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Abstract
BackgroundThe ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are now available, effective treatment options are still out of reach.ResultsTo characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P < 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3, PPP1R15A, NFKBIA, and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score < − 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection.ConclusionsThus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients’ expression profiles of BALF cells.
Highlights
The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide
Signature generation and validation To develop a gene expression signature representative of COVID-19, a computational analysis tool known as Adaptive Signature Selection and InteGratioN (ASSIGN) was used on cell lines infected with SARS-CoV-2 (GSE147507)
Genes that showed the highest discrimination between the control and SARS-CoV-2 infected training samples were selected
Summary
The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. The 2019 coronavirus pandemic (COVID-19), caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has already contributed to over 107 million confirmed cases and 2.3 million deaths worldwide [1]. Despite previous advancements in our knowledge of SARS-CoV-2, significant gaps still exist within our understanding of COVID-19 and clinical care, such as the uncertainty of mortality risk in critically ill patients. Publicly available studies and datasets can be further leveraged to learn more about COVID-19 pathophysiology and treatment [6]
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