Identification of COVID-19 prognostic markers and therapeutic targets through meta-analysis and validation of Omics data from nasopharyngeal samples

通过对鼻咽样本的组学数据进行荟萃分析和验证,确定 COVID-19 预后标记和治疗靶点

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作者:Abhijith Biji, Oyahida Khatun, Shachee Swaraj, Rohan Narayan, Raju S Rajmani, Rahila Sardar, Deepshikha Satish, Simran Mehta, Hima Bindhu, Madhumol Jeevan, Deepak K Saini, Amit Singh, Dinesh Gupta, Shashank Tripathi

Background

While our battle with the COVID-19 pandemic continues, a multitude of Omics data have been generated from patient samples in various studies. Translation of these data into clinical interventions against COVID-19 remains to be accomplished. Exploring host response to COVID-19 in the upper respiratory tract can unveil prognostic markers and therapeutic targets.

Methods

We conducted a meta-analysis of published transcriptome and proteome profiles of respiratory samples of COVID-19 patients to shortlist high confidence upregulated host factors. Subsequently, mRNA overexpression of selected genes was validated in nasal swabs from a cohort of COVID-19 positive/negative, symptomatic/asymptomatic individuals. Guided by this analysis, we sought to check for potential drug targets. An FDA-approved drug, Auranofin, was tested against SARS-CoV-2 replication in cell culture and Syrian hamster challenge model. Findings: The meta-analysis and validation in the COVID-19 cohort revealed S100 family genes (S100A6, S100A8, S100A9, and S100P) as prognostic markers of severe COVID-19. Furthermore, Thioredoxin (TXN) was found to be consistently upregulated. Auranofin, which targets Thioredoxin reductase, was found to mitigate SARS-CoV-2 replication in vitro. Furthermore, oral administration of Auranofin in Syrian hamsters in therapeutic as well as prophylactic regimen reduced viral replication, IL-6 production, and inflammation in the lungs. Interpretation: Elevated mRNA level of S100s in the nasal swabs indicate severe COVID-19 disease, and FDA-approved drug Auranofin mitigated SARS-CoV-2 replication in preclinical hamster model. Funding: This study was supported by the DBT-IISc partnership program (DBT (IED/4/2020-MED/DBT)), the Infosys Young Investigator award (YI/2019/1106), DBT-BIRAC grant (BT/CS0007/CS/02/20) and the DBT-Wellcome Trust India Alliance Intermediate Fellowship (IA/I/18/1/503613) to ST lab.

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