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BMC Syst Biol. 2012; 6: 28.
Published online Apr 30, 2012. doi:  10.1186/1752-0509-6-28
PMCID: PMC3383540
Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis
Jason E McDermott,1 Deborah L Diamond,2 Courtney Corley,3 Angela L Rasmussen,2 Michael G Katze,2 and Katrina M Waterscorresponding author1
1Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA
2Department of Microbiology, University of Washington, Seattle, WA 98195, USA
3Knowledge Systems Pacific Northwest National Laboratory, Richland, WA 99352, USA
corresponding authorCorresponding author.
Jason E McDermott: Jason.McDermott/at/pnnl.gov; Deborah L Diamond: ddiamond/at/u.washington.edu; Courtney Corley: court/at/pnnl.gov; Angela L Rasmussen: arasmus/at/u.washington.edu; Michael G Katze: honey/at/u.washington.edu; Katrina M Waters: Katrina.Waters/at/pnnl.gov
Received March 13, 2012; Accepted April 30, 2012.
Abstract
Background
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.
Results
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.
Conclusions
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.
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