Potential role of cellular miRNAs in coronavirus-host interplay

  1. Stepan Nersisyan
  2. Narek Engibaryan
  3. Aleksandra Gorbonos
  4. Ksenia Kirdey
  5. Alexey Makhonin
  6. Alexander Tonevitsky

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Host miRNAs are known as important regulators of virus replication and pathogenesis. They can interact with various viruses through several possible mechanisms including direct binding of viral RNA. Identification of human miRNAs involved in coronavirus-host interplay becomes important due to the ongoing COVID-19 pandemic. In this article we performed computational prediction of high-confidence direct interactions between miRNAs and seven human coronavirus RNAs. As a result, we identified six miRNAs (miR-21-3p, miR-195-5p, miR-16-5p, miR-3065-5p, miR-424-5p and miR-421) with high binding probability across all analyzed viruses. Further bioinformatic analysis of binding sites revealed high conservativity of miRNA binding regions within RNAs of human coronaviruses and their strains. In order to discover the entire miRNA-virus interplay we further analyzed lungs miRNome of SARS-CoV infected mice using publicly available miRNA sequencing data. We found that miRNA miR-21-3p has the largest probability of binding the human coronavirus RNAs and being dramatically up-regulated in mouse lungs during infection induced by SARS-CoV.

Article activity feed

  6. Version published to 10.7717/peerj.9994
  7. ScreenIT

    SciScore for 10.1101/2020.07.03.184846: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationFor SARS-CoV-2 thousand genomes were randomly chosen preserving the percentage of samples from each country.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    All genomes available on the NCBI Virus were used for SARS-CoV, MERS-CoV, HCoV-OC43, HCoV-NL63, HCoV-HKU1 and HCoV-229E (263, 253, 139, 58, 39 and 28 genomes, respectively).
    HCoV-NL63
    suggested: RRID:CVCL_RW88)
    Software and Algorithms
    SentencesResources
    Prediction of miRNA binding sites: To find miRNAs which can bind to the viral RNAs we used miRDB v6.0 (Chen and Wang, 2020) and TargetScan v7.2 (Agarwal et al., 2015).
    miRDB
    suggested: (miRDB, RRID:SCR_010848)
    TargetScan
    suggested: (TargetScan, RRID:SCR_010845)
    Specifically, the data was obtained from GDC Data Portal (https://portal.gdc.cancer.gov/) and included miRNA expression table whose columns correspond to 46 normal lung tissues and rows are associated with miRNAs.
    https://portal.gdc.cancer.gov/
    suggested: (Genomic Data Commons Data Portal (GDC Data Portal, RRID:SCR_014514)
    Adapters were trimmed via Cutadapt 2.10 (Martin, 2011), miRNA expression was quantified by miRDeep2 (Friedländer et al., 2012) using GRCm38.p6 mouse genome (release M25) from GENCODE (Frankish et al., 2019) and miRBase 22.1 (Kozomara et al., 2019).
    Cutadapt
    suggested: (cutadapt, RRID:SCR_011841)
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    miRBase
    suggested: (miRBase, RRID:SCR_017497)
    Gene expression profile of SARS-CoV infected mouse lungs was downloaded in form of count matrix from Gene Expression Omnibus (GEO) (Barrett et al., 2013) under GSE52405 accession number (Josset et al., 2014).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Differential expression analysis was performed with DESeq2 (Love et al., 2014).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Sequence alignment: Multiple Sequence Alignment (MSA) of viral genomic sequences was done using Kalign 2.04 (Lassmann et al., 2009).
    Kalign
    suggested: (Kalign, RRID:SCR_011810)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  8. Version published to 10.1101/2020.07.03.184846v1 on bioRxiv