Circular RNA profiling reveals abundant and diverse circRNAs of SARS-CoV-2, SARS-CoV and MERS-CoV origin

  1. Shaomin Yang
  2. Hong Zhou
  3. Ruth Cruz-Cosme
  4. Mingde Liu
  5. Jiayu Xu
  6. Xiaoyu Niu
  7. Yaolan Li
  8. Lizu Xiao
  9. Qiuhong Wang
  10. Hua Zhu
  11. Qiyi Tang

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Abstract

Circular RNAs (circRNAs) encoded by DNA genomes have been identified across host and pathogen species as parts of the transcriptome. Accumulating evidences indicate that circRNAs play critical roles in autoimmune diseases and viral pathogenesis. Here we report that RNA viruses of the Betacoronavirus genus of Coronaviridae , SARS-CoV-2, SARS-CoV and MERS-CoV, encode a novel type of circRNAs. Through de novo circRNA analyses of publicly available coronavirus-infection related deep RNA-Sequencing data, we identified 351, 224 and 2,764 circRNAs derived from SARS-CoV-2, SARS-CoV and MERS-CoV, respectively, and characterized two major back-splice events shared by these viruses. Coronavirus-derived circRNAs are more abundant and longer compared to host genome-derived circRNAs. Using a systematic strategy to amplify and identify back-splice junction sequences, we experimentally identified over 100 viral circRNAs from SARS-CoV-2 infected Vero E6 cells. This collection of circRNAs provided the first line of evidence for the abundance and diversity of coronavirus-derived circRNAs and suggested possible mechanisms driving circRNA biogenesis from RNA genomes. Our findings highlight circRNAs as an important component of the coronavirus transcriptome.

Summary

We report for the first time that abundant and diverse circRNAs are generated by SARS-CoV-2, SARS-CoV and MERS-CoV and represent a novel type of circRNAs that differ from circRNAs encoded by DNA genomes.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.07.415422: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Cell culture, plasmid DNA transfection and SARS-CoV-2 infection: Vero cells (ATCC, CCL-81) and HEK 293T(ATCC® CRL-1573™) were purchased from ATCC.
    Vero
    suggested: None
    HEK
    suggested: None
    The plasmid, pCAG-nCoV-N-FLAG (34) expresses nucleocapsid (N) gene and was transfected into HEK 293T cells by transfection reagent, Lipofectamine 3000 (cat# L3000015, Scientific Fisher, USA) according to the manufacturer’s protocol.
    HEK 293T
    suggested: None
    After keeping the tubes in room temperature for 5 min to fully lysis the cells, we took 100 μL/sample for inactivation test by performing two rounds of virus isolation in Vero E6 cells.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Adaptor trimmed reads of the same condition were pooled and aligned with BWA Aligner(25) (BWA-MEM version 0.7.17-R1188) and bowtie2 (version 2.3.5.1)(29) to host and viral reference genomes: Afircan green monkey (ChlSab1.1.101) for bioproject PRJNA168621; human (hg19) for bioproject PRJNA31257; SARS-CoV-2 (NC_045512.2) for bioproject PRJNA485481; SARS-CoV (NC_004718.3) for bioproject PRJNA485481; and MERS-CoV (NC_019843.3) for bioproject PRJNA485481.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)
    bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    Quantification and plotting: Quantification and plots were produced using python (version 3.9.0) with plotly module (https://plotly.com/python/ and R statistical environment (version 3.4.5) with R package: gggenes (https://wilkox.org/gggenes/, Figure 1B), ggplot2 (other Figures)(32).
    python
    suggested: (IPython, RRID:SCR_001658)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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

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  2. Version published to 10.1101/2020.12.07.415422v1 on bioRxiv