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. 2020 Aug 26;11(9):1002.
doi: 10.3390/genes11091002.

Prediction and Analysis of SARS-CoV-2-Targeting MicroRNA in Human Lung Epithelium

Jonathan Tak-Sum Chow  1 , Leonardo Salmena  1   2
Affiliations

Prediction and Analysis of SARS-CoV-2-Targeting MicroRNA in Human Lung Epithelium

Jonathan Tak-Sum Chow et al. Genes (Basel). .

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an RNA virus, is responsible for the coronavirus disease 2019 (COVID-19) pandemic of 2020. Experimental evidence suggests that microRNA can mediate an intracellular defence mechanism against some RNA viruses. The purpose of this study was to identify microRNA with predicted binding sites in the SARS-CoV-2 genome, compare these to their microRNA expression profiles in lung epithelial tissue and make inference towards possible roles for microRNA in mitigating coronavirus infection. We hypothesize that high expression of specific coronavirus-targeting microRNA in lung epithelia may protect against infection and viral propagation, conversely, low expression may confer susceptibility to infection. We have identified 128 human microRNA with potential to target the SARS-CoV-2 genome, most of which have very low expression in lung epithelia. Six of these 128 microRNA are differentially expressed upon in vitro infection of SARS-CoV-2. Additionally, 28 microRNA also target the SARS-CoV genome while 23 microRNA target the MERS-CoV genome. We also found that a number of microRNA are commonly identified in two other studies. Further research into identifying bona fide coronavirus targeting microRNA will be useful in understanding the importance of microRNA as a cellular defence mechanism against pathogenic coronavirus infections.

Keywords: SARS-CoV-2; cellular antiviral defence; coronavirus; lung epithelia; microRNA.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Computational identification of microRNA (miRNA) with predicted miRNA response elements (MREs) in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reference genome. (A) Computational pipeline used to identify the 128 candidate miRNA with at least one predicted MRE. The remaining number of miRNA remaining after each step is shown above. (B) Schematic of the SARS-CoV-2 reference genome (NC_045512.2) with key features shown.
Figure 2
Figure 2
Analysis of miRNA expression (log2 transformed) from 46 normal lung tissue control samples in the TGCA-LUAD dataset.
Figure 3
Figure 3
Differential miRNA expression analysis of Calu3 cells infected with SARS-CoV-2 or mock 24 h post-infection from GSE148729. (A) Multidimensional scaling analysis of samples and replicates from GSE148729. Samples infected with mock are in red and samples infected with SARS-CoV-2 are in black. (B) Significantly differentially expressed miRNA. A miRNA was considered differentially expressed if it had log(fold-change) magnitude > 1 and FDR < 0.05. Significantly downregulated miRNA are in blue and significantly upregulated miRNA are in red.
Figure 4
Figure 4
Differential miRNA expression analysis of Calu3 cells infected with SARS-CoV or mock 24 h post-infection from GSE148729. (A) Multidimensional scaling analysis between SARS-CoV and mock infected cells (top) and between SARS-CoV-2, SARS-CoV and mock infected cells. Samples infected with mock are in green, samples infected with SARS-CoV-2 are in red and samples infected with SARS-CoV are in black. (B) Significantly differentially expressed miRNA. Significantly downregulated miRNA are in blue and significantly upregulated miRNA are in red. miRNA were considered differentially expressed if the |log(fold-change)| > 1 and FDR < 0.05. (C) Commonly differentially expressed miRNA between SARS-CoV-2 and SARS-CoV infection.
Figure 5
Figure 5
Commonly identified miRNA with other computational prediction studies.
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