Updated SARS‐CoV‐2 single nucleotide variants and mortality association

  1. Shuyi Fang
  2. Sheng Liu
  3. Jikui Shen
  4. Alex Z Lu
  5. Audrey K. Y. Wang
  6. Yucheng Zhang
  7. Kailing Li
  8. Juli Liu
  9. Lei Yang
  10. Chang‐Deng Hu
  11. Jun Wan

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Abstract

By analyzing newly collected SARS‐CoV‐2 genomes and comparing them with our previous study about SARS‐CoV‐2 single nucleotide variants (SNVs) before June 2020, we found that the SNV clustering had changed remarkably since June 2020. Apart from that the group of SNVs became dominant, which is represented by two nonsynonymous mutations A23403G (S:D614G) and C14408T (ORF1ab:P4715L), a few emerging groups of SNVs were recognized with sharply increased monthly incidence ratios of up to 70% in November 2020. Further investigation revealed sets of SNVs specific to patients' ages and/or gender, or strongly associated with mortality. Our logistic regression model explored features contributing to mortality status, including three critical SNVs, G25088T(S:V1176F), T27484C (ORF7a:L31L), and T25A (upstream of ORF1ab), ages above 40 years old, and the male gender. The protein structure analysis indicated that the emerging subgroups of nonsynonymous SNVs and the mortality‐related ones were located on the protein surface area. The clashes in protein structure introduced by these mutations might in turn affect the viral pathogenesis through the alteration of protein conformation, leading to a difference in transmission and virulence. Particularly, we explored the fact that nonsynonymous SNVs tended to occur in intrinsic disordered regions of Spike and ORF1ab to significantly increase hydrophobicity, suggesting a potential role in the change of protein folding related to immune evasion.

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  1. Version published to 10.1002/jmv.27191
  2. ScreenIT

    SciScore for 10.1101/2021.01.29.21250757: (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
    Protein Structure Analysis: We adopted PyMOL (16-18) to analyze and visualize protein structures for WT (Wuhan-Hu-1) and mutated proteins with identified non-synonymous SNVs.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    Properties of AAs were retrieved from the “Table of standard amino acid abbreviations and properties” on Wikipedia.
    Wikipedia
    suggested: (Wikipedia, RRID:SCR_004897)

    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.

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  3. Version published to 10.1101/2021.01.29.21250757v1 on medRxiv