SARS-COV-2 δ variant drives the pandemic in India and Europe via two subvariants

  1. Xiang-Jiao Yang

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Abstract

SARS-COV-2 evolution generates different variants and drives the pandemic. As the current main driver, δ variant bears little resemblance to the other three variants of concern, raising the question what features future variants of concern may possess. To address this important question, I compared different variant genomes and specifically analyzed δ genomes in the GISAID database for potential clues. The analysis revealed that δ genomes identified in India by April 2021 form four different groups (referred to as δ1, δ2, δ3 and δ4) with signature spike, nucleocapsid and NSP3 substitutions defining each group. Since May 2021, δ1 has gradually overtaken all other subvariants and become the dominant pandemic driver, whereas δ2 has played a less prominent role and the remaining two (δ3 and δ4) are insignificant. This group composition and variant transition are also apparent across Europe. In the United Kingdom, δ1 has quickly become predominant and is the sole pandemic driver underlying the current wave of COVID-19 cases. Alarmingly, δ1 subvariant has evolved further in the country and yielded a sublineage encoding spike V36F, A222V and V1264L. These substitutions may make the sublineage more virulent than δ1 itself. In the rest of Europe, δ1 is also the main pandemic driver, but δ2 still plays a role. In many European countries, there is a δ1 sublineage encoding spike T29A, T250I and Q613H. This sublineage originated from Morocco and has been a key pandemic driver there. Therefore, δ variant drives the pandemic in India and across Europe mainly through δ1 and δ2, with the former acquiring additional substitutions and yielding sublineages with the potential to drive the pandemic further. These results suggest a continuously branching model by which δ variant evolves and generates more virulent subvariants.

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    SciScore for 10.1101/2021.10.16.21265096: (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
    Some cleaned Fasta files were also uploaded to SnapGene (version 5.3.2) for multisequence analysis via the MAFFT tool.
    SnapGene
    suggested: (SnapGene, RRID:SCR_015052)
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    With these factors all considered, an optimal tree was selected from the 21 trees for re-rooting via FigTree (https://github.com/rambaut/figtree/releases/tag/v1.4.4).
    FigTree
    suggested: (FigTree, RRID:SCR_008515)
    PyMol structural modeling: The PyMol molecular graphics system (version 2.4.2, https://pymol.org/2/) from Schrödinger, Inc. was used for downloading structure files from the PDB database for further analysis and image export.
    PyMol
    suggested: (PyMOL, RRID:SCR_000305)
    The images were cropped via Adobe Photoshop and further presentation using Illustrator.
    Adobe Photoshop
    suggested: (Adobe Photoshop, RRID:SCR_014199)
    Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)
    Pandemic and vaccination data: Pandemic and vaccination data were downloaded from the Our World in Data website (https://ourworldindata.org/explorers/coronavirus-data-explorer?zoomToSelection=true&time=2020-03-01..latest&facet=none&pickerSort=desc&pickerMetric=total_cases&Metric=Confirmed+cases&Interval=Cumulative&Relative+to+Population=false&Align+outbreaks=false&country=~OWID_WRL) as a .csv file for further processing via Excel and figure generation through Prism 9.0 and Adobe Illustrator.
    Excel
    suggested: None
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)

    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.

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