Factors Associated with Emerging and Re-emerging of SARS-CoV-2 Variants

  1. Austin N. Spratt
  2. Saathvik R. Kannan
  3. Lucas T. Woods
  4. Gary A. Weisman
  5. Thomas P. Quinn
  6. Christian L. Lorson
  7. Anders Sönnerborg
  8. Siddappa N. Byrareddy
  9. Kamal Singh

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Abstract

Global spread of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has triggered unprecedented scientific efforts, as well as containment and treatment measures. Despite these efforts, SARS-CoV-2 infections remain unmanageable in some parts of the world. Due to inherent mutability of RNA viruses, it is not surprising that the SARS-CoV-2 genome has been continuously evolving since its emergence. Recently, four functionally distinct variants, B.1.1.7, B.1.351, P.1 and CAL.20C, have been identified, and they appear to more infectious and transmissible than the original (Wuhan-Hu-1) virus. Here we provide evidence based upon a combination of bioinformatics and structural approaches that can explain the higher infectivity of the new variants. Our results show that the greater infectivity of SARS-CoV-2 than SARS-CoV can be attributed to a combination of several factors, including alternate receptors. Additionally, we show that new SARS-CoV-2 variants emerged in the background of D614G in Spike protein and P323L in RNA polymerase. The correlation analyses showed that all mutations in specific variants did not evolve simultaneously. Instead, some mutations evolved most likely to compensate for the viral fitness.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.24.436850: (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
    These sequences were aligned using the MAFFT [42], MEGA [43] or JalView [44] sequence alignment programs.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    JalView
    suggested: (Jalview, RRID:SCR_006459)
    The sequences were then analyzed through in-house Python or R scripts.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Relative abundance (RA) of mutations in S protein from different geographic regions was determined by an in-house python script using the scikit-learn (Python) library [45] and plotted with R (codes available upon request).
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    The structural analyses were conducted using the Schrodinger Suite (Schrodinger LLC, NY) and PyMol [46].
    PyMol
    suggested: (PyMOL, RRID:SCR_000305)

    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 10.1101/2021.03.24.436850 on bioRxiv