Switching of OAS1 splicing isoforms mitigates SARS-CoV-2 infection

  1. Kei Iida
  2. Masahiko Ajiro
  3. Yukiko Muramoto
  4. Toru Takenaga
  5. Masatsugu Denawa
  6. Ryo Kurosawa
  7. Takeshi Noda
  8. Masatoshi Hagiwara

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

The rapidly accumulating disease susceptibility information collected from coronavirus disease (COVID-19) patient genomes must be urgently utilized to develop therapeutic interventions for SARS-CoV-2 infection. Chromosome 12q24.13, which encodes the 2’-5’-oligoadenylate synthetase (OAS) family of proteins that sense viral genomic RNAs and trigger an antiviral response, is identified as one of the genomic regions that contains SNPs associated with COVID-19 severity. A high-risk SNP identified at the splice acceptor site of OAS1 exon 6 is known to change the proportions of the various splicing isoforms and the activity of the enzyme.

Methods

We employed in-silico motif search and RNA pull-down assay to define a factor responsible for the OAS1 splicing. Next, we rationally selected a candidate for slicing modulator to modulate this splicing.

Results

We found that inhibition of CDC-like kinase with a small chemical compound induces switching of OAS1 splice isoforms in human lung cells. In this condition, increased resistance to SARS-CoV-2 infection, enhanced RNA degradation, and transcriptional activation of interferon β1, were also observed.

Conclusions

The results indicate the possibility of using chemical splicing modifiers aided by genome-based precision medicine to boost the innate immune response against SARS-CoV-2 infection.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.08.23.457314: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationContamination: All cells were maintained in an incubator at 37 °C with 5 % CO2, and mycoplasma was confirmed negative in routine polymerase chain reaction tests.

    Table 2: Resources

    Antibodies
    SentencesResources
    Eluted proteins were then analyzed by western blotting with anti-U1-70k mouse monoclonal antibody (9C4.1) (
    anti-U1-70k
    suggested: None
    05-1588, Merk Millipore, Burlington, MA, USA) at a dilution of 1:500 for the detection of U1-70k, anti-SR protein (1H4G7) mouse monoclonal antibody (33-9400, Thermo Fisher Scientific, Waltham, MA, USA) at a dilution of 1:200 for phosphorylated SRSF6, and anti-β-actin (
    U1-70k , anti-SR protein ( 1H4G7 )
    suggested: None
    anti-β-actin
    suggested: None
    ACTB) mouse monoclonal antibody (Ac-15) (sc-69879, Santa Cruz Biotechnology, Dallas, TX, USA) at a dilution of 1:4,000 for ACTB.
    sc-69879
    suggested: (Santa Cruz Biotechnology Cat# sc-69879, RRID:AB_1119529)
    Experimental Models: Cell Lines
    SentencesResources
    Transcriptome analysis for CaNDY-treated Calu-3 cells: RNAs were extracted using RNeasy Mini kit (QIAGEN, Hilden, Germany) from Calu-3 cells, treated with 10 µM CaNDY or 0.1% DMSO for 18 h, and applied for RNA-Seq analysis.
    Calu-3
    suggested: None
    RNA samples were collected from the cells 24 h post-infection (pi), and the virus titers were determined by the 50% tissue culture infectious dose (TCID50) using VeroE6/TMPRSS2 cells at 48 h pi.
    VeroE6/TMPRSS2
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Software and Algorithms
    SentencesResources
    RNA-seq reads were mapped to the human genome sequences (GRCh38) using STAR (ver. 2.7.1a, https://github.com/alexdobin/STAR) with ENCODE options, using the Ensembl genome annotation (ver. 102)
    STAR
    suggested: (STAR, RRID:SCR_004463)
    https://github.com/alexdobin/STAR
    suggested: (Hamilton Microlab STAR Automated Liquid Handling, RRID:SCR_019993)
    Raw reads were counted with bam files, and TPM values were calculated using RSEM v1.2.31 (
    RSEM
    suggested: (RSEM, RRID:SCR_013027)
    For characterizing gene set and transcriptome profiles, we used the Metascape website (https://metascape.org/) and Gene Set Enrichment Analysis (GSEA, https://www.gsea-msigdb.org/gsea/).
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    The original RNA-seq data were deposited at the Gene Expression Omnibus (GEO) of National Center for Biotechnology Information (NCBI) with the accession ID GSE174398.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)

    Results from OddPub: Thank you for sharing your 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: Please consider improving the rainbow (“jet”) colormap(s) used on page 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.08.23.457314v1 on bioRxiv