Emergence of SARS-CoV-2 Omicron lineages BA.4 and BA.5 in South Africa.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
28
04
2022
accepted:
21
06
2022
pubmed:
28
6
2022
medline:
28
9
2022
entrez:
27
6
2022
Statut:
ppublish
Résumé
Three lineages (BA.1, BA.2 and BA.3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern predominantly drove South Africa's fourth Coronavirus Disease 2019 (COVID-19) wave. We have now identified two new lineages, BA.4 and BA.5, responsible for a fifth wave of infections. The spike proteins of BA.4 and BA.5 are identical, and similar to BA.2 except for the addition of 69-70 deletion (present in the Alpha variant and the BA.1 lineage), L452R (present in the Delta variant), F486V and the wild-type amino acid at Q493. The two lineages differ only outside of the spike region. The 69-70 deletion in spike allows these lineages to be identified by the proxy marker of S-gene target failure, on the background of variants not possessing this feature. BA.4 and BA.5 have rapidly replaced BA.2, reaching more than 50% of sequenced cases in South Africa by the first week of April 2022. Using a multinomial logistic regression model, we estimated growth advantages for BA.4 and BA.5 of 0.08 (95% confidence interval (CI): 0.08-0.09) and 0.10 (95% CI: 0.09-0.11) per day, respectively, over BA.2 in South Africa. The continued discovery of genetically diverse Omicron lineages points to the hypothesis that a discrete reservoir, such as human chronic infections and/or animal hosts, is potentially contributing to further evolution and dispersal of the virus.
Identifiants
pubmed: 35760080
doi: 10.1038/s41591-022-01911-2
pii: 10.1038/s41591-022-01911-2
pmc: PMC9499863
doi:
Substances chimiques
Amino Acids
0
Spike Glycoprotein, Coronavirus
0
spike protein, SARS-CoV-2
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1785-1790Subventions
Organisme : Wellcome Trust
ID : 221003/Z/20/Z
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Organisme : NCIRD CDC HHS
ID : U01 IP001048
Pays : United States
Organisme : FIC NIH HHS
ID : U54 TW012041
Pays : United States
Organisme : FIC NIH HHS
ID : D43 TW009610
Pays : United States
Organisme : NIAID NIH HHS
ID : K24 AI131924
Pays : United States
Organisme : NIAID NIH HHS
ID : K24 AI131928
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NIAID NIH HHS
ID : U01 AI151698
Pays : United States
Investigateurs
Armand Phillip Bester
(AP)
Mathilda Claassen
(M)
Deelan Doolabh
(D)
Innocent Mudau
(I)
Nokuzola Mbhele
(N)
Susan Engelbrecht
(S)
Dominique Goedhals
(D)
Diana Hardie
(D)
Nei-Yuan Hsiao
(NY)
Arash Iranzadeh
(A)
Arshad Ismail
(A)
Rageema Joseph
(R)
Arisha Maharaj
(A)
Boitshoko Mahlangu
(B)
Kamela Mahlakwane
(K)
Ashlyn Davis
(A)
Gert Marais
(G)
Koleka Mlisana
(K)
Anele Mnguni
(A)
Thabo Mohale
(T)
Gerald Motsatsi
(G)
Peter Mwangi
(P)
Noxolo Ntuli
(N)
Martin Nyaga
(M)
Luicer Olubayo
(L)
Botshelo Radibe
(B)
Yajna Ramphal
(Y)
Upasana Ramphal
(U)
Wilhelmina Strasheim
(W)
Naume Tebeila
(N)
Stephanie van Wyk
(S)
Shannon Wilson
(S)
Alexander G Lucaci
(AG)
Steven Weaver
(S)
Akhil Maharaj
(A)
Yusasha Pillay
(Y)
Michaela Davids
(M)
Adriano Mendes
(A)
Simnikiwe Mayaphi
(S)
Informations de copyright
© 2022. The Author(s).
Références
Viana, R. et al. Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa. Nature 603, 679–686 (2022).
doi: 10.1038/s41586-022-04411-y
Rahimi, F. & Talebi Bezmin Abadi, A. The Omicron subvariant BA.2: birth of a new challenge during the COVID-19 pandemic. Int. J. Surg. 99, 106261 (2022).
doi: 10.1016/j.ijsu.2022.106261
Fonager, J. et al. Molecular epidemiology of the SARS-CoV-2 variant Omicron BA.2 sub-lineage in Denmark, 29 November 2021 to 2 January 2022. Euro. Surveill 27, 2200181 (2022).
doi: 10.2807/1560-7917.ES.2022.27.10.2200181
Chen, L.-L. et al. Contribution of low population immunity to the severe Omicron BA.2 outbreak in Hong Kong. Nat. Commun. 13, 3618 (2022).
doi: 10.1038/s41467-022-31395-0
O’Toole, Á., Pybus, O. G., Abram, M. E., Kelly, E. J. & Rambaut, A. Pango lineage designation and assignment using SARS-CoV-2 spike gene nucleotide sequences. BMC Genomics 23, 121 (2022).
doi: 10.1186/s12864-022-08358-2
Rambaut, A. et al. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat. Microbiol. 5, 1403–1407 (2020).
doi: 10.1038/s41564-020-0770-5
Lucaci, A. G. et al. RASCL: rapid assessment of SARS-CoV-2 clades through molecular sequence analysis. Preprint at https://www.biorxiv.org/content/10.1101/2022.01.15.476448v1 (2022).
Motozono, C. et al. SARS-CoV-2 spike L452R variant evades cellular immunity and increases infectivity. Cell Host Microbe 29, 1124–1136 (2021).
doi: 10.1016/j.chom.2021.06.006
Greaney, A. J. et al. Mapping mutations to the SARS-CoV-2 RBD that escape binding by different classes of antibodies. Nat. Commun. 12, 4196 (2021).
doi: 10.1038/s41467-021-24435-8
Greaney, A. J. et al. Comprehensive mapping of mutations in the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human plasma antibodies. Cell Host Microbe 29, 463–476 (2021).
doi: 10.1016/j.chom.2021.02.003
Greaney, A. J. et al. Complete mapping of mutations to the SARS-CoV-2 spike receptor-binding domain that escape antibody recognition. Cell Host Microbe 29, 44–57.e9 (2021).
doi: 10.1016/j.chom.2020.11.007
Zhou, J. et al. Mutations that adapt SARS-CoV-2 to mink or ferret do not increase fitness in the human airway. Cell Rep. 38, 110344 (2022).
doi: 10.1016/j.celrep.2022.110344
Lan, J. et al. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature 581, 215–220 (2020).
doi: 10.1038/s41586-020-2180-5
Greaney, A. J., Starr, T. N. & Bloom, J. D. An antibody-escape estimator for mutations to the SARS-CoV-2 receptor-binding domain. Virus Evol. 8, veac021 (2022).
doi: 10.1093/ve/veac021
Scott, L. et al. Track Omicron’s spread with molecular data. Science 374, 1454–1455 (2021).
doi: 10.1126/science.abn4543
Sun, K. et al. SARS-CoV-2 transmission, persistence of immunity, and estimates of Omicron’s impact in South African population cohorts. Sci. Transl. Med. eabo7081. https://doi.org/10.1126/scitranslmed.abo7081 (2022)
Madhi, S. A. et al. Population immunity and Covid-19 severity with Omicron variant in South Africa. N. Engl. J. Med. 386, 1314–1326 (2022).
doi: 10.1056/NEJMoa2119658
Khan, K. et al. Omicron sub-lineages BA.4/BA.5 escape BA.1 infection elicited neutralizing immunity. Preprint at https://www.medrxiv.org/content/10.1101/2022.04.29.22274477v1 (2022).
Koeppel, K. N. et al. SARS-CoV-2 reverse zoonoses to pumas and lions, South Africa. Viruses 14, 120 (2022).
doi: 10.3390/v14010120
World Organisation for Animal Health. Statement from the Advisory Group on SARS-CoV-2 Evolution in Animals concerning the origins of Omicron variant. https://www.oie.int/en/document/statement-from-the-advisory-group-on-sars-cov-2-evolution-in-animals-concerning-the-origins-of-omicron-variant/ (2022).
Marivate, V. & Combrink, H. M. Use of available data to inform the COVID-19 outbreak in South Africa: a case study. Data Sci. J 19, 19 (2020).
doi: 10.5334/dsj-2020-019
Marivate, V. et al. Coronavirus disease (COVID-19) case data—South Africa. https://zenodo.org/record/3819126#.Yrwct0bMJPY (2020).
Msomi, N., Mlisana, K. & de Oliveira, T. & Network for Genomic Surveillance in South Africa writing group. A genomics network established to respond rapidly to public health threats in South Africa. Lancet Microbe 1, e229–e230 (2020).
doi: 10.1016/S2666-5247(20)30116-6
Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).
doi: 10.1093/bioinformatics/bty407
neherlab/nextalign. https://github.com/neherlab/nextalign (2021).
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
doi: 10.1093/molbev/msu300
Rambaut, A., Lam, T. T., Max Carvalho, L. & Pybus, O. G. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol. 2, vew007 (2016).
doi: 10.1093/ve/vew007
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).
doi: 10.1093/ve/vey016
Griffiths, R. C. & Tavaré, S. Sampling theory for neutral alleles in a varying environment. Philos. Trans. R. Soc. Lond. B Biol. Sci. 344, 403–410 (1994).
doi: 10.1098/rstb.1994.0079
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).
doi: 10.1093/sysbio/syy032
Wickham, H. ggplot2. WIREs Comp. Stat. 3, 180–185 (2011).
doi: 10.1002/wics.147
Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinformatics 69, e96 (2020).
doi: 10.1002/cpbi.96
Lemey, P., Rambaut, A., Welch, J. J. & Suchard, M. A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol. 27, 1877–1885 (2010).
doi: 10.1093/molbev/msq067
Dellicour, S., Rose, R., Faria, N. R., Lemey, P. & Pybus, O. G. SERAPHIM: studying environmental rasters and phylogenetically informed movements. Bioinformatics 32, 3204–3206 (2016).
doi: 10.1093/bioinformatics/btw384
Rambaut, A. et al. A dynamic nomenclature proposal for SARS-CoV-2 to assist genomic epidemiology. Nat. Microbiol. 5, 1403–1407 (2020).
doi: 10.1038/s41564-020-0770-5
O’Toole, Á. et al. Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool. Virus Evol. 7, veab064 (2021).
doi: 10.1093/ve/veab064
Murrell, B. et al. Detecting individual sites subject to episodic diversifying selection. PLoS Genet. 8, e1002764 (2012).
doi: 10.1371/journal.pgen.1002764
Kosakovsky Pond, S. L. & Frost, S. D. W. Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol. Biol. Evol. 22, 1208–1222 (2005).
doi: 10.1093/molbev/msi105
Shu, Y. & McCauley, J. GISAID: global initiative on sharing all influenza data—from vision to reality. Euro. Surveill. 22, 30494 (2017).
doi: 10.2807/1560-7917.ES.2017.22.13.30494
Davies, N. G. et al. Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Science 372, eabg3055 (2021).
doi: 10.1126/science.abg3055
Campbell, F. et al. Increased transmissibility and global spread of SARS-CoV-2 variants of concern as at June 2021. Euro Surveill. 26, 2100509 (2021).
doi: 10.2807/1560-7917.ES.2021.26.24.2100509
Package ‘emmeans’: Estimated Marginal Means, aka Least-Squares Means. https://cran.r-project.org/web/packages/emmeans/emmeans.pdf (2021).