Bayesian Molecular Dating Analyses Combined with Mutational Profiling Suggest an Independent Origin and Evolution of SARS-CoV-2 Omicron BA.1 and BA.2 Sub-Lineages
The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodi...
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| Published in | Viruses Vol. 14; no. 12; p. 2764 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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01.12.2022
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| Abstract | The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus's genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August-22 October 2021) and 1.435 × 10
(95% HPD = 1.021 × 10
- 1.869 × 10
) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September-28 November 2021) and 1.074 × 10
(95% HPD = 6.444 × 10
- 1.586 × 10
) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health. |
|---|---|
| AbstractList | The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus’s genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August–22 October 2021) and 1.435 × 10−3 (95% HPD = 1.021 × 10−3 − 1.869 × 10−3) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September–28 November 2021) and 1.074 × 10−3 (95% HPD = 6.444 × 10−4 − 1.586 × 10−3) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health. The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus’s genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August–22 October 2021) and 1.435 × 10 −3 (95% HPD = 1.021 × 10 −3 − 1.869 × 10 −3 ) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September–28 November 2021) and 1.074 × 10 −3 (95% HPD = 6.444 × 10 −4 − 1.586 × 10 −3 ) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health. The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus's genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August-22 October 2021) and 1.435 × 10-3 (95% HPD = 1.021 × 10-3 - 1.869 × 10-3) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September-28 November 2021) and 1.074 × 10-3 (95% HPD = 6.444 × 10-4 - 1.586 × 10-3) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health.The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus's genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August-22 October 2021) and 1.435 × 10-3 (95% HPD = 1.021 × 10-3 - 1.869 × 10-3) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September-28 November 2021) and 1.074 × 10-3 (95% HPD = 6.444 × 10-4 - 1.586 × 10-3) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health. The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus's genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August-22 October 2021) and 1.435 × 10 (95% HPD = 1.021 × 10 - 1.869 × 10 ) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September-28 November 2021) and 1.074 × 10 (95% HPD = 6.444 × 10 - 1.586 × 10 ) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health. The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus’s genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August–22 October 2021) and 1.435 × 10[sup.−3] (95% HPD = 1.021 × 10[sup.−3] − 1.869 × 10[sup.−3] ) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September–28 November 2021) and 1.074 × 10[sup.−3] (95% HPD = 6.444 × 10[sup.−4] − 1.586 × 10[sup.−3] ) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health. |
| Audience | Academic |
| Author | Sahu, Upasana Singh, Ashutosh Sanyal, Aniket Kaushik, Rahul Bhatia, Sandeep Kumar, Naveen Zhang, Kam Y J Uversky, Vladimir N |
| AuthorAffiliation | 1 Diagnostics & Vaccines Group, ICAR-National Institute of High Security Animal Diseases, Bhopal 462022, India 3 Center for Biosystems Dynamics Research, Laboratory for Structural Bioinformatics, Yokohama 230-0045, Japan 4 Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA 2 Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi P.O. Box 3692, United Arab Emirates 5 Federal Research Center ‘Pushchino, Scientific Center for Biological Research of the Russian Academy of Sciences’, Institute for Biological Instrumentation of the Russian Academy of Sciences, 142290 Pushchino, Russia |
| AuthorAffiliation_xml | – name: 1 Diagnostics & Vaccines Group, ICAR-National Institute of High Security Animal Diseases, Bhopal 462022, India – name: 5 Federal Research Center ‘Pushchino, Scientific Center for Biological Research of the Russian Academy of Sciences’, Institute for Biological Instrumentation of the Russian Academy of Sciences, 142290 Pushchino, Russia – name: 4 Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA – name: 2 Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi P.O. Box 3692, United Arab Emirates – name: 3 Center for Biosystems Dynamics Research, Laboratory for Structural Bioinformatics, Yokohama 230-0045, Japan |
| Author_xml | – sequence: 1 givenname: Naveen orcidid: 0000-0002-3326-5465 surname: Kumar fullname: Kumar, Naveen organization: Diagnostics & Vaccines Group, ICAR-National Institute of High Security Animal Diseases, Bhopal 462022, India – sequence: 2 givenname: Rahul orcidid: 0000-0002-6489-6913 surname: Kaushik fullname: Kaushik, Rahul organization: Center for Biosystems Dynamics Research, Laboratory for Structural Bioinformatics, Yokohama 230-0045, Japan – sequence: 3 givenname: Ashutosh surname: Singh fullname: Singh, Ashutosh organization: Diagnostics & Vaccines Group, ICAR-National Institute of High Security Animal Diseases, Bhopal 462022, India – sequence: 4 givenname: Vladimir N orcidid: 0000-0002-4037-5857 surname: Uversky fullname: Uversky, Vladimir N organization: Federal Research Center 'Pushchino, Scientific Center for Biological Research of the Russian Academy of Sciences', Institute for Biological Instrumentation of the Russian Academy of Sciences, 142290 Pushchino, Russia – sequence: 5 givenname: Kam Y J orcidid: 0000-0002-9282-8045 surname: Zhang fullname: Zhang, Kam Y J organization: Center for Biosystems Dynamics Research, Laboratory for Structural Bioinformatics, Yokohama 230-0045, Japan – sequence: 6 givenname: Upasana surname: Sahu fullname: Sahu, Upasana organization: Diagnostics & Vaccines Group, ICAR-National Institute of High Security Animal Diseases, Bhopal 462022, India – sequence: 7 givenname: Sandeep surname: Bhatia fullname: Bhatia, Sandeep organization: Diagnostics & Vaccines Group, ICAR-National Institute of High Security Animal Diseases, Bhopal 462022, India – sequence: 8 givenname: Aniket surname: Sanyal fullname: Sanyal, Aniket organization: Diagnostics & Vaccines Group, ICAR-National Institute of High Security Animal Diseases, Bhopal 462022, India |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36560768$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1038_s41598_024_55599_0 crossref_primary_10_1016_j_jviromet_2024_114995 |
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| Keywords | COVID-19 SARS-CoV-2 Omicron tMRCA mutational profiling selection pressure evolutionary rate |
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| Snippet | The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of... |
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| SubjectTerms | Algorithms Bayes Theorem Bayesian analysis Coronaviruses COVID-19 Datasets Dating Disease transmission Divergence Epidemiology Evolution Evolution & development evolutionary rate Genomes Humans Mutation mutational profiling Pandemics Phylogenetics Phylogeny Public health Regression analysis SARS-CoV-2 - genetics SARS-CoV-2 Omicron selection pressure Severe acute respiratory syndrome coronavirus 2 Spike Glycoprotein, Coronavirus Spike protein tMRCA |
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| Title | Bayesian Molecular Dating Analyses Combined with Mutational Profiling Suggest an Independent Origin and Evolution of SARS-CoV-2 Omicron BA.1 and BA.2 Sub-Lineages |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36560768 https://www.proquest.com/docview/2756812612 https://www.proquest.com/docview/2758105719/abstract/ https://pubmed.ncbi.nlm.nih.gov/PMC9788409 https://doaj.org/article/93428360d8ce4472b14f8dc6f3ab6219 |
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