RdRp mutations are associated with SARS-CoV-2 genome evolution

  1. Doğa Eskier
  2. Gökhan Karakülah
  3. Aslı Suner
  4. Yavuz Oktay

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Abstract

COVID-19, caused by the novel SARS-CoV-2 virus, started in China in late 2019, and soon became a global pandemic. With the help of thousands of viral genome sequences that have been accumulating, it has become possible to track the evolution of the viral genome over time as it spread across the world. An important question that still needs to be answered is whether any of the common mutations affect the viral properties, and therefore the disease characteristics. Therefore, we sought to understand the effects of mutations in RNA-dependent RNA polymerase (RdRp), particularly the common 14408C>T mutation, on mutation rate and viral spread. By focusing on mutations in the slowly evolving M or E genes, we aimed to minimize the effects of selective pressure. Our results indicate that 14408C>T mutation increases the mutation rate, while the third-most common RdRp mutation, 15324C>T, has the opposite effect. It is possible that 14408C>T mutation may have contributed to the dominance of its co-mutations in Europe and elsewhere.

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  3. Version published to 10.7717/peerj.9587
  4. ScreenIT

    SciScore for 10.1101/2020.05.20.104885: (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.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The custom ANNOVAR gene annotations for SARS-CoV-2 were obtained from ANNOVAR resources, decompressed, and placed in the sarscov2db directory.
    ANNOVAR
    suggested: (ANNOVAR, RRID:SCR_012821)
    All statistical analyses were performed using IBM SPSS version 25.0 (Chicago, IL, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

  5. Version published to 10.1101/2020.05.20.104885v1 on bioRxiv