Hepatitis C virus infection protein network

B de Chassey^1,2,a, V Navratil^2,3,a, L Tafforeau^1,2,a, M S Hiet^1,2,a, A Aublin-Gex^1,2, S Agaugué^1,2,a, G Meiffren^1,2, F Pradezynski^1,2, B F Faria^1,2, T Chantier^1,2, M Le Breton^1,2, J Pellet^1,2, N Davoust^1,2, P E Mangeot^1,2, A Chaboud^2,4, F Penin^2,4, Y Jacob⁵, P O Vidalain⁶, M Vidal⁷, P André^1,2,8, C Rabourdin-Combe^1,2 & V Lotteau^1,2,8

^aThese authors contributed equally to this work

^aPresent address: Department for Molecular Virology, University of Heidelberg, Heidelberg 69120, Germany.

^aPresent address: Service de Recherches en Hémato-Immunologie, CEA-DSV-DRM, Hôpital Saint-Louis, IUH, Paris 75475, France.

Article highlights

Synopsis

Hepatitis C virus (HCV) infection concerns 170 millions of individuals worldwide. Chronically infected patients present liver injury essentially mediated by immune mechanisms and metabolic disorders associated with hepatic steatosis, fibrogenesis and insulin resistance. Long-term-infected patients have a high risk of developing cirrhosis and hepatocarcinoma. Molecular basis of HCV pathology remains poorly understood. HCV genome is a positive-strand RNA of 9.6 kb encoding a polyprotein post-translationally processed into structural (CORE, E1, E2 and p7) and non-structural (NS2, NS3, NS4A, NS4B, NS5A and NS5B) proteins (Appel et al, 2006). Here, a proteome-wide mapping approach of interactions between HCV and cellular proteins was performed to provide a comprehensive view of viral infection. Viral baits were screened against human cDNA libraries using a highly stringent yeast two-hybrid assay (Y2H). Together with literature, the resulting HCV–human interactome is composed of 481 protein–protein interactions (PPIs) with 65% new interactions, involving 421 human proteins. NS3, NS5A and CORE are the most connected proteins, with 214, 96 and 76 cellular partners, respectively, highlighting the potential multi-functionality of these proteins during infection. A human PPI network, reconstructed from eight databases and composed of 44 223 non-redundant PPIs between 9520 different cellular proteins, revealed that cellular proteins interacting with HCV (H_HCV) are strongly interconnected (Figure 1).

Figure 1

The HCV interaction network. (A) Nomenclature. V: viral protein (black node). H_HCV: human protein interacting with HCV proteins (red node). H_Not-HCV: human protein not interacting with HCV proteins (blue node). V-H_HCV: HCV–human protein interaction (red edge). H_HCV–H_HCV: interaction between HCV-interacting human proteins (blue edge). H–H: human–human protein interaction (blue edges). V-H_HCV represents the interactions between HCV and human proteins (black box). H_HCV–H_HCV is composed of human proteins interacting with viral proteins (red box). H–H network represents interactions between human proteins (blue box). (B) Number of proteins and interactions in HCV–human interaction network. Number of human proteins interacting with HCV proteins (H_HCV) and corresponding number of protein–protein interactions (V-H_HCV PPI). Data are given for our yeast two-hybrid screens (IMAP Y2H) and for literature-curated interactions (IMAP LCI). (C) Validation of Y2H interactions by co-affinity purification assay. Nine out of 22 positive co-AP assays are shown, representing the following: NS5A-SORBS2, NS3-CALCOCO2, NS5A-BIN1, NS5A-MOBK1B, NS5A-EFEMP1, NS3-PSMB9 and NS5A-PSMB9, NS5A-PPPIRI3L, NS3-RASAL2. After pull-down with GST-tagged viral baits or with negative-control GST alone cellular preys are identified with anti-Flag antibody. Anti-GST antibody identifies either GST-alone or GST-tagged viral baits. Expression of cellular preys in cell lysate is controlled by anti-Flag (bottom panel). (D) Number of interactions by viral protein.

Full figure and legend (298K)Figures & Tables index

Topological analysis of the HCV–human interaction network

To assess how HCV proteins interplay with the cellular protein network, we focused on the centrality measures of H_HCV proteins in the human interactome. The degree of a protein corresponds to its number of direct partners and is therefore a measure of local centrality. The betweenness is a global measure of centrality that represents the information flow in the network. Comparing these values computed for H_HCV and for cellular proteins that do not interact with HCV indicates that HCV proteins have a strong tendency to interact with highly connected cellular proteins. Similar results were observed with human proteins interacting with Epstein–Barr virus proteins, suggesting that preferential attachment on central proteins may be a general hallmark of viral proteins (Calderwood et al, 2007). As the high centrality of proteins was previously shown to correlate with their functional essentiality, the data suggest that HCV proteins tend to interact with essential proteins in the cell.

Functional analysis of the HCV–human interaction network

Analysis of the data set revealed the specific targeting of three pathways associated with HCV clinical syndromes (insulin, TGF and Jak/STAT pathways) and identified focal adhesion as a novel pathway affected by HCV.

IJT network (insulin–Jak/STAT–TGF network)

Chronic infection by HCV is associated with an increased risk for metabolic disorders with development of steatosis. Although insulin, TGF and Jak/STAT pathways have been suspected to be involved in these clinical features, their related perturbation during HCV infection remains unexplained (Romero-Gomez, 2006). We thus used a network approach to identify cellular proteins targeted by HCV and localized at the interface of these pathways. The resulting interaction map was constructed to form the IJT network (insulin–Jak/STAT–TGF network; Figure 4).

Figure 4

IJT network. (A) Graphical representation of IJT network. Protein (nodes) members of insulin (blue), Jak/STAT (red) and TGF (green) pathways according to KEGG annotation, and their interactions (edges) are shown (proteins interacting with HCV proteins are named). Proteins shared by two pathways are shown in secondary colours (pink, yellow and cyan). Grey and black nodes are neighbours that connect the KEGG pathways and that interact with HCV proteins (grey: protein from the IMAP Y2H data set; black: protein from the IMAP LCI data set). Neighbours interacting with HCV but not connecting the KEGG pathways are not represented. Discussed protein examples PLSCR1 and YY1 are in box. References to visualization tools are provided in supplementary files (network visualization). IJT network construction in Supplementary methods. (B) Relative contribution of each viral protein in V–H_HCV. Percentage interactions for the three most interacting viral proteins, relative to the total number of interactions as listed in Supplementary Table SI, are shown. A total of 51.3% of CORE interactions are concentrated in the IJT network. (C) Relative contribution of each viral protein in IJT network. Percentage interactions for the three most interacting viral proteins, relative to the total number of viral protein interactions with proteins of IJT network, are shown.

Full figure and legend (611K)Figures & Tables index

Interaction of interface proteins with HCV proteins may induce functional perturbations that could expand to adjacent pathways. One of these proteins is the nuclear factor Yin Yang 1 (YY1), which exhibits a central position in the IJT network as it connects the three pathways. HCV CORE interaction with YY1 has been previously shown to be functional relieving NPM1 expression. This observation could be extrapolated to PPAR expression and SMADs transcriptional activity in support of insulin and TGF pathway modulation (Kurisaki et al, 2003; Mai et al, 2006; He et al, 2008). This is only one illustrative example of cellular target most likely to be involved in HCV-induced phenotypes. Many of the proteins at the interface are known to have an important function in the regulation of one, two or the three pathways without being officially annotated. The clinical phenotypes observed in chronic HCV infection are most likely to result from the integrative effect of protein interactions depicted in the IJT network.

Another issue that became apparent in the IJT network is that CORE appears as a major perturbator of the IJT network. Interestingly, transgenic mice expressing CORE develop insulin resistance (Pazienza et al, 2007). The IJT network provides a powerful tool to investigate the impact of CORE in HCV-associated metabolic disorders. It is also worth considering that the IJT network may identify a series of genes involved in diseases, such as steatosis and fibrogenesis, in the absence of viral infection.

Focal adhesion

Focal adhesion was specifically targeted by NS3 and NS5A proteins. Integrin-linked focal adhesion complexes control cell adhesion to extracellular matrix (ECM). Upon binding to the ECM, both and integrin subunits recruit proteins establishing a physical link between the actin-cytoskeleton and signal transduction pathways. When deregulated, this functional process can lead to detachment from ECM and tumour initiation. An adhesion assay was performed that demonstrates that, when transfected, NS3 and NS5A specifically affected adhesion of cells on fibronectin and thus could participate in tumour initiation and progression.

In a network approach of HCV infection, the interaction map identifies all potential connections needed for the virus to replicate and escape host defence. A fascinating challenge of this approach is to identify molecular signatures common to several viruses at the protein network level and to discover the vulnerable points to develop original large-spectrum anti-viral molecules. This will, however, necessitate the integration of system-level data sets of different origins that will set the stage for the complex systems analysis of viral infections.

MORE ARTICLES LIKE THIS

These links to content published by NPG are automatically generated.