43] , [44] , [45] . This is in agreement with previous studies demonstrating that endophytic bacteria isolated from nickel hyperaccumulators exhibit high nickel tolerance [46] , [47] . Plant pathogens that have not been subject to this selection may find it difficult to grow and cause disease in T . caerulescens . Metal-dependent resistance in T . caerulescens may be particularly effective against airborne , foliar pathogens such as P . syringae and powdery mildew , which may be deposited onto the surface of T . caerulescens leaves by rain and wind having previously colonized non-accumulating or metal-excluding plants , with no prior selection for metal tolerance . Thus , hyperaccumulated metals may be functionally equivalent to the diverse array of anti-microbial secondary metabolites used by plants to provide protection against infection . Only pathogens that are able to tolerate or inhibit the chemical defenses present in a specific plant species or genotype can grow in plant tissues . Similarly , in the case of metal-hyperaccumulation , only a small number of organisms – those possessing high metal tolerance – are able to grow in these plants . When the plants are deprived of this form of defense by cultivation on a low-metal growth medium , they may become vulnerable to a wider range of pathogens , explaining the spontaneous outbreaks of mildew infection observed on such plants ( Figure 1 ) . When considering the role of metals in protecting plants against infection in a natural setting it is important to note that metal concentrations in the leaves of hyperaccumulating plants are typically orders of magnitude higher than metal concentrations in the environment , as illustrated in Figure 5 . Therefore , even though the environment surrounding these plants may favour the growth of moderately metal tolerant microorganisms , many of these organisms may have insufficient metal tolerance to be able to grow in the tissues of hyperaccumulating plants . In addition , the observation that T3SS mutants of Psm were unable to infect T . caerulescens plants grown on low metal concentrations indicates that the plants possess additional defense mechanisms that act in conjunction with metal hyperaccumulation to protect plants , but which can be suppressed by the action of T3SS effectors . Thus , successful pathogens of T . caerulescens must not only be adapted for growth in a metal-rich environment , but must also possess at least some of the pathogenicity mechanisms known to be required for infection of non-accumulating plants such as Arabidopsis thaliana . We have demonstrated that hyperaccumulation of any of three metals , zinc , nickel , or cadmium , by T . caerulescens provides the plant with an elemental defense against the hemibiotrophic pathogen Psm . The validity of the defense hypothesis has been challenged by some studies in which no evidence was found that metal hyperaccumulation defended T . caerulescens from herbivory in the field [13] , [14] . Our work , however , suggests that the defensive effect of metal hyperaccumulation against pathogens remains relevant in field conditions , with only metal-tolerant bacteria found growing naturally in the leaves of T . caerulescens . Further , we have shown that metal concentrations in the leaves are sufficient to account for this defensive effect without invoking any other factors . For all of the metals hyperaccumulated by T . caerulescens , we have shown that growth is also inhibited in apoplastic fluid , and that both zinc and nickel are found in this specific compartment at concentrations sufficient to account for the defensive effect . Moreover , we have demonstrated that the zinc tolerance of Psm mutants is correlated with their ability to colonize zinc-hyperaccumulating T . caerulescens plants . This result is mirrored by our findings concerning the zinc tolerance of natural endophytes of T . caerulescens from a zinc-rich field site . We therefore believe that metal hyperaccumulation by T . caerulescens can provide an effective form of defense against a wide range of pathogens .
Seeds of Thlaspi caerulescens J . & C . Presl from Prayon , Belgium ( provided by A . J . M . Baker and C . Lefèbvre ) were cultured hydroponically on modified 0 . 1-strength Hoagland solution [20] in a glasshouse . The Prayon population of T . caerulescens was chosen as it is a widely studied , well characterized population showing typical hyperaccumulation behavior and producing relatively large amounts of biomass under the growth conditions described [20] , [22] , [24] , [25] . Natural radiation was supplemented by sodium-vapor lamps for 14 hours per day . Night temperature was maintained at 14°C and day temperature at a minimum of 24°C . Two-week-old plants were transferred to modified 0 . 1-strength Hoagland solution containing 0 . 04 , 10 , 30 , or 300 µM ZnSO4 , or 0 , 3 , 10 , or 30 µM NiSO4 or CdSO4 . The lowest zinc concentration used was 0 . 04 µM , rather than 0 µM for Ni and Cd . This is because zinc is an essential micronutrient without which the plants do not survive . The Hoagland solution used for all Ni and Cd assays contained 10 µM Zn for this reason; at 0 . 04 µM Zn , signs of zinc deficiency become apparent . T . caerulescens plants were grown on these metal treatments for a further 8 weeks , the nutrient solution being exchanged fortnightly for the first 6 weeks and weekly for the final 2 weeks . Arabidopsis thaliana ( L . ) Heynh . ( Col-0 ) were sown on peat-based compost and grown in a glasshouse under the same conditions for 6 weeks . Bacterial strains used in pathogenicity assays are listed in Table S1 . Psm ES4326 , Psm hrpN− and Psm hrpS− were provided by K . Schreiber and D . Desveaux . Psm hrpN− and Psm hrpS− were isolated from a transposon library of Psm ES4326 constructed using a kanamycin-resistant derivative of mini-Tn5 ( [48]; D . Desveaux , personal communication ) . All bacterial strains were streaked onto Luria–Bertani ( LB ) agar [49] from stocks kept in glycerol at −80°C and incubated at 28°C ( 37°C for Escherichia coli ) for 24 to 48 hours prior to use . Single colonies were transferred to LB broth and incubated at 28°C with shaking for most applications . King's B ( KB ) agar [50] , supplemented with CFC ( Cetrimide-Fucidin-Cephalosporin; Oxoid ) at half the manufacturer's recommended concentration , was used to selectively culture bacteria isolated from plant tissues for in planta growth studies . LB and KB broths were used in metal-tolerance experiments . For zinc-requirement assays , the minimal medium M9 [49] was used . To compare the in vitro growth of wild-type and mutant strains of Psm , bacteria were grown overnight on LB agar and resuspended in KB broth to give an OD600 of 0 . 1 . One hundred microliters of this suspension were added to a further 100 µl of media in each of 16 wells of a 96-well microplate . Sixteen wells were inoculated with media alone as a media control . OD was measured every 20 min for the next 48 hours using an Infinite M200 plate reader ( Tecan Group Ltd . , Männedorf , Switzerland ) . For assays to examine the ability of bacteria to cause disease symptoms in T . caerulescens , bacteria were grown overnight on LB agar and re-suspended in sterile 10 mM MgCl2 at an optical density at 600 nm ( OD600 ) of 0 . 35 . Bacterial suspensions were infiltrated into at least three fully expanded leaves from 6-week-old T . caerulescens plants grown on nutrient solution ( 10 µM zinc ) or 6-week-old A . thaliana using a blunt 1 ml syringe . Symptoms resulting from bacterial inoculation were monitored over 7 days . For growth assays , bacteria were resuspended in sterile 10 mM MgCl2 at an OD600 of 0 . 2 . This suspension was diluted 100-fold to give a suspension of approximately 106 cfu/ml and infiltrated into fully expanded T . caerulescens or A . thaliana leaves through the abaxial surface using a blunt 1 ml syringe . Nine leaves on each of six plants were inoculated within each metal treatment . Leaf discs of 10-mm diameter were taken from three of the inoculated leaves immediately , and from three further leaves at 2 and 5 days after inoculation . Leaf discs were homogenized in 10 mM MgCl2 and the resulting suspension spread onto agar plates with a minimum of three technical replicates used for each sample . After incubation at 28°C for 48 hours , the number of bacterial colonies was counted and used to estimate the number of bacterial cells per unit area of leaf . Apoplastic fluid was extracted from T . caerulescens and A . thaliana leaves by a modification of the method described by Rico and Preston [51] , using vacuum infiltration of the intercellular spaces with distilled water followed by centrifugation to extract apoplastic fluid . This fluid was centrifuged for a further 10 minutes at 4°C , filter-sterilized , and stored at −80°C . The degree of apoplast dilution was estimated as described by Rico and Preston [51] . For bacterial growth experiments , apoplast extract was freeze-dried and resuspended in an appropriate volume of distilled water to return it to its estimated concentration in planta . Apoplast extracts from T . caerulescens plants were used as a substrate for the growth of Psm in vitro . Six 100 µl samples of apoplast extract from each metal treatment were aliquoted into a 96-well microwell plate and inoculated with 5 µl of Psm suspended in 10 mM MgCl2 to give a final OD600 of 0 . 05 . The plate was incubated at 28°C with shaking and the OD600 measured at 20-minute intervals for 24 hours using the plate reader . For determination of whole-leaf metal concentrations , fresh leaf material was oven-dried at 80°C for 48 hours . Subsamples of 50 mg of dried leaf material were digested in 3 ml of concentrated ( 69% , v/v ) nitric acid for 16 hours in glass vials . Samples were then diluted 10-fold with ultrapure water and filtered using Whatman grade 3 filter paper . Metal contents of samples were measured in an air–acetylene flame by atomic absorption spectrophotometry using a double-beam optical system with deuterium arc background correction ( AAnalyst 100; Perkin-Elmer , UK ) . Samples were further diluted as necessary to fall within the linear range of calibration curves prepared using appropriate standard solutions and reagent blanks . The accuracy of the calibration curves was validated using Certified Reference Material LGC7162 ( strawberry leaves; LGC Standards , Teddington , UK ) . Three technical replicates were analysed for each measurement , and two samples from each of three plants were analysed for each metal treatment . Measurements of the fresh and dry biomass of 24 individual T . caerulescens plants were used to provide an average ratio between fresh and dry biomass , which allowed the metal content to be expressed as an approximate molar concentration in the fresh tissue . Apoplast samples were diluted appropriately with ultrapure water and measured without further treatment . The metal tolerance of Psm in vitro was tested in LB . A 5 µl aliquot of an overnight liquid culture was added to 200 µl of media supplemented with zinc , nickel , or cadmium at a range of concentrations . OD600 was read using the plate reader . Metal tolerance was also tested in apoplast extracted from T . caerulescens plants grown in 0 . 1-strength Hoagland solution ( 10 µM zinc ) . For these experiments , 2 µl of an overnight culture of P . syringae pv . maculicola M4 was added to 75 µl of apoplast extract or apoplast extract supplemented with metal . Transposon mutagenesis was carried out using the transposon miniTn5::gfp::lux cloned into pGP704 ( generous gift of Phil Hill ) . The transposon was introduced into Psm by triparental mating using the helper plasmid pRK2013 [52] . The resultant bacterial mixture was spread onto LB agar containing 50 µg/ml kanamycin and 50 µg/ml rifampicin . Resulting colonies were inoculated into 150 µl of KB broth in 96-well plates and incubated at 28°C overnight . Thirty microliters of 50% ( v/v ) glycerol was then added to each well and plates were stored at −80°C as a mutant library . Mutants were screened in a four-stage process . In round one , mutants were grown in KB in 96-well plates in which a wild-type was also included . A 5 µl aliquot of overnight culture was transferred to 200 µl of KB in a black , clear-bottomed 96-well plate and incubated at 28°C in the plate reader . Plates were maintained at 28°C with continuous shaking and the OD600 and luminescence from each well was read at hourly intervals . After 3 . 5 hours , cultures were supplemented with 5 µl of 0 . 5 mM ZnSO4 . OD600 and luminescence were read immediately and then at intervals of 3 to 9 hours for the next 48 hours . Changes in luminescence after the addition of zinc were recorded and growth was compared to wild-type . A total of 866 strains were selected with markedly increased or decreased tolerance to zinc shock . Each of these mutants was transferred to 200 µl KB in two separate wells of a 96-well plate and growth was monitored with and without 0 . 5 mM zinc over 48 hours . Of these , 134 mutants whose tolerance differed notably from the wild-type were selected and further screened for growth in 200 µl KB in 96-well plates with zinc concentrations from 0 to 3 . 5 mM for 48 hours . IC50 values were then calculated and compared to wild-type . These results were validated in a second experiment for ten mutants that showed the largest consistent changes in zinc tolerance compared to wild-type . These ten mutants were then tested for their ability to cause symptoms in T . caerulescens , A . thaliana and pak choi ( Brassica rapa spp . chinensis ) . Genomic DNA was extracted using a DNeasy Blood and Tissue kit ( Qiagen ) according to the manufacturer's protocol for Gram negative bacteria . Eight microliters of the subsequent DNA suspension were digested with 1 µl of either the restriction endonuclease SphI or NarI ( New England BioLabs ) and 1 µl of the appropriate 10× buffer according to the manufacturer's specifications . NarI cleaves the transposon DNA near the 3′ end , while SphI cleaves it towards the 5′ end; both also cleave the Psm genome frequently . Digested DNA was ethanol-precipitated , resuspended in 8 µl of ultrapure water , and self-ligated overnight at 14°C using 1 µl of T4 ligase and 1 µl of 10× buffer ( New England BioLabs ) . One µl of circularized DNA was then used as a template for inverse PCR . Primers designed to the ends of the short fragments of transposon resulting from NarI ( AACAATCTAGCGAGGGCTTGGTAAGGTGATCC and CTTGCAGTGGGCTTACATGACGATAGCTAGAC ) or SphI ( GGAACGCCGCAGGAATG and CAGCAGCTGTTACAAACTCAAGAAG ) digestion , and facing outwards , were used to amplify the flanking DNA . This was then sequenced using a primer to the end of the transposon ( CGGTTTACAAGCTAAAGCTTGC for NarI-digested DNA and either CTTCTTTAAAATCAATACC or TTCCAGTAGTGCAAATAA for SphI-digested DNA ) . Leaves of T . caerulescens were collected in the field at Hafna mine ( Snowdonia , North Wales , UK: 53°07′N , 3°49′W ) . They were then returned to the laboratory and immediately surface-sterilized by immersion in 10% ( w/v ) sodium hypochlorite for 3 minutes , followed by immersion in 100% ethanol for 3 minutes . Sterile leaves were rinsed in ultrapure water and macerated in 1 ml of 10 mM MgCl2 solution . The resulting suspension was plated onto KB-CFC agar . Plates were incubated at 28°C for 48 hours . Resultant colonies were transferred to 150 µl of KB broth in 96-well plates . After 48 hours of growth at 28°C , 30 µl of 50% ( v/v ) glycerol was added to each well and the plates stored at −80°C . For zinc tolerance experiments , bacteria were grown in 200 µl KB for 48 hours and replicated into 200 µl KB supplemented with 0 , 1 , 2 . 5 , 5 , 7 . 5 , 10 , 12 . 5 , 15 , or 20 µM ZnSO4 . Bacterial growth at each of these zinc concentrations was determined by measuring OD600 at 0 and 48 hours using the plate reader . Bacterial growth at 48 hours was then plotted against zinc concentration , from which the concentrations giving half-maximal inhibition ( IC50 ) were estimated . RNA for qRT-PCR analysis was isolated from leaves of 10-week-old T . caerulescens grown on either 0 . 04 or 300 µM zinc and from leaves of 6-week-old , non flowering A . thaliana . A . thaliana leaves inoculated with Psm at 106 cfu/ml and incubated for 24 hours under standard plant growth conditions were used as a positive control for PR-1 expression . Leaves were snap frozen in liquid nitrogen and RNA was extracted using the RNeasy kit ( Qiagen ) according to the manufacturer's instructions . After elution , RNA was precipitated in 100 µl of 8 M LiCl overnight . After two washes in 70% ( v/v ) ethanol , pellets were resuspended in 30 µl ultrapure water . RNA concentration was measured using a Nanodrop-1000 spectrophotometer ( Thermo Scientific ) and integrity was checked by electrophoresis on an ethidium bromide gel . cDNA was prepared from 1 µg RNA using the Bioline cDNA synthesis kit with oligo dT primer according to the supplier's instructions . qRT-PCR was performed using SYBR green PCR master mix ( Applied Biosystems ) in a 7300 Realtime PCR machine ( Applied Biosystems ) and analysed by calibration to a standard curve of gene expression created from pooled cDNA from all samples under test , using the 7300 SDS system software v1 . 3 . 1 ( Applied Biosystems ) . Four control genes were analysed in the same way , and PR-1 gene expression normalized to the geometric mean of the expression of these genes [53] . Gene-specific primers used are listed in Table 3 . The GenBank ( http://www . ncbi . nlm . nih . gov ) accession numbers for the genes and gene products discussed in this paper are: PSPTO_0382 ( NP_790231 . 1 ) ; PSPTO_2153 ( NP_791973 ) ; PSPTO_3533 ( NP_793313 . 1 ) ; PSPTO_5164 ( NP_794895 ) . We thank Prof . A . J . M . Baker for providing seeds of Thlaspi caerulescens; Prof . J . L . Dangl for providing Pseudomonas syringae pv . maculicola M4 , Dr . K . Schreiber and Dr . D . Desveaux for providing hrp mutants of Pseudomonas syringae pv . maculicola ES4326 , Dr . P . Hill for proving the transposon used for mutagenesis , and Prof . J . A . Langdale , Prof . N . P . Harberd and Dr . A . Buckling for helpful discussions . | Soils rich in heavy metals support communities of distinctive metal-tolerant plants , a number of which exhibit a remarkable trait known as metal hyperaccumulation . These plants accumulate exceptionally high concentrations of metallic elements in their leaves , but whether this trait confers any adaptive advantage is controversial . In this study , we test the hypothesis that metal hyperaccumulation provides protection against disease . We demonstrate that Thlaspi caerulescens becomes resistant to bacterial leaf spot caused by Pseudomonas syringae pv . maculicola ( Psm ) when it accumulates zinc , nickel , or cadmium , and show that the metal concentrations in these plants are sufficient to account for their observed disease resistance . We also show that there is a close correlation between the zinc tolerance of different strains of Psm and their ability to colonize T . caerulescens leaves with high zinc content . In a field study , bacteria isolated from the leaves of T . caerulescens plants growing on the site of a former lead–zinc mine were found to possess a higher degree of metal tolerance than bacteria isolated from crop plants . Our findings show that metal hyperaccumulation in plants can act as a mechanism to prevent attack by pathogenic microorganisms , but that metal tolerant pathogens can overcome this defense . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"plant",
"biology/plant-biotic",
"interactions",
"ecology/plant-environment",
"interactions",
"microbiology/environmental",
"microbiology",
"plant",
"biology/plant-environment",
"interactions",
"ecology/environmental",
"microbiology",
"microbiology/microbial",
"physiology",
"and",
"met... | 2010 | Metal Hyperaccumulation Armors Plants against Disease |
Visceral leishmaniasis ( VL ) is diagnosed by microscopic confirmation of the parasite in bone marrow , spleen or lymph node aspirates . These procedures are unsuitable for rapid diagnosis of VL in field settings . The development of rK39-based rapid diagnostic tests ( RDT ) revolutionized diagnosis of VL by offering high sensitivity and specificity in detecting disease in the Indian subcontinent; however , these tests have been less reliable in the African subcontinent ( sensitivity range of 75–85% , specificity of 70–92% ) . We have addressed limitations of the rK39 with a new synthetic polyprotein , rK28 , followed by development and evaluation of two new rK28-based RDT prototype platforms . Evaluation of 62 VL-confirmed sera from Sudan provided sensitivities of 96 . 8% and 93 . 6% ( 95% CI = K28: 88 . 83–99 . 61%; K39: 84 . 30–98 . 21% ) and specificities of 96 . 2% and 92 . 4% ( 95% CI = K28: 90 . 53–98 . 95%; K39: 85 . 54–96 . 65% ) for rK28 and rK39 , respectively . Of greater interest was the observation that individual VL sera with low rK39 reactivity often had much higher rK28 reactivity . This characteristic of the fusion protein was exploited in the development of rK28 rapid tests , which may prove to be crucial in detecting VL among patients with low rK39 antibody levels . Evaluation of two prototype lateral flow-based rK28 rapid tests on 53 VL patients in Sudan and 73 VL patients in Bangladesh provided promisingly high sensitivities ( 95 . 9% [95% CI = 88 . 46–99 . 1 in Sudan and 98 . 1% [95% CI = 89 . 93–99 . 95%] in Bangladesh ) compared to the rK39 RDT ( sensitivities of 86 . 3% [95% CI = 76 . 25–93 . 23%] in Sudan and 88 . 7% [95% CI = 76 . 97–95 . 73%] in Bangladesh ) . Our study compares the diagnostic accuracy of rK39 and rK28 in detecting active VL cases and our findings indicate that rK28 polyprotein has great potential as a serodiagnostic tool . A new rK28-based RDT will prove to be a valuable asset in simplifying VL disease confirmation at the point-of-care .
Leishmania parasites are transmitted to mammals by the bite of female phlebotomine sand flies and occasionally by the sharing of needles , by blood transfusion , or by congenital transmission . The life-cycle of Leishmania has two distinct forms: the flagellated promastigotes found in the gut of the arthropod vector and non motile amastigotes , which develop intracellularly in the mammalian host . Promastigotes injected into the skin during sand fly bite are internalized by dendritic cells and macrophages in the dermis where they lose their flagella as they transform into amastigotes . They multiply and survive within the phagolysosomes through a complex host-parasite interaction [1] . The prepatent period can vary from weeks to months and during this period disease symptoms may gradually appear and worsen with disease manifestations ranging from self-healing skin lesions , to diffuse cutaneous and mucosal manifestations and , in some cases , to severe visceral involvement of the spleen , liver and lymph nodes depending on the species of Leishmania . Visceral leishmaniasis , known as kala-azar ( Hindi for Black fever ) in the Indian subcontinent and Africa , is the most severe form of the disease affecting approximately 500 , 000 adults and children worldwide . Following infection , the parasites disseminate through the lymphatic and vascular systems and infect other monocytes and macrophages in the reticulo-endothelial system , resulting in infiltration of the bone marrow , hepato-splenomegaly and sometimes enlarged lymph nodes ( lymphadenopathy ) . Mortality of VL is high in the absence of treatment , which is generally lengthy , expensive and toxic . Clinical diagnosis relies on non-characteristic symptoms ( long standing fever , cachexia , anemia and hepato-splenomegaly ) which are only reliable in advanced cases and in epidemic situations . Parasitological diagnosis remains the reference standard in VL diagnosis , typically undertaken by microscopic examination of Giemsa-stained bone marrow , spleen or lymph node aspirates to detect amastigotes . This is not only invasive , but also suffers from low sensitivity , requiring both highly trained laboratory personnel to biopsy patients and high-powered microscopes that are typically available only in regional clinics . Thus , tissue aspirations for routine VL screenings are not feasible as a large-scale approach in remote field areas lacking electricity and even the most basic laboratory set-up . The development of two serological tests for diagnosis of VL , the direct agglutination test ( DAT ) and the rK39 strip tests , have started to circumvent the need for tissue aspirates in Sudan and the Indian subcontinent , respectively . DAT is the first-line VL serodiagnostic test employed in Sudan [2] and , despite being highly sensitive and specific [3] , [4] , it is not optimum as a point-of-care test as it requires a laboratory capable of holding controlled temperature with overnight incubation . The rK39 strip test became possible after the identification of the k39 kinesin gene by an immunoscreen of a L . infantum expression library with sera obtained from visceral leishmaniasis patients [5] . VL patients mount a strong antibody response to the 39-amino acid , tandem repeat units in the gene , and the recombinant form of this gene , rK39 , has been successfully used to develop an enzyme-linked immunosorbent assay ( ELISA ) [6] , [7] as well as a point-of-care RDT [8] , [9] . The rK39 RDT is a field-friendly , easy to use format that has been extensively tested in many countries . In a WHO supported multicenter trial , the FDA-approved rK39 RDT ( Kalazar Detect- Inbios , Seattle ) demonstrated excellent sensitivity ( >95% ) and specificity ( >90% ) in the Indian subcontinent ( India and Nepal ) , but only moderate sensitivity ( 75 to 85% ) and specificity ( 70–92% ) in East Africa ( Sudan , Kenya and Ethiopia ) [10] . Reasons for the suboptimal performance of the rK39 RDT in Africa are not entirely clear and have been attributed to lower antibody levels to rK39 in infected individuals . We previously identified k9 and k26 , two Leishmania genes coding for hydrophilic proteins , and demonstrated that VL patients mount strong and specific antibody responses against K26 , which can complement rK39 in a more accurate diagnosis of human VL [11] . Specific and independent antibody reactivity to each of the three antigens rK9 , rK26 and rK39 have been studied and utilized in serodiagnosis of canine VL [12] . A multi-epitope , recombinant chimeric protein for serodiagnosis of canine and human VL was evaluated by fusing L . infantum k9 gene with single repeat units of k39 and k26 genes . ELISA with this fusion protein provided 96% sensitivity for canine VL compared to only 82% for human VL , with 99% specificity for both human and canine control groups [13] . We sought to improve serodiagnosis of VL by developing enhanced RDT prototypes that can detect >90% of the African VL cases , thus increasing diagnostic accuracy and overcoming limitations of the current rK39 RDT . The development of an affordable , simple , sensitive and specific point-of-care diagnostic test would clearly have a major impact on detection , control and treatment of visceral leishmaniasis patients . A synthetic gene , k28 , was generated by fusing multiple tandem repeat sequences of the L . donovani haspb1 and k39 kinesin genes to the complete open reading frame of haspb2 , thereby increasing antigen epitope density while providing complementing epitopes in the resulting recombinant protein . The recombinant fusion protein rK28 was evaluated on ELISA with a panel of active human Sudanese VL cases and also used to develop two prototype point-of-care tests , the results of which are presented . To our knowledge , this is the first study describing the development of a second generation rapid test for the serodiagnosis of human VL . Our results confirm that rK28 is an excellent serodiagnostic tool that has the potential to replace rK39 protein pending larger field trials .
The K28 gene was synthesized at Blue Heron Biotechnology , Bothell , WA using the Genemaker technology with a six-histidine tag downstream of the N-terminal methionine . The synthetic gene includes nucleotides 142–267 encompassing three 14-amino acid ( aa ) repeats of ( L . donovani haspb1 gene , GenBank accession# AJ011810 . 1 ) , nucleotides 2110–2343 encompassing two 39-aa repeats of ( L . donovani k39 kinesin protein gene , GenBank accession# DQ831678 . 1 ) and nucleotides 1–400 ( the complete ORF ) of L . donovani haspb2 gene , GenBank accession# AJ011809 . 1 ) . The 795 base pair ( bp ) product was subcloned directionally in the Nde I/Xho I sites of pET29 ( Novagen , USA ) , and the transformants selected in XL-10 Gold cells ( Stratagene , Santa Clara , CA . ) . Following sequence verification of the insert , the recombinant plasmid was subsequently transformed into E . coli HMS-174 ( DE3 ) for expression of recombinant protein . The k28 gene sequence has been submitted to GenBank under accession number HM594686 . Recombinant K28 protein ( rK28 ) was produced by growing the transformed host cell HMS-174 ( DE3 ) in 2XYS with kanamycin using a fed-batch fermentation system . The production media was inoculated with a 10% inoculum culture in a log-growth phase . The culture was grown in a 10 L bioreactor ( New Brunswick Scientific , Edison , NJ ) to an optical density of 8–10 ( A600 ) , induced with IPTG ( 1mM final concentration ) for 2 hours , and the cells harvested by centrifugation . Cell pellets were lysed in 50 mM Tris , pH 8 . 0 using an 110S microfluidizer ( Microfluidics , Newton , MA ) and cellular debris removed by centrifugation . The supernatant containing the expressed protein was combined with Ni-NTA agarose ( QIAGEN , Valencia , CA ) in a batch-bind mode and incubated overnight at 4°C . The resin was then packed in a column and washed with 10 column volumes of 20 mM Tris-Cl , pH 8 . 0 , 250 mM NaCl , 0 . 5% CHAPS . Bound protein was eluted with 20 mM Tris-Cl pH 8 . 0 containing 400 mM imidazole . The eluted fractions were diluted 1∶3 with 20 mM Tris-Cl pH 8 . 0 and loaded onto a Q sepharose fast flow column ( GE Healthcare Biosciences , Piscataway , NJ ) . The peaks eluted at 200 to 300 mM NaCl were combined; ammonium sulfate was added to make the final concentration 2 M , following which hydrophobic interaction chromatography was performed using Octyl Sepharose 4 Fast Flow ( GE Healthcare Biosciences ) . Peaks eluted at low salt concentrations were combined , dialyzed into 20 mM Tris-Cl , pH 8 . 0 , and sterile filtered through 0 . 22 µm filter . The final protein concentration was determined using BCA assay ( Pierce Chemical , Rockford , IL ) . The lipopolysaccharide content of each protein preparation was measured by a Limulus amoebocyte lysate test ( BioWhittaker , Walkersville , MD ) and shown to be below 10 endotoxin units ( EU ) /mg of protein . Disease-positive sera from 62 VL patients were obtained from the Gedaref state of eastern Sudan . The inclusion criteria for VL sera samples used in this study were , ( i ) all patients were parasitology positive , confirmed by microscopy of lymph node or bone marrow aspirates , ( ii ) all patients had clinical symptoms and were diagnosed as active VL cases . Microscopic confirmation of parasites was performed by trained technologists . The median age of the patients was 10 years . 32% of the recruited patients were females and 68% were males . Panels of negative sera were from 25 healthy endemic controls ( EC ) from the VL endemic region of Gedaref and 20 healthy non-endemic controls ( NEC ) from Khartoum , Sudan . Sera from patients with other infections included malaria- ( n = 10 ) , tuberculosis- ( n = 10 ) and Salmonella- confirmed patients ( n = 10 ) from Khartoum , Sudan ( courtesy of Dr . Sayda El-Safi , Faculty of Medical Laboratory Sciences , Khartoum University , Sudan ) . Sera samples used in this study were collected as a part of routine diagnosis and treatment of patients in Gedaref and Khartoum , Sudan . Patients received standard treatment for the different disease indications as outlined by the Federal Ministry of Health-Sudan . All samples were subject to appropriate ethical clearance from the Faculty of Medicine , University of Khartoum and from the National Ethical Review Board at the Federal Ministry of Health-Sudan . The entire Sudanese negative-sera panel had no past history of visceral leishmaniasis . IRB approval was not sought for this study as banked sera from IRB approved protocols were used for both the ELISA and RDT testing . No personal identifiers were used nor any clinical investigation carried out as part of this study . Information about the patient's clinical diagnosis was available to us at the time this study was undertaken . Thirty normal human sera ( NHS ) from U . S residents with no history of international travel were used for testing non-specific reactivity of the recombinant proteins ( Equitech-Bio , Kerrville , TX ) . rK39 , rK28 , rK26 , rK9 were titrated ( 200–25 ηg/well ) with different dilutions of positive and negative sera ( 1∶100 , 1∶200 , 1∶400 ) using a checker board titration on flat-bottomed MediSorp™ and PolySorp™ Nunc MicroWell™ plates to determine the optimized ELISA conditions . A human immunoglobulin ( IgG ) standard curve was constructed using chrompure human IgG ( Jackson ImmunoResearch , West Grove , PA ) and used as a reference standard on every plate [14] . The first two columns on every plate were coated in duplicate with 4-fold dilutions of the standard curve ( 100 , 25 , 6 . 25 , 1 . 563 , 0 . 391 , 0 . 098 , 0 . 024 , 0 µg/well ) in 0 . 1 M bicarbonate buffer pH 9 . 6 , 0 . 01% BSA , 0 . 1% sodium azide . The rest of the plate was coated with 25 ηg /well of the antigen in 0 . 1M bicarbonate buffer pH 9 . 6 at room temperature for 2 hours . The non-specific reactivity on the plate was blocked with 1% BSA in phosphate-buffered saline pH 7 . 2 , 0 . 1% Tween 20 for a period of 2 hours at room temperature . The plates were washed in wash buffer ( PBS , 0 . 1% Tween 20 ) four times and 100 µl of 1∶400 dilution of the sera in serum diluent ( 0 . 1% BSA in phosphate buffered saline pH 7 . 2 , 0 . 1% Tween 20 ) added to the antigen wells and 100 µl of serum diluent added to the standard curve wells in duplicates and incubated at room temperature for an hour on a micro plate shaker at 500 rpm . The plates were washed in wash buffer and the bound antibodies were assayed using 100 µl per well of 1∶10 , 000 diluted Rec . Protein-G HRP ( Zymed , San Francisco , CA ) at room temperature for 1 hour . The enzyme reaction was developed with 100 µl per well of SureBlue TMB 1-component microwell peroxidase substrate ( KPL , Gaithersburg , MD ) for 5 minutes . The reaction was stopped using 50 µl/well of 1 M sulfuric acid , plates were read at 450 nm on a ThermoMax microplate reader and data analyzed using SoftMax Pro ( Molecular Devices , Sunnyvale , CA ) . The final conditions that produced the best observed test results for the Sudanese panel of sera were 25 ηg/well of antigen , 1∶400 dilution of serum , and a 1∶10 , 000 dilution of recombinant protein G-HRP as the enzyme conjugate for detection on Medisorp plate surface . Extensive optimization experiments were performed with different parameters in order to determine the final conditions that could best differentiate between strong positive and borderline positive VL sera from truly negative sera . Reproducibility of the ELISA results were confirmed by having at least two independent operators ( blinded to the identity of test samples ) perform the same assay . The human IgG standard curve was used as a reference standard to control for inter-plate variation , as well as to determine that the test was run properly . The standard curve was plotted on a 4-parameter curve fit , and an r2 value of 0 . 995 was required to validate the data from the plate . A receiver-operator characteristic ( ROC ) curve was used to evaluate all possible combinations of sensitivity and specificity and to determine an optimal cut-off that clearly discriminates between disease-positive and -negative sera [15] , [16] . The ELISA test results from 105 non-VL sera {endemic controls ( EC ) , non-endemic controls ( NEC ) , US normal human sera ( NHS ) and other infection sera} were used as the negative data set , and the results from 62 VL-confirmed sera were used as positive data set . GraphPad Prism 4 . 0 software ( GraphPad Prism Inc . , San Diego , CA ) was used to perform the statistical analyses . ROC curves were plotted using the software , and a table of sensitivity and specificity with all possible cut-offs were generated with 95% confidence intervals . The sensitivity of the test was determined as the fraction of the VL confirmed sera that were test positive , and specificity was calculated as the fraction of the EC , NEC , NHS and other infection patient sera that were identified to be truly test negative . The positive and negative predictive values of the tests were calculated . Area under the curve ( AUC ) was used as measure of diagnostic accuracy of the test providing a means to truly discriminate between disease-positive and disease-negative sera . Correlations in antibody responses were studied between individual component antigens and rK28 . A nonparametric Spearman correlation was used to calculate the correlation coefficient . Purified rK28 was provided to two manufacturers , EASE-Medtrend ( Shanghai , China ) and Chembio Diagnostic Systems ( Medford , NY ) to develop rapid tests . The EASE-Medtrend single lateral flow test utilizes a proprietary dynamic flow principle . The test antigen is immobilized on a nitrocellulose membrane within the test zone . The liquid conjugate is applied to the device through the reagent port , priming the device to facilitate the migration of serum applied in the sample port . The specific antibodies present in the serum are captured by the immobilized antigens and subsequently visualized in the form of a magenta-colored test line by the conjugate . In the control zone , a conjugate-binding reagent is immobilized on the membrane . A magenta line in the control zone appears in every valid test . The EASE-Medtrend rK28 based prototype will be referred to as K28-LF . A subset of Sudanese VL sera with low ELISA reactivity to rK39 were selected for studying the additive effect of antigens on the lateral flow format using the K28-LF RDT . The Chembio immunoassay format is called the Dual Path Platform ( DPP ) . It differs from conventional lateral-flow systems in that the test sample and the marker-detecting conjugate are delivered to the test line area independent of each other . The DPP assay has two laminated strips , connected to each other as a “T” shape inside a disposable plastic cassette . The first strip receives a sample and running buffer through the sample port . The sample migrates along the strip towards the second strip containing the test and control bands . Development of the assay is achieved by adding buffer to the development port . This step releases the conjugate ( colloidal gold ) and facilitates its migration to the test area . Antibodies , if present in the test sample , will bind to the capture reagent immobilized on the second strip , and the conjugate will react with this complex , making the test band detectable by visual evaluation . Irrespective of the presence of antibodies in the test sample , the control band should develop to assure correct DPP assay performance . The Chembio rK28-based prototype will be referred to as K28-DPP . Testing of the two K28 RDT prototypes with larger sera panels were carried out in parallel in Sudan and Bangladesh . The rK28-DPP RDTs were tested in Sudan using 73 parasitology ( LN aspirates ) confirmed VL samples , 24 healthy endemic controls , 18 tuberculosis- and 20 malaria- confirmed sera . Evaluation in Bangladesh was carried out using the rK28-LF RDT and included 53 parasitologically ( spleen aspirates ) confirmed VL samples , 20 healthy endemic controls and 20 healthy non endemic controls . The rK39-based Kalazar Detect ( InBios International Inc . Seattle , WA ) RDT was used as a comparator in all studies . Both rapid test formats use recombinant protein A-colloidal gold conjugate for detection and were performed according to manufacturer's specifications . The test results were determined in 10 minutes for the Inbios and Ease Medtrend strips and in 15–20 minutes for the Chembio DPP tests . Every sample was tested twice and the results were scored by the operator as well as independently by an individual blinded to the identity of serum samples . While microscopic confirmation of parasites is performed by well trained technologists , minimal training was required for individuals scoring the RDTs . Sera samples used in this study were collected as a part of routine diagnosis and treatment of VL patients in Gedaref Hospital , Sudan and at the Rajshahi Medical College Hospital , Bangladesh . Study protocols for the collection were approved by the Institutional Review Boards of Khartoum University and Rajshahi Medical College . In Bangladesh and Sudan , written consent was obtained from all adult patients and parents or guardians of children . For samples from illiterate adult participants and children , verbal consent was read to and discussed with adults/guardians in presence of a literate relative , the consent form was signed by the literate relative and a fingerprint was obtained from the parent/guardian . IRB approval was not sought for this study as banked sera from IRB approved protocols were used for both the ELISA and RDT testing ( retrospective study ) . No personal identifiers were used nor any clinical investigation carried out as part of this study . Information about the patient's clinical diagnosis was available to us at the time this study was undertaken .
The synthetic gene k28 was designed by fusing nucleotide sequences for three 14-aa tandem repeats of the L . donovani haspb1 gene [17] , two 39-aa tandem repeats of the L . donovani kinesin gene [18] and the entire 133 aa of the L . donovani haspb2 gene ( Figure 1A ) [17] . The 795 bp nucleotide sequence cloned in pET-29a was used to express an N-terminal 6XHis-tagged recombinant protein in E . coli . The fusion protein was purified by affinity chromatography over a Ni-NTA agarose matrix . The 264-aa sequence ( Figure 1B ) encoded an acidic protein ( pI 4 . 73 ) with a predicted molecular weight of 28 . 33 kDa . The protein migrated aberrantly around 40 kDa on SDS-PAGE ( Figure 1C ) . Hypothetically , the mobility of rK39 and rK28 should be slower than the mobility of rK26 . The predicted molecular masses of both rK39 ( 35 . 3 kDa ) and rK28 ( 28 . 33 kDa ) are indeed higher than that of rK26 ( 26 kDa ) and yet they seem to migrate faster compared to rK26 ( 26 kDa ) . The slower mobility of rK26 is due to its higher proline content compared to either rK28 or rK39 . This characteristic has been observed for other proteins with high acidity and high proline/lysine content including HASPB1 , K26 and K9 [11] , [17] , [19] . In order to evaluate the antigen-specific antibody responses against rK28 , rK39 , rK26 , and rK9 , antibody ELISA's were optimized to obtain the best signal-to-noise ratio and develop a reproducible and robust assay that was capable of capturing antibodies over a biologically relevant assay range . Sudanese parasitology confirmed VL-positive and -negative sera ( NHS , EC , NEC and other infection sera ) were tested by ELISA on rK28 , rK39 , rK26 , and rK9 to evaluate immunoreactivities ( expressed as A450nm in Fig . 2 ) against individual proteins . The overall OD responses of individual VL-confirmed sera were quite similar for rK39 and rK28 antigens ( Fig . 2A and B , respectively ) , and both were much higher than the responses to rK26 and rK9 . The ELISA cut-off values of the 4 recombinant proteins rK28 , rK39 , rK26 , and rK9 were 0 . 4151 , 0 . 3043 , 0 . 3149 , and 0 . 1589 , respectively , and were determined by ROC ( Receiver-Operator Curve ) analyses of the absorbance values at 450 nm ( Table 1 ) . The sensitivity , specificity and area under the curve ( AUC ) were calculated for all 4 recombinant proteins ( Table 1 ) . rK28 was test-positive on 60 of the 62 VL-positive serum samples , yielding a sensitivity of 96 . 8% , while rK39 had a sensitivity of 93 . 5% ( 58/62 ) . rK26 and rK9 both missed 6 out of 62 VL positive sera and had sensitivities of 90 . 3% . Specificity was calculated using a panel of 105 sera that included healthy endemic , healthy non-endemic , and other confirmed infectious disease sera , together with sera from healthy non-travelers from the United States . Based on the ELISA cut-off ( Table 1 ) , rK26 had the highest specificity of 97 . 1% , closely followed by rK28 with a specificity of 96 . 2% . rK39 had a specificity of 92 . 4% while the least specific was rK9 with a specificity of 82 . 9% . All healthy U . S donors ( 30 samples ) were test-negative on rK39 , rK28 and rK26 ELISA . rK9 was least specific , as 4/30 U . S donors reacted non-specifically on the ELISA ( data not shown ) . The area under the curve ( AUC ) is a widely accepted metric for evaluating diagnostic accuracy [20] . The greater the AUC , the better the accuracy of the diagnostic test , and an AUC of 1 represents perfect accuracy [21] . The ROC curves obtained for the ELISA using absorbance values for rK28 , rK39 , rK26 and rK9 are shown in Figure 3 . rK28 had the highest AUC ( AUCrK28 ) with a value of 0 . 98 . This was followed in order of accuracy by AUCrK26 = 0 . 97 , AUCrK39 = 0 . 96 , and finally AUCrK9 = 0 . 94 . rK28 is a fusion polyprotein comprising regions of L . donovani haspb1 ( L . infantum k26 homologue ) , L . donovani kinesin ( L . infantum k39 homologue ) and L . donovani haspb2 ( L . infantum k9 homologue ) . For many of the Sudanese VL sera tested , the relative absorbance observed were higher on the rK28 ELISA compared to rK39 , rK26 or rK9 . We also observed a subset of individual sera with very low reactivity to rK39 , but much higher reactivity to rK28 . The absorbance values ( A450 ) of individual sera to rK28 were plotted against the absorbance values of the three individual proteins rK39 , rK26 and rK9 and are shown as scatter plots ( Figure 4 ) . A nonparametric spearman correlation analysis was done to calculate the correlation coefficient r and two-tailed P values . The antibody levels measured by rK39 and rK28 ( Spearman r = 0 . 8383 , P<0 . 0001 ) ; rK26 and rK28 ( Spearman r = 0 . 7141 , P<0 . 0001 ) ; rK9 and rK28 ( Spearman r = 0 . 4112 , p = 0 . 0009 ) displayed a positive and significant correlation . Overall , there seemed to be a cumulative increase in the absolute magnitude of the antibody responses when using rK28 protein to capture serum antibodies . In order to investigate the cumulative effects of rK28 , VL sera with low K39 reactivity were selected , and the responses against individual proteins titrated against 10-fold serial dilutions of the sera ( 1∶100–1∶1000000 ) in an ELISA ( Figure 5 ) . We observed that a majority of the VL sera tested in this manner had a much higher response to rK28 and in some cases a stronger response to rK26 ( Figure 5C ) . This may be due to the fact that rK28 can capture circulating antibodies against all three component proteins ( Haspb1 , LdK39 , and Haspb2 ) leading to a more robust signal . Therefore , it is likely that tests using rK28 protein could potentially diagnose individuals that are missed by rK39 . In order to evaluate rK28 on a point-of-care test , single lateral flow RDT prototypes ( K28-LF ) were developed by EASE MedTrend ( Shanghai , PRC ) based on their proprietary dynamic flow principle . Preliminary screening of 13 VL Sudanese sera ( selected on the basis of low rK39 ELISA reactivity ) demonstrated a much higher sensitivity for the K28-LF ( 92 . 3% ) compared to a much lower sensitivity with Kalazar Detect ( 69 . 2% ) ( Table 2 ) . Neither RDTs gave any false positive results with sera from U . S healthy subjects . To obtain a more realistic performance of the RDTs in a VL endemic region , we next evaluated both the rK28-LF and Kalazar Detect on a larger panel of VL confirmed sera in Bangladesh . 53 parasitology confirmed VL sera and 40 healthy endemic control sera with no history of VL were evaluated . Once again the K28-LF RDT provided more favorable results ( 98 . 1% sensitivity , 92 . 5% specificity ) compared to Kalazar Detect ( 88 . 7% sensitivity , 100% specificity ) ( Table 3 ) . A second prototype test of rK28 ( K28-DPP ) using a distinct technology was developed by Chembio Diagnostic systems . Sera from 73 parasitology confirmed VL patients who were DAT or smear-positive and 62 negative sera ( 24 endemic controls , 20 malaria- and 18 tuberculosis-confirmed patients ) with no history of VL were evaluated in Sudan . All sera were also tested with Kalazar Detect as a comparator ( Table 4 ) . The K28-DPP RDT proved to be superior and provided a sensitivity of 95 . 9% and specificity of 100% while the Kalazar Detect yielded a sensitivity of 86 . 3% and specificity of 96 . 4% . The DAT , which was performed on every serum sample used in this study , provided a sensitivity of 94 . 5% and specificity of 100% .
Aspirates from bone marrow , lymph nodes or the spleen are typically done to confirm the diagnosis of visceral leishmaniasis . Although the specificity is high , the sensitivity of microscopy varies and is greatly influenced by the experience of the individual making the smear , the quality of the smear , and the reagents used . Microscopy is available only in tertiary care or referral centers/hospitals in endemic countries and is a time-consuming procedure . These factors make it difficult to accurately diagnose VL patients in primary care settings . The identification of rK39 as a marker of active VL disease [5] followed by its use in a rapid test format [9] has revolutionized VL diagnosis in the Indian subcontinent . While the rK39-based rapid test has greater than 95–98% sensitivity in the Indian subcontinent and is now widely used as a means of confirming diagnosis of VL patients , its sensitivity is lower in the VL endemic regions of Africa , limiting its usefulness as a widely used point-of-care serodiagnostic test . Detailed studies of human leukocyte antigen ( HLA ) polymorphisms between VL subjects from the Indian and African subcontinents would be valuable to explain differences in epitope specificities between these populations . This study was initiated with the goal of developing a highly sensitive , specific , cost-effective , and rapid point-of-care serodiagnostic test for VL diagnosis that would improve upon the rK39 RDT . Our strategy included design of a synthetic gene , k28 , harboring sequences fused from three L . donovani tandem repeat containing genes ( haspb1 , LdK39 and haspb2 ) . Previous work done by our group revealed that increasing the number of tandem repeat units exponentially increases the ability to capture antibodies in the serum [22] . We utilized this information and incorporated multiple tandem repeat regions of haspb1 and LdK39 in order to increase the antigen epitope density within the resulting fusion protein . The L . infantum homologues of these genes have previously been shown to have good serodiagnostic value for both human and canine VL [11] , [12] , [13] . The tandem repeat regions found in many protozoan proteins usually contain immunodominant B-cell epitopes capable of generating high levels of antibody response in infected individuals [23] , [24] , [25] , [26] . Serological responses of Sudanese VL patients were tested by ELISA against rK28 and compared with individual L . infantum homologue proteins rK39 , rK26 , and rK9 . In order to model a realistic scenario in a VL endemic country , our specificity data included healthy endemic and non-endemic control sera as well as sera from individuals with other diseases . Healthy human sera from the USA were also included as part of the specificity panel to evaluate if the recombinant proteins had any false positive reactivities . From the testing done on ELISA , rK28 was more sensitive and specific than rK39 . The ROC curves also predicted a higher diagnostic accuracy for rK28 compared to rK39 . We next sought to determine whether VL sera with low/borderline ELISA reactivity against rK39 could be detected with greater accuracy using rK28 . Our results showed that many of the rK39 low-reactive sera had higher reactivity to rK28 and in some instances to rK26 . The benefit of using rK28 for VL diagnosis arises from its ability to capture circulating antibodies to 3 Leishmania antigens compared to rK39 , which binds antibodies specific to a single antigen . The cumulative antibody binding observed with rK28 raises the intensity of the signal and makes the border-line positive low rK39 sera distinctly positive . The higher sensitivity of rK28 in effectively identifying low rK39 sera prompted us to exploit this characteristic for developing rK28-based point-of-care rapid tests . rK28 was provided to two independent manufacturers for prototype RDT development to ensure that test format-specific constraints would not limit product development . Also , having multiple manufacturers creates a healthy competition promoting lower costs and better quality tests for clinicians . Testing of 13 low rK39 reactive Sudanese VL sera with the rK28-LF prototype confirmed significantly higher sensitivity ( 92% ) afforded by a rK28-based test in comparison to the Kalazar Detect test ( 69% ) . To evaluate accuracy of the rK28 RDT prototypes in detecting VL patients , independent studies were conducted with larger sera samples in Sudan and Bangladesh , two countries where VL is endemic . As the prototype tests were manufactured on a small scale for conducting pilot studies , the two RDT prototypes could not be tested in both countries . The rK28-DPP afforded 96% sensitivity in detecting DAT or smear-positive active VL patients in Sudan , while the rK28-LF RDT provided 98% sensitivity in detecting microscopy-confirmed active VL cases in Bangladesh . Overall , both rK28-based prototype tests proved more sensitive in detecting VL cases compared to the rK39-based tests . rK28-DPP tests also proved to be highly specific ( 100% ) in Sudan while the rK28-LF was somewhat less specific ( 92 . 5% ) in Bangladesh . Large-scale field studies in both countries for selection of the final test format are planned . Further testing in the field and close follow-up of healthy individuals in VL endemic areas who have tested positive on the rK28 tests , but lack clinical symptoms , will further illustrate characteristics of the fusion protein and help us determine whether rK28 is capable of acting as an early marker of infection . The samples used as a part of this study were VL patients confirmed by parasitology , therefore , the role of K28 rapid test in detecting parasitology negative and DAT negative VL patients is yet to be studied . This will be crucial in determining the true accuracy of the rapid tests . Due to the lack of accurate and non-invasive field-applicable tests , VL patients ( a majority of whom are children ) undergo extreme pain and discomfort as a result of diagnosis by tissue biopsy . The K28 RDT's could become a crucial tool for VL diagnosis , providing an easy alternative to biopsies . Early diagnosis and treatment of VL are crucial for both the affected individual and for the community . Untreated VL patients act as a reservoir of disease , especially in Africa and the Indian subcontinent where the disease is anthroponotic . Early and accurate case detection and treatment are essential components in VL control and elimination programs . Identification of affected individuals using an affordable serodiagnostic test prior to using expensive confirmatory tests for parasite detection and subsequent initiation of treatment would greatly impact timely case management and disease control . Use of a low-risk , field-based diagnostic test to detect active disease with greater accuracy , as well as monitor sub-clinical infection rates would significantly impact population-based control of disease and potentially reduce time to cure for individual patients . Multicenter large scale field evaluation of these prototype formats , including the rK39-based RDT as a comparator , are being planned to enable selection of a rK28-based RDT . In conclusion , we have designed a new synthetic fusion protein for improved serodiagnosis of VL . The rK28 protein affords higher sensitivity in detecting active VL cases compared to rK39 both on ELISA and RDT format . The development of an rK28-based point-of-care test has yielded promising results and will become a valuable tool in rapid diagnosis of VL in conjunction with complementary tools such as parasite circulating antigen detection tests and nucleic acid detection tests and permit addressing the under-reporting of this neglected disease . | Visceral Leishmaniasis caused by Leishmania donovani is endemic in several parts of South Asia , East Africa , South and Central America . It is a vector-borne disease transmitted by bites of infected sand flies and often fatal in the absence of chemotherapy . Timely diagnosis is an essential first step in providing proper patient care and in controlling transmission . VL diagnosis in East Africa and Latin America are currently based on microscopic confirmation of parasites in tissue aspirates . The Kalazar Detect rapid test is widely used as a confirmatory test in India with very high accuracy , but sensitivity issues have severely limited its usefulness in the African sub-continent . Direct Agglutination Test is another confirmatory test used widely in East Africa and offers high sensitivity but is not field-friendly . We report on the design of a novel synthetic fusion protein capable of sequestering antibodies against three different Leishmania donovani antigens and the development of point-of-care tests for improving VL diagnosis . We believe the ease of use of these rapid tests and their high accuracy in detecting VL cases could make them useful as a first-line test , thereby eliminating the need for painful biopsies and ensuring better patient care . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] | 2010 | Design, Development and Evaluation of rK28-Based Point-of-Care Tests for Improving Rapid Diagnosis of Visceral Leishmaniasis |
The coordinated behaviour of populations of cells plays a central role in tissue growth and renewal . Cells react to their microenvironment by modulating processes such as movement , growth and proliferation , and signalling . Alongside experimental studies , computational models offer a useful means by which to investigate these processes . To this end a variety of cell-based modelling approaches have been developed , ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes . However , it remains unclear how these approaches compare when applied to the same biological problem , and what differences in behaviour are due to different model assumptions and abstractions . Here , we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework , Chaste ( http://www . cs . ox . ac . uk/chaste ) . This framework allows one to easily change constitutive assumptions within these models . In each case we provide full details of all technical aspects of our model implementations . We compare model implementations using four case studies , chosen to reflect the key cellular processes of proliferation , adhesion , and short- and long-range signalling . These case studies demonstrate the applicability of each model and provide a guide for model usage .
Cells in eukaryotic organisms respond to physical and chemical cues through processes such as movement , growth , division , differentiation , death and secretion or surface presentation of signalling molecules . These processes must be tightly orchestrated to ensure correct tissue-level behaviour and their dysregulation lies at the heart of many diseases . The last decade has witnessed remarkable progress in molecular and live-imaging studies of the collective self-organization of cells in tissues . In combination with experimental studies , mathematical modelling is a useful tool with which to unravel the complex nonlinear interactions between processes at the subcellular , cellular and tissue scales from which organ- and organism-level function arises . The classical approach to modelling these processes treats the tissue as a continuum , using some form of homogenization argument to average over length scales much larger than the typical diameter of a cell . It can thus be difficult to incorporate heterogeneity between cells within a population , or investigate the effect of noise at various scales , within such models . Facilitated by the reduction in cost of computing power , a number of discrete or ‘individual-based’ approaches have been developed to model the collective dynamics of multicellular tissues ( Fig 1 ) . Such models treat cells , or subcellular components , as discrete entities and provide natural candidates for studying the regulation of cell-level processes in tissue dynamics . However , they are less amenable to mathematical analysis than their continuum counterparts . The precise rules and methods of implementation differ between models and must be adapted to a particular biological system . However , they can be broadly categorised as on- and off-lattice , according to whether or not cells are constrained to lie on an artificial lattice . In the present work , we choose to focus on five of the most widely used approaches . Each of the models described below have been helpful in furthering our knowledge but , like all models , they are simplifications and so have limitations . Arguably the simplest individual-based models are cellular automata ( CA ) , where each lattice site can contain at most a single cell ( Fig 1 ( a ) ) . The system is evolved discretely , using a fixed time-stepping [1] or event-driven [2] approach , with the new state of each cell determined using deterministic or stochastic rules and the state of the system at the previous time step . The computational simplicity of CA renders them amenable to simulating large numbers of cells . Another class of on-lattice model is the cellular Potts ( CP ) model [3] , which represents each cell by several lattice sites , allowing for more realistic cell shapes ( Fig 1 ( b ) ) . The shape of each cell is evolved via some form of energy minimization . Unlike CA , the CP model can incorporate mechanical processes such as cell membrane tension , cell-cell and cell-substrate adhesion , and cell volume conservation . The CP model has been used to study biological processes ranging from cell sorting [4] and morphogenesis [5] to tumour growth [6] . The removal of a fixed-lattice geometry in off-lattice models enables the more detailed study of mechanical effects on cell populations . Two common descriptions of cell shape in off-lattice models are ( i ) ‘overlapping spheres’ ( OS ) or quasi-spherical particles [7] and ( ii ) through Voronoi tessellations ( VT ) [8]; in both approaches , the centre of each cell is tracked over time . In the former , cells are viewed as particles that are spherical in the absence of any interactions but which deform upon cell-cell or cell-substrate contact ( Fig 1 ( c ) ) . In the latter , the shape of each cell is defined to be the set of points in space that are nearer to the centre of the cell than the centres of any other cell; a Delaunay triangulation is performed to connect those cell centres that share a common face , thus determining the neighbours of each cell [9] ( Fig 1 ( d ) ) . In either case , Monte Carlo methods or Langevin equations may be used to simulate cell dynamics . An alternative off-lattice approach commonly used to describe tightly packed epithelial cell sheets is the vertex model ( VM ) framework , in which each cell is modelled as a polygon , representing the cell’s membrane ( Fig 1 ( e ) ) . Each cell vertex , instead of centre , moves according to a balance of forces due to limited compressibility , cytoskeletal contractility and cell-cell adhesion . Additional rules govern cell neighbour rearrangements , growth , mitosis and death . For the remainder of this work , we focus on the five classes of model outlined above; however , we note that a variety of other cell-based models have been developed , and are reviewed in detail elsewhere [10] , [11] , [12] . These include ( among others ) the finite element method [13] , immersed boundary method [14] and subcellular element method [15] . A key advantage of cell-based models is that they can be straightforwardly coupled to other continuous models [16] . Several cell-based models have coupled descriptions of nutrient or morphogen transport and signalling to cell behaviour [17] , [18] , [19] , [20] , [21] . For example , a hybrid CA was used by Anderson and colleagues to study the role of the microenvironment on solid tumour growth and response to therapy [22] , while Aegerter-Wilmsen et al . coupled a vertex model of cell proliferation and rearrangement with a differential algebraic equation model for a protein regulatory network to describe the interplay between mechanics and signalling in regulating tissue size in the Drosophila wing imaginal disk [17] . As the use of cell-based models becomes increasingly widespread in the scientific community , it becomes ever more useful to be able to compare competing models within a consistent computational framework , to avoid the potential danger of artifacts associated with different methods of numerical solution . To date there has not been a comparison of the classes of models described above , due in part to the lack of a common computational framework in which to carry out such a comparison . The development of Chaste , an open-source C++ library for cell-based and multiscale modelling [23] , [24] , addresses this issue . Here we present a systematic comparison of five classes of cell-based models through the use of four case studies . We demonstrate how the key cellular processes of proliferation , adhesion , and short- and long-range signalling can be implemented and compared within the competing modelling frameworks . Moreover , we provide a guide for which model is appropriate when representing a given system . We concentrate throughout on the two-dimensional case , but note that many of these models have also been implemented in three dimensions . The remainder of this paper is structured as follows . We begin by presenting the five mathematical frameworks and discuss their implementation . Next , we use our four case studies to demonstrate how the modelling frameworks compare . Finally , we discuss our results and present a guide to which framework to use when modelling a particular problem .
There are several possible ways to represent cell movement in a CA . Here we focus on compact tissues so consider movement driven by division and cell exchange , using a shoving-based approach [25] . The spatial domain is discretised into a regular lattice with cells occupying the individual lattice sites ( Fig 1 ( a ) ) . The area Ai of each cell i in this model is given by 1 squared cell diameter ( CD2 ) . In common with all of the cell-based models presented here , cell proliferation is determined by a model of how cells progress through the cell cycle , which in turn specifies when cells divide . Our model of cell-cycle progression varies across the four examples considered . However , in all cases a dividing cell selects a random lattice site from its Moore neighbourhood ( the eight cells that surround it ) , and all cells along the row , column or diagonal from the dividing cell’s location are instantaneously displaced or ‘shoved’ to make space for the new cell . We use a Metropolis-Hastings algorithm to make additional updates to the state of the tissue using asynchronous updating . At each time step Δt , after checking for and implementing any cell divisions , we sample with replacement NCells cells , where NCells is the number of cells in the tissue at time t ( thus , it may be the case that a cell is sampled more than once in a time step , while others are not sampled ) . This sweeping of the domain is also known as a Monte Carlo Step ( MCS ) . We randomly select a neighbouring lattice site from each sampled cell’s Moore neighbourhood for a potential swap . The swapping of cells is intended to model random motility and the affinity of cells to form and break connections with adjacent cells . Assigning the MCS to a time step Δt allows us to parametrize the timescale of the switching process and relate this to cell division . A probability per hour is assigned for the cells ( or empty lattice site , which we refer to as a void ) to swap locations , pswap , which is calculated as pswap=κswapmin ( 1 , e−ΔH/T ) , ( 1 ) where κswap represents the rate of switching and T represents the background level of cell switching , modelling random cell fluctuations . If T = 0 then only energetically favourable swaps happen , and we use this as the default value for our simulations; as T increases , more energetically unfavourable swaps occur . Finally , ΔH = H1 − H0 denotes the change in adhesive energy due to the swap , with H0 and H1 being the energy in the original and changed configurations respectively , which is defined to be the sum of the adhesion energy between lattice sites: H = ∑ ( i , j ) ∈ N γ ( τ ( i ) , τ ( j ) ) , ( 2 ) where γ ( a , b ) is a constant whose value depends on a and b , representing the adhesion energy between cells ( or void ) of type a and b , τ ( k ) is the type of cell k ( or void if there is no cell on the lattice site ) and N is the set of all neighbouring lattice sites . Here τ ( k ) takes the values ‘A’ , ‘B’ and ‘void’ , but can in principle be extended to more cell types . Note that while we have chosen the particular implementation of our CA to accommodate the case studies below , a variety of alternative implementations exist based on other updating schemes and cell division algorithms [26] . As in the CA , we discretize the spatial domain into a lattice . Although , as in the CA case , the structure and connectivity of this lattice may be arbitrary , for simplicity we restrict our attention to a regular square lattice of size N × N . In contrast to the CA model , each cell is represented by a collection of lattice sites , with each site contained in at most one cell with the cell type of a lattice site being referred to as its spin . The area Ai of each cell i in this model is given by the sum of the area of all the lattice sites contained in the cell . In the present study , we represent a cell by 16 lattice sites ( i . e . 1 CD2 equals 16 lattice sites ) . This is illustrated in Fig 1 ( b ) . In a similar manner to the CA , the system evolves by attempting to minimize a total ‘energy’ or Hamiltonian , H , over discrete time steps using a Metropolis-Hastings algorithm . The precise form of H varies across applications but can include contributions such as cell-cell adhesion , hydrostatic pressure , chemotaxis and haptotaxis [5] . One iteration of the algorithm consists of selecting a lattice site and a neighbouring site ( from the Moore neighbourhood ) at random and calculating the change in total energy resulting from copying the spin of the first site to the second , ΔH = H1 − H0 . The spin change is accepted with probability p copy = min 1 , e - Δ H / T , ( 3 ) where T , referred to as the ‘temperature’ , characterizes fluctuations in the system; broadly speaking , at higher values of T cells move more freely , and hence system fluctuations increase in size . At each time step , Δt , we choose to sample with replacement N × N lattice sites . Note that this established algorithm for simulating CP models permits cell fragmentation , in principle; however , recent work has overcome this limitation [27] . In this study , we use a Hamiltonian of the form H = ∑ i = 1 N Cells ( t ) [ α ( A i − A i ( 0 ) ) 2 + β ( C i − C i ( 0 ) ) 2 ] + ∑ ( i , j ) ∈ N ( 1 − δ σ ( i ) , σ ( j ) ) γ ( τ ( i ) , τ ( j ) ) , ( 4 ) where the first and second terms on the right-hand side represent the area and perimeter constraint energies , summed over each cell in the system , and the third term represents the adhesion energy . Here σ ( k ) denotes the index of the cell containing lattice site k ( note we let σ ( k ) = 0 if no cell is attached to the lattice site and we denote this to be the void ) , and δa , b is the delta function , which equals 1 if a = b and 0 otherwise . τ ( k ) denotes that cell’s ‘type’ ( with the type void if σ ( k ) = 0 ) , and γ denotes the interaction energies between cells occupying neighbouring lattice sites i and j . Again N is the set of all neighbouring lattice sites and we allow γ to take different values for homotypic and heterotypic cell-cell interfaces and for ‘boundary’ interfaces between cells and the surrounding medium . Here A k ( 0 ) and C k ( 0 ) denote a specified ‘target area’ and ‘target perimeter’ for cell k , respectively , which can depend on internal properties of the cell , allowing for cell growth to be modelled . Here we assume all cells are mechanically identical and set A k ( 0 ) = A ( 0 ) and C k ( 0 ) = C ( 0 ) . The parameters α and β influence how fast cells react to the area and perimeter constraints , respectively . Upon cell division , half the lattice sites are assigned to each daughter cell ( with 2 cells of n + 1 and n lattice sites , respectively , if the parent cell has 2n + 1 lattice sites ) . Here cells are represented by their centres , which are modelled as a set of points {r1 , … , rNCells} which are free to move in space . For simplicity , we assume all cells to have identical mechanical properties and use force balance to derive the equations of motion . We balance forces on each cell centre and , making the standard assumption that inertial terms are small compared to dissipative terms ( as cells move in dissipative environments of extremely small Reynolds number [28] ) , we obtain a first-order equation of motion for each cell centre , ri , given by η d r i d t = F i ( t ) = ∑ j ∈ N i ( t ) F i j ( t ) , ( 5 ) where η denotes a damping constant and Fi ( t ) is the total force acting on a cell i at time t which is assumed to equal the sum of all forces , coming from the connections with all neighbouring cells j ∈ N i ( t ) adjacent to i at that time , Fij ( t ) . The definition of N i ( t ) varies between the OS and VT models; in the former , it is the set of cells whose centres lie within a distance rmax from the centre of cell i , while in the latter , it is the set of cells whose centres share an edge with the centre of cell i in the Delaunay triangulation . We solve this equation numerically using a simple forward Euler scheme with sufficiently small time step Δt to ensure numerical stability: r i ( t + Δ t ) = r i ( t ) + Δ t η ∑ j ∈ N i ( t ) F i j ( t ) . ( 6 ) Upon cell division , we generate a random mitotic unit vector m ^ and the daughter cells are placed at r i ± ϵ m ^ , where ϵ is a constant division separation parameter and is dependent on the particular cell-centre model being used . In the VM a tissue is represented by a collection of non-overlapping connected polygons whose vertices are free to move and each polygon corresponds to a cell . In this model , the area Ai of a cell i is given by the area of the associated polygon . An illustration of cells in a VM is given in Fig 1 ( e ) . As in cell-centre models we consider a set of points {r1 , … , rNVertices} . Here we derive a force on each vertex from a phenomenological energy function , which we balance with a viscous drag term , leading to a first-order equation of motion ( alternative formulations assume that the tissue evolves quasistatically [32] , [33] ) : ηVdridt=−∇i[ ∑j=1NCells ( t ) ( α ( Aj−Aj ( 0 ) ) 2+β ( Cj−Cj ( 0 ) ) 2 ) ]−∇i[ ∑j=1NCells ( t ) ( ∑m=1Mjγ ( τ ( j ) , τ ( jm ) ) Lj , m ) ] , ( 11 ) where ri is the position of vertex i , ηV is an associated drag constant , ∇i is the gradient with respect to ri and NCells ( t ) denotes the number of cells in the system at time t . The variables Aj and Cj denote the area and the perimeter of cell j , respectively , and Mj is the number of vertices of cell j . Lj , m is the length of the line connecting vertices m and m + 1 in cell j and jm is the neighbour of cell j which shares the edge connecting vertices m and m + 1 in cell j . Similar to the CP model , A ( 0 ) is the cell’s natural ( or target ) area , and C ( 0 ) is its natural perimeter . Finally , α and β are positive constants that represent a cell’s resistance to changes in area or perimeter , respectively . γ again denotes the interaction energies between neighbouring cells . We allow γ to take different values for homotypic and heterotypic cell-cell interfaces and for ‘boundary’ interfaces between cells and the surrounding medium . For simplicity here we set all cells to have a target area of A ( 0 ) = 1 and therefore a target perimeter of C ( 0 ) = 2 π . See [34] for a discussion on the other growth options and their influence on simulations . Cell division is implemented by placing a new edge along the shortest axis through the dividing cell’s centroid [35] and placing two new vertices at the intersection of this edge and the cell’s perimeter , thus creating two daughter cells . To maintain a non-overlapping tessellation of the domain we need to introduce a process where cell edges can swap , known as a T1 transition . This process allows cell connectivity to change as cells grow and move and is instrumental in the process of cell sorting . When an edge between two cells , A and B , becomes shorter than a given threshold , lr , we rearrange the connectivity so that the cells A and B are no longer connected and the other cells that contain the vertices on the short edge , C and D , become connected . Other processes may also be required , such as a T2 transition where small triangular elements are removed to simulate cell death . For further details of these elementary operations , see [35] . As with all of these models , other force laws could be used to define cell interactions [36] . For full details of the forces used in the vertex model , along with how they differ in both implementation and simulation results , see [35] . Now that we have briefly introduced all the cell-based models used in this study we proceed to discuss their implementation . Each simulation takes the form given in Fig 1 ( f ) . All components of this algorithm are the same for each simulation type except for the CA model where cells may also move due to the division of other cells . All models have been non-dimensionalised so that the units of space are cell diameters ( CDs ) and time is measured in hours . Parameter values are , where possible , taken from published studies using the models . In these papers the parameters were identified by fitting global simulation behaviour to that of the biological system . Some parameters have been modified from their original values in order to make cell movement as similar as possible between models . We implement all model simulations within Chaste , an open source C++ library that provides a systematic framework for multiscale multicellular simulations [24] . Further details on the implementation of VM and CP models within Chaste can be found in [35] and [37] , respectively . All code used to generate the results presented in this paper , along with tutorials for running it , is released under an open source ( BSD ) license and is available at https://chaste . cs . ox . ac . uk/trac/wiki/PaperTutorials/CellBasedComparison2017 .
Cell-cell adhesion is a fundamental property of tissue self-organization . If embryonic cells of two or more histological types are placed into contact with each other , they can undergo spontaneous reproducible patterns of rearrangement and sorting . This process can , for example , lead to engulfment of one cell type by another . Explanations for this phenomenon include the differential adhesion hypothesis , which states that cells tend to prefer contact with some cell types more than others due to type-specific differential intercellular adhesion [38]; and the differential interfacial tension hypothesis , which states that cells of different types instead exert different degrees of interfacial tension when in contact with other cell types or any surrounding medium [39] . Computational modelling has played a key role in comparing these hypotheses [40] . As our first case study , we simulate cell sorting due to differential adhesion in a monolayer of cells in the absence of cell proliferation or respecification . We consider a mixed population of two cell types , A and B , which we assume to exhibit differential adhesion . This is implemented in the CA , CP and VM models by having different values of the parameter γ for different cell types . Specifically , we choose γ ( A , A ) = γ ( B , B ) < γ ( A , B ) and γ ( A , void ) < γ ( B , void ) to drive type-A cells to engulf type-B cells . In the cell-centre ( OS and VT ) models , we instead assume that for any pair of neighbouring cells located a distance farther apart than the rest length , the spring constant , μ , is reduced by a factor μhet = 0 . 1 if the cells are of different types . Additionally , in the OS model we use a larger interaction radius , rmax = 2 . 5 , to encourage cell sorting . In addition to the update rules and equations of motion outlined in the previous section , we consider each cell to be subject to random motion . This random motion is intrinsic to the CA and CP models and is adjusted by changing the parameter T in Eqs ( 1 ) and ( 3 ) . For the OS , VT and VM models we introduce an additional random perturbation force acting on each cell or vertex , Frand=2ξΔtη , ( 12 ) where η is a vector of samples from a standard multivariate normal distribution and ξ is a parameter that represents the magnitude of the perturbation [35] . This size is scaled by the time step to ensure that when the equations of motion are solved numerically , the amplitude of the random perturbation force is independent of the size of time step . We simulate each model ten times , starting from an initial rectangular domain of width Lx and height Ly , comprising 50% type-A cells and 50% type-B cells . For all models , the edge of the domain is a free boundary , with no modification being made in the force ( or update rule ) on each boundary cell or vertex . The time step of the CA and CP models dictates how many MCS occur per hour and , along with the temperature , T , can influence the dynamics of the simulation [37] . Here we perform an ad hoc calibration of T and Δt so that the temporal dynamics of the CA and CP models match those of the other models as far as possible [37] . A full list of parameter values is provided in Tables 1 and 2 . The results of a single simulation of each model are shown in Fig 2 . In each case , the tissue evolves to a steady state where cells of each type are more clustered than the initial configuration . In the CA , CP and VM models , type-A cells are eventually completely engulfed; note that for other parameter values , each model can exhibit dissociation or checkerboard patterning [4] , [40] . In the other models , the tissue evolves to a local steady state ( a dynamic equilibrium at a local minimum in the global energy landscape ) that does not correspond to complete engulfment . A quantitative comparison of cell sorting dynamics is shown in Fig 3 . In particular we show how cell sorting is affected by the level of random motion applied to cells by multiplying the temperature T ( for CA and CP simulations ) or perturbation force magnitude ξ ( for OS , VT and VM simulations ) by the multiplier kpert which we vary between 10−2 and 102 . This is demonstrated by computing the fractional length , defined as the total length of edges between cells of different types for each simulation . These are then normalised by the length at t = 0 for comparison . The dashed black line represents the fractional length for optimal engulfment ( a circular region of 200 type A cells surrounded by type B cells ) . We find that the CA and CP models undergo repeated annealing due to their stochastic updating , and eventually end up at the global minimum ( corresponding to complete engulfment ) . However , large amounts of noise can cause disassociation of cells in the CP model . As Fig 3 ( left ) shows , for the off-lattice models the total energy of the system evolves to a local minimum in the absence of random cell movement . However , we can recover more complete engulfment through the addition of random cell movement . A relatively large amount of noise is required to alter cell neighbours in the Delaunay triangulation , illustrated by the flat lines in Fig 3 ( Left , VT ) . However , if there is too much noise then cells can become dissociated and move amongst the ghost nodes; in this case , if a cell reaches the edge of the ghost node region , its Voronoi area becomes ill-defined and we can no longer define the fractional length and therefore halt these simulations . A similar sensitivity is exhibited by the VM; in this case , if the amount of noise is too high , cell shapes can become inverted due to vertices randomly intersecting edges , again we halt these simulations if this occurs . From the fractional length plots it is clear that for certain simulations there is an increased level of fluctuation in fractional length . In Fig 3 ( right ) we present how the level of fluctuation in fractional length varies as we increase the perturbations applied to the models . We calculate the magnitude of the fluctuations as the mean squared error between the original curves and smoothed versions of the same curves , using a 10 hour smoothing range . For all models the magnitude of the fluctuations increases as kpert is increased . The exception to this is that the fluctuations for CP simulations for large kpert are effectively zero , this is because cells have become dissociated and the fractional length is zero . In order to illustrate the effect of perturbations on the patterning of the tissue , in Fig 4 we present snapshots of the tissue at t = 100 ( where possible ) for increasing levels of perturbation ( kpert ) . We see that for each model as we increase the perturbation we move from an unsorted state to the sorted states presented in Fig 2 but as we increase the perturbations further cells become dissociated , and for VT and VM models assumptions of connectivity and concavity of cells can become void ( shown by incomplete lines in Fig 3 and missing snapshots in Fig 4 ) . To summarise , we find that the degree of cell sorting observed in our simulations depends on how much random cell movement can be accommodated within each model . We note that there is no reason a priori to suppose that the configuration corresponding to the global minimum is biologically realistic; this depends on how the typical time scale which complete sorting occurs compares to other embryogenic processes . Comparing the different models , we note that the OS and VT models considered in Fig 2 will always differ from the CA , CP and VM models , in that given sufficient time they will fully separate rather than undergo complete engulfment . Embryonic development and adult tissue self-renewal both rely on careful control of cell proliferation , differentiation and apoptosis to ensure correct cell numbers . The intestinal epithelium offers a particularly well-studied example of such tightly orchestrated cell dynamics . It is folded to form invaginations called crypts and ( in the small intestine ) protrusions called villi . The disruption of cell proliferation and migration in intestinal crypts is the cause of colorectal cancers . Experimental evidence indicates a complex pattern of cell proliferation within the crypt , in which cells located at the base of the crypt cycle significantly more slowly than those further up . One possible explanation for this is contact inhibition , in which stress due to overcrowding causes a cell to proliferate more slowly , enter quiescence or even undergo apoptosis [43] . The biological mechanism through which shear stress affects the expression of key components in the Wnt signalling pathway , which in turn plays an important role in cell proliferation and adhesion in this tissue , has been elucidated through a number of studies [44] , [45] . A variety of cell-based models have been developed to study aspects of intestinal crypt dynamics [46] , including defining the role of the Wnt signalling pathway [47] . The process and consequences of contact inhibition have also been described using cell-based modelling approaches in a more general setting [48] , [49] , [50] . A recent study used a cell-centre modelling approach to investigate how combined changes in Wnt signalling response and contact inhibition may induce altered proliferation in radiation-treated intestinal crypts [42] . As our second case study , we simulate the spatiotemporal dynamics of clones of cells within a single intestinal crypt . This example demonstrates how multicellular models and simulations ( in particular Chaste ) can include the coupling of cell-level processes to simple subcellular processes and deals with cell proliferation , death and differentiation . Our underlying model of a colonic crypt has been described in detail previously [31] , [51] , [52] . We restrict cells to lie on a fixed cylindrical crypt surface , defined by the two-dimensional domain [0 , Lx] × [0 , Ly] , where Lx and Ly denote the crypt’s circumference and height , respectively . Periodicity is imposed at the left- and right-hand boundaries x ∈ {0 , Lx} . We impose a no-flux boundary condition at the crypt base ( y = 0 ) and remove any cell that reaches the crypt orifice ( y = Ly ) . In each simulation , we start with a regular tessellation of cells occupying this domain; the crypt is then evolved for a duration tstart to a dynamic equilibrium , before cell clones are recorded and the crypt evolved for a further duration tend . For each cell-based model considered , we implement cell proliferation and differentiation as follows . Any cell located above a threshold height yprolif from the crypt base is considered to be terminally differentiated , and can no longer divide . Any cell located below yprolif can proliferate . On division a random cell cycle duration is drawn independently for each daughter cell . Specifically , we draw the duration of each cell’s G1 phase , tG1 , from a truncated normal distribution with mean μG1 = 2 , variance σ G1 2 = 1 and lower bound tG1min = 0 . 01 , and we set the remainder of the cell cycle as tS = 5 , tG2 = 4 and tM = 1 , for the durations of the S phase , G2 phase , and M phase , respectively . In addition the duration of G1 phase depends on the local stress , interpreted as the deviation from a cell’s preferred area . A cell pauses in the G1 phase of the cell cycle if Ai<rCIAi ( 0 ) , ( 13 ) where rCI is the quiescent area fraction and Ai , Ai ( 0 ) is as earlier defined for each model [53] . This description allows for quiescence imposed by transient periods of high compression , followed by relaxation . If a cell is compressed during the G2 or S phases then it will still divide , and thus cells whose areas are smaller than the given threshold may still divide . The dimensions of the crypt domain are chosen in line with [41] but are scaled to decrease simulation time . A full list of parameter values is provided in Tables 1 and 3 . The results of a single simulation of each model are shown in Fig 5 . In each case , the number of clones decreases over time as the crypt drifts to monoclonality . A more quantitative comparison of clonal population dynamics is shown in the left column of Fig 6 . For each simulation we compute the number of clones remaining in the crypt as a function of time . All models exhibit the same qualitative behaviour , with a sharp initial drop as all clones corresponding to cells outside the niche are rapidly lost , followed by a more gradual decay in the number of clones at the crypt base due to neutral drift . However , we note that the number of clones reduces more slowly in the VM than other models , since the implementation of the ‘no flux’ boundary condition at the crypt base causes cells to remain there for longer in this model . This highlights the effect that the precise implementation of boundary conditions can have in such models . Finally , we note that for models where contact inhibition can be imposed , we see a slight effect of the degree of contact inhibition on the clonal population dynamics . In most of the models contact inhibition slows the process of monoclonal conversion , due to there being more compression at the crypt base . In contrast , in the VM the number of clones present in the crypt decreases more quickly when rCI is larger . This effect is due to there being higher rates of division , resulting in cells more frequently being ‘knocked’ from the crypt base; in the other models this effect is counteracted by compression from above . A quantitative comparison of cell velocity profiles up the crypt is shown in the right column of Fig 6 . Additionally we present the average number of cells in the crypt for all simulations in Fig 7 . This extends the comparison previously made of cell-centre and vertex models of crypt dynamics in [51] . For each simulation we compute the vertical component of cell velocity at different heights up the crypt , averaging over the x direction . We find that all models are similar when considering a ‘position-based’ cell-cycle model ( in which cell proliferation occurs below a threshold height up the crypt , corresponding to a threshold Wnt stimulus ) . However we see more pronounced differences when incorporating more restrictive contact inhibition into the cell-cycle model , in particular we see that the VT model is affected much more than the other models and the CA model is unaffected ( as all cells have the same constant size ) . This is because , with the parameters being used , cells in the VT model are more compressed than in the other models , as seen by the increased number of cells in the simulation ( shown in Fig 7 ) . Due to this increased compression a greater number of cells experience contact inhibition . In fact the OS cells are also as compressed ( as seen by a similar number of cells ) but due to the different calculation of cell area fewer cells experience contact inhibition and therefore the velocity is influenced less than in the VT model . In many developmental processes , distinct states of differentiation emerge from an initially uniform tissue . Lateral inhibition , a process whereby cells evolving towards a particular fate inhibit their immediate neighbours from doing so , has been proposed as a mechanism for generating such patterns . This process is known to be mediated by the highly conserved Notch signalling pathway , which involves ligand-receptor interactions between the transmembrane proteins Notch and Delta or their homologues [54] . Lateral inhibition through Notch signalling has been the subject of several mathematical modelling studies [55] , [56] , [57] , [58] , [59] , [60] . Such models have largely focused on the conditions for fine-grained patterns to occur in a fixed cell population; little attention has been paid to its interplay with cell movement , intercalation and proliferation . To illustrate how cell-based modelling approaches may be utilised to investigate such questions , as our third case study we simulate Notch signalling in a growing monolayer . This example demonstrates how intercellular signalling may be incorporated within each cell-based model . In this example , cells proliferate if located within a radius RP from the origin , and are removed from the simulation if located more than a radius RS > RP from the origin . For each proliferative cell , we allocate a probability pdiv of division per hour , once the cell is above a minimum age , tmin . This is implemented by independently drawing a uniform random number r ∼ U [0 , 1] for each cell at each time step and executing cell division if r < pdivΔt . This description is coupled to a description of Notch signalling between neighbouring cells that is based on a simple ordinary differential equation model previously developed by Collier et al . [55] . This represents the temporal dynamics of the concentration of Notch ligand , Ni ( t ) , and Delta receptor , Di ( t ) , in each cell i in the tissue . A feedback loop is assumed to occur , whereby activation of Notch inhibits the production of active Delta . Signalling between cells is reflected in the dependence of Notch activation on the average level of Delta among a cell’s immediate neighbours . The precise set of equations for this signalling model takes the form d N i dt = D ¯ i n N k N + D ¯ i n N - N i , ( 14 ) d D i dt = r D N k D k D + N i n D - D i , ( 15 ) where D ¯ i denotes the average value of {Dj ( t ) :j∈Ni ( t ) } , and N i ( t ) is the set of neighbours of cell i . A full list of parameter values is provided in Tables 1 and 4 . At the start of the simulation , values of each Ni and Di are independently drawn from a U[0 , 1] distribution . Upon division , the values of Ni and Di are inherited by each daughter cell . Eqs ( 14 ) and ( 15 ) are coupled to the cell-based models using the following algorithm . At each time step , having updated the cell-based model , we calculate D ¯ based on the current connectivity and , assuming D ¯ remains constant on the short interval Δt , we solve the Notch signalling model numerically over the interval [nΔt , ( n + 1 ) Δt] using a Runge-Kutta method . In terms of software implementation , all Delta-Notch simulations share a common function that contains just a few lines for initialising the subcellular level of Delta-Notch . Simulation snapshots for each model are shown in Fig 8 . In each case , we see that lateral inhibition successfully leads to patterning of cells in ‘high Delta’ steady state surrounded by cells in a ‘low Delta’ steady state in the outer ring of non-proliferating cells . This patterning is disrupted in the inner proliferating region , as cells frequently change neighbours and hence are unable to synchronise their Delta-Notch dynamics . The degree of this disruption increases with cell division rate and is most apparent in the VM simulation . A lattice-induced anisotropy is clearly visible in the CA simulation , where cell shoving causes significantly more cell rearrangements and , as a result , less patterning along diagonals . This phenomenon also occurs , to a lesser extent , in the CP simulation . A quantitative comparison of the patterning dynamics across models is shown in Fig 9 ( Left ) . As a measure of patterning we plot the ratio of cells in the heterogeneous steady state to those not in this state at the end of each simulation , computed as a radial distribution across the tissue . Note that the ‘kinks’ observed in the CA results ( Fig 9 ( Left CA ) ) are due to the presence of discrete cells on a fixed lattice . We also present the level of cell compression ( represented as the number of cells per unit target area , for each model , as proliferation is varied in Fig 9 ( Right ) . We see that there is significantly less patterning in the proliferative region for all models and that as the rate of division is increased the difference is exaggerated . This is due to cells becoming more compressed in the central proliferative zone ( Fig 9 ( Right ) ) and causing cells to expand outwards faster . For higher proliferation rates this leads to exchanging neighbours more frequently , even in regions without proliferation . This is most apparent in the VT and VM simulations where there is a larger degree of compression in the proliferative zone . Note that several of the models show an increase in cell numbers ( per unit target area ) on the edge of the tissue . This is due to cells being removed once the center of the cell had passed the the right of the outer-most bin which allows more cell centers in the outer-most bin without being compressed . Morphogens are secreted signalling molecules that provide positional information to cells in a developing tissue and act as a trigger for cell growth , proliferation or differentiation . The processes of morphogen gradient formation , maintenance and interpretation are well studied , most notably in the wing imaginal disc in the fruit fly Drosophila [61] , a monolayered epithelial tissue . A key morphogen called Decapentaplegic ( Dpp ) forms a morphogen gradient along the anterior-posterior axis of this tissue . Dpp is known to determine the growth and final size of the wing imaginal disc , although the mechanism by which its gradient is established remains unclear . A number of cell-based models have been proposed for the cellular response to morphogen gradients and mechanical effects in developing tissues such as the wing imaginal disc [62] , [63] , [18] . As our final case study , we simulate the growth of an epithelial tissue in which cell proliferation is coupled to the level of a diffusible morphogen . This case study represents an abstraction of a wing imaginal disc and illustrates how continuum transport equations may be coupled to cell-based models . Our description of morphogen-dependent cell proliferation is based on that proposed by [19] and is implemented as follows . The probability of a cell dividing exactly n time steps after its last division is given by pdivunΔt , where pdiv is a fixed parameter and the weighting un satisfies the recurrence relation u n + 1 = u n ( 1 + Δ t g ( 1 + λ c n ) ( 1 − u n ) ) , ( 16 ) with u0 = uN/2 where uN denotes the parent cell’s weighting value immediately prior to division . Here λ is a fixed parameter quantifying the effect of the morphogen on cell growth , cn denotes the morphogen concentration at that cell at that time step , and g is a random variable independently drawn upon division from a truncated normal distribution with mean μg , variance σ g 2 and minimum value gmin . When initialising the simulation , a value of g is drawn independently for each cell from a truncated normal distribution ( as on division ) , and a value of u0 is drawn independently from a U[0 . 5 , 1] distribution . Each cell-based model is coupled to a continuum model of morphogen transport based on that proposed by [19] . We assume that the morphogen is secreted in a central ‘stripe’ of tissue and diffuses throughout the whole tissue , being transported by the cells , while being degraded . In this description , the morphogen concentration c ( x , t ) is defined continuously for times t ≥ 0 in the spatial domain x ∈ Ωt defined by the boundary of the cell population ( see below ) . This concentration evolves according to the reaction-advection-diffusion equation ∂c∂t+w· ( ∇c ) −∇· ( D∇c ) =f ( x ) −kcc , ( 17 ) with zero-flux boundary conditions at the edge of the domain . In line with most work , we do not account for the exclusion of diffusing chemicals from the space occupied by cells . The vector field w denotes the velocity of the cells moving in the tissue ( and is found in the weak formulation in [19] ) . Its inclusion in Eq ( 17 ) denotes the advection of Dpp with the cells . The parameters D and kc denote the morphogen diffusion coefficient and degradation coefficient , respectively . Finally , the function f specifies the rate of production of morphogen in the central stripe of tissue , and is given by f ( x , y ) = f prod for x ∈ ( - L prod , L prod ) , 0 otherwise . ( 18 ) To solve Eq ( 17 ) numerically , we first discretise the spatial domain defined by the cells to make a computational mesh . For the VT model we use the triangulation defined by the dual of the Voronoi tessellation; for the vertex model we use the triangulation defined by dividing each polygonal cell into a collection of triangles ( made up from the set of vertices and the centre of the polygon ) as in [19]; and for the CA , CP and OS models we create a triangulation by calculating the constrained Delaunay triangulation of the centres of the cells . This tessellation changes over time as the tissue grows . We solve Eq ( 17 ) using a method of lines approach along the characteristic curves d c d t = ∂ c ∂ t + w · ( ∇ c ) , ( 19 ) and a continuous Galerkin finite element approximation to the spatial derivatives . We approximate the solution of Eq ( 17 ) using a Forward Euler discretization for time and a linear finite element approximation in space . As we generate the computational mesh from the cells , the mesh moves with velocity w . We can therefore account for the advective term of Eq ( 17 ) by moving the solution with the moving cells . Finally in each model when a cell divides it creates a new node in the mesh and the solution at the new node is defined to be the same as the node attached to the parent cell . A full list of parameter values is provided in Tables 1 and 5 . Simulation snapshots for each model are shown in Fig 10 . As expected , over time the morphogen biases the shape of the tissue , which exhibits greater growth in the y direction . This is confirmed in Fig 11 ( right ) , which shows a quantitative comparison of tissue shape dynamics across models . A quantitative comparison of the spatio-temporal morphogen dynamics across models is shown in Fig 11 ( left ) . In each case , the morphogen distribution is plotted at different times as an average over the x direction and over 20 simulations . While the mean behaviour is conserved across models , the CA exhibits significantly greater variation about this mean . This is due to the discrete nature of cell movement , and hence morphogen advection , in these models . We would expect this greater variation to be less pronounced if a simpler approach often taken when simulating CA models , that of neglecting advection due to cell movement , were taken . Looking at the snapshots in Fig 10 we see that despite being an off-lattice model the VT model exhibits some regularity in shape through growth , witnessed by straighter than expected edge segments ( shown in detail in Fig 12 ) . This is due to the method for calculating connectivity in the VT model and can introduce artefacts when considering freely growing domains as seen here .
The field of mathematical modelling in biology has matured beyond recognition over the past decade . One indication of this is the move towards quantitative comparison with data taking precedence over qualitative comparison . In this context , we must investigate if the model framework chosen might amplify or diminish the effects of certain processes . To this end , the present work seeks to advance our comparative understanding of different classes of models in the context of cell and tissue biology . A variety of cell-based approaches have been developed over the last few years . These models range from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes . Such models have proven useful in gaining mechanistic insight into the coordinated behaviour of populations of cells in tissues . However , it remains difficult to accurately compare between different modelling approaches , since one cannot distinguish between differences in behaviour due to the underlying model assumptions and those due to differences in the numerical implementation . Here , we have exploited the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework , Chaste . This framework allows one to easily change constitutive assumptions within these models . In each case we have provided full details of all technical aspects of our model implementations . An important finding of this study is that , with variable levels of success , it is possible to use each model investigated to represent the various behaviours of interest . Moreover , even though individual simulations may have visual differences , the bulk properties of the simulations were comparable in almost every case . However , there were differences ( detailed below ) and these could influence biological conclusions being drawn from the simulations . We compared model implementations using four case studies , chosen to reflect the key cellular processes of proliferation , adhesion , and short- and long-range signalling . These case studies demonstrate the applicability of each model and provide a guide for model usage . While on a qualitative level each model exhibited similar behaviour , this was mainly achieved through parameter choice and fitting . Parameters were chosen to give consistent behaviour where possible . When choosing which model to use , one should bear in mind the following . Certain case studies presented in this study are more aligned with particular models . For instance , in the adhesion example the CP and VM models are designed to explicitly represent cell sorting ( through cell boundary energy terms ) whereas the other models needed modification to represent the same phenomena . In fact , in the OS and VT ( and to some extent the VM ) models , the ability to sort completely was limited by the presence of local energy minima and a noise component of cell motion was required to mitigate this . However , as the level of noise was increased , artefacts can be introduced into the models , for example the tessellation may become non-conformal leading to voids in the tissue . The implementation of other features , such as boundary conditions , can also influence simulation outcomes . This was observed in the proliferation example where the rate of neutral drift was significantly different in the VM compared to the other models , due to additional adhesion of cells to the bottom of the domain . In this study we did not implement contact inhibition for the CA model as our definitions of contact inhibition required cells to be different sizes . It is possible to implement an alternative form of contact inhibition in the CA model by restricting division events to only occur when there is sufficient free space [64]; however , this would again result in a different behaviour to our simulations . A key difference between the models we considered lies in the definition of cell connectivity . It is possible for cells in the same configuration to have different neighbours under different models . For example , when under compression , cells in the OS model can have more neighbours than similarly sized cells in the CP , VT or VM models . The effects of this can be seen in the short-range signalling example with a high degree of proliferation . Finally , the models differ vastly on how long they take to simulate . In their original uncoupled forms , the least computationally complex model to simulate is the CA , followed in order by the CP , OS , VT and VM . However , this complexity depends on what is coupled to the models , at both the subcellular and tissue levels . Specifically , in order to make the CP model equivalent to the other models when coupling to subcellular and tissue level processes , we have chosen to use a time step that is smaller than that typically used in CP simulations , increasing the computation time . In the following we compare the computational times for each case study as measured from our implementation of the different models in Chaste . To illustrate relative computation times , we record in Table 6 the run time for a typical simulation of each case study , across the five models considered . We emphasise that these times are heavily dependent on the implementation of each model within Chaste , which is more heavily optimised for off-lattice models . In particular , the computation time for the CP model is likely to be significantly reduced if other software implementations of this class of model are used [65] , [66] . We see that ( except for the CP model ) the level of computational time is roughly as expected , increasing with complexity with the OS and VT models being similar . There are exceptions to this . For example , the CA and CP simulations of the long-range signalling example take longer than may be expected . This is due to the method used to calculate the growing PDE mesh in our computational implementation in Chaste , which is optimal for off-lattice models; future work will involve developing optimised numerical techniques that exploit the lattice structure of the on-lattice models . On the other hand , the VM simulation of the proliferation example is quicker than may be expected; this is due to the choice of parameters leading to there being slightly fewer cells in the VM simulation for this example , reducing the computational demands . Parallelisation is one way to both decrease computational time and to also be able to solve larger problems . Of the models considered , the CA model is simplest to parallelise . While more advanced , the CP model has been parallelised in publicly available software packages [65] , as has the OS model [67] . In the VT and VM cases , the implementations are much more involved . These considerations are summarised in Table 7 . The present study provides a starting point for a number of further avenues for research . First , there remains a need for theoretical and computational tools with which to easily perform quantitative model comparisons . Our results indicate that for many of the sorts of questions these types of model are currently being used to address , there is likely to be little difference in model predictions . However , such models are nevertheless moving toward a more quantitative footing , particularly as the resolution of experimental data at the cell to tissue scale improves . Further progress in this area will be accelerated by advances in automating the process of model specification and implementation , for example through extended use of mark-up languages such as SBML , FieldML and MultiCellDS . Here we have made use of a consistent simulation framework , Chaste , within which to compare different classes of cell-based model . A longer-term challenge is to extend such comparison studies across simulation tools , of which there is an increasing ecosystem , including CompuCell3d [65] , Morpheus [66] , EPISIM [68] , CellSys [69] , VirtualLeaf [70] , Biocellion [71] , BioFVM [72] , LBIBCell [73] and EmbryoMaker [74] . We emphasize here the lack of ‘benchmarks’ on which to make such comparisons . We propose that the present study offers four examples that could offer such benchmarks . Since some modelling paradigms are capable of reproducing certain biological phenomena and others are not , there is no benchmark on which all models will produce the same result; here , by selecting several simple biologically-inspired test cases we have gone some way to narrowing down the search for suitable benchmarks . Throughout this study we have concentrated on 2D studies . However , many of the models considered have also been implemented in three dimensions both in previous studies and in the Chaste modelling framework , for example in the case of overlapping spheres models of the intestinal crypt [42] , [75] or 3D vertex models of the mouse blastocyst [76] . Of the models considered in the present study , vertex models are arguably the most technically challenging to extend to three dimensions , due to the complexity of the possible cell rearrangements and force calculations . Work has also been done to model individual cells at a finer resolution by considering them to be composed of mesoscopic volume elements , which enables cell geometry and mechanical response to be emergent , rather than imposed , properties . These include the subcellular element model [77] , which may be thought of as a natural extension of the cell centre model , and the finite element model [13] and immersed boundary model [14] , which use alternative approaches to decompose cell shapes into volumetric or surface elements in a much more detailed manner than the cell-based models considered in this study . | In combination with molecular and live-imaging techniques , computational modelling plays an increasingly important role in the study of tissue growth and renewal . To this end a variety of cell-based modelling approaches have been developed , ranging in complexity from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes . However , it remains unclear how these approaches compare when applied to the same biological problem , and under which circumstances each approach is valid . Here we implement five classes of such model in a consistent computational framework , Chaste . We apply each model to four simulation studies , chosen to illustrate how the cellular processes such as proliferation , adhesion , and short- and long-range signalling may be implemented in each model . These case studies demonstrate the applicability of each model and highlight where one may expect to see qualitative differences between model behaviours . Taken together , these findings provide a guide for model usage . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"cell",
"physiology",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"cloning",
"notch",
"signaling",
"cell",
"differentiation",
"simulation",
"and",
"modeling",
"developmental",
"biology",
"molecular",
"development",
"molecular",
"biology",
"technique... | 2017 | Comparing individual-based approaches to modelling the self-organization of multicellular tissues |
Crown gall tumors develop after integration of the T-DNA of virulent Agrobacterium tumefaciens strains into the plant genome . Expression of the T-DNA–encoded oncogenes triggers proliferation and differentiation of transformed plant cells . Crown gall development is known to be accompanied by global changes in transcription , metabolite levels , and physiological processes . High levels of abscisic acid ( ABA ) in crown galls regulate expression of drought stress responsive genes and mediate drought stress acclimation , which is essential for wild-type-like tumor growth . An impact of epigenetic processes such as DNA methylation on crown gall development has been suggested; however , it has not yet been investigated comprehensively . In this study , the methylation pattern of Arabidopsis thaliana crown galls was analyzed on a genome-wide scale as well as at the single gene level . Bisulfite sequencing analysis revealed that the oncogenes Ipt , IaaH , and IaaM were unmethylated in crown galls . Nevertheless , the oncogenes were susceptible to siRNA–mediated methylation , which inhibited their expression and subsequently crown gall growth . Genome arrays , hybridized with methylated DNA obtained by immunoprecipitation , revealed a globally hypermethylated crown gall genome , while promoters were rather hypomethylated . Mutants with reduced non-CG methylation developed larger tumors than the wild-type controls , indicating that hypermethylation inhibits plant tumor growth . The differential methylation pattern of crown galls and the stem tissue from which they originate correlated with transcriptional changes . Genes known to be transcriptionally inhibited by ABA and methylated in crown galls became promoter methylated upon treatment of A . thaliana with ABA . This suggests that the high ABA levels in crown galls may mediate DNA methylation and regulate expression of genes involved in drought stress protection . In summary , our studies provide evidence that epigenetic processes regulate gene expression , physiological processes , and the development of crown gall tumors .
The bacterial pathogen A . tumefaciens genetically engineers the host plant by transferring its T-DNA , a piece of DNA from the tumor-inducing ( Ti ) plasmid , into the plant genome . Expression of the T-DNA-encoded oncogenes IaaH , IaaM , and Ipt results in increased synthesis of both auxin and cytokinin [1] , [2] . High concentrations of these phytohormones not only facilitate proliferation of transformed plant cells , but also differentiation of specialized cell types within the resulting crown gall tumor [3] . In addition to auxin and cytokinin , elevated levels of the phytohormones salicylic acid , ethylene and abscisic acid ( ABA ) have been observed in crown galls on A . thaliana stems [4]–[6] . In particular , ABA was shown to be important for drought stress acclimation to ensure wildtype-like crown gall growth [7] . Moreover , approximately 20% of protein coding genes are differentially transcribed in these plant tumors compared to tumor-free stem tissue [8] . The massive changes in gene expression , together with the cooperative action of phytohormones , fulfill distinct roles in differentiation , pathogen defense , metabolic changes , and physiological adaptations in crown galls [3] , [7] , [8] . In recent years , there has been increased interest in the role of epigenetic events in regulating biotic and abiotic stress responses in plants [9] . Environmental stresses have been shown to influence epigenetic processes , inducing the release of transcriptional silencing of transgenes and several endogenous A . thaliana gene loci . Changes in the DNA methylation pattern have also been reported in cases where , similar to A . thaliana crown galls , foreign DNA is integrated into the mammalian genome prior to tumor formation . For example , mammalian tumors induced by adenovirus type 12 display extensive genome-wide hypermethylation [10] . These widespread differences in the methylation pattern during mammalian tumor formation indicate that they may be a common feature of neoplastic growth , possibly also during plant tumor development . Such an epigenetic contribution to crown gall formation was already suggested by Braun 50 years ago [11] . To date , only the integrated T-DNA has been examined with respect to DNA methylation . The T-DNA of different crown gall lines was shown to be frequently methylated . At least one T-DNA copy in each tumor genome remained unmethylated [12] , [13] , which allowed expression of oncogenes and thereby crown gall proliferation . T-DNA methylation can be induced by siRNAs which are produced by dicer activities on long dsRNA . Synthesis of the latter RNAs results from bidirectional or read-through transcription of rearranged or integrated T-DNAs . While siRNAs corresponding to T-DNA oncogenes accumulate in A . tumefaciens-infected plant tissue , synthesis of siRNA is specifically inhibited in developing tumors resulting in a potent antisilencing state [14] . In the A . thaliana genome , the highest levels of methylation are found in transposon-rich heterochromatic regions . This methylation pattern is in agreement with a primary function for methylation in transposon silencing . However , DNA methylation of protein coding genes also frequently occurs . Methylation is depleted at promoters and gene ends , indicating that it interferes with important regulatory functions in these gene segments [15] . Endoreduplication is also known to cause methylation changes as a result of increased ploidy levels in A . thaliana [16] . Furthermore , endoreduplication is a phenomenon known to occur in specialized cell types of animals and in different tissues of many plant species [17] . It extensively occurs in A . thaliana , especially if the levels of auxin and cytokinin are increased , such as in crown galls [18] , [19] . Methylation in plants differs from that in mammals in its sequence context . In plants , cytosines are methylated in three different sequence contexts ( CG , CHG and CHH , where H = A , C , T ) , whereas methylation at CG dinucleotides predominates in mammals [20] . DNA methylation in A . thaliana is established by DRM1 and DRM2 ( DOMAINS-REARRANGED-METHYLASE ) methyltransferases in all sequence contexts [21] . Methylation of specific genomic regions can be targeted by DRM proteins through interaction with ARGONAUTE4 ( AGO4 ) . Small RNAs of 21–24 nt are incorporated in AGO4 and guide DRM activity to the corresponding genomic sequences [22] . Both DRM methyltransferases and AGO4 are also essential for transgene silencing which is inducible by hairpin constructs complementary to a transgene promoter [23] . CG methylation is maintained during genome replication by the activity of MET1 ( METHYLTRANSFERASE1; [24] ) , while the plant-specific DNA methyltransferase CMT3 ( CHROMOMETHYLASE ) primarily methylates cytosines in the CHG context [25] . Furthermore , subsets of genomic DNA methylation patterns are influenced by the activity of the demethylating enzymes ROS1 ( REPRESSOR OF SILENCING ) , DME ( DEMETER ) , DML2 ( DEMETER-LIKE ) and DML3 [26] . Our study provides the results of a genome-wide methylation analysis of stem-derived A . thaliana crown galls in comparison with mock-inoculated stem tissue . This analysis indicates that the crown gall tumor genome is globally hypermethylated , while promoter regions are hypomethylated . These changes in the DNA methylation pattern seem to exert an inhibitory influence on growth of crown gall tumors , since A . thaliana mutants with reduced DNA methylation , like drm1/drm2/cmt3 ( ddc ) and ago4 , developed significantly larger crown galls . The global differences in DNA methylation between the crown gall and the tumor-free stem genome were in agreement with the transcriptomic changes of protein coding genes . For example , genes involved in ABA-dependent drought stress protection were promoter methylated and transcriptionally silenced in crown gall tumors . A . thaliana seedlings treated with the stress phytohormone demonstrated ABA-dependent methylation of the promoters of these genes . Taken together , our studies provide evidence for a role of epigenetic processes in controlling gene expression , development and physiology in crown gall tumors .
Earlier studies have shown that the T-DNA-encoded oncogenes of the virulent A . tumefaciens strain C58 are always actively transcribed in crown gall tumors of A . thaliana stems [8] . However , transgenes are known to be frequently methylated . Therefore , we analyzed the cytosine methylation pattern of a 5 , 429 bp T-DNA segment of the pTiC58 plasmid . This segment consists of the coding sequences ( CDS ) from the oncogenes IaaH , IaaM and Ipt as well as the two intergenic regions between them ( IGR1 and IGR2 , Figure 1A ) . Methylated cytosines in this region were determined by bisulfite sequencing of genomic DNA preparations of stem-derived A . thaliana crown galls . This analysis revealed that only 0 . 94% of all cytosines were methylated in the three coding sequences ( CDS ) of the oncogenes , whereas the two IGRs were completely devoid of methylated cytosines ( Figure 1B ) . The extremely low degree of T-DNA methylation in crown gall cells suggests that this is a prerequisite to maintain the expression levels of oncogenes required for tumor formation . This hypothesis was tested by the induction of oncogene promoter methylation , making use of the endogenous siRNA-directed plant methylation pathway . Plasmids containing a hairpin construct directed against the IGRs upstream of the oncogene CDSs , each fused to the CaMV35S promoter ( Figure S1 ) , were transferred into the crown gall genome by using the virulent A . tumefaciens strain C58 . Development of crown gall tumors was strongly impaired ( Figure 2A ) when A . thaliana was inoculated with strain C58 that contained hairpin sequences directed against both IGRs ( siRNA-IGR1/2 , Figure S1A ) . In contrast , no growth inhibition occurred on plants inoculated with strain C58 if only one IGR was addressed by a hairpin construct ( Figure S1B ) . Bisulfite sequencing analysis of both IGRs revealed that the IGR1/2 hairpin induced methylation of cytosines in all three sequence contexts upstream of the Ipt and IaaH CDS ( Figure 1C ) . However , IGR1 was only marginally methylated in contrast to IGR2 . Induction of IGR methylation by hairpin constructs suggests that expression of the oncogenes may be hindered . Quantification of IaaH and Ipt transcripts by qRT-PCR in A . thaliana stems six days after inoculation demonstrated that the transcription of oncogenes was indeed inhibited whenever strain C58 contained a hairpin construct directed against the corresponding IGR ( Figure 2B ) . Transcriptional silencing of IaaH was achieved despite a low cytosine methylation level , which suggests that other factors such as siRNA-mediated histone modification may also play role in silencing of this gene [27] , [28] . Wildtype A . tumefaciens lacking siRNA induced expression of the two oncogene transcripts as expected . Overall , the results demonstrate that the T-DNA sequence is susceptible to DNA methylation and crown gall development can be efficiently prevented by transcriptional silencing of oncogenes . In order to analyze whether A . tumefaciens causes genome-wide DNA methylation changes in the plant genome , we determined the methylation pattern of A . tumefaciens-induced A . thaliana crown gall tumors . Tumor growth was induced on A . thaliana stems , and tumor-free stem material was used as reference tissue . Genomic DNA from each of three independent tumor and stem samples , was randomly fragmented . Thereafter it was subjected to methylcytosine immunoprecipitation ( mCIP ) , resulting in an enrichment of methylated DNA fragments . Each mCIP sample ( 3× tumor and 3× stem ) was separately hybridized to an Affymetrix A . thaliana Tiling 1 . 0R array in parallel with three non-enriched input controls of each tissue type . This tiling array consists of oligonucleotide probes that represent the A . thaliana genome with an average resolution of 35 bp . Hybridization signals allowed the detection of genomic regions consisting of at least five genomically adjacent probes that displayed signal intensities above the local background . These regions were assumed to be enriched by the mCIP procedure and therefore considered to contain methylated cytosines . In total 15 , 431 distinct genomic regions were methylated in either tissue type . These regions cover 26 , 287 kb ( 22 . 06% ) of the A . thaliana nuclear genome . In order to verify the reliability of the methylation profile analysis the distribution of methylation signals was examined for both the tumor and stem genome in four categories of annotated loci: Protein coding genes , transposable elements , pseudogenes and non-coding RNAs ( ncRNA ) . For this purpose the proportion ( % ) of methylated genes out of the total number of genes showing methylation was calculated at 60 positions from 1 kb upstream to 1 kb downstream of genes and plotted along an abstracted model sequence for each gene category . The highest percentages of methylation at all positions were detected in pseudogenes , where they were almost evenly distributed along the entire sequence ( Figure S2 ) . In protein coding genes , methylation was especially enriched in the 3′-half of the transcribed region , whereas it decreased towards the transcription start ( TSS ) and transcription end sites ( TES ) . The methylation pattern was similar for ncRNAs , except that the overall methylated proportion was higher and did not increase in the transcribed region . In accordance with its presumed function in transposon silencing [28] , the transcribed regions of transposable elements were highly methylated . Overall , in the genomes of both tissue types the methylation patterns of the mentioned gene categories are well in agreement with those reported in earlier studies [15] , [29] . In order to identify differences in DNA methylation between crown gall tumor and stem tissue , differentially methylated regions ( DMRs ) were determined in the four categories ( Figure 3; for details see Materials and Methods ) . This analysis revealed that 2 , 876 annotated loci differed in methylation levels between crown galls and the tissue from which they originate . The majority of these loci overlapped with regions found to be hypermethylated in crown galls ( 1 , 822 ) , whereas 1 , 100 hypomethylated regions were located in the proximity or inside of annotated loci . The sum of hyper- and hypomethylated regions is higher than that of all affected loci because one locus may contain several DMRs . With respect to the total number of DMRs between crown gall and stem tissue , the majority could be assigned to protein coding genes ( 71 . 3% ) and transposable elements ( 25 . 3% , Figure 3A ) , which together account for nearly 97% of all DMRs . DMRs were also present in pseudogenes ( 1 . 8% ) and ncRNAs ( 1 . 5% ) . However , when separately calculating the number of DMRs for each of the four categories of annotated loci , the highest proportion was detected in protein coding genes ( 7 . 7% ) , followed by pseudogenes ( 6 . 1% ) and ncRNAs ( 3 . 4% ) , whereas only 2 . 4% of transposable elements were differentially methylated between the two tissue types ( Figure 3B ) . Hypermethylation was more prominent than hypomethylation in protein coding genes ( 5 . 1% vs . 1 . 6% ) , pseudogenes ( 3 . 8% vs . 2 . 3% ) and ncRNAs ( 2 . 3% vs . 1 . 1% ) , but not in transposable elements ( 1 . 2% vs . 1 . 2% ) . Taken together , the genome of the crown gall tumor is globally hypermethylated . The changes in the genome wide methylation pattern reflected the increased expression of genes involved in DNA methylation ( MET1 , DRM2 , CMT3 and AGO4 ) as well as demethylation ( ROS1/DML1 ) in crown galls ( Table S1 ) . In order to verify methylation differences by an independent method , one DMR was randomly chosen from each of the five A . thaliana chromosomes and analyzed by bisulfite sequencing . The directions of methylation changes at the tested loci ( hypermethylation: At5g58370 , At4g12460 and hypomethylation: At3g19250 , At1g20850 ) were in agreement with the results achieved by tiling array analysis , except for the locus At2g16595 from chromosome 2 ( Figure S3 ) . This locus was enriched in immunoprecipitated crown gall DNA , which indicates an increase in methylation . However , bisulfite sequencing revealed that only CG and CHH methylation was increased at this locus , but this was accompanied by a decrease in CHG methylation . Changes in the methylation pattern may be a result of altered ploidy levels which frequently occur in A . thaliana . Therefore , the ( endo ) ploidy levels of A . thaliana crown gall cells as well as tumor-free stem cells were determined by flow-cytometry ( Figure S4 ) . In both cases the first DNA peak ( 2C ) was found at similar positions , excluding a ploidy change towards tetraploidy in the crown gall tumor cells . Neither the histogram nor the cycle value , defined as the mean number of endoreduplication cycles per nucleus [30] , indicated an increased endopolyploidization rate in crown gall ( 0 . 784 ) versus tumor-free ( 0 . 899 ) tissues . Furthermore , we observed no peak shifts or changes in the peak width on the histograms derived from the crown gall tissue which might have indicated aneuploidy . These data demonstrate that hypermethylation in A . thaliana crown gall tumors is not due to an increased DNA content per nucleus . The significantly decreased 4C/2C ratio in crown galls ( 1 . 11 ) compared to stem tissue ( 2 . 32 ) indicates an increased number of 2C nuclei ( Figure S4B ) and thus an elevated rate of cell division in crown galls . Whereas in the non-tumor tissue more and more cells switch from the initial mitotic divisions to endoreduplication cycles , tumor cells tend to proliferate mitotically resulting in 2C cells at the end of each cycle . In contrast to the animal genome , a substantial amount of cytosine methylation occurs in non-CG contexts in plants . To identify the sequence motifs which were mostly affected by differential DNA methylation in A . thaliana crown gall tumors , all methylated genomic regions were grouped into three classes ( hypomethylated , unchanged or hypermethylated ) . These classes indicate the methylation levels of crown gall DNA compared to control tissue . Pairwise Wilcoxon rank sum tests were conducted to determine whether the frequencies of the three sequence motifs ( CG , CHG or CHH ) differ between the three classes . Differences in methylation frequency were much less significant for the CG motif ( P-value>0 . 01 , Figure S5 ) than for CHG- and CHH motifs ( P-value<0 . 01 ) . This suggests that methylation changes in the crown gall tumor mainly occurred at CHG and CHH motifs and to a lower extent at CG nucleotides . The significant changes in the DNA methylation pattern prompted us to test its impact on crown gall development . Several A . thaliana mutants with no obvious growth phenotype but with defects in either methylation or demethylation processes were inoculated with the virulent A . tumefaciens strain C58 . The fresh weight of mature crown galls from both mutant and wildtype plants was compared after 28 days ( Figure 4 ) . The ddc triple mutant , in which CHG and CHH methylation are strongly impaired , displayed significantly enhanced crown gall growth . A similar difference in tumor growth was found between wildtype plants and the ago4 mutant , which is impaired in RNA-dependent methylation processes . Note that the differences in tumor weights between ago4 plants and the ddc mutant are likely to be based on their genetic background . Plants in the Ler background of ago4 are known to develop much smaller crown gall tumors than plants in the Col-0 background of ddc . The growth of crown gall tumors was not altered in the rdd mutant , which demonstrates that demethylation pathways are not essential for A . tumefaciens-induced tumor development . Enhanced growth of crown galls on mutants that are affected in non-CG methylation pathways suggests that hypomethylation at CHG and CHH motifs facilitates plant tumor proliferation . Together with the increased differences in non-CG motif frequency in DMRs , these results provide further evidence for a prominent role of non-CG methylation during crown gall development . Nevertheless , we cannot rule out an involvement of CG methylation , because homozygous met1-3 mutants are not suitable for tumor growth assays due to severe developmental abnormalities . DNA methylation especially at transcriptional start sites ( TSS ) and transcriptional end sites ( TES ) of protein encoding genes and in the transcribed region of transposable elements is known to affect both gene expression and transposon mobility in plants . In order to determine where the changes in DNA methylation preferentially occur , the percentages of hyper- and hypomethylated regions out of all DMRs were calculated for the tumor genome in comparison to the uninfected stem at 60 positions from 1 kb upstream to 1 kb downstream of genes . The distribution of DMRs was plotted along a model sequence for protein coding genes and transposable elements . In the crown gall genome , hypomethylated regions dominated in transcribed regions of transposable elements , while the distal 5′- and 3′-flanking sequences were rather hypermethylated ( Figure S6A ) . Protein coding genes were preferentially hypermethylated in the 3′-half of the transcribed region , whereas both the upstream sequence and the 5′-half of transcribed region were hypomethylated in crown galls compared to stems ( Figure S6B ) . The proportion of hypomethylation in the upstream sequence and around the TSS was relatively high , which may be a mechanism to regulate gene expression in the plant tumor . A comparison of the methylome with transcriptome data from a previous study [8] supported this hypothesis . Methylation changes at the TSS or TES had an inverse effect on gene expression for the majority of genes ( negative logFC product ) . In contrast , differential methylation within the gene body was preferentially associated with gene expression changes in the same direction ( positive logFC product; Figure 5 ) . Due to the relationship between the methylation patterns and gene expression levels , a gene ontology analysis was performed to determine which pathways are mostly affected by differential methylation . According to the MapMan software [31] , [32] a large number of DMRs that target protein coding genes were enriched in the functional category “development” ( FDR≤0 . 002; Table 1 ) . DMRs were also overrepresented ( FDR<0 . 1 ) in the categories “cell” ( particularly in the subcategory “cell division” ) , as well as “signaling” , “biotic stress” and “cytochrome P450” . Most of the genes in these categories are involved in processes that are associated with crown gall development; such as transcriptional regulation , cell cycle , chromosome condensation , redox and disease resistance . These differences in methylation correlated with the differences in transcription , as exemplified in Figure 6 for genes involved in embryo development ( At2g22870 , Figure 6A ) , microtubule-based movement ( At1g63640 , Figure 6B ) , cysteine rich receptor kinase signaling ( At4g11480 , Figure 6C ) , and pathogenesis-related protein signaling ( At1g78780 , Figure 6D ) . A complete list of all genes affected by differential methylation in the respective pathways is present in Table S2 The significant changes in DNA methylation in crown gall tumors and their role in tumor development piqued our interest regarding the control of physiological processes by DNA methylation . Previously we had shown that the lack of an intact epidermis causes induction of ABA-dependent protection against drought stress in crown galls . Drought stress acclimation is associated with altered transcript levels of many genes involved in ABA-dependent signaling . Acclimation to drought stress is also important for crown gall tumor growth , which has been shown to be impaired in ABA-deficient or -signaling mutants [7] . In order to assess whether ABA influences DNA methylation processes in crown galls , genes known to be strongly transcriptionally repressed by ABA according to the Genevestigator database [33] , [34] were selected . In addition to transcriptional regulation by ABA , these genes were known to be significantly downregulated and highly methylated in their promoter sequence in the crown gall tumor . The genes are involved in chloroplast-specific processes , such as cyclic electron flow around photosystem I ( NDF4 ) , light-dependent transcription of the photosystem II subunit proteins D2 ( SIG5 ) and alpha-/beta-hydrolase activity in the chloroplast ( F12A4 . 4 ) . The influence of ABA on promoter methylation of the selected genes was studied in germination experiments with A . thaliana seeds , because these experimental conditions were used in the original study from the Genevestigator data set [34] . Methylation patterns of the promoters of the selected genes were analyzed by applying bisulfite sequencing . ABA treatment of . A . thaliana seeds provoked increased methylation levels in upstream regions ( Figure S7 ) of NDF4 ( logFC: 1 ) , SIG5 ( logFC: 0 . 52 ) and F12A4 . 4 ( logFC: 0 . 96 ) and additionally caused severely reduced transcript levels ( Figure 7A ) . This result reflects the situation observed in A . thaliana tumors , where elevated ABA levels and reduced transcription were accompanied by upstream hypermethylation of the tested genes ( Figure 7B ) . Apparently , part of the difference in methylation patterns and transcription between crown galls and the tissue of origin can be ascribed to elevated ABA levels within the tumor .
In recent years , epigenetic processes such as DNA methylation have received increasing attention focused on their function in development , biotic and abiotic stress responses , as well as genome defense [35]–[37] . In our studies we have focused on the impact of DNA methylation on development and physiology of A . tumefaciens-induced crown gall tumors . A precondition for crown gall tumor formation is expression of the oncogenes IaaH , IaaM and Ipt , which are encoded on the agrobacterial T-DNA that is integrated into the plant genome . Therefore , it is not surprising that post-transcriptional gene silencing ( PTGS ) of these genes by means of RNAi caused resistance to crown gall tumorigenesis in previous experiments [38]–[40] . In these studies the Ipt and Iaa oncogenes were silenced using hairpin constructs directed against their coding sequences . Apart from such PTGS-dependent processes , transcriptional gene silencing can be induced by de novo methylation . Previously this epigenetic mechanism has been used to initiate methylation of the agrobacterial nopaline synthase promoter [23] . However , synthesis of siRNA directed against T-DNA-encoded genes like IaaM and agropine synthase is specifically inhibited in developing crown gall tumors [14] . This observation is in agreement with our finding that the sequence of the oncogene cluster IaaH , IaaM and Ipt was unmethylated in A . thaliana crown galls [23] , despite the proposed ancient role of methylation in genome defense [37] . Thus , suppression of siRNA-mediated oncogene silencing as well as methylation-mediated silencing of oncogene promoters may guarantee unimpeded expression of T-DNA oncogenes which is indispensable for tumor growth . The view that promoter methylation induces transcriptional silencing was further supported by introducing siRNAs complementary to oncogene promoter regions . SiRNA-directed methylation of both promoter regions prevented crown gall development , whereas targeting of only one promoter region still allowed proliferation . The latter result indicates that expression of either oncogene is sufficient for tumor growth , which is in line with earlier studies [41] , [42] . Transcriptional gene silencing by promoter methylation is thus an effective tool in transgene silencing , as it was previously demonstrated for the nopaline synthase promoter [43] . Induction of siRNA synthesis complementary to oncogene promoters probably outweighs the antisilencing state induced by A . tumefaciens . Consequently , it may provide a potent mechanism to suppress tumor development after A . tumefaciens infection . Methylation analysis of A . thaliana crown gall tumors revealed that the genome was globally hypermethylated . Hypermethylation may be attributable to increased expression of DRM2 , CMT3 and AGO4 in crown galls , all of which are involved in RNA-directed DNA methylation pathways . Transcription levels of demethylating enzymes probably do not impact the global methylation level since they affect only subsets of genomic loci [26] . In the crown gall genome mainly CHG and CHH motifs were altered , whereas CG methylation was less affected . This suggests that CG methylation does not play an important role in plant tumor development , although MET1 expression is also increased in crown galls . CG methylation is known to play a major role in generating meiotically stable epialleles due to spontaneous gain or loss of DNA methylation after propagation of A . thaliana over several generations . These transgenerational effects are probably a result of epigenetic reprogramming that occurs after fertilization [44] , [45] . In contrast , reprogramming of the transformed plant cells begins with the expression of the T-DNA-encoded oncogenes , causing proliferation of the plant tumor . Epigenetic reprogramming during crown gall tumor formation is more likely to be induced by DRM and CMT3 methyltransferases which govern methylation of CHG and CHH sequence motifs [46] . Consistent with this hypothesis , A . thaliana mutants ( ddc and ago4 ) , severely defective in non-CG methylation , developed much larger tumors than the wildtype controls . In the ddc mutant , for example , CHG and CHH methylation are reduced from 22% in the wildtype to only 1% and 7% of the total methylcytosines , respectively [47] . This indicates that wildtype plants restrict tumor growth by changes in the methylation pattern of cellular genes which are altered in ddc and ago4 mutants . As the integrated oncogene sequences are already unmethylated in wildtype tumors , growth restriction is most likely a result of differential methylation of endogenous plant genes . In A . thaliana crown galls , the majority of DMRs are located in protein coding genes and transposable elements . However , differential methylation within the group of transposable elements is much lower than that of protein coding genes . This is in agreement with a report demonstrating that methylation of transposon sequences is much more stable than that of protein coding genes [48] . The higher stability of transposable element methylation is not surprising considering that loss of methylation of transposable elements has been shown to result in activation of their movement with occasionally mutagenic consequences [49]–[51] . Methylation changes in A . thaliana are known to be caused by endoreduplication which results in increased ploidy levels [12] . This is unlikely to happen in A . thaliana crown galls as the genome is rather stable in terms of ploidy level alterations . In contrast to the observed global hypermethylation , promoter sequences of protein coding genes in A . thaliana tumors were found to be rather hypomethylated and showed an increased level of gene expression . Methylation changes in transcribed regions were associated with transcriptional changes in the same direction . These observations are in accordance with previous studies of DNA methylation in A . thaliana [15] , [29] . The studies revealed that gene body methylation was preferentially found in highly expressed genes while methylation near the TSS was associated with low gene expression levels . It is widely accepted that DNA methylation at the TSS inhibits transcription by interfering with transcriptional initiation . More controversy surrounds the role of DNA methylation in transcribed regions that may be important in preventing spurious transcription from internal promoters [29] or exon definition [52] . Overall , the results of this study implicate epigenetic processes , among others , as one mechanism to control gene expression in crown galls . Little is known about plant signals that may affect the DNA methylation patterns and thereby gene expression . A process associated with A . thaliana crown gall development is ABA-dependent drought stress acclimation which was shown to be important for wildtype-like crown gall growth [7] . A . thaliana mutants in ABA-signaling ( abi1-1 , abi2-1 , abi4-1 ) and -synthesis ( aba3-1 ) display severely reduced crown gall growth . Under drought stress conditions photosynthesis is very much reduced [53] . Accordingly , in crown galls , which undergo increased water loss due to the lack of an intact epidermal layer [54] , genes involved in photosynthetic light reactions are significantly downregulated [8] . The idea that biotic and abiotic stresses give rise to an epigenetic modification by ABA signaling has been put forward earlier [36] . For example , the pea genome has been shown to be hypermethylated as a response to water deficit [55] . In addition , ABA has previously been suggested to play a role in DNA methylation in a study from Khraiwesh et al . [56] , who found that expression of stress-related genes in Physcomitrella patens is regulated by ABA in a methylation-dependent manner . In our studies , ABA-mediated methylation of promoters from photosynthesis-related genes caused their transcriptional silencing . The observed methylation and gene expression patterns were similar to those found in A . thaliana crown galls accumulating high levels of ABA . Thus , an increase of ABA levels induces promoter methylation and reduces gene expression . These observations suggest that ABA signaling pathways are interconnected with methylation processes in A . thaliana crown galls as a response to environmental stress . Unraveling the molecular mechanism underlying ABA-dependent DNA methylation will be an important task for future studies . The global methylation patterns of mammalian tumors that are induced by integration of viral DNA into chromosomes , such as from Adenovirus type 12 ( Ad12 ) , differ from those of most other mammalian tumors . In Ad12-induced tumors , DNA integration into the eukaryotic genome causes de novo methylation in cis and trans , resulting in a hypermethylated genome [10] . Accordingly , hypermethylation in crown galls may be a consequence of the integration of bacterial DNA into the plant genome and the resulting acquisition of constitutive pathways favoring crown gall formation . Alterations in DNA methylation are most likely attributable to the methylation pattern of the transformed plant cells since it has been shown that almost every cell in crown galls expresses the T-DNA-encoded oncogenes and therefore has the potential for proliferation [8] . Until now , infection of A . thaliana with Pseudomonas syringae is the only plant-pathogen interaction which has been extensively studied with respect to DNA methylation changes [57] . It is accompanied by alterations in the methylation pattern and gene expression , preferentially of defense genes . In contrast to P . syringae , infections with virulent A . tumefaciens strains induce cell proliferation and crown gall growth . Consequently , the most severe DNA methylation changes in crown gall tumors were detected in genes involved in development , cell division and signaling . Differential methylation of cell division-related genes in A . thaliana crown galls is also in line with the observed increased rate of cell division . For example , the gene encoding a kinesin motor protein ( AT1G63640 ) may contribute to cytoskeleton organization during cell division and cell growth as it is both less promoter-methylated and increasingly expressed in the tumor . Methylation seems to be linked to abiotic stress responses . Both the protein kinase CRK32 ( At4g11480 ) , which is upregulated in response to abiotic stress [58] , and ABA-dependent photosynthesis-related genes ( NDF4 , SIG5 , F12A4 . 4 ) are involved in stress-dependent signaling pathways and are differentially methylated in crown galls . The latter genes are severely downregulated in tumors , which are characterized by a heterotrophic metabolism and strong inhibition of photosynthesis genes . Thus , physiological and developmental adaptations during crown gall tumor growth seem to be controlled by epigenetic processes . Furthermore , these results suggest that in accordance with the prevailing response of the host towards a pathogen ( development or defense ) , distinct sets of genes are regulated by DNA methylation in crown gall tumors and in tissues infected with P . syringae . Taken together , this study demonstrates that essential processes during crown gall development are regulated by methylation , which alters the gene expression pattern and controls tumor development . We propose that hypermethylation of the plant tumor genome is a mechanism which restricts tumor growth , for example by affecting genes which are necessary for development and physiological adaptions . Growth restriction allows long-term coexistence of a developing tumor with the host plant and guarantees its survival .
A . thaliana plants were cultivated in growth chambers at 22°C during the light and 16°C during the dark period in 12 h intervals . Plants used in this study included the wildtype accessions WS-2 , Col-0 and Ler as well as the mutants ddc ( drm1–2 drm2–2 cmt3–11 , [59] ) , rdd ( ros1-3; dml2-1; dml3-1 , [26] ) and ago4-1 [27] . Tumors were induced by injecting the nopaline-utilizing A . tumefaciens strain C58noc ( nopaline catabolism; no . 584; Max-Planck-Institute for Plant Breeding Research ) into the base of young inflorescence stalks ( 2 to 5 cm ) . Tumor tissue was separated from the host inflorescence stalk 28 d after inoculation under a stereo-zoom microscope ( Leica MZ6 , Leica Microsystems GmbH ) using a scalpel . Mock-injected segments of tumor-free inflorescence stalks of the same age were used as reference tissue . ABA treatment was conducted according to the protocol of Nishimura et al . [34] . In brief , A . thaliana seeds ( ecotype WS-2 ) were stratified at 4°C for 4 days and were allowed to germinate on 0 . 8% agar supplemented with full strength Murashige and Skoog salts and 2% sucrose in the presence or absence of 0 . 5 µM ABA for two days . Genomic DNA of all plant material was isolated by applying the DNeasy Plant Mini Kit ( Qiagen ) as outlined in the manufacturer's protocol . Genomic DNA ( 1 . 1 µg ) was sonicated with a Bioruptor ( Diagenode ) until fragments of approximately 600 bp were obtained . The DNA fragments were heated for 10 min at 99°C and immediately cooled on ice for 10 min . One hundred nanograms of genomic DNA fragments were used as input samples for array hybridization . Immunoprecipitation was performed by incubating 1 µg of sonicated DNA with 10 µg of 5-mC monoclonal antibody ( Diagenode ) in 600 µl IP-Buffer ( 10 mM Na-Phosphate Buffer , 0 . 14 M NaCl , 0 . 05% Triton X-100 , pH 7 . 0 ) at 4°C for 12 h . Thereafter 100 µl of Dynabeads Protein G were added ( Life Technologies ) , incubated at 4°C for 3 h and washed twice with 600 µl IP-Buffer for 10 min . DNA elution was performed by vortexing the Dynabeads three times in 200 µl TE buffer with increasing SDS-concentrations ( 0 . 1% , 0 . 5% and 1 . 5% ) . The DNA was purified by phenol-chloroform extraction and ethanol precipitation . Genomic input and mCIP DNA samples were labeled using the GeneChip Mapping 10K Xba kit and hybridized to GeneChip A . thaliana Tiling 1 . 0R arrays ( both from Affymetrix ) according to the instructions of the manufacturer . Locations of genomic probes were mapped to the TAIR9 version of the A . thaliana nuclear genome sequence . Raw array data of input and mCIP pairs were loess normalized . Signal log ratios ( SLRs ) of mCIP versus input were calculated for crown gall and mock inoculated stem tissue samples and finally quantiles normalized . Probe SLRs from the three biological replicates of each of the two groups were then subjected to a 500 bp sliding window median smoothing in order to create a robust and smoothed SLR ( sSLR ) for each probe position in each group . An implementation of the CMARRT algorithm [60] , [61] was used for detection of genomic regions with consistently increased sSLRs across at least five consecutive probes for each of the two groups . For all regions displaying signal enrichments , log fold changes ( logFCs ) of crown gall tumor versus non-tumorous samples were calculated based on median region sSLRs . Thereafter , the distribution of logFCs was determined for the regions found to be enriched in the crown gall tumor as well as the tumor-free group ( Dnull ) . Tumor-enriched regions of sSLRs with logFCs greater than the 75% quantile of Dnull were classified as hypermethylated , whereas those of sSLRs enriched in the tumor-free group with logFCs less than the 25% quantile of Dnull were defined as hypomethylated . These hyper- and hypomethylated regions were classified as differentially methylated regions ( DMRs ) . All other regions were considered unchanged . The analyses were performed in R ( http://www . r-project . org ) along with the packages IRanges , Ringo and Starr ( http://www . bioconductor . org ) . Bisulfite conversion of methylated cytosine nucleotides was conducted using the Epitect Bisulfite Kit ( Qiagen ) according to the manufacturer's protocol . A different number of PCR products , depending on the length of the analyzed DNA fragment were generated from the bisulfite-treated DNA , inserted into the pGEM-T easy vector ( Promega ) and cloned in E . coli XL1-Blue MRF′ cells . For each DNA locus multiple independent clones were sequenced , ten clones each for verification of microarrays , ABA-dependent methylation samples and analysis of oncogene methylation in tumors induced by transgenic Agrobacteria , five clones each for oncogene analysis wildtype tumors . Analysis of oncogene methylation of wildtype tumors was performed by analysis of 15 separate fragments covering the sequences of IaaH , IaaM , and Ipt . Primers used for bisulfite sequencing are listed in Table S3 . The full lengths of the two IGRs between the coding sequences of IaaH , IaaM and Ipt , comprising 337 bp for IGR1 and 697 bp for IGR2 ( Figure 1A ) were separately cloned into pHellsgate12 [62] in sense and antisense orientation ( Figure S1 ) . This vector expresses the two self-complementary sequences of Ipt IGR and/or IaaH IGR separated by a PDK intron under control of the 35S promoter in planta . For generation of the construct with both IGR sequences , the hairpin cassette including 35S promoter , IGR1 hairpin and OCS terminator was PCR-amplified with primers containing SpeI adapter sequences ( Table S3 ) . Subsequently , this fragment was inserted into the recombinant pHellsgate plasmid already containing the hairpin construct of IGR2 using SpeI restriction and ligation . All recombinant pHellsgate12 plasmids were transformed into the virulent A . tumefaciens strain C58 . Nuclei of non-tumorous stem and crown gall tumor tissue were isolated one month after mock injection or injection of A . tumefaciens strain C58 , stained with propidium iodide ( PI ) as described previously [63] and analyzed using a FACStarPLUS flow cytometer ( BD Biosciences ) equipped with an INNOVA 90-C argon laser ( Coherent ) . PI fluorescence was excited with 500 mW at 514 nm and measured in the FL1 channel using a 630 nm band-pass filter . Usually 10 . 000 nuclei per sample were analyzed . RNA extraction , reverse transcription and qRT-PCR were conducted as described previously [64] . The data reported in this paper have been deposited in the Gene Expression Omnibus ( GEO ) database , www . ncbi . nlm . nih . gov/geo ( accession no . GSE37680 ) . | Until now , knowledge about the impact of DNA methylation on plant tumor development and physiology has been scant . Therefore , we studied the methylation pattern of Arabidopsis thaliana crown galls on a genome-wide and single-gene level . Crown gall tumor development requires expression of oncogenes , which are transferred on T-DNA of virulent Agrobacterium tumefaciens strains into the plant genome . We found that oncogene expression was associated with an unmethylated oncogene sequence although the promoters were susceptible to methylation . siRNA–mediated promoter methylation caused transcriptional silencing of oncogenes and prevented crown gall proliferation . Moreover , we observed that the genome-wide DNA methylation profile of crown gall tumors was significantly altered and influenced gene expression pattern as well as tumor development . Finally , we demonstrated that physiological processes important for wild-type-like crown gall growth , such as abscisic acid-dependent drought stress protection , are regulated by DNA methylation . From our data , we conclude that epigenetic processes control gene expression , development , and physiology of crown gall tumors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"plant",
"science",
"plant",
"physiology",
"rna",
"interference",
"plant",
"growth",
"and",
"development",
"plant",
"pathogens",
"genetics",
"plant",
"pathology",
"epigenetics",
"biology",
"dna",
"modification",
"plant",
"genomics",
"plant",
"biology",
"genetics",
"an... | 2013 | DNA Methylation Mediated Control of Gene Expression Is Critical for Development of Crown Gall Tumors |
Uncovering how natural selection and genetic drift shape the evolutionary dynamics of virus populations within their hosts can pave the way to a better understanding of virus emergence . Mathematical models already play a leading role in these studies and are intended to predict future emergences . Here , using high-throughput sequencing , we analyzed the within-host population dynamics of four Potato virus Y ( PVY ) variants differing at most by two substitutions involved in pathogenicity properties . Model selection procedures were used to compare experimental results to six hypotheses regarding competitiveness and intensity of genetic drift experienced by viruses during host plant colonization . Results indicated that the frequencies of variants were well described using Lotka-Volterra models where the competition coefficients βij exerted by variant j on variant i are equal to their fitness ratio , rj/ri . Statistical inference allowed the estimation of the effect of each mutation on fitness , revealing slight ( s = −0 . 45% ) and high ( s = −13 . 2% ) fitness costs and a negative epistasis between them . Results also indicated that only 1 to 4 infectious units initiated the population of one apical leaf . The between-host variances of the variant frequencies were described using Dirichlet-multinomial distributions whose scale parameters , closely related to the fixation index FST , were shown to vary with time . The genetic differentiation of virus populations among plants increased from 0 to 10 days post-inoculation and then decreased until 35 days . Overall , this study showed that mathematical models can accurately describe both selection and genetic drift processes shaping the evolutionary dynamics of viruses within their hosts .
Plant virus emergences represent near half of emerging plant infectious diseases [1] and often have detrimental consequences for food production . Emergences result from complex processes leading to novel virus-vector-plant-environment interactions [2] , [3] . At the ecosystem level , numerous ecological factors , often related to changes in agricultural practices [3] , favour emergence by impacting the very biology of viruses and vectors . At the molecular level , evolutionary factors allow viruses to jump host species barriers . As most viruses transferred to new hosts replicate poorly , the existence of already adapted variants within virus populations is often crucial to achieve a successful jump [4] . Though high mutation rates of RNA viruses favour the existence of already adapted variants , their dynamics in the reservoir hosts also depend on the strength of natural selection and genetic drift [4] , [5] . Disentangling how selection and drift shape the evolutionary dynamics of viruses is therefore required to understand emergences [2] , [5] . Mathematical models , which already play an important role in scrutinising the effects of such mechanisms , are also essential for estimating the likelihood of future emergences . Their scope of applications ranges from the management of drug-resistance in infectious diseases [6] to the achievement of durable plant resistance [7] , [8] . Natural selection is a deterministic process by which the frequencies of the fittest variants in a given environment increase [9] . Selective effects among virus variants differing only by one or two point mutations can be very strong . Indeed , viruses with small genomes , including RNA and ssDNA viruses infecting animals , plants and bacteria , are characterized by a high mutational sensitivity . Non-lethal mutations reduce fitness by 10–13% on average [10] . Genetic drift is a stochastic process by which frequencies of virus variants change due to random sampling effects . Its strength is usually characterized by the effective population size ( ) which is defined as the size of a theoretical population that would drift at the same rate as the observed population [11] . Although plant virus populations can reach extremely large sizes , estimates of during colonization of plant tissues remained relatively small , ranging from units [12] , [13] to a few hundreds [14] . These figures indicate that virus populations are often faced with narrow genetic bottlenecks that limit the fixation of advantageous mutations and allow slightly deleterious mutations to reach high frequencies [2] . While within-host genetic drift and selection act simultaneously , and thus jointly determine emergence , their intensities have rarely been estimated and modelled jointly from experimental data . Drift intensity was often measured using populations of pathogen variants with equal multiplicative fitness [14]–[16] and comparison of selection intensity acting on variants did not take into account genetic drift [17]–[19] . In the present work , we characterized experimentally and modelled the within-host population dynamics of four Potato virus Y ( PVY ) variants simultaneously submitted to genetic drift and natural selection . The four variants differ by one or two mutations that change their pathogenicity properties towards pepper genotypes carrying resistance alleles at a single locus [20] . Virus population dynamics were followed using high-throughput sequencing ( HTS ) [21] to track quantitatively the dynamics of PVY populations within a susceptible pepper host . Analysis of HTS data was performed with some sensible mathematical models which allowed inferring both the selection process between competing virus variants and the intensity of drift experienced by viruses during host plant colonization .
The pepper ( Capsicum annuum L . ) genotype used in this study was Yolo Wonder , a bell pepper cultivar susceptible to all PVY isolates . The SON41p infectious cDNA clone [22] and three derived PVY variants were used: NN , DN , NH and DH ( the latter corresponding to SON41p ) . They were named after the amino acids observed at positions 119 and 121 of the VPg ( viral protein genome-linked ) pathogenicity factor ( D , H and N representing aspartic acid , histidine and asparagine , respectively ) ( Figure 1A ) . The three mutated clones of SON41p differing by one or two substitutions in the VPg cistron were constructed using the QuikChange site-directed mutagenesis kit ( Stratagene , La Jolla , CA , U . S . A . ) [23] . Only variant DH , also termed resistance-breaking ( RB ) variant , was able to infect the pepper genotype Florida VR2 which carries the pvr22 resistance gene ( Figure 1A ) [20] . Inoculations were carried out under insect-proof greenhouse conditions . First , separate inoculations with the cDNA clones were realized by DNA-coated tungsten particle bombardments of juvenile Nicotiana clevelandii plants ( four week old ) [22] . Crude extracts of infected N . clevelandii plants were calibrated using DAS-ELISA [23] , adjusted by dilution , mixed equally and then inoculated mechanically on the two cotyledons of 40 Yolo Wonder plants approximately three weeks after sowing ( i . e . at two-leaf stage ) . The conformity of each variant was checked by direct sequencing of the RT-PCR product corresponding to the entire VPg cistron of the PVY populations present in the four plants used for the inoculum [22] . Virus populations were separately sampled from eight plants at five successive dates: 6 days post-inoculation ( dpi ) corresponding to the 3–4 leaf stage , 10 dpi ( 5–6 leaf stage ) , 15 dpi ( 7–8 leaf stage ) , 24 dpi ( 11–12 leaf stage ) and 35 dpi ( 22–23 leaf stage ) . Additionally , a sample of the mixed inoculum used for mechanical inoculations on pepper plants was also collected . At each date , all leaves of each plant were harvested , homogenized in a buffer ( 0 . 03 M phosphate buffer ( pH 7 . 0 ) supplemented with 2% ( w ∶ v ) diethyldithiocarbamate; 4 mL of buffer per gram of leaves ) and total RNAs were purified with the Tri Reagent kit ( Molecular Research Center Inc . , Cincinnati , OH , U . S . A . ) from a 150 µL aliquot of each sample . RNAs were used to amplify the central part of the VPg cistron by RT-PCR with Avian myeloblastosis virus reverse transcriptase ( Promega ) , the high-fidelity Herculase II fusion DNA polymerase ( Stratagene ) and primers PYRO-FOR ( 5′-attcatccaattcgttgatcc-3′ , nucleotide positions 5930 to 5950 ) and PYRO-REV ( 5′-tgtcacaaaccttaagtggg-3′ , nucleotide positions 6149 to 6168 ) . Emulsion-PCR and high-throughput 454 sequencing were realized by GATC-Biotech ( Konstanz , Germany ) . The genome region sequenced encompasses notably codons 101 to 123 of the VPg cistron which has been demonstrated by reverse genetics to be the only region involved in breakdown of the pvr2 resistance genes in pepper [20] . Since sampling was destructive , virus populations at the successive dates came from different plants . In addition , in order to estimate the effective population size during leaf colonization , the eight plants sampled at 15 dpi were kept till 50 dpi and then a single newly grown leaf was sampled randomly on each plant ( Figure S1 ) . These leaves were individually crushed , total RNAs were purified and HTS of the central part of the VPg cistron was performed as described above . In the analysis , the virus populations characterized at 15 dpi constituted the “initial” populations and the ones at 50 dpi the “final” populations . In this protocol , as explained in [14] , the “initial” populations defined by sampling all infected leaves at 15 dpi is likely to represent best the virus population circulating within the vascular system as infected leaves at 15 dpi have previously received ( and exported ) viruses from ( and into ) the vascular system . The “final” populations , sampled in a single systemically infected and newly grown leaf , necessarily originate from the “initial” populations regardless of the many successive stages of the systemic infection . The estimation of the effective population size arose from the comparison between the genetic variances of the “initial” and “final” populations . We obtained between 209 and 930 correctly assigned sequences per virus population of each pepper plant , corresponding to a total of 24 , 166 sequences . Alignment was done using default parameters of the software SeqMan ( DNASTAR Lasergene , Madison , U . S . A . ) . Because indels are the most frequent 454 pyrosequencing errors [24] , a program , developed using the software R version 2 . 9 . 2 ( R Development Core Team , 2009 ) , was used to remove insertions and to replace deletions by the nucleotide present at the corresponding position in the four PVY variants . Because the program removed short sequences , the total number of cleaned sequences reduced to 20 , 795 , ranging from 184 to 824 per virus population . Since no mutation with a significant frequency ( >1% ) has been observed in the sequenced region for any PVY population , the census of each variant ( NN , DN , NH and DH ) was determined for each sample according to the two polymorphic sites at codon positions 119 and 121 of the VPg coding region initially present in the PVY population ( Table S1 ) . Our goal was to determine ( i ) the forms of selection processes occurring between competing viruses within a host plant , which could be handled via the forms of the competition coefficients used to derive the overall theoretical frequencies λi ( t ) and ( ii ) the intensity and temporal variation of genetic drift experienced by viruses during host plant colonization , which could be investigated via the time dependence of the scale parameter θ ( t ) . Accordingly , six models allowing four to 14 parameters ( Table 1 ) were considered . All models included four intrinsic rates of increase ( under the constraint ) . Regarding the competition issue , three embedded hypotheses were proposed for the Lotka-Volterra competition coefficients . From general to particular: Regarding the genetic differentiation of virus populations between plants , two embedded hypotheses were considered . From general to particular: Six models , denoted ( Table 1 ) , are obtained by crossing hypotheses Di with Cj . Under the constraint and by setting K to 106 and μ to 10−5 [27] , the six models were statistically identifiable using maximum likelihood techniques . For initial values of ODE system , was arbitrarily set to 100 whereas = ( 0 . 32 , 0 . 22 , 0 . 22 , 0 . 24 ) corresponded to the observed frequencies of virus sequences in the inoculum . A note on model identifiability and likelihood-based inferences is provided in Text S1 . Computations were performed with the R software environment using “bbmle” package and “nlminb” optimization routines . Models were compared using AIC and BIC procedures ( Akaike and Bayesian Information Criteria ) to choose the model that is best supported by the data . In order to estimate the effective population size ( Ne ) , i . e . the number of founder infectious units initiating the systemic infection of a single leaf , we used a method based on FST statistics described in [14] . Population genetics theory asserted that , for a haploid organism , , where ( resp . ) corresponds to FST value at 15 dpi ( resp . 50 dpi ) . A 95% confidence interval was calculated for Ne with a bootstrap method by resampling data 1 , 000 times over plants .
HTS of the composite inoculum confirmed that DAS-ELISA used to calibrate PVY variants concentration was accurate . The frequencies observed a posteriori , i . e . 0 . 32 , 0 . 22 , 0 . 22 and 0 . 24 for the NN , DN , NH and DH variants , respectively , were quite close to the expected frequency of 0 . 25 , although an excess of NN was noticed . During the course of the experiment , variant NN was selected; its mean frequency increased from 0 . 32 in inoculum to 0 . 57 in the populations sampled at 35 dpi ( Figure 1G ) . This selection took place rapidly and could be detected as soon as at 6 dpi . Selection was also observed for variant NH , whose mean frequency increased from 0 . 22 to 0 . 35 . Conversely , variants DN and DH were counter-selected: their mean frequency decreased from 0 . 22 ( resp . 0 . 24 ) in the inoculum to 0 . 003 ( resp . 0 . 07 ) at 35 dpi ( Figure 1G ) . Besides these mean trends , the standard deviation of variant frequencies between plants exhibited remarkable dynamics ( Figures 1B to 1F , Figure 1H ) . It reached a maximum at 10 dpi , except for DN which had the lowest frequency . In four out of the eight plants analyzed at 10 dpi , a single variant ( NH in two plants and NN in two others ) dominated ( Figure 1C ) . This observation indicated that virus populations underwent strong stochastic variations until 10 dpi . Later , the genetic differentiation of virus populations between plants tended to decrease: two weeks later , at 24 dpi , three variants ( NN , NH , DH ) co-infected all the eight plants analyzed ( Figure 1E ) . A model selection procedure was used to test hypotheses concerning the selection process occurring between competing virus variants within a host plant and the intensity of drift experienced by viruses during host plant colonization . According to both AIC and BIC criteria , the model ( Table 1 ) was best supported by the data . It satisfactorily fitted the data with an r2 value of 0 . 88 between observed and predicted mean frequencies of virus variants ( Figure 2A ) and of 0 . 71 between observed and predicted standard deviations of these frequencies ( Figure 2B ) . The root mean square error ( RMSE ) was 0 . 07 . Inference was not sensitive ( percentage of variations <5% ) to 1000 fold ranges of variation of the mutation rate μ and of the number of inoculated viruses . Inference was also only slightly sensitive to a 20% random fluctuation of the inoculum initial frequencies ( Text S1 ) . Inference on intrinsic rates of increase revealed significant differences of fitness between the four variants ( Figure 3A ) . Variants NN and NH had the highest increase rates ( 1 . 048 for NN and 1 . 043 for NH ) , significantly higher than the one of DH ( 0 . 99 ) . Variant DN had significantly the lowest rate of increase ( 0 . 91 ) . Moreover , the selection of the model lent support to Lotka-Volterra competition coefficients expressed as the ratio . Regarding the intensity of drift experienced by viruses during plant colonization , selection of model indicated that the genetic differentiation of virus populations between plants varied significantly with time . So did the fixation indices , FST ( Figure 3B ) . The differentiation was maximal at 10 dpi , with an FST of 0 . 58 and minimal at 35 dpi with an FST of 0 . 069 . The long term behaviour of the model that is best supported by the data ( Lotka-Volterra system with and mutation process ) was theoretically studied . The differential system admits a single stable equilibrium which attracts all possible trajectories ( i . e . the equilibrium point does not depend on initial conditions ) . The detailed proof is provided in Text S2 . At equilibrium , all variants co-exist but no simple analytical expression of the equilibrium could be derived . Analytical results showed that the fittest variant predominates at equilibrium point whereas the density of other variants depended largely on ( i ) their genetic distance from the fittest variant and ( ii ) the difference between the intrinsic increase rate of the fittest variant and their own increase rate . According to parameter estimates , the relative frequencies of variants NN , DN , NH and DH at equilibrium were 0 . 9978 , 0 . 0021 , 7 . 18×10−5 and 4 . 34×10−7 , respectively . Ne during plant colonization was estimated by comparing the genetic variances of PVY populations sampled at 15 dpi ( initial populations ) and at 50 dpi ( final populations ) among the same plants . At 15 dpi , three variants were systematically detected in all plants , although with varying frequencies ( Figure 1D ) . The final PVY populations , sampled from a single apical leaf of each plant at 50 dpi , differed largely from the initial ones . For seven out of eight plants , the frequency of variant NN was >0 . 85 , while in the remaining plant , variant NH predominated ( Table S1 ) . This observation supported the hypothesis of large stochastic variations during the systemic infection of a newly formed leaf . Accordingly , the estimated effective population size amounted to 2 . 25 ( Table S2 ) with a 95% confidence interval ranging from 1 . 3 to 3 . 38 . Actually , the method did not allow to disentangle the effects of selection and genetic drift on . When applied only to the two variants NN and NH showing equal relative fitness ( Figure 3A ) and therefore subjected only to genetic drift , Ne was estimated to 2 . 14 with a 95% confidence interval ranging from 1 . 29 to 3 . 41 .
The present study investigated with HTS the intra-host dynamics of plant virus populations and their variability between plants . Data revealed a strong pattern of genetic differentiation of virus populations between plants with FST indices that increased from inoculation date to 10 dpi and then decreased until 35 dpi . From inoculation to 6 dpi , heterogeneity observed between plants could be potentially related to the very inoculation process and/or to the process of colonization of plants by viruses . Although we cannot exclude some random effects due to inoculation , we believe that most observed variance was due to within-plant colonization processes , as the four variants inoculated were detected still in all samples at 6 dpi ( Figure 1B ) . Severe bottlenecks act on most virus populations at all the scales within infected plants , from virus loading into individual cells ( MOI , multiplicity of infection , defined as the number of virus genomes that enter and effectively replicate in individual cells [15] , [28] ) , to colonization of tissues and organs through plasmodesmata [29] and to translocation in the whole plant through the vascular system [12] , [13] , [30] . We strengthen the latter results for another RNA virus by showing that between one to four PVY genomes initiate the population of systemically-infected leaves . However , severe bottlenecks during host colonization are not necessarily the rule for all plant viruses . Using the same protocol , several hundred genomes of CaMV , a DNA virus , were shown to initiate infection of apical leaves [14] . The scale of our study was the set of leaves of infected plants . It represents the epidemiologically-relevant part of the virus population since it is readily accessible to vectors that ensure plant-to-plant horizontal transmission . It is possible that the narrow bottlenecks and intense genetic drift observed at larger scales ( whole plant and plant organs ) are direct consequences of those incurred by virus populations at the smallest scale ( individual cell ) . Supporting this hypothesis , the time dependence of FST which we observed at the whole-plant level ( Figure 3B ) parallels that of the MOI in another plant virus [15] . MOI values observed by [15] were close to 2 at 14 dpi , increased up to 13 at 40 dpi and then decreased to the initial level at 70 dpi . Accordingly , we observed a FST decrease in the time period shared by both studies ( from 14 to 35 dpi ) . It is also possible that decreasing FST values observed at later infection times are related to the sink-source transition undergone by leaves during plant growth . Source-to-sink translocation of carbohydrates through the phloem corresponds to the direction of the systemic movement of viruses within plants [31] , [32] . As the plant grows , more and more leaves behave as virus sources , a process beginning in oldest leaves . Consequently , as the plant matures , more leaves unload their viruses into the phloem sieve elements and can contribute to the colonization of new expanding leaves at the apex of the plant , hence increasing the size of the source virus population within plants and decreasing between-plant FST values . In the context of our experiments , the timing of the sink-source transition in pepper and its comparison with FST variation remain to be determined . The two above hypotheses are not mutually exclusive , since the increasing number of source leaves during infection could also increase the MOI [15] , and , in turn , decrease between-plant FST values . Even if major stochastic events impact the evolutionary dynamics of virus populations , natural selection remains a powerful force in virus evolution [2] . In our experiment , two variants were selected ( NN and NH ) and the other two counter-selected ( DN and DH ) . The fitness effect of each mutation can be assessed from their intrinsic rates of increase . Compared to the fittest variant ( NN ) , the fitness effects of mutations N121H and N119D amounted to −0 . 45% and −13 . 2% , respectively . These figures agree with the distribution of the mutational effect of single nucleotide substitutions . Indeed non-lethal mutations reduce virus fitness by 10–13% on average [10] . The fitness cost ( 4 . 8% ) of the variant combining both mutations ( DH ) indicated a case of negative epistasis , as often observed for RNA viruses [2] , [4] . Altogether , these data determined the most likely evolutionary pathway toward the breakdown of the pepper resistance gene pvr22 ( mutation N121H followed by mutation N119D ) [4] , [33] ( Figure 3A ) . In all , the RB variant is counter-selected in virus populations . When extrapolating the dynamics of the Lotka-Volterra system , the mean frequency of the RB variant would be <4% at 50 dpi . This prediction fitted our observation of ∼0 . 5% mean frequency of the RB variant at 50 dpi , although these results should be read with caution , as the sampling scheme differed at this date . Moreover , even if the long-time behaviour of the system indicated that all variants would co-exist at an equilibrium ( which corresponds to the mutation-selection balance ) , the frequency of the RB variant would be very low ( ∼5×10−7 ) . In natural context , the RB variant would most likely appear initially by mutation at a very low frequency in a virus population largely dominated by the fittest variants NN and NH . Note also that , although multidrug-resistant variants can be generated by recombination ( e . g . [34] on HIV ) , its role is unlikely in the present case because the two critical amino acid positions are only two amino acids apart . Altogether these data provide an explanation of the scarcity of viruses able to overcome the resistance gene pvr22 in natural context [20] . By confronting the results of virus dynamics simulated under several Lotka-Volterra models differing by the form of the competition coefficients , we learnt about the selection process occurring between competing virus variants . These coefficients describe the interactions of several competing variants for host factors necessary for virus replication and movement within plants . Statistical model selection results lent support to the hypothesis that the competition coefficients βij exerted by variant j on variant i are equal to their fitness ratio , . The assumption was initially argued on a theoretical ground [35] . These authors showed that Eigen's model of molecular quasispecies [36] was to a large extent equivalent to the Lotka-Volterra competition equations when assuming that . However this did not imply that the virus populations studied behaved as quasispecies . In particular , the quasispecies model does not allow for stochastic changes in population structure [30] whereas , as discussed earlier , the present results evidenced strong effects of genetic drift . Overall , this study showed that mathematical models can accurately describe both selection and genetic drift shaping the evolutionary dynamics of viruses within hosts . A similar within-host model was coupled with an epidemiological model in [37] to assess the relative effects of ten demo-genetic and epidemiological parameters on the probability of breakdown of a plant resistance . Our present results validated a posteriori this choice . More generally , the modelling framework proposed here might provide a valuable cornerstone of models linking within- and between-host scales of disease dynamics [38] . It also might provide useful tools to study the interplay between the evolutionary and epidemiological processes acting on a virus population , at the individual host scale but also at the population host scale [39] , and ultimately to design some efficient control strategies of virus emergence . | Natural selection and genetic drift drive the evolution of virus populations within their hosts and therefore influence strongly virus emergences . To help predict future virus emergences , we developed a model that estimates simultaneously genetic drift and selection intensities using high-throughput sequence data representing the within-host population dynamics of Potato virus Y variants differing at most by two substitutions involved in pathogenicity properties . The competitiveness costs induced by these mutations as well as the mathematical expressions of the competition coefficients of virus variants were derived from Lotka–Volterra equations . High genetic differentiation of virus populations between hosts was evidenced as well as its hump-shaped behaviour with time . The modelling framework proposed here was intended to help design control strategies aiming to prevent virus emergences . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"sustainable",
"agriculture",
"statistics",
"population",
"genetics",
"microbiology",
"integrated",
"control",
"mathematics",
"effective",
"population",
"size",
"pest",
"control",
"population",
"modeling",
"biostatistics",
"evolutionary",
"modeling",
"biology",
"infectious",
... | 2012 | Modelling the Evolutionary Dynamics of Viruses within Their Hosts: A Case Study Using High-Throughput Sequencing |
Despite the recent progress in sequencing technologies , genome-wide association studies ( GWAS ) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection . The small contribution sometimes corresponds to incomplete penetrance , which may result from probabilistic effects on molecular regulations . In such cases , genetic mapping may benefit from the wealth of data produced by single-cell technologies . We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits . Phenotypic values are acquired on thousands of individual cells , and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances . No prior assumption is required on the mode of action of the genetic loci involved and , by exploiting all single-cell values , the method can reveal non-deterministic effects . Using both simulations and yeast experimental datasets , we show that it can detect linkages that are missed by classical genetic mapping . A probabilistic effect of a single SNP on cell shape was detected and validated . The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon . Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits . The method is available as an open source R package called ptlmapper .
Modern genetics aims to identify DNA variants contributing to common trait variation between individuals . A high motivation to map such variants is shared worldwide because many heritable traits relate to social and economical preoccupations , such as human health or agronomical and industrial yields . In addition to the molecular knowledge they provide , these variants fuel the development of personalized and predictive medicine as well as the improvement of economically-relevant plants , animal breeds or biotechnology materials . However , this high ambition is accompanied by a major challenge: common traits are under the control of numerous variants that each contribute little to phenotypic variation [1] , and this modest contribution of each variant hampers the statistical power to detect them . Power is further limited by the multiplicity of linkage tests when scanning whole genomes . The consequence of this has been debated under the term "missing heritability": most of the genetic variants of interest remain to be identified . Currently , this issue is handled by modelling the effect of known or hidden factors , and by scaling up sample size up to tens of thousands of individuals [2–4] . Practically , however , cohort size cannot be infinitely increased , and relevant factors are difficult to choose . Studies would therefore greatly benefit from a better detection of small genetic effects , and from a reduction of the number of genomic loci to test . Small-effect variants are typically associated with predisposition ( or incomplete penetrance ) : carriers of a mutation display a phenotype at increased frequency , but not all of them do . In this probabilistic context , the statistical properties of cellular traits may sometimes become informative: a tissue may break because cells have an increased probability to detach , a tumor may emerge because a cell type has an increased probability of somatic mutations , a chemotherapy may fail if cancer cells have an increased probability to be in a persistent state . In other words , molecular events in one or few cells can have devastating consequences at the multicellular level . As discussed previously [5] , cellular-scale probabilities are likely related to the genotype and this relation may sometimes underlie genetic predisposition [6] . Striking examples are genetic factors affecting the mutation rate of somatic divisions and thereby modifying cancer predisposition . These loci have a probabilistic effect on a cellular trait: the amount of de novo mutations in the cell's daughter . Other loci may modulate the heterogeneity between isogenic cancer cells that underlies tumour progression [7 , 8] and resistance to chemotherapy [9–11] . They would then change the fraction of problematic cells between individuals and thereby disease progression or treatment outcome . Fortunately , the experimental throughput of single-cell measurements has recently exploded . Technological developments in high-throughput flow cytometry [12] , multiplexed mass-cytometry [13] , image content analysis [14–16] and droplet-based single-cell transcriptome profiling [17 , 18] now offer the possibility to estimate empirically the statistical distribution of numerous molecular and cellular single-cell quantitative traits . We therefore propose to scan genomes for variants that modify single-cell traits in a probabilistic manner , which we call single-cell Probabilistic Trait Loci ( scPTL ) . This requires to monitor not only the macroscopic trait of many individuals but also a relevant cellular trait in many cells of these individuals . After scPTL are found , they can constitute a set of candidate loci to be directly tested for a possible small effect on the macroscopic trait of interest . Methods are needed to detect scPTL . With its fast generation time , high recombination rate and reduced genome size , the unicellular yeast Saccharomyces cerevisiae offers a powerful experimental framework for developing such methods . Using this model organism , scPTL were discovered by treating one statistical property of the single-cell trait , such as its variance in the population of cells , as a quantitative trait and by applying Quantitative Trait Locus ( QTL ) mapping to it [19 , 20] . However , this approach is limited because it is difficult to anticipate a priori which summary statistics must be used . We present here the development of a genome-scan method that exploits all single-cell values with no prior simplification of the cell population phenotype . Using simulations and existing single-cell data from yeast , we show that it can detect genetic effects that were missed by conventional linkage analysis . When applied to a novel experimental dataset , the method detected a locus of the yeast genome where natural polymorphism modifies cell-to-cell variability of the activation of the GAL regulon . This work shows how single-cell quantitative data can be exploited to detect probabilistic effects of DNA variants . Our approach is conceptually and methodologically novel in quantitative genetics . Although we validated it using a unicellular organism , it opens alternative ways to apprehend the genetic predisposition of multicellular organisms to certain complex traits .
We specify here the concepts and definitions that are used in the present study . Let X be a quantitative trait that can be measured at the level of individual cells . X is affected by the genotype of the cells and by their environmental context . However , even for isogenic cells sharing a common , supposedly homogeneous environment , X may differ between the cells . To describe the values of X among cells sharing a common genotype and environment , we define a single-cell quantitative trait density function f [5] as the function underlying the probability that a cell expresses X at a given level ( Fig 1A ) . Statistically speaking , f represents the probability density function of the random variable X . In the present study , this function f ( X ) constitutes the 'phenotype' of the individual from whom the cells are studied . As for any macroscopic phenotype , it can depend on the environmental context of the individual ( diet , age , disease… ) as well as on its genotype . Single-cell trait density functions also obviously depend on the properties of the cells that are studied , such as their differentiation state or proliferation rate . We focus here on the effect of the genotype . Conceptually , cells from one individual may follow a density function of X that is different from the one followed by cells of another individual , because of genotypic differences between the two individuals ( Fig 1B ) . The important concept is that the genetic difference has probabilistic consequences: it changes the probability that a cell expresses X at a given level , but it does not necessarily change X in most of the cells . Depending on the nature of trait X and how the two functions differ , such a genetic effect can have implications on macroscopic traits and predisposition to disease [5] . The term single-cell Probabilistic Trait Locus will refer here to a genetic locus modifying any characteristics of f ( that is , changing allele A in allele B at the locus changes the density function f of X , i . e . fB ≠ fA ) . A quantitative trait locus ( QTL ) linked to X is a location on a chromosome where a genetic variant changes the mean or the median of X in the cell population . Similarly , a varQTL is a genetic locus changing the variance of X and a cvQTL is a genetic locus changing the coefficient of variation ( standard deviation divided by the mean , abbreviated CV ) of X in the cell population . All three types of loci ( QTL , varQTL and cvQTL ) assume a change in f and they are therefore special cases of scPTL . However , not all scPTL are QTL: many properties of f may change while preserving its mean , median , variance or CV . The purpose of the present study was to develop an approach that could identify scPTL without knowing a priori how it might change f . An important question before investing efforts in scPTL mapping is whether genotypes can modify f without affecting its expected value ( the mean of X ) . If not , then QTL mapping will capture the genetic modifiers of f and searching for more complex scPTL is not justified . In contrast , if other-than-mean genotypic changes of f are frequent , then scPTL can considerably complement QTL to control single-cell traits . In this case , scPTL mapping becomes important . In multicellular organisms , cell types and intermediate differentiation states constitute the predominant source of cellular trait variation . Studying their single-cell statistical characteristics requires accounting for the developmental status of the cells . This constitutes a major challenge that can be avoided by studying unicellular organisms . The yeast S . cerevisiae provides the opportunity to study individual cells that all belong to a single cell type , in the context of a powerful genetic experimental system . By analysing specific gene expression traits in this organism , we and others identified loci that meet the definition of scPTL but not of QTL [20 , 21] . This illustrated that , for some traits , scPTL mapping could complement classic quantitative genetics to identify the genetic sources of cellular trait variation . To estimate if non-QTL scPTL are frequent , we re-analysed an experimental dataset corresponding to the genetic segregation of many single-cell traits in a yeast cross ( Fig 2A ) . After a round of meiosis involving two unrelated natural backgrounds of S . cerevisiae , individual segregants had been amplified by mitotic ( clonal ) divisions and traits of cellular morphology were acquired by semi-automated fluorescent microscopy and image analysis [22] . This way , for each of 59 segregants , 220 single-cell traits were measured in about 200 isogenic cells , which enabled QTL mapping of these traits . We reasoned that if all scPTL of a trait are also QTL , then a high genetic heritability of any property of f should coincide with a high genetic heritability of the expected value of f . In particular , the coefficient of variation ( CV ) of a single-cell trait should then display high heritability only if the mean value of the trait also does . To see if this was the case , we computed for each trait the broad-sense genetic heritabilities of both the mean and CV of the trait . Note that the genetic heritability computed here is not the same as the mitotic heritability of cellular traits transmitted from mother to daughter cells . Here , a value ( mean or CV ) is computed on a population of cells , and its heritability corresponds to the proportion of its variation that can be attributed to genetic differences between the cell populations ( see methods ) . Overall , heritability of mean was higher than heritability of CV , and the two types of heritabilities were correlated ( Fig 2B ) . We also observed that several traits had high heritability of CV and low heritability of their mean value , or vice versa . This indicates that , for some traits , genetic factors exist that modify the trait CV but not the trait mean . This observation is in agreement with the complex CV-vs-mean dependency previously reported in this type of data [23 , 24] . We therefore sought to develop a method that can detect scPTL that do not necessarily correspond to QTL . One way to identify scPTL from experimental measures is to compute a summary statistic of the trait distribution , such as one of its moments , and then scan for QTL controlling this quantity . This approach is particularly appropriate when searching for specific genetic effects on f , such as a change in the level of cell-to-cell variability , and a few previous studies successfully used it to map varQTL and cvQTL [19 , 20 , 22 , 25 , 26] . However , it is less adapted when nothing is known on the way f may depend on genetic factors . Scanning for scPTL considers the entire distribution of single-cell trait values as the phenotype of interest and searches the genome for a statistical association with any change in the distribution . We assume that for a set of genotypic categories ( individuals for multicellular organisms , or populations of cells for unicellular ones ) , a cellular trait has been quantified in many individual cells of the same type . This way , the observed distribution of the trait constitutes the phenotypic measure of individuals . We also assume that a genetic map is available and the individuals have been genotyped at marker positions on the map . The method we propose is based on three steps . First , a distance is computed for all pairs of individuals in order to quantify how much their phenotype differs . We chose the Kantorovich metric ( also known as the Wasserstein distance or the earth-mover's distance ) to measure this distance because , unlike the Kullback-Leibler divergence , it satisfies the conditions of non-negativity , symmetry and triangle inequality and , unlike the Hellinger distance , it does not converge to a finite upper limit when the overlap between distributions diminishes [27] . The Kantorovich metric can be viewed as the minimum energy required to redistribute one heap of earth ( one f-function ) into another heap ( a second f-function ) . It has enabled developments in various fields , ranging from mathematics [28] to economy ( the minimal transportation problem ) [29 , 30] to the detection of states from molecular dynamics data [27] . The next two steps are inspired from methods used in ecology , where spatial distinctions between groups are often searched after determining distances between individuals [31 , 32] . In step 2 of our method , individuals are placed in a vectorial space while preserving as best as possible the distance between them ( Fig 3A ) . This is achieved by multi-dimensional scaling , a dimension-reduction algorithm [33] . The third step is the genetic linkage test itself . At every genetic marker available , a linear discriminant analysis is performed to interrogate if individuals of different genotypic classes occupy distinct sectors of the phenotypic space ( Fig 3B and 3C ) . The optimal choice of dimensionality is determined dynamically and a permutation test assesses statistical significance in the context of the corresponding degrees of freedom . Note that if the dimensions have been reduced to a single one , then canonical analysis is not needed: the phenotypic value of each individual has become a scalar and linkage can be performed by standard QTL mapping . Finally , scPTL linkage is scored using the Wilks' lambda statistics . Statistical inference is made using empirical p-values produced by permutations where the identities of individuals are re-sampled . The full procedure is described in details in the methods section . We first evaluated if our method could detect scPTL from simulated datasets . To do this , we considered a probabilistic single-cell trait governed by a positive feedback of molecular regulations . This is representative of the expression level of a gene with positive autoregulation . As depicted in Fig 4A , the employed model is based on three parameters . For each individual , a set of parameter values was chosen and single-cell values of expression were generated by stochastic simulations . We chose to simulate a scPTL that modified the expected values of the parameters so that the skewness of cellular trait distribution is affected . To do so , we considered a panel of individuals and their genotype at 200 markers evenly spaced every 5cM . Parameter values of each individual were drawn from Gaussians and the mean of these Gaussians depended on the genotype at the central marker . This defined two sets of phenotypes that are depicted by blue and red histograms in Fig 4B . A universal noise term η was added to introduce intra-genotype inter-individual variation which , in real datasets , could originate from limited precision of measurements or from non-genetic biological differences between individuals . For each of five increasing values of η , about 130 individuals were simulated . We first scanned the generated dataset by QTL mapping , treating either the mean trait or its variance as the phenotype of interest . This way , the central scPTL locus was detected only when intra-genotype noise was null or very low ( Fig 4C ) . This was anticipated because the mean and variance of the simulated trait values slightly differed between the two sets of individuals . In contrast , our new method allowed to robustly detect the scPTL locus even in the presence of high ( up to 20% ) intra-genotype noise ( Fig 4D and 4E ) . The results described above using a simulated dataset suggest that the method can complement usual QTL mapping strategies . To explore if this was also the case when using real experimental data , we applied scPTL scans to the dataset of Nogami et al . [22] mentioned above ( Fig 2A ) where 220 single-cell traits were measured in about 200 cells from segregrants of a yeast cross . We applied three genome x phenome scans , each one at FDR = 10% . Two consisted of QTL interval mapping and were done by considering either the mean cellular trait value of the population of cells or the coefficient of variation of the cellular trait as the population-level quantitative trait to be mapped . The third scan was done using the novel method described here to map scPTL . Significant linkages obtained from this scan are available in S1 Table . As shown in Fig 5 , the three methods produced complementary results . We detected more linkages with the scPTL method than with the 2 QTL scans combined ( 71 vs . 61 traits mapped ) . This illustrates the efficiency of using the full data ( whole distribution ) of the cell population rather than using a summary statistic ( mean or CV ) . In addition , we expected that a fraction of scPTL would match QTL , because QTL controlling the mean or CV of cellular traits are specific types of scPTL . This was indeed the case , with 67% of scPTL corresponding to loci that were detected by at least one of the two QTL scans . For 11 cellular traits , a locus was found by QTL or cvQTL mapping but it was missed by the scPTL scan . This illustrates that the methods have different power and sensitivity . Importantly , 22 cellular traits were associated to scPTL that were not detected by the QTL search , suggesting that some probabilistic effects may affect poorly the trait's mean or CV . Altogether , these observations highlight the complementarity of the different approaches and show that scPTL mapping can improve the detection of genetic variants governing the statistical properties of single-cell quantitative traits . Examples of scPTL of yeast cellular morphology are shown in Fig 6 . One of the cellular traits measured was the distance between the center of the mother cell and the brightest point of DNA staining ( Fig 6A ) . No QTL was found when searching genetic modifiers of the mean or CV of this trait , but a significant scPTL was mapped on chromosome II . When displaying trait distributions , it was apparent that segregants carrying the BY genotype at the locus had reduced cell-cell variability of the trait as compared to segregants having the RM genotype ( Fig 6A , right panel ) . Consistently , a small cvQTL peak was seen on chromosome II , although this peak did not reach genome-wide statistical significance . This trait , which relates to the statistical properties of DNA migration during the early phase of cell division , provided a biological example where scPTL scan identified a genetic modulator of cell-to-cell variability that was missed by the QTL approach . Three other traits were of particular interest because they mapped to a position on chromosome VIII where a functional SNP was previously characterized in this cross . This SNP corresponds to a non-synonymous I->S mutation at position 469 in the Gα protein Gpa1p . It targets a domain that is essential for physical interaction with pheromone receptors Ste2p and Ste3p [34 , 35] . In the presence of pheromone , Gpa1p is released from the receptor and triggers a signalling cascade of molecular response that causes cell-cycle arrest and cell elongation ( a process called 'shmooing' ) . In the absence of pheromone , improper binding of Gpa1pI469S to the receptor causes residual activation of the pathway in the BY strain , as seen by transcriptomic profiling [36] , which explains why BY cells are more elongated [24] and proliferate slower [37] than RM cells . Here we saw that this locus is a scPTL , but not a QTL , of the degree to which cells are elliptical ( Fig 6B ) . Displaying the distributions of this trait in each segregant revealed a remarkable amount of variability between the segregants , and that the BY allele at the locus corresponded to a modest reduction of the trait value as compared to the RM allele ( sharper mode at slightly lower value ) . To see if this was due to the GPA1I469S mutation , we examined the data from a BY strain where this mutation was cured [22] . Remarkably , the single amino-acid substitution caused a mild but statistically significant redistribution of the trait values ( Fig 6B ) . This change was comparable to the difference seen among the segregants , demonstrating the causality of the GPA1I469S SNP . Another trait , corresponding to the distance between the bud tip and the short axis of the mother cell , also mapped to this locus , with the RM allele associated to greater cell-cell variability , and data from the GPA1I469S allele-replaced strain validated this SNP as the causal polymorphism ( Fig 6C ) . These observations suggest that either the residual activation of the pathway in absence of pheromone is not uniform among BY cells , or the proper inactivation of the pathway is not complete in all RM cells . This , and the fact that the mutation does not prevent BY cells from proliferating ( as compared to pheromone-arrested cells ) , indicate that the detachment of Gpa1pI469S from the receptor is a rare event that has probabilistic effects on the cellular phenotype . Further investigations based on biochemistry , dynamic recording of individual cells and stochastic modelling are needed to understand how variation in binding affinity accounts for this effect . The results described here illustrate that scPTL scans can identify individual SNPs that modify single-cell trait distributions without necessarily affecting the trait mean . Finally , another trait corresponding to the angle of bud site position mapped to two scPTL loci and no QTL . One of these loci contained the GPA1 gene on chromosome VIII . Although the phenotype of bud site selection is not related to 'shmooing' , we examined if the GPA1I469S SNP was involved and found that it was not: the allele-replaced strain did not show a different trait distribution than its control ( Fig 6D ) . Thus , other genetic polymorphisms at the locus should participate to the statistical properties of cellular morphology , by affecting the position of budding sites . We then explored if scPTL scanning could provide new results when applied to a molecular system that had been extensively characterized by classical genetics . The system we chose was the yeast GAL regulon which , in addition to be one of the best described regulatory network , presented several advantages . Natural strains of S . cerevisiae are known to display differences in its regulation [38 , 39] and the transcriptional response of cell populations can be tracked by flow cytometry . This provides data from large numbers of cells and therefore a good statistical power to compare single-cell trait distributions . In addition , acquisitions on many genotypes are possible using 96-well plates . We reasoned that if features of the cell population response segregate in the BY x RM cross ( described above for morphology ) , then scPTL scanning might identify genetic variants having non-deterministic effects on the regulation of GAL genes . We first compared the dynamics of transcriptional activation of the network in the two strains BY and RM . This was done by integrating a PGal1-GFP reporter system in the genome of the strains , stimulating them by addition of galactose in the medium , and recording the response by flow cytometry . As shown in Fig 7 , both strains responded and full activation of the cell population was reached after ~2 hours of induction . Interestingly , remarkable differences were observed between the two strains regarding the distribution of the cellular response . The BY strain showed a gradual increase of expression through time that was relatively homogeneous among the cells ( unimodal distribution with relatively low variance ) , whereas the RM strain showed elevated cell-cell heterogeneity at intermediate activation time points ( higher variance , with fraction of non-induced cells ) . This suggested that genetic polymorphisms between the strains might control the level of heterogeneity of the cellular response at these intermediate time points . We sought to map one or more of these genetic factors . To do so , we acquired the response of 60 meiotic segregants of the BY x RM cross . Using the data collected at each time point , we scanned the genome for scPTL of the reporter gene expression level using the novel genome-scan method described above . The procedure identified a locus on chromosome V position 350 , 744 that was highly significant ( genome-wide p-value < 0 . 001 ) at 30 minutes post induction , the time at which heterogeneity markedly differed between the BY and RM strains ( Fig 7B and 7C ) . The locus was also significant at times 20 min ( p < 0 . 005 ) and 40 min ( p < 0 . 005 ) post induction . Visualizing the distributions of single-cell expression levels at 30 minutes revealed that the RM and BY genotypes at this locus corresponded to high and low cell-cell heterogeneity , respectively ( Fig 7D and 7E ) . Thus , this locus explains , at least in part , the different levels of heterogeneity observed between the parental strains . It should therefore also be detected as a varQTL or cvQTL . This was indeed the case: the LOD score linking the locus to the variance of expression was 4 . 5 and reached statistical significance ( P = 0 . 005 ) . Importantly , the scPTL was not a QTL: the locus genotype did not correlate with the mean level of expression of the population of cells ( LOD score < 2 . 8 ) . When surveying the genomic annotations of the locus [40] , we realized that it contained no obvious candidate gene that would explain an effect on the heterogeneity of the response ( such as genes known to participate to the transcriptional response ) . One potentially causal gene was DOT6 , which encodes a poorly characterized transcription factor that was shown to shuttle periodically between the cytoplasm and nucleus of the cells in standard growth conditions [41] . Given that i ) the shuttling frequency of such factors can sometimes drive the response to environmental changes and ii ) numerous non-synonymous BY/RM genetic polymorphisms were present in the gene , we constructed an allele-replacement strain for DOT6 and tested if the gene was responsible for the scPTL linkage . This was not the case . Strains BY and BY-DOT6RM ( isogenic to BY except for the DOT6 gene which was replaced by the RM allele ) displayed very similar transcriptional responses at intermediate times of induction ( S1 Fig ) . Fine-mapping of the locus and a systematic gene-by-gene analysis are now needed to precisely identify the polymorphisms involved . By highlighting a novel genetic locus modulating cell-cell variability of the transcriptional response to galactose , our results show that scPTL scanning can provide new knowledge on the fine structure of a well-studied system .
When considering macroscopic phenotypes , it is important to distinguish the situations where scPTL mapping is biologically relevant from those where it is not . The determinants of human height , for example , act via countless cells , of multiple types , and over a very long period of time ( ~ 16 years ) . In such cases , the macroscopic trait results from multiple effects that are cumulated and considering the probabilistic individual contribution of specific cells is inappropriate . Similarly , many tissular traits heavily rely on communications between cells and probabilistic changes in a few may not affect the collective output of the cell population . In contrast , a number of macroscopic traits can be affected by particular events happening in rare cells or at a very precise time ( see below ) . In these cases , studying the probabilities of a biological outcome in the relevant cells or of a molecular event within the critical time interval can provide invaluable information on the emergence of the macroscopic phenotype , and scPTL mapping then becomes relevant . A striking example of such traits is cancer . Genetic predisposition is conferred by variants affecting somatic mutation rates and these loci are special cases of scPTL: the cellular trait they modify is the amount of de novo mutations in the cell's daughter . These variants have classically been identified by genetic linkage of the macroscopic trait ( disease frequency in families and cohorts ) , and their role on the maintenance of DNA integrity was deduced afterwards by molecular characterizations . For a review on the genetics of cancer syndrome predisposition , see [42 , 43] . scPTL mapping is also relevant to the non-genetic heterogeneity of cancer cells which was shown to be associated with tumour progression [7 , 8] and treatment efficiency [9–11] . Genetic loci changing the fraction of problematic cells are likely modulators of the prognosis . If the functional properties ( expression level , phosphorylation status , subcellular localization ) of a key molecular player , such as a critical tumor-suppressor gene , can be monitored in numerous individual cells , then scPTL mapping , as presented here , may help identify genetic factors that modulate the activity of this gene in a probabilistic manner . Once identified , the association of these loci with the macroscopic phenotype can then be tested directly , avoiding at least partly the statistical challenges of whole-genome scans . To illustrate this , we considered an idealized case where three scales are bridged: at the molecular level , a scPTL affects the expression of a protein X ( same regulation as in Fig 4 ) ; at the cellular level , cells have higher probability to divide if their level of X is low ( Fig 8A ) ; and at the whole-organism level , disease appears if too many cells are present . Using a stochastic model of this scenario , we simulated a cohort of individuals and recorded the state and number of cells in every individual over time ( Fig 8B , see methods for details ) . Disease appeared in all individuals , between age 22 and 29 . Using the data at age 23 , we compared the power of GWAS and scPTL mapping . For GWAS , the trait of individuals was whether they had declared the disease or not . For scPTL mapping , the trait was the expression level of X in 10 , 000 of their cells . As expected by the moderate effect on disease frequency , GWAS failed to detect the locus ( Fig 8C ) . In contrast , scPTL detection was highly significant from the same cohort of individuals ( Fig 8D ) . Importantly , although not significant genome-wide , the GWAS score at the locus had a nominal p-value lower than 0 . 01 ( Fig 8C ) . The locus would therefore be considered significant if it had been the only one tested . This illustrates the added value of scPTL mapping: while keeping cohort size constant , it can highlight candidate loci of the genome that can then be tested individually for association to the disease . This power clearly results from i ) additional traits ( cellular ones ) that are included before scanning the genome and ii ) relaxation of multiple-testing correction when testing association to disease . Note that other system genetics methods , such as expression QTL ( eQTL ) mapping , improve power in a similar way: they highlight relevant candidates via the addition of intra-individual traits ( molecular ones ) [44] . Note also that recruiting large cohorts remains important: Methods detecting scPTL and eQTL can improve genetic mapping but their detection power remain strictly dependent on the number of individuals available in the study . In real studies , external knowledge is needed on the link between the cellular trait and the disease: what single-cell trait should be measured ? Can it be measured in a sufficiently large number of cells ? If a reporter system of de novo mutations , for example based on the intracellular distribution of a fluorescently tagged repair protein [45 , 46] , can be introduced in a relevant and large population of cells , then the high number of cell measurements may allow to detect loci that modify even slightly the mutation rate . For non-genetic features of problematic cells , choice of the trait can be driven by investigations at the molecular level , such as stochastic profiling [47] , and at the cellular level , such as recording the response of cell populations to treatment or differentiation signals [9 , 10] . For example , the distribution of the biomarker JARID1B ( a histone demethylase ) in populations of melanoma cells is indicative of an intra-clonal heterogeneity that is important for tumour progression [7] , biomarkers CD24 and CD133 can distinguish rare cells that persist anti-cancer drug treatments [10] and multiplexed markers of signalling response can reveal patterns of population heterogeneity that predict drug sensitivity [48] . When relevant markers are not known , a possibility is to screen for them using stochastic profiling [47] . This method interrogates the transcriptomic variability between pools of few cells in order to identify transcripts displaying elevated cell-to-cell variability in specific biological contexts . It allowed the discovery of two molecular states of extracellular matrix-attached cells that can be distinguished by the jun D proto-oncogene and markers of TGF-β signalling [8] . Such markers of isogenic cellular subtypes may allow the development of scPTL mapping in humans . An important statistical requirement to identify scPTL is the abundance of cells on which the probabilistic trait is quantified . For human studies , peripheral blood offers access to many cells but , unfortunately , many internal organs do not . This requirement also implies using technologies where the throughput of quantitative acquisitions is high . This is the case for flow-cytometry and , although at higher costs , for high-content image analysis [14 , 15] and digital microfluidics [17 , 18] . For these practical reasons , it is possible that mouse immunological studies will help making progress in mammalian scPTL mapping . For example , the work initiated by Prince et al . [49] describing pre- and post-infection flow-cytometry profiles of F2 offsprings from different mouse strains may provide an interesting pilot framework . The interest of scPTL mapping is not restricted to cancer biology . Developmental processes and cellular differentiation are also vulnerable to mis-regulations happening in few cells or during short time intervals . Their macroscopic outcome can therefore be affected by probabilistic events at the cellular scale . For example , stochastic variation in the expression of the stem cell marker Sca-1 is associated with different cellular fates in mouse hematopoietic lineages [50] , suggesting that genetic factors changing this stochastic variation may impact blood composition . Similarly , embryonic stem cells co-exist in at least two distinct molecular states that are sensitive to epigenetic and reprogramming factors [51] . Genetic variants modulating these factors may change the statistical partitioning of these states . Two observations made on flies remarkably support the existence of natural genetic factors altering developmental processes in a probabilistic manner . The first one is the fact that high levels of fluctuating asymmetry can be fixed in a wild population of D . melanogaster under artificial selection [52] . The second one comes from a comparative study of Drosophila species [53] . Embryos of D . santomea and D . yakuba display high inter-individual variability of expression of the signal transducer pMad at the onset of gastrulation , as compared to D . melanogaster embryos . This increased variability was attributed to a reduced activity of the homeobox gene zerknüllt thirty minutes before this stage . Very interestingly , it is accompanied by phenotypic variability ( inter-individual variance of the number of amnioserosa cells ) in D . santomea but not in D . yakuba . These and other examples [54] illustrate how developmental variability and phenotypic noise can evolve in natural populations . Applying scPTL mapping may allow to dissect the genetic factors responsible for this evolution . Our new method based on the Kantorovich distance is not the only one by which scPTL can be identified . Applying classical QTL mapping to summary statistics of the cellular traits can also be efficient . We emphasize that the two approaches are complementary . For example , our method missed to detect linkage for 9 yeast morphological traits for which cvQTL scans were successful , but it detected several significant scPTL that were missed by the QTL-based approach ( Figs 5 and 6 ) . Second , we observed that scPTL detection was often efficient when the mean value of cellular trait differed among genotypic categories . As shown on Fig 5B , traits successfully mapped tended to display high heritability of the mean . Thus , after a scPTL is detected , it is necessary to examine the effect on the trait distributions and to determine if it is a QTL or not . Third , alternative ways of mapping scPTL are open and may prove more appropriate in some contexts . For example , if a cellular trait becomes preoccupying when it exceeds a certain threshold value , then the fraction of cells above this threshold can be used as a macro-trait to be mapped by QTL analysis . This way , the focus is made on the relevant aspect of the cellular trait , avoiding variation in other parts of the distribution . We therefore recommend conducting Kantorovich-based scPTL mapping in addition to classical methods and not as a replacement strategy . While the principle of genotype-phenotype genetic linkage dates back to several decades ago , the statistical methods that test for linkage are still being improved , especially regarding multi-loci interactions or population structure corrections [55 , 56] . The present study provides a priming of a generic scPTL mapping approach ( exploiting thousands of single-cell trait values ) and demonstrates its feasibility and potential ( new loci were detected ) . Since it is new , we anticipate that it will also evolve in the future . It is currently based on three steps: ( i ) computing pairwise distances between individuals by using the Kantorovich metric , ( ii ) using the resulting distance matrix to construct a relevant phenotypic space and ( iii ) testing for genetic linkage by LDA . A number of methodological considerations can be made in anticipation to future developments and applications . Estimating the proportion of variance explained by scPTL is not straightforward: the 'captured variance' as quantified by the eigenvalues of the LDA is not the same as the 'explained variance' which must be re-computed by regression; and if linearity of the data is questionable , the method remains a useful tool if it detects scPTL but interpreting variance proportions need justifications . A phenotypic space can be constructed by alternative ways that do not require the Kantorovich metric . For example , we considered representing individuals in a "space of moments" , where the coordinate of every individual on the i-th axis was the i-th moment of the cellular trait distribution associated to this individual . We applied this to the yeast morphological data and we searched for genetic linkages by linear discriminant analysis as described above . This approach detected many significant scPTL but we encountered a difficulty that was avoided by our Kantorovich-metric based method . When searching for significant linear discrimination , the dimensionality of the phenotypic space is important . At high dimensionality , discriminant axes are more likely to be found . This improves detection in the actual data but at the expense of increasing the degrees of freedom and therefore the false positive hits estimated from the permuted data . In a "space of moments" , the properties of the single-cell trait distributions are very important because they define which axis ( moments ) are relevant to separate individuals . Keeping the 4-th axis may be crucial even if all individuals have very similar first , second or third moments . Choosing the appropriate dimension for LDA is then arbitrary and it becomes difficult to keep a good detection power while still controlling the FDR . In fact , applying QTL mapping on the 3rd and 4th moments of all traits was fruitless because the FDR could not be controlled at the genome-by-phenome scale . This issue is avoided in the case of Kantorovich distances because multi-dimensional scaling can be applied without normalization and the axes of the phenotypic space are ranked by descending order of their contribution to the inter-individual differences . The 4-th axis , for example , contributes less than the first three axes to the separation of individuals in the space . If keeping the 4-th dimension prior to LDA is beneficial for linkage , then keeping the first three axes is also highly relevant , and this is true regardless of the properties of the single-cell trait distributions . We found this very useful: our algorithm adds dimensions one by one and evaluates the benefit of each increase ( see methods ) . There are at least three lines along which our method may be further improved . First , LDA is only appropriate if genotypic categories can be distinguished along linear axis . If individuals in the phenotypic space are separated in non-linear patterns , other methods such as those based on kernel functions [57] may be more appropriate . Second , we propose to compute confidence intervals of scPTL position by bootstrap , following a method sometimes applied to QTL positions [58] . As expected , resampling not only affected scPTL position but also the optimal dimensionality retained ( S2A Fig ) . A deeper investigation of the simultaneous variation of these two outputs could help improve the precision of mapping . And third , single-cell data acquisitions often generate multiple trait values for each individual cells . This is the case for morphological profiling as in the dataset we used here , but also for gene expression [59] or parameters describing the micro-environment of the cells [60] . It would therefore be interesting to search for scPTL affecting multiple cellular traits simultaneously instead of treating cellular traits one by one . A multidimensional analysis could be performed in order to extract a set of informative meta-traits , such as principal components or representative medoids and scPTL of these meta-traits could be searched using our method . This dimension-reduction approach would benefit from the redundant information available from correlated traits ( e . g . the perimeter of a cell and its area are two measurements of its size ) , but the biological interpretation of a probabilistic effect on a meta-trait may not be straightforward . Alternatively , one might want to identify scPTL affecting the joint probability distribution of multiple cellular traits . In this case , a natural extension of our method would be to compute Kantorovich distances between multivariate distributions . However , the Kantorovich metric cannot be easily computed for more than two marginals ( i . e . cellular traits in our case ) . In fact , its existence as a unique solution to the multi-dimensional transportation problem was itself a subject of research [61] . A possible alternative could be to compute a Euclidean distance in the "space of moments" mentioned above and then apply multi-dimensional scaling . Furthermore , although our study was focused on probability density functions , steps ( ii ) , constructing the phenotypic space , and ( iii ) , testing for genetic linkage , could in principle be applied to other types of functions , provided that a relevant metric estimating the dissimilarity between such functions exists . This could be interesting in the case of function-valued traits , such as speech sound or other time-series functions . The evolution of these functions is being studied using phylogenetic methods that present challenging statistical issues [62 , 63] . Extending our approach to such functions may open the possibility to study them from a ( complementary ) quantitative genetics angle . Finally , we can anticipate that gene-gene and gene-environment interactions also shape the probability density function of cellular traits . Our results on the activation of the yeast galactose network remarkably illustrate this: the effect of the scPTL on chromosome V is apparent only transiently , and in response to a change of environmental conditions . It is tempting to extrapolate that signaling pathways in plants and animals may be affected by scPTL that act at various times and steps along molecular cascades . In conclusion , our study provides a novel method that can detect genetic loci with probabilistic effects on single-cell phenotypes , with no prior assumption on their mode of action . By exploiting the power of single-cell technologies , this approach has the potential to detect small-effect genetic variants that may underlie incomplete trait penetrance at the multicellular scale .
Single-cell gene activity was modeled by a stochastic variable X that represented the number of proteins in one cell at a given time . Under the model , the dynamics of X is controlled by two processes: ( 1 ) protein production with rate α and ( 2 ) protein degradation with rate β . We assume that the gene is positively auto-regulated by a 4-mer complex , meaning that α is an increasing function of X with a typical Hill-like shape α = α0+α1 ( XK ) 41+ ( XK ) 4 with α0 the leaky production rate in absence of X 4-mers at the promoter , α1 the production rate in presence of 4-mers , and K the dissociation constant of the 4-mer . We set β = 1 , which corresponds to scaling time units . The dynamics of the mean value of X in a population of isogenic cells follows the equation shown in Fig 4A . To obtain the probability distribution of X , we performed exact stochastic simulations of the chemical system defined by the two reactions rate α and β , using the Stochastic Simulation Algorithm [64] . To generate two groups of individuals , we assumed that the set of parameters ( α0 , α1 , K ) was controlled by one locus that could exist in two alleles ( A and B ) with mean values ( μ0A/B , μ1A/B , μKA/B ) and , for simplicity , that the individuals were haploids . To account for sources of inter-individual variability within genotypic groups , the values of the parameters for one individual were drawn from normal distributions of mean values μ0A/B , μ1A/B and μKA/B and of standard deviations ημ0A/B , ημ1A/B and ημKA/B where η represented the strength of inter-individual variability . η was assumed to be the same for A and B alleles . Values were: μ0A = 6 . 3 , μ0B = 0 . 1 , μ1A = 12 , μ1B = 10 , μKA = 10 and μKB = 1 . 6 . All statistical analysis were done using R ( version 3 . 1 . 2 ) [65] . The data from Nogami et al . [22] consisted of 220 traits , acquired on >200 cells per sample . Note that most traits are related to one of three division stages . Each trait was therefore measured on a subset of cells of the sample ( less than 200 ) . There were nine samples of the BY strain , nine of the RM strain , and three of each of 59 segregants of the BY x RM cross . For each trait , we computed the genetic heritabilities of the mean and CV as follows . The mean and CV of the cellular trait in each sample were computed , leading to two scalar values per sample that we call macro-traits hereafter ( to distinguish them from the single-cell values ) . The broad-sense genetic heritability of each macro-trait was H2 = ( varT − varE ) / varT , with varT and varE being the total and environmental variance , respectively . For Fig 2B , we estimated varT by randomly choosing one of the three replicate sample of each segregant and computing the variance across these 59 values . This was repeated 100 times and the estimates were averaged . Our estimate of varE was the pooled variance of varBY , varRM , varSeg1 , varSeg2 , … , varSeg59 which were the between-replicates variance of each strain . Confidence intervals on H2 values were computed by bootstrapping the strains . For the filtering step prior to linkage , H2 was computed slightly differently in order to be consistent across mapping methods ( see below ) . We first normalized the distributions as densities ( division of all bin counts by half the total number of cells ) . Following [27] , we then computed the Kantorovich distance between two distributions f1 and f2 as the area under the absolute value of the cumulative sum of the difference between the two distributions: KD ( f1 , f2 ) = ∫−∞+∞|∫−∞xf1 ( t ) −f2 ( t ) dt|dx Multi-dimensional scaling of the resulting distance matrix was then performed using the R function cmdscale ( ) from the stats package . The number of dimensions retained ( ndim ) was the number of eigenvalues exceeding the expected value under the hypothesis of no structure in the data ( i . e . mean of all eigenvalues , Kaiser criterion ) . We computed the heritability of each yeast morphological trait in this multidimensional space . This was done as above for one dimension , by computing the total variance of the data , and estimating the environmental variance from the replicated experiments made on the parental strains . For 147 traits , heritability was greater than 0 . 5 and scPTL were searched . Details on how these steps were implemented in R are described in S1 Methods , and the code is available in the open source ptlmapper R package ( https://github . com/fchuffar/ptlmapper ) . The yeast genotypes we used were from Smith and Kruglyak [66] . For the morphological traits , we pooled triplicates together in order to increase the number of cells per sample . The data then corresponded to 220 traits , measured on >600 cells per sample , with 3 BY samples , 3 RM samples , and 1 sample per segregant . We scanned the genetic map with two methods . First , we considered the coordinates of each segregant on the first axis of the multi-dimensional scaling , and we considered this coordinate as a quantitative trait that we used for interval mapping using R/qtl [67] . Secondly , we applied a linear discrimination analysis ( LDA ) on the phenotypes data , using the genotype at every marker as the discriminating factor . An important issue in this step is the multidimensionality of the data: axis 2 , 3 and more may contain useful information to discriminate genotypic groups , but if too many dimensions are retained , a highly-discriminant axis may be found by chance only . To deal with this issue , we evaluated the output of LDA at all dimensions d ranging from 2 to ndim . For each value of d , we applied LDA at every marker position and we recorded the Wilks' lambda statistics: Λ = ∏j = 1d 11+λj where λj was the j-th eigenvalue of the discriminant analysis . Low values of this statistics allow to reject the null hypothesis of no discrimination by the factor of interest [68] which , in our case , is the genotype . We defined a linkage score ( W score ) as: W = −Log10 ( P ) where P is the p-value of the Wilk's test ( deviation of Λ from the F-statistics with relevant degrees of freedom ) . Note that P is not interpreted directly as a significance value for linkage ( see the permutation test below ) . We then quantified how much the best marker position was distinguished from the rest of the genome by computing a Z-score: Z = Wbest − Wσw where Wbest , <W> and σW were the highest , the mean and the standard deviation of all W scores found on the genome , respectively . Finally , we chose the dimension that maximized this Z-score ( i . e . dimension where the linkage peak had highest contrast ) . Very importantly , the same degrees of freedom ( exploration of the results at various dimensionalities ) were allowed when applying the permutation test of significance ( see below ) . The distribution of the dimensionalities retained for the morphological traits is shown in S2B Fig . Additional details are provided in S1 Methods and the code is available in the open source ptlmapper R package ( https://github . com/fchuffar/ptlmapper ) . QTL-based mapping was performed as follows . A quantitative trait was considered at the cell-population level . This macro-trait was either the mean ( for QTL ) , the coefficient of variation ( standard deviation divided by mean , for cvQTL ) or the variance ( for varQTL ) of the cellular trait in the population of cells . For the yeast morphology data , we selected the traits with H2 > 0 . 5 prior to linkage . To do so , we re-computed H2 values in a way that was consistent with the heritability calculation of the phenotypic space prior to scPTL mapping , where replicates of segregants were pooled together before analysis of inter-strain variation ( see above ) . In this case , only 3 replicates of BY and 3 replicates of RM are then available to estimate the environmental variance . Therefore , we estimated varT as the variance of the 59 macro-trait values of the segregants and varE as ( varBY + varRM ) / 2 , with varBY ( resp . varRM ) being the variance of the three macro-trait values of the BY ( resp . RM ) strain . We then scanned the genome using the scanone function from r/qtl [67] with a single QTL model and the multiple imputation method [69] . Our code implementing the calls to r/qtl is available in the open source ptlmapper R package ( https://github . com/fchuffar/ptlmapper ) . We first explain the case where a single trait is studied . When the trait was mapped using R/qtl , significance was assessed by the permutation test implemented in function scanone ( ) of the package [67] . For scPTL , we implemented our own permutation test as follows . The significance of an scPTL is the type one error when rejecting the following null hypothesis: "there is no marker at which the genotype of individuals discriminates their location in the phenotypic space" , where one 'individual' refers to one population of isogenic cells , and where the 'phenotypic space' is the multi-dimensional space built above by computing Kantorovich distances and applying multi-dimensional scaling . The relevant permutation is therefore to randomly re-assign the phenotypic positions to the individuals before scanning genetic markers for discrimination . We did this 1 , 000 times . Each time , LDA was applied at dimensions 2 to ndim , the dimension showing the best contrast ( high Z score ) was retained , and the highest W score obtained at this dimension was recorded . The empirical threshold corresponding to genome-wide error rates of 0 . 1% , 1% and 5% were the 99 . 9th , 99th and 95th percentiles of the 1 , 000 values produced by the permutations , respectively . These thresholds are typically those employed in whole-genome scans for a single trait . We now explain the case of the morphological study , where multiple traits ( 220 ) were considered . This case is similar to system genetics studies , where the FDR must be controlled . Keeping it below 10% ensures that 9 out of 10 results are true positives , which is often considered as acceptable . Four different methods were used . For three of them , single-cell trait values were resumed to a scalar macro-trait and QTL was searched . The three methods differed by the choice of this macro-trait , which was either the mean or the coefficient of variation of single-cell traits , or the coordinate of individuals on the first axis of the phenotypic space . For each of the three methods , morphological traits with less than 50% genetic heritability ( see above ) were not considered further , and QTL was searched for the remaining Ntraits traits only . For each of these traits , LOD scores were computed on the genome by interval mapping using the macro-trait value as the quantitative phenotype of interest . Significance was assessed by random re-assignment of the macro-trait values to the individuals ( yeast segregants ) . We did 1 , 000 such permutations . For each one , the genome was scanned as above and the highest LOD score on the genome was retained . This generated a 1 , 000 x Ntraits matrix Mperm of the hits expected by chance . At a LOD threshold L , the FDR was computed as: FDR = NFalseL / NActualL where NActualL was the number of linkages obtained from the actual dataset at LOD > L , and NFalseL was the expected number of false positives at LOD > L , which was estimated by the fraction of elements of Mperm exceeding L . The fourth method considered all coordinates of the individuals in the phenotypic space . At this step , for each morphological trait , a phenotypic space of ndim dimensions had been built as explained above by computing Kantorovich distances and applying multi-dimensional scaling . Let P1 , P2 and PS be the phenotypic matrices of parent 1 ( strain BY ) , parent 2 ( strain RM ) and segregants , respectively , with rows being the samples ( replicates for P1 and P2 , and segregants for PS ) and columns being the ndim coordinates of each sample in the phenotypic space . These matrices had dimensions 3 x ndim for P1 and P2 and 59 x ndim for PS . Genetic heritability was computed as H2 = ( varT—varE ) / varT , where the total variance varT was the variance of the samples in PS , and where the environmental variance varE was estimated as ( varP1 + varP2 ) / 2 , with varP1 ( resp . varP2 ) being the variance of the samples in P1 ( resp . P2 ) . Morphological traits showing H2 < 0 . 5 were discarded , and scPTL mapping was applied to the remaining Ntraits traits as described above ( choice of dimensionality with highest contrast and recording of the best W score obtained on the genome at this dimensionality ) . Significance of W scores was assessed as described above for the LOD scores , by performing 1 , 000 permutations and determining the FDR associated to various thresholds of W scores . For each cell , the probability to divide depended on the concentration of gene product X according to the following Hill-like function ( Fig 8A ) : P ( X ) = β01+ ( Xθ ) n+ β∞ with β0 = 0 . 2 , ϑ = 2 . 5 , β∞ = 0 . 05 and n = 2 . 5 . The regulation of X was governed by the same model as above , with η = 0 . 16 . For each individual , parameters alpha0 , alpha1 and K were drawn from the same normal distributions as above , where mean and variance depended on the genotype ( A or B ) . At age 0 , a population of 1 , 000 cells was initiated with X = 5 . This population was then evolved by Stochastic Simulation Algorithm [64] , with a constant rate of cell death of 0 . 0001 until the age of 30 . The python code implementing this simulation is provided in S2 Methods . The yeast strains and oligonucleotides used in this study are listed in S2 Table . To construct the Gal-GFP reporter , we first removed the MET17 promoter of plasmid pGY8 [19] by digestion with restriction enzymes BspEI and SpeI followed by Klenow fill-in and religation . This generated plasmid pGY10 . The GAL1 promoter fragment was digested ( BglII-BamHI ) from pFA6a-His3MX6-PGAL1 [70] and cloned in the BamHI site of pGY10 . A small artificial open reading frame upstream GFP was then removed by digestion with EcoRV and BamHI , Klenow fill-in end blunting and religation . This generated plasmid pGY37 , carrying a PGAL1-yEGFP-NatMX cassette that could be integrated at the HIS3 genomic locus . Plasmid pGY37 was linearized at NheI and integrated at the HIS3 locus of strain BY4716 ( isogenic to S288c ) , YEF1946 ( a non- clumpy derivative of RM11-1a ) and in 61 F1 non-clumpy segregants from BY471xRM11-1a described in [22] to generate strains GY221 , GY225 , and the S288c x RM11-1a HIS3:PGAL1-yEGFP-NatMX:HIS3 set , respectively . In parallel , we also constructed a GAL1-GFPPEST reporter coding for a destabilized fluorescent protein [71] . We derived it from pGY334 , where GFPPEST was under the control of the PGK promoter . pGY334 was constructed in several steps . The PGK promoter was PCR-amplified from pJL49 ( gift from Jean-Luc Parrou ) using primers 1A23 and 1A24 , digested by BamHI and cloned into the BamHI site of pGY10 . The resulting plasmid was digested with EcoRV and XbaI , subjected to Klenow fill-in end blunting and religated , generating plasmid pGY13 carrying a HIS3:PPGK-yEGFP-NatMX:HIS3 cassette . The lox-CEN/ARS-lox sequence from pALREP [20] was amplified by PCR using primers 1I27 and 1I28 and cloned by homologous recombination into pGY13 , generating plasmid pGY252 . The GFPPEST sequence was PCR-amplified from pSVA18 [71] using primers 1I92 and 1I93 and cloned in vivo into pGY252 ( digested by MfeI and DraIII ) , leading to pGY334 . The GAL1 promoter fragment was amplified by PCR from pGY37 using primers 1J33 and 1I42 and cloned into plasmid pGY334 by recombination at homologous sequences flanking the BamHI site of the plasmid . The CEN/ARS cassette of the resulting plasmid was excised by transient expression of the Cre recombinase in bacteria [20] , generating the final integrative plasmid pGY338 carrying the HIS3:PGAL1-GFPPEST-NatMX:HIS3 cassette . pGY338 was linearized by NheI and integrated at the HIS3 locus of BY4724 ( isogenic to S288c ) and GY1561 to create GY1566 and GY1567 strains , respectively . Strain GY1561 is a non-clumpy derivative of RM11-1a where the KanMX4 cassette was removed . It was obtained by first transforming RM11-1a with an amplicon from plasmid pUG73 [72] obtained with primers 1E75 and 1E76 and selecting a G418-sensitive and LEU+ transformant ( GY739 ) which was then transformed with pSH47 [73] for expression of the CRE recombinase . After an episode of galactose induction , a LEU- derivative was chosen and cultured in non-selective medium ( URA+ ) for loss of pSH47 , leading to strain GY744 , which was then crossed with GY689 [74] to generate GY1561 . Liquid cultures in synthetic medium with 2% raffinose were inoculated with a single colony and incubated overnight , then diluted to OD600 = 0 . 1 ( synthetic medium , 2% raffinose ) and grown for 3 to 6 hours . Cells were then resuspended in synthetic medium with 2% raffinose and 0 . 5% galactose and grown for the desired time ( 0 , 10 , 20 , 30 , 40 , 60 , 80 , 100 , 130 , 160 , 205 and 250 minutes ) . Cells were then washed with PBS1X , incubated for 8 min in 2% paraformaldehyde ( PFA ) at room temperature , followed by 12 min of incubation in PBS supplemented with Glycine 0 . 1M at room temperature and finally resuspended in PBS . They were then analyzed on a FACSCalibur ( BD Biosciences ) flow cytometer to record 10 , 000 cells per sample . Flow cytometry data was analysed using the flowCore package from Bioconductor [75] . Cells of homogeneous size were dynamically gated and normalized as follows: ( i ) removal of events with saturated signals ( FSC , SSC or FL1 ≥ 1023 or ≤ 0 ) , ( ii ) correction of FL1 values by subtracting the mean ( FL1 ) observed on the same strain at t = 0 , ( iii ) computation of a density kernel of FSC , SSC values to define a perimeter of peak density containing 60% of events , ( iv ) cell gating using this perimeter and ( v ) removal of samples containing less than 3 , 000 cells at the end of the procedure . The GFP expression values were the corrected FL1 signal of the retained cells . The DOT6RM allele was amplified by PCR from genomic DNA of the RM strain using primers 1K87 and 1K88 . It was then cloned into plasmid pALREP [20] by homologous recombination at sequences flanking the HpaI site of the plasmid . The CEN/ARS cassette of the resulting plasmid was excised by transient expression of the Cre recombinase in bacteria , as previously described [20] , generating plasmid pGY389 , which was linearized at EcoRI ( a unique site within the DOT6 gene ) and integrated in strain GY1566 ( isogenic to BY , and carrying the HIS3:PGAL1-GFPPEST:HIS3 cassette ) . The pop-in pop-out strategy was applied as previously described [20] and four independent transformants were selected ( GY1604 , GY1605 , GY1606 and GY1607 ) where PCR and sequencing validated the replacement of the DOT6 allele . The yeast morphological data corresponds to the experiments described in [22] . For the present study , raw images were re-analyzed using CalMorph 1 . 0 . The single-cell values and genotypes used are provided in S1 Dataset of this article . The flow cytometry data corresponding to yeast galactose response is made available from http://flowrepository . org under accession number FR-FCM-ZZPA . The simulated data of Fig 4 is available as an R package ( ptldata ) from https://github . com/fchuffar/ptldata . The scPTL mapping method is made available as an open source R package ( ptlmapper ) which can be downloaded from https://github . com/fchuffar/ptlmapper . A tutorial of this package explains how to run the analysis on the simulated dataset . | Genetic association studies are usually conducted on phenotypes measured at the scale of whole tissues or individuals , and not at the scale of individual cells . However , some common traits , such as cancer , can result from a minority of cells that adopted a special behavior . From one individual to another , DNA variants can modify the frequency of such cellular behaviors . The body of one of the individuals then harbours more misbehaving cells and is therefore predisposed to a macroscopic phenotypic change , such as disease . Such genetic effects are probabilistic , they contribute little to trait variation at the macroscopic level and therefore largely escape detection in classical studies . We have developed a novel statistical method that uses single-cell measurements to detect variants of the genome that have non-deterministic effects on cellular traits . The approach is based on a comparison of distributions of single-cell traits . We applied it to colonies of yeast cells and showed that it can detect mutations that change cellular morphology or molecular regulations in a probabilistic manner . This opens the way to study multicellular organisms from a novel angle , by exploiting single-cell technologies to detect genetic variants that predispose to certain diseases or common traits . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"chemical",
"compounds",
"quantitative",
"trait",
"loci",
"quantitative",
"traits",
"carbohydrates",
"galactose",
"organic",
"compounds",
"fungi",
"mathematics",
"molecular",
"biology",
"techniques",
"discrete",
"mathematics",
"combinatorics",
"research",
"and",
"analysis",... | 2016 | Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects |
Central mechanisms by which specific motor programs are selected to achieve meaningful behaviors are not well understood . Using electrophysiological recordings from pharyngeal nerves upon central activation of neurotransmitter-expressing cells , we show that distinct neuronal ensembles can regulate different feeding motor programs . In behavioral and electrophysiological experiments , activation of 20 neurons in the brain expressing the neuropeptide hugin , a homolog of mammalian neuromedin U , simultaneously suppressed the motor program for food intake while inducing the motor program for locomotion . Decreasing hugin neuropeptide levels in the neurons by RNAi prevented this action . Reducing the level of hugin neuronal activity alone did not have any effect on feeding or locomotion motor programs . Furthermore , use of promoter-specific constructs that labeled subsets of hugin neurons demonstrated that initiation of locomotion can be separated from modulation of its motor pattern . These results provide insights into a neural mechanism of how opposing motor programs can be selected in order to coordinate feeding and locomotive behaviors .
The recruitment of appropriate motor programs to changing environmental conditions is an essential aspect of animal behavior [1] . The nervous system of invertebrates and vertebrates includes a broad variety of motor programs with neuronal circuits having the intrinsic property to generate rhythmic motor output , termed central pattern generators ( CPGs ) . These motor programs underlie the spatial and temporal activation of specific muscle groups that characterize movements like chewing , swallowing , walking , breathing , and locomotion [2] , [3] . The mechanisms by which a specific motor program is selected from a repertoire of potential motor programs are not well understood . In vertebrates , the motor system for locomotion has been extensively studied with various methods including pharmacological , electrophysiological , and more recently genetic tools [4]–[9] . Isolated spinal cord preparations have been used to demonstrate the existence of locomotive CPGs in the mammalian spinal cord [10] , [11] . The neural networks comprising the motor programs and the motor neurons for the single limbs are located in the spinal cord , and the motor network of a limb can be divided into motor subprograms and sets of motor neurons for each joint of a limb . These spinal–cortical networks are activated via reticulospinal neurons by command centers in the mesencephalon and diencephalon , which in turn are controlled by neuronal structures in basal ganglia [1] , [12] . Neurotransmitters such as serotonin , for example , have been shown to be necessary to induce motor patterns in isolated brainstem–spinal preparations [13] , [14] . Specific neurotransmitter-expressing cells that are involved in regulating the speed of locomotion have also been identified by genetic tools in lamprey , zebrafish , and mouse [15]–[19] . Currently , little is known about the cellular circuits in the brainstem or descending cortical pathways which regulate the locomotion CPGs in the spinal cord [20] . In addition to the highly conserved locomotor motor behaviors , those related to feeding are critical for growth and survival . These encompass movements involving the whole body for searching and getting access to food sources , local parts of the body for actual food intake , as well as organ-specific movements for post-ingestive phases of feeding . In invertebrates , the rhythmic nature of swallowing and food transport has been utilized as a model to study the structure of CPGs that generate oscillating motor patterns [3] , [21] , as well as providing insights into the physiological parameters that drive feeding behavior [22]–[25] . This has also been the case in mammalian systems , where the discovery of leptin provided a major nucleation point for analyzing how peripheral signals influence central circuits that regulate food intake behavior and energy homeostasis [26] , [27] . Simpler genetic systems such as Drosophila and Caenorhabditis elegans are increasingly being used to study the genes and neural circuits that control feeding and feeding-related processes . These studies include the identification of the first gene involved in food search behavior [28] , metabolic genes that influence feeding , as well as numerous neuropeptide- and neurotransmitter-encoding genes , to name a few [29]–[32] . Studies in Drosophila have , to date , focused mostly on analyzing feeding behavior in response to chemosensory or metabolic cues [33]–[39] . These studies have used sophisticated genetic tools to manipulate specific neuronal populations in the brain [40] , [41] , but what has lagged behind is a high resolution readout of such manipulations on motor programs . Most have used behavioral paradigms as readout assays , for example extension of the proboscis towards a food source , measurement of food ingested , the direction a fly takes in two-choice food assays . Although providing valuable information , these approaches determine the summation of many motor programs , and it is difficult to deconstruct the distinct motor programs that produce the observed behavioral output . In addition , most of the feeding behavior assays are performed in response to sensory stimuli , and it is not possible to distinguish which step in the sensorimotor pathway is primarily being affected . Thus , it is not surprising that , in comparison with chemosensory circuits [42]–[44] , much less is known about the motor circuits that underlie feeding behavior . Recently , an electrophysiological approach was used in semi-intact preparations to monitor the rhythmic motor patterns that comprise the Drosophila larval feeding cycle [45] . These analyses led to the identification of three motor patterns derived from three distinct nerves that innervate the feeding apparatus and which together comprise larval feeding behavior: motor output of antennal nerve ( AN ) results in pharyngeal pumping , motor output of maxillary nerve ( MN ) drives mouth hook movements , and that of prothoracic accessory nerve ( PaN ) causes head tilting movements [45] . In addition to providing higher resolution dissection of feeding motor patterns , this approach also overcomes an important issue relevant for studying motor circuits in general: it eliminates external inputs provided by a wide variety of sensory organs , as well as by internal peripheral tissues that can affect feeding responses , such as the gut , fat body , or the oenocytes [46]–[48] . The approach provides an opportunity to combine molecular genetics with electrophysiology in order to study how the central nervous system ( CNS ) selects and executes motor programs . In this study , we used behavioral , genetic , imaging , and electrophysiological approaches to study central mechanisms that modulate feeding-related behaviors . We first identified neurotransmitter and neuropeptide clusters that modulate subsets of motor programs for feeding . This revealed that a small neuronal cluster can oppositely regulate feeding and locomotive motor programs . The cells of this cluster express the gene hugin , which encodes a neuropeptide homolog to mammalian neuromedin U and which was previously proposed as being involved in food intake and food search behaviors [23] , [49] . Increased neuromedin U signaling in mammals has been shown to suppress feeding and increase locomotion [50] , [51] . We show here that activation of hugin neurons suppresses the motor program for feeding and simultaneously initiates the motor program for locomotion . Our results provide a model for how selection of coordinately regulated motor programs can be brought about through activation of a single cluster of neurons in the brain .
We previously characterized the major muscles and the nerves driving the movements that underlie feeding behavior [45] , [47]: AN , MN , and the PaN ( see also Figure 1A ) . Our next goal in characterizing the feeding motor system was to identify central components of the motor hierarchy that could modulate the motor pattern recorded from the three pharyngeal nerves . The strategy was to activate specific neurotransmitter- and neuropeptide-expressing neurons in an inducible manner , and assay their effect on motor programs of feeding-related behavior ( Figure 1 ) . Directing the expression of the temperature-sensitive cation channel dTrpA1 [52] via the Gal4-UAS system enabled us to characterize the effect of activating distinct neuronal populations in a temporally controlled manner ( Figure S1 ) . We initially prescreened 11 lines , representing major neurotransmitter and selected neuropeptide lines , by a food intake assay ( Figure S2 ) ; those that showed significant effect on food intake were taken for electrophysiological as well as additional feeding analysis . Five lines selected for this study were those labeling glutamatergic ( Glu ) , cholinergic ( ACh ) , serotonergic ( 5-HT ) , dopaminergic ( DA ) , and hugin ( Hug ) neurons . The effect of temperature-induced activation of neuronal populations on the motor patterns was then monitored with single extracellular recordings of the three pharyngeal nerves ( AN , MN , and the PaN ) to distinguish neuronal populations that would affect the feeding motor pattern either globally or as just a subset ( Figure 1 ) . We then compared the changes in cycle frequency , which is a crucial feature of rhythmic behavior: classical studies on crustacean stomatogastric nervous systems revealed that all known modulatory inputs affect the cycle frequency of pyloric motor rhythm by altering the endogenous properties of at least one component of the CPG [3] . Neuronal activation of the Glu population resulted in a reversible state of tonic excitation in the motor patterns of all three pharyngeal nerves ( Figure 1B ) . This was expected since the Gal-4 driver line ( OK371 ) drives target gene expression in nearly all Glu neurons of the CNS that comprise the motor neurons [53] . Activating the ACh neurons showed a significant increase in cycle frequency of all motor patterns; in some instances the pattern approached the tonic excitation seen for Glu neurons . Activation of 5-HT neurons also caused an increase in cycle frequency; the effect on the 5-HT neurons stood out because of the remarkable regularity of the accelerated motor pattern in all three pharyngeal nerves . By contrast , activation of DA and Hug neurons decreased rhythm frequency . Moreover , these showed differential effect on the feeding motor patterns: only the AN motor pattern was affected , and not the MN or the PaN . ( Figure 1B ) . These results indicated that certain neuronal classes affected all , whereas others affected only a subset , of the feeding motor programs . Food intake studies further confirmed the roles of these neurones in feeding behavior . A short-term yeast intake assay was used in order to minimize longer-acting peripheral influence on the feeding response ( Figure 2A ) . Four neuronal populations significantly decreased yeast intake: Glu , ACh , DA , and Hug neurons . Only one increased yeast intake: 5-HT ( Figure 2A ) . Contraction of the cibarial dilator muscles ( CDM ) , which is due to the AN motor program , is the movement most dedicated to food intake per se as compared to those driven by MN or PaN motor programs . Contractions of CDM presumably generate a negative pressure , resulting in ingestion of liquidized food: ‘pharyngeal pumping’ . Thus , we also performed video-based monitoring of the CDM contractions in semi-intact larvae to see how this particular movement could be correlated with the electrophysiology and food intake data ( CDM tracking , Figure 2B ) . There is indeed a good correlation between the CDM contraction pattern and the AN recordings , which may explain the food intake results . For Glu , the tonic-like excitation resulted in convulsive contractions of the CDM , leading to essentially no food intake . For ACh , the CDM relaxed incompletely between successive contractions , causing less effective pharyngeal pumping , which likely accounts for the decreased food intake despite increased pumping rate . For DA and Hug , the frequency of the contraction was reduced ( Figure 2B ) ; the effect was more drastic for Hug , as seen by the strength of each contraction . The decreased food intake in both cases is as expected . For 5-HT , there was a rapid increase in the rate of CDM contractions , consistent with the increase in AN recording cycle frequency and food intake . The combined electrophysiological and behavioral analyses opened up several avenues to pursue , as all the lines revealed interesting features relating to selection and modulation of motor patterns . For example , the unique finding that the serotonergic line , when activated , was the only one of 11 lines tested which resulted in increased food intake . The dopaminergic and hugin lines were interesting since they affected only a subset of the motor programs ( i . e . , the AN , but not MN or PaN motor programs ) , thus demonstrating a specificity in recruitment of different motor programs that comprise the feeding system . For the current study , we decided to focus on the hugin neuronal cluster for one critical reason , namely the relative simplicity of the expression pattern generated by the HugS3-Gal4 line in both numerical and spatial terms . Previous studies showed that this line drives reporter gene expression precisely in 20 cells , all tightly clustered in the subesophageal ganglion ( SOG ) [49] ( Figure S3 ) , and send projections to the ventral nerve cord and the protocerebrum , which is the higher brain center . We first wanted to verify the effect of hugin on the feeding motor system using an independent method to activate neurons . Thus , we used NaChBac and recorded the AN motor pattern [54] , [55] . The recordings showed a significant suppression of AN motor activity , further strengthening the view that hugin neurons suppress feeding motor patterns ( Figure S4 ) . We also wanted to perform the converse experiment by inhibiting hugin neuronal activity through the use of temperature-sensitive shibire ( shibirets ) , which blocks synaptic transmission [56] . However , we did not observe any difference in the frequency of the AN motor pattern ( Figure S5A ) . This indicated that activating hugin neurons suppresses AN motor activity , but inhibiting them does not increase it . We do not think this is due to the normal larval feeding motor system operating at a maximal level ( since larvae are continuous feeders ) , since we can in fact observe an increase in motor activity when serotonergic neurons are activated . Instead , we believe that this reveals insights into the mechanism by which hugin neurons function in modulating the feeding motor system ( see Discussion ) . Consistent with this view , ablating the hugin cells ( by expressing reaper-hid to induce apoptosis [57] ) or inhibiting the neuronal activity using Kir2 . 1 also had no effect on the AN motor pattern ( Figure S5B ) . Based on these observations , we next wanted to analyze the alterations in feeding behavior when hugin neurons were activated in more detail . Specifically , we wanted to determine if the suppressed food intake was accompanied by alterations in a food-related locomotory behavior , namely the wandering-like behavior . This is a behavior that is observed in certain mutant larvae which are defective in food intake , where they move away from the food source and wander about the surrounding area [47]–[49] . Indeed , in addition to suppression of food intake , a significant wandering-like behavior is also observed when hugin neurons are activated ( Figure 3 ) . Due to the alteration in locomotive behavior , we next asked if the activity of the abdominal segmental muscles that underlie locomotion were affected by activating the hugin neurons . The Drosophila larval neuromuscular junctions of the ventral longitudinal muscle ( M6 and M7 ) are well established and have provided valuable insight into synapse function and muscle membrane excitability [58] , [59] . The rhythmic motor outputs recorded from abdominal muscle M6 are representative for locomotory patterns generated by the larval CNS and likely reflect crawling behavior [60] , [61] . We therefore monitored the activity of the abdominal muscle M6 by intracellular recordings ( Figure 4A ) . Interestingly , we observed an accelerating effect on abdominal muscle contraction pattern upon activation of the hugin neurons ( Figure 4B–D ) . We note at this point that we also recorded the M6 muscle motor pattern when shibirets was used to silence hugin neurons , but as with the pharyngeal motor pattern above , no effect was observed ( Figure S5C ) . We then asked if pharyngeal pumping and abdominal activity could be coordinately regulated . Therefore , we performed double intracellular recordings of the CDM and the abdominal muscle M6 ( Figure 4E ) . Remarkably , hugin neuron activation resulted in a concomitant decrease in feeding and increase in locomotion motor program: the post-synaptic potentials ( PSPs ) of CDM are completely suppressed , whereas those of abdominal muscle ( M6 ) persist and the motor pattern increased in cycle frequency ( Figure 4F–H ) . In wild type situations , an increase in temperature results in the usually observed temperature effect where both activities are increased ( Figure 4H ) . It is well established that temperature has a profound effect on intrinsic network properties that influence the setting of rhythm frequencies in the CNS of invertebrates and vertebrates [62] , [63] . In HugS3>dTrpA1 , the increased abdominal motor activity is accompanied by a concomitant decrease in CDM motor activity . These results indicated that hugin neurons can modulate two opposite motor programs simultaneously: the feeding program and the locomotory program . Consistent with the previous results , shibirets also had no effect on the CDM motor pattern recordings ( Figure S5A ) and the underlying feeding behavior ( Figure S6 ) . Since the results described above indicated that activation of the hugin neurons leads to suppression of feeding , we next wanted to determine if the hugin neuropeptide is required for this suppression . The strategy was to decrease hugin neuropeptide levels in the hugin neurons through RNA interference ( RNAi ) and see if activating the hugin neurons would still result in suppression of feeding behavior . First we determined the effectiveness of several RNAi lines to decrease hugin neuropeptide levels ( Figure 5A; Figure S7 ) . We chose two independent constructs that were effective in reducing hugin neuropeptide levels ( HugRNAi1A and Hug-TriPJF03122 ) . Animals which only expressed the hugin RNAi gene construct did not show any alterations in the feeding phenotype , in line with the results , described in the previous section , showing that inhibiting or ablating hugin neurons also had no effect ( Figure S5 ) . However , if hugin neurons were activated with dTrpA1 in animals expressing the HugRNAi construct , the suppression of AN motor pattern was no longer observed ( Figure 5B , top panel ) . Similar results were observed with food intake and wandering-like behavior . In both cases , the HugRNAi lines significantly prevented the hugin neurons from exerting their suppressive effect ( Figure 5C ) . Interestingly , the increase in cycle frequency of M6 motor pattern was not affected—that is , activating the hugin neurons still resulted in increased cycle frequency ( Figure 5B , bottom panel ) . Thus , the induction of wandering-like behavior can be decoupled from modulation of the locomotory motor program . Taken together , these results show that hugin neuropeptide is required for modulating food intake but not for the locomotion motor program; it is also required for initiating wandering-like behavior . The hugin neuronal cluster comprises just 20 cells , whose soma are all located in the SOG . Earlier work showed that the hugin neurons form four distinct subclasses , each having different projection targets [23] , [49] . One subclass sends projections down the entire length of the ventral nerve cord ( VNC ) [64] , suggesting a possible role in locomotion . To explore this , we made several deletion constructs of the hugin cis-regulatory region in order to see if the different subclasses were under the control of separable enhancers . In one construct ( Hug0 . 8 ) there was a complete absence of expression in the four hugin cells that project to the VNC ( Figure 6A–C; G ) , whereas the other 16 neurons were present . Furthermore , using this promoter element in cell ablation experiments resulted in the loss of the 16 cells , whereas the four hugin VNC neurons remained ( Figure S8 ) , demonstrating the specificity of this promoter element . To analyze the behavioral consequence , we carried out both food-intake and wandering-like locomotion assays . The 16-cell construct ( Hug0 . 8 ) , in which the VNC projections were missing , could still suppress food ingestion as well as induce wandering-like behavior ( Figure 6H , I ) . We then performed the converse experiment: to determine the function of the 4-cell hugin cluster that projects to the VNC . We therefore made a promoter construct from a region that was deleted in Hug0 . 8 construct relative to the HugS3 construct . This line drove target gene expression in precisely the four hugin cells that project down the VNC ( Figure 6D–F , G ) . dTrpA1 activation of this 4-cell VNC cluster had no effect on food intake or wandering-like behavior ( Figure 6 H , I ) . Next we measured cycle frequency of the AN motor pattern after dTrpA1 activation of these two nonoverlapping neuronal clusters . The hugin-0 . 8 line suppressed the AN motor pattern , whereas the VNC-line could not ( Figure 7A–C ) , supporting the food intake data . However , when M6 abdominal muscle recordings were performed , we observed the acceleration of the motor pattern with the 4-cell element but not with the 16-cell element ( Figure 7D–F ) . Similar results were obtained when we performed simultaneous double recordings from CDM ( for pharyngeal pumping ) and M6 abdominal muscles ( Figure 7 G–I ) . Taken together , these results indicated that food intake ( motor program for pharyngeal pumping ) and initiation of wandering-like behavior can be decoupled from modulation of the speed of abdominal muscle contraction . The 4-cell hugin VNC cluster can thus regulate locomotion speed separately from pharyngeal pumping . Therefore , although activation of the entire 20-cell hugin cluster coordinately suppresses feeding and enhances locomotion speed , the two motor programs are under the control of distinct hugin neuronal subclasses . Both the suppression of food intake and the induction of wandering-like behavior are performed by the 16-cell cluster , whereas the 4-cell VNC cluster is required to increase the cycle frequency of the locomotor motor pattern . The above results indicated that the 16 cell hugin cluster mediates the suppressive effect of hugin neurons on the AN motor pattern . These comprise three different subclasses of hugin neurons [49] , [64]: two of these have projections which leave the CNS and target the periphery ( to the pharynx , and the ring gland ) , and one has projections to the protocerebrum . In an effort to start addressing the issue of whether the protocerebrum is required for hugin function in modulating feeding motor pattern , we used a classical lesion approach in combination with dTrpA1 activation . The experimental strategy was to make lesions to the isolated CNS preparation and record the AN motor pattern upon dTrpA1 activation of hugin neurons ( Figure 8 ) . At 18°C , when dTrpA1 is not activated , lesioning the VNC or the brain hemispheres ( H ) still resulted in a rhythmic motor pattern from the AN ( Figure 8 , 18°C ) , although there were some noticeable variations relative to the pattern generated by an intact CNS . Upon dTrpA1 activation , the suppression of AN motor pattern was still observed when the VNC was lesioned ( Figure 8B ) . However , when the hemispheres were lesioned , we no longer observed this suppression ( Figure 8C ) . These results suggested that the protocerebrum is required for hugin neuronal function in suppressing the AN motor pattern underlying pharyngeal pumping . Furthermore , these results demonstrate that the CPG for the AN motor pattern is located in the SOG .
Behavioral modules can be seen to be composed of distinct motor programs that are differentially recruited based on adaptive needs [1] . These can be cooperative or antagonistic , and the right combinations must be selected in order to bring about the required behavior . For feeding , this requires motor programs that allow actual ingestion of food as well as those locomotor programs involved in food search or food avoidance behaviors . This implies that animals must distinguish motor programs that run serially or in parallel , and those that are essentially mutually exclusive . In humans for example , feeding normally requires arm movements to bring the food to the mouth , followed by biting and chewing , and finally swallowing; but the act of swallowing can occur in the absence of the earlier movements; conversely , similar arm movements to those made during eating can be observed during running . Within a given motor program there are additional levels of modulation—for example , the speed with which a given movement is made . The behavioral module that comprises Drosophila larval feeding is also composed of distinct motor programs , as shown by motor patterns of three pharyngeal nerves , the AN , MN , and the PaN [45] . Our electrophysiology screen reveals distinct populations of central neurons that can regulate motor patterns in a different manner . Some , such as serotonergic neurons , affect all three motor programs; others , like hugin neurons , act on a subset . These differences can be viewed as having varying degrees of functional overlap . Pharyngeal pumping ( due to the AN motor program ) is the movement most dedicated to food intake; at the other end of the functional spectrum , the segmental longitudinal muscle contractions would be most dedicated to locomotion . Mouth hook and head tilt movements ( due to MN and PaN motor programs , respectively ) are likely involved in both feeding and locomotion . These motor programs can be separately regulated and recruited for different behavioral modules . Such mechanisms have been demonstrated at the CPG level in other invertebrates , where the same neurons can be used in different CPGs [21] , [65] . For both feeding and locomotion , the cellular identities of the CPGs remain largely unknown . Previous studies have demonstrated the existence of feeding CPG ( s ) in the Drosophila larval CNS [45] . We have now localized one of these , the AN motor pattern underlying pharyngeal pumping , to the SOG by lesion experiments . Spieß et al . [66] has provided evidence that the motor neurons of the AN are also located in the SOG . Although the lesioning of VNC or brain hemisphere can still generate a rhythmic motor pattern , it is not identical to that generated when both are present , indicating that inputs from the VNC and brain hemispheres have a modulatory effect on the pharyngeal pumping CPG . Our results on feeding are complementary to earlier findings on the motor program for locomotion . Forward crawling of Drosophila larvae is composed of repetitive wave-like contractions of the segmental body wall musculature from posterior to anterior [67] . Several studies indicated that the neural networks of the crawling motor program ( CPGs for crawling ) are located in the thoracic and abdominal segments of the central nervous system [60] , [68] , and genetic manipulations showed that the brain hemispheres and the SOG are not required to produce a rhythmic motor pattern in the VNC and crawling behavior , although the rhythmic motor pattern is required for directed movements in response to chemosensory cues [68] . In this context , a major issue is that of cellular specificity: which of the cells targeted by these neurotransmitter Gal4 lines are responsible for the observed effects on the feeding and locomotor program ? For example , serotonin is expressed in about 84 cells in the larval CNS: ∼56 in the VNC , ∼8 in the SOG , and ∼20 in the protocerebrum [69] . As mentioned above , the activation of the serotonergic cells results in an increased cycle frequency of all three motor programs; it also enhances the locomotion program in the VNC ( see Figure S9 , Figure 9A ) . However , we do not know which of the serotonergic cells contribute to which of these programs . In addition , different groups of serotonergic cells may have different , even opposite functions , and it may be that the promoting effect dominates when all groups become activated . The various sparse lines and intersectional strategies to narrow down the types of cells being manipulated will be valuable in addressing this issue . This can be combined with the ability to record , from isolated CNS , both feeding and locomotor motor patterns , permitting the identification of central neurons that coordinate the motor programs underlying different behavioral modules . A striking finding from our study is the fact that activating a small cluster of 20 neurons in the SOG , all expressing the neuropeptide hugin , leads to a simultaneous suppression of a motor program for feeding and induction of one for locomotion . This is observed both at the behavioral and electrophysiological level . Thus , the hugin cluster can regulate two essentially competing programs since larvae , as with most animals , do not feed and move at the same time . A notable feature of the hugin neuronal cluster is that we have not been able to observe any difference to the control situation when hugin neuronal activity is decreased . For both pharyngeal pumping and wandering-like behavior , it is only when the hugin neurons are activated that we see a modulatory effect . Similarly , the increase in the frequency of M6 abdominal muscle contraction is observed only under activation of hugin neurons . We believe these observations provide insights into the mechanism by which the hugin neurons act . This can be illustrated in terms of how a brake and gas pedal function to coordinate two mutually exclusive operations of a car . Activating hugin neurons decreases feeding , but inhibiting them does not increase feeding: applying a brake causes deceleration , but removing it does not cause acceleration . Similarly , activating the hugin neurons enhance abdominal muscle contraction , but their inhibition does not slow down contraction: stepping on the gas pedal increases speed , but taking it off does not actively decrease speed . This scenario can be used to explain the requirement of hugin neuropeptide in our RNAi experiments . Lowering the level of hugin neuropeptide in activated hugin neurons no longer affected the motor patterns underlying food intake and locomotion , indicating that hugin neuropeptide is necessary for the hugin neurons to suppress feeding and induce wandering-like behavior . It is of interest to note that hugin neuropeptide does not seem to be required for speeding up the motor program for locomotion . This could be because of the residual quantity of hugin neuropeptide or to some compensation mechanism; more likely , the accelerating effect is due to a different neurotransmitter . At this point , we do not know which classical neurotransmitters are expressed in the hugin cells . In mammals , it has been shown that serotonergic and cholinergic systems influence the speed of motor neuron firing in the spinal cord that underlies locomotion [6] , [10] . Furthermore , our results show that modulation of the speed of locomotion motor program can be decoupled with the initiation of wandering-like behavior . The decision to both stop feeding and to move out of the food is mediated by a separate cluster of 16 hugin cells , eight of which project to the protocerebrum . A possible scenario is that the cells which adjust the speed of locomotion are recruited during or after the selection of motor programs for suppressing food intake and initiating wandering-like behavior . In many vertebrates , the center for swallowing is thought to be localized in the brainstem [70]–[72] . The cranial nerves that innervate muscles involved in chewing and swallowing descend from the brainstem . The neuronal components are much less understood relative to the spinal cord , although identifying the specific brain areas that regulate food intake is a focus of intense study in the mouse [73] . In Drosophila , the larval SOG occupies a central position within the CNS to integrate information on feeding and locomotion , as it connects the VNC with the brain hemispheres . The pharyngeal nerves that innervate the feeding musculature originate from the SOG , and gustatory sensory neurons send projections to the SOG [43] , [45] . The brainstem in vertebrates is analogously positioned , being located at the junction between the brain and the spinal cord , and the cranial nerves that innervate the pharynx originate from this part of the CNS [74] , suggesting that the SOG could represent an analogous structure to the vertebrate brainstem . It has recently been postulated that the insect central complex might play an analogous role to the basal ganglia [75]–[77] . Although a canonical central complex has not yet been identified in the Drosophila larval brain , a functional analogue is probably located in the protocerebrum . The neuroanatomy of the hugin neurons , especially exemplified by the projection pattern that connects the SOG to the protocerebrum , suggests that the SOG/protocerebrum corridor encompassing the hugin neuronal projections may play an important role in action selection of motor programs underlying feeding and locomotion ( Figure 9B ) . The hugin projections to the protocerebrum and the connections to the gustatory cells and the insulin-producing cells [49] , [78] , would process external and internal sensory cues , and determine which motor programs modulating feeding and locomotion are selected . A major future challenge will be to determine how the different neuronal components of the feeding motor hierarchy are interconnected . One essential effort will be to analyze the receptor for the hugin neuropeptides . Two putative receptors have already been identified and it would be necessary to determine the cells that express the receptors [79] , [80] . Another effort will be to localize the classical neurotransmitters that may be expressed in the different hugin cells . Complementary to these would be to exploit the high-resolution connectivity mapping of the larval CNS that is currently being done through serial EM reconstructions [81] , as has been done in the classic work for C . elegans [82] . Working on a small brain with its limited behavioral repertoire may thus lead to a functional map superimposed on the connectome of the larval motor system .
The following Gal4 driver and UAS effector lines were used: OK371-Gal4 ( Bloomington #26160 ) , Cha-Gal4 ( Bloomington #6798 ) , GAD-Gal4 [83] , TRH-Gal4 [84] , TH-Gal4 ( Bloomington #8848 ) , DDC-Gal4 ( Bloomington #8849 ) , TDC2-GaL4 ( Bloomington #9313 ) , DILP2-Gal4 [85] , HugS3-Gal4 [49] , NPF-Gal4 ( Bloomington #25682 ) , sNPF-Gal4 ( Kyoto DGRC #113901 ( NP6301 ) ) , UAS-dTrpA1 ( Bloomington #26263 ) , UAS-eYFP ( Bloomington #6659 ) , UAS-mCD8-mRFP ( Bloomington #27398 ) , 10×UAS-mCD8-GFP ( Bloomington #32184 ) , UAS-LacZRNAi ( a gift from M . Jünger ) , UAS-shibirets ( a gift from A . Thum ) , and UAS-TRiP . JF03122 ( Bloomington#28705 ) . Stable homozygous lines of tubulin-Gal80ts and UAS-NaChBac ( a gift from R . Jackson ( Tufts University ) ) and of tubulin-Gal80ts ( Bloomington #7108 ) and UAS-eYFP ( Bloomington #6660 ) as control for NaChBac experiments were used . For control experiments OregonR ( wild type ) or w1118 was used . Adult flies and larvae were reared on standard fly food and kept at 25°C unless otherwise stated . All experiments were performed with third instar larvae 98±2 h AEL ( after egg laying ) . Four hours egg collections were made on apple juice-agar plates with yeast-water paste . After 48 h , larvae were transferred into vials containing standard fly food . For experiments with shibirets larvae were raised at 18°C to avoid temperature-induced developmental defects [56] . Experiments were performed with third instar larvae 8 days old . In the experiments using tubulin-Gal80ts and UAS-NaChBac/UAS-eYFP larvae were raised on 18°C for 7 days and were transferred on 30°C for 8–12 h prior to the experiment to induce the expression of the NaChBac/eYFP . For Hug0 . 8-Gal4 line , a 793 bp hugin promoter fragment was amplified by primer1: CATTGACATTGCCCCCATT and primer2: GGGACAACTGATGCCAGC , subcloned into TOPO TA pCRII vector ( Invitrogen ) , digested with BamHI and NotI and ligated into the pCasperAUG-Gal4-X vector ( Addgene plasmid 8378 , [86] ) . The construct was injected into w[1118] ( Bloomington#3605 ) . For HugVNC-Gal4 , a 403 bp hugin promoter fragment was amplified by primer1: ATCGCAGTGCTCACAATCTG and primer2: GTGGGGCATCCTGTTTAATG from wild type DNA and subcloned into TOPO TA pCRII vector . A BamHI/NotI digestion product was ligated into pENTR4 Gateway Entry vector ( Invitrogen ) and cloned into the destination vector pBPGUw ( Addgene plasmid 17575 ) , [87] , by using LR Clonase II enzyme mix ( Invitrogen ) . Transgenic lines were generated using standard methods for PhiC31 integrase-mediated genomic integration into y , w; P{CaryP}attP2 ( BestGene Inc , USA ) . Reduced semi-intact preparations were made of third instar larva consisting of the CNS , CPS , and associated pharyngeal nerves and muscles . Detailed description of the dissection has been described earlier [45] . All dissections and experiments were performed in saline solution composed of ( in mM ) : 140 NaCl , 3 KCl , 2 CaCl2 , 4 MgCl2 , 10 sucrose , and 5 HEPES [88] . For en passant extracellular recording , the nerve was insulated with a surrounding petroleum jelly border on a piece of Parafilm . Recording electrodes were made of silver wire ( diameter: 25–125 µm , Goodfellow ) . Motor output was measured by differential recordings of the deafferented nerve with a preamplifier connected to a four-channel amplifier/signal conditioner ( Model MA 102/103; Ansgar Büschges group electronics lab ) . All recorded signals were amplified ( amplification factor: 5000 ) and filtered ( bandpass: 0 . 1–3 kHz ) . The recordings were sampled at 20 kHz . Data was acquired with Micro3 1401 or Power 1401 mk2 A/D board ( Cambridge Electronic Design ) and Spike2 software ( Cambridge Electronic Design ) . For intracellular muscle recordings , semi-intact CDM/M6 preparations of third instar larvae were used . PSPs of the muscle M6 of 4th abdominal segment and CDM were recorded using glass microelectrodes filled with 3 M KCl solution ( tip resistance: 20–30 MΩ ) connected to an intracellular amplifier ( BRAMP-01R , npi electronic GmbH ) . All recordings were digitally sampled by a Micro3 1401 or Power 1401 mk2 A/D board ( Cambridge Electronic Design ) at 20 kHz . Data was acquired with Spike2 software ( Cambridge Electronic Design ) . For analysis , data pairs of successive 60 s or 120 s recording-sections under unstimulated and stimulated conditions were analyzed . Processing of the electrophysiological recordings was performed with a modified script of Spike2 ( provided by Cambrigde Electronic Design ) . For a pair of successive recording-sections , fold change in cycle frequency was calculated . The dTrpA1-experiments revealed an endogenous temperature effect which could mask the impact of dTrpA1-activated GAL4-driver lines on the rhythmic motor output . Due to this , the mean fold change in cycle frequency of the respective control experiments was subtracted for each data point , termed relative change in cycle frequency . For dTrpA1-experiments ( nerve/muscle recording and CDM tracking ) thermal stimuli were applied to the dorsal side of CNS . The custom-made stimulator consisted of a silver wire ( diameter: 4 mm ) attached to a Peltier element with thermally conductive adhesive . Peltier element was driven by a voltage-regulated power supply ( VSP 2405 , Voltcraft ) connected to an A/D board . The end of the thermal stimulator was filed to a tip and insulated with nail polish . Applied temperature was measured by digital thermometer ( GMH 3210 , Greisinger electronic ) . The sensor for the thermometer was placed 5 mm from the tip ( for temperature calibration see Figure S1 ) . Temperature signals were acquired with the A/D board . The thermal stimulator was regulated by a script-based feedback loop via the A/D-board . For measurement of yeast ingestion , larvae were first washed and then starved in a Petri dish lined with tap water-moistened tissue for 30 min on RT . Afterwards they were transferred on colored yeast ( colored with crimson red powder ) on pre-warmed ( 30 min at 32°C ) apple juice-agar plates and incubated for 20 min at 32°C . Afterwards the larvae were removed from the yeast and placed in 65°C hot water . For analysis larvae were photographed using a digital camera ( Axiocam ICc 1 , Zeiss ) mounted on a binocular ( Stemi 2000-CS , Zeiss ) . For each individual , the amount of yeast ingested was calculated as area of the alimentary tract stained by colored yeast divided by body surface area using the software ImageJ ( Fiji ) . Data on the feeding assay is represented as percentage of ingested yeast relative to the body surface . For simultaneous investigation of feeding and wandering-like behavior , five larvae were placed on a pre-heated/-cooled apple juice agar plate ( 18°C or 32°C ) . 20 min videos at 18°C and 32°C were acquired using a digital camera ( Quickcam 9000 Pro , Logitech ) and the software VirtualDub . The measurement of yeast ingestion was performed as listed in the previous paragraph . The locomotion data was analyzed using the tracking software MTrack2 ( Fiji ) . Analysis of larvae leaving the yeast spot was carried out using a custom-made macro for ImageJ ( Fiji ) . CDM contractions were studied in semi-intact larvae . The preparation consisted of the CNS , the abdominal body wall , and the feeding apparatus ( CPS including associated muscles ) . Thermal stimulation was applied directly to the CNS . Consecutive videos of 60 s at 18°C and 60 s at 32°C were recorded using a digital camera ( Axiocam ICc 1 , Zeiss ) mounted on a binocular ( Stemi 2000-CS , Zeiss ) . CDM contractions were tracked by measuring the length-difference of pharyngeal lumen ( Δd ) over time relative to the maximal contractions at 18°C . The measurements were performed using the software ImageJ ( Fiji ) . Dissected larval brains were fixed in paraformaldehyde ( 4% ) . For the antibody staining of hug-eYFP , primary antibody was rabbit-antiGFP ( 1∶500 , Abcam plc ) and the secondary antibody was rabbit-antiGFP Cy2 ( 1∶200 , Dianova GmbH ) . The antibody staining of HugVNC>Cam2 . 1 was performed with chicken anti-GFP ( 1∶500 , Abcam plc ) and as secondary antibody anti-chicken Alexa488 ( 1∶200 , Invitrogen ) was used . Antibody staining of hugin was performed with guinea pig anti-Hugin ( 1∶200 , Pankratz laboratory; for hug0 . 8>rpr/hid ) or rabbit anti-Hugin ( 1∶500 , Pankratz laboratory; hug0 . 8>eYFP ) . Antibody stainings for RNAi experiments were done using rabbit anti-Hugin ( 1∶500 ) . Secondary antibodies were: anti-rabbit Cy3 , anti-guinea pig Cy3 ( 1∶200 , Jackson ImmunoResearch ) , and mouse anti-GFP ( 1∶500 , Sigma-Aldrich ) . Nuclei were counter stained with DAPI or Draq5 . Labeled larval brains were mounted in Mowiol . Imaging was carried out using Laser Scanning Microscope ( ZEISS LSM780 ) . The obtained images were arranged using Zen LE and Photoshop CS5 ( Adobe ) ( for detailed staining procedures see [64] ) . All images were obtained by using a confocal microscope Zeiss LSM 780; non-specific background fluorescence of the in vivo images was reduced by the Median Filter of the Zeiss Zen Software . Hugin cDNA PCR fragment flanked by a BamHI and a KpnI restriction sites was cloned into pHIBS vector [89] ( primer sequences GGATCCGTTCCATTCGATCGTCCGAC and GGTACCGTGGCACTGGCCTTCTGG ) . The 394 bp hugin fragment represents bases 41 to 434 of 1033 bp hugin full length cDNA ( flybase . org ) . A 478 bp SalI/KpnI fragment of hugin-HIBS was then cloned into XhoI/KpnI cut pUdsGFP [89] . Next a 407 bp BamHI/EcoRI fragment of hugin-HIBS was cloned into the EcoRI/BglII cut hugin-pUdsGFP . The pUdsGFP plasmid harboring two hugin fragments in opposite orientation was used for standard germline transformation [90] . The line used in the text is referred to as HugRNAi1A . For the lesion experiments we used the standard reduced semi-intact preparations of third instar larvae as mentioned above ( see Electrophysiology ) . VNC or brain hemispheres were removed by a microdissecting scissor ( Fine Science Tools ) . Five minutes after the lesion of the neuronal tissue , extracellular recording of antennal nerve was started . Thermal stimuli were applied by the above described protocol for temperature stimulation . Consecutive 60 s sections of the AN motor output at 18°C and 32°C were analyzed . The cycle frequency of AN motor pattern at 18°C showed no significant difference between OrgR×dTrpA1 and HugS3>dTrpA1 for each lesion . Therefore the data is presented as fold change in cycle frequency of AN motor pattern between both genotypes at 32°C ( during dTrpA1 activation ) for each experiment . All electrophysiological and behavioral experiments were tested for significance with the Mann-Whitney-Rank-Sum-test ( *p≤0 . 05 , **p≤0 . 01 , ***p≤0 . 001 ) . | In the animal kingdom , two of the most essential behaviors are locomotion and feeding . The motor programs underlying these behaviors are controlled by higher-order circuits in the central nervous system . However , how an organism selects a particular motor program based on inputs from the information-processing higher brain centers to generate an adaptable behavior is not well understood . Here , we analyze the behavior of Drosophila larvae after activating a small cluster of neurons in the brain and show that the animals simultaneously stop eating and start moving . These neurons express the neuropeptide hugin , which is homologous to the mammalian neuromedins . We show that the reduction of food intake depends on hugin and that the cluster of hugin neurons is functionally divided into distinct subgroups that both accelerate the motor program for locomotion and decelerate the motor program for feeding . We propose that hugin neurons represent a control system between the higher brain circuits that process information and those that execute motor programs . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience",
"biology",
"and",
"life",
"sciences"
] | 2014 | Selection of Motor Programs for Suppressing Food Intake and Inducing Locomotion in the Drosophila Brain |
The reproductive parasites Wolbachia are the most common endosymbionts on earth , present in a plethora of arthropod species . They have been introduced into mosquitos to successfully prevent the spread of vector-borne diseases , yet the strategies of host cell subversion underlying their obligate intracellular lifestyle remain to be explored in depth in order to gain insights into the mechanisms of pathogen-blocking . Like some other intracellular bacteria , Wolbachia reside in a host-derived vacuole in order to replicate and escape the immune surveillance . Using here the pathogen-blocking Wolbachia strain from Drosophila melanogaster , introduced into two different Drosophila cell lines , we show that Wolbachia subvert the endoplasmic reticulum to acquire their vacuolar membrane and colonize the host cell at high density . Wolbachia redistribute the endoplasmic reticulum , and time lapse experiments reveal tight coupled dynamics suggesting important signalling events or nutrient uptake . Wolbachia infection however does not affect the tubular or cisternal morphologies . A fraction of endoplasmic reticulum becomes clustered , allowing the endosymbionts to reside in between the endoplasmic reticulum and the Golgi apparatus , possibly modulating the traffic between these two organelles . Gene expression analyses and immunostaining studies suggest that Wolbachia achieve persistent infections at very high titers without triggering endoplasmic reticulum stress or enhanced ERAD-driven proteolysis , suggesting that amino acid salvage is achieved through modulation of other signalling pathways .
The alpha-proteobacteria Wolbachia -Wb- are the most common endosymbionts encountered in nature , present in a plethora of terrestrial arthropod hosts , and in filarial nematode species . These reproductive parasites have developed a wide range of symbiotic interactions , from facultative to mutualistic [1] . In all instances , they are vertically transmitted through the female germline but also colonize the soma [2] . The tissues that are infected can differ from one host species to another , as well as the Wolbachia intracellular titer . Although the highest titers are often observed in the germline , they vary considerably among wild isolates of specimens within a single species [3] . While Wolbachia intrinsic factors can be responsible for targeting specific cell types acting as reservoirs , i . e . the somatic stem cell niche in the Drosophila ovary [4] , they can also influence the degree of intracellular replication . Such is the case for the pathogenic Wolbachia strain wMelPop , that possesses a region of eight genes called octomom , whose degree of amplification dictates the bacterial titer and the virulence [5] . Conversely , the host genetic background also exerts a profound influence on the bacterial ability to replicate . When the wMel strain naturally hosted in the fruit fly Drosophila melanogaster is transferred into the closely related Drosophila simulans species , mature oocytes appear dramatically more infected [6] . Therefore , depending on the permissivity of the genetic background , different cell types can harbor a wide range of endosymbiontic titers . As a consequence , the impact of a given Wolbachia strain on the cellular homeostasis , and the degree of subversion exerted on organelles to satisfy their obligate intracellular lifestyle can potentially induce variable phenotypes , i . e . in terms of nutrient demand , stress or cell innate immune responses . These past years have seen a resurgence of interests in Wolbachia because they can be a drug target to fight parasitic filarial diseases [7] , and because of their ability to compromise transmission of vector-borne arboviruses [8] . In the latter case , the wMel strain has been favored and introduced into mosquito vectors because it does not induce a fitness cost [9 , 10] , allowing a spread through wild populations of mosquitos . Although the mechanisms by which Wolbachia block the pathogen transmission are not fully understood , a clearer picture starts to emerge . However among recent studies , somewhat contradictory results have been reported , reflecting a variety of phenotypes under environmental influence ( for a review see [11] ) . Typically , the role of Wolbachia-induced innate immunity priming in pathogen interference is still an object of debate , although viral replication inhibition can be achieved by wMel without inducing an upregulated expression of anti-microbial peptide genes [12 , 13] . Wolbachia depend on host nutrients such as amino acids and lipids [14 , 15] , but they potentially provision their hosts to act in some instances as nutritional symbionts . Hence , the cost and benefit associated with a Wolbachia infection are certainly variable . Nonetheless their intracellular lifestyle involves a competition with viruses for subverting the same limited resources . Cholesterol and lipid homeostasis are modulated in the presence of Wolbachia [16] and account for their pathogen-blocking effect , limiting the viral access to these metabolites essential to their replication [17 , 18] . If a persistent infection with Wolbachia endosymbionts exerts a cellular stress , it should not affect the host viability . An Endoplasmic Reticulum -ER- stress response has been described to be associated with Wolbachia [18 , 19] . The ER is involved in lipid metabolism , protein synthesis and their proper folding as well as post-translational modifications , and is the source of vesicular trafficking with the Golgi apparatus [20] . Because of its central role in the host cell metabolism , the ER is often subverted by viruses and intracellular bacteria [21 , 22] . When the cell homeostasis is perturbed to the point that misfolded proteins accumulate in the ER , an Unfolded Protein Response -UPR- is triggered . In order to restore homeostasis , the ER protein folding capacity is increased through chaperone release in the ER lumen and upregulation of chaperone and UPR sensor genes; translation is reduced; and an ER-associated degradation ERAD- pathway is upregulated . If the stress is prolonged , cell dysfunctions occur and cell death is eventually induced [23] . Accordingly , some intracellular bacteria have learned to subvert and control the UPR to avoid such fate [22] . It is therefore intriguing that an ER stress has been reported or suggested by some studies and up to date invoked as a consequence of a Wolbachia infection . More specifically , proteomic studies suggest a mild upregulation of some UPR related genes , although it should be noted that they were carried out with the life-shortening pathogenic strain wMelPop [18] . A recent study using RNAi screening in Drosophila cells coupled to electron microscopy observations , highlights the requirement of an ERAD ubiquitin ligase to maintain a normal Wolbachia titer , and reports a close subcellular vicinity between Wolbachia and a morphologically aberrant ER [19] . This study suggests that an ERAD-derived proteolysis is induced by Wolbachia to salvage amino acids . In the present study , we seek to clarify the link between Wolbachia and the ER by exploring the physical relationship between the endosymbiont intracellular population and this organelle at the cellular level as well as the functional consequences of a Wolbachia infection on the ER . To avoid cell line-specific phenotypes and to take in account the impact of the host genetic background , two cell lines showing different gene expression profiles have been infected with the same wMel strain . Specifically , live studies and observation of fixed cells reveal a complex and dynamic interaction between wMel and the ER . This organelle , and not the Golgi apparatus as previously suggested , appears to be the source of the endosymbiont vacuolar membrane . Wolbachia redistribute the ER without triggering pathological morphologies . In addition , gene expression analyses indicate that UPR and ERAD key players are not upregulated upon Wolbachia infection , and immunostaining studies of ubiquitin chains with degradative roles confirm that ERAD-derived proteasomal degradation is not increased , suggesting that Wolbachia do not induce ER stress and proceed through subversion of other host pathways to salvage amino acids .
In order to gain insights into the general mechanisms of host cell subversion operated by the Wolbachia strain wMel in its natural host D . melanogaster to sustain its intracellular lifestyle , and to minimize cell line-specific phenotypes , we established new wMel infections in two D . melanogaster cell lines described to display distinct gene expression profiles [24] . The two selected cell lines are adherent , facilitating cellular analyses on live and fixed samples . While both cell lines express about 6 , 000 genes , nearly half of them show considerable expression variations between cell lines . 1182-4H is an acentriolar haploid cell line derived from maternal haploid mh 1182 mutant embryos [25 , 26] . S2R+ are tetraploid male cells derived from the original Schneider’s cell line [27 , 28] . We chose to introduce in these two different genetic backgrounds a wMel strain derived from JW18 , very closely related to the wMel genome of reference [28] . The infected JW18 cell line has been commonly used in numerous studies as a reference cell line to explore the Wb-host interactions and the Wb- induced viral protection at the cellular and molecular levels [19 , 28–31] . To infect naive cell lines , wMel bacteria were purified from JW18 cell cultures and added to flasks of uninfected 1182–4 and S2R+ cells ( See Methods ) . JW18 cells harbor fluorescent GFP-Jupiter decorated microtubules . This helped us to confirm the exclusion of cell contaminant during the infection process . After one month , we found the infection to be partial in both cell lines , and an infection dynamics time course experiment confirmed the slow progress of the infection ( S1A Fig ) . Another round of infection was then repeated , leading to stably infected cell lines as determined by immunofluorescence with an anti-Wolbachia surface protein -WSP- antibody ( See Methods and Fig 1A to 1C' ) , named hereafter 1182–4 Wb and S2R+ Wb . The vast majority of cells is infected in 1182–4 Wb , and the infection is total in S2R+ Wb . The Wb titer is also much higher in S2R+ Wb compared to 1182–4 Wb , reaching several hundreds of endosymbionts per individual cells ( Fig 1B' and 1C'; S1 and S2 Movies ) . These high Wb titers do not significantly affect the host cell viability ( Sup1B Fig ) . We used the WSP-associated fluorescence area , expressed as a percentage of the total cell surface , acquired from full confocal image projections as a proxy to quantify the Wb titer in both cell lines ( Fig 1D ) . We concluded that the S2R+ genetic background is more permissive to the wMel infection . Using a moderate and variable Wb titer in 1182–4 Wb on the one hand , and a remarkably high Wb titer in S2R+ Wb on the other hand , we sought to describe the influence of the Wb endosymbionts on the host cell physiology , taking into account the Wb level . The subcellular distribution of organelles is tightly linked to their function [32] , and can be affected together with their morphology , by intracellular pathogens [33] . The Wb reside in a vacuole made of a host-derived membrane . Previously Wb and the Golgi cisternae were described to reside in the same subcellular compartment close to centrioles in the Drosophila embryo , therefore the Golgi apparatus has been proposed to be the source of the Wb-containing vacuole [34] . Moreover the Golgi apparatus can be subverted and fragmented by intracellular pathogens such as Chlamydia , that are surrounded by Golgi ministacks to facilitate lipid acquisition [35] . We reasoned that the amount , the localization and the morphology of the organelle providing membranes to the Wb-containing vacuoles may be potentially affected in a Wb titer-dependent manner . To investigate the relationship between Wb and the Golgi apparatus , S2R+ and acentriolar 1182–4 cells were both co-stained with an anti- Wb surface protein—anti-WSP- and a cis-Golgi marker -GM130- , in presence and absence of endosymbionts ( Fig 2A ) . When the Wb do not fill the entire cytoplasm , i . e . in 1182–4 Wb cells , a thorough visual inspection did not allow us to draw a correlation of subcellular localization between the endosymbionts and the Golgi apparatus . In addition , the number and size of GM130-positive foci did not appear influenced by the abundance of Wb endosymbionts in either infected cell lines ( i . e . Fig 2A dashed lines for cells with either high or low Wb levels , and B ) . Unlike in a previous report establishing the Golgi apparatus as a source of vacuolar membrane , we never observed GM130-positive Wb vacuoles [34] . We next checked the morphology of the Golgi apparatus in presence of Wb by ultrastructural studies ( Fig 2C ) . The Golgi cisternae appeared properly arranged , and we could not detect any morphologies that would differ from non-infected cells , despite heavy loads of endosymbionts in the S2R+ Wb cell line . Together , this data set suggests that the Golgi apparatus does not appear to be subverted by Wolbachia at the subcellular level , and does not support the hypothesis of this organelle being a source of membrane for the endosymbionts . A previous study based on electronic microscopy has reported observations of Wb in close contact with ER tubules , and in some instances a continuum between the ER and the Wb vacuolar membrane [19] . To better understand how and to what extent the Wb intracellular population interact globally with the ER , we performed simultaneous live observations of the endosymbionts and of this organelle . To this end , we used the SYTO 11 DNA live dye that stains preferentially Wb [36] , and an ER tracker , that recognizes the sulfonylurea receptors of ATP-sensitive K+ channels located on ER membranes . We first performed confocal time lapse fluorescence imaging of 1182–4 Wb cells . Cortical areas enriched in tubular ER were chosen for time lapse analyses because they offer a better resolution of these dynamic structures ( Fig 3A ) . We typically observed three categories of Wb . Some peripheral Wb clusters did not show any obvious interactions with the ER ( Fig 3A grey arrows ) , some were juxtaposed to the ER and displayed tightly coupled dynamics ( Fig 3A orange arrow ) , while few Wb appeared to be localized within dynamic ER tubules ( Fig 3A yellow arrowhead , and see S3 Movie that recapitulates these observations ) . We next used the same fluorescent markers in 1182–4 Wb and S2R+ Wb cells to score the different types of interaction between Wb and the ER ( Fig 3B ) . Striking differences appeared in these two different cellular environments . While in random focal planes 62% of Wb did not reside in close ER vicinity in 1182–4 Wb cells , only 2% were distant from the ER in S2R+ Wb cells . Hence a majority -80%- of endosymbionts were in close contact with the ER in S2R+ Wb , while only 34% contacted the ER in the 1182–4 genetic background . Interestingly 17% in S2R+ Wb and 9% in 1182–4 Wb appeared either inside the ER and/or surrounded by an ER tracker-positive membrane ( Fig 3C ) . Together this dataset shows that the physical interaction of Wb with the ER is highly dynamic . The presence of ER tracker around some endosymbionts strongly suggests that this organelle is a source of vacuolar membrane . Some Wb were detected in ER tubules , and only a minority of endosymbionts display an ER tracker-positive vacuolar membrane , leading us to hypothesize that they may represent newly acquired membranes , whose composition is subsequently modified by Wb ( i . e . less or no ATP-sensitive K+ channels leading to ER tracker-negative Wb vacuoles ) . In addition , time lapse recordings showing Wb-ER coupled dynamics reveal a tight physical interaction between the Wb vacuole and this organelle , suggesting potential signaling events and/or possible nutrient uptake . The increased association of Wb with the ER in a S2R+ genetic background , highly permissive to the Wb infection , suggests that the ability to subvert the ER is crucial for Wb to thrive intracellularly . Because the ER-Wb contacts are prominent in S2R+ , we first examined the ER by confocal microscopy to assess the impact of Wb on its distribution . In non-infected cells , the ER appears principally composed of a dense perinuclear network of tubules and vesicles , while cisternae are less detectable . The cell periphery and cortical areas are enriched with ER tubules , which are often branched ( Fig 4A ) . In contrast , in infected cells a fraction of the ER becomes heavily clustered close to the nucleus ( Fig 4A cyan arrows ) , while cytoplasmic regions harboring Wb are highly enriched in tubular ER ( Fig 4A yellow arrowheads and bottom row ) . We defined this mass of ER as "ER clusters" , which is greatly enhanced by the presence of Wb in both cell lines ( Fig 4B ) . We wondered whether this ER distribution was a consequence of an ER stress , and S2R+ were treated with tunicamycin , an ER stress inducer , which did not increase the occurrence of this phenotype compared to untreated S2R+ cells ( Fig 4B ) . ER morphological aberrations that may not be detectable by confocal fluorescence microscopy have been reported in Wb-infected cells such as ER tubule swelling and an increase in cisternae [19] , leading us to perform EM ultrastructural studies on S2R+ Wb and 1182–4 Wb cells , and on their naive counterparts ( Fig 4C and 4D ) . The dark ER mass is easily distinguishable in infected cells -thick red arrows- . A closer look at this cluster reveals it is composed of randomly—thin green arrows- and orderly -thin red arrow- packed tubules or cisternae . No swollen structure was detected within these clusters in either cell types . In the periphery , multiple Wb share very often a same vacuole , tightly apposed to rough ER -cyan arrows , and bottom image- . Incidentally , these multi-Wb vacuoles were encountered much more frequently in the highly permissive S2R+ genetic background compared to 1182–4 . We then searched for a size increase of cisternae and swollen ER tubules without success in S2R+ Wb . Measurements of ER inter membrane distances by electron microscopy however revealed very marginal ER swelling in 1182–4 Wb , not affecting the average thickness of ER in this cell line ( Fig 4D ) . Last , because of the dramatic ER redistribution observed in S2R+ Wb occurring in more than half of these infected cells , we investigated at the individual cell level the impact of this ER defect on the Golgi apparatus distribution ( Fig 4E ) . In non-infected cells , the Golgi foci are surrounded throughout the cell periphery by large amounts of the ER ( Fig 4E left upper and lower panels , yellow arrowheads point to Golgi foci ) . In Wb-infecting cells showing ER clusters , the Golgi units do not coalesce toward the ER mass ( Fig 4E right panel top images ) , and their distribution does not appear significantly perturbed . Although they remain associated with some ER ( Fig 4E yellow arrowheads on bottom images ) , the overall distance between most of the ER and the Golgi apparatus is increased . In conclusion , Wolbachia dramatically redistribute the ER without affecting its luminal width , since we did not observe any ultrastructural variations in presence of the endosymbionts . The high titer in S2R+ Wb correlates with a tight association of Wb with the ER , and in general a large fraction of this organelle becomes spatially restricted , close to the nucleus , upon a Wb infection . This defect could potentially affect its function and interactions with other organelles such as the Golgi apparatus . Attempts to phenocopy the ER compaction with tunicamycin did not succeed , suggesting that this redistribution may be operated by Wb independently of a potential ER stress . We next sought to examine the impact of a Wb infection on the ER functions . To ensure protein homeostasis in the cell , one of the role of the ER is to control the proper folding and maturation of proteins through the unfolded protein response -UPR- , upregulated when misfolded proteins accumulate . When these adaptive responses are not sufficient , the endoplasmic-reticulum-associated protein degradation -ERAD- pathway is in turn activated to target and retrotranslocate ER misfolded proteins to the cytosol , where they are addressed towards a degradation pathway by the ubiquitin-proteasome machinery [37] . We first checked whether the ERAD function was subverted in order to provision Wb with amino acids derived from an increased proteolytic activity , as previously suggested in Wb-infected JW18 cells [19] . We first stained cells with the FK2 antibody recognizing all mono- and polyubiquitylated proteins , but not the free ubiquitin , considered as a good proxy to assess proteasomal degradation-associated polyubiquitylation marks—K48 and K11 poly-Ub— , since these degradation marks are the most abundant among polyubiquitylated chains in the cell [38] ( See Methods and Fig 5A ) . We quantified the total fluorescence surface associated with the polyubiquitylation foci on full confocal projections , and we found the presence of Wb to correlate with 2 . 5 and 4 . 2 times as many polyubiquitylation in 1182–4 and S2R+ genetic backgrounds , respectively ( Fig 5A and 5B ) . We reasoned that a proteasomal degradation-linked poly-Ub signal , reflecting a Wb-dependent amino acid demand , should vary according to the endosymbiont titer , that is variable between cells in a given infected cell line . We chose the 1182–4 Wb cell line showing fewer heavily infected cells to perform a linear regression highlighting the amount of FK2 foci in function of an increasing Wb titer ( Fig 5C ) . We found no correlation between the Wb titer and the number of FK2 foci . This suggests that the observed FK2 signal is unlikely to account for an increased proteasomal degradation . To verify this result , we next checked specifically the levels of K11 poly-Ub chains by immunostaining analyses . K11 is the ubiquitin linkage primarily generated by the ERAD pathway [39] . We failed to detect any differences between infected and non-infected cells ( Fig 5D ) . In the fraction of S2R+ Wb cells endowed with high Wb levels , the ER becomes clustered in an area from which the endosymbionts are excluded . Focusing our attention on these areas to detect a possible enrichment of ER-associated K11 poly-Ub , we did not detect an increase of this ERAD-associated degradation mark ( Fig 5D , dashed yellow circle ) . Both K11- and K48- linked poly-Ub chains are involved in ERAD [40] , therefore we checked the levels of K48 poly-Ub , that also appeared indistinguishable in infected cells compared to their non-infected counterparts ( S2 Fig ) . Together these results indicate that the global increase of cellular polyubiquitylation in presence of Wolbachia does not reflect an increase in proteasomal degradation- associated K11/K48 polyubiquitylation marks . We decided to perform quantitative PCR analyses to investigate the UPR and ERAD responses at the gene expression level in the presence of Wb , in order to characterize the level of ER stress potentially generated by the endosymbionts . Briefly , upon a stress leading to accumulation of misfolded proteins , the ER transmembrane stress sensors PERK , ATF6 , and IRE1 release the chaperone Bip in the ER lumen , and an UPR response is activated . This response aims at decreasing protein translation and enhancing the protein folding capacity in the ER , by upregulating the expression of chaperones and UPR sensors ( Fig 6A and [41] ) , while the ERAD pathway drives misfolded protein to undergo proteolysis . We first selected D . melanogaster genes confirmed to respond to tunicamycin-induced ER stress , and that are involved in both UPR and ERAD responses [42] . We next monitored these candidate genes in the 1182–4 genetic background by submitting the cell line to a tunicamycin treatment for 48 hours at 10 μg/mL ( Fig 6B ) . We found a ~2 fold gene expression upregulation for the three UPR sensors perk/gcn2 , atf6 and ire1 ( Fig 6A and 6B top graphs ) . In addition , a number of ERAD key players , the derlin orthologs der-1 and der-2 , sel1L/hrd3 and hrd1/sip3 whose products associate to form a complex , as well as members of the ubiquitin ligase complex were upregulated from 3 to more than 5 folds . With this experiment validating the 1182–4 cell line responsiveness to ER stress , we next measured the impact of Wb on this stress in the 1182–4 Wb ( Fig 6B bottom graphs ) . We did not detect any induction of the UPR sensors or downstream targets . Similarly , none of the ERAD key players that responded to tunicamycin were affected by the presence of Wb . This shows that Wolbachia do not trigger an ER stress response leading to increased UPR and ERAD activities in 1182–4 Wb cells . Last , we verified the level of ER stress in S2R+ Wb cells using a fluorescent ATF-4 activity reporter gene -the translational inhibitor 4E-BP- that responds to the PERK/GCN2- ATF4 pathway through ATF4 binding sites [43] . The fluorescence was monitored 48 hours after transfection with the 4E-BP intron dsRed reporter , and a tunicamycin treatment was added as a positive control of ER stress ( Fig 6C , and Methods ) . Transfected cells showed in presence of tunicamycin high nuclear and cytoplasmic fluorescence levels . Quantification of the fluorescence revealed a level of ATF4 signaling activity upon ER stress 4 times higher on average compared to non-treated S2R+ cells . The fluorescence levels expressed in S2R+ Wb cells appeared similar to what observed in S2R+ cells , suggesting that the presence of Wb do not cause a significant stress in the S2R+ genetic background . Altogether , this data set suggests that in these two host cell genetic backgrounds , the Wolbachia can proliferate and persist in a stable manner without triggering ER stress and in particular the ERAD pathway , implying that other mechanisms than ERAD-induced proteolysis should exist to salvage amino acids .
A number of studies these past years have started to investigate the basis of the Wb intracellular lifestyle and their impact on the cell homeostasis using in vitro cell culture models ( i . e . [13 , 16 , 18 , 19 , 44 , 45] ) . The results of these studies can be variable depending on the Wb strain and the infected insect cell lines . In order to minimize the bias of a cellular context potentially leading to cell line-specific phenotypes , we infected two genetic backgrounds presenting an important variation at the level of the expressed genes [24] . Additionally , the two cell lines were infected with a single wMel strain , that derives naturally from D . melanogaster . Here we identified the endoplasmic reticulum as a source of vacuolar membranes for Wolbachia in D . melanogaster species , and we observed close appositions between the replicative vacuole of these endosymbionts and the ER membrane . These appositions are likely to lead to the biogenesis of ER-derived Wb vacuoles , while sometimes allowing fusion with this organelle . Coupled dynamics between Wb and the ER tubules seen in time lapse microscopy reveals tight and prolonged interactions , supporting as well the possibility of nutrient uptake from the ER . The cellular context greatly influences the Wb titer , and a permissive environment correlates with more apposition events with the ER , suggesting that the ability of Wb to subvert the ER in a given environment correlates with growth and replication . A Wb infection redistributes the ER , and while a tubular network associates with the endosymbionts , a significant fraction of this organelle shrinks to become compacted close the cell nucleus . Although the functional impact of this ER clustering remains unclear , the ultrastructural ER organization does not reveal swollen compartments or more cisternae . Gene expression analyses of central ER stress players , as well as immunofluorescent studies of ERAD-induced proteolysis key marks indicate that the Wb-induced ER subversion does not trigger the UPR nor an increased proteolysis . Hence the Wb level , whether low or high , does not seem to perturb the ER-regulated mechanisms of cell homeostasis in a significant manner . Incidentally , these results indicate that Wb is likely to rely on other sources than ERAD-induced proteolysis to salvage amino acids . The Wolbachia endosymbionts are transmitted vertically in their arthropod or filarial nematode hosts , from mothers to their offspring . Once in the egg they next colonize specific somatic tissues and the germline during embryonic and larval developmental stages , following asymmetric segregation during cell mitotic divisions [2] . Although a germline tropism has been described , implying that Wb can pass from cell to cell either artificially in Drosophila through abdominal injections of purified Wb , or through a developmentally regulated colonization of the filarial nematode ovary [46 , 47] , they do not share with most intracellular pathogens the ability to easily infect naïve cells , thus limiting their horizontal transfers . It has been demonstrated that Wb can pass from infected to non-infected cell in in vitro assays , without requiring cell-to-cell contact , possibly through secretion [30] . If active mechanisms of cell entry are not precisely described , passive uptake mechanisms through phagocytosis explain at least in part their entry in cell culture assays . To optimize the infection of naive cell lines , we set up a protocol of Wb enrichment from a Wb-infected cell culture . This allowed us to expose cells to very high bacterial concentrations . Although D . melanogaster cell cultures have a strong capacity of engulfment–which does not make them an ideal model to study mechanisms of bacterial cell entry- , artificial infections of naïve cell culture with Wolbachia remain nonetheless a slow process . The fact that a significant proportion of cells remained uninfected after one month suggests indeed that extracellular Wb originating from possible secretion or dead cells do not have strong infection capacities and that colonization of a naïve environment remains a challenge . This is in part due to their slow replication cycle estimated to last 14 hours [48] , but it is also very likely that some Wb do not succeed in escaping autophagy . Those nonetheless succeeding at surviving and replicating not only need to modify the phagosome membrane along the endocytic pathway to avoid the cell surveillance , but also need to acquire new membranes and nutrients . The ER represents a nutrient-rich compartment devoid of antimicrobial functions , and several intracellular bacteria derive their vacuole from , and/or replicate in , this organelle [49] . Such is the case of Legionella pneumophila and Brucella abortus that possess like Wb a type IV secretion system they employ upon infection to secrete an array of effectors subverting cellular machineries to gain access to ER . L . pneumophila regulate membrane trafficking through modulations of GTPase signalling pathways interfering with early secretory vesicles to ultimately allow fusion of the Legionella vacuole with ER-derived membranes [50] . Along the endocytic pathway , B . abortus co-opt the ER exit sites–ERES- , involved in the vesicular trafficking towards the Golgi , thus acquiring an ER-derived vacuolar membrane [51] . Similar to observations of these pathogens , our ultrastructural studies have revealed a tight association of Wb with rough ER membranes . In addition , live experiments have demonstrated that some Wb-containing vacuoles appear positive for a fluorescent and specific ER tracker , and in some instances Wb were located within ER tubules , strongly suggesting that the ER is a source of membrane for Wb . We hypothesize that the presence of ER tracker-negative Wb-containing vacuoles indicates a maturation process in the biogenesis of the membrane surrounding Wb , although we cannot rule out other sources of membranes . In both 1182–4 Wb and S2R+ Wb cell lines we observed a compaction of ER . It is established that the Wb-containing vacuoles move along microtubules , using host motors such as Kinesins and Dynein [52 , 53] . Intracellular motor-based transport of organelles such as the ER is important to regulate their distribution and morphology [54] . A high Wolbachia load may reduce the interactions between the cytoskeleton and some organelles through titration of key microtubule molecular motors . As a consequence of the ER clustering , Wb reside in between ER and the Golgi apparatus , which could potentially favor Wb interactions with the ERES . Wb could benefit from co-opting the COPII vesicles routing towards the Golgi to acquire membranes , lipids and other nutrients . This is in accordance with the discovery that in presence of the pathogenic strain wMelPop , cholesterol homeostasis is affected [18] . Not only Wb likely incorporate cholesterol into their membranes as a substitute for lipopolysaccharide , but also proper ER-to-Golgi vesicular trafficking requires cholesterol [55] . Hence Wb may interfere with the anterograde trafficking . In addition , a lipidomic analysis has shown that the wMel affect the sphingolipid metabolism and deplete mosquito cells from ceramide and derived sphingolipids [16] . Ceramides are synthesized in the ER and exported to the Golgi [56] . They play an important role during bacterial infections as part of a pro-apoptotic lipid signalling [57] and sphingolipids regulate autophagosome biogenesis and endocytic trafficking [58] , suggesting that a Wb-induced decreased availability of these lipids may prevent xenophagy and/or apoptosis . It is then possible that the interaction of Wolbachia with the ER and the derived intracellular vesicular trafficking plays also a central role in immune escape and control of apoptosis . In S2R+ Wb cells , the bacterial titer is exceptionally high compared to other infected insect cell lines , and Wb often fill the cytoplasm entirely when observed in confocal microscopy with an anti-WSP staining . In this cellular environment unable to efficiently control the Wb titer , electron microscopy analyses revealed a high frequency of poly Wb- containing vacuoles , possibly resulting from a limited access to new membranes . It is nonetheless interesting to observe that under these conditions the infection is persistent and does not compromise the host cell viability . Since ER tracker-negative Wb are often observed in the cell periphery , the interaction with ER may be necessary for an active replication . Wb infections are usually characterized by very high intracellular loads of bacteria , usually above a hundred bacteria per cell , similar to other Rickettsiales . Despite the peculiar relationship between Wb and the ER , we did not detect an ER stress above levels found in non-infected cells suggesting that a Wb infection either does not require this cell response or is able to prevent it . Moreover , prolonged ER stress leads to cell death and seems incompatible with endosymbiosis [59 , 60] . This conclusion is in addition justified by several lines of evidence . First , although the ER appears redistributed , we did not detect morphological signs of enhanced ER activities linked to ER stress , such as swollen tubules and cisternae , in contrast to a previous study performed with wMel-infected LDW1 cells [19] . Second , we monitored the gene expression levels for the three UPR sensors , downstream targets , and ERAD key players , either by quantitative PCR or by fluorescent assay approaches . We could not find altered gene expressions indicating that a persistent Wb infection triggers an ER stress . Last , immunofluorescence studies of polyubiquitin linkages associated with ERAD-driven proteolysis ( K11 and K48 polyUb ) revealed that these marks are not increased in presence of Wb . Since the monoclonal antibody FK2 targets all covalently linked mono- and poly-ubiquitins , it is likely that the increased amount of FK2 foci in presence of Wb corresponds to either mono-ubiquitylated proteins; and/or to proteins decorated with polyubiquitin chains on possibly the five other lysine residues of ubiquitin with non-degradative roles , reported to be involved in: K6 -mitophagy- , K27 -protein secretion and autophagy- , K63 -endocytosis , signalling , activation of NF-kappa-B-; K33 -kinase modification- , and K29 -lysosomal degradation- [38] . It is hence possible that Wolbachia , directly or indirectly , influence a number of cellular mechanisms through modulation of polyubiquitylation-dependent signalling events , and this field remains to be explored . Recent proteomic studies provide conflicting evidence regarding Wb and the UPR , possibly due to the differences in the Wb stains and the host cells employed . The pathogenic strain wMelPop slightly increases ( up to 1 . 36 fold ) some UPR–related genes identified by gene ontology analysis [18] while the wStr infection in Aedes albopictus cells rather leads to a decrease of proteins involved in ER protein folding [44] . Nonetheless a genome-wide RNAi screen has revealed the importance of UBC6 , an ubiquitin-conjugating enzyme part of the ERAD pathway , to sustain the wMel titer [19] . Although we found no evidence for an increased ERAD-induced proteolysis through ubiquitin-targeted proteasomal degradation in presence of Wb , this does not rule out the requirement of intact UPR/ERAD response for Wb survival . Alternatively , UBC6 may either be involved in a non-ERAD-related function , or since the Wb vacuolar membrane appears ER-derived , these endosymbionts may have subverted an ERAD machinery at the level of their own vacuole . The apociplast of apicomplexan parasites is an organelle derived from an algal endosymbiont that has retooled the host ERAD into an apicoplast-localized ERAD-like protein import machinery [61] . The UPR response can be modulated by intracellular pathogens to their advantage , and the three branches–IRE1 , PERK , ATF6- can be individually upregulated or inhibited in order to modulate i . e . the host defense through apoptosis or innate immunity response , or to build a replicative niche [62] . Hence , further investigations will be needed to clarify the role of the UPR in a Wb infection . However , the absence of an enhanced ERAD-proteasomal degradation pathway suggests that amino acid salvage does rely on mechanisms other than an increased proteolysis . Several studies have shown that the Wb infection decreases the global protein translation in the host cell [28 , 44] . While the mechanisms are still unknown , TORC1 and insulin pathways regulate protein translation based on environmental conditions , and greatly influence the Wb titer in Drosophila [63] . Future studies will determine whether Wolbachia can directly subvert growth signalling pathways to down-regulate translation and therefore increase the pool of free amino acids . In conclusion , there is no doubt that in an effort to elucidate the mechanisms of intracellular survival employed by Wolbachia , the comprehension of subversion strategies will be key: how are ubiquitylation pathways modulated and what are their targets ? How do Wb acquire ER-derived membranes on the one hand , and how do they modulate signalling or synthesis pathways to acquire amino acids and lipids on the other hand ? These are the next questions to be addressed . In parallel , the current growing efforts to express the putative Wb effectors into surrogate systems [64 , 65] , yeast or Drosophila cell cultures , should accelerate our knowledge of one of the most commonly encountered endosymbiont .
All the cell lines are derived from primary cultures of D . melanogaster cells . JW18 is a kind gift from William Sullivan [66] , 1182–4 was obtained from Alain Debec [25 , 26] , and S2R+ from François Juge [27] . JW18 , 1182–4 , and 1182-4Wb cells were maintained in a Shields and Sang M3 insect medium ( Sigma ) supplemented with 10% decomplemented fetal bovine serum and were passaged twice a week at a 1/4 dilution . S2R+ and S2R+Wb cells were maintained in a Schneider insect medium ( Dominique Dutscher ) supplemented with 10% decomplemented fetal bovine serum and were passaged twice a week at a 1/2 dilution . Cell lines were kept at 25°C . The content of ten 25 cm2 cell culture flasks reaching confluency with Wolbachia-infected JW18 adherent cells was pooled in two 50 mL Falcon tubes and centrifuged at 1200 rpm for 5 minutes at room temperature . Next , each pellet was resuspended by pipetting on ice with 3 ml of pre-cooled Nalgene-filtered extraction buffer ( 220 mM sucrose , 3 . 8 mM monopotassium phosphate , 8 mM dipotassium phosphate , and 10 mM magnesium chloride ) . Cell suspensions were transferred into two 15 ml Falcon conical tubes on ice containing 2 g of sterile 3 mm-glass beads and vortexed vigorously 3 times for 30 seconds with a 30-second incubation period on ice between each round of vortexing . Each lysate was transferred to a new 15 ml Falcon tube on ice and centrifuged at 1200 rpm for 5 minutes at 4°C . Then , the Wolbachia-containing supernatant was transferred to 1 . 5 mL Eppendorf tubes and centrifuged at 10 000 rpm for 10 minutes at 4°C to pellet Wolbachia . The bacterial pellet of one of the Eppendorf tubes was resuspended in 500 μL of cell culture medium and its content transferred from one tube to another in order to resuspend all the bacterial pellets and collect them in one final tube . An extract of Wolbachia was transferred into a 25 cm2 cell culture flask containing confluent 1182–4 or S2R+ cells in a 4 mL volume of cell culture medium . After two days cells were passaged twice a week for a 1-month duration and then , the infection process was repeated to obtain stably infected 1182-4Wb and S2R+Wb cell lines . To follow the infection dynamics , cells were plated on 18 mm x 18 mm coverslips in a plastic 6-well cell culture plate , and after adherence were fixed in PBS with 3 . 2% paraformaldehyde for 10 minutes at room temperature , washed for 5 minutes with PBS , and incubated for 2 hours at 37°C in the dark with Alexa Fluor 488 phalloidin A12379 ( Life technologies ) at a 1/50 dilution . After a 5-minute wash with PBS , coverslips were mounted on glass slides using Fluoroshield with DAPI and observed with an inverted laser scanning confocal microscope ( SP5-SMD , Leica Microsystems ) using a 63x/1 . 4 HCX PL APO CS oil objective and images taken with a z-stack interval of 0 . 5 μm . The viability of 1182–4 versus 1182-4Wb and S2R+ versus S2R+Wb was evaluated using an automated cell counter ( Countess Invitrogen ) relying on a trypan blue ( Life Technologies ) exclusion method according to the protocol of the manufacturer . The cells were passaged the day before the viability measurements were taken . Cells were plated on 18 mm x 18 mm coverslips in a 6-well cell culture plate 24 hours before fixation in PBS with 3 . 2% paraformaldehyde for 10 minutes at room temperature . Next , coverslips were dried and immersed in -20°C pre-cooled methanol and kept for 10 minutes at -20°C . Then , coverslips were dried out from residual methanol at room temperature and incubated in a humid chamber for 10 minutes with PBS , BSA 2% . After a PBS wash , cells were incubated for 2 hours at 37°C with the primary antibody or antibodies , added as a 50 μL drop . Following 3 washes of 5 minutes with PBS 1x , cells were incubated for 2 hours at 37°C with the secondary antibody or antibodies . Then , the cells were washed 3 times; each for 5 minutes with PBS 1x and mounted using fluoroshield with DAPI . All primary antibodies were used at a 1/400 dilution: rabbit polyclonal anti-GM130 antibody ab30637 ( Abcam ) and rabbit monoclonal anti- K48 linkage polyubiquitin antibody ab140601 ( Abcam ) . Mouse monoclonal anti-FK2 ubiquitin antibody AB120 ( LifeSensors ) . Rabbit monoclonal anti-ubiquitin K11 linkage , clone 2A3/2E6 ( Millipore ) . Mouse monoclonal anti-Wolbachia surface protein ( BEI resources , NIAID , NIH ) . Secondary antibodies were used at a 1/500 dilution . Goat anti-mouse IgG antibody coupled to Alexa Fluor 488 ab150117 ( Abcam ) , goat anti-rabbit IgG antibody coupled to Cy3 A10520 ( Invitrogen ) . An inverted laser scanning confocal microscope ( SP5-SMD; Leica Microsystems ) at a scanning speed of 400 Hz equipped with a 63x/1 . 4 HCX PL APO CS oil objective was used to take images with a z-stack of 0 . 5 μm and in the case the images needed deconvolution ( Deconvolution software: Huygens Professional version 18 . 04 ) , the z-stack was of 0 . 2 μm . Cells were incubated with tunicamycin ( Sigma-Aldrich ) at 10 μg/mL for 48 hours [67] . Cells were plated on concanavalin A-coated glass bottom fluorodishes 48 hours before observation . One batch of the S2R+ cell line was treated with tunicamycin as described above . To stain the ER , the cell culture medium was aspirated , cells washed with PBS 1x and incubated for 30 minutes at 25°C with 1 μM live ER-tracker red dye ( Molecular Probes ) diluted in PBS . The ER-tracker solution was replaced by a 1/20 000 solution of SYTO-11 ( Molecular Probes ) DNA dye for 10 minutes at 25°C diluted in the appropriate cell culture medium prior to confocal microscopy observations . The temperature of the microscope chamber was set at 25°C prior to observation . For concomitant stainings of the ER and the Golgi apparatus , cells were first incubated for 30 minutes at 4°C with 5 μM of BODIPY FL C5-ceramide ( Molecular Probes ) in PBS . Next , the cells were rinsed 3 times for 2 minutes and incubated for 30 minutes at 25°C with the live ER-tracker red dye as described above . For SP5 confocal time-lapse recordings , stacks of three images , z = 0 . 5μm , were taken each 5 seconds , with a line average = 8 , in bidirectional , resonance mode with a SP5 confocal microscope . To monitor ATF4 activity , cells were plated on concanavalin A-coated glass bottom fluorodishes . Upon cell adherence , the cells were transfected with a 4E-BP intron-dsRed reporter plasmid [43] using the lipofectamine kit ( Invitrogen ) according to the instructions of the manufacturer . Twenty-four hours post-transfection , one of the fluorodishes containing Wolbachia-free cells was treated with tunicamycin ( 10 μg/ml for 48 hours ) . The image analysis software used is ImageJ version 1 . 48 . The ImageJ macros were developed in collaboration with the MRI-CRBM-Optics platform , Montpellier , France and are available upon request . The graphing software used was GraphPad Prism version 7 . 00 . For each cell line , the content of a 25 cm2 flask at cell confluence , three days after medium change , was washed and transferred to a 1 . 5 mL Eppendorf tube and centrifuged at 2000 rpm for 2 minutes at room temperature . The cell pellet was fixed for 1 hour by resuspension in a 2 . 5% gluteraldehyde -PHEM solution pH = 7 . 4 . Fixed cells were kept overnight at 4°C . Cells were next rinsed in PHEM buffer and post-fixed in 0 . 5% osmic acid for 2 hours at room temperature in the dark . After two PHEM washes , cells were dehydrated in a graded series of ethanol solutions ( 30–100% ) before being embedded in EmBed 812 using an automated microwave tissue processor for electron microscopy ( Leica AMW ) . Thin sections of 70 nm were collected at different levels of each block using the Ultracut E microtome ( Leica-Reichert ) . These sections were counterstained with uranyl acetate and lead citrate and observed using a transmission electron microscope ( Tecnai F20 ) at 200 kV . RNA extraction was performed in biological triplicates for each sample . Precisely , the RNA was extracted from confluent flasks of 25 cm2 containing approximately 106 cells . The culture medium was aspirated and replaced by 1 ml PBS 1x . Cells were scraped and transferred to 1 . 5 ml Eppendorf tubes and centrifuged at 1200 rpm for 5 minutes . Following that , the supernatant was discarded and the cells were resuspended in 300 μl of the Quick-RNA MicroPrep kit ( Zymo Research ) lysis buffer . The next steps were performed according to the RNA purification protocol detailed in the kits’ instructions but the in-column DNaseI treatment step was omitted and replaced with a TURBO DNase ( Ambion ) treatment . RNA was purified using the RNA Clean & Concentrator-5 kit ( Zymo Research ) . cDNA was produced from 2 μg of RNA using the SuperScript VILO cDNA synthesis kit ( Invitrogen ) and diluted at 1/25 for the RT-qPCR experiments . Primer pairs were selected according to Primer3 version 0 . 4 . 0 , synthesized by Eurofins Genomics ( S1 Table ) . Primer pairs with an efficiency close to 100% were selected for qPCR experiments . RT-qPCR reactions were performed using SYBR Green 10x with Platinum Taq ( Invitrogen ) . Amplifications were performed using a Mx3000P instrument ( Agilent Technologies ) and the MxPro QPCR Software ( Agilent Technologies ) . The RT-qPCR cycling program consists of a pre-amplification cycle of 2 minutes at 94°C followed by 40 amplification cycles of 30 seconds at 94°C , 30 seconds at 55°C , and 20 seconds at 72°C . The RT-qPCR cycle ends with a dissociation/melt cycle of 1 minute at 94°C , 30 seconds at 55°C , and 30 seconds at 94°C . For each gene , RT-qPCR is performed in technical and biological triplicates . The changes in expression were calculated according to the 2-ΔΔCt method [68] and were plotted using the GraphPad Prism software version 7 . 0 . | Wolbachia are a genus of intracellular bacteria living in symbiosis with millions of arthropod species . They have the ability to block the transmission of arboviruses when introduced into mosquito vectors , by interfering with the cellular resources exploited by these viruses . Despite the biomedical interest of this symbiosis , little is known about the mechanisms by which Wolbachia survive and replicate in the host cell . We show here that the membrane composing the Wolbachia vacuole is acquired from the endoplasmic reticulum , a central organelle required for protein and lipid synthesis , and from which originates a vesicular trafficking toward the Golgi apparatus and the secretory pathway . Wolbachia modify the distribution of this organelle which is a potential source of membrane and likely of nutrients as well . In contrast to some intracellular pathogenic bacteria , the effect of Wolbachia on the cell homeostasis does not induce a stress on the endoplasmic reticulum . One of the consequences of such a stress would be an increased proteolysis used to relieve the cell from an excess of misfolded proteins . Incidentally , this suggests that Wolbachia do not acquire amino acids from the host cell through this strategy . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"vacuoles",
"pathology",
"and",
"laboratory",
"medicine",
"intracellular",
"pathogens",
"pathogens",
"endoplasmic",
"reticulum",
"cell",
"processes",
"biological",
"cultures",
"animals",
"wolbachia",
"animal",
"mod... | 2019 | Wolbachia endosymbionts subvert the endoplasmic reticulum to acquire host membranes without triggering ER stress |
The model plant species Arabidopsis thaliana is successful at colonizing land that has recently undergone human-mediated disturbance . To investigate the prehistoric spread of A . thaliana , we applied approximate Bayesian computation and explicit spatial modeling to 76 European accessions sequenced at 876 nuclear loci . We find evidence that a major migration wave occurred from east to west , affecting most of the sampled individuals . The longitudinal gradient appears to result from the plant having spread in Europe from the east ∼10 , 000 years ago , with a rate of westward spread of ∼0 . 9 km/year . This wave-of-advance model is consistent with a natural colonization from an eastern glacial refugium that overwhelmed ancient western lineages . However , the speed and time frame of the model also suggest that the migration of A . thaliana into Europe may have accompanied the spread of agriculture during the Neolithic transition .
Arabidopsis thaliana is an important model organism for plant biology , serving as a focal species for studies of plant physiology , molecular biology , and genetics [1]–[4] . Its use as a model species is facilitated by its short generation time in the laboratory , its production of large numbers of seeds , and its reproduction primarily by self-fertilization . Many of the same traits that contribute to the utility of A . thaliana as a model organism are important in determining the niche of the species in its natural environment . Its rapid flowering , self-fertilization , and extensive seed production are characteristic of colonizing species that grow in open or recently disturbed habitats [5] , [6] . From an ecological standpoint , due to its status as a colonizing species , A . thaliana can be viewed as a weed . A . thaliana is frequently described as native to the Eurasian landmass [6] , [7] , and in recent times it has been among the group of weeds from Europe that have invaded North America and Australia since the time of European colonization [8] , [9] . However , relatively little is known about the prehistoric spread of the species into Europe . Because pollen from A . thaliana is very similar to that of many other species from the Brassicaceae family [10] , it is often undetectable in surveys of past plant geographic distributions . Thus , investigations of patterns of present-day genetic variation have provided an important alternative method for understanding the recent history of the species . Most European species are believed to have been restricted to southern refugia at the height of glaciation ∼18 , 000 BP—many in the peninsulas of Iberia , Italy , and the Balkans , and some near the Caucasus region and the Caspian Sea [11]–[13] . When the climate warmed and the ice retreated , these species expanded their ranges northwards , starting ∼16 , 000 BP [14] . For Arabidopsis thaliana , on the basis of population-genetic data , Sharbel et al . [15] proposed a scenario of post-glacial re-colonization of Europe from two refugia , one in the Iberian Peninsula and the other in central Asia , followed by admixture of the two ancestral populations in central and eastern Europe . However , contradicting the predictions of this model , Schmid et al . [16] found that linkage disequilibrium was more extensive in the putative source regions of Iberia and central Asia than in central Europe . Furthermore , although some population-genetic studies in A . thaliana have identified relatively unstructured patterns of genetic variation compatible with rapid range expansions from glacial refugia [17]–[20] , the most recent studies of large data sets have found that genetic variation in A . thaliana shows evidence of considerable population structure [16] , [21] , [22] . This structure has not been extensively analyzed to determine the likely explanations for its origin , and hypotheses about the location of origin and the timing of the spread of A . thaliana have been under some debate [20] , [23] , [24] . In this article , we consider an alternative model for the spread of A . thaliana in Europe . Using recently developed approximate Bayesian computation and spatial modeling techniques , we re-analyzed the data of Nordborg et al . [21] , one of the largest population-genetic data sets collected to date in A . thaliana . We find evidence that a migration wave from east to west is responsible for most of the genetic ancestry of European A . thaliana . We discuss this result in relation to the hypothesis of an eastern refugium , and in relation to the hypothesis that the migration of A . thaliana may have been precipitated by the spread of agriculture into Europe .
To investigate spatial population structure in European accessions of Arabidopsis thaliana , we used model-based clustering as implemented in the TESS computer program [25] , [26] . Our analysis used the molecular data from 75 European accessions plus one accession from Libya ( Mt-0 ) , a total set of 876 alignments described in the study of Nordborg et al . [21] ( Table S1 ) . Using TESS , we performed an admixture analysis incorporating individual spatial coordinates and accounting for natural obstacles ( see Methods and Figure S1 ) . The program allows individuals to be distributed over Kmax clusters , estimating the most likely value for the number of clusters as a value K less than or equal to Kmax ( see Methods ) . The TESS runs with the smallest values of the Deviance Information Criterion , a penalized measure of how well the model underlying TESS fits the data , were obtained for Kmax greater than four ( see Methods ) . In Figure 1 , we report results for Kmax = 5 clusters . The cluster membership coefficients estimated for the central European and western European accessions suggest that clinal variation occurs along an east-west gradient separating two clusters . The western cluster grouped accessions mainly from the British Isles , France and Iberia . The eastern cluster grouped all accessions from central Europe , southern Sweden , Poland , Russia , Ukraine , and Estonia . German and Swiss accessions shared almost the same amount of membership in the western and eastern clusters . The eight northern Swedish accessions and two Finnish accessions were grouped into a separate cluster . In previous analysis of the same data set [21] , it was observed that when individual genomes were clustered by genetic similarity using the program STRUCTURE [27] , European accessions sorted into K = 8 clusters , some of them corresponding to small geographic regions [21] . The TESS analysis identified a substantially lower number of actual clusters ( Figure 1 ) , consistent with more continuous allele frequency variation across geographic space and with significant isolation by distance [15] , [16] , [22] . Although the northern European cluster was also identified from STRUCTURE runs with K = 3 [21] , some differences were found by TESS in the two continental clusters . In [21] , the Iberian accessions clustered with the eastern populations , whereas TESS grouped them with the western accessions ( France , British Isles ) . More strongly than in the STRUCTURE analysis , the TESS results suggest clinal variation of allele frequencies within central and western Europe , with Germany possibly serving as a hybrid zone separating the two clusters corresponding to these regions . To better evaluate the direction of variation in the continental cluster , we regressed heterozygosity on geographic distance . This analysis used the approach of Ramachandran et al . [28] , who showed that recurrent founder events can cause a decrease in genetic diversity in colonizing populations . Assuming a unique origin , genetic diversity is then predicted to decrease approximately linearly with geographic distance from the origin . All accessions from the northern Sweden sample , as well as a few accessions that were poorly geographically connected to other accessions , were removed from the regression analysis . The remaining accessions were grouped into seven samples ( Table S2 ) , defined on the basis of geographic and genetic proximity . To minimize the sensitivity of the regression analysis to a particular geographic pooling of European accessions , we repeated the regression study for several combinations of seven modified samples , and the results reported can be viewed as representative of these various combinations . For each of 300×180 points on a two-dimensional lattice covering Europe , we computed distances from each lattice point considered as a potential source for the geographic expansion of A . thaliana . The Pearson correlation coefficients of genetic diversity with distance from the source were estimated and plotted on the grid . The correlations were negative ( ∼ −0 . 5 ) in the east , and they were positive ( ∼ +0 . 3 ) in southwestern Europe . Assuming a unique site of origin , Figure 2 provides evidence that the pattern of heterozygosities is best explained by spatial expansion originating from the east . Because this analysis is based on a relatively limited geographic sample , it is possible that it is affected by the peculiarities of this sample . Therefore , to assess the possibility of bias due to non-uniform and sparse geographic sampling , we performed spatially explicit range expansion simulations that reproduced the geographic sampling scheme of the actual data ( Text S1 ) . Assuming an origin in Anatolia ( west Asia ) , we indeed observed a considerable shift of the position of the estimated origin to the southwest of the true origin ( Figure S2 ) . Because our data analysis identified a best-fitting origin in the Balkan region , it is thus possible that the true origin is potentially localized farther to the northeast . Inference of demographic parameters and the choice of a best-fitting demographic model for the data were performed using an approximate Bayesian computation ( ABC ) analysis [29]–[31] . ABC approaches bypass the computational difficulties of using explicit likelihood functions by simulating data from a coalescent model . These methods rely on the simulation of large numbers of data sets using parameter values sampled from prior distributions . A set of summary statistics is then calculated for each simulated sample , and each set of summaries is compared with the values for the observed sample , sobs . Parameter values that have generated summary statistics close enough to those of the observed data are retained to form an approximate sample from the posterior distribution , enabling parameter estimation and model choice ( see Methods ) . The ABC analysis was limited to a subset of 64 individuals representing the central European and western European populations . We restricted the analysis to the non-coding part of the genomic data , using the intron and the intergenic sequences only ( 648 loci ) . Simulated data also included 648 corresponding loci , each paired to have the same length as a locus in the observed data . The loci were assumed to be in linkage equilibrium , in agreement with the median ∼100 kb distance between fragments in the genome-wide data [21] and with levels of linkage disequilibrium that decay within ∼10 kb in A . thaliana [21] , [32] . Coalescent simulations were performed under four demographic scenarios ( Models A–D ) . Model A has a constant population size , N0 . Model B has an exponentially growing population size ( present size , N0 , ancestral size , N1 , time since the onset of expansion , t0 ) . In model C , the population size was constant in the distant past as well as in the recent past , and the growth was exponential between the two periods of constant population size ( present size , N0 , ancestral size , N1 , time since the onset of expansion , t0 , time since the end of expansion , t1 ) . Model D is similar to model B , but it includes an ancient bottleneck before expansion . The prior distributions used in the four models are described in Table S3 . Twelve summary statistics were used to capture genomic information at the 648 loci ( see Methods ) . To make quantitative model comparison possible , we evaluated the evidence of model 1 against model 2 ( where 1 and 2 are chosen among A , B , C and D ) using an approximation of the Bayes factor [33] . Pritchard et al . [30] computed the Bayes factor as the ratio of the acceptance rates in Models 1 and 2 . Including smooth weighting to more heavily weight the simulations that produced results that more closely matched the observed data [29] , we approximated the Bayes factor aswhere Kδ is the Epanechnikov kernel and si , 1 and si , 2 are the ith vectors of summary statistics simulated under models 1 and 2 ( see Methods ) . Among all the scenarios , variants of the four models with variable mutation rates across loci were given higher statistical support , measured by the Bayes factor , than were models with fixed mutation rates - reflecting the high heterogeneity of diversity estimates among loci [21] . The best-supported model was model C with variable mutation rates , which assumed a past rapid expansion followed by a constant-size population phase ( see Figure 3 ) . The Bayes factor BA , B = 0 indicates that the model with constant population size ( model A ) was totally unsupported . The exponential growth model ( model B ) was the second best-supported model , and the evidence supporting model C against model B was moderate ( BC , B = 1 . 9 , see Figure 3 ) . The scenario in which the population experienced a bottleneck before expansion was rejected , but less decisively than model A ( model D , BD , B = 0 . 7 ) . Table 1 displays the estimates of the parameter values under the variants of model B and C with variable mutation rates . The time of onset of the expansion was dated at t0 = 10 , 000 BP ( model B ) and t0 = 12 , 000 BP ( model C ) using the Maximum A Posteriori ( MAP ) estimate ( Figure S3 ) . As a consequence of using broad prior bounds in the ABC analysis , similarly to [34] , we observed large 95% credibility intervals . The ratio of the ancestral population size to the present population size was estimated at N1/N0 = 0 . 3 , but the large credibility interval ( 0 , 0 . 6 ) makes it impossible to eliminate the hypothesis of a wider expansion . The MAP estimate of the mutation rate was μ = 2 . 0×10−8 with credibility intervals ranging from 0 . 9×10−8 to 12 . 6×10−8 . The MAP estimate for the date of the end of the expansion was t1 = 5 , 000 BP ( see Table S4 and Table S5 for posterior estimates and Bayes factors for all eight models ) . To investigate the relationship between the time of onset , t0 , and the length of the expansion , t0−t1 , the joint posterior distribution of these two quantities was computed . Figure 4 displays this joint distribution , and it indicates a positive correlation between the two values . Because we observed considerable difference in the TESS analysis between the northernmost accessions and the main European populations ( Figure 1 ) , we performed model fitting to assess various scenarios for the split of the northern cluster . Quantifying the genetic divergence between the central European population and the northern Swedish and Finnish population by the mean number of distinct haplotypes and the mean number of private haplotypes [35] , we obtained estimates of these statistics for subsamples of size two to ten . The patterns of haplotype diversity in the central European and northern European populations were typical for pairs of separated populations in which one population has larger size than the other [36] . The central European population had , on average , 3 . 85 distinct haplotypes for a sample of ten individuals , and the northern European population had , on average , 2 . 61 distinct haplotypes for a sample of ten individuals . However , in each population , about half of the haplotypes were unique to the population ( Figure 5 ) , and the genetic variation in the northern European population was not a subset of that in the central European population . To study the split between the northern and central European populations , we used a coalescent model for the divergence between two populations at some time T in the past , with subsequent migration at rate m between these two populations ( where m is the rate in each direction ) . We simulated the same number of fragments as in the data for both populations , and we determined the mean across 100 replicates of the sum of squared differences ( SSD ) between the simulated and the observed summary statistics . In a first set of simulations we increased the split time T from 0 to 135 , 000 years in a model with no migration ( m = 0 ) . Figure 6 shows the results for T = 0 to T = 27 , 000 BP superimposed on the same summary statistics computed for the observed data . For small values of T , the fit of the simulated data to the observed data was poor , with an improvement as T increased ( SSD for T = 1 , 350 BP , distinct haplotypes: 2 . 56 , private haplotypes: 2 . 12 ) . When T was equal to 7 , 000 BP , the simulated data fit the observed data quite well ( SSD for T = 7 , 000 BP , distinct haplotypes: 0 . 06 , private haplotypes: 0 . 10 ) . When T increased beyond 13 , 500 BP , the fit became poorer . In a second set of simulations , we used a population divergence model that incorporated migration , and we increased the values of the migration rate , m . Figure 7 shows simulated results superimposed on the observed results . The simulations fit the data relatively well for m in the range [1] , [3] when T equalled 13 , 500 BP , and the best values were obtained for m = 3 ( SSD for m = 3 , distinct haplotypes: 0 . 04 , private haplotypes: 0 . 08 ) . As m increased above the value 3 , the fit of the mean number of distinct haplotypes deteriorated . We also tested values of T>13 , 500 together with m>3 , without finding a close fit to the observed data , and the best fit was found for a model with low migration rates . A model with high migration rates was not able to replicate the observed data under the tested conditions . Thus , it is unlikely that the split occurred more recently than ∼7 , 000 years ago . In the ABC analysis the scenarios that consisted solely of population size change produced patterns of DNA sequence diversity similar to those resulting from a rapid spatial range expansion [37] . To better include geographic sampling in the analysis and to estimate the rate of spread , we modeled the process of colonization of Europe in a more explicit manner [38] , [39] . Range expansion was simulated under a two-dimensional wave-of-advance model [40] . We included environmental heterogeneity , borrowing topographic information from a Geographic Information System . Assuming an origin of the colonization process to the north of the Black Sea ( 48°N , 35°E ) , we divided Europe into an array consisting of 130×180 = 23 , 400 demes , each representing an area of ∼2 , 500 km2 . To account for the fact that in Europe , A . thaliana grows mainly in low-altitude landscapes , carrying capacities were set to their highest values for altitudes below 200 m and were linearly decreased for altitudes higher than 1 , 500 m . It has been previously recognized that the frequency spectrum may be influenced by signals of past demographic events [41] , [42] . Consequently , the fit of simulated data to the pattern of polymorphism of A . thaliana was evaluated by comparing the non-coding empirical folded frequency spectrum and frequency spectra obtained from simulated individuals located at the same coordinates as the real accessions . Simulated and observed frequency spectra were compared by using the χ2 distance ( see Methods ) . A coarse preliminary search found that values of migration rates and growth rates corresponding to the saturation of a deme in 100–300 years and lengths of the colonization phase around 3 , 000–6 , 000 years followed by an equilibrium migration phase yielded non-significant χ2 P-values . Thus , these values provide a reasonable explanation for the observed data . They translate into a wave-of-advance of around 0 . 5 to 1 km/year . In a second stage of the analysis , we investigated the time at which the range expansion began , varying this time from t0 = 5 , 000 BP to t0 = 20 , 000 BP assuming a growth rate of r = 0 . 6 for the oldest dates . For the most recent dates , we increased r to 0 . 7 , 0 . 9 and 1 . 2 so that the colonization phase ended before the present day . This analysis supported the values found by the MAP estimate from the ABC analysis . Figure 8A shows that dates around 10 , 000–12 , 000 BP are consistent with the pattern of polymorphism observed today . To better locate the origin of A . thaliana , we investigated several potential locations , and we plotted χ2 distances between simulated spectra and the empirical spectrum on an interpolated map ( Figure 8B ) . The χ2 values ranged from 0 . 03 ( East ) to 0 . 3 ( Spain - North Africa ) . Although the map does not provide an accurate localization of the onset of range expansion , it is similar to Figure 2 , providing further support to the hypothesis of an eastern origin . Figure 9A demonstrates that the empirical folded frequency spectrum computed from non-coding nucleotides deviates from neutrality through an excess of rare alleles . Figure 9B shows one simulated folded spectrum obtained from the estimated parameters ( m = 0 . 25 , r = 0 . 6 , N1 = 5 , 000 and t0 = 10 , 000 , χ2 = 0 . 03 , P = 0 . 68 ) . For this set of parameters , the estimated speed of the wave-of-advance was ∼0 . 9 km/year . It is clear from the search strategy used here that these parameter settings are only likely to represent a local maximum of the probability of an evolutionary scenario , and that other settings may also provide a reasonable fit to the data .
From a biogeographic point of view , Europe is a large peninsula with an east-west orientation , delimited in the south by a strong Mediterranean barrier . During glaciation epochs , many species likely went through alternating contractions and expansions of range , involving extinctions of northern populations when the temperature decreased , and spread of the southern populations from different refugial areas after glaciation . Such colonization processes were likely characterized by recurrent bottlenecks that would have led to a loss of diversity in the northern populations . The idea that the refugia were localized in three areas ( Iberia , Italy , Balkans ) is now well-established [12] , although recent studies , particularly of tree species , have begun to suggest that northern and eastern refugia could have existed [43] , [44] . Comparison of colonization routes has highlighted four main suture-zones where lineages from different refugia meet [11] . Two of these suture-zones correspond to the Alps and the Pyrenees , while the two others are in Germany and in Scandinavia . We observed that genetically diverse populations of A . thaliana were localized at intermediate latitudes , as a potential consequence of the admixture of divergent lineages colonizing the continent from separate refugia . These results are potentially consistent with the pattern expected if the species colonized Europe from two separate refugia , one in the Iberian peninsula and the second in the east , as suggested by the model of Sharbel et al . [15] . Similarity with patterns of cpDNA diversity in 22 plant species that have genetically divergent populations in Mediterranean regions was also observed for the seven geographic samples considered in the regression analysis ( [13] and Figure S4 ) . Furthermore , the presence of a highly divergent accession ( Mr-0 ) in Italy , south of the Alpine barrier , is also compatible with the view that A . thaliana was present in Mediterranean refugia during the last glaciation . We observed that intraspecific diversity declines away from the southeast , as predicted by a model of successive founder events during colonization . We also inferred that the putative origin of most accessions in the sample is localized somewhere in a vast eastern region , encompassing refugia such as the Caucasus region and the Balkans . The direction of diffusion from the east towards the British Isles coincides with the post-glacial re-colonization of Europe for many species such as beech , alder and ash trees , or flightless grasshopers [45] , [46] , and it is possible that , to a large extent , this wave of expansion erased any contribution of ancient western lineages that originated in Mediterranean refugia . The boreal regions , in which environmental conditions are often very severe , contain the northern distribution limit of many European plants . These regions are often characterized by larger fluctuations in population size , which increase the effect of drift and can lead to increased genetic differentiation [47] . Fennoscandia has recovered its flora after the last ice age , less than 10 , 000 years ago , via many different routes . The presence of a suture-zone in Scandinavia indicates that this area may have been colonized by A . thaliana both from the south and from the northeast . The estimated separation time of the northern European A . thaliana population and the central European population , at least 7 , 000 years ago , indicates that the split between the continental and northern populations took place during the early history of the re-colonization of Europe by the species . An alternative hypothesis to the idea of a natural spatial expansion of A . thaliana is that its spread might have accompanied the spread of farming into Europe , perhaps following an earlier post-glacial wave of colonization . Between 9 , 000 and 5 , 500 BP Neolithic farming spread across Europe from the Near East , primarily northwestwards along the Danube-Rhine axis [48]–[50] . Several aspects of our results are consistent with the hypothesis that A . thaliana was part of a group of weeds that accompanied the spread of agriculture into Europe . First , the evidence for an eastern source for European A . thaliana parallels the evidence that agriculture spread into Europe from the east [48] , [49] . Putative origins in the Danube basin , west of the Black Sea , received high explanatory power in our analysis , and this area was an important way-point in the route followed by the spread of agriculture . Second , the estimated time for the beginning of the A . thaliana population size expansion parallels the time for the spread of agriculture . Third , the estimated rate of westward spread of A . thaliana , ∼0 . 9 km/year , fits within the range 0 . 6–1 . 3 km/year estimated for the rate of agricultural expansion [48] , [51] . It is believed that Neolithic agriculture advanced into Europe along two preferred routes , a Mediterranean route and a Danubian route [52] , [53]; our analysis suggests that if A . thaliana followed the spread of agriculture , then it likely followed the Danubian route . The possible prehistoric anthropogenic spread of A . thaliana in Europe is an instance of a more general pattern documented in historical times , in which land disturbances instigated through long-distance human migrations co-occur with the spread of opportunistic organisms unintentionally brought by the migrating populations from their home region . This phenomenon of “ecological imperialism” has been used to explain the current prominence of European weeds in regions of the Americas , Australia , and New Zealand that have recently been transformed by European agriculture [54] . Several lines of evidence support the view that a similar process for the spread of weeds acted during the transformation of European landscapes by the spatial advance of agriculture - that is , that a large fraction of weeds in Europe trace their geographic distributions to the Neolithic expansion of European farming . For example , based on palaeobotanical data , Pyšek et al . [55] estimated that of the presently known prehistoric alien species of central Europe , 35% arrived there during the first thousand years after the advent of agriculture . Kreuz et al . [56] detected a chronological correlation in the number of introduced weed species in central Europe and the development of the agriculturalist Bandkeramik culture . In two weedy species of Lolium , Balfourier et al . [57] found patterns of population structure explicable by the spread of agriculture , supporting the view that the A . thaliana results could be part of a general trend for prehistoric European weeds . Another source of evidence for a large-scale prehistoric agriculturalist spread of weeds into Europe is a comparison of weed species in modern plots of land in the Czech Republic . In the study of Pyšek et al . [58] , introduced weeds that entered Europe in prehistoric times were comparatively more numerous in land farmed with crops dating to the origin of European agriculture ( e . g . barley and wheat ) than in land farmed with more recently introduced crops ( e . g . maize and rapeseed ) , where recently introduced weeds were more numerous . Thus , the success in modern times of A . thaliana and other weedy plants brought from Europe to temperate regions worldwide may be the result of long-lasting associations with European agriculture that these plants have had since the time of the Neolithic revolution . While our results might be explained by the simultaneous expansion of A . thaliana into Europe from multiple glacial refugia , we find that a perspective incorporating agriculture explains the data as parsimoniously as a model relying exclusively on natural dispersal . Because the sampling of accessions was denser at intermediate latitudes than it was in southern Europe , we were not able to exclude roles for Spanish or Italian refugia or for a Mediterranean route of agriculture in producing the pattern of variation in current genomes . One possibility is that A . thaliana did follow the agricultural expansion , but only after it had already arrived in Europe via a natural colonization from glacial refugia . Similarly to the diffusion of human agriculturalist genes , the continuous pattern of variation in A . thaliana would then be explained by the genetic dilution of the eastern genes that might have resulted from admixture with local populations during the agricultural expansion phase . Although the current data set has a large representation of individuals along the Danubian route of agricultural expansion , genomic analysis of a larger sample from Spain and the Balkans , as well as from the key eastern region of Asia Minor , will have greater potential to distinguish among possible models for the evolutionary history of A . thaliana in Europe .
A set of 76 individuals containing both hierarchical population samples and stock center accessions was extracted from the sample of 96 individuals studied by Nordborg et al . [21] . The subset included all accessions within an interval of latitudes of ( 32°N , 65°N ) and within an interval of longitudes of ( −10°E , 40°E ) , i . e . all European accessions plus one from Libya ( Mt-0 ) . For the 76 individuals , the total set of 876 reliable alignments representing 0 . 48 Mb of the genome was used . A thorough description of the data set can be found in the Materials and Methods of [21] . The list of accessions used in this study can be found in Table S1 . Since Arabidopsis thaliana is largely homozygous , we used a haploid setting . To enable comparisons with results obtained in [21] from the program STRUCTURE version 2 . 0 , each fragment was treated as a multiallelic locus , so that two accessions had a different allele if they differed at any site in the fragment . To determine which clusters are generally robust to the assumption of continuous variation , we used a modified algorithm that includes spatially explicit prior distributions describing which sets of individuals are likely to have similar cluster membership [25] . In this approach , implemented in the program TESS [26] , clusters correspond to spatially and genetically continuous units separated by small discontinuities that occur where genetic barriers are crossed . The incorporation of a spatial component into the clustering model has the potential to determine if clines provide a sensible description of the underlying pattern of variation . We performed an admixture analysis using TESS version 1 . 1 , whose individual-based spatially explicit Bayesian clustering algorithm uses a hidden Markov random field model to compute the proportion of individual genomes originating in K populations [25] , [26] . The hidden Markov random field accounts for spatial connectivities by representing them as links in a network of individuals . In addition , the hidden Markov random field also incorporates decay of membership coefficient correlation with distance ( computed on the network ) , a property similar to isolation-by-distance . The network topology merely conveys information about which pairs of individual genomes are more likely to be assigned to the same clusters , and the network was automatically generated by the TESS program using a Dirichlet tessellation obtained from the accession spatial coordinates . To better account for potential geographic barriers , we modified the network by removing several links . For our application to A . thaliana , we imposed a network topology in which the skeleton of the topographic structure of European landmasses was mimicked ( Figure S1 ) . This topology was obtained after removing the longest Dirichlet edges in the automatically generated graph . Two values of the TESS interaction parameter were used , ψ = 0 . 6 and ψ = 1 , which can be viewed as a moderate and a strong value . This hyperprior parameter weights the relative importance given to spatial connectivities ( the value ψ = 0 recovers the model underlying STRUCTURE ) . Similar results were obtained from both the moderate and strong values , and only those for ψ = 0 . 6 are reported . TESS and STRUCTURE proceed with the determination of the number of clusters K in a similar way . However the TESS algorithm incorporates a regularization procedure that perhaps leads to a less ambiguous decision regarding K . Indeed K can be determined by sequentially increasing the maximal number of clusters , Kmax , and by running the program until the final inferred number of clusters , K , becomes less than Kmax . We used the admixture version of TESS , and we set the admixture parameter to α = 1 . The algorithm was run with a burn-in period of length 20 , 000 cycles , and estimation was performed using 30 , 000 additional cycles . We increased the maximal number of clusters from Kmax = 3 to Kmax = 8 ( 20 replicates for each value ) . Runs with Kmax = 5 led to either K = 3 or to K = 4 . For each run we computed the Deviance Information Criterion ( DIC ) [59] , a model-complexity penalized measure of how well the model fits the data . The smallest DIC values were obtained for Kmax = 5 . One accession , Mr-0 ( Italy ) , shared nearly equal membership in each of the Kmax clusters , regardless of the value of Kmax ( see the clustering tree in [60] for identification of Mr-0 as an outgroup accession ) . To a lesser extent , Bur-0 ( Ireland ) and Fei-0 ( Portugal ) exhibited similar patterns of shared membership . For Kmax = 5 , we performed 100 additional runs ( interaction parameter ψ = 0 . 6 , admixture parameter α = 1 ) , and we averaged the estimated admixture coefficients ( Q matrix ) over the ten runs with the smallest values of the DIC ( DIC ∼72 , 000 , s . d . = 30 ) . To account for label switching and to decide which of the clusters of each run corresponded to a specific label , we used the software CLUMPP version 1 . 1 [61] , whose greedy algorithm computed a symmetric similarity coefficient equal to 0 . 788 ( 100 random input sequences , G statistic ) . Spatial interpolation of admixture coefficients was performed according to the kriging method as implemented in the R packages ‘spatial’ and ‘fields’ [62] , [63] . One difficulty with fitting trend surfaces arises when the observations are not regularly spaced . To handle this issue we took the spatial correlation of the fitting errors into account by assuming that the errors had non-null covariance . Trend surfaces of degree two were adjusted using generalized least squares and exponential covariance with decay parameter h = 5 . The regression analysis of heterozygosities on geographic distances was based on 57 central European , eastern and western European accessions . The 57 individuals were grouped into seven samples as described in Table S2 . The seven samples were defined on the basis of geographic and genetic proximity , and provided a balance between pooled individual accessions and actual population samples . We did not include nine individuals that were either ambiguously assigned to clusters by TESS or that were geographically isolated . The German sample was restricted to six accessions , and diversity for this sample was estimated by using a resampling procedure ( mean over 100 replicates ) . We also ran a simulation study to evaluate the influence of the resampling strategy ( Text S1 ) . We used an ABC approach for inferring demographic parameters under four models of population growth . In the ABC approach , we assume that there is a multidimensional parameter of interest θ , and the observed value sobs of a set of summary statistics , S , is calculated for the data . The basic rejection sampling method generates random draws ( θi , si ) , where θi is sampled from the prior distribution , and si is measured from synthetic data , simulated from a generative model with parameter θi . Fixing the tolerance error , δ , only parameters θi such that |sobs−si|<δ are retained to form an approximate sample of size M from the posterior distribution , where | . | is the Euclidean norm . We used tolerance errors such that fractions of either 5% or 1% of the total number of simulations were retained . The four demographic scenarios were described in the text as Models A–D . The six-dimensional parameter θ included the mutation rate per bp per generation , μ ( ×10−8 ) , the population size at the onset of expansion , N1 , the time since the onset of expansion , t0 , the growth rate , r , the present equilibrium population size , N0 , and the time elapsed since the equilibrium phase , t1 . The variable mutation rate models included locus-specic rates , μj , obtained as independent realizations of an exponential prior distribution for which the hyperparameter was exponentially distributed with mean μ . Coalescent simulations were performed with the software MS [64] . Recombination within each locus was assumed , using an exponentially distributed prior of mean 0 . 3 for the effective recombination rate [65] . The prior distributions used in the four models are described in Table S3 . Twelve summary statistics were used to capture genomic information at the 648 loci , defined as the 25% , 50% and 75% quantiles ( quartiles ) of each of the distributions of the number of segregating sites , the mean number of pairwise differences between sequences , the Tajima D statistic , and the number of distinct haplotypes . The summary statistics were rescaled before comparison to the observed statistics . We divided each simulated summary statistic by the median absolute deviation – a robust estimate of the standard deviation – of the simulated statistics . Our ABC approach partially followed Beaumont et al . [29] , who added regression adjustment and smooth weighting to the Bayesian rejection algorithm of Pritchard et al . [30] . We dropped the regression adjustment step because it led to a poor fit during preliminary runs ( R2<0 . 25 ) . The second improvement of the original method – namely , smooth weighting – was retained in our analysis . Smoothing was implemented using the Epanechnikov kernel Kδ with window size δ to weight the parameters by Kδ ( |si−sobs| ) [29] . The same weights were also used when estimating the mean , the quartiles and the maximum of the posterior distribution . We computed the Bayes factor when evaluating the evidence of model 1 against model 2 ( where 1 and 2 are chosen among A , B , C and D ) as described in Results . The new formula can be seen as an improvement of the method that used the ratio of acceptances under the two models to approximate the Bayes factor , originally formulated aswhere Iδ is the indicator function Iδ ( t ) = 1 if t<δ , 0 otherwise . Note that in our case , Jeffreys' scale on degrees of belief should be interpreted more cautiously than the usual scale based on exact Bayesian computation [33] . The Bayes factors in Figure 3 and Table S5 correspond to the ratio of the weight of evidence of each model to the weight of evidence of the variant of model B with variable mutation rates . Two tolerance errors , δ0 . 01 and δ0 . 05 , corresponding to the 1% and 5% quantiles of the distance between the summary statistics obtained under the variant of model B with variable mutation rates and the observed summary statistics , were used when computing the Bayes factors . We selected 64 individuals from central Europe and western Europe and ten individuals from northern Europe ( northern Sweden and Finland ) . From the 876 fragments , we removed indels , sites with more than 20% missing data , and monomorphic sites . A total of 795 fragments and 11 , 134 SNPs remained . For each site , the remaining missing data was replaced by sampling alleles from the allele frequency distribution so that the final data set did not contain any missing data . We simulated data from model C using MS [64] . Forward in time , there is a period of constant population size followed by a period of growth and finally a period of constant population size ending in the present . We used the model parameters from the MAP estimates of model C ( see Table 1 ) , which received the most statistical support from the ABC analysis . We considered variable mutation rates per simulated fragment , taken from the same exponential distribution as used in the ABC analysis ( also in agreement with [66] ) . The recombination rate in a simulated fragment was set to 0 . 3 [65] . We assumed that the population split into two subpopulations some time T in the past , scaled by NCE = 135 , 000 , the estimated size of the central European population , and that migration occurred at rate m , scaled by NCE . The size of the northern population , NNE , was assumed to be 1/4 of the estimated size of the central European population , NCE . The growth scenario was assumed to be the same in the two populations , with only the population sizes differing . To approximate the likelihood of the parameters , we used two haplotype diversity statistics , the mean number of distinct haplotypes and the mean number of private haplotypes . To correct the number of distinct haplotypes and the number of private haplotypes statistics for sample size differences , we used the rarefaction method [35] , [36] to get estimates of these statistics for samples of size two to ten ( the sample size of the northern Swedish and Finnish population was equal to ten ) . This was done separately for each fragment , and finally we took the average across fragments . Simulations of a two-dimensional stepping stone model were performed using the program SPLATCHE 1 . 1 [40] . We modeled Europe using an array of demes that included topographic information borrowed from the online Geographic Information System GEODAS of the National Geographic Data Center . The map covered latitudes from 32°N to 65°N and longitudes in an interval of −10°E to 40°E . Topography was used to define carrying capacities for each deme . We divided Europe into an array consisting of 130×180 = 23 , 400 demes , each representing an area of ∼600 km2 . To account for the fact that A . thaliana inhabits lower altitude landscapes , carrying capacities were set to their highest values for altitudes below 200 m ( N = 5 , 000 ) . They were progressively decreased to N = 100 for altitudes higher than 1500 m using a nonincreasing step function ( N = 2 , 500 for altitudes between 200 m and 500 m , N = 1 , 000 for altitudes between 500 m and 1000 m , N = 500 for altitudes between 1000 m and 1500 m ) . At the beginning of the colonization process , a single deme was occupied . To date the onset of the spread , we based the origin at the north of the Black Sea ( 48°N , 35°E ) . We chose a logistic population growth model to describe the dynamics of population demography within each deme . The growth rate r was identical in each deme . Following [66] we set the mutation rate per base pair and per generation around u ∼10−8 , and the generation time corresponded to one year . Because the memory requirements of SPLATCHE are particularly high , we modified the mutation rate and the effective size in order to accelerate the generation time from one year to ten years ( this means that the model was simulated ten generations at a time ) . Values of the original population size were taken equal to N1 around 10 , 000 ( 5 , 000–15 , 000 ) . DNA sequences were simulated using the modified mutation rate v = 10−5 . Rescaling the generation time to a value tR = 10 years produced a level of nucleotide diversity close to the one present in the data ( Ne u = N1×v/tR ∼10−2 ) . Note that N1 cannot be compared to the value used in the non-spatial ABC simulations unless we restore the original mutation rates and generation times . After the correction , the values used in the spatial and non-spatial scenarios were actually similar . In simulations , we assumed that the population remained constant ( equal to N1 ) during 100 Ky before range expansion . To compare with the data in western and central Europe , we simulated the genealogies of 66 individuals located at the same spatial coordinates as the set of 66 accessions that excluded those from northern Sweden and Finland . The fit of simulated data to the real data was assessed by evaluating the distance between the empirical folded frequency spectrum computed from the non-coding sequences , and frequency spectra obtained from individuals simulated at the same locations . The distance used to compare folded spectra was the χ2 distance defined from four classes as follows: Class 1 ) minor allele frequency 1 ( total 28% ) ; Class 2 ) minor allele frequency 2–4 ( total 26% ) ; Class 3 ) minor allele frequency 5–12 ( total 25% ) ; and Class 4 ) minor allele frequency 13–33 ( total 21% ) . Five model parameters were varied: the time of the onset of spatial expansion t0 , the migration rate m , the growth rate within a newly colonized deme r , the effective population size at the beginning of range expansion N1 ( resized ) , and the location of the origin . Ideally one would use an ABC analysis to choose a subset of parameters that maximizes the posterior probability of the corresponding evolutionary scenario given prior distributions over these parameters . However performing an ABC analysis with geographically explicit simulations is prohibitively time-consuming , due to the large cost of a single simulation . In practice , we first performed a coarse search using fixed values of the starting date t0 ( equal to 8 , 000–12 , 000 BP ) and a random sampling design for the other parameters , exploring migration rates ( m ) within the range 0 . 1–0 . 8 and population size expansion rates ( r ) within the range 0 . 2–1 . 5 , and assuming that the starting point was located at coordinates ( 48°N , 35°E ) . This preliminary search found that values of migration rates around 0 . 2–0 . 3 , growth rates between 0 . 6 and 1 , and initial sizes of 5 , 000–10 , 000 individuals yielded non-significant χ2 P-values . These ranges of parameter settings for m and r corresponded to the saturation of a deme in 100–300 years . For most of the simulations , the length of the colonization phase was around 3 , 000–6 , 000 years , which corresponded to waves of advance varying from 0 . 5 to 1 km/year . In a second stage , we investigated the time at which the range expansion began , varying this time from t0 = 5 , 000 BP to t0 = 20 , 000 BP using r = 0 . 6 for the oldest dates . For the most recent dates , we increased r to 0 . 7 ( t0 = 10 , 000 ) , 0 . 9 ( t0 = 7 , 000 ) and 1 . 2 ( t0 = 5 , 000 ) , so that the colonization phase ended before the present day . Finally , we studied the explanatory power of twenty-four potential spatial origins throughout central and western Europe ( m = 0 . 25 , r = 0 . 6 , Figure 8B ) . | The demographic forces that have shaped the pattern of genetic variability in the plant species Arabidopsis thaliana provide an important backdrop for the use of this model organism in understanding the genetic determinants of plant natural variation . We investigated the demographic history of A . thaliana using novel population-genetic tools applied to a combination of molecular and geographic data . We infer that A . thaliana entered Europe from the east and spread westward at a rate of ∼0 . 9 kilometers per year , and that its population size began increasing around 10 , 000 years ago . The “wave-of-advance” model suggested by these results is potentially consistent with the pattern expected if the species colonized Europe as the ice retreated at the end of the most recent glaciation . Alternatively , it is also compatible with the possibility that A . thaliana—a weedy species—may have spread into Europe with the diffusion of agriculture , providing an example of the phenomenon of “ecological imperialism” described by A . Crosby . In this framework , just as weeds from Europe invaded temperate regions worldwide during European human colonization , weeds originating from the source region of farming invaded Europe as a result of the disturbance caused by the spread of agriculture . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"plant",
"biology/plant",
"genomes",
"and",
"evolution",
"ecology/evolutionary",
"ecology",
"ecology/spatial",
"and",
"landscape",
"ecology",
"genetics",
"and",
"genomics",
"genetics",
"and",
"genomics/population",
"genetics"
] | 2008 | Demographic History of European Populations of Arabidopsis thaliana |
Salmonella Typhimurium sequence type ( ST ) 313 produces septicemia in infants in sub-Saharan Africa . Although there are known genetic and phenotypic differences between ST313 strains and gastroenteritis-associated ST19 strains , conflicting data about the in vivo virulence of ST313 strains have been reported . To resolve these differences , we tested clinical Salmonella Typhimurium ST313 and ST19 strains in murine and rhesus macaque infection models . The 50% lethal dose ( LD50 ) was determined for three Salmonella Typhimurium ST19 and ST313 strains in mice . For dissemination studies , bacterial burden in organs was determined at various time-points post-challenge . Indian rhesus macaques were infected with one ST19 and one ST313 strain . Animals were monitored for clinical signs and bacterial burden and pathology were determined . The LD50 values for ST19 and ST313 infected mice were not significantly different . However , ST313-infected BALB/c mice had significantly higher bacterial numbers in blood at 24 h than ST19-infected mice . ST19-infected rhesus macaques exhibited moderate-to-severe diarrhea while ST313-infected monkeys showed no-to-mild diarrhea . ST19-infected monkeys had higher bacterial burden and increased inflammation in tissues . Our data suggest that Salmonella Typhimurium ST313 invasiveness may be investigated using mice . The non-human primate results are consistent with clinical data , suggesting that ST313 strains do not cause diarrhea .
Salmonella enterica serovar Typhimurium generally causes gastroenteritis in immunocompetent individuals . However , there is growing evidence to suggest that in certain susceptible populations , such as infants in sub-Saharan Africa and HIV-positive adults , Salmonella Typhimurium infection manifests as septicemia without any gastroenteritis [1 , 2 , 3] . There is a novel genotype of Salmonella Typhimurium circulating in Africa called sequence type ( ST ) 313 ( based on multi-locus sequence typing ) [4] . In contrast , the most common ST present throughout the rest of the world is ST19 [5] . Kingsley et al [4] compared the genome of an ST313 strain , D23580 , from Malawi , with the genomes of Salmonella Typhimurium ST19 gastroenteritis-associated strains and observed that D23580 appeared to have undergone genome degradation similar to what has been observed for Salmonella Typhi and Paratyphi A [6] . Okoro et al [7] further describe two lineages of invasive Salmonella Typhimurium , lineage I and lineage II , which emerged at the same time as HIV in Africa . The authors hypothesize that lineage I isolates have been replaced by lineage II isolates due to the use of chloramphenicol for treatment of invasive non-typhoidal Salmonella ( iNTS ) . Phenotypic analysis has also shown that Salmonella Typhimurium ST313 strains differ from ST19 strains . Two groups determined that ST313 isolates produce less flagella and are less pro-inflammatory than ST19 isolates [8 , 9] . We previously showed that ST313 strains are less motile than ST19 [9] whereas Yang et al [10] observed that D23580 ( ST313 ) is just as , if not more , motile than Salmonella Typhimurium SL1344 ( ST19 ) . In vitro , Ramachandran et al [9] showed that invasive ST313 isolates from Mali survive well within macrophages and cause less host cell death than clinical ST19 isolates . The in vivo infection data for Salmonella Typhimurium ST313 has been mixed , with several groups showing conflicting data . ST313 strains have been shown to colonize systemic sites of C57BL/6 and BALB/c mice [10 , 11] . Yang et al [10] found that D23580 ( ST313 ) was able to more rapidly colonize the spleen than SL1344 ( ST19 ) . Similarly , D23580 caused severe invasive infection in chickens and rapidly infected the spleen and liver by day 3 post-infection ( p . i . ) [12] . In chickens , ST313 was less able to colonize the gastrointestinal tract than ST19 [12] . Several groups have attempted to determine whether ST313 strains are able to elicit inflammation and gastroenteritis . Okoro et al [11] tested the ability of ST313 isolates to induce an inflammatory response in streptomycin-treated C57BL/6 mice and found no significant difference in colonization of the cecum but saw reduced inflammation compared to SL1344 ( ST19 ) . In contrast , Singletary et al [13] observed no significant difference in intestinal pathology of streptomycin-treated CBA/J mice infected with either D23580 ( ST313 ) or IR715 ( ST19 ) . Fluid accumulation and inflammation of ST313 has also been evaluated in ileal loop models . In bovine ileal loops , ST313 strains induced significantly less fluid accumulation than ST19 isolates [11] . In a rhesus macaque ileal loop model , no difference was observed between D23580 ( ST313 ) and IR715 ( ST19 ) in terms of fluid accumulation or inflammatory cytokine expression [13] . In this study , our goal was to evaluate virulence of Salmonella Typhimurium ST313 strains isolated from the blood of infants in Mali in various animal models . In particular , we sought to confirm whether previous findings from other groups are robust and to definitively assess whether ST313 can produce gastroenteritis in a rhesus macaque infection model . This would provide critical evidence to support the clinical data which suggests that ST313 strains do not produce diarrhea in infants and children .
All animal experiments were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All protocols were reviewed and approved by the Animal Care and Use Committee at the University of Maryland , School of Medicine . The IACUC protocol numbers for the mouse experiments were #0715010 and #0212016 and for the rhesus macaque experiment the protocol number was #1115011 . Details of primate welfare , including information about housing , feeding and environmental enrichment , pain relief and method of euthanasia are described in S1 Text . The anonymized invasive NTS isolates from Mali were previously collected under a clinical protocol reviewed by the Ethics Committee of the Faculté de Medécine de Pharmacie et d'Odontostomatologie , University of Mali , and by the Institutional Review Board of the University of Maryland , Baltimore as described [14] . Invasive Salmonella Typhimurium ST313 ( D65 , Q55 , S11 ) and Salmonella Typhimurium ST19 ( I77 , I41 , S52 ) have previously been isolated from the blood of infants in Mali , West Africa [14 , 15 , 16] . These clinical strains were maintained on animal product-free Hy-Soy ( HS ) agar ( 0 . 5% Hy-yest [Kerry Biosciences , Beloit , WI] , 1% Soytone [TEKNova , Hollister , CA] , 0 . 5% NaCl [American Bio , Natick , MA] ) . Data were generally analysed by using Student’s unpaired t-test or Mann-Whitney test , two-tailed . Analysis of diarrheal episodes was carried out using the Mann-Whitney test , one-tailed , and Fisher’s exact test , one-tailed . Statistical analysis was completed using Prism 5 ( GraphPad software , Inc , La Jolla , CA ) .
No significant difference in LD50 was observed for ST313 versus ST19 strains . Four-week-old ( juvenile ) BALB/c mice were challenged perorally ( p . o . ) with three strains of Salmonella Typhimurium ST19 ( I77 , I41 , S52 ) and ST313 ( D65 , Q55 , S11 ) . The oral LD50 values were highly variable between strains , but showed no clear trend between sequence types ( Table 1 ) . When eight-week-old BALB/c mice were infected p . o . , the geometric mean oral LD50 for the ST19 strains was determined to be 1 . 2 x 104 CFU and for the ST313 strains , 5 . 0 x 104 CFU . Similarly , in CD-1 mice , the i . p . LD50 was 1 . 1 x 104 CFU and 5 . 1 x 104 CFU for the ST19 and ST313 strains , respectively . We next investigated the infection kinetics of Salmonella Typhimurium ST19 and ST313 strains from Mali in vivo . CD-1 mice were orally infected and blood was collected at 3 , 24 , 72 and 168 h p . i . and bacterial loads were determined by performing viable counts . There were no significant differences between Salmonella Typhimurium ST19 and ST313 strains in bacterial loads of blood , spleen or liver at any of the time-points evaluated ( Fig 1A , 1B and 1C ) . However , there was a trend towards higher bacteremia at 24 h in S . Typhimurium ST313-infected CD-1 mice . When we tested the bacterial loads in the blood of BALB/c mice , we did not observe a significant difference in the bacterial counts between Salmonella Typhimurium ST19 and ST313 at 3 h p . i . , however , a significant increase in bacterial load was observed for Salmonella Typhimurium ST313 ( P < 0 . 001; Student’s t-test , two-tailed ) at 24 h when compared to Salmonella Typhimurium ST19 ( Fig 1D ) . Indian rhesus macaques were challenged perorally with either Salmonella Typhimurium I77 ( ST19; n = 3 ) or Salmonella Typhimurium D65 ( ST313; n = 3 ) . Monkeys were monitored for diarrhea , dysentery , and lethargy . The ST19-infected animals had a significantly higher number of days with moderate-to-severe diarrhea than those infected with ST313 ( P = 0 . 05 , Mann-Whitney , one-tailed; Table 2 ) . In addition , by using moderate-to-severe diarrhea as an endpoint , significantly more ST19-infected animals had diarrhea than those infected with ST313 ( P = 0 . 05 , Fisher’s exact test ) . By Day 10 , all diarrhea had completely resolved . On days 3 and 4 , lethargy and dysentery , respectively , were also observed in monkey DFK0 ( infected with strain I77 ) . Monkeys infected with Salmonella Typhimurium D65 presented with very mild diarrhea . The monkeys were monitored for changes in body temperature and body weight on Days 0 , 1 , 3 , 7 , 15 and 21 or 22 . Close to 10% and 7% reduction in body weight was observed in two monkeys ( DFK0 and DFP4 ) infected with Salmonella Typhimurium I77 ( ST19 ) at 3 days p . i . ( Fig 2A ) . At 7 days p . i . , an 8% reduction in body weight was observed for DG15 which was infected with Salmonella Typhimurium I77 ( ST19 ) . Only one monkey ( DFV4 ) infected with Salmonella Typhimurium D65 ( ST313 ) showed weightloss , with a 7% reduction in body weight 3 days p . i . A similar pattern was observed for body temperature ( Fig 2B ) . Most of the monkeys maintained their body temperature p . i . However , two monkeys infected with Salmonella Typhimurium I77 ( ST19 ) had a mild reduction in body temperature at day 3 p . i . Complete blood count analysis was performed on days 0 , 7 , and 15 . Monkeys DFV4 and DFN1 infected with Salmonella Typhimurium D65 ( ST313 ) had stable white blood cell ( WBC ) ( Fig 2C ) and neutrophil ( Fig 2D ) counts . Only one monkey ( DFT9 ) infected with Salmonella Typhimurium D65 ( ST313 ) showed an increase in WBC and neutrophil counts on day 7 p . i . This animal also had increased lymphocytic count ( 6240/μl ) by day 15 p . i . when compared to its baseline lymphocytic count of 4002/μl on day 0 of the study ( pre-infection ) . All three monkeys infected with Salmonella Typhimurium I77 ( ST19 ) displayed a trend towards an increase in WBC and neutrophil counts on day 7 p . i . ( P > 0 . 05; Fig 2C and 2D ) . Fecal samples from infected monkeys were collected on days 1 to 7 , 9 , 11 , 15 , 18 and 21 or 22 . High levels of the bacteria were detected for both groups of monkeys . On days 1 and 2 , there were no significant differences in bacterial load in the feces ( P > 0 . 05 ) ( Fig 3 ) . However , on days 3 and 4 p . i . , there was a significant increase in bacterial shedding in the feces of monkeys infected with Salmonella Typhimurium I77 ( ST19 ) , compared to monkeys infected with Salmonella Typhimurium D65 ( ST313 ) ( P = 0 . 006; Student’s t-test , two-tailed ) . All the monkeys that were infected with Salmonella Typhimurium D65 ( ST313 ) stopped shedding bacteria by day 10 p . i . However , two monkeys infected with Salmonella Typhimurium I77 ( ST19 ) , continued to shed bacteria in the feces up to 18 days p . i . Blood was collected on days 1 , 3 , 15 and 21 or 22 and tested by blood culture . One animal ( DFT9 ) infected with Salmonella Typhimurium D65 ( ST313 ) and another animal ( DG15 ) infected with Salmonella Typhimurium I77 ( ST19 ) had circulating bacteria in their blood on days 1 and 3 , respectively , p . i . ( Table 3 ) . At euthanasia , qualitative culture from tissues was conducted by obtaining swabs of spleen , liver , lung , ileum , colon , MLN and axillary lymph nodes and the presence of Salmonella Typhimurium was detected by culture . Salmonella Typhimurium D65 ( ST313 ) was detected in the colon of one animal ( Table 3 ) . Spleen , liver , ileum , colon and MLN were also collected from all the monkeys at euthanasia and quantitative bacterial counts were performed . One monkey ( DFP4 ) infected with Salmonella Typhimurium I77 ( ST19 ) had 1 . 9 x 103 CFU/g of ileum and 5 . 7 x 104 CFU/g of colon ( Table 3 ) . Two monkeys ( DFK0 and DG15 ) infected with Salmonella Typhimurium I77 ( ST19 ) also had 4 x 102 CFU/g and 2 x 102 CFU/g , respectively , isolated from their mesenteric lymph nodes . Hematoxylin and eosin-stained tissue sections of liver , spleen , colon , ileum and MLN from monkeys infected with either Salmonella Typhimurium I77 ( ST19 ) or D65 ( ST313 ) were analyzed for histopathology . Monkey DFK0 ( infected with Salmonella Typhimurium I77 [ST19] ) displayed pathological alterations in the liver , colon , ileum and MLN . Liver sections from this animal displayed multiple foci of lymphocytic infiltration and areas of necrosis ( Fig 4 ) . Focal necrosis and erosion of portions of the colonic epithelium was also evident . Areas of the colon displayed moderate multifocal lymphoplasmacytic colitis . The colonic mucosal epithelium was superficially eroded . The mucosal epithelium that was intact was lined by bacterial debris observed in the lumen and brush border of the colonic epithelium . Infiltration of a moderate number of lymphocytes , plasma cells and eosinophils in the lamina propria areas of the colon were evident . Areas of lymphoid hyperplasia were observed in the submucosal areas of the colon . Sections of the ileum from this animal showed moderately to severely blunted villi displaying moderate amounts of submucosal edema . The lamina propria displayed moderate numbers of lymphocytes , plasma cells and scattered eosinophils . The ileum displayed moderate multifocal lymphoplasmacytic and eosinophilic ileitis with blunting and lymphangiectasia . Multiple areas of granulomas composed of giant cells and eosinophils were seen in the MLN of this animal . The MLN changes indicated lymphoid hyperplasia and moderate granulomatous and eosinophilic lymphadenitis . Overall all animals infected with the Salmonella Typhimurium I77 strain ( ST19 ) displayed varying degrees of pathology in the liver , ileum , colon and MLN . Monkeys belonging to the Salmonella Typhimurium D65 ( ST313 ) infected group did not show any significant pathology in any of the organs evaluated ( Fig 4 ) .
In our present study , we evaluated the virulence of Salmonella Typhimurium ST19 and ST313 clinical strains in different animal models of infection . We first determined the i . p . LD50 of three Salmonella Typhimurium ST19 and ST313 strains in CD-1 mice and in adult and juvenile BALB/c mice ( peroral infection ) . In all three models , our LD50 data suggests that the ST19 and ST313 genotypes are equally virulent in mice . These findings corroborate a recent study that showed that Salmonella Typhimurium ST313 isolates are not human host-restricted and instead produce an invasive phenotype in experimentally infected chickens [12] . When we infected BALB/c mice with Salmonella Typhimurium D65 ( ST313 ) and I77 ( ST19 ) we found significantly more ST313 bacteria in the blood at 24 h p . i . We hypothesize that this model of p . o . infection of BALB/c mice for 24 h could be used to examine the pathogenesis of ST313 strains and host responses to these bacteria . We further tested the reference ST19 ( SL1344 ) and ST313 ( D23580 ) strains in BALB/c mice to determine differences in bacterial burden post peroral infection . In contrast to the results obtained by Yang et al [10] , who showed a higher bacterial burden for D23580 ( ST313 ) than SL1344 ( ST19 ) in the spleen at day 3 and 5 p . i . , we observed no difference in bacterial load between D23580 and SL1344 in the spleen , liver or blood ( S2 Text and S1 Fig ) . Further work will need to be performed to determine if all the different iNTS models in mice are robust and can be repeated by various laboratories . Interestingly , we also were not able to replicate the motility data reported by Yang et al [10] ( S2 Text and S2 Fig ) . We have previously found that ST313 strains from Mali are less motile than ST19 strains [9] . Here , we repeated our analysis of I77 ( ST19 ) and D65 ( ST313 ) and also included some of the strains that were tested by Yang et al [10]; D23580 , SL1344 and A130 . We were able to replicate our previous findings whereby ST19 strains ( SL1344 and I77 ) were more motile than ST313 strains ( D65 , D23580 , A130 and 5579 ) . In contrast , Yang et al [10] found that D23580 was more motile than SL1344 , which in turn , was more motile than A130 . Our data ( previous and present ) is supported by Carden et al [8] who showed that ST313 isolates express less fliC than ST19 isolates . Collectively , our data differs from Yang et al [10] in terms of in vivo virulence as well as motility . This could potentially be due to differences in the method of bacterial propagation or perhaps passaging of the bacteria has resulted in changes in the strains . Our streptomycin model data was similar to Okoro et al [11] in that we did not observe a difference in bacterial load of the cecum ( S2 Text and S3 Fig ) . However , unlike this previous study , we did not detect any differences in pathology . Classical studies performed in the 1970’s showed that when 2–3 kg rhesus macaques were given 5 x 1010 CFU Salmonella Typhimurium , 80% of challenged animals exhibited diarrhea which lasted until 48–72 h p . i . [17 , 18 , 19] . We previously evaluated Salmonella Typhimurium I77 ( ST19 ) and a candidate live-attenuated Salmonella Typhimurium vaccine in SIV-infected and SIV-uninfected Indian rhesus macaques with collaborators at the Vaccine Research Center , NIH [20] where we observed peak diarrhea 4–6 days p . i . in I77-infected animals . In the present study , we wanted to determine whether ST313 causes gastroenteritis using this model . For the first time , we have successfully shown that an ST313 strain does not elicit gastroenteritis in non-human primates . This data is supported by a previous study in which ST313 isolates were less able to induce fluid accumulation than ST19 isolates in bovine ligated loops [11] . Singletary et al [13] evaluated D23580 in rhesus macaque ligated ileal loops and found no difference in fluid accumulation compared to IR715 ( ST19 ) . They also saw no difference in inflammatory cytokine gene expression . Similarly , we saw no difference in proinflammatory cytokines circulating in sera of D65 and I77 infected rhesus macaques ( S2 Text and S4 Fig ) . It is not clear why Singletary et al [13] did not observe any difference in fluid accumulation between ST19 and ST313 . In this study , as well as our previous study [20] , we used Indian rhesus macaques . Other groups have shown that the origin of non-human primates plays a role in their susceptibility to specific pathogens . A study conducted in rhesus macaques of Indian and Chinese origin displayed varying degrees of SIV pathogenesis in the two groups of animals [21] . Differential responses to Shigella infection have been reported in cynomolgus macaques of Chinese versus Mauritian origin [22] . If Singletary et al [13] used Chinese rhesus macaques , this may explain why their results differed from our own . There were several limitations of our study . Firstly , we were not able to determine whether ST313 is indeed more invasive than ST19 . Using blood culture , we detected I77 in one animal 3 days p . i . and D65 in one animal 1 day p . i . We believe that we were not able to detect bacteria in blood in additional animals due to the low volume tested by blood culture ( 3 ml ) and also due to the limitations of blood culture itself which has low sensitivity [23] . We also were not able to evaluate bacteria in deep organs at early time-points as we wanted to monitor clinical signs and observe the entire progression of disease . We detected I77 in the ileum , colon and MLN at euthanasia ( day 21 or 22 ) but only detected D65 in the colon of one animal . Now that we have established the model , we believe that additional experiments will allow us to determine whether ST313 is indeed more invasive than ST19 . This could be achieved by examining bacterial burden in deep organs at early time-points ( e . g . , 3 days p . i . ) and assessing inflammation by measuring pro-inflammatory cytokine production as well as pathological changes . Based on our in vitro data , we hypothesize that ST313 strains will be found in the blood and in deep organs ( spleen and liver ) at higher levels than ST19 bacteria; and will induce less pro-inflammatory cytokines . Taken together , our data suggest that Salmonella Typhimurium ST313 isolates from sub-Saharan Africa are more invasive than ST19 isolates and do not elicit gastroenteritis . These findings support the clinical data whereby ST313 strains do not produce diarrhea in infants and children . | Salmonella Typhimurium sequence type ( ST ) 19 causes self-limiting gastroenteritis throughout the world . In recent years , a new ST of Salmonella Typhimurium , ST313 , has been circulating in sub-Saharan Africa and causes fever in infants and immunocompromised individuals in the absence of gastroenteritis . The differences in pathogenesis between Salmonella Typhimurium ST313 and ST19 is not clearly understood in animal models . In our current study , we examined possible differences between ST19 and ST313 strains in different animal models . We found that ST313 strains could replicate in the blood of mice to higher levels than ST19 strains . Furthermore , when rhesus macaques were orally infected with Salmonella Typhimurium ST19 or ST313 , we observed increased diarrhea , inflammation and higher bacterial burden in ST19-infected monkeys compared to ST313-infected monkeys . Our data supports the clinical evidence which suggests that Salmonella Typhimurium ST313 do not cause gastroenteritis and instead are more invasive than ST19 strains . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"vertebrates",
"animals",
"salmonellosis",
"mammals",
"primates",
"animal",
"models",
"bacterial",
"diseases",
"diarrhea",
"model",
"org... | 2017 | Virulence of invasive Salmonella Typhimurium ST313 in animal models of infection |
Trachomatous trichiasis ( TT ) needs to be managed to reduce the risk of vision loss . The long-term impact of epilation ( a common traditional practice of repeated plucking of lashes touching the eye ) in preventing visual impairment and corneal opacity from TT is unknown . We conducted a randomized controlled trial of epilation versus surgery for the management of minor TT ( fewer than six lashes touching the eye ) in Ethiopia . Here we report the four-year outcome and the effect on vision and corneal opacity . 1300 individuals with minor TT were recruited and randomly assigned to quality trichiasis surgery or repeated epilation using high quality epilation forceps by a trained person with good near vision . Participants were examined six-monthly for two-years , and then at four-years after randomisation . At two-years all epilation arm participants were offered free surgery . At four-years 1151 ( 88 . 5% ) were re-examined: 572 ( 88% ) and 579 ( 89% ) from epilation and surgery arms , respectively . At that time , 21 . 1% of the surgery arm participants had recurrent TT; 189/572 ( 33% ) of the epilation arm had received surgery , while 383 ( 67% ) declined surgery and had continued epilating ( “epilation-only” ) . Among the epilation-only group , 207 ( 54 . 1% ) fully controlled their TT , 166 ( 43 . 3% ) had minor TT and 10 ( 2 . 6% ) had major TT ( >5 lashes ) . There were no differences between participants in the epilation-only , epilation-to-surgery and surgery arm participants in changes in visual acuity and corneal opacity between baseline and four-years . Most minor TT participants randomised to the epilation arm continued epilating and controlled their TT . Change in vision and corneal opacity was comparable between surgery and epilation-only participants . This suggests that good quality epilation with regular follow-up is a reasonable second-line alternative to surgery for minor TT for individuals who either decline surgery or do not have immediate access to surgical treatment .
Trachoma is the leading infectious cause of blindness worldwide [1] . Trachomatous trichiasis ( TT ) is the late stage scarring sequelae of repeated conjunctival Chlamydia trachomatis infection and inflammation in which the upper eyelid is distorted and rolled inwards ( entropion ) and the eyelashes turn towards the eye [2] . Trachoma leads to visual impairment through the damaging effect of trichiasis on the cornea . The risk of sight loss is directly correlated with disease severity , becoming more frequent with increasing severity of trichiasis [3–6] . The clinical phenotype ranges from a single aberrant eyelash touching the eye ( without entropion ) to the whole eyelid rolled inwards [7] . Some lashes may scratch the cornea directly while others are peripheral . Trichiasis is usually grouped , based on the number of eyelashes touching the eye into minor TT ( 1–5 lashes touching the eye ) and major TT ( >5 lashes touching the eye ) [5 , 8] . Globally , the most recent World Health Organisation ( WHO ) estimate suggested 8 million people had trichiasis in 2009 [1] . Updated disease estimates will become available in the next few years from the Global Trachoma Mapping Programme . Eyelid surgery is performed to correct the anatomical abnormality , in the expectation that this reduces the risk of sight loss [4 , 9] . The WHO advises that “all patients should be offered surgery for entropion trichiasis” [9] . However , up to half of the individuals with trachomatous trichiasis may not have significant entropion [7] . Therefore , there is a degree of ambiguity about how programmes should manage patients with non-entropic trichiasis , particularly those with only a few lashes touching the eye . Despite considerable efforts to scale-up surgery programmes only around 150 , 000 people per year have been reported as treated surgically in recent years worldwide [10] . It has been estimated at the current rate the trichiasis backlog ( ignoring incident cases ) would not be dealt with until 2032 , twelve years after the 2020 target for controlling trachoma [11] . Given the current surgical rate in Ethiopia , the country with the greatest burden of trichiasis , it will take more than 10 years to clear the backlog [12] . Many individuals with trichiasis , particularly those with mild disease , decline surgery , even when this is provided free and close to home [13–16] . Lack of time and fear of surgery are leading reasons for poor surgical uptake , suggesting a need for non-surgical , community-based management strategies for those declining surgery [8 , 13 , 17] . Poor surgical outcomes ( recurrent trichiasis or an unsatisfactory cosmetic appearance ) may also deter people from accepting surgery [5 , 18–20] . Epilation is a widespread traditional practice in many trachoma endemic societies , with up to 70% of people with trichiasis using this treatment strategy [3 , 6 , 13 , 21] . It involves the repeated plucking of lashes touching the eye with forceps [3 , 4 , 8] . Many individuals who decline surgical treatment consider epilation an acceptable alternative [13] . In view of the problems in delivering the necessary volume of surgery , the high rate of refusals in some areas and concerns about the quality of programmatic surgical outcomes , we conducted a randomized controlled non-inferiority trial of epilation versus surgery for minor trichiasis in Ethiopia; the two-year follow-up results have been reported [22] . With respect to the primary outcome of progression to major trichiasis there was an inconclusive result , relative to the predetermined non-inferiority margin of 10% . However , at two years there was no difference in the change in visual acuity or change in corneal disease between the two groups . At the two-year time-point all individuals who had been randomized at baseline to epilation were offered free surgery , however , only one third accepted . Here we report the four-year outcomes of study participants .
This study was approved by the National Health Research Ethics Review Committee ( NHRERC ) of the Ethiopian Ministry of Science and Technology , the London School of Hygiene and Tropical Medicine Ethics Committee and Emory University Institutional Review Board . Potential participants were provided with both written and oral information in Amharic about the trial . For those agreeing to participate , written informed consent in Amharic was required prior to enrolment . If the participant was unable to read and write , the information sheet and consent form were read to them and their consent recoded by witnessed thumbprint . The detailed Trial Protocol is described in S1 Text and the CONSORT statement in Text S2 of the report of the two-year results [22] . The trial methods and results up to two-years have been published previously [22] . Briefly , 1300 individuals aged 18 years or over with previously un-operated minor trichiasis were recruited in West Gojjam , Amhara Region , Ethiopia from March to June 2008 . At baseline , unaided and pinhole LogMAR visual acuities were measured at 4 metres using an ETDRS equivalent Tumbling-E LogMAR chart and the eyes were examined using 2 . 5x magnification loupes by a single ophthalmologist ( SR ) , and graded according to the detailed WHO FPC Trachoma grading system . Standardised high-resolution digital photographs were taken of each of the clinical features . In individuals with bilateral trichiasis one eye was randomly designated as the “study eye” although both eyes were treated . Following baseline assessment , participants were randomised to one of two intervention groups: ( 1 ) posterior lamella tarsal rotation surgery , or ( 2 ) repeated epilation using high quality , machine-manufactured epilation forceps ( Tweezerman ) . Surgery was performed by five experienced Integrated Eye Care Workers ( IECWs ) , chosen on the basis of the quality of their surgery . The surgeons received refresher training and underwent a standardisation process . Individuals randomised to the epilation group were each given two pairs of epilation forceps; the participant and an accompanying adult ( “epilator” ) with good near vision were trained to perform epilation . The procedure was explained and demonstrated to them by a field worker , who then in turn watched and checked the technique of the relative / patient in performing epilation . Participants were followed-up at 6 , 12 , 18 and 24 months and re-assessed using the same protocol . Participants who showed evidence of disease progression during the follow-up period , defined as five or more lashes touching the eye or corneal changes related to observed lashes , were immediately offered primary surgery ( epilation arm ) or repeat surgery ( surgery arm ) to be performed by a senior surgeon . New epilating forceps were provided for epilation arm participants as required . Individuals with other ophthalmic pathology ( e . g . cataract ) were referred to the regional ophthalmic service in Bahirdar . At the end of the trial at two-years all epilation arm participants were offered free trichiasis surgery in the community . Some individuals accepted this , but the majority chose to continue epilating . About four years after enrolment participants were invited for a follow-up assessment ( March to August 2012 ) . They were notified by a letter sent out through the village administration teams , which explained the purpose , date and place of follow-up assessment . People not able to come to the health facilities for assessment were assessed in their homes . Reasons for loss to follow-up were identified and documented . Participants were interviewed in Amharic about their vision , ocular symptoms , epilation forceps retention and history of epilation and/or surgery since the two-year follow-up . Individuals were considered to be “frequent epilators” if they performed epilation at least once in two months . Participants enrolled into the epilation arm were asked about their views on epilation and epilation practices . Unaided and pinhole LogMAR visual acuities were measured at 4 metres . Ophthalmic examinations were conducted in the same manner as the previous follow-ups by a single observer ( EH ) who had also conducted the 6 and 18-month follow-ups . Grades of trichiasis , entropion , and corneal opacity were documented and the eyes were photographed . The examiner was masked to the intervention allocation . The treatment allocation code had been previously broken for the two year analysis . Recurrent trichiasis was defined as one or more lashes touching the eye or evidence of epilation or repeat surgery . Clinical evidence of epilation was identified by the presence of broken or newly growing lashes , or areas of absent lashes . Change in corneal opacity was assessed by direct comparison of the baseline and four-year cornea photographs . Photographs were viewed on a computer screen at about 10x magnification by a single masked ophthalmologist ( MJB ) . These were graded as improved , no change , or worse . Patients initially randomised to epilation were sub-divided according to whether or not they had surgery during the four-year follow-up period . These are subsequently referred to as epilation-to-surgery and epilation-only groups , respectively . Baseline and four-year follow-up demographic and clinical characteristics , and the change in clinical phenotypes during follow-up were compared between the surgery-only , epilation-only and epilation-to-surgery participants using X2 tests . The Wilcoxon rank-sum test was used to test for significant differences in number of lashes . Logistic regression was used to assess factors associated with trichiasis progression within the epilation-only group , to identify predictors of surgery uptake in all epilation arm participants and corneal opacity deterioration in all study participants . Ordinal logistic regression was used to assess factors associated with reduction in visual acuity by four-years in all study participants . Variables that were associated with the outcome on univariable analyses at a level of p<0 . 05 were retained in the multivariable logistic regression models .
At baseline , 1300 individuals were recruited , of whom 650 were randomised to immediate surgery and 650 to epilation ( Fig . 1 ) . The baseline demographic and clinical characteristics of all 1300 participants have been previously described along with the results up to two-years follow-up [22] . At four-years 1151 ( 88 . 5% ) were re-examined: 579 ( 89% ) participants from the surgery arm and 572 ( 88% ) from the epilation arm . The baseline demographic and clinical characteristics of these individuals are shown in Table 1 ( columns A and B ) . They were all Amharan with an average age of 50 . 3 years ( SD 14 . 4; range 18–95 ) at baseline . The majority were female ( 767 , 66 . 6% ) and illiterate ( 1034 , 89 . 8% ) . Of those initially randomised to the epilation arm , 189 ( 33% ) had undergone trichiasis surgery by the four-years follow-up , while the other 383 ( 67% ) were still epilating only ( Fig . 1 ) . There were 149 participants who were not re-examined at four-years . The reasons for not being re-examined are shown in Fig . 1 . In summary , at baseline this group was slightly older ( p <0 . 001 ) , had worse presenting LogMAR visual acuity ( p <0 . 001 ) and had slightly more corneal opacification ( p = 0 . 04 ) than the 1151 re-examined at four-years . Other variables such as sex , literacy and other baseline clinical characteristics including trichiasis severity and corneal opacity were comparable between those re-examined and not re-examined at four-years . Amongst those re-examined at four-years , there were more female participants randomised to surgery compared to those randomisation to epilation ( p = 0 . 03 ) , however , there was no difference in age or literacy status ( Table 1 ) . Baseline clinical characteristics were balanced between the randomisation arms ( Table 1 ) , with the exception of central corneal opacity ( CC2/CC3 ) , which was more frequent in the epilation group ( 128; 22 . 4% ) than the surgery group ( 95; 16 . 4% ) . The 383 epilation arm participants who were still only epilating at four-years ( epilation-only ) were slightly older than both the 579 surgery arm and 189 epilation-to-surgery participants ( mean ages 50 . 0 , 49 . 0 and 46 . 4 years respectively; p<0 . 002 , Table 1 ) . Within the epilation arm , the epilation-only group had slightly less baseline trichiasis than the epilation-to-surgery group ( p<0 . 001 ) . The epilation-only group had slightly less baseline entropion than both the surgery arm ( p = 0 . 02 ) and epilation-to-surgery participants ( p = 0 . 001 ) . The baseline LogMAR visual acuity of the epilation-only group was slightly worse than both the surgery-arm ( p = 0 . 007 ) and the epilation-to-surgery arm ( p = 0 . 02 ) , Table 1 . During the four-year period , recurrent trichiasis developed in 122 ( 21 . 1% ) of the 579 participants randomised to surgery . Of these 122 , 61 ( 50 . 0% ) were practicing epilation at four years . Twenty-one ( 3 . 6% ) had undergone repeat surgery during the four years . Among the 189 epilation-to-surgery participants , 42 ( 22 . 2% ) had failed surgery ( recurrent trichiasis ) , of whom 27 ( 64 . 2% ) were epilating . At four-years , among the 383 epilation-only participants , 207 ( 54 . 1% ) were successfully epilating ( had no lashes touching the eye ) , 166 ( 43 . 3% ) had minor trichiasis ( <6 lashes ) and 10 ( 2 . 6% ) had major trichiasis ( >5 lashes ) . Overall , the epilation-only group had more trichiasis , entropion and lid margin conjunctivalisation than either the surgery arm or the epilation-to-surgery group ( Table 2 ) . Changes in clinical phenotype between baseline and four-years are shown in Table 3 . The outcomes of surgery in terms of trichiasis , entropion and conjunctivalisation were mostly very good . In the epilation-only group , the number of lashes touching the eye increased in 82 ( 21 . 5% ) , however , this was mostly a minor increase , with progression from minor to major trichiasis in six ( 1 . 6% ) of the 383 epilation only patients . The majority of individuals had less or the same level of trichiasis . Independent risk factors for 5+ lashes touching were baseline age ≥50 years , ≥3 lashes at baseline , and infrequent epilation ( Table 4 ) . At four-years the surgery arm and epilation-to-surgery participants had slightly better LogMAR visual acuity than the epilation-only group ( Table 2 ) . However , this difference is attributable to the pre-existing difference in baseline vision ( reported above , Table 1 ) , as there was no difference between the different groups in terms of change in visual acuity between baseline and four-years ( Table 3 ) . Age ≥50 years , male gender , detection of other visually impairing conditions ( e . g . cataract ) , baseline corneal opacification ( CC2/CC3 ) and incident/progressive corneal opacification were independently associated with deterioration in visual acuity ( Table 5 ) . Overall , few individuals had a change in corneal opacification , determined by the comparison of baseline and four-year photographs ( Table 3 ) . There was no difference in change of corneal opacification between the surgery arm and the epilation-only group or the epilation-to-surgery group ( Table 3 ) . Incident or progressive corneal opacification was independently associated with age ≥50 years and the presence of some baseline corneal opacification ( CC2/CC3 ) , Table 6 . Among the epilation-only group 259 ( 67 . 6% ) were “frequent epilators” ( at least once in two months ) between the two and four-year follow-ups . They were asked about their experience: 185 ( 72% ) reported “no problem” , 37 ( 14 . 3% ) did not always find the trained epilators when needed , 17 ( 6 . 6% ) had found epilation uncomfortable , the trained epilators of 9 ( 3 . 5% ) reported difficulty epilating , and 7 ( 2 . 7% ) had found people unwilling to epilate them . Among the 124 who were not frequently epilating , 119 ( 96% ) did not have a specific reason for not epilating other than not needing to; the other five had nobody to perform epilation . Epilation frequency was not associated either with age ( p = 0 . 31 ) or gender ( p = 0 . 60 ) . Compared to the “infrequent epilators” , the “frequent epilators” had a slightly higher lash burden at baseline ( Median: 1 vs 1 , Wilcoxon rank-sum test , p = 0 . 19 ) , but lower lash burden at four-years ( Median: 0 vs 1 , Wilcoxon rank-sum test , p = 0 . 073 ) . The epilation-only group were asked if they had tried to obtain trichiasis surgery at any time between the two and four-year follow-up: 352 ( 92% ) replied “Never” and 325 ( 85% ) reported that they were happy epilating . There was no statistically significant difference in the average lash burden at four-years between those who were happy epilating and those that were not ( 1 . 11 v 1 . 31 , p = 0 . 30 ) . Participants who were not happy epilating were more likely to have tried to obtain surgical treatment for their trichiasis between two and four-year follow-ups ( Fisher’s exact test , p = <0 . 001 ) . At the two-year follow-up , 589 / 603 ( 98% ) of epilation arm participants still had their epilation forceps . At the four-year follow-up , 351 / 383 ( 92% ) of the epilation-only group had retained at least one pair of epilation forceps . Females were more likely to have retained their forceps than males ( OR 2 . 38; 95%CI 1 . 15–4 . 96; p = 0 . 020 ) . At four-years , new forceps were provided to those who had lost their forceps , and did not want surgery . Univariate and multivariable associations with having surgery in epilation arm participants at any point during the four years of follow-up are shown in Table 7 . Having surgery was independently associated with age less than 50 years , ≥3 lashes or corneal lashes at baseline , and frequent baseline epilation . At the four-year follow-up , all participants with recurrent trichiasis in the surgery arm and all participants in the epilation arm who had not previously had surgery were offered free surgery: only 17 / 383 ( 4 . 4% ) of the epilation only participants accepted surgery , the remaining 366 ( 95 . 6% ) preferred to continue epilating .
Trachomatous trichiasis has a wide disease spectrum , with many individuals having relatively few lashes touching the eye [4 , 7] . This may partly explain the observation in this study that at two-years , despite being offered surgery free of charge and close to home , more than two-thirds of people practicing epilation declined surgery . Most ( 92% ) of the epilation-only patients had not sought trichiasis surgery during the two to four-year follow-up period , and the majority ( 85% ) reported that they were still happy epilating . This was also reflected in 96% of the epilation-only patients declining the offer of free community-based surgery at the time of the four-year follow-up . This finding is consistent with two Gambian cohort studies , in which 50–70% of individuals with major trichiasis declined trichiasis surgery [8 , 13] . Presence of symptoms interfering with work was a predictor for accepting surgery [13] . In our study , younger patients and those with higher baseline lash burden , corneal lashes and frequent epilation at baseline were more likely to accept surgery . It seems likely that these individuals are more symptomatic and therefore more motivated to find a potential long-term solution in surgery . It is encouraging to note that patients with corneal lashes and higher lash burden are more willing to accept surgical management , as these are strong indications for surgery . In this study , surgery was better than epilation at correcting entropion and controlling trichiasis . However , it should be noted that at four-years 76 . 2% of the epilation-only participants had mild or no entropion , and 63 . 4% had no entropion progression . The epilation-only group generally controlled their trichiasis well by epilation , with only a few showing signs of significant progression . At four-years 2 . 6% of this group had major trichiasis ( >5 lashes ) , which is low compared to the Gambian longitudinal study , in which 37% of the eyes progressed from minor to major trichiasis over four years [8] . However , in the Gambian study participants used low quality traditional epilation forceps without training . In our original trial analysis with follow-up to two-years , the primary endpoint was the presence of 5+ lashes touching or a history of surgery . At four-years only 6 . 8% of the epilation only group had 5+ lashes . This is somewhat less that the cumulative total of 13 . 9% individuals who had reached the primary endpoint by two-years , many of whom had accepted surgery at two years . The proportion of participants in the surgery arm with recurrent trichiasis at four-years was relatively low compared to other trials and longitudinal studies [18 , 23 , 24] . This is because the risk of recurrence is heavily influenced by pre-operative disease severity; all the participants in our study had minor trichiasis at baseline; other studies have enrolled patients with more severe disease [5 , 22 , 25 , 26] . The epilation-only group had poorer baseline visual acuity compared to both the surgical arm participants and the epilation-to-surgery group . However , there was no difference in visual acuity change ( baseline to four-years ) between the epilating and surgery groups , which is similar to what we reported at two-years [22] . Several studies have reported an overall improvement in visual acuity after trichiasis surgery [5 , 27] . However , participants in these studies , unlike those in our present study , had a wider range of baseline trichiasis severity and were therefore more likely to have an improvement in vision following trichiasis surgery . Consistent with other studies , older age , presence of other blinding conditions , baseline corneal opacity and progressive corneal opacification were associated with deterioration of vision [8] . It is likely that much of the reduction in visual acuity over the four years is due to age related changes such as cataract . Interestingly , we found that female participants had 33% less risk of loss of vision than males . The explanation for this is unknown . The proportion of participants with visual impairment and blindness increased markedly at four-years in all groups , pointing to a major burden of blindness in the study area from other causes such as cataract in this older group of people . The epilation arm as a whole and the epilation-only sub-group had more baseline corneal opacity than the surgery arm participants . However , there was no significant difference in change of corneal opacity between the different groups at four-years . This result is consistent with our findings at two-years and a report from a longitudinal study in The Gambia , which compared change in corneal opacity in individuals with minor trichiasis who had undergone surgery with those who had declined surgery and practiced epilation [8] . In farming communities , corneal opacity can occur from other causes such as corneal infections and injuries . Similarly , new corneal opacity has been reported after surgery without the presence of recurrent trichiasis [18] . Corneal opacity development or progression was associated with old age and the presence of some pre-existing opacification , similar to other studies [5 , 6] . More than two-thirds of the epilation-only participants reported frequent epilation . Most reported no problems . Difficulty of getting the trained epilator when they needed help was cited as the main problem , encountered by 14% . However , this could be addressed by training more than one family member . The high retention rate of forceps in this study is encouraging , suggesting that the forceps are valued . This study has a number of limitations . This four-year follow-up and analysis was not pre-specified in the original trial protocol , which covered the period up to the two-year follow-up . The study ceased to have a fully randomised treatment allocation at two-years when all the epilation arm participants were offered free surgery , and hence we have adjusted follow-up outcomes for baseline imbalances . Those not examined at four-years were older and had worse baseline presenting visual acuity than those seen at four-years . This could have underestimated change in vision over time as older age is associated with greater visual impairment . However , this is unlikely to introduce bias in vision change between the surgery arm and epilation only group as those lost to follow-up were equally distributed between these groups . We found that surgery was more effective for controlling trichiasis than epilation; however there was no difference in change in visual acuity and corneal opacity . The progression of minor trichiasis can be effectively mitigated with frequent epilation . We found low rates of surgery uptake among people with mild disease , even with free community-based surgery . There is a need for clear guidelines on how programmes should manage patients with a few non-entropic lashes who refuse surgery . Trichiasis in general and particularly major trichiasis warrants surgical treatment . However , the results of this study and the reality of low surgical uptake in many regions , suggest that good quality epilation , in the context of regular follow-up by a service that can provide surgery if subsequently needed , is a reasonable second-line alternative to surgery for minor trichiasis for individuals who either decline surgery or do not have immediate access to surgical treatment . | Trachoma causes visual impairment through the effect of in-turned eyelashes ( trichiasis ) on the surface of the eye . Epilation is a common traditional practice of intermittent plucking of lashes touching the eye , however , its long-term effectiveness in preventing visual impairment is unknown . We conducted a randomized controlled trial of epilation versus eyelid surgery ( the main treatment option ) in 1300 people with mild trichiasis in Ethiopia . We defined mild trichiasis as fewer than six lashes touching the eye . We have previously reported results to two years and have now re-assessed these individuals at four years . Overall , we found no difference between the epilation and surgery groups in terms of change in vision and corneal opacity between baseline and four years . Most mild trichiasis participants randomised to the epilation arm continued epilating and controlled their trichiasis . This suggests that good quality epilation is a reasonable second-line alternative to surgery for mild trichiasis for individuals who either decline surgery or do not have immediate access to surgical treatment . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Epilation for Minor Trachomatous Trichiasis: Four-Year Results of a Randomised Controlled Trial |
We report a phenomenon wherein induction of cell death by a variety of means in wing imaginal discs of Drosophila larvae resulted in the activation of an anti-apoptotic microRNA , bantam . Cells in the vicinity of dying cells also become harder to kill by ionizing radiation ( IR ) -induced apoptosis . Both ban activation and increased protection from IR required receptor tyrosine kinase Tie , which we identified in a genetic screen for modifiers of ban . tie mutants were hypersensitive to radiation , and radiation sensitivity of tie mutants was rescued by increased ban gene dosage . We propose that dying cells activate ban in surviving cells through Tie to make the latter cells harder to kill , thereby preserving tissues and ensuring organism survival . The protective effect we report differs from classical radiation bystander effect in which neighbors of irradiated cells become more prone to death . The protective effect also differs from the previously described effect of dying cells that results in proliferation of nearby cells in Drosophila larval discs . If conserved in mammals , a phenomenon in which dying cells make the rest harder to kill by IR could have implications for treatments that involve the sequential use of cytotoxic agents and radiation therapy .
In metazoa where cells exist in the context of other cells , the behavior of one affects the others . The consequences of such interactions include not just cell fate choices but also life and death decisions . In wing imaginal discs of Drosophila melanogaster larvae , dying cells release mitogenic signals [1] , [2] , [3] . Signaling from dying cells , or dying cells kept alive by the caspase inhibitor p35 ( the so-called ‘undead’ cells ) , in wing discs operate through activation of Wingless ( Drosophila Wnt ) and JNK , and through repression of the tumor suppressor Salvador/Warts/Hippo pathway . A crosstalk between JNK and Hpo has also been reported [1] . The consequences on the neighbors include increased number of cells in S phase and activation of targets of Yki , a transcription factor that is normally repressed by Hpo signaling [3] . Mitogenic signals from dying cells results in increased proliferation of neighbors , which is proposed to compensate for cell loss and help regenerate the disc . A target of Yki is bantam microRNA [4] , but ban was not examined in above-described studies . ban was first uncovered in a genetic screen for promoters of tissue growth when overexpressed in Drosophila [5] . Further study found a role for ban in both preventing apoptosis and promoting proliferation [6] . A key target of ban in apoptosis is hid , a Drosophila ortholog of mammalian SMAC/Diablo proteins . These proteins antagonize DIAP1 to liberate active caspases and allow apoptosis . Hid is pro-apoptotic; repression of Hid by ban via binding sites in hid 3′UTR curbs apoptosis [6] . Since the initial characterization of ban , the role of this miRNA has expanded to include coordinating differentiation and proliferation in neural and glial lineages , cell fate decisions in germ line stem cells , in circadian rhythm , and in ecdyson hormone production [7] , [8] , [9] , [10] , [11] . In these and other contexts , ban is regulated by a number of transcriptional factors and signaling pathways including , Hpo/Yki , Wg , Myc , Mad , Notch and Htx [6] , [12] , [13] , [14] , [15] . The regulatory region of ban gene is likely to be complex and substantial; p-element insertions more than 10 kb away from ban sequences produce ban phenotypes [5] . The experimental evidence in Drosophila that dying cells promote proliferation presaged by several years the experimental evidence for a similar but mechanistically different phenomenon in mammals . A response called ‘Phoenix Rising’ occurs in mice after cell killing by ionizing radiation . Here , the activity of Caspase 3 and 7 is required in dying cells and mediates the release of prostaglandin E2 , a stimulator of cell proliferation [16] . These signals act non-autonomously to stimulate proliferation and tissue regeneration . A follow-up study in mice found a requirement for Caspase 3 in tumor regeneration after radiation treatment [17] . Not all consequences on neighboring cells are protective or mitogenic . In the classical ‘radiation bystander effect’ , seen in cell culture and in mice , the effect of irradiated cells on the neighbors is destructive , making the latter more prone to death [18] , [19] , [20] . There is evidence for a soluble signal; media from irradiated cells can induce the bystander effect on naïve cells . Inhibitors of the bystander effect include antioxidants [21] , suggesting that oxidative stress and energy metabolism may be involved in radiation bystander effect . We showed previously that ban activity increased after exposure to ionizing radiation ( IR ) in wing imaginal discs of Drosophila larvae [22] . IR-induced increase in ban activity required caspase activity: expression of a viral caspase inhibitor , p35 , or mutations in p53 that reduced and delayed the onset of caspase activation attenuated ban activation . We noted that while IR-induced cell death was scattered throughout the disc , ban activation was homogeneous . This suggested a non-cell-autonomous component in activation of ban . The current study came out of our efforts to understand how ban is activated in response to IR . We identified Drosophila tie , which encodes a receptor tyrosine kinase of VGFR/PDGFR family , as an important mediator of IR-induced changes in ban . Previous knowledge of Tie function in Drosophila was limited to long range signaling for border cell migration during oogenesis [23] . We report here that Tie was needed to activate ban in response to cell death . One consequence of ban activation , we found , was that remaining cells were harder to kill by IR .
To detect ban activity , we used a published GFP transgene that is expressed from the tubulin promoter and includes 2 perfect ban target sequences in its 3′UTR , allowing for repression by ban [6] . We reproduced our published data that ban activity increased in wing discs after exposure to IR [22] ( Fig . S1 ) . A published control sensor that lacks sequences for ban binding did not show a change in GFP under the same conditions ( Fig . S1 ‘control sensor’ ) . Next , we investigated whether the ban sensor was responsive to cell killing by another method; we were concerned that although only some cells died in irradiated discs , all were exposed to a death-inducing DNA-damaging agent that might have caused changes in ban . FLP-recombinase mediated ‘flip-out’ method was used to express GAL4 in random , scattered cells in 3rd instar larval wing discs . GAL4 then drove the expression of pro-apoptotic genes , hid and reaper , to result in cell kill . In these experiments , FLP was driven from a heat shock promoter using a brief ( 10–20′ ) heat pulse at 37°C . Constitutive expression of GAL80ts from a tubulin promoter kept GAL4 repressed , and allowed clones to form . To induce cell death , GAL80 was inactivated by an incubation at 29°C for 8–12 h ( 10′ heat shock and 8 h incubation at 29° shown in Fig . 1 ) . Cells expressing GAL4 were marked with RFP . In irradiated discs , a drop in GFP from the sensor was most prominent at >20 h after robust cell death [22]; this was expected because GFP has a half-life of 26 h [24] . Therefore , we monitored the sensor 22–24 h after de-repression of GAL4 ( Fig . 1G ) . We found that GFP was reduced in discs with Hid/Rpr clones compared to discs with RFP-only clones ( Fig . 1C vs . D ) . In discs with flip-out clones , surviving cells had not been exposed to a death-inducing DNA-damaging agent . Yet , these also showed a drop in sensor , reflecting ban activation . Co-expression of p35 with Hid/Rpr in clones prevented the decrease in GFP ( Figure S1 ) . This is in agreement with our published finding that p35 also prevented a decrease in the GFP ban sensor in irradiated discs [22] . In irradiated wing discs or in wing discs with Hid/Rpr clones , cell death was scattered but GFP ban sensor decreased throughout the disc . We infer that at least some instances of ban activation occurred in non-apoptotic cells , that is , non-autonomously . To address this idea more rigorously , we killed cells in a known , defined , and invariant location in wing discs . Expression of a dsRNA against de2f1 under the control of patched-GAL4 ( to be called ‘ptc4>dE2f1RNAi’ ) reduced E2F1 protein levels and the expression of an E2F1 target reporter in a stripe of anterior compartment cells along the Anterior/Posterior ( A/P ) compartment boundary of wing discs [25] . And the corresponding region in the adult wings was reduced in size . Mutations in dE2F1 caused cell-autonomous apoptosis [26] . Therefore , we asked whether depletion of dE2F1 by RNAi also killed cells . We detected little apoptosis in wing discs of ptc4>dE2f1RNAi 3rd instar larvae raised at 25°C ( Fig . 2 ) , even though we could see the expression of a UAS-eGFP reporter in the same larvae ( Fig . S2 ) . Presumably , RNAi-mediated knockdown of dE2F1 was sub-optimal under these conditions . A temperature-dependence in the effect of ptc4>dE2f1RNAi on the size of the inter-vein region of the adult wing was noted before [25] . Therefore , in an attempt to enhance the killing effect of dE2f1RNAi , we shifted the larvae from 25°C to 29°C , at 72 h after egg deposition ( AED ) . We observed robust caspase activity in the ptc domain 24 hours ( h ) after the temperature shift ( Fig . 2C ) . Active caspase staining was stronger in the pouch than the rest of the disc; therefore , we focused on the pouch in subsequent analyses of the consequence of cell death . Similar results were seen by shifting larvae at 96 h AED instead of 72 h . At these times , ptc domain had already narrowed to a vertical stripe [Fig . S2 in [27]; Fig . S2] . To see if ptc4>dE2f1RNAi-induced cell death resulted in changes in ban activity , we monitored GFP ban sensor after an additional 20 h to allow for the half-life of GFP , that is , at 44 h after temperature shift . In wing discs from control larvae without ptc4>dE2f1RNAi , pouch cells in the Anterior ( A ) and the Posterior ( P ) compartments showed similar GFP signals , producing an A/P ratio of 1 ( Fig . 2B , quantified in Table 1 ) . A stripe of cells at the dorsal/ventral ( D/V ) boundary showed high sensor signal ( low ban activity; between brackets ) . This was noted before and represents repression of ban by N/wg along the D/V boundary [6] , [15] . In wing discs from ptc4>dE2f1RNAi larvae , cells anterior to the ptc domain showed lower GFP signal than cells posterior to the ptc domain ( Fig . 2D , black vs . white arrowheads; quantified in Table 1 ) . This reduction was small , reducing the A/P ratio by about 20% , but was reproducible and bore physiological consequences as described later . The P compartment of some but not all ptc4>dE2f1RNAi discs also showed signs of reduced GFP ( Fig . 2D and B , white arrows ) . But this difference was weaker than the change in the A compartment ( thus producing A/P ratios <1 in discs with ptc4>dE2f1RNAi ) and not reproducible . Shifting larvae to 29°C at 96 h AED instead of 72 h produced similar results . We conclude that cell death in the ptc domain resulted in a localized decrease in GFP ban sensor , reflecting activation of ban . Expression of UAS-Hid/Rpr from the ptc-GAL4 driver also produced cell death . In fact , we needed to keep GAL4 repressed with GAL80ts in order to allow survival to 3rd instar . De-repression of GAL4 by incubation at 29°C for 12 h was sufficient to induce robust cell death in the ptc domain ( Fig . 2H ) . This was followed by a decrease in GFP throughout the disc 24 h later ( Fig . 2I ) . We conclude that with more robust cell death in the ptc domain , a greater area of the disc activated ban . In some wing discs expressing ptc4>dE2f1RNAi , bright specks of GFP were seen in the ptc domain as cells underwent apoptosis ( e . g . * in Fig . 2D ) . Elevated GFP was also seen in the dying cells in ptc4>Hid/Rpr discs immediately after GAL4 de-repression ( * in Fig . 2G ) but not at later times ( Fig . 2I ) , and rarely in irradiated discs with cell death ( Fig . S1 ) . We do not know the reason for elevated GFP in dying cells in some of these cases , but we emphasize that a decrease in the sensor occurred in the survivors outside the domain of cell death in all cases , which we focused on in subsequent sections . ban is anti-apoptotic and acts by repressing Hid expression [6] . hid , along with rpr and skl are induced by radiation in a p53-dependent manner ( e . g . [28] , [29] ) . Hid , Rpr and Skl antagonize DIAP1 to activate caspases and induce apoptosis . Reduction of hid gene dosage by half has been shown to reduce IR-induced apoptosis [28] , [29] . If ban was activated in the surviving cells in the experiments described above , the former may show altered sensitivity to apoptotic-inducing stimuli . To test this possibility , we irradiated larvae with ptc-GAL4-driven cell death and monitored IR-induced cell death using an antibody against cleaved Caspase 3 . In y1w1118 control discs ( Fig . 3B ) , there was robust cell death in both A and P compartments of the wing pouch . Along the D/V boundary , low ban activity correlated with high caspase activity as expected ( brackets ) . To test for any protective effects of cell death , we used two additional death-inducing constructs . Loss of Drosophila ATM , encoded by telomere fusion , resulted in cell autonomous apoptosis due to telomere dysfunction and activation of the DNA damage responses [30] . This method removed complications due to reduced dE2F1 . Cell killing by ATMRNAi , we found , was not as robust as cell killing by dE2f1RNAi ( Fig . 3C vs . E; Fig . S2 ) . We also used UAS-Rpr to induce death by ptc-GAL4 . As in the case of UAS-Hid/Rpr , UAS-Rpr also had to be kept repressed with GAL80ts to allow larval survival to 3rd instar and was able to induce cell death with a 12 h de-repression of GAL4 . But the resulting cell death was less robust than UAS-Hid/Rpr under the same experimental conditions ( Fig . 3 G vs . I ) . Irradiation of these discs showed that prior cell death in the ptc domain accompanied a reduction in IR-induced caspase activation ( Fig . 3D , F , H and J arrows ) . The protected area was localized in D and F but expanded in H and J , showing an increase with increasing level of prior death . In discs expressing dE2f1RNAi ( Fig . 3F ) or Hid/Rpr ( Fig . 3J ) , the protected area corresponded to the area with reduced ban sensor ( Fig . 2D and I ) . We conclude that prior cell death in the ptc domain resulted in ban activation and protection from IR-induced cell death in the rest of the disc , in a dose-dependent manner . Cells in the same compartment as the dying cells ( A compartment ) were the first to benefit but the protective effect was able to reach the P compartment with increased ptc-GAL4-driven cell death in the case of Rpr and Hid/Rpr ( compare Fig 3J to B ) . To rule out the possibility that the protection is due simply to disruptions in the ptc domain , we induced cell death in random clones; this too resulted in protection ( Fig . 4 , described in detail below ) . To rule out unwanted consequences of depleting dE2F1 earlier in development , we repeated the experiments with ptc4>dE2f1RNAi but used GAL80tts to repress GAL4 until 96 h AED . GAL4 was then de-repressed for 24 h and protection from IR-induced apoptosis assayed ( Fig . 5D ) . The results were indistinguishable from what we saw without GAL80 ( Fig . 3F ) . We also ruled out the possibility that dE2f1RNAi in the ptc domain altered cell proliferation in the pouch to interfere with IR-induced cell death; we saw no change in the mitotic index in A and P compartments in discs with ptc4>dE2f1RNAi ( Fig . S3 ) . We also ruled out the possibility that death in the ptc domain interfered with developmental signaling; we saw no change in Dpp-lac Z reporter expression in control and ptc4>dE2f1RNAi discs ( Fig . S4 ) . Death within the ptc domain accompanied increased ban activity and protection from IR-induced cell death outside the ptc domain . Death in Hid/Rpr clones also increased ban activity outside the clones ( Fig . 1 ) . Therefore , we asked if Hid/Rpr clones also resulted in protection from IR-induced cell death . Discs like those in Fig . 1C and D were irradiated and IR-induced caspase activity was monitored . We found robust caspase activation in areas outside RFP-only clones ( Fig . 4A–D ) . In contrast , areas outside RFP-marked Hid/Rpr clones showed reduced caspase activation ( Fig . 4E–H ) . We conclude that Hid/Rpr clones also resulted in protection from IR-induced death and that the protected area included cells outside the clones . Wing discs expressing ptc4>dE2f1RNAi showed an intermediate level of protection in the A compartment ( Fig . 3F and 5D ) . The P compartments in these discs showed similar level of caspase activation as the P compartments of controls ( Fig . 5B vs . D ) ; the average mean caspase fluorescence in the P compartments was 182±32 arbitrary units for ptc4>dE2f1RNAi and 163±16 for yw in two different experiments . In contrast , the A compartment of ptc4>dE2f1RNAi pouches show reduced caspase stain compared to P cells in the same disc ( Fig . 5D ) . This allowed us to use the P compartment as an internal control to account for variations in antibody staining , and quantify the reduction in the A compartment in terms of reduced A/P ratio . The ptc domain was visible in the DNA images as a ‘depression’ as dead cells were extruded from the cell layer ( Fig . 5C ) . Therefore , we used the DNA images as a guide to locate A and P compartments . The normalized A/P ratio for caspase fluorescence in ptc4>dE2f1RNAi discs was 0 . 60±0 . 11 and was significantly different from the controls ( Table 1 ) . TUNEL assay produced similar results ( Fig . 5F and Table 1 ) . This quantitative assay for protection allowed us to test the role of other factors . Co-expression of caspase inhibitor p35 in dying cells attenuated the protective effect ( Table 1 ) . This is in agreement with our data that p35 abolished changes in ban sensor after irradiation [22] and in discs with Hid/Rpr clones ( Fig . S2 ) . Reduction of ban gene dosage using heterozygotes of a null allele , banD1 , attenuated the protection ( Fig . 5H and Table 1 ) ; this was consistent with the observation that ban was activated in the protected regions ( Fig . 2D ) . One copy of the UAS-banA transgene , which encodes the primary transcript and rescued the lethality and growth defects of ban null mutants without a GAL4 driver [6] , also rescued the protection in banD1/+ ( Fig . 5J and Table 1 ) . We also addressed the role of ban in cell death-induced protection in ptc4>Hid/Rpr discs . In initial experiments , we were unable to see significant effects of banD1/+ using a protocol in which GAL4 was de-repressed for 12 h ( Fig . 3K ) . We reasoned this could be because the protective effect of Hid/Rpr was too strong to overcome by removing just one copy of ban . Removing both copies of ban was not an option because we found before that homozygous ban mutant cells in the wing disc underwent spontaneous apoptosis [22] . To get around this problem , we de-repressed ptc4>Hid/Rpr for 6 h instead of 12 h ( Fig . 6 ) . This reduced cell death in the ptc domain to the level seen after de-repressing ptc4>Rpr for 12 h ( compare Fig . 6B to Fig . 3G ) . The protective effect was also reduced ( Fig . 6D ) and resembled the protection with Rpr de-repressed for 12 h ( Fig . 3H ) than Hid/Rpr de-repressed for 12 h ( Fig . 3J ) . Importantly , banD1 heterozygosity further compromised the protection such that we could see caspase activation throughout A and P compartments ( Fig . 6F arrows , compare to arrows in 6D ) . The protection was not completely abolished , however . This could be because protective mechanisms could still act on the remaining copy of ban . We conclude that protection by ptc4>Hid/Rpr was also sensitive to ban gene dosage . To better understand the role of ban in radiation responses , we performed a forward genetic screen for modifiers of a ban phenotype . ban mutant larvae are hypersensitive to IR but this sensitivity can be rescued by reducing the gene dosage of hid , a known target of ban [22] . Using a similar experimental protocol , we sought to identify dominant modifiers of IR sensitivity of ban mutant larvae . We used 4000R of X-rays because this dose resulted in an intermediate level of lethality in ban mutants such that both suppressors and enhancers may be uncovered . Chromosomal deficiencies ( Df ) used in the screen , on their own , could show IR sensitivity and modify ban through simple additive effects . To exclude these , we measured the % survival of ban mutants and each Df , and used these to compute the expected survival for an additive interaction ( Fig . S5A ) . We then identified ban/Df combinations that produced observed survival that was significantly different from the expected , by X2 analysis . We focused on deficiencies that showed consistent and statistically significant effect on both ban1170 and banEP3622 alleles . Of 78 Df that cover 85% of chromosome 3L and part of 3R , 3 were significant modifiers . Df ( 3L ) 9028 and Df ( 3L ) 6115 enhanced and Df ( 3L ) 6086 suppressed the IR sensitivity of ban mutants . We focused our efforts on Df ( 3L ) 9028 because it deleted a single gene , tie ( Fig . S5B ) . tie encodes a Drosophila homolog of mammalian Tie ( Tyrosine kinase with Ig and epidermal growth factor homology domain ) [31] . We confirmed the deletion by PCR of genomic DNA ( data not shown ) , and also by q-RT-PCR for tie expression ( Fig . S5C ) . Df ( 3L ) 9028 is homozygous viable . The data from the screen suggested that tie mutants were IR sensitive . We confirmed this using three additional alleles; larvae homozygous of all four alleles show significant and reproducible sensitivity to X-rays ( Fig . 7A ) . Importantly , all three alleles show genetic interaction with ban seen with Df ( 3L ) 9028; each allele , in heterozygous state , reduced the radiation survival of ban1170 heterozygous larvae to levels below what was expected for an additive effect between two mutants ( Fig . 7B , data not shown ) . If tie regulates ban to promote survival after irradiation , increasing ban gene dosage may rescue radiation survival in tie mutants . We found that UAS-banA did rescue the survival in irradiated tie heterozygous larvae ( Fig . 7C ) , but did not change the survival of irradiated w1118 controls in the same experiment ( p = 0 . 6 ) . The data described so far are consistent with tie acting upstream of or parallel to ban to confer survival after irradiation . To distinguish between these possibilities , we investigated whether tie was needed for IR-induced changes in ban . We found that while GFP ban sensor decreased after irradiation in control larvae , reflecting activation of ban ( ‘wt’ in Fig . 8C , F and I ) , this decrease was attenuated in tie mutants . All three allelic combination of tie studied showed this defect , with Df ( 3L ) 9028 homozygotes showing the greatest defect , i . e . the smallest change in GFP between –IR and +IR discs ( Fig . 8C ) . In q-RT-PCR analysis , tie was the only gene whose expression was reduced in mutants used here; neither of the flanking genes showed a consistent change in expression ( Fig . S5C ) . In addition , a UAS-tie transgene rescued the change in ban sensor in Df ( 3L ) 9028 homozygotes after irradiation ( Fig . 8J-L ) . Similar to UAS-banA [6] , the rescue occurred without GAL4 ( see also Discussion ) . Collectively , these data support the idea that tie is needed to activate ban after irradiation . Previous work showed that IR-induced caspase activity was needed for ban activation following irradiation [22] . Therefore , it is possible that tie mutants could not activate ban because they could not activate caspases . We do not favor this possibility , however , because we found that wing discs from tie mutant larvae were capable of activating caspases after irradiation , as were tie homozygous mutant clones of cells ( Fig . S6 ) . We conclude that tie is not required for IR-induced apoptosis . Because both tie and caspase activity were required to activate ban but tie was not required to activate caspase activation , we conclude that tie acts after caspase activation to activate ban . To address how ban was activated , we monitored mature ban miRNA levels by q-RT-PCR . ban increased 2-fold at 2 h after irradiation and returned to control levels by 18 h ( Fig . 8M ) . This timing was consistent with the change in GFP ban sensor at 20+ hours after irradiation , given that the half-life of GFP is 26 h . The IR-induced increase in ban was abolished in homozygotes of two tie alleles examined , suggesting that tie was required to increase ban levels after IR . We conclude that tie-dependent increase in ban activity after IR was due , at least in part , to a tie-dependent increase in ban levels . Using the ptc4>dE2f1RNAi system , we addressed the role of tie in the protective effect of dying cells . Wing discs from tie Df ( 3L ) 9028 heterozygotes showed an attenuation of both ban activation , as seen by changes in the GFP ban sensor ( Fig . S7 and Table 1 ) , and the protective effect of cell death in the ptc domain ( Fig . 5L , Table 1 ) . This defect was rescued by one copy of UAS-banA ( Fig . 5N and Table 1 ) . We conclude that tie was required to activate ban and to confer protection in response to ptc-GAL4-induced cell death . Haplo-insufficiency of tie in these experiments was consistent with the identification of tie as a dominant modifier of ban in our screen . The ligands for mammalian Tie-2 are Angiopoietin 1 and 2 . Although Drosophila genome includes five predicted Ang homologs [31] , there is no evidence for these being ligands for Drosophila Tie . Instead , there is evidence for a Drosophila PDGF/VGF-like protein , Pvf1 , in border cell migration , a process in which Tie is known to function [23] . We found that mRNA for Pvf1 , Pvf2 and CG10359 , which encodes one of the putative Ang homologs , were induced at 2 h after IR in wing imaginal discs ( [32]; Supplemental Table 1 ) . In p53 mutants , where caspase activation and apoptosis after irradiation were reduced and delayed [29] , these genes were no longer induced . An independent RNAseq analysis confirmed these results ( our unpublished data ) . In these experiments , mRNA levels for tie , another Pvf homolog , Pvf3 , and four remaining predicted Ang homologs were unchanged ( Table S1 and data not shown ) . Analysis of Pvf1 and Pvf2 expression using enhancer trap lines corroborated these results; we found that both genes were induced in the ptc domain after expression of ptc4>dE2f1RNAi ( Figure S8 ) . These data suggest that cell death induced Pvf1 , Pvf2 and/or CG10359 to activate Tie . To address this possibility , we monitored the protective effect of ptc4>dE2f1RNAi but in Pvf1 and Pvf2 mutants and a chromosomal deficiency that deletes CG10359 . The results with Pvf2 and CG10359 were negative ( not shown ) , but a hemizygote of a Pvf1 allele , shown before to express no detectable mRNA or protein [33] , showed reduced protection ( Fig . 5P and Table 1 ) .
We document a previously unknown phenomenon in wing imaginal discs of Drosophila larvae; dying cells protected nearby cells from death . We found that killing cells by any one of three methods __ ptc-GAL4-driven expression of dE2F1RNAi or pro-apoptotic genes hid and rpr , exposure to ionizing radiation ( IR ) and clonal induction of Hid/Rpr __ activated an anti-apoptotic microRNA , bantam . Death by ptc-GAL4 or clonal expression of Hid/Rpr also made surviving cells more resistant to killing by IR . The protective effect was sensitive to ban gene dosage . We have named this phenomenon ‘Mahakali effect’ , after the Hindu goddess of death who protects her followers . Mahakali effect differs from classical radiation ‘bystander effect’ in which byproducts from cell corpses make surviving cells more prone to death [18] , [19] , [20] . The Mahakali effect appears to operate in a non-cell-autonomous fashion . Disc-wide protection by ptc4>Rpr and Hid/Rpr that included even cells in the P compartment that did not express ptc , provides the strongest evidence for non-autonomy . This idea is supported by the finding that IR-induced caspase activation was reduced in cells outside Hid/Rpr flip-out clones . A recent paper describes a non-autonomous induction of apoptosis by apoptotic cells [34] . We do not believe these results necessarily contradict what is reported here . Most of the experiments in the published work used undead cells kept alive by p35; Mahakali effect is seen without p35 . Non-autonomous apoptosis was assayed at , typically , 3–4 days after induction of undead cells; we detect Mahakali effect 6 hr after cell death induction using similar death-inducing stimuli ( Hid/Rpr ) . It would be interesting to see how long Mahakali effect persists and whether non-autonomous apoptosis , occurring at longer time points , also produces Mahakali effects of its own . Another recent paper describes tissue regeneration after massive cell ablation in wing discs [35] . It would also be interesting to see if the Mahakali effect operates among regenerating cells . The data shown here suggest that the basic components of the Mahakali effect are caspase activity in dying cells ( because expression in dying cells of p35 , an inhibitor of effector caspases , blocked ban activation ) , ban ( because ban activation resulted from cell death and the protective effect was sensitive to ban gene dosage ) , and tie ( because tie was required to activate ban and the protective effect was sensitive to tie gene dosage ) . We propose a model in which caspase activity in dying cells acts through Tie to cause non-autonomous activation of ban and the Mahakali effect ( Figure 9 ) . A validated target of ban in apoptosis inhibition is hid , whose 3′UTR includes 4 potential ban binding sites . We have shown previously that a GFP sensor with hid 3′UTR is reduced after IR [22] , reflecting repression of hid by ban . Deletion of two potential ban-binding sites in the hid 3′UTR abolished the IR-induced changes in GFP . The Mahakali effect differs in two ways from previously described effects of dead/dying cells in wing discs . First , the Mahakali effect extended further than previously reported signaling from dead/dying cells . In the extreme case of ptc4>Hid/Rpr , the protection reached as far as the edge of the disc . This distance , on of order of 100 or more mm ( scale bar in Fig . 3 ) is comparable to the distance of border cell migration [36] , in which Tie is known to function . In contrast , the mitogenic effect that occurs through JNK/Wingless in response to undead cells in the wing disc is seen up to 5 cells away [2] . Activation of proliferation through the Hpo/Yki axis also spans 3–5 cells away . This can be seen as activation of Yki targets such as DIAP1 [3] . We could reproduce this result: ptc4>dE2f1RNAi activated a Yki target , DIAP1 , but only within or close to the ptc domain ( Fig . S4 ) . YkiB5 allele , which disrupts cell death-induced proliferation [3] , did not alter the Mahakali effect ( Fig . S9 ) , further supporting the idea that the two effects are different . Second , ban activation in response to cell death was sensitive to the caspase inhibitor p35 . In contrast , the mitogenic effect of dying cells in wing imaginal discs is not sensitive to p35 [2] , [37] , [38] , [39] , [40] , [41] . We note that the mitogenic effect of dying cells is inhibited by p35 in the differentiating posterior region of eye imaginal discs [42] , which is similar to what we saw for ban activation in the wing discs . We found that tie was required for IR-induced activation of ban and for larval survival after irradiation . There were similarities as well as differences in the role of ban and tie . tie mutants were IR-sensitive ( this study ) , as are viable alleles of ban [22] . Tissue-specific overexpression of ban results in abnormal growth [5]; we found that 6 independent UAS-tie transgenic lines were lethal when driven by actin-GAL4 ( data not shown ) . Thus , too much ban or tie has consequences . On the other hand , reducing tie or ban gene dosage by half attenuated the Mahakali effect . Thus , too little ban or tie also has consequences . In fact , UAS-ban or UAS-tie without a GAL4-driver was sufficient to rescue ban and tie mutant phenotypes [[6] and this study] . Thus , intermediate levels of expression may be important for the function of these genes . The biggest difference between ban and tie , of course , was that while tie homozygous larvae were viable ( this study ) , ban homozygous larvae are lethal [6] . tie became necessary only after radiation exposure . This suggests that tie was needed to regulate ban not during normal development but after radiation exposure . How is IR and cell death linked to Tie ? We found that mRNA for Pvf1 , a ligand for Tie in border cell migration , was induced by IR and that this induction appeared to be dependent on cell death ( abolished in p53 mutants ) . Pvf1EP1624 mutants that are mRNA and protein null [33] , also showed reduced Mahakali effect . The degree of reduction was significant but not back to the level seen in control discs without ptc4>dE2f1RNAi , suggesting the involvement of additional ligands or mechanisms for Tie activation . In agreement , we could not see ban activation or the Mahakali effect after overproduction of Pvf1 ( data not shown ) . Pvf1 was necessary but insufficient to produce these effects without cell death . Tie activated ban , at least in part by increasing ban levels . How IR and caspase activity promotes Pvf1 expression and how Tie activity increases ban levels will be key questions to address in the future . Testing the role of known apoptosis regulators , such as Diap1 , and signaling molecules , such as Wg , may help address these questions . We also plan to complete the genetic screen that identified Tie; it has the potential to identify additional components of the Mahakali effect . Pvr , a PDGF/VEGF receptor homolog that function redundantly with Tie in border cell migration , also plays an anti-apoptotic role in embryonic hemocytes [43] . A recent study in wing discs found that Pvr is activated in neighbors of dying cells in a JNK-dependent manner , to result in cytoskeletal changes that allow the engulfment of the dead cell by the neighbor [44] . It is interesting that two PDGF/VEGF receptor homologs that function redundantly in cell migration during oogenesis may also play non-redundant roles in non-autonomous responses to cell death in wing discs . Cancer therapy routinely comprises the application of two or more cytotoxic agents ( taxol and radiation , for example ) to cancer cells . A phenomenon in which cell killing by one agent influence resistance to the second agent is , therefore , of potential clinical significance . The bulk of our analysis focused on protection from IR-induced cell death . But we also have preliminary indication that the Mahakali effect can also protect against cell death induced by maytansinol ( Fig . S10 ) , a microtubule depolymerizing agent with relevance to cancer therapy that we found before to induce cell death in Drosophila wing discs [45] . An important question is whether a phenomenon like Mahakali effect exists in mammals and acts as a survival mechanism in response to cell death . Ang-1 , a ligand for mammalian Tie-2 , is a pro-survival factor for endothelial cells during serum deprivation and after irradiation in cell culture models [46] , [47] , [48] . Interestingly , Ang1 is produced not by endothelial cells but by neighbors , at least in cell culture [49] . Based on these data , we think it possible that radiation exposure results in Ang1 production by dead/dying cells that promote the survival of endothelial cells via Tie-2 . Consistent , an Ang-1 derivative that is a potent activator of Tie-2 has been shown to protect endothelial cells from radiation-induced apoptosis [50] .
ban mutants: ban1170 , banEP3622 , banD1 [5] . tie mutants:y1 w1118; PBac{3HPy+}TieC098 ( Bloomington #16280 ) ; PBAC{RB}Tiee03394 ( Harvard Exelixis Collection ) ; PBAC{RB}Tiee02680 ( Harvard Exelixis Collection ) ; w1118; Df ( 3L ) Exel9028 , PBac{RB5 . WH5}Exel9028 ( Bloomington #7925 ) . Pvf1 mutant: pvf1EP1624 [33] . Clonal induction of GAL4: hs-FLP22; 20 . X/T ( 2;3 ) CyO Tb/Act<FRT>GAL4>UAS-RFP; Generated using w1118; P{GAL4-Act5C ( FRT . CD2 ) . P}S , P{UAS-RFP . W}3/TM3 , Sb1 ( Bloomington#30558 ) and crossed to UAS-hid , rpr on Chr I [51] . Mitotic clones: FRT80B-Df ( 3L ) Exel9028 crossed to FRT80B-Ubi-GFP Other stocks: ptc-GAL4 and UAS-dsRNA against dE2F1 or ‘PE3’ on Chr II [25]; ban sensor ‘20 . X’ on Chr II [6]; Dpp-lac Z reporter , P{BS3 . 0}H1-1 , cn1; ry506 ( Bloomington#5527 ) ; DIAP1-lac Z reporter [52]; Yki5B [53]; UAS-ATMRNAi on Chr III ( VDRC stock #22502 ) ; Ptub-GAL80ts on Chr III ( Bloomington stock center ) ; Pvf1 enhancer trap , w1118 Mi{ET1}Pvf1MB01242 ( Bloomington#23032 ) ; Pvf2 enhancer trap , w1118 Mi{ET1}Pvf2MB03230 ( Bloomington#24055 ) . To clone a Tie cDNA , total RNA was isolated from whole Sevelin pupae 2-3 days post-white pupa stage , reverse transcribed ( RT primer: ATGCGCTGCACGCCTAAATCA3 ) , PCR-amplified with tie specific primers ( amplification primers:5′ CGTGTGTGTATGTGTGTGTCG and 3′ GGGTAGGGGTTGGCTCAGTCA ) , and cloned into a pCR-XL-Topo vector ( Invitrogen ) . Tie cDNA was sub-cloned into pUAST and injected into w1118 embryos to make transgenic stocks at a commercial facility ( Best Gene , Inc . ) . The integrity of the cDNA was verified by DNA sequence analysis after each cloning steps . Eight independent lines on Chromosome II and III were obtained . The data shown is with one Ch II line . Larvae in food were irradiated in a Faxitron Cabinet X-ray System Model RX-650 ( Lincolnshire , IL ) at 115 kv and 5 . 33 rad/sec . Irradiated larvae were incubated at 25°C for indicated amounts of time before dissection . Cleaved Caspase 3 ( 1∶100 , rabbit polyclonal , Cell Signaling Cat# 3661 ) was used as described before [29] . Phospho-Histone H3 ( Upstate Biotech , 1∶1000 ) and Engrailed ( 1∶500 , Developmental Hybridoma Bank Cat#4D9 ) were used as described before [54] . Secondary antibodies were used at 1∶500 ( Jackson ) . For TUNEL , wing discs were dissected in PBS , fixed in 4% para-formaldehyde in PBS for 20 minutes , and washed three times in 0 . 3% Triton X-100/PBS for at least 20 minutes total . The discs were permeabilized overnight at 4°C in 0 . 3% Triton X-100/PBS , followed by three washes in 0 . 3% Triton X-100/PBS for at least 10 minutes total . The discs were processed using an Apoptag Red kit ( Millipore ) , according to manufacturer's instructions . For X-gal staining , wing discs were extirpated in PBS and fixed in PBS +4% formaldehyde for 10 min . The discs were washed three times for 5 min each in PBT ( PBS +0 . 2% Tween 20 ) . The discs were stained overnight in 1 mg/ml X-gal in staining solution ( 50 ml contains: 0 . 684 ml 1 M Na2HPO4 , 0 . 316 ml 1 M Na2PO4 , 1 . 5 ml 5 M NaCl , 0 . 05 ml 1 M MgCl2 , 0 . 065 g K4Fe ( II ) ( CN ) 6 , 0 . 051 g K3Fe ( II ) ( CN ) 6 , 0 . 15 ml Triton X 100 , 47 ml H20 ) . The discs were washed in PBT . The discs were counter-stained with 10 ug/ml Hoechst33258 in PBT or PBTx ( 0 . 1%TritonX-100 ) for 2 min , washed 3 times , and mounted on glass slides in Fluoromount G ( SouthernBiotech ) . Acridine Orange staining was as described before [29] . AO is excluded from live cells and has been shown to specifically stain apoptosis but not necrotic cells in Drosophila [55] . To quantify the GFP sensor , wing imaginal discs were extirpated in PBS , mounted between a glass slide and a glass coverslip , and imaged live . For mean GFP signal per disc , the images were acquired on a Leica DMR fluorescence microscope using Slidebook ( Intelligence Imagine ) , and compiled in Photoshop . For relative GFP signal between different parts of the same disc , images were acquired using a Perkin Elmers spinning disc confocal on a Leica DMR microscope . 26–36 Z sections 1 mm apart were collected and collapsed by maximal projection using Image J ( NIH opensource ) , and mean GFP signal for defined areas measured . Anterior signals were normalized by dividing with the posterior signals and averaged for each sample . To quantify caspase and TUNEL , discs were imaged on a Perkin Elmers spinning disc confocal attached to a Leica DMR microscope . 26–36 Z-sections 1 mm apart were collected per disc and collapsed using ‘maximum projection’ in Image J . Collapsed images were corrected for background using the ‘threshold’ function in Image J . The mean fluorescence from the area of interest was measured and averaged for all discs in a sample . To quantify mitotic indices , cells showing phosphor-Histone H3 stain were manually counted . Larvae were irradiated at 96±2 hr AEL . Between 15–20 wing discs were dissected per sample per time point and flash frozen in PBS using liquid nitrogen . Total RNA was isolated using Invitrogen TRIzol kit according to the manufacturer's instructions , and treated with DNase I ( Amplification Grade , Invitrogen ) . RT reactions were performed with Superscript III ( Invitrogen ) according to the manufacturers instructions , using primers to amplify NT1 , tie , CG11353 or a-tubulin mRNA as control . The primers used were: tie: 5′ GGCGACGGGAAAGCCGAAA 3′ GGTGCGACGAGCAGCCAACA NT1: 5′ GGCGGATGAGGGATTGCGCC 3′ TGCCAAACATCATGCGAACCTGT CG11353: 5′ AGCGCGGCATACTCGGCAAA 3′ GGTCTTTGGACGCCGCGACA a-Tub84B ( a-tubulin ) : 5′TCCAATCGCAACAAAAAATTCA 3′ TCGTTTTCGTATGCTTTTCAGTGT For mature bantam miRNA , custom primers were purchased and used according to manufacturer's instructions ( TaqMan , MicroRNA Assay , Applied Biosystems ) . Q-PCR was performed using 1 X SYBR Green Mix ( Applied Biosystems ) and 4 ng of each cDNA for 35 cycles using the indicated primers . Standard curves using 0 . 01-20 ng of cDNA pools were used . Plates were read in an Applied Biosystems 7900HT Real-time PCR instrument ( Absolute Quantification Method ) . Values were normalized to those of a-tubulin . p-values were calculated using 2-tailed Student's t-test except in the screen that used X2 tests . | In multicellular organisms where cells exist in the context of other cells , the behavior of one affects the others . The consequences of such interactions include not just cell fate choices but also life and death decisions . In the wing primordia of Drosophila melanogaster larvae , dying cells release mitogenic signals that stimulate the neighbors to proliferate . Such an effect is proposed to compensate for cell loss and help regenerate the tissue . We report here that , in the same experimental system , dying cells activate a pro-survival microRNA , bantam , in surviving cells . This results in increased protection from the killing effect of ionizing radiation ( IR ) . Activation of ban requires tie , which encodes a receptor tyrosine kinase . tie and ban mutant larvae are hypersensitive to killing by IR , suggesting that the responses described here are important for organismal survival following radiation exposure . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"cell",
"biology",
"genetics",
"developmental",
"biology",
"biology"
] | 2014 | Dying Cells Protect Survivors from Radiation-Induced Cell Death in Drosophila |
Divergence of upstream regulatory pathways of the transcription factor Doublesex ( Dsx ) serves as a basis for evolution of sex-determining mechanisms in animals . However , little is known about the regulation of Dsx in environmental sex determination . In the crustacean Daphnia magna , environmental sex determination is implemented by male-specific expression of the Dsx ortholog , Dsx1 . Transcriptional regulation of Dsx1 comprises at least three phases during embryogenesis: non-sex-specific initiation , male-specific up-regulation , and its maintenance . Herein , we demonstrate that the male-specific up-regulation is controlled by the bZIP transcription factor , Vrille ( Vri ) , an ortholog of the circadian clock genes—Drosophila Vri and mammalian E4BP4/NFIL3 . Sequence analysis of the Dsx1 promoter/enhancer revealed a conserved element among two Daphnia species ( D . magna and D . pulex ) , which contains a potential enhancer harboring a consensus Vri binding site overlapped with a consensus Dsx binding site . Besides non-sex-specific expression of Vri in late embryos , we found male-specific expression in early gastrula before the Dsx1 up-regulation phase begins . Knockdown of Vri in male embryos showed reduction of Dsx1 expression . In addition , transient overexpression of Vri in early female embryos up-regulated the expression of Dsx1 and induced male-specific trait . Targeted mutagenesis using CRISPR/Cas9 disrupted the enhancer on genome in males , which led to the reduction of Dsx1 expression . These results indicate that Vri was co-opted as a transcriptional activator of Dsx1 in environmental sex determination of D . magna . The data suggests the remarkably plastic nature of gene regulatory network in sex determination .
The diversity and evolution of sex-determining pathways among animals are fundamental issues in developmental and evolutionary biology . The primary cues to trigger sexual development have been varied across evolution [1 , 2] , and can be broadly divided into two categories: a strict genetic cue or merely an environmental signal [3] . There are numerous studies about genetic sex determination ( GSD ) mechanisms from various model organisms , including the mouse , nematode , and fruit fly . These studies have shown that , through interactions of several genes in a hierarchical manner , initial cues finally lead to sex-specific expression of the major effector of sexual differentiation , a DM-domain gene that encodes a transcription factor containing a DNA binding domain called DM-domain [4] . In addition , pioneering studies using model organisms have demonstrated that sex-determining genes differ among species upstream of the hierarchies [4 , 5] . In contrast , little is known about the mechanisms of environmental sex determination ( ESD ) because organisms with ESD systems are poor genetic models . The crustacean waterflea , Daphnia magna , has emerged as a model organism for understanding ESD because of its fully sequenced genome [6 , 7] and advances in genetic manipulations through RNAi [8] , ectopic expression [9] , CRISPR/Cas9 [10] and TALEN systems [11–13] . In healthy populations , Daphnia normally produces female clones through parthenogenesis , but switches to sexual reproduction when environmental qualities for growth and reproduction decline [14] . In unfavorable environments such as shortened photoperiod , lack of food and/or high population density , Daphnia produces clonal males that allow fertilization of haploid eggs , which results in the production of resting eggs as a survival strategy upon harsh conditions [15] . We and others have shown that juvenile hormone analogs ( JHAs ) induce male production in cladoceran crustaceans without environmental cues [16 , 17] . A developing oocyte is sensitive to JH or JHA and a period when eggs are destined to be males by these chemicals is four to ten hours before ovulation ( Fig 1A ) [16 , 17] , suggesting that environmental cues for sex determination are converted to JH signals neuroendocrinically . We also found that , during embryogenesis , a DM domain gene name Doublesex1 ( Dsx1 ) is exclusively expressed in male-specific tissues and regulates the male trait development in D . magna [18] , which provides evidence that both GSD and ESD have the same origin and share similar genetic components in their sex-determining pathways . To understand mechanisms of JH-dependent Dsx1 activation in D . magna , we had previously examined temporal change of its expression during embryogenesis [18] . Of the two Dsx1 mRNAs ( Dsx1-α , Dsx1-β ) which differ only at the 5′ UTR , zygotic transcription of Dsx1-α mRNA is largely divided into three phases ( Fig 1A ) , non-sex specific transcription prior to early gastrula at 6-hour post ovulation ( hpo ) ( initiation ) , male-specific activation during gastrulation from 6- to 9-hpo ( up-regulation ) , and constant transcription during late embryogenesis ( maintenance ) . Male-specific transcription of Dsx1-pemRNA starts three hours later than Dsx1-s mRNA ( around 9-hpo ) and thereafter become more abundant in male embryos . We also generated transgenic D . magna to visualize spatiotemporal expression patterns and discovered that male-specific Dsx1 expression starts in a presumptive primary organizer that migrates from the rostral to the caudal side on a ventral region at 11-hpo and thereafter gradually becomes specialized in male traits [19] . These previous findings suggest that JH activates Dsx1-α mRNA transcript in a specific population of gastrula cells . However , there is a significant time lag between the critical period of the JH action and onset of up-regulation of Dsx1-α mRNA levels ( Fig 1 ) , suggesting that Dsx1 is not a primary JH-responsive gene regulated by the JH receptor protein , Methoprene-tolerant ( MET ) [20] , but unknown transcription factors control its male-specific up-regulation in gastrula . In this study , we aimed to identify the transcription factor responsible for male-specific up-regulation of Dsx1-α mRNA transcription that starts at 6-hpo . We searched for potential transcription factor binding sites at a region upstream of the transcription start site of Dsx1-α transcript . We found a potential enhancer that contains a consensus sequence of the Dsx binding site and an overlapping element for binding of an ortholog of the bZIP transcription factors , Drosophila Vrille ( Vri ) and vertebrate E4BP4/NFIL3 , which are known to be involved in various general development processes including growth [21 , 22] , circadian clock regulation [23 , 24] , metamorphosis [25] , apoptosis [26] , and human T cell function [27] . In D . magna , Vri showed male-specific transient expression at 6-hpo . Loss- and gain-of-function analyses showed Vri to be necessary and sufficient for Dsx1 activation . In addition , the disruption of the enhancer suggested Vri-dependent Dsx1 activation . We infer co-option of the transcription factor Vri to the environmental sex-determination cascade .
To find candidate transcription factors ( TFs ) that activate Dsx1 male-specific expression from 6-hpo , we analyzed a sequence within 7 , 899 base pairs upstream from the transcription start site of Dsx1-α mRNA . First , elements similar to known TF binding sites were searched with the TFBIND program [28] using the transcription factor database TRANSFAC R . 3 . 4 . Next , because Dsx1 up-regulation and maintenance phases suggest positive feedback regulation of this gene , we investigated consensus binding sites of Drosophila melanogaster Dsx . Of the thousands of potential TF binding sites found in this study , we focused on an element similar to the fat body enhancer of the Drosophila yolk protein gene 1 that contains a Dsx binding site and an overlapping bZIP protein binding site [29] . We confirmed conservation of its position and sequence in the related daphniid species Daphnia pulex ( Fig 1B and 1C ) . In this Daphnia species , a binding site for bZIP protein in the potential Dsx1 enhancer matched to a consensus binding site for mammalian E4BP4/NFIL3 , suggesting that an ortholog of E4BP4/NFIL3 may function as a transcriptional activator of Dsx1 in Daphnia . To investigate the existence of an E4BP4/NFIL3 ortholog in D . magna , we performed a BLAST search using an amino acid sequence of the human E4BP4/NFIL3 against the D . magna genome database and found one ortholog that shows high homology in the bZIP domain to E4BP4/NFIL3 proteins ( S1 Fig ) . We determined the cDNA sequence by 5′ and 3′ RACE reactions and obtained a 2 , 394 bp nucleotide sequence that codes for 797 amino acids ( S2 Fig ) . Phylogenetic analysis using bZIP domains from various animals revealed that the Daphnia E4BP4/NFIL3 ortholog is most closely related to the insect E4BP4/NFIL3 ortholog Vrille ( S3 Fig ) . Therefore , we designated this gene as Vrille ( Vri ) . We then analyzed the temporal expression profile of Vri by qRT-PCR during embryogenesis ( Fig 2 ) . At early stages of embryogenesis ( 0 , 3 , and 6-hpo ) , Vri expression in males was higher than that in females . At 6-hpo , Vri transcripts transiently became more abundant and retained the sexually dimorphic expression pattern ( Fig 2A ) . At later embryonic stages ( 18 and 36-hpo ) , Vri expression increased both in males and in females and lost its sexual dimorphism ( Fig 2B ) . The existence of a Vri binding site in the Dsx1 promoter sequence and the male-specific expression of Vri prior to Dsx1 up-regulation led us to hypothesize that Vri could regulate male-specific Dsx1 up-regulation in gastrula . To investigate this hypothesis , we performed RNAi-mediated knockdown analysis as described previously [8 , 30] . To confirm specificity of phenotypes induced by Vri RNAi , we designed two siRNAs , Vri_siRNA_1 and Vri_siRNA_2 , which differ at their target sequences ( S2 Fig ) . To observe the cells and tissues influenced by Vri RNAi during embryogenesis , we used transgenic H2B-GFP expressing Daphnia that allows us to visualize individual cells in an embryo [31] . We injected each siRNAs into the eggs induced to become males by exposure to the JH agonist Fenoxycarb . Based on H2B-GFP expression patterns , development of both Vri_siRNA_1- and Vri_siRNA_2-injected embryos seemed to be normal at around 10 to 11-hpo . At 20-hpo , Vri_siRNA_1-injected embryos developed cephalic appendages such as second antennae but did not start thoracic segmentation in contrast to control embryos ( Fig 3A ) . Vri_siRNA_2-injected embryos died because of more severe phenotypes in which the segmental structures were not formed . At 30-hpo , Vri_siRNA_1-injected embryos showed abnormal segmentation of thoracic appendages , and undeveloped posterior and anterior regions of the embryos ( Fig 3A ) , which prevented us from investigating sex-reversal in sexually dimorphic structures such as the 1st antennae . These RNAi-dependent severe deformities were also observed in females ( S5 Fig ) . To exclude the possibility that the developmental defect affects Dsx1 expression , we analyzed Dsx1 expression levels in RNAi embryos at 11-hpo by qRT-PCR and validated that Vri expression level was negligible in both of the RNAi embryos ( S4 Fig ) . qRT-PCR analysis also revealed that both Vri_siRNA_1 and Vri_siRNA_2 reduced Dsx1 expression ( Fig 3B ) . To further analyze where Vri RNAi reduced Dsx1 expression , we used another transgenic Daphnia , a Dsx1 reporter strain that expresses mCherry , the red fluorescence protein under the endogenous Dsx1 promoter/enhancer [19] . At 20-hpo , in control male and female Daphnia , the mCherry fluorescence appeared exclusively in male embryos and is localized in the 1st antennae , which are the first organs to show a male-specific trait in Daphnia . In addition , mCherry-expressing cells could be seen in thoracic appendages , which may be supplied from the posterior growth zone [19] ( Fig 3C ) . In Vri_siRNA_1-injected male embryos , mCherry signal could be seen only in the posterior growth zone but its signal was weaker . Vri_siRNA_2 injected embryos did not show any red fluorescence ( Fig 3C , Table 1 ) , although due to severe effect of Vri silencing on embryonic processes , we could not exclude the possibility that some of the structures which normally express the mCherry reporter were not properly formed when Vri was silenced . To test whether transient expression of Vri in early embryos is sufficient to activate Dsx1 and trigger male development , we induced transient ectopic expression of Vri in females by delivering capped , polyadenylated mRNAs into ovulated eggs via microinjection . We first attempted to establish a system to mimic transient expression of Vri in early male embryos . We constructed GFP mRNAs harboring the 5′ UTR and 3′ UTR sequences obtained from Xenopus laevis β-globin gene and injected this chimeric GFP mRNAs into female eggs . This injection led to expression at early embryogenesis ( 3 to 10-hpo ) but not in the later stages ( S6 Fig ) . Therefore , we linked the X . laevis β-globin UTRs to the Vri CDS and injected this chimeric Vri mRNA into wild-type eggs that would develop into females . Although this chimeric mRNA induced high embryonic lethality ( Table 2 ) , the juveniles that survived showed partial elongation of the 1st antennae in an mRNA concentration-dependent manner ( Fig 4A , Table 2 ) . Consistent with this masculinized phenotype , we could confirm up-regulation of Dsx1 expression levels in Vri RNA-injected daphniids by qRT-PCR at 48 to 50-hpo ( Fig 4B ) . Low viability prevented us from evaluating further masculinization in injected female animals . In addition , by using the Dsx1 reporter strain , we tested the effects of the same chimeric Vri mRNA on Dsx1 activation in females and detected high and widespread mCherry expression mainly in thoracic appendages at 50-hpo ( Fig 4C , Table 3 ) . To confirm whether Vri’s DNA binding activity was necessary for Dsx1 activation , we injected mRNA encoding a mutated form of Vri that lacked the bZIP domain ( S2 Fig ) . This mutated Vri could increase Dsx1 expression levels but showed lower transactivation activity ( Fig 4C , Table 3 ) . Taken together , these loss-and-gain-of-function analyses show that Vri functions as a transcription activator for Dsx1 expression in D . magna . To test whether the enhancer element is required for Dsx1 activation and male trait development , we tried to disrupt its sequence on the genome by using the CRISPR/Cas9 system . Because the low GC content ( 23% ) of the enhancer prevented us from designing enhancer-targeting TALENs and gRNAs , we designed two separate gRNAs , gRNA-1 and gRNA-2 , near to the enhancer ( Fig 5A ) and confirmed the functionality of the gRNAs by Cas9 in-vitro cleavage assay ( S7 Fig ) . We then co-injected the two gRNAs with Cas9 protein into the Dsx1 reporter strain [19] that would develop into males and evaluated effects of enhancer disruption on Dsx1 and the morphological phenotypes . At 36-hpo , we found four different phenotypes from the 12 injected embryos . Four embryos ( #1 , #2 , #3 , and #4 ) exhibited phenotype-1 ( Fig 5B ) , in which embryonic development was delayed and the embryos showed weaker mCherry fluorescence than control but at later stages , they could have normal male traits development . Two embryos ( #5 and #6 ) showed phenotype-2 wherein egg development was disturbed and mCherry signal was weak with abnormal localization . Phenotype-3 was observed in three embryos ( #7 , #8 , and #9 ) showing the most severe deformities and no mCherry expression . The remaining three embryos ( #10 , #11 , and #12 ) showed no apparent change in phenotype compared to control ( Phenotype-4 ) . The abnormal development of these eggs prevents us from observing the sex-specific traits or feminized phenotypes . To examine the correlation between the introduced mutations and the observed phenotypes , we extracted genomic DNA from each embryo and performed genomic PCR to amplify the enhancer region . Native PAGE electrophoresis of PCR products showed either bands of smaller sizes than what was expected from wild-type sequence , or wild-type bands of reduced intensity , suggesting that large and small deletions in the enhancer region had occurred ( Fig 5C ) . We measured the intensity of each band and calculated the ratio of intensity of expected to smaller bands , and observed that the more severe the phenotype of injected embryo was , the higher was the ratio . These results indicate that the enhancer may be a cis-regulatory element for male-specific Dsx1 expression . In addition , we attempted to generate the enhancer knockout mutants by injecting the Cas9 protein-gRNAs complexes and collecting offspring of the injected daphniids . In injection into eggs that develop into females , neither somatic nor heritable mutations were detected ( S1 Table , S2 Table ) . In male daphniids , the injection led to high embryonic lethality ( >90% ) ( S2 Table ) . We could collect offspring by feminizing the survived males using Dsx1 RNAi , but no mutant line was generated .
We had previously found that JH and Dsx1 are essential for environmental sex determination in D . magna [18] . JH drives commitment to male development in oocytes at 4 to 10 h before ovulation [32] . In response to JH signal , Dsx1 is up-regulated from early gastrula at 6 h post-ovulation and is maintained in late embryos for the control of male trait development [19] . However , the molecular mechanisms that mediate JH signaling and Dsx1 up-regulation have remained unknown . In this study , we identified the bZIP transcription factor , Vri as a candidate transcriptional activator by sequence analysis of the Dsx1 promoter/enhancer . Further studies involving expression pattern analysis , loss- and gain-of-function analyses and disruption of an enhancer harboring a Vri consensus binding site indicated that it is required for male-specific Dsx1 up-regulation . Our findings provided evidence that Vri has been co-opted as a component upstream of Dsx1 in the environmental sex-determining pathway . Over the past several years , new sex-determining genes have been identified in genetic sex-determining pathways in several animals , which reveals the importance of gene co-option . Mechanisms for co-option of new sex-determining genes are largely divided into three categories: 1 ) allelic diversification , 2 ) duplication of genes related to sexual development and 3 ) recruitment of a novel gene with no homology to any known sexual regulators [33] . First , by allelic diversification , transcription factor SOX3 was recruited as a master regulator for sex determination in mice [34] and Indian ricefish [35] . By the same mechanism , the DM-domain gene Dmrt1 and the gonadal soma-derived growth factor ( Gsdf ) were also co-opted at the top of sex-determining pathways in birds [36] and Luzon ricefish [37] respectively . Second , in frog [38] and Medaka [39] , the Dmrt1 gene was duplicated and one of the duplicates gained function as a master sex-determining gene . In insects , transformer orthologs that are conserved components of the sex-determining cascades , were duplicated in honeybee [5 , 40] , resulting in upstream regulators named the Csd . These findings suggested that orthologous genes are repeatedly co-opted for genetic sex-determining pathways in independent animal lineages [33] even though , in the silkworm and the rainbow trout , novel factors , a piRNA [41] and the interferon regulatory factor irf9 [42] seems to have evolved as sex determiners . The Vri gene was previously identified as one of genes regulated by Dsx in male Drosophila [43] . As well as most of previously identified sex-determining genes , Vri might be repeatedly employed in the sex-determining regulatory networks . In environmental sex-determining D . magna , without allelic diversification and duplication , Vri would have been co-opted in upstream of Dsx1 . Sex-related roles of Vri in various organisms should be examined in future . Our findings indicate that Vri functions as an activator of the Dsx1 gene in Daphnia . In Drosophila , Vri regulates various developmental processes such as cell growth , proliferation and flight [21 , 44] , as well as metamorphosis [25] and tracheal integrity [22] . In addition to these processes , Vri is required for circadian oscillation by repression of Clock transcription [24] . In mammals , the Vri ortholog E4BP4/NFIL3 is also reported as a clock-controlled gene . It competes for the binding site of the PAR-protein . Both Drosophila Vri and mammalian E4BP4/NFIL3 are well known as transcriptional repressors . However , in the human immune response system , E4BP4/NFIL3 was identified as an activator of the IL3 promoter [27] and was also shown to up-regulate IL-10 and IL-13 [45] . It is essential for lineage commitment of innate lymphoid cells ( ILCs ) [46] . In natural killer cell development , E4BP4/NFIL3 interacts with the histone ubiquitinase MYSM1 and maintains an active chromatin state at the Id2 locus [47] . In Daphnia sex determination , Vri works at the gastrulation stage when lineage commitment occurs . These similarities in regulation at the genetic and cellular levels may suggest that the molecular mechanism of Vri-dependent Dsx1 activation is similar to that of E4BP4/NFIL-3 function in human ILCs . Based on the timing of action of JH , Vri , and Dsx1 , we were able to propose a hierarchy of signal transduction in environmental sex determination ( Fig 6A ) . In this hierarchy , JH first stimulates expression of Vri , which in turn activates Dsx1 expression . To examine the possibility that the JH-receptor MET directly regulates Vri activation , we searched for sequences similar to the MET-binding site for the Vri promoter/enhancer and found one candidate sequence that is conserved in two Daphnia species ( Fig 6B ) , suggesting that this motif functions as an element to regulate the JH-dependent gene expression . However , because there is still time lag between JH signaling and Vri activation , there might be other molecules that respond to JH signal and then direct the male-specific Vri transcription . Thus , discovering these early response genes of JH signal may improve our understanding of hormonal signaling and the environmental sex determination pathway . Interestingly , in the initiation phase of Dsx1 transcription , Dsx1 is transcribed both in males and in females at 3 to 6-hpo . We hypothesize that in males , Vri might form a heterodimer with Dsx1 , bind to the enhancer and up-regulates Dsx1 expression at 6 to 9-hpo . Drosophila Dsx is known to form a heterodimer with the bZIP-domain transcription factor and binds to the fat body enhancer ( FBE ) of the yolk protein gene [29] . Transactivation of Dsx1 by a truncated Vri lacking the bZIP domain in this study also suggests that heterodimer formation allowed the mutated Vri to access the target binding site . These suggest that the heterodimeric combination of Dsx and bZIP transcription factors has functioned as a transcriptional regulator before divergence of insects and crustaceans . Even though we provide substantial genetic evidence of Dsx1 activation by Vri in early embryos , because the loss- and gain-of- Vri function led to embryonic lethality , we still cannot conclude that Vri is the sole upstream component acting as a Dsx1 activator that is necessary for male trait development . To understand more about Vri function in environmental sex determination , we will need to clarify localization of Vri in early embryos and perform knockdown/overexpression in cells that express Vri endogenously , which would avoid alternation of non-sex specific functions of Vri in later embryos . In targeted mutagenesis using Cas9 , we could not introduce any mutation into the Vri binding site at the Dsx1 promoter/enhancer on the genome in females . In contrast , this mutagenesis introduced deletion at the target site on the genome in males and reduced Dsx1 expression . These results suggest that this enhancer may be silenced via closed chromatin in females but is required for Dsx1 activation in males . We also found that deletion of the enhancer led to embryonic lethality in males although we could not shed light on the mechanism underlying this high mortality . However , these clear differences of phenotypes between males and females in our targeted mutagenesis experiments indicate a male-specific role of this enhancer . Further study is needed to understand the epigenetic regulation at Dsx1 locus . In conclusion , we demonstrate co-option of the bZIP transcription factor Vrille upstream of the Dsx1 in the environmental sex-determining cascade of the crustacean D . magna . Vri is transiently expressed in early gastrula in response to juvenile hormone and controls male-specific up-regulation of Dsx1 in late gastrula . This is the first finding that Vri is recruited into sex determining pathways . Our finding reveals the remarkably plastic nature of Dsx regulation , which will contribute to understanding of the diversity and evolution of the sex-determining pathways in organisms .
All of the wild-type ( WT ) and transgenic lines share the same genetic background ( NIES clone ) . They were cultured in ADaM medium [48] as described previously [49] . Male Daphnia were obtained by exposing female adults ( 2–3 weeks old ) to 1 μg/L of the synthetic juvenile hormone analog , Fenoxycarb ( Wako Pure Chemical; Osaka , Japan ) [50] . We utilized previously established transgenic lines of D . magna . One of the transgenic lines ( HG-1 ) expresses H2B-GFP protein under the control of D . magna Elongation Factor 1 α-1 ( EF1α-1 ) promoter/enhancer [31] . Another was the Dsx1 reporter strain , which was generated by introducing mCherry gene upstream of Dsx1 coding sequence in the genome of the HG-1 [19] . Total RNA was extracted from female and male embryos in triplicates using Sepasol-RNAI solution ( Nacalai Tesque; Kyoto , Japan ) . The RNA was subjected to cDNA synthesis using random primers ( Invitrogen; Carlsbad , CA , USA ) and the SuperScriptIII Reverse Transcriptase ( Invitrogen ) . qPCR was conducted with the SYBR GreenER qPCR Supermix Universal ( Invitrogen ) using the Mx3005P real time ( RT ) -PCR system ( Agilent Technologies; Santa Clara , CA , USA ) . Vri expression was quantitated and was normalized with the ribosomal protein L32 expression level using the primers listed in S3 Table . The primers used to amplify the Dsx1 and the ribosomal protein L32 gene were the same as described previously [18] . For normalization of Dsx1 expression level in the Vri knockdown using Vri_siRNA_1 and overexpression , expressions of three other reference genes , ribosomal L8 gene , β-actin gene and Cyclophilin gene [51] were analyzed using the primers listed in S3 Table . The geometric mean of the reference genes were calculated and used for normalization as described previously [52] . The Vri cDNA sequence was amplified from Daphnia by 5′ and 3′ rapid amplification of cDNA ends ( RACE ) methods as described previously [30] . The primer sequences used for cDNA fragment amplification were as follows: Vri 5′ RACE gene specific primer ( 5′-TGTTGCTGCCGATTGCGCTGACACTG-3′ ) ; Vri 5′ RACE nested primer ( 5′-CTCGGTCGAACGCCGTCCGCTACTG-3′ ) ; Vri 3′ RACE gene specific primer ( 5′-CCGGCCGTGTACTGCCGCTCAAACTA-3′ ) ; and Vri 3′ RACE gene nested primer ( GGCTGCCGCTGTTCTGCTGACACTCA-3′ ) . The resulting PCR products were excised from an agarose gel after electrophoresis , purified and were cloned into a TOPO vector ( Invitrogen ) for sequencing analysis . We then used the DNA sequence for homology search and phylogenetic analyses using BLAST and MEGA ( version 7 . 0 . 21 ) as mentioned previously [30] . The Vri cDNA sequence is available from the DDBJ database ( http://getentry . ddbj . nig . ac . jp/getentry/na/LC230164/ ? format=flatfile&filetype=html&trace=true&show_suppressed=false&limit=10 ) ( Accession number LC230164 ) . To knockdown the Vri gene , 100 μM of Vri_siRNA_1 and Vri_siRNA_2 ( sequences indicated in S2 Fig ) were used . A previously used control siRNA ( 5′- GGUUAAGCCGCCUCACAUTT-3′ ) was utilized as a negative control [53] . The siRNA oligonucleotides were dissolved in DNase/RNase-free water ( Life Technologies Inc . ; Grand Island , NY , USA ) . To overexpress the Vri gene , chimeric Vri cDNA harboring the 5′ UTR and 3′ UTR of X . laevis β-globin gene was designed and subcloned downstream to the T3 promoter on the pRN3 vector [54] . The Vri CDS of this plasmid was replaced with the CDS of GFP fused with minos transposase for preparation of control mRNA for investigating effects of β-globin UTRs on mRNA stability and/or translation efficiency . These plasmids were linearized by BsaAI restriction enzyme , purified with phenol/chloroform extraction and used as templates for mRNA synthesis . In vitro transcription by T3 RNA polymerase and poly-A tail addition were performed according to the manufacturers’ protocol of the commercial kits mMessage mMachine T3 kit ( Life Technologies Inc . ) and Poly ( A ) Tailing kit ( Life Technologies Inc . ) , respectively . The synthesized mRNAs were column purified by RNeasy Mini kit ( Qiagen; Tokyo , Japan ) , followed by phenol/chloroform extraction , ethanol precipitation , and dissolution in DNase/RNase-free water . For the syntheses of gRNAs , the templates were prepared by the cloning free method [55] . The sense synthetic oligo contains three main parts: a T7 promoter ( shown in bold ) , a variable targeting sequence ( N18 ) and the first 20 nt of the Cas9 binding scaffold sequence . The full sequence is as follows: ( 5′- GAAATTAATACGACTCACTATAGGNNNNNNNNNNNNNNNNNNGTTTTAGAGCTAGAAATAGC-3′ ) . The anti-sense oligo contains 80 nt full sequence of the Cas9 binding scaffold: ( 5′-AAAA GCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAAC-3′ ) where the underlined nucleotides denote the complementary sequence between two oligo sequences . The PCR reaction was performed with PrimeSTAR polymerase ( Takara Bio; Shiga , Japan ) . After purification by phenol/chloroform extraction , the DNA fragments were used as templates for in vitro transcription with the MEGAscript T7 kit ( Life Technologies Inc . ) , followed by column purification with mini Quick Spin RNA columns ( Roche diagnostics GmbH; Mannheim , Germany ) , phenol/chloroform extraction , ethanol precipitation , and dissolution in DNase/RNase-free water . Microinjection was performed as described previously [8] . Eggs were obtained from adult Daphnia at 2–3 weeks of age , directly after ovulation and placed in ice-cold M4 media contained 80 mM sucrose . The specific RNAs for each experiment were mixed with either Alexa Fluor 568 dye ( Life technologies Inc . ) or Lucifer Yellow dye ( Life technologies Inc . ) with final concentrations of 0 . 01 μM and 1 μM respectively , as an injection marker . The microinjection was performed on ice and the injected eggs were incubated in a 96-well at 23°C for the appropriate time . We mixed in vitro synthesized RNA with Cas9 protein to make gRNA-Cas9 complexes . Cas9 protein was prepared as described previously [56] . They were incubated for 5 min at 37°C and injected into wild type D . magna eggs , as described previously [8] . To characterize the somatic mutation on Vri binding site generated by Cas9 protein , target loci were amplified by PCR from genomic DNA isolated from each injected egg . To extract the genomic DNA , injected embryos were homogenized individually in 90 μL of 50 mM NaOH with zirconia beads . The sample was heated at 95°C for 10 min , followed by a neutralization step by adding 10 μL of 1 M Tris-HCl ( pH 7 . 5 ) . Before this DNA extract was used as a PCR template , the sample was centrifuged at 13 , 000 g for 5 min . The PCR was performed with HS Ex Taq polymerase ( Takara Bio ) using a primer pair designed as follows: Vri-bs forward ( 5′-GATGTCACGAAATCTGAGGTC-3′ ) and Vri-bs reverse ( 5′-GATCTAAACACCTTGGCGTAAC-3′ ) , which amplified 214 bp including the enhancer region . The PCR products were analyzed with native PAGE gel electrophoresis . To characterize the heritable mutagenesis , injected Daphnia were cultured separately until they produced offspring . The offspring were pooled ( up to 8–10 daphniids ) and genomic DNA extraction and genomic PCR were performed as mentioned above . | Sex is widespread for reproduction of offspring in the animal kingdom . In the sex determination process , through interactions of several genes in a hierarchical manner , an initial cue leads to sex-specific expression of the major effector of sexual differentiation , Doublesex ( Dsx ) . Although how genetic factors on sex chromosomes control Dsx expression has been extensively studied in model organisms such as mouse , fruit fly , and nematodes , little is known about dependence of Dsx regulation on environmental signals . We used the crustacean , Daphnia magna , owing to its advantages for analyzing environmental sex determination: 1 ) fully sequenced genome , 2 ) recent advancement of genome engineering and 3 ) artificial control of sex by juvenile hormone treatment . We found that early male embryos transiently express the bZIP transcription factor , Vrille ( Vri ) , known to be a circadian regulator , before male-specific Dsx1 activation begins . Disruption of a potential Vri-binding site in the Dsx1 regulatory region , and gain- and loss-of-function analyses revealed that Vri regulates male-specific Dsx1 activation in Daphnia . We infer that a novel gene can be co-opted as a regulator of Dsx in environmental sex-determining pathway . Our results would expand our understanding about the diversity and evolution of the sex-determining pathways in animals . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"gene",
"regulation",
"regulatory",
"proteins",
"messenger",
"rna",
"dna-binding",
"proteins",
"animals",
"dna",
"transcription",
"developmental",
"biology",
"transcription",
"factors",
"embryos",
"morphogenesis",
"small",
"interfering",
"rnas",
"embryology... | 2017 | Co-option of the bZIP transcription factor Vrille as the activator of Doublesex1 in environmental sex determination of the crustacean Daphnia magna |
Chagas disease remains a major cause of mortality in several countries of Latin America and has become a potential public health problem in non-endemic countries as a result of migration flows . Cardiac involvement represents the main cause of mortality , but its diagnosis is still based on nonspecific criteria with poor sensitivity . Early identification of patients with cardiac involvement is desirable , since early treatment may improve prognosis . This study aimed to assess the role of diastolic dysfunction , abnormal myocardial strain and elevated brain natriuretic peptide ( BNP ) in the early identification of cardiac involvement in Chagas disease . Fifty-four patients divided into 3 groups—group 1 ( undetermined form: positive serology without ECG or 2D-echocardiographic abnormalities; N = 32 ) , group 2 ( typical ECG abnormalities of Chagas disease but normal 2D-echocardiography; N = 14 ) , and group 3 ( regional wall motion abnormalities , left ventricular [LV] end-diastolic diameter >55 mm or LV ejection fraction <50% on echocardiography; N = 8 ) —and 44 control subjects were studied . Patients with significant non-cardiac diseases , other heart diseases and previous treatment with benznidazol were excluded . The median age was 37 ( 20–58 ) years; 40% were men . BNP levels , longitudinal and radial myocardial strain and LV diastolic dysfunction increased progressively from group 1 to 3 ( p for trend <0 . 01 ) . Abnormal BNP levels ( >37 pg/ml ) were noted in 0% , 13% , 29% and 63% in controls and groups 1 to 3 , respectively . Half of patients in the undetermined form had impaired relaxation patterns , whereas half of patients with ECG abnormalities suggestive of Chagas cardiomyopathy had normal diastolic function . In group 1 , BNP levels were statistically higher in patients with diastolic dysfunction as compared to those with normal diastolic function ( 27±26 vs . 11±8 pg/ml , p = 0 . 03 ) . In conclusion , the combination of diastolic function and BNP measurement adds important information that could help to better stratify patients with Chagas disease .
Chagas disease , a major cause of morbidity and mortality in several countries of Latin America [1] , has become a potential public health problem in countries where the disease is not endemic as a result of migration flows [2] , [3] , [4] . Chagas cardiomyopathy is the most serious form of the chronic phase of the disease and represents the major cause of mortality in these patients . For this reason , accurate diagnosis of cardiac involvement is critical . However , Chagas disease remains a neglected disease [5] and the diagnosis of Chagas cardiomyopathy is still based on simple and nonspecific criteria including an increased cardiothoracic ratio ( >0 . 5 ) or ECG abnormalities such as complete right bundle-branch block , left anterior hemiblock , complete left bundle-branch block , as well as other conduction and rhythm disturbances [6] , [7] . Echocardiography refined the diagnosis of Chagas cardiomyopathy , and regional wall motion abnormalities , reduced left ventricular ejection fraction ( LVEF ) <50% and increased left ventricular ( LV ) end-diastolic diameter >55 mm are now included as diagnostic criteria in some publications [8] , [9] . In spite of that , the sensitivity of these parameters is far from perfect and they may indeed misclassify patients with early myocardial involvement into the undetermined form , as conventional 2D echocardiography only detects advanced myocardial involvement . On the other hand , patients without cardiac disease but having one of the described as typical but unspecific ECG findings could be considered to have Chagas cardiomyopathy . Therefore , a more accurate classification model , particularly to identify patients with early cardiac involvement from the undetermined form would be desirable , since an early treatment and closer follow-up might be beneficial on these patients [8] . Analysis of diastolic function by echocardiography , cardiac magnetic resonance ( CMR ) and several biomarkers , including brain natriuretic peptide ( BNP ) and inflammation markers , have emerged as useful tools in the diagnosis and monitoring of heart failure in different conditions . In fact , BNP has recently been included in the guidelines for the diagnosis and management of congestive heart failure [10] . Comprehensive evaluation of diastolic dysfunction and myocardial strain imaging has provided more accuracy and sensitivity to detect early myocardial involvement in different cardiomyopathies [11] , [12] . However , these methods have been seldom utilized in characterizing patients with established Chagas cardiomyopathy [13] , [14] , [15] , and its role in the identification of cardiac involvement in the earlier phases of the disease is unclear [16] , [17] , [18] . We conducted a prospective study aimed to analyze the added value of different techniques in identifying cardiac involvement in the undetermined stage of Chagas disease . Diastolic function , natriuretic peptide and inflammatory markers levels in different phases of the disease were measured and correlated with longitudinal and radial myocardial strain and delayed enhancement on CMR to find out their value in improving the stratification of patients with Chagas disease .
A cross-sectional analysis was performed in a prospective cohort of consecutive adult patients evaluated at our Institution from January 2008 to June 2009 . Diagnosis of Chagas disease was based on a clinical record compatible with the epidemiology of the disease ( individuals from endemic zones of Chagas disease ) and microbiologic diagnosis by any combination of at least two positive commercial serological tests using different antigens: ELISA using a T . cruzi lysate ( Ortho-Clinical Diagnostics® ) , ELISA with recombinant antigens ( BioELISA Chagas® , Biokit S . A . ) and indirect immunofluorescence ( Inmunofluor Chagas , Biocientifica® ) . In an attempt to avoid factors that could have an effect on diastolic function , myocardial strain , natriuretic peptides or inflammatory markers levels , patients with severe non-cardiac diseases , prior diagnosis of heart disease from other etiology ( ischemic , hypertensive or alcoholic ) , hypertension , diabetes mellitus , active infections by other causal agent or previous treatment with benznidazol were excluded . All patients gave written consent for inclusion . The research protocol was approved by the Ethics Committee of our institution . Patients meeting the inclusion criteria were categorized into 4 groups: Group 0 ( control group , subjects from endemic areas with negative serology for Chagas disease ) ; Group 1 ( patients in the undetermined form of Chagas disease defined as those with positive serology of Chagas disease without any abnormal ECG finding , normal LV dimensions and LV global and regional systolic function with conventional 2D echocardiography ) ; Group 2 ( patients with typical ECG abnormalities of cardiac involvement by Chagas disease such as complete right bundle-branch block and/or left anterior hemiblock , complete left bundle-branch block , ventricular premature beats , primary abnormalities of ventricular repolarization , electrically inactive zones , low voltage QRS , sinus bradycardia <50 beat/min , advanced atrioventricular block or cardiac pacemaker , but normal LV dimensions and global and regional systolic function by 2D-echocardiography ) ; and Group 3 ( patients with Chagas cardiomyopathy with any regional wall motion abnormality and/or LV end-diastolic diameter >55 mm and/or LVEF <50% by 2D-echocardiography ) . Clinical examination , blood analysis including ions , creatinin , inflammatory markers and natriuretic peptides levels as well as a comprehensive 2D-echocardiogram with diastolic function and myocardial strain analysis were obtained from all patients . CMR studies were performed in an unselected sample of patients with Chagas disease ( Groups 1–3 ) . Measurements of plasmatic levels of endothelin 1 , tumor necrosis factor-α ( TNFα ) , interleukin 6 ( IL-6 ) , atrial natriuretic peptide ( ANP ) and BNP levels was carried out through peripheral venous puncture , after a 30 minutes rest , and quantified using commercially available kits . BNP levels were measured using a fully automated two-site sandwich BNP immunoassay on an Advia Centaur ( Siemens Diagnostics , Zurich , Switzerland ) . Minimum sensitivity and upper limit of normal values are 2 and 37 pg/ml respectively for the BNP assay . The precision of this technique is 1 . 8–4 . 3% . Echocardiographic studies were performed with a commercially available system ( VIVID 7 , General Electrics; Milwaukee , WI ) . Images were digitally stored for later off-line analysis with a commercial software package ( EchoPac , General Electrics; Milwaukee , WI ) . LV volumes and LVEF were calculated using the modified Simpson rule ( biplane method ) . Left atrium area was measured at the end-ventricular systole excluding the confluences of the pulmonary veins and the left atrium appendage . LV volumes and left atrium area were indexed to body surface area . Analysis of diastolic function was performed evaluating the mitral inflow pattern with pulsed Doppler: E and A waves and deceleration time of the E wave ( DT ) ; the pulmonary vein flow and the mitral annulus velocities with Doppler Tissue Imaging ( Em and Am ) . Patients were classified according to diastolic function patterns ( normal , impaired relaxation or stage I , pseudonormal or stage II and restrictive pattern or stage III ) following current recommendations [19] . The ratio of early diastolic mitral flow velocity to early diastolic mitral annulus velocity ( E/Em ) was used as a surrogate of LV filling pressures . LV segmental myocardial longitudinal 2D-strains were acquired from two and four-chamber apical views and radial strains from the parasternal short axis view at the level of the papillary muscles . Averages of longitudinal and radial strains were obtained by dividing the sum of all segmental strains by the number of analyzed segments . Images were optimized to obtain a frame rate >50 fps . CMR studies were performed using a 1 . 5 T clinical scanner ( Signa CV HDxt , General Electric , Milwaukee WI ) . Functional assessment was studied with a standard cine steady-state free precession sequence and delayed-enhanced images were acquired using a gradient-echo segmented inversion recovery technique , 10 minutes after intravenous administration of gadodiamide at a dose of 0 . 2 mmol/Kg ( Omniscan , GE Healthcare , Madrid ) . LV end-diastolic , end-systolic volumes and LVEF were calculated using Mass 4 . 0 . 1 software analysis ( MEDIS , The Netherlands ) . All echocardiographic and CMR analyses were performed by experienced independent observers blinded to BNP measurements and clinical data . Continuous baseline variables were expressed as mean±SD or median ( interquartile range ) values depending on normality assessed by the Shapiro-Wilks test . Categorical variables were expressed as total number ( percentages ) and compared between groups using Chi-square test or Fisher's test . Differences in continuous variables were analyzed using either ANOVA test or Kruskall Wallis test depending on variable distribution . Post-hoc analysis using either T-test or Wilcoxon test corrected by Bonferroni method was carried out to detect differences between each pair of groups . Trends in continuous variable changes across Chagas's disease severity and diastolic function impairment were analyzed using either ANOVA test for trend or Jonckheere-Terpstra test . Correlations between natriuretic peptides and LV volumes and LVEF were assessed using Pearson coefficient . Receiver-operating characteristic ( ROC ) curves were constructed to estimate the accuracy of BNP and ANP to detect any grade of diastolic dysfunction . Statistical analysis was performed with SPSS 15 . 0® .
A total of 98 consecutive subjects were included . Median age was 37 ( range 20 to 58 ) years and 40% were men . There were 44 subjects in Group 0 , 32 patients in Group 1 , 14 in group 2 , and 8 patients in group 3 . Clinical characteristics , hemodynamic data , and levels of inflammatory markers and natriuretic peptides are shown in Table 1 . Creatinin and sodium plasmatic levels were normal for all patients . Only two patients were under pharmacological treatment: one patient in group 1 was treated with betablockers and one patient in group 3 was under angiotensin converting enzyme inhibitors . There were no differences in clinical and hemodynamic characteristics between groups except for the New York Heart Association ( NYHA ) functional class . The majority of patients were in NYHA functional class I; no patient had NYHA functional class III or IV . TNFα levels were higher in group 1 as compared to group 0 and a significant trend towards increasing levels was observed according worsening clinical forms of Chagas disease . Abnormally high BNP levels ( >37 pg/ml ) were noted in 0% , 13% , 29% and 63% of patients in groups 0 , 1 , 2 and 3 , respectively . BNP and ANP levels in group 3 were significantly elevated as compared to those in groups 0 and 1 . There were no statistically significant differences in IL6 and endothelin 1 levels between groups . IL6 levels were undetectable in 68% of patients . Echocardiographic data regarding LV volumes , LVEF , LV myocardial strains and diastolic function are shown in Table 2 . Patients in the undetermined form ( group 1 ) showed no differences in LV dimensions or global LVEF as compared to the control group but had significantly reduced Em and lengthened DT ( 0 . 14±0 . 03 m/s vs . 0 . 16±0 . 03 m/s and 238 . 5 ms vs . 200 ms , respectively; p<0 . 001 for both ) . When patients were classified according to the diastolic function pattern , 50% of patients in groups 1 and 2 had an impaired relaxation pattern . On contrast , every patient in group 3 had a certain degree of diastolic dysfunction ( Table 3 ) . Two ( 5% ) subjects in group 0 had impaired relaxation pattern . Natriuretic peptides and LV myocardial strain averages were progressively different as diastolic dysfunction severity increased ( p for trend <0 . 01 ) ( Table 4 ) . The accuracy of BNP and ANP to detect any grade of diastolic dysfunction , as assessed by the area under the ROC curve , was 0 . 73 , 95%CI 0 , 60–0 . 85 for BNP and 0 . 70; 95%CI 0 . 58 – 0 . 81 for ANP . In addition , both BNP and ANP significantly correlated with LV end-diastolic volumes ( r = 0 . 35; p = 0 . 001 and r = 0 . 26; p = 0 . 013 , respectively ) , LV end-systolic volumes ( r = 0 . 44; p<0 . 001 and r = 0 . 36; p<0 . 001 respectively ) and LVEF ( r = −0 . 44; p<0 . 001 and r = −0 . 44; p<0 . 001 , respectively ) . When only patients in the undetermined form of the disease ( group 1 ) were considered , BNP levels were higher in patients with diastolic dysfunction compared to those with normal diastolic pattern ( 27±26 versus 11±8 pg/ml , respectively , p = 0 . 03 ) . A similar result was obtained in group 2 , thus , a trend towards increasing BNP levels was observed from normal diastolic pattern to abnormal relaxation and pseudonormal pattern ( 11±4 , 37±36 and 41±3 , pg/ml respectively , p = 0 . 06 ) . The areas under the ROC curves for BNP and ANP to detect mild diastolic dysfunction in patients in the undetermined form were 0 . 69; 95%CI 0 , 49–0 . 89 for BNP and 0 . 62; 95%CI 0 . 43–0 . 82 for ANP . Additionally , every patient with abnormally high levels of BNP ( >37 pg/ml ) had diastolic dysfunction ( Figure 1 ) . A CMR was performed in 21 Chagas disease patients , 7 patients in each group ( groups 1–3 ) according to the standard classification [20] , [21] . Two ( 28% ) patients in group 1 ( Figure 2 ) , 1 ( 14% ) patient in group 2 and 3 ( 43% ) patients in group 3 had gadolinium delayed enhancement compatible with scar or fibrosis . However , when these patients were classified according to the diastolic function pattern , none with normal diastolic function had delayed enhancement , whereas 40% of patients with impaired relaxation pattern and 50% with pseudonormal pattern showed delayed enhancement ( Figure 3 ) .
The main finding of our investigation is that diastolic dysfunction occurs before any LV dilatation , regional or global systolic abnormalities or significant increased filling pressures as assessed by E/Em ratio . In this regard , we found significant reduction in Em and lengthening in DT in patients in the undetermined form compared to control individuals , in spite of preserved LV volumes and systolic function . The fact that all the rest of the echocardiographic measurements remained similar in both the undetermined form and control individuals , suggests that Em and DT could be the most sensitive echocardiographic parameters to detect cardiac involvement by Chagas disease . In addition , BNP levels identify patients with diastolic dysfunction among those in the undetermined form of Chagas disease with high specificity . The study also confirmed the fact that as the disease progresses from the undetermined form to Chagas cardiomyopathy with abnormal 2D-echocardiography , including enlargement of LV volumes and a deterioration of global LV function , diastolic function deteriorates . This is supported also by the fact that global LV strain , both longitudinal and radial , also progressively decreases along with diastolic function impairment , suggesting the existence of myocardial damage , despite preserved LVEF . Previous studies analyzing diastolic function in the undetermined form of Chagas disease have shown conflicting results ( 15–21 ) . Barros et al . were the first to report early LV diastolic dysfunction in patients in the undetermined form of Chagas disease as they demonstrated a lengthening of DT and isovolumic relaxation time [16] . Cianciulli et al . similarly observed that transmitral Doppler flow allowed to identify early abnormalities of diastolic function in patients with normal ECG and conventional 2D-echocardiogram [17] . On the other hand , Pazin-Filho et al . also focused on patients in the undetermined form and showed that patients with normal global and segmental LV systolic function by 2D echocardiography did not show any abnormality of diastolic function [18] . However , in a meticulous evaluation of this small study a trend for higher LA volumes , DT lengthening and Em reduction could be observed among groups , and therefore , an insufficient statistical power might have contributed to the negative result . Despite being only performed in a small subgroup of patients , the results of CMR also sustain that diastolic dysfunction may be the first manifestation of myocardial involvement , before systolic dysfunction occurs as it happens in other cardiomyopathies . Therefore , the comprehensive analysis of diastolic dysfunction could be more sensitive in terms of early diagnosis of myocardial involvement compared to the standard classification as it was shown that some patients with normal ECG and normal conventional 2D-echocardiography had myocardial fibrosis detected by delayed enhancement , whereas no patient with normal diastolic function had enhancement on CMR . A prior study showed that delayed enhancement in CMR can be present in up to 20% of patients who are in the undetermined form of the disease [22] , suggesting that this technique has an extended value for the diagnosis in early stages of cardiac involvement . However , CRM availability is limited particularly in areas where Chagas disease is endemic . The association between a normal diastolic function by echo and absence of fibrosis on CMR has to be confirmed in larger series . Nevertheless , although diastolic function analysis seems to have a high sensitivity , it has to be acknowledged that the complexity of diastolic dysfunction measurements may preclude its use in large populations , especially in underdeveloped geographical areas . In this regard , the initial screening with BNP determination could be helpful , especially with the use of simplified , point-of-care kits . In fact , every patient with abnormally high BNP levels had diastolic dysfunction in our study ( Figure 1 ) . Natriuretic peptides have been shown to be involved in the pathogenesis of Chagas disease in animal experiments [23] , [24] and some clinical studies have reported that BNP levels are increased in Chagas cardiomyopathy [25] , [26] , [27] and correlates with prognosis [28] . Ribeiro et al . demonstrated a high specificity with moderate sensitivity for BNP to detect LVEF≤40% in infected patients with an abnormal ECG or chest X-ray [25] . In a second study , the same group reported that BNP levels correlated with LV dimensions and LVEF in patients with Chagas disease and also that patients with mild degree of cardiac dysfunction , defined as no more that minor alterations in their echocardiography , had intermediate BNP levels compared to control individuals and patients in the cardiac form of the disease [27] . In a third study , they compared the diagnostic accuracy of the combination of BNP plasmatic levels and ECG vs . the standard strategy ( ECG and chest X-ray ) to detect LVEF ≤40% and demonstrated a significant improvement in specificity although the new strategy had less sensitivity [26] . However , even though the correlation between BNP levels and systolic dysfunction in Chagas disease has been well described , few studies have correlated BNP levels and diastolic function in Chagas disease [14] , [29]; indeed , all of them have been done in patients in the cardiac form of Chagas disease . Barbosa et al [14] evaluated 59 patients with dilated cardiomyopathy due to Chagas disease and reported a marked elevated concentration of the amino-terminal portion proBNP specifically in patients with a restrictive diastolic pattern . Oliveira et al [29] evaluated 36 patients , all of them with diffuse or segmental ventricular motion abnormalities , and described a significant correlation between BNP and E/E' ratio in the inferior wall . To our knowledge there are no studies that have specifically assessed the association between BNP and diastolic function in patients in the undetermined form . In our population , the accuracy of BNP to detect any degree of diastolic dysfunction was good ( area under curve of 0 . 73 ) ; additionally , the ability to detect mild diastolic dysfunction in the group of patients in the undetermined form was also good ( area under the curve of 0 . 69 ) . The specificity of BNP levels >37 pg/ml to detect mild diastolic dysfunction in patients in the undetermined form of Chagas disease was 100% . The power of ANP levels to detect diastolic dysfunction was slightly inferior” . Different from ANP , BNP is mainly secreted in the ventricles in response to wall stress , ischemia or fibrosis . Indeed , myocardial fibrosis has been described to strongly trigger BNP synthesis [30] . Chagas cardiomypathy is a predominately fibrogenetic cardiomyopathy . Cardiac fibrosis is evident even in early stages of the disease [22] , [31] . This fact might explain why BNP levels could be elevated even in patients with normal NYHA functional class , normal LVEF and ventricular filling pressures , and supports the idea that BNP levels measurement could be useful to early detect cardiac involvement in Chagas disease . Enhanced fibrosis , compared with other cardiomyopathies , could also explained higher BNP levels in patients with Chagas disease as compared to patients with cardiac disease of different etiologies in the same NYHA functional class [32] . Our study also aimed to assess the plasmatic levels of TNFα , IL6 and endothelin 1 , in patients with different clinical forms of Chagas cardiomyopathy . These biomarkers have been described to be elevated in Chagas cardiomyopathy [33] , [34] , [35] . We found statistically significant elevated levels of TNFα in patients in the undetermined form as compared to control individuals and a significant trend towards increasing levels was observed along clinical severity groups . Our finding is in concordance with previously published literature suggesting heart inflammation in patients with Chagas disease even in the absence of heart failure . Talvani et al [35] had previously reported higher TNFα levels in patients with severe Chagas cardimyopathy; in this study although TNFα levels were slightly higher in patients in the undetermined form as compared to those in healthy individuals , no statistically significant differences were reached . Similarly , in our study , a trend towards greater IL6 levels could be also observed along with progressive clinical severity and diastolic function impairment . However , as this measurement was undetectable in a significant proportion of patients , its interpretation is limited . The use of high-sensitivity kits for IL6 detection could be valuable in this context . Finally , our study failed to demonstrate differences in plasmatic levels of endothelin 1 in the different forms of Chagas disease . A privious study reported elevated endothelin plasmatic levels in patients with Chagas cardiomyopathy [33] . However , in this study seropositive patients had similar endothelin plasmatic levels than control individuals , and the group of patients in the undetermined form had even lower levels than controls . Therefore , the usefulness of plasmatic measurement of endothelin is not clear and more studies are required to clarify its role in the clinical evaluation of patients with Chagas disease . The main limitation of our study is its cross-sectional design and , consequently progression of cardiomyopathy could not be evaluated . Longitudinal studies in Chagas disease are difficult due to the slow progression of the disease and the frequent change of residency that particularly patients who live in non-endemic areas have , making follow-up difficult . In conclusion , the initial screening with measurement of BNP , an easy test with high specificity for LV damage , combined with a comprehensive analysis of diastolic function that contributes with high sensitivity seems to be a more accurate strategy to early diagnose LV involvement in Chagas disease . Our findings could help to better stratify patients with Chagas disease . The association between normal diastolic function , normal BNP levels , absent fibrosis on CMR and no progression of the disease warrants confirmation in larger and prospective longitudinal studies . Also , new studies are required to demonstrate that early treatment and closer follow-up slow the disease progression and consequently improve the prognosis in these patients . | Chagas disease remains a major cause of morbidity and mortality in several countries of Latin America and has become a potential public health problem in countries where the disease is not endemic as a result of migration flows . Cardiac involvement represents the main cause of mortality , but its diagnosis is still based on nonspecific criteria with poor sensitivity . Early identification of patients with cardiac damage is desirable , since early treatment may improve prognosis . Diastolic dysfunction and elevated brain natriuretic peptide levels are present in different cardiomyopathies and in advanced phases of Chagas disease . However , there are scarce data about the role of these parameters in earlier forms of the disease . We conducted a study to assess the diastolic function , regional systolic abnormalities and brain natriuretic peptide levels in the different forms of Chagas disease . The main finding of our investigation is that diastolic dysfunction occurs before any cardiac dilatation or motion abnormality . In addition , BNP levels identify patients with diastolic dysfunction and Chagas disease with high specificity . The results reported in this study could help to early diagnose myocardial involvement and better stratify patients with Chagas disease . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"cardiovascular",
"disorders/coronary",
"artery",
"disease",
"cardiovascular",
"disorders/myocardial",
"infarction",
"cardiovascular",
"disorders/cardiovascular",
"imaging"
] | 2010 | Chagas Cardiomiopathy: The Potential of Diastolic Dysfunction and Brain Natriuretic Peptide in the Early Identification of Cardiac Damage |
Rabbit hemorrhagic disease virus ( RHDV ) is an important member of the Caliciviridae family and a highly lethal pathogen in rabbits . Although the cell receptor of RHDV has been identified , the mechanism underlying RHDV internalization remains unknown . In this study , the entry and post-internalization of RHDV into host cells were investigated using several biochemical inhibitors and RNA interference . Our data demonstrate that rabbit nucleolin ( NCL ) plays a key role in RHDV internalization . Further study revealed that NCL specifically interacts with the RHDV capsid protein ( VP60 ) through its N-terminal residues ( aa 285–318 ) , and the exact position of the VP60 protein for the interaction with NCL is located in a highly conserved region ( 472Asp-Val-Asn474; DVN motif ) . Following competitive blocking of the interaction between NCL and VP60 with an artificial DVN peptide ( RRTGDVNAAAGSTNGTQ ) , the internalization efficiency of the virus was markedly reduced . Moreover , NCL also interacts with the C-terminal residues of clathrin light chain A , which is an important component in clathrin-dependent endocytosis . In addition , the results of animal experiments also demonstrated that artificial DVN peptides protected most rabbits from RHDV infection . These findings demonstrate that NCL is involved in RHDV internalization through clathrin-dependent endocytosis .
Rabbit hemorrhagic disease virus ( RHDV ) is a non-enveloped , single-stranded , positive sense RNA virus , belonging to the Caliciviridae family [1] , and it is the causative agent of a highly contagious and lethal disease in rabbits which is strongly associated with liver degeneration and diffuse hemorrhage [2 , 3] . RHDV was first isolated in China in 1984 [4] and it has been subsequently detected in rabbit populations throughout Asia , Europe , Australia , and the Americas , resulting in the death of millions of wild and domestic adult rabbits [3] . It is well known that the first step in viral infection is viral entry . However , a suitable cell culture capable of supporting authentic RHDV has not yet been established , thereby greatly impeding the progress of investigations into the mechanisms underlying the pathogenesis , translation , and replication of RHDV . Consequently , studies on the viral entry of RHDV have relied on the self-assembly of the capsid protein into virus-like particles when expressed in Escherichia coli or insect cells . RHDV has been shown to bind to oligosaccharides H type 2 and A type 2 , which are histo-blood group antigens ( HBGAs ) expressed on the surfaces of cells lining the duodenal surface and trachea of rabbits , thereby presenting two possible viral entry points [5] . RHDV isolates from six different genetic groups bind specifically to different HBGAs , which act as attachment factors that facilitate infection [6 , 7] . Following attachment of RHDV to the cell surface , internalization by an unknown mechanism and desencapsidation occur , leading to the release of the viral genome into the cytoplasm of the host cell . In 2017 , we successfully constructed and cultured a mutant RHDV ( mRHDV ) in RK-13 cells in vitro , which has a specific receptor-recognition motif ( Arg-Gly-Asp ) on the surface of the capsid protein that is characterized by two amino acid ( aa ) substitutions . mRHDV is recognized by the intrinsic membrane receptor ( integrin α3β1 ) of RK-13 cells and then gains entry , replicates , and imparts apparent cytopathic effects [8] . In previous studies , we found that the RHDV capsid protein ( VP60 ) was associated with many host proteins , including NCL , through affinity purification of infected cells [8] . NCL is a phosphoprotein that is ubiquitously and abundantly expressed in many growing eukaryotic cells and highly conserved during evolution , as it is involved in a remarkably large number of cellular activities [9] . Generally , NCL is mainly distributed in the nucleolus , but also exists in the nucleoplasm and cytoplasm , and on the cell surface , where its specific functions vary [10 , 11] . NCL controls a wide range of fundamental cellular processes , such as ribosome biogenesis , proliferation , and cell cycle regulation , and also plays important roles in the replication and intracellular trafficking of multiple viruses [12–16] . For example , the interaction between NCL and the 3′ untranslated region ( UTR ) of tombusvirus RNA has been shown to inhibit replication by interfering with the recruitment of viral RNA [17] . Similarly , the interaction between NCL and the UTRs of feline calicivirus [18] and poliovirus [19 , 20] stimulate translation of viral proteins . In addition , NCL binds to a protein of herpes simplex virus type 1 to facilitate exportation of US11 from the cell nucleus to the cytoplasm [21] . Notably , cell surface NCL is involved in viral infection by promoting viral attachment and internalization . For example , NCL acts as a receptor of human respiratory syncytial virus [22] and as a low-affinity receptor of human immunodeficiency virus [23] . NCL also mediates cellular attachment and internalization of enterovirus 71 [24] . Moreover , the internalization of multiple influenza A viruses , including H1N1 , H3N2 , H5N1 , and H7N9 , is reduced by suppression of NCL function and expression on the cell surface [25–29] . A recent study showed that NCL interacts with the capsid protein of dengue virus , suggesting a role in viral morphogenesis [30] . In addition , NCL is important for lyssavirus infection [31] . Here , we demonstrated that RHDV VP60 protein directly interacts with NCL . Of note , this interaction was found to play a key role in RHDV internalization . Furthermore , our data showed that NCL is involved in clathrin-dependent endocytosis , which is an important pathway of RHDV infection , by interacting with the C-terminal residues of clathrin light chain A ( CLTA ) . Collectively , these findings further illustrate the molecular mechanism underlying RHDV infection .
We previously reported a novel strategy for the construction of an mRHDV that could be recognized by host cells and grows well in RK-13 cells [8] . Furthermore , we performed affinity purification with RHDV capsid protein VP60 mAb from RK-13 cells , which were infected with mRHDV for 48 h , followed by mass spectrometry analysis . As shown in Fig 1A , many host proteins were associated with VP60 , including NCL [8] . In this study , we found that RHDV and NCL were co-localized on the cell membrane surface at the early stage of mRHDV infection . This phenomenon was most apparent at 2–3 hours post-infection ( hpi ) ( Fig 1B ) . Therefore , we hypothesized that NCL is important for RHDV entry . To determine if NCL is required for RHDV infection , the expression level of NCL was changed by siRNA ( S1 Fig ) or transfection with plasmids ( S2A Fig ) . For the attachment assay , RK-13 cells were pre-transfected with Flag-tagged NCL plasmids or NCL siRNA for 24 h , or pre-incubated with anti-NCL mAb for 1 h at 37°C . The cells were then chilled and mRHDV was added at a multiplicity of infection ( MOI ) of 1 ( S3A Fig ) . After viral attachment at 4°C for 2 h , the RK-13 cells were washed and lysed . qRT-PCR determined the number of attached viral RNA copies . Percent changes in VP60 RNA copy numbers were derived by comparing the number of VP60 RNA copies in the treated samples to that of the untreated RK-13 cells . As shown in Fig 1C , mRHDV attachment to RK-13 cells was not obviously affected by changing or blocking NCL regulation . Next , we investigated whether NCL was required for RHDV internalization . For the internalization assays , RK-13 cells were incubated with mRHDV ( MOI = 1 ) for 2 h at 4°C and then washed with chilled PBS . The temperature was then shifted to 37°C to facilitate viral internalization . At 30 min post infection , when RHDV internalization was proportionally increasing ( S3B Fig ) , the cells were washed in pre-chilled acidic PBS ( pH = 2 . 5 ) to remove non-internalized viruses . After cell lysis , the amount of internalized mRHDV VP60 RNA copies was determined by qRT-PCR . As shown in Fig 1D , the percentage of mRHDV internalized in RK-13 cells was 40% or 38% after treatment with NCL siRNA or NCL Ab , respectively . In contrast , overexpression of NCL increased the percentage of internalized mRHDV by 25% . Collectively , these data show that NCL is required during RHDV internalization , but not attachment . Previous studies have reported that NCL is involved in the entry of dengue virus and enterovirus 71 by interacting with the capsid proteins [24 , 30] . The only capsid protein present in RHDV , VP60 , is responsible for antigenicity and binding to host proteins , and plays a key role in RHDV entry [7] . To determine if NCL binds to VP60 during RHDV internalization , we assessed the interaction between NCL and VP60 in RK-13 cells in the presence and absence of mRHDV infection for 2h at 37°C . The results of an immunoprecipitation ( IP ) assay performed on cell lysates using NCL mAb showed that NCL and VP60 associated in infected cells ( Fig 2A ) . Moreover , to determine whether NCL directly interacts with VP60 , co-IP assays were employed with a myc mAb in RK-13 cells , which were co-transfected with pVP60-myc and pNCL-Flag eukaryotic expression plasmids . Western blot analysis using a mAb against the Flag tag showed a band corresponding to NCL in the myc co-IP assay , indicating a direct interaction between RHDV VP60 and NCL ( Fig 2B ) . In addition , an IFA was performed using mAbs against VP60 and NCL in RK-13 cells co-transfected with pVP60-myc and pNCL-Flag plasmids for 24 h . As shown in Fig 2C , NCL was co-localized with RHDV VP60 in the RK-13 cell cytoplasm . To characterize the critical domain of NCL for NCL-RHDV VP60 interactions , GST-fusion proteins corresponding to NCL and sub-fragments of NCL ( GST-NCL , GST-NCL-NTD , GST-NCL-RBD and GST-NCL-CTD , respectively ) were prepared for use as bait proteins in glutathione pull-down assays to determine their abilities to interact with the VP60 protein expressed in RK-13 cells ( Fig 2D ) . The results of these assays showed that both GST-NCL and GST-NCL-NTD bound to RHDV VP60 , but did not to GST-NCL-RBD , GST-NCL-CTD , and GST ( Fig 2E ) . Subsequently , sub-fragments of NCL-NTD GST-fusion proteins ( i . e . , GST-NTD-A , GST-NTD-B , and GST-NTD-C ) were prepared for use as bait proteins in the GST pull-down assay ( Fig 2F ) . As shown in Fig 2G , both GST-NTD-C and GST-NCL-NTD bound to RHDV VP60 , while the other proteins were undetectable . In addition , NCL-NTD-C was split into the fragments NTD-C1 ( aa 214–249 ) , NTD-C2 ( aa 250–284 ) , and NTD-C3 ( aa 285–318 ) , which were fused with GST and expressed ( Fig 2H ) . The results of further pull-down assays showed that NTD-C3 and NTD-C bound to VP60 , while the other proteins did not ( Fig 2I ) . Together these results suggest that RHDV VP60 directly and specifically interacts with the N-terminal residues 285–318 of NCL . RHDV VP60 is composed of three domains: N-terminal arm ( NTA ) , shell ( S ) , and protrusion ( P ) . The P domain is further divided into the P1 and P2 sub-domains [32] . To identify functional areas of VP60 that interacts with NCL , the GST-VP60 fusion protein and its sub-fragments ( i . e . , GST-VP60-NTA , GST-VP60-S , GST-VP60-P1 , GST-VP60-P2s , and GST-VP60-P1s ) ( Fig 3A ) were prepared for use as bait proteins in GST pull-down assays to determine their abilities to interact with the NCL protein . As shown in Fig 3B , only GST-VP60-P1s and GST-VP60 bound to NCL . These results confirm that binding to NCL requires the P1s domain of VP60 . To further map the VP60 P1s segments responsible for the VP60-NCL interactions , GST-fusion proteins corresponding to sub-fragments of the VP60-P1s domain were prepared by removing 50 aa residues from the N- and C- terminals of VP60-P1s ( i . e . , GST-P1s-N1 , GST-P1s-N2 , GST-P1s-N3 , GST-P1s-N4 , GST-P1s-C1 , GST-P1s-C2 , and GST-P1s-C3 ) ( Fig 3C ) . The results of a set of pull-down assays showed that GST-P1s-N1 , GST-P1s-N2 , GST-P1s-N3 , GST-P1s-C1 , and GST-P1s interacted with NCL , while the other proteins did not ( Fig 3D ) . These findings indicate that RHDV VP60 interacts with NCL via aa residues 468–484 . To pinpiont the key aa responsible for binding of the RHDV VP60 P1s domain with NCL , blocks of three or four aa substitutions were introduced within and beyond the conserved sequence motif . The following non-conservative substitutions to the GST-tagged RHDV VP60 protein were made: 468RRTG468DDPP , 472DVN472RSP , 475AAA475FPQ , and 482GTQ482KPA . Furthermore , the sequence GSGSGS was inserted after aa residue 483 ( Fig 4A ) . It has been reported that GSGSGS is a flexible peptide for use in different expression systems to separate functional proteins [33] . The wild-type and mutant RHDV VP60 P1s proteins were used as bait proteins in the GST pull-down assays to determine their abilities to bind to NCL . As shown in Fig 4B , the 472DVN472RSP mutation was not able to bind to NCL , while the binding capacities of the other mutations to the residues at positions 468–484 were reduced to different extents . Moreover , as predicted with the SWISS-MODEL online tool ( https://swissmodel . expasy . org/ ) , wild-type DVN peptides formed a structure similar to a “claw , ” which provides a structural basis for interactions with NCL . However , once the key aa were changed ( DVN mutated to RSP ) , the “claw” structure is broken and loses the ability to bind to NCL ( Fig 4C ) . These results indicate that this “claw” structure is the structural basis for the interaction between VP60 and NCL . In addition , analysis of the VP60 sequence of the G1–G6 genotypes of RHDV showed that the DVN motif was highly conserved ( Fig 4D ) . These observations confirm that RHDV VP60 interacts with NCL via the DVN ( Asp-Val-Asn ) motif , which is a conserved sequence in RHDV . The above results indicate that NCL was required during RHDV internalization and interacted with the RHDV capsid protein ( VP60 ) . Therefore , we speculated that NCL is involved in RHDV internalization via binding to VP60 . To verify this hypothesis , we blocked the binding site of NCL-VP60 with a synthesized DVN peptide ( RRTGDVNAAAGSTNGTQ ) and examined its effect on RHDV entry . The results of the attachment and internalization assays showed that mRHDV internalization was drastically reduced in RK-13 cells treated with the DVN peptide , with only 40% internalization , but there was no obvious effect on mRHDV attachment ( Fig 5A ) . To image the internalization of single RHDV particles in live cells , the fluorescent dye Alexa Fluor 488 was conjugated to purified virions . This labeling process did not significantly reduce the viral titer , as measured by the qRT-PCR assay ( Fig 5B ) . Alexa Fluor 488-conjugated mRHDV ( RHDV-FITC ) was used to evaluate the efficiency of viral internalization in response to different doses of the DVN peptide as well as a control peptide ( RHDV VP60 residues 434–450: VTYTPQPDRIVTTPGTP ) . RK-13 cells were pretreated with DVN peptide or a control peptide at a concentration of 5 , 10 , 20 , 40 , 80 , or 100 μg/mL , respectively for 6 h , and then incubated with RHDV-FITC ( MOI = 1 ) for 2 h at 4°C and washed with chilled PBS . The temperature was then shifted to 37°C to facilitate viral internalization . At 30 min post infection , when RHDV internalization was proportionally increasing , the cells were washed in pre-chilled acidic PBS ( pH = 2 . 5 ) to remove non-internalized viruses . The amount of internalized mRHDV was quantitated by flow cytometry . As shown in Fig 5C , with the increase in the DVN peptide , the amount of RHDV internalized in RK-13 cells was significantly reduced , and when the cells were treated with DVN peptide ( 80 μg/mL ) , the amount of virus in the treated cells was only about half of the control group . In addition , fluorescence microscopy also showed that the amount of internalized mRHDV was drastically reduced in RK-13 cells treated with either the DVN peptide or NCL mAb ( Fig 5D ) . These results suggest that blocking the binding site of the interaction between VP60 and NCL resulted in a significant reduction in RHDV internalization . It is well known that viruses typically make use of various pinocytic mechanisms of endocytosis that serve the cell by promoting the uptake of fluid solutes , and small particles . The mechanisms include clathrin-dependent endocytosis , the most commonly used mechanism for virus , macropinocytosis , a transient , ligand-induced , actin-dependent mechanism , and lipid raft-dependent endocytosis , a lipid raft-dependent mechanism mainly used by polymaviruses [34] . A previous study reported that RHDV VLPs use clathrin-dependent endocytosis to enter mouse and human dendritic cells [35] . However , the mechanism by which RHDV enters rabbit cells remains unclear . In order to determine the type of endocytosis involved in RHDV internalization , a series of experiments were conducted to examine RHDV entry into RK-13 cells . First , the cells were treated with 30 mM CPZ , an inhibitor of clathrin-dependent endocytosis , which inhibits clathrin triskelions assembly , 100 mM Nys , an inhibitor of lipid raft-dependent endocytosis , which disrupts membrane lipid rafts , 50 mM 5-EIPA , an inhibitor of macropinocytosis , which is a selective blocker of Na+/H+ pump exchanger , or DMSO , respectively . The effect of inhibitors on RHDV-FITC internalization was quantified by qRT-PCR , flow cytometry , and fluorescence microscopy . The results showed that CPZ , as well as NCL siRNA , inhibited RHDV internalization , as the percentage of internalization was about 40% , but not Nys or EIPA ( Fig 6A–6C ) , suggesting that clathrin-dependent endocytosis is involved in the process of RHDV entry into rabbit cells . Our results are consistent with those of previous studies , suggesting that clathrin-dependent endocytosis is involved in RHDV internalization . Previous studies have shown that NCL is required for clathrin-dependent endocytosis in human and murine cells [27 , 36] . Here , FITC-conjugated ligands were used to evaluate the efficiency of cargo uptake by siRNA-treated and untreated RK-13 cells . The effect of NCL siRNA on all endocytic pathways was quantified by flow cytometry . The results showed that NCL siRNA inhibited the uptake of transferrin and EGF , which were used as representative markers of clathrin-dependent endocytosis [37] , but had no effect on the uptake of the representative markers of the lipid raft-dependent endocytosis ( CD4 [38] ) and macropinocytosis ( dextran [39] ) ( S4A–S4D Fig ) . These results showed that NCL also plays a key role in clathrin-dependent endocytosis in RK-13 cells . However , the molecular mechanism underlying NCL involvement in clathrin-dependent endocytosis remains unclear . To elucidate the molecular mechanism , RK-13 cell lysates were used for affinity purification with NCL mAb . Briefly , the eluted protein complexes were resolved by SDS-PAGE and the protein bands were visualized by silver staining and identified by mass spectrometry analysis . As shown in Fig 7A , many host proteins were bound to NCL , including adaptor protein 2 ( AP2 ) and CLTA , which are necessary for clathrin-dependent endocytosis . CLTA participates in several membrane traffic pathways involving both clathrin and actin by binding with actin-organizing huntingtin-interacting proteins [40] . Moreover , IP experiments were performed with an NCL mAb in RK-13 cells . Western blot analysis using a mAb against AP2 or CLTA showed a band corresponding to CLTA , but not AP2 , indicating an interaction between CLTA and NCL ( Fig 7B ) . Furthermore , the crucial domain of the interactions between NCL and CLTA was investigated using the glutathione pull-down assays . The GST-fusion proteins corresponding to CLTA and sub-fragments of CLTA ( GST-CLTA , GST-CLTA-N , GST-CLTA ( aa 81–120 ) , GST-CLTA ( aa 121–160 ) , and GST-CLTA-C , respectively ) were prepared for use as bait proteins to determine their abilities to interact with the NCL protein expressed in RK-13 cells ( Fig 7C ) . These results showed that CLTA-GST and GST-CLTA-C bound to NCL , but no GST-CLTA-N , GST-CLTA ( aa 81–120 ) , GST-CLTA ( aa 121–160 ) or GST ( Fig 7D ) . Taken together , these results suggest that NCL plays a key role in clathrin-dependent endocytosis by directly and specifically interacting with CLTA via the C-terminal aa residues 161–219 . The results of the above studies showed that NCL is involved in RHDV internalization by binding to VP60 . In addition , clathrin-dependent endocytosis is involved in RHDV internalization and NCL participates in clathrin-dependent endocytosis by interacting with CLTA . To investigate the role of the interaction between CLTA and NCL in RHDV internalization , the expression level of CLTA was changed by siRNA ( Fig 8A ) or myc-CLTA plasmid ( S2B Fig ) transfection . The results of the attachment and internalization assays showed that mRHDV internalization was reduced in RK-13 cells treated with CLTA or NCL siRNA ( Fig 8C ) , and increased by overexpression of CLTA ( Fig 8D ) , athough there was no obvious influence on mRHDV attachment ( Fig 8B ) . Of note , the effect on mRHDV internalization is dependent on the dose of CLTA siRNA , NCL siRNA or the myc-CLTA plasmid . The percentage of mRHDV internalization in RK-13 cells was about 41% , 56% and 125% , following treatment with CLTA siRNA ( 100 pmol ) , NCL siRNA ( 100 pmol ) or myc-CLTA ( 2 μg ) , respectively ( Fig 8C and 8D ) . These data showed that CLTA is involved in RHDV internalization , confirming that the interaction between CLTA and NCL plays an important role in RHDV internalization . The above experimental results were obtained with RK-13 cells infected with mRHDV . In order to assess the role of NCL-VP60 interaction during infection with wild-type RHDV , rabbits were immunized with the DVN peptide , control peptide , commercial inactivated RHDV vaccine , or PBS . The VP60-specific Ab responses of immunized rabbits were determined using an indirect ELISA . As shown by the results presented in Fig 9A , the Ab titers of VP60 from the DVN peptide , control peptide , and commercial vaccine groups gradually increased . Statistically , the Ab titers were significantly higher ( p < 0 . 05 ) in the DVN peptide , control peptide , and commercial vaccine groups than in the PBS group at 0 , 1 and 2 weeks post-immunization . Simultaneously , in order to investigate cell-mediated immune responses , the amounts of IFN-γ , IL-2 , and IL-4 were measured by ELISA . As shown in Fig 9B–9D , there was no significant difference in the levels of IFN-γ , IL-2 , and IL-4 over time between the DVN peptide and control peptide groups . However , the levels of IFN-γ , IL-2 , and IL-4 were significantly higher ( p < 0 . 05 ) in the commercial vaccine group than in the PBS group at 1 and 2 weeks post-immunization . These results indicated that the DVN peptide was unable to activate the cellular immune response in rabbits . At 21 days post immunization , all rabbits were challenged intramuscularly with wild-type RHDV . As shown in Fig 9E , the survival rates of rabbits against virulent RHDV in the DVN peptide and commercial vaccine groups were 60% and 80% , respectively . However , all rabbits in the control peptide group and PBS group ( negative control ) died within 48–72 hpi with virulent RHDV . These negative control animals exhibited clinical symptoms of RHDV infection . Moreover , histopathological analysis indicated that typical pathological changes , such as diffuse bleeding of tissues and organs , were observed in the control peptide and PBS groups , but the tissues and organs of rabbits in the DVN peptide and commercial vaccine groups showed no pathological changes . Similarly , immunohistochemical analysis indicated large amounts of RHDV virions in the tissues of rabbits in the control and PBS groups , but not in the DVN peptide and commercial vaccine groups ( Fig 9F ) . These data show that the DVN peptide provides partial protection against RHDV in rabbits , suggesting that the interaction between VP60 and NCL plays a key role in RHDV internalization .
NCL is well known as a multifunctional protein that is mainly localized in the nucleolus , but is also found in the nucleoplasm and cytoplasm , as well as on the cell membrane [10] . The multi-functionality of NCL mainly results from its multi-domain structure . Sequence comparison of different species revealed a high degree of evolutionary conservation of this large protein , and biophysical and biochemical studies have shown that NCL is composed of three main structural domains: the N-terminal domain , the central domain , and the C-terminal domain [41] . The N-terminal domain of NCL contains acidic regions , rich in glutamic acid and aspartic acid , which are the sites of phosphorylation , and participate in the transcription of rRNA and interact with components of the pre-rRNA processing complex [16] . The central domain of NCL contains four RNA-binding domains ( RBDs ) , also known as RNA-recognition motifs , which are involved in a variety of biological processes , including RNA packaging , pre-mRNA splicing , poly-A tail synthesis and maturation , translational control , and mRNA stability [11] . The C-terminal region of NCL contains a glycine- and arginine-rich domain , through which NCL interacts with target mRNAs and proteins , including ribosomal proteins [9] . NCL is involved in several cellular functions , including ribosome biogenesis , DNA and RNA metabolism , cell proliferation , apoptosis regulation , and stress responses [42] . Moreover , increasing evidence suggests that NCL is also involved in the pathogenesis of many viruses . For viral infection , cell surface NCL has been shown to be involved by promoting either initial attachment of the virus to the cell surface or entry into host cells . For instance , NCL was found to serve as a cellular receptor during infection with human respiratory syncytial virus [22] . Cell surface NCL also mediates the binding and internalization of enterovirus 71 [43] . By targeting NCL on the cell surface , HB-19 pseudo-peptides inhibit attachment of human immunodeficiency virus to the cell surface as well as subsequent viral entry into the host cell [44] . Furthermore , cell surface NCL ligands , such as midkine and lactoferrin , have similar effects during viral infection [45] . In addition , NCL serves as a conserved cellular factor that is required for cell entry of multiple influenza A viruses , including H1N1 , H3N2 , H5N1 , and H7N9 . Suppression of the expression or function of cell surface NCL by siRNA or blocking Abs substantially reduced internalization of the influenza virus [9 , 25 , 26 , 29] . In this study , we found that NCL is also involved in RHDV infection and plays a key role in RHDV internalization . It has been reported that HBGAs are functional receptors of RHDV and play key roles in viral attachment to the cell surface [5] . Data derived from the three-dimensional structure of RHDV suggest that HBGAs mainly bind to the P region of the RHDV capsid protein , thereby triggering RHDV infection of host cells [7 , 9] . However , the molecular mechanism underlying RHDV invasion of host cells remains unknown . The results of the present study showed that NCL was involved in RHDV internalization . In other words , following RHDV binding to the cell receptor ( HBGA ) , the viral internalization process is completed with the help of NCL , which acts as a bridge that connects RHDV VP60 and CLTA . In this study , the NTD domain of NCL was found to bind to the RHDV VP60 protein via a conserved sequence at the P1s domain of VP60 , and the internalization efficiency of RHDV was significantly inhibited by blocking these interactions . This finding was also confirmed by the animal experiments . Rabbits were partially protected from RHDV assault ( ~ 60% ) after inoculation with synthetic DVN peptides . In the designed experiment , activation of the humoral immune response of rabbits and resulted in the production of specific Abs against DVN . However the cellular immune response was very low . We speculated that the specific Abs induced by DVN peptides blocked the internalization of RHDV , and provided protection to the rabbits . However , the role of NCL in RHDV infection differs from that of other viruses . The results of this study showed that NCL does not directly mediate viral entry into cells as a functional receptor for RHDV , but still plays a key role in the RHDV internalization stage , as NCL was found to mediate the entry of RHDV into the cells by clathrin-dependent endocytosis via binding to the C-terminus of CLTA . It is well known that clathrin-dependent endocytosis is a classical endocytic pathway that transports extracellular substances into the cell , and most enveloped and non-enveloped viruses enter the host cell by this endocytic pathway [34] . Clathrin consists of light and heavy chains , and forms a triangular complex structure . When an extracellular substance binds to the receptor , the clathrin complex is recruited to the cell membrane surface in response to endocytic signaling and pulls the substance down to the cell membrane , where it becomes trapped and forms clathrin-invaded pits . Under the action of dynein , the invaginated pits leave the cell membrane and form clathrin-coated vesicles that eventually pass the endocytosed material to endosomes [40] . Previous studies have shown that NCL is involved in the internalization of the EGF receptor , which is mediated by clathrin-dependent endocytosis [46] . Also , NCL is involved in clathrin-dependent endocytosis through interactions with the C-terminus of CLTA . This discovery should prove helpful to further understand clathrin-dependent endocytosis . Inhibition of clathrin-dependent endocytosis by drugs inhibited the internalization of RHDV , which further demonstrated that clathrin-dependent endocytosis indeed plays an important role in RHDV infection . Together , these results demonstrated that RHDV is internalized by the host cell via clathrin-dependent endocytosis , which is mediated by NCL . In conclusion , this study is the first to report that NCL plays a key role in the infection mechanism of RHDV . As shown in Fig 10 , the capsid protein of RHDV and the host clathrin are connected through NCL and form a triprotein complex , thereby completing the intrusion process of RHDV through clathrin-dependent endocytosis . To our knowledge , this is the first report of the infection mechanism of RHDV at the molecular level . These results enrich current knowledge of the pathogenic mechanism of RHDV and provide important clues for the development of novel vaccines against RHDV infection .
All experiments were performed in a secondary biosecurity laboratory . All experiments involving rabbits and mice were carried out in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the Ministry of Science and Technology of the People’s Republic of China , and all efforts were made to minimize suffering . All animal procedures were approved by the Institutional Animal Care and Use Committee of the Shanghai Veterinary Research Institute , Chinese Academy of Agricultural Sciences ( permit number: SHVRIAU-7-0103 ) . Rabbit kidney cells ( RK-13 , ATCC CCL37 ) and 293T cells ( ATCC , CRL-3216 ) were grown in minimal essential medium ( Life Technologies , Carlsbad , CA , USA ) or Dulbecco’s modified Eagle’s medium ( DMEM ) ( Life Technologies ) supplemented with 10% fetal bovine serum under an atmosphere of 5% CO2 at 37°C . RHDV strain JX/CHA/97 was isolated in 1997 during an outbreak of RHDV in China and stored in our laboratory . mRHDV was constructed in 2017 with a reverse genetics technique and stored in our laboratory [8] . The p3×FLAG-CMV-14 vector ( Sigma-Aldrich Corporation , St . Louis , MO , USA ) and pCMV-Myc ( Clontech Laboratories , Inc . , Mountain View , CA , USA ) were used to create mammalian expression constructs . The pGEX-4T-1 vector ( GE Healthcare Life Sciences , Chicago , IL , USA ) was expressed in competent E . coli BL21-CodonPlus ( DE3 ) cells . NCL ( GenBank accession number XM_017343189 . 1 ) and CLTA ( GenBank accession number XM_002707987 . 3 ) sequences were amplified by reverse transcription polymerase chain reaction ( RT-PCR ) from a RK-13 cell cDNA library . Total RNA was isolated from RK-13 cells using TRIzol reagent ( Invitrogen Corporation , Carlsbad , CA , USA ) according to the manufacturer’s instructions . DNA was removed from the isolated RNA using DNaseI ( Takara Bio , Inc . , Shiga , Japan ) and cDNA was produced with Moloney murine leukemia virus reverse transcriptase ( Promega Corporation , Madison , WI , USA ) and random hexamers . The RHDV VP60 gene was amplified by RT-PCR from RHDV cDNA . The genomic sequence of RHDV CHA/JX/97 was retrieved from the GenBank database ( accession number DQ205345 ) . Viral cDNA was generated as described in our previous report [47] . All plasmids were created with In-Fusion HD Cloning Kits ( Clontech Laboratories , Inc . ) according to the manufacturer’s instructions . All RT-PCR amplifications for cloning were performed with TransStart FastPfu Fly DNA Polymerase ( TransGen Biotech Co . , Ltd . , Beijing , China ) according to the manufacturer’s instructions . RT-PCR products were separated by agarose gel electrophoresis and purified with the SanPrep Column DNA Gel Extraction Kit ( Sangon Biotech Co . , Ltd . , Shanghai , China ) . Restriction digests were performed using commercial kits ( New England Biolabs , Ipswich , MA , USA ) according to the manufacturer’s instructions . All plasmid sequences were amplified by RT-PCR and analyzed by Sanger sequencing to verify the sequence fidelity and reading frames ( Sangon Biotech Co . , Ltd . ) . Details of all constructs used in the study , including residue numbers , expression vectors , and tags , are summarized in S1 Table . In addition , the primers used in this research are listed in S2 Table . All proteins were expressed in competent E . coli BL21-CodonPlus ( DE3 ) cells ( TransGen Biotech Co . , Ltd . ) that were seeded in 1 mL of an overnight starter culture and then grown in 100 mL of lysogeny broth at 220 rpm and 37°C until the mid-log phase ( OD600 = ~0 . 6–0 . 8 ) . Then , the cells were typically induced with 0 . 2 mM isopropyl β-D-1-thiogalactopyranoside and incubated for approximately 16 h at 16°C and 220 rpm . Cells were pelleted by centrifugation at ~5 , 000 g and stored at -80°C . Bacterial pellets were resuspended in lysis buffer ( 20 mM Tris/HCl pH 7 . 4 , 60 mM NaCl , 1 mM ethylenediaminetetraacetic acid , 1 mg/mL lysozyme , 1 mM dithiothreitol , and 0 . 1% Triton X-100 ) supplemented with complete protease inhibitor cocktail ( Thermo Fisher Scientific , Waltham , MA , USA ) for 1 h on ice . Nuclease was then added and the lysate was incubated for 1 h at ambient temperature under rotation . The lysates were centrifuged at 4°C for 10 min at 12 , 000 × g . Glutathione-Sepharose 4B beads ( Pierce Biotechnology , Waltham , MA , USA ) were added to the clarified supernatants and the mixtures were incubated overnight at 4°C under rotation . The beads were washed with lysis buffer , followed by three washes with phosphate-buffered saline ( PBS ) , and then stored at 4°C in an equal volume of PBS . For the in vitro binding assay , Flag- or myc-tagged NCL , CLTA , and VP60 proteins were expressed in RK-13 cells . In accordance with the manufacturer’s instructions , the GST pull-down assay was performed by incubating 50 μL of a 50% slurry of glutathione-sepharose beads containing 25 μM GST fusion protein in lysis buffer with a three-fold molar excess of prey protein ( Pierce Biotechnology ) . The bound proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) and then subjected to western blot analysis . RK-13 cells were co-transfected with the bait and prey plasmids . At 48 h after transfection , total protein was isolated from RK-13 cells using IP lysis buffer . Co-IP analysis was conducted using a commercial Co-IP kit ( Pierce Biotechnology ) according to the manufacturer’s instructions . AminoLink Plus Coupling Resin was incubated with anti-NCL monoclonal antibody ( mAb ) ( Santa Cruz Biotechnology , Inc . , Dallas , TX , USA ) or anti-VP60 mAb , which was prepared in our laboratory , and then subjected to SDS-PAGE . Immunoblot analysis of the proteins was subsequently conducted using mAbs against NCL , VP60 , CLTA , and AP2 ( Abcam , Cambridge , UK ) . Protein samples were separated on 12% gels and then transferred to nitrocellulose membranes ( Hybond-C; Amersham Life Sciences , Little Chalfont , UK ) using a semi-dry transfer apparatus ( Bio-Rad Laboratories , Hercules , CA , USA ) . The membranes were blocked with 5% ( w/v ) nonfat milk in TBST buffer ( 150 mM NaCl , 20 mM Tris , and 0 . 1% Tween-20 , pH 7 . 6 ) for 3 h at 4°C and then stained overnight at 4°C with a primary antibody ( Ab ) . After washing three times for 10 min each time , the membranes were incubated with a secondary Ab against immunoglobulin G ( IgG ) conjugated to horseradish peroxidase ( Sigma-Aldrich Corporation ) in PBST buffer ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 2 mM KH2PO4 , and 0 . 1% Tween-20 pH 7 . 4 ) for 1 h at room temperature ( RT ) . Finally , after washing three times for 10 min each time , the proteins were detected using an automatic chemiluminescence imaging analysis system ( Tanon Science & Technology Co . , Ltd . , Shanghai , China ) . Cells were fixed in 3 . 7% paraformaldehyde in PBS ( pH 7 . 5 ) at RT for 30 min and subsequently permeabilized by incubation in methanol at -20°C for 30 min . The fixed cells were blocked with 5% ( w/v ) nonfat milk in PBST buffer for 3 h at 4°C and then stained with a primary Ab for 2 h at 37°C . After washing three times for 10 min each time , the cells were incubated with a secondary Ab against IgG conjugated to fluorescein isothiocyanate ( FITC ) ( Sigma-Aldrich Corporation ) in PBST buffer for 1 h at RT . Finally , after washing three times for 10 min each time the samples were observed under a fluorescence microscope equipped with a video documentation system ( Nikon Corporation , Tokyo , Japan ) . The kinetics of RHDV attachment was studied by adding the virus at a MOI of 0 . 25–2 to chilled RK-13 cells . After incubation for 2 h at 4°C , the cells were washed extensively with chilled PBS and then lysed . For the internalization analysis , the cells were washed extensively with chilled PBS following attachment for 2 h and the temperature was shifted to 37°C to allow internalization of the attached viruses . At specific time points post-internalization , the cells were washed extensively with acidic PBS ( pH 2 . 5 ) and PBS to remove viruses attached to the cell surface . The washed cells were lysed and the total RNA of each lysate sample was extracted with the Spin Column RNA Cleanup & Concentration Kit ( Sangon Biotech Co . , Ltd . ) . NCL siRNA-treated ( 24 h post-transfection ) RK-13 cells were serum-starved in DMEM for 3 h , rinsed extensively with chilled PBS , and cold-bound with Alexa Fluor 488-conjugated epidermal growth factor ( EGF ) , Alexa Fluor 488-conjugated transferrin , Alexa Fluor 488-conjugated dextran , and Alexa Fluor 488-conjugated CD4 at concentrations of 2 μg/mL for 1 h at 4°C , as recommended by the manufacturer ( Life Technologies ) . Unbound ligands were removed by extensive washing with cold PBS and the cells were incubated in warm DMEM at 37°C for the designated times . At the indicated time-points , non-internalized ligands were removed by washing with acidic PBS ( pH 2 . 5 ) . Internalized fluorescent signals in the cells were measured by flow cytometry . To image the internalization of single RHDV particles in live cells , purified virions were conjugated to the fluorescent dye Alexa Fluor 488 ( Molecular Probes , Invitrogen Corporation ) , solubilized in dimethyl sulfoxide ( DMSO ) at 10 mg/mL , and incubated at a final concentration of 50 μg/mL with purified RHDV ( 1 mg/mL ) in 0 . 1 M NaHCO3 ( pH 8 . 3 ) for 90 min at RT . The viruses were separated from free dye by differential centrifugation , solubilized in PBS containing 10 mM HEPES ( 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid; pH 7 . 4 ) , and stored at -80°C . To measure the effect of labeling on viral internalization in RK-13 cells , equivalent amounts of total viral protein from labeled and unlabeled preparations were titrated by quantitative real-time polymerase chain reaction ( qRT-PCR ) . RK-13 cells were pretreated for 30 min at 37°C with each of the inhibitors followed by cold-synchronized infection . Where indicated , inhibitors were added to previously infected cells at 2 or 3 hpi . Stock solutions of 50 mM nystatin ( Nys ) ( Sigma-Aldrich Corporation ) and 50 mM 5-ethylisopropyl amiloride ( EIPA ) ( Sigma-Aldrich Corporation ) were dissolved in DMSO . Water was used as a solvent for 10 mM chlorpromazine ( CPZ ) ( Sigma-Aldrich Corporation ) . All inhibitors were present throughout the experiments . This study was required given that we used the highest doses reported in the literature to ensure the efficacy of the inhibitors . We also assessed the cytotoxic activity of the organic solvent DMSO . Based on the results of these experiments , optimal non-toxic working concentrations were selected for the internalization assay . For the blocking experiments , RK-13 cells were pretreated in serum-free minimal essential medium containing DMSO as a control , 100 mM Nys , 50 mM EIPA , or 30 mM CPZ . These drug-treated cells were subsequently infected with RHDV at a MOI of 1 , incubated in growth medium for 1 h at 37°C , and harvested for flow cytometry or qRT-PCR analysis . The vRNA-VP60 copy numbers of attached and internalized mRHDV were estimated by qRT-PCR . The amount of mRNA in each sample was normalized to that of rabbit glyceraldehyde 3-phosphate dehydrogenase ( GADPH ) . Total RNA was extracted from cell lysate using TRIzol reagent ( Invitrogen Corporation ) according to the manufacturer’s instructions . All qRT-PCR reactions were performed with the SYBR PreMix Ex Taq II kit ( Takara Bio , Inc . ) with an ABI 7500 Fast Real Time PCR system ( Applied Biosystems , Carlsbad , CA , USA ) . For VP60 , the qRT-PCR reaction was performed as follows: one cycle at 95°C for 30 s , then 40 cycles at 95°C for 5 s , 58°C for 5 s and 72°C for 30 s , and one cycle to generate a melting curve . The primers are listed in S2 Table . Twenty 8-week-old New Zealand male rabbits seronegative for RHDV were randomly distributed into four groups ( n = 5/group ) and housed in individual ventilated cages . All experimental protocols were reviewed by a state ethics commission and have been approved by the competent authority . The details of the protection assay are shown in S4 Table . Rabbits were immunized with DVN peptide ( 1 mg ) , a control peptide ( 1 mg ) , a commercial inactivated RHDV vaccine ( Nanjing Tianbang Bio-industry Co . , Ltd . , Nanjing , China ) , or PBS , respectively . All of the groups were immunized on day 0 . Blood samples were collected from the marginal ear vein before each immunization and 2 weeks after the last injection to analyze the level and specificity of the Ab response against RHDV . Ab titers were assessed using an enzyme-linked immunosorbent assay ( ELISA ) . Briefly , RHDV strain JX/CHA/97 ( 100 μL , 1 μg/mL , incubated overnight at 4°C ) was used to capture Abs in the sera ( incubated for 1 h at 37 °C ) and then detected with 100 μL of horseradish peroxidase-conjugated mouse anti-rabbit IgG ( Sigma-Aldrich Corporation ) per well ( diluted 1:5000 in PBS containing 0 . 5% Tween 20 and 10% fetal bovine serum ) , followed by 100 μL of tetramethylbenzidine liquid substrate ( Sigma-Aldrich Corporation ) per well for 30 min at RT in the dark . End-point titers were defined as the highest plasma dilution that resulted in an absorbance value ( OD450 ) . Data are presented as log10 values . In addition , all rabbits were challenged intramuscularly with 100 × the median lethal dose ( LD50 ) of RHDV at 21 days after immunization . The rabbits were clinically examined daily for 7 days post-challenge . All animals were necropsied and serum and organs were collected and stored at -30°C . For histopathological analysis , samples were fixed in 10% neutral buffered formalin solution , sectioned , and stained with hematoxylin and eosin . Alternatively , immunohistochemical analysis was conducted with a VP60-specific mAb . To evaluate the efficiency of the cellular responses , the serum levels of interferon ( IFN ) -γ , interleukin ( IL ) -2 and IL-4 at weeks 0 , 1 , and 2 post-immunization were measured using commercially available ELISA kits ( R&D Systems , Inc . , Minneapolis , MN , USA ) according to the manufacturer’s instructions . Jingjie PTM BioLab Co . , Ltd . ( Hangzhou , China ) performed all mass spectrometry analyses . Statistical analyses were conducted with the Student’s t-test and one-way analysis of variance using SAS 9 . 1 software ( SAS Institute , Cary , NC , USA ) . Probability ( p ) values of < 0 . 05 and < 0 . 01 were considered as significant and extremely significant , respectively . | Rabbit hemorrhagic disease virus ( RHDV ) is the causative agent of a highly contagious and lethal disease in rabbits . Since , at present , there is no robust cell culture system for the propagation of the virus , the molecular mechanism of RHDV internalization remains poorly understood . Here , we demonstrated that rabbit nucleolin ( NCL ) efficiently mediates RHDV internalization by interacting with the RHDV capsid protein ( VP60 ) . The exact function domains of the interaction between NCL and VP60 were also determined . Notably , these functional domains are highly conserved in all RHDV genotypes . Further study results revealed that NCL was involved in clathrin-dependent endocytosis through interactions with the C-terminal residues of clathrin light chain A . In addition , the artificial peptide ( RRTGDVNAAAGSTNGTQ; DVN peptide ) based on the functional domain , which is responsible for RHDV VP60 binding to NCL , is able to inhibit RHDV infection , indicating that the DVN peptide might be a candidate target for the design of antiviral drugs against RHDV infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"affinity",
"chromatography",
"viral",
"transmission",
"and",
"infection",
"gene",
"regulation",
"cell",
"processes",
"microbiology",
"rabbits",
"vertebrates",
"animals",
"mammals",
"animal",
"models",
"experimental",
"organism",
"systems",
"sequence",
"motif",
"analysis"... | 2018 | Nucleolin mediates the internalization of rabbit hemorrhagic disease virus through clathrin-dependent endocytosis |
Gaseous neurotransmitters such as nitric oxide ( NO ) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network , independent of synaptic connectivity . NO provides homeostatic control of intrinsic excitability . Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks . We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations . However , the resulting networks differ , with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons , a feature disrupted in networks with non-diffusive homeostasis . This results in networks capable of representing input heterogeneity , and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis . We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined . These results suggest a mechanism for dynamically maintaining neural heterogeneity , and expose computational advantages of non-local homeostatic processes .
Nitric oxide ( NO ) is a diffusive neurotransmitter which is widely synthesized in the central nervous system , from the retina to the hippocampus [1 , 2] . Its properties as a small nonpolar gas molecule allows rapid and unconstrained diffusion across cell membranes , a phenomenon often called volume transmission [3] . An important role of NO signaling is to regulate neural excitability through the modulation of potassium conductances in an activity-dependent manner , effectively mediating a form of homeostatic intrinsic plasticity ( HIP ) . Experiments characterizing this effect also demonstrated that NO-synthesizing neurons can induce changes in the excitability of neurons located up to 100 μm away [4 , 5] . These findings are corroborated by a recent study demonstrating neurovascular coupling mediated through activity-dependent NO diffusion [6] . We build upon these observations , postulating a general form of HIP mediated by a diffusive neurotransmitter such as NO which we will refer to as diffusive homeostasis . This contrasts with canonical models of HIP , here referred to as non-diffusive homeostasis , which assume that each neuron has access to only its own activity [7] . Theoretical studies of HIP have generally focused on its role in maintaining stable network dynamics [8 , 9] . It has also been recently demonstrated that HIP can improve the computational performance of recurrent networks by increasing the complexity of network dynamics [10] . However , little is known about the effects of HIP on the heterogeneity typically observed in cortical networks; in particular , a growing body of evidence supports the finding that even neurons of the same type have a broad and heavy-tailed distribution of firing rates [11] . Rather than an epiphenomenon of biological noise , neural heterogeneity has been proposed to improve stimulus encoding by broadening the range of population responses [12 , 13] . However , this form of heterogeneity is difficult to reconcile with canonical models of HIP , which generally suppress cell-to-cell variability [14] . While some degree of heterogeneity in populations of the same type of neuron may emerge naturally [15] , we found that such independent sources of variability will generally limit the responsiveness of a network through neuronal saturation . Using network models and dynamic mean field analysis , here we show that networks with HIP mediated by diffusive neurotransmission exhibit a very different and unexpected behavior . Firstly , we report that diffusive homeostasis provides a natural substrate for flexibly maintaining substantial heterogeneity across a network . Secondly , the resulting population heterogeneity enables linear network responses over a wide range of inputs . This not only improves population coding , but enables a good use of available resources by ensuring that all neurons remain functionally responsive to changes in network dynamics . Finally , we demonstrate that these effects are preserved in networks whose recurrent synaptic inputs undergo Hebbian plasticity .
Fig 1C illustrates that both forms of homeostasis stabilized network activity following an increase in input . There was however a crucial difference in how the neurons reacted to this change . While for non-diffusive homeostasis each neuron simply returned to its target firing rate , diffusive homeostasis caused each neuron to sense a mixture of its own activity level and that of the rest of the network . This can be seen in the spatial concentration profiles in Fig 1C . It is important to note that the spatial position of each neuron was random and independent of its connections , meaning that there was no explicitly defined structure in the NO concentrations . As a result , these networks exhibited a very different steady state behavior . The firing rate distribution was narrow as expected for non-diffusive homeostasis , but broad and heavy-tailed for diffusive homeostasis ( Fig 1D ) . The latter is consistent with recent experimental results indicating that firing rate distributions in cortex are generally heavy-tailed , approximating log-normal distributions [11] . There were no noticeable differences in inter-spike interval statistics between networks with diffusive and non-diffusive homeostasis ( not illustrated ) . We investigated the difference in firing rate distributions by modeling the relation between activity read-out and homeostatic compensation in these two cases using a dynamic mean-field model ( see Methods ) . This approach considered an unconnected population of neurons with random inputs , where each of the two scenarios was simulated by using an appropriate activity read-out . HIP was implemented as in the full spiking model , but the degree of diffusive signaling was now controlled by a single parameter , α ( Eq 11 in Methods ) , which determined the balance between local and global activity read-out . If small , neurons used primarily their own activity to modulate their firing threshold , while increasing α caused the firing threshold to depend more strongly on the average population activity . Setting , for instance , α = 0 . 8 led to a broad and heavy-tailed rate distribution similar to the full model , while α = 0 yielded a narrow distribution as in the non-diffusive case ( Fig 1E ) . This model provides a simple and intuitive explanation for this effect . For a non-interacting population , non-diffusive homeostasis can be thought of as precisely matching a neuron’s input μi and its threshold θi to maintain the target firing rate . We can imitate this by introducing a covariance σ ( μ , θ ) between μi and θi , such that a high input rate implies a high firing threshold and a low input rate a low firing threshold . Since setting α > 0 ( analogous to diffusive homeostasis ) introduces a correlation between a neuron’s threshold θi and the average population threshold θ ¯ , this effectively results in a decorrelation of μi and θi in comparison with setting α = 0 ( analogous to non-diffusive homeostasis ) . In line with the previous results , populations with for instance σ ( μ , θ ) = 0 . 6 yielded a broader and more heavy-tailed distribution of firing rates than populations with σ ( μ , θ ) = 0 . 99 ( Fig 1F ) . Since non-diffusive homeostasis directly relates the firing threshold of a neuron to its input , we observed a wider distribution of firing thresholds , which in turn ensured that all neurons assumed similar firing rates . Diffusive homeostasis , on the other hand , yielded similar firing thresholds across the population ( Fig 3A and 3B ) . When combined with the nonlinear input-output relation of neurons [17] , this gave rise to the broad firing rate distributions we observed ( see also Discussion ) . This result was robust to changes in the rate of NO diffusion . While decreasing the rate of diffusion , D , did result in slightly narrower firing rate distributions , they were broader than in networks with non-diffusive homeostasis across a wide range of values ( Fig 3C ) . A similar trend was observed when varying the width of the external input rate distribution . While decreasing this width led to a decrease in the width of the firing rate distribution , they were consistently broader in networks with diffusive homeostasis ( Fig 3D ) . Since one may argue that diffusive homeostasis is merely adding variability to each neuron’s homeostatic signal due to the influence of neighboring neurons’ activity , we now ask whether it is possible to achieve broad firing rate distributions with non-diffusive homeostasis . Indeed , by introducing variability of homeostatic targets ( see Methods ) , we could produce a distribution of firing rates similar to that observed with diffusive homeostasis ( Fig 1D , red histogram ) . However , as we will show next , the effect of diffusive homeostasis is quite distinct from that of activity-independent , ‘quenched’ heterogeneity arising from randomly distributed homeostatic targets . To investigate the functional consequences of heterogeneity caused by a diffusive homeostatic process , we next simulated specific changes in external input . First , we stimulated small random groups of neurons at higher input rates of 5 Hz and 10 Hz ( versus a baseline of 2 . 5 Hz ) , as illustrated in Fig 4 . Such inputs may , for instance , reflect developmental or other plastic changes that lead to a long-lasting change in network input . In these simulations , the average network firing rate was reliably brought back to the original target firing rate by both forms of homeostasis ( Fig 4A–4C , black traces ) . As above , in networks with non-diffusive homeostasis this was achieved by returning the rate of each neuron to the target firing rate regardless of their external input ( Fig 4A , colored traces ) . In contrast , for networks with diffusive homeostasis , we found that the separability of firing rates of individual groups are maintained according to their input , while the firing rates of all groups were simultaneously reduced so that the average network firing rate again reached the target ( Fig 4B , colored traces ) . Introducing variability in homeostatic targets for the non-diffusive case , as described previously , did not maintain separability of individual groups as in the diffusive case . Instead , the different groups returned to their mean firing rates that existed before inputs were elevated ( Fig 4C ) . The distribution of final firing thresholds explains these differences ( Fig 4D–4F ) . For non-diffusive homeostasis , neurons in the group receiving 10 Hz input had the highest thresholds since they needed to reduce their firing rate the most , followed by the 5 Hz and 2 . 5 Hz groups respectively . This led to the final threshold of each neuron reflecting its input . Note that the distribution of firing thresholds is broader in this setup than in ( Fig 3A and 3B ) , as a broader range of inputs is given to the network . For a diffusive signal , a neuron’s firing threshold is modulated by the activity of nearby neurons . Since group membership of a neuron is independent of its position , this effect again introduced a correlation between each neuron’s threshold and the mean threshold of the entire network , resulting in a distribution of final thresholds which are less segregated according to their input compared to a network with non-diffusive homeostasis . Thus , firing thresholds in neurons undergoing diffusive homeostasis were more weakly related to their external input . This in turn preserves local firing rate differences in input groups while maintaining constant average network activity . Introducing variable targets for non-diffusive homeostasis caused the thresholds to depend more strongly on their external input , similar to the original non-diffusive case . We could broadly reproduce the distinctions between diffusive and non-diffusive homeostasis in the dynamic mean-field approach by varying α . For α = 0 , modeling non-diffusive homeostasis , we obtained identical firing rates in input groups , as in the recurrent network ( Fig 4G ) . Note that changing the input of groups of neurons in the recurrent network also affects the activity of neurons with fixed input ( Fig 4B , red traces ) due to recurrent connections , an effect that is obviously absent in the dynamic mean-field description . Increasing α led to local firing rate differences persisting for longer periods of time . However , these differences eventually decay very slowly , only remaining stable for the case where α = 1 ( Fig 4H and 4I ) . The reason this occurs is , even after the population activity has quickly reached its homeostatic target , the deviations of the input groups still exert a small homeostatic force when α < 1 . For example , if α = 0 . 95 , there will be a relatively fast change in thresholds as the population activity reaches its target , followed by much slower changes , at 1 − α = 0 . 05 times the speed ( Fig 4I ) . This does not happen to the same extent in the spiking network simulations with diffusive homeostasis , as diffusion of NO ensure that deviations from the population activity are directly compensated for by neighboring neurons . Differences persist for 3245 ± 440 s , compared with 115 ± 6 s and 140 ± 40 s for non-diffusive homeostasis with uniform and variable homeostatic targets , respectively ( ± symbol denotes standard error of the mean of 6 independent network realizations in each case , see Methods ) . Since we have increased the speed of homeostasis in order to reduce simulation time ( see Methods ) , a more realistic time course of 15 minutes for NO modulation would cause input differences to persist in networks with diffusive homeostasis for many hours to days [5] . Taken together , this shows that diffusive homeostasis can retain input heterogeneity due to the influence of neighboring neurons’ activity on an individual neuron’s firing threshold . In the simulations shown so far , each neuron received a static input throughout since we were interested in the final network states . We now investigate how these networks respond to fast changes in input; specifically how faithfully each neuron represents its change in input . Since networks with diffusive homeostasis simultaneously maintain constant average network activity and firing rate heterogeneity , we expected that this should allow input modulations to be followed more precisely due to a greater representational capability . After the network reached steady state under an initial distribution of external inputs , we froze homeostasis so as to simulate fast changes in activity , since we assume that homeostasis is not active over these time scales . We then regenerated the external inputs to each neuron from the same distribution presented during homeostasis . This can be thought of as a re-configuration of inputs due to external fluctuations . To best represent such changes in a simple population coding paradigm , each neuron should respond linearly to a change in input; non-linear transformations may lead to an information loss and hence affect neural computations , although this may indeed be desirable in some brain regions . We interpreted the range of changes in input over which this response is linear , or non-saturating , as the range over which homeostasis does not interfere with the network response . Fig 5A–5C show the change in input rate versus change in output rate of each neuron . A highly nonlinear response was observed in networks with non-diffusive homeostasis , with rectification for large decreases in input and superlinear responses for large increases in input . This effect was quantified by an R2 value of 0 . 57 from a linear regression . Conversely , networks with diffusive homeostasis exhibited a linear response across the entire range of input changes , with an R2 value of 0 . 85 . Population heterogeneity can also be achieved , as discussed before , by introducing target variability during non-diffusive homeostasis . This yielded a similar non-linear response as in the non-diffusive network with homogeneous targets , with an R2 value of 0 . 38 . A consequence of the asymmetry in responses to input changes for networks with non-diffusive homeostasis was that the population rate increases upon regenerating inputs , despite the fact that mean input to the network remained unchanged ( Fig 5D ) . This did not occur for networks with diffusive homeostasis , suggesting that these networks are more adept at maintaining a target level of activity in conditions where external inputs are dynamic and fast-changing . Crucially , the benefits of a diffusive homeostatic signal can be achieved by a relatively broad range of values for the rate of diffusion , D , indicating that the effects we describe are robust to precise parameter choices ( Fig 5E ) . Increasing the rate of NO decay , λ ( see Methods ) , has a similar effect to decreasing D ( S1 Fig ) . This difference in responses to input changes could again be reproduced in the dynamic mean-field approach . This allowed us to characterize population responses across different effective diffusive ranges , using the R2 value from a linear regression as a measure of response linearity . Fig 5F shows R2 values across a range of different input distribution widths , δ , as α is varied to model different diffusion coefficients ( see Methods ) . This revealed a dependency on δ: While values of α ∼ 1 exhibited the best response for smaller δ , hence cases where the inputs are rather narrow , the optimal α decreased as δ increased , as well as the overall response linearity . This dependence on input width can be explained by considering the manner in which a population of neurons with a distribution of dynamic ranges span a range of inputs . If this range of inputs is small , then all neurons will span it regardless of their dynamic range ( determined by their firing threshold ) , hence the high values of R2 for δ = 0 . 1 . For an intermediate range of inputs , neurons whose dynamic range is best adapted to the average input are most responsive . This is achieved by increasing α . If the range of inputs is very large ( δ = 1 . 0 ) , R2 values are low since the dynamic ranges of the population cannot span the inputs . This effect is stronger at high α , as firing thresholds are more correlated , and the dynamics range of most neurons cannot capture the full input variance . Since connection probability falls off with spatial distance in cortical networks [18] , we additionally simulated recurrent networks featuring such connectivity profiles . These networks exhibited qualitatively similar behavior under diffusive and non-diffusive homeostasis compared to networks without any spatial dependence in connectivity ( S2 Fig ) . Up until this point we have presented diffusive and non-diffusive homeostatic mechanisms as dichotomies , which has enabled a clear investigation of their distinct effects on network properties . However , it is more biologically relevant to investigate networks in which both mechanisms are simultaneously active . Fig 6 shows that the increased neural heterogeneity and response linearity observed in networks with diffusive homeostasis are also present in networks with both diffusive and non-diffusive homeostasis , and that the degrees of neural heterogeneity ( Fig 6A ) and response linearity ( Fig 6B ) are determined by the relative timescales of these mechanisms ( see Methods ) . As the ratio of timescales of non-diffusive homeostasis to diffusive homeostasis is increased ( i . e . as non-diffusive homeostasis becomes slower than diffusive homeostasis ) , the network goes from narrow steady state firing rate distributions to broad , and exhibits an increase in response linearity , thus becoming more similar to networks with only diffusive homeostasis . Overall , these results suggest that networks undergoing diffusive homeostasis are better suited to linearly represent a range of inputs . We investigated this by presenting the networks with time-varying inputs after freezing homeostasis . Groups of excitatory neurons received additional inputs which were randomly and independently generated after fixed time intervals ( see Methods ) . Fig 7A shows the representation of such a time-varying input pattern ( dotted black line ) for each network ( colored lines ) . Networks which have undergone diffusive homeostasis were capable of tracking this input significantly better than their non-diffusive counterparts , as characterized by the RMS error between the network response and input pattern ( 0 . 12 for diffusive homeostasis; 0 . 23 and 0 . 19 for non-diffusive homeostasis with uniform and variable targets , respectively; Fig 7B ) . We can explore these differences further by constructing a simplified task in which a population of orientation-selective neurons respond to the orientation of a stimulus ( see Fig 7C–7F , Methods ) . This is not intended to represent circuits which perform this task in the brain , but to serve purely as a demonstration of the relative merits of linear and non-linear network responses . Neurons in the network are randomly assigned a preferred stimulus orientation , independent of their spatial position . A stimulus of a certain orientation can then be presented to the network by varying the external input rates of each neuron , with neurons whose preferred orientation is closest to the stimulus orientation receiving the highest input rate . The stimulus orientation can be decoded from the network by taking the vector average of the stimulus response across all neurons . The orientation of this vector average , or population vector , is the decoded stimulus orientation . Networks with linear responses perform better than those with non-linear responses in decoding stimulus orientation , as measured by the standard deviation of errors in the orientation of the population vector compared to the stimulus orientation ( 41° , 63° and 72° for diffusive homeostasis , non-diffusive , and non-diffusive with variable targets respectively , Fig 7G ) . In the networks described so far , we have used static and uniform synaptic weights for recurrent connections . We next considered whether the observed properties of diffusive homeostasis are altered by the presence of plastic synaptic weights , in particular when Hebbian spike-timing-dependent plasticity ( STDP ) is introduced ( see Methods ) . Using a standard model of STDP with additive depression and potentiation for all recurrent excitatory synapses , we simulated networks with both STDP and homeostasis active until synaptic weight and firing rate distributions reached a steady state [19] . As before , firing rate distributions were broader in networks with diffusive homeostasis ( Fig 8Bi ) . Broad distributions could also be achieved by introducing variability in homeostatic targets . Spiking activity remained asynchronous after STDP , as shown by the distribution of inter-spike intervals and the spike autocorrelograms , although STDP caused weakly synchronous activity in networks without any form of homeostasis ( Fig 8Bii–8Biv ) [20] . The additive STDP rule led to a bimodal distribution of synaptic weights ( Fig 8A ) , as previously reported [19] . STDP amplified the differences in response linearity that were observed between homeostatic cases . Inputs to each neuron were regenerated from the same distribution presented during plasticity , and the corresponding change in output rate was compared to the change in input rate , as in Fig 5A–5C . While the response linearity , given by the mean R2 value , for networks with diffusive homeostasis was 0 . 16 , networks with non-diffusive homeostasis exhibited much lower mean values of 0 . 01 and 0 . 02 , for uniform and variable homeostatic targets respectively ( Fig 8C ) . Networks without any homeostasis had a mean value of 0 . 1 . These R2 values were lower than those from networks without any STDP ( Fig 5E ) , which was likely due to a combination of weaker external input given to these networks ( gext of 40 ns compared to 80 ns , see Methods ) , and stronger recurrent excitation received by neurons in these networks due to potentiation during STDP . This was tested by assessing response linearity in networks with static weight matrices obtained by shuffling the steady-state weight matrix of a network which had undergone STDP without any homeostasis ( see Methods ) . These networks were run for each different homeostatic case until a steady state was reached , and inputs to each neuron were regenerated in order to measure response linearity . While shuffling synaptic weights did increase R2 values across all networks ( Fig 8C , crosshatched bars ) , indicating that STDP plays a role in decreasing response linearity , they remained lower than in networks from Fig 5E , confirming that the reduced influence of external input compared with recurrent input is largely responsible for this difference . We observed qualitatively similar retention of broad firing rate distributions and response linearity with diffusive homeostasis when a weight-dependent update rule was used ( not illustrated ) , which has been argued to lead to more realistic weight distributions [21] .
Neural homeostasis is commonly thought of as a local process , where neurons individually sense their activity levels and respond with a compensatory change if activity changes . Here we investigated a complementary mechanism , where homeostasis is mediated by a diffusive molecule such as NO that acts as a non-local signal . Using a generic recurrent network model , we show that this form of homeostasis can have unexpected consequences . First , we found that it enables and maintains substantial population heterogeneity in firing rates , similar to that observed experimentally in intact circuits [11] , and that input heterogeneities can be preserved in the population activity . Second , the specific form of neural heterogeneity brought about by diffusive homeostasis is particularly suited to support linear network responses over a broad range of inputs . It is important to note that this behavior differs from that of networks where heterogeneity is simply introduced by randomly assigning a different target to each neuron . These results predict that disrupting neural diffusive NO signaling can affect perceptual and cognitive abilities through changes of neural population responses . While other non-diffusive homeostatic mechanisms would continue to stabilize neural activity , the lack of a signal related to the average population activity may disrupt the flexible maintenance of firing rate heterogeneity , and as a result the ability to represent network inputs . Mean-field analysis revealed that these differences are essentially due to the diffusive messenger providing each neuron with a combination of the average network activity and its own activity as the homeostatic signal . Diffusion of the signal from highly active neurons causes a reduction in the activity of their neighbors , such that firing rates of highly active neurons do not have to be completely reduced in order for the population to achieve a target rate . As a consequence , diffusive homeostasis furnishes a network with an efficient way of flexibly maintaining heterogeneity of firing rates . These effects can also be understood by considering the neural transfer functions , as illustrated in Fig 9A and 9B , which provides an intuitive explanation for the differences in firing rate distributions observed under diffusive homeostasis [17 , 22] . For non-diffusive homeostasis the transfer function of each neuron is brought to center around its input , leading to a narrowing of the firing rate distribution . Diffusive homeostasis decorrelates the input and threshold of individual neurons , resulting in a population of neurons residing along the entire transfer function . This preserves the non-linear shape of the transfer function , causing broad and heavy-tailed firing rate distributions . Narrow firing rate distributions are an obvious consequence of local homeostatic processes , as for instance shown recently with homeostasis implemented as local synaptic metaplasticity [14] . This is in apparent conflict with the growing body of experiments documenting broad and heavy-tailed distributions of firing rates in cortex [11] . One could argue that a straightforward explanation is a process , for example genetic or developmental , which randomly assigns neurons heterogeneous homeostatic targets . While we show here that this can result in broader firing rate distributions , we also found that this generally leads to networks with a mismatch between the neural dynamic ranges and input statistics , which in turn limits the responsiveness of the network . A striking feature of diffusive homeostasis is the lack of requirement for any such distribution of homeostatic targets , as the diffusive signal can be effectively exploited through providing a context for heterogeneity—neurons which maintain a significantly higher firing rate than the rest of the network also synthesize a higher level of the diffusive signal , thus ensuring that their deviation from the average firing rate is counterbalanced by lowering neighboring neurons’ firing rates . This mechanism essentially allows neurons to differ in activity from the population as long as the population as a whole provides some compensation for these deviations . Moreover , this mechanism is compatible with the recent finding that a minority of cells were found to consistently be the most highly active and informative across brain states [23] . While non-diffusive homeostasis would have a disruptive effect on such a ‘preserved minority’ of neurons by reducing their activity towards those of the less active majority , diffusive homeostasis provides a substrate for maintaining their differentiated activity . A significant distinction between the effects of diffusive and non-diffusive homeostasis appears when network responses to rapidly changing input are considered ( Fig 5 ) . We show that networks with diffusive homeostasis represent input changes more faithfully than those with non-diffusive homeostasis . Saturation of neurons’ responses to large changes are observed in networks without diffusion—this effect is further illustrated in Fig 9C and 9D . Across a spatially homogeneous network , diffusing signals act to effectively shift the transfer function of each neuron towards the average network input , ensuring that neurons are responsive across the entire range of inputs presented to a network . This is in contrast to networks with non-diffusive homeostasis , in which individual neurons are only responsive in a range around their current input . Moreover , the asymmetric response of networks with non-diffusive homeostasis causes the average network activity to increase after fast input changes , while it is constant for a network with diffusive homeostasis ( Fig 5D ) . The latter case is consistent with observations that mean population firing rates are preserved across novel and familiar environments and across different episodes of slow-wave sleep [24 , 25] Networks with diffusive homeostasis have an improved ability to accurately track time varying inputs ( Fig 7A and 7B ) as a direct consequence of their linear responses . Beneficial effects of neural heterogeneity for population coding have been suggested before [13 , 26] , but here we find that the broad linear response regime maintained by diffusive homeostasis further improves network performance . This improvement in network performance is also observed in a simplified stimulus orientation decoding task ( Fig 7C–7G ) . Networks with diffusive homeostasis perform better than those with non-diffusive homeostasis when a population vector is constructed from the neural responses in order to decode stimulus orientation ( Fig 7G ) . Although there exist alternative methods for decoding stimuli , the population vector has been shown to exhibit performance close to the optimal maximum likelihood procedure for broad tuning , as was used in our example [27] . These distinctions between diffusive and non-diffusive homeostasis are conserved in networks with STDP ( Fig 8 ) . This demonstrates that the limitations of non-diffusive homeostasis in maintaining neural heterogeneity and responsiveness extend beyond the case of static inputs , towards more realistic situations in which neurons receive ongoing and diverse perturbations . Indeed , networks with non-diffusive homeostasis lost almost all sensitivity to external inputs after STDP , while networks with diffusive homeostasis retained this sensitivity ( Fig 8C ) . The consequences of diffusive and non-diffusive homeostasis coexisting were also explored , by implementing these mechanisms simultaneously in a single network ( Fig 6 ) . Stable activity could be robustly maintained , with the resulting network behavior depending on the relative timescales of the non-diffusive and diffusive mechanisms . If non-diffusive homeostasis acted faster than diffusive homeostasis , the network exhibited a narrow rate distribution and a low responsiveness to input changes . Conversely , if diffusive homeostasis acted faster than non-diffusive homeostasis , the network exhibited broad firing rate distributions and linear responses to input changes . This is a plausible scenario , as NO modulation of ion channels occurs over a timescale of 15 minutes [5] , while other homeostatic processes which require transcriptional changes occur over a timescale of hours to days [28] . These results reflect what is observed as α is varied in the dynamic mean-field analysis , as local and global homeostatic mechanisms are simultaneously active for values in the range 0 < α < 1 . It is important to note that modeling HIP as a force acting on the threshold of an integrate-and-fire neuron in order to achieve a target firing rate is a significant simplification . More physiologically realistic descriptions of homeostatic processes reveal the complex relationship between ion channel concentrations and the regulation of a wide range of neural activity [28] . Moreover , a number of previous studies have explored the effects of volume transmission on network dynamics , including its potential in implementing a winner-take-all function [29] , the ability of a diffusive signal to reflect the average activity of a group of neurons [30] , and the role of another diffusive neurotransmitter , TNFα , in epileptogenesis [31] . Here , we add a functional interpretation by exploring its effects on neural heterogeneity and responsiveness within a network . While NO is involved in a wide variety of neural processes throughout development and learning [32–34] , these were ignored throughout for the sake of simplicity and tractability . Nonetheless , the impaired performance of nNOS knock-out mice in cognitive tasks [35] and the prevalence of epilepsy following nNOS inhibition [36] could be linked to diminished homeostatic control of neural excitability . Finally , the outcome of this study is not necessarily confined to NO , and could equally apply to other diffusive neurotransmitters observed in the brain such as hydrogen sulfide and carbon monoxide [37] . Indeed , we conclude that it demonstrates the potential role of diffusive neurotransmitters as an economical and reliable signal of activity across a population of neurons .
We simulated a spiking network of leaky integrate-and-fire ( LIF ) model neurons with conductance-based synapses and injected Ornstein-Uhlenbeck noise , as described by d v d t = 1 τ m ( E l - v ) + g e J e ( E e - v ) + g i J i ( E i - v ) + σ O U η ( t ) + J e x t ( E e - v ) ( δ ( t - t e x t ) ) ( 1 ) d g e d t = - g e τ e + ∑ k δ ( t - t k ) , d g i d t = - g i τ i + ∑ k δ ( t - t k ) ( 2 ) where v is the membrane potential , τm the membrane time constant , El the leak conductance reversal potential , and σOU the variance of the injected noise . η ( t ) is an Ornstein-Uhlenbeck process with zero mean , unit variance , and correlation time τOU = 1 ms [38] . ge and gi are the excitatory and inhibitory synaptic currents respectively , given by Eq ( 2 ) , where tk denotes the time of all k incoming spikes . The reversal potential of the synapses are denoted by Ee and Ei , the synaptic conductances by Je and Ji , and the synaptic time constants by τe and τi . The external input conductance is given by Jext , and text denotes the arrival time of external input , modeled as an independent homogeneous Poisson process for each neuron i with rate μi . A spike is emitted whenever the membrane potential v exceeds the firing threshold θ , and the membrane potential is then reset to the resting potential value , vr , after a refractory period , τref . The network was made up of N neurons; 0 . 8N excitatory and 0 . 2N inhibitory , with excitatory and inhibitory synaptic conductances scaled so that the network was in a balanced state [16] . Recurrent connections were random and sparse , with connection probability ϵ = C N independent of neuron type , where C defines the mean number of synapses per neuron . The balanced state was achieved in the network through scaling the inhibitory synaptic conductances by a factor of g , such that Ji = gJe . We assumed that neuronal NO synthase ( nNOS ) is activated by Ca2+ influx following a spike Eq ( 3 ) and describe the relationship through the Hill Equation , Eq ( 4 ) , which results in a sigmoidal concentration dependence , where n and K are parameters of the Hill equation [39] and τCa2+ and τnNOS are the timescales of Ca2+ decay and nNOS activation respectively . d [ Ca 2 + ] d t = - [ Ca 2 + ] τ Ca 2 + + [ Ca spike 2 + ] δ ( t spike ) ( 3 ) d [ nNOS ] d t = 1 τ nNOS [ Ca 2 + ] n [ Ca 2 + ] n + K n ( 4 ) Throughout this paper we considered the case where all neurons , inhibitory and excitatory , express nNOS . The 2D diffusion equation , Eq ( 5 ) , was solved numerically using a spatial resolution ds , diffusion coefficient D and a decay term λ [40] . Neurons were represented by a point source according to their activated nNOS concentration . This is a reasonable simplification , in particular as the networks we study are assumed to be large compared to the dimensions of individual somata . Periodic boundary conditions were used , as we assume we are simulating a subsection of a cortical network embedded in a larger cortical network with similar network activity . This resulted in a 2D toroidal surface on which diffusion was simulated . Boundary conditions with fixed concentration or fixed zero gradient were also tested , with no appreciable differences in the spatial distribution of NO . However , boundary conditions with nonzero fixed gradients of NO , approximating a large NO sink or source , can affect the spatial distribution of NO , and could hinder the ability of NO to act as a homeostatic readout . For further discussions of the diffusive properties of nitric oxide in neural tissue , see [41] . d [ NO ] d t - D ∇ 2 [ NO ] = [ nNOS ] - λ [ NO ] ( 5 ) The homeostatic effect of NO was represented in neuron i by an increase in θi , the firing threshold , according to the relative difference in intracellular NO concentration [NO] and a target concentration [NO]0; d θ i d t = 1 τ HIP [ NO ] - [ NO ] 0 [ NO ] , ( 6 ) where τHIP is the timescale of homeostasis . For simplicity , the implementation of non-diffusive homeostasis is almost identical to that of diffusive homeostasis , in that the putative non-diffusive neuromodulator [NOnon-diffusive] is synthesized through Eqs ( 3 ) and ( 4 ) , and modulates firing thresholds through Eq ( 6 ) . The only difference is that the diffusion term in Eq ( 5 ) is removed , so that [NOnon-diffusive] is entirely determined by the rate of intracellular synthesis and decay; d [ NO non-diffusive ] d t = [ nNOS ] - λ [ NO non-diffusive ] ( 7 ) For a detailed derivation of equations used in our dynamic mean-field analysis , see [16] and [17] . Briefly , under the assumptions that the network is in an asynchronous regime and that a single EPSP is sufficiently small compared to the voltage required to elicit a spike from resting membrane potential , we can extract the mean firing rate of an LIF neuron in a recurrent network by solving a pair of equations under the condition of self-consistency . The synaptic current for a neuron i in a time interval τ can be described by its mean μi and standard deviation σi as follows: μ i = J C ν τ , σ i = J C ν τ , ( 8 ) where J is the synaptic efficacy , C the number of synapses per neuron and ν the average population firing rate . The expected mean firing rate ϕi ( μi , σi ) of an LIF neuron with this synaptic current is given by ϕ i ( μ i , σ i ) = [ π τ m ∫ v r - μ i σ i θ i - μ i σ i d v e v 2 erfc ( - v ) ] - 1 , ( 9 ) where erfc is the complementary error function . Since the firing rate described by Eq ( 9 ) is determined by the synaptic current parameters μi and σi , which are in turn determined by the population firing rate ν , self-consistency requires that the rate which determines the synaptic current parameters must also be equal to the rate which is produced by these parameters , that is: ν = ϕ i ( μ i ( ν ) , σ i ( ν ) ) . ( 10 ) We simulated a non-interacting population of neurons described by the mean-field theory , in which all neurons are identical except for their threshold θi . Although there is no recurrent excitation within the population , the synaptic current statistics are comparable to that which a neuron within a recurrent network would receive . This enabled us to consider the firing rate distributions arising from presenting single neurons with distributions of synaptic currents , similar to the approach by [17] . We assumed that a neuron embedded in a homogeneous network receiving a diffusive homeostatic signal is analogous to a neuron using a combination of its own firing rate and the average population firing rate as a signal . The network can then be reduced to a population in which the firing threshold θi of each neuron i is modulated according to d θ i d t = 1 τ HIP ( ( 1 - α ) ϕ i - ϕ 0 ϕ i + α ϕ ¯ - ϕ 0 ϕ ¯ ) , ( 11 ) where ϕ0 is the target firing rate and ϕi and ϕ ‾ are the firing rate of the neuron i and the population respectively . α was varied between 0 and 1 and can be thought of as the proportion of NO which a neuron receives due to diffusion from other neurons , with α = 0 indicating that each neuron senses only its own activity and α = 1 indicating that all neurons share an identical population-wide signal . In order to implement homeostasis in this setup , we iterated through Eqs ( 8 ) and ( 9 ) until Eq ( 10 ) is satisfied to a precision of 10−4Hz , where Eq ( 9 ) returns ϕi for each neuron in the population , and ϕ ¯ = ∑ ϕ i N is used as ν in Eq ( 8 ) . At each timestep the thresholds θi of each neuron were modulated according to Eq ( 11 ) , and rates ϕi were subsequently recalculated from Eq ( 9 ) . External input rates μi for each neuron i were randomly drawn from a Gaussian distribution such that μi ∼ 𝓝 ( 10 , 10 2 ) Hz . Since the mean NO concentration takes time to reach a steady state in the recurrent network simulations , we ran the network for 100 s without homeostasis and with all neurons receiving 5 Hz input , defining the target NO concentration [NO]0 to be the mean NO concentration across all neurons at 100 s . For the dynamic mean field analysis , we chose parameters which roughly match the rate statistics of the recurrent network simulations . Inputs to each neuron were drawn from a Gaussian distribution such that μi ∼ 𝓝 ( 5 . 7 , δ2 ) , σ i = μ i . δ = 0 . 4 is the width of the distribution of mean inputs to the population . Note that the parameter δ referred to here differs from the δ in Eqs ( 1 ) – ( 3 ) . In order to match the distribution of effective targets observed during diffusive homeostasis for networks with non-diffusive homeostasis , we assigned each neuron in the non-diffusive network a different homeostatic target , [NO0]i . A network without any homeostasis is presented with input statistics ( μi ∼ 𝓝 ( 2 , 52 ) Hz ) , tuned such that the firing rate distribution match that of the network with diffusive homeostasis . [NO0]i for each neuron i can then be drawn from the distribution of steady-state intracellular concentrations of NO for this network . This results in a broad and heavy-tailed distribution of homeostatic targets , as opposed to the single homeostatic target which is used in networks with diffusive homeostasis and the unmodified non-diffusive homeostasis . A similar approach was adopted in the dynamic mean-field analysis , with each neuron assigned a target firing rate ϕ0 , i from the steady-state firing rate distribution of a network with α = 0 . 8 . External input for each neuron i was μi = 2 . 5 Hz ( N = 2500 ) . NO0 was set as described previously , although with an input rate of 2 . 5 Hz . 2 groups of 250 excitatory neurons each were randomly chosen , independent of neuron position , and stimulated with μgreen = 5 Hz and μblue = 10 Hz , keeping the inputs to the remaining neurons unchanged . Firing rates plotted in Fig 4A–4C were smoothed with a uniform time window of 20 s . Persistence of input differences were calculated by measuring the length of time it took for the signal-to-noise ratio between the two groups receiving elevated inputs to fall below 0 . The signal-to-noise ratio is defined as ( μ1 − μ2 ) / ( σ1 + σ2 ) , where μi and σi correspond to the mean and standard deviation of the firing rates of group i . Fig 5A–5D were generated using the same simulation setup as described previously . After the network has reached the homeostatic target firing rate , we froze homeostasis . Input rates to each neuron were then regenerated from the same original input distribution , such that μ i after ∼ 𝓝 ( 10 , 10 2 ) Hz . Δ μ i = μ i after − μ i before is the difference in input rate each neuron experiences upon this change , and Δ ν i = ν i after − ν i before is the corresponding change in output rate for each neuron . The black lines in Fig 5A–5C are from least-squares linear regression , and the R2 values given were derived from this fit . A similar approach was used in the dynamic mean-field analysis , while varying δ , the width of the input distribution . In networks with both diffusive and non-diffusive homeostasis active , Eq ( 6 ) is replaced with Eq ( 12 ) , where τdiffusive and τnon-diffusive represent the timescales of diffusive and non-diffusive homeostasis respectively , and the dynamics of [NO] and [NOnon-diffusive] are as described in Eqs ( 5 ) and ( 7 ) respectively . d θ i d t = [ NO ] - [ NO ] 0 τ diffusive [ NO ] + [ NO non-diffusive ] - [ NO non-diffusive ] 0 τ non-diffusive [ NO non-diffusive ] ( 12 ) Fig 6A was generated using the same simulation parameters as in Fig 1 , while varying τnon-diffusive from 1000 to 15000 ms and keeping τdiffusive fixed at 2500 ms . These networks were run for 350 s in order to ensure they reached a steady state . Likewise , Fig 6B was generated using the same simulation parameters as in Fig 5A–5D , while varying the ratio of timescales . In addition to the external input μi previously described , the network was randomly separated into groups of 250 neurons . Each group j was stimulated with external Poisson input with a rate given by μj , t ∼ 𝓝 ( 0 , 252 ) Hz . These inputs were regenerated at each timestep t of length 1 s . The time-varying input μj , t was also presented during homeostasis . The dotted black line in Fig 7A shows the normalized μj , t , while colored lines show the normalized rate deviation of a randomly chosen group j from the mean population firing rate . Each excitatory neuron was randomly assigned a preferred orientation . After the network reached a steady state , homeostasis was frozen . For each trial , each neuron i with preferred orientation θi was stimulated with external Poisson input at a rate given by μb + 𝓝 ( θs , σs , θi ) , where μb = 20Hz is the base input rate and 𝓝 ( θs , σs , θi ) is the amplitude at θi of a Gaussian tuning curve centered around the stimulus orientation θs , with a width given by σs = 90° and a peak amplitude of 2 . 5 Hz . The angle decoded using the population vector method is the angle of the vector sum of all neural responses . A spike-timing dependent plasticity rule , as described in [19] , was implemented in each recurrent excitatory-excitatory synapse . Both potentiation and depression are additive in this rule , with no weight dependence . For each nearest neighbor pair of pre- and post-synaptic spikes separated by a time Δt , the synaptic weight is updated by a value Δw given by Δ w = { A + exp ( Δ t / τ + ) g max , if Δ t < 0 . - A - exp ( Δ t / τ - ) g max , if Δ t ≥ 0 . ( 13 ) Above , τ+ and τ− denote the timecourse over which potentiation and depression occur respectively , while A+ and A− denote the relative strengths of potentiation and depression . Weights are bounded by 0 < w < gmax , therefore leading to a bimodal weight distribution . Initial weights are generated from a Gaussian distribution , given by w0 ∼ 𝓝 ( 7 . 5 , 2 . 52 ) nS . Depression is set to be slightly stronger than potentiation ( A+ < A− ) in order to prevent runaway potentiation , thus ensuring that irregular firing is maintained within a reasonable range of rates . The external input in networks with STDP was given by μi ∼ 𝓝 ( 10 , 102 ) Hz , and Jext = 40 nS , C = 250 . STDP and homeostasis were frozen after 2000 s in order to facilitate measurements of the steady state for Fig 8A and 8B , and to measure the network response linearities in Fig 8C . Steady-state firing rate distributions for Fig 8Bi were obtained as in the procedure for Fig 1D . The autocorrelogram in Fig 8Biv was obtained by measuring the average rate of coincident spikes in a temporal bin of width 2 . 5 ms at each time lag , averaged across all neurons . Network response linearities were obtained as in the procedure for Fig 5A–5D . The R2 values for networks with shuffled , static synaptic weights were obtained by taking the steady-state synaptic weight matrix from a network with STDP and without any homeostasis , randomly shuffling this final weight matrix , and using these weight matrices in new networks for each different case of homeostasis , without STDP active . Using shuffled weight matrices obtained from networks with other forms of homeostasis present did not qualitatively affect the results . Unless explicitly defined , the parameters used throughout the paper are given in Table 1 . For synthesis , diffusion , and decay of NO we have attempted to match data when available [40 , 42] , although the dearth of experimental measurements does not permit for great precision [43 , 44] . Additionally , parameters were chosen such that the timescale of homeostasis is separated from that of firing rate fluctuations . This is a reasonable assumption , given that activity-dependent NO modulation likely acts within 10 minutes or slower [5] , although NO diffusion occurs on the order of 10 seconds . τHIP was chosen to be long enough so as to avoid oscillations but short enough so as to allow feasible large scale simulations . This is a common assumption in computational studies [10] . Larger simulations , up to N = 25000 , were run with no discernible difference in results . All numerical simulations were implemented using the Brian simulator , v1 . 4 . 1 [45] , and the mean-field analysis was implemented using IPython Notebook [46] . The 2D diffusion equation was solved numerically using an explicit finite difference equation method , using the numpy python package [47] . Data analysis was performed with the numpy Python package and plotting with the matplotlib package and seaborn library [47 , 48] . Simulation code and IPython Notebooks which perform the data analysis and plotting are available at https://github . com/yannaodh/sweeney-2015 . | Neural firing rates must be maintained within a stable range in the face of ongoing fluctuations in synaptic connectivity . Existing cortical network models achieve this through various homeostatic mechanisms which constrain the excitability of individual neurons according to their recent activity . Here , we propose a new mechanism , diffusive homeostasis , in which neural excitability is modulated by nitric oxide , a gas which can flow freely across cell membranes . Information about a neurons’ firing rate can be carried by nitric oxide , meaning that an individual neurons’ excitability is affected by neighboring neurons’ firing rates as well as its own . We find that this allows a neuron to deviate from the target population activity , as its neighbors will counteract this deviation , thus maintaining stable average activity . This form of neural heterogeneity is more flexible than assigning different target firing rates to individual neurons . Consequently , networks endowed with this diffusive mechanism have an improved representational capability compared to canonical , local homeostatic mechanisms , and allow for more efficient use of neural resources . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks |
Protein synthesis is a template polymerization process composed by three main steps: initiation , elongation , and termination . During translation , ribosomes are engaged into polysomes whose size is used for the quantitative characterization of translatome . However , simultaneous transcription and translation in the bacterial cytosol complicates the analysis of translatome data . We established a procedure for robust estimation of the ribosomal density in hundreds of genes from Lactococcus lactis polysome size measurements . We used a mechanistic model of translation to integrate the information about the ribosomal density and for the first time we estimated the protein synthesis rate for each gene and identified the rate limiting steps . Contrary to conventional considerations , we find significant number of genes to be elongation limited . This number increases during stress conditions compared to optimal growth and proteins synthesized at maximum rate are predominantly elongation limited . Consistent with bacterial physiology , we found proteins with similar rate and control characteristics belonging to the same functional categories . Under stress conditions , we found that synthesis rate of regulatory proteins is becoming comparable to proteins favored under optimal growth . These findings suggest that the coupling of metabolic states and protein synthesis is more important than previously thought .
Translation is involved in the multi-layer process of the gene expression and allows the transfer of gene coding information from RNA to protein through ribosome action . Translation is composed of three successive steps: initiation , elongation and termination . During initiation , a ribosome binds to an mRNA at the ribosome-binding site to initiate translation at the beginning of the coding sequence ( Figure 1 ) . Next , the ribosome moves forward on the mRNA reading the sequence of codons and synthesizes the corresponding sequence of amino acids . Several ribosomes are translating simultaneously the same mRNA molecule , and this mRNA-ribosome complex is called polyribosomes or polysomes . When a ribosome reaches the stop codon translation ends with the termination step during which the native protein and the ribosome are released from the mRNA . Using the polysome size , we can define the ribosomal density , ρ ( see Materials and Methods ) . It goes from 0 ( empty mRNA ) to 1 ( mRNA full of ribosomes ) and takes into account the length of the gene , its polysome size and the size of a ribosome . The ribosomal density influences translation efficiency: it is generally postulated that the higher the number of bound ribosomes , the greater the number of protein molecules produced from a transcript . However , we observed in our modeling and computational studies that this is not in general true because it appears that ribosome traffic jam can emerge and slow down translation [1] , [2] . In prokaryotes , Hatzimanikatis and co-workers investigated the relation between protein synthesis rate , rate limitation and ribosomal density [1]–[4] . They used a kinetic model for translation based on works from MacDonald and Gibbs [5] and Heinrich and Rapoport [6] . In these recent studies [1]–[4] , the model was extended to account for all elementary steps of translation in Escherichia coli and the authors applied a metabolic control analysis framework to determine when translation is initiation- , elongation- or termination-limited . They found that translation rate increased with increasing ribosomal density , reached a maximum and then decreased . For almost the entire range of ribosomal densities , the translation kinetics was either initiation- or elongation-limited , with the maximum protein synthesis occurring at a ribosomal density corresponding to elongation-limitation [1] , [2] . However , these studies were based only on modeling and simulations , since no experimental data for genome-wide in vivo ribosomal densities was yet available in prokaryotes . Indeed , up to now , such data was only available in eukaryotes [7]–[10] . This nevertheless changed recently: the ribosomal density of each mRNA present in a cell was for the first time experimentally measured in a bacterium ( Lactococcus lactis ) [11] . In these studies , a great variability of ribosomal densities was observed . There , Girbal , Cocaign-Bousquet and co-workers estimated the relative contribution of various factors in explaining these polysome data ( such as mRNA concentration , mRNA half-life , gene length , CAI and specific codon sequences ) . The aim of this study was to analyze protein synthesis rate and control at the genome-wide scale in a prokaryotic organism , the bacterium Lactococcus lactis . One of the challenges was to estimate ribosomal density from genome-wide polysome size measurements due to the complexity that arises from the fact that all components are mixed in a single compartment in prokaryotes , potentially leading to simultaneous transcription and translation . Here we further integrated and analyzed these data , along with similar studies from different physiological conditions , and we designed a data analysis procedure to estimate robustly the polysome density based on the experimental data . From these values of estimated polysome densities we then used a mechanistic model to determine the protein synthesis rates for individual genes and we quantified the rate limiting steps of translation . These results were further analyzed in view of the gene functionalities .
L . lactis subsp . lactis IL1403 was grown in batch cultures in a modified chemically defined medium in exponential conditions ( growth rate of 0 . 88 h−1 ) and isoleucine starvation conditions ( growth rate of 0 . 05 h−1 ) [12] . Translatome experiments were performed to determine genome-wide ribosomal density and ribosome occupancy ( fraction of mRNA molecules engaged in translation ) in normal and stress conditions ( Figure S1 ) [11] , [13] . Briefly , after translation arrest and cell disruption , size fractionation of mRNA-ribosome complexes on sucrose gradient was processed . In the elution fractions corresponding to different polysome sizes , total RNA was extracted and hybridized to microarrays . For each microarray series , normalization steps including intra-series and inter-series normalization , correction of intensity values to the total RNA quantity and their centering reduction were performed to determine the number of bound ribosomes on each mRNA molecules . The fraction of ribosomes engaged in translation , noted , was experimentally estimated by area integration of the polysomal profile and equaled to 0 . 61 in normal and stress conditions [11] . Under similar conditions , genome-wide transcriptomic-based methods were previously used to determine mRNA concentrations and mRNA half-lives in L . lactis [11] , [13] . Translation was modeled by considering the individual motion of the ribosomes along the mRNA chain ( Figure 1 ) [3] , [6] . The first step is the binding of the ribosome to the initiation site . The ribosome is then considered as a hard body that covers a number L of codons , ( considered to be 10 in our study [14] ) that can move along the mRNA . At the final stage , when reaching the termination codon , the ribosome releases its newly formed protein and the ribosome unbinds from the mRNA . For the mathematical formulation of the model , we consider the mass balance equations for the codons occupied by the front of a ribosome: ( 1 ) where is the copy number of the mRNA species l engaged in translation , is the probability of having a ribosome front in codon i of mRNA species l , are the various fluxes of transitions of the ribosomes , defined in the following way: ( 2 ) The initiation , elongation and termination rate constants are given by , and respectively . represents the free ribosomes . is the probability that the initiation site of mRNA species l is empty , and is the probability that codon is empty knowing that the front of a ribosome is on codon i . These probabilities are given by: ( 3 ) ( 4 ) It is therefore needed to solve for each species nonlinear ordinary differential equations . Note however that we are looking for the steady state solutions of the system . The absolute protein synthesis rate of gene species l is given by of equation 2 . This corresponds to the rate of synthesis of proteins from this species from all mRNA copies engaged in translation of this species . The experiment could not determine the absolute concentrations of the mRNA species , but it was possible to obtain relative concentrations between the species . We can therefore get a “normalized absolute protein synthesis rate” for each species and compare their values ( see Normalized absolute protein synthesis rate section below ) . The specific protein synthesis rate is defined as the rate of synthesis of proteins per mRNA copy . It is therefore given by: ( 5 ) The ribosomal density as defined in [1] , [2] , noted , is proportional to polysome size , the number of ribosomes bound to a single mRNA molecule . ( 6 ) varies therefore between zero ( no ribosome loaded on mRNA ) and one ( full coverage of the mRNA by ribosomes ) . is the polysome size of mRNA species l . In these equations we have kept the elongation rate constant as codon-dependent ( i . e . in equation 2 ) however for the rest of this study we will use an averaged value , codon-independent , noted simply . For this value , we used the cell-averaged value of 23 amino acids per second and per ribosome , which we computed for L . lactis ( see Text S1 ) . In order to determine the value of the free parameters and for each gene we made an assumption: the steady state protein synthesis rate of each mRNA is maximized by the cell under the constraint , gene specific , given by the polysome size of each mRNA ( estimated experimentally ) . Indeed , protein synthesis is a very expensive process and we can therefore assume the cell has optimized this process to be the most efficient possible , reducing the cost of wasted energy ( otherwise the cell would need to use more ribosomes and mRNA copies in order to reach the same production of proteins ) . Note that this assumption is equivalent to having the termination rate constant as big as possible , so that it is usually not rate limiting . With this assumption and knowing the experimental polysome size of each gene [11] , we could determine the unique pair of initiation and termination rate constants that was resulting in this polysome size and maximum specific protein synthesis rate , by solving the system of equations 1–4 and 6 together [15] . Briefly , this is done with the following principle: the termination rate constant is first fixed to a high , non-limiting value; then the initiation rate constant is increased , starting from 0 and the polysome size is recorded in function of the initiation rate constant; if the target polysome size is reached the wished pair of initiation/termination is obtained . However there is the possibility that the target polysome size is not reached and that any further increase in initiation rate constant does not lead to further increase in polysome size . In such a case , it is now the termination rate constant that is varied , by decreasing its value until the target polysome size is reached . For the computations an additional assumption was made: all ribosomes on the gene were considered to be active . In eukaryotes , it has been observed that some ribosomes could bind in the 5′ UTR [16] and would therefore not really be active . However 5′ UTR are usually shorter in prokaryotes and present fewer regulations , therefore this should only have a small impact .
Translatome data was obtained for L . lactis cells in the exponential phase for 1619 genes and their polysome sizes were assigned with confidence for 1177 genes [11] . In such experiments , a chromatogram is used to elute the mRNA copies according to their polysome sizes into different elution fractions , with help of a sucrose gradient ( Figure S1 ) . It is generally assumed that all full size mRNA copies of a given gene have in average the same polysome size , i . e . that the mRNAs of a given gene have a uniform polysome size in the cells population , and therefore they should belong to a single elution fraction or to some adjacent fractions , as is observed for most eukaryote genes [7] , [10] . Some eukaryote genes had yet their mRNA copies distributed with peaks between two non-adjacent fractions . Nevertheless , such odd behavior seems to be much more common in prokaryotes , as is observed in the experimental data for L . lactis: the mRNA copies from many genes are distributed across all the seven elution fractions ( Figure S2 ) . The two first elution fractions , B and C , represent transcripts that are still ribosome-free ( fraction B ) or only in co-sedimentation with one ribosomal sub-unit ( fraction C ) , while the other fractions are composed of mRNAs engaged in translation with average number of loaded ribosomes from 1 to 14 [11] . A distribution of the mRNA copies with multiple peaks at different polysome sizes is characteristic for many genes ( Figure S2 ) , with one peak around empty mRNA copies , one peak between polysome sizes 4 . 1 and 7 . 4 and a third peak in the last elution fraction ( polysome size 14 ) , instead of a single narrow peak as it would be expected if all the copies of the same mRNA species had the same ribosomal density . Therefore , we first investigated the origin of these observations by testing the following five hypotheses: ( i ) influence of the stochasticity and intrinsic noise; ( ii ) effects of the partially transcribed or decaying mRNA; ( iii ) impact of the biophysics of co-elution; ( iv ) variations in the initiation process; and ( v ) influence of the operonic structures . In order to investigate each of these hypotheses , we analyzed the data in depth , using alternative modeling and computational approaches . These results are presented and discussed in the following subsections . Overall our analyses below suggest that the main contributors are the impact of co-elution and a modified initiation process . From the analysis of the ribosomal density ( previous section ) , we could estimate ( i ) the polysome sizes of 1'108 genes ( and hence their ribosomal densities ) , and ( ii ) the corresponding fraction of mRNA copies that were truly engaged in translation . We next used these two values for each gene , and a mathematical model to estimate the maximum specific protein synthesis rate for each gene ( see Materials and Methods ) . The specific protein synthesis rate is defined as the number of protein molecules synthesized per second and per mRNA copy of the gene . For each gene , under the assumption of maximal synthesis rate , we determined a characteristic pair of initiation and termination rate constants that correspond to the gene's ribosomal density [15] . In the 1108 characterized L . lactis genes and for increasing ribosomal density the specific protein synthesis rate increased , it reached a maximum and then decreased ( Figure 3A ) . A maximal rate of 1 . 3 s−1 ( i . e . 1 . 3 molecule of protein synthesized per second and per mRNA copies ) was reached for ribosomal densities between 0 . 7 and 0 . 8 , for L . lactis cells grown at 0 . 88 h−1 . In order to characterize the changes occurring at the translatome level under stress conditions and at reduced growth rate , we analyzed an experiment where L . lactis cells were challenged with depletion of isoleucine and the translatome studied on the genome-scale [35] . A recalibration of the data was first performed , as done earlier for the normal conditions ( see Text S1 ) , resulting in a total of 1405 characterized genes . Interestingly we observed that in the stress condition a higher proportion of genes was mainly under elongation control or under control shared between elongation and initiation ( Figure S6C ) . Under these conditions , very few genes also appeared to be under termination control . As this experiment was performed under isoleucine depletion , we hypothesize that this observed increased elongation control is mainly due to an increased control from isoleucine codons , where ribosomes are probably forming queues along the mRNA . Comparing the absolute protein synthesis rates grouped by ( sub ) categories with those under normal conditions ( Figure 4 , 5 and S6 ) , it emerges that stress conditions cause a decrease in the rates of several biogenesis-related functional categories ( FAT , REP , PUR and TRD ) , which is in agreement with the reduction of growth . For most of these functional categories ( i . e . FAT , PUR and TRD ) a decrease of protein levels was previously observed when growth rate is reduced [17] . It should however be underlined that stressed L . lactis cells were still metabolically active with low but significant glucose consumption and lactate production rates ( Figure S1 ) . Interestingly , we observed that the genes for the regulatory category ( REG ) increased synthesis rate in comparison to their value under the normal conditions , probably due to a need of the cells to cope with the stress conditions . This result about the key role of regulatory functions in adaptation is also consistent with the high protein level measured in the regulatory category during starvation in earlier studies [29] . Additionally , genes of the subcategory AMIbba ( for branched chain amino acid biosynthesis ) had an increased absolute synthesis rate relative to the normal conditions ( Figure S6B and S6D ) . This result is supported experimentally by the increase of in vivo protein concentration for two proteins IlvD and LeuC of the isoleucine biosynthetic pathway ( respectively , 4-fold and 2 . 5-fold in stress compared to normal conditions ) [12] . De novo isoleucine synthesis was therefore metabolically active and allowed L . lactis to survive and even to grow in the total depletion of isoleucine in the growth medium . Consistently , the other amino acid biosynthetic pathways did not show any specific increase in synthesis rate as they were supplied by the growth medium . In this paper , we developed a novel strategy to analyze translatomics data in bacteria , allowing robust estimation of polysome size and ribosome occupancies at the genome-scale level . We used a mechanistic model of protein synthesis to analyze the translatome in L . lactis cells in exponential phase and also under stress condition using experimentally determined ribosomal densities . Other groups have previously studied translatome in eukaryotic systems , with help of various techniques ( e . g . with polysome gradients similar to the data used here; or with help of the novel technique of ribosome profiling , which asses the position of ribosomes on the mRNA but does not strictly measure the polysome size , see also below for discussion ) [7] , [8] , [16] , [36] , [37] . Additionally the ribosome profiling method was recently applied to prokaryotes systems in order to get information on the positioning of ribosomes along the mRNA [38] , [39] . Nevertheless , this is the first study of polysome gradients for prokaryotic cells . In order to resolve some important issues in the quantification of ribosomal density in prokaryotes , we first developed a method for data preprocessing and analysis to better determine the ribosomal density . The analysis has further estimated protein synthesis rates at the genome scale level and deciphered the key regulatory steps for translation . Analysis of the model allowed us to identify the rate limiting steps for protein synthesis of each gene . We specifically quantified the distribution of control between initiation , elongation , and termination , and our results support the concept of mixed control of translation in bacteria , in the particular case of L . lactis . Contrary to general belief , it emerges from the study that translation elongation has a significant impact on a large proportion of genes . Most of the genes are under initiation and elongation control , and fewer are under elongation and termination control . In addition , we have provided new results on the link between translation regulation and metabolism . Functional enrichment analysis suggests that genes with similar function share common synthesis rate and rate limitation properties . In L . lactis , genome-scale translation regulation was used to adapt the metabolic network to growth conditions . In both optimal and stress environments , particular metabolic pathways were more or less favored by regulating protein synthesis rates . Furthermore , depending on gene function , the protein synthesis rate was controlled by the nature of the rate-limiting step to be sensitive or not to translational limitations . In particular , we identified main differences between the regulation of various functional categories: the genes affected most by perturbation on elongation rates were those related to translation , probably enabling a fast redistribution of translation component when needed , while genes with stronger initiation control were related to some essential elements for the cell ( AMI , CEL , COF , NRJ , PUR ) , possibly ensuring that these proteins keep an abundance independent of elongation perturbations . Comparative , model-based analysis of the translatome under different physiological states ( optimal growth conditions vs . amino-acid starvation ) provided significant insights on the specific role of protein synthesis , for groups of proteins and individual proteins . We observed a redistribution of protein synthesis rate and control limitations for some functional categories , and mainly for regulatory proteins . Overall , the methods and the analysis procedure developed here is general and it can serve as a reference procedure for translatome analyses of other prokaryotic systems . Our findings about the importance of the elongation in translation control suggests that it is important to further characterize the position of the ribosomes along the mRNA in addition to the number of ribosomes per chain . If ribosomes are queuing near specific codons , this might cause additional redistribution of the elongation control along the mRNA sequence , with important implications . In order to measure such positioning of ribosomes along the mRNA sequence , one could build an experiment similar to the ribosome density mapping developed by Arava et al . in Saccharomyces cerevisiae [8] . There they measured the polysome sizes of fragments of mRNA after cutting the mRNA sequences in two to three fragments , and they observed that the ribosome distribution stayed uniform over the whole gene . Such an experiment could then support the assumptions of the present model , where termination rate constant is assumed non-limiting for most genes and elongation rate constant is approximated as uniform along the full sequence: for example if the termination rate constant would be limiting , then such an experiment would observe a queuing of ribosomes near of the stop codon . By cutting the mRNAs near of the start codon , one could additionally assess the validity of considering that all the ribosomes are active in translation , or in contrary observe that some ribosomes are bound in the 5′ UTR . The positioning of ribosomes along the mRNA sequence could also be measured , with the ribosome profiling strategy first presented in S . cerevisiae [16] , which was subsequently also applied to E . coli and B . subtilis [38] , [39] . Note however the major difference between ribosome profiling and polysome gradients: with ribosome profiling , it is possible to estimate the position of ribosomes on mRNA , however one cannot tell if the ribosomes are coming from the same mRNA copy or from different copies ( i . e . the method is not able to tell if it is one mRNA copy that had 10 ribosomes and a second copy of the same gene that was empty , or if the two mRNA copies had 5 ribosomes each for example ) , and in the analysis presented in the present study , it is exactly such information on the number of ribosomes on each mRNA that was needed , which is exactly what the polysome gradients gives . In order to further assess the power of the presented framework , one could also build an additional experiment that would measure the in vivo synthesis rate of some labeled protein species and measure simultaneously the polysome sizes of their mRNA to observe if there is a good concordance between the simulations and measurements . | Post-transcriptional regulation is important for the understanding of gene expression control . Our work is a genome-scale analysis of the translation steps of protein synthesis from transcripts . We have developed a mathematical model to integrate and analyze experimental ribosome density of hundreds of transcripts of Lactococcus lactis , providing robust estimation of polysome sizes . Using a mechanistic approach we have modeled for the first time in bacteria the protein synthesis rate for each gene and determined by control analysis the limiting rate between initiation , elongation and termination . Highly expressed proteins belonged to the group of the proteins with high synthesis rate and were controlled by elongation . Unexpectedly , a significant number of genes under elongation limitation were found although initiation was generally believed to be limiting . In addition , we showed that translation rate and control were in agreement with cellular requirements in cells growing in optimal environment but also in cells under nutritional limitation . This work provided a better understanding of translational regulation in bacteria and demonstrated how protein synthesis control was closely related to cellular metabolic states . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results/Discussion"
] | [] | 2013 | A Genome-Scale Integration and Analysis of Lactococcus lactis Translation Data |
The role of Wnt signaling in embryonic development and stem cell maintenance is well established and aberrations leading to the constitutive up-regulation of this pathway are frequent in several types of human cancers . Upon ligand-mediated activation , Wnt receptors promote the stabilization of β-catenin , which translocates to the nucleus and binds to the T-cell factor/lymphoid enhancer factor ( TCF/LEF ) family of transcription factors to regulate the expression of Wnt target genes . When not bound to β-catenin , the TCF/LEF proteins are believed to act as transcriptional repressors . Using a specific lentiviral reporter , we identified hematopoietic tumor cells displaying constitutive TCF/LEF transcriptional activation in the absence of β-catenin stabilization . Suppression of TCF/LEF activity in these cells mediated by an inducible dominant-negative TCF4 ( DN-TCF4 ) inhibited both cell growth and the expression of Wnt target genes . Further , expression of TCF1 and LEF1 , but not TCF4 , stimulated TCF/LEF reporter activity in certain human cell lines independently of β-catenin . By a complementary approach in vivo , TCF1 mutants , which lacked the ability to bind to β-catenin , induced Xenopus embryo axis duplication , a hallmark of Wnt activation , and the expression of the Wnt target gene Xnr3 . Through generation of different TCF1-TCF4 fusion proteins , we identified three distinct TCF1 domains that participate in the β-catenin-independent activity of this transcription factor . TCF1 and LEF1 physically interacted and functionally synergized with members of the activating transcription factor 2 ( ATF2 ) family of transcription factors . Moreover , knockdown of ATF2 expression in lymphoma cells phenocopied the inhibitory effects of DN-TCF4 on the expression of target genes associated with the Wnt pathway and on cell growth . Together , our findings indicate that , through interaction with ATF2 factors , TCF1/LEF1 promote the growth of hematopoietic malignancies in the absence of β-catenin stabilization , thus establishing a new mechanism for TCF1/LEF1 transcriptional activity distinct from that associated with canonical Wnt signaling .
The Wnt/β-catenin signaling pathway plays an essential role during embryonic development and as a major regulator of stem/progenitor cell maintenance in a number of postnatal organs and tissues , including the gastrointestinal tract , the skin and the hematopoietic system [1]–[3] . Genetic alterations that lead to aberrant activation of this pathway occur very commonly in certain tumors , including colon cancer , hepatocellular carcinomas and adrenocortical adenoma [4] , [5] . In other types of tumors , alternative mechanisms are more frequently responsible for Wnt/β-catenin constitutive up-regulation . Indeed , a Wnt autocrine transforming activity was initially discovered in the mouse model three decades ago [6] , and we have established that this mechanism also occurs frequently in different human cancers , including breast cancer [7] , non small cell lung cancer [8] and sarcoma [9] . The Wnt/β-catenin , or canonical , pathway is initiated by Wnt-mediated coupling of the seven transmembrane domain receptor Frizzled and the single-membrane-spanning low-density receptor-related protein 5/6 ( LRP5/6 ) , followed by phosphorylation of the LRP5/6 intracellular domain [10] , [11] . Through a mechanism not yet fully understood , phosphorylated LRP5/6 leads to the inhibition of the so-called β-catenin destruction complex , composed of axin , glycogen synthase kinase 3 , dishevelled ( Dvl ) , casein kinase 1 and the tumor suppressor adenomatous polyposis coli , resulting in the accumulation of β-catenin in the cytoplasm and the nucleus [11] , [12] . In the absence of Wnt activation , the four members of the TCF/LEF family of transcription factors form a complex with Groucho/TLE repressors and inhibit gene expression [3] , [13] , [14] , [15] , [16] , [17] . Through either direct competition [18] or XIAP-mediated ubiquitylation [19] , nuclear β-catenin displaces Groucho/TLE and binds to TCF/LEF factors , thus promoting a transcriptional switch that allows the expression of Wnt target genes , including Myc , cyclin D1 , Axin 2 and Lef1 [4] , [20] . While its role in the control of gene expression is thought to depend mainly on the interaction with TCF/LEF proteins , β-catenin can act in some contexts through binding to other transcription factors , including the homeodomain protein Prop1 [21] , various nuclear receptors [17] , [22] , the forkhead box O factors [23] and the Krueppel-like factor 4 [24] . TCF/LEF factors can also interact with other proteins , including ALY [25] , Smad [26] , [27] and c-Jun [28] , allowing the formation of large nuclear complexes that control the expression of genes containing a particular combination of regulatory sequences in their promoter/enhancer . While providing an additional level of regulation , such interactions do not challenge the general notion that nuclear β-catenin is required for TCF/LEF transcriptional activity , which is widely supported by biochemical and genetic data [2] , [15] , [29] . An exception to this paradigm may involve the hematopoietic system , where intriguing discrepancies in the phenotypes of TCF1/LEF1 and β-catenin null mice have suggested that in particular contexts canonical Wnt signals could be transduced independently of β-catenin [30]–[36] . While the β-catenin pathway has been reported to be upregulated in cancer stem cells of different leukemias [37]–[40] and in the transition from chronic to acute myelogenous leukemia [41] , mutations in intracellular components that are frequently responsible for Wnt pathway activation in solid tumors are uncommon in hematopoietic tumors . Here we show that some hematopoietic tumor cells display β-catenin-independent TCF/LEF activity , whose down-regulation inhibits the expression of TCF/LEF target genes and cell growth . Using both in vitro and in vivo approaches , we further demonstrate that TCF1 retains transcriptional activity in the absence of β-catenin . Finally , we establish that ATF2 family members physically and functionally interact with TCF1/LEF1 factors to promote target gene expression and hematopoietic tumor cell growth . Together , our results uncover a new mechanism that activates TCF1/LEF1 transcriptional activity independently of β-catenin and the Wnt canonical pathway .
Using lentiviral wild-type ( Top ) and mutant ( Fop ) TCF/LEF reporters , we investigated the status of Wnt signaling in cells derived from several types of human cancer . As a result of this screen , we identified up-regulated TCF/LEF activity in different hematopoietic tumor cells , including Ramos , K562 and Jurkat ( Figure 1A ) . As shown in Figure S1A , TCF/LEF reporter levels in these cells were comparable to those observed in solid tumor cells exhibiting autocrine Wnt activation [8] , [9] . To assess the levels of stabilized , transcriptionally active β-catenin in these same hematopoietic tumor lines , we used an approach based on the capture of uncomplexed β-catenin with GST-E-cadherin beads [7]–[9] . Surprisingly , none showed detectable levels of stabilized β-catenin ( Figure 1B ) . It has been reported that γ-catenin can also play a role in Wnt signaling , probably through a mechanism involving stabilization of β-catenin [42] , [43] . However , neither down-regulation of β-catenin nor γ-catenin had any effect on the TCF/LEF reporter in Ramos and K562 cells ( Figures 1C and S1B ) , indicating that β-catenin and γ-catenin were dispensable for TCF/LEF transcriptional activation in these hematopoietic tumor cells . To gain insights into the biological relevance of this β-catenin-independent TCF/LEF activity , we tested the effects of a GFP-fused DN-TCF4 in Ramos lymphoma cells using a lentiviral tetracycline ( Tet-off ) inducible system . As a negative control for these experiments , we utilized U937 lymphoma cells , which lacked detectable constitutive TCF/LEF activity ( Figure 1A ) . Doxycyline withdrawal triggered expression of DN-TCF4 to a similar extent in Ramos and U937 cells , as measured by FACS analysis ( Figure 1D ) . Under these conditions , DN-TCF4 expression inhibited colony formation by Ramos cells but had no effect on U937 cells ( Figure 1E ) . Of note , induction of DN-TCF4 expression in Ramos cells also reduced the mRNA levels of LEF1 and Axin 2 ( Figure 1F ) , two well-established Wnt target genes . Consistent with these results , DN-TCF4 also inhibited TCF/LEF reporter activity as well as the expression of LEF1 and Axin 2 in K562 ( Figures S1C and S1D ) . Together , these results indicated that some hematopoietic tumor lines exhibit elevated β-catenin-independent TCF/LEF activity , which positively influences the expression of Wnt target genes and tumor cell growth . We extended our analysis to other hematopoietic tumor cell lines as well as primary tumors . Several mantle cell lymphoma lines , including Granta-519 , HBL-2 , JEKO-1 and JVM-2 , exhibited TCF/LEF reporter levels as high or higher than Ramos ( Figure S1E ) and also contained undetectable levels of uncomplexed β-catenin ( Figure S1F ) , implying their activation of β-catenin-independent TCF/LEF transcriptional activity . We also surveyed a series of primary hematopoietic tumors for expression of Axin 2 and LEF1 as compared to levels present in Ramos and U937 cells . Several exhibited high expression levels of these TCF/LEF target genes in the absence of detectable uncomplexed β-catenin ( Figures 1G and S1G ) , arguing that activation of this novel pathway was not limited to hematopoietic tumor lines but occurred in primary human hematopoietic tumors as well . TCF1 and LEF1 were cloned in 1991 from hematopoietic cells [44]–[46] and belong to a four-member family of transcription factors containing a N-terminal β-catenin binding domain and a high mobility group DNA binding domain located closer to the C-terminus [15] . To investigate whether these factors could act as transcriptional activators in the absence of β-catenin , we expressed TCF1 , LEF1 or TCF4 in 293T cells , which lack constitutive Wnt activation . Figure 2A shows that TCF1 and LEF1 , but not TCF4 , stimulated TCF/LEF reporter activity in these cells . The stimulatory effect of TCF1 was observed using three different reporter constructs , containing respectively 2 , 4 and 8 TCF/LEF repeats generated using different backbone vectors ( Figure S2A ) . As an additional specificity control , TCF1-induced reporter activity was strongly inhibited by an excess of DN-TCF4 ( Figure S2B ) . Of note , deletion of the first 65 aa , containing the β-catenin binding domain , only partially reduced the TCF/LEF activation induced by TCF1 ( Figure 2A ) , indicating that β-catenin was not strictly required for this effect . To inhibit any residual nuclear β-catenin potentially present in 293T cells , we generated a decoy construct containing the TCF1 β-catenin binding domain fused to the Gal4 DNA binding domain ( BCBD ) . While almost completely abolishing the up-regulation of the TCF/LEF reporter induced by Wnt3a ( Figure 2B ) , BCBD did not affect TCF1 transcriptional activity ( Figure 2C ) , strongly arguing against the involvement of β-catenin in this activity . Based on the crystal structure of the β-catenin-TCF3 complex , we mutated two residues in the TCF1 β-catenin binding domain , D21A and E29K , which should impair the ability of this transcription factor to bind to β-catenin [47] . Figures 2D and S3A show that this mutant , designated TCF1mt , was unable to physically interact with β-catenin by co-immunoprecipitation and failed to synergize with β-catenin or γ-catenin to stimulate the Wnt responsive reporter , instead exerting an antagonistic effect ( Figures 2E , S3B and S3C ) . In contrast , both wild type TCF1 and TCF1mt expression stimulated TCF/LEF activity in 293T cells to similar extents ( Figure 2F ) , implying that TCF1 was able to act as a transcriptional activator independently of β-catenin . During the earliest stages of Xenopus embryonic development , locally activated Wnt signaling induces the asymmetric expression of genes of the dorsal organizer , thus allowing the establishment of dorsal-ventral polarity [48]–[50] . We used this in vivo model to assess the β-catenin-independent activity of TCF1 . As previously reported [51] , injection of tcf1 , but not tcf4 , mRNA induced ectopic expression of the Wnt direct target gene Xnr3 in animal cap explants ( Figure 3A ) . Of note , we found that both TCF1mt and the N-terminal truncated TCF1 ( del65TCF1 ) were able to trigger Xnr3 expression , indicating that β-catenin was not required for this effect . Under these conditions , Wnt3a , but not the TCF1 constructs , increased the expression of another organizer gene , Chordin , likely reflecting its ability to induce a higher level of Wnt pathway activation . As a specificity control , neither TCF1 nor Wnt3a had an effect on the TGF-β target gene Xbra ( Figure 3A ) . Consistent with these data , all TCF1 constructs , but not TCF4 , induced axis duplication in the embryos ( Figure 3B ) . Together , our findings indicate that in the absence of β-catenin TCF1 does not strictly act as a transcriptional repressor , but instead can stimulate the expression of Wnt canonical target genes and functions in vivo independently of β-catenin . To gain insights into the mechanisms responsible for β-catenin-independent TCF1/LEF1 transcriptional activity , we next investigated the contribution of different TCF1 domains to its ability to stimulate TCF/LEF reporter activity . Alternative splicing gives rise to several isoforms of each TCF/LEF protein , which in some cases can affect their activity [14] , [15] . While the majority of these splicing events involve the C-terminus , experiments in Xenopus have suggested that the central exon IVa might be responsible for the balance between the activating and repressive functions of these transcription factors [52] , [53] . We found that the presence or the absence of exon IVa did not affect TCF/LEF1 reporter activity induced by human TCF1 in 293T cells ( Figure S4 ) , indicating that this region is not involved in β-catenin-independent TCF1 transcriptional activity . It has been reported that certain C-terminal TCF splice variants can bind to repressors , including CtBP [54] , although the biological relevance of such interactions is controversial [15] , [55] . To assess whether the β-catenin independent activity of TCF1 was associated with a particular C-terminal domain , we generated a chimeric protein containing the first 299 aa of TCF1 fused to the DNA-binding domain and C-terminus of TCF4 ( TCF1/4 ( 1–299 ) ; Figure 4A ) . We observed that TCF1 and TCF1/4 ( 1–299 ) stimulated the TCF/LEF reporter to a similar extent , while TCF4 was inactive ( Figure 4B ) , indicating that TCF1 C-terminus and DNA binding domains were not required for this effect . Using a similar approach , we showed that swapping the N-terminal 100 aa of TCF1 with the corresponding domain of TCF4 resulted in nearly inactive fusion proteins ( Figure 4B ) . These results implied that this region was important for TCF1 transcriptional activity but not sufficient to confer β-catenin-independent transcriptional activity to TCF4 . We extended TCF1 mapping and found that deletion of the region between aa 56/101 and aa 211 partially reduced TCF1 transcriptional activity in the absence of β-catenin ( Figure 4C and S5A ) but had no effect on the synergy between TCF1 and β-catenin ( Figure S5B ) . As shown in Figure 4C , a TCF1/4 fusion protein containing TCF1 aa 1–211 and the TCF4 DNA binding domain and C-terminus showed decreased TCF/LEF activity , implying that aa 212–299 are also involved in β-catenin-independent TCF1 activity . As a complementary approach , we assessed the activity of different TCF1 domains fused to the Gal4 DNA binding domain . Consistent with the results shown in Figures 4B and 4C , TCF1 aa 100–211 strongly up-regulated the activity of a Gal4 reporter , while aa 37–122 had no effect ( Figure 4D ) . Of note , expression of TCF1 ( 37–122 ) -Gal4 , but not TCF1 ( 100–211 ) -Gal4 , inhibited TCF/LEF reporter activity induced by TCF1 in 293T cells ( Figure 4E ) , suggesting that TCF1 ( 37–122 ) -Gal4 may interfere with the binding to a TCF1 molecular partner , while TCF1 ( 100–211 ) may have an intrinsic transactivation activity . Together , these results indicated that these three TCF1 domains have distinct roles in its β-catenin independent activity: the region between aa 100–211 is partially active on its own , while the N-terminal 100 aa and aa 211–299 are required for full transcriptional function . We used a candidate approach to identify partners for TCF1/LEF1 potentially involved in their β-catenin independent activity . It has been shown that c-Jun binds to TCF4 and β-catenin to promote intestinal cancer development [28] . We tested the effects of TCF1 and c-Jun co-expression in 293T cells and found that c-Jun actually decreased TCF1-induced reporter activity ( Figure 5A ) . A similar inhibitory effect was observed upon expression of the c-Jun-related JunB ( Figure 5A ) , indicating that the c-Jun family has opposite effects in β-catenin-dependent and β-catenin-independent TCF/LEF signaling . These results prompted us to investigate the effects of other activator protein-1 ( AP-1 ) factors and Jun binding partners on TCF1 transcriptional activity . While c-Fos had no effect ( data not shown ) , ATF2 strongly synergized with TCF1 in stimulating the TCF/LEF reporter ( Figure 5B ) . Of note , expression of ATF2 alone moderately increased reporter activity ( Figure 5B ) , presumably through endogenous TCF1/LEF1 . ATF2 showed a similar synergistic effect when co-expressed with TCF1mt , as well as in the presence of either Dvl or β-catenin shRNA ( Figure S6 ) , implying that the cooperation between TCF1 and ATF2 was independent of β-catenin . The fact that ATF2 also enhanced LEF1-induced reporter activity ( Figure 5C ) suggested that the synergy we uncovered between TCF1 and ATF2 could be part of a more general cooperation between the TCF1/LEF1 and ATF2 families of transcription factors . Indeed , expression of the two other ATF2-like proteins , i . e . activating transcription factor 7 ( ATF7 ) and cAMP responsive element binding protein 5 ( CREB5 ) , also increased the TCF/LEF transcriptional activity of TCF1 and LEF1 ( Figures 5D , 5E and 5F ) . Consistent with our previous findings , TCF4 was unable to stimulate the TCF/LEF reporter even in the presence of overexpressed ATF2 , CREB5 or ATF7 ( Figure S7A ) . Of note , various combinations of TCF1/LEF1 and ATF2 factors displayed different degrees of cooperation , with TCF1-ATF2 and LEF1-CREB5 showing the strongest synergy , likely reflecting preferential interactions among distinct members of these two transcription factor families . To gain insights into the mechanisms involved in the synergistic functional interactions of TCF1/LEF1 and ATF2 , we investigated the ability of these proteins to form complexes . LEF1 formed a complex with CREB5 ( Figure 5G ) , and TCF1 did so with endogenous ATF2 in 293T cells ( Figure 5H ) , while coupling between endogenous LEF1 and ATF2/ATF7 was observed in Ramos and K562 cells ( Figure S8 ) . Of note , TCF4 showed weaker interaction with CREB5 compared to TCF1 ( Figure S7B ) , suggesting that TCF4's lack of transcriptional activity could reflect a lower binding affinity for ATF2 factors . These findings indicated that TCF1/LEF1 proteins interact physically as well as functionally with ATF2 transcription factors . Finally , we asked whether the association between TCF1/LEF1 and ATF2 factors plays a role in the β-catenin-independent up-regulation of TCF/LEF activity identified in some human hematopoietic tumor lines . We observed that shRNA-mediated down-regulation of ATF2 and ATF7 in Ramos cells significantly inhibited their endogenous TCF/LEF reporter activity ( Figure 6A ) , as well as the expression of the Wnt target gene Axin 2 ( Figure 6B ) . Consistent with our results using DN-TCF4 ( Figure 1 ) , the repression of TCF/LEF transcriptional activity induced by shATF2/7 in these cells was accompanied by inhibition of colony formation ( Figure 6C ) . Together , our results provide compelling evidence for a new mechanism of constitutive TCF/LEF activation in tumor cells , which is independent of β-catenin and involves cooperation with ATF2 transcription factors .
In the present study , we uncovered a novel mechanism of TCF/LEF dependent transcription that bypasses β-catenin and increases expression of Wnt target genes through interaction of TCF1/LEF1 and ATF2 transcription factors ( Figure 6D ) . We utilized several different approaches , including shRNA and decoy constructs , as well as TCF1 mutants unable to bind to β-catenin , to demonstrate that TCF1/LEF1 possess transcriptional activity that is independent of β-catenin . Expression of these proteins was able to stimulate the TCF/LEF reporter in mammalian cells , and to trigger Wnt target genes in vivo , accompanied by induction of the characteristic axis-duplication phenotype in Xenopus embryos . We showed further that this mode of TCF/LEF activation in the absence of β-catenin stabilization occurs constitutively in some human hematopoietic tumors , where it plays a role in stimulating cell growth . Thus , this cooperation between TCF1/LEF1 and ATF2 represents an unexpected new strategy used by tumor cells to aberrantly activate TCF1/LEF1 signaling independently of β-catenin and the Wnt canonical pathway . Several arguments support a role of TCF/LEF dependent Wnt signaling in the hematopoietic system [56] , and it has recently been shown that moderate levels of canonical Wnt activation promote the function and/or the maintenance of various hematopoietic lineages [57] . However , the involvement of β-catenin in these cells is highly controversial [56] . While the knock-out of TCF1 and/or LEF1 demonstrated that these transcription factors are required for normal T and B cell development [30]–[32] , β-catenin conditional deletion in the mature hematopoietic system using different CRE drivers only showed mild [37] or undetectable effects [33] , [36] . In fact , using two different Wnt responsive gene reporters , Jeannet et al . demonstrated that thymocytes exhibited constitutive TCF/LEF activity , which was strongly inhibited upon knock-out of TCF1 , but not β-catenin or γ-catenin [34] . In light of our present findings , it is tempting to speculate that this new mechanism of β-catenin-independent activation of TCF1/LEF1 identified by us here may allow a cell autonomous up-regulated level , required for the maintenance of certain hematopoietic lineages . We identified three TCF1 domains involved in β-catenin-independent activity , all of which localized N-terminally of the high mobility group DNA binding domain . Whereas the region between aa 100 and 211 displayed some intrinsic transactivation properties when fused to the Gal4 DNA binding domain , the N-terminal 100 aa proved to be particularly important , albeit not sufficient , to confer transcriptional activity to TCF1 . The N-terminal domain of TCF/LEF proteins has been previously associated with binding to β-catenin [14] , [15] , [47] , and our demonstration that this region also participates in β-catenin-independent signaling may help in the interpretation of several previous observations . For example , it has been assumed that binding to β-catenin is required for the role of TCF1 in T cell development , since the defects in thymocyte maturation and survival observed in TCF1 null mice could be rescued by expression of the long TCF1 isoform , but not using a short TCF1 lacking the N-Terminal 116 aa [58] . The fact that the N-terminal 100 aa of TCF1 also participate in its β-catenin-independent transcriptional activity may help to reconcile these findings with the normal phenotype of β-catenin null thymocytes [33]–[35] . Human sebaceous tumors have been reported to contain LEF-1 N-terminal domain mutations with decreased ability to interact with β-catenin [59] , and expression of a LEF1 construct lacking the first 32 aa driven by the keratin 14 promoter provoked the formation of sebaceous skin tumors in mice [60] . Our results indicate that N-terminally deleted LEF1 should retain β-catenin independent transcriptional activity , suggesting that the induction of such tumors might be due to this activity rather than to inhibition of Wnt canonical signaling . We established that both TCF1 and LEF1 interact with ATF2 transcription factors to promote TCF/LEF activity , and inhibition of such activity using either DN-TCF4 or shATF2/7 decreased the expression of Wnt target genes , as well as lymphoma cell growth . The role of ATF2 proteins in cancer depends on cell context , as these factors have been associated with both oncogenic or tumor suppressive functions [61] . Contrary to what was previously reported for the β-catenin-TCF4 complex [28] , we found that c-Jun and JunB inhibited TCF1-induced transcription , suggesting that Jun proteins may compete with TCF1/LEF for binding to ATF2 . While c-Jun is an oncogene overexpressed or amplified in different types of cancer , including sarcomas [61] , it has been demonstrated that conditional JunB inactivation [62] , [63] or PU . 1 related downregulation of both JunB and c-Jun [64] provoke myeloproliferative disorders and different types of leukemia in mice . It is tempting to speculate that decreased levels of JunB and/or c-Jun may perturb the balance between different AP1 factors , thus facilitating the interaction between ATF2 and TCF1/LEF1 . With the exception of few general target genes , including LEF1 and Axin 2 , the transcriptional outcome of activated Wnt signaling depends on the cell/tissue context . Gene array experiments have identified hundreds of genes whose expression is modified by Wnt , with often little overlap between different cell models [15] . A number of variables likely contribute in determining which genes are regulated in a particular cell or tissue type , including the strength of the signal , cooperation with other pathways or transcription factors , expression of different LEF/TCF isoforms [65] and , possibly , even post-translational modifications of TCF/LEF factors [66] , [67] . We showed that down-regulation of β-catenin-independent TCF/LEF activity inhibited the expression of LEF1 and Axin 2 genes . However , further studies will be needed to obtain a broader view of the genes regulated through this new mechanism of TCF/LEF activation and to assess similarity and differences with classical Wnt/β-catenin signaling . In a recent study , TCF1 was identified as one of the most up-regulated genes in self-renewing versus partially differentiated hematopoietic multipotential precursor cells [68] . These same authors found by ChIP-seq that TCF1 primarily binds to up-regulated genes , many of which are involved in self-renewal [68] . Yet , expression of Wnt ligands was extremely low in these cells , consistent with activation of TCF1 being independent of β-catenin and canonical Wnt signaling . The integration of this type of high-throughput dataset with those generated in other systems , in which the Wnt canonical pathway is active , may aid in dissecting the different functions of β-catenin-dependent and -independent TCF/LEF signaling .
Ramos ( Burkitt's lymphoma ) , U937 ( histiocytic lymphoma ) , K562 ( chronic myelogenous leukemia ) and Jurkat ( acute T-cell leukemia ) cells were maintained in RPMI-1640 ( Lonza ) supplemented with 10% fetal bovine serum ( FBS; Sigma ) . Human mantle cell lymphoma lines including Granta-519 , HBL-2 , JEKO-1 and JVM-2 were generously provided by Dr . Samir Parekh , Icahn School of Medicine at Mount Sinai , and were also cultured in this medium . AB5 ( immortalized human breast epithelial ) , PA1 ( ovarian teratocarcinoma ) , 293T ( human embryonic kidney ) , NIH-3T3 ( mouse fibroblasts ) and Mel888 ( melanoma ) cells were maintained in DMEM ( Lonza ) supplemented with 10% FBS . Transient transfection was performed using Fugene 6 ( Roche ) according to the manufacturer's instructions or with polyethylenimine ( Polysciences ) . For lentivirus production , 293T cells were co-transfected with the lentiviral vector , pCMV Δ8 . 91 and pMD VSV-G plasmids . The conditioned medium containing the viral particles was collected two , three and four days after transfection , supplemented with 8 µg/ml polybrene and added to a pellet of hematopoietic tumor cells , followed by centrifugation for 1 h at 500 g , 4°C and overnight incubation at 37°C , 5% CO2 . Two days after transduction , the cells were selected in 2 µg/ml puromycin or 10 µg/ml blasticidin . Primary human lymphoma samples were obtained either as part of standard excisional biopsy or from peripheral blood samples from patients at the Icahn School of Medicine at Mount Sinai with informed consent reviewed and approved by the Institutional Review Board and in accordance with the Declaration of Helsinki . Specimens were processed to viable , sterile single-cell suspensions . Briefly , lymph node tissue was diced and forced through a metal sieve in a laminar flow hood into RPMI tissue culture medium . Peripheral blood mononuclear cells or disaggregated follicular lymphoma biopsy cells were pelleted by low-speed centrifugation , resuspended in media composed of 90% fetal calf serum and 10% DMSO ( Sigma ) , frozen slowly in the vapor phase of liquid nitrogen in multiple cryotubes , and stored in liquid nitrogen . The frozen cells were thawed and maintained for 1–3 days in RPMI-1640 supplemented with 10% fetal bovine serum prior to RNA extraction for qPCR analysis or preparation of cell lysates for analysis of uncomplexed β-catenin ( see below ) . Lentiviral TCF/LEF luciferase and GFP reporters , β-catenin and Dvl shRNAs , inducible and constitutive DN-TCF4 vectors and the SuperTop reporter ( pTA-Luc vector ) were previously described [8] , [69] . The pOT ( pGL3 ) reporter was kindly provided by B . Vogelstein . The Top-Glow reporter was purchased from Millipore . Human TCF4 was cloned by PCR from 293T cells into pcDNA3HA vector . Human TCF1 was cloned from CCRF-CEM cells into pcDNA3HA and pcDNA3flag vectors . TCF1 mutant ( D21A; E29K ) was generated using the QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) . TCF1 N-terminal deletion and TCF1-TCF4 fusion constructs were generated by PCR in pcDNA3HA . The sequence corresponding to TCF1 β-catenin binding domain ( N-terminal 60 aa ) , aa 37–122 and aa 100–211 were cloned downstream of Gal4 DNA binding domain into the pBIND vector from the CheckMate Mammalian Two-Hybrid System ( Promega ) . Full-length LEF1 was cloned in pCAN-myc2 and pcDNA3HA ( Figure 2A ) vectors . Myc-tagged β-catenin and HA-tagged Wnt3a were cloned into pCCBS vector . Human ATF2 and CREB5 were cloned into pEF-flag vector . c-Jun and Jun-B were cloned into pcDNA3HA . The ATF7 construct was kindly provided by P . J . Hamard . The lentiviral shRNA constructs targeting β-catenin , γ-catenin , ATF2 and ATF7 were generated in VIRDH-EP or VIRHD-bla vectors , using the following targeting sequences: GTACGAGCTGCTATGTTCC ( β-catenin ) , CACCATTCCCCTGTTTGTG ( γ-catenin ) , AGCCCTCAGGAAGTTGATTAAA ( ATF2 ) and CGAAGAACTCACTTCTCAGAA ( ATF7 ) . All constructs were sequence verified . The following antibodies were purchased from commercial sources: mouse anti-β-catenin , mouse anti-γ-catenin ( BD Biosciences ) , mouse anti-tubulin , mouse anti-Flag , mouse anti-HA ( Sigma ) , rabbit anti-ATF2 , rabbit anti-HA ( Santa Cruz ) , goat anti-Axin ( R&D Systems ) , rabbit anti-ATF7 ( Abcam ) , mouse anti-LEF1 ( Millipore ) . Mouse anti-myc clone 9E10 was obtained from the Mount Sinai Hybridoma Core Facility . For immunoblot , cells were washed once with phosphate-buffered saline ( PBS ) and lysed on ice in lysis buffer containing 50 mM Hepes pH 7 . 6 , 150 mM NaCl , 5 mM EDTA , 1% Nonidet P-40 , 20 mM NaF , 2 mM sodium orthovanadate , supplemented with the Complete Mini proteinase inhibitor cocktail tablets ( Roche ) . Lysates were cleared by centrifugation at 20 , 000× g for 15 min at 4°C and protein concentrations were determined by using the Bio-Rad protein assay ( Bio-Rad ) . Sodium dodecyl sulfate ( SDS ) loading buffer was added to equal amounts of lysate , followed by SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) and transfer to Immobilon-P membranes ( Millipore ) . Antibodies used in immunoblot analysis were revealed by chemiluminescence using ECL Western Blotting Substrate ( Thermo Fisher Scientific ) or using the Odyssey Infrared Imaging System ( LI-COR Biotechnology ) . For immunoprecipitation , equal amounts of cell lysates were incubated either with anti-Flag M2 agarose beads ( Sigma ) for 3 hrs at 4°C or with mouse anti-myc or mouse anti-HA antibodies for 1 hr at 4°C , followed by 3 hr incubation with Protein G Sepharose 4 Fast Flow beads ( GE Healthcare ) . For LEF1 immunoprecipitation , equal amounts of cell lysates were incubated with aLEF1 antibody for 2 hrs at 4°C , followed by overnight incubation with Protein G Sepharose 4 Fast Flow beads . Beads were washed three times with lysis buffer and resuspended in SDS loading buffer , followed by SDS-PAGE and immunoblot . Uncomplexed β-catenin was measured using glutathione S-transferase ( GST ) –E-cadherin or GST-TCF1 beads as described previously [70] . Briefly , bacterially expressed E-cadherin or TCF1 β-catenin binding domains fused to GST were bound to glutathione-Sepharose beads ( GE Healthcare ) and incubated with equal amounts of cell lysates . After pull-down , the samples were subjected to immunoblot analysis using anti-β-catenin antibody . For luciferase reporter assay , the cells were transfected or transduced with the TCF/LEF firefly luciferase reporters and the renilla luciferase control reporters or transfected with the Gal4 responsive vector pG5luc ( Promega ) and the renilla containing pBind plasmid . The cells were lysed and luciferase activity was measured using the Dual-Luciferase Reporter Assay System ( Promega ) according to the manufacturer's instructions . For GFP reporter assay , cells transduced with the Top or Fop TCF/LEF GFP reporter were transferred to polystyrene tubes ( Falcon ) and subjected to FACS analysis ( FACScan; Becton Dickinson ) using CellQuest 3 . 2 software ( Becton Dickinson ) . Growth of hematopoietic tumor cells in soft agarose was determined by seeding 2×103 cells per 60-mm dish in 0 . 5% sea plaque agarose ( Cambrex ) in RPMI supplemented with 10% FBS on a semisolid bottom layer of growth medium containing 1% agarose . Cells were fed once weekly with 0 . 3 mL of medium and stained after 17 days with iodonitrotetrazolium ( Sigma ) . The cells containing Tet-off inducible DN-TCF4-GFP vector were grown overnight in the presence or the absence of 100 ng/ml doxycycline , followed by extensive washing in PBS . The cells were then counted and seeded in soft agarose dishes in the presence or the absence of 100 ng/ml doxycyline . The remaining cells were grown for one additional day with or without 100 ng/ml doxycyline , and FACS analysis was performed to assess the level of induction of DN-TCF4-GFP . Total RNA was extracted using the Trizol Reagent ( Invitrogen ) , incubated with DNase I ( Invitrogen ) and reverse transcribed in the presence of random primers using SuperScript II reverse transcriptase ( Invitrogen ) according to the manufacturer's protocol . Quantitative PCR was performed using FastStart SYBR Green Master ( Roche ) on a MJ Opticon ( Bio-Rad ) . The primers used for qPCR were: TATA Binding Protein ( 5′-ATCAGTGCCGTGGTTCGT and 5′-TTCGGAGAGTTCTGGGATTG ) , 18S ( 5′-GTAACCCGTTGAACCCAT and 5′-CCATCCAATCGGTAGTAG ) , Axin 2 ( 5′-ACTGCCCACACGATAAGGAG and 5′-CTGGCTATGTCTTTGGACCA ) , LEF1 ( 5′-CTTTATCCAGGCTGGTCTGC and 5′-TCGTTTTCCACCATGTTTCA ) . RNA was synthesized in vitro in the presence of cap analog using the mMessage mMachine kit ( Ambion ) . Microinjection , explant dissection , cell culture and whole-mount antibody staining were performed as described [71] . The 12/101 antibody ( Developmental Studies Hybridoma Bank , University of Iowa ) was used at a 1∶1 dilution . Secondary antibody was a donkey anti-mouse IgG coupled to horseradish peroxidase ( Jackson Laboratories ) , and was used at 1∶1000 dilution . Color reactions were performed using the Vector SG kit ( Vector Laboratories ) . For RT-PCR , Xenopus laevis embryos were staged and harvested at appropriate stages according to morphological criteria . RNA was prepared using RNA Bee RNA isolation reagent ( Tel-Test Inc . ) . RT-PCR was performed as described [72] . Primers used in this study are as follows: Xbrachyury ( 5′-GGATCGTTATCACCTCTG and 5′-GTGTAGTCTGTAGCAGCA ) , chordin ( 5′-CAGTCAGATGGAGCAGGATC and 5′-AGTCCCATTGCCCGAGTTGC ) , EF1-α ( 5′-CAGATTGGTGCTGGATATGC and 5′-ACTGCCTTGATGACTCCTAG ) , Xnr3 ( 5′-GTGAATCCACTTGTGCAGTT and 5′-ACAGAGCCAATCTCATGTGC ) . Studies on Xenopus laevis embryos were performed in accordance with the guidelines of the American Veterinary Medical Association , and under the auspices of the Queens College Institutional Animal Care and Use Committee ( IACUC ) . All experiments were undertaken with the highest regard for scientific , ethical , and humane principles . | The Wnt signaling pathway plays a crucial role during embryonic development and in the maintenance of stem cell populations in various organs and tissues . Aberrant activation of this pathway through different mechanisms participates in the onset and progression of several types of human cancers . In the presence of Wnt ligands , stabilized β-catenin acts as a transcriptional activator to induce the expression of target genes through binding to the TCF/LEF family of transcription factors . Using in vitro and in vivo models , we show that TCF/LEF proteins can be activated independently of β-catenin through cooperation with members of the ATF2 subfamily of transcription factors . This novel alternative mechanism of TCF/LEF activation is constitutively up-regulated in certain hematopoietic tumor cells , where it regulates the expression of TCF/LEF target genes and promotes cell growth . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology",
"medicine",
"developmental",
"biology",
"biology",
"molecular",
"cell",
"biology"
] | 2013 | β-Catenin-Independent Activation of TCF1/LEF1 in Human Hematopoietic Tumor Cells through Interaction with ATF2 Transcription Factors |
Fused in Sarcoma ( FUS ) proteinopathy is a feature of frontotemporal lobar dementia ( FTLD ) , and mutation of the fus gene segregates with FTLD and amyotrophic lateral sclerosis ( ALS ) . To study the consequences of mutation in the fus gene , we created transgenic rats expressing the human fus gene with or without mutation . Overexpression of a mutant ( R521C substitution ) , but not normal , human FUS induced progressive paralysis resembling ALS . Mutant FUS transgenic rats developed progressive paralysis secondary to degeneration of motor axons and displayed a substantial loss of neurons in the cortex and hippocampus . This neuronal loss was accompanied by ubiquitin aggregation and glial reaction . While transgenic rats that overexpressed the wild-type human FUS were asymptomatic at young ages , they showed a deficit in spatial learning and memory and a significant loss of cortical and hippocampal neurons at advanced ages . These results suggest that mutant FUS is more toxic to neurons than normal FUS and that increased expression of normal FUS is sufficient to induce neuron death . Our FUS transgenic rats reproduced some phenotypes of ALS and FTLD and will provide a useful model for mechanistic studies of FUS–related diseases .
Amyotrophic lateral sclerosis ( ALS ) and frontotemporal lobar degeneration ( FTLD ) are two common neurodegenerative diseases [1] , [2] . ALS is characterized by degeneration of motor neurons , denervation atrophy of skeletal muscles , and progressive paralysis of limbs [3] , [4] . FTLD mainly affects cortical neurons and causes cortical dementia [5] . ALS patients may develop cortical dementia that overlaps with FTLD in pathology [2] , [6] . ALS and FTLD share a common feature of pathology—ubiquitin-positive inclusion [7]–[10] . Although selective groups of neurons are primarily affected in each disease condition [2] , increasing evidence suggests that ALS and FTLD may fall the same disease spectrum . Fused in Sarcoma ( FUS ) has recently been linked to both ALS and FTLD [11] , [12] . FUS is a highly conserved ribonucleoprotein that mainly resides in the nucleus while shuttling between the cytoplasm and the nucleus [13]–[15] . Fus was initially reported to translocate and fuse with one of several genes to form chimeric oncogenes in leukemia and liposarcoma [16] , [17] . The N-terminus of the FUS protein is rich in glutamine , serine , and tyrosine residues , and may be responsible for transactivation activity of FUS oncogenic fusion [18] , [19] . The C-terminal part of the FUS protein contains several structural motifs important for nucleic acid binding [18] , [20] , [21] . FUS may also play an important role in regulating mRNA [14] , [22] , [23] . Deletion of the fus gene results in chromosomal instability and perinatal death in inbred mice [24] , but causes only male sterility in outbred mice [25] . FUS-positive inclusion is considered a hallmark of some sporadic FTLD [9] , [26] . FUS , Tau , and TDP-43 are the important components of ubiquitinated proteins in FTLD , but exclude one another in ubiquitin-positive inclusion [8]–[10] , [27] . Mutations in the fus gene segregate with ALS and FTLD [11] , [12] , [28] , [29] , implying a pathogenic role of FUS in these diseases . Given the importance of FUS in human diseases , the consequences of mutation in the fus gene must be examined . Here we show that overexpression of a mutant , but not normal , human FUS in rats induced progressive paralysis resembling ALS . Mutant FUS transgenic rats developed severe axonopathy of motor neurons , denervation atrophy of skeletal muscles , and a substantial loss of cortical and hippocampal neurons . At advanced ages , normal FUS transgenic rats displayed deficits in spatial learning and memory , and a loss of cortical and hippocampal neurons . Neuronal loss was accompanied by ubiquitin aggregation and glial reaction . Our FUS transgenic rats recapitulated some features of ALS and FTLD .
To study the consequences of mutation in the fus gene , we created transgenic rats expressing the human fus gene with or without mutation ( Table S1 ) . Most mutations in the fus gene are a single amino acid alteration , as exemplified by the substitution of arginine for cysteine at residue 521 ( R521C ) that is identified in geographically unrelated patients [11] , [12] , [30] . We therefore chose R521C as an example of fus mutation for our transgenic studies . The ribonucleoproteins FUS and TDP-43 are both linked to ALS and FTLD [11] , . FUS and TDP-43 are robustly and ubiquitously expressed in rodents during development [33] , implying an important role for these genes in development . Constitutive expression of a mutant TDP-43 causes early death to transgenic founder rats [34] , preventing transgenic lines from establishment . To overcome this potential difficulty , we used a tetracycline-inducible system to express human fus transgenes in a controlled manner [34] . From 26 transgenic founders carrying the normal ( 12 rats ) or the mutant ( 14 rats ) fus transgene , we established four transgenic lines ( line number corresponding to copy number of the transgenes ) that expressed human FUS , under tight control by Doxycycline ( Dox ) , at substantial levels ( Figure 1A , Figure S1 , and Table S1 ) . FUS transgenic rats were crossed with a CAG-tTA transgenic line to produce double transgenic offspring that expressed human FUS transgene in the absence of Dox [35] . Breeding female rats were given Dox in their drinking water until delivery such that expression of the fus transgenes would be recovered in the offspring after Dox withdrawal ( Figures S1 and S2 ) . Immunoreactivity to human FUS was detected in the brain and spinal cord ( gray and white matter ) of FUS transgenic rats ( Figure 1B , 1D , and 1E ) , but not in tissues of nontransgenic rats ( Figure 1C and 1F ) . While transgenic rats of lines 16 , 20 , and 22 expressed human FUS at comparable levels ( Figure 1A ) , only the mutant FUS transgenic rats ( lines 16 and 22 ) developed paralysis resembling ALS ( Figure 1G-1J and Videos S1 and S2 ) . Similar disease phenotypes were observed in two independent lines of mutant FUS transgenic rats ( Figure 1G–1J ) , suggesting that the disease phenotypes resulted from expression of the mutant fus gene . Pathological analysis revealed that few motor neurons in the spinal cord were undergoing degeneration ( Figure 2A–2E ) . Degenerating axons were detected in the dorsal corticospinal tracts ( Figure 2G ) , the ventral roots ( Figure 2I and 2M ) , and the dorsal roots ( Figure 2K ) of mutant FUS transgenic rats at paralysis stages . As a result of motor axon degeneration , groups of skeletal muscle cells were atrophied ( Figure 2O ) , although there were some perimysial cells with small nuclei suggestive of inflammation . These pathological changes were not observed in nontransgenic rats ( Figure 2A ) and also not observed in age-matched , wild-type FUS transgenic rats ( Figure 2B , 2D , 2F , 2H , 2J , 2L , and 2N ) expressing human FUS at comparable levels ( Figure 1A ) . Collectively , these findings suggest that mutation of the fus gene is pathogenic . Electromyography of the gastrocnemius muscle revealed fibrillation potential , a characteristic of denervation atrophy ( Figure 2P ) . Confocal microscopy showed that a substantial number of neuromuscular junctions were denervated in paralyzed FUS transgenic rats ( Figure 2Q and 2R ) . Through stereological cell counting , we estimated the number of spinal motor neurons and did not detect a significant loss of motor neurons , although a trend of neuron loss was observed in the mutant FUS rats at paralysis stages ( Figure 2S ) . Our results suggest that degeneration of motor axons contributed to paralysis in the mutant FUS transgenic rats . ALS and FTLD somewhat overlap in pathology [2] , and mutation of the fus gene is linked to both ALS and FTLD [28] , [29] . We therefore examined the pathology in the brains of mutant FUS transgenic rats . Through stereological cell counting ( Figure S3 ) , we detected a significant loss of neurons in the frontal cortex and dentate gyrus of mutant FUS transgenic rats at paralysis stages ( Figure 3 ) . This neuronal loss was not observed in age-matched , normal FUS transgenic rats of line 20 , although they expressed human FUS at comparable levels ( Figure 1A and Figure 3 ) . While cortical neurons are the primary targets of degeneration in FTLD , hippocampal neurons could be affected particularly at advanced disease stages [36] , [37] . Our results show that overexpression of mutant FUS induced a substantial loss of cortical and hippocampal neurons in FUS transgenic rats , a phenotype of FTLD in rat models . FUS proteinopathy is a hallmark of some sporadic FTLD cases [9] , [26] . How normal FUS is related to neurodegeneration in the disease remains to be examined . Our wild-type ( line 20 ) and mutant ( line 16 ) FUS transgenic rats expressed human FUS at comparable levels ( Figure 1A ) , but only the mutant FUS transgenic rats developed paralysis at an early age ( Figure 1G–1I ) . We further examined the normal FUS transgenic rats at advanced ages ( Figure 4 ) . Although the normal FUS transgenic rats were asymptomatic by the age of 1 year , they displayed a deficit in spatial learning and memory at the advanced age ( Figure 4J and 4K ) . By stereological cell counting , we detected a moderate , but significant , loss of neurons in the frontal cortex and dentate gyrus of the normal FUS transgenic rats at advanced ages ( Figure 4L and 4M ) . These findings suggest that increased expression of normal FUS is sufficient to induce neurodegeneration and that mutant FUS is more toxic to neurons than is normal FUS . Ubiquitin-positive inclusion is a hallmark of ALS and FTLD [8]–[27] . Accumulated ubiquitin was detected in the cortex ( Figure 5D–5F ) and spinal cord ( Figure 5J–5L ) of mutant FUS transgenic rats at paralysis stages , but was not detected in the tissues of age-matched normal FUS transgenic rats ( Figure 5A–5C and 5G–5I ) . In the normal FUS transgenic rats , ubiquitin aggregates were observed only when neuronal loss was detected at advanced ages ( Figure 4 ) , suggesting that ubiquitin aggregation accompanied neurodegeneration . Ubiquitin inclusions were detected only in FUS-expressing cells , but were not colocalized with FUS ( Figure 4G–4I and Figure 5 ) . Ubiquitinated aggregates were positive for the mitochondrial marker COXIV ( Figure S4 ) , suggesting that damaged mitochondria may be ubiquitinated for degradation . No typical FUS inclusion was detected in FUS transgenic rats ( Figure 1B and 1E , and Figure 5 ) . FUS mainly resided in the nucleus , but was also diffusely located in the cytoplasm ( Figure 1E ) . The C-terminus of FUS contains a nuclear localization signal that is necessary for the nuclear import of FUS . Most mutations occur within the C-terminus of FUS and disrupt this nuclear localization signal [38] , leading to cytoplasmic accumulation of FUS . The R521C mutation tested in our transgenic studies affects FUS distribution to a minimal extent [38] , and may be less potent in eliciting redistribution and aggregation of FUS in transgenic rats . Glial cells are key players in neurodegeneration [39] . Here we found that astrocytes and microglia proliferated in the brain ( Figure 6A–6F ) and spinal cord ( Figure 6H–6K ) of FUS transgenic rats at paralysis stages . Our results indicate that neurodegeneration was accompanied by ubiquitin aggregation and glial reaction .
ALS and FTLD are two related neurodegenerative diseases [2] , [6] and may fall within the same disease spectrum . While a subset of FTLD patients develop motor neuron disease [40] , ALS patients may develop the symptoms and pathology of FTLD [41]–[43] . FUS and TDP-43 are two ribonucleoproteins and their mutant forms are linked to both ALS and FTLD [7]–[29] . We obtained two FUS transgenic lines expressing a mutant or normal human fus transgene at comparable levels . Transgenic rats expressing a mutant FUS developed progressive paralysis secondary to axonal degeneration and displayed a substantial loss of neurons in the cortex and hippocampus , reproducing some phenotypes of ALS and FTLD . While the mutant FUS transgenic rats developed some phenotypes of ALS and FTLD , the age-matched normal FUS transgenic rats were asymptomatic . Our findings in FUS transgenic rats confirm that mutation of the fus gene is related to these two diseases and suggest that mutation of the fus gene is pathogenic . FUS proteinopathy characterizes a subset of sporadic FTLD , in which ubiquitin-positive inclusions are negative for TDP-43 and tau but positive for FUS protein [27] , [44] . However , it is not known how normal FUS is related to neurodegeneration in these diseases . While overexpression of mutant FUS induced severe phenotypes in young animals , overexpression of the normal FUS also induced neuron death as well as learning and memory deficits in aged rats . Mutated FUS appeared more toxic in transgenic rats , but an increase in the expression or function of the fus gene may elicit neurotoxicity . The effects of gene mutation include gain-of-function , loss-of-function , and dominant-negative effects . Overexpression of either the mutant or wild-type FUS induced disease phenotypes in transgenic rats , suggesting that mutation of the fus gene may cause the disease by a gain of toxic properties . Since gain-of-function and dominant-negative mutations can induce similar effects in transgenic models , more sophisticated genetic approaches , such as gene knockin , may be required for determining the nature of FUS mutations . FUS and TDP-43 show a similarity in disease induction . Mutant forms of these genes are more toxic than the normal genes [34] , and increased expression of the normal genes is sufficient to induce neurodegeneration [45] , [46] . Both FUS and TDP-43 are ribonucleoproteins and may have overlapping functions . Indeed , FUS and TDP-43 are found in one protein complex regulating HDAC6 mRNA [47] . Like TDP-43 , FUS predominantly resides in the nucleus , but also shuttles between the nucleus and the cytoplasm to perform multiple functions [13] . Similar to results for TDP-43 [34] , we found that FUS was diffusely located in the cytoplasm in transgenic rats . Possibly , redistribution of FUS may alter the functions of this multifunctional protein , incurring cellular toxicity . In summary , our results suggest that mutant FUS is more toxic to neurons than normal FUS and that increased expression of normal FUS is sufficient to induce neuron death . Our FUS transgenic rats reproduced some phenotypes of ALS and FTLD . The establishment of these FUS transgenic rat lines will allow for more detailed studies of FUS-related diseases .
Animal use followed NIH guidelines . The animal use protocol was approved by the Institutional Animal Care and Use Committees ( IACUC ) at Thomas Jefferson University . The open reading frame ( ORF ) of the normal human fus gene was PCR-amplified from a human cDNA pool ( Invitrogen ) and the mutation was introduced by site-directed mutagenesis ( Stratagene ) . The normal and mutant human FUS ORF were inserted downstream of the TRE promoter as described previously [34] . Linearized transgenic DNA was purified from agar gel and injected into the pronuclei of fertilized eggs of Sprague-Dawley ( SD ) rats to produce transgenic founder rats [34] , [35] . Transgenes were maintained on the SD genomic background and were identified by PCR analysis of rat's tail DNA . Grip strength of the rat's fore and hind paws was measured twice a week ( Columbus Instruments ) and used for determining disease onset and progression . Disease onset was defined as an unrecoverable reduction in the grip strength of fore or hind paws . Disease end-stage was defined as paralysis in two or more legs or as a 30% reduction in body weight . Spatial learning and memory tasks were examined with a Barnes Maze ( Med Associates ) . Compared to Morris Water Maze or Radial Arm Maze , the Barnes Maze not only avoids dietary restriction and intense stress , but also gives comparable results on rodent's spatial learning and memory tasks [48] , [49] . The Barnes Maze consists of a white , acrylic , circular disk ( 122 cm diameter ) with 18 holes ( 9 . 5 cm diameter ) spaced every 20° and a high stand ( 140 cm height ) supporting the disk that is designed to discourage animals from jumping to the floor . Rats were given one training session and four test sessions for 5 consecutive days . During training or testing sessions , rats were placed in the same initial orientation inside a transparent cylinder ( start box ) that was located at the center of the maze disk and the rats remained in the start box for 1 minute such that a standard starting context was ensured . When a lamp above the maze was turned on to make the surface of the maze aversive , the start box was removed to allow the animal to escape the maze surface by locating and crawling through the correct hole under which a black safe box was located . When the animal entered the safe box , the light was turned off and the safe box was covered with a black sheet . The animal was allowed to stay in the safe box for 1 minute before it was placed back to its home cage . Before training , each rat was given 2 minutes to explore the maze and then placed inside the safe box for 1 minute for habituation . During training , each rat was guided to the safe box twice and then given two trials to locate the safe box by itself . During the test , rats were placed inside the start box for 1 minute to locate the fixed safe box . The number of incorrect hole pokes ( error ) and the latency to locate the safe box were recorded . An incorrect hole poke was indicated when an animal closely approached and visually inspected a wrong hole . Latency to locate the safe box was calculated from the time testing started to the time when the animal entered , or its four paws touched , the safe box . The maze was wiped clean with 70% ethanol and then with dry paper towel after each test to prevent animals following odor trails . An antibody to human FUS was produced by immunizing rabbits with a synthetic peptide ( Genemed ) : ( N-terminal ) -SYGQPQSGSYSQQPS . Antiserum was affinity-purified with a peptide-affinity column ( Pierce ) . Anesthetized rats were transcardially perfused with 4% paraformaldehyde ( PFA ) dissolved in 1X PBS buffer and tissues were dissected after perfusion . Tissues were cryopreserved in 40% sucrose and cut into sections on a Cryostat . Tissue sections of 12 µm were immunostained with the following antibodies: rabbit polyclonal antibody to human FUS ( made in-house ) , chicken antibody to ubiquitin ( Sigma ) , mouse monoclonal antibodies against Iba-1 ( Wako Chemical ) or GFAP ( Millipore ) , and mouse monoclonal antibody against NeuN ( Millipore ) . For histochemistry , immunostained sections were visualized with an ABC kit in combination with diaminobenzidine ( Vector ) and counterstained with hematoxylin to display nuclei . For immunofluorescent staining , tissue sections were incubated first with specific primary antibodies and then with secondary antibodies labeled with fluorescent dyes ( Jackson Immunoresearch ) . Primary antibodies were diluted at 1∶1000 and secondary antibodies diluted at 1∶200 . The primary antibodies were incubated overnight at 4°C and the secondary antibodies were incubated for 2 hours at room temperature . For detection of degenerating neurons , paraffin-embedded spinal cords were cut into transverse sections of 10 µm and stained using a protocol for Bielschowsky silver staining [34] . As described in a previous publication [34] , neuromuscular junctions ( NMJ ) were visualized by immunofluorescent staining and confocal microscopy . PFA-fixed gastrocnemius muscles were cut into sections of 100 µm on a Cryostat . Muscle sections were incubated with α-bungarotoxin ( Invitrogen ) for 30 minutes at room temperature and subsequently immunostained with mouse monoclonal antibodies to neurofilament ( Sigma ) and synaptophysin ( Millipore ) . Both primary and secondary antibodies were diluted at 1∶1000 . The primary antibodies were incubated overnight at 4°C and the secondary antibodies were incubated for 2 hours at room temperature . NMJ images were captured with a Zeiss LSM510 META confocal system and the NMJ was reconstructed through z-stack projections from serial scanning every 1 µm . As described previously [34] , anesthetized rats were perfused with a mixture of 4% PFA plus 2% glutaraldehyde . Cervical spinal cords and L3 ventral and dorsal roots were dissected and post-fixed in the same fixative at 4°C overnight . Fixed tissues were embedded in Epon 812 ( Electron Microscopic Sciences , PA ) and cut into semithin and thin sections . Semithin sections ( 1 µm ) were stained with 1% toluidine blue and visualized under a light microscope . Thin sections ( 80 nm ) were stained with uranyl acetate and lead citrate and observed under a transmission electron microscope ( Hitachi H7500-I ) . Anesthetized rats were examined by EMG . Fibrillation and fasciculation potentials of gastrocnemius muscles were recorded with an EMG machine ( CMS6600; COTEC Inc . ) as previously described [34] . Motor neurons in the ventral horn of the L3 lumbar cord were stereologically counted as previously described [34] . Neurons larger than 25 µm in diameter were counted in the ventral horns on both sides . For estimation of neurons in the frontal cortex and dentate gyrus , one hemisphere of the brain was used for cell counting . The forebrain was cut into coronal sections of 30 µm between the apical rostral part of the brain and the first occurrence of hippocampus , and every 12th section ( a total of 15 to 18 sections ) was counted for neurons in the defined frontal cortex ( Figure S3 ) . The portion of the brain containing the dentate gyrus was cut into consecutive sections ( 20 µm ) and every 12th section ( a total of 16 to 21 sections ) was counted for neurons in the dentate gyrus . Tissue sections were stained with Cresyl violet and mounted in sequential order ( rostral-caudal ) . The number of targeted neurons was estimated using a fractionator-based unbiased stereology software program ( Stereologer ) run on a PC computer that was attached to a Nikon 80i microscope fitted with a motorized XYZ stage ( Prior ) . At low magnification , the targeting area was outlined and a random sampling grid was created . At high magnification , an optical dissector probe in the designated area was randomly generated by the program . The presence of clearly definable neurons was noted according to defined inclusion and exclusion limits of the dissector . This process was repeated on all selected sections . The total number of defined neurons was calculated by the software according to the result of random counts as previously described [34] . The number of defined neurons in the defined region was statistically compared between groups of transgenic rats and comparison among experimental groups was performed by one-way ANOVA followed by Tukey's post-hoc test . The null hypothesis was rejected at the level of 0 . 05 . | Amyotrophic lateral sclerosis and frontotemporal lobar degeneration are two related diseases characterized by degeneration of selected groups of neuronal cells . Neither of these diseases has a clear cause , and both are incurable at present . Mutation of the fus gene has recently been linked to these two diseases . Here , we describe a novel rat model that expresses a mutated form of the human fus gene and manifests the phenotypes and pathological features of amyotrophic lateral sclerosis and frontotemporal lobar degeneration . Establishment of this FUS transgenic rat model will allow not only for mechanistic study of FUS–related diseases , but also for quick development of therapies for these devastating diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] | [
"medicine",
"neurological",
"disorders",
"neurology",
"genetics",
"biology",
"neuroscience",
"genetics",
"and",
"genomics"
] | 2011 | FUS Transgenic Rats Develop the Phenotypes of Amyotrophic Lateral Sclerosis and Frontotemporal Lobar Degeneration |
Numerous genome-wide screens for polymorphisms that influence gene expression have provided key insights into the genetic control of transcription . Despite this work , the relevance of specific polymorphisms to in vivo expression and splicing remains unclear . We carried out the first genome-wide screen , to our knowledge , for SNPs that associate with alternative splicing and gene expression in human primary cells , evaluating 93 autopsy-collected cortical brain tissue samples with no defined neuropsychiatric condition and 80 peripheral blood mononucleated cell samples collected from living healthy donors . We identified 23 high confidence associations with total expression and 80 with alternative splicing as reflected by expression levels of specific exons . Fewer than 50% of the implicated SNPs however show effects in both tissue types , reflecting strong evidence for distinct genetic control of splicing and expression in the two tissue types . The data generated here also suggest the possibility that splicing effects may be responsible for up to 13 out of 84 reported genome-wide significant associations with human traits . These results emphasize the importance of establishing a database of polymorphisms affecting splicing and expression in primary tissue types and suggest that splicing effects may be of more phenotypic significance than overall gene expression changes .
The release of the HapMap data in 2003 and the availability of immortalized cell lines from HapMap participants initiated a new era of research investigating how SNPs affect how genes are expressed at the mRNA level . In 2005 , two landmark publications evaluated how SNPs affect overall transcription in immortalized cell line samples collected from unrelated individuals [1 , 2] . Since those initial publications , the work has advanced with additional studies using more sophisticated microarrays , larger more diverse sample sets , and with studies of heritability of transcript and exon-level expression [3–6] . The work to date however has been limited in scope , largely focusing on the control of overall gene expression in immortalized cells , which may not be representative of in vivo patterns in specific cellular populations [7] . Only two genome-wide studies have focused on human primary cells [8 , 9] , and most studies have considered only overall expression with no attempt to identify polymorphisms that have their effects primarily on alternative splicing . Here we extend the previous body of work by studying the genetic control of both exon-level and whole-transcript level variation in expression in two primary cell types , including peripheral blood mononucleated cells ( PBMCs ) and cortical brain tissue from a set of control individuals , combined with parallel genome-wide genotyping of these samples . The implementation of identical genome-wide screens in two primary tissue types has allowed us to identify polymorphisms with clear effects on both overall expression and splicing , and to show that these effects are often tissue specific . We have also established an easy-to-use database that allows users to assess whether a given polymorphism is associated with any local changes in expression and have shown that these data suggest possible underlying causes of several published disease associations .
Exon-level microarrays were used to quantify expression levels of fully annotated coding sequences , EST-predicted exons , and bioinformatically predicted exons across the genome . These data allow direct inferences about expression levels of specific exons . By averaging sets of exons it is also possible to estimate expression levels for transcript species ( Figure 1 , top panel ) . While the exon-level expression data do not allow inference about the representation of specific ( full ) transcripts resulting from a given alternative splicing event , they do reflect splicing events in how they influence the proportion of transcripts with and without a given exon ( Figure 1 , middle and bottom panels ) . We first used a principal component analysis to evaluate overall variation in exon-level expression data , and found that tissue source is the most important determinant of that variation ( Figure 2 ) . We have therefore implemented genome-wide screens for SNPs controlling gene expression and splicing ( referred to as expression quantitative trait locus [eQTL] and splicing quantitative trait locus [sQTL] , respectively ) separately in the tissue types . Our screen for ( cis-acting ) polymorphisms controlling expression and splicing evaluated SNPs in or near ( within 100 kb ) either the target gene or exon . We limited this screen to SNPs with a minor allele frequency ( MAF ) > 0 . 04 in our sample sets ( requiring at least six alleles to be present in the tissue type investigated ) . The screen for cis-acting sites controlling overall expression and those regulating exon expression levels required approximately ten and 85 million tests , respectively . On average 40 SNPs were considered for each of the ∼22 , 000 genes , including ∼12 transcripts per gene and ∼four exons per transcript . Thus , thresholds for study-wide significance were 5 × 10−9 for transcript level associations and 6 × 10−10 for exon-level associations . We identified 584 study-wide significant eQTLs meeting the MAF requirements , but many of these were associated with one another and therefore appeared to reflect the same causal eQTL . We used stepwise regression to eliminate associated SNPs , separately evaluating the two tissue types , and identified 81 independent eQTLs . Significant associations that overlapped between the two tissue types were merged , resulting in 77 transcript level associations . Associations were separated into high confidence ( Table 1 ) and low confidence ( Table S2 ) , depending on whether the transcripts were core transcripts , which indicates the highest level of confidence . For exon-level assessments 5 , 357 significant associations were identified in the two tissue types combined and 1 , 554 remained after removal of associated SNPs . We also removed associations where the probeset contained the associated SNP or a SNP in high linkage disequilibrium ( LD , r2 > 0 . 5 ) , leaving 985 associations . Significant associations that overlapped between the two tissue types were merged , resulting in 929 unique exon-level associations . We also identified a subset of these as high confidence ( see Table 2 and Table S3 ) on the basis of the following criteria: ( 1 ) p < 10−12 , ( 2 ) no reported SNPs within the regions covered by the associated probesets , and ( 3 ) no suggested cross-hybridization of the associating probeset . For all high confidence associations identified , we evaluated how often the expression effects of SNPs were observed in the other tissue , and found that 74% of eQTLs and 51% of sQTLs appeared to act exclusively in one tissue or the other . These data clearly indicate a significant role of tissue-specific genetic regulation . To confirm the accuracy of the exon array technology , and in particular the conclusion of tissue specificity , we selected a subset of sQTLs to evaluate with quantitative real-time PCR ( qRT-PCR ) . Events were selected to replicate the detected event as closely as possible , and also to establish that tissue specificity was not the result of low resolution of the array when exons are differentially expressed in a tissue type . We found a highly significant correlation between measurements using both technologies ( Figure 3 ) with an overall associated p-value comparing the two methodologies ( linear regression ) of 1 × 10−35 . Importantly , we found clear replication of tissue specificity . We also evaluated our associations for overlap with previously reported expression QTLs . We confirmed associations with a previously reported eQTL in LRAP ( renamed ERAP2 ) [1] , and also SNP regulation of RPS26 expression [2 , 8] . While previous reports document an overall transcript change [2 , 8 , 9] , we identified the effects of the SNP to be localized to specific exons in the RPS26 transcript . This discrepancy is probably due to microarray platform differences . We also confirmed in our PBMC sample set several previously reported sQTLs established in HapMap cell lines , including sQTLs in ULK4 , PARP2 , C17orf57 , and others [5] . Taking the full list of high confidence sQTLs we evaluated how often LD extends into or surpasses regions known to be important in splicing ( Figure 4 ) [10] . We found that 78% of study-wide significant sQTLs , or their extended regions of SNPs in high LD ( r2 > 0 . 2 ) , were located near the exons they regulated for at least one transcript containing the exon . The remaining approximately 22% likely reflect unknown exons not screened for in the array , and also possibly novel regulatory regions that regulate splicing outside of these well-documented regions . Amongst all the study-wide significant sQTLs , only two of them are themselves located in a consensus splicing sequence for the relevant exons , rs10814567 in POLRE1 and rs7770794 in PIP3-E . We note , however , that the probeset screening for the associated exon in POLRE1 contains a SNP in perfect LD with the consensus site SNP and therefore may be the result of poor hybridization to the target . Given that most common polymorphisms are now known , it is surprising that there are so few cases where a candidate polymorphism responsible for a splicing change is in the consensus sequence , although this scarcity may be due to low primary representation of these SNPs on the array . To further evaluate the role of polymorphisms in consensus sequences , we identified all known polymorphisms in the conserved region located at the exon boundaries of the close to 300 , 000 core exons measured on the Affymetrix array ( three basepairs into the exon and eight into the intron , Ensembl database , National Center for Biotechnology Information [NCBI] Build 36 hg18 ) and assessed how these influenced the expression levels of neighboring exons . A total of 2 , 078 SNPs were identified with an MAF > 0 . 1 , of which 1 , 011 were represented by a proxy on the Illumina genotyping chip ( r2 = 1 with the splicing SNP in Centre d'Etude du Polymorphisme Humain from Utah ( CEU ) HapMap samples ) . For both tissue types , fewer than 7% of consensus site SNPs associated with relevant exon expression levels ( Table S4 ) . While it is likely that some associations are missed because of unknown exons not included on the array , this number was surprisingly low given the common conception that disruption of this highly conserved region would very likely disrupt exon assembly . We emphasize that this analysis only evaluates systematically the effects of common SNPs in consensus regions at the exon boundaries and note that rare variation may produce profoundly different effects . We also assessed transcript-level associations for proximity of LD regions associated with the eQTL to promoter regions ( within 10 kb upstream of the transcript start ) or in the 3′UTR regions of transcripts , key regions involved in transcription and stability of mRNA transcripts [11] . Twenty-one out of 23 high confidence eQTLs or SNPS in the LD region ( r2 > 0 . 2 ) were found to be located in or extending beyond these relevant regions involved in the steady state expression level of mRNA transcripts . One motivation for the current project is to facilitate rapid evaluation of whether polymorphisms implicated in human disease influence gene expression or splicing in relevant tissue types . We have therefore established a user interface called SNPExpress , which permits rapid interrogation of the localized effects of common SNPs on exon and transcript level expression ( Figure 5 ) . This resource is freely available at: http://people . genome . duke . edu/∼dg48/SNPExpress/ . As of April 2008 , >60 genome-wide associations studies were published identifying SNPs with convincing associations to complex human traits . While the association of these SNPs to the study phenotype is secure , how these polymorphisms ( or variants associated with them ) confer their effects is largely unknown . Of these published genome-wide association scans , 41 papers document genome-wide significant findings for 50 different traits ( 84 variants ) . Interestingly , outside of identifying nonsynonymous coding SNPs , only six claim to have identified a functional molecular-level consequence that may contribute to the phenotype , all of which are expression changes at the mRNA transcript level [12–18] . To test the utility of the SNPExpress database , we evaluated the 84 variants ( Table S5 ) for localized associations within the transcript/exon containing this SNP or transcripts/exons within 100 kb of the SNP and determined thirteen to have a strong ( p < 1 × 10−5 ) effect on an exon or transcript-level expression level ( Figure 5 , top panel; Table 3 ) . Of these , rs11171739 , associated with type 1 diabetes [12] , was found through the use of an Illumina proxy to have an association with the exon-level expression of the RPS26 gene . In a follow-up analysis , a SNP in LD with rs11171739 ( rs2292239 r2 = 0 . 71 with rs11171739 ) was found to be more highly associated with type 1 diabetes [19] . Interestingly , a SNP located upstream of both of these SNPs , rs10876864 , was found in our dataset to have the strongest association with a splicing event in RPS26 . This observation extends what was recently reported by Schadt et al . [9] by specifically identifying RPS26 splicing as responsible for the expression association with the implicated polymorphism in type 1 diabetes . More generally , however , these results illustrate the exceptional difficulty of moving from phenotypic associations to underlying biological mechanisms . While the strong splicing association with RPS26 makes it a convincing candidate for being responsible for the diabetes risk , the originally reported polymorphism is located in the ERBB3 gene , which also has been suggested as having direct relevance in type 1 diabetes [19] . Fortunately , the effects may in this case be resolvable because rs10876864 has a stronger association with the splicing change than does rs2292239 . The association between these polymorphisms , while high , is not complete and it should be possible to resolve which SNP is the more likely causal variant by testing whether the originally identified polymorphism has a stronger association with type 1 diabetes than does the polymorphism more strongly associated with the splicing change . This scan of genome-wide associations for effects on expression changes also identified splicing effects of rs6678677 , a SNP originally identified as a risk factor in rheumatoid arthritis and later a contributor to type 1 diabetes predisposition , in the PTPN22 gene [12 , 19–21] . We note that the associated SNP is located directly in the region targeted by the associated probeset , which may result in a false positive association , however the SNP effects were not observed in the brain tissue despite a similar expression level in the minor allele homozygotes . Additional work is needed to confirm this association . Other e- or sQTLs that overlapped with associations in genome-wide association studies were found for SNPs previously implicated in ankylosing sponylitis , asthma , celiac disease , Crohn disease , HDL cholesterol , lupus , multiple sclerosis rheumatoid arthritis , and type 1 diabetes . It is unclear why the majority of the splicing/expression associations we have found are for SNPs originally implicated in autoimmune diseases . Although this result could reflect a particular importance of splicing variation in autoimmunity , two other possibilities seem more plausible . First , the imbalance could be the result of a methodological bias in evaluating a tissue type clearly relevant to immune system function ( PBMCs ) . While brain tissue was included , little progress has been made identifying common variants that influence brain-specific phenotypes in genome-wide studies . Interestingly , for each e- or sQTL the association in the immune system relevant PBMCs in all cases was stronger compared to that observed in the brain tissue samples ( Table 3 ) , which argues for the importance of assessing expression and splicing effects in tissue types most relevant to the disease under study . The second possible explanation for the clear excess of candidate mechanisms in the case of autoimmune diseases is more fundamental and relates to the growing recognition of the importance of rare variants in common disease [22–24] . It is generally assumed that when a common SNP is associated with disease in a genome-wide study , that it , or some other common variant in LD with it , is responsible for the association . It is theoretically possible , however , that many of the associations observed are not due to single common variants , but rather due to a constellation of more rare disease-causing variants that happen to occur , by chance , more frequently along with one of the common alleles at given SNP as opposed to the other . In such a case , the signal of association credited to a common SNP is actually a synthetic association resulting from the contributions of multiple rare SNPs . In such cases a screen for a common SNP associated with an underlying biological effect ( such as expression or splicing ) is not likely to identify a causal site . Our failure to identify any good strong candidate SNPs controlling expression or splicing associated with disease implicated SNPs in conditions other than autoimmune conditions could reflect a difference in the importance of common variants in autoimmune disease versus other diseases . Such a difference in the role of common variants could be an indirect consequence of selection [25] related to infectious disease , which has created predispositions to autoimmune conditions . In short , outside of autoimmunity , it is possible that many of the reported associations are synthetic , due to multiple rare variants , and therefore the reason that no clear expression or splicing effects have been consistently identified at these loci . A key challenge in human disease genomics is establishing appropriate resources to elucidate the underlying biological causes of polymorphisms that are associated with disease . As demonstrated here , one key element in this effort is the development of appropriate databases that describe the relationship between polymorphisms and patterns of gene expression and splicing in multiple human primary tissue types . As the field transitions to the study of rare variants it will be critical to supplement these datasets with complete DNA resequencing data to comprehensively characterize the full spectrum of genetic regulation of expression .
Brain tissue samples ( frontal cortex ) from neurologically healthy control individuals were obtained from the National NeuroAIDS Tissue Consortium ( NNTC ) , the Kathleen Price Bryan Brain Bank ( KPBBB ) at Duke University , and the Oregon Brain Bank . PBMCs from healthy living participants were purchased from Seracare Bioservices , Cellular Technology Ltd . , and also provided by the Duke Human Vaccine Institute . All samples used in this analysis were of European ancestry . Sample demographics are included in Table S1 . PBMCs obtained from living participants were completely de-identified and obtained according to standards set forth by the Duke University Institutional Review Board . Affymetrix Human ST 1 . 0 exon arrays were used to assess exon and transcript expression levels for all samples used in the study . Genome-wide genotyping was performed using Illumina Human Hap550K chips . DNA and RNA were extracted using standard Qiagen protocols . Exon array sample preparation from total RNA was conducted based on standard Affymetrix protocols . Exon array data were evaluated using a series of quality control steps defined by Affymetrix for uniform hybridization intensity , abnormal background signals , and sample outliers . The data were normalized across all samples for a tissue type on an exon and transcript level ( four separate normalizations ) per Affymetrix PLIER protocol with a sketch-quantile normalization procedure ( Affymetrix Expression Console ) . This algorithm also removed undetectable signals for the dataset using a screen for signals below a group of antigenomic probesets . Principal component analysis ( PCA ) was performed to look secondarily to identify sample outliers using Partek Genomics Suite . Individual sample positions on top principal component ( PC ) axes were exported and the effects postmortem interval ( applicable only to brain tissue analyses ) , age , gender , and sample source/processing day were tested for significance using STATA/IC 10 . 0 . All of the four covariates were deemed to impact the sources of variability on both an exon and transcript level , and were therefore included in subsequent genetic association analyses as covariates in linear regression models . A combined normalization on both brain tissue samples and PBMCs also was performed on both the exon and transcript level . Principal components analyses were performed for a combined normalization in order to demonstrate the unique expression patterns in cortical tissue and PBMCs . Genotyping quality was assessed using previously published methods [13] . Briefly , all SNPs that we called with a genotyping frequency of >99% across individuals ( 1% rule ) were included in the analysis . All participants were also required to have a genotyping success rate of >99% for all SNPs that passed the 1% rule . Finally , each study-wide significant SNP identified in this analysis was manually evaluated in the Illumina Bead Studio files for genotyping quality/accuracy . Taqman-based real-time PCR was used to confirm exon-level expression changes . Primers and fluorescently-labeled probes were custom designed for specific detection of exon-level expression . The follow primers/probes were used: ULK4 , TCTCGTCCTAAAGCTTCTTCAGATT; ULK4 , CTTTTCTGAGGATCTCTTTGAAGT; ULK4 . PROBE , VIC- ATTAATTTGCTTGATGGGTT; SLC12A7F , ATCCTGGGCGTCATCCTCT; SLC12A7R , CACATGGCCACGATGAGG; SLC12A7 . PROBE , VIC-CTGGTGTCCTGGAGTCCT; KLHL24F , TGGTACTAATATTGGGACGCAGAC; KLHL24R , CGCTTAGTTGCTGGGGAATC; KLHL24 . PROBE , VIC- TAAACAGAGAGGATCTTGGG . FAM-labeled β-actin was used as an internal control in a multiplex reaction . Assays were performed according to standard methods ( 900-nM primer and 250-nM probe in 20-μl reaction mix , Applied Biosystems ) . Fluorescence outputs were quantified in real time using a 7900HT Fast Real Time PCR System and the data were analyzed using SDS software v . 2 . 2 . 2 ( Applied Biosystems ) . To screen for cis-acting genetic regulation of splicing/expression genetic association analyses were conducted to search for cis-acting SNPs that regulate exon-level and gene-level expression . Specifically , associations were limited to SNPs lying in or 100 kb surrounding the region of the transcripts or exons . Linear regression incorporating all the covariates was performed using PLINK genome-wide association analysis toolkit ( http://pngu . mgh . harvard . edu/∼purcell/plink/ ) [26] . To control for the possibility of spurious associations resulting from population stratification , we used a modified EIGENSTRAT method [13 , 27] . A total of four separate analyses were conducted , including PBMC transcript level , PBMC exon level , brain transcript level , and brain exon level . Thresholds for significance at each level were calculated based on the total number of association tests conducted within the four separate analyses . Initially , all SNPs were excluded from analysis if the minor allele was not present at least six times in the sample group , translating to an MAF cutoff of 0 . 04 . All significant observations that had p-values below the threshold and that met the MAF cutoff requirement were exported and the list was evaluated based on the following criteria: ( 1 ) Associations that were present on the exon or transcript list that were actually due to the opposing level were moved to the appropriate list . Specifically , a study-wide significant transcript level association was reported if the p-value achieved the threshold requirements , the affected transcript contained >2 exons , and >40% of exons contained in a transcript were significantly associated at exon-level study-wide significance level . Consistent with those rules , transcripts containing <2 exons associating with genotype that were study-wide significant and/or <40% of exons within a transcript were affected were removed and allowed on the exon-level list if they achieved exon-level study-wide significance . ( 2 ) Using stepwise linear regression ( STATA/IC 10 . 0 ) associations were removed that were redundant due to LD between SNPs . Specifically , following inclusion of the most significant SNP-probeset association , only SNPs that contributed significantly above and beyond the initial association at a p-value of <10−6 were considered as a separate association . ( 3 ) Finally , if the sQTL or a SNP in LD ( r2 > 0 . 5 ) was located in a probeset for the exon-level associations , it was excluded from any list of significant associations . Step 3 was not applied to transcript level associations as these expression levels were determined over a range of exons thereby reducing the contribution of SNPs to the expression levels . Post hoc evaluation of the transcript level associations we identified were manually inspected for effects of SNPs or SNPs in LD contributing to the association by exporting the raw values , eliminating probesets that contained a SNP , and recalculating the expression level . None of the reported associations could be accounted for by a SNP in a probeset measuring the transcript . We also assessed whether associations were present in the other sample type . The p-value for declaring an effect in the other tissue was based on a p-value cutoff of 0 . 05 . Directionality was confirmed to be the same in the two tissue types for all overlapping associations . All uncorrected p-values for observations in the other tissue types are provided in the relevant tables . See Figure 4 for methodological details regarding the screen for proximity of sQTLs to affected exons . To screen for effects of consensus site SNPs , we first identified all known SNPs located in highly conserved consensus site regions at the exon-intron boundary including all SNPs eight basepairs into an intron and three basepairs into an exon ( Ensembl database ) . A total of 2 , 078 common consensus site SNPs were identified , of which 1 , 011 had proxies ( r2 = 1 ) with one or more SNPs on the Illumina Human Hap550K chip allowing assessments of their effects in the present dataset . Specifically , consensus site SNPs , or their proxies , were assessed for significant associations ( uncorrected p < 0 . 05 ) with the expression level of the immediate exon or exons located up- or downstream for all transcripts containing that exon . | Although humans have a relatively small complement of genes , the proteins encoded by those genes and their biologic function are far more complex . The increased complexity is achieved in part through processes that create different messages from the same gene sequence ( alternative splicing ) and that regulate the expression of those messages in a tissue-specific fashion . These processes expand the functional capacity of the human genome , but also can create predisposition to disease when these processes go awry . In this study , we investigated how single nucleotide polymorphisms influence both overall gene expression and alternative splicing in two important cell types ( brain and blood ) highly relevant to human disease . Extensive and tissue-specific regulation of gene expression and alternative splicing were observed in the two tissue types , and some of these polymorphisms were shown to be connected to other polymorphsims that have been recently implicated in human diseases through genome-wide association studies . Most of these connections appeared to relate to alternative splicing as opposed to overall expression changes , suggesting that changes in splicing patterns may be more consequential for disease than those affecting only expression . These data emphasize the importance of comprehensive studies into genetic regulation of gene expression in all human tissue types in order to help understand how genetic variation influences risk of common diseases . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [
"genetics",
"and",
"genomics"
] | 2008 | Tissue-Specific Genetic Control of Splicing: Implications for the Study of Complex Traits |
DNA binding transcriptional activators play a central role in gene-selective regulation . In part , this is mediated by targeting local covalent modifications of histone tails . Transcriptional regulation has also been associated with the positioning of genes within the nucleus . We have now examined the role of a transcriptional activator in regulating the positioning of target genes . This was carried out with primary β-cells and hepatocytes freshly isolated from mice lacking Hnf1α , an activator encoded by the most frequently mutated gene in human monogenic diabetes ( MODY3 ) . We show that in Hnf1a−/− cells inactive endogenous Hnf1α-target genes exhibit increased trimethylated histone H3-Lys27 and reduced methylated H3-Lys4 . Inactive Hnf1α-targets in Hnf1a−/− cells are also preferentially located in peripheral subnuclear domains enriched in trimethylated H3-Lys27 , whereas active targets in wild-type cells are positioned in more central domains enriched in methylated H3-Lys4 and RNA polymerase II . We demonstrate that this differential positioning involves the decondensation of target chromatin , and show that it is spatially restricted rather than a reflection of non-specific changes in the nuclear organization of Hnf1a-deficient cells . This study , therefore , provides genetic evidence that a single transcriptional activator can influence the subnuclear location of its endogenous genomic targets in primary cells , and links activator-dependent changes in local chromatin structure to the spatial organization of the genome . We have also revealed a defect in subnuclear gene positioning in a model of a human transcription factor disease .
The recognition of nucleotide sequences in the vicinity of genes by DNA binding factors is central to the regulation of gene-specific transcription [1] . The mechanism by which DNA binding transactivators lead to gene activation is in part dependent on their ability to promote the remodeling of chromatin structure and the covalent modification of nucleosomal histone tails [1]–[3] . Numerous studies have linked different covalent histone modifications with the transcriptional state of gene loci [4] . Amongst these , the methylation of H3-Lys4 at gene promoters has been linked to gene activity [5] , [6] , whereas transcriptional silencing correlates with increased methylation of H3-Lys9 or H3-Lys27 [7]–[10] . For example , trimethylated histone H3 Lysine 9 ( H3-Lys9me3 ) is enriched at pericentromeric repeats forming constitutive heterochromatin [9] , [11] , while trimethylated H3-Lys27 ( H3-Lys27me3 ) has been linked to other forms of inactive chromatin , including chromosome X facultative heterochromatin , imprinted loci , and Polycomb-mediated silencing of homeobox gene clusters [7] , [8] , [10] . It has recently become apparent that the positioning of gene loci within the three dimensional structure of the nucleus may provide a further level of regulation ( reviewed in [2] , [12] , [13] . Gene activation has been linked to selective looping of loci away from chromosome territories [14] , [15] , and appears to be associated with an increased likelihood that a locus intermingles with heterologous chromosome territories [16] . Transcribing genes have also been shown to colocalize with nuclear domains that are visibly enriched in RNA polymerase II [17] , [18] . Other observations revealed that active loci localize in the nuclear interior , whereas inactive genes have been found to be preferentially positioned at the nuclear periphery [19] , [20] . Moreover , repositioning to centromeric regions has been shown for several hematopoietic genes during differentiation-related silencing , and correlates with mutation-induced silencing of the brown locus in Drosophila [21]–[24] . The precise relationships between gene positioning and transcriptional regulation , however , are not understood . Some studies suggest that gene compartmentalization may play a decisive regulatory role . An example is the demonstration that artificial recruitment of genes to the nuclear lamina results in transcriptional repression in certain , though not all , experimental settings [25] , [26] . On the other hand , several studies show that gene-rich regions tend to locate outside of their respective chromosome territories or occupy more central nuclear positions [27]–[30] . Therefore , the extent to which a gene's subnuclear position in a given cell type depends on its gene-specific transcriptional activity or on the regional organization of the chromosome territory is unclear . Furthermore , little is known about the mechanisms that govern gene positioning , or the possible role of transcriptional activators in this process . Recent studies have shown that fusion proteins containing activation domains can cause dynamic subnuclear relocation of artificial multicopy genomic targets [31] , [32] . However , so far , no study has addressed the role of endogenous activators in the positioning of endogenous genes . Furthermore , the possible interplay of gene positioning with other activator-dependent effects , such as site-specific chromatin modifications , is poorly understood . In the current study , we explored the relationships between transcriptional activator function , chromatin structure , and subnuclear gene positioning . This was addressed using mice with targeted ablation of the Hnf1a gene ( also known as Tcf1 ) . Hnf1α ( Hepatocyte nuclear factor 1α ) is a homeodomain protein encoded by the gene implicated in MODY3 ( Maturity-onset diabetes of the young 3 ) , the most common form of human monogenic diabetes [33] . Studies performed with knock-out mice have shown that Hnf1α is dispensable for organogenesis , but is essential for the activity of several direct target genes involved in differentiated functions of liver , kidney , and pancreatic β-cells [34]–[38] . Using immuno-FISH , we studied the subnuclear position of endogenous direct targets of Hnf1α in freshly isolated primary hepatocytes and islet-cells from Hnf1a-null mutant vs . control mice . This model enabled us to ascribe observed changes to the presence or absence of an activator , in contrast to previous studies comparing the position of a locus among cell-types or developmental stages which potentially differ markedly in their chromosomal configurations [39] . At the same time , this model overcomes the limitations of using transformed cell lines and artificial overexpression systems . The results provide genetic evidence that a transcriptional activator influences the subnuclear position of its endogenous genomic targets in primary cells . In addition , we present data to support that activator-dependent changes in local histone modifications and chromatin condensation may play a role in regulating the spatial organization of the cell nucleus . Collectively , the results provide novel insights into the in vivo functions of a transcriptional activator and increase our understanding of the cellular defects underlying a human transcriptional disease .
Earlier studies showed that Hnf1α-dependent transcription is dependent on the recruitment of histone acetyltransferases and the local acetylation of nucleosomal histones [40] , [41] . We have now examined the methylation state of histone H3 in target genes . For this analysis we selected the most profoundly downregulated genes identified in expression profiling experiments of Hnf1a−/− hepatocytes ( Afm , Cyp2j5 and Pah ) and islets ( Kif12 ) , all of which are specifically downregulated in their respective Hnf1α-deficient cell-types ( Figure 1A ) . The four genes contain evolutionary conserved high-affinity Hnf1 binding sites in their promoter regions , and were experimentally shown to be directly bound by Hnf1α ( Figure 1B and not shown ) . As shown in Figure 1C–D , dimethylated H3-Lys4 ( H3-Lys4me2 ) was decreased in the 5′ region of such genes in hepatocytes from Hnf1a-deficient mice , while no changes were observed in control genes . H3-Lys9me3 , an established repressive mark associated with constitutive heterochromatin [9] , was not increased in the 5′ region of these genes in Hnf1a−/− hepatocytes ( Figure 1E ) , but was readily detected in minor satellite positive control sequences ( data not shown ) . In contrast , methylated H3-Lys27 was increased in Hnf1α-dependent targets in Hnf1a−/− hepatocytes to a similar extent as in two constitutively silenced genes known to be enriched in this repressive mark ( Nanog and Hoxa9 ) , whereas no changes were observed in non Hnf1α-dependent control genes ( Figure 1F–H and not shown ) . Increased methylated H3-Lys27 was primarily the trimethylated form , as it was detected with selective antisera for H3-Lys27me2 , 3 and H3-Lys27me3 , but not H3-Lys27me2 ( Figure 1F–H and not shown ) . Interestingly , increased H3-Lys27me2 , 3 was spread throughout the Cyp2j5 locus , rather than being circumscribed to discrete segments ( Figure 1H ) . Dimethylated H3-Lys9 , another histone mark previously associated with facultative heterochromatin , was also increased by 3 . 5 to 5-fold in inactive Hnf1α-targets in Hnf1a−/− cells ( data not shown ) . We also examined the consequences of Hnf1α-deficiency on target chromatin condensation . General DNAse I sensitivity studies revealed reduced degradation of Cyp2j5 chromatin in Hnf1a−/− vs . Hnf1a+/+ hepatocytes , whereas no differences were observed between genotypes for the control gene Actb ( Figure 1I ) . Thus , in direct Hnf1α target genes that are inactive due to Hnf1α-deficiency , there is a switch from an active chromatin conformation enriched in methylated H3-Lys4 , to a more compacted state enriched in trimethylated H3-Lys27 . To explore possible relationships between Hnf1α-dependent gene activity , site-specific histone modifications , and nuclear organization , we first assessed subnuclear distributions of histone modifications in primary hepatocytes and pancreatic islet-cells . Both of these cell types are largely quiescent under normal conditions . The results showed that H3-Lys4me2-rich subnuclear regions displayed a high degree of colocalization with regions that are enriched in RNA polymerase II phosphorylated on serine 5 of the C terminal repeat , the predominant polymerase form in the transcriptional initiation complex [42] ( hereafter referred to as RNA polymerase II ) ( Figure 2A ) . In sharp contrast , gene-silencing marks H3-Lys9me3 and Lys27me3 were more abundant in regions that were not enriched in RNA polymerase II ( Figure 2A ) . These subnuclear distributions were independent of the fixative and processing methods used , and were observed with different H3-Lys27me3 antibodies ( Figure S1A , B ) . Furthermore , the H3-Lys27me3 immunostaining pattern was distinct from that of Histone H3 and other modifications including H3-Lys4me2 , H3-Lys27me1 , H3-Lys27me2 , H3-Lys9me3 , as well as the DNA stain TO-PRO-3 , indicating that it does not merely reflect chromatin density ( Figure S1C–F , Figure S2 , and not shown ) . We next examined the radial distribution of histone modifications . H3-Lys27me3 was markedly enriched whereas H3-Lys4me2 displayed relative depletion in the immediate vicinity of the inner nuclear membrane , as shown by co-immunostaining of Lamin A/C ( Figure 2B ) . Erosion analyses using non-thresholded images furthermore revealed markedly different radial enrichment patterns for RNA polymerase II , H3-Lys4me2 , and H3-Lys27me3 ( Figure 2C ) . Thus , RNA polymerase II and H3-Lys4me2 were significantly depleted in peripheral nuclear zones compared to more interior nuclear regions ( Figure 2C , ANOVA p values 5 . 4×10−40 and 8 . 9×10−23 ) . In contrast , H3-Lys27me3 was significantly enriched in the outermost zones , compared to more internal regions ( Figure 2C , ANOVA p value 8 . 7×10−18 ) . These results are largely consistent with recent studies describing distinct nuclear patterns of histone modifications in cultured cell lines [43] , but extend it by showing that H3-Lys4me2 exhibits preferential colocalization with RNA polymerase II in central nuclear domains , while H3-Lys27me3 is particularly abundant in peripheral domains lacking enrichment in RNA polymerase II , H3-Lys4me2 , or H3-Lys9me3 . Immunofluorescence analysis indicated that Hnf1α is clearly enriched in H3-Lys4me2- and RNA polymerase II-rich , H3-Lys27me3-poor subnuclear domains , suggesting that there might be a subnuclear compartmentalization of Hnf1α function ( Figure S3 ) . We therefore tested if Hnf1α promotes not only changes in site-specific histone modifications , but also in the subnuclear positioning of its targets relative to histone modification domains . To address this question we performed DNA immuno-FISH experiments and non-thresholded images were analyzed to determine the enrichment of defined histone modifications and RNA polymerase II at Hnf1α-dependent loci in control vs . null-mutant nuclei ( Figure 3 and Figure S4 ) . Importantly , the compartmentalization of histone marks was conserved after the immuno-FISH procedure , and the spatial patterns of histone modifications were unaltered in Hnf1a−/− cells ( Figure S1A and Figure S5 ) . In several studies it has been observed that gene silencing is associated with relocation to constitutive heterochromatic domains enriched in satellite repeat sequences [21]–[24] , [44] . In mice , H3-Lys9me3 is enriched at pericentromeric regions [11] . In agreement , this was also observed under the conditions used here , where pericentromeric regions clustering at chromocenters were highlighted by TO-PRO-3 staining ( Figure S2 ) . However , inactive Hnf1α-targets in mutant hepatocytes ( Cyp2j5 ) and islets ( Kif12 ) were not positioned in domains that are enriched in H3-Lys9me3 or TO-PRO-3 as compared to wild-type cells ( Figure 3J , Figure S2D , and not shown ) . Furthermore , the distance of Cyp2j5 to H3-Lys9me3-rich chromocenters , and the frequency with which the two were in contact , did not differ in wild-type vs . null- mutant cells ( 0 . 94±0 . 10 vs . 0 . 88±0 . 09 µm , and 10% vs 7 . 7% , respectively ) . Thus , Hnf1α-deficiency does not result in repositioning of inactive Hnf1α-targets to pericentromeric heterochromatin clustering at chromocenters enriched in H3-Lys9me3 . In sharp contrast , Cyp2j5 alleles in activator-deficient cells were positioned in nuclear domains that are relatively enriched in H3-Lys27me3 ( Figure 3K ) . Analogous results were observed for the pancreatic islet Hnf1α-dependent gene Kif12 ( Figure S4I ) . These observations were specific for Hnf1α-dependent loci because they were not observed in 4 control loci in hepatocytes ( Hnf1b , Ly9 , Actb and Nanog ) ( Figure 3F and Figure S6 ) or one control locus in islet-cells ( Figure S4E ) . Furthermore , silent Cyp2j5 and Kif12 loci in Hnf1a-deficient cells were located in subnuclear domains with decreased H3-Lys4me2 and RNA polymerase II ( Figure 3L , M and Figure S4J , K ) . Simultaneous imaging of two protein marks at each locus allowed us to more accurately assess the extent to which loci were differentially positioned in domains enriched in distinct marks . We found that the average ratio of non-thresholded H3-Lys27me3/RNA polymerase II fluorescence signal intensity measured at individual Cyp2j5 and Kif12 FISH signals was 8- and 2 . 3- fold higher in Hnf1a−/− vs . Hnf1a+/+ cells , respectively , but remained unaltered at control loci ( Figure 3I , N; Figure S4H , L; and Figure S6 ) . We also classified alleles according to their presence in domains enriched in histone modifications and RNA polymerase II , using a 75th percentile enrichment criterion , as described above . This analysis showed that Hnf1α-dependent genes Cyp2j5 and Kif12 were located in domains selectively enriched in H3-Lys27me3 2 . 6 and 3 . 3 times more frequently in Hnf1a−/− cells compared to Hnf1a+/+ cells , respectively ( Figure 3P and Figure S4N ) . This finding did not reflect just delocalization from RNA polymerase II-rich domains , as Cyp2j5 and Kif12 in null-mutant cells were not more frequently in RNA polymerase II-poor/H3-Lys27me3-poor domains ( Figure 3P and Figure S4N ) , nor in RNA polymerase II-poor/H3-Lys9me3-rich or RNA polymerase II-poor/TO-PRO-3-rich domains ( data not shown ) . The results remained significant using the median ( 50th percentile ) of nuclear epitope intensity as an alternate threshold to define epitope enrichment ( 2 . 6-fold and 2 . 2-fold increased presence of Cyp2j5 and Kif12 in H3-Lys27me3-rich/RNA polymerase II-poor domains in Hnf1a−/− vs . Hnf1a+/+ cells , respectively; Fisher's exact test , p<0 . 01 ) . Differences were again not observed in four control genes using similar criteria ( Figure 3O , Figure S4M , and not shown ) . In concordance with the preferential nuclear compartmentalization of Hnf1α in RNA polymerase II- and H3-Lys4me2-rich domains ( Figure S3 ) , target loci were also preferentially localized in Hnf1α-rich domains in hepatocytes , in contrast to the inactive control locus Ly9 ( Figure S7 , and not shown ) . However , we found no evidence that this preferential localization reflected the existence of an activator-specific subnuclear domain , because an Hnf1α-independent active control gene ( Actb ) exhibited a similar subnuclear compartmentalization with Hnf1α as Cypj5 ( Figure S7 ) . We also compared the radial positioning of Hnf1α-dependent loci by erosion analysis in wild-type vs . mutant hepatocytes and islet cells , respectively . In contrast to the unchanged radial positioning of the control locus Ly9 , significantly increased percentages of Cyp2j5 and Kif12 alleles localized in the most peripheral nuclear zone , where H3-Lys27me3 is mostly enriched , in mutant nuclei compared to wild-type ( p = 0 . 002 and p = 0 . 01 , respectively ) ( Figure 4 ) . Conversely , a significant decrease in the number of Kif12 loci in mutant cells was observed in the interior shell 3 ( p = 0 . 04 ) ( Figure 4 ) . Thus , in the presence of Hnf1α its direct target genes Cyp2j5 and Kif12 are positioned in more central nuclear domains enriched in RNA polymerase II and H3-Lys4me2 , whereas in the absence of Hnf1α inactive targets are positioned in more peripheral , H3-Lys27me3-rich domains . Interestingly , these subnuclear histone modification enrichment patterns parallel those observed locally in Hnf1α-target nucleosomes . Altered positioning of Hnf1α targets in null mutant cells could represent a localized activator-dependent phenomenon , or a more global effect of Hnf1α-deficiency on the configuration of nuclear structures . To address the mechanisms involved , we performed two-color DNA FISH using contiguous BAC probes mapping to sites adjacent to the Cyp2j5 locus ( Figure 5 ) . Despite their proximity , signals from adjacent clones could be clearly separated by dual FISH analysis in a substantial number of nuclei ( Figure 5A ) , thus enabling us to test how genomic regions in the vicinity of Cyp2j5 were positioned relative to subnuclear domains in wild-type and mutant cells . To assist the interpretation of results , we first analyzed the gene content in these regions . We noted that there were two additional Hnf1α-dependent genes immediately centromeric to Cyp2j5 , while an extensive telomeric region was completely devoid of any experimentally defined spliced transcripts ( Figure 5B , Table S2 ) . Parallel ImmunoFISH studies showed that unlike Cyp2j5 , the adjacent regions 12L1 , 68H9 , and 114C9 were not differentially distributed with respect to nuclear RNA polymerase II or histone marks domains in Hnf1a−/− vs . Hnf1a+/+ cells ( Figure 5C , F and G ) . Nonetheless , the region marked by clone 263F12 that is in immediate proximity to the Hnf1α-dependent gene ( Cyp2j6 ) did show differential positioning similar to Cyp2j5 ( Figure 5D–E , Table S2 ) . These findings indicated that Hnf1α-dependent positioning of Cyp2j5 into histone modification/RNA polymerase II subnuclear domains is a locally restricted phenomenon , encompassing a somewhat extended domain of up to 300 Kb containing at least two additional coordinately regulated Hnf1α-dependent genes . We next sought to determine if Hnf1α-dependent positioning of the Cyp2j5 locus can be elicited relative to adjacent genomic regions , thus providing reference points that are independent of histone mark and RNA polymerase II spatial distributions . We used two-color DNA FISH to measure the distance of Cyp2j5 to adjacent loci in wild-type vs . null-mutant cells . We found that the distance between Cyp2j5 and the two telomeric clones 68H9 and 114C9 was significantly increased in wild-type compared with Hnf1a−/− hepatocytes ( 0 . 36±0 . 02 vs . 0 . 28±0 . 02 µm , and 0 . 46±0 . 02 vs 0 . 36±0 . 02 µm , respectively , Mann-Whitney test p<0 . 001 ) ( Figure 5J , K ) . The distance between Cyp2j5 and the most proximal centromeric 263F12 region was not affected by Hnf1a-deficiency ( in keeping with the lack of differences in RNA polymerase II/K27me3 colocalization studies ) , but for the more distal clone 12L1 it was decreased from 0 . 40±0 . 03 µm in wild-type cells to 0 . 29±0 . 02 µm in Hnf1a−/− cells ( Mann-Whitney test p<0 . 001; Figure 5H–I ) . Accordingly , the percentage of non-overlapping loci , which was systematically defined as those located at >0 . 4 µm center to center distance , was higher in wild-type vs . null mutant cells for Cyp2j5-68H9 ( 39 vs . 17% , Fisher's exact test p<0 . 001 ) , Cyp2j5-114C9 ( 57 vs . 42% , p<0 . 05 ) , and Cyp2j5-12L1 ( 35 vs . 24% , p<0 . 05 ) comparisons ( Figure 5H–K ) . In contrast , the distances separating 68H9 and 114C9 , which do not contain Hnf1α-dependent genes , do not differ between control and null-mutant cells ( Figure 5L ) . Thus , Cyp2j5 showed altered Hnf1α-dependent positioning relative to neighboring centromeric and telomeric chromosomal regions . We further assessed Hnf1α-dependent positioning of Cyp2j5 with respect to its chromosomal territory . We observed that Hnf1α-deficiency did not affect the position of the nearby 114C9 genomic region relative to its chromosomal territory , whereas Cyp2j5 alleles less frequently extended away from their territory surface in Hnf1a−/− cells versus wild-type cells ( Figure S8 ) . Collectively , these findings reveal the existence of Hnf1α-dependent , spatially restricted positioning of a target locus relative to chromosomal reference landmarks and subnuclear RNA polymerase II/histone modification domains . The analysis of distances between adjacent regions and relative to the chromosome territory furthermore indicates that Hnf1α-dependent positioning involves chromatin decondensation of the Cyp2j5 locus .
We have used a genetic model to show that a transcriptional activator regulates the subnuclear positioning of its direct endogenous targets in primary differentiated cells . We documented Hnf1α-dependent differential gene positioning with respect to: a ) subnuclear regions enriched in H3-Lys27me3 , H3-Lys4me2 , and phosphoserine-5 RNA polymerase II ( Figure 3 ) , b ) radial nuclear zones ( Figure 4 ) , c ) genomic regions adjacent to an Hnf1α-dependent gene ( Figure 5 ) , and d ) chromosomal territories ( Figure S8 ) . The analysis of four control loci in trans allowed us to conclude that the observed Hnf1α-dependent spatial changes are specific . Experiments comparing the position of an Hnf1α-dependent locus to adjacent chromosomal regions and its chromosomal territory further demonstrated specificity , and revealed that changes were locus-selective and did not reflect broad chromosomal reconfigurations . Although numerous studies have shown a relationship between gene transcription and subnuclear positioning , several variables that are only indirectly related to gene transcription , such as regional gene density or nucleotide composition , also appear to impact the subnuclear location of genomic regions , independent of their actual transcriptional activity [27]–[29] , [45] . Our new findings demonstrate that transactivator-dependent functions are dominant over such variables in the regulation of subnuclear gene positioning . Earlier reports have linked the function of sequence specific-DNA binding proteins such as Ikaros and NF-E2p18 with the repositioning of endogenous loci [21] , [46] , [47] . In such examples , repressor-mediated repositioning of silenced loci to pericentromeric compartments was observed during developmentally regulated gene-silencing processes . This clearly represents a different situation compared to the current analysis where gene inactivity results from the sheer lack of an activator and gives rise to a different pattern of subnuclear positioning that does not involve association with chromocenters . Previous evidence supporting the role of transactivators in gene positioning comes from studies of transgenes . Some of these studies took advantage of a lac repressor-VP16 acidic activation domain fusion protein , which was shown to cause repositioning of targeted multicopy loci away from the nuclear periphery [31] , [32] . Another study has analyzed transgenes with intact or mutated transactivator binding sites and showed that intact sites prevent association of transgenes with pericentromeric heterochromatin [48] . The role of transactivators in the positioning of endogenous loci , however , has not been directly assessed . One study showed that the deletion of a 24 Kb endogenous genomic region containing the β-globin locus control region results in gene silencing and increased perinuclear localization of the endogenous locus [49] . These effects were probably due to activator functions because the deleted region contained multiple binding sites for essential transcription factors . Nevertheless , it could not be excluded that structural changes due to deletion of an extended genomic segment also affected nuclear positioning by transactivator independent mechanisms . Our results provide genetic evidence in primary cells that positioning of endogenous genes can be dependent on a single transactivator . Together with previous studies , this suggests that the regulation of the subnuclear location of target gene loci might be a general function of sequence-specific DNA binding transcriptional regulators . Earlier studies describing correlations between gene silencing and perinuclear positioning were based on the comparisons of different cell types or developmental stages [19] , [20] , [50] , [51] . Such studies can theoretically be confounded by cell-specific differences in global spatial chromosomal arrangements [39] . It is thus important that peripheral positioning is now elicited in a model where transcriptional inactivity is ascribed to the selective absence of a direct transactivator . Previous studies have also shown that genes are preferentially transcribed in nuclear subdomains enriched in RNA polymerase II [17] , [52] , [53] . This has led to models postulating that active loci loop into domains with high local RNA polymerase II concentrations [17] , [54] . Our findings confirm that gene activity is associated with localization to phosphoserine-5 RNA polymerase II domains in primary cells , and furthermore demonstrate that association with such domains is linked to the function of a transcriptional activator . Importantly , the new results extend our understanding of this phenomenon by showing that relocation does not only occur with respect to domains enriched in RNA polymerase II , but also involves repositioning amongst compartments that differ in the composition of histone modifications known to be critically involved in transcriptional regulation , and that such domains display distinct radial distributions . Our integrated analysis of a transactivator-deficient model thus suggests that transcription-related gene positioning with respect to RNA polymerase II foci , distinct radial nuclear zones , and domains enriched in specific histone modifications might reflect different experimental measurements of a single biological phenomenon . Together with previous findings , our data shows that binding of Hnf1α to target loci promotes local histone tail hyperacetylation , methylation of H3-Lys4 , and chromatin decondensation , while preventing methylation at H3-Lys27 [38] , [40] , [41] . H3-Lys27 methylation thus appears to represent a default state , consistent with genetic studies showing that the H3-Lys4-specific methyltransferase Trithorax suppresses default gene silencing mediated by methylated H3-Lys27 [55] . Concomitant with local chromatin changes , Hnf1α binding also causes the recruitment of targets to predominantly central subdomains that are enriched in phosphoserine-5 RNA polymerase II and concordant histone modifications ( see model in Figure 6 ) . How Hnf1α controls subnuclear positioning of its targets remains to be clarified . Treatment with the RNA polymerase II inhibitors α-amanitin and 5 , 6-dichlorobenzimidazole riboside ( DRB ) does not alter the preferential positioning of Kif12 in H3-Lys4me-rich/H3-Lys27me-poor domains in a β-cell line with normal Hnf1α expression ( Figure S9 ) . This suggests that Kif12 compartmentalization is not solely dependent on ongoing transcriptional activity per se , and points to the involvement of other activator-dependent functions . Changes in local chromatin structure represent another potential mechanism . Our results showed that Hnf1α regulates not only local chromatin decompaction , but also the decondensation of the Cyp2j5 locus that is reflected by changes in distances between adjacent loci and relative to chromosomal territories . Our findings also show that the histone modification enrichment pattern of Hnf1α-dependent genes in nuclear domains with which they associate coincides with the local post-translational histone modification profile . This raises the possibility that histone modifications may be partly instrumental in gene positioning . Although similar measurements of locus positioning relative to histone modification domains have not been carried out before , two studies previously showed that treatment with histone deacetylase inhibitors causes repositioning of inactive genes away from the nuclear periphery [20] , [56] . Local histone modifications could affect compartmentalization of gene loci by regulating interactions with the nuclear lamina [56] and could also affect mobility , since acetylated histones have been previously shown to increase chromatin fiber flexibility [57] . Taken together , these findings support the proposal that local Hnf1α-dependent chromatin decompaction and histone modifications might result in augmented mobility and loop formation , thus increasing the likelihood of accessing and establishing dynamic interactions with components of transcriptionally active nuclear regions ( Figure 6 ) . Local activator-dependent changes in chromatin structure may thus play a role in regulating the spatial organization of the genome . Emerging evidence indicates that gene transcription is an integrated process involving multiple levels of regulation [2] , [3] . The data presented here link the in vivo function of an activator to different levels of regulation , namely the binding to specific target sequences , the local modification of target chromatin , and the positioning of targets in distinct subnuclear domains . This demonstration is provided in a genetic model of human diabetes , indicating cellular defects at multiple regulatory levels in a human transcriptional disease . Thus , our findings provide not only new insights into the complexity of trans-activator functions and transcriptional regulation , but are also important for understanding mechanisms underlying human disease .
Hepatocytes and pancreatic islets were isolated from 4–6 week-old Hnf1a+/+ and Hnf1a−/− mice [36] by local perfusion of the organ with collagenase for digestion and subsequent isolation of the cells as described [34] , [40] . For immnostaining and FISH studies , after isolation islets were gently dissociated for 2 min in pre-warmed trypsin solution . Cells were processed for chromatin , RNA , immunofluorescence , and FISH analysis immediately after isolation . RNA isolation , reverse transcription and PCR were carried out as described [34] . Isolated hepatocytes ( 30–40 . 106 ) were resuspended in 10 mL NI buffer ( 15 mM Tris-HCl pH7 . 5 , 300 mM sucrose , 15 mM NaCl , 60 mM KCl , 4 mM MgCl2 and 0 . 5 mM DTT ) , and 10 mL of NI buffer supplemented with 1% NP40 was added for 10 min incubation in ice . Nuclei were collected at 500×g for 3 min , washed in 4 mL NI buffer , resuspended in a final volume of 700 µL NI buffer and distributed in 100 µL aliquots for DNAse I digestion . To each suspension of 100 µL NI buffer , either 0 , 10 , 20 , 30 , 40 , 50 or 80 µg DNAse I was added for 10 min on ice . The reaction was stopped with Nuclei Lysis Solution from Wizard Genomic DNA purification kit ( Promega ) , and DNA was extracted as indicated by the manufacturer . DNAs were resuspended in 20 µL DNA rehydration solution and 1 µL was used for PCR amplification . Oligonucleotides are presented in Table S1 . Approximately 2 . 106 isolated hepatocytes or MIN6 beta-cells were used per immunoprecipitation as described [40] , [58] , with modifications . Immunoprecipitations were carried out overnight at 4°C with 7 . 5 µg rabbit anti-HNF1 ( H-205 ) ( Santa Cruz , sc8986 ) , 2 µg rabbit anti-H3-Lys4me2 ( Upstate , 07-030 ) , 2 µg mouse anti-H3-Lys27me2 , 3 [59] ( D . Reinberg , University of Medicine and Dentistry of New Jersey ) , 10 µg rabbit anti-H3-Lys27me3 [11] ( T . Jenuwein , The Vienna Biocenter and Upstate , 07-449 ) or 20 µg rabbit anti-H3-Lys9me3 [11] ( T . Jenuwein and Upstate , 07-442 ) . For anti-H3-Lys4me2 and H3-Lys27me2 , 3 , 1% Triton was added to the antibody binding solution . For anti-H3-Lys27me2 , 3 , 3 µg rabbit anti-mouse IgG ( Sigma ) was added for a further 3 hr incubation at 4°C . Immune complexes were collected by adsorption to protein A+G Sepharose ( Amerhsam ) . Beads were washed and eluted as described , except for the anti-H3-Lys9me3 immunoprecipitation that was washed with 250 mM NaCl . Purified immunoprecipitated DNA was analyzed in duplicate by SYBR green real-time PCR , and compared to a standard curve generated with serial dilutions of input chromatin DNA . Oligonucleotides are shown in Table S1 . 5 . 105 isolated hepatocytes or islet-cells were lightly cytospun and fixed at room temperature for 5 min in freshly prepared 4% paraformaldehyde . In control experiments immunostaining was carried out as described [60] except that antibody retrieval was not employed . Primary antibodies were used with the following dilutions: rabbit anti-H3 ( Abcam , Ab1791 ) ( 1/500 ) , rabbit anti H3-Lys4me2 ( Upstate , 07-030 ) ( 1/500 ) , rabbit anti H3-Lys9me3 [11] ( T . Jenuwein , and Upstate , 07-442 ) ( 1/500 ) , rabbit anti H3-Lys9me2 [11] ( T . Jenuwein , and Upstate , 07-442 ) ( 1/500 ) , mouse anti-H3-Lys27me3 ( Abcam , Ab 6002 ) ( 1/50 ) , rabbit anti H3-Lys27me3 [11] ( T . Jenuwein and Upstate , 07-449 ) ( 1/500 ) , rabbit anti H3-Lys27me2 [11] ( T . Jenuwein ) ( 1/500 ) , rabbit anti H3-Lys27me1 ( Upstate , 07-448 ) ( 1/500 ) , mouse anti-phospho serine 5 RNA polymerase II CTD4H8 ( Abcam , Ab 5408 ) ( 1/1000 ) , goat anti-Lamin A/C N18 ( Santa Cruz ) ( 1/200 ) , mouse anti-HNF1α ( Transduction Laboratories , H69220 ) ( 1/50 ) , and rabbit anti-HNF1 ( H-205 ) ( Santa Cruz , sc8986 ) ( 1/100 ) . The specificity of methylated H3-Lys27 stainings was verified by co-staining with two different highly specific antibodies directed against the same epitope and using alternate fixation ( methanol at −20°C for 10 min ) . The specificity of anti-Hnf1α staining patterns was verified using Hnf1a−/− cells and alternate fixation procedures ( Figure S3 ) . Secondary donkey antibodies anti-mouse Cy2 , anti-mouse Cy3 , anti-mouse IgM Cy3 , anti-goat Cy2 , anti-rabbit Cy5 and anti-rabbit Cy3 were from Jackson ImmunoResearch , and used at 1/200 . Nuclear DNA was counterstained with TO-PRO-3 ( 1/50 , 000 ) . We used purified BAC DNAs ( Table S2 ) for labeling with Dig-nick translation or BioNick kits ( Roche ) . Immuno-FISH was based on modifications of the protocol described by Brown et al [21] . Cells were immunostained essentially as described above except that nuclei were fixed in 4% paraformaldehyde for 15 min and heated in a microwave in 10 mM citrate buffer , pH 6 , for 5 min before permeabilization . Immunostained nuclei were then post-fixed in 4% paraformaldehyde for 15 min , denatured in NaOH 0 . 1M in PBS , pH 13 for 110 sec , and washed in cold PBS and 2× SSC . One µL digoxigenin-labeled probe in 14 µL hybridization buffer ( 50% formamide , 2× SSC , 125 µg/mL Cot-1 and 10% dextran sulphate ) and 1 µg mouse Cot-1 ( Invitrogen ) were denatured for 5 min at 90°C . Probes were hybridized overnight at 37°C , and sequentially washed in 2×SSC , 1×SSC , PBS-triton 0 . 2% , and PBS for 5 min at room temp . Slides were then sequentially incubated with Sheep anti-digoxigenin antibody ( Roche ) ( 1/300 ) for 3 h , and donkey anti-sheep Cy3 antibody ( Jackson Immunoresearch ) ( 1/200 ) for 2 h at room temp . , with washes after each step . Cells were mounted with ProLong Antifade ( Amersham ) . Cells were fixed in 4% paraformaldehyde for 15 min at room temperature , washed in PBS and permeabilized for 30 min in PBS-0 . 5% Triton X-100 . Cells were then heated in 10 mM citrate buffer , pH 6 for 5 min and post-fixed in 4% paraformaldehyde , washed in PBS , and incubated in 2× SSC . One µL digoxigenin-labeled probe and one µL biotin-labeled probe were added to 14 µL hybridization buffer as described above . Both probes and cells were simultaneously heated at 90°c for 5 min to denature DNA , and hybridized and washed essentially as in the immuno-FISH protocol . The digoxigenin-labeled probe was detected as in the immuno-FISH procedure , and the biotin-labeled probe was detected with AF488-streptavidin ( Molecular Probes ) ( 1/500 ) . Cells were washed and counterstained with TO-PRO-3 ( Molecular Probes ) ( 1/50 , 000 ) and mounted with ProLong Antifade ( Amersham ) . Cells were fixed and permeabilized as described above . After permeabilization , hepatocytes were incubated with 100 µg/mL RNase A at 37°C for 30 min . Nuclei were then denatured at 74°C in 2×SSC-70% formamide for 3 min followed by 1 min in 2×SSC-50% formamide . Ten µL of chromosome 4 biotin-labeled probe ( Cambio ) was denatured at 70°C for 10 min in the supplied buffer ( Cambio ) and pre-annealed for 20 min at RT . Subsequently 1 µL of either Cyp2j5 or 114C9 digoxigenin-labeled probe denatured at 90°C for 5 min was added for overnight hybridization at 37°C . After sequential washes of 5 min at 45°C in 2×SSC-50% formaldehyde , 1×SSC , PBS-0 . 2% Triton and PBS , biotin and digoxigenin-labeled probes were detected and processed for confocal image acquisition as described above . Confocal images for each fluorochrome were acquired sequentially at room temperature with a Leica TCS SL laser scanning confocal spectral microscope , using a 63× oil immersion objective lens ( NA 1 . 32 ) . Focal Check Fluorescent microspheres ( Molecular Probes ) were used before image capture to align laser lines . Non-saturated , unprocessed images were further analyzed with ImageJ . Contrast-stretch and gamma adjustments were made using Photoshop ( Adobe ) only for display . This analysis was carried out to determine colocalization between the most intense nuclear signals of each epitope . Ten to twenty nuclei were assessed in each double immunofluorescence experiment , and pixels with values exceeding the 75th percentile in each channel were selected for further analysis . The rationale for this threshold is that nuclear RNA polymerase II and H3-Lys27me3 signal intensities do not adhere to a normal distribution , and the 75th percentile enabled separation of visually evident RNA polymerase II and H3-Lys27me3-enriched domains from most remaining nuclear signals . Signals filtered in this manner were used to calculate Manders' coefficient of colocalization using the appropriate ImageJ plug-in ( Wayne Rasband and Tony Collins , www . uhnresearch . ca/wcif ) . Manders' coefficient calculates , for each channel , the proportion of colocalizing pixels respect to the summed up intensities of all pixels in the nucleus . Comparable results were obtained by subtracting pixels lower than the 50th percentile in each channel , and then applying a modified Mander's coefficient using the Colocalization threshold ImageJ plug-in , that first calculates an automated threshold ( % colocalized pixels above threshold: RNA polymerase II vs . H3-Lys9me3: 4% . RNA polymerase II vs . H3-Lys27me3: 19% . RNA polymerase II vs . H3-Lys4me2: 73% . H3-Lys27me3 vs . H3-Lys4me2: 22 . 5% . H3-Lys27me3 vs . H3-Lys9me3: 31% ) . Erosion analyses were performed with single light optical sections and mid-nuclear planes were analyzed . Radial positioning of FISH signals was analyzed as described [20] . With respect to immunostaining data erosion analyses were performed as follows . Based on the DNA counterstaining signal , the nuclear plane was subdivided into five concentric zones , each having a thickness of 20% of the nuclear radius . The numbers of pixels in each zone were counted and the grey value of each pixel was determined separately for each channel . Each grey value ( I ) was multiplied by its frequency N ( I×N ) and the sum of all values obtained for a given zone was determined ( Σ I×N ) . The result obtained for each zone was divided by the sum of results obtained for all nuclear zones in order to determine the percentage of total nuclear fluorescence intensity in each nuclear zone . The values obtained were normalized to the relative nuclear areas occupied by the different zones . Thus a value of 1 was obtained if the percentage of the total nuclear fluorescence intensity corresponded to the percentage of the total nuclear area occupied by a given zone . It should be noted that this procedure did not involve any thresholding . For each condition , typically 100 alleles ( range 70–200 ) from at least two independent experiments were analyzed blindly in unprocessed images to quantify signal intensities of histone marks and RNA polymerase II . In H3-Lys27me3 experiments Barr bodies were avoided . The 9-pixel area containing the brightest and most central pixels of each FISH signal was selected by inspection of single color images , and the average signal for each channel in this area was obtained using RGB Measure ( ImageJ ) . Each non-thresholded immunofluorescence signal at a FISH-detected locus was divided by the median value of the entire nucleus in the same cell to correct for cell to cell and inter-assay technical variability . The mean intensity for each channel was also calculated from a broad cytoplasmic area in every stack and used to subtract non-specific background from both FISH and nuclear signals . This background value was similar to the non-specific nuclear signal elicited in control Immuno-FISH experiments in which primary antibodies were omitted . The resulting value was referred to as normalized signal in Figure 3 and Figure S4 ) . To classify alleles according to the presence or absence of enrichment in either RNA polymerase II or a histone mark , we used the 75th percentile of nuclear pixel intensities in each channel as the threshold , as described above . The results presented here remained statistically significant with alternate thresholds to define enrichment , such as the nuclear median ( see results ) . Approximately 100 allele pairs were analyzed blindly in unprocessed images to quantify the distance ( in µm ) between the center of adjacent FISH signals defined by the 9-pixel square area containing the brightest and most central pixels , essentially as described for immuno-FISH analysis . Non-overlapping loci were defined as those with signal center distances exceeding 0 . 4 µm , thus providing a uniform criteria that is not affected by variable FISH signal intensities and shapes . After identification of locus-specific probes in the same z plane as its chromosome territory , the image background of the chromosome territory was blindly modified until a clear visualization of the territory edge was obtained . The distance ( in µm ) between the center of the locus-specific FISH signal and the nearest chromosome surface was then measured as described for the two-color DNA FISH analysis . One hundred alleles from each genotype were classified as being located either within a territory and >0 . 4 µm from the edge , outside and >0 . 4 µm from the edge , or in contact if they were <0 . 4 µm from the edge . A two-tailed Student's t-test was used for comparison of ChIP values . Mann-Whitney test was used for comparisons of immuno-FISH and allele distance values , which did not adhere to a normal distribution . ANOVA was used for erosion analysis . Fisher's exact test was used for comparison of qualitative two-color DNA FISH , chromosomal territory , and immuno-FISH results . | All cells in an organism share a common genome , yet distinct subsets of genes are transcribed in different cells . Selectivity of gene transcription is largely determined by transcription factors that bind to target genes and promote local changes in chromatin . Such changes are thought to be instrumental for transcription . Emerging evidence indicates that the position of genes in the 3-dimensional structure of the nucleus may also be important in transcriptional regulation . However , the role of transcription factors in gene positioning , and its possible relationship with chromatin modifications , is poorly understood . To examine this , we employed a genetic approach . We used mice lacking Hnf1α , a transcription factor gene that is mutated in an inherited form of diabetes . We studied genes that are directly bound by Hnf1α , as well as various control genomic regions , and determined their position in nuclear space in liver and insulin-producing β-cells . The results showed that the absence of Hnf1α causes local changes in the chromatin of target genes . At the same time , it modifies the position of target genes in nuclear space . The findings of this study lead us to propose a model whereby transcription factor dependent local chromatin modifications are linked to subnuclear gene positioning . They also revealed abnormal subnuclear positioning in a model of a human transcription factor disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/epigenetics",
"genetics",
"and",
"genomics/nuclear",
"structure",
"and",
"function",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"genetics",
"and",
"genomics/chromosome",
"biology"
] | 2008 | Targeted Deficiency of the Transcriptional Activator Hnf1α Alters Subnuclear Positioning of Its Genomic Targets |
Chagas disease ( CD ) is a life-threatening illness caused by the protozoan parasite Trypanosoma cruzi , which is transmitted by triatomine bugs . Triatomine bugs inhabit poorly constructed homes that create multiple hiding spots for the bugs . Modifying the actual structure of a home , along with the homeowners’ practices , can reduce triatomine infestation . This research was designed to collect culturally-relevant information to develop a health campaign to decrease risk of CD transmission by promoting home maintenance and better hygiene in rural communities of southern Ecuador . The Health Belief Model ( HBM ) guided focus group discussions and the interpretation of the results . Four focus groups ranging from 4 to 10 participants were conducted between May and June 2014 in three communities of Loja province in Southern Ecuador . A thematic analysis was used to identify within the data related to perceptions of susceptibility , severity , benefits , barriers and self-efficacy related to CD and its prevention . The results provide clear guidance for the development of Chagas-prevention messages . Data obtained emphasize the importance of standardizing messages presented to the communities for CD prevention . Messages should provide more information on the protective nature of the behaviors promoted for CD prevention; overcoming barriers such as cost and convenience , and build on facilitating factors , including community members’ interest on quality of life , protection of their families , and relationship with the land .
The World Health Organization categorizes CD as a neglected tropical disease ( NTD ) [11] . Chagas and other neglected tropical diseases are poverty-habilitated issues also derived from insufficient access to drinking water , sanitation , inadequate housing , education and health services [12–15] . One key to long-term CD prevention is to prevent infestation of the home . Inadequate housing is associated with higher infestation [12] . Physical barriers between the home and surrounding environments can prevent triatomines from populating the home [16] . These insects enter through and live in the cracks of poorly constructed homes in rural areas; dirt floors , thatched roofs , and exposed eaves also provide entry points . Housing improvements can lower the infestation rate of the insect and increase the quality of life of individuals [17] . Although issues of home construction are significant [8 , 10] , practices in and around the home ( the peridomicile ) also increase the risk of infestation [12 , 18] . Field observations and prior research indicate that in poorer houses , with lower incomes and fewer residents with formal education , community members perform behaviors that create suitable conditions for triatomine infestation [8 , 12] . The strongest four determinants of triatomine infestation are the number of dogs allowed to enter the home [19] , having chickens in a corral or not [8 , 9 , 18] , cleaning of trash from the peridomicile or not [9 , 11] , and being located in the boundary of the village or not [12 , 18] . Firewood and rock piles also can increase risk of infestation [8 , 19] . Promoting healthier housing practices will create a sustainable solution for triatomine prevention , lowering exposure to CD [6 , 7] . To protect adequately humans from contracting CD there is a need to effectively communicate the risk and propose viable preventive measures , particularly the control of triatomine vectors [20] . The health belief model ( HBM ) provides a framework for this kind of communication . The HBM can be used to predict preventive health behaviors and to develop interventions [21 , 22] . This model has been used extensively in multiple health contexts [23–25] . The HBM is comprised of several constructs that , in combination , predict behavior . They are: perceived threat ( comprised of perceived susceptibility [an individual’s assessment of the likelihood of a negative health condition occurring] and perceived severity [an individual’s assessment of the seriousness of contracting the health condition and its consequences] ) ; likelihood of action ( the weighing of perceived benefits [rewards attributed to engaging in the recommended behavior] against perceived barriers [obstacles that deter an individual from executing the recommended behavior change]; cues to action ( motivating factor that provokes or encourages change ) ; and , self-efficacy ( one’s belief in one’s ability to actually perform ( and maintain ) the desired behavior change ) . The HBM guided our investigation of factors affecting the adoption of CD prevention behaviors . Specifically , we asked the following research questions , RQ1: What is the perceived threat ( perceived severity and perceived susceptibility ) of triatomine infestation among community members ? RQ2: What factors affect the likelihood of action ( perceived barriers and perceived benefits ) to prevent triatomine infestation among community members ? RQ3: What level of self-efficacy for triatomine control is expressed in these communities ? RQ4: What cues to action for triatomine control do community members recall ?
Ohio University Institutional Review Board reviewed and approved the protocol , including administration of oral informed consent , all recruitment materials and data collection procedures involving human participants ( OU IRB 14-E-158 ) . All human subjects were adults . Oral informed consent was collected in this case because the research is minimal risk and cultural norms make participants wary of signing documents . This study took place in Loja province , Ecuador , which has a high rate of triatomine infestation and , consequently , a high risk for CD presence [8 , 26 , 27] . Since 2010 , HLI has collaborated with three adjacent rural communities–Guara , Chaquizhca and Bellamaria–in this province ( 5 ) . These three communities were selected as pilot communities for HLI because of the high rate of infestation of triatomines [8 , 10] . All three communities have similar characteristics , with most of the inhabitants participating in subsistence farming . The three communities comprise ~150 family homes spread through mountainous terrain in Ecuador’s southern highlands . These communities face limited access to clean water and sanitation facilities , along with insufficient transportation to reach the larger nearby town of Cariamanga , the main commerce point in this area . The homes are far apart . Community members often must walk one or two hours to get to the main road to find transportation to go to the city . Most of the homes in these communities–excepting those ( re ) constructed in HLI interventions–have structural , behavioral , and peridomiciliary conditions associated with triatomine infestation . Focus groups were conducted to identify community perceptions of CD and their views of healthy practices identified by HLI as important . All male and female adults ( over age 18 ) who lived in the communities of Guara , Chaquizhca and Bellamaria were invited to participate . The first author and a field community worker made home visits and spoke with community members at local gathering points to ask for participation in the focus groups . After three weeks of recruitment , meeting dates and locations were arranged with potential participants . Men and women were separated for most of the focus groups to promote open dialogue [28]; however , a mixed focus group was also conducted to allow us to assess the possible influence of gender roles . Four focus group discussions were held . A mixed group , with five women and four men , met in the Bellamaria community center . A second group , also in the community center , consisted of four women . A focus group in Guara , comprised of nine women , and a focus group in Chaquizhca , comprised of four men , were held in the communities’ respective elementary schools . Prior to conducting focus groups in the selected communities , a moderator guide was designed to systematically incorporate the HBM constructs and to encourage dialog between group members . Following initial construction , we consulted with HHHL staff that work in the communities on a daily basis [28] to modify the guide . This allowed us to employ terms and expressions used by community members . There was also a practice focus group conducted with HHHL staff to consolidate the discussion and to adjust the ordering of questions , as well as to assess the natural flow of conversation . The focus group team consisted of a moderator , note taker , and assistant . The focus group moderator conducted the actual focus groups . The note taker made observational notes of the groups and individuals expressions . The note taker also wrote down the participants’ comments about the behavior that were easy/hard . The assistant helped arrange the room prior to the focus groups and helped explain the task of identify behaviors as easy/hard . The room was set up with chairs in a circle allowing all participants and the moderator to look at one another . Upon arrival , participants were told the purpose of the research . They were then asked to provide individual oral informed consent . A short icebreaking activity followed . Participants stated their names and described the part of their homes they liked most . This question helped build comfort in talking about their homes and to build good rapport . The question also assisted the transcriptionist in identifying the voices of the participants . Following the icebreaker , the moderator started the official focus group . The focus group discussions followed constructs of the HBM . The first section asked participants to explain what they knew about CD and triatomines . Next , the participants viewed an educational video . The educational video highlighted the pathogenesis of CD , the transmission via triatomines and then described behavior changes that can help prevent triatomine infestation . Following the videos , participants contributed to an interactive exercise in which they rated behavior changes listed in the video as easy to execute , hard to execute , or neither . Participants placed different color sticky notes on poster boards to represent each behavior as “easy” , “hard” , or neither . The activity enabled participants to move around the room and discuss the behaviors with one another . Following the participatory activity , the participants discussed why they chose to answer the way they did , and their responses were written on the poster board by the note taker . The participants concluded by providing suggestions for promoting the behavioral practices in their communities , including possible media and channels . The focus group data were analyzed , verified , and reported . The first author transcribed the focus groups verbatim in Spanish; then the transcripts were translated into English for reporting . A thematic analysis was conducted to find information useful for designing a health campaign [29] . Commonalities in the focus group dialogs were identified as themes; these themes were assigned specific codes following Faraday and Muir-Cochran’s stages of coding method [30] . The data were coded through MAXQDA-10 . An initial coding manual was developed . Two bilingual researchers then coded the transcripts and compared notes for reliability . The first author developed the initial codes; the fourth author used the same codes to test their reliability . The fourth author also added her own codes as needed . The two coders discussed the codes before analyzing all the focus groups using the common codes for a second time . The codes were then summarized under Health Belief Model constructs for reporting .
The first research question asked what the perceived threat of triatomine infestations was in these communities . The perceived threat for adults was low because , although members of the community believed they were susceptible to insect bites , the consequences of these bites were minimal . Threats to children were seen as higher because children were viewed as suffering more severe consequences . In each focus group , the facilitator asked about the possibility of community members being bitten by triatomines and whether they thought CD was present in their communities . Few participants mentioned knowing individuals affected by the disease in their communities . One man from the Bellamaria group stated: Some participants mentioned that there were fewer triatomines in their communities than in other communities in the country . One woman , also from Bellamaria , stated , “It seems that in other regions there are more [triatomines] than there are in our neighborhood Bellamaria because other places are cooler . ” Other participants , in other focus groups , agreed that because of the warmer climate in Loja there were lower rates of infection . One theme consistent in all focus groups was that being exposed to triatomines was not associated with an elevated risk of contracting CD . Triatomines were recognized as being present in the participant’s homes; however; many participants stated that the triatomine infestation had decreased in recent years due to the HLI project . One female participant from the mixed focus group expressed her view of triatomine bugs , saying: This woman noted that the triatomines have been in her home and had bitten her and her family , but she did not mention any ill health effects . Other community members agreed , as they too had experienced similar interactions with the triatomines . Her statement , and those who agreed with her , reflects a belief that susceptibility to a bug bite is high , but the consequences of the bug bite are not severe . Because threat is a combination of both severity and susceptibility , perceived threat in these communities may be low . Even after viewing the video with information about being exposed to triatomines , participants did not indicate a high threat of Chagas disease from the bugs . In the men’s focus group and the mixed focus group , lack of connection between vectors and disease was also present . Participants expressed little concern about their own risk of contracting CD . Other participants held similar sentiments when discussing past interactions with the triatomines from both male and female groups . The focus group moderator probed about the perceptions held by the larger community . One woman summarized the communities feelings well: “Almost everyone knows about it [CD] but nobody takes it seriously” . Although adults did not feel a threat from triatomines or CD , they did feel that their children were at risk . In the women’s group in Bellamaria and the women’s group in Guara , participants felt that there was a higher possibility of their family being affected by CD . Many of the women’s comments argued that triatomine bugs could harm their children . As one woman said , “We need to be very cautious and think about the kids , because it is serious… I know they have bitten us and it’s not life threatening for us but for our children” . One woman from Bellamaria shared her personal story of how triatomines could hurt children . She said that her brother died of CD . She stated that her brother lived with their family until fifteen years ago , when he moved to the capital city , Quito . In Quito , her brother started having symptoms and received treatment . She expressed that , when they were younger , they were likely to have been bitten by triatomines . She was not sure whether her brother contracted CD , but she thought his early life in Bellamaria was why he contracted this illness . Men were also concerned about their children’s risks . They mentioned that children are more susceptible to contracting CD than adults . A male participant from Bellamaria stated , “We need to be really careful and think about the children , as it is very serious for them . ” He described having seen triatomines in his community . He did not state anything about being at risk himself , only his children . Another man , from the Guara group , stated , “When a chinchorro bites a person , at least when it bites a child , it affects it more because the body reacts in a bad way like they said in the video , where the heart gets bigger . ” The majority of participants expressed these sentiments both before and after the educational video . Thus , even if there is little personal threat perceived by adults , there are threats they perceive to the younger members of their communities . The second research question asked what factors in the community affected the likelihood of action . Perceived barriers to action were mainly issues of cost and lack of storage for animals and material . The overarching barrier is these communities believe they have few economic resources to allow them to enact the desired changes . The perceived benefits of controlling triatomines were a sense of pride in the cleanliness of homes and the ability to protect children , yet these benefits were also seen as attainable through pesticide spraying . The third research questions asked what the level of self-efficacy expressed in the communities was . Participants rarely expressed statements related to self-efficacy . They did not discuss confidence or lack of confidence in several skills related to CD prevention , such as ability to identify triatomines or to trap the bugs . In one of the few moments where self-efficacy was discussed , when asked what happens if they see the triatomines in their homes , one woman stated , “Now I catch the bugs in a plastic bag or grab them with toilet paper . ” Other participants stated that they simply kill the triatomines in their homes , but there was little depth of discussion . The final research question asked what cues to action for triatomine control the community members recalled . Participants identified few cues to action or reminders to engage in healthy home behaviors . Although assessing the effects of previous HLI efforts was not a focus of this research , participants clearly indicated that most of their knowledge about CD and triatomines was due to the efforts of our research and intervention group . Specifically , identifying different species of the triatomines and their different stages of development was connected to the educational calendars that were distributed to community members in 2012 . Participants from the focus groups stated that the majority of the information they possessed on CD originated from the interaction with the health workers who fumigated their homes and the calendar . A few community members stated that they like to decorate their homes with pictures and memorabilia and the calendars added to their decor . We also asked whether the community needed a communication campaign to help educate and motivate community members . They were receptive to the development of a health campaign , suggesting it would be beneficial for others in their communities to have access to the educational video that they watched during the focus groups . After viewing the informational video , one participant from the mixed focus group stated: “I know that some people [in the community] lack some of this information , but with these videos that’s when people start to become concerned with their health and the health of their families” . Participants from all focus groups expressed that it was important for the communities to collaborate in efforts to improve their living conditions . When prompted about communication channels , all of the participants stated that they used cell phones more than any other communication means . Following cell phones , face-to-face conversations were a common communication channel . Television and radio were used rarely because some participants did not have the electronic devices in their homes . The participants who stated that they used radio or television also mentioned that reception was precarious .
This research was conducted in small rural communities ( 152 homes total ) , mainly populated by families relying on agriculture-based activities . Local families are usually busy in their fields , which limit participation in non-essential activities . The number of focus groups conducted was sufficient for the small size of these communities; however , the participants who had sufficient time to participate may have differed from other members in the community who did not have this time . In addition , because these three communities have been exposed to previous HLI interventions , they may be more aware of triatomines and CD than other similarly situated communities in Loja province . Finally , although the focus group moderator was fluent in Spanish , some local colloquial words may have been lost in translation . This article used the health belief model to examine factors that predict behaviors associated with decreased triatomine infestation , and thus decreased Chagas disease transmission , in rural Ecuador . Neglected tropical diseases such as CD are difficult diseases for which to design communication campaign because there are few visible symptoms and consequences . Understanding perceptions of community members in terms of threat , likelihood of action , self-efficacy , and cues to action may enhance prevention efforts . To identify best practices , further research is needed on best health campaign practices in the context of communities that are at risk of neglected tropical diseases . A key element to decreasing exposure for CD and other NTD , comes from designing campaigns that help communities adopt preventive behaviors most fitting for them . | This study focuses on Chagas disease ( CD ) prevention in southern Ecuador . This region has a high rate of triatomine infestation . We used the Health Belief Model ( HBM ) to understand why people do or do not engage in CD preventive behaviors , particularly those related to home improvement . Additionally , we wanted to learn how to communicate most effectively about CD prevention . We gathered information about community member’s perceptions of CD in four focus groups , with a total of 26 participants . Our results indicate that communities do not see triatomines and CD as a threat . Lack of structures , cost , and convenience were the three key barriers mentioned by research participants for implementing CD preventive behaviors . However , participants were open to work with prevention programs since they saw it as an opportunity for community members to work together under the motivation of improving their living conditions . Capitalizing on motivators and removing barriers will be important for subsequent communication campaigns . Messages evoking fear of triatomine bugs and CD will contradict participants’ personal experience; instead , messages focusing how these behaviors will improve communities’ quality of life , protect their families , and strengthen their relationship with the land , will be more acceptable and appealing . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"ecuador",
"behavioral",
"and",
"social",
"aspects",
"of",
"health",
"tropical",
"diseases",
"geographical",
"locations",
"vertebrates",
"social",
"sciences",
"parasitic",
"diseases",
"animals",
"pest",
"control",
"neglected",
... | 2018 | Using the health belief model to identify communication opportunities to prevent Chagas disease in Southern Ecuador |
Over the last decade , unusually high losses of colonies have been reported by beekeepers across the USA . Multiple factors such as Varroa destructor , bee viruses , Nosema ceranae , weather , beekeeping practices , nutrition , and pesticides have been shown to contribute to colony losses . Here we describe a large-scale controlled trial , in which different bee pathogens , bee population , and weather conditions across winter were monitored at three locations across the USA . In order to minimize influence of various known contributing factors and their interaction , the hives in the study were not treated with antibiotics or miticides . Additionally , the hives were kept at one location and were not exposed to potential stress factors associated with migration . Our results show that a linear association between load of viruses ( DWV or IAPV ) in Varroa and bees is present at high Varroa infestation levels ( >3 mites per 100 bees ) . The collection of comprehensive data allowed us to draw a predictive model of colony losses and to show that Varroa destructor , along with bee viruses , mainly DWV replication , contributes to approximately 70% of colony losses . This correlation further supports the claim that insufficient control of the virus-vectoring Varroa mite would result in increased hive loss . The predictive model also indicates that a single factor may not be sufficient to trigger colony losses , whereas a combination of stressors appears to impact hive health .
Pollination by wild or managed species of pollinators is essential to agricultural productivity . Honey Bees ( Apis mellifera ) play an essential role in this process by pollinating many important crops such as apples , almonds , and alfalfa [1 , 2] . In the United States ( USA ) , almond-bearing acres have grown by 130% since 1982 and now rely on 1 . 6 million colonies ( 65%-70% of all USA colonies ) to pollinate 740 thousand acres of almond trees [3] . In years prior to 2007 , winter losses of hives averaged 10%–15% , represented by general decline in hive health , brood density , and total honeybee number . In 2007 beekeepers began to report unusually high losses of hives ranging from 30% to 90% [4] . The losses were associated with an unusual phenomenon of sudden disappearance of bees , with very few dead bees located near the colony . This phenomenon was designated Colony Collapse Disorder ( CCD ) [5 , 6] . Metagenomics analysis performed in 2007 identified the Israeli Acute Paralysis Virus ( IAPV ) as a potential cause of CCD [7 , 8] , but further research showed that IAPV was present in the USA before the CCD epidemic [9] . Other research on CCD hives failed to show an association with IAPV [4] . Since 2007 , colony losses have been monitored across the USA and found to average around 30% [10] . Recent research indicates that the decline of managed hives during winter months is influenced by a combination of several factors , including pests , parasites , bacteria , fungi , viruses , pesticides , nutrition , management practices , and environmental factors [4 , 11 , 12] . There is no consensus , however , regarding the relative importance of these factors , singly or in combination , in causing CCD [11] . Several studies have been performed in the USA and other world regions to identify the most significant factors associated with hive decline [4 , 7 , 13–18] . Most have focused on hive pathogens such as bee viruses , Nosema , and the Varroa mite . Cornman et al . performed a survey of pathogens in CCD and non-CCD hives , showing an increase of pathogens in collapsed hives; no association was determined for other factors such as weather , pests , or nutrition [17] . Runckel et al . performed a 10-month pathogen investigation using hives under migration stress and antibiotic treatment , and were able to show a correlation of hive collapse to various bee viruses and Nosema [16] . The ectoparasitic Varroa destructor mite , in combination with various bee viruses , is also associated with colony losses [7 , 19–26] . The Varroa mite is currently considered to be the most serious threat to honey bee populations worldwide [27] . Varroa has adapted to the developmental stages of the honey bee , entering uncapped brood cells to reproduce and feeding on the larval hemolymph after capping , causing nutrient depletion and weakening the larvae . Several bee viruses have been reported to be transmitted by and replicated in Varroa mites who act as an alternative host . These include Deformed Wing Virus ( DWV ) , Kashmir Bar Virus ( KBV ) , Sacbrood Virus ( SBV ) , Acute Bee Paralysis Virus ( ABPV ) , and Israeli Acute Paralysis Virus ( IAPV ) [24 , 28–32] . Surveys monitoring virus and Varroa mite levels have been supplemented by modeling approaches that are found to predict and understand the dynamics of the Varroa-virus interaction in the hive and their effect on hive health [33–35] . Beekeepers monitor Varroa mite levels extensively and use several acaricides to maintain low Varroa levels . However , determining presence of pathogenic bee viruses and whether they replicate is complex and not always available to beekeepers . Furthermore , not all bees exhibit a phenotype when virally infected , thus complicating any diagnosis and making prediction of viral outbreaks even more difficult . To further dissect factors that influence hive health , we conducted large-scale controlled winter trials at three locations across the USA . Each site contained approximately 60 monitored hives that were not treated with antibiotics or acaricides in order to better understand the effect of Varroa destructor , bee viruses , and Nosema on hive health . The trials were conducted without exposing hives to migratory stress . A weather station was placed at each location for daily monitoring of temperature , humidity , and precipitation . The hives were assessed four times during a period of 7 months . The assessment included Varroa levels , prevalence and replication of 8 bee viruses , Nosema ceranae levels , hive strength , measured by the Almond Grower Method ( AGM ) , and total adult bee number .
The trial was carried out in 3 different locations across the US to represent 3 different climate and geographical conditions: mountain area of Northern California ( site 1 ) , costal area of Florida ( site 2 ) and Southeast Texas ( site 3 ) ( Fig 1 ) . Hives were internally fed monthly with sugar syrup and were assessed four times for hive strength , virus levels , Nosema ceranae , and Varroa mite counts ( Fig 1 ) . Two methods were used to assess hive strength: the Almond Grower Method ( AGM ) , used by beekeepers to assess hive strength as number of bee covered frames before almond pollination; and imaging software , counting bees from frame images [36] . The number of bees in the hive provides a reliable proxy to the comparative strength of the hive . Fig 2A shows different results between the two methods; while the AGM method showed equal hive strength at start point in all three sites , the frame imaging method indicates that Site 3 had significantly ( P<0 . 05 ) fewer bees than the other two sites . To allow for consistency between sites , different inspectors and beekeepers , we defined in the trial protocol a collapsed hive as a hive with AGM assessment of less than or equal to 1 bee frame coverage . In our study the probability of overwinter survival for a hive , based on bee population , is estimated to be 50% when bee number drops below 2500 and 90% when at least 4000 bees are present ( Fig 2B ) . Eight bee viruses were assessed for prevalence ( defined as percentage of hives where the virus was detected ) throughout the trial using QuantiGene Plex 2 . 0 platform . Bee virus prevalence reported here is a snapshot of the prevalence for those hives that were classified as live at the sampling time . As the study progressed , the number of sampled hives decreased due to hive loss . The temporal patterns in virus prevalence were similar for the subset of hives that had measurements at every sampling period and those reported here for all live hives at each sampling period ( S1 Table ) . KBV , ABPV , and IAPV exhibited similar patterns , starting with relatively low levels at the beginning of the trial ( October time point Fig 3A , 3B , and 3E ) and increasing by trial end to >65% across sites . DWV was found at high prevalence ( 75%-95% ) throughout the trial with no significant difference among sites . BQCV and CBPV ranged between 25%-95% prevalence across sites throughout the trial . Lake Sinai Virus ( LSV ) was present in >90% of the hives across sites and time points . One striking difference was found at Site 1 , where prevalence of paralysis viruses ( KBV , ABPV , BQCV , CBPV , IAPV ) dropped dramatically in the February assessment , while DWV and LSV prevalence remained high . Nosema ceranae was analyzed by QuantiGene Plex 2 . 0 platform using two different probes to verify consistency of results . Nosema prevalence averaged 60–85% across sampling times ( Table 1 ) , but differences in prevalence were noted among sites at different sampling times . Significant increases in Varroa counts per 100 bees were observed across time at sites 2 and 3 while a significant decrease was found at Site 1 ( Fig 4 ) . The relationship between DWV and IAPV virus levels in bees and Varroa infestation , defined as the number of phoretic mites per 100 bees , is depicted in Fig 5A and 5B . Results show a significant linear association of virus levels of the two viruses in bees and Varroa at high Varroa infestation ( >3 Varroa mites per 100 bees ) . At low Varroa infestation ( ≤3 Varroa mites per 100 bees ) , there were insufficient numbers of colonies with non-zero levels of DWV and IAPV in bees to determine a correlation . DWV can be found at high virus levels in Varroa , but not in bees at low Varroa infestation ( Fig 5A ) . Two sites exhibited a positive linear relationship between Varroa mite infestation and levels of at least one virus . Varroa infestation was positively associated with DWV ( Sites 1 and 2 ) , IAPV ( Site 2 ) , and VDV ( Site 2 ) ( Table 2 ) . No significant association between Varroa infestation and viral load was found at Site 3 . The variation in Varroa mite number was narrower at Site 3 than Sites 1 and 2 , which may have limited the ability to detect significant relationships . Positive linear relationships were found between Nosema ceranae load and levels of the Dicistroviridae family of viruses ( ABPV , BQCV , CBPV , IAPV , KBV ) and LSV at all sites . Associations with DWV or VDV-1 were less consistent ( Table 2 ) . Replication of DWV , but not IAPV or LSV , increased significantly with an increase in Varroa infestation at two sites ( Table 3 ) . Increase in Nosema was significantly associated with increase in replicating IAPV at 3 sites , with DWV at 2 sites and with LSV at 1 site ( Table 3 ) . Data were not sufficient to determine association between replication of ABPV or KBV with Varroa mite increase or Nosema ceranae . Table 4 shows the colony loss pattern throughout the trial . During the January and February assessments , all sites exhibited similar losses of about ≃22%; while in April there was an increase in colony losses at site 2 and 3 compared to previous months , and in comparison to site 1 ( 30 colonies at Site 2 and 13 colonies at Site 3 ) . Temperature loggers at each site indicated that , while at Site 1 minimum temperatures remained below 6°C throughout the trial , Sites 2 and 3 showed average temperature increases to above 7°C in April ( Table 5 ) . At site 1 replicating DWV was greater in collapsed hives in the October measurement but not in January or February ( S2 Table ) . At Site 2 , DWV load was consistently greater in collapsed hives and significantly so ( P<0 . 05 ) at the February collection period after which the greatest loss in hives occurred ( Fig 6A ) . IAPV and replication of DWV were also significantly greater at the February collection ( Fig 6B and 6C ) . Although not significant , we found that the replication of IAPV was higher in collapsed hives ( P<0 . 15 Fig 6D ) . At Site 3 , DWV and IAPV loads were significantly greater in collapsed hives in January ( S2 Table ) . Replication of IAPV , however , was found to be consistently greater in survived hives and significantly greater at the February time period ( S2 Table ) . Varroa mite counts increased throughout the trial at Site 2 and differed significantly between collapsed and survived hives in February ( Fig 6E ) , where levels of Varroa mites in collapsed hives averaged 15 mites per 100 bees . A similar pattern of increasing Varroa infestation over time with greater levels in collapsed hives and significantly so at the February collection was noted at Site 3 but not at Site 1 . The predictor variables in the final logistic model were DWV replication , VDV load , average 10-day minimum temperature , average maximum temperature , IAPV replication , and location . A consistent linear association was found between collapsed hives and DWV replication . At DWV replication levels of >32 QG units , 80% of the hives collapsed . In the predictive model , increased DWV replication , VDV load , sustained cold temperatures , and sustained warm temperatures were found to increase the probability of colony losses . A counter-intuitive association between high IAPV replication and hive survival was found . Further examination of this factor revealed 10 surviving hives at Site 3 with high levels of IAPV replication and DWV replication . Replicating IAPV helped to correctly predict survival of those hives . At the other two sites , high levels of IAPV replication were associated with colony losses . Nosema ceranae was not chosen as a predictive marker by the model . Varroa also was not selected as a predictor due to lack of a linear association across all levels of Varroa infestation . However , at loads ≥8 Varroa/100 bees , 87% of the hives collapsed . The relative contribution of each factor to colony losses is depicted in Fig 7 . Although a linear relationship between collapse and Varroa load was not found , Varroa is included in the figure to account for collapse in hives with high infestation . Collapse was attributed to Varroa at ≥8 mites/100 bees , to DWV replication at ≥32 QG units and to VDV at ≥300 QG units . Collapse of hives that were correctly predicted as collapsed but did not meet any individual threshold level was attributed to a combination of factors at lower threshold levels . Cold weather in combination with at least one other factor , but not by itself , was included as contributing to collapse . Warm weather was associated with increased Varroa , DWV replication or VDV levels and was not considered as a direct factor in collapse . Varroa infestation , DWV replication , VDV loads , and cold weather accounted for 69% of the collapsed hives . The reason for collapse in the remaining 31% is unknown . The same criteria , when applied to the surviving hives , predicted 19% should have collapsed .
Several pathogens , such as bee viruses , Nosema ceranae and Varroa destructor have been proposed as contributing to increased winter losses of bees [15 , 37 , 40–43] . In 2012 , the National Honey Bee Pests and Diseases Survey Report published the prevalence of bee viruses Nosema and Varroa destructor across the USA between the years 2009–2011 [18] . Similar levels of bee viruses , with slight differences among sites , are reported in this study ( Fig 3 ) . The 2012 Survey Report found that IAPV prevalence varies from year to year and increases between January and April . A similar increase from January to April , with higher levels of IAPV , KBV , ABPV , and BQCV , was reported here . LSV was surveyed by Runckel at al . showing a peak in prevalence in January , whereas here we show high virus prevalence throughout the trial [16] . Difference in detection methods ( QuantiGene Plex 2 . 0 platform vs . QPCR ) , year of sampling , or the fact that the hives in this study were not treated could account for differences noted between the two studies . Francis et al . compared IAPV , KBV , and ABPV viral levels in their study in mitocide-treated and untreated hives and showed an increase in viral load in the untreated groups [43] . This increase could be correlated to an increase in Varroa mite infestation reported in our study ( Fig 4 ) , as the mite was found to transmit these viruses to bees [21 , 29–31 , 43] . Prevalence of these viruses exhibited reduction at the February time point at Site 1 , potentially caused by virus-carrying bee mortality between the January and February time points . Unlike Cornman et al . , in our study , the overall picture of virus prevalence was similar across all sites and no difference was found between the different regions in the USA [17] . The microsporidia Nosema ceranae is associated with colony losses , especially in Europe [44 , 45] . Botias et al . showed presence of Nosema spores in honey and honey bee samples taken as early as 2000 , and an increase in prevalence from 30% in 2002 to 47% in 2007 [46] . Our data from 2012/2013 show the prevalence of Nosema to be 60–80% , depending on the site and month of sampling ( Table 1 ) . Similar data was reported by others [17 , 18] . Runckel et al . reported lower levels of Nosema , but in their study , hives were treated with fumagillin [16] . Varroa destructor is considered to be one of the main causes of hive decline; therefore , we monitored its levels ( expressed as number of mites per 100 bees ) throughout the trial . Rennich et al . reported Varroa mite levels of 2–8 per 100 bees depending on the season and year of sample [18] . The average mite number in their survey is only from hives where Varroa was found , whereas we show an average count of all hives ( Fig 4 ) . Sites 2 and 3 started with very low levels of Varroa , as the participating beekeepers treated against Varroa prior to start point . Mite levels at these sites increased during the trial , averaging as high as 15 mites per 100 bees at Site 2 . Unlike Sites 1 and 3 , the temperature at Site 2 ( Table 5 ) was high throughout the trial . Bee number of survived hives ( S3 Table ) at Site 2 did not decrease for the first 5 monitored months , indicating the presence of brood throughout the trial , hence the opportunity for Varroa mite to propagate . Sites 1 and 3 experienced a roughly 50% drop in the bee population during the first 5 months of our study ( S3 Table ) , most likely due to low winter temperatures ( Table 4 ) , thus preventing bee brood development as well as Varroa reproduction . Previous studies have demonstrated the correlation between Varroa mite infestation and viruses , especially DWV [21 , 24 , 28–31] , the association between viral replication in mites and development of crippled wings [19 , 24 , 47 , 48] , and the correlation between DWV viral copies in bees and mite infestation [43] . This study supported a direct association between 3 parameters: viral load ( DWV or IAPV ) in bees , viral load in mites , and mite infestation level . Given the observational nature of this study , the reported associations do not imply causation . The DWV viral load in bees can remain low in the presence of a high DWV copy number in Varroa as long as Varroa infection is low ( ≤3 mites/100 bees ) . At mite levels >3 mites/100 bees , a linear association can be found between the DWV and IAPV loads in the mite and in the bees . Martin et al . showed in their survey that , once Varroa penetrated the Hawaiian islands , DWV increased both in prevalence and in copy number [24] . Similarly , Mondet et al . showed that presence of DWV or KBV in bees correlates to their presence in the mites [49] . The penetration of Varroa mite to New Zealand also increased the number of different bee viruses in infested hives as compared to hives from Varroa-free areas . These results support the theory of direct transmission of bee viruses between Varroa to bees . To further characterize the relationship between Varroa infestation and different bee viruses , the association between Varroa mite infestation and viral load and replication was tested at each site . At Site 1 , only DWV was positively correlated with mite infestation , while at Site 2 , DWV , VDV-1 , and IAPV were positively associated . At site 3 , non-significant negative correlations were found between mite infestation , KBV and ABPV . Similar negative correlation was reported by Mondet et al . once Varroa penetrated New Zealand [49] . They also showed that different viruses can be found in the bees in correlation to the number of years Varroa infested the island . The differences between sites could also result from different viruses being carried by Varroa at different locations as reported by others [24 , 30 , 31] . DWV replication increased proportionately to mite infestation , but the replication of IAPV and LSV did not associate with mite infestation . Sumpter et al . have used a mathematical model to investigate the relationship between mite load and viral epidemic potential within a colony , demonstrating that , because the paralysis viruses ( IAPV/ABPV/KBV ) are harmful to bees at low levels , a pupa infected in a brood cell by a mite with APV will die before reaching adulthood [35] . Pupal mortality indicates that the virus will not spread in the hive unless transmitted by the Varroa to adult bees . DWV is less virulent in its effects and infected pupae most probably will emerge with the virus allowing the epidemiological correlation of replicating virus with Varroa infestation [35] . Our study shows that increase in Nosema ceranae is associated with an increase of all tested paralysis viruses , while having low correlation , if any , with DWV , VDV-1 , or LSV . These results are supported by data obtained by Martin at al . , in which no correlation was found between Nosema ceranae and DWV viral load [50] . The association between the microsporidia to the paralysis viruses has been demonstrated; Toplak et al . showed potential synergistic effects when co-infecting bees with CBPV and Nosema ceranae , yet this association does not necessarily imply direct causation and could result from effects of high Nosema or viral load on the immune system of the bee [51] . Antunez et al . showed that infection of Nosema ceranae leads to suppression of bee immune response , which could lead to an increase in bee pathogens . [52] . Suggested reasons for hive decline have been numerous: Varroa mite , bee viruses , Nosema , nutrition , extreme cold , beekeeping practices , and pesticides . This study addressed colony loss as a multi-factorial problem and identified DWV replication , Varroa infestation and VDV loads as influential in colony losses . Weather could not be considered as a stand alone factor for collapse since all hives at each location were exposed to the same weather patterns . There were , however , notable differences in weather patterns among sites and the predictive model found both sustained cold and sustained warm weather to increase the probability of colony collapse . A sustained cold period at site 1 was predicted to influence the February colony loss . The sustained warm weather at site 2 was accompanied by increased levels of at least one other risk factor . Late colony collapse at this site was attributed to high levels of Varroa infestation , DWV replication or VDV which may have increased under mild winter weather conditions . Factors contributing to colony losses were Varroa infestation ( ≥8/100 bees ) , DWV replication ( ≥32 QG units ) , VDV-1 load ( ≥300 QG units ) , combinations of Varroa infestation , DWV replication and VDV-1 at lesser threshold levels , and cold weather in combination with at least one other contributing factor ( Fig 7 ) . The model suggests that non-treated hives with increased mite populations are more likely to decline due to mite infestation , as reported by others [37 , 42] . Based on the factors measured in this study , DWV replication has the greatest impact on colony loss in treated hives where mite population is controlled . Interestingly , the model predicted that a portion of the surviving hives had a greater than 50% probability of collapse . These hives were largely subject to high levels of single risk factors; further suggesting collapse is enhanced by the presence of multiple factors . Our study supports the hypothesis that combinations of factors contribute to colony losses . With no available treatment against Varroa , its levels can exceed 8 mites per 100 bees , causing hives to collapse . At lower mite infestation rates , replication of bee viruses takes an active role in the collapse . According to our model , approximately 70% of hive collapse is caused by Varroa and bee viruses . Control of mite and viral levels may mitigate colony loss , resulting in levels more acceptable to the apiary industry .
Three commercial beekeepers participated in the trial . At each site hives were re-queened with queens of the same age and genetic background , and equalized to have 7 frames covered with bees and 3–4 frames covered with capped brood . Queens were purchased from Kona Queen Hawaii , Inc . ; at study initiation , queen acceptance was verified . Bees intended for Quantigene Plex 2 . 0 assay and Nosema counts were collected from the outer frame in a 50mL tube . Immediately following collection , samples were placed on dry ice and were kept at -80°C until analyzed . For Varroa counts , half a cup of bees was sampled from the inner frame into Wide-Mouth HDPE Packaging Bottles with PP closure ( Thermo Scientific cat 03-313-15D ) , which contained 70% alcohol solution . To collect Varroa mites for virus analysis , the sugar shake method , used by beekeepers to monitor mite level or as treatment against Varroa , was used . A cup of powdered sugar was placed on top of the frames and spread using a hive tool or a paint brush , allowing adult bees to be covered with sugar powder . The sugar powder causes the majority of mites to dislodge from their host and fall down onto the bottom board . A white paper was placed on a bottom board of each hive . After five minutes the bottom board was removed and live Varroa mites were collected into 50mL tubes using a paint brush . The mites were immediately placed on dry ice and kept at -80°C until analyzed . Almond grower assessment method ( AGM ) was performed by the beekeepers or the assigned study monitor that was trained to perform the assessment . Hives were opened and graded by the number of covered bee frames assessed after looking at the top and bottom of each hive . A weather collection station monitoring temperature , humidity , and precipitation was placed at each site ( Phytech , ILS ) . The data was transmitted in real-time over a cellular network and collected in our computers . Frames with bees were slowly removed to avoid disruption and placed on a frame holder . Photos were taken from both sides of the frame . Total number of bees on each frame was determined using image recognition software ( IndiCounter , WSC Regexperts ) . Quantigene , a quantitative , non-amplification-based nucleic acid detection analysis , was performed on total lysate from frozen honey bees or Varroa mite samples . The oligonucleotide probes used for the QuantiGene Plex 2 . 0 assay were designed and supplied by Affymetrix , using the sense strand of bee virus sequences as template or negative strand for replicating virus . The probe , designed to detect the sense strand , reflects the presence of virus ( viral load ) and probe designed to detect the anti-sense strand reflects level of viral replication . Housekeeping gene probes were designed from sequences of Apis mellifera mellifera Actin , Ribosomal protein subunit 5 ( RPS5 ) , and Ribosomal protein 49 ( RP49 ) . For Varroa mites , actin and α tubulin were used as housekeeping gene references . The QuantiGene assay was performed according to the manufacturer’s instructions ( Affymetrix , Inc . , User Manual , 2010 ) with the addition of a heat denaturation step prior to hybridization of the sample with the oligonucleotide probes . Samples in a 20 μL volume were mixed with 5 μl of the supplied probe set in the well of a PCR microplate , followed by heating for 5 minutes at 95°C using a thermocycler . Heat-treated samples were maintained at 46°C until use . The 25 μl samples were transferred to an Affymetrix hybridization plate for overnight hybridization . Before removing the plate from the thermocycler , 75 μl of the hybridization buffer containing the remaining components were added to each sample well . The PCR microplate was then removed from the thermocycler; the content of each well ( ~100 μl ) was then transferred to the corresponding well of a Hybridization Plate ( Affymetrix ) for overnight hybridization . After signal amplification , median fluorescence intensity ( MFI ) for each sample was captured on a Luminex 200 machine ( Luminex Corporation ) . Bees were collected in 70% alcohol solution and shaken for 10 minutes on a Burrel ( Model 75 ) Wrist Action Shaker . Bottles were emptied onto a VWR 1/8 inch US Standard Testing Sieve ( Cat # 57334–242 ) to collect the Varroa shaken off the bees . Washed Varroa fell through the sieve onto a weigh boat , and the sieve , with the bees on top , was shaken by hand to collect any Varroa mites that had not washed off immediately . The bottle was checked for any Varroa that had not poured out onto the sieve . Varroa mites were then counted . To determine the number of bees in each bottle , 10 bees from each bottle were weighed and average bee weight was calculated . Weight of all bees was then divided by the average bee weight to calculate number of bees . The Varroa count was divided by number of bees and multiplied by 100 to determine number of Varroa/100 bees . Differences among sites for bee number at study initiation was tested using one-way analysis of variance with site as the sole fixed effect . Mean separation was performed using Fisher’s Protected LSD . A logistic regression was used to predict the probability of hive survival ( AGM score >1 ) given bee counts . The number of bees associated with a collapsed hive at the first observation of collapse was considered as the bee count for collapsed hives . For non-collapsed hives , the minimum bee count across the 4 measurement periods was used as the bee count for the hive . The logistic regression modeled the binomial response of collapsed or live hives as a function of bee number , site , and the interaction between site and bee number . The relationship was not found to differ by site and the across site model is reported here . Viruses were considered present if the Quantigene Plex 2 . 0 value was above the background level . Percent prevalence was calculated as the number of hives with the virus over the number of hives that were sampled . As the study progressed , the number of sampled hives decreased due to hive loss . Pearson’s Product-Moment Correlation analysis was performed to test for linear relationships between viral loads in Varroa and bees for DWV and IAPV . A logarithmic transformation was applied to the Quantigene units data before conducting the correlation analysis . The relationship between Varroa number and viral load was examined using a repeated measures analysis of covariance . The viral load ( log transformed ) was fitted as a function of Varroa number and collection time , while modeling the repeated measures on hives across time with an autoregressive covariance structure of order 1 . The analysis was conducted by location and virus . The same analysis was performed for the relationship between viral load and Nosema load . Hives that had an AGM score of ≤1 at any time during the study were considered to be collapsed . Data from the first 3 collection periods was used in a repeated measures analysis that fitted individual viral loads or Varroa load as a function of the binary variable of collapsed or live hives , collection time , and the interaction between hive status and collection time . The data from the April collection period was not used since it was unknown if the hives collapsed at a subsequent time period and viral data from collapsed hives was limited at the final collection period . The analysis was conducted by location and virus . A multi-factor model to predict colony losses was developed by first selecting a subset of predictor variables from the profile of nine viruses , Varroa mite counts , Nosema load , 3- , 7- , 10- , and 14-day moving averages for minimum and maximum temperatures ( defined as extreme weather conditions ) , and average minimum and average maximum temperature . For collapsed hives , the virus profile , Nosema load , and Varroa counts at the time of collapse or at the previous collection period were used in the model and the weather variables for the time period between the time of collapse and the previous time period were used . For live hives , the profile at the February collection period and the weather variables between the January and February collection periods were used . The February profiles were selected for live hives as they represented the time period when most colony losses occurred . The subset of variables included in the final modeling process was selected by consensus of variable importance ranking by Random Forest [53] and LASSO regression techniques [54] . Stepwise logistic regression was applied to the subset of variables to develop a final model . Results are considered significant at P<0 . 05 . The Random Forest and LASSO regressions were performed in R [55 , 56] . All other statistical analyses were conducted using SAS/STAT software . | Roughly one third of the food supply relies on pollinating insects . The number of colony losses of the domesticated Honey Bee ( Apis mellifera ) has grown significantly in the past eight years , endangering pollination of crops like almonds . Recent research indicates that colony losses are influenced by a combination of several factors . We conducted an extensive and controlled study that allowed us to look at the contribution of different factors to colony loss . Results helped us build a predictive model showing that a single factor is often insufficient to trigger colony loss . Combination of stressors has shown to have greater impact on hive health; replication of the Deformed Wing Virus , stressful weather conditions , and Varroa destructor comprise the primary identified causes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Prospective Large-Scale Field Study Generates Predictive Model Identifying Major Contributors to Colony Losses |
Facioscapulohumeral dystrophy ( FSHD ) is an autosomal dominant muscular dystrophy in which no mutation of pathogenic gene ( s ) has been identified . Instead , the disease is , in most cases , genetically linked to a contraction in the number of 3 . 3 kb D4Z4 repeats on chromosome 4q . How contraction of the 4qter D4Z4 repeats causes muscular dystrophy is not understood . In addition , a smaller group of FSHD cases are not associated with D4Z4 repeat contraction ( termed “phenotypic” FSHD ) , and their etiology remains undefined . We carried out chromatin immunoprecipitation analysis using D4Z4–specific PCR primers to examine the D4Z4 chromatin structure in normal and patient cells as well as in small interfering RNA ( siRNA ) –treated cells . We found that SUV39H1–mediated H3K9 trimethylation at D4Z4 seen in normal cells is lost in FSHD . Furthermore , the loss of this histone modification occurs not only at the contracted 4q D4Z4 allele , but also at the genetically intact D4Z4 alleles on both chromosomes 4q and 10q , providing the first evidence that the genetic change ( contraction ) of one 4qD4Z4 allele spreads its effect to other genomic regions . Importantly , this epigenetic change was also observed in the phenotypic FSHD cases with no D4Z4 contraction , but not in other types of muscular dystrophies tested . We found that HP1γ and cohesin are co-recruited to D4Z4 in an H3K9me3–dependent and cell type–specific manner , which is disrupted in FSHD . The results indicate that cohesin plays an active role in HP1 recruitment and is involved in cell type–specific D4Z4 chromatin regulation . Taken together , we identified the loss of both histone H3K9 trimethylation and HP1γ/cohesin binding at D4Z4 to be a faithful marker for the FSHD phenotype . Based on these results , we propose a new model in which the epigenetic change initiated at 4q D4Z4 spreads its effect to other genomic regions , which compromises muscle-specific gene regulation leading to FSHD pathogenesis .
FSHD is the third most common heritable muscular dystrophy [1] . It is characterized by progressive weakness and atrophy of facial , shoulder , and upper arm musculature , which can spread to the abdominal and foot-extensor muscles [2] . It can be accompanied by hearing loss and retinovasculopathy . The genetics underlying FSHD are highly unusual , as no pathogenic mutation ( s ) of a disease causing gene ( s ) has been identified . Instead , the majority ( >95% ) of FSHD cases involve mono-allelic deletion of D4Z4 repeat sequences at the subtelomeric region of chromosome 4q ( termed “4q-linked” FSHD , FSHD1A ( OMIM 158900 ) ; designated as “4qF” in this study ) [2] . There are between one and ten repeats in the contracted 4qter allele in FSHD patient cells , in contrast to up to 11∼150 copies in normal cells . In addition , <5% of FSHD cases are not associated with D4Z4 repeat contraction ( termed “phenotypic” FSHD , FSHD2; referred to as “PF” in this study ) , and their etiology remains undefined . How contraction of the 4qter D4Z4 repeats causes muscular dystrophy is not understood . A previous study reported the YY1-nucleolin-HMGB2 repressor complex binding to D4Z4 , and it was postulated that reduction of the repeat number may result in decreased repressor complex binding , leading to derepression of neighboring genes [3] . Consistent with this model , overexpression of the neighboring 4q35 genes was demonstrated in the same study , and the same group recently showed that muscle-specific overexpression of the neighboring gene FRG1 indeed causes muscular dystrophy in mice [4] . Curiously , however , microarray and quantitative expression studies by other laboratories revealed that many genes located elsewhere in the genome important for myoblast differentiation are dysregulated , but unanimously provided no evidence for abnormal upregulation of FRG1 and other 4q35 genes in FSHD [5]–[7] . Furthermore , the model cannot explain the mechanism of phenotypic FSHD in which there is no D4Z4 repeat contraction . Cytological analyses revealed that the 4q telomeric region uniquely associates with the nuclear periphery , consistent with the hypothesis that this region is heterochromatic [8] , [9] . However , since the D4Z4 repeat contraction in 4qF did not lead to any significant localization changes , the functional relevance to FSHD remains uncertain [8] , [9] . A recent study demonstrated that the 4qter D4Z4 region is hypermethylated at the DNA level in normal cells , but is hypomethylated in both 4q-linked and phenotypic FSHD [10] . This was the first evidence that 4qter D4Z4 is also involved in phenotypic FSHD . DNA methylation is an important mechanism for epigenetic regulation of gene transcription , and is generally associated with transcriptional silencing [11] . Thus , the results suggested that the D4Z4 repeat array organizes a transcriptionally suppressive heterochromatic environment , which is disrupted in FSHD . However , DNA hypomethylation , more severe than that seen in FSHD , at D4Z4 was also observed in another hereditary disorder , the “immunodeficiency , centromere instability and facial anomalies ( ICF ) ” syndrome , due to a mutation in DNA methyltransferase 3B ( DNMT3B ) [10] , [12] . Since the clinical presentation of ICF syndrome shares no similarity with the FSHD disease phenotype [13] , the relevance of DNA methylation changes in FSHD is unclear and the molecular events underlying the D4Z4-linked disease process remain an open question . Here we report the characterization of the chromatin of the 4q and 10q D4Z4 repeats and a comparison between normal , FSHD and other muscular dystrophy cells . Our results demonstrate that there is a distinct change of histone modification and downstream factor binding that is specifically associated with both 4q-linked and phenotypic FSHD , suggesting that epigenetic alteration plays a critical role in FSHD pathogenesis .
We found trimethylation of H3K9 ( H3K9me3 ) and H3K27 ( H3K27me3 ) at D4Z4 , both of which frequently represent transcriptionally repressive heterochromatin [15] , [16] , as well as H3K4 dimethylation ( H3K4me2 ) and H3 acetylation ( H3Ac ) , which mark transcriptionally permissive euchromatin [17] ( Figure 2A ) . H3K9me3 signals were confirmed by two different antibodies specific for H3K9me3 which have slightly different binding preferences [18] ( Figure 2A , lanes 10–14 ) . Recent studies demonstrated that H3K9me3 can also be associated with transcriptionally active gene regions [18] , [19] . However , no significant H3K4me3 , which is coupled to transcription-associated H3K9me3 [18] , was detected using the same primer pairs ( Figure 2A , lane 4 ) . Although it is possible that H3K4me3 may be present elsewhere in the D4Z4 repeat , it is at least not present within the promoter and 5′ regions of the putative open reading frame ( ORF ) for DUX4 where 4qHox and the Q-PCR primers bind ( Figure 1A ) . Furthermore , double-ChIP analysis revealed that H3K9me3 coincides with H3K27me3 , but not H3K4me2 , suggesting that the D4Z4 repeat cluster contains a distinct heterochromatic domain marked by both H3K9me3 and H3K27me3 as well as a euchromatic domain containing H3K4me2 ( Figure 2B ) . Notably , PCR amplification of the first proximal D4Z4 repeat revealed that this end is euchromatic , consistent with a previous report that the region proximal to the D4Z4 repeat is euchromatic [6] ( Figure 1A and Figure 2C ) . Both H3K4me2 and H3K9me3 are present at D4Z4 in human embryonic stem ( hES ) cells , suggesting that D4Z4 chromatin domains are marked by these histone modifications early in development and are maintained during differentiation ( Figure 2D ) . This is in contrast to H3Ac , which is absent in hES cells and appears to be added at later stages ( compare Figure 2A , lane 7 and Figure 2D , lane 5 ) . Taken together , unlike the previous model that implies that D4Z4 is a uniformly transcriptionally repressive domain [3] , we found that D4Z4 repeats are composed of both euchromatic and heterochromatic domains with possibly the proximal repeats being euchromatic . Importantly , the presence of both 4q- and 10q-specific nucleotide polymorphisms ( Figure 1C ) was confirmed by sequencing of the ChIP DNA , indicating that a similar spectrum of histone modifications are present in the 4q and 10q D4Z4 regions ( Table S1 ) . We next examined the chromatin modifications in FSHD patient-derived primary cells compared to normal cells from healthy individuals . The H3K9me3 signal at D4Z4 was significantly decreased in D4Z4-contracted FSHD myoblasts and fibroblasts while H3K27me3 and H3K4me2 remained unaffected ( Figure 3A and 3B , 4qF ) . Importantly , the loss of H3K9me3 is site-specific because no significant change was observed at the ribosomal DNA ( rDNA ) region ( Figure 3A , lower panels , and Figure 3B , lanes 7–11 ) or in the amount of total H3K9me3 detected by western blot ( data not shown ) . Similarly , no loss of H3K9me3 was observed at other repeat sequences , including chromosome 1 α-satellite and satellite 2 , chromosome 4 α-satellite , NBL2 , DXZ4 , and RS447 , in FSHD patient cells compared to normal cells ( Figure S1 ) . The failure to detect H3K9me3 at D4Z4 is not due to an insufficient number of D4Z4 copies since the ChIP signals were normalized to input DNA to reflect D4Z4 repeat number changes , and the loss of H3K9me3 was also observed in phenotypic FSHD ( PF ) cells with no repeat contraction . It is unlikely to be the result of a drastic change in antibody accessibility since H3K27me3 , which resides in the same region according to the double-ChIP results ( Figure 2B ) , is unchanged ( Figure 3A and 3B ) . The persistence of H3K27me3 at D4Z4 also eliminates the possibility that only one allele is intrinsically organized as heterochromatin and deletion of this particular allele leads to FSHD . This is in agreement with previous observations that there is no clear paternal or maternal bias of disease transmission suggestive of imprinting , which could differentially organize the chromatin structure of the two alleles [2] , [20] . Consistent with this , no significant difference in subnuclear localization of the two 4qter regions was found by the previous FISH analyses [8] , [9] . Interestingly , the total numbers of D4Z4 repeat copies ( i . e . the numbers of 4q and 10q repeats combined ) are comparable between normal and FSHD patient cells ( Figure 3B , bottom panel ) . Since the analysis in normal cells indicate that 10q D4Z4 also contains similar H3K9me3 modification ( see above ) , the low level of H3K9me3 ChIP signal in FSHD patient cells cannot simply be attributed to the chromatin change at 4q D4Z4 . This suggests that the loss of H3K9me3 also occurs at 10q D4Z4 . This is further supported by the fact that both 4q and 10q polymorphisms were found in the residual H3K9me3 ChIP Q-PCR products of PF ( KII-I ) and 4qF ( RD217 ) samples ( Table S1 ) . The results provide the first evidence that 10q D4Z4 chromatin is co-regulated with 4q D4Z4 chromatin and undergoes similar loss of H3K9me3 in FSHD . The loss of H3K9me3 at D4Z4 was observed not only in FSHD patient myoblasts and fibroblasts , but also in lymphoblasts , indicating that this chromatin change is not a mere non-specific epiphenomenon associated with the dystrophic state of the muscle cell ( Figure 3A and 3B and Figure 4A ) . Presently , we have examined 14 normal and 14 FSHD patient cell samples of different origins and obtained consistent results . Importantly , no significant loss of H3K9me3 at D4Z4 was observed in cells from Duchenne muscular dystrophy ( DMD ) , limb-girdle muscular dystrophy ( LGMD ) , oculopharyngeal muscular dystrophy ( OPMD ) , and inclusion body myopathy associated with Paget's disease of bone and frontotemporal dementia ( IBMPFD ) ( Figure 3C ) . Therefore , the loss of H3K9me3 at 4q and 10q D4Z4 appears to be a specific change uniquely associated with both 4q-linked ( 4qF ) and phenotypic ( PF ) FSHD . DNA and heterochromatic histone methylation are often co-regulated [21] . Although DNA methylation is more frequently a downstream consequence of H3K9 methylation [22] , DNA methylation in some instances was shown to promote H3K9me3 [23] . Thus , we next addressed whether the loss of H3K9me3 is simply a downstream event of DNA hypomethylation previously observed in FSHD and clinically unrelated ICF syndrome cells [10] . We found that H3K9me3 is largely intact at D4Z4 in ICF cells , though there appears to be an increase in H3K4me2 and H3Ac , as indicated by ChIP analysis ( Figure 3C and Figure 4A ) . Similarly , H3K9me3 is unaffected at another non-satellite repeat sequence called NBL2 in ICF cells , which was also shown to be DNA-hypomethylated in these cells ( Figure 4B ) [12] . Furthermore , no significant loss of H3K9me3 was observed in cells from a clinically unaffected individual with significant DNA hypomethylation at D4Z4 ( Figure 3B , KI-II ) [10] . Finally , treatment of cells with 5-Azacytidine ( 5-AzaC ) , which blocks DNA methylation , did not affect H3K9me3 despite the significant reduction of DNA methylation at D4Z4 ( Figure 5E ) . Taken together , DNA methylation is not required for H3K9me3 at D4Z4 , and H3K9me3 loss clearly distinguishes FSHD from ICF , implying that loss of H3K9me3 at D4Z4 , rather than DNA hypomethylation , is causally involved in FSHD . What happens as a result of the loss of H3K9me3 at D4Z4 ? To investigate the consequences of H3K9me3 loss in FSHD , we examined factors that bind to this region . Heterochromatin binding protein HP1 is recruited to heterochromatic regions by direct binding to the methylated H3K9 residue and plays an important role in transcriptional silencing [24] , [25] . Swi6 , an HP1 homolog in S . pombe , was also shown to recruit the essential sister chromatid cohesion complex “cohesin” to the pericentromeric heterochromatin where it mediates centromeric sister chromatid cohesion critical for mitosis [26] , [27] . Although the study in yeast indicated that cohesin does not play any role in transcriptional repression at heterochromatic regions [27] , HP1 and cohesin are valid candidates for the downstream effectors of H3K9me3 at D4Z4 in human cells . In mammals , there are three HP1 variants: HP1α , HP1β and HP1γ . We found that HP1γ specifically binds to D4Z4 ( Figure 5A ) . Cohesin binding to D4Z4 was also detected using antibodies against two of its subunits ( i . e . , hSMC1 and hRad21 ) , indicating the presence of the holo-complex ( Figure 5B ) . Cohesin binding was observed in both undifferentiated myoblasts and differentiated ( mitotically inactive ) myotubes , suggesting a role beyond mitosis at this site ( Figure 5C ) . Importantly , similar to H3K9me3 , HP1γ and cohesin binding was also compromised at D4Z4 , but not at the rDNA , DXZ4 , and chromosome 1 α-satellite and satellite 2 repeat regions where H3K9me3 appears intact , in both 4qF and PF cells ( Figure 5D and Figure S2 ) . The results indicate that H3K9me3 , HP1γ and cohesin form heterochromatin at D4Z4 , and suggest that the loss of HP1γ and cohesin binding to D4Z4 is a significant downstream consequence of the loss of H3K9me3 in FSHD . Similar to H3K9me3 , treatment of cells with 5-AzaC did not affect cohesin and HP1γ binding to D4Z4 , further separating H3K9me3 and HP1γ/cohesin binding from DNA methylation ( Figure 5E ) . The methyltransferase responsible for H3K9me3 and the relationship between H3K9me3 , HP1γ and cohesin were addressed using small interfering RNAs ( siRNAs ) . SiRNA against SUV39H1 , which has no effect on SUV39H2 , abolished H3K9me3 at D4Z4 but not at rDNA , suggesting that SUV39H1 has a non-redundant function at D4Z4 ( Figure 6A ) . Supporting this notion , depletion of G9a , another H3K9 methyltransferase , decreased H3K9me3 at the c-Myc region [28] , but had no effect at D4Z4 or rDNA ( Figure 6A ) . Abolishment of H3K9me3 by SUV39H1 depletion also impaired HP1γ and cohesin binding at D4Z4 but not at rDNA , confirming that SUV39H1-mediated H3K9me3 is necessary for HP1γ and cohesin binding specifically at D4Z4 . Neither HP1γ nor cohesin depletion affected the level of H3K9me3 at D4Z4 , placing them downstream of H3K9me3 ( see Figure 7A , lane 5 ) . Interestingly , HP1γ and cohesin binding to D4Z4 is significantly low in normal lymphoblasts , even with intact H3K9me3 at D4Z4 ( Figure 4A , lane 4 ) , when compared to other cell types ( compare Figure 6B to Figure 5 ) . This is not due to a general decrease of HP1γ and cohesin binding in lymphoblasts since HP1γ and cohesin binding was clearly observed at four other repeat sequences tested ( i . e . , rDNA , α-satellite and satellite 2 on chromosome 1 , and DXZ4 ) in both normal and FSHD lymphoblasts , similar to myoblasts and fibroblasts ( Figure 6B and Figure S2 ) . Furthermore , the total level of H3K9me3 is comparable between HeLa and both normal and FSHD lymphoblasts ( Figure 6B , lanes 11–13 ) . The results indicate that H3K9me3 is not sufficient and suggest that an additional factor ( s ) , which may be expressed in a cell type-specific manner , is required for HP1γ and cohesin binding to D4Z4 . The requirement for an additional factor ( s ) is also supported by the observation that not all H3K9me3-positive repeat sequences are bound by HP1γ and cohesin , even in the same cell sample ( Figure 6C ) . Similar to the recruitment of cohesin to pericentromeric heterochromatin in S . pombe [26] , [27] , HP1 is required for cohesin binding at D4Z4 ( Figure 7A ) . Interestingly , depletion of HP1γ alone abolished cohesin binding at D4Z4 , indicating that HP1α and HP1β cannot compensate for this function of HP1γ at this site . In contrast , depletion of HP1γ had no effect on cohesin binding to the rDNA region , α-satellite and satellite 2 repeats on chromosome 1 , and DXZ4 , most likely due to functional redundancy with other HP1 variants ( Figure 7A ( lanes 7–12 ) and B ) . Consistent with this notion , HP1α binding was detected at the α-satellite repeat , but not at D4Z4 ( Figure 5A , lane 4; data not shown ) . Thus , HP1γ is uniquely involved in heterochromatin formation at D4Z4 . We found that the cohesin loading factor Scc2 [29] also binds to D4Z4 , which was significantly decreased by depletion of HP1γ to an extent similar to the decrease caused by depletion of Scc2 itself ( Figure 7C ) . Consistent with this , we found an interaction between the endogenous HP1γ and Scc2 by in vivo coimmunoprecipitation ( co-IP ) ( Figure 7D ) . Although weak , the interaction is specific and partially resistant to a 1 M salt wash ( Figure 7D , “eluate” ) . We found that HP1γ mainly interacts with Scc2 , rather than cohesin ( Figure 7D ) . Although it was originally shown that HP1 interacts with cohesin in S . pombe [27] , the interaction of Scc2 with HP1 variants was reported in human cells [30] and more recently in S . pombe [31] . Interestingly , CTCF , another factor recently shown to recruit cohesin to its binding sites [32]–[34] , interacts preferentially with cohesin but not Scc2 ( Figure 7D ) , suggesting distinct modes of cohesin recruitment by these factors . In S . pombe , cohesin is downstream of HP1 , and does not play any role in HP1 recruitment [27] . Interestingly , we found that depletion of hSMC1 impairs HP1γ binding to D4Z4 ( Figure 7A ) . Similarly , depletion of Scc2 abolished D4Z4 binding of not only cohesin but also HP1γ . Thus , the results provide the first evidence for an active role of cohesin in heterochromatin organization . This appears to be context-dependent , since the rDNA region , α-satellite and satellite 2 repeats on chromosome 1 , and the DXZ4 region showed no effect on HP1γ binding following depletion of hSMC1 or Scc2 ( Figure 7A and 7B ) .
Although D4Z4 was thought to be a uniformly transcriptionally repressive domain [3] , [10] , we found that D4Z4 regions contain a mixture of euchromatic and heterochromatic histone modifications; specifically , H3K4me2 and H3Ac as well as H3K9me3 and H3K27me3 . These euchromatic and heterochromatic modifications are present in distinct domains within D4Z4 repeat clusters with the first proximal repeat being euchromatic ( Figure 8B ) . Interestingly , only H3K9me3 is lost in FSHD , but not H3K27me3 from the heterochromatic region ( Figure 8C ) . Thus , the chromatin change in FSHD is not a total loss of transcriptionally repressive heterochromatin . This is consistent with the fact that there apparently is no significant compensatory increase of euchromatic modifications , suggestive of expansion of euchromatic domains within D4Z4 , in FSHD . PF and 4qF are genetically distinct . While the etiology of PF is unknown , our results revealed a correlation between the repeat contraction and the loss of H3K9me3 at D4Z4 in 4qF patient cells . This raises the possibility that repeat contraction leads to the loss of H3K9me3 at D4Z4 in 4qF . It is also formally possible that the upstream event that initially caused the repeat contraction might have also caused the loss of H3K9me3 . It is less likely that the loss of H3K9me3 is the cause of repeat contraction , since there is no repeat number instability in phenotypic FSHD despite the similar loss of H3K9me3 . Detection of H3K9me3 at D4Z4 in hES cells and multiple cell types indicates that H3K9me3 at this region is normally established early during development at a pluripotent stage , and is maintained throughout multi-lineage differentiation . The fact that H3K9me3 is lost even in lymphoblasts in FSHD patients indicates that this establishment process during early development may have gone awry . Interestingly , our results indicate that contraction of one allele not only triggers the histone modification change ( loss of H3K9me3 ) on the disease allele , but also affects H3K9me3 levels on other non-contracted 4q and 10q D4Z4 alleles , suggesting a functional communication between these homologous sequences perhaps reminiscent of transvection in Drosophila [35] ( Figure 8B ) . This is in contrast to DNA hypomethylation , which appears to be restricted to the disease chromosome in FSHD [10] , [13] . The dominant effect of contraction of one 4q D4Z4 allele on H3K9me3 at other D4Z4 alleles is consistent with the dominant nature of the disease and is in agreement with our results indicating that DNA hypomethylation is not required for the loss of H3K9me3 . This strongly argues against the theory that only the contracted D4Z4 allele is involved in FSHD pathogenesis [3] . Rather , it is possible that both alleles of 4q D4Z4 as well as 10q D4Z4 may be involved in the disease process . Consistent with the coordinated chromatin changes observed , somatic pairing of 4q and 10q D4Z4 has been reported [36] . Although the mechanism is currently unclear , the results provide the first evidence that the initial genetic change ( repeat contraction ) spreads its effect to other genomic regions in 4qF . A similar coordinated loss of H3K9me3 at 4q and 10q D4Z4 was observed in PF , further emphasizing the significance of this phenomenon . We identified the histone methyltransferase ( HMTase ) SUV39H1 , but not other HMTases , to be responsible for D4Z4 H3K9me3 ( Figure 8A ) . This raises the possibility that misregulation of this enzyme activity is linked to the etiology of FSHD . However , no mutation in SUV39H1 itself ( either at the promoter or gene region ) in FSHD patient cells was found [13] . Consistent with this , the total level of H3K9me3 in the nucleus is similar between normal and FSHD cells . This suggests that a specific cofactor of SUV39H1 , possibly important for its recruitment , and/or a specific histone demethylase acting at D4Z4 , may be compromised in FSHD . It is plausible that PF results from a genetic mutation of such a factor . Further investigation of the site-specific SUV39H1 ( or antagonizing histone demethylase ) regulation will be important to understand FSHD's etiology and pathogenesis , and may shed new light onto the yet to be identified cause of PF . It is also interesting to note that there is a slight but consistent decrease in HP1γ binding to other repeat sequences tested in PF , but not 4qF , cells ( Figure S2 ) . Although the significance of this small decrease is currently unclear , this may reflect the distinct etiologies of PF and 4qF and may provide another clue to identify the genetic defect in PF . We established the loss of H3K9me3 at D4Z4 to be the signature change in both types of FSHD , but how does this epigenetic change lead to muscular dystrophy ? We identified two major downstream effectors of H3K9me3 , the heterochromatin binding protein HP1γ and cohesin , whose binding to D4Z4 is H3K9me3-dependent and , consequently , is severely compromised in FSHD . The data presented here argue for both factors having a role in FSHD pathogenesis . Importantly , while H3K9me3 at D4Z4 is seen in all cell types tested , the binding of HP1γ and cohesin to D4Z4 is cell type-specific , suggesting that their binding is involved in cell type-specific chromatin organization ( Figure 8A ) . This restricted HP1γ/cohesin binding to D4Z4 may explain the tissue-specific FSHD disease phenotype , as their loss may be particularly deleterious to muscle function . Interestingly , recent evidence suggests that cohesin is also involved in gene regulation . Although initially identified as a factor essential for mitosis , discoveries of mutations of cohesin components and the essential cohesin chromatin loading factor NIPBL/Scc2 in the developmental disorder Cornelia de Lange Syndrome ( CdLS ) strongly suggested the involvement of cohesin in developmental gene regulation [37]–[39] . The sequence-specific DNA binding transcription factor CTCF was found to recruit cohesin to many of its binding sites , where cohesin is involved in CTCF-dependent transcriptional regulation [32]–[34] . Accumulating evidence indicates that gene regulation can be affected by physical interaction between two distant chromosomal regions in cis and in trans in mammalian cells [40]–[43] . CTCF is known to be one such factor that exerts its transcriptional activity by directing long-distance chromatin interactions and loop formation , for example , in imprinting and X inactivation [44] , [45] . Thus , the discovery that cohesin is an important mediator of CTCF transcriptional function raised the intriguing possibility that cohesin may dictate gene expression by facilitating such higher-order chromatin organization . Recent reports support this notion for cohesin at certain CTCF binding insulator sites [46] , [47] . Similar to what was proposed for sister chromatid cohesion [48] , cohesin may trap two distant chromatin fibers inside of its ring . We failed to detect any significant binding of CTCF concomitant with cohesin at D4Z4 ( data not shown ) , which is consistent with the fact that CTCF and heterochromatin are mutually exclusive [49] . However , cohesin may still function in a similar manner mediating long-distance chromatin interactions , together with HP1γ in the case of D4Z4 heterochromatin . In Drosophila , it was suggested that HP1 promotes interchromosomal association of heterochromatin , which may be important for coordinated gene silencing [50] . Evidence for gene silencing by association with distant heterochromatin was also found in mammalian cells , in which the temporal association of the terminal transferase ( Dntt ) gene with pericentromeric heterochromatin correlates with its silencing during thymocyte maturation in mice [51] . Thus , one possibility for the involvement of D4Z4 heterochromatin in gene regulation is that it makes contact with , and represses , distant target genes via long-distance chromatin: chromatin interactions by spreading a silencing effect in normal cells ( Figure 8C ) . H3K27me3 found in the same region may also contribute to this by possibly recruiting the polycomb silencing complex . We hypothesize that in FSHD the loss of H3K9me3 , and therefore of HP1γ and cohesin , results in the loss of this chromatin interaction , thereby causing abnormal derepression of these distant target genes that leads to the dystrophic phenotype ( Figure 8C ) . There may be different sets of target genes for 4q and 10q D4Z4 , both of which would be affected in FSHD due to the concomitant loss of H3K9me3 . Interestingly , some evidence for change in local higher-order chromatin organization and nuclear matrix association in 4q-linked FSHD was recently reported [52] . However , this change appears to occur in the nearby regions outside of the D4Z4 cluster , and how D4Z4 contraction affects this is unclear . The same phenomenon has not been confirmed in phenotypic FSHD . In addition , since this change was shown to be restricted to the contracted allele and not other D4Z4 alleles , the relationship to the spreading of D4Z4 chromatin changes observed in the current study remains to be investigated . Further studies to examine the possible chromatin interactions and organization involving D4Z4 and their changes in FSHD may provide critical insight into the mechanism of FSHD pathogenesis .
HeLa cells were grown as described previously [53] . The undifferentiated and differentiated normal myoblasts and the FSHD patient myoblasts were grown in SkBM-2 ( Skeletal Muscle Cell Basal Medium , Cambrex Bio Science , NJ ) . Myoblast differentiation was induced by 2% horse serum as previously described [5] . Five normal and five 4q-linked FSHD myoblast lines were used . Control ( KI-I , KI-II , NFGr ) , ICF ( ICF1 and ICF2 ) , 4q-linked FSHD ( 91RD217 , 423/16 , F2625 , 508 ) and phenotypic FSHD ( KII-I , KII-II , Rf394 . 2 , RF394 . 3 ) fibroblasts were grown in DMEM/F-12 ( 1∶1 ) supplemented with 10% FBS , penicillin/streptomycin , 2 mM GlutaMAX-I ( Invitrogen-Gibco , CA ) , 10 mM HEPES buffer and 1 mM sodium pyruvate [10] , [54] . For comparison among different muscular dystrophies , one 4q-linked FSHD ( 508 ) and two phenotypic FSHD ( Rf394 . 2 and Rf394 . 3 ) patient fibroblast samples , five OPMD patient fibroblast samples ( 376 , 395 , 396 , 54030922 , and 203241 ) , four DMD patient fibroblast samples ( d1137 . 5 , 6103 , 5639 . 1 , and dl90 . 3 ) , three LGMD patient fibroblast samples ( 00–288 , 01–196 , 99–305 ) [55] , [56] , two ICF patient fibroblast samples [57] , and four IBMPFD patient samples ( two fibroblast and two lymphoblast ) ( JH-FIB , MJ-FIB , 307/98 , and RS-LCL ) [58] were used . Control ( 256 . 1 LCL ) , ICF ( 10759 ICF LCL ) , and FSHD ( B8-1 ) lymphoblast cells were grown in RPMI-1640 supplemented with 10% FBS , penicillin/streptomycin , and 2 mM L-Glutamine ( Invitrogen-Gibco , CA ) . Human ES cells H1 and H9 were grown as described [59] . Mouse somatic cell hybrids containing chromosome 4 , 10 , 13 , 14 , 15 or 21 ( GM11687 , 11688 , 11689 , 10479 , 11715 , 08854 , respectively , from Coriell Cell Repositories , Camden , NJ ) were grown in DMEM/F-12 ( 1∶1 ) medium with the same supplements as the fibroblasts . Chromosomes 13 , 14 , 15 , and 21 are known to contain D4Z4-like repeat sequences [60] . The NIGMS Human/Rodent Somatic Cell Hybrid Mapping Panel #2 , version 3 was from Coriell Cell Repositories , in which chromosome 1 , 16 , 17 , 20 , and 21 hybrids are from mice while the others are from Chinese hamsters . Antigen affinity-purified rabbit polyclonal antibodies specific for Rad21 , hSMC1 , hCAP-G , and the pre-immune IgG control were published previously [53] , [61] . Antibodies against H3K4me2 , H3K4me3 , H3K9me3 , H3K27me3 , H3 Ac , H4 Ac , HP1γ , SUV39H1 , and G9a ( Upstate Biotech , MA ) , against H3K9me3 ( Abcam , Cambridge , MA ) and against HP1α ( Novus Biologicals , CO ) were used . Antibody against 5-methylcytidine was from Eurogentec North America ( San Diego , CA ) . The ChIP analysis was performed as recommended by the Upstate ChIP assay kit . Briefly , we crosslinked the cells with 1% formaldehyde and used 1×106 cells for one histone ChIP and 3×106 cells for the other ChIP assays . Protein A beads were preincubated with 1 mg/ml BSA and 0 . 2 mg/ml ssDNA for 20 min at 4°C . Typically , 4–8 µg of affinity-purified IgG was used per assay . The mixtures of antibody and nuclear extracts pre-cleared with protein A beads were incubated at 4°C overnight followed by precipitation with protein A beads . After washing , immunoprecipitated materials were eluted with 0 . 1 M NaHCO3 and 1% SDS , and crosslinks were reversed at 65°C for 4–6 hrs . Primer sequences are listed in Table S2 . PCR primers specific for chromosome 1 α-satellite ( α-sat ) and satellite 2 ( sat2 ) , chromosome 4 α-satellite ( α-sat ) , DXZ4 , RS447 , and NBL2 sequences were used [6] , [12] , [62] . In addition , a PCR primer pair specific for the c-Myc region was used as a control for G9a depletion as previously described [28] . The primers for rDNA are located in the intergenic region . All of the end-point PCR experiments were repeated at least three times . The endpoint gel quantitation of the ChIP-PCR products was carried out using the Gel-Doc Imager and Quantity One software ( Bio-Rad ) . Real-time Q-PCR primers were designed using Lasergene software . Q-PCR was performed using the iCycler iQ Real-time PCR detection system ( Bio-Rad ) with iQ SYBR Green Supermix ( Bio-Rad ) . The ChIP PCR signal was normalized by the subtraction of the preimmune IgG ChIP PCR signal , which was further divided by input genomic PCR ( for normalization of different D4Z4 repeat numbers in different cells ) minus PCR with no template . Results were an average of three PCR reactions , and the arbitrary value of 1 . 0 was assigned to the normal control sample . Double-ChIP analysis was performed according to the published protocol [63] . The 5-AzaC treatment was performed as previously described [64] . Briefly , 50 µM of 5-AzaC was added to HeLa cells at 80% confluency and after 24 hr incubation , the cells were harvested for ChIP experiments . The MeCIP assay was performed according to the published protocol [65] . After the cell samples were harvested and sonicated , they were treated with proteinase K overnight and the DNA from these samples was purified by the QIAquick gel purification kit ( QIAGEN ) . Four µg of the purified DNA was used per MeCIP assay . The DNA was denatured at 95°C for 10 min and incubated with 4 µl antibody against 5-methylcytidine in 500 µl IP buffer ( 10 mM sodium phosphate , pH 7 . 0 , 140 mM NaCl , 0 . 05% Triton X-100 ) at 4°C for 2 hrs . The DNA: antibody mixtures were further incubated with protein A beads at 4°C for an additional 2 hrs . The beads were washed with 700 µl IP buffer three times and treated with proteinase K at 50°C for 3 hrs . Finally , the DNA was recovered using the gel purification kit and analyzed by PCR . HeLa cells were transfected three times 24 hours apart with siRNAs at a final concentration of 10 nM using HiPerFect Transfection Reagent per manufacturer's instructions ( Qiagen ) . The target sequences for SUV39H1 and G9a were previously described [66] , [67] . Other siRNA target sequences include hSMC1 ( 5′-CACCATCACACTTTAATTCCA-3′ ) , HP1γ ( 5′-CTAAGTTAAATGAACATTTAA-3′ ) , Scc2 ( 5′-CTAGCTGACTCTGACAATAAA-3′ ) , and negative control ( 5′-AATTCTCCGAACGTGTCACGT-3′ ) . Cells were used for ChIP and western blot analyses at 48 hours after the third transfection . HeLa nuclear extracts were used for co-IP using antibody specific for Scc2 or cohesin ( Rad21 ) as previously described [53] , [61] . Briefly , precipitated materials were washed four times with a buffer containing 0 . 1 M KCl , then eluted with 1 . 0 M KCl ( “wash” ) and finally eluted with 2 . 0 M guanidine-HCl ( “eluate” ) . Proteins in the wash and eluate fractions were precipitated by trichloroacetic acid ( TCA ) and analyzed by SDSPAGE and western blotting using antibody specific for HP1γ . | Most cases of facioscapulohumeral muscular dystrophy ( FSHD ) are associated with a decrease in the number of D4Z4 repeat sequences on chromosome 4q . How this leads to the disease remains unclear . Furthermore , D4Z4 shortening is not seen in a small number of FSHD cases , and the etiology is unknown . In the cell , the DNA , which encodes genetic information , is wrapped around abundant nuclear proteins called histones to form a “beads on a string”–like structure termed chromatin . It became apparent that these histones are modified to regulate both maintenance and expression of genetic information . In the current study , we characterized the chromatin structure of the D4Z4 region in normal and FSHD patient cells . We discovered that one particular histone modification ( trimethylation of histone H3 at lysine 9 ) in the D4Z4 repeat region is specifically lost in FSHD . We identified the enzyme responsible for this modification and the specific factors whose binding to D4Z4 is dependent on this modification . Importantly , these chromatin changes were observed in both types of FSHD , but not in other muscular dystrophies . Thus , this chromatin abnormality at D4Z4 unifies the two types of FSHD , which not only serves as a novel diagnostic marker , but also provides new insight into the role of chromatin in FSHD pathogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"molecular",
"biology/histone",
"modification",
"molecular",
"biology/chromatin",
"structure",
"molecular",
"biology/dna",
"methylation"
] | 2009 | Specific Loss of Histone H3 Lysine 9 Trimethylation and HP1γ/Cohesin Binding at D4Z4 Repeats Is Associated with Facioscapulohumeral Dystrophy (FSHD) |
Systemic Lupus Erythematosus ( SLE ) pathology has long been associated with an increased Epstein-Barr Virus ( EBV ) seropositivity , viremia and cross-reactive serum antibodies specific for both virus and self . It has therefore been postulated that EBV triggers SLE immunopathology , although the mechanism remains elusive . Here , we investigate whether frequent peaks of EBV viral load in SLE patients are a consequence of dysfunctional anti-EBV CD8+ T cell responses . Both inactive and active SLE patients ( n = 76 and 42 , respectively ) , have significantly elevated EBV viral loads ( P = 0 . 003 and 0 . 002 , respectively ) compared to age- and sex-matched healthy controls ( n = 29 ) . Interestingly , less EBV-specific CD8+ T cells are able to secrete multiple cytokines ( IFN-γ , TNF-α , IL-2 and MIP-1β ) in inactive and active SLE patients compared to controls ( P = 0 . 0003 and 0 . 0084 , respectively ) . Moreover , EBV-specific CD8+ T cells are also less cytotoxic in SLE patients than in controls ( CD107a expression: P = 0 . 0009 , Granzyme B release: P = 0 . 0001 ) . Importantly , cytomegalovirus ( CMV ) -specific responses were not found significantly altered in SLE patients . Furthermore , we demonstrate that EBV-specific CD8+ T cell impairment is a consequence of their Programmed Death 1 ( PD-1 ) receptor up-regulation , as blocking this pathway reverses the dysfunctional phenotype . Finally , prospective monitoring of lupus patients revealed that disease flares precede EBV reactivation . In conclusion , EBV-specific CD8+ T cell responses in SLE patients are functionally impaired , but EBV reactivation appears to be an aggravating consequence rather than a cause of SLE immunopathology . We therefore propose that autoimmune B cell activation during flares drives frequent EBV reactivation , which contributes in a vicious circle to the perpetuation of immune activation in SLE patients .
Systemic lupus erythematosus ( SLE ) is a chronic autoimmune disorder . Common manifestations include inflammation and tissue damage of skin and joints as well as inner organs , such as brain and kidneys , in severe cases . The disease can be fatal , but with recent medical advances , mortality is reduced significantly . The course of the disease is unpredictable , with peak periods of illness ( active SLE ) alternating with periods of remission ( inactive SLE ) . SLE-related autoimmune symptoms can be triggered by environmental factors , such as ultraviolet light , drugs and viruses . [1] , [2] In this regard , it has been reported that lupus patients have elevated antibody responses to the gamma-herpesvirus EBV [3] , [4] and that this antibody response shows cross-reactivity to nuclear self antigens . [5] , [6] , [7] , [8] Primary EBV infection typically occurs during childhood without apparent clinical symptoms and evolves into a non-symptomatic life-long virus carrying latency . Rare cases of infection in early adulthood lead to infectious mononucleosis ( IM ) , which has been linked to increased risk of Hodgkin's lymphoma [9] and to the onset of autoimmune diseases , such as Multiple Sclerosis ( MS ) [10] and less documented cases of rheumatoid arthritis ( RA ) and SLE , as reviewed by Münz et al . [2] Detectable levels of lytic EBV antigen , BZLF1 , were observed more frequently in SLE patients ( 35% ) than in healthy controls ( 0% ) , suggesting recurrent EBV replication in SLE patients . [11] In line with this observation , several groups demonstrated that EBV viral load is elevated in SLE patients , [12] , [13] and that the number of infected B cells monitored longitudinally is positively correlated with the SLE disease activity index ( SLEDAI ) . [11] However , the mechanisms linking EBV to SLE immunopathology still remain elusive . On the one hand , EBV-related disorders are often observed as a consequence of immunodeficiency in hosts , such as bone marrow transplant patients . [14] On the other hand , it is debated that EBV transformation can support the survival of self-reactive B cells . [2] It has furthermore been demonstrated that EBV nuclear antigen 1 ( EBNA1 ) is capable of inducing T [15] , [16] and B cell responses [5] , [6] , [7] , [8] cross-reactive to auto-antigens , and thus potentially induce auto-immunity . Of note , IM patients have cross-reactive antibody responses to EBNA1 and the common lupus spliceosomal autoantigen Sm B' during the most severe acute phase of IM , [17] suggesting a connection between the immunopathology of EBV-induced IM and SLE . [18] It was reported in an early study that T cells from SLE patients are unable to control immunoglobulin production from EBV-exposed B cells . [19] Subsequently , Kang et al . observed that lupus patients had elevated frequencies of interferon-γ ( IFN-γ ) secreting EBV-specific CD4+ T cells , whereas no significant modification was observed for IFN-γ secreting EBV-specific CD8+ T cells . [12] Similarly , Berner et al . reported that the frequency of EBV-specific CD8+ T cells did not differ between SLE patients and healthy controls , when analysed using peptide-MHC tetramer probes . However , the capacity of EBV-specific CD8+ T cells to secrete IFN-γ seemed reduced in SLE patients compared to healthy controls . [20] Altogether , whether the defective control of latent EBV infection in SLE patients is related to a CD8+ T cell defect remains controversial . [11] , [12] , [13] Furthermore , it is unclear whether the defect is EBV-specific or global . Finally , the sequence in which EBV re-activation and disease onset occurs is unresolved . Here , we assess quantitative and qualitative attributes of EBV-specific CD8+ T cells from SLE patients . We show that the frequencies of IFN-γ , tumour necrosis factor-α ( TNF-α ) , interleukin-2 ( IL-2 ) and Macrophage Inflammatory Protein 1β ( MIP-1β or CCL4 ) secretion by EBV-specific CD8+ T cells upon antigen stimulation are diminished in SLE patients compared to healthy controls . We furthermore demonstrate that EBV-specific T cells from SLE patients exhibit a marked impairment in their cytotoxic granule exocytosis process . We finally associate the dysfunctional T cell phenotype with the up-regulation of the inhibitory receptor programmed death 1 ( PD-1 ) , and strengthen this association by reversing the dysfunctional T cell phenotype through specific blockade of the PD-1 signaling pathway . In line with previous findings , EBV viral load was found to be elevated in SLE patients compared to healthy controls . Interestingly , longitudinal monitoring revealed that bursts of viral load always occurred in a delayed manner with respect to disease flare onset .
To study the impact of EBV infection on SLE immunopathology , we established a cohort of SLE patients and age- and sex-matched healthy controls . Patient characteristics and treatments are presented in Table 1 . We validated that the patients displayed the EBV associated features identified in literature , [3] , [4] such as increased EBV seroprevalance ( P = 0 . 006 ) and augmented anti-EBV antibody titers ( P<0 . 0001 ) ( Table 1 ) . Furthermore , we confirm that cell-associated EBV viral load is augmented in EBV seropositive SLE patients , when compared with EBV seropositive healthy controls . [12] , [13] Thus , cell-associated EBV DNA is more frequently above detection threshold in SLE patients than in healthy controls ( Figure 1 ) . In comparison , CMV was below detection threshold in the majority of study subjects ( Healthy: 0 of 18; SLE: 5 of 93 , P = 0 . 59 ) . We then explored whether cell-associated EBV viral load is linked with disease activity . As shown , EBV was as frequently detectable in inactive as in active patients ( Figure 1 ) . EBV viral loads were not influenced by any treatment-related parameters ( corticosteroids , hydroxychloroquine and other immunosuppressors – see Table 1 ) according to a multivariate analysis ( P = 0 . 40 , 0 . 21 and 0 . 24 , respectively , n = 118 ) . In order to address whether increased EBV viral loads in SLE patients could be due to a T cell functional defect , we compared phenotypic and functional characteristics of lytic ( BMLF1 , BMRF1 , BZLF1 ) and latent ( EBNA3A and EBNA3B ) EBV-specific CD8+ T cell responses between patients with SLE and healthy controls . Using HLA/peptide tetramers , we quantified circulating lytic and latent EBV- and CMV pp65-specific CD8+ T cells in patients and controls ( Figure 2A and Figure S1A in Text S1 ) . As shown , inactive and active SLE patients have slightly elevated frequencies of lytic EBV- , and comparable frequencies of latent EBV- and CMV-specific CD8+ T cells compared to healthy controls ( Figure 2B and Figure S1B in Text S1 ) . However , the elevated lytic EBV-specific CD8+ T cell frequency is counterbalanced by a general lymphopenia ( Figure S2A in Text S1 ) . Thus , absolute counts of lytic EBV-specific CD8+ T cells in SLE patients are comparable ( inactive SLE patients ) or even slightly decreased ( active SLE patients ) as compared to healthy controls ( Figure S2B in Text S1 ) . MHC class I tetramer positive EBV- and CMV-specific CD8+ T cells were then tested for their capacity to secrete IFN-γ , TNF-α , IL-2 and MIP-1β in response to stimulation with EBV and CMV cognate antigens ( Figure 2A ) . We found that CD8+ T cells from inactive and active SLE patients specific for lytic EBV antigens are functionally impaired in their capacity to secrete IFN-γ ( P = 0 . 003 and 0 . 021 , respectively ) , TNF-α ( P = 0 . 005 and 0 . 004 , respectively ) , IL-2 ( P = 0 . 004 and 0 . 0001 , respectively ) and MIP-1β ( P = 0 . 001 and 0 . 0001 , respectively ) compared to T cells from healthy controls ( Figure 2C – upper panel ) . The impairment is also observed as a decline in the absolute number of circulating cytokine-secreting EBV-specific CD8+ T cells ( Figure S2C in Text S1 ) . Moreover , the proportion of EBV-specific CD8+ T cells able to secrete multiple cytokines is reduced in patients compared to controls ( Figure 2D – upper panel ) . Similarly , we observed that CD8+ T cells from SLE patients specific for latent EBV antigens tend to have reduced capacity to secrete IFN-γ ( Figures S1A and S1C in Text S1 ) . In contrast , CMV-specific cytokine responses are well preserved in inactive and active SLE patients ( Figure 2C – lower panel ) . Likewise , polyfunctionality of CMV-specific CD8+ T cells do not differ significantly between patients and controls ( Figure 2D – lower panel ) . Importantly , impaired functionality of EBV-specific CD8+ T cells is not related to treatments ( corticosteroids , hydroxychloroquine and other immunosupressors ) according to a multivariate statistical analysis ( All treatment parameters were non-significant for the prediction of IFN-γ- , IL-2- , MIP-1β- and TNF-α-secretion , n = 46 ) . We then investigated whether EBV-specific CD8+ T cells from SLE patients are also less cytotoxic than their healthy counterparts . We measured the capacity of EBV-specific CD8+ T cells to degranulate by monitoring the appearance of degranulation marker LAMP-1 ( CD107a ) on the cell surface ( Figures 3A–B ) and granzyme B release ( Figures 3C–D ) , prior to and following stimulation with cognate antigen . Surface exposed CD107a is inversely correlated with granzyme B release , and thus a marker of recent history of cytotoxic activity . [21] As shown , CD8+ T cells from SLE patients specific for lytic EBV antigens carry similar loads of granzyme B ( Figure 3D – upper left panel ) , but are dramatically less able to degranulate ( P = 0 . 0009 , Figure 3B upper panel ) and release their cytotoxic content ( P = 0 . 0001 , Figure 3D – upper right panel ) following stimulation , compared to EBV-specific CD8+ T cells from healthy controls . A similar impairment of cytotoxic activity was observed for CD8+ T cells specific for latent EBV antigens ( CD107a , P = 0 . 050 ) ( Figures S1A and S1C in Text S1 ) . In contrast , CMV-specific CD8+ T cells from SLE patients retain their cytotoxic potential ( Figures 3B and 3D lower panels ) . We conclude from this first set of experiments that there is an EBV-specific CD8+ T cell functional defect in SLE patients , the latter cells being impaired in their capacity to secrete multiple effector cytokines and in their cytotoxic granule exocytosis process . To investigate the mechanism of EBV-specific CD8+ T cell dysfunction , we performed a comparative combinatorial analysis of markers expressed by SLE versus control CD8+ T cells . We measured expression levels of a range of differentiation ( CD45RA , CCR7 , CD27 , CD57 , FoxP3 ) , co-stimulatory/co-inhibitory ( CTLA-4 , ICOS , PD-1 , CD80 , CD86 , 41BBL , ICOSL and PD-L1 ) , activation ( HLA-DR , CD69 and CD38 ) and proliferation ( Ki-67 ) markers on EBV-specific cells and total CD8+ T cells . We found that the balance between central memory , effector memory and naïve CD8+ T cell subsets is not altered in SLE patients , compared to healthy controls ( data not shown ) . However , proliferation ( Ki-67 ) and activation ( HLA-DR , CD69 and CD38 ) markers are significantly up-regulated on total CD8+ T cells in active SLE patients and less pronounced in inactive SLE patients compared to controls ( Figures S3A–B in Text S1 ) . Also EBV-specific T cells show a trend to be more activated in SLE patients compared to healthy controls ( Figure S3C in Text S1 ) . In addition , we found that whereas inhibitory receptor CTLA-4 expression is conserved ( Figure S4A in Text S1 ) , PD-1 expression is up-regulated on total CD8+ T cells ( p = 0 . 005 and 0 . 008 for inactive and active SLE , respectively ) compared to healthy controls ( Figure S4B in Text S1 ) . Interestingly , polyclonal stimulation of CD8+ T cells with Staphylococcal Enterotoxin B ( Figure S4C in Text S1 ) , anti-CD3 and anti-CD28 antibodies ( Figure S4D in Text S1 ) or PMA-Ionomycin ( Figure S4E in Text S1 ) mounted lower responses in SLE patients compared to healthy controls . Importantly , EBV-specific CD8+ T cells represent one of the T cell subsets expressing high PD-1 levels in SLE , compared to controls ( Figure 4A; p = 0 . 0004 ) . In contrast , CMV-specific CD8+ T cells from SLE patients do not express elevated levels of PD-1 ( Figure 4A ) . Since PD-1 expression has previously been associated with impaired cellular functionality , [22] we then asked whether increased PD-1 expression by EBV-specific CD8+ T cells from SLE patients could account for their impaired functional capacity . In HIV-infected patients , it was shown that blockade of the PD-1 inhibitory pathway can restore CD8+ T cell functionality . [23] We therefore tested the influence of the PD-1 signaling pathway on EBV-specific CD8+ T cells by blocking PD-1 signaling with antagonistic antibodies specific for PD-1's two known ligands , PD-L1 and PD-L2 . Blockade of PD-1 signaling during lytic and latent EBV antigen stimulation substantially boosted general T cell proliferation ( Figure 4B ) , EBV-specific T cell expansion ( Figure 4C ) and IFN-γ secretion ( Figure 4D ) in PBMC cultures from SLE patients but not from healthy controls . In contrast , blockade of PD-1 signalling during CMV antigen stimulation neither boosted general T cell proliferation ( Figure 4B ) nor CMV-specific T cell expansion ( Figure 4C ) or IFN-γ secretion ( Figure 4D ) . We conclude that the PD-1 inhibitory pathway appears to have a particularly important deleterious impact on lytic and latent EBV-specific CD8+ T cell responses in SLE patients . Although EBV replication was found increased both in active and inactive patients , we reasoned that only longitudinal studies would clearly decipher whether EBV viral bursts precede or follow disease flares . In order to address this issue , SLEDAI and EBV viral load were longitudinally recorded from initiation of disease flare to clinical and biological recovery in 6 established SLE patients ( Figure 5A ) and 5 healthy controls ( Figure 5B ) . An increase of EBV viral load was observed in all SLE patients ( Figure 5A ) . In contrast , EBV remained below detection levels in the 5 healthy controls monitored during the 8 weeks follow-up ( Figure 5B ) . Importantly , viral replication peaked 1 week or more post flare onset in all 6 patients followed longitudinally , EBV being below detection level in 4 of these patients at time of hospital admission ( Figure 5A ) . We confirmed in the cross-sectional series of flaring patients that EBV was below detection levels in 5 out of 7 cases studied at the time of their hospital admission . We conclude from these cross-sectional and longitudinal studies that early clinical symptoms of SLE do not coincide with high EBV viral load .
Alterations in the control of EBV infection in individuals susceptible to lupus are suspected to promote the development of autoimmunity through multiple mechanisms , such as cross-reactive antibody and T cell responses . [24] Here we show that SLE patients have recurrent bursts of EBV viral load . We furthermore associate this altered control of EBV infection with a PD-1 induced impairment of T cell mediated immune surveillance of EBV . Virus-specific T cells play a crucial role in the control of EBV infection , and have already been the focus of previous studies in human SLE . [12] , [19] Berner et al . addressed the issue by combining MHC-peptide tetramer staining with IFN-γ ELISPOT analysis . Based on these tests , it was suggested that EBV-specific T cells from SLE patients might have impaired IFN-γ secreting capacity . [20] The latter study was however hampered by limitations in cohort size , and by the fact that function and frequency of EBV-specific CD8+ T cells were not monitored simultaneously at the single cell level . The present study was designed to concurrently assess the quality and quantity of EBV-specific CD8+ T cell responses . This was achieved by combining the analysis of IFN-γ , TNF-α IL-2 , MIP-1β , CD107a and granzyme B on MHC class I tetramer-stained EBV-specific CD8+ T cells stimulated with their cognate antigen . Being able to enumerate not only frequencies of responses , but also proportions of functional cells among EBV-specific CD8+ T cells , we clearly establish that EBV-specific CD8+ T cells are present at slightly elevated frequency but functionally impaired in SLE patients . Indeed , EBV-specific T cells from SLE patients exhibit a reduced capacity to secrete IFN-γ , TNF-α , IL-2 and MIP-1β and an impaired cytotoxic granule exocytosis process . The increased frequency of CD8+ T cells specific for lytic EBV antigens is most likely due to recurrent EBV replication . However , the elevated frequency is counterbalanced by a global T cell lymphopenia , which is a common clinical feature of SLE . [25] Furthermore , functional impairment at the single-cell level coincides with a diminished absolute number of functional EBV-specific CD8+ T cells in SLE patients . Interestingly , there was no direct inverse correlation between EBV-specific cell function ( cytokine secretion and cytotoxicity ) and EBV viral load ( data not shown ) . This is probably related to the fact that EBV viral loads fluctuate relatively rapidly ( Figure 5 ) and frequently enough to have a long lasting imprint on T cell functions . A link between CMV and SLE has also been debated due to the fact that more frequent CMV seropositivity and elevated CMV viral loads have been reported in SLE patients in a single study . [26] SLE patients from the present study were also found more frequently seropositive for CMV than healthy controls ( Table 1 ) . However , CMV viral loads were not found elevated and dysfunctional anti-CMV T cell responses were not observed in SLE patients , compared to healthy controls . Altogether , the immune alterations described in our study affect preferentially EBV-specific responses and not responses to another herpesvirus , CMV . The impaired functional status of EBV-specific T cells in SLE patients could be due to an alteration in their phenotype , possibly caused by recurrent exposure to EBV antigens . We observed ( Figure S3 in Text S1 ) that proliferation marker Ki-67 and activation markers CD69 , HLA-DR and CD38 were up-regulated on CD8+ T cells from SLE patients as previously reported . [20] , [27] , [28] Taken together , this demonstrates that T cell hyper activation and hyper proliferation are essential factors in SLE pathophysiology . PD-1 has previously been associated with diminished functional capacity [22] and up-regulation is commonly observed on chronically stimulated antiviral T cells . [23] , [29] Of note , a single nuclear polymorphism ( SNP ) within the gene encoding the PD-1 receptor has been identified as an inheritable risk factor of SLE . [30] We therefore reasoned that the PD-1 receptor could be involved in the EBV-related immune alterations observed in SLE patients . As shown , compared to control lytic EBV-specific CD8+ T cells , PD-1 surface expression levels are indeed up-regulated on lytic EBV-specific CD8+ T cells from SLE patients . The functional relevance of this marker was corroborated by the fact that blocking PD-1 signaling restores both lytic and latent EBV-specific CD8+ T cell function . PD-1 expression is not only up-regulated on EBV-specific CD8+ T cells but also , most likely , on pathogenic T cells , since elevated PD-1 levels are observed on the global CD8+ T cell compartments ( Figure S4B in Text S1 ) . We also observed that not only EBV-specific T cells show signs of impairment in SLE patients as polyclonal stimulation reveal significantly diminished cytokine responses in the global CD8+ T cell compartment ( Figures S4C-E in Text S1 ) . Therefore PD-1 up-regulation in SLE patients might represent an important regulatory mechanism , limiting the severity of pathogenic T cell responses . This view is also supported by the fact that a recessive PD-1 knock-out SNP is overrepresented in families of individuals suffering from SLE , [30] suggesting a protective role for PD-1 regulation in SLE immunopathogenesis . It is still debated whether EBV reactivation is a cause or consequence of SLE disease activity . We first noted that EBV replication in our initial cross-sectional studies is usually undetectable at time of hospital admission for SLE flare ( 5 out of 7 cases ) . To address this issue more directly we longitudinally followed patients starting at their first hospital visit after initiation of disease flare until flare resolution . In this way we observed that EBV replication is maximal post flare onset . The relatively narrow window of EBV replication assessed through longitudinal analysis suggests that cross-sectional studies most probably underestimate the occurrence of EBV reactivation in active patients . This would explain why no significant differences were recorded between active and inactive patients in terms of EBV viral loads ( Figure 1 ) . More longitudinal studies will be necessary to formally rule out the implication of EBV in the triggering of SLE flares . In particular , it would be interesting to monitor EBV not only at flare onset , but also shortly before active disease . Nevertheless our results strongly suggests that EBV replication is more likely a result of B cell activation associated with active disease , rather than a triggering factor for disease re-activation . However , EBV can contribute to the vicious circle of autoimmunity in several ways . As previously mentioned , EBV can be responsible for the induction of cross-reactive B and T cell responses . [15] , [16] Moreover , it was shown in healthy individuals that EBV induces type 1 interferon ( IFN ) production by plasmacytoid dendritic cells , [31] a subset of cytokines which are central features of SLE active disease . [32] Thus , iterative episodes of viral replication could account , at least in part , for the over-expression of IFN and IFN-induced genes observed in SLE . [33] , [34] The potential implications of EBV in SLE immunopathology in relation to an impaired EBV-specific T cell response suggest that pharmaceutical or immunological anti-EBV interventions might potentially be beneficial to these patients . In conclusion we propose a model where autoimmune-driven B cell activation [35] , [36] induces an activation of the EBV lytic cycle in infected B cells , which leads to a burst of EBV replication . In response , EBV-specific T cells are activated in order to control viral replication and may eventually cross-react with self antigens and lead to auto-immune manifestations . EBV-induced IFN may also take part in SLE immunopathology . Repetitive episodes of viral replication ultimately results in PD-1 mediated impairment of EBV-specific cytotoxic and cytokine-secreting T cells . This impairment partially limits the risks of cross-reactive tissue injuries , but at the same time explains why EBV replication is less suppressed in SLE patients . Association between SLE and EBV has been studied for 40 years , and EBV remains suspected to induce SLE early on in life . [37] , [38] In established SLE disease , it is debated whether autoimmunity is triggered by reactivation of pathogens , such as EBV or vice versa . [2] In our study of adults with established disease , frequent EBV reactivation appears to be an aggravating consequence , rather than a cause , of SLE immunopathology . Future studies are needed to elucidate whether EBV contributes to the initiation of disease in young healthy individuals .
All samples were obtained following acquisition of the study participants' and/or their legal guardians' written informed consent . The study protocol was reviewed and approved by the local ethics committees ( Comité de Protection des Personnes Ile de France VI ) . We enrolled a total of 149 study subjects , including 118 consecutive SLE patients , defined according to the American College of Rheumatology classification criteria , [39] as well as 31 healthy ( H ) control subjects . SLEDAI for individual SLE patients was determined at the time of sample collection . [40] SLE patients were subdivided in two groups consisting of 76 inactive ( SLEDAI<6 ) and 42 active ( SLEDAI≥6 ) SLE patients . Included subjects were then selected according to their HLA genotype ( HLA-A*0201 , A*1101 , B*0702 , B*0801 ) , for which well characterized EBV and CMV peptide antigens have been described . [41] , [42] , [43] , [44] The serological status of EBV and CMV were measured by serum ELISA ( BIO Advance , France ) according to the manufacturer's instructions . Both EBV and CMV DNA loads were measured using in-house real-time PCR assays . EBV and CMV PCRs were carried out on the same DNA extract obtained from peripheral blood mononuclear cells ( PBMCs ) or total blood for longitudinal studies , using the QIamp Blood DNA kit ( Qiagen , France ) according to the manufacturer's instructions . Real-time quantitative PCRs based on hydrolysis probe technology were carried out on a LightCycler 480 ( Roche Diagnostics , France ) as previously described by Deback et al . [45] Real-time PCR accuracy was previously confirmed by the Quality Control for Molecular Diagnosis ( QCMD ) 2008 proficiency panel . The human albumin gene was quantified in each DNA sample , to enable quantitation of the copy number per million cells of EBV and CMV . Directly conjugated and unconjugated antibodies were obtained from the following providers: BD Biosciences ( San Jose , CA ) : Ki-67 [FITC] , HLA-DR [PE–cyanin 7] , CD38 [Alexa Fluor 700] , CTLA-4 [cyanin 5-PE] , CD107a [cyanin 5–PE] , Granzyme B [A647] , IFN-γ [Alexa Fluor 700] , IL-2 [APC] and TNF-α [PE–cyanin 7]; R&D Systems ( Abingdon , UK ) : MIP-1β [FITC] , PD-1 [FITC]; Caltag ( Burlingam , CA ) : CD8 [Alexa Fluor 405]; Dako ( Glostrup , Denmark ) : CD3 [cascade yellow] and BioLegend ( San Diego , CA ) : CD69 [APC-Cy7] . Peptide/MHC tetramers were produced as previously described [41] and included the following epitopes: HLA-A*0201 CMV pp65-NV9; HLA-A*0201 EBV BMLF1-GL9 and BMRF1-YV9; HLA-A*1101 EBV EBNA-3B IK9 , EBNA-3B AK10; HLA-B*0702 CMV pp65-TM10; HLA-B*0801 EBV BZLF1-RL8 and EBNA-3A-FL9 . PBMCs isolated on ficoll gradients ( PAA , France ) were stained with titrated antibodies specific for cell surface markers , followed by staining for intra-cellular Ki-67 , according to manufacturer's recommendation . For polyfunctional analysis , PBMCs were stimulated in the presence of peptide antigen ( 5 µM ) and PE-Cy5 conjugated anti-CD107a antibody over night at 37°C in a 5% CO2 incubator . Cytokine secretion was blocked by the addition of 2 . 5 µg/ml monensin and 5 µg/ml Brefeldin A ( Sigma-Aldrich , St . Louis , MO ) . Cells were then stained with corresponding PE-conjugated peptide MHC class I tetramer ( 0 . 5 µg per 106 cells ) and directly conjugated anti-CD3 and anti-CD8 antibodies . Cells were then fixed and permeabilized with Cytofix/Cytoperm ( BD Biosciences ) according to manufacturer's instructions . Finally , cells were stained with anti-cytokine antibodies and/or anti-granzyme B antibody for 15 minutes at room temperature . Samples were acquired on a BD LSRII flow cytometer ( Becton Dickinson ) with appropriate isotype controls and color compensation . Data were analysed with FACSDiva ( BD Biosciences ) and FlowJo ( TreeStar Inc ) softwares . Unstimulated cells for each sample , treated under the same experimental conditions served as negative controls , and background values were subtracted from the analysis of the stimulated samples . PBMCs were cultured for 10 days at 37°C 5% C02 , in RPMI supplemented with 5% human serum and a cytokine cocktail mix ( 20 ng/ml of IL-7 and 20 ng/ml IL-2 ( R&D Systems , Minneapolis , MN ) ) . Cells were stimulated with or without EBV or CMV peptide ( 1 µg/ml ) in the presence of either isotype control antibodies or both anti-PD-L1 and anti-PD-L2 ( 10 µg/ml ) . On day 10 , cells were re-stimulated with peptide ( 1 µg/ml ) overnight and proliferation and functionality was assessed by cell counting and flow cytometry . Antagonistic antibodies were kindly provided by Pr . Gordon Freeman ( Dana Farber Institute , Boston ) . Clinical information and flow cytometric analysis were gathered in a database ( Office Access 2003 , Microsoft France , Issy-les-Moulineaux , France ) . Differences of continuous variables between patient groups were tested using the Mann-Whitney U-test ( unpaired ) and the Wilcoxon matched pairs test ( paired ) . Differences of categorical variables , such as sex and detectable viral load , between groups were tested with Fisher's exact test . All tests were 2-sided and a p value <0 . 05 was considered statistically significant . To exclude the influence of treatment-related factors on EBV viral load and EBV specific CD8+ T cell cytokine secretion we built multivariate regression models . In these models , EBV viral load and EBV specific CD8+ T cell cytokine secretion were used as dependent variables , and all treatment-related variables were included as explanatory variables . Statistical analysis was performed using GraphPad Prism Ver . 4 . 03 ( GraphPad Software Inc ) , JMP7 ( SAS Software , NC , USA ) , Pestle Ver . 1 . 6 . 2 and Spice Ver . 4 . 2 . 3 ( Mario Roederer , ImmunoTechnology Section , VRC/NIAID/NIH ) softwares . [46] | Systemic Lupus Erythematosus ( SLE ) has been associated with Epstein-Barr Virus ( EBV ) infection for decades , however the mechanistic links have remained elusive . Most human adults are infected by EBV and carry the virus for life without clinical symptoms . However , for unknown reasons EBV induces infectious mononucleosis in some individuals , during which cross-reactive antibodies specific for both virus and self have been detected . Interestingly , such cross-reactive antibodies are also frequently found in SLE patients . Since , EBV seropositivity and viremia are more frequent in SLE patients than in healthy individuals , it has been postulated that EBV trigger autoimmunity . Here we show that SLE patients are indeed less capable of controlling EBV viremia , since their EBV-specific CD8+ T cells have diminished capacity to secrete effector molecules ( e . g . cytokines and chemokines ) and to kill EBV-infected targets as a consequence of their Programmed Death 1 ( PD-1 ) receptor up-regulation . Longitudinal studies further reveal that disease flares precede EBV viremia . Thus , contrary to expectations , EBV reactivation appears to be an aggravating consequence , rather than a cause , of SLE immunopathology . Our results pave the way for immunological interventions that restore the host-EBV balance , which may result in decreased levels of aggravating cross-reactive antibodies and ultimately be beneficial to SLE patients . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"immune",
"cells",
"clinical",
"immunology",
"immunity",
"lupus",
"erythematosus",
"autoimmune",
"diseases",
"t",
"cells",
"immunity",
"to",
"infections",
"viral",
"diseases",
"immunology",
"epstein-barr",
"virus",
"infectious",
"mo... | 2011 | Exhausted Cytotoxic Control of Epstein-Barr Virus in Human Lupus |
Here we report that the change from the red seeds of wild rice to the white seeds of cultivated rice ( Oryza sativa ) resulted from the strong selective sweep of a single mutation , a frame-shift deletion within the Rc gene that is found in 97 . 9% of white rice varieties today . A second mutation , also within Rc , is present in less than 3% of white accessions surveyed . Haplotype analysis revealed that the predominant mutation originated in the japonica subspecies and crossed both geographic and sterility barriers to move into the indica subspecies . A little less than one Mb of japonica DNA hitchhiked with the rc allele into most indica varieties , suggesting that other linked domestication alleles may have been transferred from japonica to indica along with white pericarp color . Our finding provides evidence of active cultural exchange among ancient farmers over the course of rice domestication coupled with very strong , positive selection for a single white allele in both subspecies of O . sativa .
Human efforts to domesticate plant and animal species began thousands of years before recorded history , leaving us to guess at the methods that transformed wild species into agriculturally important crops and livestock . Rice , Oryza sativa , was domesticated in Asia and is now grown on every continent throughout the world , with the exception of Antarctica . The species consists of two major subspecies , indica and japonica , whose separate genetic identities were recognized in ancient times and are maintained by sterility barriers coupled with the inbreeding habit of O . sativa [1–3] . The indica and japonica subspecies can be further subdivided into five genetically distinct subpopulations . Several estimates have been made for the time of divergence between indica and japonica based on intron sequence and retrotransposon insertions , and all of these calculations place the time to the recent common ancestor at more than 100 , 000 years ago [4–6] . This divergence time is an order of magnitude larger than the oldest estimates for rice domestication . The data strongly support at least two independent domestications of O . sativa from predifferentiated pools of the O . rufipogon wild ancestor [3 , 4 , 7 , 8] . With such evidence for independent domestications , we would expect to see different mutations conditioning domestication traits fixed within the different subspecies . We recently cloned the Rc gene , a bHLH protein , which is the only reported locus in rice that effects a change from red grain to white grain [9] . The change in pericarp color from red to white is an important hallmark of rice domestication . Most rice cultivars grown and consumed throughout the world today have a white or beige pericarp , the color of unpolished “brown rice” while all known accessions of the wild ancestor , O . rufipogon , invariably have grains with a red pericarp . Here , we investigate the number and origin of mutations that give rise to diverse white-grained rice cultivars from around the world , and the extent to which they were disseminated throughout the genetically and geographically diverse range of O . sativa . The evolutionary history of the Rc locus provides clear documentation that a single mutation moved rapidly from the japonica into the indica subspecies , while an independent mutation in the aus subpopulation was not widely disseminated during rice domestication .
To determine the frequency and distribution of the 14-bp deletion in the Rc gene that has been shown to cause white pericarp in rice [9 , 10] , a set of 440 geographically and genetically diverse rice cultivars were genotyped using the rice indel RID 12 primers reported in Sweeney et al . ( 2006 ) . Our panel of varieties was selected to represent the range of diversity within Asian cultivated rice . It included both landrace and modern varieties from the five well-defined subpopulations of O . sativa [3] ( Figure 1 ) ( 204 indica , 33 aus , 87 temperate japonica , 99 tropical japonica , and 17 aromatic rices ) collected in 24 different countries on three different continents ( Table S1 ) . Molecular polymorphism data groups the indica and aus subpopulations within the indica subspecies and the tropical japonica , temperate japonica , and aromatic subpopulations within the japonica subspecies [2 , 3] . One hundred percent of the varieties from the indica , tropical japonica , and temperate japonica populations ( n = 311 ) with white pericarp contained an identical 14-bp deletion in the bHLH gene; this deletion was found in none of those with red pericarp ( n = 103 ) ( Figure 2 ) . This mutation was also found in 15 of the 17 white aromatic and four of the nine white aus varieties . Thus , a single 14-bp deletion in the Rc gene is present in both subspecies and in all five subpopulations and is responsible for white pericarp in 97 . 9% ( 330/337 ) of rice varieties surveyed . To identify mutations that could explain the lack of pigment in the seven white rice varieties that did not contain the 14-bp deletion , we sequenced the entire coding region of Rc in two of the white aus varieties and one white aromatic variety that lacked the 14-bp deletion . As a control , nine red pericarp varieties from different subpopulations were also sequenced . A SNP ( C→A ) in exon 6 of the Rc gene distinguished these white- and red-grained rices . This SNP introduces a premature stop codon that truncates the protein before the bHLH domain . Further sequencing confirmed that this SNP was predictive of white pericarp in all seven white rices that did not contain the 14-bp deletion in Rc ( DQ902346-DQ902352 ) . Thus , the C→A SNP in exon 6 represents an independent mutation in the Rc gene that was not found in any of the indica , tropical , or temperate japonica varieties but exists at moderate frequency in white aus varieties ( five of nine , 55 . 5% ) and in two accessions out of 17 aromatic cultivars ( Figure 2 ) . To determine the subpopulation origin of the Rc mutations , we examined ancestral haplotypes across the Rc coding sequence and promoter region in 103 genetically diverse , red-grained rices . Four rice insertion/deletion polymorphism ( RID ) and two rice microsatellite ( RM ) polymorphisms were used to construct haplotypes across the 6 . 5-kb region containing the Rc gene ( Figure 2 ) . A single haplotype , H1 , was detected among the 44 red-grained tropical and temperate japonica plants; we will refer to this as the ancestral japonica haplotype . No red rices belonging to the aromatic subpopulation were available for in this study . Five closely related haplotypes were identified among the 24 red aus and 35 red indica landraces , and all are clearly differentiated from the japonica haplotype . Haplotypes 6–7 and 9–11 ( Figure 2 ) are derivatives of each other and the similarity among them reflects the close evolutionary relationship between the indica and aus subpopulations [3] . When all red-grained indica , aus , and japonica accessions ( n = 103 ) are compared , it can be seen that they share the wild-type alleles at both functional markers ( the non-deletion allele at RID12 and the C allele at SNP marker RS40 , Figure 2 ) . All flanking marker alleles were unique to one of the subspecies or subpopulation groups . Additionally , we collected ∼6 , 350 bp of sequence data for the Rc gene and ∼650 bp of downstream DNA from four red indicas and four red japonica varieties . Within this 7-kb region there are 21 SNPs whose alleles are subspecies specific . Thus , there are clearly differentiated haplotypes that distinguish the ancestral japonica gene pool ( Haplotype Group A ) from the ancestral indica and aus gene pools ( Haplotype Group B ) across this genic region . Based on these ancestral haplotypes , we were able to trace the origin of both functional mutations leading to white pericarp . The most common haplotype of white pericarp rices , H2 , contained the 14-bp deletion at RID12 , and differed from the ancestral japonica haplotype , H1 , only at this functional mutation . In contrast , the white H2 haplotype differed from the aus and indica red haplotypes ( Haplotype Group B ) at every marker tested ( Figure 2 ) . This provides strong support for the conclusion that the H2 haplotype originated in a japonica ancestor . White haplotypes H3 and H4 are derived from H1 , differing at a single marker locus in each case . Haplotype 5 provides evidence of a putative recombination event . It is found only in indica accessions and cultivars carrying this haplotype have ancestral indica alleles across the promoter and first intron of Rc and ancestral japonica alleles across the remaining sequence of the gene , including the 14-bp deletion . These data strongly support a single origin of the 14-bp deletion from a japonica background that then was introgressed into indica and aus . Sequence data ( 7 kb across Rc , Table S2 ) from white accessions with the deletion ( eight indica , two aus , eight tropical japonica , two temperate japonica , and one aromatic ) and eight red accessions from both the indica and japonica subspecies were used to create a gene tree ( Figure 3 ) . If the Rc gene tree and the tree created using genome-wide SSR data [3] had similar typologies , it would suggest an independent origin of white pericarp in indica and japonica . However , these trees are not consistent , suggesting introgression from one subspecies into the other . Accessions with red pericarp share a similar distribution pattern in both tress , but all whites with the deletion have japonica alleles at the 21 subspecies specific SNPs within the Rc gene and form a cluster with the red japonicas regardless of their subpopulation identity ( Figure 3 ) . Thus , phylogenetic analysis confirms the japonica origin of the 14-bp deletion . Together , the lack of concordance between the Rc gene and rice genomic trees and the marker analysis provides strong support for the conclusion that the 14-bp deletion conferring white pericarp in rice arose once in the japonica gene pool and was widely introgressed into indica and aus landraces . The presence of this deletion within 97 . 9% of white-grained rice varieties found throughout the world today suggests either that the gene was dispersed during the early phases of domestication and is common by descent in modern varieties or a that very strong , positive selection for the allele lead to its introgression and maintenance in already established gene pools . The second mutation Rc-s is only found in haplotype 8 and is restricted to six aus and two aromatic accessions . These white-grained varieties share no marker alleles with any of the tropical or temperate japonica accessions , suggesting that the origin of this allele is different from that of the 14-bp deletion . H8 differs from H7 , a red aus haplotype , by only the functional C→A SNP . Since H7 is restricted to the aus subpopulation , we conclude that the C→A functional polymorphism originated in the aus subpopulation and that this allele was not widely disseminated during the domestication of O . sativa . How much DNA of japonica ancestry was introgressed into indica cultivars along with the 14-bp deletion conferring white pericarp is of particular interest because the Rc locus falls within the map positions of several quantitative trait loci associated with other domestication-related traits ( dormancy , shattering , tillering , and panicle architecture ) on Chromosome 7 [11–17] . It is possible that an array of domestication alleles arose in the japonica background and were transferred as a block into indica cultivars . In order to define the extent of the introgression , we designed a series of indel and SNP markers that clearly distinguished the ancestral indica and japonica gene pools across Chromosome 7 . To identify these markers , we evaluated polymorphism on a panel of 30 japonicas and 15 red-seeded indicas . Red indicas contain ancestral indica sequence in this region while both red and white japonicas ( as well as white indicas ) are expected to contain ancestral japonica sequence . FST values provide a quantitative estimate of the degree of allelic differentiation between subpopulations . The indel and SNP markers used to distinguish the ancestral gene pools all had FST values above 0 . 8 ( see upper portion of Figure 4 ) . These markers were used to genotype 88 diverse white indica varieties to explore the extent of japonica DNA in the Rc region ( Table S3 ) . In this study , nine extended haplotype patterns were identified and they showed varying sizes of japonica introgressions ( Figure 4 , upper portion ) . Ninety-one percent of the indica varieties contained less than 1 Mb of japonica derived DNA in the region , with asymmetric distribution around rc . This is similar to haplotype patterns described for the Arabidopsis FRI gene [18] The haplotype with the smallest introgression , H2 , which was observed in two varieties , was only 247–371 kb in size ( Figure 4 , lower portion ) and contained approximately 100 genes . Ten white indica varieties , H8 and H9 , contained an introgression that included the recently mapped shattering locus , Hsh [14] , although in most white indicas the japonica introgression does not extend that far . In an extreme case , H9 , eight indica landraces collected from an isolated population in Kalimantan , Indonesia ( on the island of Borneo ) had japonica-derived alleles across the entire length of Chromosome 7 ( 29 . 7 Mb ) ( Figure 4 ) . When FST values were calculated using data from 88 white indicas and 30 japonicas , we observed a dramatic drop around Rc , from values of over 0 . 75 , down to 0 and back above 0 . 75 within a 1-Mb region ( Figure 4 , upper portion ) . FST values around 0 indicate no differentiation between subpopulations and the drop around Rc illustrates the location and size of the japonica introgression ( Figure 4 ) . The difference in FST values outside the Rc region between the red or white indicas compared to japonica is due to the inclusion of the eight white indicas from Kalimantan that carry japonica alleles across the entire length of Chromosome 7 . As more domestication genes are cloned from this region , the pattern and extent of introgression reported here can be used to determine if japonica alleles for other domestication traits hitchhiked along with the rc mutation when it was introduced into indica landrace varieties . To investigate the reduction in diversity around the 14-bp deletion in Rc , we used sequences from a portion of the Rc gene in 21 diverse varieties of white rice carrying the 14-bp deletion and eight red varieties representing indica , aus , tropical , and temperate japonica subpopulations ( Table S2 ) . A summary of DNA polymorphism in the Rc gene is given in Table 1 . Levels of polymorphism are reduced by 98% in the white pericarp rices , compared to the red landraces . Notably , the levels of DNA polymorphism in red landraces is comparable to randomly chosen loci in an unbiased sample of O . sativa landraces . For red landraces in the Rc region π ( the level of nucleotide diversity ) = 0 . 0025 per base pair , whereas in a survey of randomly chosen loci in O . sativa , π = 0 . 0032 ( Ana Caicedo and Scott Williamson , personal communication , University of Massachusetts , Amherst , Massachussetts and Cornell University , respectively ) . Thus , other than the extreme reduction in diversity in white pericarp rices , the Rc region is not atypical of the rice genome . Furthermore , the site-frequency spectrum of the white rice sample is completely skewed towards polymorphisms with rare derived alleles . All of these observations are consistent with very strong , recent selection in favor of the 14-bp deletion in the Rc gene .
In an outcrossing species , it is not surprising to see the rapid spread of a favorable mutation . However , O . sativa is 97%–98% inbreeding [1 , 22] , has a closed-flower morphology , and short-lived pollen grains . Gene flow is further constrained by the presence of a complex network of sterility barriers between the two independently domesticated indica and japonica subspecies of rice [1 , 23] . Even among largely out-crossing wild Oryza relatives , viable pollen rarely travels more than 10 m [24] , making it difficult for a favorable allele to be widely disseminated through natural pollen flow . Thus , it is unlikely that the rc mutation would have traveled far beyond its point of origin if not for the activities of humans who valued the white grains as a source of food and presumably as a commodity for trade . The importance of the rc mutation to early agriculturalists is evidenced by the fact that it moved around the Himalayan mountain range that is found between the proposed centers of indica and japonica domestication [4 , 8] , and , having traversed this substantial geographic barrier , was rapidly introgressed into all major subpopulations of rice despite an emerging fertility barrier . Three key genetic features of white pericarp contributed to the rapid spread of this trait . First , rc is a single gene mutation that causes a qualitative change in phenotype so it is straightforward to visually distinguish red from white grains . However , the color of the seed coat is not visible on plants in the field because each grain of rice is completely covered by a hull , or glume . As dehulling reduces the germination rate , seeds that were to be planted the following season would be maintained with the hulls on . The pericarp color would only have been obvious to those who dehulled the grains , something that is normally done just prior to cooking . The pericarp is a maternally derived cell layer and its color is determined by the maternal genotype rather than the genotype of the fertilized embryo . Therefore , all seeds on any one plant are the same color . By dehulling only a few grains on any plant , it would be apparent which plants carried red or white seeds , allowing for selection . Third , the rc mutation is recessive , and in a highly self-pollinated species such as O . sativa , this means that the trait breeds true; seeds with white pericarp will produce offspring having grains with white pericarp for generations to come . Almost all wild plants have pigmented seed coats , skin , and flesh , yet humans seem to prefer white starchy staples , and selection against pigmentation is a common theme in the evolutionary history of crop plants . The reasons our ancestors selected against pigmentation in these staple foods are not entirely clear , although it is tempting to speculate . The novelty of a different color rice may have contributed to its popularity . White rice has the very practical advantage that it is far easier to detect and eliminate insects and pathogens against a light background than a darker red background . Alternatively , differences in cooking properties between red and white rice may have resulted in the preference for white . Colored rice has a harder seed coat than white rice . This means longer cooking times as well as more time spent finding fuel with which to cook . In many cultures , the rice hull and bran layers were removed by pounding . As red rice has a harder seed coat than white , it requires more work to remove the bran layer from reds . No matter what the reason for selecting against red pericarp in rice , selection was strong enough to catalyze a major change from red to white grains across a vast geographic area in a surprisingly short period of time . Despite the lack of human records , the history of domestication is written in the genomes of the plants and animals that have been changed by our choices . As we begin to decipher these stories , we gain new insight into our own history as part of the domestication process . In rice , at least two important and unlinked domestication alleles , rc ( discussed in this paper ) and the shattering allele , sh4 ( located on Chromosome 4 ) [25] , are now known to have arisen only once in evolution and to have been introgressed across the indica–japonica divide , with almost complete prevalence in modern forms of cultivated rice . Other loci affecting domestication traits , such as the Rc-s allele for white pericarp ( this paper ) or the sh1 allele for non-shattering [26] , confer alternative or complementary alleles for the same domestication phenotypes , but their distribution is restricted to a specific subpopulation . The fact that cultivars belonging to the deeply divergent indica and japonica subspecies have both common and distinct domestication genes suggests that the process of domestication in O . sativa involves complex patterns of subpopulation isolation and convergence , underwritten by a rich tapestry of cross-talk among ancient farmers . Whether a majority of key domestication alleles are subpopulation-specific or are shared among all Asian rice varieties , and whether the shared alleles arose predominantly within a single subspecies or subpopulation , is not yet known . The answer to this question will illuminate the paths traveled by ancient peoples and their innovations in the rice-growing world .
List of primers in Table S4 All primers have an annealing temperature of 55 °C . For the introgression study , primers were designed to amplify small ( 4–30 bp ) insertion/deletion events that were originally detected computationally by aligning the sequences from cv Nipponbare ( japonica ) and cv 9311 ( indica ) [27 , 28] . As 9311 is a white indica , it carries a japonica introgression around rc . In order to identify indels between ancestral indica and japonicas in this region , short stretches of sequence from the red indica cv Mudgo were aligned with Nipponbare . To determine the pericarp color , ten seeds per plant were dehulled and then visually inspected . Rc DNA sequenced using overlapping primers . SNPs were confirmed with multiple reads through independent amplicons . Amplified products were sequenced at the Cornell Biotechnology Resource Center . Alignment adjusted manually in MacClade 4 . 05 ( http://macclade . org/macclade . html ) . A heuristic parsimony search was conducted on the data matrix , excluding gapped characters , with TBR branch swapping . This identified a single tree at 53 steps , with a consistency index of 0 . 98 ( 0 . 97 without autapomorphies ) and a retention index of 0 . 99 . Bootstrap support was estimated using 100 replications of the same search strategy .
The sequences discussed in this paper were assigned National Center for Biotechnology Information ( NCBI ) GenBank ( http://www . ncbi . nlm . nih . gov/gquery/gquery . fcgi ) accession numbers DQ885795–DQ885823 and DQ902346–DQ902352 . | Understanding the history and origin of genetic mutations that have changed wild plants into crops can help us understand the history of the people who cultivated these plants . Rice is one of the oldest crops grown in Asia and it contains two different subspecies that are believed to have been domesticated in different locations by different people . Surprisingly , some of the genetic mutations responsible for domestication are common in all rice . We here show that a mutation in the Rc gene that changed the red seed of wild rice into the white seeds of modern rice is shared by a large majority of all rice varieties , regardless of subspecies . This transfer of genes requires contact among rice types and implies contact among the people who cultivated the different subspecies . We have traced the origin of the mutation in Rc to the japonica subspecies . As additional domestication genes are cloned and their evolutionary history described , we will see how many times and in how many directions such gene transfers have occurred . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] | [
"evolutionary",
"biology",
"oryza",
"genetics",
"and",
"genomics",
"plant",
"biology"
] | 2007 | Global Dissemination of a Single Mutation Conferring White Pericarp in Rice |
Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously ( i . e . , within 5 ms of each other ) . This spike synchrony may occur by chance , based solely on the neurons’ fluctuating firing patterns , or it may occur too frequently to be explicable by chance alone . When spike synchrony above chances levels is present , it may subserve computation for a specific cognitive process , or it could be an irrelevant byproduct of such computation . Either way , spike synchrony is a feature of neural data that should be explained . A point process regression framework has been developed previously for this purpose , using generalized linear models ( GLMs ) . In this framework , the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions . An important possible source of spike synchrony is network-wide oscillations , which may provide an essential mechanism of network information flow . To establish the statistical link between spike synchrony and network-wide oscillations , we have integrated oscillatory field potentials into our point process regression framework . We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates . We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1 ) simulated neurons , 2 ) in vitro recordings of hippocampal CA1 pyramidal cells , and 3 ) in vivo recordings of neocortical V4 neurons . Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony .
A leading theory of current neuroscience is that synchronous firing of neurons driven by network-wide oscillations may encode and transmit information within and across brain regions [1–9] . Supporting this theory , a number of studies have suggested that synchronous firing of action potentials or “spikes” may indeed occur in conjunction with oscillations in local field potential ( LFP ) [10–14] . However , a missing link in this theory has been the ability to dissociate enhanced spike synchrony due to network-wide oscillations from enhanced spike synchrony that may be due to other measured or unmeasured sources . Recently , we developed a statistical framework in which the association between spike synchrony and measured covariates may be assessed [15 , 16] . Here we show how this approach may be applied to describe the relationship between spike synchrony and oscillatory activity . Using point process regression models , which take the form of generalized linear models ( GLMs ) , our statistical framework compares the observed number of synchronous spikes within a small time window ( here , 5 ms ) to the number predicted by chance , under varying assumptions about the factors that affect the firing of each individual neuron [15 , 16] . The number of synchronous spikes predicted “by chance” refers here to the number predicted under conditional independence after conditioning on the various measured factors that have been hypothesized to affect individual-neuron spiking . For example , two neurons having fluctuating stimulus-driven firing rates will produce some number of synchronous spikes even if they are acting independently . The point process regression method fits fluctuating firing rate functions for each neuron separately , then predicts the number of synchronous spikes under conditional independence ( i . e . , after conditioning on these fluctuating firing rates ) , and compares the prediction to the observed number of synchronous spikes . In this way , a single factor may be either included or excluded from the regression model in order to quantify that factor’s ability to explain the observed spike synchrony . In this article , we consider the contribution of network-wide oscillations by comparing observed and predicted spike synchrony after conditioning on the phase of an LFP representing a network-wide oscillation . Thus , we predict spike synchrony with and without inclusion of LFP phase as an explanatory variable for each neuron separately . To demonstrate that increased spike synchrony is associated with a network-wide oscillation , we would begin by establishing that , without considering LFP phase , the observed number of synchronous spikes is greater than the predicted number by a statistically significant magnitude , after conditioning on both stimulus-driven firing rates and recent post-spike history effects . This would indicate a failure of the phase-free model to accurately account for spike synchrony . We would then include the LFP phase in the model , and if it succeeded in predicting spike synchrony , then we would conclude that LFP phase can explain the remaining spike synchrony . Furthermore , we could estimate the proportion of excess synchronous spikes accounted for by the LFP phase . The same procedure could be used , instead to demonstrate the role of network-wide oscillations in suppressing spike synchrony . In order to carry out this general procedure , we first need to model an individual neuron’s spiking probability in terms of LFP phase . We follow [17] , which recently described and assessed point process regression models that include a sinusoidal phase term . We enhance their approach by weakening the sinusoidal assumption , allowing the phase relationship to be nonparametric as in [18] , and we add to the favorable results of [17] by showing that , in estimating phase relationships , the point process regression model can reduce bias and mean-squared error in comparison with the more familiar spike phase histogram approach . Using this point process regression model , we are then able to quantify the dependence of synchronous spiking on an oscillatory modulation . We illustrate the method using simulated neurons , in vitro recordings of hippocampal CA1 pyramidal cells , and in vivo recordings of neocortical V4 neurons from a behaving monkey .
We assume that the spiking of each neuron follows a point process and , following [19] ( page 592 ) , we write its conditional intensity function as λ ( t∣Ht , Xt ) , where Ht represents the spike history ( auto-history ) , and the covariate Xt represents other external factors . In this work , we let Xt include the stimulus and the LFP phase , denoted by Xt = ( St , Φt ) . We assume the conditional intensity takes a multiplicative form , which becomes additive on the log scale: log λ ( t | H t , X t ) =f 1 ( S t ) + f 2 ( H t ) + f 3 ( Φ t ) =log λ 1 ( t ) + log λ 2 ( t - t * ) + log λ 3 ( Φ t ) ( 1 ) where t* is the last spike time preceding t ( see Materials and Methods ) . We use splines to capture stimulus and auto-history effects , and circular splines to capture LFP phase effects . Our point process model thus takes the form of a standard generalized linear model ( GLM ) . We also ensure identifiability by imposing a set of restrictions ( Eqs ( 20 ) and ( 21 ) ) , which are implemented within a maximum likelihood estimation ( MLE ) algorithm . The parametric bootstrap is used for acquiring 95% confidence bands . To illustrate the ability of the MLE algorithm to recover the model in Eq ( 1 ) , we simulated 100 spike trains ( Fig 1A ) with known functions λ1 ( t ) , λ2 ( t − t* ) and λ3 ( ϕ ) . Using the simulated spike trains and phase of the oscillatory drive ( representing a network-wide oscillation ) , the MLE algorithm accurately fit the underlying spike history ( Fig 1B ) , stimulus ( Fig 1C ) and phase modulation ( Fig 1D ) effects . Our approach can thus accurately recover the statistical relationships between firing rate and various external factors . The model in Eq ( 1 ) is a “full” model including stimulus , auto-history , and an oscillatory factor . Importantly , we can remove selected factors from the full model ( e . g . , the LFP phase modulation ) and still fit the spike trains using the same procedure . Indeed , in the following results , we also fit a simplified model lacking the oscillatory factor , log λ ( t | H t , X t ) = log λ 1 ( t ) + log λ 2 ( t - t * ) . ( 2 ) Many researchers have reported that firing rate is modulated by the phase of specific network-wide oscillations in different brain areas , such as monkey V1 [20] , rat hippocampus [21] , rat prefrontal cortex [22] , mouse olfactory bulb [23] , human pedunculopontine nucleus [24] , lamprey reticulospinal neuron [25] , and so on . Almost all of these results [20–22 , 24 , 25] used spike phase histograms to show how firing rate is modulated by the oscillation . The significance of phase locking can be evaluated using Rayleigh’s Z statistic [22] . The model in Eq ( 1 ) offers an alternative method of computing LFP phase modulation . We simulated N spike trains and estimated LFP phase modulation using two different methods: 1 ) the classical spike phase histogram , and 2 ) by fitting λ3 ( ϕ ) with the point process regression of model ( 1 ) . The true LFP phase modulation function is defined as λ3 ( ϕ ) . The discrepancy between the estimated λ ^ 3 ( ϕ ) and λ3 ( ϕ ) is measured by the integrated squared error ( ISE ) : ISE = ∫ - π π [ λ ^ 3 ( ϕ ) - λ 3 ( ϕ ) ] 2 d ϕ Using each method , we can derive point-by-point standard errors and 95% confidence bands for the LFP phase modulation . The mean integrated squared error ( MISE ) is then defined as: MISE =1 n ∑ i = 1 n ISE i =1 n ∑ i = 1 n ∫ - π π [ λ ^ 3 i ( ϕ ) - λ 3 ( ϕ ) ] 2 d ϕ , where n is the total number of data sets and i is the index of ith data set . λ ^ 3 i ( ϕ ) is computed given N repeated trials of spike train in ith data set . We can decompose MISE in terms of the sample mean λ ‾ 3 ( ϕ ) in the form of: ∫ - π π { 1 n ∑ i = 1 n [ λ ^ 3 i ( ϕ ) - λ ¯ 3 ( ϕ ) ] 2 + [ λ ¯ 3 ( ϕ ) - λ 3 ( ϕ ) ] 2 } d ϕ which provides an estimator of variance plus bias squared . The histogram method is highly dependent on the bin size for smoothing . We picked the optimal bin size that minimizes the MISE . Fig 2C illustrates how the MISEs of the two methods are dependent on number of trials N . Both methods achieve smaller MISEs when more data are used , but the spike phase histogram method consistently exhibits a much larger MISE than the GLM method . Indeed , the spike phase histogram MISE reaches an asymptote for high N that is much larger than the MISE of the GLM method . In Fig 2F we show the variance and bias separately for the two methods . These results show that the spike phase histogram method retains a large bias , explaining the MISE asymptote in Fig 2C . The LFP phase modulation estimated by the spike phase histogram method additionally exhibits significantly larger variance than the GLM method for small sample sizes ( < 17 trials ) . Two additional comments can be made about the results shown above ( Fig 2 ) . First , when few trials or samples are available , only the GLM method can provide an accurate estimation of the LFP phase modulation of a neuron’s firing . Second , for moderately large samples , the error in the estimation of the LFP phase modulation by the spike phase histogram method arises primarily from estimation bias . We can explain this second point by considering the definitions of the two methods . The term λ3 ( ϕ ) describes how an oscillation changes the firing rate and is independent of other factors ( stimulus , auto-history , etc . ) . In contrast , the spike phase histogram method provides the distribution of phases when a spike occurs , denoted as Pdata ( ϕ ) . Since the generation of a spike train is influenced by factors other than the oscillation , especially for a non-Poisson process , Pdata ( ϕ ) is conceptually different than λ3 ( ϕ ) . Below , we explore this conceptual difference further . Spike field coherence ( SFC ) is commonly used to report interactions between spikes and specific oscillations in LFP . Lepage et al . [17 , 26] showed that SFC is dependent on the expected rate of spiking , and they proposed to use intensity field coherence , which is a rate-independent measure , for inference of spike field synchrony . They also used GLMs to estimate spike field association [17] . In their work , they assumed that the LFP phase modulation is a sinusoidal function with period of 2π , which might not be accurate enough in some cases [22 , 23] . In our model , to approximate this periodic function we use circular splines [18] , which remain easy to fit while being more flexible than a sinusoidal function . Here , we provide two examples showing that when estimating spike field relationships , the SFC can be misleading . First , we simulated spike trains with three different mean firing rates , then computed SFCs with GNU software Chronux [27] . Fig 4A shows that the three SFCs are different even though they were generated by the same λ3 ( ϕ ) = 1 + 0 . 4cos ( ϕ + π ) . On the other hand , when we use our model to fit the LFP phase modulation functions , Fig 4C shows that there is no difference in phase modulation strength in these three cases . Second , we show that two neurons exhibiting different LFP phase modulations can have the same SFC ( Fig 4B ) because they have different firing rates . Again , we can use our model to distinguish these two conditions by their respective LFP phase modulation curves ( Fig 4D ) . We now use point process regression of our GLMs Eqs ( 1 ) and ( 2 ) to analyze the contribution of network-wide oscillations to the synchronous spiking of two neurons . We first present numerical simulation results where ground truth is known and then apply the same technique to experimental neural recordings . To further demonstrate the value of our approach , we next examined the relationship between an oscillatory signal and spike synchrony in experimental neural recordings from two distinct preparations: hippocampal CA1 pyramidal cells recorded in vitro and V4 neurons recorded in vivo .
In this paper , we have shown how the GLM methods of [15–17 , 26] may be combined in order to assess the potential contribution of network-wide oscillations to neural synchrony . The novel approach presented in this study complements existing alternatives [31–33] by: introducing models of single neuron firing based on stimulus-related fluctuations as well as a network-wide oscillatory signal; using those models to make predictions about spike synchronization; and quantifying departures from those predictions in the observed data . We demonstrated the advantages of this novel approach using both neural simulations and experimental neural recordings in vitro and in vivo . In our analyses , we have utilized a repeated-trial structure , which allowed us to estimate the stimulus effects as a function of time , λ1 ( t ) . We note , however , that the same approach could be applied using a linear response filter [34–36] or analogous nonlinear methods . Previous work has shown the close relationship between GLM neurons and integrate-and-fire neurons [37–39] . We only considered one band of oscillation in simulation and experimental examples , but it is straightforward to extend this method to the case of multiple oscillations by including additional terms in the model of Eq ( 1 ) . Sometimes the firing probability may be related to the amplitude of the oscillation At , or the magnitude of an LFP Bt ( cf . [17] ) . If so , we can change f3 ( Φt ) to f3 ( At ) or f3 ( Bt ) . Overall , the key step of this method is to build an approximately correct GLM . The specific form of GLM depends on the data and we can check model performance using time rescaling [40] . We have also included a simulation to show that even when the model is mis-specified , and therefore less sensitive , it can detect spike-LFP relationships ( S3 Fig ) . We have also defined spike synchrony to involve the firing of two neurons within a few milliseconds of each other ( i . e . , with zero lag on average ) . In other contexts , however , interest may focus on two neurons firing in procession with a consistent positive or negative lag of many milliseconds . Our approach could be easily applied to such lagged-synchrony cases as well . In this paper , we consider only pairwise synchrony . By combining our approach with the procedure proposed by [16] , we can also test the role of oscillations in three-way synchrony . Briefly , we fit all single neuron firing probabilities and then compute the pairwise synchrony coefficients ζ ^ i j; we can then use an iterative algorithm to estimate the three-way synchrony coefficient ζ ^ i j k , and to test the null hypothesis of two-way interactions , instead of three-way interaction . In principle the same steps may be followed for more than three neurons , but simulations in [16] show that very large data sets would be needed in order to demonstrate higher-order interactions convincingly in the absence of stronger assumptions about the nature of those interactions . It has been argued that synchronous firing resulting from network-wide oscillations could provide an essential mechanism of network information flow , and further serve as a a marker distinguishing normal from diseased states ( e . g . , see [41–48] ) . On the other hand , there has been considerable debate on this subject ( see [49] and references therein ) . We remain agnostic on this , and importantly , the value of our methods does not depend on the ultimate outcome of this debate . Instead , we view synchrony , more descriptively , as a feature of spike train data that needs to be explained . To this end , the framework that we have introduced here is useful for quantifying the extent to which oscillations , as a feature of neural activity , are associated with synchronous spiking among neurons . Armed with this method , future experiments can measure oscillations and synchrony in a statistical framework in which their contributions to cognitive and behavioral processes can be accurately quantified .
In a continuous time interval ( 0 , T] , a neuron can fire a spike at any discrete time point ui . A series of spikes {ui} for 1 ≤ i ≤ N forms the spike train , where 0 ≤ u1 < ⋯ < uN ≤ T . We take the spike train to be a point process , which is characterized by its conditional intensity function λ ( t | H t , X t ) = lim Δ → 0 P [ N ( t + Δ ) - N ( t ) = 1 | H t , X t ] Δ ( 6 ) where N ( t ) is the total number of spikes prior to time t , Ht is neuron’s own spiking history prior to time t , and Xt includes all other relevant covariates . When Δ is small , λ ( t∣Ht , Xt ) ⋅ Δ approximates to the firing probability in the time interval ( t , t + Δ ) . To determine how different factors contribute to firing rate we write λ ( t∣Ht , Xt ) as a function of ( Ht , Xt ) λ ( t | H t , X t ) = f ( H t , X t ) . ( 7 ) We can include different factors into this model and study their effects . Usually the stimulus S ( t ) is included when neurons show selectivity to stimuli . In this work , because we are interested in phase modulation by an oscillatory signal , the phase of the specific oscillation Φ ( t ) is also included . To take advantage of the generalized linear model ( GLM ) framework we divide T into K equally spaced bins , thus taking the bin width to be Δ = T/K . Δ is small enough that no more than one spike event in each bin , e . g . Δ = 1 ms . Therefore the probability of observing one spike in kth bin is p k = λ ( t k | H t k , X t k ) · Δ , k = 1 , 2 , ⋯ , K ( 8 ) Using the vector Y ⊂ ℝK × 1 to represent the spike train {ui} , yk is the number of spikes in kth bin . Since we choose small bin width Δ , yk is not bigger than 1 , i . e . yk ∈ {0 , 1} and , from the Poisson approximation to the binomial for small p we take the probability of observing yk given Htk and Xtk to be p ( y k | H t k , X t k ) = p k y k y k ! e - p k ( 9 ) where tk = kΔ . The loglikelihood function is L = ∑ k = 1 K [ y k · log ( p k ) - p k ] = ∑ k = 1 K [ y k · log ( λ ( t k | H t k , X t k ) Δ ) - λ ( t k | H t k , X t k ) Δ ] ( 10 ) and this is maximized to determine the MLE fit . We assume that log[λ ( t∣Ht , Xt ) ] can be written as a sum of specific functions of each covariate . Here we are studying three factors , stimulus , recent post-spike auto-history , and oscillatory phase , and we write log [ λ ( t | H t , X t ) ] =f 1 ( stimulus ) + f 2 ( auto-history ) + f 3 ( oscillation ) ( 11 ) =f 1 ( S t ) + f 2 ( H t ) + f 3 ( Φ t ) . ( 12 ) Here , S ( t ) is a possibly time-varying stimulus and f1 ( stimulus ) determines the trial-independent time-varying firing rate , i . e . , the effect that is usually associated with the peri-stimulus time histogram ( the PSTH ) , which may be estimated due to the repeated trial structure of the experiment . The recent post-spike auto-history effect is assumed here to be dominated by effects subsequent to the most recent spike t* prior to time t , as in [50] , so we assume f2 ( auto-history ) has the form f2 ( t − t* ) . The oscillatory term f3 ( oscillation ) is defined as f3 ( Φt ) , where Φt is the phase of specific oscillation . In summary , our spike train model has the form log ( λ ( t | H t , X t ) ) =f 1 ( t ) + f 2 ( t - t * ) + f 3 ( Φ t ) ( 13 ) =log λ 1 ( t ) + log λ 2 ( t - t * ) + log λ 3 ( Φ t ) ( 14 ) and we will assume f1 ( ⋅ ) , f2 ( ⋅ ) and f3 ( ⋅ ) are smooth functions . To fit the smooth functions f1 ( ⋅ ) , f2 ( ⋅ ) and f3 ( ⋅ ) we use cubic splines of the form log ( λ ( t | H t , X t ) ) = ∑ i a i ( t ) α i + ∑ j b j ( t - t * ) · β j + ∑ k r k ( Φ t ) · γ k ( 15 ) where {ai ( t ) } is a B-spline basis set for f1 ( t ) within the range t ∈ ( 0 , T] , { b j ( t − u t* ) } is a B-spline basis set for f2 ( t − t* ) , and {rk ( ϕ ) } is circular spline basis set for f3 ( Φt ) . Thus , we use maximum likelihood to fit the coefficients Θ = {α , β , γ} . We used open source software FDAfuns [51] to create each B-spline basis sets after manually selecting knots . For the circular spline we pick knots equally spaced in [−π , π] . Once we get all knots {ϕi} , acquiring the related basis function is straightforward [18] using r k ( ϕ ) = ∑ m = 1 ∞ 2 ( 2 π m ) 4 cos ( 2 π m ( ϕ - ϕ k ) ) . ( 16 ) In numerical implementations , we usually cut the summation from m = 1 to m = 4 because amplitude of each term decreases quickly . Because L in Eq ( 10 ) is a concave function , we can use iteratively reweighted least squares ( IRLS ) , as in typical GLM implementations . From Eqs ( 8 ) , ( 10 ) , and ( 15 ) , we can rewrite loglikelihood in matrix form L = Y T · log μ - I 1 × K · μ ( 17 ) log μ = [ A · α + B · β + R · γ ] Δ ( 18 ) Here we have three parameter sets to fit {α , β , γ} . If we fit all three parameter sets together , the dimension space of this GLM model is relative large . To make model fitting efficient , we prefer back-fitting , i . e . , fitting each parameter set separately , and iterating cyclically . For example , when we fit the parameters {α} , we hold the parameters {β , γ} constant and rewrite Eq ( 18 ) as log μ = V · θ + log μ t 0 ( 19 ) where θ ∈ {α , β , γ} and V is the corresponding covariate matrix . We fit {α , β , γ} in a sequence and then iterate the loop until convergence . We also must place identifiability restrictions on {β , γ} because both the auto-history and oscillatory effects modulate the spike trains and the parameters must be constrained to provide unique solutions . We use the constraints ∫ 0 T exp [ ∑ j b j ( τ ) · β j ] d τ T = 1 ( 20 ) ∫ - π π exp [ ∑ k r k ( Φ ) γ k ] d Φ 2 π = 1 . ( 21 ) To avoid over-fitting of the model , we also add an l2 penalty into the objective function . Now the problem becomes minimizing objective function Q = - L + λ 2 | Θ | 2 = - Y T · ( V · Θ + log μ t 0 ) + I 1 × K · exp ( V · Θ + log μ t 0 ) + λ 2 · Θ T Θ . ( 22 ) Because the objective function Q is convex , we can iteratively maximize Θ by following the updating rule Θ i + 1 = Θ i - H - 1 · ∇ Q ( 23 ) where H is the Hessian of Q and ∇Q is the gradient of the function , which are obtained as ∇ Q = V T [ exp ( V · Θ + log μ t 0 ) - Y ] + λ Θ ( 24 ) H = V T · W · V + λ ( 25 ) where W is a diagonal matrix W i , j = { exp ( V · Θ + log μ t 0 ) i if i = j 0 otherwise . ( 26 ) The algorithm is summarized as Algorithm 1 , shown below . Algorithm 1: IRLS method for finding argminΘ Q ( Θ ) Data: Y , V , Θ 0 , log μ t 0 , λ Result: Θ* = argminΘ Q ( Θ ) begin Q1 ← Q ( Θ0 ) ; repeat Q0 ← Q1; ∇ Q ← V T [ exp ( V ⋅ Θ 0 + log μ t 0 ) − Y ] + λ Θ 0; W ← diag { exp ( V ⋅ Θ 0 + log μ t 0 ) }; H ← VT ⋅ W ⋅ V+λ; Θ1 ← Θ0 − H−1 ⋅ ∇Q; Q1 ← Q ( Θ1 ) ; Θ0 ← Θ1; until ∣Q1 − Q0∣ ≤ δ; return Θ1; end For a pair of neurons labeled 1 and 2 , we fit conditional firing rate for each of them to get λ ^ 1 ( t ∣ H t , X t ) and λ ^ 2 ( t ∣ H t , X t ) . Then we can predict the number of synchronized spikes given temporal bins with Δ = 5ms , as in [15] , using N p r e d = ∫ λ ^ 1 ( t | H t , X t ) · λ ^ 2 ( t | H t , X t ) d t . ( 27 ) Given spike trains from these two neurons , we can also get the observed number of synchronized spikes Nobs by counting . If a pair of spikes from two neurons has an time interval less than 5 ms , then this pair is counted as a synchronized spike . Synchrony is measured by taking the ratio of these two numbers ζ ^ or logζ ^ ζ ^ = N o b s N p r e d . ( 28 ) When two neurons are conditional independent , Eq ( 27 ) can make relative good predictions and ζ ^ ≈ 1 ( or logζ ^ ≈ 0 ) . Once we have logζ ^ , we also need to determine its standard error and confidence interval . Furthermore , a p value is required to test the hypothesis logζ ^ = 0 . We use a parametric bootstrap method for these purposes , as in [16] . For example , given λ ^ 1 ( t ∣ H t , X t ) and λ ^ 2 ( t ∣ H t , X t ) we can obtain the p value as follows: Statistical power is the probability of correctly rejecting the null hypothesis when it is false . We used the GLM model in Eq ( 1 ) to study power as a function of ζ and N ( N being the number of trials ) . We simulated N trials of spike train data for each of two neurons , independently , using Eq ( 1 ) with intensity functions λ ( 1 ) ( t∣Ht , Xt ) for the first neuron and λ ( 2 ) ( t∣Ht , Xt ) for the second . The synchronous spikes in the resulting spike trains occur with probability corresponding to ζ = 1 ( independence ) . In order to obtain sets of spike trains for other values of ζ we removed all the synchronous spikes from the N simulated spike trains and replaced them with synchronous spikes generated from an intensity function ζ ⋅ λ1 ( t∣Ht , Xt ) ⋅ λ2 ( t∣Ht , Xt ) , i . e . , for each time bin of width δ , synchronous spikes occurred with probability ζ ⋅ λ1 ( t∣Ht , Xt ) ⋅ λ2 ( t∣Ht , Xt ) δ2 . However , while this is the desired probability of synchronous spikes , it leaves the wrong marginal probability of spiking for each neuron . To adjust these we consider the spike trains made up of only the non-synchronous spikes , and we thin these with probabilities p ( j ) ( t ) given by p ( j ) ( t ) = λ ( j ) ( t | H t , X t ) - ζ · λ ( 1 ) ( t | H t , X t ) · λ ( 2 ) ( t | H t , X t ) δ λ ( j ) ( t | H t , X t ) - λ ( 1 ) ( t | H t , X t ) · λ ( 2 ) ( t | H t , X t ) δ for j = 1 , 2 . Note that when we multiply the numerator and denominator of this expression by δ we have the ratio of the desired probability of a non-synchronous spike to the probability of a non-synchronous spike under independence ( the latter probability corresponding to the process we are thinning ) . After obtaining all N trials we then fitted the model to these simulated spike trains , found the estimate ζ ^ , and applied the hypothesis test using the bootstrap method . This procedure was carried out for each ζ and N in our simulation . Because the simulation is computationally time-consuming , for the benefit of any future efforts along these lines , we also derived a formula to approximate the number of trials needed to get 0 . 8 power . Suppose we have N trials , each trial is T seconds , the bin size for synchrony detection is δ . Denote the instantaneous firing rates for two neurons on trial i by λ t , i ( 1 ) and λ t , i ( 2 ) . The number of synchronized spikes within the tth bin is y t , i ( 12 ) and y t , i ( 12 ) ∼ Poisson ( ζ ⋅ λ t , i ( 1 ) λ t , i ( 2 ) ⋅ δ 2 ) , where ζ is the synchrony coefficient . The total number of observed synchronized spikes given λ t , i ( 1 ) and λ t , i ( 2 ) is N o b s∣λ t , i ( 1 ) , λ t , i ( 2 ) = ∑ i = 1 N ∑ t = 1 T / δ y t , i ( 12 ) . Then we compute ζ ^ conditioned on λ t , i ( 1 ) and λ t , i ( 2 ) , ζ ^ | λ t , i ( 1 ) , λ t , i ( 2 ) = N o b s | λ t , i ( 1 ) , λ t , i ( 2 ) N p r e d = ∑ i = 1 N ∑ t = 1 T / δ y t , i ( 12 ) ∑ i = 1 N ∑ t = 1 T / δ λ t , i ( 1 ) λ t , i ( 2 ) · δ 2 . Since y t , i ( 12 ) ∼ Poisson ( ζ ⋅ λ t , i ( 1 ) ⋅ λ t , i ( 2 ) δ 2 ) , we can easily get E [ ζ ^ | λ t , i ( 1 ) , λ t , i ( 2 ) ] =E [ ∑ i = 1 N ∑ t = 1 T / δ y t , i ( 12 ) ] ∑ i = 1 N ∑ t = 1 T / δ λ t , i ( 1 ) λ t , i ( 2 ) · δ 2 = ∑ i = 1 N ∑ t = 1 T / δ ζ · λ t , i ( 1 ) λ t , i ( 2 ) δ 2 ∑ i = 1 N ∑ t = 1 T / δ λ t , i ( 1 ) λ t , i ( 2 ) · δ 2 = ζ V a r ( ζ ^ | λ t , i ( 1 ) , λ t , i ( 2 ) ) =V a r ( ∑ i = 1 N ∑ t = 1 T / δ y t , i ( 12 ) ) ( ∑ i = 1 N ∑ t = 1 T / δ λ t , i ( 1 ) λ t , i ( 2 ) δ 2 ) 2 = ∑ i = 1 N ∑ t = 1 T / δ ζ · λ t , i ( 1 ) λ t , i ( 2 ) δ 2 ( ∑ i = 1 N ∑ t = 1 T / δ λ t , i ( 1 ) λ t , i ( 2 ) δ 2 ) 2 =ζ ∑ i = 1 N ∑ t = 1 T / δ λ t , i ( 1 ) λ t , i ( 2 ) δ 2 . Assuming λ t , i ( 1 ) and λ t , i ( 2 ) are independent , we have E [ ∑ i = 1 N ∑ t = 1 T / δ λ t , i ( 1 ) λ t , i ( 2 ) δ 2 ] = ∑ i = 1 N ∑ t = 1 T / δ E [ λ t , i ( 1 ) ] E [ λ t , i ( 2 ) ] δ 2 = N T λ 1 λ 2 δ , where λ1 and λ2 are the mean firing rates of two neurons . Then we have E [ ζ ^ ] =E [ E [ ζ ^ | λ t , i ( 1 ) , λ t , i ( 2 ) ] ] = ζ V a r ( ζ ^ ) =V a r ( E [ ζ ^ | λ t , i ( 1 ) , λ t , i ( 2 ) ] ) + E [ V a r ( ζ ^ | λ t , i ( 1 ) , λ t , i ( 2 ) ) ] =ζ N T λ 1 λ 2 δ + O ( ζ ( N T λ 1 λ 2 δ ) 3 ) E [ log ζ ^ ] ≈log ζ - 1 ζ 2 V a r ( ζ ^ ) = log ζ + O ( 1 N T λ 1 λ 2 δ ζ ) V a r ( log ζ ^ ) ≈1 ζ 2 V a r ( ζ ^ ) - 1 4 ζ 2 V a r ( ζ ^ ) 2 = 1 ζ 1 N T λ 1 λ 2 δ + O ( 1 ( N T λ 1 λ 2 δ ) 2 ) . We next assume that the distribution of logζ ^ is ( approximately ) normal , i . e . , logζ ^ ∈ N ( logζ , 1 ζ 1 N T λ 1 λ 2 δ ) , so that to get the power to equal 0 . 8 with type I error . 05 we need Φ ( x - log ζ 1 ζ 1 N T λ 1 λ 2 δ ) = 0 . 2 Φ ( x - log 1 1 N T λ 1 λ 2 δ ) = 0 . 95 , where x is the threshold of rejecting null hypothesis log ζ = 0 . We can then solve for N as the number of needed trials for detecting excess synchony: N = ⌈ 1 T λ 1 λ 2 δ ( Φ - 1 ( 0 . 95 ) - Φ - 1 ( 0 . 2 ) / ζ log ζ ) 2 ⌉ . | Spike synchrony , which is widely reported in neural systems , may contribute to information transmission within and across brain regions . Critical to this theory is the potential link between oscillatory activity and synchronous spiking . In this article we provide a method for establishing the statistical association of spike synchrony with an oscillatory local field potential . We demonstrate the value of this technique by numerical simulation together with application to both in vitro and in vivo neural recordings . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Establishing a Statistical Link between Network Oscillations and Neural Synchrony |
Spinocerebellar ataxia type 1 ( SCA1 ) is a dominantly inherited neurodegenerative disease caused by expansion of a CAG repeat that encodes a polyglutamine tract in ATAXIN1 ( ATXN1 ) . Molecular and genetic data indicate that SCA1 is mainly caused by a gain-of-function mechanism . However , deletion of wild-type ATXN1 enhances SCA1 pathogenesis , whereas increased levels of an evolutionarily conserved paralog of ATXN1 , Ataxin 1-Like , ameliorate it . These data suggest that a partial loss of ATXN1 function contributes to SCA1 . To address this possibility , we set out to determine if the SCA1 disease model ( Atxn1154Q/+ mice ) and the loss of Atxn1 function model ( Atxn1−/− mice ) share molecular changes that could potentially contribute to SCA1 pathogenesis . To identify transcriptional changes that might result from loss of function of ATXN1 in SCA1 , we performed gene expression microarray studies on cerebellar RNA from Atxn1−/− and Atxn1154Q/+ cerebella and uncovered shared gene expression changes . We further show that mild overexpression of Ataxin-1-Like rescues several of the molecular and behavioral defects in Atxn1−/− mice . These results support a model in which Ataxin 1-Like overexpression represses SCA1 pathogenesis by compensating for a partial loss of function of Atxn1 . Altogether , these data provide evidence that partial loss of Atxn1 function contributes to SCA1 pathogenesis and raise the possibility that loss-of-function mechanisms contribute to other dominantly inherited neurodegenerative diseases .
Polyglutamine diseases are caused by the expansion of an unstable translated CAG repeats that encode a polyglutamine tract in unrelated proteins [1] , [2] . There are nine dominantly inherited neurodegenerative disorders caused by expanded polyglutamine tracts: Huntington's disease ( HD ) , spinobulbar muscular atrophy ( SBMA ) , dentatorubropallidoluysian atrophy ( DRPLA ) , and six spinocerebellar ataxias ( SCA1–3 , 6 , 7 and 17 ) [1]–[7] . Several genetic studies have revealed that loss of the involved proteins in humans and mice does not cause neurodegeneration , leading to the conclusion that the polyglutamine expanded protein causes disease by a dominant gain-of-function mechanism whereby it confers toxic properties to the host proteins [8]–[14] . The importance of protein context and sub-cellular localization has been highlighted in SCA1 pathogenesis because expansion of the polyglutamine tract is necessary but not sufficient to cause neurodegeneration . For example , overexpression of polyglutamine-expanded ATXN1 that has a single serine residue mutated to alanine ( S776A ) does not lead to Purkinje cell degeneration , and overexpression of polyglutamine-expanded ATXN1 lacking a functional nuclear localization signal or lacking the AXH domain is not toxic in mice [15]–[17] . These data revealed key domains in ATXN1 that are critical for SCA1 pathogenesis , and indicated that mutant ATXN1 must be localized in the nucleus to exert toxicity . Furthermore , these data suggest that perhaps normal interactions or functions of ATXN1 are relevant to SCA1 pathogenesis . Several protein interactors of ATXN1 have been identified to date . Among these , there are various transcriptional regulators , including the Capicua homolog CIC , SMRTER , HDAC3 , GFI-1 and RORα . Some of these factors modify the pathogenesis of SCA1 in mice and fly models [15] , [18]–[20] . For instance , Rorα haploinsufficiency results in enhanced pathogenesis in SCA1 transgenic mice [20] . Furthermore , SCA1 transgenic mice share common gene expression changes with the staggerer mice , which have a spontaneous mutation in the Rorα gene that leads to cerebellar defects and ataxia [20]–[22] . Recent evidence shows that altered interactions of ATXN1 with its native partners contribute to SCA1 pathogenesis . Studies in the knock-in mouse model of SCA1 , Atxn1154Q/+ , show that polyglutamine-expanded Atxn1 prefers the formation of a protein complex with the RNA splicing factor RBM17 while concomitantly diminishing the formation of Atxn1 complexes with CIC , a transcriptional repressor [23] . These data suggest an endogenous role of ATXN1 in transcriptional regulation that might be altered in SCA1 . Even though much of the genetic evidence suggests that SCA1 is mainly caused by a gain-of-function mechanism , additional data also suggest that partial loss of Atxn1 function might contribute to pathogenesis . We demonstrated that removing the wild-type copy of Atxn1 in the knock-in mouse model of SCA1 ( Atxn1154Q/− ) leads to worsened SCA1 phenotypes [23] . Moreover , phenotypes and neuropathology in Atxn1154Q/+mice are partially suppressed by the mild overexpression of an evolutionarily conserved paralog , Ataxin-1-Like ( Atxn1L ) [19] , [24] . Atxn1L shares high homology with Atxn1 , however it lacks the polyglutamine tract , and it interacts with all tested proteins that bind to Atxn1 [19] , [24] , [25] . Together , these studies indicate that altering the levels of wild-type Atxn1 and its paralog Atxn1L results in strong modulation of SCA1 phenotypes . Taken together , these data led us to hypothesize that SCA1 pathogenesis results from a gain-of-function mechanism , with partial loss of Atxn1 function potentially contributing to the disease . Since Cic protein levels are also reduced in the Atxn1−/− mouse model [18] , we predict that loss of functional Atxn1-Cic complexes could lead to common transcriptional defects in Atxn1−/− and Atxn1154Q/+ mice . Given the predicted role of Atxn1 in transcriptional regulation , we set out to test this possibility by performing microarray analyses on Atxn1−/− and Atxn1154Q/+cerebella . We show that Atxn1−/− and Atxn1154Q/+mice share many common transcriptional and molecular phenotypes , some potentially involving alterations in Atxn1-Cic-mediated transcriptional repression . Loss of Atxn1 function also results in several cerebellar transcriptional changes in common with mice lacking Rorα , another transcription factor that genetically and physically interacts with ATXN1 . Finally , we demonstrate that overexpression of the Atxn1-related gene Atxn1L can rescue some of the molecular and behavioral defects caused by loss-of-function of Atxn1 , strongly suggesting that Atxn1L-mediated suppression of SCA1 neuropathology could be due to restoration of the partial loss of Atxn1 function component in the disease .
To identify gene expression changes in SCA1 that might be due to partial loss of function of Atxn1 , we surveyed transcriptional changes in Atxn1−/− and Atxn1154Q/+mouse cerebella using the Affymetrix mouse Exon Array ST 1 . 0 and searched for shared expression alterations . These arrays potentially enable the detection of even small fold changes due to the existence of multiple probes sets for most transcripts . Early symptomatic ( 7-week-old ) Atxn1154Q/+mice were used to reflect early changes in SCA1 pathogenesis ( Figure 1A ) . Due to a lack of overt phenotypes in 7-week-old Atxn1 null mice [13] , 16-week-old Atxn1−/− mice were chosen in order to maximize the potential number of gene expression changes detected . Each mutant allele was studied and compared to age-matched wild-type littermates controls . We observed a highly significant rate of concordance and overlap between cerebellar expression profiles of Atxn1−/− and Atxn1154Q/+ mice ( z = 11 . 9485 , P-value<2 . 2e-16 , Kendall-τ test ) . Applying a very stringent cutoff value ( false discovery rate ( FDR ) –corrected P<0 . 01 , Fold change ≥|±0 . 1| log2 ) , we identified 197 transcripts that are significantly dysregulated in both mouse models ( Figure 1A ) . These 197 transcriptional changes account for 22 . 1% of all gene expression changes detected in Atxn1154Q/+cerebella ( Figure 1A and Table S1 ) . Remarkably , a majority of the differentially regulated transcripts ( 135 out of 197 , 68 . 5% ) are in the same direction in both mouse models . ( Figure 1B ) . A preponderance of down-regulated genes in both models is observed ( 90 out of 197 ) , followed by genes up-regulated in both models ( 45 out of 197 ) , with only less than a third ( 62 out of 197 ) altered in an opposite direction ( Figure 1B ) . These data suggest that at least 15 . 1% ( 135 out of 892 ) of the cerebellar transcriptional changes found in the SCA1 knock-in model ( Atxn1154Q/+ ) could be attributed to loss of Atxn1 function . To verify the microarray results , we selected 15 of these shared gene expression changes for independent validation by quantitative real-time reverse transcription polymerase chain reaction ( qRT-PCR ) using cerebellar RNA samples from an independent set of 16-week-old Atxn1−/− and Atxn1154Q/+mice , and their respective littermates . Ten out of 15 gene expression changes , or 66 . 6% , were positively validated in Atxn1−/− mice , while 13 out of 15 genes , or 86 . 7% , were validated in Atxn1154Q/+cerebella ( Table 1 ) . Most importantly , of the 10 genes validated in Atxn1−/− cerebella , 9 were also validated in Atxn1154Q/+cerebella ( Table 1 ) . Taken together , the comparison of cerebellar microarray studies of Atxn1 null and polyglutamine-expanded Atxn1 knock-in mouse models demonstrate that a significant amount of transcriptional changes are shared between these models , supporting the notion that some loss of Atxn1 endogenous function contributes to disease . In order to gain insight into the potential molecular pathways commonly affected in Atxn1−/− and Atxn1154Q/+ mice , we performed Gene Ontology ( GO ) analysis and pathway analysis based on the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) ( Figures S1 , S2 , S3 , S4 , S5 , S6 , and Table S2 ) . GO analysis revealed some biological functions that are enriched in commonly dysregulated genes . For example , cell junction and synapse , guanyl-nucleotide exchange factor activity and GTPase activity categories were enriched in the common set of up-regulated genes ( Figures S1 , S2 , S3 ) . In the down-regulated gene set , genes encoding for calcium ion binding were the most significantly affected both in Atxn1154Q/+ and Atxn1−/− cerebella ( Figures S4 , S5 , S6 ) . Using KEGG pathway analysis , among the top enriched categories for genes commonly down-regulated in both Atxn1−/− and Atxn1154Q/+ mice are the phosphatidylinositol and calcium signaling , Long Term Depression ( LTD ) associated genes , and Alzheimer's disease pathways ( Table S2 ) . These results strongly suggest that loss of Atxn1 results in transcriptional changes that are potentially pathogenic , since in addition to the enrichment for genes involved in neurodegenerative disease , the phosphatidylinositol and calcium signaling pathways are also known to be dysregulated in SCA1 models [26]–[28] . Interestingly , commonly up-regulated genes in Atxn1−/− and Atxn1154Q/+ mice include genes that are involved in cancer pathways ( Table S2 ) . This could potentially point to a novel function of Atxn1 that remains to be clarified in proliferating cells . The categories enriched for genes going in opposite directions between Atxn1−/− and Atxn1154Q/+ mice involve citrate cycle and ubiquitin-mediated proteolysis . These genes going in opposite directions are of interest , since they might reflect potential differences reflecting the main gain of function mechanism in SCA1 pathogenesis . Given the significant overlap between the transcriptional profiles of Atxn1−/− and Atxn1154Q/+cerebella , we were interested in examining whether these transcriptional changes are due to Atxn1 function being affected in both mouse lines and not simply due to cerebellar dysfunction . Previous studies showed that ATXN1 and RORα interact genetically and biochemically , and deficiency of Rorα enhances phenotypes in a transgenic model of SCA1 [20] . Interestingly , we observed a significant overlap between the genetic profiles of Atxn1−/− mice , and those common between staggerer mice and SCA1 transgenic mice ( Table 2 ) [20] , [21] . Most of these shared changes between staggerer and SCA1 mice are not present in Sca7266Q/+ cerebella , a knock-in mouse model for SCA7 [27] , suggesting that most of these changes are not due to cerebellar dysfunction or polyglutamine disease in general ( Table 2 ) . We were able to confirm some of these changes by real-time quantitative RT-PCR in cerebella from 16-week-old Atxn1−/− mice , validating 4 out of 6 transcriptional changes tested ( Figure S7 ) . Given the known genetic and physical interaction between ATXN1 and RORα [20] , these results could potentially indicate that Rorα -dependent transcriptional regulation is altered by loss of Atxn1 function . In contrast to SCA1 transgenic mice , it is important to note that Rorα levels are not changed in Atxn1−/− mice [20] . Together , these findings suggest that loss of endogenous function of Atxn1 results in transcriptional changes that could potentially contribute to cerebellar pathogenesis . ATXN1 forms stable complexes in vivo with the Capicua homolog ( CIC ) , a transcriptional repressor that exhibits reduced levels in Atxn1154Q/+ mice [18] , [23] . To test the hypothesis that reduced Atxn1-Cic complexes lead to dysregulated gene expression in Atxn1154Q/+mice , we searched our microarray data for up-regulated genes that are direct targets of Cic . Microarray analysis revealed at least 3 significantly up-regulated genes ( False Discovery Rate-corrected P<0 . 05 ) in Atxn1−/− cerebella that have been identified as direct targets of Cic-mediated repression , namely Etv1 , Etv5 [29] , and Ccnd1 ( Fryer and Zoghbi , unpublished data ) . Interestingly , the microarray data show that Etv5 and Ccnd1 are also significantly up-regulated in Atxn1154Q/+mice cerebella . These results could reflect the fact that Cic protein levels are diminished both in Atxn1−/− and Atxn1154Q/+mice [18] , [23] . Since Atxn1 and Cic form stable complexes in vivo , we rationalized that both proteins should bind the promoter regions of target genes if they mediate transcriptional repression together as a complex . To test this possibility , we performed chromatin immunoprecipition analysis , followed by PCR for the promoter regions of Etv5 and Ccnd1 ( ChIP-PCR ) . ChIP-PCR analysis using antibodies against Cic confirmed that Cic is present on the promoters of Etv5 and Ccnd1 in vivo ( Figure 2A ) . To determine if Atxn1 binds to the promoters of Cic targets and if the binding is altered due to polyglutamine expansion , we prepared cross-linked chromatin from mice expressing one wild-type Atxn1 allele ( Atxn1+/− ) and compared it to mice expressing one polyglutamine-expanded Atxn1 allele ( Atxn1154Q/− ) . We used Atxn1−/− mice as a negative control for testing Atxn1 antibody specificity . As predicted , in chromatin extracts prepared from Atxn1+/− cerebella and immunoprecipitated using Atxn1 antibody , wild-type Atxn1 was detected on the promoters of Etv5 and Ccnd1 ( Figure 2B ) . In contrast , we could not detect any specific signal for Atxn1[154Q] above background levels in Atxn1154Q/− cerebellar chromatin immunoprecipitations using Atxn1 antibodies ( compared to Atxn1−/− and pre-immune sera controls ) ( Figure 2B ) . These data suggest that there is minimal association of polyglutamine-expanded Atxn1[154Q] to the promoters of target genes in vivo . Alternatively , it is possible that a conformational change in Atxn1[154Q] renders it inaccessible for immunoprecipitation , resulting in reduced signal . Given that Atxn1[154Q]-specific signal is not detected on the promoters of Etv5 and Ccnd1 , we next asked if Cic protein can be detected at these promoters in Atxn1154Q/− mice using Cic antisera . We detected Cic binding to the promoter regions of Etv5 and Ccnd1 ( Figure 2C ) on all genotypes tested ( Atxn1+/− , Atxn1154Q/− and Atxn1−/− ) , despite the fact that Cic protein levels are reduced in Atxn1−/− and Atxn1154Q/− mice [18] , [23] . To better quantify potential differences in Cic binding between the different genotypes , we performed ChIP followed by quantitative PCR . Primer sets designed for conserved regions in the promoters of Etv5 and Ccnd1 that contain or are adjacent to Cic binding sites ( CBS ) ( TGAATGAA or TGAATGGA ) were able to amplify with no significant difference in all three genotypes ( Atxn1+/− , Atxn1154Q/− and Atxn1−/− ) , both for Etv5 and Ccnd1 ( Figure 2D–2E ) . To test for the specificity of Cic-binding to its consensus sequences , primers were designed for regions lacking predicted Cic-binding sites sequences , either upstream or downstream of the CBS-containing regions in Etv5 and Ccnd1 promoters ( Figure 2D–2E ) . As expected , quantitative PCR for these regions show less binding relative to the corresponding positive regions of Etv5 and Ccnd1 by quantitative PCR . These findings suggest that although Cic still binds the promoter , its function in repression is less efficient in the absence of wild-type Atxn1 or that Atxn1L partially compensates for the loss of Atxn1 at the promoters . The gene expression and ChIP-PCR data suggest that polyglutamine-expanded Atxn1 is less efficient in Cic-dependent repression . More importantly , they provide evidence for a model in which loss of Atxn1/Cic function can result in transcriptional dysregulation , contributing to SCA1 pathogenesis . Given that mild overexpression of the Atxn1 paralog , Atxn1L , can displace both wild-type and polyglutamine-expanded Atxn1 from large native complexes in a dose-dependent manner , we wondered if Atxn1L could partially replace Atxn1 as a Cic binding partner , especially because Atxn1L interacts with Cic in wild-type cerebellum [19] , [24] . This led us to propose that in addition to competing with Atxn1[154Q] in the large native complexes [24] , increased Atxn1L levels can suppress a putative loss of Atxn1 function in Atxn1154Q/+ mice by directly substituting for Atxn1 in Cic-containing complexes . To test this possibility , we generated Atxn1−/− and Atxn1−/−; Atxn1Ldp/+ littermates , and performed western blot analysis for Cic . As shown in Figure 3A , Atxn1L overexpression results in restoration of Cic levels back to wild-type levels in Atxn1−/− cerebellum . We then tested if overexpression of Atxn1L increased formation of Atxn1L-Cic complexes , thus stabilizing Cic . For this , we performed co-immunoprecipitation studies using Cic antibody on cerebellar extracts of Atxn1−/− mice with or without the Atxn1Ldp allele , followed by western blot analysis for Atxn1L . As expected , Atxn1L co-immunoprecipitates with Cic in wild-type cerebella ( Figure 3B ) . However , despite the reduced levels of Cic protein in Atxn1−/− cerebella ( Figure 3A and [18] ) , the relative fraction of Atxn1L co-immunoprecipitated with Cic in Atxn1−/− protein extracts was greater than in wild-type cerebella ( Figure 3B ) . Atxn1L overexpression further increased the levels of Atxn1L-Cic co-immunoprecipitation in Atxn1−/−; Atxn1Ldp/+ mice ( Figure 3B ) . These results suggest that Atxn1L overexpression enhances the formation of Atxn1L-Cic complexes in Atxn1−/− mice , thus stabilizing Cic protein levels . We further asked whether Atxn1L-Cic complexes were proficient in Cic-dependent transcriptional repression . For this , we co-transfected HEK293T cells with a luciferase reporter construct containing a tandem array of Cic binding sites [18] , [29] , along with Atxn1L and Cic-expressing plasmids . Consistent with previous studies [18] , co-transfection of Cic and Atxn1 resulted in synergistic repression of the Cic-responsive luciferase reporter ( Figure 3C ) . Interestingly , co-transfection of constructs expressing Cic and Atxn1L resulted in synergistic repression of the reporter similar to co-transfection of Cic and wild-type Atxn1 ( Figure 3C ) . These results strongly suggest that Atxn1L-Cic and Atxn1-Cic complexes are functionally redundant in Cic-dependent transcriptional repression . Thus , we conclude that mild overexpression of Atxn1L in Atxn1−/− mice might partially rescue a loss of Atxn1 endogenous function related to reduced Atxn1-Cic complexes . Given that mild Atxn1L overexpression rescues Cic levels in Atxn1−/−; Atxn1Ldp/+ mice , we predicted that Atxn1L should rescue some of the transcriptional changes in Atxn1−/− cerebella if it can functionally substitute for Atxn1 . To test this , we performed qRT-PCR on cerebellar RNA isolated from Atxn1−/−; Atxn1Ldp/+ mice and Atxn1−/− littermates at 16 weeks of age . We focused our qRT-PCR analysis on the 9 genes that are commonly dysregulated in Atxn1154Q/+ and Atxn1−/− cerebella ( Table 1 ) , and also on the 4 down-regulated Rorα targets validated by qRT-PCR in Atxn1−/− cerebellum ( Figure S7 ) . Interestingly , 5 out of 13 genes tested ( Ccnd1 , Igfbp5 , Apba2bp , Robo1 and Grid2 ) were partially or completely restored back to wild-type levels in Atxn1−/−; Atxn1Ldp/+ , compared to Atxn1−/− cerebella ( Figure 4A–4E ) . Ccnd1 , one of the up-regulated genes that is potentially a direct target of Atxn1-Cic complexes ( Figure 4A ) , and Igfbp5 , an early key pathogenic marker in SCA1 disease ( Figure 4B ) , were among the genes rescued by Atxn1L overexpression in Atxn1−/− mice . Additionally , the Rorα target Grid2 is also significantly rescued in Atxn1−/−; Atxn1Ldp/+ mice . Thus , mild overexpression of Atxn1L in vivo results in partial rescue of several transcriptional changes related to SCA1 pathogenesis in a loss-of-function model of Atxn1 . Since Atxn1L binds to transcriptional regulators that interact with ATXN1 , such as CIC and SMRT-NCoR , it is possible that Atxn1L rescues transcriptional changes by functionally replacing polyglutamine-expanded Atxn1 in these transcriptional complexes . Although Atxn1−/− mice do not exhibit overt ataxia phenotypes or progressive neurodegeneration , they do exhibit a variety of neurological deficits ( [13] and Figures S8 , S9 , S10 , S11 ) . Interestingly , Atxn1−/− mice exhibit deficits in spatial learning and memory , and in motor learning and coordination , phenotypes shared with Atxn1154Q/+mice ( [13] and Figures S10 and S11 ) . This raises the possibility that partial loss of Atxn1 function of could also contribute to these phenotypes in Atxn1154Q/+mice . The fact that the Atxn1Ldp allele can suppress behavioral phenotypes in Atxn1154Q/+mice [24] also raises the possibility that Atxn1L suppresses SCA1 pathogenesis by functionally replacing those Atxn1 functions altered by polyglutamine-expanded Atxn1 . To investigate whether Atxn1L and Atxn1 are functionally redundant in vivo , we tested if the Atxn1Ldp allele can ameliorate behavioral defects in Atxn1−/− mice . We focused on two characteristic phenotypes clinically associated with SCA1 disease: cognitive deficits ( learning and memory ) and deficits in motor coordination and balance [30] , [31] . We performed the conditioned fear paradigm on Atxn1−/− mice carrying the Atxn1Ldp allele and compared them to Atxn1−/− littermates . As shown in Figure 5A , and in agreement with previous data [32] , Atxn1−/− mice exhibited significant deficits , as determined by reduced freezing behavior , in the contextual fear-conditioning test compared to wild-type and Atxn1+/− littermates . Wild-type and Atxn1+/− mice expressing the Atxn1L dp allele performed similarly to the controls ( Figure 5A ) . Interestingly , the duplication of Atxn1L rescued the freezing behavior due to loss of Atxn1 ( compare Atxn1−/− mice to Atxn1−/−; Atxn1Ldp/+ mice; Figure 5A ) . Having shown that mild overexpression of Atxn1L rescues Pavlovian contextual learning in Atxn1−/− mice , we then wanted to assess the effects of increased Atxn1L levels on motor coordination and balance deficits in Atxn1−/− mice , another phenotype common to SCA1 mouse models . In order to discriminate between motor learning impairments and motor coordination deficits , we chose to use the dowel rod and wire hang tests . These paradigms do not rely on consecutive daily training , as does the rotating rod test , therefore we selected them in order to discern motor coordination and balance defects from cerebellar learning deficits [33] , [34] . We generated an independent cohort of Atxn1−/− mice and Atxn1−/−; Atxn1Ldp/+ mice , with Atxn1+/− and Atxn1+/−; Atxn1dp/+ littermates as controls , and tested them at 8 weeks of age . We measured the latency to reach the side ( first touch ) and frequency of walking off the rod ( number of side touches in 120 seconds ) ( Figure 5B and 5C ) . All genotypes tested remained on the dowel for the maximum time ( data not shown ) , but Atxn1−/− mice moved much less than Atxn1+/− and Atxn1+/−; Atxn1dp/+ littermates on the rod . The latency of Atxn1−/− mice to reach the side for the first time was increased ( Figure 5B ) , and consequently they walked off the rod fewer times ( Figure 5C ) . It is noteworthy that Atxn1−/− mice are active and travel the same distance as wild-type littermates the open field analysis ( Figure S5 ) . Thus the hesitancy to move on the dowel suggests that in addition to learning and memory deficits , Atxn1−/− mice have balance or motor coordination impairments , evident at an early age ( 8-week-old ) . In contrast , Atxn1−/− mice carrying the Atxn1Ldp allele take less time to walk off the dowel ( Figure 5B ) , and they also crossed the dowel more times in 120 s than Atxn1−/− littermates ( Figure 5C ) . Thus , mild Atxn1L overexpression partially rescues the dowel phenotype caused by loss of Atxn1 . The wire-hang paradigm assesses motor coordination and grip strength [34] . In this paradigm , mice are hanging onto the center of an elevated wire from their forepaws , and they need to get to the sides for relief , which requires coordination and normal strength . We measured the time and frequency to reach the sides in a 120 s interval , and found no significant falling off the wire in Atxn1−/− mice compared to the control littermates , suggesting that they have reasonable grip strength ( data not shown ) . However , Atxn1−/− mice showed increased latency to reach the sides for the first time compared Atxn1+/− and Atxn1+/−; Atxn1Ldp/+ controls ( Figure 5D ) . Additionally , Atxn1−/− mice reached the sides fewer times than control littermates ( Figure 5E ) . In contrast , Atxn1−/− mice overexpressing Atxn1L exhibited marked reduction in the time for the first touch and increased number of side touches in the 120 s interval , when compared to Atxn1−/− mice ( Figure 5D and 5E ) . Taken together , these behavioral data demonstrate that a 50% increase in the levels of AtxnlL is sufficient to partially rescue several behavioral deficits caused by loss of Atxn1 function . Furthermore , this rescue correlates with the molecular data , demonstrating that Atxn1L is a functional homolog of Atxn1 in vivo .
Recently , we proposed the possibility that in addition to toxic gain-of-function due to polyglutamine-expanded ATXN1 , a concomitant partial loss of ATXN1 function might contribute to SCA1 pathogenesis [23] . It is challenging to establish the extent of the contribution of a potential loss-of-function mechanism to SCA1 pathogenesis in models carrying the mutant protein , since the severe gain-of-function effects might mask any subtle loss-of-function component , thus confounding the interpretation of the results . In the present study , we sought to distinguish between gain- and loss-of-function mechanisms by focusing on transcriptional defects in Atxn1−/− mice , and comparing them to the knock-in model of SCA1 , Atxn1154Q/+ mice . Using this approach , we identified several molecular changes that could be attributable to loss of ATXN1 function in SCA1 . We found that loss-of-function of Atxn1 in mice is sufficient to cause many transcriptional changes common to the Atxn1154Q/+ knock-in mice , a model of SCA1 that faithfully replicates many features of the disease , and with SCA1[82Q] transgenic mice . It has been reported that ATXN1 interacts with several factors involved in transcriptional regulation , including CIC , SMRTER , HDAC3 , Gfi1 and RORα [15] , [18]–[20] . Therefore , these shared expression changes might be indicative of altered transcriptional functions of ATXN1 in SCA1 pathogenesis . Furthermore , we showed that a majority of the shared transcriptional changes go in the same direction in both Atxn1−/− and Atxn1154Q/+ mice , strongly arguing that part of the transcriptional dysregulation in SCA1 might be explained by a partial loss-of-function of Atxn1 . The up-regulation of direct targets of Atxn1-Cic provides evidence for this concept . Another important finding of this study is that there are many transcriptional changes that are unique to the Atxn1154Q/+ model , which could potentially be related to toxicity of polyglutamine-expanded Atxn1 . We propose that a combination of toxic gain-of-function and mild loss-of-function mechanisms contribute to SCA1 pathogenesis , with the partial loss-of-function of ATXN1 being sufficient to cause some transcriptional changes that are pathogenic in the cerebellum . Previous studies using microarray analysis reported on the down-regulation of the dopamine receptor D2 ( Drd2 ) in Atxn1−/− mouse cerebella [35] . However , with the exception of a couple of genes ( e . g . Pafahb3 , Sp1 ) , we were unable to find extensive overlap between the changes reported by Goold et al . and the microarray analysis presented in this study . The differences in genetic background , microarray platform , and the age of the Atxn1−/− animals ( 5-week-old ) in the Goold et al . studies [35] , compared to 16 weeks in our studies , are likely to contribute to the minimal overlap in gene expression changes in the two studies . Bioinformatics analyses of the genes commonly altered in Atxn1−/− and Atxn1154Q/+ cerebella show enrichment for categories associated with pathological pathways involved in neurodegeneration ( Alzheimer's disease ) , and also pathways previously implicated in pathogenesis both in knock-in and transgenic SCA1 mouse models , such as the phosphatidylinositol and calcium signaling pathways [26]–[28] . These results strongly suggest that Atxn1−/− mice have dysfunctional cerebella due to a loss of endogenous Atxn1 function . We also found that Atxn1−/− mice share significant overlap in cerebellar transcriptional profiles with staggerer mice , which have a spontaneous loss-of-function mutation in the gene encoding the transcription factor Rorα . Rorα-regulated genes involved in calcium signaling ( Itpr1 and Calb1 ) and glutamatergic signaling ( Grm1 and Slc1a6 ) are significantly down-regulated in Atxn1−/− cerebellum , as determined by microarray analysis and real-time qRT-PCR . It is noteworthy that loss-of-function mutations in several of these genes result in ataxic phenotypes ( e . g . Itpr1 , Slc1a6 , and Grm1 ) [36]–[38] , raising the possibility that simultaneous down-regulation of several of these genes could contribute to the motor coordination impairments observed in Atxn1−/− mice . Rorα mRNA transcript and protein levels appear normal in Atxn1−/− cerebellum [20] , ruling out that changes in Rorα targets are due to reduced Rorα protein levels in Atxn1−/− mice . Since ATXN1 and Rorα physically interact via Tip60 , it is conceivable that loss of Atxn1 affects Rorα-dependent transcription directly [20] . Altogether , these data support two important conclusions: first , that Atxn1−/− cerebellum exhibits pathological molecular changes , even in the absence of progressive neurodegeneration , and second , that transcriptional changes in the loss-of-function model of Atxn1 could identify endogenous pathways that might also be altered by the expression of mutant Atxn1 . We previously described a reduction of Atxn1-Cic complexes in Atxn1154Q/+cerebella , with Atxn1[154Q] favoring the formation of enhanced toxic gain-of-function complexes with RBM17 [23] . It is interesting that among the genes up-regulated both in Atxn1−/− and Atxn1154Q/+cerebella , we identified two potential direct targets of Cic-dependent repression , Ccnd1 and Etv5 , the genes encoding for Cyclin D1 and Ets variant 5 , respectively ( [29] and J . Fryer , unpublished data ) . We demonstrated that wild-type Atxn1 and Cic are bound to the promoter regions of Ccnd1 and Etv5 . Interestingly , we failed to detect mutant Atxn1[154Q] on these promoters in mice only expressing expanded Atxn1 ( Atxn1154Q/− ) . One interpretation of this result is that Atxn1[154Q] has diminished association to the promoters , resulting in reduced Atxn1-Cic dependent repression . Alternatively , it is possible that polyglutamine-induced conformational changes make the Atxn1[154Q]-Cic complexes less accessible for antibody recognition , resulting in reduced chromatin immunoprecipitation . Irrespective of the basis of the inability to detect Atxn1[154Q] binding on these promoters , the data strongly suggest that polyglutamine-expanded Atxn1 and Cic have reduced transcriptional repression function on these specific promoters in vivo . These results provide a mechanistic explanation on how diminished Atxn1-Cic function can contribute to transcriptional defects in SCA1 . Mild overexpression of the evolutionarily conserved gene Atxn1L partially suppresses the neuropathology caused by polyglutamine-expanded ATXN1 in flies and mice [19] , [24] . Increased Atxn1L levels induce the sequestration of polyglutamine-expanded Atxn1 into nuclear inclusions , leading to a proposed model in which Atxn1L suppresses toxicity by displacement of mutant Atxn1 from its major endogenous complexes that contain Cic [24] . In the present study , we show an additional mechanism contributing to this rescue , by demonstrating that mild overexpression of Atxn1L suppresses several molecular and behavioral phenotypes in Atxn1−/− mice , potentially by replacing Atxn1 in Cic-containing complexes ( Figure 6 ) . The motor coordination and learning deficits suppressed by Atxn1L are common to both Atxn1−/− and polyglutamine-expanded Atxn1154Q/+ mouse models . Therefore , these data provide evidence for an additional mechanism in which Atxn1L can functionally compensate for a partial loss of Atxn1 function to suppress SCA1 pathogenesis . Although our studies demonstrate that Atxn1L is a functional homolog of Atxn1 in Cic-mediated transcriptional repression , we cannot rule out that Atxn1L overexpression can restore other yet to be determined Atxn1-related functions not addressed in these studies . In sum , based on our previous data and this study , we propose that partial loss of ATXN1 function actively contributes to SCA1 pathogenesis as part of a two-pronged mechanism , in which enhanced toxic gain-of-function of polyglutamine-expanded ATXN1 leads to neurodegeneration , while a simultaneous loss-of-function of other stable endogenous protein complexes , Atxn1-Cic , contributes to the SCA1 phenotypes ( Figure 6B ) . It was previously reported that reduction of normal functions of genes involved in other polyglutamine diseases results in enhanced pathology , providing evidence for concomitant gain- and loss-of-function mechanisms in polyglutamine disorders [39]–[43] . In SBMA transgenic mouse models expressing polyglutamine-expanded androgen receptor ( AR ) , loss of endogenous AR protein resulted in accelerated motor neuron degeneration [39] . These studies suggested two independent pathways contributing to SBMA pathogenesis: gain-of-function due to mutant AR nuclear toxicity and loss of AR trophic effects on motor neurons [39] . Conditional deletion of Htt in mouse forebrain leads to several features reminiscent of Huntington disease , including motor deficits , tremors and progressive degeneration of the striatum and cortex , hinting that loss of htt function could contribute to these phenotypes in HD [42] . Moreover , loss of wild-type Htt function leads to enhanced neurodegeneration in transgenic models expressing polyglutamine-expanded Htt , while overexpression of wild-type Htt reduces toxicity caused by mutant htt [40]–[43] . The mechanism by which loss of Htt contributes to HD is unclear at this time; it might involve either its anti-apoptotic properties , its role in BDNF-mediated neuroprotection , both , or some other yet to be determined function [44]–[47] . Our studies comparing Atxn1−/− and SCA1 knock-in mice pinpoint Cic-dependent transcriptional repression as one of the molecular pathways mediating the partial loss-of-function component in SCA1 pathogenesis . Loss of normal endogenous function of mutant proteins may also play a role in other dominant neurodegenerative diseases caused by gain-of-function mutations . The Parkinson's disease model mice overexpressing the mutant A53T SNCA gene lacking endogenous alpha-synuclein , exhibit worsened synucleinopathy when compared to littermates carrying wild-type Snca alleles [48] . In Alzheimer disease ( AD ) , increased aggregation of the amyloid beta peptides induced by AD-related presenilin mutations is thought to be a consequence of a dominant gain-of-function mechanism [49] . However , loss of function of presenilin in the mouse brain results in phenotypes strikingly reminiscent of AD ( progressive memory loss and neurodegeneration ) in the absence of beta-amyloid deposition [50] , [51] . These results suggest that altered pathways leading to Alzheimer disease can be caused from a combination of dominant gain of function and/or loss of function mechanisms . The potential prevalence of mutations that lead to both loss- and gain-of-function in human neurological diseases underscores the importance of understanding the endogenous functions of causative genes through the careful analysis of loss-of-function models , which may uncover critical pathways leading to pathogenesis .
All mouse lines used in this study have been previously described [9] , [13] , [24] . Atxn1154Q/+ and Atxn1−/− mice have been backcrossed into the C75Bl/6J strain for over ten generations . In the case of Atxn1L duplication ( Atxn1Ldp ) mice , all experiments described in this study were done in mice backcrossed for at least 7 generations into the C75Bl/6J strain . Atxn1−/− mice were bred to Atxn1Ldp/+ mice , and Atxn1−/− mice carrying the Atxn1L duplication locus were generated by intercrossing the progeny . Mouse experiments followed protocols approved by the Baylor College of Medicine Institutional Animal Care and Use Committee ( IACUC ) . Total RNA isolation from adult mouse cerebella was performed as described elsewhere [52] . Briefly , RNA was extracted from cerebella of 16-week-old mice using TRIzol reagent ( Invitrogen Corporation , Carlsbad , CA ) , DNaseI-treated , and purified using the RNeasy mini kit according to the manufacturer's protocol ( Qiagen , Valencia , CA ) . We used the Affymetrix Mouse Exon 1 . 0 ST microarray , which carries 1 . 2 million probe-sets covering one million exon clusters , with an average of 40 probes per gene . The exon array data were analyzed as previously described [52] . Briefly , raw data were processed in the R statistical programming environment using locally developed methods and the exonmap package . RMA normalization was applied , and linear models were calculated to analyze genotype effects for each gene . Genomic annotations were obtained from UCSC ( http://genome . ucsc . edu ) . The normalized probe level data were then averaged within each exon to produce exon-level data for each gene for each animal . A two-way ANOVA with main effects for genotype and exonic region was calculated for each gene . The ANOVA model was fit using weighted least squares analysis where the weights were determined according to the probe counts within each exon . Since separate wild-type ( WT ) control littermates were used in the Atxn1−/− and Atxn1154Q/+ experiments , a separate linear model was estimated for each gene in each model ( one fit for each WT background ) . A linear contrast was calculated comparing the WT and mutant cross-exon means for each gene . The cut-off rule for determining genes was a fold change threshold of +/−0 . 1 in both experiments , and a linear step up false discovery rate ( FDR ) of less than 0 . 01 value for the T-statistic corresponding to the linear contrast comparing each WT strain with its corresponding mutant . The gene set determined by this fold change and FDR multiplicity corrected cutoff , corresponds to a median raw marginal p-value of less than 0 . 00015 for the underlying T-statistics . We performed Gene Ontology ( GO ) analysis on the obtained data using locally developed software and methods [52] . Briefly , the gene ontology vocabulary and current mouse annotations were obtained from the GO website ( 9/1/2007 build ) . The mouse exon array was mapped to Entrez identifiers , and these identifiers were mapped to the GO data structure using the available annotations . Using our local ontology analysis system ( OntologyTraverser ) , we tabulated the genes annotated at or below each GO node for the entire exon array . We then used a hypergeometric sampling model to examine the statistical representation of each GO node for genes in our gene sets . In order to make comparisons between sets , we took differences between the standardized scores determined for each gene set . Because of the extreme overlapping structure of the GO , many GO nodes report duplicate or redundant information . To avoid this problem , we calculated the GO covariance structure and used this estimate to compute de-correlated GO scores . For the KEGG pathway analysis , the overlapping gene list containing the 197 most significant genes was uploaded and analyzed using the web-based Functional Annotation Bioinformatics Microarray Analysis DAVID 6 . 7 ( National Institute of Allergy and Infectious Diseases NIH , david . abcc . ncifcrf . gov/ ) . For the independent validation of the exon array data , real time qRT-PCR assays were performed on more than eight mice of each genotype . cDNA was synthesized from 1 µg of RNA using the RT2 First Strand Kit ( SuperArray Bioscience Corporation , Frederick , MD ) . Quantitative real-time PCR reactions were performed on 10 ng of cDNA using RT2 SYBR Green/ROX PCR master mix and commercially available primers ( SuperArray Bioscience Corporation , Frederick , MD ) . All RNA samples were analyzed in triplicate and normalized relative to Gapdh levels . For validation of Rorα targets , cDNA was synthesized from RNA isolated from seven Atxn1−/− mice and control littermates , using Superscript III ( Invitrogen ) . Quantitative real-time PCR reactions were performed on 10 ng of cDNA using the SYBR Green PCR Master Mix ( Applied Biosystems ) using the following primer sets: Itpr1 fw 5′- GGGCCAACAGCACTACAGATG-3′ rv 5′-CTTCTTTTCCAAGTCTGCAGCAT-3′ Grid2 fw 5′- CCCGCATTGAGAGCTCCAT-3′ rv 5′- GCCATAAGGGATATCTGTTTGCTT-3′ Inpp5a fw 5′- TGGTCAAGAAAAGGCTTCATCA-3′ rv 5′- CCAAGTCGAAGGCACAGTCA-3′ Grm1 fw 5′- TCCTCTGACCTGAGACCAATAGC-3′ rv 5′- CGCGTTAGTGGCCATAAGCT-3′ ChIP was performed as previously described [52] . Cerebella were dissected from mice of each genotype at 7–8 weeks of age and incubated in 1% formaldehyde for 10 minutes at room temperature to cross-link DNA to associated proteins . Chromatin was treated with micrococcal nuclease and sheared by sonication to generate fragments with an average length of ∼100–200 bps , as determined by agarose gel electrophoresis . For immunoprecipitation , 200 µl of chromatin was diluted 1∶10 in ChIP dilution buffer ( Millipore Corporation , Billerica , MA ) and 1% of the diluted sample was saved as input . The sample was first precleared with protein A Dynabeads ( Invitrogen Corporation , Carlsbad , CA ) , then incubated overnight with protein A Dynabeads that were pre-blocked with salmon sperm DNA and coupled to 20 µl of affinity purified rabbit anti-Atxn1 ( 11NQ ) antibody , or 4 µl of guinea pig polyclonal anti-Cic antibody . Mock immunoprecipitations using nonspecific preimmune sera for each antibody were included as negative controls . After immunoprecipitation , the beads were washed at room temperature with low salt buffer , followed by high salt buffer , LiCl buffer ( Millipore Corporation , Billerica , MA ) , and TE buffer ( 10 mM Tris-HCl pH 7 . 4 , 1 mM EDTA pH 8 . 0 ) . Elution was performed twice in 250 µl of fresh elution buffer ( 1% SDS , 0 . 1 M NaHCO3 ) for 15 minutes at room temperature . The eluates were combined , the crosslinks were reversed , and DNA was purified with Qiagen PCR cleanup kit ( Qiagen , Valencia , CA ) and recovered in 30 µl of 10 mM Tris-HCl pH 8 . 0 . One or two µl of DNA were used for each PCR reaction using primer pairs for the promoter regions of the following genes: Etv5 fw 5′-GGGGAAGCTTAGCTGAGTCAGTGAA-3′ rv 5′- GTTTCTGTGTGTGGAATGACGAATTC-3′ Ccnd1 fw 5′-GGTTAACTGAATGGACTCCTAAGTTT-3′ rv 5′-GGAAATGTGTGTGAATAGTTCGCCTA-3′ Capicua binding to the promoters was also analyzed using ChIP followed by quantitative real-time PCR ( SYBR green ) . Quantitative real-time PCR experiments were performed in triplicate on three independent sets of samples . Relative amounts of immunoprecipitated DNA were determined based on the threshold cycle ( Ct ) value for each PCR reaction . In order to control for variation between ChIP fractions , for every gene promoter studied , a ΔCt value was calculated for each sample ( Atxn1+/− , Atxn1154Q/+ , Atxn1−/− ) by subtracting the Ct value for the input ( CtInput ) from the Ct value for the immunoprecipitated sample ( Ctantibody or Ctpreimmune ) . Since the input DNA fraction represents only 1% of the total material , the CtInput value was first adjusted for this dilution factor by subtracting 6 . 644 cycles ( Log2 of 100 ) , then substracted from the immunoprecipitated samples using the following formula:Differences between the specific immunoprecipitation and the preimmune serum background were then determined and plotted as fold enrichment over the preimmune serum ( for each genotype sample: ΔCtantibody/ΔCtpreimmune ) . Primer and sequences for the promoter regions used were as follows: Etv5 qA ( highly conserved; two predicted Cic-binding sites ) : fw 5′- TTGCTCCTGATCACACATGC -3′ rv 5′- GCTGGAACCTCGTGAATGAT -3′ Etv5 qB ( conserved; ∼1 Kb upstream of positive region Etv5 qA ) fw 5′- AATCAGCACCGGCTTGTTTA-3′ rv 5′- CTAAGCTTCCCCCTCAGGTC-3′ Ccnd1 qA ( highly conserved; ∼150 bp upstream of predicted Cic-binding site ) fw 5′- AAATTTGCATGAGCCAATCC-3′ rv 5′- GCAGAGCTCAACGAAGTTCC-3′ Ccnd1 qB ( poorly conserved; ∼400bp downstream of predicted Cic-binding site ) fw 5′- GGTCGTGGTTAACTGAATGGA -3′ rv 5′- AGGTGGTGGAACCGCTTTAT-3′ Cerebella from three mice of each genotype were dissected , and dounce-homogenized in 1 ml of ice-cold TST buffer ( 10 mM Tris–HCl , pH 7 . 5 , 0 . 9% NaCl , 0 . 05% Tween 20 ) . Protein was adjusted to 0 . 5 mg in 200 µl with TST buffer ( 10 mM Tris–HCl , pH 7 . 5 , 0 . 9% NaCl , 0 . 05% Tween 20 ) and then added 800 ml cold PBS . Fifteen microlitters of the extract were saved as input . The extract was pre-cleared using protein A Sepharose beads . BSA-blocked protein A Sepharose beads were coupled to 4 µl of polyclonal guinea pig anti-Cic serum or pre-immune serum for 1 hr at room temperature , and the diluted extract was incubated with the antibody-coupled beads overnight at 4°C . Four washes were carried out using ice-cold TST buffer and the pellet was resuspended in sample buffer . Input and pellets were analyzed by SDS-PAGE and protein blot . Luciferase reporter assays for Cic-dependent repression were performed as previously described [18] . HEK293T cells in 24-well plates were co-transfected using Lipofectamine 2000 ( Invitrogen ) with 50 ng of the pGL3-Promoter ( Promega ) containing six copies of CIC binding sites ( TGAATGAA or TGAATGGA ) , 10 ng of pRL-TK , and 10 ng of expression plasmids for Atxn1[2Q] ( wild-type ) , Atxn1L and Cic-expressing plasmids as indicated . All constructs have also been previously described [18] , [24] . The total amount of DNA transfected was kept constant by adding pcDNA3 . 1 ( - ) ( Invitrogen ) . Luciferase activities were measured using the dual luciferase reporter assay system ( Promega ) . For this task , the mice were trained in a novel environment with a neutral stimulus ( a tone ) paired with a foot shock . The mice are placed in a different context with the same tone ( cued test ) or back in the same environment without a tone ( contextual test ) 24 h later . Mice of each genotype were placed in a Med Associates/Actimetrics chamber system where a 30 second tone was followed by a 2 second foot shock at 1 . 5 mA . The tone–foot shock were repeated at 2 min . Twenty-four hours later , the mice were tested for freezing in the same chamber with no tone to evaluate contextual fear . Scoring for freezing is automated in this system . Analysis was performed using ANOVA and t-test analysis . Mice were placed on the center of a 0 . 9 cm wooden dowel suspended between two platforms . If the mouse walked off of the dowel onto the platform , the mouse was placed back in the center . The test lasted 2 min beginning when the mouse was placed on the dowel . Data were analyzed using ANOVA and Student's t-test . | Spinocerebellar Ataxia type 1 ( SCA1 ) is one of nine neurodegenerative diseases caused by an increase in the number of the amino acid glutamine in their respective proteins . Genetic studies have pointed to the fact that the glutamine expansion in Ataxin-1 causes SCA1 by causing Ataxin-1 to gain some function ( s ) . Here , we demonstrate that in addition to the toxic gain-of-function mechanism , partial loss of the normal functions of Ataxin-1 contributes to SCA1 . Ataxin-1 forms protein complexes with Capicua , a protein that silences expression of other genes , and we found that in SCA1 mouse models the levels of these complexes are reduced , resulting in increased expression of some genes . We also demonstrate that increased levels of Ataxin-1-Like , a protein that is similar to Ataxin-1 and protects against mutant Ataxin-1 in mice , rescues molecular and behavioral defects in mice deficient in Ataxin-1 . These results show that Ataxin-1-Like compensates for loss of Ataxin-1 and that Ataxin-1 and Ataxin-1-Like share some normal functions . Together , these findings suggest that rescue of SCA1 symptoms by Ataxin-1-Like could be partly due to restoration of lost normal functions of Ataxin-1 in mice that express the mutant polyglutamine-expanded Ataxin-1 . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience/behavioral",
"neuroscience",
"neurological",
"disorders/movement",
"disorders",
"genetics",
"and",
"genomics/disease",
"models",
"genetics",
"and",
"genomics/gene",
"function",
"neurological",
"disorders/neurogenetics",
"cell",
"biology/gene",
"expression"
] | 2010 | Partial Loss of Ataxin-1 Function Contributes to Transcriptional Dysregulation in Spinocerebellar Ataxia Type 1 Pathogenesis |
Hypoxia-inducible factor ( HIF ) is a nuclear transcription factor that responds to environmental and pathological hypoxia to induce metabolic adaptation , vascular growth , and cell survival . Here we found that HIF subunits and HIF2α in particular were normally expressed in the mediobasal hypothalamus of mice . Hypothalamic HIF was up-regulated by glucose to mediate the feeding control of hypothalamic glucose sensing . Two underlying molecular pathways were identified , including suppression of PHDs by glucose metabolites to prevent HIF2α degradation and the recruitment of AMPK and mTOR/S6K to regulate HIF2α protein synthesis . HIF activation was found to directly control the transcription of POMC gene . Genetic approach was then employed to develop conditional knockout mice with HIF inhibition in POMC neurons , revealing that HIF loss-of-function in POMC neurons impaired hypothalamic glucose sensing and caused energy imbalance to promote obesity development . The metabolic effects of HIF in hypothalamic POMC neurons were independent of leptin signaling or pituitary ACTH pathway . Hypothalamic gene delivery of HIF counteracted overeating and obesity under conditions of nutritional excess . In conclusion , HIF controls hypothalamic POMC gene to direct the central nutrient sensing in regulation of energy and body weight balance .
Hypoxia-inducible factor ( HIF ) is the central nuclear transcription factor that is induced under environmental ( e . g . , high altitude ) and pathological ( e . g . , cancer ) hypoxia [1] , [2] . HIF exists as the heterodimer of an α subunit and a β subunit; HIFα protein levels are regulated based on tissue oxygen availability , while HIFβ ( also called aryl hydrocarbon receptor nuclear translocator , or ARNT ) is constitutively present [1]–[4] . Under normoxia , HIFα undergoes protein hydroxylation , ubiquitination , and degradation , and this process is mediated by prolyl hydroxylases ( PHDs ) and ubiqutin E3 ligase pVHL—the product of von Hippel-Lindau ( VHL ) gene [1]–[4] . Under hypoxia , PHDs are suppressed , leading to HIFα protein stabilization and thus the transcriptional action of HIFα/β in inducing genes that classically regulate metabolic adaptation , vascular growth , and cell survival [1]–[4] . Among three HIFα isoforms ( HIF1α , HIF2α , and HIF3α ) , HIF1α and HIF2α have been extensively studied in the literature [1]–[4] . While both HIF1α and HIF2α mediate hypoxia adaptation , HIF2α can control a distinct set of target genes [5] , [6] . Consistently , the biological consequences of HIF2α versus HIF1α ablation in mice are different [7]–[9] , suggesting that HIF2α and HIF1α have divergent physiological functions . Recent research has elucidated that regulation of HIF1/2α by hypoxia involves metabolic mediators , such as reactive oxygen species that can modulate mitochondrial complex III [1]–[4] , [10]–[12] . In addition , HIF is subjected to normoxic regulation , and the underlying basis has been related to several metabolic signaling pathways including the PI3K-mTOR cascade [13]–[15] and SIRT1 [16] . The biochemical regulation of HIF by various metabolic signals has been implicated to underlie the metabolic programming of tumorigenesis [1]–[4] . However , it remains unexplored whether a reverse relationship might exist , i . e . , if HIF could be a primary regulator of metabolic physiology at an organism level and hence a critical target for controlling metabolic disease , and if so , what could be the responsible tissue/cell types and the underlying molecular basis . The hypothalamus in the central nervous system ( CNS ) is the master regulator of energy intake ( feeding ) , energy expenditure , and body weight balance [17]–[21] . The responsible neuronal regulation involves not only hormonal sensing by molecules such as leptin and insulin [17]–[21] but also nutrient sensing by species such as glucose , amino acid , and fatty acids [22]–[32] . Compared to the long-term homeostatic regulation of body weight by hypothalamic hormonal signaling , hypothalamic glucose sensing is rapid and predicted to provide an acute and real-time regulation on metabolic homeostasis . Proopiomelanocortin ( POMC ) -expressing neurons , termed POMC neurons , have been identified to account for hypothalamic glucose sensing [27] , [33] . In this report , we demonstrate that hypothalamic glucose sensing is mediated by HIF activation and resulting up-regulation of POMC gene and that HIF loss-of-function in POMC neurons causes glucose desensitization to promote energy imbalance and obesity development .
In order to screen nuclear transcription factors that control hypothalamic neuropeptide genes , we analyzed the DNA sequence of POMC promoter and identified a HIF-responsive element at the proximal promoter region across species from rodents to humans . POMC is the precursor of hypothalamic neuropeptide , α-melanocyte-stimulating hormone ( α-MSH ) , which is an important hypothalamic regulator of feeding and energy balance , and mutation of POMC gene is sufficient to cause severe obesity and diabetes in both rodents [34] and humans [35] , [36] . Notably , the HIF-binding DNA element ( 5′-GCGTG-3′ ) is located immediately upstream of the transcriptional initiation site ( TATA box ) in the POMC promoter ( Figure 1A ) . In contrast , the DNA elements for STAT3 , the most established nuclear transcription factor for POMC gene in leptin signaling [37]–[39] , are located further upstream . Using a luciferase reporter system , we found that overexpression of HIF1α and HIF2α increased the activities of transfected POMC promoter by 12 folds and 26 folds , respectively ( Figure 1B ) . When HIF1α or HIF2α was co-overexpressed with HIFβ , the heterodimeric complex activated POMC promoter by 362–466-fold ( Figure 1B ) . On the other hand , deletion of the 5-bp HIF-binding DNA element ( 5′-GCGTG-3′ ) substantially prevented HIF from activating the mutant POMC promoter ( Figure 1B ) . All these data suggest that POMC gene represents a HIF target . To explore whether there was an anatomic basis to support a metabolic role of hypothalamic HIF , we then profiled HIF isoform distribution in the hypothalamus as well as other brain regions . Western blot analysis of HIF2α showed high protein levels in the hypothalamus but low levels in many other brain regions , including cortex , thalamus , olfactory bulb , pons , and cerebellum ( Figure 1C ) . Unlike HIF2α , HIF1α protein expression was normally weak throughout the brain ( Figure 1C ) . We then performed brain immunostaining of HIF2α versus HIF1α . The specificity of HIF2α and HIF1α antibodies for immunostaining were both verified through exogenous expression and co-immunostaining with conjugated epitope tags . In these experiments , hypothalamic GT1-7 cells were transfected with pcDNA3 . 1 plasmid , which expressed myc-tagged HIF2α or myc-tagged HIF1α . The induction of HIFα isoforms was detected by the immunostaining of anti-HIF1α or anti-HIF2α antibody , and the specificity was confirmed by co-immunostaining with anti-myc antibody . The results showed that anti-HIF2α antibody ( Figure S1 ) and anti-HIF1α antibody ( Figure S2 ) provided equal sensitivity and did not yield cross-reactions . Subsequently , we employed these two antibodies to map HIF2α versus HIF1α in the brain of normal C57BL/6 mice . Immunostaining revealed that HIF2α was abundant in neurons of the mediobasal hypothalamus that comprised the arcuate nucleus ( Figure 1D ) , but less abundant in many other brain regions ( unpublished data ) . Compared to HIF2α , HIF1α was weakly expressed in the hypothalamus ( Figure 1D ) and barely detectable in many other brain regions . Altogether , these data suggest that HIFα , in particular HIF2α , might be involved in hypothalamic regulation of whole-body physiology . POMC neurons have been known as a major hypothalamic neuronal type that mediates glucose sensing of the hypothalamus [27] , [33] . Hence , we investigated whether HIF inactivation in POMC neurons could affect hypothalamic glucose sensing in regulation of feeding . To test this question , we chose to ablate HIFβ gene , since HIFβ is mandatory for the DNA binding and activation of both HIF1 and HIF2 complexes [1]–[4] . By crossing POMC-Cre mice [40] with HIFβlox/lox mice [41] , we created a knockout mouse model with HIFβ gene ablated in hypothalamic POMC neurons , termed POMC/HIFβlox/lox mice . To evaluate the efficiency of HIFβ ablation in the knockout mice , we further crossed POMC/HIFβlox/lox mice with ROSA-flox-STOP-flox-YFP mice in order to visualize POMC neurons in brain sections . Using this tool , we revealed that HIFβ protein was disrupted in the majority ( ∼90% ) of POMC neurons in POMC/HIFβlox/lox mice ( Figure 2A&B ) . The specificity of anti-HIFβ antibody was verified in cultured cells by co-staining of transfected HIFβ with the conjugated tag ( Figure S3 ) . The total number and morphology of hypothalamic POMC neurons were not affected by HIFβ gene ablation ( Figure 2C ) , suggesting that HIF inactivation did not impair the development of POMC neurons . POMC/HIFβlox/lox mice were developmentally indistinguishable from littermate control HIFβlox/lox mice . POMC/HIFβlox/lox mice and controls at young ages ( 1∼3 mo old ) had similar body weight . This knockout mouse model was then employed to test whether HIFβ ablation could compromise nutrient-induced hypothalamic POMC mRNA expression . Two experimental paradigms were used: supply of general nutrients through re-feeding post-fasting and glucose administration through third-ventricle infusion . First , it was found that re-feeding post-fasting significantly increased hypothalamic levels of POMC mRNA in control mice , but failed to do so in POMC/HIFβlox/lox mice ( Figure 2D ) . HIFβ ablation did not alter mRNA levels of hypothalamic neuropeptides CART , AGRP , and NPY ( Figure 2D ) or hindbrain neuropeptide nesfatin-1 ( Figure S4A ) . We also examined a few other nuclear transcription factors including BSX , FoxO1 , and CREB , which can also control neuropeptide expression . The expression levels of these genes were unchanged in the hypothalamus of POMC/HIFβlox/lox mice compared to controls ( Figure S4B ) . Second , we performed the experiment using third-ventricle glucose infusion . Similar to re-feeding , glucose infusion up-regulated hypothalamic POMC mRNA levels in control mice; however , this up-regulation was not induced in POMC/HIFβlox/lox mice ( Figure 2E ) . In sum , the data suggested that HIF mediates glucose-dependent hypothalamic POMC gene expression . Subsequently , we investigated whether the HIFβ ablation could compromise the feeding-restricting effect of hypothalamic glucose sensing . Following a prolonged fasting ( 24 h ) , POMC/HIFβlox/lox mice and their littermate controls received third-ventricle injection of glucose via pre-implanted cannula . Indeed , glucose suppressed fasting-induced feeding in control mice . This anorexic effect occurred rapidly within 6 h post-injection ( Figure 2F ) and lasted throughout 24-h follow-up period ( Figure 2G ) . In contrast , glucose-induced appetite suppression was substantially abolished in POMC/HIFβlox/lox mice ( Figure 2F&G ) . Hence , HIF in POMC neurons is required for glucose-dependent hypothalamic regulation of feeding behavior . POMC cells are present in the hypothalamus as well as the pituitary; thus , both places were targeted by the Cre-loxp technique for HIFβ ablation in the knockout mice . Following the above observations in POMC/HIFβlox/lox mice , we examined whether the pituitary POMC cells were affected by HIFβ ablation in the knockout mice . Because pituitary POMC is the precursor of adrenocorticotropic hormone ( ACTH ) , we evaluated the pituitary ACTH synthesis via ACTH immunostaining . Data revealed that the numbers of pituitary ACTH-positive cells and ACTH expression levels were similar between POMC/HIFβlox/lox mice and littermate controls ( Figure S5A&B ) . Consistently , pituitary morphology ( Figure S5A ) , ACTH release ( Figure S5C ) , and pituitary mass ( Figure S5D ) in the POMC/HIFβlox/lox mice were normal . Also , since the main function of ACTH is to control adrenal growth and corticosterone release , we analyzed the histology of adrenal glands from POMC/HIFβlox/lox mice and matched controls . Indeed , the adrenal morphology and mass were comparable between POMC/HIFβlox/lox mice and the controls ( Figure S5E&F ) . In line with this profile , blood corticosterone concentrations in POMC/HIFβlox/lox mice and controls were also similar ( Figure S5G ) . Altogether , these data indicated that the pituitary POMC-ACTH system was not involved in glucose-related feeding dysregulation of POMC/HIFβlox/lox mice . The next question was: How could glucose activate hypothalamic HIF ? Since mRNA levels of hypothalamic HIF2α and HIF1α were not affected by third-ventricle glucose infusion ( Figure S4C ) , glucose regulation of hypothalamic HIF was not mediated via HIF mRNA expression . In contrast , third-ventricle glucose infusion significantly increased HIF2α protein levels in the hypothalamus ( Figure 3A&B , Figure S6A ) . This effect was not evident in peripheral tissues of mice that were i . p . injected with glucose ( Figure S6B&C ) . To understand the molecular basis for glucose-induced hypothalamic HIF up-regulation , we tested if it involved PHDs-dependent HIFα hydroxylation and degradation—which is the classical molecular cascade in regulation of HIF activity [1] , [2] . Using the GHO assay , which was established in the literature [42] , we found that third-ventricle glucose delivery suppressed the hydroxylation activities of PHDs in the hypothalamus ( Figure 3C ) . We also examined pVHL , an E3 ubiquitin ligase that mediates ubiquitination and degradation of hydroxylated HIFα . Glucose did not change pVHL protein levels in the hypothalamus ( Figure 3A ) , suggesting that pVHL was not a primary factor for glucose-induced HIFα up-regulation . Thus , glucose employs the PHD-pVHL system to induce hypothalamic HIF up-regulation , although the magnitude of this effect was much smaller than that of hypoxia ( Figure 3B&C ) . We also analyzed the binding of HIF2α to p300 , since the complex functions in the nucleus to exert the transcriptional activity . As shown in Figure 3D , increased hypothalamic HIF2α protein levels were proportionally associated with the increased binding of HIF2α to p300 , suggesting that up-regulation of hypothalamic HIF2α by glucose is transcriptionally functional . We further asked whether HIF2α might be involved in the action of leptin , a well-established hormone that employs nuclear transcription factor STAT3 to mediate hypothalamic regulation of feeding [17]–[19] . First , in contrast with the effect of glucose , leptin administration via the third ventricle did not alter HIF2α protein levels in the hypothalamus ( Figure S7A ) . Then , we investigated whether HIF might be required for the signaling and function of leptin in the hypothalamus . To test this question , we employed a loss-of-function strategy by analyzing hypothalamic leptin signaling and leptin-dependent feeding regulation in POMC/HIFβlox/lox mice . Compared to the control mice , POMC/HIFβlox/lox mice showed similar levels of leptin-induced STAT3 phosphorylation in the hypothalamus including the comprised POMC neurons ( Figure S7C&D ) . Thus , hypothalamic HIF was responsive to glucose but not leptin , which aligns with the observation that hypothalamic glucose sensing did not involve the induction of leptin signaling ( Figure S7B ) . To evaluate the physiological relevance of this finding , experiments were performed to compare leptin-dependent feeding regulation in POMC/HIFβlox/lox mice versus control mice . Data showed that food intake in POMC/HIFβlox/lox mice and the matched controls were suppressed by leptin in a similar manner ( Figure S7E ) . Altogether , while STAT3 is a critical nuclear transcription factor in leptin signaling , HIF represents a nuclear transcription factor that crucially mediates the glucose-sensing process of the hypothalamus . It has been recently demonstrated that glucose metabolites fumarate and succinate can inhibit PHDs to activate HIF in cancer cells to promote tumorigenesis [4] . Hinted by this information , we questioned if fumarate and succinate could mediate glucose-dependent HIF activation in the hypothalamus . To examine this idea , we first confirmed the prediction that glucose delivery via the third ventricle increased the production of fumarate and succinate in the hypothalamus ( Figure 4A&B ) . Then , after having established the appropriate dose- and time-course conditions ( Figure S8A–D ) , we revealed that HIF2α protein levels in the hypothalamus of normal C57LB/6 mice were significantly increased by a third-ventricle delivery of either fumarate or succinate ( Figure 4C , Figure S8E&F ) . Furthermore , fumarate and succinate were both found to suppress the PHD hydroxylation activities in the hypothalamus ( Figure 4D ) . These data indicated that these two glucose metabolites activated hypothalamic HIF via the PHD-pVHL pathway . Subsequently , we examined whether manipulation of hypothalamic fumarate or succinate could affect feeding in mice . As established in the literature [43] , [44] , succinate and fumarate can be accumulated by using inhibitors of either a succinate dehydrogenase , thenoyltrifluoroacetone ( TTFA ) , or a fumarate hydratase inhibitor , trans-aconitate , or 3-nitropropionic acid ( 3-NPA ) . We found that individual delivery of these chemicals via third-ventricle injection inhibited fasting-induced food intake of mice ( Figure S9A ) without evident toxic/aversive effects ( Figure S9B ) . Then , we tested if succinate or furmarate administration into the third ventricle of mice could affect their feeding activities . Indeed , we found that a third-ventricle injection of either fumarate or succinate suppressed food intake in the control mice . In contrast , such effects were significantly reduced in POMC/HIFβlox/lox mice ( Figure 4E&F ) . Hence , fumarate and succinate are two glucose metabolites that can mediate glucose up-regulation of hypothalamic HIF . Recent research has revealed AMPK as an “energy gauge” in hypothalamic regulation of energy balance [30]–[32] . In this context , we asked if AMPK could be involved in hypothalamic HIF signaling and action . Using AMPKα phosphorylation to reflect AMPK activity , we found that an intra-third ventricle injection of glucose inhibited hypothalamic AMPK ( Figure 5A&B ) . Then , we performed intra-third-ventricle injection of glucose in the presence or absence of AICAR , an established AMPK activator . Data revealed that AICAR markedly reduced the effect of glucose in hypothalamic HIF up-regulation ( Figure 5A&B ) . This result indicated AMPK might work as an inhibitory regulator for glucose-dependent HIF activation in the hypothalamus . Also , it has recently been reported that activation of hypothalamic AMPK by AICAR can promote food intake in mice [45] , [46] . After having confirmed this effect in our experiment , we tested whether hypothalamic HIF could prevent the feeding-promoting effects of AICAR . To do this , we delivered HIF2α/HIFβ complex into the neurons in the mediobasal hypothalamus , since POMC neurons are predominantly localized in this region . Through a neuron-specific lentiviral vector in which dual synapsin promoters were used to direct co-expression of two genes ( Figure S10A ) , HIF2α and HIFβ were co-delivered into the mediobasal hypothalamus of standard C57BL/6 mice ( Figure S10B&C ) . Mice receiving lentiviral delivery of GFP were used as controls ( Figure S10B ) . As shown in Figure 5C&D , a third-ventricle administration of AICAR promoted food intake in control mice as expected; however , this effect of AICAR was eliminated by the exogenous expression of HIF2α/HIFβ complex . Thus , AMPK suppression by glucose is mechanistically involved in glucose sensing of the hypothalamic HIF pathway . We also examined the potential relevance of mTOR and its downstream component S6K , since mTOR/S6K can promote HIF1/2α protein synthesis in various experimental models [1] , [2] , and also AMPK can inhibit mTOR/S6K [47] , [48] . We observed that glucose-dependent hypothalamic HIF2α up-regulation was associated with increased S6K activities ( Figure 6A&B ) . Conversely , glucose induction of hypothalamic HIF2α was significantly reversed by third-ventricle injection of mTOR inhibitor , rapamycin ( Figure 6A&B ) . Supported by recent research that has shown that hypothalamic mTOR [24] , [49] and S6K [50] restrict feeding and weight gain , we further evaluated if mTOR/S6K might participate in the action of hypothalamic HIF in regulation of feeding . Using the site-specific gene delivery approach described above , we delivered lentiviruses expressing constitutively active Rheb ( CARheb ) to directly activate mTOR in the mediobasal hypothalamus of POMC/HIFβlox/lox mice versus littermate controls ( Figure 6C–F ) . As revealed in Figure 6F , while CARheb decreased food intake in control mice , this effect was reduced in POMC/HIFβlox/lox mice . In summary , glucose sensing of HIF in POMC neurons critically involves mTOR/S6K signaling . Following the studies addressing glucose sensing of hypothalamic HIF ( Figures 2–6 ) , we examined whether HIF inhibition in POMC neurons could be sufficient to affect the steady-state levels of feeding and energy homeostasis . First , energy ( food ) intake and expenditure were profiled in POMC/HIFβlox/lox mice and littermate controls under normal chow feeding . Compared to the controls , POMC/HIFβlox/lox mice were found hyperphagic ( Figure 7A ) with impaired energy expenditure ( Figure 7B&C ) , but resulting in only a mild overweight condition ( unpublished data ) . Despite the lack of dramatic body weight effect , DEXA scanning revealed that fat mass of POMC/HIFβlox/lox mice increased evidently ( Figure 7D ) . Morphological examination of various fat tissues further confirmed that the size of fat cells isolated from the knockout mice increased ( Figure 7E ) . These physiological changes in the knockout mice were associated with impaired thermogenic response of brown fat tissue to re-feeding ( Figure S11 ) . We also tested if the obesogenic effect of hypothalamic HIF loss-of-function could result from leptin resistance . To do this , ob/ob mice received mediobasal hypothalamic injection of lentiviruses expressing dominant-negative HIFα , which has been established to inhibit both HIF1α and HIF2α [51] , [52] . The results revealed that the obesity-promoting effect of HIF loss-of-function remained in ob/ob mice ( Figure S12 ) , indicating that leptin signaling was not involved in the metabolic action of hypothalamic HIF . Taken together , HIF loss-of-function in POMC neurons causes positive energy balance in favor of obesity development . To further elucidate the significance of hypothalamic HIF inhibition in obesity development , we maintained POMC/HIFβlox/lox mice and the littermate controls under high-fat diet ( HFD ) feeding since weaning . Despite caloric abundance ( 58 . 5 Kcal% fat ) in the HFD , POMC/HIFβlox/lox mice continued to display an overeating behavior ( Figure 7F ) . Thus , the knockout mice were insensitive to the enriched levels of calories from HFD , again supporting the notion that HIF inactivation in POMC neurons reduced the nutrient-sensing function of the hypothalamus . We monitored the longitudinal course of body weight gain and obesity development in HFD-fed POMC/HIFβlox/lox mice versus controls . Compared to HFD-fed control mice , HFD-fed POMC/HIFβlox/lox mice gained body weight more rapidly and displayed obesity in an exacerbated manner ( Figure 7G ) . Thus , when challenged with obesity-prone conditions ( such as HFD feeding ) , the susceptibility of disease development is highly increased by HIF dysfunction in hypothalamic POMC neurons , supporting the importance of HIF in the pathogenesis of obesity-diabetes syndrome . Finally , we performed animal studies to evaluate whether hypothalamic HIF could be targeted to generate strong therapeutic effects against obesity . Although HIF2α is the major subunit in the hypothalamus ( Figure 1C&D ) , we designed experiments to evaluate the potential use of both HIF2α and HIF1α . Using the lentiviral co-expression system shown in Figure S10A&B , we delivered HIF2α/HIFβ versus HIF1α/HIFβ into the mediobasal hypothalamus of normal C57BL/6 mice . Matched mice with gene delivery of GFP were used as the controls . Following viral injection , all mice were maintained on an HFD and longitudinally followed up for feeding and weight gain . As shown in Figure 8A&B , the control mice gained body weight , rapidly leading to overt obesity over a 3-mo period . In contrast , delivery of either HIF2α/HIFβ or HIF1α/HIFβ markedly reduced obesity development ( Figure 8A&B ) . The anti-obesity effect of HIF gain-of-function was clearly attributed to feeding restriction ( Figure 8C&D ) presumably resulting from POMC gene expression up-regulation ( Figure S10D ) . In conclusion , although hypothalamic HIF2α/HIFβ is more physiologically relevant to metabolic regulation , both HIF2α and HIF1α hold significant therapeutic potentials and can be targeted , individually or in combination , in order to counteract obesity and related metabolic diseases .
This study demonstrates that HIF is present in the hypothalamus and sensitively up-regulated by the local availability of glucose and its metabolites . Glucose up-regulation of hypothalamic HIF is mediated by PHD/pVHL-dependent HIFα degradation and AMPK/mTOR-dependent HIFα synthesis . POMC neurons in the hypothalamus are critical for the metabolic role of hypothalamic HIF through its direct transcriptional regulation on POMC gene . HIF loss-of-function in POMC neurons can cause overeating and weight gain to promote obesity development , while HIF gain-of-function can provide strong therapeutic benefits against obesity ( and related diseases ) ( Figure 9 ) . Taken together , all these unexpected findings reveal an unappreciated role for neuronal HIF in the brain regulation of energy , body weight , and metabolic balance . Energy homeostasis is fundamental for living , and this physiological process relies on the complex property of the life' regulatory system in coordinately sensing and transducing the metabolic dynamics in the body . The hypothalamus is known as the “headquarters” for the regulation of energy balance [17] , [18] , [53] , [54] . This work discovered that the HIF complex plays a critical role in linking hypothalamic glucose sensing to glucose-dependent hypothalamic control of feeding and energy balance . Known as a nuclear transcription factor that mediates hypoxia adaptation , the physiological and disease significance of HIF has traditionally been explored for the pathological roles in diseases , particularly cancers [1]–[3] . Recently , research in cancer biology has begun to appreciate that tumorigenesis involves the connection between HIF and metabolism at cellular levels [55] , [56] . Here , we have demonstrated an important but unappreciated role of HIF in the hypothalamus at an organism level from the perspective of feeding and body weight regulation as well as metabolic disease , especially obesity and related health problems . Indicated by our findings , further lines of research can be expected to explore possible roles of neuronal HIF in other physiological functions and disease relevance of the hypothalamus . For almost a decade , the hypothalamus has been established as a lipostatic feeding regulator through hormonal action of leptin , which involves activation of POMC by STAT3 [37]–[39] , [57] . Recently , the role of the hypothalamus in nutrient sensing has become a major interest in the research field , leading to the elucidation of hypothalamic glucose sensing involving AMPK [30]–[32] and KATP channels [27] and hypothalamic amino acid-sensing process involving mTOR [24] . In this study , we discovered that HIF in the hypothalamus acts as a nutrient sensor . In alignment with the fast turnover rate of HIF proteins ( half life: 5∼10 min ) [1] , [2] , the HIF pathway in hypothalamic POMC neurons likely directs the real-time homeostasis of feeding and energy homeostasis . This regulation complements STAT3-dependent POMC gene expression in leptin signaling and its long-term regulation on feeding and body weight [39] , [57] , [58] . Additional research seems needed to address if HIF pathway can work in other neuronal types in addition to POMC neurons in mediating hypothalamic control of metabolic physiology . Overall , this work has specifically focused on the glucose-sensing process of the hypothalamus , and along this line , it deserves further endeavors to study whether HIF pathway can function in the central sensing process of other nutrient species ( such as amino acids and fatty acids ) and their metabolites . The hypothalamus has been well appreciated as the master regulator of body weight and metabolic balance and a pathogenic culprit for obesity and related disease [17]–[21] . Recent research attentions have been extensively directed to hypothalamic hormonal dysregulations and most notably the development of leptin resistance and insulin resistance [59]–[63] . However , it remains poorly understood how hypothalamic nutrient sensing is mediated . More importantly , it is still unclear if a molecular pathway in central nutrient sensing could be targeted to effectively counteract obesity and related disease . In this work , we found that enhancing hypothalamic glucose sensing through HIF induction per se is effective in treating obesity . Our findings are supported by a recent work showing an anti-obesity effect of HIF activation through the ablation of an HIF inhibitor FIH throughout the brain [64] . We recognize that there is a great deal of research efforts aimed to develop HIF inhibitors for cancer therapeutics , but concerns were recently raised about some serious problems that can arise from HIF inhibition in certain tissues and cells [65] . Here our research further points out that HIF inhibition in the hypothalamus can result in adverse metabolic outcomes and needs to be avoided in drug designs . On the other hand , selective activation of neuronal HIF especially in the hypothalamus could be developed to provide a new therapeutic avenue against obesity and related metabolic disease . Neuron-specific HIF activation might not have critical concerns in terms of oncogenesis since neurons are non-replicable , but the potential application of this strategy in treating metabolic disease will certainly require future technology development and safety assessment .
HIFβlox/lox mice [41] and POMC-Cre mice [40] described previously were maintained on the C57BL/6 strain background . ROSA-flox-STOP-flox-YFP mice were from Jackson Laboratory . All the mice were housed in standard conditions . High-fat diet ( 58 . 5 Kcal% fat ) was purchased from Research Diets , Inc . Mice were measured for body weight and food intake on either a daily or regular basis . DEXA scanning was performed using the DEXA scanner at the Primate Center at the University of Wisconsin . The physiological markers of energy expenditure , including O2 consumption and CO2 production , were measured using the metabolic chambers ( Columbus Instrument , Inc . ) at the DRTC core at Albert Einstein College of Medicine . The Institutional Animal Care and Use Committee approved all the procedures . As previously described [59] , an ultra-precise small animal stereotactic apparatus ( 10-µm resolution , David Kopf Instrument ) was used to implant a guide cannula into the third ventricle of anesthetized mice at the midline coordinates of 1 . 8 mm posterior to the bregma and 5 . 0 mm below the bregma . The mice were allowed 2 wk for surgical recovery . Angiotensin II-stimulated drink response was used to verify success of implantation . Individual mice were restrained in a mouse restrainer , and infused with an indicated reagent over the indicated time period a 26-gauge guide cannula and a 33-gauge injector ( Plastics One ) connected to a Hamilton Syringe and an infusion pump ( Harvard Apparatus ) . Glucose , diethyl fumarate ester , and diethyl succinate ester were from Sigma . Nutrient/chemical infusion experiments for terminal molecular assays: glucose ( 10 nmol/min ) , diethyl fumarate ( 50 nmol/min ) , and diethyl succinate ( 50 nmol/min ) were infused over 2 to 8 h . Rapamycin and AICAR injections were , respectively , 50 ng and 15 µg per injection ( at hour 0 , 2 , and 4 during the 5-h experimental period ) . The injection experiments for acute physiological tests used a single injection of glucose ( 40 nmol ) , diethyl fumarate ester ( 0 . 2 µmol ) , diethyl succinate ester ( 0 . 2 µmol ) , and AICAR ( 3 µg ) in a 2 µl vehicle over 5 min . Glucose and rapamycin were from Sigma , and AICAR from Toronto Research Chemicals . Artificial cerebrospinal fluid ( aCSF ) was used as the vehicle for glucose , leucine , insulin , leptin , and AICAR . Full-length cDNAs for HIF1α , HIF2α , and HIFβ , provided by D . Peet , were sub-cloned into pcDNA3 . 1 plasmids ( Invitrogen ) . DNA for POMC promoter ( provided by S . Melmed ) was sub-cloned to pGL3 ( Promega ) . Mutant POMC promoter was generated by deleting 5′-GCGTG-3′ in the WT version of POMC promoter . For the lentivirus that co-expresses HIF subunits , we introduced the encoding cDNA of HIF1α or HIF2α and HIFβ into the lentiviral vector Lox-Syn-Syn ( provided by G . Francisco ) in which two synapsin promoters control neuron-specific co-expression of two inserts . For lentivirus that directs expression of single gene , we sub-cloned PCR fragment of dominant-negative HIF2α ( HIF2α amino acids 1–485 ) [51] , [52] or constitutively active Rheb ( CARheb ) ( provided by J . Avruch ) into pLenti6/V5 vector ( Invitrogen ) . The lentiviruses were produced from HEK293T cells through co-transfecting the target plasmid with two package plasmids ( VSVg and delta 8 . 9 ) using Ca3 ( PO4 ) 2 . Lentiviruses were purified by ultracentrifugation . Ultracentrifuge purified lentivirus in 0 . 2 µl aCSF was injected over 10 min through a 26-gauge guide cannula and a 33-gauge injector ( Plastics One ) connected to a Hamilton Syringe and an infusion pump ( WPI Instruments ) . As previously described [59] , bilateral injections to the mediobasal hypothalamus were directed using an ultra-precise stereotax with 10-µm resolution ( Kopf Instruments ) to the coordinates of 1 . 5 mm posterior to the bregma , 5 . 8 mm below the bregma , and 0 . 3 mm lateral to midline . As we previously described [59] , the hypothalamus was dissected along the anterior border of the optic chiasm , posterior border of the mammillary body , upper border of the anterior commissure , and lateral border halfway from the lateral sulcus in the ventral side of brain . Animal tissues were homogenized , the proteins dissolved in a lysis buffer , and Western blot was performed as previously described [59] , [66] , [67] . Proteins dissolved in a lysis buffer were separated by SDS/PAGE and identified by immunoblotting or immunoprecipitation followed by immunoblotting . Primary antibodies included anti-HIF1α , anti-HIF2α , and anti-PHD2 ( Novus Biologicals ) ; anti-p300 ( Santa Cruz ) ; and anti-VHL , anti-pS6K , anti-S6K , anti-pAMPKα , anti-AMPKα , and anti-β-actin ( Cell Signaling ) . Secondary antibodies included HRP-conjugated anti-rabbit and anti-mouse antibodies ( Pierce ) . Western blots were quantified using NIH Image J software . Tissue histology: Various tissues were removed from mice and fixed in Bouin' solution ( Sigma ) . Parafilm sections were prepared , stained with H&E , and examined under a bright field microscope . PHD activity was determined using the GHO assay as established in the literature [42] . Briefly , a wheat germ in vitro transcription-translation ( IVTT ) system ( Promega ) was used to produce unhydroxylated HA-tagged GHO protein ( substrate of PHDs ) . Hydroxylation of GHO protein was performed by incubation with hypothalamic protein lysates ( dissolved in HEB buffer ) at 37°C for 15 min in the presence of 1 mM ascorbate and 100 µM FeSO4 ( Sigma ) . The reaction was terminated by adding SDS loading buffer , and hydroxylated versus unhydroxylated GHO protein was separated by PAGE gels and detected by Western blot analysis of HA tag . Fumarate and succinate were measured using Fumarate Assay Kit ( BioVison ) and Succinic Acid Kit ( Megazyme ) . HEK 293 and HEK 293T ( ATCC ) were maintained in DMEM with 5%–10% FBS , glutamate , antibiotics , and in 5%–10% CO2 at 37°C . Transfection of cultured cells with luciferase plasmids and expression plasmids was performed through Lipofectamine 2000 ( Invitrogen ) . Co-transfection of pRL-TK ( Promega ) was used to internally control firefly activity . Empty plasmids pGL3 and pcDNA3 . 1 were used as negative controls . We extracted total RNA from the homogenized hypothalamus using TRIzol ( Invitrogen ) . Complementary DNA was synthesized using the M-MLV RT system ( Promega ) . PCR amplification was quantified using SYBR®Green PCR Master Mix ( Applied Biosystems ) . Results were normalized against the expression of house-keeping genes including TATA box-binding protein ( TBP ) and GAPDH . Mice under anesthesia were trans-heart perfused with 4% PFA , and the brains were removed , post-fixed in 4% PFA for 4 h , and infiltrated in 20%–30% sucrose . Brain sections ( 20-µm thickness ) were made using a cryostat at −20°C . For cell culture , cells were cultured in coverslips and fixed using 4% PFA . Fixed tissues or cells were blocked with serum of appropriate species , penetrated with 0 . 2% Triton-X 100 , treated with primary antibodies including rabbit anti-HIF1α , anti-HIF2α ( Novus Biologicals ) , anti-HIFβ ( Cell Signaling ) , mouse anti-HuCD ( Molecular Probes ) , and subsequently followed by a fluorescent reaction with Alexa Fluor 488 or 555 secondary antibody ( Invitrogen ) . Naïve IgGs of the appropriate species were used as negative controls . DAPI staining was used to reveal all the cells in the slides . A con-focal microscope was used to image fluorescence . | The hypothalamus in the brain is a master regulator of feeding and body weight . The regulation of it is mediated by the ability of the hypothalamus to sense nutrients ( most importantly glucose ) and hormones ( such as insulin and leptin ) . While hormone has been extensively studied , we know less about how the hypothalamus can sense nutrients . It is also unclear whether changes in hypothalamic nutrient sensing can influence the development of obesity and related disease , and could therefore be targeted for disease intervention . In this study , we show that a protein termed hypoxia-inducible factor ( HIF ) is normally present in the hypothalamus and able to respond to glucose . This glucose response leads to the up-regulation of a hypothalamic neuropeptide , POMC , a pivotal molecule that controls feeding and body weight balance . We then developed a mouse model in which HIF is disrupted in hypothalamic cells that express POMC . These mice displayed reduced hypothalamic sensitivity to glucose , resulting in overeating and susceptibility to obesity . Furthermore , we found that delivery of the HIF gene into the hypothalamus has strong anti-obesity effects in mice . We conclude that HIF is a molecular mediator of hypothalamic glucose sensing and can be potentially targeted for obesity therapeutics . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine"
] | 2011 | Hypoxia-Inducible Factor Directs POMC Gene to Mediate Hypothalamic Glucose Sensing and Energy Balance Regulation |
Leptospirosis is a worldwide bacterial zoonosis . Outbreaks of leptospirosis after heavy rainfall and flooding have been reported . However , few studies have formally quantified the effect of weather factors on leptospirosis incidence . We estimated the association between rainfall and leptospirosis cases in an urban setting in Manila , the Philippines , and examined the potential intermediate role of floods in this association . Relationships between rainfall and the weekly number of hospital admissions due to leptospirosis from 2001 to 2012 were analyzed using a distributed lag non-linear model in a quasi-Poisson regression framework , controlling for seasonally varying factors other than rainfall . The role of floods on the rainfall–leptospirosis relationship was examined using an indicator . We reported relative risks ( RRs ) by rainfall category based on the flood warning system in the country . The risk of post-rainfall leptospirosis peaked at a lag of 2 weeks ( using 0 cm/week rainfall as the reference ) with RRs of 1 . 30 ( 95% confidence interval: 0 . 99–1 . 70 ) , 1 . 53 ( 1 . 12–2 . 09 ) , 2 . 45 ( 1 . 80–3 . 33 ) , 4 . 61 ( 3 . 30–6 . 43 ) , and 13 . 77 ( 9 . 10–20 . 82 ) for light , moderate , heavy , intense and torrential rainfall ( at 2 , 5 , 16 , 32 and 63 cm/week ) , respectively . After adjusting for floods , RRs ( at a lag of 2 weeks ) decreased at higher rainfall levels suggesting that flood is on the causal pathway between rainfall and leptospirosis . Rainfall was strongly associated with increased hospital admission for leptospirosis at a lag of 2 weeks , and this association was explained in part by floods .
Leptospirosis is a worldwide bacterial zoonosis caused by pathogenic spirochetes of the genus Leptospira . The disease is most commonly transmitted through skin , especially abraded skin exposed to water contaminated with the urine of infected rodents or other wild or domesticated animals [1–3] . Globally , there are approximately one million cases of human leptospirosis with 58 , 900 related deaths occurring every year [4] . The disease is usually diagnosed clinically because diagnostic tests are often not available in many resource-limited settings [5 , 6] . Clinical presentations of the disease can range from subclinical or mild fever , mimicking other types of febrile illness such as dengue , typhoid fever and pneumonia , to a severe form known as Weil’s disease with symptoms that may include jaundice , bleeding tendency , renal failure , and pulmonary hemorrhage [1–3] . Because of the wide range of manifestations , diagnosis is often difficult , particularly in the absence of diagnostic resources , often leading to underreporting and even neglect [4] . Earlier studies have suggested that rainfall and floods might be important risk factors for leptospirosis in humans [7 , 8] . An association between rainfall and leptospirosis has been reported in Sri Lanka , Reunion Island and Thailand [9–11] . Additionally , outbreaks of leptospirosis have been observed after floods in India , Philippines , Nicaragua , and other countries [12–16] . In the Philippines , given its location in the tropics along the typhoon belt , leptospirosis is endemic and outbreaks tend to occur after flooding or heavy rainfall [12 , 17] . A recent study using data from this location has indicated correlations between the seasonal cycle of temperature , rainfall and humidity and the disease , but without controlling for potential mutual confounding [18] . In addition , no study has so far considered the non-linear or lagged associations between rainfall and leptospirosis , which are likely to be important due to qualitatively different mechanisms of disease transmission under light , heavy , or extreme rainfall , as well as the many sources of variability in the temporal lag between exposures and leptospirosis . Against this backdrop , in the current study , we used distributed lag non-linear model ( DLNM ) –a method that allows simultaneous representation of non-linear exposure-response relationships and delayed effects–to examine the short-term association between rainfall and leptospirosis , and the potential influence of floods in an urban setting .
This study utilized secondary data . No human or animal subjects were used . Metro Manila is the capital of the Philippines covering an area of about 614km2 with an estimated population of 13 million [19] . Approximately a third of the population are living in slums , where many are at high risk for various infectious diseases because of poor sanitation [20] . The city has a tropical monsoon climate characterized by heavy rains from June to November , and recorded about 20 typhoons , cyclones or tropical depressions per year on average . The wet climate and the flat and low-lying geography of the city explain the frequent flooding , further made worse by poor drainage and waste management [21] . Data on leptospirosis cases were obtained from the San Lazaro Hospital ( SLH ) admission database . SLH is a national referral hospital for infectious diseases in Manila . It has 500 beds and accepts a large number of patients from low social-economic background from the city and the surrounding regions . Leptospirosis is usually diagnosed clinically at SLH without serological confirmation because of limited diagnostic resources . A recent study at the same hospital suggests that the clinical diagnosis agreed with serological diagnoses in 63% of the cases [22] . Among the patients admitted during the period from 1 January 2001 to 31 December 2012 , 3 , 590 were diagnosed with leptospirosis by physicians . Among these , 3 , 078 patients who were residing in Metro Manila were enrolled in the current study . We collected the demographic ( age and sex ) and clinical information ( date of admission , final diagnosis , and outcome ) , and classified the patients into two age groups: “child” ( 0–15 years old ) and “adult” ( over 15 years old ) . To maintain patient confidentiality , we deleted individual-identifiable information and assigned identification number to each enrolled case . This study was approved by the ethical committee of SLH . Meteorological data were obtained from an online database [23] . We extracted daily rainfall ( mm ) and mean temperature ( °C ) for the study period from a meteorological station in Manila city about 3km south of the hospital ( S1 Fig ) . Flood information was obtained from an online database of the Centre for Research on the Epidemiology of Disasters ( CRED ) at the Catholic University of Louvain , Belgium [24] . We collected data on “flood” and “storm” that occurred in Metro Manila from 2001 to 2012 . This database includes only major events that fulfill at least one of the following criteria: ( 1 ) 10 or more people killed; ( 2 ) 100 or more people affected; ( 3 ) significant disaster; and/or ( 4 ) declaration of a state of emergency or an appeal for international assistance . The CRED database classifies disasters according to the nature of the primary event . For example , if a major storm was followed by flooding , the event is recorded in the database with the identifier 'storm' . Therefore , we collected data on 'flood' and 'storm' . These conditions exclude minor floods , which were therefore not considered in our analysis . For each event , we identified the start and end dates and created a binary variable to indicate the presence of a flood or storm on at least one day in a week . The relationships between rainfall , flooding and leptospirosis hypothesized in this study are shown in Fig 1 . Briefly , humans can become infected with Leptospira through exposure to contaminated water during floods . Flooding can also force humans and rodents into closer contact , which can result in further contamination of surrounding water . Although rodent populations may possibly affect the associations between rainfall and leptospirosis , we were unable to account for this interaction because we had no information about rodents [1 , 3] . We performed a time-series regression analysis by applying a DLNM in a quasi-Poisson regression framework in order to concurrently describe the non-linear relationship between rainfall and weekly hospital admissions for leptospirosis over multiple weeks of lag [25] . We allowed lags up to 4 weeks ( lags 0–4 ) for rainfall , which were sufficient to capture attenuation of the association based on initial analysis ( S2 Fig ) . For flood indicator , lags up to 7 weeks ( lags 0–7 ) were included using a similar approach . We included weekly means of temperature averaged over lags 1–3 to adjust for the variable . The choice of lag period was based on previous studies [10 , 11] and the understanding of the underlying mechanism linking Leptospira to human infection [1–3] . To control for seasonality and long-term trends in leptospirosis incidence due to effects other than the short-term impacts of rainfall , flooding , or temperature , we incorporated two natural cubic splines: one for the week of year with 3 degrees of freedom ( DF ) and another for year with 2 DF . The selection of DF was guided by the quasi-Akaike’s Information Criterion [26] . In the case of the rainfall , flood and temperature variables , we also considered the attenuation of the lag structure . The model has the following form: log[E ( Yt ) ]=α+β1Rt , l+β2NCS ( temperature , df=2 ) +β3NCS ( weekoy , df=3 ) +β4NCS ( year , df=2 ) where Yt is the number of leptospirosis cases in week t , α is the intercept , Rt , l is the matrix obtained by applying cross-basis function to rainfall through DLNM using natural cubic spline functions for rainfall and its lags , both at 3 DF , l is the lag of week , NCS is a natural cubic spline , “temperature” , “weekoy” and “year” denote the weekly mean temperature , week of year , and year , respectively . To account for the potential effect of floods , this model was expanded to include a cross-basis function of flood with strata parameterization for the variable space and natural cubic spline for the lag space with 5 DF . The collinearity between this indicator and rainfall was examined by checking their variance inflation factors ( VIF ) . We computed the relative risks ( RRs ) for rainfall at each lag using 0 cm/week rainfall as the reference . The RRs were expressed according to the flood warning system by the meteorological agency of the Philippines , PAGASA [27] . The system classifies the amount of rainfall into five categories–light , moderate , heavy , intense , and torrential–correspond to about 2–5 , 5–15 , 16–31 , 32–63 , and >63 cm/week of rainfall , respectively , with the assumption that rainfalls continue for 3 hours per day for 7 days . We presented the RRs at 2 , 5 , 16 , 32 , and 63 cm/week rainfall cut-off points . We also performed subgroup analyses by age and sex using the flood-unadjusted model . We assessed the sensitivity of our results by changing the DF ( 3 to 7 ) for the week-of-year spline . We repeated the analysis to exclude extreme events using two subsets of data as follows . Subset 1 in which extreme rainfall in the 32nd week of 2012 was removed , and Subset 2 in which , in addition to removing extreme rainfall in the 32nd week of 2012 , as in Subset 1 , two outbreaks in the 41st week of 2009 and the 34th week of 2012 were removed ( Fig 2 ) . The weekly outcomes corresponding to these extreme values were regarded as missing during the sensitivity analysis . The exclusion of extreme rainfall resulted in no observation with >63 cm/week of rainfall and so no results are reported for this category . Analysis was conducted using Stata version 12 ( StataCorp LP , College Station , TX , USA ) and R version 3 . 3 . 1 ( R Foundation for Statistical Computing , Vienna , Austria ) . We used R packages dlnm ( version 2 . 2 . 6 ) and tsModel ( version 0 . 6 ) .
There were 3 , 078 cases of leptospirosis admitted to SLH from January 2001 to December 2012 ( Table 1 ) . During this period , 253 of these patients died , translating to a case fatality rate of 8 . 4% . As observed in previous studies , leptospirosis was more frequently reported among adults ( 88 . 8% ) and males ( 89 . 2% ) [4 , 28–30] . The weekly number of admitted leptospirosis cases , cumulative rainfall and mean temperature are shown in Fig 2 ( The summary statistics were shown in S1 Table ) . Leptospirosis admissions displayed apparent seasonality with higher number of cases during the rainy season from June to November . Huge outbreaks were documented in 2009 and 2012 . These outbreaks were preceded by heavy rainfalls , particularly in 2012 when an extreme level of rainfall was recorded . Mean temperature did not vary much , except for a slight increase around April and May . The estimated association between leptospirosis and rainfall , and the lagged pattern are shown in Fig 3 . The risk of admission increased as the amount of rainfall increased , and this occurred mainly at lag 2 ( Fig 3A ) . The cross-sectional plots by lag ( Fig 3B , left panels ) show a non-linear positive association between increasing rainfall and leptospirosis hospitalizations , with the strongest effects observed for heavy to torrential rainfall at a 2-week lag ( Fig 3B , right panels ) . When adjusted for flood occurrence , RRs for rainfall of 16cm/week and 32cm/week at lag 2 decreased , but the lag structure was stable ( Fig 3C ) . The estimated RRs for the association are shown in S2 Table . The RRs of light , moderate , heavy , intense and torrential rainfall categories at a lag of 2 weeks were 1 . 30 ( 95% CI: 0 . 99–1 . 70 ) , 1 . 53 ( 95% CI: 1 . 12–2 . 09 ) , 2 . 45 ( 95% CI: 1 . 80–3 . 33 ) , 4 . 61 ( 95% CI: 3 . 30–6 . 43 ) , and 13 . 77 ( 95% CI: 9 . 10–20 . 82 ) , respectively , compared with the reference of no rainfall ( 0 cm/week ) . The RRs were lower at lags 1 and 3 but significant at higher rainfall categories ( S2 Table ) . There was no evidence of association at any rainfall category at lag 4 . When adjusted for flood occurrence , the RRs at lag 2 reduced for rainfall levels categorized as heavy , intense and torrential ( S3 Table ) . The RRs for heavy , intense and torrential rainfall at lag 2 decreased by 14 . 3% , 34 . 9% , and 57 . 3% , respectively . The lag pattern was similar to that obtained with the flood-unadjusted model ( S3 Table ) . In the subgroup analysis , RRs at lag 2 were higher in the child groups for all rainfall categories but the difference was not significant . The lag patterns were similar among the subgroups ( S4 Table and S5 Table ) . Positive associations of flooding with leptospirosis were observed at lags of 1–2 weeks . The associations became negative at lag 4 and 5 , while no association was observed at lags 6 and 7 . The lag structure remained similar after removing extreme values . ( Fig 4 and S6 Table ) . The VIFs for the flood indicator and rainfall cross-basis were less than 2 . In sensitivity analyses , the RRs associated with each level of rainfall remained stable after a week of extreme rainfall was removed from the dataset ( Subset 1 ) , and , with the exception of RRs associated with the ‘intense’ rainfall category , after the two most extreme weeks of outcome data were also removed ( Subset 2 ) ( S2 Table , S3 Table and S3 Fig ) . The RRs for all rainfall levels were not sensitive to the DF of the week-of-year spline ( S7 Table ) and rainfall remained a significant risk factor at lag 2 in all cases .
In this study , we investigated the short-term association between rainfall and leptospirosis . We found positive non-linear associations between rainfall and leptospirosis , with the strongest associations observed at a lag of 2 weeks . After adjusting for flooding , this association weakened , suggesting that floods might partially explain the effect of high rainfall on leptospirosis . The time-series regression analysis suggested a significant positive association between the number of weekly admitted leptospirosis cases and the total weekly rainfall , with the greatest risk occurring 2 weeks after a heavy , intense , or torrential rainfall . This time lag is consistent with reports of leptospirosis outbreaks after floods . According to studies in India and Hawaii , leptospirosis outbreaks occurred 2 to 3 weeks after a flood [14 , 16 , 31] . The lag of 2 weeks might reflect the period from contaminated flood water exposure to hospital admission , which spans the incubation period as well as the onset and deterioration of the patient’s condition . At lower rainfall levels , the observed association between rainfall and leptospirosis at lags shorter than 2 weeks might be explained by rainfalls that gradually tapered off over a few weeks after an extreme event . The association of flood with leptospirosis admissions was positive at lag week 1 and 2 , but became negative at lag week 4 and 5 , and null thereafter . This pattern resembles the so-called harvesting [32 , 33] , a term used to describe the forward displacement of health events such as leptospirosis admissions following an environmental trigger such as major flood . The increased risk observed at lag week 1 and 2 suggests that major floods have led to many admissions cases , resulting in a temporary reduction of susceptible population at lag week 4 and 5 , which would explain the negative associations . However , its mechanism in the context of leptospirosis is not well understood . The observed lag pattern might be due to harvesting as explained , or other mechanisms that might produce similar harvesting-like time-series dynamics which would require further investigations . Huge outbreaks of leptospirosis occurred in 2009 and 2012 , both at times shortly after heavy rainfall and flooding were reported . The epidemic curve of leptospirosis was very steep , which suggests that many people were infected with the pathogen almost simultaneously or in a very short time , probably due to contact with flood water . This study did not find association between rainfall and leptospirosis at a lag of 4 weeks or longer , although some previous studies have reported associations at lags lasting several months [9–11] . Studies in Sri Lanka and Reunion Island showed a 2-month lag in rainfall-case association in some parts of the study area [10 , 11] . A study in the north and northeast Thailand showed 8 to 9-month lags in rainfall-case association in some parts of the study area [9] . It is unclear exactly why the results of the present study are different from these previous studies; however , differences in the geographic and demographic characteristics of the study sites may have contributed to this disparity . The previous studies were conducted in relatively rural areas , while this study was conducted in an urban area . The different conditions of the study area may have a different effect on the transmission of leptospirosis . In rural areas , farmers are exposed through their daily work to soil and water that is potentially contaminated with Leptospira . An increase in rainfall may make the soil moister , which helps Leptospira survive for a longer period of time . This lengthened lifespan could lead to an increased exposure to the bacteria among rodents , which might result in a higher number of Leptospira-infected rodents . Such situations would gradually increase occupational exposure to Leptospira among humans working in paddy fields in Sri Lanka or Thailand , or in sugar cane fields in Reunion Island . Through these processes , a corresponding increase in the number of leptospirosis cases could take several months from the observed rainfall . In contrast , in urban areas , most of the cases are likely infected by direct exposure to flood water . In such situations , the time from rainfall to a corresponding increase in the number of leptospirosis cases might only be slightly longer than the incubation time of leptospirosis . The finding that the rainfall effects decreased after adjusting for flood at high rainfall level ( > 16cm/week ) suggests that flood is on the causal pathway between rainfall and the incidence of leptospirosis . Flood may increase the risk of leptospirosis because it increases the chance for exposure due to forced movement outside for evacuation and injuries to the skin . However , our flood indicator covers only relatively large-scale flood events , and smaller-scale floods were not considered in the analysis . The residual risks associated with high rainfall after adjusting for extreme floods could , in part , have to do with the role of smaller flood events . This is probably because people often wade in flooded streets in the rainy season in Manila . There are several limitations to this study . First , the accuracy of the diagnosis for leptospirosis is uncertain because almost all cases of leptospirosis were diagnosed clinically in reference to the modified Faine’s diagnostic criteria [34 , 35] . The criteria ask for history of contact with animal or contaminated water , which might possibly result in over diagnosis after flood . Laboratory tests such as IgM-ELISAs and MATs were seldom performed , and consistent serologically confirmed time series data were not available for analysis . A recent study showed that the clinical diagnoses of leptospirosis in SLH were consistent with serological diagnoses in 63% of the cases [22] , suggesting possible over-diagnosis which is an important consideration when interpreting the results of the current study . Second , we did not take period from onset to hospitalization into account because we could not obtain information about date of onset . Leptospirosis has two-phase manifestations: early-phase manifestations with febrile illness which does not deteriorate shortly after the onset; severe late-phase manifestations with jaundice , renal failure , bleeding tendency and pulmonary haemorrhage which usually occur 4 to 6 days after the onset of illness . Although hospitalization occurs in late phase in general [3] , the period from disease onset to hospital visit may vary . Third , this is a hospital-based study that included only admitted cases , which do not represent the population of all leptospirosis patients in the study area . However , this is unlikely to alter the observed association in the current study because of the similar pathways of infection . Fourth , population immunity to leptospirosis epidemics is uncertain , and immunity to leptospirosis is strongly restricted to homologous serovars or closely related serovars of Leptospira . In a huge leptospirosis outbreak , if some percentage of the population were infected with Leptospira but were asymptomatic or experienced only mild cases , an outbreak of the same Leptospira serovar may not occur again for several years . We did not take immunity effect into account in this study [36 , 37] . Finally , although rodent populations might possibly affect the associations between rainfall and leptospirosis , we were unable to account for this interaction because we had no information about rodents . Improved understanding of environmental factors associated with leptospirosis may help to improve the ability to predict outbreaks , and contribute to preparing hospitals and clinics for increased number of patients in case of an outbreak . In addition , identification of the specific environmental factors underlying the disease incidence is a critical step towards understanding how global climate change might affect patterns of leptospirosis .
This study found that high rainfall in Manila was strongly associated with increased leptospirosis hospitalizations two weeks later , while controlling for confounding by seasonality and between-year variation in leptospirosis admissions , as well as effects of temperature . The association between rainfall and leptospirosis admissions could be explained , in part , by major floods . | Leptospirosis is a worldwide bacterial zoonosis which is mainly transmitted through contact with water contaminated by rodents’ urine . It manifests with various symptoms , ranging from fever and muscle pain to a severe syndrome characterized by jaundice , renal failure and pulmonary hemorrhage . Outbreaks of leptospirosis after heavy rainfall and flooding have been reported , but few studies have evaluated the effect of weather factors on leptospirosis . We estimated the association between rainfall and leptospirosis cases in an urban setting in Manila , the Philippines , and examined the potential intermediate role of floods in this association . The risk of post-rainfall leptospirosis peaked at a lag of 2 weeks . After adjusting for floods , the effect of rainfall at lag 2 decreased at higher rainfall levels suggesting that flooding is on the causal pathway between heavy rainfall and leptospirosis . The results are useful for public health interventions to prepare hospitals and clinics for increased number of patients in case of an outbreak , which can help reduce the disease burden . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusions"
] | [
"medicine",
"and",
"health",
"sciences",
"leptospira",
"pathology",
"and",
"laboratory",
"medicine",
"engineering",
"and",
"technology",
"atmospheric",
"science",
"pathogens",
"tropical",
"diseases",
"microbiology",
"vertebrates",
"animals",
"mammals",
"health",
"care",
... | 2018 | The non-linear and lagged short-term relationship between rainfall and leptospirosis and the intermediate role of floods in the Philippines |
Merkel cell polyomavirus ( MCPyV ) is an etiological agent of Merkel cell carcinoma ( MCC ) , a highly aggressive skin cancer . The MCPyV small tumor antigen ( ST ) is required for maintenance of MCC and can transform normal cells . To gain insight into cellular perturbations induced by MCPyV ST , we performed transcriptome analysis of normal human fibroblasts with inducible expression of ST . MCPyV ST dynamically alters the cellular transcriptome with increased levels of glycolytic genes , including the monocarboxylate lactate transporter SLC16A1 ( MCT1 ) . Extracellular flux analysis revealed increased lactate export reflecting elevated aerobic glycolysis in ST expressing cells . Inhibition of MCT1 activity suppressed the growth of MCC cell lines and impaired MCPyV-dependent transformation of IMR90 cells . Both NF-κB and MYC have been shown to regulate MCT1 expression . While MYC was required for MCT1 induction , MCPyV-induced MCT1 levels decreased following knockdown of the NF-κB subunit RelA , supporting a synergistic activity between MCPyV and MYC in regulating MCT1 levels . Several MCC lines had high levels of MYCL and MYCN but not MYC . Increased levels of MYCL was more effective than MYC or MYCN in increasing extracellular acidification in MCC cells . Our results demonstrate the effects of MCPyV ST on the cellular transcriptome and reveal that transformation is dependent , at least in part , on elevated aerobic glycolysis .
Human polyomaviruses are a diverse family of small DNA tumor viruses that typically cause asymptomatic , lifelong infections in healthy individuals [1 , 2] . However , immune deficiencies enable more severe polyomavirus induced diseases including Merkel cell carcinoma ( MCC ) . MCC is a rare and aggressive skin cancer that primarily affects the elderly and immunocompromised [3 , 4] . Transcriptome sequencing of MCC led to the discovery of Merkel cell polyomavirus ( MCPyV ) and demonstration that viral DNA was clonally integrated in approximately 80% of MCC tumors [5] . The integrated MCPyV early-region ( ER ) expresses wild-type small T antigen ( ST ) and a truncated form of large T antigen ( LT ) . The truncated LT retains the LXCXE motif that binds the retinoblastoma protein ( RB1 ) but is unable to support viral replication due to loss of the DNA binding and helicase domains [6 , 7] . In some MCC tumors , ST can be detected in the absence of LT , suggesting that ST is required for tumorigenesis [7] . The precise mechanisms for how MCPyV ST promotes cellular transformation are still unresolved . The contribution of the related polyomavirus simian virus 40 ( SV40 ) ST to transformation has been shown to be dependent on its ability to bind and inhibit protein phosphatase 2A ( PP2A ) activity that , in turn , perturbs a wide range of signaling pathways [8 , 9] . However , PP2A binding by MCPyV ST may not be necessary for transformation [7] . Compared to ST from other polyomaviruses , MCPyV ST has unique properties including inhibition of the E3 ubiquitin ligase FBXW7 and an ability to increase cap-dependent translation through hyperphosphorylation of 4E-BP1 [10–12] , activities which likely contribute to the initiation and maintenance of an oncogenic state . Under normal physiological conditions , cells can convert one glucose molecule into two pyruvate molecules followed by pyruvate oxidation in mitochondria resulting in the synthesis of 38 ATP molecules per molecule of glucose [13] . In hypoxic conditions , oxidative phosphorylation is inhibited and anaerobic glycolysis is activated , leading to the production of only 2 ATP molecules and secretion of lactate into the extracellular space [14] . Cancer cells may convert pyruvate to lactate under normoxic conditions resulting in aerobic glycolysis , known as the Warburg effect . Increased aerobic glycolysis has been recognized as a hallmark of cancer due to the requirement for large quantities of biosynthetic intermediates to be generated for sustained tumorigenesis [15 , 16] . Excess lactate production increases the acidity of the tumor cell microenvironment that favors two additional hallmarks , tumor cell invasion and metastasis [17] . Viruses typically do not express their own metabolic enzymes and instead rely on manipulation of host signaling and metabolic pathways to establish productive infections [18] . For example , RNA viruses such as hepatitis C and dengue virus promote efficient replication by altering host lipid metabolism [19] . Adenovirus can perturb the function of the transcription factor MYC to promote glucose and glutamine metabolism [20] , and Kaposi’s Sarcoma-associated Herpesvirus ( KSHV ) has also been shown to induce MYC expression leading to increased glutaminolysis [21] . To gain insight into the impact of MCPyV ST on gene expression , we generated normal human cells with inducible expression of MCPyV ST or GFP and performed transcriptome profiling . The resulting analysis revealed that ST significantly altered metabolism-related pathways with upregulation of glycolysis and metabolite transporter genes . Correspondingly , we found that increased ST levels led to increased aerobic glycolysis . Furthermore , inhibition of the major monocarboxylate transporter MCT1 suppressed ST induced cellular proliferation , transformation and MCC viability .
Given the unique properties of MCPyV ST compared to ST from other human polyomaviruses [1 , 11] , we sought to obtain a global view of MCPyV ST induced transcriptional perturbations in normal human cells . IMR90 human diploid lung fibroblasts were selected because of their wide use in transcriptome and genomic analyses , including viral oncoprotein perturbations [22] . Cells were transduced with lentiviral vectors capable of doxycycline ( dox ) inducible MCPyV ST or GFP expression . Cells were treated with dox for 96 hours and harvested every 8 hours followed by processing for RNAseq in triplicate ( Fig 1A ) . ST transcript levels were rapidly induced with peak levels between 24 to 40 hours after dox addition , followed by a gradual decrease in levels ( Fig 1B ) . ST protein levels followed the transcript profile , peaking between 48 to 64 hours before declining ( Fig 1C ) . Similarly , GFP protein levels were detectable within 8 hours and decreased by 64 hours after dox addition . Genes that were most differentially expressed between ST and GFP were ranked by LIMMA ( see Methods ) with 2854 genes passing p-value and fold-change cutoffs [23] . Model-based clustering grouped the differentially expressed genes into 50 clusters ( Fig 1D and S1 Fig and S1 Table ) . Clusters were evaluated for significant enrichment in biological processes , including Gene Ontology ( GO ) terms ( S2 Table ) and the Cancer Hallmark gene sets in the Molecular Signatures Database ( MSigDB ) ( S3 Table ) [24] . ST expressing cells were significantly enriched for gene clusters with the GO terms for energy coupled proton transport , isoprenoid and L-serine biosynthetic processes , and glutamine , lysine and arginine transport . ST expressing cells were also significantly enriched for the Cancer Hallmarks including epithelial to mesenchymal transition ( EMT ) , TNFA signaling via NF-κB , hypoxia , mTORC1 , oxidative phosphorylation , glycolysis , MYC , and cell cycle including E2F targets , G2/M checkpoint and mitotic spindle . Elevated aerobic glycolysis is a hallmark present in many cancers and represents a potential vulnerability for targeting cancer cell proliferation [15 , 16 , 25] . Expression profiling of MCPyV ST cells revealed a high prevalence of significantly altered metabolism-related genes , specifically those involved in glycolysis such as Hexokinase 2 ( HK2 ) in cluster 6 ( Fig 2A ) and metabolite transport . Transport of metabolites is mediated in large part by members of the SLC gene family [26] . A large proportion of all SLC transporter genes were significantly upregulated following ST expression ( S2 Fig ) . A consequence of increased aerobic glycolysis is elevated acidification of the surrounding microenvironment . Notably , expression of SLC16A1 ( MCT1 ) , the major monocarboxylate transporter for lactate and pyruvate [27 , 28] , was significantly increased after ST induction ( Fig 2B and 2C ) . Levels of MCT1 were increased in IMR90 cells expressing ST compared to GFP following dox addition . Activation of glycolysis is normally accompanied by an increase in the rate of glucose import . Consistent with this , we found that MCPyV ST increases the expression of two glucose transporters , GLUT1 ( SLC2A1 ) and GLUT3 ( SLC2A3 ) . We validated the increase in the expression of GLUT1 in ST expressing using RT-qPCR ( Fig 2E ) . Among the larger family of hexose transporters ( SLC2A1-14 ) we also observed upregulation of SLC2A8 , 13 , and 14 ( Fig 2F ) . Additionally , we found that ST cells have higher expression of the carbohydrate response element binding proteins ( ChREBPs ) MLX and MLXIP , which can bind and activate the promoters of genes encoding glycolytic enzymes , thus increasing the rate of glycolysis ( S3A Fig ) . Glycolysis is a multistep biochemical process . HK2 serves as an upstream regulator that irreversibly commits glucose to enter the pathway . A byproduct of aerobic glycolysis is lactate . Production of lactate from pyruvate is mediated by lactate dehydrogenase ( LDH ) , comprised of homo- or heterotetramers of two subunits , LDHA and LDHB . A major function of MCTs , including MCT1 , is to prevent the toxic buildup of lactate in the intracellular milieu by co-exporting lactate together with protons out of the cell [27] . We observed that levels of glucose were depleted from the media and lactate levels increased at a significantly higher rate following ST expression compared to GFP cells ( Fig 3A ) . Given the significant increase in lactate levels in the media as early as 2 days after ST induction , we measured the extracellular acidification rate ( ECAR ) of IMR90 cells inducibly expressing ST before and after dox addition for 48 hours . We found a significant increase in the ECAR of these cells following ST induction , consistent with an increase in aerobic glycolysis ( Fig 3B and 3C ) . Inhibition of ATP synthase by oligomycin treatment led to an increase in the glycolytic rate of cells in response to the lack of ATP production from oxidative phosphorylation ( Fig 3C ) [29] . As expected , we found the ECAR to be significantly decreased in both ST and GFP expressing cells treated with the MCT inhibitor α-cyano-4-hydroxycinnamate ( CHC ) ( Fig 3C ) . There was no significant difference in the oxygen consumption rate ( OCR ) of ST cells compared to GFP cells ( Fig 3D ) . This suggests that the level of oxidative phosphorylation is maintained in ST cells despite the increased rate of glucose being converted to lactate . ST cells may use alternative carbon sources , like glutamine , to fuel the TCA cycle . Consistent with this hypothesis , we found that ST cells upregulate the expression of the glutamine transporter SLC1A5 , as well as the enzymes glutaminase ( GLS ) and glutamate dehydrogenase ( GLUD1 ) , which are necessary to convert glutamine to the TCA cycle intermediate α-ketoglutarate through the metabolic pathway called glutaminolysis ( S3B Fig ) . To determine the importance of MCT1 activity on the fitness of ST expressing cells , we measured the growth of IMR90 cells expressing either ST or GFP following CHC or DMSO treatment ( Fig 3E ) . CHC significantly suppressed the growth rate of ST expressing cells , while GFP cells were largely unaffected . We assessed the glycolytic pathway in three MCPyV-positive MCC cell lines . ECAR measurements revealed that MKL-1 and MKL-2 cells had similar rates of glycolysis , while WaGa cells had a lower glycolytic rate ( Fig 4A ) . Conversely , following oligomycin treatment , MKL-1 and MKL-2 cells increased their ECAR levels higher than WaGa cells consistent with their higher level of glycolysis . Inhibition of MCT1 in a highly glycolytic cell can lead to intracellular acidification through the accumulation of monocarboxylates and protons . MCT1 inhibition has previously been shown to be toxic to certain tumors with high MCT1 expression [27 , 30] . A number of MCT1 inhibitors are current in clinical trials for treating advanced solid tumors , with promising results in cancers with elevated MCT1 expression [31 , 32] . SR13800 and SR13801 are pyrole pyrimidine-based molecules with high specificity for MCT1 [33] . We tested the effects of the MCT1 inhibitors CHC , SR13800 and SR13801 on the viability of MKL-1 , MKL-2 and WaGa MCC cell lines over 7 days ( Fig 4B , 4C and 4D , respectively ) . All three MCC cell lines showed high sensitivity to CHC treatment , while only MKL-1 and MKL-2 cells were affected by SR13800 and SR13801 . MKL-1 and MKL-2 cells were more dependent than WaGa cells on MCT1 activity and glycolysis for continued proliferation reflecting their higher ECAR levels . The expression of HK2 , LDH , MCT1 and other glycolytic genes are regulated , at least in part , by MYC [25 , 34] . Given the pronounced effects of ST on glycolysis , we sought to determine how sensitive MCPyV-positive MCC cell lines were to perturbations in this pathway . Since the MCC cell lines MKL1 , MKL-2 , WaGa and BroLi were previously uncharacterized with regards to MYC , we assessed the levels of the different MYC isoforms in these cells . By immunoblotting , these MCC cell lines had no detectable MYC , but did have detectable levels of MYCN and MYCL ( S4A Fig ) . We next determined the ability of each MYC family member to regulate glycolytic gene expression and aerobic glycolysis in MCC cell lines . MKL-1 and WaGa cells were transduced with dox inducible vectors expressing MYC , MYCN or MYCL . Following selection , cells were treated with dox for 72 hours , and then lysates were harvested for immunoblotting ( Fig 5A ) . We observed that MYC and MYCN but not MYCL led to increased levels of HK2 and LDHA in MKL-1 and WaGa cells . MYC and MYCL led to increased levels of MCT1 while MYCN led to decreased MCT1 levels in WaGa cells . Given how the various MYC isoforms differentially affected HK2 , LDHA and MCT1 levels , we compared the effects of MYC expression in MKL-1 cells on aerobic glycolysis by measuring the ECAR following 72 hours of dox treatment ( Fig 5B ) . GFP and MYCN expressing cells had similar basal ECAR , while MYC and MYCL expressing cells had higher ECAR levels . Notably , the MYCL expressing MKL-1 cells had significantly higher ECAR following oligomycin treatment , indicating that MYCL was more effective in facilitating the glycolytic capacity of these cells compared to the other MYC isoforms . We sought to identify specific signaling pathways that ST utilized to increase glycolytic and MCT1 gene expression and if these pathways contributed to cellular transformation . Given the significant enrichment of genes from both the MYC and NF-κB Cancer Hallmark gene sets across several ST induced gene clusters ( Fig 1D ) , we examined the MCT1 promoter region [-1000 , +100] for MYC and NF-κB binding sites ( Fig 6A ) . Chromatin accessibility patterns in the parental IMR90 cells were assessed in the ENCODE DNase I hypersensitivity experiments ( GEO: GSM468801 , GSM530665 , GSM530666 , GSM468792 ) . A MYC binding site was located within a relatively open region in the MCT1 promoter , while a NF-κB binding site was located in an area of reduced hypersensitivity . To investigate the regulation of glycolysis gene expression in the context of a MCPyV-transformed cell line , IMR90 cells were serially transduced with retroviral constructs expressing a dominant-negative form of p53 ( p53DD ) , telomerase reverse transcriptase ( hTERT ) , and a tumor-derived form of MCPyV early-region ( ER ) that expresses ST plus truncated LT to generate p53DD-hTERT-ER ( PHE ) cells . Unlike SV40 large T antigen ( LT ) , MCPyV LT cannot bind and inhibit p53; therefore p53DD is required to bypass senescence and apoptotic checkpoints in these IMR90 cells [35 , 36] . To determine if MYC family proteins could cooperate with MCPyV IMR90 cells , we treated cells stably expressing ST , GFP , or p53DD + hTERT ( PH ) and MCPyV tumor-derived early-region ( PHE ) with inducible expression of MYC , MYCN or MYCL with dox for 48 hours and immunoblotted for HK2 , MCT1 and LDHA ( Fig 6B ) . ST alone could induce MCT1 levels and both PH and PHE cells had higher levels of MCT1 expression than ST alone . Consistent with the effects seen in MCC cell lines ( Fig 5A ) , MYC and MYCN induction but not MYCL led to increased levels of HK2 and MCT1 in PH and PHE cells . The presence of MCPyV ER did not appear to affect induction of HK2 or MCT1 by MYC or MYCN . In contrast , PHE cells with MYCL had very high levels of MCT1 protein in the uninduced state , likely due to leakiness of the vector ( S4B and S4C Fig ) . These results indicate that MYCL but not MYC or MYCN was able to cooperate with MCPyV ER to induce MCT1 . In addition to MYC , NF-κB is a prominent inducer of metabolic and growth-promoting genes and has been shown to independently regulate MCT1 [33 , 37 , 38] . RelA , also known as p65 , is a key subunit of canonical NF-κB signaling and forms a homo- or heterodimer with other NF-κB subunits to activate target genes including IκBα [39] . To assess the role of NF-κB in the regulation of MCT1 in the context of MCPyV , IMR90 PH and PHE cells with inducible MYC or MYCL were transfected with siRNA targeting RelA ( siRelA ) or a non-targeting control ( siCtrl ) . Cells were re-fed with or without dox-containing media 24 hours after transfection , then lysed 48 hours later for immunoblotting ( Fig 6C ) . Following siRelA but not siCtrl treatment , reduced levels of RelA and the downstream target IκBα were observed in both MYC and MYCL expressing PH and PHE lines . Knockdown of RelA did not affect the ability of MYC to induce HK2 in the PH and PHE cells . In contrast , MCT1 levels were reduced following knockdown of RelA across all conditions , indicating that canonical NF-κB signaling likely has a complementary role in regulating MCT1 levels . We assessed whether MCPyV-mediated transformation could be attenuated by MCT1 inhibition . We found that overexpression of MYCL in PHE ( PHEL ) IMR90 cells led to robust IMR90 anchorage-independent growth in soft agar , while PH , PHL and PHE cells lacked significant colony formation ( Fig 7A ) . We chose MYCL in this context as it was previously shown that MYCL is amplified in MCC tumors , and may therefore have oncogenic potential in the presence of MCPyV [40] . We measured basal ECAR of IMR90 cells stably expressing p53DD , PH , PHE and PHEL ( Fig 7B ) and found that PH , PHE and PHEL cells had significantly higher ECAR than p53DD cells , with PHE cells maintaining the highest rate while PHEL cells had a significantly lower ECAR than PHE cells . To determine if MCT1 activity was necessary to support cellular transformation of IMR90 cells by MCPyV , we assessed anchorage-independent growth by culturing PHE and PHEL cells in soft agar in the presence of the MCT1 inhibitors or DMSO . Proliferation of PHEL cells was highly attenuated by CHC treatment , while SR13800 and SR13801 inhibitors had modest but significant effects on growth compared to DMSO treatment ( Fig 7C ) . PHEL cell growth in soft agar was significantly inhibited by CHC , SR13800 and SR13801 compared to DMSO treated cells ( Fig 7D ) . These results indicate that MCT1 activity was required for transformation of IMR90 cells by MCPyV and MYCL .
Metabolic perturbations represent a key hallmark in many cancers , as the energetic and biosynthetic demands of tumor cells increase to sustain proliferation . MCPyV ST is a potent oncoprotein with transforming potential in vitro and in vivo and contributes to MCC . By performing temporal transcriptional profiling and metabolic analysis of ST expressing cells , we determined that ST significantly increases aerobic glycolysis and that inhibition of this pathway can suppress MCPyV-induced transformation as well as MCC growth . Cancers with viral etiology are particularly likely to undergo metabolic alterations due to the fundamental need for viruses to create a pro-replicative environment . Many viruses , including adenovirus , hepatitis C virus and HIV , induce aerobic glycolysis in infected cells to support viral replication [18] . Our results indicate that MCPyV ST can specifically alter the metabolic state of a cell . We designed a time-series RNA-sequencing experiment to characterize the dynamics of gene expression in cells after expression of MCPyV ST . Comparing with statistically distinct behavior in the ST-expressing cells relative to GFP-expressing cells , we found that most of the differential expression trends appeared already at 16 hours post-induction , with down-regulated genes first reaching a minimum at around 32 hours and up-regulated genes building more gradually to peak at the 48 hour mark . Most genes exhibited only down- or up-regulation throughout the time course of 96 hours . We grouped differentially expressed genes into clusters to build a global picture of how ST remodels the transcriptional landscape . Among the 50 resulting clusters and their GO term and pathway enrichment , we observed a strong signature of metabolism-related changes ( Fig 1D and S1 Fig ) . Many of the up-regulated clusters were enriched for the glycolysis pathway , rRNA processing , amino acid transport and response to glucose starvation . Among down-regulated clusters , there was enrichment in fatty acid oxidation , purine and pyrimidine metabolic processes , lipid metabolism , and mitochondrial respiration and ATP synthesis genes . The transcriptional signature of ST-expressing cells exhibited many of the characteristics associated with activation of aerobic glycolysis . In particular , we found that ST upregulated glucose import , lactate export and ChREBPs , transcription factors that specifically activate glycolytic enzymes . In addition , we found evidence that ST cells maintain normal levels of oxidative phosphorylation through anaplerosis , through increased levels of glutamine transporter and GLS and GLUD1 , critical for glutaminolysis . MCPyV ST also induced changes in many genes that were not annotated to be involved in metabolic processes . There were 14 out of the 50 clusters that showed enrichment for GO terms not involved in metabolism ( S2 Table ) . The up-regulated genes were enriched for the mitotic cell cycle ( clusters 46 , 12 , 31 , 39 ) , SMAD and BMP pathways ( clusters 23 and 15 ) , vascular permeability ( 3 ) , keratinocyte migration ( 9 ) and pinocytosis ( 14 ) . The down-regulated genes were involved in synapse assembly ( 19 ) , SMAD protein import into the nucleus ( 43 ) , extracellular matrix organization ( 38 ) , cell adhesion ( 24 ) , and negative regulation of viral genome replication ( 40 ) . Therefore , ST appears to have a major impact on genes in metabolic and cell cycle pathways as well as several additional transcriptional programs , including the SMAD/BMP differentiation pathway and cell adhesion . Given the functional enrichment for glycolysis-related genes in ST expressing cells ( Fig 1D and S3A Fig and S3 Table ) , we focused on characterizing the role of MCT1 ( SLC16A1 ) , a major monocarboxylate transporter that most closely followed the ST expression pattern . Genetic and pharmacological inhibition of MCT1 has been an effective strategy for targeting highly glycolytic tumors , inhibiting tumor growth through a combination of effects including accumulation of intracellular lactate , altering the production of glycolytic intermediates , reducing glucose transport and ATP levels , and reducing glutathione levels [33 , 37 , 41–43] . Cancer cells with elevated MCT1 expression have also been found to be exquisitely sensitive to the glycolysis inhibitor 3-bromopyruvate [44] . Through extracellular flux experiments , we found a significant increase in the ECAR of ST expressing IMR90 cells ( Fig 3C ) , accompanied by increased sensitivity to MCT1 inhibition ( Fig 3E ) . We observed that MCPyV-transformed IMR90 cells exhibited significantly higher ECAR levels compared to untransformed IMR90 cells ( Fig 7B ) . Furthermore , MCPyV-transformed IMR90 cells ( Fig 7D ) and MCPyV-positive MCC cell lines ( Fig 4 ) were sensitive to MCT1 inhibition . The sensitivity of these MCC cell lines to MCT1 inhibition corresponded to their relative ECAR levels . These results indicate that ST , together with LT , can manipulate cellular energy states to meet the demands of tumorigenesis . We have demonstrated that ST plays a significant role in altering the metabolic state of the host cell , but our results do not rule out the possibility that further modulation by LT is required for its effect on transformation . The presence of MCPyV LT in the transformed cell experiments using PHE and PHEL cells could have an effect on the ability of ST to induce glycolysis genes . Previous transcriptional profiling of LT did not indicate any significant perturbation of the metabolic pathways being investigated here [22] . As MCPyV LT can still bind pRB , there could be perturbations of metabolic genes that cooperate with ST [45] , as seen in PHE cells where HK2 and LDHA levels were increased compared to PH and ST-only cells ( Fig 6B ) . The regulation of MCT1 expression has been proposed to involve numerous factors , including MYC [33] and NF-κB [37] . Intriguingly , one study found that a significant number of MCC tumors contained genomic amplification of MYCL [40] , a close relative of MYC that is also amplified in small cell lung cancer [46] . MYCN , another MYC isoform , is amplified in pediatric neuroblastoma and has been shown to regulate metabolic pathways in a similar way as MYC [47] . Given these findings , we investigated whether MYC , MYCN and MYCL could cooperate with MCPyV to regulate glycolysis gene expression . By generating a series of dox inducible constructs expressing the MYC , MYCN and MYCL isoforms in both MCC cells and IMR90 lines , we found that MYC and MYCN could robustly affect the expression of MCT1 and the critical glycolysis enzyme HK2 ( Figs 5A and 6B ) . We found that unlike IMR90 cells , MCC cell lines lacked endogenous MYC expression but did express MYCL and MYCN ( S4A Fig ) . MYCL overexpression led to increased levels of MCT1 in MCC cells , but decreased levels in IMR90 PHE cells . This suggests that the observed lack of MYC expression in these MCC cells may alter the transcriptional activity of MYCL . MCPyV ER expression independently upregulated LDHA expression in IMR90 cells in a manner not dependent on MYC signaling , whereas MYC affected LDHA expression in MKL-1 cells , suggesting that metabolic regulation by MCPyV may involve cell type-specific factors . Interestingly , PHEL cells had significantly lower ECAR than PHE cells ( Fig 7B ) , although PHEL cells are fully transformed and are sensitive to specific MCT1 inhibitors ( Fig 7C and 7D ) . This may be due to the observation that expression of MYCL in PHE cells decreased MCT1 expression ( Fig 6B and 6C ) . Taken together , these results suggest that MYC and MYCN behave quite differently from MYCL , with MYCL appearing to have a particular synergy with MCPyV ST that influences both gene expression and transformation . Soft agar experiments , besides testing for anchorage-independent growth , also place cells in an environment that likely has a lower diffusion rate for extracellular metabolites compared to the typical environment encountered on a plastic dish containing liquid growth media . While the effects of SR13800 and SR13801 are modest in the standard cell culture plate setting used in Fig 7C , the significant decrease in transformation shown in Fig 7D by these inhibitors is likely due to the fact that as a colony of cells grows in size in soft agar , it is forced to become more glycolytic as hypoxia increases . This increased glycolytic load , compounded with the inhibition of MCT1 , may lead to toxic intracellular acidification that results in the significant colony formation defect . Previous work from our lab and others has suggested that MCPyV ST has specific effects on NF-κB signaling [22 , 48] . We found that MCT1 expression could be efficiently suppressed through RNAi knockdown of the canonical NF-κB subunit RelA in IMR90 PH and PHE cells ( Fig 6C ) . Overexpression of MYC could still induce MCT1 in PHE cells following RelA depletion , although to a lower degree , suggesting that MYC is the primary regulator of MCT1 expression in these cells with NF-κB potentially having a supplemental role . These results agree with our promoter analysis ( Fig 6A ) and earlier studies indicating that MCT1 is regulated by multiple transcription factors [33 , 37 , 38] . Other transcription factors for genes encoding glycolytic enzyme , PPP enzymes and glucose transporters , such as the carbohydrate responsive element binding proteins ( CHREBPs ) [49] , would be interesting candidates to pursue in future studies . We focused on the MYC family in this study as they are a major driver of glycolysis gene expression , have been implicated in the regulation of MCT1 and in particular , MYCL has been found amplified in MCC . MCT1 primarily mediates intracellular or extracellular acidification depending on the cell and tumor type [27 , 50] . Our data indicates that MCT1 inhibition in MCPyV-expressing IMR90 fibroblasts dramatically alters extracellular acidification . The effect of CHC compared to the more specific MCT1/2 inhibitors suggests that broad MCT inhibition may be more potent across MCC cell lines . This is supported by observations that MCT4 can also export lactate in a redundant fashion [51] . WaGa cells may have higher levels of MCT4 that could contribute to monocarboxylate transport and thereby limit viability defects from specific MCT1 inhibition , or could uniquely utilize lactate for energy production . Our results here represent the first temporal transcriptome analysis of a DNA tumor virus protein , leading to the identification of key metabolic perturbations that contribute to the proliferative effects of MCPyV ST . We have also shown the differential effects of MYC , MYCN , MYCL and NF-κB on aerobic glycolysis and MCT1 regulation . Recent genetic analysis of patients with severe ketoacidosis identified several inactivating mutations in MCT1 , correlating with disease severity , MCT1 protein levels and transport capacity , highlighting yet another critical role of this transporter in human health [52] . The key role of MCT1 in MCC viability should be considered in future treatment regimens , perhaps in combination with metformin or other metabolic agents that have previously shown promise when combined with MCT1 inhibition [51] .
293T and IMR90 cells were obtained from ATCC . The MKL-1 and MKL-2 cell lines were kind gifts from Masahiro Shuda and Yuan Chang ( University of Pittsburgh , PA ) , BroLi cells from Roland Houben ( University of Wuerzburg , Germany ) and WaGa cells were from Jürgen Becker ( Medical University Graz , Austria ) . 293T cells were cultured in Dulbecco’s modified Eagle medium ( DMEM ) ( Cellgro ) supplemented with 1% Pen Strep ( GIBCO ) , 1% Glutamax ( GIBCO ) , and 10% fetal bovine serum ( FBS ) ( Sigma ) . IMR90 cells were cultured with a similar media composition as the 293T cells with the exception of 15% FBS and addition of 1% non-essential amino acids ( GIBCO ) . MCC cell lines were cultured in RPMI 1640 media ( GIBCO ) supplemented with 1% Pen Strep , 1% Glutamax , and 10% FBS . Packaging and envelope plasmids were co-transfected with lentiviral or retroviral expression vectors into 293T cells using Lipofectamine 2000 ( Life Technologies ) . Two days after transfection , 293T cell supernatant was clarified with a 0 . 45 μm filter and supplemented with 4 μg/mL polybrene ( Santa Cruz ) before transducing recipient cells . Stable cell lines were generated after selection with 2 μg/mL puromycin ( Sigma ) , 5 μg/mL blasticidin ( Invivogen ) , 500 μg/mL neomycin ( Sigma ) and 50 μg/mL hygromycin ( Santa Cruz ) as required by each vector . For inducible cell line experiments , doxycycline ( Clontech ) was used at 1 μg/mL . For MCT1 transport inhibitor experiments , dimethyl sulfoxide ( DMSO ) ( Sigma ) , α-cyano-4-hydroxycinnamate ( CHC ) ( Sigma ) ( 5 mM ) , SR13800 ( Calbiochem ) ( 100nM ) , and SR13801 ( Tocris ) ( 100 nM ) were used at the indicated concentrations . MCPyV ST , MYC-T58A , MYCN , MYCL and GFP Gateway-compatible cDNA entry clones were transferred from pDONR221 donor vectors to the pLIX_402 doxycycline inducible lentiviral Gateway destination vector ( a gift from David Root; Addgene plasmid # 41394 ) via Gateway cloning ( Life Technologies ) . pBabe-neo-p53DD was a gift from William C . Hahn ( Dana-Farber Cancer Institute ) . pBabe-hygro-hTERT was a gift from Bob Weinberg ( Addgene plasmid # 1773 ) [53] . Tumor-derived ( MCCL21 ) MCPyV ER cDNA was generated as previously described [36] and cloned into the pLenti CMV Blast DEST ( 706–1 ) vector ( a gift from Eric Campeau; Addgene plasmid # 17451 ) [54] . Lentiviral packaging plasmid psPAX2 and envelope plasmid pMD2 . G were gifts from Didier Trono ( Addgene #12260 , #12259 ) . Retroviral packaging plasmid pUMVC3 was a gift from Bob Weinberg ( Addgene # 8449 ) [55] and envelope plasmid pHCMV-AmphoEnv from Miguel Sena-Esteves ( Addgene # 15799 ) [56] . Dox inducible IMR90 lines expressing ST or GFP were seeded in 6 cm dishes 24 hours before initiation of time course with dox-containing DMEM . Cells were harvested every 8 hours for 96 hours and total RNA was purified using RNeasy Plus Mini Kit ( Qiagen ) and mRNA was isolated with NEBNext Poly ( A ) mRNA Magnetic Isolation Module ( New England BioLabs ) . IMR90 cells were refed after 48 hours with dox-containing media . Sequencing libraries were prepared with NEBNext mRNA library Prep Master Mix Set for Illumina ( New England BioLabs ) , passed Qubit , Bioanalyzer and qPCR QC analyses and sequenced on HiSeq 2000 system ( Illumina ) . The complete set of RNAseq data can be accessed from the Gene Expression Omnibus ( GEO ) repository GSE79968 . The following antibodies were used: MCPyV Ab5 [36 , 57]; GFP ( D5 . 1 , Cell Signaling ) ; vinculin ( H-10 , Santa Cruz ) ; actin ( D6A8 , Cell Signaling ) ; HK2 ( C64G5 , Cell Signaling ) ; LDHA ( EP1565Y , Abcam ) ; MCT1 ( A1512 , NeoBiolab ) ; LAT1 ( 5347 , Cell Signaling ) ; RelA ( D14E12 , Cell Signaling ) ; IκBα ( L35A5 , Cell Signaling ) ; MYC ( 9E10 , Santa Cruz ) ; MYCN ( 9405 , Cell Signaling ) ; MYCL ( AF4050 , R&D Systems ) . Whole cell lysates were prepared using RIPA buffer ( Boston BioProducts ) supplemented with protease inhibitor cocktail set I ( Calbiochem ) and phosphatase inhibitor cocktail set I ( Calbiochem ) . Clarified protein extracts were boiled in SDS sample buffer ( Boston BioProducts ) , resolved by SDS-PAGE ( Criterion TGX precast gels; Bio-Rad ) , transferred to nitrocellulose membranes ( Bio-Rad ) , blocked and incubated with the appropriate primary antibody in TBS-T overnight at 4°C . Detection of proteins was performed with horseradish peroxidase-conjugated secondary antibodies ( Rockland ) , developed using Clarity Western ECL substrate ( Bio-Rad ) , and imaged with a G:BOX Chemi detection system ( Syngene ) . Total RNA was purified using RNeasy Plus Mini Kit ( Qiagen ) . cDNA was synthesized from the RNA using a High-Capacity cDNA Reverse Transcription kit ( Thermo Fisher ) . qPCR was performed using Brilliant III SYBR Master Mix ( Agilent Genomics ) following the manufacturer’s instructions . RPLP0 was used as an internal loading control to normalize RNA levels . IMR90 PH and PHE cells were seeded in 6 cm dishes ( 5 x 105 cells/dish ) and were transfected with 100 nM siRNA ON-TARGETplus SMARTpool siRNA against human RelA ( L-003533-00-0005 ) or ON-TARGETplus Non-Targeting siRNA#1 ( D0018100105 ) from GE Healthcare Dharmacon using Lipofectamine RNAiMAX ( Life Technologies ) . After 24 hours , cells were refed with media with or without dox . Cells were then harvested for subsequent immunoblotting after 48 hours . Reads were mapped to a transcriptome index generated from the hg19 human reference genome and the Merkel cell polyomavirus sequence , using Tophat 2 . 0 . 4 and Bowtie1 with default parameters . Novel junctions were not allowed . MCPyV-aligned reads were transformed into gene-level counts using HTSeq . Human-aligned reads were quantified at the gene level using Cufflinks 1 . 3 . 0 along with its ancillary algorithms that correct for biases and multi-mapped reads . Log-transformed FPKM ( fragments per kilobase of transcript per million mapped reads ) values were input into further statistical analysis of human transcript levels . All genes that had a zero FPKM value in any sample were deemed to have low expression and removed from the analysis . The R package limma was used to rank genes by their differential expression in the ST cell line between every time point and the zero time point , relative to the same comparison in the GFP cell line [23] . We selected all genes with P < 0 . 01 and with total absolute fold change across all time points ( relative to GFP ) above a cutoff of 4 . This resulted in a list of 2854 genes that were differentially perturbed over time by ST . We used the R package mclust to cluster the genes , based on their expression across all time points in the ST inducible cell line . Expression values were mean-centered and scaled by the standard deviation across the ST samples . The 50 clusters were tested for GO term enrichment using the R package GOstats with p-values adjusted for multiple testing by the Benjamini-Hochberg method . Similarly , the clusters were also tested for enrichment in pathways representing the hallmarks of cancer downloaded from the Molecular Signatures Database ( MSigDB ) [58] . The significance of each overlap was evaluated using a hypergeometric test and adjusted for multiple testing using Benjamini-Hochberg . Position weight matrices ( PWMs ) for NFKB1 and MYC::MAX were extracted from the R package MotifDB . The SLC16A1 promoter from -1000 to +100 relative to TSS was mapped to the binding motifs using a cutoff of 0 . 8 for the estimated probability of a match between the promoter sequence and the PWM [59] . DNAse I hypersensitivity data for IMR90s was downloaded from the Gene Expression Omnibus ( GEO ) , accession numbers GSM468801 , GSM530665 , GSM530666 , and GSM468792 . Extracellular acidification rate ( ECAR ) was measured using the Seahorse Bioscience XFe24 Extracellular Flux Analyzer according to the manufacturer’s protocol . For IMR90 extracellular flux analysis , cells were seeded into assay culture plates ( 2 x 104 cells/well ) 24 hours prior to the assay . For MCC cell line analysis , assay culture plates were coated with Cell-Tak ( Corning ) following protocol from Seahorse Bioscience . Cells ( 1 x 105 cells/well ) were adhered to coated assay plate wells via centrifugation . Cells were rinsed and cultured in XF Base Medium ( Seahorse Bioscience ) supplemented with 10 mM glucose ( GIBCO ) , 1 mM sodium pyruvate ( Sigma ) , 1% Glutamax , and pH was adjusted to 7 . 4 prior to performing the assay . Where described , DMSO and CHC were added to complete XF media before the start of the assay . Real-time OCR and ECAR data are representative of two biological replicates , with values representing the means and error bars representing standard deviation of five technical replicates at each time point . For media glucose and lactate measurements , IMR90 cells inducibly expressing either ST or GFP were seeded in duplicate ( 1 x 105 cells/well ) in a 24-well plate using standard IMR90 culture media supplemented with 5 mM lactate ( Sigma ) . Dox was added and media was collected daily for 5 days . Day 0 media corresponds to a sample of fresh growth medium . Glucose and lactate was measured using a YSI Biochemistry Analyzer . IMR90 anchorage-independent growth was performed as described [36] using 6-well dishes with SeaPlaque Agarose ( Lonza ) at concentrations of 0 . 3% top and 0 . 6% bottom layers . Agarose was diluted with 2X MEM ( Gibco ) supplemented with 2X Glutamax , 2X Pen Strep , and 30% FBS . IMR90 cells ( 104 ) were seeded in triplicate in the top agarose layer . Wells were fed with top agarose twice per week . After 4 weeks , cells were stained with 0 . 005% crystal violet ( Sigma ) in PBS and colonies were counted . MCT1 inhibitors were included into soft agar layers at the concentrations described above . IMR90 proliferation assays were performed as previously described [60] . Briefly , IMR90 cell lines were seeded in triplicate in 24-well plates ( day 0; 5 x 103 cells per well ) . Cell density was measured by crystal violet staining at intervals after plating as previously described [22] . MCC cell line proliferation assays were performed in triplicate in 48-well plates using XTT assay ( Roche ) following the manufacturer’s protocol . | In 2008 , Merkel cell polyomavirus ( MCPyV ) was identified as clonally integrated in a majority of Merkel cell carcinomas ( MCC ) , a rare but highly aggressive neuroendocrine carcinoma of the skin . Since then , studies have highlighted the roles of the MCPyV T antigens in promoting and sustaining MCC oncogenesis . In particular , MCPyV small T antigen ( ST ) has oncogenic activity in vivo and in vitro . We performed transcriptome analysis of normal human fibroblasts with inducible expression of MCPyV ST and observed significant alterations in levels of metabolic pathway genes , particularly those involved in glycolysis . MCT1 , a major monocarboxylate transporter , was rapidly induced following ST expression and inhibition of MCT1 activity reduced the ST growth promoting and transforming activities . The metabolic perturbations induced by this oncogenic human polyomavirus reflect a potent transforming mechanism of MCPyV ST . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"cell",
"physiology",
"chemical",
"compounds",
"gene",
"regulation",
"metabolic",
"processes",
"carbohydrates",
"cell",
"metabolism",
"organic",
"compounds",
"glucose",
"glycolysis",
"genome",
"analysis",
"small",
"interfering",
"rnas",
"gene",
"expression",
"metabolic",
... | 2016 | Merkel Cell Polyomavirus Small T Antigen Promotes Pro-Glycolytic Metabolic Perturbations Required for Transformation |
Pneumolysin ( PLY ) is a key Streptococcus pneumoniae virulence factor and potential candidate for inclusion in pneumococcal subunit vaccines . Dendritic cells ( DC ) play a key role in the initiation and instruction of adaptive immunity , but the effects of PLY on DC have not been widely investigated . Endotoxin-free PLY enhanced costimulatory molecule expression on DC but did not induce cytokine secretion . These effects have functional significance as adoptive transfer of DC exposed to PLY and antigen resulted in stronger antigen-specific T cell proliferation than transfer of DC exposed to antigen alone . PLY synergized with TLR agonists to enhance secretion of the proinflammatory cytokines IL-12 , IL-23 , IL-6 , IL-1β , IL-1α and TNF-α by DC and enhanced cytokines including IL-17A and IFN-γ by splenocytes . PLY-induced DC maturation and cytokine secretion by DC and splenocytes was TLR4-independent . Both IL-17A and IFN-γ are required for protective immunity to pneumococcal infection and intranasal infection of mice with PLY-deficient pneumococci induced significantly less IFN-γ and IL-17A in the lungs compared to infection with wild-type bacteria . IL-1β plays a key role in promoting IL-17A and was previously shown to mediate protection against pneumococcal infection . The enhancement of IL-1β secretion by whole live S . pneumoniae and by PLY in DC required NLRP3 , identifying PLY as a novel NLRP3 inflammasome activator . Furthermore , NLRP3 was required for protective immunity against respiratory infection with S . pneumoniae . These results add significantly to our understanding of the interactions between PLY and the immune system .
Streptococcus pneumoniae is responsible for millions of deaths annually from pneumonia , meningitis and septicaemia while also causing other less serious infections , such as otitis media and sinusitis . Pneumolysin ( PLY ) is a major virulence factor that is expressed by virtually all clinical isolates of the bacterium . The toxin is a member of the cholesterol-dependent cytolysins , a family that includes perfringolysin O and streptolysin O , expressed by Clostridium perfringens and Streptococcus pyogenes , respectively . A classical feature of these toxins is their ability to create transmembrane pores in cholesterol-containing membranes and thereby cause cell lysis ( reviewed in [1] ) . The importance of PLY as a pneumococcal virulence factor is well established , with several studies showing reduced pathogenesis in mice infected with PLY-deficient strains of S . pneumoniae , compared to isogenic toxin-producing strains [2] , [3] , [4] , [5] . Furthermore , application of PLY directly into the lungs of rats induced an acute inflammatory response similar to that observed during pneumococcal pneumonia [6] . At sublytic concentrations , the toxin has been reported to promote activation of host complement [7] , potentiation of neutrophil activity [8] , [9] , activation and chemotaxis of CD4+ T cells [10] and enhanced production of proinflammatory cytokines in macrophages and monocytes [11] , [12] . Studies have been carried out to address the mechanisms underlying the immunomodulatory effects of PLY and particularly the role of TLRs . It has been proposed that the ability of PLY to induce cytokine production [13] and apoptosis [14] in peritoneal macrophages was TLR4-dependent . In contrast , the activation of p38 mitogen-activated protein kinase in epithelial cells [15] and the activation of nuclear factor of activated T cells ( NFAT ) [16] by PLY were reported to be TLR4-independent . Furthermore , there are conflicting reports on the role of TLR4 in defence against pneumococcal infection . TLR4 was reported to be required for host defence against PLY-producing pneumococci , as mice lacking functional TLR4 were more susceptible to disease after nasopharyngeal challenge [13] . However , other studies have shown a more limited or indeed a redundant role for TLR4 in host resistance to pneumococcal disease , depending on the bacterial dose and the model of infection [17] , [18] . Thus , the role of TLR4 in immune responses to the pneumococcus , and particularly to PLY , is unclear and must be resolved . T cells play a key role in protection against pneumococcal infection [4] , [10] so it is important to determine the factors underlying pneumococcus-induced T cell responses . In this regard it is noteworthy that studies in IFN-γ−/− mice indicate a protective role for IFN-γ during bacteremic pneumococcal pneumonia [19] and administration of recombinant IFN-γ to mice promoted protection from disease following intratracheal infection with S . pneumoniae [20] . Furthermore , an essential role was proposed for IL-17A in protection against pneumococcal nasopharyngeal colonization following intranasal immunization of mice with killed pneumococci and cholera toxin adjuvant , as protection was abrogated in mice deficient in the IL-17A receptor [21] . Thus , there is a strong rationale for the development of vaccination approaches that induce IFN-γ- and IL-17A-producing cells and for understanding the mechanisms by which pneumococci may either promote or evade such responses . The activation and differentiation of naïve CD4+ T cells following immunization or infection depends on interactions with DC [22] . T cell activation requires antigen presentation on MHC class II molecules , as well as costimulatory signals provided by molecules including CD80 and CD86 on the DC surface . The differentiation of Th1 and Th17 cells requires polarizing cytokines which can be produced by DC . IL-12 and IL-18 are two of the key cytokines involved in Th1 cell differentiation , while IL-23 , IL-1 and IL-6 promote Th17 cell development . To date , little is known about the interaction of PLY with DC . Therefore , in this study the effects of PLY on DC maturation and cytokine production and the role of TLR4 in these processes were determined . In addition to the key role of DC in dictating T cell differentiation and polarization , important roles for natural killer ( NK ) cells and γδ T cells have also been described . In particular , IFN-γ produced by NK cells plays a key role in the instruction of Th1 responses [23] , while IL-17A derived from γδ T cells promotes Th17 responses [24] . We demonstrate that endotoxin-free PLY alone does not induce cytokine production by DC or macrophages but it can synergize with TLR agonists to enhance cytokine secretion . Furthermore , PLY promotes the secretion of cytokines including IFN-γ and IL-17A by splenocytes and is essential for S . pneumoniae to promote the production of IFN-γ by NK cells and IL-17A by γδ T cells in the lung following respiratory infection . In contrast to previous publications showing a role for TLR4 in PLY-induced immune responses , we show that the ability of the toxin to enhance cytokine secretion does not require TLR4 . However , NLRP3 activation was required for PLY- and live S . pneumoniae-mediated enhancement of IL-1β secretion and NLRP3 was required for protection against respiratory infection with S . pneumoniae .
IFN-γ and IL-17 responses play an essential role in protective immunity against pneumococcal disease [19] , [20] , [21] . We therefore investigated the ability of PLY to promote these cytokines in vitro and the requirement for TLR4 . In the absence of any stimulation with PMA or anti-CD3 , PLY alone was unable to promote cytokine secretion by splenocytes ( data not shown ) . However , we did find significant enhancing effects of PLY on IFN-γ secretion by splenocytes stimulated with heat-killed S . pneumoniae ( HkSp ) and PMA/ionomycin ( Fig . 1A ) . This was true for spleen cells from both C3H/HeN and C3H/HeJ mice , indicating that the combination of PLY and HkSp is a potent stimulus for TLR4-independent IFN-γ secretion in spleen cells ( Fig . 1A ) . The IFN-γ inducing ability of the toxin was not dependent on its cytolytic activity as a mutant of PLY ( W433F ) with greatly reduced cytotoxic activity was also capable of stimulating IFN-γ production by splenocytes ( Fig . 1A ) . Indeed , concentrations of W433F from 0 . 32–200 ng/ml enhanced pneumococci-induced IFN-γ production , whereas the wild-type toxin only significantly augmented IFN-γ secretion at a concentration of 0 . 32 ng/ml ( Fig . 1A ) . The pore-forming activity of the toxin may , therefore , interfere with its ability to stimulate IFN-γ production in splenocytes . Furthermore , in the presence of anti-CD3 , PLY significantly augmented IFN-γ secretion by splenocytes ( Fig . 1B ) . We also examined the ability of PLY to induce the secretion of other cytokines by anti-CD3-stimulated splenocytes . At a concentration of 1 . 6 ng/ml , PLY significantly enhanced IL-5 ( 3-fold; P<0 . 05 ) , IL-10 ( 3-fold; P<0 . 05 ) and IL-17A ( 2-fold; P<0 . 05 ) , in addition to IFN-γ ( 14-fold; P<0 . 001 ) , secretion by splenocytes from TLR4 hyporesponsive C3H/HeJ mice ( Fig . 1B ) . These data demonstrate that PLY induces cytokine secretion by spleen cells in a TLR4-independent manner . PLY at 1 . 6 ng/ml was more effective at promoting cytokine secretion than at 40 ng/ml ( Fig . 1B ) . However , a concentration of 40 ng/ml PLY may induce early apoptosis in splenocytes , as dual staining of these cells with AnnexinV and propidium iodide ( PI ) revealed slight increases in AnnexinV+/PI- cells following incubation with the toxin for 6 hours ( Fig . S1A ) . These increases were more apparent following stimulation with 200 ng/ml PLY ( Fig . S1A ) . In contrast , when splenocytes were incubated with 200 ng/ml PLY for 24 hours or 96 hours there was no increase in AnnexinV+/PI- cells ( data not shown ) , although an increase in the percentage of PI+ cells was evident ( Fig . S1B and S1C ) . Higher concentrations of PLY ( 1–5 µg/ml ) induced the death of >25% of splenocytes from either C3H/HeN or C3H/HeJ mice after 24 hours ( Fig . S1B ) and this increased to >50% by 96 hours ( Fig . S1C ) . Having shown that PLY enhanced the secretion of cytokines , including IFN-γ and IL-17A by splenocytes stimulated in vitro ( Fig . 1 ) , we next determined the contribution of PLY to pneumococcus-induced cytokine production using two different murine models of pneumococcal infection in vivo . Using a model of acute pneumonia [4] , we found that infection with a PLY-deficient strain of pneumococcus ( PLN-A ) induced significantly less IFN-γ in the lungs of mice compared to infection with its PLY-positive parental strain ( Fig . 2A ) . As has been demonstrated previously in this pneumonia model [3] , [4] , [5] , the PLY-deficient strain exhibited reduced virulence in the lungs compared to the wild-type strain ( Fig . S2A ) . Furthermore , using a model of resolving pneumonia [25] , [26] , we found significantly lower concentrations of IL-17A in the lungs of mice infected with PLN-A compared to those infected with wild-type pneumococci , at both 24 and 48 hours post-infection ( Fig . 2B ) . There were no significant differences in bacterial CFU at time 0 in the lungs of mice infected with wild-type bacteria compared to PLY-deficient bacteria and lung CFU of both pneumococcal strains were reduced at 24 and 48 hours post-infection ( Fig . S2B ) . We then investigated the cellular source of these cytokines by intracellular cytokine staining . NK cells were the principal source of IFN-γ in the lungs 48 hours following infection , although IFN-γ was also produced by γδ T cells and other T cells ( Fig . 2C and 2E ) . In the case of IL-17A , the enhanced cytokine production was predominately by γδ T cells with limited IL-17A production in other T cells and NK cells ( Fig . 2D and 2E ) . Having shown that PLY promotes IFN-γ and IL-17A production in vitro and in vivo , we next established the effects of the toxin on DC . DC play an important role in the activation and polarization of naïve T cells but to date the effects of PLY on DC have not been widely investigated . We therefore examined the effects of PLY on DC cytokine production and costimulatory molecule expression . While the TLR2 agonist PAM3Csk4 promoted the secretion of proinflammatory cytokines from DC , endotoxin-free PLY , at concentrations of up to 1 µg/ml , did not induce the secretion of IL-6 , IL-12p40 or TNF-α ( Fig . 3A ) . Higher concentrations were not tested because PLY at 2 µg/ml caused some cell death in DC , as determined by PI staining ( Fig . S3A ) . Indeed , concentrations of PLY ranging from 3 µg/ml to 6 µg/ml were highly toxic to DC , with PLY at 6 µg/ml inducing death in approximately 90% of DC from either C3H/HeN or C3H/HeJ mice after 24 hours ( Fig . S3A and S3B ) . AnnexinV/PI staining of DC incubated with this higher concentration of the toxin for 6 hours indicated that PLY induced limited apoptosis ( AnnexinV+/PI− ) , but greatly increased the percentage of dead AnnexinV+/PI+ cells ( Fig . S3C ) . Since previous studies demonstrated that PLY could directly induce cytokine production in monocytes and macrophages [12] , [13] , it was important to establish if the finding that endotoxin-free PLY does not promote cytokine secretion was specific to DC . Endotoxin-free PLY was also unable to induce secretion of significant concentrations of IL-6 , TNF-α , IL-12p40 ( Fig . 3B ) , IL-1β or IL-23 ( data not shown ) from murine bone-marrow derived macrophages ( BMDM ) . These data indicate that PLY alone does not stimulate inflammatory cytokine production by DC or macrophages . Since co-stimulation is required for DC to promote T cell activation , we assessed the effects of PLY on the expression of co-stimulatory molecules and MHC class II on DC and compared these effects to those induced by the TLR4 agonist LPS . Stimulation of DC with endotoxin-free PLY enhanced the expression of MHC class II and co-stimulatory molecules , particularly CD86 . Interestingly , similar findings were observed with PLY-stimulated DC from both C3H/HeN and LPS-hyporesponsive C3H/HeJ mice , indicating that the enhancement of DC maturation by the toxin was TLR4-independent . In contrast , LPS strongly enhanced the expression of co-stimulatory molecules on DC from C3H/HeN mice only ( Fig . 3C ) . Therefore , although endotoxin-free PLY does not induce cytokine secretion by DC , it can promote their maturation independently of TLR4 . To determine the functional significance of the direct effects of PLY on DC , cells were incubated with antigen ( KLH ) alone or KLH and PLY overnight and injected into mice . The exposure of DC to PLY increased their ability to promote antigen-specific T cell proliferation in splenocytes 7 days following DC transfer ( Fig . 3D ) . We did not detect enhanced antigen-specific cytokine production by these cells ( data not presented ) . However , the T cell proliferation data indicate that PLY can act directly on DC to enhance their T cell stimulatory activity . Since exposure of DC to PLY enhanced their ability to promote adaptive immune responses , we determined the adjuvant properties of endotoxin-free PLY when co-injected with KLH . Injection of PLY with KLH did not enhance antigen-specific cellular immune responses ( Fig . S4A ) . In contrast , antigen-specific antibody responses were significantly enhanced by co-immunization with antigen and PLY ( Fig . S4B and S4C ) . The ability of PLY to promote antigen-specific antibody responses applied to both IgG1 and IgG2a subclasses and the toxin was equally effective in C3H/HeN and C3H/HeJ mice . Thus , the toxin has the ability to enhance antigen-specific immune responses independently of TLR4 signalling . Although PLY alone did not induce cytokine production by DC or macrophages , we investigated if the toxin could synergize with other stimuli to enhance cytokine secretion . We found that PLY ( 1 µg/ml ) synergized with HkSp to significantly enhance the secretion of IL-6 , IL-12p40 , IL-23 and TNF-α by DC ( Fig . 4A ) . Notably , this synergistic effect was observed in DC from both C3H/HeN and C3H/HeJ mice , indicating that the enhancement of cytokine secretion by PLY did not require the presence of functional TLR4 . Furthermore , PLY was also able to synergize with HkSp to significantly enhance IL-6 and TNF-α production in BMDM ( Fig . S5 ) . PLY alone did not induce significant secretion of IL-1α or IL-1β by DC ( Fig . 4B ) or BMDM ( data not shown ) . However , PLY synergized with HkSp to significantly enhance IL-1β secretion by DC ( Fig . 4B ) . Importantly , the ability of PLY to promote IL-1β secretion was not limited to pneumococci as the toxin also significantly enhanced IL-1β secretion in response to a range of TLR and NLR agonists; PAM3Csk ( TLR1/2 ligand ) , zymosan ( TLR2/6 ligand ) , CpG ( TLR9 ligand ) and MDP ( Nod2 ligand ) . Similarly , IL-1α secretion was significantly enhanced in DC stimulated with these TLR/NLR agonists and PLY compared to the agonists alone . Furthermore , by using DC from C3H/HeJ mice , we showed that PLY-induced enhancement of IL-1α and IL-1β secretion by TLR agonists was independent of TLR4 ( Fig . 4B ) . Since it has been shown that IL-1β is required for resistance to pneumococcal infection [27] , we investigated the mechanism underlying PLY-induced IL-1β further . Firstly , to determine the importance of haemolytic activity in the promotion of IL-1β by PLY , DC were incubated with HkSp or CpG in the presence or absence of PLY or the W433F hemolysin mutant . PLY-driven IL-1β secretion was hemolysin-dependent as the ability of W433F to promote secretion of the cytokine was significantly less than wild-type PLY ( Fig . 4C ) . IL-1β is synthesised inside cells as a biologically inactive precursor that is processed by caspase-1 prior to its secretion . Caspase-1 is itself produced as an inactive zymogen that must be cleaved to generate the active p10 and p20 subunits . Since the secretion of IL-1β requires both the transcription of IL-1β and caspase-1 activation , we investigated how PLY promoted IL-1β secretion in DC . We found that PLY alone promoted caspase-1 activation in DC and that this was independent of TLR4 , as processed ( p10 ) caspase-1 was detected in DC lysates from both C3H/HeN and C3H/HeJ mice ( Fig . 4D ) . Pre-treatment of DC with the caspase-1 inhibitor YVAD-fmk completely blocked the synergy between PLY and PAM3Csk for IL-1β secretion ( Fig . 4E ) , confirming the importance of caspase-1 in PLY-induced IL-1β secretion . In contrast , the enhancement of IL-1α secretion by the toxin was not significantly affected , indicating that caspase-1 was not required ( Fig . 4E ) . The importance of caspase-1 in PLY-induced IL-1β secretion was also confirmed using DC from caspase-1 knockout mice , as the synergy between PLY and either PAM3Csk or HkSp for IL-1β was dramatically reduced in caspase-1−/− DC compared to wild-type DC ( Fig . 5A ) . The activation of caspase-1 is regulated by the assembly of inflammasomes , cytoplasmic multiprotein complexes that contain a nucleotide-binding oligomerization domain ( NOD ) -like receptor ( NLR ) family member , such as NLRP3 or NLRC4 and caspase-1 [28] . We investigated the role of the NLRP3 inflammasome in PLY-driven IL-1β secretion and found that the ability of PLY to promote IL-1β secretion was compromised in cells from NLRP3−/− mice compared to wild-type mice ( Fig . 5B ) , but not in DC from mice deficient in NLRP6 or NLRP12 ( Fig . S6 ) . In particular , the synergy between PLY and HkSp for IL-1β secretion was abrogated in DC from NLRP3−/− mice ( Fig . 5B ) . This marked defect in the ability of NLRP3−/− DC to secrete IL-1β in response to stimulation with PLY was due to differences in IL-1β processing , as Western blot analysis did not reveal altered proIL-1β production in DC from NLRP3−/− mice compared to DC from wild-type mice ( Fig . S7 ) . In contrast to IL-1β secretion , PLY significantly enhanced bacteria-induced IL-1α secretion in DC from both wild-type and NLRP3−/− mice ( Fig . 5B ) . The adapter protein ASC ( apoptosis-associated speck like protein containing CARD ) is necessary for caspase-1 activation via NLRP3 . PLY-mediated enhancement of IL-1β secretion was also reduced in DC from ASC−/− mice , compared to wild-type mice ( Fig . S8 ) . DC from caspase-1−/− ( Fig . 5A ) or NLRP3−/− ( Fig . 5B ) mice secreted comparable concentrations of TNF-α to wild-type mice when stimulated with PLY and TLR agonist , indicating that the ability of these cells to produce inflammatory cytokines was not globally compromised . Indeed , the ability of PLY to promote the secretion of TNF-α , IL-6 , IL-23 , IL-12 and IL-10 was independent of NLRP3 ( Fig . 6 ) . CpG was used as the TLR for these experiments as it was the most effective TLR agonist to determine synergy with PLY for this panel of cytokines , particularly IL-12p70 . The IL-10 data indicate that PLY can promote secretion of both pro- and anti-inflammatory cytokines , although the overall profile suggests that PLY is a highly pro-inflammatory factor . Having demonstrated that purified endotoxin-free PLY can promote IL-1β secretion via the NLRP3 inflammasome , we next investigated the role of NLRP3 in IL-1β production by DC in response to live S . pneumoniae . Wild-type S . pneumoniae induced robust IL-1β secretion by DC and this was dependent on PLY since the PLY-deficient strain induced very little IL-1β secretion ( Fig . 7 ) . In contrast , IL-1α , TNF-α and IL-6 secretion in response to the PLY-deficient strain was comparable to , or greater than , that induced by the wild-type bacteria . Thus , PLY appears to play a vital and selective role in pneumococcus-induced IL-1β secretion by DC . Furthermore , the induction of IL-1β secretion by the pneumococcus was strongly dependent on NLRP3 ( Fig . 7 ) . In order to further investigate the mechanisms by which PLY may trigger caspase-1 activation and IL-1β secretion in DC , we examined the involvement of some known factors implicated in NLRP3 inflammasome activation such as reduced cytoplasmic potassium concentration . It has been reported that the pore forming toxins , Staphylococcus aureus α-toxin [29] and Aeromonas hydrophilia aerolysin [30] induce the secretion of IL-1β and the activation of caspase-1 , respectively , by allowing the efflux of intracellular potassium ( K+ ) . We therefore assessed whether K+ efflux could also be the trigger for caspase-1 activation by PLY . The addition of extracellular KCl to the medium ( 50 mM ) significantly inhibited IL-1β secretion by DC in response to PLY and PAM3Csk ( Fig . 8A ) . Interestingly , IL-1α secretion was also significantly reduced in the presence of high potassium concentrations ( Fig . 8A ) . It has been proposed that the NLRP3 inflammasome may be activated by lysosomal damage and the subsequent release of cathepsin B into the cytoplasm of cells [31] . We investigated the effect of inhibiting cathepsin B on PLY-induced IL-1 secretion and found that inhibition with the specific cathepsin B inhibitor CA-074-Me resulted in a significant reduction in IL-1β , but not IL-1α , secretion by DC stimulated with PLY and PAM3CSk ( Fig . 8B ) . Furthermore , treating DC with bafilomycin A , which inhibits the H+ ATPase system required for lysosomal acidification [32] , significantly reduced the enhancement of IL-1β and IL-1α , but not TNF-α , by PLY ( Fig . 8B ) . To investigate the significance of PLY and pneumococcal-induced inflammasome activation , NLRP3−/− and wild-type control C57BL/6 mice were intranasally infected with S . pneumoniae ( strain D39 ) . In contrast to control mice , which showed a significant reduction in lung CFU 24 hr post-infection , bacterial clearance was significantly compromised in NLRP3−/− mice and no reduction in bacterial CFU was evident in the lungs of these mice at this time-point ( Fig . 9A ) . To determine the role of PLY , mice were also infected with a PLY-deficient isogenic mutant strain of D39 ( PLN-A ) . Bacterial CFU of PLN-A in lungs of control C57BL/6 mice at 24 hr post-infection were significantly lower than in lungs of NLRP3−/− mice ( Fig . 9B ) . In addition , although there were reductions in bacterial CFU of PLN-A in lungs of both NLRP3−/− and control mice over time , the reduction in control mice was significantly greater than observed in NLRP3−/− mice ( Fig . 9B ) . Therefore , NLRP3 appears to play a particularly important role in infection with PLY-expressing S . pneumoniae , although the data with PLN-A suggest that even in the absence of PLY , NLRP3 may play a role in protective immunity . These data indicate for the first time a role for NLRP3 in protection against S . pneumoniae infection and this is the first description of a role for NLRP3 in protection against a Gram-positive bacterial pathogen .
In mice , the cytokines IL-1β [27] , IFN-γ and IL-17A [19] , [20] , [21] have been shown to play important protective roles in immunity against pneumococci . Here we show that PLY strongly enhances the secretion of IFN-γ by splenocytes in vitro and is required for IFN-γ and IL-17A responses during pneumococcal infection in vivo . NK cells are the principal cellular source of IFN-γ in the lungs following infection while γδ T cells are the major producers of IL-17A . This supports recent findings that γδ T cells are a key source of IL-17A production in vitro and in vivo in response to IL-1 stimulation [24] and pathogen-derived products [33] . The latter study found that IL-17-producing γδ T cells share a number of characteristic features with Th17 cells . IL-1 and IL-23 play key roles in the induction of IL-17A production by γδ T cells and in the differentiation and expansion of Th17 cells [24] . Other cytokines , including IL-6 and TNF-α , have also been shown to be important in Th17 cell differentiation [34] , [35] , [36] . Importantly , our data show that PLY can synergize with TLR agonists to enhance the secretion of IL-1α , IL-1β , IL-23 , TNF-α and IL-6 by DC , which can induce IL-17A production by γδ T cells and promote the differentiation and expansion of Th17 cells . The ability of Freund's complete adjuvant or LPS to induce antigen-specific Th17 cell responses in vivo requires IL-1R1 , indicating a key role for IL-1 in adjuvant-driven Th17 cell responses [36] . Pneumolysin also strongly enhanced TLR agonist-induced IL-12 secretion , which together with cytokines such as IFN-γ and IL-18 , is important for Th1 differentiation and stabilization [37] . Like IL-1β , active IL-18 also requires cleavage of its precursor form by caspase-1 and enhanced levels have been reported in macrophages co-stimulated with recombinant PLY and PLY-deficient S . pneumoniae [38] . Since IFN-γ produced by NK cells is important for the instruction of Th1 responses [23] , our demonstration of NK cells producing IFN-γ in the lungs of infected mice could suggest subsequent promotion of pathogen-specific Th1 responses . Likewise , it has been suggested that IL-17A derived from γδ T cells can promote Th17 responses [24] and we demonstrate that there are IL-17A-producing γδ T cells in the lungs of mice infected with pneumococcus . Both of these effects were strongly dependent on pneumolysin , suggesting that the toxin is a key factor in pneumococcus-induced IFN-γ secretion by NK cells and IL-17A production by γδ T cells . Furthermore , since IL-23R signalling promotes the expansion and maintenance of γδ T cells in response to intracellular bacterial infection [39] , the ability of pneumolysin to promote both IL-1 and IL-23 production is likely to contribute strongly to these γδ T cell responses . During pneumococcal infection , PLY may synergize with pneumococcal or endogenous danger signals to induce the secretion of inflammatory cytokines . Reduced concentrations of plasma IL-6 have been reported from mice infected with PLY-deficient S . pneumoniae compared to isogenic wild-type pneumococci [40] . In addition , IL-1 and TNF-α are induced during murine pneumococcal disease [41] and elevated levels of both have been reported in patients with pneumococcal meningitis . Notably , studies using mice deficient in IL-1 receptor type 1 ( IL-1R1−/− ) [42] or IL-1β [27] have shown the importance of IL-1 in conferring resistance to pneumococcal meningitis and pneumonia respectively . The latter study found that IL-1β , but not IL-1α , plays a major role in resistance to pneumococcal infection . Therefore , the inflammasomes , which are required for IL-1β processing and secretion , are likely to be crucial components of the host defense to S . pneumoniae . Shoma et al . recently reported that recombinant PLY stimulated the secretion of IL-1α , IL-1β and IL-18 in a caspase-1 dependent manner in macrophages and that this was augmented by co-stimulation with pneumococci [38] . Importantly , we show here for the first time that the enhancement of IL-1β secretion by PLY is NLRP3-dependent . The ability of live pneumococci to promote IL-1β secretion by DC is also strongly dependent on NLRP3 . One of the key observations in this study is that NLRP3 is required for protective immunity against pneumococcal infection . The first demonstration of a role for NLRP3 in defence against a Gram-negative pathogen , Salmonella typhimurium , was recently described [43] , but our study is the first report to show that NLRP3 is required for protection against a Gram-positive bacterium . NLRP3 activation by PLY required lysosomal damage and the release of cathepsin B , as inhibitors of these processes reduced PLY-induced IL-1β secretion . Phagosomal rupture has previously been shown to be important in NLRP3 inflammasome activation by other compounds including silica crystals , aluminium salts and microparticles [31] , [32] , [44] and a role for cathepsin B was recently reported in the promotion of IL-1β secretion by the pore-forming toxin tetanolysin O [45] . Furthermore , the ability of PLY to activate NLRP3 was dependent on the haemolytic activity of the toxin . Taken together , our data suggest that PLY-induced pore formation results in K+ efflux from DC and intracellular changes including lysosomal destabilization . The release of lysosomal products , such as cathepsin B , into the cytosol may promote the generation of danger signals , which are detected by NLRP3 or intermediary factors , resulting in inflammasome assembly and caspase-1 activation . Active caspase-1 could then process pro-IL-1β , which is generated in response to a second stimulus such as a TLR/NLR ligand , into mature IL-1β which is released from the cell . Although a direct role for PLY in the induction of IL-1 or TNF-α during pneumococcal disease has not been established , the reduced inflammation and pathology in the lungs of mice infected with PLY-deficient pneumococci compared to wild type strains [4] suggests a diminished inflammatory cytokine response . In particular , lower levels of T cell infiltration and neutrophil recruitment into the lungs have been reported from mice infected with PLY-deficient S . pneumoniae compared to wild-type pneumococci [4] . TNF-α and IL-1β are two of the key cytokines involved in neutrophil recruitment in the lungs of mice infected intratracheally with wild-type S . pneumoniae [46] . Additionally , IL-17A plays a key role in neutrophil recruitment and its induction in vivo is strongly dependent on IL-1 [34] , [36] . An additional major finding of this study is that the stimulatory effects of PLY on DC and splenocyte cytokine secretion are independent of TLR4 . While we demonstrate that PLY can promote expression of costimulatory molecules and proinflammatory cytokines in DC , these effects are independent of TLR4 . We also show that PLY exerts adjuvant effects , promoting antibody responses against a co-administered antigen in both wild-type and TLR4-defective mice , indicating that the immunostimulatory effects of PLY in vivo do not require TLR4 . Therefore , further studies into the involvement of pathogen recognition receptors in sensing PLY in innate cells are warranted . Streptococcus pneumoniae is a pathogen of significant clinical importance and understanding its interaction with the immune system is crucial . We propose that upon infection with S . pneumoniae , PLY , in synergy with pneumococcal PAMPs promotes the secretion of proinflammatory cytokines , particularly IL-1β , that promote an inflammatory response and mediate protective immunity . We identify PLY as a novel NLRP3 inflammasome activator and show that the ability of the live bacterium to promote IL-1β secretion is also strongly NLRP3-dependent . More importantly , NLRP3 is required for protective immunity against respiratory pneumococcal infection .
Experiments on mice carried out at Trinity College Dublin were conducted under Irish Department of Health guidelines with ethical approval from the TCD ethics committee . Mice experiments carried out at the University of Leicester were done under UK Home Office guidelines with ethical approval from the UK Home Office . CpG ODN 1826 was from Oligos Etc . E . coli LPS , Serotype R515 was obtained from Alexis Biochemicals while all other TLR agonists were obtained from InvivoGen . Recombinant PLY was expressed in E . coli and purified as previously described [47] . Unless otherwise stated the specific haemolytic activity of PLY was 100 , 000 HU/mg . The toxin was passed three times through an EndoTrap endotoxin removal column ( Profos AG , Germany ) after which LPS was undetectable using the PyroGene Recombinant Factor C assay ( Lonza; detection limit 0 . 01 EU/ml ) . S . pneumoniae serotype 2 , strain D39 ( NCTC 7466 ) , was obtained from the National Collection of Type Culture , London , UK . The pneumolysin-negative mutant , PLN-A , was made by insertion duplication mutagenesis as described previously [2] . Heat-killed S . pneumoniae ( HkSp ) D39 was obtained by boiling 1×108 CFU in phosphate buffered saline ( PBS ) for 20 minutes and checking for viability by colony counts and plate streaking . PLY W433F was a gift from The Netherlands Vaccine Institute . Female BALB/c , C57BL/6 , C3H/HeN and C3H/HeJ mice were obtained from Harlan Olac ( UK ) and were used at 9–16 weeks old . NLRP3−/− mice were bred in the Bioresources Unit in Trinity College Dublin . Animals were maintained according to the regulations of the EU and the UK or the Irish Department of Health as appropriate . Caspase1−/− DC were kindly provided by Dr . Katherine Fitzgerald ( UMass , USA ) . DC and BMDM were prepared by culturing murine bone marrow cells using protocols adapted from Lutz et al . [48] and Davies et al . [49] . Briefly , bone marrow cells were flushed aseptically from the femurs and tibia of mice . For culture of DC , cells were grown in RPMI 1640 medium ( Biosera ) containing 8% v/v fetal calf serum ( FCS; Biosera ) , 100 U/ml penicillin , 100 µg/ml streptomycin , and 100 mM L-glutamine ( Gibco ) and supplemented with supernatant from a granulocyte-macrophage colony-stimulating factor ( GM-CSF ) -expressing cell line ( final concentration of 20 ng/ml GM-CSF ) . Macrophages were grown in Dulbecco's Modified Eagle's Medium containing 20 ng/ml M-CSF . Cultures were maintained in a humidified atmosphere ( 5% CO2 ) at 37°C , and medium was replaced on days 3 , 6 and 8 for DC or days 2 and 4 for BMDM . On day 6 ( BMDM ) or day 10 ( DC ) of culture , cells were plated and stimulated with PLY and/or TLR agonists 24 hours later . DC ( 6 . 25×105/ml ) were cultured at 37°C for 24 h with medium , PLY or W433F alone , TLR and NLR agonists ( PAM3Csk4 , LPS , zymosan , MDP , CpG ) alone , HkSp alone or PLY or W433F and TLR agonists or HkSp together . In certain experiments , DC were incubated with YVAD-fmk ( Bachem ) , KCl ( Sigma ) , CA-074-ME ( Sigma ) or Bafilomycin A ( Sigma ) 30 mins before the addition of PLY and/or TLR agonists . At the end of the incubation , supernatants were removed and cytokine concentrations were determined by ELISA using pairs of antibodies purchased from BD Biosciences ( IL-6 and IL-12p40 ) or R&D Systems ( IL-1α , IL-1β , TNF-α , IL-12p70 , IL-10 or IL-23 ) according to the manufacturer's specifications . Alternatively , cells were recovered and used for immunofluorescence analysis or Western Blotting . The expression of DC surface markers was assessed using fluorochrome-labelled antibodies against murine CD80 , CD86 , I-A/I-E , CD11c and CD40 ( BD Biosciences ) . After blocking for 10 minutes on ice with anti-mouse CD16/CD32 ( BD Biosciences ) followed by incubation with antibodies for 30 minutes on ice , cells were washed and immunofluorescence analysis was performed on a CyAN ( Dako ) flow cytometer using FlowJo software ( Tree Star ) . For Western blot analysis , cells were lysed in Laemmli sample buffer , samples were boiled and proteins separated by SDS/PAGE . Caspase-1 p10 was detected as previously described [44] . DC ( 6 . 25×105/ml ) from C57BL/6 and NLRP3−/− mice were cultured at 37°C in RPMI 1640 medium ( Biosera ) containing 8% v/v FCS ( Biosera ) and 100 mM L-glutamine ( Gibco ) in the absence of antibiotics . Cells were stimulated with PBS or with wild-type ( D39; 10 bacteria:1 DC ) or PLY-deficient S . pneumoniae ( PLN; 10 bacteria:1 DC ) in PBS . Following 24 h incubation , supernatants were removed and analysed for cytokine production by ELISA . DC ( 1×106/ml ) were incubated overnight with medium alone or with KLH ( 10 µg/ml ) with or without PLY ( 1 µg/ml ) . After extensive washing , cells ( 1×105/mouse ) were injected subcutaneously into the footpads of mice . After 7 days , splenocytes were isolated and resuspended at 2×106 cells/ml in RPMI medium , supplemented with 8% v/v FCS . Cells were cultured in triplicate with KLH ( 2 or 50 µg/ml ) or with medium only as a negative control . Proliferative responses were assessed after 4 days of culture by [3H]-thymidine incorporation . Splenocytes were isolated from C3H/HeN or C3H/HeJ mice by passing spleens through a 70 µm cell strainer ( BD Falcon ) . After lysing erythrocytes in 0 . 88% w/v NH4Cl solution for 5 minutes and washing , cells were incubated in 96-well plates ( 1×106/ml ) in RPMI medium with 8% v/v FCS plus antibiotics . Splenocytes were stimulated for 72 hours with PLY in the presence of plate-bound anti-CD3 ( BD Biosciences ) or HkSp and in some cases for a further 24 hours with PMA ( Sigma Aldrich ) and ionomycin ( Sigma Aldrich ) . Supernatants were removed after 72 or 96 hours and tested by ELISA for IL-10 , IL-17A ( R&D Systems ) , IFN-γ and IL-5 ( BD Biosciences ) . The pneumococcal infection studies were done at the University of Leicester under UK Home Office guidelines or in Trinity College Dublin under Irish Department of Health guidelines . Wild-type S . pneumoniae serotype 2 strain D39 , NCTC 7466 ( NCTC , London , UK ) was used . An isogenic PLY-negative mutant , PLN-A , has been described previously [2] . Pneumococci were cultured on blood agar base with 5% v/v horse blood , or in brain heart infusion broth ( BHI; Oxoid , Basingstoke , UK ) with 20% v/v foetal bovine serum ( Gibco , Paisley , UK ) , supplemented with 1 µg/ml erythromycin ( Sigma , Poole , UK ) for PLN-A . Before use , pneumococci were passaged through mice , as described previously [4] and aliquots stored at −80°C . When required , the suspension was thawed at room temperature , and bacteria were harvested by centrifugation before resuspension in sterile PBS . Female outbred MF1 , BALB/c , C57BL/6 ( Harlan , Bichester , UK ) or NLRP3−/− ( bred in the Bioresources Unit of Trinity College Dublin ) mice ( 9–10 weeks old , 30–35 g ) were used for infection studies . Mice were lightly anaesthetized , as described previously [4] , and 50 µl PBS containing 1×106 CFU S . pneumoniae was administered into the nostrils . The inoculum dose was confirmed by viable count following infection . At pre-chosen time intervals following infection , mice were sacrificed and lungs removed , weighed , and homogenised with an Ultra-Turrax T8 homogeniser ( IKA , Germany ) . CFU bacterial counts were determined by viable count on blood agar plates as described previously [4] . IFN-γ and IL-17A concentrations in lung homogenates were measured by ELISA ( BD Biosciences ) . Forty-eight hours post-infection of BALB/c mice with D39 wild-type S . pneumoniae ( as described in the previous section ) , mice were sacrificed , lungs removed and cells isolated as described previously [4] . For intracellular cytokine staining , lung cells were cultured for five hours at 5×105 cells/well in round-bottomed 96-well plates in RPMI 10% FCS supplemented with GolgiPlug ( BD Biosciences ) , according to manufacturer's instructions , to block cellular secretion of cytokines . Media was further supplemented with 500 ng/ml ionomycin and 50 ng/ml PMA . Following culture , cells were washed and stained with antibodies against extracellular markers ( anti-CD3 , anti-CD4 , anti-CD8 , anti-NKp46 , anti-NK1 . 1 , anti-CD45 or anti-γδTCR; BioLegend or eBioscience ) . Cells were then incubated with Fix/Perm solution ( BD Biosciences ) for 20 minutes before washing in Perm/Wash buffer ( BD Biosciences ) . Staining with antibodies against intracellular cytokines was performed with anti-IL17A and anti-IFN-γ antibodies diluted in Perm/Wash buffer . After staining , cells were washed and resuspended in PBS 3% ( v/v ) FCS prior to data collection . Unless otherwise stated , data were compared by one-way ANOVA . The Tukey-Kramer multiple-comparison test was used to identify significant differences between individual groups . Methods for supporting data are described in Methods S1 . | Streptococcus pneumoniae ( pneumococcus ) is a pathogen of global significance , causing diseases including pneumonia , meningitis and septicaemia . In order to develop improved pneumococcal vaccines it is essential to understand how the bacterium interacts with the host immune system . Pneumococci produce a range of pathogenicity factors , among which the toxin pneumolysin plays a central role and has potential as a vaccine candidate . Here , we demonstrate that pneumolysin can directly activate innate immune cells and dramatically amplify the production of pro-inflammatory cytokines . These enhancing effects of the toxin do not require Toll-like receptor ( TLR ) 4 . In particular , the toxin exerts a potent effect on interleukin ( IL ) -1 , which is an endogenous pyrogen and powerful activator of IL-17A production . This effect results from activation of the NLRP3 inflammasome complex and NLRP3 is required for protection against the pathogen in vivo . To induce protective immunity against pneumococci , IFN-γ and IL-17A are thought to be essential . We show that pneumolysin plays a key role in promoting these cytokines both in vitro and in vivo during respiratory infection . The results add significantly to our understanding of the interactions between pneumococci and the immune system and support investigations into the inclusion of pneumolysin or its derivatives in novel pneumococcal vaccines . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"immunology/immunomodulation",
"immunology/immunity",
"to",
"infections",
"immunology/innate",
"immunity"
] | 2010 | Pneumolysin Activates the NLRP3 Inflammasome and Promotes Proinflammatory Cytokines Independently of TLR4 |
Control of onchocerciasis as a public health problem in Africa relies on annual mass ivermectin distribution . New tools are needed to achieve elimination of infection . This study determined in a small number of Onchocerca volvulus infected individuals whether moxidectin , a veterinary anthelminthic , is safe enough to administer it in a future large study to further characterize moxidectin's safety and efficacy . Effects on the parasite were also assessed . Men and women from a forest area in South-eastern Ghana without ivermectin mass distribution received a single oral dose of 2 mg ( N = 44 ) , 4 mg ( N = 45 ) or 8 mg ( N = 38 ) moxidectin or 150 µg/kg ivermectin ( N = 45 ) with 18 months follow up . All ivermectin and 97%–100% of moxidectin treated participants had Mazzotti reactions . Statistically significantly higher percentages of participants treated with 8 mg moxidectin than participants treated with ivermectin experienced pruritus ( 87% vs . 56% ) , rash ( 63% vs . 42% ) , increased pulse rate ( 61% vs . 36% ) and decreased mean arterial pressure upon 2 minutes standing still after ≥5 minutes supine relative to pre-treatment ( 61% vs . 27% ) . These reactions resolved without treatment . In the 8 mg moxidectin and ivermectin arms , the mean±SD number of microfilariae/mg skin were 22 . 9±21 . 1 and 21 . 2±16 . 4 pre-treatment and 0 . 0±0 . 0 and 1 . 1±4 . 2 at nadir reached 1 and 3 months after treatment , respectively . At 6 months , values were 0 . 0±0 . 0 and 1 . 6±4 . 5 , at 12 months 0 . 4±0 . 9 and 3 . 4±4 . 4 and at 18 months 1 . 8±3 . 3 and 4 . 0±4 . 8 , respectively , in the 8 mg moxidectin and ivermectin arm . The reduction from pre-treatment values was significantly higher after 8 mg moxidectin than after ivermectin treatment throughout follow up ( p<0 . 01 ) . The 8 mg dose of moxidectin was safe enough to initiate the large study . Provided its results confirm those from this study , availability of moxidectin to control programmes could help them achieve onchocerciasis elimination objectives . ClinicalTrails . gov NCT00300768
Onchocerciasis is caused by the filarial nematode Onchocerca volvulus and is transmitted among humans through the bites of blackfly vectors , in Africa mainly by Simulium damnosum s . l . . Around 99% of people at risk live in Sub-saharan Africa . The African Programme for Onchocerciasis Control ( APOC ) estimated 89 million Africans at risk and 37 million infected in 19 APOC countries [1] based on rapid epidemiological mapping [2] . Since its launch in 1995 , APOC and the public health systems have established annual community-directed treatment with ivermectin ( CDTI ) to eliminate onchocerciasis as a public health problem in the 17 APOC countries with areas where onchocerciasis is meso- and/or hyperendemic [3] . At the time , it was considered impossible for CDTI to interrupt transmission across Africa [4] . Thus , the UNICEF/UNDP/World Bank/World Health Organization Special Programme for Research and Training in Tropical Diseases ( WHO/TDR ) continued research for drugs or drug combinations which could eliminate onchocerciasis infection ( e . g . [5]–[7] ) . Today , prospects for elimination of infection with CDTI appear better [8]–[10] . Questions remain as to whether CDTI alone can eliminate onchocerciasis in highly endemic areas [11]–[13] . Moxidectin , a milbemycin macrocyclic lactone , is registered worldwide as an anthelmintic in cattle , sheep , swine , horses and dogs [14] . Initiation of the clinical development of moxidectin was based on ( i ) published data [15]–[18] and investigator reports to TDR from in vitro ( O . volvulus , O . lienalis , O . gutturosa ) and in vivo models ( O . cervicalis in horses , O . lienalis in mice , O . ochengi in cattle , Brugia pahangi in dogs and jirds ) of onchocerciasis and lymphatic filariasis and ( ii ) toxicology data from development as a veterinary drug [19] . The objective of the development for human use is to assess through a series of non-clinical and clinical studies whether moxidectin could be safe for mass treatment for onchocerciasis control with an efficacy which modelling studies suggest could result in permanent interruption of transmission of O . volvulus after substantially less rounds of mass-treatment than ivermectin . Data from moxidectin use for veterinary parasites [14] , in vivo models of human filarial infections and on the effects of ivermectin , an avermectin macrocyclic lactone , on O . volvulus , suggest several possible effects of moxidectin on O . volvulus . These include a microfilaricidal effect combined with an embryostatic effect after a single dose and/or an adult worm ( macrofilariae ) sterilizing effect and/or a macrofilaricidal effect upon repeated exposure . Furthermore , repeated exposure to moxidectin might reduce the life time of the macrofilariae as discussed by Geary and Mackenzie [20] for the effect of long term treatment with ivermectin . Given a half life of 20–40 days in healthy volunteers [21]–[25] ( around 20 days in the participants in this study , unpublished data , manuscript in preparation ) , a potential effect of moxidectin on the viability or development of transmitted L3 larvae could also be considered . The data from two studies in healthy volunteers [21] , [24] and all toxicology data available at the time resulted in the decision to initiate the first study in individuals infected with O . volvulus [19] , [26] . It was not known whether the putative microfilaricidal activity of moxidectin would be associated with a combination of severe Mazzotti reactions ( i . e . the complex , acute inflammatory response of the body to the effect of the drug on microfilariae ) , similar to those seen after diethylcarbamazine treatment which make diethylcarbamazine unsuitable for mass treatment [27]–[32] . Consequently , the study was designed to determine in a small number of participants whether moxidectin induces severe reactions at a frequency suggesting that development of moxidectin should be discontinued . If that was not the case , the study was designed to determine the moxidectin dose ( s ) with an adverse reaction profile suitable for further clinical testing . Further clinical testing in a large number of participants would allow to better define moxidectin's safety profile and to quantify the difference in the effect of moxidectin and ivermectin on skin microfilariae levels . The participant-safety driven design resulted in a study duration of ≥1 . 5 years . Therefore , participant follow up was expanded beyond that required for assessment of Mazzotti reactions to obtain pharmacokinetic data as well as initial data on moxidectin's effect on the parasite relative to that of ivermectin . This paper summarizes the safety data with focus on statistically significant differences to ivermectin , presents the effect on the skin microfilariae ( mf ) and reports the results of the histological examination of the macrofilariae from subcutaneous nodules excised 18 months after treatment .
This study was approved by the Ghana Food and Drugs Board , the Ghana Health Service Ethics Review Committee and the WHO Ethics Review Committee . Study conduct according to the principles laid down in the Declaration of Helsinki and in compliance with Good Clinical Practice and the protocol was monitored regularly . Participants gave informed consent to study participation and testified to this by signature or thumbprint , as specified in the protocol approved by the Ethics Committees , in the presence of an independent literate witness in their villages before initiation of any study related procedures . The severity of many Mazzotti reactions correlates with the skin mf density [31] . Therefore , each of three dose levels of moxidectin ( 2 mg , 4 mg , 8 mg , established on the basis of the pharmacokinetic data from healthy volunteers [21] , [24] , 34–136 µg/kg or 0 . 05–0 . 21 µmol/kg for 59 kg body weight ) was evaluated sequentially in three cohorts of participants with different levels of skin mf density and ocular involvement pre-treatment . In the first cohort , participants had a skin mf density <10 mf/mg skin and no ocular involvement ( subsequently referred to as ‘mildly infected’ ) . In the second cohort , participants had a skin mf density of 10 mf/mg to 20 mf/mg skin and the sum of microfilariae in both anterior chambers of the eye had to be ≤10 ( subsequently referred to as ‘moderately infected’ ) . Participants of the third cohort had skin mf density >20 mf/mg skin without or with any level of ocular involvement ( subsequently referred to as ‘severely infected’ ) ( Figure 1 ) . In each mildly and moderately infected cohort , 16 participants were planned to be enrolled and randomized in a ratio of 3∶1 to moxidectin or 150 µg/kg ivermectin ( as per ivermectin labelling for use in onchocerciasis ) . This provided 4 ivermectin treated participants as concurrent controls for the safety data in the planned 12 moxidectin treated participants in each cohort . To increase the probability of detection of adverse events in participants with high skin mf density ( who are most likely to experience Mazzotti reactions [31] ) , 32 severely infected participants were planned to be enrolled for each moxidectin dose level and randomized 3∶1 to moxidectin∶ivermectin . This provided 8 ivermectin treated participants as concurrent controls for the safety data of 24 moxidectin treated participants . Across all 9 cohorts , the planned number of participants resulted in 48 participants in each treatment group for comparison of the safety data as well as the effects on the parasite . Figure 1 shows the number of participants actually treated in each cohort and provides the screen failure reasons which resulted in these numbers being lower than the planned numbers . Mazzotti reactions usually subside within one or two weeks after treatment [30] . Since no data on Mazzotti reactions following moxidectin treatment were available , the decision to treat the next cohort within one moxidectin dose level was made based on the safety data obtained during the first month after treatment of the previous cohort . Progression to the next dose level was decided upon based on all data available to Month 1 follow up of the last cohort at the previous dose level ( Figure 2 ) . To further decrease potential risk to participants , all participants remained in the study center for 18 days after treatment . Subsequent follow up to 18 months was on an outpatient basis . Participants were recruited from onchocerciasis endemic villages between 0°30′ and 0°45′E , 6°45′ and 7°0′N within the River Tordzi basin in the Volta Region of South-eastern Ghana . The vast majority ( 90% ) of participants came from the villages Honuta-Gbogame , Kpedze-Anoe , Togorme , Aflakpe , Luvudo , Kpoeta-Ashanti and Hoe , the remainder from 11 other villages in the area ( Figure 3 ) . This area was not included in vector control activities under the Onchocerciasis Control Programme because at the time of the OCP it was forested . Simuliid species were Simulium yahense and Simulium squamosum [33] . At the time of this study , the area was not yet included in the ivermectin mass distribution programme of the National Onchocerciasis Control Programme because it is overall hypoendemic with small meso- or hyperendemic foci . The area is not endemic for lymphatic filariasis or loiasis . A total of 172 of 196 planned individuals meeting the intensity of infection criteria described above but otherwise regarded as healthy based on physical examination , electrocardiography , medical and medication history , serum biochemistry , haematology and semiquantitative urinalysis participated in the study . Volunteers with a history of or current neurological or neuropsychiatric disease or epilepsy , orthostatic hypotension at screening , hyperreactive onchodermatitis and antifilarial therapy within the previous 5 years as well as pregnant and breastfeeding women were excluded . Women of child-bearing potential who wanted to participate had to agree to contraception ( depo-medroxyprogesterone acetate or levonorgestrel implants ) during the first 150 days after treatment . The pre-treatment evaluations included those detailed in the footnote to Table 1 and height measurement . Vital signs were obtained 12 times during the pre-treatment evaluations and the mean was used to assess changes post-treatment . The 3 mg ivermectin tablets ( purchased from Merck and Co . Inc ) , 2 mg moxidectin tablets developed for human use , as well as placebo were provided by Wyeth in identical looking capsules . Inactive ingredients of the moxidectin tablets were microcrystalline cellulose , anhydrous lactose , sodium croscarmellose , sodium lauryl sulfate , colloidal silicon dioxide , and magnesium stearate . Placebo capsules contained the inactive ingredients of the moxidectin tablets . Each participant received 4 capsules provided in an envelope labelled only with subject identifying information , resulting in participants and investigative team being blinded . In each cohort , participants were stratified by sex and randomly allocated by a pharmacist in a ratio of 3∶1 to receive a single oral dose of moxidectin or ivermectin ( 150 µg/kg ) based on computer-generated randomization schedules with a block size of 4 provided by the sponsors . The pharmacist prepared for each participant an envelope which contained 4 capsules , including 1 , 2 or 4 capsules containing a 2 mg moxidectin tablet or 2 , 3 or 4 capsules containing a 3 mg ivermectin tablet and the complementary number of placebo capsules . The pharmacist gave the sealed envelopes to the investigative team and was not otherwise involved in the study . Treatment was administered on day 1 between 7:00 and around 7:40 under observation by members of the investigative team after an overnight fast and vital sign measurement . Treatment effects were evaluated daily during the first 18 days and 1 , 2 , 3 , 6 , 12 and 18 months after treatment ( Table 1 ) . Safety outcomes: Adverse events ( AEs ) , including ( i ) clinically significant changes in laboratory values from pre-treatment , ( ii ) clinically significant adverse changes from pre-treatment in systemic or ocular symptoms detected through examinations , spontaneous reporting by participants or questioning of participants , and ( iii ) changes in vital signs , whether clinically significant or not . AEs were to be classified as serious ( SAE ) if they met the criteria in the SAE definition in the ‘Guideline for Good Clinical Practice’ of the ‘International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use’ , i . e . if they resulted in death , persistent or significant disability or incapacity , a congenital anomaly or a birth defect or were life-threatening or required inpatient hospitalization or prolongation of an existing hospitalization [34] or if they were important medical events , including cancer , that could jeopardize the subject and require medical or surgical intervention to prevent one of the outcomes above . The severity of all AEs was graded ( grade 1–4 ) according to the Onchocerciasis Chemotherapy Research Center ( OCRC ) Common Toxicity Criteria ( OCRC CTC ) version 2 . 0 ( Supporting Table S1 ) , an expansion of the criteria developed at OCRC for quantitation of Mazzotti reactions [35] , [36] . Severity of AEs not included in the OCRC CTC was graded according to the National Cancer Institute Common Toxicity Criteria version 2 . 0 [37] . All AEs were categorized by the Principal Investigator ( KA ) as Mazzotti reactions , non-Mazzotti adverse drug reactions or drug-unrelated adverse events . Efficacy outcomes: Skin snips were obtained with a 2 mm Holth or Walser corneoscleral punch . One snip each was taken from the right and left iliac crest as well as the right and left calf for a total of 4 snips/participant pre-treatment , 8 days and 1 , 2 , 3 , 6 , 12 and 18 months after treatment . Each skin snip was weighed on an electronic balance to an accuracy of 0 . 1 mg . Snips were incubated individually in a well of a 96-well plate with flat bottom for at least 8 hours in 0 . 9% normal saline at approximately 22°C . Microfilariae were counted under an inverted microscope at a magnification of 60× supplemented by 100× when necessary . The skin mf density of each participant at each follow up time point was calculated as the arithmetic mean number of mf/mg across the four skin snips . The change from pre-treatment in O . volvulus skin mf densities was calculated as the difference between skin mf density at follow up and pre-treatment in absolute terms and as the percentage of pre-treatment density for each follow up time point . The proportion of participants with undetectable levels of skin mf was calculated as the proportion of participants without mf in each of the four skin snips . The palpable nodules from the participants who attended the 18 month follow up visit and agreed to their excision were excised at that time ( 35/38 in the 2 mg moxidectin group , 37/37 in the 4 mg moxidectin group , 24/37 in the 8 mg group , 30/42 in the ivermectin group ) . Nodules were processed as described previously [6] , [38] . The histological assessment was based on the examination of three 4 µm sections obtained along the longest axis of each nodule in a way that each third of a nodule was sampled . Worms were examined and classified by a single observer ( SKA ) according to the criteria described previously [38] , [39] . Each macrofilaria identified was classified by sex and as alive , moribound , dead or dead and calcified based on the presence or absence of normal or degenerate cuticle , hypodermis and muscles , of normal , degenerate and/or pigmented intestine and genital tract wall , deposits in the body cavity , nearly complete resorption of the worm , calcification of organs or of the worm , and presence of giant cells . Female macrofilariae were furthermore classified as young or not . The reproductive status of each female macrofilaria was classified based on the presence or absence of an empty genital tract , normal or degenerate oocytes , morulae , gastrulae , coiled mf , stretched mf , sperm in the uterus , polymorphous material , presence of oocytes and remant mf only , or mixed development . Gravid female macrofilariae were categorized as in full production of developmental stages , as in less than full production of developmental stages , as having resumed production of developmental stages and showing the presence of pre-microfilarial stages ( morulae and gastrulae ) or as going out of embryonic production and showing the presence of coiled mf ( pretzels ) and stretched mf . Furthermore , the number of mf in the nodule was determined . The reproductive status of male macrofilariae was categorized based on an empty genital tract and presence or absence of normal or degenerate spermatogonia , primary spermatocytes , secondary spermatocytes , spermatids , or spermatozoa [38] , [39] . The sample size for each pre-treatment intensity of infection category ( mild , moderate , severely infected ) and treatment arm was chosen to minimize the number of participants exposed to moxidectin while providing a relatively high probability of detecting frequent adverse events . The probabilities of events with given true event rates occurring with a given sample size can be calculated using the binomial distribution [40] . The planned sample size for each pre-treatment intensity of infection category combined with the 3∶1 moxidectin∶ivermectin randomization ratio provides for each moxidectin dose level a probability of >90% to detect at least 1 event with a true event rate of 20% among the 12 mildly or moderately infected participants , at least 1 event with a true event rate of 10% among the 24 severely infected participants and at least 1 event with a true event rate of 5% among the total of 48 participants with any intensity of infection exposed to a specific moxidectin dose or ivermectin . The planned 48 participants per treatment group chosen in view of the adverse event detection probabilities , provided approximately 92% power to detect a statistically significant difference between any 2 treatment groups when the percentage of participants with undetectable levels of skin mf was approximately 99 . 9% in one and 80% in the other group . This estimate is based on a 2-sided Fisher's Exact test with Type I error of 0 . 05 without adjustment for multiple comparisons which was not necessary due to the hierarchical way comparisons for efficacy were performed ( see Statistical Methods ) .
A total of 172 of 196 planned participants were included in the study and treated ( Figure 1 ) . The difference between the number of planned and treated participants is due to the fact that it was not possible to recruit within the protocol specified timelines for each cohort 16 participants who met the eligibility criteria . Screen failure reasons included not meeting criteria for intensity of infection for the cohort for which screening was conducted ( 56% ) , laboratory values outside the protocol specified range ( 26% ) , ocular disease not meeting the criteria for the cohort for which screening was conducted ( 7% ) , hypertension ( 6% ) and others ( 5% , including age outside protocol specified range , orthostatic hypotension , pregnancy , weight lower than specified in the protocol , history of neurologic/neuropsychiatric disorder/epilepsy ) . Of the 172 treated participants ( mITT population ) , 6 discontinued from the study before the 18 months follow up examination ( Figure 1 ) , including 1 who died due to a snake bite , 1 who decided to withdraw from the study and 4 who were lost to follow up , i . e . could not be located despite several attempts to find them . Table 2 shows the demographics and O . volvulus infection related pre-treatment characteristics . Testing for differences between treatment groups in age , sex , height , weight and subjects by intensity of infection showed no statistically significant differences . With the eligibility criteria precluding ocular involvement in approximately 25% of participants ( mildly infected cohorts ) and limiting it in a further approximately 25% ( moderately infected cohorts ) , ocular involvement overall was very low . Table 2 also shows the number of onchocercal and non-onchocercal nodules determined at histological assessment of all excised nodules at Month 18 . Table 3 shows the number of subjects with different categories of AEs , with Mazzotti reactions for which Fisher's exact test between at least one moxidectin treatment group and the ivermectin treatment group resulted in a p-value of <0 . 05 , and with severe symptomatic postural hypotension ( SSPH ) . The skin mf densities in the individual participants before ( 0 ) and after treatment are shown in Figure 5 . Figure 4 shows the percent reduction from pre-treatment in mean mf density after treatment in each treatment group across all participants who completed the study ( e-mITT population ) and after treatment with ivermectin and 8 mg moxidectin for the subgroup with >20 mf/mg skin pre-treatment . The decrease in skin mf density after ivermectin treatment ( for overview of decrease seen in other studies see [44] ) was also observed after treatment with 2 mg , 4 mg or 8 mg moxidectin , but was faster and more extensive ( Figure 4 , Figure 5 ) . From day 8 onward , the decrease in skin mf density from pre-treatment was significantly higher after treatment with any of the moxidectin doses than after treatment with ivermectin in both the e-mITT and the mITT populations ( <0 . 005 ) . Supporting Table S3 shows descriptive statistics and the results of the statistical analysis of the data for the e-mITT population . The faster and more extensive decrease in skin mf density was reflected in the proportion of participants with undetectable levels of skin mf ( Figure 6 ) . This proportion was significantly higher than in the ivermectin group already on day 8 in the 4 mg and 8 mg moxidectin groups ( p<0 . 02 ) and from month 1 onward in all moxidectin groups in both the e-mITT and the mITT populations ( p<0 . 01 ) . Supporting Table S4 shows the descriptive statistics and the results of the statistical analysis of the data for the e-mITT population . In three ivermectin treated participants in the e-mITT population ( skin mf density pre-treatment 29 . 7–62 . 5 mf/mg ) , as well as in one participant ( skin mf density pre-treatment 19 . 3 mf/mg ) who discontinued from the study after the 12 month follow up , the reduction in skin mf density from pre-treatment to day 8 was <60% and the skin mf density was >6% of the pre-treatment value 3 months after treatment . Thus , the reduction in skin mf density in these participants did not meet the criteria for an ‘adequate parasite response’ to ivermectin defined by Awadzi and coworkers ( ≥60% reduction in skin mf level from pre-treatment level on day 8 post-treatment , skin mf density ≤6% of pre-treatment density 3 months post-treatment ) [41] . These participants are referred to below as ‘Suboptimal Microfilariae Responders’ ( SOMR ) to be distinguished from ‘Suboptimal Responders’ reported in other studies [41] , [42] , [45] , [46] in whom the reduction in skin mf levels is as expected , but the increase in skin mf levels following the initial decrease is faster and/or more extensive than considered consistent with ‘adequate parasite response’ by Awadzi and coworkers ( skin mf density ≤40% of pre-treatment density at 12 months after treatment ) . Figure 4 B shows the percentage reduction from pre-treatment across all severely infected ivermectin treated participants who completed the study as well as after exclusion of the SOMRs . The conclusions from the statistical analysis of the change in skin mf densities between moxidectin and ivermectin treated groups did not change when the three SOMRs in the e-mITT population were excluded . A response to ivermectin not meeting the criteria for adequate response was also observed in four other ivermectin treated participants in the e-mITT population . These participants are not considered here as SOMRs because the intensity of infection pre-treatment ( 10 . 2 mf/mg , <1 mf/mg for three of them ) was below that from which the criteria were derived and at low intensity of infection the variability between mf counts in individual snips can have too large an impact on mean skin mf density to support definitive conclusions , even when the skin snip weight is taken into account . No participant treated with moxidectin showed a response fitting the criteria for SOMRs . Figure 7 A and B show for the e-mITT population and for the subgroup of severely infected participants excluding the three SOMRs , respectively , the time from treatment to recorded skin mf density nadir . Figure 7 C and D show the time of start of recorded sustained increase in skin mf density . In the ivermectin group , three ( 7 . 7% ) participants showed an increase in skin mf levels as early as 2 months after treatment ( 0 . 1–2 . 53 mf/mg ) while three other participants reached the nadir only at 3 or 6 months ( undetectable – 2 . 89 mf/mg ) . Overall , 85% of ivermectin treated participants who completed the study had undetectable levels of skin mf recorded at one point , including 76% of severely infected participants . An increase in skin mf levels was first observed among 2 mg moxidectin treated participants 3 months after treatment ( n = 1 , 0 . 14 mf/mg skin ) , among 4 mg treated participants 6 months after treatment ( n = 4 , 0 . 08–0 . 69 mf/mg skin ) and among 8 mg moxidectin treated participants 12 months after treatment ( n = 12 , 0 . 24–3 . 3 mf/mg skin , 0 . 5%–12% of pre-treatment value ) . The proportion of participants with undetectable levels of skin mf ( Figure 6 ) was statistically significantly higher among moxidectin treated than ivermectin treated participants through month 12 for the 2 mg and 4 mg doses ( p<0 . 02 ) and through month 18 for the 8 mg dose in both the mITT and the e-mITT population ( p<0 . 05 , Supporting Table S4 ) . The reduction in mean skin mf densities relative to pre-treatment densities was statistically significantly higher among moxidectin than ivermectin treated participants through month 12 for the 2 mg moxidectin dose ( p<0 . 0001 ) and through month 18 for the 4 and 8 mg moxidectin dose in both the mITT and the e-mITT population ( p<0 . 005 , Supporting Table S3 ) . The average annual reduction in skin mf density from pre-treatment was 88% ( median 94% , range 24%–99% , average 89% if the SOMRs are excluded ) after ivermectin treatment , 97% ( median 98% , range 81%–99% ) after 2 mg moxidectin , 98% ( median 99% , range 90%–99% ) after 4 mg moxidectin and 98% ( median 99% , range 96% to 99% ) after 8 mg moxidectin . A total of 245 nodules were excised 18 months after treatment . Histological assessment showed 214 ( 87 . 3% ) nodules to be onchocercal , including 46/56 ( 82 . 1% ) nodules from ivermectin treated participants , 66/76 ( 86 . 8% ) nodules from 2 mg moxidectin treated participants , 57/62 ( 91 . 9% ) nodules from 4 mg moxidectin treated participants and 45/51 ( 88 . 2% ) nodules from 8 mg moxidectin treated participants . Non-onchocercal nodules ( 1–2/subject ) included lipoma , lymphnodes and granulomas around foreign bodies . The types of non-onchocercal nodules include those found in other studies , but the frequency of non-onchocercal nodules was higher than observed in these studies [47] , [48] . In 30/166 ( 18 . 1% ) of participants who completed the study , the number of nodule sites palpated at Month 18 was lower than that palpated pre-treatment , suggesting that some of the nodules palpated pre-treatment were not onchocercal or onchocercal nodules whose resorption had been completed by Month 18 . The number of nodule sites palpated at month 18 was higher than pre-treatment by 1 in 23/163 ( 14 . 1% ) and by 2 in 4/163 ( 2 . 5% ) of participants who completed the study and agreed to nodule palpation . Investigator observations suggest that participants becoming more aware of nodule sites after the pre-treatment examination and nodule palpation for preparation of nodulectomies at month 18 follow up occurring under better lighting conditions contributed to the higher number of nodules palpated after than before treatment . An increase in the number of palpable onchocercal nodule sites due to new infections is also possible but unlikely to be significant given that some of the ‘additional’ nodule sites were already observed 1–6 months after treatment . The number of excised onchocercal nodules/participant ranged from 0 ( no palpable nodules or all palpable nodules were non-onchocercal ) to 8 . There was no trend suggesting a relationship between the age or sex of the participant and the number of excised onchocercal nodules ( Figure 8 A ) , the skin mf density pre-treatment ( Figure 8 B ) or the skin mf level when the body surface area was taken into account [49] ( data not shown ) . Figure 9A shows the pre-treatment skin mf densities by sex of the host vs . the number of onchocercal nodules for the 135 participants with a number of palpable nodule sites at month 18≤ the number of palpable nodule sites pre-treatment and who had agreed to excision of all palpable nodules , or who had no palpable nodules . High skin mf densities were observed in some participants with 0 or only 1 onchocercal nodule . This indicates that the palpable onchocercal nodules can represent only a small fraction of the nodules in the body as previously concluded by others ( e . g . [50] and references therein ) . Pre-treatment skin mf densities of some participants with 0 or 1 excised onchocercal nodule were several times higher than those in some of the participants with ≥3 excised onchocercal nodules . This suggests that the fraction of onchocercal nodules accessible for excision varies significantly between individuals . Figure 9B shows for the same set of participants as for Figure 9A that there was no correlation between the number of live or the total number of live and dead female macrofilariae and the skin mf density pre-treatment . This suggests that the macrofilariae accessible through nodulectomy represent only a fraction of those present in the body as well as considerable inter-individual variability in this fraction . Since assessment of macrofilariae was only a secondary objective , nodules and macrofilariae were assessed by only one parasitologist ( SKA ) . Given a level of agreement on the onchocercal nature of nodules and total number of female macrofilariae of 98% and 88% , respectively , between SKA and another parasitologist in a previous study [7] , it is unlikely that reading of the slides from this study by a second parasitologist would have resulted in a significantly better correlation between number of nodules or total number of female macrofilariae and skin microfilariae than shown in Figure 9 . Furthermore , a high degree of uncertainty about the true female adult parasite burden has been deduced previously from examination of the relationship between macrofilariae in palpable nodules and skin mf density in Burkina Faso and Liberia [51] . Figure 10 shows for each treatment group the skin mf density 18 months after treatment vs . the number of excised live female , live young female and live male macrofilariae . In all treatment groups , undetectable levels of skin mf as well as skin mf densities ≥5 mf/mg occurred in some participants with 0 or 1 live female or male macrofilaria as well as in some participants with ≥4 live female macrofilariae . This suggests again that the excised macrofilariae are not necessarily representative of the macrofilariae in the body . Summary statistics of the results of the histological assessment are provided in Supporting Table S5 . The percentage of female macrofilariae assessed as dead and/or dead and calcified was 50% , 36 . 5% , 32 . 2% and 27 . 7% in the ivermectin , 2 mg , 4 mg and 8 mg moxidectin treatment groups , respectively . Since a single dose of 150 µg/kg ivermectin does not kill macrofilariae ( see e . g . [52]–[54] ) , this indicates a pre-treatment imbalance in the proportions of live and dead female macrofilariae between the ivermectin treatment group and the moxidectin treatment groups . Even if the excised macrofilariae were representative of all macrofilariae in the body , this imbalance makes conclusions about the relative effect of ivermectin and moxidectin on macrofilariae viability impossible . Consequently , the only conclusion on the effect of moxidectin on the macrofilariae the histology data support is that a single dose of 2 mg , 4 mg or 8 mg had neither sterilized all excised macrofilariae to month 18 nor killed all excised macrofilariae by 18 months follow up . Thus , the histology data do not provide any indication of the biological basis of the differences in the skin mf densities seen between treatment groups .
The clinical development plan for moxidectin includes ( i ) five pharmacokinetic and safety studies in healthy volunteers [21]–[25] , ( ii ) the study reported here , ( iii ) a large ( Phase 3 ) study in 1500 O . volvulus infected individuals ≥12 year old to determine adverse reactions , including those with a true frequency too low to have been detected in the study reported here , and to assess the relative efficacy of 8 mg moxidectin and ivermectin in individuals from different areas in Africa , and ( iv ) a paediatric pharmacokinetic and safety bridging study as per discussions with the European Medicines Agency . The primary role of the study reported here was to determine in a small number of infected individuals whether moxidectin-associated Mazzotti reactions are infrequent enough or have a level of severity which allows to give moxidectin to several hundred people in the Phase 3 study . Review of the blinded data obtained to 1 month after treatment of the last cohort by the sponsors and an external advisory committee , as well as review by the external advisory committee with access to the treatment codes , resulted in the conclusion that 8 mg moxidectin is safe enough to initiate the Phase 3 study which compares 8 mg moxidectin to ivermectin . The investigators agreed based on their blinded assessment that the safety profile in the study was not different from what they had seen in previous studies with ivermectin using similarly close monitoring of participants for Mazzotti reactions . It is noteworthy that the publications of the safety data from these studies did , in contrast to Table 3 here , not include the total number of patients who had experienced at least one Mazzotti reaction ( see e . g . [5] , [55] , [56] ) . The Phase 3 study has been completed ( NCT00790998 ) . In the study reported here , there were no significant adverse drug reactions other than Mazzotti reactions , which is consistent with the data obtained in healthy volunteers [21]–[25] . The Mazzotti reactions pruritus , rash , increased pulse rate and decreased mean arterial pressure after 2 minutes standing still following ≥5 minutes supine occurred significantly more frequently among 8 mg moxidectin than ivermectin treated participants . The majority of these reactions were mild or moderate and all , including severe ones , resolved without treatment . This contributed to the decision to initiate the Phase 3 study . While Fisher's exact test did not return a p value<0 . 05 for the comparison of the frequency of severe symptomatic postural hypotension ( SSPH ) after moxidectin and ivermectin treatment , consideration is given here also to SSPH . SSPH is a transient phenomenon observed at the Onchocerciasis Chemotherapy Research Center ( OCRC ) when a person cannot tolerate standing still for 2 minutes following ≥5 minutes supine ( see OCRC Common Toxicity Criteria in Supporting Table S1 ) . SSPH disappears rapidly after lying down [43] and OCRC staff observations of participant behaviour following an SSPH episode show that it does not interfere with resumption of their normal daily activities . In OCRC studies , SSPH incidence among participants treated with 150 µg/kg ivermectin was variable and sometimes high ( e . g . 11% [57] or 22% [58] ) . In contrast , SSPH incidence reported from large scale use of ivermectin is in most cases low or 0 , even in hyperendemic areas , when higher than standard doses were used or when significant decreases in standing MAP were measured [43] , [59]–[64] . SSPH has furthermore not been an impediment to CDTI covering by now >75 million people [65] . OCRC experience shows that SSPH occurs when study participants get up ( e . g . as part of the procedure to assess postural hypotension or after a bed rest ) and immediately or shortly thereafter have to stand still ( e . g . during the OCRC procedure or while urinating ) . SSPH is not usually seen when participants get up and move around naturally . This link between SSPH and standing still could explain the discrepancies between the results in OCRC studies and reports from large scale use of ivermectin . Underreporting by the treated individuals during large scale use is another possible explanation consistent with the OCRC observations that the symptoms of SSPH ( severe dizziness , weakness , faintness ) are shortlived and do not interfer with resumption of normal activities . This could result in SSPH not being a ‘memorable’ experience reported to the investigators . Consequently , the data to date do not suggest that a higher frequency of SSPH after moxidectin vs . ivermectin treatment , if shown to be statistically significant in the Phase 3 study ( which used OCRC procedures ) , will be an impediment for evaluating moxidectin in community studies . These studies would allow to assess the potential significance of SSPH for mass treatment when appropriate advice is given to participants as in the early community studies of ivermectin [59] . Four participants treated with ivermectin did not show a decrease in skin mf levels meeting the criteria of adequate response to the microfilaricidal effect of ivermectin defined by Awadzi et al . [41] . Given that the participants were recruited from an area in Ghana without mass-treatment with ivermectin at the time of recruitment for this study , it is unlikely that this reflects drug pressure-induced selection of parasites with low susceptibility to ivermectin's microfilaricidal activity . It , thus , needs to be considered that the variability of the response of O . volvulus to the microfilaricidal activity is larger than had been observed by Awadzi et al . at the time they analysed their available data to derive criteria for adequate parasite response [41] . A larger variability of the response of O . volvulus to the embryostatic effect of ivermectin than considered ‘adequate’ in some studies [41] , [66] has been deduced from a meta-analysis of the data from a large number of clinical and field studies [67] . In this study , a single dose of 2 mg , 4 mg or 8 mg moxidectin resulted in a significantly lower skin mf density and higher proportion of participants with undetectable skin mf earlier and for a longer period of time than ivermectin . Detectable levels of skin mf 18 months after treatment in 83% , 73% and 65% of participants treated with 2 mg , 4 mg and 8 mg , respectively , show that a single dose of moxidectin did not prevent skin repopulation with mf in all participants and thus did not kill or sterilize ( permanently or to month 18 ) all macrofilariae in all participants . The histology data do not allow further conclusions about the biological basis of the long term differences in skin mf densities between the treatment groups , because of the pre-treatment imbalance between treatment groups in the percentage of dead and/or dead and calcified female macrofilaria ( Supporting Table S5 ) and because the macrofilariae in the palpable onchocercal nodules were not representative of all macrofilariae in the body ( Figures 9 , 10 ) . This is not surprising given the small sample size , chosen based on safety considerations , and not in view of allowing conclusions on treatment differences in the effect on the macrofilariae . Determining a sample size sufficient to conclude with a pre-specified power and significance level that the effect of two treatments differs by at least a pre-specified amount requires a good estimate of the effect size in the comparator arm and the variance in the efficacy parameter ( see e . g . [68] , [69] ) . For macrofilariae , the comparator effect size may vary with the endemicity of the area and treatment history of the study area and participant . The effect size variance depends on biological and methodological factors which include: ( i ) inter-individual variability in the fraction of total nodules in the body which the excised nodules represent ( Figure 9A ) , ( ii ) variability in the fraction of total worms of each category ( female , male , live , dead , … ) which the excised worms represent and which is not necessarily the same as the fraction of total nodules ( Figure 9 , Supporting Table S5 ) , ( iii ) variability between the macrofilariae within each participant in the variable evaluated ( e . g . age , reproductive activity [70] ) , ( iv ) method-specific factors such as for histology the extent to which the number of sections per nodule examined permits representative characterization of all worms in the nodule , quantitative vs . semiquantitative assessment , inter-observer variability ( see e . g . [7] , [71] ) and ( v ) variability in macrofilariae exposure and susceptibility to the drug ( e . g . macrofilariae age dependent , nodule location dependent ) . Analysis of the pooled raw data from different past studies may help quantitate some of these variabilities and provide the basis for calculating sample sizes for future studies . This may also help resolve the question of the extent to which cumulative doses of ivermectin affect macrofilariae reproductive capacity and viability . As pointed out for the reproductive capacity , this has significant implications for whether , where and how elimination of O . volvulus infection with ivermectin can be achieved [72] . The pre-treatment imbalance in this study in the percentage of female macrofilariae assessed as dead and/or dead and calcified between the ivermectin and moxidectin treatment groups ( Supporting Table S5 ) shows the importance of stratification of study participants for randomization to treatment groups by number or at least proportion of dead and live macrofilariae in the palpable nodules ( determined with a non-invasive method such as ultrasonography [73] ) to reduce the probability of false conclusions from post-treatment data . Given the absence of data on the biological basis for the low skin mf levels 12–18 months after moxidectin treatment , the relative value of moxidectin and ivermectin for reducing disease transmission will be considered without any assumptions about this basis . The transmission model developed by Duerr and coworkers [12] , [13] includes examination of the effect of a drug which reduces skin mf density irrespective of the effect on the macrofilariae . It shows that mass-treatment with a skin mf reducing drug can lead to elimination by increasing the threshold biting rate ( TBR ) , i . e . the annual biting rate ( ABR , number of vectors taking a blood meal from one person/year ) below which onchocerciasis cannot remain endemic . With increasing efficacy of the intervention , quantified as the annual average reduction ( AAR ) in skin mf density in the population , the TBR increases in a non-linear manner: in an area with a TBR without intervention of 700 bites per person per year , the model predicts a TBR of approximately 1200 , 2000 and 5000 when the AAR is around 65% , 80% and 95% , respectively . The AAR after treatment with ivermectin and 8 mg moxidectin in this study were 88% and 98% , respectively . The AARs after treatment of communities would be different because of different distribution of pre-treatment skin mf densities and treatment coverage . However , and provided the relative superiority of 8 mg moxidectin over ivermectin is confirmed in the Phase 3 study , moxidectin would have a higher AAR than ivermectin suggesting that , according to this model , moxidectin could lead to elimination in areas with higher ABRs than ivermectin . CDTI occurs at a time chosen by the community and is in many cases furthermore dependent on logistical conditions ( e . g . availability of funds for activities the public health system needs to conduct to enable CDTI ) . This results in CDTI in areas with seasonal transmission not always happening within the time window optimal for achieving elimination , i . e . a time that results in the lowest skin mf levels in the population when the vector population is largest . The longer period of undetectable skin mf levels after moxidectin compared to ivermectin would reduce the negative impact of community treatment occurring outside the optimal time window on transmission . | Around 100 million Africans live in onchocerciasis endemic areas . Control of onchocerciasis as a public health problem and possibly even elimination of onchocerciasis infection relies on annual community-directed mass treatment with ivermectin . Given concerns about possible emergence of ivermectin resistance of the parasite Onchocerca volvulus and elimination of infection in areas where very high numbers of vectors can result in continued parasite transmission even when only few parasites are present in only a few people , research for drugs with higher effect on the parasite remains important . A series of non-clinical and clinical studies was planned to find out whether moxidectin , a veterinary anthelminthic , is sufficiently safe for mass treatment and has a better effect on the parasite than ivermectin . We report here results from the first study in infected people conducted to assess in small numbers of individuals the adverse reactions to the killing of parasites by moxidectin . A single dose of 8 mg moxidectin reduced skin parasite numbers better and for a longer time than ivermectin . The frequency and severity of adverse reactions was so low that a larger study to better characterize the adverse reactions to moxidectin and compare its efficacy with that of ivermectin was initiated . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"research",
"design",
"infectious",
"diseases",
"helminth",
"infections",
"medicine",
"and",
"health",
"sciences",
"clinical",
"medicine",
"clinical",
"research",
"design",
"phase",
"i",
"clinical",
"investigation",
"clinical",
"trials",
"phase",
"ii",
"clinical",
"in... | 2014 | A Randomized, Single-Ascending-Dose, Ivermectin-Controlled, Double-Blind Study of Moxidectin in Onchocerca volvulus Infection |
Identifying the genetic basis for mitochondrial diseases is technically challenging given the size of the mitochondrial proteome and the heterogeneity of disease presentations . Using next-generation exome sequencing , we identified in a patient with severe combined mitochondrial respiratory chain defects and corresponding perturbation in mitochondrial protein synthesis , a homozygous p . Arg323Gln mutation in TRIT1 . This gene encodes human tRNA isopentenyltransferase , which is responsible for i6A37 modification of the anticodon loops of a small subset of cytosolic and mitochondrial tRNAs . Deficiency of i6A37 was previously shown in yeast to decrease translational efficiency and fidelity in a codon-specific manner . Modelling of the p . Arg323Gln mutation on the co-crystal structure of the homologous yeast isopentenyltransferase bound to a substrate tRNA , indicates that it is one of a series of adjacent basic side chains that interact with the tRNA backbone of the anticodon stem , somewhat removed from the catalytic center . We show that patient cells bearing the p . Arg323Gln TRIT1 mutation are severely deficient in i6A37 in both cytosolic and mitochondrial tRNAs . Complete complementation of the i6A37 deficiency of both cytosolic and mitochondrial tRNAs was achieved by transduction of patient fibroblasts with wild-type TRIT1 . Moreover , we show that a previously-reported pathogenic m . 7480A>G mt-tRNASer ( UCN ) mutation in the anticodon loop sequence A36A37A38 recognised by TRIT1 causes a loss of i6A37 modification . These data demonstrate that deficiencies of i6A37 tRNA modification should be considered a potential mechanism of human disease caused by both nuclear gene and mitochondrial DNA mutations while providing insight into the structure and function of TRIT1 in the modification of cytosolic and mitochondrial tRNAs .
Mitochondrial diseases are characterised by biochemical defects in oxidative phosphorylation ( OXPHOS ) enzyme activity and arise as a consequence of nuclear- or mitochondrial-encoded gene mutations [1] . Generalised disorders of mitochondrial protein synthesis resulting in OXPHOS defects are increasingly reported as causing a clinically heterogeneous group of neonatal and infantile mitochondrial disease presentations associated with isolated or multi-organ involvement [2] . The advent of whole exome capture and sequencing technologies has revolutionised the molecular diagnosis of this patient group [3]; the molecular disease mechanism can implicate nuclear gene products involved in mitochondrial DNA ( mtDNA ) replication , synthesis and repair [4] , mitochondrial aminoacyl tRNA synthetases [5] , mitochondrial translation elongation and release factors [6] , structural ribosomal proteins and assembly factors , and enzymes involved in mt-RNA modification [7] . Post-transcriptional modification of tRNAs is crucial for folding , stability and function in deciphering the genetic code during translation . Modifications of cytosolic-tRNAs ( cy-tRNA ) and mt-tRNAs occur notably at nucleotide positions 34 and 37 in the anticodon loop ( ACL ) , serving to promote translational fidelity and efficiency by optimising codon-anticodon fit within the ribosome [8] . The homologous tRNA isopentenyltransferases ( IPTases ) are conserved from bacteria to humans and introduce an evolutionarily ancient modification , an isopentenyl group onto N6 of adenine at position 37 ( i6A37 ) . In bacteria , i6A37 is further modified by methylthiolation to ms2i6A37 which uses its methyl-sulphur group to stabilise the intrinsically weak A–U pairing between anticodon A36 and the first base of UNN codons [9] . However , the methylthiolation enzymes are not present in eukaryotes , leaving i6A37 without further modification . To date , functional analysis in eukaryotes comes from studies in yeast which have shown that i6A37 promotes translational efficiency and fidelity in a codon-specific manner cognate with the i6A37-tRNAs [10] . The presence of i6A37 increases the specific activity of a tRNA for its codon about four-fold in S . pombe [10] . Prior work on orthologues including MiaA ( E . coli ) , Tit1 ( S . pombe ) , Mod5 ( S . cerevisiae ) , and TRIT1 ( human ) has revealed specificity for subsets of cy- and mt-tRNAs that bear the single-stranded anticodon loop recognition sequence , ‘A36-A37-A38’ , although this motif alone is not always sufficient ( [11] and refs therein ) . However , it has also become clear that due to sequence variability in the tRNAs and the specificities of the transferases , different species contain different subsets of i6A37-modified tRNAs [10] , [12] . Determining the subsets of specific mRNAs that are sensitive to i6A37 deficiency and how this contributes to phenotype is a contemporary challenge [10] . We used whole exome sequencing to identify a homozygous p . Arg323Gln mutation in the TRIT1 gene that segregates within a consanguineous UK-Pakistani family in which affected children present with encephalopathy and myoclonic epilepsy due to multiple OXPHOS deficiencies in skeletal muscle . We confirm that this mutation is responsible for a severe deficiency in the i6A37 content of cy- and mt-tRNAs , as it can be reversed by rescue with wild type TRIT1 in the patient's fibroblasts . We show that TRIT1 is targeted to mitochondria and provide evidence in both humans and yeast that this gene is required for efficient mitochondrial function . Furthermore , we have demonstrated that a previously-reported pathogenic A38G mutation of mt-tRNASer ( UCN ) , causes i6A37 deficiency , strengthening the conclusion that TRIT1-related human disease can arise from mutation of either the enzyme or its tRNA substrate .
We investigated a family with clinical indications of mitochondrial disease in two affected children . A skeletal muscle biopsy ( subject II-3 , detailed clinical report in Text S1 ) showed normal morphology and a mosaic pattern of cytochrome c oxidase ( COX ) deficiency ( Figure 1A ) , which can be associated with mutations in nuclear genes involved with mtDNA translation and maintenance or mtDNA mutations . We also observed biochemical evidence of a mitochondrial respiratory chain deficiency involving complexes I ( 10% of controls ) and IV ( 60% of controls ) , with apparent sparing of complex II and III activity ( Figure 1B ) . Together these data confirmed the presence of a combined OXPHOS deficiency . Micro-scale oxygraphy analysis provided evidence of mitochondrial respiratory dysfunction in patient fibroblasts ( Figure 1C and D ) . Basal oxygen consumption rate ( OCR ) was significantly decreased ( P = 0 . 0451 ) in the patient compared to controls , as was maximal OCR ( P = 0 . 0078 ) . The spare respiratory capacity ( SRC ) ( Figure 1C ) was significantly reduced ( P = 0 . 0102 ) in patient cells whilst the coupling efficiency of ATP synthesis to respiration , a measure of proton leak , was unchanged ( Figure 1D ) . In vitro metabolic labelling of mitochondrial translation identified a generalised decrease in the synthesis of mtDNA-encoded proteins with particularly notable loss of ND1 and ND5 of Complex I , CYTB of Complex III and COXI , COXII and COXIII of Complex IV ( Figure 1E ) . This was supported by immunoblotting , which revealed decreased steady-state levels of mtDNA-encoded OXPHOS components in the patient fibroblasts ( Figure 1F ) and a moderate decrease in SDHA protein levels which was surprising given that complex II activity in skeletal muscle was normal ( Figure 1F ) . Levels of TOMM20 were unchanged in patient cells confirming a specific defect of OXPHOS protein synthesis rather than general loss of mitochondrial proteins . Having excluded mtDNA rearrangements , copy number abnormalities and point mutations ( Table S1 ) , we employed whole exome sequencing of both affected siblings to elucidate a potential genetic basis of the defect . This analysis identified 3970 novel homozygous protein altering variants shared between siblings ( Table S2 ) , of which 40 were rare ( Minor Allele Frequency <0 . 01 ) . Based on predicted mitochondrial localisation and an autosomal recessive inheritance pattern , variant filtering identified a single candidate homozygous missense mutation shared by both affected siblings in TRIT1 ( c . 968G>A predicting p . Arg323Gln ) . This mutation was predicted to be pathogenic by PolyPhen-2 ( http://genetics . bwh . harvard . edu/pph2/ ) with a score of 0 . 999 . Targeted resequencing of the proband and familial relatives confirmed the homozygous mutation in the affected siblings and demonstrated disease segregation as both parents and an unaffected sibling were heterozygous carriers ( Figure 2A ) . Importantly , the TRIT1 c . 968G>A variant was not observed by the 1000 Genomes Project , the NHLBI Exome Sequencing Project nor a panel of 120 ethnically-matched control chromosomes ( data not shown ) . The p . Arg323Gln mutation occurs in exon 8 of the TRIT1 gene , which also has a putative mitochondrial targeting sequence in exon 1 and a matrin-type zinc finger domain contributed by exons 10 and 11 ( Figure 2B ) . Mitochondrial targeting of TRIT1 is supported by prediction using the freely available online tool , MitoProt II ( http://ihg . gsf . de/ihg/mitoprot . html ) [13] , with a confidence of 94% . Both cytosolic and mitochondrial localization is predicted by other available databases , as is also the case for its homologs Mod5 , Tit1 , and GRO-1 . Indeed , immunoblotting of whole cell extracts and isolated mitochondrial subfractions confirmed that TRIT1 was present in the cytosolic fraction ( Figure 2C , lane 2 ) and also detectable in proteinase K-treated mitoplasts ( lane 6 ) , consistent with the enzyme being present in the mitochondrial matrix , where tRNA molecules and the translation apparatus are active during protein synthesis . However , at this stage we cannot exclude the possibility that a significant fraction of the cytosolic portion of TRIT1 may be associated with the outer mitochondrial membrane . The corresponding position of the affected amino acid , p . Arg323 , is occupied by a basic side chain in all homologues from a range of species ( Figure 2D ) , whereas glutamine at this position in the proband is polar but uncharged . Based on the available Mod5-tRNA co-crystal structure [14] ( Figure 2E–G ) , the position and chemical nature of the mutation was not expected to affect mitochondrial localization , general solubility or gross structural alterations of the enzyme . In Mod5 this position is occupied by p . Lys294 whose basic side chain extends from an α-helix that lies adjacent to , but pointing away from , the catalytic site containing A37 [14] . Both this and adjacent conserved basic side chains comprise part of a series of residues on the same side of the α-helix , including Arg298 ( also arginine in TRIT1 ) which are involved in binding the phosphate groups of nucleotides 27–29 of the AC stem ( Figure 2E–G ) [14] . Based on this we suspected that the mutation would not impair catalytic activity per se but might impair proper binding of the enzyme to its tRNA substrates . However , since this mutation would appear to affect only one of a series of contacts with the surface of the tRNA backbone , it was not necessarily expected to cause a severe deficiency of isopentenyltransferase activity . Purified recombinant TRIT1 was previously used to examine i6A modification activity in vitro using an established assay employing synthetic RNA that matches the anticodon stem loop ( ASL ) of a substrate tRNA [11] . By this assay , the isopentenyl group of DMAPP ( dimethylallyl pyrophosphate ) is transferred to N6 of A37 in substrate tRNAs by the IPTase TRIT1 ( [11] and references therein ) . His-tagged TRIT1-WT and His-tagged TRIT1-Arg323Gln were purified from E . coli in parallel and compared by gel electrophoresis ( Figure 3A ) . The modification activity of mutant TRIT1 was negligible relative to that of wild-type TRIT1 using the standard assay ( Figure 3B ) . We reasoned that if the mutation led to decreased affinity for its substrate , as suggested by the co-crystal structure of Mod5-tRNA , we might be able to obtain activity by increasing substrate concentration . Some activity of mutant TRIT1 could indeed be observed by increasing the concentration of the RNA substrate 4-fold , but even under these conditions it was much less active than the wild-type TRIT1 ( Figure 3C ) . Although increasing the concentrations of enzyme and substrate further was technically-limited in these reactions , the data suggest that higher activity might be achieved with higher concentrations . A previously characterized S . pombe strain with a deletion of the tit1+ gene ( a homologue of TRIT1 ) , yNB5 , exhibits two distinct phenotypes [10] , [11] that were examined for their sensitivity to wild-type TRIT1 and the p . Arg323Gln TRIT1 mutant . The first phenotype is manifested by a red-white colony colour assay , that monitors tRNASer ( UCA ) -mediated suppression of a UGA nonsense mutation in ade6-704 . This in vivo assay reports on the codon-specific translational activity of the suppressor-tRNASer ( UCA ) to decode the ade6-704 UGA codon , which was previously shown to be highly dependent on i6A37 [10] , [11] . In this assay , absence of i6A37 decreases the translational activity of the suppressor-tRNA and the cells accumulate red pigment [11] . The yNB5 strain ( tit1-Δ ) transformed with the empty vector is red as expected , whilst the yYH1 strain ( tit1+ ) is white . yNB5 transformed with either wild-type TRIT1 or wild-type tit1+ are white , indicating successful complementation , whilst yNB5 transformed with mutant TRIT1 is red ( Figure 3D ) . The second phenotype of the yNB5 strain is slow growth in glycerol , which is a manifestation of mitochondrial respiratory dysfunction . This growth defect could be rescued by tit1+ but not by a catalytically debilitated mutant-tit1 carrying a point mutation [10] . The positive control , yYH1 ( tit1+ ) , grows well when transformed with an empty vector , but yNB5 transformed with an empty vector grows relatively poorly on glycerol . Transformation with wild-type tit1+ or wild-type TRIT1 rescued the growth defect of yNB5 . However , whilst growth on glycerol after transformation with the mutant TRIT1 was slightly better than with the empty vector , rescue was less complete compared to wild-type TRIT1 ( Figure 3E ) . This partial rescue may reflect a low level of i6A37 modification activity by the mutant TRIT1 enzyme . We also generated a strain of S . cerevisiae with knock-out MOD5 ( a homologue of TRIT1 ) which was transformed with either wild-type MOD5 , an empty vector , mod5K294R ( humanised MOD5 , which carries the Lys294Arg mutation ) or mod5K294Q ( mutant MOD5 , which carries the Lys294Gln mutation ) . As observed in S . pombe , mod5-Δ yeast transformed with mutant MOD5 showed a reduced growth rate in a oxidative carbon source such as ethanol compared to mod5-Δ yeast transformed with either wild-type MOD5 or humanised MOD5 ( Figure S1A ) . Oxidative growth defects were due to reduced respiratory activity since mod5-Δ strain transformed with an empty vector showed a significantly decreased respiration rate ( P = 0 . 0002 ) in comparison to yeast transformed with wild-type MOD5 , whilst mutant MOD5 failed to rescue the respiration rate of the transformed yeast as efficiently as wild-type or humanised MOD5 ( Figure S1B ) . Immunoblotting of TRIT1 in fibroblasts from the proband demonstrated no significant loss of protein levels in comparison to control fibroblasts , using β-actin as a loading control ( Figure 4A ) . A second , smaller band that was barely detectable in the control cell extract but more abundant in the patient cell extract was not always reproducible , likely reflecting nonspecific protein degradation . Notably , our evidence indicates that mitochondrial TRIT1 is the same molecular weight as the major band observed in the extracts ( see Figure 2C ) . We next examined the in vivo levels of mitochondrial and cytosolic tRNA-i6A37 in patient and control fibroblasts using the Positive Hybridisation in the Absence of i6A ( PHA6 ) assay [11] . As described previously , in the PHA6 assay , strong binding of the ACL probe occurs only in the absence of i6A37 , whilst body probes ( BP ) efficiently bind the tRNA whether or not i6A37 is present and were used to indicate relative levels of the tRNAs [11] . Equal loading of the RNA was confirmed by ethidium bromide imaging of the gel ( Figure 4B , upper panel ) and by hybridization with appropriate BPs . Both cy-tRNASer ( UGA ) and mt-tRNASer ( UCN ) had considerably decreased i6A37 modification in patient fibroblasts compared to control . In contrast , there was no difference in the ACL probing of mt-tRNACys , which does have a A36A37A38 target site for TRIT1 but is not modified , consistent with previous results [12] . The cy-tRNASer ( UGA ) appears to be fully modified in control fibroblasts ( undetectable with ACL probe ) consistent with prior results using HeLa cells [12] but largely unmodified in patient fibroblasts ( Figure 4B ) . The mt-tRNASer ( UCN ) shows a significant , albeit decreased difference in the ACL signal between control and patient compare to cy-tRNASer ( UGA ) . This is due in part to a significant fraction of unmodified mt-tRNASer ( UCN ) in the control cells , again consistent with prior results using HeLa cells [12] . This suggests that wild-type TRIT1 is only partially active on mt-tRNASer ( UCN ) in control fibroblasts . The more similar mt-tRNASer ( UCN ) ACL signals in control and patient fibroblasts is also due in part to a significantly lower amount of the overall level of mt-tRNASer ( UCN ) in the patient , as revealed by the mt-tRNASer ( UCN ) BP . We note that whilst cy-tRNASer ( UGA ) showed similar steady-state levels in control and patient cells , the mt-tRNASer ( UCN ) showed a 40% decrease in steady-state level ( Figure 4B , quantification not shown but see Figure 5E ) , calculated using the BPs of mt-tRNASer ( UCN ) and mt-tRNACys as internal calibration standards [10] . Attempts to rescue the i6A37 hypomodification observed in patient fibroblasts were met with significant technical challenges that produced variable levels of transient transfection efficiency and ectopic TRIT1 and therefore only partial rescue of the i6A37 hypomodification ( data not shown ) . We therefore used a retrovirus vector-based transduction approach to optimize the percentage of cells expressing ectopic TRIT1 . Transduction of control and patient fibroblasts resulted in very high levels of overexpression as compared to the empty vector ( Figure 5A ) , allowing us to examine the i6A37 content of tRNAs from patient cells ( Figure 5B ) . Importantly , wild-type TRIT1 completely reversed the cy-tRNASer ( UGA ) hypomodification defect in the patient cells , whereas the empty vector did not , providing strong evidence that the native endogenous mutant TRIT1 protein is responsible for the hypomodification ( Figure 5B , compare lanes 7 & 8 with 9 & 10 ) . Given the very high overexpression of TRIT1 in these cells ( Figure 5A ) , it was not surprising based on our mutation structure analysis , in vitro modification results and partial rescue of slow growth in glycerol by the mutant TRIT1 protein , that mutant TRIT1 was no less efficacious than wild-type TRIT1 in rescuing the i6A37 hypomodification of cy-tRNASer ( UGA ) in patient cells ( Figure 5B ) . In notable contrast to the rescue of cy-tRNASer ( UGA ) hypomodification , the hypomodification of mt-tRNASer ( UCN ) was rescued more efficiently by wild-type TRIT1 than mutant TRIT1 ( Figure 5B ) . Moreover , restoration of i6A37 to mt-tRNASer ( UCN ) was specifically associated with an overall increase in the steady state levels of this tRNA as reflected by the mt-tRNASer ( UCN ) BP ( Figure 5B; compare lanes 7 and 8 with 9 and 10 ) . Quantification of independent triplicate sample sets revealed that whilst wild-type TRIT1 could effectively recover the i6A37 modification level of mt-tRNASer ( UCN ) to that observed in the control fibroblasts , ∼60% ( P = 0 . 6286 ) , mutant TRIT1 was significantly less efficient , at ∼20% ( P = 0 . 0146 ) ( Figure 5C ) . Using U5 snRNA as a loading control and mt-tRNACys and mt-tRNALeu ( UUR ) as non-substrate mitochondrial controls , the steady-state levels of both cy-tRNASer ( UGA ) ( Figure 5D ) and mt-tRNASer ( UCN ) ( Figure 5E ) ( calculated using BPs ) in transduced fibroblasts were determined . Curiously , cy-tRNASer ( UGA ) levels relative to U5 RNA were reproducibly found to be significantly higher in patient fibroblasts as compared to the control cells , regardless of whether the transducing vector encoded TRIT1 or not ( Figure 5D ) . However , mt-tRNASer ( UCN ) levels , which were relatively lower in patient fibroblasts , were more efficiently rescued by wild-type TRIT1 ( elevated relative to U5: P = 0 . 0162 ) than by mutant TRIT1 ( unchanged relative to U5: P = 0 . 2038 ) or the empty vector ( decreased relative to U5: P = 0 . 0055 ) when compared to control fibroblasts transduced with empty vector ( Figure 5E , black bars ) . This quantitative trend was more significant when calibrating the mt-tRNASer ( UCN ) levels relative to the non-substrate control , mt-tRNACys in the patient cells ( Figure 5E , grey bars ) . We tried various approaches to rescue the biochemical , respiratory and molecular phenotypes in the patient fibroblasts . However due to limitations associated with the manipulation and transfection of patient and control fibroblasts , we were unable to do so with either wild-type or mutant TRIT1 despite multiple attempts ( not shown ) . It appears that the cells had become less dependent on and/or less expressive of respiratory function with passage and handling . As noted in the Introduction , the tRNA substrates of all characterized isopentenyltransferases have an enzyme recognition sequence of A36A37A38 in their anticodon loops [11] . Thus we decided to further investigate a previously reported patient with mitochondrial myopathy [15] due to a pathogenic ( m . 7480A>G ) mutation at position 38 in mt-tRNASer ( UCN ) , a substrate of TRIT1 ( Figure 6A ) . The PHA6 assay using a double ACL probe that matches both the wild type and mutant mt-tRNASer ( UCN ) ( see Methods ) performed on total RNA extracted from homogenised patient skeletal muscle showed reduced i6A37 modification of mt-tRNASer ( UCN ) , to ∼14% of control levels ( Figure 6B , quantification not shown ) . Furthermore , the steady-state level of mt-tRNASer ( UCN ) was also decreased by ∼30% in patient skeletal muscle compared to control ( quantification not shown ) . Interestingly , when comparing control skeletal muscle to the previously described control fibroblasts ( Figure 4B ) , it appears that skeletal muscle harbours relatively more mt-tRNASer ( UCN ) lacking the i6A modification . The in vitro modification assay was also performed on several synthetic tRNA ASLs , substantiating our finding of a deficiency in modification activity ( Figure 6C ) . Isopentenyl modification of wild-type mt-tRNASer ( UCN ) ( lane 3 ) in vitro is comparable to that observed for the positive controls , cy-tRNASec ( UCA ) ( Lane 1 ) and cy-tRNASer ( UGA ) ( Lane 2 ) . However , the m . 7480A>G mutation significantly abolishes in vitro activity of TRIT1 on mt-tRNASer ( UCN ) ( lane 4 ) to the level observed in the non-substrates , mt-tRNACys ( Lane 5 ) and mt-tRNALeu ( UUR ) ( Lane 6 ) . Hybridization of ASL probes to synthetic substrates modified in vitro by DMAPP was observed by the PHA6 assay to be substantially decreased compared to hybridization of the same probes to unmodified synthetic substrates ( Figure 6D ) . Hybridization of ASL probes to the non-DMAPP substrate mt-tRNACys was unaffected by treatment with TRIT1 . Notably , hybridisation of ASL probes to mutant mt-tRNASer ( UCN ) ( m . 7480A>G ) was unchanged by the DMAPP modification reaction , confirming the loss of i6A37 modification in the mutant tRNA . These data validate the PHA6 assay for detection of changes in the i6A modification status of substrate mitochondrial and cytosolic tRNAs .
Here we describe the investigation of a consanguineous kindred in which affected children presented with encephalopathy and myoclonic epilepsy associated with a disorder of mitochondrial translation . Analysis of whole exome sequencing data indicated that this was due to a recessively-inherited p . Arg323Gln mutation in TRIT1 , the gene encoding the tRNA isopentenyltransferase ( IPTase ) responsible for i6A modification at position 37 in the anticodon loop of a subset of tRNAs [16] , including mt-tRNASer ( UCN ) , consistent with a previous report on i6A in bovine mt-tRNASer ( UCN ) [17] . In addition to the TRIT1 mutation-associated disorder of mitochondrial dysfunction reported here , we also demonstrated that a m . 7480A>G point mutation of mt-tRNASer ( UCN ) , previously reported as a cause of progressive mitochondrial myopathy [15] , results in i6A37 hypomodification . In this case , the point mutation was in the TRIT1 sequence-specific recognition site , A36A37A38 , of mt-tRNASer ( UCN ) . The convergence of two different mechanisms , one due to a mutation in the TRIT1 enzyme and the other to a mutation in its substrate mt-tRNASer ( UCN ) , that both cause i6A37 hypomodification of mt-tRNASer ( UCN ) and mitochondrial myopathy , provide strong genetic evidence of the critical importance of i6A37 in mitochondrial translation . TRIT1 should therefore be added to the increasing list of genes encoding mitochondrial tRNA-modifying enzymes , including MTU1 [18] , PUS1 [19] , MTO1 [20] , MTFMT [21] and the various mitochondrial aminoacyl-tRNA synthetases [5] , that have been associated with human disease . It is also worth noting that TRIT1 has been reported as a tumor suppressor and certain rare variant alleles are associated with poor survival from lung cancer in some ethnic groups [22] , [23]; other mitochondrial-disease associated genes such as GRIM19 have also been implicated as a tumor suppressor [24] . However , it is not clear whether this is due to a relationship between cellular respiration and the mitochondrial function of TRIT1 in the lungs and/or the enzyme's cytosolic role . In addition , recent work has demonstrated an effect of Mod5 in tRNA-gene mediated gene silencing of RNA polymerase II promoters , suggesting a role for eukaryotic IPTases beyond their tRNA modification activity [25] . IPTases are conserved in sequence , structure and catalytic mechanism from bacteria to humans , particularly in the sequence surrounding the TRIT1 p . Arg323Gln mutation site . Indeed , in all of the IPTase sequences examined including E . coli MiaA , the targeted residue is either Arg or Lys . The mod5-i6A37-tRNA crystal structure shows that this residue comprises one of several basic side chains that contact the acidic backbone of the tRNA anticodon stem suggesting that the semi-conservative Arg to Gln mutation might compromise but not ablate enzyme activity . We therefore expected that any effect of the p . Arg323Gln mutation on TRIT1 activity would not be extreme and that the phenotype would be associated with a moderate decrease in tRNA i6A37 modification . However , to our surprise the mutation severely impaired the activity of the enzyme in vitro and caused a dramatic loss of i6A37 from both cy-tRNASer ( UGA ) and mt-tRNASer ( UCN ) in patient fibroblasts . It is important to note that our observation of i6A37 modification of cy-tRNASer ( UGA ) contrasts with a previous study that could not detect this modification in human cy-tRNASer ( UGA ) expressed in monkey-derived CV-1 cells [26] . In all eukaryotes examined , the IPTases modify both cytosolic and mitochondrial tRNAs and mitochondrial localization is a significant part of the biology . S . cerevisiae employs an intricate system for maintaining proper distribution of Mod5 to the mitochondria , nucleus and cytoplasm [27]–[29] . A prominent phenotype of S . pombe tit1-Δ mutants is slow growth on glycerol , a manifestation of mitochondrial respiratory dysfunction [10] . In C . elegans , the extended life span phenotype as well as deregulated development and other phenotypes of gro-1 mutants can be rescued by the mitochondrial isoform of the GRO-1 IPTase but not the nuclear and cytoplasmic isoform [30] . It is therefore noteworthy that while both cytosolic and mitochondrial tRNAs are lacking i6A37 in patient cells , the manifestations of disease clearly localize to a mitochondrial cause . Therefore , it may be important that in addition to hypomodification of mt-tRNASer ( UCN ) , the overall levels of the mt-tRNASer ( UCN ) were significantly lower in patient fibroblasts . This suggests that in addition to the ∼4-fold loss of tRNA specific activity due to lack of i6A37 [10] , mitochondrial translation would be even further compromised by a decrease in the absolute level of mt-tRNASer ( UCN ) . This may contribute to a molecular basis for the apparent sensitization of the mitochondrial-associated phenotype in the patients described here . Another informative finding was that the TRIT1-mutant could modify its substrate tRNAs with i6A37 when greatly over-expressed in the transduced patient fibroblasts . This was not completely unexpected on the basis of two experimental observations . As noted above , structure modelling suggested that the mutation would affect substrate binding but not i6A37 catalytic activity . Indeed , we observed increased transferase activity with increased substrate concentration , at least within the technical limits of the assay ( Figures 3B and 3C ) . Second , the TRIT1 p . Arg323Gln mutant manifested partial activity to complement the slow growth in glycerol phenotype in the S . pombe tit1-Δ strain ( Figure 3E ) . Furthermore , previous studies have shown that this phenotype remains uncomplemented by a prior characterized tit1-T12A catalytic mutant that is inactive for i6A37 modification of tRNA [10] , [11] . Thus , the partial complementation of this phenotype ( Figure 3E ) suggests that the TRIT1 mutant retains some i6A37 activity , consistent with high activity of the nmt1+ promoter in the multi-copy expression vector . Recent studies have concluded that although some tRNAs in human cells contain the A36A37A38 TRIT1 recognition motif they accumulate in the i6A37-unmodified form [12] . Somewhat similarly , the A36A37A38-containing tRNATrp ( CCA ) in S . cerevisiae remains unmodified [11] . This further suggests that the subset of i6A37-containing tRNAs may change under different conditions , due to varying concentrations of the enzyme or substrate , a situation that may occur during development and/or other physiological conditions .
Written informed consent was obtained from the family in accordance with the Declaration of Helsinki and the study was approved by the Newcastle and North Tyneside 1 Ethics Committee . Standard histological and histochemical analyses , including cytochrome c oxidase ( COX ) , of a skeletal muscle biopsy were performed according to established protocols [31] , on fresh-frozen skeletal muscle sections ( 10 µm ) . Mitochondrial respiratory chain complex activities were determined in skeletal muscle homogenates as previously described , and expressed relative to the activity of the matrix marker enzyme , citrate synthase [32] . Total DNA was extracted by standard procedures from all available tissues obtained with consent from familial relatives , and mtDNA rearrangements were excluded by long-range PCR . Direct sequencing of the entire mitochondrial genome was performed on homogenate skeletal muscle DNA . Genomic DNA from the two affected siblings ( II–1 and II–3 ) was isolated from blood ( DNeasy , Qiagen , Valencia , CA ) ; fragmented to 150–200 bp with the use of Adaptive Focused Acoustics ( Covaris ) ; end-repaired , adenylated , and ligated to adapters ( Illumina Paired-End Sample Preparation Kit ) . Ligated libraries were hybridized with whole-exome baits that covered 27 , 184 genes ( Agilent SureSelect Human All Exon Kit Version 2 ) with modifications for the SureSelect Human All Exon Kit Illumina Paired-end Sequencing Library ( Version 2 . 0 . 1 ) . Captured fragments were purified , clonally amplified and sequenced on 2 lanes of an Illumina Genome Analyser IIx using 75 bp paired-end reads . The sequence was aligned to the human reference genome ( UCSC hg19 ) with Burrows Wheeler Aligner ( BWA ) [33] , then reformatted with the use of SAMtools v0 . 1 . 18 [34] . 83 . 1% of exon target sequence was covered by >10 reads . Single base variants were identified with Varscan v2 . 2 [35] and Indels were identified with Dindel v1 . 01 [36] . Variants were annotated using wANNOVAR [37] . Lists of on-target variants were filtered against data from the National Heart , Lung and Blood Institute ( NHLBI , NIH , Bethesda , MD ) Exome Sequencing Project ( ESP ) 6500 exomes , the 1000 Genomes project , and the exome sequences of 315 unrelated in-house control exomes to identify rare homozygous variants with a Minor allele frequency ( MAF ) <0 . 01 . Variant filtering led to a final list of 40 rare , homozygous , protein-altering variants of which 4 were mitochondrial according to the Gene-Ontology database . These genes included TRIT1 , CCDC19 , ARSB and SYNJ2 of which TRIT1 segregated with disease in the family . Targeted resequencing and familial segregation studies were performed by cycle sequencing using an ABI 3130xl ( Applied Biosystems ) system and BigDye Terminator v3 . 1 technology . The following primer pairs , including universal tags , were employed: forward primer , 5′-TGTAAAACGACGGCCAGTAGGGAAAATGCACACTGGAG-3′ , and reverse primer , 5′-CAGGAAACAGCTATGACCTTCCCTTAGGTCAGATCCAAAA-3′ . Analysis of the evolutionary conservation of the mutated amino acid across a range of homologous proteins was performed by the freely available Clustal Omega multiple sequence alignment software ( http://www . ebi . ac . uk/Tools/msa/clustalo/ ) [38] . Primary human fibroblast cell lines were established from the patient as well as from controls according to standard protocols and cultured at 37°C , in a humidified , 5% CO2 atmosphere . Fibroblasts were maintained as monolayers in Minimum Essential Media ( MEM ) ( Life Technologies #21090 ) supplemented with FBS to 10% , 1x MEM-vitamins , 21 mM L-Glutamine , 1 mM sodium pyruvate , 1x penicillin/streptomycin , 1x non-essential amino acids , and 0 . 41 µM uridine . TRIT1 wild-type and p . Arg323Gln mutant open reading frames were cloned into the pOP retroviral vector ( Radichev et al . , 2006 ) in frame with FLAG and HA epitope tags at the 5′ end using the XhoI and NotI sites , and sequencing was performed for confirmation . The preparation of the retroviral supernatants and the transduction of the control and patient fibroblasts were done as described [39] . Live cell respiration studies were performed by micro-scale oxygraphy using the Seahorse XFe Extracellular Flux Analyzer 24 ( Seahorse Bioscience ) according to manufacturer's instructions . Fibroblasts were seeded at a density of 30 , 000 cells/well . Mitochondrial function was assayed through the sequential addition of oligomycin ( to 1 . 3 µM ) to block the ATP synthase , 2 additions of carbonyl cyanide 4- ( trifluoromethoxy ) -phenylhydrazone ( FCCP ) , a respiratory uncoupler which drives maximal respiration ( to 2 µM and then to 3 µM ) , and antimycin ( to 2 . 5 µM ) to inhibit Complex III . Oxygen consumption rate ( OCR ) and proton production rate ( PPR ) measurements for each well were normalized by cell number . Non-mitochondrial respiration was subtracted from all OCR values prior to analysis; spare respiratory capacity ( SRC ) equals maximal OCR - basal OCR , ATP coupling efficiency equals ( basal OCR - oligomycin-inhibited OCR ) / ( basal OCR*100 ) . Seven separate control cell lines underwent multiple testing and the means were combined to calculate control data ( mean ± SD; n = 7 ) . Patient fibroblasts were tested multiple times ( n = 21 ) . An unpaired , two-tailed Student's t-test was performed to determine the significance of differences between the data sets and P-values were considered significant at the 95% confidence interval . Total cellular protein was extracted from patient and control fibroblasts ( as well as transfected cell lines ) , size separated on a 10% separating gel by SDS-PAGE and transferred to a methanol-activated PVDF membrane . Immunoblotting was performed using primary antibodies to NDUFA9 , NDUFB8 , NDUFA13 , SDHA , UQCRC2 , MTCOI , MTCOII , COXIV and ATPB ( all from Abcam ) , TRIT1 ( GeneTex GTX120508 ) and β-actin ( Sigma A5316 ) as a loading control and TOMM20 ( Abcam ) as a non-respiratory chain protein mitochondrial control . Chemiluminescent detection of the bands was achieved using the Amersham ECL Prime Kit ( GE Healthcare ) for signal development , following manufacturer's instructions and the membrane was viewed using the ChemiDocTMMP Imaging System ( Bio-Rad ) . Subcellular fractions were prepared as described previously [40] . The same amount of protein ( 40 µg ) from whole cell lysate , post-mitochondrial supernatant and mitochondrial subfractions was loaded onto a 12% SDS-PAGE gel , transferred to a PVDF membrane and analysed by immunoblotting using primary antibodies to TOMM20 ( Santa Cruz ) , AIF ( NEB ) , GDH ( custom made against mature recombinant protein ) , NDUFA9 ( Mitosciences ) , eIF4E ( Cell Signaling ) . Chemiluminescent detection of the bands was achieved as described before . The translation of proteins encoded by the mtDNA in patient fibroblasts was assessed by labelling with 35S-methionine/35S-cysteine ( Perkin Elmer ) as described previously [41] . Cytosolic translation was inhibited by co-incubation of the radioisotopes with 100 µg/ml emetine dihydrochloride . Total cell protein was extracted from both control and patient fibroblasts and 50 µg loaded onto a 15%–20% gradient gel for SDS-PAGE . Assessment of protein loading was achieved by Coomassie blue staining , and the gel was visualised by exposure to a blank PhosphorImager screen that was imaged using a Typhoon system ( GE Healthcare ) . The in vitro modification activity of both wild-type and mutant TRIT1 was determined as previously described [11] using recombinantly-expressed enzyme from E . coli recovered using a Histidine tag [42] . Synthetic RNA minihelixes representing the target anticodon stem/loop ( ASL ) sequences of various tRNAs were used as templates for modification with 14C-labelled dimethylallyl pyrophosphate ( DMAPP ) . In this assay , the isopentenyl group of DMAPP is transferred to A37 in substrate tRNAs by the IPTase TRIT1 . The following RNA oligos were designed with an additional G-C base pair added to each ASL to stabilize the stem , and purchased from Integrated DNA Technologies ( IDT ) : rGrUrGrCrArGrGrCrUrUrCrArArArCrCrUrGrUrArC ( cy-tRNASec ( UCA ) ) , rGrArUrGrGrArCrUrUrGrArArArUrCrCrArUrC ( cy-tRNASer ( UGA ) ) , rGrGrGrUrUrGrGrCrUrUrGrArArArCrCrArGrCrUrC ( mt-tRNASer ( UCN ) ) , rGrGrGrUrUrGrGrCrUrUrGrArArGrCrCrArGrCrUrC ( mt-tRNASer ( UCN ) -A7480G ) , rGrUrUrGrArArUrUrGrCrArArArUrUrCrGrArC ( mt-tRNACys ) and rGrUrArArArArCrUrUrArArArArCrUrUrUrArC ( mt-tRNALeu ( UUR ) ) . Decreased hybridization efficiency of complimentary DNA oligos due to the incorporation of the isopentenyl group in synthetic RNA minihelixes was confirmed by running an in vitro modification reaction with unlabeled DMAPP and recombinant His-TRIT1 . We used the same protocol for the in vitro modification reaction as described previously [11] , replacing 14C-labeled DMAPP with unlabeled DMAPP ( 100 nmol ) . For each sample , two reactions were performed . In mock-treated samples , all the components of the in vitro reaction were added except His-TRIT1 . After the reaction , the RNA sample was purified by a phenol-chloroform extraction and loaded on a 15% TBE-Urea gel . The RNA was transferred to a GeneScreen Plus Hybridization Transfer Membrane ( Perkin Elmer , catalog # NEF986001PK ) and hybridized with 32P-labeled anticodon loop ( ACL ) oligos as described in the legend of Figure 6D . The sequences of DNA oligos used for this experiment are the same as used elsewhere in this paper . Total RNA isolation from skeletal muscle and human primary cell lines was performed using TRIzol according to manufacturer's protocol . The impact of the TRIT1 mutation on in vivo levels of the i6A37 modification in both cytosolic and mitochondrial tRNAs was assessed by the Positive Hybridisation in the Absence of i6A ( PHA6 ) assay , which is an adaptation of high-resolution northern analysis [11] . The following anticodon loop ( ACL ) and body ( BP ) probes were used ( all written as 5′-3′ ) : mt-tRNACys ACL , TCTTCGAATTTGCAATTCAATATG and BP , AGCCCCGGCAGGTTTGAAGCT , cy-tRNASer ( UGA ) ACL , CCCATTGGATTT CAAGTCCAACGC , and BP , GCAGGATTCGAACCTGCGCGGG , wild-type mt-tRNASer ( UCN ) ACL , CAAAGCTGGTTTCAAGCCAACCCC ( used for analysis of the patient carrying the TRIT1 mutation ) , mutant mt-tRNASer ( UCN ) ACL , CAAAGCTGGCTTCAAGCCAACCCC ( both the wild-type and mutation-bearing probes were used together as a ‘double ACL probe’; the complement of the mutated base is underlined ) and mt-tRNASer ( UCN ) BP , AAGGAAGGAATCGAACCCCCC , mt-tRNALeu ( UUR ) BP , GTTAAGAAGAGG AATTGAACCTC and U5 probe , TCCTCTCCACGGAAATCTTTA . The wild-type TRIT1 or mutant TRIT1 was cloned into the pREP82X plasmid under the nmt1+ promoter before transformation into a yNB5 ( tit1-Δ ) strain of S . pombe . The experiments related to tRNA-mediated anti-suppression and growth deficiency in glycerol were performed as described previously [10] , [11] . The S . cerevisiae yeast strain used was BY4742 mod5-Δ ( MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 mod5::KanMX4 ) from the Euroscarf collection . The MOD5 gene was PCR-amplified with Kod HiFi Polymerase using primers MOD5CFw ( gactagaaaatcgatgtgtcagg ) and MOD5CSalIRv ( ccgccGTCGACgcttgtcat cctccctttcc ) , digested with KpnI and SalI and cloned in the centromeric vector , pFL38 [43] , thus obtaining the plasmid pFL38MOD5 . The mod5K294R humanized and mod5K294Q mutant alleles were obtained by site-directed mutagenesis as described previously [44] , on a MOD5 gene fragment obtained through amplification with the upstream forward primer MOD5MUTFw ( ggagcccctgcagcttcatg ) and the reverse mutagenic primer MOD5hK294RFw ( cgagaacacgtcaatacgcaCGCaggcaggtaaaatggatcaag ) or MOD5K294QFw ( cgagaacacgtc aatacgcaCaaaggcaggtaaaatggatcaag ) . The amplified fragments were digested with BamHI and SalI and subcloned in BamHI-SalI-digested pFL38MOD5 . Plasmids were introduced in a BY4742 mod5Δ strain according to [45] . Growth assays were performed at 28°C in SD medium ( 0 . 69% YNB ( Formedium , Norfolk , UK ) , without amino acids for which the strain is auxotroph ) supplemented with 2% glucose ( w/v ) or 2% ethanol ( v/v ) . Images of the colonies in the spots were acquired at 40X magnification with a Zenith inverted microscope through an Optikam 3 Digital Camera ( Optika ) . Oxygen consumption rate was measured at 30°C from suspensions of yeast cells cultured for 24 hours at 28°C in SD medium supplemented with glucose at a non-repressing concentration of 0 . 6% using a Clark-type oxygen electrode ( Oxygraph System Hansatech Instruments England ) with 1 ml of air-saturated respiration buffer ( 0 . 1 M phthalate – KOH , pH 5 . 0 ) , 0 . 5% glucose . The reaction was started by addition of 20 mg of wet-weight cells . | Mitochondrial disorders are clinically diverse , and identifying the underlying genetic mutations is technically challenging due to the large number of mitochondrial proteins . Using high-throughput sequencing technology , we identified a disease-causing mutation in the TRIT1 gene . This gene encodes an enzyme , tRNA isopentenyltransferase , that adds an N6-isopentenyl modification to adenosine-37 ( i6A37 ) in a small number of tRNAs , enabling them to function correctly during the synthesis of essential mitochondrial proteins . We show that this mutation leads to severe deficiency of tRNA-i6A37 in the patient's cells that can be rescued by introduction of the wild-type TRIT1 protein . A deficiency in oxidative phosphorylation , the process by which energy ( ATP ) is generated in the mitochondria , leads to a mitochondrial disease presentation . Introducing the mutant protein into model yeast species and measuring the resulting impairment provided further evidence of the pathogenic effect of the mutation . Additional studies investigating a previously reported pathogenic mutation in a mitochondrial tRNA gene demonstrated that a mutation in a substrate of TRIT1 can also cause a loss of the modification , providing evidence of a new mechanism causing mitochondrial disease in humans . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"rna",
"rna",
"structure",
"medicine",
"and",
"health",
"sciences",
"genetic",
"dominance",
"nucleic",
"acids",
"autosomal",
"recessive",
"traits",
"gene",
"identification",
"and",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecula... | 2014 | Defective i6A37 Modification of Mitochondrial and Cytosolic tRNAs Results from Pathogenic Mutations in TRIT1 and Its Substrate tRNA |
Chemotaxis enhances the fitness of Salmonella enterica serotype Typhimurium ( S . Typhimurium ) during colitis . However , the chemotaxis receptors conferring this fitness advantage and their cognate signals generated during inflammation remain unknown . Here we identify respiratory electron acceptors that are generated in the intestinal lumen as by-products of the host inflammatory response as in vivo signals for methyl-accepting chemotaxis proteins ( MCPs ) . Three MCPs , including Trg , Tsr and Aer , enhanced the fitness of S . Typhimurium in a mouse colitis model . Aer mediated chemotaxis towards electron acceptors ( energy taxis ) in vitro and required tetrathionate respiration to confer a fitness advantage in vivo . Tsr mediated energy taxis towards nitrate but not towards tetrathionate in vitro and required nitrate respiration to confer a fitness advantage in vivo . These data suggest that the energy taxis receptors Tsr and Aer respond to distinct in vivo signals to confer a fitness advantage upon S . Typhimurium during inflammation by enabling this facultative anaerobic pathogen to seek out favorable spatial niches containing host-derived electron acceptors that boost its luminal growth .
The enteric pathogen S . Typhimurium uses its virulence factors , two type III secretion systems ( T3SS-1 and T3SS-2 ) , to trigger acute intestinal inflammation [1] . Interestingly , the pathogen can benefit from the host inflammatory response , as indicated by its enhanced luminal growth during colitis [2] , [3] . The resulting bloom of S . Typhimurium in the lumen of the inflamed gut enhances its rate of transmission [4] . However , the properties that enhance the fitness of S . Typhimurium during colitis remain incompletely understood . Flagella-mediated motility is required for efficient growth of S . Typhimurium in the lumen of the inflamed intestine [5] , but not in the absence of intestinal inflammation [6] , suggesting that the inflammatory host response creates an environment that favors the growth of motile bacteria . The fitness advantage conferred by flagella in the gut is dependent on chemotaxis [6] , a property that is predicted to enable S . Typhimurium to seek out favorable metabolic niches . It has been proposed that motility and chemotaxis enable S . Typhimurium to migrate towards complex polysaccharides available for fermentation . Galactose residues are present in high abundance at the cecal mucosa [6] and might attract S . Typhimurium through Trg , a methyl-accepting chemotaxis protein ( MCP ) that senses galactose and ribose [7] , [8] , [9] . Chemotaxis is required for S . Typhimurium to get close to the mucosal surface and it has been hypothesized that a concentration gradient of galactose residues might be a possible chemotactic cue that attracts S . Typhimurium towards the intestinal epithelium [6] . However , there is currently no direct evidence to support the proposition that chemotaxis towards fermentable sugars allows S . Typhimurium to enhance its growth during intestinal inflammation . Mucus production in the gut is raised as part of the mucosal defense against S . Typhimurium infection [6] , [10] , which is consistent with the idea that the availability of fermentable sugars becomes elevated during infection . On the other hand , mucus production is not abrogated in the absence of intestinal inflammation . Furthermore , no chemotactic signals that are absent in the non-inflamed gut , but generated during inflammation have been identified . We thus reasoned that further investigation into the mechanisms by which chemotaxis enhances the fitness of S . Typhimurium during colitis was warranted . To this end , we determined how mutations in genes encoding different MCPs affect growth in the inflamed intestine and performed follow-up studies to elucidate the mechanisms by which chemotaxis confers a growth advantage during intestinal inflammation .
We used the mouse colitis model [11] to investigate why chemotaxis provides an advantage in the environment of the inflamed gut . In this model , mice are inoculated with streptomycin one day prior to infection with S . Typhimurium . Preconditioning with streptomycin disrupts the resident microbiota and allows S . Typhimurium to elicit acute cecal inflammation , which is an animal model for human gastroenteritis ( reviewed in [12] ) . We reasoned that a first step in elucidating the mechanism by which chemotaxis increases the fitness of S . Typhimurium during inflammation would be to determine which MCP ( s ) confer ( s ) a luminal growth advantage in the mouse colitis model . To this end we generated S . Typhimurium strains carrying mutations in mcpA ( FR37 ) , mcpB ( FR36 ) , mcpC ( FR35 ) , trg ( FR42 ) , aer ( FR5 ) or tsr ( FR4 ) . As a positive control , we included a chemotaxis-deficient S . Typhimurium cheY mutant ( FR13 ) , which lacks a core chemotaxis component that is essential for signal transduction from MCPs to the flagellar apparatus . Each mutant was tested for its ability to compete with wild-type S . Typhimurium ( IR715 ) for luminal growth in the mouse colitis model ( competitive infection assay ) . Infection of streptomycin pre-treated mice with the S . Typhimurium wild type ( IR715 ) and one of the above mutants resulted in acute cecal inflammation ( Fig . S1 ) and markedly increased cecal mRNA levels of the Kc and Nos2 genes ( Fig . 1A ) . Kc and Nos2 encode the inflammatory markers keratinocyte-derived cytokine ( KC ) and inducible nitric oxide synthase ( iNOS ) , respectively . Wild-type S . Typhimurium ( IR715 ) was recovered in significantly higher numbers ( P<0 . 05 ) from the colon contents than a chemotaxis-deficient cheY mutant ( FR13 ) four days after infection ( Fig . 1B ) , which confirmed previous observations in this model [6] . Consistent with the prediction that chemotaxis towards galactose residues might enhance the fitness of S . Typhimurium in the inflamed gut [6] , the trg mutant ( FR42 ) was recovered in lower numbers than the wild type . In contrast , mutations in mcpA , mcpB or mcpC did not reduce the fitness of S . Typhimurium ( Fig . 1B ) . Remarkably , the S . Typhimurium wild type ( IR715 ) was recovered in significantly ( P<0 . 05 ) higher numbers from colon contents than an aer mutant ( FR5 ) or a tsr mutant ( FR4 ) ( Fig . 1B ) , which identified Tsr and Aer as two new MCPs required for luminal growth during colitis . The growth advantage of wild-type S . Typhimurium ( IR715 ) over an aer tsr mutant ( FR6 ) was not significantly greater than that over a tsr mutant ( FR4 ) . In S . Typhimurium , aer is located upstream of mcpC , a gene encoding a MCP that is absent from Escherichia coli [13] , [14] ( Fig . S2A ) , thereby raising the possibility that an insertion in aer could be polar on expression of the downstream mcpC gene . However , inactivation of aer did not alter mcpC mRNA levels in vitro ( Fig . S2B ) , which made it unlikely that polar effects on mcpC could account for the phenotype of the aer mutant ( FR5 ) in the mouse colitis model ( Fig . 1B ) . Mutational inactivation of mcpC did not reduce fitness ( Fig . 1B ) , suggesting that Aer , but not McpC , provides a competitive luminal growth advantage in the mouse colitis model . Furthermore , we cloned the aer gene under the control of its native promoter on a low-copy number plasmid ( pFR5 ) and introduced this plasmid into the aer mutant . Streptomycin pre-treated mice were infected with an equal mixture of the aer mutant carrying a control vector ( pWSK129 ) and an aer mutant complemented with the cloned aer gene cloned on pFR5 . The complemented aer mutant ( aer[pFR5] ) was recovered in significantly ( P<0 . 05 ) higher numbers from colon contents than the aer mutant carrying a control vector ( aer[pWSK129] ) ( Fig . 1B ) , thereby providing strong support for the idea that Aer confers a growth benefit in the mouse colitis model . S . Typhimurium causes intestinal inflammation by using T3SS-1 for epithelial invasion and T3SS-2 for macrophage survival [1] . Inactivation of T3SS-1 ( through a mutation in invA ) and T3SS-2 ( through a mutation in spiB ) renders S . Typhimurium unable to trigger gut inflammation in the mouse colitis model [10] . To investigate whether migration towards galactose residues would confer a luminal growth advantage in the absence of intestinal inflammation , streptomycin pre-treated mice were infected with an equal mixture of an invA spiB mutant ( SPN452 ) and an invA spiB trg mutant ( FR43 ) . Mice infected with this mixture did not exhibit elevated expression of inflammatory markers ( Fig . 1A ) and the invA spiB trg mutant was recovered in lower numbers than the invA spiB mutant ( Fig . 1B ) , suggesting that Trg conferred a luminal growth advantage in the absence of intestinal inflammation . To determine whether the aer gene and the tsr gene conferred a fitness advantage in the absence of intestinal inflammation , streptomycin pre-treated mice were infected with an equal mixture of an invA spiB mutant ( SPN452 ) and an invA spiB aer mutant ( FR11 ) or an invA spiB mutant ( SPN452 ) and an invA spiB tsr mutant ( FR10 ) . Mice infected with these mixtures neither developed intestinal pathology ( Fig . S1 ) nor exhibited elevated expression of inflammatory markers ( Fig . 1A ) . An equal recovery of both strains from colon contents ( Fig . 1B ) suggested that neither Aer nor Tsr boosted luminal growth of S . Typhimurium in the absence of intestinal inflammation . To confirm that equal recovery of an invA spiB mutant and an invA spiB aer mutant from mice was due to lack of inflammation , we performed an additional experiment in which inflammation was restored by adding wild-type S . Typhimurium . To this end , streptomycin pre-treated mice were infected with a mixture of the S . Typhimurium wild type ( ATCC14028 ) , an invA spiB mutant ( SPN452 ) and an invA spiB aer mutant ( FR11 ) . Mice infected with this mixture developed acute intestinal inflammation , as indicated by elevated expression of inflammatory markers ( Fig . 1A ) . The invA spiB mutant was recovered in higher numbers than the invA spiB aer mutant ( Fig . 1B ) , suggesting that inflammation induced by the S . Typhimurium wild type generated an environment in which the aer gene conferred a fitness advantage upon the invA spiB mutant . Similar results were obtained when mice were infected with the S . Typhimurium wild type ( ATCC14028 ) , an invA spiB mutant ( SPN452 ) and an invA spiB tsr mutant ( FR10 ) ( Fig . 1A and 1B ) . Collectively , these data supported the working hypothesis that signals generated by the host inflammatory response are detected through Aer and Tsr , thereby enabling S . Typhimurium to migrate towards favorable luminal niches to boost its growth . However , the nature of these signals remained obscure . Enhanced luminal growth of S . Typhimurium during colitis has been linked to the transmigration of neutrophils into the intestinal lumen [15] . Neutrophils release reactive oxygen species ( ROS ) in an attempt to kill bacteria . A by-product of releasing ROS is the oxidation of endogenous sulfur compounds , such as thiosulfate ( S2O32− ) generated by the colonic epithelium [16] , [17] , to tetrathionate ( S4O62− ) [18] . Since tetrathionate becomes available only during inflammation [18] , we investigated whether this compound could serve as a signal for Aer and/or Tsr during colitis . We tested this hypothesis using the motility plate assay , in which formation of a halo by migration of S . Typhimurium from a point of inoculation into the surrounding motility agar was measured after anaerobic incubation for 6 . 5 hours . Halo formation was not observed with non-motile ( flgK mutant , SW215 ) or chemotaxis-deficient ( cheY mutant , FR13 ) strains . The tsr mutant ( FR4 ) formed halos with significantly ( P<0 . 05 ) reduced diameter compared to those produced by the S . Typhimurium wild type ( IR715 ) both in the presence or in the absence of tetrathionate ( Fig . S3 ) , suggesting that Tsr responded to signals other than ( or in addition to ) tetrathionate in this assay . In contrast , the wild type ( IR715 ) formed halos with a significantly ( P<0 . 05 ) larger diameter than an aer mutant ( FR5 ) when tetrathionate was present in the motility agar , but not when tetrathionate was absent ( Fig . S3A and S3B ) . Anaerobic growth in LB broth inoculated with the wild type ( IR715 ) and an aer mutant ( FR5 ) or the wild type and a tsr mutant ( FR4 ) indicated that inactivation of these chemotaxis receptors did not produce a growth defect ( Fig . S3B ) . These data supported the idea that Aer mediates chemotaxis towards tetrathionate . Since tetrathionate is not known to bind Aer directly , we were interested in the mechanism by which Aer could detect this compound . The ttrBCA gene cluster encodes a tetrathionate reductase that enables S . Typhimurium to oxidize NADH/H+ by transferring electrons from this donor through the electron transport chain to the terminal electron acceptor tetrathionate ( tetrathionate respiration ) [19] . Aer possesses a flavin adenine dinucleotide ( FAD ) -binding domain that enables this MCP to respond to changes in the redox status of the bacterial cell [20] , [21] , a process termed energy taxis [22] . We thus reasoned that by reducing tetrathionate , the TtrBCA tetrathionate reductase might change the redox status of the bacterial cell , thereby generating a signal that is sensed by Aer . This hypothesis would predict that a tetrathionate respiration-deficient mutant would be unable to migrate towards tetrathionate by Aer-mediated energy taxis . To test this prediction , we assessed the ability of a tetrathionate respiration-deficient ( ttrA ) mutant to migrate towards tetrathionate in the motility plate assay . Consistent with our hypothesis , a ttrA mutant ( SW661 ) formed halos with a significantly ( P<0 . 05 ) smaller diameter than the wild type ( IR715 ) when tetrathionate was present in the motility agar , but not when this terminal electron acceptor was absent ( Fig . S3A and S3B ) . To further investigate whether tetrathionate respiration serves as a signal for Aer , we used the capillary assay [23] , in which a glass capillary was submerged in a reservoir containing an equal mixture of two S . Typhimurium strains and migration into the capillary was measured after a 45 minute anaerobic incubation . In the absence of tetrathionate , the wild type ( IR715 ) and an aer mutant ( FR5 ) were recovered in equal numbers from the capillary . In contrast , the wild type was recovered in significantly higher numbers when the capillary contained tetrathionate ( Fig . 2A and Table S1 ) . Introduction of the cloned aer gene ( pFR5 ) into the aer mutant resulted in increased recovery from a capillary containing tetrathionate . These data provided further support for the idea that Aer mediates chemotaxis towards tetrathionate . When a capillary containing tetrathionate was submerged in a reservoir filled with a ttrA mutant and a ttrA aer mutant , both strains were recovered in equal numbers ( Fig . 2A ) , suggesting that Aer-mediated chemotaxis towards tetrathionate was dependent on tetrathionate respiration in vitro . Consistent with the fact that genes for the utilization of inferior electron acceptors are repressed in the presence of oxygen [24] , we did not observe energy taxis towards tetrathionate when the assay was repeated under aerobic conditions ( Fig . 2A ) . To test the idea that Aer detects tetrathionate by sensing changes in the redox status of the cell , we generated a strain carrying a point mutation in the aer open reading frame ( C65T ) leading to an amino acid substitution ( S22F ) that inactivates the FAD-binding domain of Aer ( aerS22F mutant , FR47 ) [25] . Under anaerobic conditions , the S . Typhimurium wild type was recovered in significantly higher numbers than the aerS22F mutant from a tetrathionate-containing capillary ( Fig . 2A ) , which supported the idea that Aer senses tetrathionate by measuring the redox status of the cell . Interestingly , the wild type ( IR715 ) and a tsr mutant ( FR4 ) were recovered in equal numbers from a capillary containing tetrathionate ( Fig . 2A ) , suggesting that Tsr does not mediate taxis towards tetrathionate in vitro . To investigate whether the fitness advantage conferred by the aer and/or tsr genes was dependent on the ability of S . Typhimurium to perform tetrathionate respiration in vivo , we infected groups of streptomycin pre-treated mice with equal mixtures of either a tetrathionate respiration-deficient mutant ( ttrA mutant , SW661 ) and a ttrA aer mutant ( FR9 ) or a ttrA mutant ( SW661 ) and a ttrA tsr mutant ( FR8 ) . The ttrA mutant ( SW661 ) outcompeted the ttrA tsr mutant ( FR8 ) ( Fig . 1B ) , suggesting that the fitness advantage conferred by Tsr was independent of tetrathionate respiration . In contrast , the fitness advantage conferred by Aer was abrogated during competition of the ttrA mutant ( SW661 ) with the ttrA aer mutant ( FR9 ) . These data suggested that the ability of S . Typhimurium to utilize tetrathionate as an electron acceptor was required to see a benefit of Aer during growth in the inflamed gut . Finally , the S . Typhimurium wild type outcompeted the aerS22F mutant ( FR47 ) in streptomycin pre-treated mice , suggesting that redox sensing by Aer conferred a fitness advantage in the mouse colitis model . To rule out that our phenotype was a result of disrupting the microbiota by treating mice with streptomycin , we infected genetically resistant ( CBA ) mice in the absence of streptomycin ( mouse typhoid model ) . In this model , mice develop cecal inflammation by day 10 after infection [26] . Mice were infected with an equal mixture of the S . Typhimurium wild type ( IR715 ) and an aer mutant ( FR5 ) or with an equal mixture of a ttrA mutant ( SW661 ) and a ttrA aer mutant ( FR9 ) . Wild-type S . Typhimurium was recovered in significantly higher numbers ( P<0 . 05 ) from colon contents than an aer mutant at 10 days and 28 days after infection ( Fig . S4 ) . At 10 days after infection , the fitness advantage conferred by Aer was significantly ( P<0 . 05 ) reduced during competition of the ttrA mutant with the ttrA aer mutant . By day 28 after infection , strains defective for tetrathionate respiration ( i . e . the ttrA mutant and the ttrA aer mutant ) were no longer recovered from colon contents . Inflammation induces expression of inducible nitric oxide synthase ( iNOS ) , an enzyme catalyzing the production of nitric oxide ( NO ) . Nitric oxide can react with ROS to generate nitrate ( NO3− ) , an electron acceptor that becomes available during inflammation [26] . Unlike tetrathionate reductases , nitrate reductases transport protons by scalar chemistry [24] . Since Tsr is proposed to sense changes in the proton-motive force [20] , we reasoned that this MCP might sense nitrate during colitis . We used the capillary assay under anaerobic conditions to test this idea . In the absence of nitrate , the wild type ( IR715 ) and a tsr mutant ( FR4 ) were recovered in equal numbers from the capillary . However , when the capillary contained 0 . 1 mM nitrate , the wild type was recovered in significantly higher numbers than the tsr mutant ( Fig . 2B and Table S1 ) . Introduction of the cloned tsr gene ( pFR6 ) into the tsr mutant resulted in increased recovery from a capillary containing 0 . 1 mM nitrate . Interestingly , the wild type was recovered in similar numbers as an aer mutant from a capillary containing 0 . 1 mM nitrate . However , Aer was able to mediate energy taxis towards higher concentrations of nitrate , because the wild type was recovered in significantly higher numbers than an aer mutant from a capillary containing 1 mM nitrate . Neither Aer nor Tsr mediated energy taxis towards nitrate when the assay was repeated under aerobic conditions . In conclusion , Tsr , but not Aer , functioned in mediating energy taxis towards low concentrations of nitrate in an anaerobic environment . S . Typhimurium encodes three nitrate reductases , encoded by the narGHJI , narZYWV and napFDAGHBC operons . Inactivation of these three nitrate reductases in a napA narZ narG mutant abrogates the ability of S . Typhimurium to perform nitrate respiration [26] . A napA narZ narG mutant ( CAL50 ) and a napA narZ narG tsr mutant ( FR46 ) were recovered in similar numbers from a capillary containing 0 . 1 mM nitrate ( Fig . 2B ) , suggesting that Tsr-mediated taxis towards nitrate required bacteria to perform nitrate respiration . To investigate whether the fitness advantage conferred by the tsr gene was dependent on the ability of S . Typhimurium to perform nitrate respiration in vivo , we infected groups of streptomycin pre-treated mice with an equal mixture of a nitrate respiration-deficient mutant ( napA narZ narG mutant , CAL50 ) and a napA narZ narG tsr mutant ( FR46 ) . The fitness advantage conferred by Tsr was abrogated during competition of the napA narZ narG mutant with the napA narZ narG tsr mutant ( Fig . 1B ) . These data suggested that the ability of S . Typhimurium to utilize nitrate as an electron acceptor was required to see a benefit of Tsr during growth in the inflamed gut . Tetrathionate respiration has recently been shown to enhance growth of S . Typhimurium in bovine ligated ileal loops [18] . Since bacteria are recovered at a defined early time point ( 8 hours ) after infection , we reasoned that this model would be well suited for chemotaxis experiments . The events leading to the formation of tetrathionate ( i . e . the generation of thiosulfate by the colonic epithelium and the respiratory burst of transmigrating neutrophils ) are expected to occur in close proximity to the epithelium . To investigate the benefit of tetrathionate respiration in different luminal compartments , we infected bovine ligated ileal loops with an equal mixture of an aer mutant ( FR5 ) and a S . Typhimurium wild-type strain that was marked by a phoN::Kan insertion to facilitate recovery ( AJB715 ) ( competitive infection assay ) . Remarkably , higher numbers ( P<0 . 05 ) of the wild-type strain ( AJB715 ) were found in close association with tissue , while equal numbers of both strains were recovered from the luminal fluid 8 hours after infection ( Fig . 3A ) . No benefit of energy taxis was observed when loops were infected with an equal mixture of an invA spiB mutant ( SPN452 ) and an invA spiB aer mutant ( FR11 ) ( Fig . 3A ) . Measurements of fluid accumulation , a surrogate for the severity of intestinal inflammation [27] , suggested that the severity of inflammatory changes was significantly ( P<0 . 05 ) reduced in loops infected with a mixture of the invA spiB mutant and the invA spiB aer mutant compared to loops infected with a mixture of the wild type and aer mutant ( Fig . 3B ) . The wild type ( AJB715 ) was recovered in higher numbers than a tsr mutant ( FR4 ) from tissue-associated samples , but not from the luminal fluid of loops infected with a mixture of both strains ( Fig . 3A ) . We conclude that Tsr and Aer-mediated energy taxis increased bacterial numbers in spatial niches that were in close proximity to the inflamed mucosal surface . Finally , to determine the consequences of energy taxis on bacterial growth after oral infection with individual strains , streptomycin pre-treated mice were inoculated with sterile LB broth ( mock infection ) , the S . Typhimurium wild type ( IR715 ) , an aer mutant ( FR5 ) , a tsr mutant ( FR4 ) or a chemotaxis-deficient mutant ( cheY mutant , FR13 ) and organs were collected for analysis four days later . Mice infected with the S . Typhimurium wild type developed acute cecal inflammation ( Fig . S5 ) . There was a modest reduction in the severity of cecal inflammation in mice infected with an aer mutant or a cheY mutant , but the severity of lesions was similar in mice infected with the tsr mutant . Importantly , the aer mutant , the tsr mutant and the cheY mutant were recovered in significantly ( P<0 . 05 ) reduced numbers from the colon contents of mice than wild-type S . Typhimurium ( Fig . 4 ) . Collectively , our data suggested that Tsr and Aer-mediated energy taxis towards electron acceptors generated by the host inflammatory response conferred a fitness advantage in the mouse colitis model ( Fig . S6 ) .
Motility and chemotaxis confer a fitness advantage when S . Typhimurium resides in the lumen of the inflamed gut , but not during luminal growth in the non-inflamed intestine [6] . One important difference between the non-inflamed and the inflamed large bowel is that growth by anaerobic respiration becomes possible only in the latter niche [18] , [26] . Changes in internal energetic conditions that accompany the switch from fermentative growth to growth by anaerobic respiration can be sensed by MCPs , which transduce this signal to the flagella apparatus to enable bacteria to approach spatial niches containing optimal concentrations of respiratory electron acceptors , a process termed energy taxis [22] . Colonic hydrogen sulfide produced by the fermenting microbiota is converted to thiosulfate by the colonic epithelium [16] , [17] . During inflammation , neutrophils transmigrate into the intestinal lumen where they release ROS that oxidize thiosulfate to tetrathionate [18] . Furthermore , ROS can react with nitric oxide produced by iNOS to form the electron acceptor nitrate [28] . S . Typhimurium readily consumes these electron acceptors to boost its luminal growth in the inflamed gut [18] , [26] . Tetrathionate and nitrate may be a limiting resource , requiring S . Typhimurium to actively seek out niches containing these electron acceptors . Here we show that energy taxis enhances the fitness of S . Typhimurium in the inflamed intestine , by enabling the pathogen to migrate towards favorable metabolic niches ( Fig . S6 ) . While Aer and Tsr , both mediate energy taxis , our data suggest that the functions of these MCPs were not redundant in vivo . To migrate towards an anaerobic niche containing tetrathionate by Aer-mediated energy taxis , bacteria need to transfer electrons from NADH/H+ through the quinone pool onto the terminal electron acceptor tetrathionate ( tetrathionate respiration ) , thereby altering the redox state of the cell . The flavin cofactor bound by Aer is proposed to sense changes in the redox state by becoming oxidized or reduced through interaction with a component of the electron transport chain , a process that generates the on and off signal for Aer-mediated energy taxis [21] , [26] . Due to this mechanism , the ability to respire tetrathionate was essential for migrating towards higher concentrations of tetrathionate by energy taxis . S . Typhimurium strains that were unable to respire tetrathionate neither exhibited Aer-mediated energy taxis towards tetrathionate in vitro ( Fig . 2 ) nor benefited from Aer-mediated energy taxis in vivo ( Fig . 1 ) . Collectively , these data implied that Aer mediates energy taxis towards spatial niches containing tetrathionate in the inflamed intestine . In contrast , the competitive growth benefit conferred by Tsr did not require S . Typhimurium to respire tetrathionate ( Fig . 1 ) , indicating that Tsr does not mediate energy taxis towards this electron acceptor . Respiration of 1 . 3 mol of tetrathionate yields 1 mol of ATP [29] , suggesting that tetrathionate respiration is not a strong generator of proton-motive force . This might explain why the proton-motive force sensor Tsr [20] did not mediate energy taxis towards tetrathionate . The nitrate/nitrite redox couple has a much higher standard redox potential ( E° = 433 mV ) [30] than the tetrathionate/thiosulfate redox couple ( E° = 170 mV ) [31] and nitrate reductases transport protons by scalar chemistry [24] . The fact that nitrate is a better generator of proton motive force might explain why Tsr sensed nitrate , but not tetrathionate . While Aer mediated energy taxis towards high concentrations of nitrate , only Tsr was functional when concentrations of nitrate were low . Our data show that in vivo Tsr-mediated energy taxis was nitrate respiration-dependent , while Aer-mediated energy taxis required tetrathionate respiration . Collectively , these observations suggest that concentrations of nitrate in the gut lumen of mice infected with S . Typhimurium are relatively low , thereby rendering Tsr the sole energy taxis sensor of nitrate in vivo . Homologues of Aer have been characterized in E . coli [21] , Vibrio cholerae [32] , P . aeruginosa [33] and Campylobacter jejuni [34] . It has been proposed that energy taxis might be important in the upper gastrointestinal tract [22] where oxygen is a limiting parameter for growth [35] . Our work is the first to suggest that in the increasingly anaerobic environments of the terminal ileum and large bowel , energy taxis becomes important during conditions of intestinal inflammation , because ROS and RNS species generated during this response produce more favorable spatial niches containing respiratory electron acceptors . The ability to use energy taxis to migrate towards such favorable niches confers a growth benefit upon S . Typhimurium , and likely upon other facultative anaerobic bacteria . It has been shown recently that nitrate generated as a by-product of the host inflammatory response supports a bloom of commensal E . coli by anaerobic respiration [28] . The picture emerging from this and previous studies [18] , [26] , [36] is that anaerobic respiration is one of the fundamental principles that boost growth of both commensal and pathogenic Enterobacteriaceae over the obligate anaerobic Clostridia and Bacteroidia during inflammation . The resulting outgrowth could lead to a competition between commensal Enterobacteriaceae and S . Typhimurium over nutrients , provided both microbes are present in the same animal . One factor important during the ensuing battle might be antimicrobial proteins , such as lipocalin-2 . Lipocalin-2 is released into the intestinal lumen during inflammation [10] and prevents bacterial iron acquisition by binding enterobactin , a siderophore commonly produced by commensal Enterobacteriaceae [37] , [38] , [39] . The iroN iroBCDE gene cluster of S . enterica confers resistance to the antimicrobial protein lipocalin-2 and provides a growth advantage in the inflamed gut [10] . Lipocalin-2 resistance might confer an advantage upon S . enterica during its competition with commensal Enterobacteriaceae , provided that the latter rely on enterobactin for iron acquisition . However , additional work is needed to elucidate the mechanisms used by S . Typhimurium to compete with commensal Enterobacteriaceae in the lumen of the inflamed gut .
The bacterial strains used in this study are listed in Table 1 . Unless indicated otherwise , bacteria were grown aerobically at 37°C in LB broth ( 10 g/l tryptone , 5 g/l yeast extract , 10 g/l NaCl ) or on LB agar plates ( 15 g/l agar ) . Antibiotics were added to the media at the following concentrations: 0 . 03 mg/ml chloramphenicol ( Cm ) , 0 . 1 mg/ml carbenicillin ( Carb ) , 0 . 05 mg/ml kanamycin ( Kan ) , 0 . 05 mg/ml nalidixic acid ( Nal ) , and 0 . 01 mg/ml tetracycline ( Tet ) . When appropriate , the chromogenic substrate of the acidic phosphatase PhoN ( 5-Bromo-4-chloro-3-indolyl phosphate ) was added to solid media at a concentration of 40 mg/l . To determine competitive indices ( CI ) after in vitro growth , LB broth or LB broth containing either 10 mM tetrathionate or 10 mM nitrate was inoculated with an equal mixture of two bacterial strains ( at a density corresponding to a 1∶200 dilution of an overnight culture ) and grown anaerobically at 37°C ( Bactron I anaerobic chamber; Sheldon Manufacturing ) overnight . The cultures were spread on agar plates containing the appropriate antibiotics to determine the ratio of recovered bacteria . Three technical replicates were performed for each experiment and each experiment was repeated at least three times independently . The plasmids and primers used in this study are listed in Tables 2 and 3 , respectively . PCR products were routinely cloned into pCR2 . 1 using the TOPO TA cloning kit ( Invitrogen , Carlsbad ) and sequenced ( SeqWright Fisher Scientific , Houston ) prior to subcloning into appropriate vectors . An internal fragment of the aer gene was amplified by PCR using the primers listed in Table 3 . The aer PCR product was then cloned into the suicide plasmids pGP704 and pEP185 . 2 using the SalI and SacI restriction enzyme sites to construct pFR2 and pFR4 , respectively . A fragment of the tsr gene was amplified by PCR and subcloned into pEP185 . 2 using the SalI and SacI restriction enzyme sites to create pFR3 . The KSAC kanamycin resistance cassette of pBS34 was subcloned into the XbaI restriction site of pSPN57 , generating a Kan-marked allelic exchange invA deletion construct ( pSPN60 ) . A suicide plasmid for generating a deletion of the aer gene was constructed by using primers del_A and del_B to PCR amplify a 404 bp 5′ flanking sequence that included 9 nucleotides of the aer coding region . Primers del_C and del_D were used to PCR amplify a 573 bp fragment that included 24 nucleotides of the 3′ coding region . The two PCR fragments were joined using the splicing overlap extension ( SOE ) technique [40] , [41] and the resulting PCR product containing an in-frame aer deletion was digested with BamHI and SalI and cloned into pRDH10 to generate plasmid pFR7 . A suicide plasmid for introducing a point mutation into the aer open reading frame was constructed by PCR amplifying a 5′ fragment of the aer gene using primer del_A , which anneals 395 bps upstream of the gene and primer S22F_B , which contains a missense mutation ( C65T ) . Primer S22F_C , which encodes the same missense mutation and primer del_D were used to amplify the remaining aer ORF plus 549 bps downstream of the aer stop codon . The two PCR fragments were joined using the SOE technique [40] , [41] and the resulting PCR product containing an aer allele with a C65T substitution was digested with BamHI and SalI and cloned into pRDH10 to generate pFR8 . For complementation of the aer mutant , the aer open reading frame including the promoter region was amplified using the primers listed in Table 3 . The resulting PCR fragment was cloned into pWSK29 using XbaI and SacI to create pFR5 . For complementation of the tsr mutant , the tsr open reading frame including the promoter region was amplified using the primers listed in Table 3 . The resulting PCR fragment was cloned into pWSK29 using XbaI and SacI to create pFR6 . Suicide plasmids were propagated in E . coli DH5α λpir and introduced into the S . Typhimurium strain IR715 by conjugation using E . coli S17-1 λpir as a donor strain . Transconjugants were selected on LB plates containing nalidixic acid ( IR715 ) and antibiotics selecting for integration of the suicide plasmid . Integration of the suicide plasmids was verified by PCR . Using this methodology , pFR2 , pFR4 and pFR3 were integrated into the aer and tsr genes of IR715 to yield strains FR5 , FR7 and FR4 , respectively . An mcpA mutant ( MB193 ) mcpB mutant ( QW111 ) , mcpC mutant ( SM19 ) and trg mutant ( SM15 ) were constructed by the one-step mutagenesis method [42] . Primers were designed with overhang sequences complementarily to the beginning and end of the Cm cassette in pKD3 to replace most of the mcpC gene or the trg gene with a Cm cassette , generating SM19 and SM15 , respectively . Primers were designed with overhang sequences complementarily to the beginning and end of Kan cassette in pKD4 to replace most of the mcpA or mcpB open reading frames with a Kan cassette to generate strains MB193 and QW111 , respectively . The invA locus of IR715 was deleted with the KSAC Kan resistance cassette by conjugating pSPN60 from S17-1 λpir into IR715 , selecting for double-crossover transconjugants on LB plates containing Nal and Kan . A strain positive for amplification with primers directed outwards from KSAC and primers directed inwards from beyond the flanking regions employed in the deletion of invA was labeled SPN453 . An unmarked invA deletion mutant was then generated by conjugating pSPN57 from S17-1 λpir into SPN453 by following a previously described methodology [30] . A strain positive by PCR for the unmarked invA deletion and negative for invA amplification was termed SPN455 . The aer was deleted by conjugation of pFR7 from E . coli S17-1 λpir into S . Typhimurium strain IR715 . Transconjugants were selected for on LB plates containing Nal and Cm . Sucrose counter-selection was performed as described previously [43] . A strain that was sucrose-tolerant and Cms was verified by PCR to carry a deletion in aer and was denoted FR38 . The aerS22F mutation was constructed by conjugation of pFR8 from E . coli S17-1 λpir into S . Typhimurium strain FR38 . Transconjugants were selected for on LB plates containing Nal and Cm . Sucrose counter-selection was performed as described previously [43] . A strain that was sucrose-tolerant and Cms , denoted FR47 , was verified by PCR amplification and sequence analysis . Phage P22 HT105/1 int-201 was used for generalized transduction . Transductants were routinely purified from phage contamination on Evans blue-Uranine agar and then cross-struck against P22 H5 to confirm phage sensitivity . The ttrA::pSW171 mutation from SW661 was introduced into FR4 and FR7 to yield strains FR8 and FR9 , respectively . The tsr::pFR3 or aer::pFR2 mutations were introduced into the SPN452 to create strains FR10 and FR11 , respectively . The cheY::Tn10 mutation from AT350 was transduced into IR715 to generate strain FR13 . The mcpC::Cm mutation from SM19 was transduced into IR715 to generate FR35 . The mcpB::Kan mutation from QW111 was transduced into IR715 to generate FR36 . The mcpA::Kan mutation from MB193 was transduced into IR715 to generate FR37 . The trg::Cm mutation from SM15 was transduced into IR715 to generate FR42 . An unmarked spiB deletion was generated in SPN455 by transducing the merodiploid ΔspiB::pSPN56 ( ΔspiB ) locus from SPN458 followed by sucrose selection as previously described [30] . A strain positive by PCR for the unmarked spiB deletion and negative for spiB amplification was dubbed SPN487 . The trg::Cm mutation of SM15 was then transduced into SPN487 , creating FR43 . Mouse and calf experiments adhered to USDA guidelines and were approved by the Institutional Animal Care and Use Committees at the University of California at Davis and Texas A&M University , respectively . Soft agar motility plates containing LB and 0 . 3% ( w/v ) agar or soft agar plates supplemented with 5 mM tetrathionate were inoculated with single colonies and incubated at 37°C anaerobically ( GasPak system , BD Biosciences ) for 6 . 5 hours and the area of the halo was determined ( Labworks 4 . 6 software , UVP ) . Each experiment was performed in triplicate . Capillary assays were performed as described previously [23] with the modifications indicated below . An overnight culture was diluted 1∶100 in LB broth and grown anaerobically at 37°C ( Bactron I anaerobic chamber; Sheldon Manufacturing ) to late exponential phase ( OD600 between 0 . 6–0 . 8 ) . An equal volume of each strain was added to a tube and washed twice ( 3 , 220 g , 5 minutes ) in 0 . 5 ml of chemotaxis buffer ( CB; 10 mM potassium phosphate buffer ( pH 7 . 4 ) , 0 . 1 mM EDTA ) . The pellet was resuspended in CB to a concentration of between 1×107 and 1×108 CFU/ml and 100 µl of the culture was added to a 1 . 5 ml tube . To make the capillaries , 1 µl glass capillaries that had been sealed on one end were briefly flamed and then added to 1 . 5 ml microcentrifuge tubes containing tetrathionate or nitrate at the indicated concentrations . The glass capillary was then placed into a 1 . 5 ml microcentrifuge tube containing S . Typhimurium strains and incubated at 37°C inside the anaerobe chamber for 45 minutes . The glass capillary was removed , and wiped off and dilutions of the contents of the capillary tube were spread on agar plates containing the appropriate antibiotics . Three technical replicates were performed for each experiment and each experiment was repeated at least three times independently . Eukaryotic gene expression was determined by real-time PCR as previously described [18] . Briefly , eukaryotic RNA was isolated using TRI reagent ( Molecular Research Center , Cincinnati ) . A Reverse transcriptase reaction was performed to prepare complementary DNA ( cDNA ) using TaqMan reverse transcription reagents ( Applied Biosystems , Carlsbad ) . A volume of 4 µl of cDNA was used as template for each real-time PCR reaction in a total reaction volume of 25 µl . Real-time PCR was performed using SYBR-Green ( Applied Biosystems ) along with the primers listed in Table 3 . Data were analyzed using the comparative Ct method ( Applied Biosystems ) . Transcript levels of Nos2 and Kc were normalized to mRNA levels of the housekeeping gene Gapdh . Formalin fixed cecal tissue sections were stained with hematoxylin and eosin , and a veterinary pathologist performed a blinded evaluation using criteria published previously [36] . Representative images were obtained using an Olympus BX41 microscope and the brightness adjusted ( Adobe Photoshop CS2 ) . Fold changes of ratios ( bacterial numbers or mRNA levels ) were transformed logarithmically prior to statistical analysis . An unpaired Student's t-test was used to determine whether differences in fold changes between groups were statistically significant ( P<0 . 05 ) . Significance of differences in histopathology scores was determined by a one-tailed non-parametric test ( Mann-Whitney ) . | The ability to thrive in the inflamed gut is crucial for Salmonella serotypes to ensure their transmission to a new host . Salmonella serotypes can use alternative electron acceptors that become available during colitis to support their growth by anaerobic respiration , therefore giving the pathogen an advantage over the fermenting microbiota . While it is known that S . Typhimurium benefits from chemotaxis and motility during colitis , the chemotaxis signals the pathogen detects in vivo have not been elucidated . Here we demonstrate that during intestinal inflammation , S . Typhimurium is able to seek energetically favorable niches by detecting the presence of nitrate and tetrathionate , two respiratory electron acceptors that are generated as by-products of the host inflammatory response . Through this mechanism , energy taxis provides a fitness advantage for S . Typhimurium , and likely other facultative anaerobic bacteria , during in vivo growth in the intestinal lumen . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"microbial",
"metabolism",
"infectious",
"diseases",
"microbial",
"physiology",
"microbial",
"pathogens",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"gastrointestinal",
"infections"
] | 2013 | Salmonella Uses Energy Taxis to Benefit from Intestinal Inflammation |
Typhoid toxin is a virulence factor for Salmonella Typhi and Paratyphi , the cause of typhoid fever in humans . This toxin has a unique architecture in that its pentameric B subunit , made of PltB , is linked to two enzymatic A subunits , the ADP ribosyl transferase PltA and the deoxyribonuclease CdtB . Typhoid toxin is uniquely adapted to humans , recognizing surface glycoprotein sialoglycans terminated in acetyl neuraminic acid , which are preferentially expressed by human cells . The transport pathway to its cellular targets followed by typhoid toxin after receptor binding is currently unknown . Through a genome-wide CRISPR/Cas9-mediated screen we have characterized the mechanisms by which typhoid toxin is transported within human cells . We found that typhoid toxin hijacks specific elements of the retrograde transport and endoplasmic reticulum-associated degradation machineries to reach its subcellular destination within target cells . Our study reveals unique and common features in the transport mechanisms of bacterial toxins that could serve as the bases for the development of novel anti-toxin therapeutic strategies .
Typhoid toxin is a unique virulence factor for the typhoidal Salmonella enterica serovars Typhi and Paratyphi [1–4] , the cause of typhoid fever in humans , a systemic disease that remains a major global public health concern [5–9] . When administered to experimental animals , typhoid toxin can reproduce many of the pathognomonic acute symptoms of typhoid fever [1] . The architecture of typhoid toxin is unusual among member of the AB5-toxin family in that it is composed of two enzymatic A subunits , PltA and CdtB , linked to a single pentameric B subunit , PltB [1] . CdtB is a deoxyribonuclease , which causes DNA damage and cell cycle arrest in intoxicated cells , while PltA is an ADP ribosyl transferase with as of yet unidentified targets . The biology of typhoid toxin is uniquely adapted to the intracellular lifestyle of Salmonella . In fact , the toxin is only expressed by intracellularly localized bacteria [2 , 4 , 10] , and after its secretion into the lumen of the Salmonella-containing vacuole by a specific protein secretion system [11] , it is packaged into vesicle carrier intermediates and exported to the extracellular space [2 , 12] . Once exported , typhoid toxin can target a variety of cells by engaging specific cell surface receptors [1] . The autocrine and paracrine pathways are consequently the only mechanism by which the toxin can reach its targets after its interaction with cell surface receptors [2] . Therefore , S . Typhi-infected cells potentially lacking receptors for the toxin would not be susceptible to intoxication although they would be competent to harbor bacteria to produce it , a mechanistic feature that may be relevant for the toxin’s proposed role during persistent infection [13] . Consistent with the stringent specificity of S . Typhi and S . Paratyphi for their human hosts , typhoid toxin has adapted to exert its function preferentially in human cells exhibiting exquisite preference for surface glycoproteins sialoglycans terminated in acetyl neuraminic acid , which are preferentially expressed by human cells [1 , 14] . How typhoid toxin reaches its cellular targets after receptor binding is currently unknown . Bacterial toxins utilize a variety of mechanisms to gain access to their cellular targets that most often involve the hijacking of specific cellular machinery for their transport within cells . Here we have used a multidisciplinary approach to define the transport pathway of typhoid toxin within human cells . Through a genome-wide CRISPR/Cas9 screen , we have identified cellular components that are required for typhoid toxin transport within cells . This study provides a detailed view of the transport mechanisms that deliver typhoid toxin from the cell surface to its destination within target cells , and identifies cellular components that are unique to the transport of this toxin as well as components that are also exploited for the transport of other bacterial toxins , thus providing the foundation for the development of novel anti toxin strategies .
All AB5 toxins whose transport mechanisms have been characterized to date are internalized by receptor-mediated endocytosis and subsequently delivered by retrograde transport first to the Golgi and then to the endoplasmic reticulum , where the holotoxins are disassembled and the enzymatic subunits are translocated to the cytosol [15–19] . To determine whether typhoid toxin follows an analogous uptake pathway , we applied fluorescently-labeled typhoid toxin to cultured cells and examined its fate over time . To synchronize the intoxication process , cultured cells treated with typhoid toxin were incubated at 4°C to allow toxin binding while preventing toxin internalization . After toxin binding cells were switched to 37°C and the fate of the labeled toxin over time was monitored by immunofluorescence microscopy . Consistent with its known interaction with surface glycoproteins [1] , typhoid toxin was initially observed bound to the cell surface plasma membrane ( Fig 1A ) . At later ( 30 min ) time points and after the temperature switch , the toxin was observed within fluorescent puncta that most likely represent early endocytic compartments ( Fig 1A ) . Two hours after the temperature shift , the toxin was observed co-localized with a compartment that could be labeled with an antibody to the Golgi marker GM130 ( Fig 1A ) , and later ( 8 hs after the temperature shift ) within puncta spread throughout the cell cytosol . To verify that typhoid toxin reaches the Golgi , we used a biochemical assay based on the expression of a SNAP-tagged reporter targeted to the Golgi apparatus [20 , 21] . The SNAP tag covalently and irreversibly reacts with benzylguanine ( BG ) therefore BG-labeled proteins can be captured by spatially localized SNAP-tagged reporters where the labeled proteins and the reporter intersect [22] ( Fig 1B ) . We applied BG-labeled typhoid toxin to cells expressing the SNAP-tagged Golgi resident protein GalT and monitored the potential arrival of the toxin to the Golgi over time by a gel mobility assay to detect the typhoid toxin-SNAP-GalT ( TT-SNAP-GALT ) complex . We found that after treating cells with BG-labeled typhoid toxin we were able to detect the presence of a BG-typhoid toxin-SNAP-tagged complex as demonstrated by the presence of a CdtB band with a shifted mobility ( Fig 1C ) . These results showed that after its internalization , typhoid toxin is delivered to the Golgi , presumably by its retrograde transport from early endocytic compartments . The PltA and CdtB enzymatic subunits of typhoid toxin are linked to one another by a disulfide bond between two spatially-coordinated cysteine residues [1] . Consequently , disassembly of the holotoxin complex requires the reduction of this disulfide bridge , which can be monitored by western blot analysis . By analogy to other AB5 toxins , reduction of the disulfide bridges should occur upon the toxin’s arrival to the endoplasmic reticulum , most likely mediated by resident disulfide reductases [23] . We therefore reasoned that the reduction of the disulfide bond that tethers CdtB to PltA could serve as a reporter for the arrival of typhoid toxin to the endoplasmic reticulum ( Fig 1D ) . We incubated cultured cells with typhoid toxin at 37°C and the integrity of the disulfide bond that tethers CdtB to PltA over time was monitored in host cell lysates using SDS-PAGE in the presence or absence of a reducing agent , and western blot analysis with an antibody to CdtB ( Fig 1E ) . Early after switching the toxin-treated cells to 37°C ( 30 min ) we detected CdtB with a mobility corresponding to a molecular weight consistent with a CdtB-PltA complex ( Fig 1E ) . In the presence of DTT , the observed mobility of CdtB corresponded to its predicted molecular weight , thus confirming that the slower migrating species observed in the absence of the reducing agent corresponded to the CdtB-PltA complex . Starting at 60 minutes after switching the treated cells to 37°C , the migration of CdtB indicated that the disulfide bond that tethers it to PltA had been reduced upon its arrival to the ER and by the two-hour time point the CdtB-PltA complex was no longer detectable ( Fig 1E ) . We also investigated whether CdtB was translocated to the cell cytosol by applying a differential membrane permeabilization and fractionation protocol to toxin treated cells . We found that starting at 2 hours after treating cells with typhoid toxin , CdtB could be readily detected in the cytosolic fraction of intoxicated cells , an indication of its translocation from the endoplasmic reticulum ( Fig 1F ) . Taken together , these results indicate that , similar to other AB5 toxins , typhoid toxin is transported to the endoplasmic reticulum through retrograde traffic , where the holotoxin is disassembled prior to the translocation of its enzymatic subunits to the cell cytosol . The results shown above provided a framework for the transport of typhoid toxin after its internalization . However , these studies did not provide insight into the cellular machinery associated with this process . To unravel the mechanisms of toxin transport , we used the CRISPR/Cas9 genome-editing system [24] to conduct a genome-wide screen to identify genes whose disruption conferred resistance to typhoid toxin . It was expected that disruption of toxin transport should protect cells from the toxin’s activity . HEK293T cells constitutively expressing Cas9 were independently transduced with two different sgRNA libraries packaged into lentiviral particles , designed to target each of ~ 20 , 000 human genes with 3 unique sgRNAs ( Fig 2A ) . Cells transduced with either of the two sgRNA libraries were treated with an amount of typhoid toxin that pilot experiments had determined to result in the death of 80–90% of the treated cells . The premise of the screen was that treatment of the transduced cells with typhoid toxin would enrich for sgRNA-directed mutations that result in cells exhibiting resistance to typhoid toxin due to the inactivation of genes required for intoxication . The pool of cells surviving toxin treatment were collected and compared to parallel untreated samples by deep sequencing of the integrated sgRNAs . To increase the robustness of the screen , all procedures were performed in triplicate and the entire screen was conducted three times . All samples were then compared using the model-based analysis of genome-wide CRISPR/Cas9 knockout ( MAGeCK ) algorithm [25] . With a false discovery rate ( FDR ) cutoff of 15% , our screen identified 26 genes whose inactivation led to increased resistance to typhoid toxin treatment , 11 common to both libraries and 5 and 11 unique to libraries A and B , respectively ( Fig 2B and 2C and S1 and S2 Tables ) . Analysis of the identified genes using GO ( http://www . geneontology . org ) showed a significant enrichment for pathways for protein glycosylation , lipid metabolism , and more prominently , many pathways involved in vesicle transport to the Golgi and the ER ( Fig 2D ) . More specifically , the screen identified genes encoding components of well-characterized multi-protein complexes such as the Golgi-associated retrograde protein ( GARP ) ( VPS51 , VPS52 , VPS53 , VPS54 ) [26] and the conserved oligomeric Golgi ( COG ) ( COG1 , COG4 , COG5 , COG6 , COG7 , COG8 ) complexes [27] , as well as components of the ER-associated degradation ( ERAD ) retro-translocation machinery ( SEL1L and SYVN1 ) [28 , 29] . These pathways presumably captured the different processes involved in TT transport from its uptake to its delivery to the particular subcellular destination where the active subunits exert their function . To validate the results of the CRISPR/Cas9 screen we generated cell lines individually defective in a subset of the identified genes or pathways ( Fig 3A ) . Specifically , using CRISPR/Cas9 genome editing we generated HEK-293T-cells defective for VPS51 , VPS54 , COG1 , COG5 , YKT6 , TMED2 , YIPF5 , SEL1L , SYVN1 , YKT6 , YIPF5 , and SCYL1 and tested the resulting cell lines for their susceptibility to typhoid toxin ( Fig 3A and 3B ) . To assay for typhoid toxin toxicity we examined the ability of the toxin to stimulate G2/M cell cycle arrest in intoxicated cells as a consequence of DNA damage inflicted by its CdtB subunit ( Fig 3B ) . We found that removal of VPS51 and VPS54 , two components of the GARP complex [26] , confer significant resistance to typhoid toxin . Similarly , removal of the members of the COG complex , COG1 or COG5 , which are involved in Golgi trafficking [27] , as well SEL1L [28] and SYVN1 [29] , which are critical components of the ER-associated degradation ( ERAD ) pathway , also resulted in significant resistance to intoxication . TMED2 , also identified in our screen , is a member of the p24 protein family , which has been implicated in vesicle traffic between the ER and Golgi complex [30 , 31] . Consistent with the results of the screen , we found that inactivation of TMED2 conferred significant resistance to typhoid toxin ( Fig 3B ) . In contrast , inactivation of YKT6 , YIPF5 , or SCYL1 , which were also identified in our screen , did not increase resistance to typhoid toxin suggesting that these proteins may not be involved in typhoid toxin transport ( Fig 3B ) . In fact , inactivation of YKT6 appeared to sensitize cells to typhoid toxin . It is unclear why these mutations may not lead to toxin resistance in the context of stable cell lines but we hypothesize that under these conditions , inactivation of these genes may lead to compensatory changes that may facilitate toxin transport through alternative pathways as YKT6 , YIPF5 , or SCYL1 have been implicated in retrograde transport [32] [33] . Taken together , these findings validate the results of our screen and implicate components of the retrograde and Golgi transport , and ERAD machinery in the transport of typhoid toxin . We next sought to investigate the specific contribution of the pathways identified in the screen in the transport of typhoid toxin to the Golgi . We treated cells deficient in specific transport pathways with fluorescently labeled typhoid toxin and examined its co-localization with the Golgi marker GM130 . We found that cells deficient in the GARP complex components VPS51 and VPS54 exhibited significantly reduced toxin co-localization with GM130 relative to wild-type cells ( Fig 4A and 4B ) . In contrast cells deficient in TMED2 , SEL1L , and SYVN1 exhibited equivalent levels of toxin co-localization with GM130 to those observed in wild type ( Fig 4A and 4B ) . Cells deficient in the COG complex components COG1 and COG5 showed an intermediate phenotype with some reduction in the level of toxin co-localization with GM130 but not as marked as what was observed in cells deficient in GARP complex components ( Fig 4A and 4B ) . We also applied BG-labeled typhoid toxin to cells expressing the SNAP-tagged Golgi resident protein GalT and monitored the formation of a TT-SNAP-GalT complexes . Consistent with the GM130 co-localization studies , typhoid toxin arrival to the Golgi was drastically reduced in cells deficient in the GARP complex components VPS51 and VPS54 ( Fig 4C and 4D ) . In contrast , cells deficient in COG complex components , which showed reduced but still significant level of toxin-GM130 co-localization , showed equivalent levels of TT-SNAP-GalT complex formation than those observed in wild type cells ( Fig 4C and 4D ) . These observations indicate that , consistent with its proposed function [27] , the COG complex may be important for mobilizing typhoid toxin through the Golgi but not for its delivery to the Golgi . Consistent with their predicted function [28 , 29] , cells deficient in TMED2 and the components of the ERAD machinery SEL1L , and SYVN1 showed no defects in typhoid toxin delivery to the Golgi ( Fig 4C and 4D ) . In fact , inactivation of SYVN1 resulted in a slight increase in the levels of TT-SNAP-GalT complex formation ( Fig 4C and 4D ) . It is possible that alteration in the ERAD pathway may result in a slow-down in transit of typhoid toxin through the Golgi , which could lead to more efficient TT-SNAP-GalT complex formation . None of the defective cells showed any reduction in the levels of toxin binding to the cell surface , indicating that disruption of the different components of the transport pathway did not affect the localization of the sialoglycan-containing surface molecules that serve as toxin receptors ( S1 Fig ) . Taken together , these results indicate that the GARP complex is required for the endosome-to-Golgi transport of typhoid toxin while the COG complex most likely contributes to its transport within the Golgi apparatus . These findings are entirely consistent with the involvement of these molecules in vesicle transport . The CdtB and PltA subunits of typhoid toxin are tethered by a disulfide bond that must be reduced prior to their translocation from the ER to the cell cytosol ( See Fig 1D and 1E ) , a process that must be mediated by ER-resident reductases . We therefore tested the disassembly of typhoid toxin in the different defective cell lines as a surrogate assay for its arrival to the ER . We found that consistent with their role in the endosome-to-Golgi transport , cells defective in the GARP complex components Vps51 and Vps54 showed a significant defect in toxin processing as shown by the significant proportion of fully assembled toxin remaining in these cells , an indication of the failure of the toxin to arrive to the ER ( Fig 5A and 5B ) . Like cells defective in the GARP complex , cell lines defective in COG1 and COG5 also showed a defect in toxin transport to the ER ( Fig 5A and 5B ) . As these cells showed reduced but not abolished toxin co-localization with the Golgi marker GM130 ( Fig 4A and 4B ) and wild type levels of the formation of the TT-SNAP-GALT complex ( a reporter for typhoid toxin’s arrival to the Golgi ) ( Fig 4C and 4D ) , these results are consistent with the notion that the COG complex is involved in typhoid toxin intra-Golgi transport . Cells defective in the ERAD components SEL1L and SYVN1 did not show defects in typhoid toxin processing indicating that transport to the ER is unaffected in these cell lines ( Fig 5A and 5B ) . However , cells defective in TMED2 showed a marked defect in typhoid toxin disassembly ( Fig 5A and 5B ) . Since these mutant cells showed no measurable defect in the ability of typhoid toxin to arrive to the Golgi ( Fig 4A–4D ) , these results indicate that TMED2 is specifically required for the Golgi-to-ER toxin transport . Although TMED2 has been implicated in vesicle traffic between the ER and Golgi complex [30 , 31 , 34] , it has not been previously reported to be involved in the retrograde transport of AB5 toxins , therefore these findings revealed unique properties in the intracellular transport mechanisms of typhoid toxin . Upon trafficking to the ER , the typhoid toxin A subunits PltA and CdtB dissociate from their B subunit prior to their translocation to the cell cytosol ( Fig 1D and 1E ) . CdtB , which possess a nuclear localization signal , must then be transported from the cytosol to the nucleus where it exerts its function . Our screen identified SEL1L and HRD1 ( SYVN1 ) , two components of the endoplasmic-reticulum-associated protein degradation ( ERAD ) pathway [28 , 35] , as required for intoxication ( Fig 2A–2D ) . The observation that typhoid-toxin-transport to the ER is unaffected in cell lines deficient in these ERAD components ( Fig 5A and 5B ) suggested that SEL1L and HRD1 might be involved in the translocation of typhoid toxin components from the ER to the cytosol . To examine this possibility we used a selective permeabilization protocol to probe for the presence of the typhoid toxin subunit CdtB in the cell cytosol . We found that the levels of CdtB in the cytosol of the SEL1L and HRD1 deficient cells was significantly reduced ( Fig 5C and 5D ) , indicating that , similar to other AB5 toxins , typhoid toxin usurps the ERAD pathway for its retrotranslocation to the cell cytosol . Cytolethal distending toxin ( CDT ) is encoded by several pathogenic bacteria including C . jejuni , some serovars of Salmonella enterica , and some pathogenic isolates of E . coli [36 , 37] . It is composed of three subunits , CdtA and CdtC , which serve as its heterodimeric B subunit , and CdtB , which acts as its single A subunit and is a close homolog of typhoid toxin’s CdtB . In fact , in vitro experiments have shown that the similarity is such that CdtB from CDT can form a functional complex with PltA and PltB if a Cys residue is added to form the disulfide bond that links it to PltA [38] . In cultured cells , CDT and typhoid toxin shared the ability to stimulate cell cycle arrest due to DNA damage [4 , 39] . Like typhoid toxin , CDT is also delivered to cells via retrograde transport mechanisms [40] . However , the specific details of its transport pathway are incompletely characterized . Since typhoid toxin and CDT do not share the same surface receptors , we hypothesized that at least some aspects of their transport mechanism may differ . To identify potentially unique specific aspects in the retrograde transport of these toxins , we examined the susceptibility to CDT of cell lines carrying inactivating mutations in genes involved in typhoid toxin transport . We found that cells deficient in the GARP complex components Vps51 and Vps54 , or in TMED2 showed resistance to CDT intoxication , an indication of similarities in the retrograde transport from endosomal compartments as well as from the Golgi to the ER ( Fig 5E ) . Similarly , cells deficient in ERAD components ( i . e . SEL1L and HRD1 ) showed resistance to CDT indicating that , as previously reported [40] , CDT utilizes this machinery for its retrograde transport to the cytosol ( Fig 5E ) . In contrast to typhoid toxin , however , cells deficient in the COG complex components COG1 and COG5 were found to be susceptible to CDT intoxication indicating differences in some aspects of the intracellular transport of these toxins ( Fig 5E ) . Taken together , these findings revealed common and unique features in the transport mechanism responsible for the traffic of typhoid toxin and CDT to their cellular destinations .
Typhoid toxin is unique in that , prior to reaching its host cell targets , it must traffic within the cell in opposite directions: 1 ) after its synthesis within the Salmonella-containing vacuole it must be transported to the extracellular space hijacking elements of the cell’s exocytic machinery [2 , 41]; and 2 ) after its transport to the extracellular space , it must enter and traffic within target cells to reach its cellular targets . Here , we have dissected the second of these trafficking events and identified cellular machinery that transports typhoid toxin from the cell surface to its final destination within the intoxicated cells . To reach their targets within cells , bacterial toxins generally must traverse multiple membrane barriers to gain access to the cell cytosol . These crucial steps involve a variety of strategies , which will ultimately determine the toxin’s transport pathway . While some toxins reach the cell cytosol by directly traversing the cell’s plasma membrane [42 , 43] , others do so from within various endosomal compartments [19 , 44] . Most AB5 toxins gain access to the cell cytosol by hijacking machinery from the ERAD pathway , whose normal function is to remove misfolded proteins from the ER so that they can be transported to the cytosolic proteasome for degradation [19 , 45] . Therefore to reach their translocation site , AB5 toxins must be transported from the plasma membrane to the endoplasmic reticulum through a process collectively referred to as retrograde transport . We found that after its receptor-mediated uptake , typhoid toxin follows this overall retrograde transport pathway to the ER . In this compartment , typhoid toxin is disassembled after the reduction of the disulfide bond that tethers its PltA and CdtB enzymatic subunits together , so that they can be individually translocated to the cell cytosol . To gain insight into the cellular machinery involved in the transport of typhoid toxin , we carried out a genome-wide CRISPR/Cas9 screen in cultured human cells for genes whose inactivation confer resistance to typhoid toxin . As defects in toxin transport should lead to resistance to intoxication , it was expected that the identity of at least some of these genes should provide a road map for the typhoid toxin transport pathway . This analysis was followed by a more detailed analysis of a selected group of genes , which allowed us to obtain a comprehensive view of typhoid toxin transport ( Fig 6 ) . To gain access to cells , typhoid toxin must first recognize specific acetyl neuraminic acid-terminated sialoglycans on surface glycoproteins or gangliosides [1 , 14] . This receptor redundancy most likely results in multiple entry routes , which presumably leads to multiple initial sorting events . This is reflected in our genetic screen in that it did not identify genes that could be assigned to these early events in typhoid toxin transport . For example , although some toxins require clathrin for their internalization [46–48] , we found no evidence for clathrin involvement in typhoid toxin uptake , neither in our screen ( S1 and S2 Tables ) nor by directly targeting clathrin with CRISPR/Cas9-mediated genome editing ( S2 Fig ) . These potentially redundant transport pathways likely converge downstream probably at the level of the Golgi-associated retrograde protein ( GARP ) complex . Indeed , our screen identified all the components ( VPS51-Vps54 ) of the GARP complex as playing a central role in typhoid toxin transport . The GARP complex is a vesicle-tethering factor that participates in retrograde transport by facilitating the fusion of early and late endosomes-derived vesicle carriers with the Golgi [26 , 49] . Therefore this complex is positioned to orchestrate the transport of typhoid toxin to the Golgi regardless of its internalization route . The GARP complex has been shown to play a central role in the transport of other AB5 toxins , therefore emerging as a major hub in the retrograde transport of bacterial toxins [50 , 51] . Consistent with the requirement of the GARP complex for typhoid toxin transport , our screen also identified Arl1 , a GTPase that is thought to play a regulatory role for GARP complex function [52] ( Fig 2 and S1 and S2 Tables ) . It is likely that the GARP complex works in conjunction with additional proteins to facilitate the transport of typhoid toxin from early and late endosome to the Golgi . Candidate proteins identified in our screen ( Fig 2 and S1 and S2 Tables ) that may work in concert with GARP include UNC50 , which has been implicated in a similar function for Shiga toxin [53] , and COPB1 and COPB2 , which are components of the COP1 coat involved in vesicle transport [54] , further supporting the involvement of this traffic machinery in typhoid toxin retrograde transport . Our screen identified several genes encoding proteins or protein complexes involved in Golgi transport , notably all but one of the 8 components of the conserved oligomeric Golgi ( COG ) complex ( COG1 , COG2 , COG4 , COG5 , COG6 , COG7 and COG8 ) [27 , 55] ( Fig 2 and S1 and S2 Tables ) . The COG complex functions as a vesicular tether during retrograde intra-Golgi trafficking . Consistent with this function , typhoid toxin was unable to reach the ER in cultured cell lines engineered to be deficient in specific COG complex components ( Fig 5A and 5B ) . However , typhoid toxin was able to reach the TGN in these cells as demonstrated by its ability to interact with the SNAP-tagged Golgi resident protein GalT ( Fig 4C and 4D ) , indicating that , as predicted by its function , the COG complex may coordinate typhoid toxin transport through the Golgi . The role of other Golgi resident proteins that our screen determined to be necessary for efficient intoxication , such as solute carrier family protein 35A1 and 35A2 ( SLC35A1 and SLC35A2 ) [56] , transmembrane protein 65 ( TMEM165 ) [57] , Core 1 UDP-Galactose:N-Acetylgalactosamine-Alpha-R Beta 1 , 3-Galactosyltransferase 1 ( C1GALT1 ) [58] , and O-Linked Mannose N-Acetylglucosaminyltransferase 2 ( POMGNT2 ) [59] ( Fig 2 and S1 and S2 Tables ) is less clear as these proteins are involved in glycosylation reactions and therefore may indirectly alter typhoid toxin transport . The same may apply to N-myristoyl transferase-1 ( NMT1 ) , which has been shown to alter Golgi transport by affecting the Golgi membrane/cytosol partitioning of ADP-ribosylation factor ( Arf ) proteins [60] . We found that the resident Golgi protein TMED2 plays an essential role in typhoid toxin’s transport from the Golgi to the ER ( Fig 5A and 5B ) . This result is noteworthy , as TMED2 has not been previously implicated in toxin transport . These findings are also consistent with the proposed role of this Golgi-resident p24 protein family member in the transport between the cis-Golgi network and the ER [30 , 31 , 34] . TMED2 possess a large luminal N-terminus and a short cytoplasmic C-terminal tail at the cytosol . Via its cytoplasmic tail , TMED2 interacts with ADP-ribosylation factor 1 ( ARF1 ) , COPI , and COPII subunits , which suggest that TMED2 can act as cargo receptor and coat protein in vesicle transport . Consistent with this notion , our screen identified the COP components COPB1 and COPB2 as required for typhoid toxin intoxication . Our results clearly implicate the endoplasmic reticulum associated degradation ( ERAD ) pathway in the translocation of typhoid toxin from the ER to the cell cytosol ( Fig 5C and 5D ) . The ERAD is involved in the transport of misfolded proteins from the ER to the cytosol for their subsequent delivery to and degradation by the proteasome [23 , 61] . There are different mechanisms by which proteins are translocated via this pathway , which are largely dependent on whether the misfolded proteins are located in the lumen of the ER , within the ER membrane , or on the cytosolic side of the ER membrane . Our screen identified the E3 ubiquitin ligase HRD1 ( SYVN1 ) , which forms a channel through which misfolded proteins and presumably unfolded toxins pass through the ER membrane [62] ( Fig 5C and 5D ) . Other postulated components of the translocation machinery include HRD3 , USA1 , DER1 , and YOS9 , none of which were identified in our screen . However , our screen identified suppressor/enhancer of Lin-12-like ( Sel1L ) as essential for typhoid toxin translocation from the ER ( Fig 5C and 5D ) . Sel1L has been shown to be an integral component of the HRD1 complex , playing an essential role in the transport of a subset of ERAD substrates including cholera toxin [50 , 63] . Overall , our screen identified several components that previous studies have implicated in the retrograde transport of other bacterial toxins , including the GARP , COG , and various components of the core ERAD pathway ( Fig 6 ) . Therefore these core toxin transport components can be viewed as hubs that are central for toxin transport and thus can potentially serve as targets for the development of novel broadly-acting antitoxin strategies . However , our screen also identified proteins that , although implicated in various transport functions , including the transport of other bacterial toxins , their mechanism of action is less well understood . These include Golgi-localized G protein couple receptor 107 ( GPR107 ) and Transmembrane 9 Superfamily Member 2 ( TM9SF2 ) ( Fig 2 and S1 and S2 Tables ) , which have been implicated in the transport Pseudomonas aeruginosa exotoxin A , CDT , and Shiga toxins [40 , 51 , 64 , 65] . The study of toxin transport mechanisms could thus provide major insight into the activity of these proteins , which have also been implicated in human pathologies including periodontal disease , viral infections , and cancer [66–70] . Our study revealed common features between the toxin transport mechanism of typhoid toxin and C . jejuni CDT ( Fig 5E ) . This observation is relevant since both toxins share the active subunit in whose activity this genetic screen was based . However , despite the existence of common core factors involved in toxin transport , it is clear that there are unique features in the transport mechanisms of specific bacterial toxins . For example , our study revealed that while the COG complex components COG1 and COG5 are required for typhoid toxin intoxication , these proteins were dispensable for CDT intoxication . Furthermore , comparison of our results with those of other genome wide screens conducted with other toxins demonstrate that , despite sharing some common core components of the cellular transport machinery , each toxin does exhibit unique aspects in their transport pathways ( Fig 6 ) [50 , 51 , 65] , which may revealed variations in vesicle transport mechanisms that have not yet been captured by more directed cell biological studies . This in turn illustrates the value of the study of bacterial toxins as tools to gain insight into basic cellular functions . In summary , our study has provided a road map for the transport pathway of typhoid toxin in intoxicated cells . These findings can provide a framework for the development of novel therapeutic strategies to combat typhoid fever and other infectious diseases .
All plasmids used in this study are listed in S4 Table and were constructed using the Gibson strategy [71] and were verified by nucleotide sequencing . Antibodies to Myc ( Cell Signaling Technology , Cat . #2276 ) , TMED2 ( Santa Cruz Biotechnology , Cat . # sc376458 ) , and GM130 ( BD Bioscience , Cat . # 610822 ) were purchased from the indicated commercial sources . Antibodies to purified recombinant typhoid toxoid were generated by Pocono Rabbit Farm & Laboratory . BG-GLA-NHS ( Cat . #S9151 ) was purchased from New England Biolabs and the Human GeCKOv2 libraries ( Cat . #1000000049 ) and the plasmid psPAX2 ( Cat . #12260 ) were purchased from Addgene . All cell lines were grown in Dulbecco’s modified Eagle medium ( DMEM , Gibco ) supplemented with 10% fetal bovine serum ( FBS ) at 37°C with 5% CO2 in a humidified incubator , and were routinely checked for mycoplasma with a Mycoplasma Detection Kit ( SouthernBiotech , Cat# 13100–01 ) . To generate cells stably expressing wild-type Cas9 endonuclease ( HEK293T-Cas9 ) , HEK293T cells ( ATCC ) were transduced with lentiviral particles produced from lentiCas9-Blast ( Addgene , #52962 ) and selected for blasticidin resistance . The preparation of lentiCRISPR library A and B was carried out as described previously [24] . Briefly , HEK293T cells were seeded on thirty-five 100 × 20 mm tissue culture dishes and grown to 30% confluence . Each plate was then transfected with 7 μg of the human CRISPR Knockout Pooled Library DNA ( GeCKO v . 2 library ) , and 3 . 5 and 5 μg of pVSVg and psPAX2 plasmid DNA , respectively , using Lipofectamine 2000 with PLUSTM reagent ( Life Technologies ) . After 5hr , the media was changed to DMEM supplemented with 10% FBS and 1% BSA ( Sigma ) . The culture media was pooled , centrifuged at 3 , 000 rpm for 10 min at 4°C to pellet cell debris , and supernatants were filtered through 0 . 45 μm low-protein-binding membranes . To concentrate the pooled library , viral particles were centrifuged at 24 , 000 rpm for 2 hr at 4°C and pellets resuspended in DMEM with 1% BSA for further use in cell transduction . The viral libraries were titered as follows . HEK293T-Cas9 cells were seeded in 6-well plates and transduced with varying amounts of the viral preparations in the presence of polybrene ( 8 μg/ml ) . The 6-well plates were centrifuged at 2 , 000 rpm for 2 hr at 37°C , and the infection media replaced with fresh media . After 24 hr , cells were detached using trypsin and split into duplicate wells with or without puromycin ( 0 . 5 μg/ml ) . After 1–2 days , cells were counted to calculate the percentage of transduction . Large-scale transduction of 4 × 107 cells was carried out in the same manner and incubated with media containing puromycin for 7 days . HEK293T-Cas9 transduced with the lentivirus GeCKOv2 libraries targeting human genes were grown on 6-well plates and subjected to puromycin-resistance selection for 7 days as indicated above . Transduced cells ( 3 × 107 ) were then treated with media alone ( control group ) or 40 ρM of typhoid toxin ( typhoid toxin treated group ) at 37°C for 60 min and changed to normal culture media . The control group was harvested 2 to 3 days post treatment when cells reached 90% confluence . The surviving cells from the typhoid toxin treated group were harvested 15 days post treatment , re-seeded onto 15 cm dishes and then harvested when they reached 90% confluence . Genomic DNAs from toxin or mock treated cells were purified with Blood & Cell Culture Midi kit ( Qiagen ) . A two-step PCR amplification protocol with Illumina sequencing adapters and sample barcodes was applied as described previously using primers listed in S3 Table . Briefly , DNA fragments containing lentiCRISPR sgRNA sequences were first amplified using primers CRISPR-F1 and R1 ( see S3 Table ) . A second PCR was conducted to attach Illumina adaptors and barcode samples using CRISPR-F2 and a R2 primer contanining a unique barcode . PCR products were separated on 2% agarose gels and extracted with the QIAquick Gel Extraction kit ( Qiagen ) . Samples were sequenced on a HiSeq 2500 ( Illumina ) at the Yale Center for Genomic Analysis suing CRISPR sequencing primers ( see S3 Table ) . To identify sgRNA sequences , the sequence reads were trimmed for quality and length using the Cutadapt program ( http://journal . embnet . org/index . php/embnetjournal/article/view/200 ) . Bowtie v1 . 1 . 2 ( http://bowtie-bio . sourceforge . net/index . shtml ) was then used to align the sequence reads back to a reference file of all sgRNA sequences in Library A or B ( provided by Addgene ) . The MAGeCK algorithm was used to identify positively selected genes in each library separately [25] . A total of 6 independent screens were conducted , 3 for each of the lentiCRISPR libraries . Purification of typhoid toxin and cytolethal distending toxin ( CDT ) was conducted as described previously [1 , 72] . Briefly , the genes encoding typhoid toxin in Salmonella Typhi ( pltA/pltB/6xHis-cdtB ) or CDT in Campylobacter jejuni ( cdtA/cdtC/6xHis-cdtB ) were cloned into the pET28a ( Novagen ) expression vector . Escherichia coli strains carrying the different plasmids were grown at 37°C in LB media to an OD600 of ~0 . 6 , toxin expression was induced by the addition of 0 . 5 mM IPTG , and cultures were further incubated at 25°C overnight . Bacterial cell pellets were resuspended in a buffer containing 15 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 0 . 1 mg/ml DNase , 0 . 1 mg/ml lysozyme , and 0 . 1% PMSF and lysed by passage through a cell disruptor ( Constant Systems Ltd . ) . Toxins were then purified from bacterial cell lysates through affinity chromatography on a Nickel-resin ( Qiagen ) , ion exchange , and gel filtration ( Superdex 200 ) chromatography as previously described [1 , 72] . Purified toxins were examined for purity on SDS-PAGE gels stained with coomassie blue . CRISPR/Cas9-edited cell lines were generated as previously described [12] . Briefly , HEK293T cells were transfected with plasmids encoding the different sgRNA , Cas9 and puromycin resistance genes using Lipofectamine 2000 . Transfected cells were then treated with puromycin for selection and isolated clones were further screened by PCR genotyping using the primers listed in S3 Table . At least two independently isolated clones per cell line were characterized for the relevant phenotypes . In all cases , the different cell lines exhibited equivalent phenotypes . Cell-cycle arrest after typhoid toxin intoxication was examined by flow cytometry as previously described [2] . Briefly , cells were collected and fixed overnight with 70% ethanol in DPBS at -20°C . Fixed cells were washed with DPBS and resuspended in 0 . 5 ml of DPBS containing 50 μg/ml propidium iodide , 0 . 1 mg/ml RNase A and 0 . 5% Triton X-100 and incubated for 30 min at 37°C . Cells were then washed with DPBS , filtered , and analyzed by flow cytometry on a BD Accuri C6 flow cytometer . The DNA content of cells was determined using FlowJo ( https://www . flowjo . com/ ) . Relative toxicity was determined by measuring the concentration of typhoid toxin resulting in 50% of the treated cells ( wild type and CRISPR/Cas9 edited cell lines ) in G2/M . Briefly , the different cells were treated with a serial dilution of a typhoid toxin preparation , and the percentage of cells in G2/M was determined by flow cytometry as described above . Values were fitted to an orthogonal polynomial regression of degree 2 to estimate the relationship between toxin concentration and % of cells in G2/M using the R software version 3 . 4 . 4 ( https://www . r-project . org ) . Purified typhoid toxin was fluorescently labeled with Oregon Green ( OG ) -488 dye ( Invitrogen ) according to the vendor’s recommendations . Briefly , purified toxin preparations ( 1 mg/ml ) were incubated with the OG-488 dye in 100 mM bicarbonate buffer for 1 h at room temperature and applied to a size-exclusion chromatography column to separate the toxin from the free dye . Typhoid toxin binding was assayed by flow cytometry as previously described [1] . Briefly , wild type and CRISPR/Cas9-edited HEK293T cells were seeded in 24-well plates for 24 hr and incubated with 0 . 2 μg of OG-488-labeled typhoid toxin for 60 min at 4°C . Cells were then fixed with 1% of paraformaldehyde , subjected to flow cytometric analyses on a BD Accuri C6 flow cytometer , and the resulting data were analyzed with FlowJo . To evaluate toxin binding and internalization by immunofluorescence microscopy , wild type and CRISPR/Cas9-edited HEK293T cells were treated with fluorescently-labeled toxin for 30 min at 4°C , washed with PBS twice , and then switched to 37°C for 0 . 5 , 2 , and 8 hr . Cells were then fixed with 4% paraformaldehyde , and stained with an antibody directed to the cis-Golgi marker GM130 ( BD Bioscience ) overnight at 4°C , and an Alexa 594-conjugated anti-mouse antibody ( Invitrogen ) for 1 hr at room temperature . Cells were then observed under Nikon TE2000 fluorescence or a Leica TCS SP6 Confocal microscopes . The co-localization of typhoid toxin with the cis-Golgi marker GM130 was quantified by fluorescent microscopy using the Coloc 2 plugin of the open source software ImageJ https://imagej . nih . gov/ij/ Purified typhoid toxin ( 50 μg ) was incubated for 2 hr at room temperature with BG-NHS ( New England BioLabs , Cat #S9151S ) ( 20 mM stock solution in DMSO ) at a molar ratio of 1:3 . The unreacted esters were quenched with 50 mM Tris ( pH 8 ) and excess BG-NHS was removed with an Amicon ultra spin column . Wild type and the different CRISPR/Cas9-edited HEK293T cells ( 6 X 105 /ml ) were seeded on 6-well plates and transfected with a plasmid encoding myc-epitope tagged GalT-SNAP using Lipofectamine 2000 . Next day , cells were treated with either 0 . 5 μg of BG-labeled or unlabeled typhoid toxin , harvested 6 hr after treatment , and cell lysates were analyzed by Western blot with an anti-Myc antibody . The amount of typhoid toxin-SNAP-GalT complex in wild type and the different CRISPR/Cas9 edited cell lines was determined by measuring the densities of all bands associated with this complex ( as shown by the shift their molecular weight ) using the Image Studio Lite software ( Li-COR Biosciences ) normalized for loading , relative to wild type , which was given a value of 100 . Wild type and CRISPR/Cas9-edited HEK293T cells ( 1 X 107 ) were seeded on 10 cm dishes and subsequently treated with 100 ng of purified His-tagged typhoid toxin at 37°C for 30 minutes . Cells were washed in DPBS to remove unbound typhoid toxin , incubated in media containing 10% FBS for indicated times , lysed in lysis buffer [ ( 150 mM NaCl , 50 mM Tris-HCl ( pH 7 . 4 ) , 0 . 5% Triton-100 , 1X protease inhibitor cocktail ( Roche ) ] for 30 min at 37°C , and centrifuged at 14 , 000 rpm for 15 min . Typhoid toxin from the soluble fractions was recovered by affinity chromatography through a nickel resin ( Qiagen ) after overnight incubation at 4°C and subsequent elution in 30 μl of an elution buffer containing 200 mM imidazole and 0 . 15 M Tris-HCl ( pH 6 . 8 ) for 20 min at room temperature . The protein eluates were analyzed by western blot with a rabbit anti-typhoid toxin antibody and a secondary HRP-conjugated goat anti-rabbit antibody in the presence or absence of DTT . Blots were quantified with Image Studio Lite ( Li-COR Biosciences ) and the proportion of assembled and dissembled toxin was determined by the ratio of the intensities of the bands corresponding to the CdtB-PltA complex and the CdtB monomer . Relative disassembly was determined by comparing the values to those of wild type , which was considered 100 . Wild type and CRISPR/Cas9-edited HEK293T cells ( 1 X 107 ) were seeded on 10 cm dishes and treated with 1 μg of purified His-tagged typhoid toxin for 30 min at 37°C . Lysates were resuspended in 500 μl of HCN buffer containing 50 mM HEPES ( pH 7 . 5 ) , 150 mM NaCl , 2 mM CaCl2 , 0 . 04% Digitonin , and 1X of a protease inhibitor cocktail ( Roche ) for 10 min at 4°C . Cytosolic ( soluble ) and membrane ( pellet ) fractions were separated by centrifugation at 14 , 000 rpm for 10 min . Pellets were resuspended in 2 x Laemmli buffer and soluble fractions were subjected to nickel affinity chromatography to recover typhoid toxin as described above . The relative amounts of typhoid toxin in the different samples was then assayed by Western blot with an anti toxin antibody using the Image Studio Lite ( Li-COR Biosciences ) software as described above . The p values were calculated using a two-tailed , unpaired Student’s t test for two group comparisons in GraphPad Prism ( GraphPad software ) . P values <0 . 05 were considered significant . Details of the statistical tests used to evaluate the significance of all observations ( including the statistical test , precision and dispersion metrics , the n values used as well as how significance is defined ) , is provided in the corresponding figure legends . The methods of statistical analysis are also described for individual experimental approaches in the Methods section above . The following software was used in this study: Graphpad Prism ( plotting data ) , Micro-Manager , Slidebook 6 , and Leica Application Suite Advanced Fluorescence ( image acquisition ) , Adobe Illustrator & Adobe Photoshop ( image preparation ) , FlowJo ( analysis of flow cytometry data ) , R project ( scatter plots of CRISPR screen results ) , Bowtie v1 . 1 . 2 ( alignment of the sequence reads ) , and Image Studio Lite ( Li-COR Biosciences ) ( quantification of the band intensity of western blot ) . | Typhoid toxin is an important virulence factor for the human pathogen Salmonella Typhi , the cause of typhoid fever . This toxin is composed of a pentameric “B” subunit linked to two enzymatic “A” subunits , resulting in an unusual A2B5 configuration . The B subunit targets the toxin’s enzymatic activities by interacting with specific surface receptors . Once internalized , the toxin must be transported to its final subcellular destination by specific transport mechanisms . Here we have used a multidisciplinary approach to define the details of the intracellular transport mechanisms utilized by typhoid toxin . Through a genome-wide screen , we found that typhoid toxin utilizes components of the retrograde transport cellular machinery to arrive to the endoplasmic reticulum , from where it is transported to the cell cytosol by the endoplasmic reticulum-associated degradation pathway . By comparing typhoid toxin’s transport pathway with the transport mechanisms utilized by other toxins we have defined unique a common components that transport these toxins to their cellular destinations . These studies may provide the based for the development of novel anti-toxin therapeutic strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"flow",
"cytometry",
"medicine",
"and",
"health",
"sciences",
"toxins",
"pathology",
"and",
"laboratory",
"medicine",
"endoplasmic",
"reticulum",
"cell",
"processes",
"toxicology",
"toxic",
"agents",
"bacterial",
"diseases",
"golgi",
"apparatus",
"cellular",
"structures... | 2019 | Unique features in the intracellular transport of typhoid toxin revealed by a genome-wide screen |
Many viruses attach to target cells by binding to cell-surface glycans . To gain a better understanding of strategies used by viruses to engage carbohydrate receptors , we determined the crystal structures of reovirus attachment protein σ1 in complex with α-2 , 3-sialyllactose , α-2 , 6-sialyllactose , and α-2 , 8-di-siallylactose . All three oligosaccharides terminate in sialic acid , which serves as a receptor for the reovirus serotype studied here . The overall structure of σ1 resembles an elongated , filamentous trimer . It contains a globular head featuring a compact β-barrel , and a fibrous extension formed by seven repeating units of a triple β-spiral that is interrupted near its midpoint by a short α -helical coiled coil . The carbohydrate-binding site is located between β-spiral repeats two and three , distal from the head . In all three complexes , the terminal sialic acid forms almost all of the contacts with σ1 in an identical manner , while the remaining components of the oligosaccharides make little or no contacts . We used this structural information to guide mutagenesis studies to identify residues in σ1 that functionally engage sialic acid by assessing hemagglutination capacity and growth in murine erythroleukemia cells , which require sialic acid binding for productive infection . Our studies using σ1 mutant viruses reveal that residues 198 , 202 , 203 , 204 , and 205 are required for functional binding to sialic acid by reovirus . These findings provide insight into mechanisms of reovirus attachment to cell-surface glycans and contribute to an understanding of carbohydrate binding by viruses . They also establish a filamentous , trimeric carbohydrate-binding module that could potentially be used to endow other trimeric proteins with carbohydrate-binding properties .
Viral infections are initiated by specific attachment of a virus particle to receptors at the surface of the host cell . This process , which serves to firmly adhere the virus to its cellular target , is rarely a bimolecular interaction between one viral attachment protein and one receptor . In most cases , several receptors are employed , and recognition events are frequently accompanied by substantial structural rearrangements that serve to expose new binding sites , strengthen the initial interaction , and prime the virus for cell entry . Structure-function analyses of virus-receptor interactions have provided detailed insights into the attachment strategies of viruses belonging to several different families [1]–[18] . However , much less is known about structure-function interrelationships between different binding sites for distinct receptors on the same viral attachment molecule . Reoviruses are useful experimental models for studies of virus-receptor interactions and viral pathogenesis . Moreover , the recent development of plasmid-based reverse genetics for reovirus provides an opportunity to manipulate these viruses for oncolytic and vaccine applications . Reoviruses form icosahedral particles approximately 850 Å in diameter . At the virion five-fold symmetry axes , the trimeric attachment protein , σ1 , extends from pentameric turrets formed by the λ2 protein . A similar arrangement of a trimeric attachment protein inserted into a pentameric base is also observed for the adenovirus attachment protein , fiber . The σ1 protein is about 400 Å long and consists of three discrete domains , termed tail , body , and head [19] . Residues 1 to 160 encompass the tail domain , which partially inserts into the virion capsid [20]–[22] . This region of the molecule is predicted to form an α-helical coiled-coil structure . The body domain encompasses residues 170 to 309 and contains β-spiral repeat motifs [22] . Lastly , the globular head domain incorporates residues 310 to 455 and folds into an 8-stranded β-barrel [22] , [23] . Reovirus attachment is thought to proceed via a two-step adhesion-strengthening mechanism , in which σ1 first engages widely distributed carbohydrate receptors with lower affinity . The three prototype reovirus strains , type 1 Lang ( T1L ) , type 2 Jones ( T2J ) , and type 3 Dearing ( T3D ) recognize different carbohydrate structures , which may account for the serotype-specific differences in routes of spread in the host and end-organ tropism . In the case of serotype 3 ( T3 ) reoviruses , the carbohydrate bound is α-linked sialic acid [24]–[26] . This initial contact , which has lower affinity and may allow for lateral diffusion of the particle at the membrane [27] , is followed by high-affinity interactions with junctional adhesion molecule-A ( JAM-A ) [28] , a component of tight junctions [29]–[31] . All reoviruses , including prototype and field-isolate strains , use JAM-A as a high-affinity receptor [28] , [32] , [33] . Firm adherence to the cell triggers uptake of the particle , which is dependent on β1 integrins [34] , [35] . Discrete regions of σ1 mediate binding to its cell-surface receptors . Structural and functional analyses show that the σ1 head , which projects farthest from the virus capsid , engages JAM-A [33] , [36] , [37] . In contrast , sequences in the σ1 body bind to carbohydrates [38] . Sequence analysis of reovirus variants identified three residues , Asn198 , Arg202 , and Pro204 , as likely critical for the interaction of T3 σ1 with sialic acid . These residues lie near the midpoint of the protein , at the lower end of the body domain , about 100 Å away from residues in the head that interact with JAM-A . Earlier structural analyses of T3D σ1 [22] , [23] , [36] were based on constructs that did not include this putative carbohydrate-binding site . It is therefore currently unclear how σ1 achieves its specificity for sialic acid , whether the large distance between the two receptor-binding sites on σ1 is relevant for binding , or whether σ1 undergoes rearrangements after engaging its carbohydrate receptor . To enhance an understanding of mechanisms by which viral attachment proteins engage cell-surface glycans , we determined the crystal structure of T3D σ1 in complex with α-2 , 3-sialyllactose , α-2 , 6-sialyllactose , and α-2 , 8-disiallylactose . All three carbohydrates terminate in sialic acid but feature different linkages that are present in various physiologic glycans . In addition , we used plasmid-based reverse genetics to engineer reoviruses that express mutagenized forms of σ1 to define residues required for functional binding to sialic acid . These studies shed light on the structural basis of σ1-sialic acid interactions and define a new carbohydrate-binding structural motif in a viral attachment protein .
The σ1 protein belongs to a class of fiber proteins constructed from triple β-spirals , a motif that was first identified in the adenovirus fiber [39] . In a previous study , we crystallized a smaller region of σ1 , spanning residues 246 to 455 and containing three β-spiral repeats as well as the globular head domain [22] . While this structure provided no insights into the carbohydrate-binding region of σ1 , it served as a basis to predict that β-spiral repeats form the entire body domain of the protein ( residues 167–309 ) [22] . Near residue 170 , the body domain transitions into a long α-helical coiled-coil region that forms the N-terminal tail domain ( residues 1–156 ) . To determine the structure of a longer fragment of σ1 including the predicted sialic-acid binding residues 198 , 202 , and 204 , we designed a construct for the expression of residues 170–455 . This construct excluded the long α-helical coiled-coil region to simplify protein expression , purification , and crystallization . Prototype strain T3D σ1 is sensitive to trypsin-mediated cleavage after Arg245 [40] . However , a sequence polymorphism occurring in the majority of T3 field-isolate strains , Thr249Ile , renders the protein resistant to trypsin [40] . A construct containing Ile249 was therefore used in our study . Trimerization was promoted by using a hexahistidine-tagged trimerization domain , a modified GCN4 sequence [41] , at the N-terminus of the expressed protein . This domain was proteolytically removed before final purification and crystallization . The structure of σ1 residues 170 to 455 reveals a highly elongated , symmetric trimer that measures about 200 Å in length ( Table 1 and Figure 1A , B ) . Tail residues N-terminal to amino acid 170 , which were not included in the crystallized protein , are predicted to form an α-helical coiled-coil structure that adds another 200 Å in length to the protein ( Figure 1C ) . As expected , the structure of the globular head domain ( residues 310 to 455 ) is essentially identical to that described previously [22] . However , the body domain displays a number of unusual features . Although sequence-based predictions suggested that this region would be composed of eight consecutive triple β-spiral repeats [22] , we find that the body domain contains a mixture of α-helical coiled-coil and β-spiral repeats ( Figure 1 ) . Four β-spiral repeats at the N-terminus ( β1–β4 , residues 170 to 235 ) are followed by a short α-helical coiled-coil ( cc , residues 236 to 251 ) and three additional β-spiral repeats ( β5-β7 , residues 252 to 309 ) ( Figure 2 ) . Inspection of the sequence indicates a likely reason for the deviation from the β-spiral fold at the center of the body ( Figure 2B ) . Three hydrophilic residues ( Thr236 , Ser244 , and Ser252 ) are located at positions that are typically occupied by hydrophobic side chains in β-spirals . Moreover , Ser241 replaces a characteristic proline or glycine at the turn in a β-spiral repeat . While some deviations from the β-spiral consensus sequence can be tolerated , even residues replacing the glycine or proline ( e . g . , residues Gln224 or Thr278 ) , the cumulative effect of the four non-consensus residues results in a β-spiral no longer being the optimal fold . The α-helical coiled-coil structure contains two heptad-repeat sequences , starting with Phe239 and ending with Gln251 ( Figure 2A , C ) . To elucidate the structural basis of the interaction of the reovirus attachment protein σ1 with its carbohydrate coreceptor , we prepared a complex by soaking crystals of σ1 with 10 mM α-2 , 3-sialyllactose , a compound that terminates in α-linked sialic acid . The subsequent structure , determined at 2 . 25 Å resolution ( Table 1 ) , unambiguously demonstrated the location of the carbohydrate in an unbiased difference electron-density map ( Figure 3A ) . The oligosaccharide binds in a shallow groove next to the loop connecting the second and third β-spiral repeats . The σ1 protein contains three identical binding sites , one on each chain , and all three are occupied by α-2 , 3-sialyllactose molecules , with the sialic acid making identical and extensive contacts in each chain ( Figure 3B , C ) . The lactose moieties face different directions , probably as a result of internal flexibility and participation in crystal contacts ( Figure 3C ) . Sialic acid contains four characteristic functional groups: a carboxylate at C1 , a hydroxyl group at C4 , an N-acetyl group at C5 , and a glycerol chain at C6 . All four groups are recognized by σ1 ( Figure 3B ) . Arg202 forms a bidentate salt bridge with the carboxyl group . A single hydrogen bond links the hydroxyl group at C4 to the carbonyl of Gly205 . The amide of the N-acetyl group is engaged in a hydrogen bond with the backbone carbonyl of Leu203 , and the N-acetyl methyl group is facing into a partially hydrophobic cavity . The glycerol chain lies parallel to the peptide backbone , forming direct hydrogen bonds with the backbone carbonyl of Ile201 and the amide nitrogen of Leu203 and in some of the binding sites water-mediated hydrogen bonds with the Asn210 side chain and the amide nitrogen of Ile211 . We note that Arg202 , which was previously shown to influence sialic acid binding [42] , provides a key contact to the ligand . Moreover , Pro204 , which also had been implicated in sialic acid binding [42] , is part of a structure that shapes the ligand-binding site . As contacts in the complex of σ1 with α-2 , 3-sialyllactose exclusively involve the sialic acid moiety , we hypothesized that σ1 should be capable of binding sialic acid in different naturally occurring linkages , including α-2 , 6- and α-2 , 8-linked sialic acid . We therefore determined crystal structures of σ1 in complex with α-2 , 6-sialyllactose ( Figure 4A ) and α-2 , 8-disialyllactose ( Figure 4B ) . Refinement statistics for both structures are provided in Table 1 . In each case , only two of the binding sites are occupied , as the third is partially blocked by crystal contacts . For the α-2 , 6-sialyllactose complex , the electron density allowed us to unambiguously identify all three sugar residues ( Figure 4A ) . The electron density for the α-2 , 8-disialyllactose complex did not allow us to model the terminal glucose . Comparison of these structures with each other and with the α-2 , 3-sialyllactose complex shows that the terminal sialic acid is bound in the same conformation and with identical contacts in all three cases . However , the remaining moieties of the glycans differ in conformation and contacts with σ1 . The α-2 , 3-sialyllactose and α-2 , 8-disialyllactose ligands assume an elongated shape in which the lactose groups face away from the protein ( Figure 3C , Figure 4B ) . Inspection of the α-2 , 8-disialyllactose complex shows that the N-acetyl group of the second sialic acid forms a hydrogen bond to the side chain of Ser195 . In contrast , σ1 binds α-2 , 6-sialyllactose in a folded-back conformation ( Figure 4A ) . This conformation is stabilized by an intramolecular hydrogen bond and the galactose O2 and O3 hydroxyl groups , which form hydrogen bonds to the backbone carbonyl atoms of Ser195 and Leu194 , respectively . To identify sequences that influence sialic acid binding , we used plasmid-based reverse genetics [43] , [44] to introduce point mutations into the σ1 protein of reovirus strain T3D . Mutant viruses were isolated following co-transfection of murine L929 cells with RNA-encoding plasmids corresponding to the T3D L1-L3 , M1-M3 , and S2-S4 genes and a plasmid corresponding to the σ1-encoding S1 gene incorporating site-specific mutations . Thus , each recombinant virus is isogenic , with the exception of the S1 gene and its protein product , σ1 . Guided by the structure of the σ1-sialic acid complexes , we engineered individual alanine substitutions of amino acids ranging from Asn189 to Asn210 . By their location in the structure , we hypothesized that these residues would be required for functional sialic acid binding . In addition , substitutions N198D , R202W , and P204L , which have been implicated in sialic acid binding by sequence comparisons of reovirus strains that differ in sialic acid utilization [26] , [45] and genetic analysis of reovirus mutants adapted to growth in murine erythroleukemia ( MEL ) cells [42] , were engineered to define the effect of these polymorphisms in an otherwise isogenic background . After confirming the σ1-encoding S1 gene nucleotide sequences , the mutant viruses were tested for hemagglutination ( HA ) capacity ( Figure 5 ) and growth in L929 cells and MEL cells ( Figure 6 ) . In comparison to rsT3D , rsT3D-σ1N198D , rsT3D-σ1R202A , rsT3D-σ1R202W , rsT3D-σ1L203A , rsT3D-σ1P204A , rsT3D-σ1P204L , and rsT3D-σ1G205A produced little or no agglutination of calf erythrocytes , a sensitive assay for sialic acid binding [26] . However , rsT3D-σ1N189A , rsT3D-σ1S195A , and rsT3D-σ1N210A produced HA titers that were comparable to those of wild-type rsT3D . Each of the point-mutant viruses produced approximately 1000-fold yields of viral progeny after growth in L929 cells ( Figure 6 ) , a cell line that does not require sialic acid binding for reovirus to replicate [45] . In contrast , those containing mutations N198D , R202A , R202W , L203A , P204A , P204L , and G205A displayed attenuated growth in MEL cells ( Figure 6 ) , a cell line permissive only to sialic acid binding reovirus strains [45] . These findings indicate that viruses with mutations of residues 198 , 202 , 203 , 204 , and 205 are altered in sialic acid binding efficiency , suggesting that these residues serve a functional role in T3D σ1-sialic acid interactions .
Although all known reovirus strains engage cells by binding to the tight junction protein JAM-A [33] , the major reovirus serotypes differ in the routes of dissemination in the host and tropism for host tissues [46]–[48] . These differences are linked to the σ1-encoding S1 gene segment and most likely attributable to serotype-specific interactions of σ1 with different cell-surface receptors . T3 reoviruses require sialic acid as a coreceptor , but the context in which sialic acid is bound is unknown . To define this interaction , we determined crystal structures of reovirus σ1 in complex with three sialylated glycans that incorporate a terminal sialic acid moiety in different linkages . These structural analyses were complemented with mutagenesis experiments that establish the physiologic relevance of the observed interactions . The σ1 protein uses a complex network of contacts to engage terminal sialic acid , which is a common feature of all three glycans studied here . The interactions involve σ1 residues at the lower end of the body domain , between β-spirals 2 and 3 . At this location , the sialic acid moiety docks into a shallow pocket that is formed mainly by residues in the third β-spiral . All four functional groups of sialic acid make contacts with σ1 through an elaborate network of hydrogen bonds and van der Waals interactions . Mutations that alter these contacts lead to significantly reduced sialic acid binding as assessed by HA profiles and diminished infection of MEL cells . Although all three ligands used for complex formation with σ1 contain additional carbohydrates , these make very few interactions . The complex with α-2 , 8-disialyllactose identified a hydrogen bond between the N-acetyl group of the second sialic acid and the side chain of Ser195 ( Figure 4B ) . However , the results from mutagenesis experiments demonstrate that a Ser195A mutation has no effect on either HA capacity or viral growth . Therefore , the observed contact is unlikely to have physiologic relevance . The interactions between σ1 and α-2 , 6-sialyllactose identified two hydrogen bonds that link the galactose to the protein and may help to stabilize the folded-back conformation of the ligand ( Figure 4A ) . As both contacts involve main chain atoms of σ1 , their functional significance cannot be easily probed by site-directed mutagenesis . Nevertheless , it is likely that the observed contacts lead to a modest increase in the affinity of σ1 for compounds terminating in α-2 , 6-linked sialic acid . It is unclear if such an increase is biologically significant . Naturally occurring sequence variability at three amino acid positions ( residues 198 , 202 , and 204 ) has been linked to the sialic acid-binding capacity of T3 σ1 [26] , [42] . Our structures readily identify two of these residues , Arg202 and Pro204 , as key determinants of sialic acid binding . The side chain of Arg202 forms a salt bridge with the sialic acid carboxylate group , while the Pro204 side chain stacks against the Arg202 guanidinium group . Moreover , the carbonyl oxygen in the peptide bond linking Leu203 and Pro204 forms a hydrogen bond with the sialic acid . Substitutions of either Arg202 or Pro204 , as seen in the R202W and P204L variants , would decrease the affinity for sialic acid , and this is confirmed by the mutagenesis data . In contrast , the critical role of residue 198 in ligand recognition is not apparent from the crystal structures . Our mutagenesis data ( Figure 5 and Figure 6 ) , in conjunction with previous results [42] , clearly demonstrate that Asn198 is required for successful sialic acid-dependent infection , with viruses carrying an N198D mutation having substantially reduced infectivity in MEL cells . However , the crystal structures show that Asn198 is not involved in direct or water-mediated contacts to any of the three oligosaccharides . Furthermore , the Asn198 side chain is solvent-exposed , forming a single hydrogen bond with the Asn189 side chain . Mutation of Asn189 to alanine does not affect sialic acid binding ( Figure 5 and Figure 6 ) , suggesting that the observed Asn198-Asn189 hydrogen bond is not relevant for ligand recognition . It is possible that the introduction of a negatively charged side chain at position 198 , as is the case with the N198D mutation , leads to long-range electrostatic effects or structural rearrangements that indirectly affect receptor binding . However , given the distance of Asn198 from the binding site and its surface-exposed location , this possibility appears remote . We think it more likely that Asn198 serves as a contact point with a part of the functionally relevant glycan , which has not been included in the structural analysis . Although our results define the interactions of σ1 with terminal sialic acid , the actual receptor may be a more complex sialylated glycan , perhaps carrying several branches . Such complex receptor structures , which can be attached to proteins or lipids , have recently been identified as the true ligands for several adenovirus and polyomavirus capsid proteins [16]–[18] . Therefore , Asn198 may well define a second receptor contact point for reovirus σ1 . A large collection of structures of viruses or viral attachment proteins in complex with sialylated oligosaccharide receptors is available , and these have produced significant insights into mechanisms of sialic acid binding , receptor specificity , and viral pathogenesis [1]–[3] , [5] , [9] , [11] , [14] , [16]–[18] , [49]–[52] . However , the interactions observed between T3D σ1 and sialic acid differ in important ways from those found in all other virus-receptor complexes , offering new insights into the parameters that guide viral attachment and specificity . In all cases in which structures are available , the receptors are bound by a globular domain in a region that projects farthest from the viral capsid and is easily accessible for interactions with the cell surface . In contrast , the highly elongated T3D σ1 protein engages its carbohydrate ligand at its midpoint , about 150 Å away from the region that projects farthest from the virion . Although the σ1 protein possesses some flexibility at defined regions [19] , [22] , the location of the sialic acid-binding site would not appear optimal for engagement of membrane-bound receptors that feature sialylated ligands close to the membrane . The region of JAM-A that is engaged by the σ1 head domain is fairly close to the membrane [36] . Even when allowing for considerable flexibility between the σ1 head and body , it is difficult to envision a conformation in which the tail of σ1 is still inserted into the virus and the sialic acid binding site can closely approach the membrane . However , σ1 could more easily engage sialic acid that projects far above the membrane , perhaps by being located on a large protein or projecting from prominent loops . Prior to this study , structural information had been available only for the C-terminal portion of the σ1 protein [22] . Based on analysis of that structure , as well as sequence comparisons with the related adenovirus fiber protein , full-length σ1 was predicted to fold into three distinct regions: an N-terminal α-helical coiled coil ( termed the tail ) , a region containing eight consecutive β-spiral repeats ( the body ) , and a globular β-barrel ( the head ) . Our structural analysis of a fragment comprising the body and head domains show that this model must be revised , as we find an insertion of a short α-helical coiled coil that interrupts the β-spiral sequence in the body , replacing one β-spiral repeat with a helical structure . Thus , it is clear that the structure of σ1 features several transitions between α-helical and β-spiral regions . This topological relationship differs from that of the adenovirus fiber , in which the shaft domain is thought to consist entirely of β-spiral repeats [39] . Examination of the T3D body domain sequence shows that it contains a nearly perfect heptad repeat pattern , which is typical for α-helical coiled coils , in a short stretch of 14 residues ( Figure 2 ) . A similar pattern is observed in the T1L and T2J σ1 sequences , but a proline residue within the consensus makes it unlikely that these proteins also feature a continous α-helical coiled coil at the equivalent location . To our knowledge , the structures presented here are the first examples of any fibrous viral protein engaging a ligand via its repetitive fiber region . Other viral attachment proteins contain fibrous- or stalk-like structures , but they usually engage receptors with globular head domains placed on top of these structural elements , as observed in complexes of adenovirus fiber proteins with their receptors [7] , [15] , [18] . Globular head domains offer higher variability in engaging ligands and can more easily create recessed binding pockets suitable for high-affinity binding . Instead , fiber-like structures generally feature short connections between their repeating units and a relatively flat surface , limiting binding options . However , inspection of the β-spirals in σ1 reveals subtle modifications in a single repeat that allow it to create a shallow binding site for sialic acid . One of the hallmarks of β-spirals is a highly conserved β-turn between two strands , involving residues at positions g , h , i , and j ( Figure 2 ) . The residue at position j is usually a proline or glycine . This turn is enlarged by two amino acids in the σ1 repeat that engages sialic acid , transforming the turn into a small loop ( Figure 7 ) . Interestingly , Pro204 introduces a kink after a β-strand , causing the chain to deviate from the β-spiral motif at this position to provide a pocket for the ligand . Thus , alteration of the typical repeating motif identifies a ligand-binding site in the case of σ1 . It is conceivable that similar aberrations in other fibrous protein sequences might also indicate binding sites . The location of a sialic acid binding site in an elongated fiber-like structure also raises the possibility of creating a small sialic acid binding cassette that could be transferred into a variety of trimeric fiber-like proteins constructed from α-helical coiled coils or β-spirals . Our work thus enhances an understanding of reovirus-glycan interactions and may also guide the construction of new sialic acid binding platforms to facilitate structure-function analyses and sialic acid-mediated cell targeting .
The expression of soluble and properly folded T3D σ1 trimers was facilitated by appending a trypsin-cleavable trimerization domain based on the GCN4 leucine zipper [41] N-terminally to a cDNA encoding the entire σ1 body and head domains ( amino acids 170–455 ) . The construct was cloned into the pQE-80L expression vector , which encodes a non-cleavable N-terminal hexahistidine-tag . The protein was expressed in E . coli Rosetta 2 DE3 ( Novagen ) at 20°C for 16 h post-induction or by autoinduction at 20°C for 48–72 h . Bacteria were lysed by two passages through an EmulsiFlex ( Avestin ) homogenizer and purified by Ni-IMAC using His-Trap-FF columns ( GE-Healthcare ) . The immobilized protein was eluted by on-column digestion with 0 . 1 mg/ml trypsin at a flowrate of 0 . 1 ml/min for 12 h . Size-exclusion chromatography ( Superdex-200 , GE-Healthcare ) was used as the final purification step . Crystals were grown using 15% PEG200 , 0 . 1 M MES ( pH 6 . 5 ) as a precipitant . The crystals belong to space group P21212 and contain one trimer in the asymmetric unit . Complexes with carbohydrate ligands were prepared by soaking crystals with the respective carbohydrate prior to data collection . The crystals were transferred into mother liquor supplemented with 10 mM carbohydrate , incubated for 5 min , and cryoprotected by incubation for 15 s in 35% PEG200 , 0 . 1 M MES , 10 mM carbohydrate ( pH 6 . 5 ) . Diffraction data were collected at the beamlines PXI ( SLS ) and ID14-4 ( ESRF ) . Diffraction data were integrated and scaled using XDS [53] , and the structure was solved by molecular replacement with AMoRe [54] using the structure of the T3D σ1 head ( PDB ID 1KKE ) as a search model . Refinement was performed with Refmac5 [55] and Phenix [56] , and model building was done in Coot [57] . Ligands were fitted into weighted Fo-Fc difference density maps at a contour level of 3σ and refined using the CCP4 library and user-defined restraints . Coordinates and structure factors for all three complexes have been deposited in the PDB data bank ( www . rcsb . org ) with accession codes 3S6X ( complex with α-2 , 3-sialyllactose ) , 3S6Y ( complex with α-2 , 6-sialyllactose ) and 3S6Z ( complex with α-2 , 8-di-sialyllactose ) . L929 cells [58] were maintained in Joklik's minimum essential medium ( Sigma-Aldrich ) supplemented to contain 5% fetal bovine serum , 2 mM L-glutamine , 100 U/ml of penicillin , 100 µg/ml of streptomycin , and 25 ng/ml of amphotericin B . MEL cells , previously designated T3cl . 2 cells [59] , were maintained in Ham's F-12 medium ( CellGro ) supplemented to contain 10% fetal bovine serum , 2 mM L-glutamine , 100 U/ml penicillin , 100 µg/ml streptomycin , and 25 ng/ml amphotericin B . Recombinant reoviruses were generated by plasmid-based reverse genetics [43] , [44] . Reovirus strains rsT3D ( wild type ) , rsT3D-σ1N198D , rsT3D-σ1R202W , and rsT3D-σ1P204L were recovered using monolayers of L929 cells at approximately 90% confluence ( 3×106 cells ) in 60-mm dishes ( Costar ) infected with rDIs-T7pol [60] at an MOI of ∼0 . 5 TCID50 . At 1 h post-infection , cells were co-transfected with ten plasmid constructs representing the cloned T3D genome using 3 µl of TransIT-LT1 transfection reagent ( Mirus ) per µg of plasmid DNA [43] . Reovirus strains rsT3D-σ1N189A , rsT3D-σ1S195A , rsT3D-σ1R202A , rsT3D-σ1L203A , rsT3D-σ1P204A , rsT3D-σ1G205A , and rsT3D-σ1N210A were recovered using BHK-T7 cells at 90% confluence ( approximately 3×106 cells ) seeded in 60-mm dishes . Cells were co-transfected with five plasmids representing the cloned T3D genome using 3 µl of TransIT-LT1 transfection reagent ( Mirus ) per µg of plasmid DNA [44] . The amount of each plasmid used for transfection was identical to that described for L929 cell transfections . Following 3 to 5 days of incubation , recombinant viruses were isolated from transfected cells by plaque purification using monolayers of L929 cells [61] . For the generation of σ1 mutant viruses , pT7-S1T3D [43] was altered by QuikChange ( Stratagene ) site-directed mutagenesis . To confirm sequences of the mutant viruses , viral RNA was extracted from purified virions and subjected to Onestep RT-PCR ( Qiagen ) using L1- or S1-specific primers . ( Primer sequences are available from the corresponding authors upon request . ) The purified PCR products were subjected to sequence analysis for the presence of the introduced mutation in the S1 gene segment and the noncoding signature mutation in the L1 gene segment [43] . Purified reovirus virions were prepared using second-passage L929-cell lysate stocks of twice plaque-purified reovirus as described [20] . Viral particles were Freon-extracted from infected cell lysates , layered onto CsCl gradients , and centrifuged at 62 , 000 × g for 18 h . Bands corresponding to virions ( 1 . 36 g/cm3 ) [62] were collected and dialyzed in virion-storage buffer ( 150 mM NaCl , 15 mM MgCl2 , 10 mM Tris-HCl pH 7 . 4 ) . The concentration of reovirus virions in purified preparations was determined from an equivalence of one OD unit at 260 nm equals 2 . 1×1012 virions [62] . Viral titers were determined by plaque assay using L929 cells [61] . Purified reovirus virions ( 1011 particles ) were distributed into 96-well U-bottom microtiter plates ( Costar ) and serially diluted twofold in 0 . 05 ml of PBS . Calf erythrocytes ( Colorado Serum Co . ) were washed twice with PBS and resuspended at a concentration of 1% ( vol/vol ) . Erythrocytes ( 0 . 05 ml ) were added to wells containing virus particles and incubated at 4°C for at least 2 h . A partial or complete shield of erythrocytes on the well bottom was interpreted as a positive HA result; a smooth , round button of erythrocytes was interpreted as a negative result . HA titer is expressed as 1011 particles divided by the number of particles/HA unit . One HA unit equals the number of particles sufficient to produce HA . HA titers from three independent experiments were compared using an unpaired Student's t test as applied in Microsoft Excel . P values of less than 0 . 05 were considered statistically significant . L929 cells or MEL cells ( 2×105 cells/well ) were plated in 24-well plates ( Costar ) and incubated at 37°C for at least 2 h . Cells were adsorbed with reovirus strains at an MOI of 1 PFU/cell . Following incubation at room temperature for 1 h , cells were washed three times with PBS and incubated at 37°C for 24 or 48 h . Samples were frozen and thawed twice , and viral titers were determined by plaque assay [61] . For each experiment , samples were infected in triplicate . Mean values from three independent experiments were compared using an unpaired Student's t test as applied in Microsoft Excel . P values of less than 0 . 05 were considered statistically significant . | Human reoviruses bind first with low affinity to a carbohydrate receptor that brings the virus in close proximity to the host cell . This interaction then facilitates high-affinity binding to a second receptor , the tight junction component junctional adhesion molecule-A ( JAM-A ) . While all human reoviruses bind JAM-A , they differ in carbohydrate receptor specificity , and this difference may influence the distinct disease patterns of reovirus serotypes . We present here the structure of the attachment protein of type 3 reovirus in complex with carbohydrates that naturally occur on human cells . Our results show that the protein forms an elongated trimer , with the carbohydrate binding site being located close to the midpoint of the molecule in a fiber-like region . Our findings provide insights into mechanisms of reovirus attachment to cell-surface glycans and contribute to an understanding of carbohydrate binding by viruses . They also establish a filamentous , trimeric carbohydrate-binding module that could potentially be used to introduce carbohydrate-binding properties into other trimeric proteins . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"chemistry",
"biology"
] | 2011 | Crystal Structure of Reovirus Attachment Protein σ1 in Complex with Sialylated Oligosaccharides |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.