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  • 标题:Synthetic lethality-based prediction of anti-SARS-CoV-2 targets
  • 本地全文:下载
  • 作者:Lipika R. Pal ; Kuoyuan Cheng ; Nishanth Ulhas Nair
  • 期刊名称:iScience
  • 印刷版ISSN:2589-0042
  • 出版年度:2022
  • 卷号:25
  • 期号:5
  • 页码:1-19
  • DOI:10.1016/j.isci.2022.104311
  • 语种:English
  • 出版社:Elsevier
  • 摘要:SummaryNovel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal and synthetic dosage lethal (SL/SDL) partners of such altered host genes. Pursuing this disparate antiviral strategy, here we comprehensively analyzed multiplein vitroandin vivobulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL/SDL with altered host genes. The predicted SL/SDL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. We further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming noninfected healthy cells.Graphical abstractDisplay OmittedHighlights•Identified anti-SARS-CoV-2 targets using synthetic lethality from infected datasets•Predicted targets are enriched by infected cell inhibiting genes from CRISPR/Cas9 data•Experimental validation of selected SL targets in siRNA assay from human Caco-2 cells•Predicted targets are made publicly available forin vivotesting and validationDrugs; Virology; Synthetic biology
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