Paper Info
Title
A chemokine gene expression signature derived from meta-analysis predicts the pathogenicity of viral respiratory infections.
Abstract
During respiratory viral infections host injury occurs due in part to inappropriate host responses. In this study we sought to uncover the host transcriptional responses underlying differences between high- and low-pathogenic infections.From a compendium of 12 studies that included responses to influenza A subtype H5N1, reconstructed 1918 influenza A virus, and SARS coronavirus, we used meta-analysis to derive multiple gene expression signatures. We compared these signatures by their capacity to segregate biological conditions by pathogenicity and predict pathogenicity in a test data set. The highest-performing signature was expressed as a continuum in low-, medium-, and high-pathogenicity samples, suggesting a direct, analog relationship between expression and pathogenicity. This signature comprised 57 genes including a subnetwork of chemokines, implicating dysregulated cell recruitment in injury.Highly pathogenic viruses elicit expression of many of the same key genes as lower pathogenic viruses but to a higher degree. This increased degree of expression may result in the uncontrolled co-localization of inflammatory cell types and lead to irreversible host damage.
Year
DOI
Venue
2011
10.1186/1752-0509-5-202
BMC systems biology
Keywords
Field
DocType
chemokines,virulence,algorithms,systems biology,cluster analysis,gene expression profiling,bioinformatics
Immunology,Biology,Respiratory tract infections,Gene expression,Respiratory system,Influenza A virus subtype H5N1,Bioinformatics,Meta-analysis,Chemokine,Virology,Virulence,Gene expression profiling
Journal
Volume
Issue
ISSN
5
202
1752-0509
Citations 
PageRank 
References 
6
0.38
3
Authors
5
Name
Order
Citations
PageRank
Stewart T. Chang1161.30
Nicolas Tchitchek2151.22
Debashis Ghosh349649.16
Arndt Benecke4706.28
Michael G Katze5977.73