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

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 themore » 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.« less

Authors:
 [1];  [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1];  [3];  [3];  [4];  [3];  [5]; ORCiD logo [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Univ. of Wisconsin, Madison, WI (United States)
  3. Univ. of North Carolina, Chapel Hill, NC (United States)
  4. National Institute of Hygiene and Epidemiology (Vietnam)
  5. Univ. of Wisconsin, Madison, WI (United States); Univ. of Tokyo (Japan)
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE; National Institutes of Health (NIH)
OSTI Identifier:
1562984
Alternate Identifier(s):
OSTI ID: 1568819
Report Number(s):
PNNL-SA-142083
Journal ID: ISSN 2296-634X
Grant/Contract Number:  
AC05-76RL01830; U19AI106772
Resource Type:
Published Article
Journal Name:
Frontiers in Cell and Developmental Biology
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2296-634X
Publisher:
Frontiers Media S.A.
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; bioinformatics; computational biology; network topology; Influenza; SARS; EGFR; network bottlenecks; systems biology; data integration

Citation Formats

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, and Waters, Katrina M. The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections. United States: N. p., 2019. Web. doi:10.3389/fcell.2019.00200.
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. The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections. United States. https://doi.org/10.3389/fcell.2019.00200
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, and Waters, Katrina M. Fri . "The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections". United States. https://doi.org/10.3389/fcell.2019.00200.
@article{osti_1562984,
title = {The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections},
author = {Mitchell, Hugh D. and Eisfeld, Amie J. and Stratton, Kelly G. and Heller, Natalie C. and Bramer, Lisa M. and Wen, Ji and McDermott, Jason E. and Gralinski, Lisa E. and Sims, Amy C. and Le, Mai Q. and Baric, Ralph S. and Kawaoka, Yoshihiro and Waters, Katrina M.},
abstractNote = {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},
journal = {Frontiers in Cell and Developmental Biology},
number = ,
volume = 7,
place = {United States},
year = {2019},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.3389/fcell.2019.00200

Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

Figures / Tables:

FIGURE 1 FIGURE 1: Target pathogenicity profile. (A) Median lethal dose 50 (MLD50) values for the six strains/mutants in the influenza virus pathogenicity gradient. Mouse MLD50 data was previously published in Tchitchek et al. (2013). (B) Target pathogenicity profile based on MLD50 values.

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Works referencing / citing this record:

Scl004
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvscl004/1661963

Scl010
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvscl010/1661968

Scl011
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvscl011/1661969

Sm001
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm001/1661974

Sm003
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm003/1661975

Sm004
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm004/1661976

Sm005
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm005/1661977

Sm007
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm007/1661978

Sm008
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm008/1661979

Sm009
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm009/1661980

Sm010
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm010/1661981

Sm011
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm011/1661982

Sm012
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm012/1661983

Sm013
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm013/1661984

Sm014
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm014/1661985

Sm015
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm015/1661986

Sm017
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm017/1661987

Sm018
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm018/1661988

Sm019
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm019/1661989

Sm020
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm020/1661990

Sm021
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm021/1661991

Sm023
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm023/1661992

Sm028
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm028/1661993

Sm029
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm029/1661994

Sm031
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm031/1661995

Sm033
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm033/1661996

Sm034
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm034/1661997

Sm035
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm035/1661998

Sm036
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm036/1661999

Sm038
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm038/1662000

Sm039
dataset, January 2020

  • Waters, Katrina; Anderson, Lindsey; McDermott, Jason
  • Pacific Northwest National Laboratory 2; EMSL; PNNL
  • DOI: 10.25584/lhvsm039/1662001

PNNL DataHub Project Omics-LHV Profiling of Host Response to Influenza Infection Post-Processed Data Package DOIs
dataset, January 2021

  • Anderson, Lindsey; McDermott, Jason; Waters, Katrina
  • Pacific Northwest National Laboratory 2; PNNL
  • DOI: 10.25584/lhvflu/1773428

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.