DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.
DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.
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DOI：
10.1016/j.isci.2021.103331
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发表时间：
2021-11-19
期刊：
影响因子：
5.8
通讯作者：
Sauter T
中科院分区：
文献类型：
--
作者：
Kishk A;Pacheco MP;Sauter T
The 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). 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 pathways Virology; Pharmaceutical science; Bioinformatics
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DOI：
10.1073/pnas.2025581118
发表时间：
2021-05-11
影响因子：
11.1
作者：
Morselli Gysi D;do Valle Í;Zitnik M;Ameli A;Gan X;Varol O;Ghiassian SD;Patten JJ;Davey RA;Loscalzo J;Barabási AL
通讯作者：
Barabási AL
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
3.7
作者：
Choon YW;Mohamad MS;Deris S;Illias RM;Chong CK;Chai LE;Omatu S;Corchado JM
通讯作者：
Corchado JM
影响因子：
14.8
作者：
Heirendt, Laurent;Arreckx, Sylvain;Fleming, Ronan M. T.
通讯作者：
Fleming, Ronan M. T.
影响因子：
64.8
作者：
Gordon, David E.;Jang, Gwendolyn M.;Krogan, Nevan J.
通讯作者：
Krogan, Nevan J.