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FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2.

Authors
  • Renz, Alina1, 2
  • Widerspick, Lina1
  • Dräger, Andreas1, 2, 3
  • 1 Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI).
  • 2 Department of Computer Science, University of Tübingen, Tübingen 72076, Germany. , (Germany)
  • 3 German Center for Infection Research (DZIF), partner site Tübingen, Germany. , (Germany)
Type
Published Article
Journal
Bioinformatics (Oxford, England)
Publication Date
Dec 30, 2020
Volume
36
Issue
Suppl 2
Identifiers
DOI: 10.1093/bioinformatics/btaa813
PMID: 33381848
Source
Medline
Language
English
License
Unknown

Abstract

The novel coronavirus (SARS-CoV-2) currently spreads worldwide, causing the disease COVID-19. The number of infections increases daily, without any approved antiviral therapy. The recently released viral nucleotide sequence enables the identification of therapeutic targets, e.g. by analyzing integrated human-virus metabolic models. Investigations of changed metabolic processes after virus infections and the effect of knock-outs on the host and the virus can reveal new potential targets. We generated an integrated host-virus genome-scale metabolic model of human alveolar macrophages and SARS-CoV-2. Analyses of stoichiometric and metabolic changes between uninfected and infected host cells using flux balance analysis (FBA) highlighted the different requirements of host and virus. Consequently, alterations in the metabolism can have different effects on host and virus, leading to potential antiviral targets. One of these potential targets is guanylate kinase (GK1). In FBA analyses, the knock-out of the GK1 decreased the growth of the virus to zero, while not affecting the host. As GK1 inhibitors are described in the literature, its potential therapeutic effect for SARS-CoV-2 infections needs to be verified in in-vitro experiments. The computational model is accessible at https://identifiers.org/biomodels.db/MODEL2003020001. © The Author(s) 2020. Published by Oxford University Press.

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