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Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa
Open accessAbstract
The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively 1–3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function 4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.
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FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
Posterior summarization in Bayesian phylogenetics using tracer 1.7
Rambaut, A., Drummond, A. J., Xie, D., Baele, G., & Suchard, M. A. (2018) · Systematic Biology · doi:10.1093/sysbio/syy032 · Cited by 11,159
Self-citationBEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchêne, S., Fourment, M., Gavryushkina, A., Heled, J., Jones, G., Kühnert, D., De Maio, N., et al. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006650 · Cited by 4,556
Self-citationEstimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England
Davies, N. G., Abbott, S., Barnard, R. C., Jarvis, C. I., Kucharski, A. J., Munday, J. D., Pearson, C. A. B., Russell, T. W., Tully, D. C., Washburne, A. D., et al. (2021) · Science · doi:10.1126/science.abg3055 · Cited by 2,581
Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England
Davies, N. G., Abbott, S., Barnard, R. C., Jarvis, C. I., Kucharski, A. J., Munday, J. D., Pearson, C. A. B., Russell, T. W., Tully, D. C., Washburne, A. D., et al. (2020) · doi:10.1101/2020.12.24.20248822 · Cited by 2,557
A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology
Rambaut, A., Holmes, E. C., O’Toole, Á., Hill, V., McCrone, J. T., Ruis, C., du Plessis, L., & Pybus, O. G. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0770-5 · Cited by 2,543
Self-citationDetection of a SARS-CoV-2 variant of concern in South Africa
Tegally, H., Wilkinson, E., Giovanetti, M., Iranzadeh, A., Fonseca, V., Giandhari, J., Doolabh, D., Pillay, S., San, E. J., Msomi, N., et al. (2021) · Nature · doi:10.1038/s41586-021-03402-9 · Cited by 2,510
Self-citationOmicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies
Cao, Y., Wang, J., Jian, F., Xiao, T., Song, W., Yisimayi, A., Huang, W., Li, Q., Wang, P., An, R., et al. (2022) · Nature · doi:10.1038/s41586-021-04385-3 · Cited by 2,006
Data, disease and diplomacy: GISAID's innovative contribution to global health
Elbe, S., & Buckland‐Merrett, G. (2017) · Global Challenges · doi:10.1002/gch2.1018 · Cited by 1,928
Detecting Individual Sites Subject to Episodic Diversifying Selection
Murrell, B., Wertheim, J. O., Moola, S., Weighill, T., Scheffler, K., & Kosakovsky Pond, S. L. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002764 · Cited by 1,811
Research works printed but not linked: 55
- Tegally, H. et al. Sixteen novel lineages of SARS-CoV-2 in South Africa. Nat. Med. 27, 440–446 (2021). · DOI · Paper page
- Tegally, H. et al. Rapid replacement of the Beta variant by the Delta variant in South Africa. Preprint at medRxiv https://doi.org/10.1101/2021.09.23.21264018 (2021). · DOI
- Martin, D. P. et al. Selection analysis identifies unusual clustered mutational changes in Omicron lineage BA.1 that likely impact Spike function. Preprint at bioRxiv https://doi.org/10.1101/2022.01.14.476382 (2022). · DOI
- Eales, O. et al. SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2. Preprint at medRxiv https://doi.org/10.1101/2021.12.17.21267925 (2021). · doi:10.1101/2021.12.17.21267925
- Wilkinson, E. et al. A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa. Science 374, 423–431 (2021). · DOI · Paper page
- Scheepers, C. et al. The continuous evolution of SARS-CoV-2 in South Africa: a new lineage with rapid accumulation of mutations of concern and global detection. Preprint at medRxiv https://doi.org/10.1101/2021.08.20.21262342 (2021). · DOI
- Kleynhans, J. et al. SARS-CoV-2 seroprevalence in a rural and urban household cohort during first and second waves of infections, South Africa, July 2020-March 2021. Emerg. Infect. Dis. 27, 3020–3029 (2021). · DOI · Paper page
- Vermeulen, M. et al. Prevalence of anti-SARS-CoV-2 antibodies among blood donors in South Africa during the period January-May 2021. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-690372/v1 (2021). · DOI
- Madhi, S. et al. Population immunity and Covid-19 severity with Omicron variant in South Africa. New Engl. J. Med. https://doi.org/10.1056/NEJMoa2119658 (2022). · DOI · Paper page
- Shu, Y. & McCauley, J. GISAID: global initiative on sharing all influenza data—from vision to reality. Euro Surveill. 22, 30494 (2017). · DOI · Paper page
- Stadler, T., Kühnert, D., Bonhoeffer, S. & Drummond, A. J. Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc. Natl Acad. Sci. USA 110, 228–233 (2013). · DOI · Paper page
- Starr, T. N. et al. Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell 182, 1295–1310 (2020). · DOI · Paper page
- Cele, S. et al. Omicron extensively but incompletely escapes Pfizer BNT162b2 neutralization. Nature 602, 54–656 (2022). · doi:10.1038/d41586-021-03824-5 · Paper page
- Planas, D. et al. Considerable escape of SARS-CoV-2 Omicron to antibody neutralization. Nature 602, 671-675 (2022). · DOI · Paper page
- Keeton, R. et al. T cell responses to SARS-CoV-2 spike cross-recognize Omicron. Nature https://doi.org/10.1038/s41586-022-04460-3 (2022). · DOI · Paper page
- Brown, J. C. et al. Increased transmission of SARS-CoV-2 lineage B.1.1.7 (VOC 2020212/01) is not accounted for by a replicative advantage in primary airway cells or antibody escape. Preprint at BioRxiv https://doi.org/10.1101/2021.02.24.432576 (2021). · DOI
- Saito, A. et al. Enhanced fusogenicity and pathogenicity of SARS-CoV-2 Delta P681R mutation. Nature 602, 300–306 (2022). · DOI · Paper page
- Mlcochova, P. et al. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion. Nature 599, 114–119 (2021). · DOI · Paper page
- Martin, D. P. et al. The emergence and ongoing convergent evolution of the SARS-CoV-2 N501Y lineages. Cell 184, 5189–5200 (2021). · DOI · Paper page
- Wu, H. et al. Nucleocapsid mutations R203K/G204R increase the infectivity, fitness, and virulence of SARS-CoV-2. Cell Host Microbe 29, 1788–1801 (2021). · DOI · Paper page
- Kosakovsky Pond, S. L., Posada, D., Gravenor, M. B., Woelk, C. H. & Frost, S. D. W. GARD: a genetic algorithm for recombination detection. Bioinformatics 22, 3096–3098 (2006). · DOI · Paper page
- Lam, H. M., Ratmann, O. & Boni, M. F. Improved algorithmic complexity for the 3SEQ recombination detection algorithm. Mol. Biol. Evol. 35, 247–251 (2018). · DOI · Paper page
- Martin, D. P. et al. RDP5: a computer program for analyzing recombination in, and removing signals of recombination from, nucleotide sequence datasets. Virus Evol. 7, veaa087 (2021). · DOI · Paper page
- Boni, M. F., Posada, D. & Feldman, M. W. An exact nonparametric method for inferring mosaic structure in sequence triplets. Genetics 176, 1035–1047 (2007). · DOI · Paper page
- Posada, D. & Crandall, K. A. Evaluation of methods for detecting recombination from DNA sequences: computer simulations. Proc. Natl Acad. Sci. USA 98, 13757–13762 (2001). · DOI · Paper page
- van der Walt, E. et al. Rapid host adaptation by extensive recombination. J. Gen. Virol. 90, 734–746 (2009). · DOI
- Wisotsky, S. R., Kosakovsky Pond, S. L., Shank, S. D. & Muse, S. V. Synonymous site-to-site substitution rate variation dramatically inflates false positive rates of selection analyses: ignore at your own peril. Mol. Biol. Evol. 37, 2430–2439 (2020). · DOI
- Smith, M. D. et al. Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015). · DOI · Paper page
- Kosakovsky Pond, S. L., Poon, A. F. Y., Leigh Brown, A. J. & Frost, S. D. W. A maximum likelihood method for detecting directional evolution in protein sequences and its application to influenza A virus. Mol. Biol. Evol. 25, 1809–1824 (2008). · DOI
- 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
- Wickham, H. ggplot2. WIREs Comp. Stat. 3, 180–185 (2011). · DOI · Paper page
- Msomi, N., Mlisana, K. & de Oliveira, T. A genomics network established to respond rapidly to public health threats in South Africa. Lancet Microbe 1, e229–e230 (2020). · DOI
- Freed, N. & Silander, O. SARS-CoV2 genome sequencing protocol (1200bp amplicon “midnight” primer set, using Nanopore Rapid kit). Protocols.io https://doi.org/10.17504/protocols.io.bwyppfvn (2021). · doi:10.17504/protocols.io.bwyppfvn
- Cleemput, S. et al. Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes. Bioinformatics 36, 3552–3555 (2020). · DOI · Paper page
- Bragg, L. M., Stone, G., Butler, M. K., Hugenholtz, P. & Tyson, G. W. Shining a light on dark sequencing: characterising errors in Ion Torrent PGM data. PLoS Comput. Biol. 9, e1003031 (2013). · DOI · Paper page
- Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011). · DOI · Paper page
- Hatcher, E. L. et al. Virus Variation Resource—improved response to emergent viral outbreaks. Nucleic Acids Res. 45, D482–D490 (2017). · DOI · Paper page
- National Library of Medicine. NCBI Virus: SARS-CoV-2 Data Hub; https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Nucleotide&VirusLineage_ss=SARS-CoV-2,%20taxid:2697049 (accessed 1 December 2021).
- Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002). · DOI · Paper page
- Kosakovsky Pond, S. L. et al. HyPhy 2.5—a customizable platform for evolutionary hypothesis testing using phylogenies. Mol. Biol. Evol. 37, 295–299 (2020). · DOI · Paper page
- Benton, D. J. et al. Receptor binding and priming of the Spike protein of SARS-CoV-2 for membrane fusion. Nature 588, 327–330 (2020). · DOI · Paper page
- Aksamentov, I., Roemer, C., Hodcroft, E. & Neher, R. Nextclade: clade assignment, mutation calling and quality control for viral genomes. J. Open Source Softw. 6, 3773 (2021). · DOI · Paper page
- Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791 (1985). · DOI · Paper page
- Sagulenko, P., Puller, V. & Neher, R. A. TreeTime: maximum-likelihood phylodynamic analysis. Virus Evol. 4, vex042 (2018). · DOI · Paper page
- Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinform. 69, e96 (2020). · DOI · Paper page
- Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018). · DOI · Paper page
- Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012). · DOI · Paper page
- Benvenuto, D. et al. The global spread of 2019-nCoV: a molecular evolutionary analysis. Pathog. Glob. Health 114, 64–67 (2020). · DOI · Paper page
- Byrne, A. W. et al. Inferred duration of infectious period of SARS-CoV-2: rapid scoping review and analysis of available evidence for asymptomatic and symptomatic COVID-19 cases. BMJ Open 10, e039856 (2020). · DOI · Paper page
- 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 · Paper page
- 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 · Paper page
- Althaus, C. L. et al. A tale of two variants: spread of SARS-CoV-2 variants Alpha in Geneva, Switzerland, and Beta in South Africa. Preprint at medRxiv https://doi.org/10.1101/2021.06.10.21258468 (2021). · DOI
- Ganyani, T. et al. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill. 25, 2000257 (2020). · DOI · Paper page
- Huisman, J. S. et al. Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2. Preprint at medRxiv https://doi.org/10.1101/2020.11.26.20239368 (2020). · DOI
- Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014). · DOI · Paper page
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Considerable escape of SARS-CoV-2 Omicron to antibody neutralization
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- Viana, R., Moyo, S., Amoako, D. G., Tegally, H., Scheepers, C., Althaus, C. L., Anyaneji, U. J., Bester, P. A., Boni, M. F., Chand, M., Choga, W. T., Colquhoun, R., Davids, M., Deforche, K., Doolabh, D., du Plessis, L., Engelbrecht, S., Everatt, J., Giandhari, J., Giovanetti, M., Hardie, D., Hill, V., Hsiao, N.-Y., Iranzadeh, A., Ismail, A., Joseph, C., Joseph, R., Koopile, L., Kosakovsky Pond, S. L., Kraemer, M. U. G., Kuate-Lere, L., Laguda-Akingba, O., Lesetedi-Mafoko, O., Lessells, R. J., Lockman, S., Lucaci, A. G., Maharaj, A., Mahlangu, B., Maponga, T., Mahlakwane, K., Makatini, Z., Marais, G., Maruapula, D., Masupu, K., Matshaba, M., Mayaphi, S., Mbhele, N., Mbulawa, M. B., Mendes, A., Mlisana, K., Mnguni, A., Mohale, T., Moir, M., Moruisi, K., Mosepele, M., Motsatsi, G., Motswaledi, M. S., Mphoyakgosi, T., Msomi, N., Mwangi, P. N., Naidoo, Y., Ntuli, N., Nyaga, M., Olubayo, L., Pillay, S., Radibe, B., Ramphal, Y., Ramphal, U., San, J. E., Scott, L., Shapiro, R., Singh, L., Smith-Lawrence, P., Stevens, W., Strydom, A., Subramoney, K., Tebeila, N., Tshiabuila, D., Tsui, J., van Wyk, S., Weaver, S., Wibmer, C. K., Wilkinson, E., Wolter, N., Zarebski, A. E., Zuze, B., Goedhals, D., Preiser, W., Treurnicht, F., Venter, M., Williamson, C., Pybus, O. G., Bhiman, J., Glass, A., Martin, D. P., Rambaut, A., Gaseitsiwe, S., von Gottberg, A., & de Oliveira, T.
- Venue
- Nature
- Published
- 2022-01-07
- Volume
- 603
- Issue
- 7902
- Pages
- 679-686
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4225691034
- Cited by
- 1,690
- References
- 25
- FWCI
- 178.17
- Type
- journal article
- Language
- en
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- creativecommons.org/licenses/by/4.0 (vor)
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