Genome surveillance of SARS-CoV-2 variants and their role in pathogenesis focusing on second wave of COVID-19 in India

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Abstract

India had witnessed unprecedented surge in SARS-CoV-2 infections and the dire consequences during the second wave of COVID-19, but the detailed report of the epidemiological based spatiotemporal incidences of the disease is missing. Here in, we have applied various statistical methods like correlation, hierarchical clustering to know the pattern of pathogenesis of the circulating VoCs. B.1.617.1 (Kappa) was the predominant VoC during the early phase of second wave. Delta (B.1.617.2) or Delta-like (AY.x) VoC constitutes majority (>90.17) of the cases during the peak of second wave. The correlation plot showed Delta/Delta-like lineage is inversely correlated with other lineages including B.1.617.1 (kappa), B.1.1.7, B.1, B.1.36.29 and B.1.36. Delta/Delta-like surge coincided with second wave whereas all other lineages (B.1.617.1, B.1.36.29, etc.) occurred during the prior phase of the second wave. The spatiotemporal analysis showed that most of the Indian states were affected during the peak of the second wave due to delta surge and fall under the same cluster. The second cluster populated mostly by north-eastern states and islands of India were minimally affected. The presence of signature mutations (T478K, D950N, E156G) along with L452K, D614G and P681R within the spike protein of Delta or Delta-like might cause elevation in host cell attachment, increased transmission and altered antigenicity which in due course of time has replaced the other circulating variants. The timely assessment of new VoCs will provide a rationale for updating the diagnostic, vaccine development by medical industries and decision making by various agencies including government, educational institutions, and corporate industries.

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  1. SciScore for 10.1101/2022.01.28.22269987: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The metadata file obtained from GISAID databases for B.1.617.2 (Delta) incidences per month is analysed with respect to different states and UTs and plotted using R/ R studio with ggplot2 and relevant packages.
    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.