The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections.
Identifieur interne : 000866 ( PubMed/Curation ); précédent : 000865; suivant : 000867The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections.
Auteurs : Hugh D. Mitchell [États-Unis] ; Amie J. Eisfeld [États-Unis] ; Kelly G. Stratton [États-Unis] ; Natalie C. Heller [États-Unis] ; Lisa M. Bramer [États-Unis] ; Ji Wen [États-Unis] ; Jason E. Mcdermott [États-Unis] ; Lisa E. Gralinski [États-Unis] ; Amy C. Sims [États-Unis] ; Mai Q. Le [Viêt Nam] ; Ralph S. Baric [États-Unis] ; Yoshihiro Kawaoka [États-Unis] ; Katrina M. Waters [États-Unis]Source :
- Frontiers in cell and developmental biology [ 2296-634X ] ; 2019.
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.
DOI: 10.3389/fcell.2019.00200
PubMed: 31616667
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Mitchell</name><affiliation wicri:level="1"><nlm:affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pacific Northwest National Laboratory, Richland, WA</wicri:regionArea></affiliation></author><author><name sortKey="Eisfeld, Amie J" sort="Eisfeld, Amie J" uniqKey="Eisfeld A" first="Amie J" last="Eisfeld">Amie J. Eisfeld</name><affiliation wicri:level="1"><nlm:affiliation>Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI</wicri:regionArea></affiliation></author><author><name sortKey="Stratton, Kelly G" sort="Stratton, Kelly G" uniqKey="Stratton K" first="Kelly G" last="Stratton">Kelly G. 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Mcdermott</name><affiliation wicri:level="1"><nlm:affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pacific Northwest National Laboratory, Richland, WA</wicri:regionArea></affiliation></author><author><name sortKey="Gralinski, Lisa E" sort="Gralinski, Lisa E" uniqKey="Gralinski L" first="Lisa E" last="Gralinski">Lisa E. Gralinski</name><affiliation wicri:level="1"><nlm:affiliation>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC</wicri:regionArea></affiliation></author><author><name sortKey="Sims, Amy C" sort="Sims, Amy C" uniqKey="Sims A" first="Amy C" last="Sims">Amy C. Sims</name><affiliation wicri:level="1"><nlm:affiliation>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC</wicri:regionArea></affiliation></author><author><name sortKey="Le, Mai Q" sort="Le, Mai Q" uniqKey="Le M" first="Mai Q" last="Le">Mai Q. Le</name><affiliation wicri:level="1"><nlm:affiliation>National Institute of Hygiene and Epidemiology, Hanoi, Vietnam.</nlm:affiliation><country xml:lang="fr">Viêt Nam</country><wicri:regionArea>National Institute of Hygiene and Epidemiology, Hanoi</wicri:regionArea></affiliation></author><author><name sortKey="Baric, Ralph S" sort="Baric, Ralph S" uniqKey="Baric R" first="Ralph S" last="Baric">Ralph S. Baric</name><affiliation wicri:level="1"><nlm:affiliation>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC</wicri:regionArea></affiliation></author><author><name sortKey="Kawaoka, Yoshihiro" sort="Kawaoka, Yoshihiro" uniqKey="Kawaoka Y" first="Yoshihiro" last="Kawaoka">Yoshihiro Kawaoka</name><affiliation wicri:level="1"><nlm:affiliation>Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI</wicri:regionArea></affiliation></author><author><name sortKey="Waters, Katrina M" sort="Waters, Katrina M" uniqKey="Waters K" first="Katrina M" last="Waters">Katrina M. Waters</name><affiliation wicri:level="1"><nlm:affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pacific Northwest National Laboratory, Richland, WA</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in cell and developmental biology</title><idno type="ISSN">2296-634X</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">31616667</PMID><DateRevised><Year>2020</Year><Month>04</Month><Day>06</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2296-634X</ISSN><JournalIssue CitedMedium="Print"><Volume>7</Volume><PubDate><Year>2019</Year></PubDate></JournalIssue><Title>Frontiers in cell and developmental biology</Title><ISOAbbreviation>Front Cell Dev Biol</ISOAbbreviation></Journal><ArticleTitle>The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections.</ArticleTitle><Pagination><MedlinePgn>200</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fcell.2019.00200</ELocationID><Abstract><AbstractText>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.</AbstractText><CopyrightInformation>Copyright © 2019 Mitchell, Eisfeld, Stratton, Heller, Bramer, Wen, McDermott, Gralinski, Sims, Le, Baric, Kawaoka and Waters.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Mitchell</LastName><ForeName>Hugh D</ForeName><Initials>HD</Initials><AffiliationInfo><Affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Eisfeld</LastName><ForeName>Amie J</ForeName><Initials>AJ</Initials><AffiliationInfo><Affiliation>Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stratton</LastName><ForeName>Kelly G</ForeName><Initials>KG</Initials><AffiliationInfo><Affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Heller</LastName><ForeName>Natalie C</ForeName><Initials>NC</Initials><AffiliationInfo><Affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bramer</LastName><ForeName>Lisa M</ForeName><Initials>LM</Initials><AffiliationInfo><Affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wen</LastName><ForeName>Ji</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>McDermott</LastName><ForeName>Jason E</ForeName><Initials>JE</Initials><AffiliationInfo><Affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gralinski</LastName><ForeName>Lisa E</ForeName><Initials>LE</Initials><AffiliationInfo><Affiliation>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sims</LastName><ForeName>Amy C</ForeName><Initials>AC</Initials><AffiliationInfo><Affiliation>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Le</LastName><ForeName>Mai Q</ForeName><Initials>MQ</Initials><AffiliationInfo><Affiliation>National Institute of Hygiene and Epidemiology, Hanoi, Vietnam.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Baric</LastName><ForeName>Ralph S</ForeName><Initials>RS</Initials><AffiliationInfo><Affiliation>Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kawaoka</LastName><ForeName>Yoshihiro</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Division of Virology, Department of Microbiology and Immunology, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>International Research Center for Infectious Diseases, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Waters</LastName><ForeName>Katrina M</ForeName><Initials>KM</Initials><AffiliationInfo><Affiliation>Pacific Northwest National Laboratory, Richland, WA, United States.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U19 AI106772</GrantID><Acronym>AI</Acronym><Agency>NIAID NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>09</Month><Day>20</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front Cell Dev Biol</MedlineTA><NlmUniqueID>101630250</NlmUniqueID><ISSNLinking>2296-634X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">SARS-CoV</Keyword><Keyword MajorTopicYN="N">data integration</Keyword><Keyword MajorTopicYN="N">influenza</Keyword><Keyword MajorTopicYN="N">network topology</Keyword><Keyword MajorTopicYN="N">systems biology</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate 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