Coincident rapid expansion of two SARS-CoV-2 lineages with enhanced infectivity in Nigeria

  1. Egon A. Ozer
  2. Lacy M. Simons
  3. Olubusuyi M. Adewumi
  4. Adeola A. Fowotade
  5. Ewean C. Omoruyi
  6. Johnson A. Adeniji
  7. Taylor J. Dean
  8. Janet Zayas
  9. Pavan P. Bhimalli
  10. Michelle K. Ash
  11. Adam Godzik
  12. Jeffrey R. Schneider
  13. João I. Mamede
  14. Babafemi O. Taiwo
  15. Judd F. Hultquist
  16. Ramon Lorenzo-Redondo

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Abstract

The emergence of new SARS-CoV-2 variants with enhanced transmissibility or decreased susceptibility to immune responses is a major threat to global efforts to end the coronavirus disease 2019 (COVID-19) pandemic. Disparities in viral genomic surveillance capabilities and efforts have resulted in gaps in our understanding of the viral population dynamics across the globe. Nigeria, despite having the largest population of any nation in Africa, has had relatively little SARS-CoV-2 sequence data made publicly available. Here we report the whole-genome sequences of 74 SARS-CoV-2 isolates collected from individuals in Oyo State, Nigeria in January 2021. Most isolates belonged to either the B.1.1.7 Alpha “variant of concern” or the B.1.525 Eta lineage, which is currently considered a “variant of interest” containing multiple spike protein mutations previously associated with enhanced transmissibility and possible immune escape. Nigeria has the highest reported frequency of the B.1.525 lineage globally with phylogenetic characteristics consistent with a recent monophyletic origin and rapid expansion. Spike protein from the B.1.525 lineage displayed both increased infectivity and decreased neutralization by convalescent sera compared to Spike proteins from other clades. These results, along with indications that the virus is outpacing the B.1.1.7 lineage in Nigeria, suggest that the B.1.525 lineage represents another “variant of concern” and further underline the importance of genomic surveillance in undersampled regions across the globe.

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    SciScore for 10.1101/2021.04.09.21255206: (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
    Sequencing reads were trimmed to remove adapters and low quality sequences using Trimmomatic v0.36
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Pileups were generated from the alignment using samtools v1.9 and consensus sequence determined using iVar v1.2.2 (ref PMID: 30621750) with a minimum depth of 10, a minimum base quality score of 20, and a consensus frequency threshold of 0 (i.e. majority base as the consensus).
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    iVar
    suggested: None
    Phylogenetic analysis: Genome sequences were aligned using MAFFT v7.453 software and manually edited using MEGA v6.06.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    All Maximum Likelihood (ML) phylogenies were inferred with IQ-Tree v2.0.5 using its ModelFinder function before each analysis to estimate the nucleotide substitution model best-fitted for each dataset by means of Bayesian information criterion (BIC).
    IQ-Tree
    suggested: (IQ-TREE, RRID:SCR_017254)
    We used BEAST v2.5.2 to estimate the date and location of the most recent common ancestors (MRCA) as well as to estimate the rate of evolution of the virus.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    BEAST priors were introduced with BEAUTI v2.5.2 including an uncorrelated relaxed molecular clock model with a lognormal distribution of the evolutionary rate, previous estimated evolutionary rates (8×10-4) as the prior for the mean, and a standard deviation of 0.1 after optimization with preliminary runs.
    BEAUTI
    suggested: (BEAST2, RRID:SCR_017307)
    We assumed a GTR substitution model with invariant sites, as the best-fitted model obtained with ModelFinder, and a Coalescence Bayesian Skyline to model the population size changes through time.
    ModelFinder
    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: 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.

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  2. Version published to 10.1101/2021.04.09.21255206 on medRxiv