https://socictopen.socict.org/files/original/9cd91169012de2bf167485d0ed02838a.pdf e22e1a54ca21143ba74e23b5fa195f07 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections Creator An entity primarily responsible for making the resource Hugh D. Mitchell, Amie J Eisfeld, Kelly G. Stratton, Natalie C. Heller, Lisa M. Bramer, Ji Wen, Jason E. McDermott, Lisa E Gralinski, Amy C. Sims, Mai Q. Le, Ralph S. Baric, Yoshihiro Kawaoka, Katrina M. Waters Description An account of the resource 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. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource Systems Biology, network topology, influenza, SARS-CoV, Data integration Identifier An unambiguous reference to the resource within a given context DOI: 10.3389/fcell.2019.00200 Source A related resource from which the described resource is derived Frontiers in Cell and Developmental Biology Publisher An entity responsible for making the resource available Frontiers Media S.A. Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General) Language A language of the resource EN