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A chemokine gene expression signature derived from meta-analysis predicts the pathogenicity of viral respiratory infections

Stewart T Chang1, Nicolas Tchitchek2, Debashis Ghosh3, Arndt Benecke2 and Michael G Katze14*

Author Affiliations

1 Department of Microbiology, University of Washington, Seattle WA, USA

2 Institut des Hautes Etudes Scientifiques, Bures-sur-Yvette, France

3 Department of Statistics, Pennsylvania State University, University Park PA, USA

4 Washington National Primate Research Center, Seattle WA, USA

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BMC Systems Biology 2011, 5:202  doi:10.1186/1752-0509-5-202

Published: 22 December 2011



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.