Analysis of SARS-CoV-2 genomes from across Africa reveals potentially clinically relevant mutations
Bibliographic record
Abstract
Abstract SARS-CoV-2 is a betacoronavirus, the etiologic agent of the novel Coronavirus disease 2019 (COVID-19). The World Health Organization officially declared COVID-19 as a pandemic in March 2020 after the outbreak in Wuhan, China, in late 2019. Across the continents and specifically in Africa, all index cases were travel-related. Understanding how the virus’s transportation across continents and different climatic conditions affect the genetic composition and the consequent effects on transmissibility, infectivity, and virulence of the virus is critical. Thus, it is crucial to compare COVID-19 genome sequences from the African continent with sequences from selected COVID-19 hotspots/countries in Asia, Europe, North and South America and Oceania. To identify possible distinguishing mutations in the African SARS-CoV-2 genomes compared to those from these selected countries, we conducted in silico analyses and comparisons. Complete African SARS-CoV-2 genomes deposited in GISAID and NCBI databases as of June 2020 were downloaded and aligned with genomes from Wuhan, China and other SARS-CoV-2 hotspots. Using phylogenetic analysis and amino acid sequence alignments of the spike and replicase (NSP12) proteins, we searched for possible vaccine coverage targets or potential therapeutic agents. Identity plots for the alignments were created with BioEdit software and the phylogenetic analyses with the MEGA X software. Our results showed mutations in the spike and replicate proteins of the SARS-Cov-2 virus. Phylogenetic tree analyses demonstrated variability across the various regions/countries in Africa as there were different clades in the viral proteins. However, a substantial proportion of these mutations (90%) were similar to those described in all the other settings, including the Wuhan strain. There were, however, novel mutations in the genomes of the circulating strains of the virus in African. To the best of our knowledge, this is the first study reporting these findings from Africa. However, these findings’ implications on symptomatic or asymptomatic manifestations, progression to severe disease and case fatality for those affected, and the cross efficacy of vaccines developed from other settings when applied in Africa are unknown.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".