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

作者：Chang Stewart T; Tchitchek Nicolas; Ghosh Debashis; Benecke Arndt; Katze Michael G^*

来源：BMC Systems Biology, 2011, 5: 202.

DOI：10.1186/1752-0509-5-202

Background: 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.
Results: 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.
Conclusions: 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.

• 出版日期2011-12-22