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.

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.

Innate immunity has evolved complex molecular pathways to protect organisms from viral infections. One pivotal line of cellular defense is the induction of the antiviral effect of interferon. To circumvent this primary response and achieve their own replication, viruses have developed complex molecular strategies. Here, we provide a systems-level study of the human type I interferon system subversion by the viral proteome, by reconstructing the underlying protein-protein interaction network. At this network level, viruses establish a massive and a gradual attack, from receptors to transcription factors, by interacting preferentially with highly connected and central proteins as well as interferon-induced proteins. We also demonstrate that viruses significantly target 22% of the proteins directly interacting with the type I interferon system network, suggesting the relevance of our network-based method to identify new candidates involved in the regulation of the antiviral response. Finally, based on the comparative analysis of interactome profiles across four viral families, we provide evidence of common and differential targeting strategies.

Background: High-throughput screening of protein-protein interactions opens new systems biology perspectives for the comprehensive understanding of cell physiology in normal and pathological conditions. In this context, yeast two-hybrid system appears as a promising approach to efficiently reconstruct protein interaction networks at the proteome-wide scale. This protein interaction screening method generates a large amount of raw sequence data, i.e. the ISTs (Interaction Sequence Tags), which urgently need appropriate tools for their systematic and standardised analysis.

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces proteinprotein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of An erratum to this chapter is available at

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There is a critical need for new and efficient computational methods aimed at discovering putative transcription factor binding sites (TFBSs) in promoter sequences. Among the existing methods, two families can be distinguished: statistical or stochastic approaches, and combinatorial approaches. Here we focus on a complete approach incorporating a combinatorial exhaustive motif extraction, together with a statistical Twilight Zone Indicator (TZI), in two datasets: a positive set and a negative one, which represents the result of a classical differential expression experiment. Our approach relies on the existence of prior biological information in the form of two sets of promoters of differentially expressed genes. We describe the complete procedure used for extracting either exact or degenerated motifs, ranking these motifs, and finding their known related TFBSs. We exemplify this approach using two different sets of promoters. The first set consists in promoters of genes either repressed or not by the transforming form of the v-erbA oncogene. The second set consists in genes the expression of which varies between self-renewing and differentiating progenitors. The biological meaning of the found TFBSs is discussed and, for one TF, its biological involvement is demonstrated. This study therefore illustrates the power of using relevant biological information, in the form of a set of differentially expressed genes that is a classical outcome in most of transcriptomics studies. This allows to severely reduce the search space and to design an adapted statistical indicator. Taken together, this allows the biologist to concentrate on a small number of putatively interesting TFs.