Hui Ying, Xueyan Wu, Xiaojing Jia, Qianqian Yang, Haoyu Liu, Huiling Zhao, Zhihe Chen, Min Xu, Tiange Wang, Mian Li, Zhiyun Zhao, Ruizhi Zheng, Shuangyuan Wang, Hong Lin, Yu Xu, Jieli Lu, Weiqing Wang, Guang Ning, Jie Zheng, Yufang Bi
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We also carried out additional validations including colocalization, linkage disequilibrium check and host-pathogen interactome predictions. We integrated MR findings with clinical trial evidence from several drug gene related databases to identify drugs with repurposing potential. Finally, we developed a tier system and identified immune-cell-based prioritized drug targets for COVID-19.</p><p><strong>Findings: </strong>We identified 132 putative causal genes in 14 immune cells (343 MR associations) for COVID-19, with 58 genes that were not reported previously. 145 (73%) gene-COVID-19 pairs showed effects on COVID-19 in only one immune cell type, which implied widespread immune-cell specific effects. For pathway analyses, we found the putative causal genes were enriched in natural killer (NK) recruiting cells but de-enriched in NK cells. Using a deep learning model, we found 107 (81%) of the putative causal genes (41 novel genes) were predicted to interact with SARS-COV-2 proteins. 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Single-cell transcriptome-wide Mendelian randomization and colocalization reveals immune-mediated regulatory mechanisms and drug targets for COVID-19.
Background: COVID-19 continues to show long-term impacts on our health. Limited effective immune-mediated antiviral drugs have been launched.
Methods: We conducted a Mendelian randomization (MR) and colocalization analysis using 26,597 single-cell expression quantitative trait loci (sc-eQTL) to proxy effects of expressions of 16,597 genes in 14 peripheral blood immune cells and tested them against four COVID-19 outcomes from COVID-19 Genetic Housing Initiative GWAS meta-analysis Round 7. We also carried out additional validations including colocalization, linkage disequilibrium check and host-pathogen interactome predictions. We integrated MR findings with clinical trial evidence from several drug gene related databases to identify drugs with repurposing potential. Finally, we developed a tier system and identified immune-cell-based prioritized drug targets for COVID-19.
Findings: We identified 132 putative causal genes in 14 immune cells (343 MR associations) for COVID-19, with 58 genes that were not reported previously. 145 (73%) gene-COVID-19 pairs showed effects on COVID-19 in only one immune cell type, which implied widespread immune-cell specific effects. For pathway analyses, we found the putative causal genes were enriched in natural killer (NK) recruiting cells but de-enriched in NK cells. Using a deep learning model, we found 107 (81%) of the putative causal genes (41 novel genes) were predicted to interact with SARS-COV-2 proteins. Integrating the above evidence with drug trial information, we developed a tier system and prioritized 37 drug targets for COVID-19.
Interpretation: Our study showcased the central role of immune-mediated regulatory mechanisms for COVID-19 and prioritized drug targets that might inform interventions for viral infectious diseases.
Funding: This work was supported by grants from the National Key Research and Development Program of China (2022YFC2505203).
EBioMedicineBiochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍：
eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.
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