Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets

  1. Sonia Verma
  2. Abhisek Dwivedy
  3. Neeraj Kumar
  4. Bichitra K. Biswal

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Over the past two decades, there has been a continued research on the role of small non-coding RNAs including microRNAs (miRNAs) in various diseases. Studies have shown that viruses modulate the host cellular machinery and hijack its metabolic and immune signalling pathways by miRNA mediated gene silencing. Given the immensity of coronavirus disease 19 (COVID-19) pandemic and the strong association of viral encoded miRNAs with their pathogenesis, it is important to study Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) miRNAs. To address this unexplored area, we identified 8 putative novel miRNAs from SARS-CoV-2 genome and explored their possible human gene targets. A significant proportion of these targets populated key immune and metabolic pathways such as MAPK signalling pathway, maturity-onset diabetes, Insulin signalling pathway, endocytosis, RNA transport, TGF-β signalling pathway, to name a few. The data from this work is backed up by recently reported high-throughput transcriptomics datasets obtains from SARS-CoV-2 infected samples. Analysis of these datasets reveal that a significant proportion of the target human genes were down-regulated upon SARS-CoV-2 infection. The current study brings to light probable host metabolic and immune pathways susceptible to viral miRNA mediated silencing in a SARS-CoV-2 infection, and discusses its effects on the host pathophysiology.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.11.02.365049: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    KEGG is a database used for analysing enriched pathways of the selected genes to further understand gene functions.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    To conduct GO and KEGG pathway analysis for the target mRNAs, the ClueGO (26), CluePedia (58), CytoKEGG, and KEGGParser (59) plugins on Cytoscape (27) were used.
    ClueGO
    suggested: (ClueGO, RRID:SCR_005748)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    ClueGO was used to identify the significant GO Terms enriched with the gene clusters retrieved in MCODE.
    MCODE
    suggested: (MCODE, RRID:SCR_015828)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.

  2. Version published to 10.1101/2020.11.02.365049v1 on bioRxiv