Genomic Variations in SARS-CoV-2 Genomes From Gujarat: Underlying Role of Variants in Disease Epidemiology

  1. Madhvi Joshi
  2. Apurvasinh Puvar
  3. Dinesh Kumar
  4. Afzal Ansari
  5. Maharshi Pandya
  6. Janvi Raval
  7. Zarna Patel
  8. Pinal Trivedi
  9. Monika Gandhi
  10. Labdhi Pandya
  11. Komal Patel
  12. Nitin Savaliya
  13. Snehal Bagatharia
  14. Sachin Kumar
  15. Chaitanya Joshi

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Abstract

Humanity has seen numerous pandemics during its course of evolution. The list includes several incidents from the past, such as measles, Ebola, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS), etc. The latest edition to this is coronavirus disease 2019 (COVID-19), caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of August 18, 2020, COVID-19 has affected over 21 million people from 180 + countries with 0.7 million deaths across the globe. Genomic technologies have enabled us to understand the genomic constitution of pathogens, their virulence, evolution, and rate of mutation, etc. To date, more than 83,000 viral genomes have been deposited in public repositories, such as GISAID and NCBI. While we are writing this, India is the third most affected country by COVID-19, with 2.7 million cases and > 53,000 deaths. Gujarat is the 11th highest affected state with a 3.48% death rate compared to the national average of 1.91%. In this study, a total of 502 SARS-CoV-2 genomes from Gujarat were sequenced and analyzed to understand its phylogenetic distribution and variants against global and national sequences. Further variants were analyzed from diseased and recovered patients from Gujarat and the world to understand its role in pathogenesis. Among the missense mutations present in the Gujarat SARS-CoV-2 genomes, C28854T (Ser194Leu) had an allele frequency of 47.62 and 7.25% in deceased patients from the Gujarat and global datasets, respectively. In contrast, the allele frequency of 35.16 and 3.20% was observed in recovered patients from the Gujarat and global datasets, respectively. It is a deleterious mutation present in the nucleocapsid (N) gene and is significantly associated with mortality in Gujarat patients with a p -value of 0.067 and in the global dataset with a p -value of 0.000924. The other deleterious variant identified in deceased patients from Gujarat ( p -value of 0.355) and the world ( p -value of 2.43E-06) is G25563T, which is located in Orf3a and plays a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct clusters under the GH clade of GISAID. This study will shed light on the viral haplotype in SARS-CoV-2 samples from Gujarat, India.

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  1. Version published to 10.3389/fgene.2021.586569
  2. Version published to 10.1101/2020.07.10.197095v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.07.10.197095: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Sample collection and processing: Nasopharyngeal and oropharyngeal swabs from a total of 277 individuals tested positive for COVID-19 from 38 locations representing 18 districts of Gujarat were collected after obtaining informed consent and appropriate ethics approval.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Raw data quality assessment and filtering: Quality of data was assessed using FASTQC v.
    FASTQC
    suggested: (FastQC, RRID:SCR_014583)
    The multiple sequence alignment was performed using MAFFT (Katoh and Standley 2013) implemented via a phylodynamic alignment pipeline provided by Augur (https://github.com/nextstrain/augur).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The selected metadata information is plotted in the time resolved phylogenetic tree was constructed using TreeTime (Sagulenko et al. 2018), annotated and visualized in the FigTree (Rambaut et al., 2018).
    FigTree
    suggested: (FigTree, RRID:SCR_008515)

    Results from OddPub: Thank you for sharing your data.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  4. Version published to 10.1101/2020.07.10.197095v1 on bioRxiv