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Liu W, Huang Z, Xiao J, Wu Y, Xia N, Yuan Q. Evolution of the SARS-CoV-2 Omicron Variants: Genetic Impact on Viral Fitness. Viruses 2024; 16:184. [PMID: 38399960 PMCID: PMC10893260 DOI: 10.3390/v16020184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/25/2024] Open
Abstract
Over the last three years, the pandemic of COVID-19 has had a significant impact on people's lives and the global economy. The incessant emergence of variant strains has compounded the challenges associated with the management of COVID-19. As the predominant variant from late 2021 to the present, Omicron and its sublineages, through continuous evolution, have demonstrated iterative viral fitness. The comprehensive elucidation of the biological implications that catalyzed this evolution remains incomplete. In accordance with extant research evidence, we provide a comprehensive review of subvariants of Omicron, delineating alterations in immune evasion, cellular infectivity, and the cross-species transmission potential. This review seeks to clarify the underpinnings of biology within the evolution of SARS-CoV-2, thereby providing a foundation for strategic considerations in the post-pandemic era of COVID-19.
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Affiliation(s)
- Wenhao Liu
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen 361000, China; (W.L.); (N.X.)
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361102, China
| | - Zehong Huang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen 361000, China; (W.L.); (N.X.)
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361102, China
| | - Jin Xiao
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen 361000, China; (W.L.); (N.X.)
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361102, China
| | - Yangtao Wu
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen 361000, China; (W.L.); (N.X.)
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361102, China
| | - Ningshao Xia
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen 361000, China; (W.L.); (N.X.)
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361102, China
| | - Quan Yuan
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen 361000, China; (W.L.); (N.X.)
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361102, China
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Li X, Yan H, Wong G, Ouyang W, Cui J. Identifying featured indels associated with SARS-CoV-2 fitness. Microbiol Spectr 2023; 11:e0226923. [PMID: 37698427 PMCID: PMC10580940 DOI: 10.1128/spectrum.02269-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 07/14/2023] [Indexed: 09/13/2023] Open
Abstract
As an RNA virus, severe acute respiratory coronavirus 2 (SARS-CoV-2) is known for frequent substitution mutations, and substitutions in important genome regions are often associated with viral fitness. However, whether indel mutations are related to viral fitness is generally ignored. Here we developed a computational methodology to investigate indels linked to fitness occurring in over 9 million SARS-CoV-2 genomes. Remarkably, by analyzing 31,642,404 deletion records and 1,981,308 insertion records, our pipeline identified 26,765 deletion types and 21,054 insertion types and discovered 65 indel types with a significant association with Pango lineages. We proposed the concept of featured indels representing the population of specific Pango lineages and variants as substitution mutations and termed these 65 indels as featured indels. The selective pressure of all indel types is assessed using the Bayesian model to explore the importance of indels. Our results exhibited higher selective pressure of indels like substitution mutations, which are important for assessing viral fitness and consistent with previous studies in vitro. Evaluation of the growth rate of each viral lineage indicated that indels play key roles in SARS-CoV-2 evolution and deserve more attention as substitution mutations. IMPORTANCE The fitness of indels in pathogen genome evolution has rarely been studied. We developed a computational methodology to investigate the severe acute respiratory coronavirus 2 genomes and analyze over 33 million records of indels systematically, ultimately proposing the concept of featured indels that can represent specific Pango lineages and identifying 65 featured indels. Machine learning model based on Bayesian inference and viral lineage growth rate evaluation suggests that these featured indels exhibit selection pressure comparable to replacement mutations. In conclusion, indels are not negligible for evaluating viral fitness.
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Affiliation(s)
- Xiang Li
- CAS Key Laboratory of Molecular Virology & Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
- AI for Science, Shanghai Artificial Intelligence Laboratory, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hongliang Yan
- AI for Science, Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Gary Wong
- CAS Key Laboratory of Molecular Virology & Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Wanli Ouyang
- AI for Science, Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jie Cui
- CAS Key Laboratory of Molecular Virology & Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
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Li K, Melnychuk S, Sandstrom P, Ji H. Tracking the evolution of the SARS-CoV-2 Delta variant of concern: analysis of genetic diversity and selection across the whole viral genome. Front Microbiol 2023; 14:1222301. [PMID: 37614597 PMCID: PMC10443222 DOI: 10.3389/fmicb.2023.1222301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/17/2023] [Indexed: 08/25/2023] Open
Abstract
Background Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has diversified extensively, producing five highly virulent lineages designated as variants of concern (VOCs). The Delta VOC emerged in India with increased transmission, immune evasion, and mortality, causing a massive global case surge in 2021. This study aims to understand how the Delta VOC evolved by characterizing mutation patterns in the viral population before and after its emergence. Furthermore, we aim to identify the influence of positive and negative selection on VOC evolution and understand the prevalence of different mutation types in the viral genome. Methods Three groups of whole viral genomes were retrieved from GISAID, sourced from India, with collection periods as follows: Group A-during the initial appearance of SARS-CoV-2; Group B-just before the emergence of the Delta variant; Group C-after the establishment of the Delta variant in India. Mutations in >1% of each group were identified with BioEdit to reveal differences in mutation quantity and type. Sites under positive or negative selection were identified with FUBAR. The results were compared to determine how mutations correspond with selective pressures and how viral mutation profiles changed to reflect genetic diversity before and after VOC emergence. Results The number of mutations increased progressively in Groups A-C, with Group C reporting a 2.2- and 1.9-fold increase from Groups A and B, respectively. Among all the observed mutations, Group C had the highest percentage of deletions (22.7%; vs. 4.2% and 2.6% in Groups A and B, respectively), and most mutations altered the final amino acid code, such as non-synonymous substitutions and deletions. Conversely, Group B had the most synonymous substitutions that are effectively silent. The number of sites experiencing positive selection increased in Groups A-C, but Group B had 2.4- and 2.6 times more sites under negative selection compared to Groups A and C, respectively. Conclusion Our findings demonstrated that viral genetic diversity continuously increased during and after the emergence of the Delta VOC. Despite this, Group B reports heightened negative selection, which potentially preserves important gene regions during evolution. Group C contains an unprecedented quantity of mutations and positively selected sites, providing strong evidence of active viral adaptation in the population.
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Affiliation(s)
- Katherine Li
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Stephanie Melnychuk
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Paul Sandstrom
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Hezhao Ji
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Aguilar Rangel M, Dolan PT, Taguwa S, Xiao Y, Andino R, Frydman J. High-resolution mapping reveals the mechanism and contribution of genome insertions and deletions to RNA virus evolution. Proc Natl Acad Sci U S A 2023; 120:e2304667120. [PMID: 37487061 PMCID: PMC10400975 DOI: 10.1073/pnas.2304667120] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/07/2023] [Indexed: 07/26/2023] Open
Abstract
RNA viruses rapidly adapt to selective conditions due to the high intrinsic mutation rates of their RNA-dependent RNA polymerases (RdRps). Insertions and deletions (indels) in viral genomes are major contributors to both deleterious mutational load and evolutionary novelty, but remain understudied. To characterize the mechanistic details of their formation and evolutionary dynamics during infection, we developed a hybrid experimental-bioinformatic approach. This approach, called MultiMatch, extracts insertions and deletions from ultradeep sequencing experiments, including those occurring at extremely low frequencies, allowing us to map their genomic distribution and quantify the rates at which they occur. Mapping indel mutations in adapting poliovirus and dengue virus populations, we determine the rates of indel generation and identify mechanistic and functional constraints shaping indel diversity. Using poliovirus RdRp variants of distinct fidelity and genome recombination rates, we demonstrate tradeoffs between fidelity and Indel generation. Additionally, we show that maintaining translation frame and viral RNA structures constrain the Indel landscape and that, due to these significant fitness effects, Indels exert a significant deleterious load on adapting viral populations. Conversely, we uncover positively selected Indels that modulate RNA structure, generate protein variants, and produce defective interfering genomes in viral populations. Together, our analyses establish the kinetic and mechanistic tradeoffs between misincorporation, recombination, and Indel rates and reveal functional principles defining the central role of Indels in virus evolution, emergence, and the regulation of viral infection.
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Affiliation(s)
| | - Patrick T. Dolan
- Department of Biology, Stanford University, Stanford, CA94305
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA94143
| | - Shuhei Taguwa
- Department of Biology, Stanford University, Stanford, CA94305
- Research Institute for Microbial Diseases, Osaka University, Yamadaoka, Suita, Osaka565-0871, Japan
| | - Yinghong Xiao
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA94143
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA94143
| | - Judith Frydman
- Department of Biology, Stanford University, Stanford, CA94305
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Tang Z, Yu P, Guo Q, Chen M, Lei Y, Zhou L, Mai W, Chen L, Deng M, Kong W, Niu C, Xiong X, Li W, Chen C, Lai C, Wang Q, Li B, Ji T. Clinical characteristics and host immunity responses of SARS-CoV-2 Omicron variant BA.2 with deletion of ORF7a, ORF7b and ORF8. Virol J 2023; 20:106. [PMID: 37248496 DOI: 10.1186/s12985-023-02066-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023] Open
Abstract
BACKGROUND The pathogenicity and virulence of the Omicron strain have weakened significantly pathogenesis of Omicron variants. Accumulating data indicated accessory proteins play crucial roles in host immune evasion and virus pathogenesis of SARS-CoV-2. Therefore, the impact of simultaneous deletion of accessory protein ORF7a, ORF7b and ORF8 on the clinical characteristics and specific immunity in Omicron breakthrough infected patients (BIPs) need to be verified. METHODS Herein, plasma cytokines were identified using a commercial Multi-cytokine detection kit. Enzyme-linked immunosorbent assay and pseudovirus neutralization assays were utilized to determine the titers of SARS-CoV-2 specific binding antibodies and neutralizing antibodies, respectively. In addition, an enzyme-linked immunospot assay was used to quantify SARS-CoV-2 specific T cells and memory B cells. RESULTS A local COVID-19 outbreak was caused by the Omicron BA.2 variant, which featured a deletion of 871 base pairs (∆871 BA.2), resulting in the removal of ORF7a, ORF7b, and ORF8. We found that hospitalized patients with ∆871 BA.2 had significantly shorter hospital stays than those with wild-type (WT) BA.2. Plasma cytokine levels in both ∆871 BA.2 and WT BA.2 patients were within the normal range of reference, and there was no notable difference in the titers of SARS-CoV-2 ancestor or Omicron-specific binding IgG antibodies, neutralizing antibody titers, effector T cells, and memory B cells frequencies between ∆871 BA.2 and WT BA.2 infected adult patients. However, antibody titers in ∆871 BA.2 infected adolescents were higher than in adults. CONCLUSIONS The simultaneous deletion of ORF7a, ORF7b, and ORF8 facilitates the rapid clearance of the BA.2 variant, without impacting cytokine levels or affecting SARS-CoV-2 specific humoral and cellular immunity in Omicron-infected individuals.
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Affiliation(s)
- Zhizhong Tang
- Urology Surgery Department, Maoming People's Hospital, Maoming, 525000, People's Republic of China
| | - Pei Yu
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China
| | - Qianfang Guo
- Guangdong Provincial Key Laboratory of Pathogen Detection for Emerging Infectious Disease Response, Institute of Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangdong, 511430, People's Republic of China
| | - Mingxiao Chen
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China
| | - Yu Lei
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China
| | - Lei Zhou
- Department Of Pathology Laboratory, Maoming People's Hospital, Maoming, 525000, People's Republic of China
| | - Weikang Mai
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China
| | - Lu Chen
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China
| | - Min Deng
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China
| | - Weiya Kong
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China
| | - Chuanying Niu
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510535, People's Republic of China
| | - Xiaoli Xiong
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510535, People's Republic of China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou, 510005, People's Republic of China
| | - Wenrui Li
- Clinical Laboratory Medicine Department, Dongguan Ninth People's Hospital, Dongguan, 523016, People's Republic of China
| | - Chunbo Chen
- Intensive Care Unit Department, Maoming People's Hospital, Maoming, 525000, People's Republic of China
| | - Changchun Lai
- Clinical Laboratory Medicine Department, Maoming People's Hospital, Maoming, 525000, People's Republic of China.
| | - Qian Wang
- State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, People's Republic of China.
| | - Baisheng Li
- Guangdong Provincial Key Laboratory of Pathogen Detection for Emerging Infectious Disease Response, Institute of Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangdong, 511430, People's Republic of China.
| | - Tianxing Ji
- Clinical Laboratory Medicine Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, People's Republic of China.
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, 511495, People's Republic of China.
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Ma Y, Li P, Hu Y, Qiu T, Wang L, Lu H, Lv K, Xu M, Zhuang J, Liu X, He S, He B, Liu S, Liu L, Wang Y, Yue X, Zhai Y, Luo W, Mai H, Kuang Y, Chen S, Ye F, Zhou N, Zhao W, Chen J, Chen S, Xiong X, Shi M, Pan JA, Chen YQ. Spike substitution T813S increases Sarbecovirus fusogenicity by enhancing the usage of TMPRSS2. PLoS Pathog 2023; 19:e1011123. [PMID: 37196033 DOI: 10.1371/journal.ppat.1011123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 05/30/2023] [Accepted: 04/24/2023] [Indexed: 05/19/2023] Open
Abstract
SARS-CoV Spike (S) protein shares considerable homology with SARS-CoV-2 S, especially in the conserved S2 subunit (S2). S protein mediates coronavirus receptor binding and membrane fusion, and the latter activity can greatly influence coronavirus infection. We observed that SARS-CoV S is less effective in inducing membrane fusion compared with SARS-CoV-2 S. We identify that S813T mutation is sufficient in S2 interfering with the cleavage of SARS-CoV-2 S by TMPRSS2, reducing spike fusogenicity and pseudoparticle entry. Conversely, the mutation of T813S in SARS-CoV S increased fusion ability and viral replication. Our data suggested that residue 813 in the S was critical for the proteolytic activation, and the change from threonine to Serine at 813 position might be an evolutionary feature adopted by SARS-2-related viruses. This finding deepened the understanding of Spike fusogenicity and could provide a new perspective for exploring Sarbecovirus' evolution.
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Affiliation(s)
- Yong Ma
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Pengbin Li
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yunqi Hu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Tianyi Qiu
- Institute of Clinical Science, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lixiang Wang
- Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Hongjie Lu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Kexin Lv
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Mengxin Xu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Jiaxin Zhuang
- The Center for Infection and Immunity Study and Molecular Cancer Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Xue Liu
- The Center for Infection and Immunity Study and Molecular Cancer Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Suhua He
- Molecular Imaging Center, Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Bing He
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Shuning Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Lin Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yuanyuan Wang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Xinyu Yue
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yanmei Zhai
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Wanyu Luo
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Haoting Mai
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yu Kuang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Shifeng Chen
- The 74(th) Group Army Hospital, Guangzhou, China
| | - Feng Ye
- The 74(th) Group Army Hospital, Guangzhou, China
| | - Na Zhou
- The 74(th) Group Army Hospital, Guangzhou, China
| | - Wenjing Zhao
- The Center for Infection and Immunity Study and Molecular Cancer Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Jun Chen
- Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Shoudeng Chen
- Molecular Imaging Center, Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Xiaoli Xiong
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Mang Shi
- The Center for Infection and Immunity Study and Molecular Cancer Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Ji-An Pan
- The Center for Infection and Immunity Study and Molecular Cancer Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yao-Qing Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- National Medical Products Administration Key Laboratory for Quality Monitoring and Evaluation of Vaccines and Biological Products, Sun Yat-sen University, Guanzhou, China
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Zheng SY, Zhang YP, Liu YX, Zhao W, Peng XL, Zheng YP, Fu YH, Yu JM, He JS. Tracking of Mutational Signature of SARS-CoV-2 Omicron on Distinct Continents and Little Difference was Found. Viruses 2023; 15:v15020321. [PMID: 36851535 PMCID: PMC9967123 DOI: 10.3390/v15020321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The Omicron variant is currently ravaging the world, raising serious concern globally. Monitoring genomic variations and determining their influence on biological features are critical for tracing its ongoing transmission and facilitating effective measures. Based on large-scale sequences from different continents, this study found that: (i) The genetic diversity of Omicron is much lower than that of the Delta variant. Still, eight deletions (Del 1-8) and 1 insertion, as well as 130 SNPs, were detected on the Omicron genomes, with two deletions (Del 3 and 4) and 38 SNPs commonly detected on all continents and exhibiting high-occurring frequencies. (ii) Four groups of tightly linked SNPs (linkage I-IV) were detected, among which linkage I, containing 38 SNPs, with 6 located in the RBD, increased its occurring frequency remarkably over time. (iii) The third codons of the Omicron shouldered the most mutation pressures, while the second codons presented the least flexibility. (iv) Four major mutants with amino acid substitutions in the RBD were detected, and further structural analysis suggested that the substitutions did not alter the viral receptor binding ability greatly. It was inferred that though the Omicron genome harbored great changes in antigenicity and remarkable ability to evade immunity, it was immune-pressure selected. This study tracked mutational signatures of Omicron variant and the potential biological significance of the SNPs, and the linkages await further functional verification.
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Affiliation(s)
| | | | | | | | | | | | | | - Jie-Mei Yu
- Correspondence: (J.-M.Y.); (J.-S.H.); Tel.: +86-10-51684358 (J.-M.Y.)
| | - Jin-Sheng He
- Correspondence: (J.-M.Y.); (J.-S.H.); Tel.: +86-10-51684358 (J.-M.Y.)
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8
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Huang Q, Zhang Q, Bible PW, Liang Q, Zheng F, Wang Y, Hao Y, Liu Y. A New Way to Trace SARS-CoV-2 Variants Through Weighted Network Analysis of Frequency Trajectories of Mutations. Front Microbiol 2022; 13:859241. [PMID: 35369526 PMCID: PMC8966897 DOI: 10.3389/fmicb.2022.859241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 02/18/2022] [Indexed: 11/13/2022] Open
Abstract
Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19.
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Affiliation(s)
- Qiang Huang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Qiang Zhang
- College of Computer, Chengdu University, Chengdu, China
| | - Paul W Bible
- College of Arts and Sciences, Marian University, Indianapolis, IN, United States
| | - Qiaoxing Liang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Fangfang Zheng
- School of Traditional Chinese Medicine Healthcare, Guangdong Food and Drug Vocational College, Guangzhou, China
| | - Ying Wang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yuantao Hao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yu Liu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
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