Perversely expressed long noncoding RNAs can alter host response and viral proliferation in SARS-CoV-2 infection

  1. Rafeed Rahman Turjya
  2. Md. Abdullah-Al-Kamran Khan
  3. Abul Bashar Mir Md. Khademul Islam

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Abstract

Background

Since December 2019, the world is experiencing an unprecedented crisis due to a novel coronavirus, SARS-CoV-2. Owing to poor understanding of pathogenicity, the virus is eluding treatment and complicating recovery. Regulatory roles of long non-coding RNAs (lncRNAs) during viral infection and associated antagonism of host antiviral immune responses has become more evident in last decade. To elucidate possible functions of lncRNAs in the COVID-19 pathobiology, we have utilized RNA-seq dataset of SARS-CoV-2 infected lung epithelial cells.

Results

Our analyses uncover 21 differentially expressed lncRNAs whose functions are broadly involved in cell survival and regulation of gene expression. By network enrichment analysis we find that these lncRNAs can directly interact with differentially expressed protein-coding genes ADAR, EDN1, KYNU, MALL, TLR2 and YWHAG ; and also AKAP8L, EXOSC5, GDF15, HECTD1, LARP4B, LARP7, MIPOL1, UPF1, MOV10 and PRKAR2A , host genes that interact with SARS-CoV-2 proteins. These genes are involved in cellular signaling, metabolism, immune response and RNA homeostasis. Since lncRNAs have been known to sponge microRNAs and protect expression of upregulated genes, we also identified 9 microRNAs that are induced in viral infections; however, some lncRNAs are able to block their usual suppressive effect on overexpressed genes and consequently contribute to host defense and cell survival.

Conclusions

Our investigation determines that deregulated lncRNAs in SARS-CoV-2 infection are involved in viral proliferation, cellular survival, and immune response, ultimately determining disease outcome and this information could drive the search for novel RNA therapeutics as a treatment option.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.29.177204: (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
    Mapping of reads was done with TopHat (tophat v2.1.1 with Bowtie v2.4.1) (
    tophat
    suggested: (TopHat, RRID:SCR_013035)
    Bowtie
    suggested: (Bowtie, RRID:SCR_005476)
    After mapping, we used SubRead package featureCount v2.21 (80) to calculate absolute read abundance (read count, rc) for each transcript/gene associated to the Ensembl genes.
    SubRead
    suggested: (Subread, RRID:SCR_009803)
    featureCount
    suggested: None
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    For differential expression (DE) analysis we used DESeq2 v1.26.0 with R v3.6.2 (2019-07-05) (81) that uses a model based on the negative binomial distribution.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Edge information for the network built with these proteins were extracted from the STRING (82)
    STRING
    suggested: (STRING, RRID:SCR_005223)
    A network file was prepared in SIF format to be visualized using Cytoscape v3.7.2 (83)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    Retrieval of RNA-RNA interactions and lncRNA functions: RNA-RNA interactions between the DE lncRNAs and other RNAs were retrieved from NPInter v4.0 (84).
    NPInter
    suggested: (NPInter, RRID:SCR_007825)
    DIANA-LncBase v3 (87) was used to retrieve miRNAs that target the DE lncRNAs, with high-confidence interactions considered only.
    DIANA-LncBase
    suggested: (DIANA-LncBase, RRID:SCR_010840)
    The set of upregulated target genes for each miRNA were analyzed using NetworkAnalyst 3.0 (88
    NetworkAnalyst
    suggested: (NetworkAnalyst, RRID:SCR_016909)

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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 10.1101/2020.06.29.177204 on bioRxiv