@article{a44c0faaec2840d6881a8f58a19c075d,
title = "Synthetic lethality-based prediction of anti-SARS-CoV-2 targets",
abstract = "Novel 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 multiple in vitro and in vivo bulk 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.",
keywords = "Drugs, Synthetic biology, Virology",
author = "Pal, {Lipika R.} and Kuoyuan Cheng and Nair, {Nishanth Ulhas} and Laura Martin-Sancho and Sanju Sinha and Yuan Pu and Laura Riva and Xin Yin and Fiorella Schischlik and Lee, {Joo Sang} and Chanda, {Sumit K.} and Eytan Ruppin",
note = "Publisher Copyright: {\textcopyright} 2022",
year = "2022",
month = may,
day = "20",
doi = "10.1016/j.isci.2022.104311",
language = "אנגלית",
volume = "25",
journal = "iScience",
issn = "2589-0042",
publisher = "Elsevier Inc.",
number = "5",
}