High diversity in Delta variant across countries revealed by genome‐wide analysis of SARS‐CoV‐2 beyond the Spike protein

  1. Rohit Suratekar
  2. Pritha Ghosh
  3. Michiel J M Niesen
  4. Gregory Donadio
  5. Praveen Anand
  6. Venky Soundararajan
  7. A J Venkatakrishnan

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Abstract

The highly contagious Delta variant of SARS‐CoV‐2 has become a prevalent strain globally and poses a public health challenge around the world. While there has been extensive focus on understanding the amino acid mutations in the Delta variant’s Spike protein, the mutational landscape of the rest of the SARS‐CoV‐2 proteome (25 proteins) remains poorly understood. To this end, we performed a systematic analysis of mutations in all the SARS‐CoV‐2 proteins from nearly 2 million SARS‐CoV‐2 genomes from 176 countries/territories. Six highly prevalent missense mutations in the viral life cycle‐associated Membrane (I82T), Nucleocapsid (R203M, D377Y), NS3 (S26L), and NS7a (V82A, T120I) proteins are almost exclusive to the Delta variant compared to other variants of concern (mean prevalence across genomes: Delta = 99.74%, Alpha = 0.06%, Beta = 0.09%, and Gamma = 0.22%). Furthermore, we find that the Delta variant harbors a more diverse repertoire of mutations across countries compared to the previously dominant Alpha variant. Overall, our study underscores the high diversity of the Delta variant between countries and identifies a list of amino acid mutations in the Delta variant’s proteome for probing the mechanistic basis of pathogenic features such as high viral loads, high transmissibility, and reduced susceptibility against neutralization by vaccines.

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  1. Version published to 10.15252/msb.202110673
  2. ScreenIT

    SciScore for 10.1101/2021.09.01.458647: (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
    To check the effect size, Cohen ‘s d was calculated (Equation 2).where, M: mean, n: sample size, SD: standard deviation Probability distributions of pairwise cosine similarities were calculated by binning frequencies (bins = 25), and their Jensen-Shannon divergence (with base 2) was calculated using the jensenshannon function available in SciPy [v1.7.0]31. p-value was calculated using bootstrapping with 1000 iterations.
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    For calculating 95% confidence interval, we calculated Jensen-Shannon divergence (JSD) and Cohen ‘s d for each bootstrap iteration.
    Cohen
    suggested: None

    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: We detected the following sentences addressing limitations in the study:
    This study has a few limitations. Since this study is based on publicly available data from the GISAID database, it may carry biases associated with sequencing disparities across countries and reporting delays. Though there is extensive genomic surveillance, there is a lack of clinical annotation of the genomes, limiting our ability to assess the clinical impact of the country-specific differences in the variants. The GISAID database does not record mutations in the recently discovered ORFs in the SARS-CoV-2 genome such as ORF10, ORF9b, and ORF9c. The assignment of the mutations in these ORFs may reveal further differences between SARS-CoV-2 variants. Though mass vaccination efforts are underway around the world, there are huge differences in the population immunity of countries due to the differences in the vaccines approved regionally and the extent of vaccination coverage in populations. These differences contribute to the risk of emergence of new SARS-CoV-2 variants and continued genome-surveillance is imperative for developing comprehensive global and country-specific preventive and therapeutic measures to end the ongoing pandemic.

    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.
    • No funding statement was detected.
    • 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.

  3. Version published to 10.1101/2021.09.01.458647 on bioRxiv