The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Creator
  • McDermott, Jason E
    • Other Affiliation: Pacific Northwest National Laboratory
  • Mitchell, Hugh D
    • Other Affiliation: Pacific Northwest National Laboratory
  • Gralinski, Lisa E
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Eisfeld, Amie J
    • Other Affiliation: University of Wisconsin-Madison
  • Josset, Laurence
    • Other Affiliation: University of Washington
  • Bankhead, Armand
    • Other Affiliation: Oregon Health and Science University
  • Neumann, Gabriele
    • Other Affiliation: University of Wisconsin-Madison
  • Tilton, Susan C
    • Other Affiliation: Pacific Northwest National Laboratory
  • Schäfer, Alexandra
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Li, Chengjun
    • Other Affiliation: University of Wisconsin-Madison
  • Fan, Shufang
    • Other Affiliation: University of Wisconsin-Madison
  • McWeeney, Shannon
    • Other Affiliation: Oregon Health and Science University
  • Baric, Ralph S.
    • Affiliation: School of Medicine, Department of Microbiology and Immunology
  • Katze, Michael G
    • Other Affiliation: University of Washington
  • Waters, Katrina M
    • Other Affiliation: Pacific Northwest National Laboratory
Abstract
  • Abstract Background The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation. Results We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation. Conclusions The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation.
Date of publication
DOI
Identifier
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • The Author(s).
Journal title
  • BMC Systems Biology
Journal volume
  • 10
Language
  • English
Bibliographic citation
  • BMC Systems Biology. 2016 Sep 23;10(1):93
Publisher
  • BioMed Central
Date uploaded
  • September 30, 2016

Relations

Parents:

This work has no parents.

In Collection:

Items

Thumbnail Title Date Uploaded Visibility Actions
file details 12918_2016_article_336.pdf 2019-05-07 Public Download
file details 12918_2016_336_moesm5_esm.xlsx 2019-05-07 Public Download
file details 12918_2016_336_moesm1_esm.xlsx 2019-05-07 Public Download
file details 12918_2016_336_moesm6_esm.pdf 2019-05-07 Public Download
file details 12918_2016_336_moesm4_esm.pdf 2019-05-07 Public Download
file details 12918_2016_336_moesm2_esm.xlsx 2019-05-07 Public Download
file details 12918_2016_336_moesm3_esm.xlsx 2019-05-07 Public Download