SARS‐CoV‐2, SARS‐CoV, and MERS‐CoV encode circular RNAs of spliceosome‐independent origin

  1. Shaomin Yang
  2. Hong Zhou
  3. Mingde Liu
  4. Dabbu Jaijyan
  5. Ruth Cruz‐Cosme
  6. Santhamani Ramasamy
  7. Selvakumar Subbian
  8. Dongxiao Liu
  9. Jiayu Xu
  10. Xiaoyu Niu
  11. Yaolan Li
  12. Lizu Xiao
  13. Sanjay Tyagi
  14. Qiuhong Wang
  15. Hua Zhu
  16. Qiyi Tang

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Abstract

Circular RNAs (circRNAs) are a newly recognized component of the transcriptome with critical roles in autoimmune diseases and viral pathogenesis. To address the importance of circRNA in RNA viral transcriptome, we systematically identified and characterized circRNAs encoded by the RNA genomes of betacoronaviruses using both bioinformatical and experimental approaches. We predicted 351, 224, and 2764 circRNAs derived from severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), SARS‐CoV, and Middle East respiratory syndrome coronavirus, respectively. We experimentally identified 75 potential SARS‐CoV‐2 circRNAs from RNA samples extracted from SARS‐CoV‐2‐infected Vero E6 cells. A systematic comparison of viral and host circRNA features, including abundance, strand preference, length distribution, circular exon numbers, and breakpoint sequences, demonstrated that coronavirus‐derived circRNAs had a spliceosome‐independent origin. We further showed that back‐splice junctions (BSJs) captured by inverse reverse‐transcription polymerase chain reaction have different level of resistance to RNase R. Through northern blotting with a BSJ‐spanning probe targeting N gene, we identified three RNase R‐resistant bands that represent SARS‐CoV‐2 circRNAs that are detected cytoplasmic by single‐molecule and amplified fluorescence in situ hybridization assays. Lastly, analyses of 169 sequenced BSJs showed that both back‐splice and forward‐splice junctions were flanked by homologous and reverse complementary sequences, including but not limited to the canonical transcriptional regulatory sequences. Our findings highlight circRNAs as an important component of the coronavirus transcriptome, offer important evaluation of bioinformatic tools in the analysis of circRNAs from an RNA genome, and shed light on the mechanism of discontinuous RNA synthesis.

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  1. Version published to 10.1002/jmv.27734
  2. 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

    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.

  3. Version published to 10.1101/2020.12.07.415422v1 on bioRxiv