Estimated Spike Evolution and Impact of Emerging SARS-CoV-2 Variants

  1. Yong Lu
  2. Kun Han
  3. Gang Xue
  4. Ningbo Zheng
  5. Guangxu Jin

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Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, has been mutating and thus variants emerged. This suggests that SARS-CoV-2 could mutate at an unsteady pace. Supportive evidence comes from the accelerated evolution which was revealed by tracking mutation rates of the genomic location of Spike protein. This process is sponsored by a small portion of the virus population but not the largest viral clades. Moreover, it generally took one to six months for current variants that caused peaks of COVID-19 cases and deaths to survive selection pressure. Based on this statistic result and the above speedy Spike evolution, another upcoming peak would come around July 2021 and disastrously attack Africa, Asia, Europe, and North America. This is the prediction generated by a mathematical model on evolutionary spread. The reliability of this model and future trends out of it comes from the comprehensive consideration of factors mainly including mutation rate, selection course, and spreading speed. Notably, if the prophecy is true, then the new wave will be the first determined by accelerated Spike evolution.

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    SciScore for 10.1101/2021.05.06.21256705: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We used BLAST for nuclei (blastn) as the alignment tool to align the 817,351genome sequences to the WIV04-reference.
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    The newest version of BLASTN was downloaded from the National Institutes of Health BLAST website (https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/), which was ncbi-blast-2.11.0+.
    BLASTN
    suggested: (BLASTN, RRID:SCR_001598)
    The regression methods were automatically selected by the ggplot2 package.
    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.05.06.21256705 on medRxiv