Mutation Landscape of SARS COV2 in Africa

  1. Angus A. Nassir
  2. Clarisse Musanabaganwa
  3. Ivan Mwikarago

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

COVID-19 disease has had a relatively less severe impact in Africa. To understand the role of SARS CoV2 mutations on COVID-19 disease in Africa, we analysed 282 complete nucleotide sequences from African isolates deposited in the NCBI Virus Database. Sequences were aligned against the prototype Wuhan sequence (GenBank accession: NC_045512.2 ) in BWA v. 0.7.17. SAM and BAM files were created, sorted and indexed in SAMtools v. 1.10 and marked for duplicates using Picard v. 2.23.4. Variants were called with mpileup in BCFtools v. 1.11. Phylograms were created using Mr. Bayes v 3.2.6. A total of 2,349 single nucleotide polymorphism (SNP) profiles across 294 sites were identified. Clades associated with severe disease in the United States, France, Italy, and Brazil had low frequencies in Africa (L84S=2.5%, L3606F=1.4%, L3606F/V378I/=0.35, G251V=2%). Sub Saharan Africa (SSA) accounted for only 3% of P323L and 4% of Q57H mutations in Africa. Comparatively low infections in SSA were attributed to the low frequency of the D614G clade in earlier samples (25% vs 67% global). Higher disease burden occurred in countries with higher D614G frequencies (Egypt=98%, Morocco=90%, Tunisia=52%, South Africa) with D614G as the first confirmed case. V367F, D364Y, V483A and G476S mutations associated with efficient ACE2 receptor binding and severe disease were not observed in Africa. 95% of all RdRp mutations were deaminations leading to CpG depletion and possible attenuation of virulence. More genomic and experimental studies are needed to increase our understanding of the temporal evolution of the virus in Africa, clarify our findings, and reveal hot spots that may undermine successful therapeutic and vaccine interventions.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.12.20.423630: (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
    ) (NCBI, 2020; Wang et al., 2020a) using the mem command in BWA v.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    The SAM file was converted to BAM file using SAMtools v.
    SAMtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Duplicate marking and addition of read groups was done using Picard v.
    Picard
    suggested: (Picard, RRID:SCR_006525)
    The BLAST2 dump file was converted into a Nexus file using the European Bioinformatics Institute (EBI) platform (Madeira et al., 2019).
    BLAST2
    suggested: None
    MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms was used to select the general time reversible (GTR) evolutionary model (BIC score=340376.778) (Kumar, Stecher, Li, Knyaz, and Tamura 2018).
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    The nexus translation tree was visualized using FigTree v 1.4.4 (Rambaut, 2010).
    FigTree
    suggested: (FigTree, RRID:SCR_008515)
    ProtParam tool in the ExPasy server was used to compute thermal stability and other physicochemical properties of the mutated proteins (Gasteiger et al., 2005).
    ExPasy
    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:
    Limitations of the Study: Out of the 18,820 global SARS CoV2 sequences deposited in NCBI, African sequences accounted for 280 or just 1.5% of all sequences and Egypt accounts for 80% of these sequences collected in Africa at the time of the analysis. Sub-Saharan Africa accounted for 0.29% of all sequences. Only 9 African countries had some SARS CoV2 sequences; not a single sequence was seen for 45 other countries. Evidently, very little effort is being made to sequence samples collected in Africa and understand the mutation patterns in this continent. The small sample size may not be sufficient to make sweeping generalizations. The genetic picture captured in this study is a temporal screenshot that explains the genetic variation present months ago. The mutation landscape is a constantly changing mosaic that is temporal in nature and which requires constant genomic analysis for continuous tracking.

    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

    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.

  2. Version published to 10.1101/2020.12.20.423630v1 on bioRxiv