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Review
. 2022 Sep 10;14(9):2009.
doi: 10.3390/v14092009.

The Rise and Fall of SARS-CoV-2 Variants and Ongoing Diversification of Omicron

Tanner Wiegand  1 , Artem Nemudryi  1 , Anna Nemudraia  1 , Aidan McVey  1 , Agusta Little  1 , David N Taylor  2 , Seth T Walk  1 , Blake Wiedenheft  1
Affiliations
Review

The Rise and Fall of SARS-CoV-2 Variants and Ongoing Diversification of Omicron

Tanner Wiegand et al. Viruses. .

Abstract

In late December of 2019, high-throughput sequencing technologies enabled rapid identification of SARS-CoV-2 as the etiological agent of COVID-19, and global sequencing efforts are now a critical tool for monitoring the ongoing spread and evolution of this virus. Here, we provide a short retrospective analysis of SARS-CoV-2 variants by analyzing a subset (n = 97,437) of all publicly available SARS-CoV-2 genomes (n = ~11.9 million) that were randomly selected but equally distributed over the course of the pandemic. We plot the appearance of new variants of concern (VOCs) over time and show that the mutation rates in Omicron (BA.1) and Omicron sub-lineages (BA.2-BA.5) are significantly elevated compared to previously identified SARS-CoV-2 variants. Mutations in Omicron are primarily restricted to the spike and nucleocapsid proteins, while 24 other viral proteins-including those involved in SARS-CoV-2 replication-are generally conserved. Collectively, this suggests that the genetic distinction of Omicron primarily arose from selective pressures on the spike, and that the fidelity of replication of this variant has not been altered.

Keywords: BA.4; BA.5; COVID-19; Omicron; SARS-CoV-2; viral surveillance.

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Conflict of interest statement

B.W. is the founder of SurGene LLC, and VIRIS Detection Systems Inc. B.W., A. Nemudryi and A. Nemudraia, and A.S.-F. are inventors on patents related to CRISPR-Cas systems and applications thereof.

Figures

Figure 1
Figure 1
Phylogenetic relationship of named SARS-CoV-2 variants. Variants of concern (VOC) are represented by a colored node. The phylogenetic tree was adapted from data provided by NextStrain, CoVariants (i.e., covariants.org, http://covariants.org (accessed on 18 July 2022)), and Pangolin (i.e., cov-lineages.org, http://cov-lineages.org (accessed on 18 July 2022)) [1,8,13].
Figure 2
Figure 2
(A) Non-synonymous mutations acquired over time in 26 SARS-CoV-2 protein sequences extracted from 97,437 genomes from 19 December 2021, to 17 June 2022 (GISAID accessions available at: https://github.com/WiedenheftLab/Omicron (accessed on 18 July 2022) and DOI: https://doi.org/10.55876/gis8.220721mv (accessed on 18 July 2022)). Genomes included in this analysis are a random sampling of 11,336,176 million SARS-CoV-2 from GISAID, which were quality filtered using NextClade (“good” overall QC status) then sampled with the Filter utility in NextStrain (selecting up to 120 genome per country per month of the pandemic) [1,14]. Variants of concern (VOC) are shown in bold and are colored as in Figure 1. Vertical lines mark the date the first sequence for each lineage was identified. The time elapsed between first detection and VOC designation by the WHO is shown as dotted lines above the graph. Dots are colored similar to variant names, and grey circles represent non-VOC lineage genomes. A linear regression line is shown in black. Omicron variants deviate from the trend. (B) Synonymous mutations in the SARS-CoV-2 genomes shown in panel (A). (C) Non-synonymous mutations in the Omicron RNA replication proteins (n = 13,094 genomes) are shown as red lines on schematic representations of each protein, and frequencies of each mutation shown as vertical lines (red). Most non-synonymous mutations are found in less than 1% of Omicron sequences. (D) Schematic depiction of SARS-CoV-2 protein coding sequences, with each gene colored according to its respective amino acid mutation rate. These rates are normalized to account for the length of each protein (i.e., substitutions/amino acids in protein/month of the pandemic).
Figure 3
Figure 3
SARS-CoV-2 genomic sequences submitted to the GISAID data repository between January 2020 and June 2022 [12].
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References

    1. Rambaut A., Holmes E.C., O’Toole A., Hill V., McCrone J.T., Ruis C., du Plessis L., Pybus O.G. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat. Microbiol. 2020;5:1403–1407. doi: 10.1038/s41564-020-0770-5. - DOI - PMC - PubMed
    1. World Health Organization Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern. [(accessed on 12 December 2021)]. Available online: https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.....
    1. Callaway E., Ledford H. How bad is Omicron? What scientists know so far. Nature. 2021;600:197–199. doi: 10.1038/d41586-021-03614-z. - DOI - PubMed
    1. Chen J., Wang R., Gilby N.B., Wei G.W. Omicron Variant (B.1.1.529): Infectivity, Vaccine Breakthrough, and Antibody Resistance. J. Chem. Inf. Model. 2022;62:412–422. doi: 10.1021/acs.jcim.1c01451. - DOI - PMC - PubMed
    1. Hadfield J., Megill C., Bell S.M., Huddleston J., Potter B., Callender C., Sagulenko P., Bedford T., Neher R.A. Nextstrain: Real-time tracking of pathogen evolution. Bioinformatics. 2018;34:4121–4123. doi: 10.1093/bioinformatics/bty407. - DOI - PMC - PubMed

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