SARS-CoV-2: Possible recombination and emergence of potentially more virulent strains

This article has been Reviewed by the following groups

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Abstract

COVID-19 is challenging healthcare preparedness, world economies, and livelihoods. The infection and death rates associated with this pandemic are strikingly variable in different countries. To elucidate this discrepancy, we analyzed 2431 early spread SARS-CoV-2 sequences from GISAID. We estimated continental-wise admixture proportions, assessed haplotype block estimation, and tested for the presence or absence of strains’ recombination. Herein, we identified 1010 unique missense mutations and seven different SARS-CoV-2 clusters. In samples from Asia, a small haplotype block was identified, whereas samples from Europe and North America harbored large and different haplotype blocks with nonsynonymous variants. Variant frequency and linkage disequilibrium varied among continents, especially in North America. Recombination between different strains was only observed in North American and European sequences. In addition, we structurally modelled the two most common mutations, Spike_D614G and Nsp12_P314L, which suggested that these linked mutations may enhance viral entry and replication, respectively. Overall, we propose that genomic recombination between different strains may contribute to SARS-CoV-2 virulence and COVID-19 severity and may present additional challenges for current treatment regimens and countermeasures. Furthermore, our study provides a possible explanation for the substantial second wave of COVID-19 presented with higher infection and death rates in many countries.

Article activity feed

  1. SciScore for 10.1101/2020.11.11.20229765: (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
    Alignment and annotation of amino acid sequence variation: Multiple sequence alignment was performed using MAFFT v7.407 [15] (retree: 5, maxiter: 1000).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Alignments’ gaps were trimmed using TrimAL (automated1) [16]
    TrimAL
    suggested: (trimAl, RRID:SCR_017334)
    SNP-sites [18] and Annovar [19] were used to extract and annotate single nucleotide variants (SNV).
    Annovar
    suggested: (ANNOVAR, RRID:SCR_012821)
    We used Haploview [21] to visualize the haplotype blocks.
    Haploview
    suggested: (Haploview, RRID:SCR_003076)
    To detect recombination within datasets, we tested for pairwise homoplasy index using PhiPack software [22]
    PhiPack
    suggested: None
    Replicate runs were further processed using CLUMPAK [24] and results for the major modes were illustrated using R software’s ggplot2 package (
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    https://www.r-project.org/).
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    PyMol (Molecular Graphics System, Version 2.0, Schrodinger, LLC) was used to generate structural images.
    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.