U.S. Department of Energy

Pacific Northwest National Laboratory

A network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses.

TitleA network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses.
Publication TypeJournal Article
Year of Publication2013
AuthorsMitchell HD, Eisfeld AJ, Sims AC, McDermott JE, Matzke MM, Webb-Robertson B-JM, Tilton SC, Tchitchek N, Josset L, Li C, Ellis AL, Chang JH, Heegel RA, Luna ML, Schepmoes AA, Shukla AK, Metz TO, Neumann G, Benecke AG, Smith RD, Baric RS, Kawaoka Y, Katze MG, Waters KM
JournalPLoS One
Abstract

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel "crowd-based" approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse 'omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

DOI10.1371/journal.pone.0069374
Alternate JournalPLoS ONE
PubMed ID23935999
PubMed Central IDPMC3723910
Grant ListHHSN272200800060C / / PHS HHS / United States
P41 GM103493 / GM / NIGMS NIH HHS / United States
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