The landscape of SARS-CoV-2 RNA modifications

  1. Milad Miladi
  2. Jonas Fuchs
  3. Wolfgang Maier
  4. Sebastian Weigang
  5. Núria Díaz i Pedrosa
  6. Lisa Weiss
  7. Achim Lother
  8. Anton Nekrutenko
  9. Zsolt Ruzsics
  10. Marcus Panning
  11. Georg Kochs
  12. Ralf Gilsbach
  13. Björn Grüning

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Abstract

In 2019 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the first documented cases of severe lung disease COVID-19. Since then, SARS-CoV-2 has been spreading around the globe resulting in a severe pandemic with over 500.000 fatalities and large economical and social disruptions in human societies. Gaining knowledge on how SARS-Cov-2 interacts with its host cells and causes COVID-19 is crucial for the intervention of novel therapeutic strategies. SARS-CoV-2, like other coronaviruses, is a positive-strand RNA virus. The viral RNA is modified by RNA-modifying enzymes provided by the host cell. Direct RNA sequencing (DRS) using nanopores enables unbiased sensing of canonical and modified RNA bases of the viral transcripts. In this work, we used DRS to precisely annotate the open reading frames and the landscape of SARS-CoV-2 RNA modifications. We provide the first DRS data of SARS-CoV-2 in infected human lung epithelial cells. From sequencing three isolates, we derive a robust identification of SARS-CoV-2 modification sites within a physiologically relevant host cell type. A comparison of our data with the DRS data from a previous SARS-CoV-2 isolate, both raised in monkey renal cells, reveals consistent RNA modifications across the viral genome. Conservation of the RNA modification pattern during progression of the current pandemic suggests that this pattern is likely essential for the life cycle of SARS-CoV-2 and represents a possible target for drug interventions.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.18.204362: (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
    Virus cultivation: SARS-CoV-2 isolates were propagated on VeroE6 cells (ATCC® CRL-1586) in Dulbecco’s Modified Eagle Medium (DMEM) with 2% FCS.
    VeroE6
    suggested: None
    Cells and infection: Calu-3 cells (ATCC® HTB-55™) were cultivated in DMEM supplemented with 5 % fetal bovine serum at 37°C and 5 % CO2.
    Calu-3
    suggested: ATCC Cat# HTB-55, RRID:CVCL_0609)
    Software and Algorithms
    SentencesResources
    The final library was loaded on a FLO-MIN106 flowcell and sequenced on a MinION (Oxford Nanopore Technologies) for 48 - 72 h, depending on the active channel count (MinKnow v3.6.5, Guppy v3.2.10).
    MinION
    suggested: (MinION, RRID:SCR_017985)
    MinKnow
    suggested: None

    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 10.1101/2020.07.18.204362v1 on bioRxiv