Vincent Navratil

Hepatitis C virus infection protein network

A proteome‐wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein–protein interactions between HCV and human proteins was identified by yeast two‐hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFβ pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins.

IRGM is a common target of RNA viruses that subvert the autophagy network.

Autophagy is a conserved degradative pathway used as a host defense mechanism against intracellular pathogens. However, several viruses can evade or subvert autophagy to insure their own replication. Nevertheless, the molecular details of viral interaction with autophagy remain largely unknown. We have determined the ability of 83 proteins of several families of RNA viruses (Paramyxoviridae, Flaviviridae, Orthomyxoviridae, Retroviridae and Togaviridae), to interact with 44 human autophagy-associated proteins using yeast two-hybrid and bioinformatic analysis. We found that the autophagy network is highly targeted by RNA viruses. Although central to autophagy, targeted proteins have also a high number of connections with proteins of other cellular functions. Interestingly, immunity-associated GTPase family M (IRGM), the most targeted protein, was found to interact with the autophagy-associated proteins ATG5, ATG10, MAP1CL3C and SH3GLB1. Strikingly, reduction of IRGM expression using small interfering RNA impairs both Measles virus (MeV), Hepatitis C virus (HCV) and human immunodeficiency virus-1 (HIV-1)-induced autophagy and viral particle production. Moreover we found that the expression of IRGM-interacting MeV-C, HCV-NS3 or HIV-NEF proteins per se is sufficient to induce autophagy, through an IRGM dependent pathway. Our work reveals an unexpected role of IRGM in virus-induced autophagy and suggests that several different families of RNA viruses may use common strategies to manipulate autophagy to improve viral infectivity.

VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus–host interaction networks

Infectious diseases caused by viral agents kill millions of people every year. The improvement of prevention and treatment of viral infections and their associated diseases remains one of the main public health challenges. Towards this goal, deciphering virus–host molecular interactions opens new perspectives to understand the biology of infection and for the design of new antiviral strategies. Indeed, modelling of an infection network between viral and cellular proteins will provide a conceptual and analytic framework to efficiently formulate new biological hypothesis at the proteome scale and to rationalize drug discovery. Therefore, we present the first release of VirHostNet (Virus–Host Network), a public knowledge base specialized in the management and analysis of integrated virus–virus, virus–host and host–host interaction networks coupled to their functional annotations. VirHostNet integrates an extensive and original literature-curated dataset of virus–virus and virus–host interactions (2671 non-redundant interactions) representing more than 180 distinct viral species and one of the largest human interactome (10 672 proteins and 68 252 non-redundant interactions) reconstructed from publicly available data. The VirHostNet Web interface provides appropriate tools that allow efficient query and visualization of this infected cellular network. Public access to the VirHostNet knowledge-based system is available at http://pbildb1.univ-lyon1.fr/virhostnet.

Host-Pathogen Interactome Mapping for HTLV-1 and -2 Retroviruses

BackgroundHuman T-cell leukemia virus type 1 (HTLV-1) and type 2 both target T lymphocytes, yet induce radically different phenotypic outcomes. HTLV-1 is a causative agent of Adult T-cell leukemia (ATL), whereas HTLV-2, highly similar to HTLV-1, causes no known overt disease. HTLV gene products are engaged in a dynamic struggle of activating and antagonistic interactions with host cells. Investigations focused on one or a few genes have identified several human factors interacting with HTLV viral proteins. Most of the available interaction data concern the highly investigated HTLV-1 Tax protein. Identifying shared and distinct host-pathogen protein interaction profiles for these two viruses would enlighten how they exploit distinctive or common strategies to subvert cellular pathways toward disease progression.ResultsWe employ a scalable methodology for the systematic mapping and comparison of pathogen-host protein interactions that includes stringent yeast two-hybrid screening and systematic retest, as well as two independent validations through an additional protein interaction detection method and a functional transactivation assay. The final data set contained 166 interactions between 10 viral proteins and 122 human proteins. Among the 166 interactions identified, 87 and 79 involved HTLV-1 and HTLV-2 -encoded proteins, respectively. Targets for HTLV-1 and HTLV-2 proteins implicate a diverse set of cellular processes including the ubiquitin-proteasome system, the apoptosis, different cancer pathways and the Notch signaling pathway.ConclusionsThis study constitutes a first pass, with homogeneous data, at comparative analysis of host targets for HTLV-1 and -2 retroviruses, complements currently existing data for formulation of systems biology models of retroviral induced diseases and presents new insights on biological pathways involved in retroviral infection.

VirHostNet 2.0: surfing on the web of virus/host molecular interactions data

VirHostNet release 2.0 (http://virhostnet.prabi.fr) is a knowledgebase dedicated to the network-based exploration of virus–host protein–protein interactions. Since the previous VirhostNet release (2009), a second run of manual curation was performed to annotate the new torrent of high-throughput protein–protein interactions data from the literature. This resource is shared publicly, in PSI-MI TAB 2.5 format, using a PSICQUIC web service. The new interface of VirHostNet 2.0 is based on Cytoscape web library and provides a user-friendly access to the most complete and accurate resource of virus–virus and virus–host protein–protein interactions as well as their projection onto their corresponding host cell protein interaction networks. We hope that the VirHostNet 2.0 system will facilitate systems biology and gene-centered analysis of infectious diseases and will help to identify new molecular targets for antiviral drugs design. This resource will also continue to help worldwide scientists to improve our knowledge on molecular mechanisms involved in the antiviral response mediated by the cell and in the viral strategies selected by viruses to hijack the host immune system.

Comparative analysis of response to selection with three insecticides in the dengue mosquito Aedes aegypti using mRNA sequencing

BackgroundMosquito control programmes using chemical insecticides are increasingly threatened by the development of resistance. Such resistance can be the consequence of changes in proteins targeted by insecticides (target site mediated resistance), increased insecticide biodegradation (metabolic resistance), altered transport, sequestration or other mechanisms. As opposed to target site resistance, other mechanisms are far from being fully understood. Indeed, insecticide selection often affects a large number of genes and various biological processes can hypothetically confer resistance. In this context, the aim of the present study was to use RNA sequencing (RNA-seq) for comparing transcription level and polymorphism variations associated with adaptation to chemical insecticides in the mosquito Aedes aegypti. Biological materials consisted of a parental susceptible strain together with three child strains selected across multiple generations with three insecticides from different classes: the pyrethroid permethrin, the neonicotinoid imidacloprid and the carbamate propoxur.ResultsAfter ten generations, insecticide-selected strains showed elevated resistance levels to the insecticides used for selection. RNA-seq data allowed detecting over 13,000 transcripts, of which 413 were differentially transcribed in insecticide-selected strains as compared to the susceptible strain. Among them, a significant enrichment of transcripts encoding cuticle proteins, transporters and enzymes was observed. Polymorphism analysis revealed over 2500 SNPs showing > 50% allele frequency variations in insecticide-selected strains as compared to the susceptible strain, affecting over 1000 transcripts. Comparing gene transcription and polymorphism patterns revealed marked differences among strains. While imidacloprid selection was linked to the over transcription of many genes, permethrin selection was rather linked to polymorphism variations. Focusing on detoxification enzymes revealed that permethrin selection strongly affected the polymorphism of several transcripts encoding cytochrome P450 monooxygenases likely involved in insecticide biodegradation.ConclusionsThe present study confirmed the power of RNA-seq for identifying concomitantly quantitative and qualitative transcriptome changes associated with insecticide resistance in mosquitoes. Our results suggest that transcriptome modifications can be selected rapidly by insecticides and affect multiple biological functions. Previously neglected by molecular screenings, polymorphism variations of detoxification enzymes may play an important role in the adaptive response of mosquitoes to insecticides.

ViralORFeome: an integrated database to generate a versatile collection of viral ORFs.

Large collections of protein-encoding open reading frames (ORFs) established in a versatile recombination-based cloning system have been instrumental to study protein functions in high-throughput assays. Such ‘ORFeome’ resources have been developed for several organisms but in virology, plasmid collections covering a significant fraction of the virosphere are still needed. In this perspective, we present ViralORFeome 1.0 (http://www.viralorfeome.com), an open-access database and management system that provides an integrated set of bioinformatic tools to clone viral ORFs in the Gateway® system. ViralORFeome provides a convenient interface to navigate through virus genome sequences, to design ORF-specific cloning primers, to validate the sequence of generated constructs and to browse established collections of virus ORFs. Most importantly, ViralORFeome has been designed to manage all possible variants or mutants of a given ORF so that the cloning procedure can be applied to any emerging virus strain. A subset of plasmid constructs generated with ViralORFeome platform has been tested with success for heterologous protein expression in different expression systems at proteome scale. ViralORFeome should provide our community with a framework to establish a large collection of virus ORF clones, an instrumental resource to determine functions, activities and binding partners of viral proteins.

Identifying genomic changes associated with insecticide resistance in the dengue mosquito Aedes aegypti by deep targeted sequencing

The capacity of mosquitoes to resist insecticides threatens the control of diseases such as dengue and malaria. Until alternative control tools are implemented, characterizing resistance mechanisms is crucial for managing resistance in natural populations. Insecticide biodegradation by detoxification enzymes is a common resistance mechanism; however, the genomic changes underlying this mechanism have rarely been identified, precluding individual resistance genotyping. In particular, the role of copy number variations (CNVs) and polymorphisms of detoxification enzymes have never been investigated at the genome level, although they can represent robust markers of metabolic resistance. In this context, we combined target enrichment with high-throughput sequencing for conducting the first comprehensive screening of gene amplifications and polymorphisms associated with insecticide resistance in mosquitoes. More than 760 candidate genes were captured and deep sequenced in several populations of the dengue mosquito Ae. aegypti displaying distinct genetic backgrounds and contrasted resistance levels to the insecticide deltamethrin. CNV analysis identified 41 gene amplifications associated with resistance, most affecting cytochrome P450s overtranscribed in resistant populations. Polymorphism analysis detected more than 30,000 variants and strong selection footprints in specific genomic regions. Combining Bayesian and allele frequency filtering approaches identified 55 nonsynonymous variants strongly associated with resistance. Both CNVs and polymorphisms were conserved within regions but differed across continents, confirming that genomic changes underlying metabolic resistance to insecticides are not universal. By identifying novel DNA markers of insecticide resistance, this study opens the way for tracking down metabolic changes developed by mosquitoes to resist insecticides within and among populations.

Two-dimensional statistical recoupling for the identification of perturbed metabolic networks from NMR spectroscopy.

The development of Statistical Total Correlation Spectroscopy (STOCSY), a representation of the autocorrelation matrix of a spectral data set as a 2D pseudospectrum, has allowed more reliable assignment of one- and two-dimensional NMR spectra acquired from the complex mixtures that are usually used in metabolomics/metabonomics studies, thus, improving precise identification of candidate biomarkers contained in metabolic signatures computed by multivariate statistical analysis. However, the correlations obtained cannot always be interpreted in terms of connectivities between metabolites. In this study, we combine statistical recoupling of variables (SRV) and STOCSY to identify perturbed metabolite systems. The resulting Recoupled-STOCSY (R-STOCSY) method provides a 2D correlation landscape based on the SRV clusters representing physical, chemical, and biological entities. This enables the identification of correlations between distant clusters and extends the recoupling scheme of SRV, which was previously limited to the association of neighboring clusters. This allows the recovery of only meaningful correlations between metabolic signals and significantly enhances the interpretation of STOCSY. The method is validated through the measurement of the distances between the metabolites involved in these correlations, within the whole metabolic network, which shows that the average shortest path length is significantly shorter for the correlations detected in this new way compared to metabolite couples randomly selected from within the entire KEGG metabolic network. This enables the identification without any a priori knowledge of the perturbed metabolic network. The R-STOCSY completes the recoupling procedure between distant clusters, further reducing the high dimensionality of metabolomics/metabonomics data set and finally allows the identification of composite biomarkers, highlighting disruption of particular metabolic pathways within a global metabolic network. This allows the perturbed metabolic network to be extracted through NMR based metabolomics/metabonomics in an automated, and statistical manner.

Orthogonal filtered recoupled-STOCSY to extract metabolic networks associated with minor perturbations from NMR spectroscopy.

Supervised multivariate statistical analyses of NMR spectroscopic data sets are often required to identify metabolic differences between sample classes, and the use of orthogonal filters has proven to be highly efficient even when dealing with weak perturbations. In this note, we associate orthogonal filters to the recently reported recoupled-statistical total correlation spectroscopy (RSTOCSY). An initial supervised deflation of the spectral matrix is applied to remove all information orthogonal to the effect of interest and is followed by an RSTOCSY analysis to extract a list of pairs of metabolites that experience correlated perturbations. This list can then be used to find possibilities for the perturbed metabolic network. This supervised RSTOCSY approach, dubbed OR-STOCSY, yields metabolites related to perturbations of biological interest, even if they make a minor contribution to the global variance of a complex data set compared to other (possibly confounding) effects under study. The method is demonstrated with the application to genetic phenotypes in Caenorhabditis elegans.