Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection

Komudi Singh, Yun-Ching Chen, Jennifer T Judy, Fayaz Seifuddin, Ilker Tunc, View ORCID ProfileMehdi Pirooznia
doi: https://doi.org/10.1101/2020.05.13.092536
Komudi Singh
1Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yun-Ching Chen
1Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jennifer T Judy
1Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fayaz Seifuddin
1Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ilker Tunc
1Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mehdi Pirooznia
1Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mehdi Pirooznia
  • For correspondence: mehdi.pirooznia@nih.gov
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Lack of effective treatment strategy and vaccine makes SARS-CoV-2 infection a big threat to mankind. Analyzing the host transcriptional changes in response to virus infection will help delineate the biological processes impacted by the virus and will potentially facilitate drug development. Using RNA seq datasets of virus infected lung cell lines A549 (infected with either SARS-CoV-2 or Influenza A virus (IAV)) and Calu3 (infected with either SARS-CoV-2 or MERS-CoV), we present a detailed analysis of genes expression changes in response to each of these viral infections. Upregulation of the antiviral interferon signaling was observed with all three viral infections. However, upregulation of the cytokine/inflammatory processes, downregulation of mitochondrial organization and respiration processes, and perturbation in the autophagic processes were specifically observed in SARS-CoV-2 infected cells, which were absent in IAV infected cells. Upregulation of the inflammatory processes was concordant with the gene expression signature of COVID-19 lungs and with inflammatory symptoms observed in severe cases of COVID-19 patients. Coexpression networks analysis also facilitated the identification of protein-protein interaction (PPI) subnetworks of genes in the inflammation and mitochondrial processes that were either coordinately up or downregulated in SARS-CoV-2 infected cells, respectively. Comparing the expression of marker genes of lung cell types from single cell RNA seq data with expression profile of A549 cells revealed that they likely represent the lung epithelial lineage cells. The cellular processes uniquely perturbed in infected cells that were identified in this analysis likely delineates lung epithelial cells response to the SARS-CoV-2 infection.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.
Back to top
PreviousNext
Posted May 14, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection
Komudi Singh, Yun-Ching Chen, Jennifer T Judy, Fayaz Seifuddin, Ilker Tunc, Mehdi Pirooznia
bioRxiv 2020.05.13.092536; doi: https://doi.org/10.1101/2020.05.13.092536
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection
Komudi Singh, Yun-Ching Chen, Jennifer T Judy, Fayaz Seifuddin, Ilker Tunc, Mehdi Pirooznia
bioRxiv 2020.05.13.092536; doi: https://doi.org/10.1101/2020.05.13.092536

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4117)
  • Biochemistry (8824)
  • Bioengineering (6528)
  • Bioinformatics (23481)
  • Biophysics (11800)
  • Cancer Biology (9218)
  • Cell Biology (13333)
  • Clinical Trials (138)
  • Developmental Biology (7440)
  • Ecology (11420)
  • Epidemiology (2066)
  • Evolutionary Biology (15166)
  • Genetics (10447)
  • Genomics (14054)
  • Immunology (9180)
  • Microbiology (22183)
  • Molecular Biology (8820)
  • Neuroscience (47610)
  • Paleontology (350)
  • Pathology (1430)
  • Pharmacology and Toxicology (2492)
  • Physiology (3735)
  • Plant Biology (8085)
  • Scientific Communication and Education (1438)
  • Synthetic Biology (2222)
  • Systems Biology (6042)
  • Zoology (1254)