tetano
Editor, Senior Moderator
PLoS One. 2013 Jul 25;8(7):e69374. doi: 10.1371/journal.pone.0069374. Print 2013.
A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses.
Mitchell HD, Eisfeld AJ, Sims AC, McDermott JE, Matzke MM, Webb-Robertson BJ, 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.
Source
Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America.
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.
PMID:
23935999
[PubMed - in process]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/23935999
A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses.
Mitchell HD, Eisfeld AJ, Sims AC, McDermott JE, Matzke MM, Webb-Robertson BJ, 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.
Source
Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America.
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.
PMID:
23935999
[PubMed - in process]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/23935999