文章基本信息

标题：DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling
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作者：Ali Kishk ; Maria Pires Pacheco ; Thomas Sauter 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：11
页码：1-20
DOI：10.1016/j.isci.2021.103331
语种：English
出版社：Elsevier
摘要：SummaryThe 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).Graphical abstractDisplay OmittedHighlights•Metabolic modeling of COVID-19 utilized public RNA-Seq of SARS-CoV-2-infected lung•In silico knockout identified 23 human essential genes for SARS-CoV-2 replication•Drug repositioning predicted single drugs targeting essential gene pairs•Among others, pyrimidine metabolism and ferroptosis are candidate druggable pathwaysVirology; Pharmaceutical science; Bioinformatics