AUTHOR=Mitchell Hugh D. , Eisfeld Amie J. , Stratton Kelly G. , Heller Natalie C. , Bramer Lisa M. , Wen Ji , McDermott Jason E. , Gralinski Lisa E. , Sims Amy C. , Le Mai Q. , Baric Ralph S. , Kawaoka Yoshihiro , Waters Katrina M. 

TITLE=The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 7 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2019.00200

DOI=10.3389/fcell.2019.00200

ISSN=2296-634X

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 using large transcriptomic and proteomic datasets from mice infected with six influenza virus strains or mutants to identify critical functions related to influenza virus pathogenicity. Our analysis revealed two pathogenicity-related gene expression clusters corroborated by matching proteomics data and identified parallel downstream processes that were altered during influenza pathogenesis. We found that network bottlenecks were highly enriched in pathogenicity-related genes, while network hubs were significantly depleted in these genes, and confirmed this trend also persisted for 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, using association networks, bottleneck genes lacking hub characteristics can be used to predict a gene’s involvement in influenza virus pathogenicity, and demonstrate the utility of employing multiple network approaches to analyzing host response data from viral infections.