Transcriptional and epi-transcriptional dynamics of SARS-CoV-2 during cellular infection

  1. Jessie J.-Y. Chang
  2. Daniel Rawlinson
  3. Miranda E. Pitt
  4. George Taiaroa
  5. Josie Gleeson
  6. Chenxi Zhou
  7. Francesca L. Mordant
  8. Ricardo De Paoli-Iseppi
  9. Leon Caly
  10. Damian F.J. Purcell
  11. Tim P. Stinear
  12. Sarah L. Londrigan
  13. Michael B. Clark
  14. Deborah A. Williamson
  15. Kanta Subbarao
  16. Lachlan J.M. Coin

Reviewed by

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

SARS-CoV-2 uses subgenomic (sg)RNA to produce viral proteins for replication and immune evasion. We applied long-read RNA and cDNA sequencing to in vitro human and primate infection models to study transcriptional dynamics. Transcription-regulating sequence (TRS)-dependent sgRNA was upregulated earlier in infection than TRS-independent sgRNA. An abundant class of TRS-independent sgRNA consisting of a portion of ORF1ab containing nsp1 joined to ORF10 and 3’UTR was upregulated at 48 hours post infection in human cell lines. We identified double-junction sgRNA containing both TRS-dependent and independent junctions. We found multiple sites at which the SARS-CoV-2 genome is consistently more modified than sgRNA, and that sgRNA modifications are stable across transcript clusters, host cells and time since infection. Our work highlights the dynamic nature of the SARS-CoV-2 transcriptome during its replication cycle. Our results are available via an interactive web-app at http://coinlab.mdhs.unimelb.edu.au/ .

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.12.22.423893: (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 AuthenticationContamination: The cell lines were tested for presence of mycoplasma using the MycoAlert Mycoplasma Detection Kit (Lonza)

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    , Caco-2 (human intestinal epithelial cells, ATCC HTB-37) and Calu-3 (human lung epithelial cells, ATCC HTB-55) and maintained at 37 °C, 5% (v/v) CO2.
    Caco-2
    suggested: None
    Oxford Nanopore Technologies library preparation and sequencing: Direct cDNA sequencing libraries were prepared using an input of 3 μg of total RNA (equivalent to approximately 150 ng of poly(A) + RNA) for Vero cell infections, and 1-2 μg of total RNA (equivalent to approximately 50 – 100 ng of poly(A) + RNA) for Caco-2 and Calu-3 cell infections per triplicate.
    Calu-3
    suggested: KCLB Cat# 30055, RRID:CVCL_0609)
    + RNA) for Vero cell infections and 3 μg of total RNA (1 μg per triplicate equivalent to ∼150 ng poly(A)
    Vero
    suggested: None
    Software and Algorithms
    SentencesResources
    All libraries were sequenced with MinION R9.4.1 flow cells.
    MinION
    suggested: (MinION, RRID:SCR_017985)

    Results from OddPub: Thank you for sharing your code and 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: 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 10.1101/2020.12.22.423893 on bioRxiv