Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis

• Published in: BMC systems biology Vol. 6; no. 1; p. 28
• Main Authors: McDermott, Jason E, Diamond, Deborah L, Corley, Courtney, Rasmussen, Angela L, Katze, Michael G, Waters, Katrina M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30.04.2012; BioMed Central
• Subjects: Analysis; Biology; Cell culture; Cell Line, Tumor; Development and progression; Experiments; Health aspects; Hepacivirus - pathogenicity; Hepatitis; Hepatitis C; Hepatitis C - metabolism; Hepatitis C - virology; Hepatitis C virus; Hepatology; High-throughput screening (Biochemical assaying); Humans; Liver; Liver - metabolism; Liver - virology; Liver diseases; Medical research; Medicine, Experimental; Methods; Pathogenesis; Protein Interaction Maps; Proteins; Proteomics; Proteomics - methods; Studies
• ISSN: 1752-0509; 1752-0509
• DOI: 10.1186/1752-0509-6-28

High-throughput methods for obtaining global measurements of transcript and protein levels in biological samples has provided a large amount of data for identification of 'target' genes and proteins of interest. These targets may be mediators of functional processes involved in disease and therefore represent key points of control for viruses and bacterial pathogens. Genes and proteins that are the most highly differentially regulated are generally considered to be the most important. We present topological analysis of co-abundance networks as an alternative to differential regulation for confident identification of target proteins from two related global proteomics studies of hepatitis C virus (HCV) infection. We analyzed global proteomics data sets from a cell culture study of HCV infection and from a clinical study of liver biopsies from HCV-positive patients. Using lists of proteins known to be interaction partners with pathogen proteins we show that the most differentially regulated proteins in both data sets are indeed enriched in pathogen interactors. We then use these data sets to generate co-abundance networks that link proteins based on similar abundance patterns in time or across patients. Analysis of these co-abundance networks using a variety of network topology measures revealed that both degree and betweenness could be used to identify pathogen interactors with better accuracy than differential regulation alone, though betweenness provides the best discrimination. We found that though overall differential regulation was not correlated between the cell culture and liver biopsy data, network topology was conserved to an extent. Finally, we identified a set of proteins that has high betweenness topology in both networks including a protein that we have recently shown to be essential for HCV replication in cell culture. The results presented show that the network topology of protein co-abundance networks can be used to identify proteins important for viral replication. These proteins represent targets for further experimental investigation that will provide biological insight and potentially could be exploited for novel therapeutic approaches to combat HCV infection.