Abstract
Despite high sequence similarity between pandemic and seasonal influenza viruses, there is extreme variation in host pathogenicity from one viral strain to the next. Identifying the underlying mechanisms of variability in pathogenicity is a critical task for understanding influenza virus infection and effective management of highly pathogenic influenza virus disease. We applied a network-based modeling approach to identify critical functions related to influenza virus pathogenicity using large transcriptomic and proteomic datasets from mice infected with six influenza virus strains or mutants. Our analysis revealed two pathogenicity-related gene expression clusters; these results were corroborated by matching proteomics data. We also identified parallel downstream processes that were altered during influenza pathogenesis. We found that network bottlenecks (nodes that bridge different network regions) were highly enriched in pathogenicity-related genes, while network hubs (highly connected network nodes) were significantly depleted in these genes. We confirmed that this trend persisted in a distinct virus: Severe Acute Respiratory Syndrome Coronavirus (SARS). The role of epidermal growth factor receptor (EGFR) in influenza pathogenesis, one of the bottleneck regulators with corroborating signals across transcript and protein expression data, was tested and validated in additional mouse infection experiments. We demonstrate that EGFR is important during influenza infection, but the role it plays changes for lethal versus non-lethal infections. Our results show that by using association networks, bottleneck genes that lack hub characteristics can be used to predict a gene's involvement in influenza virus pathogenicity. We also demonstrate the utility of employing multiple network approaches for analyzing host response data from viral infections.