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Cavalcanti-Dantas VDM, Fernandes B, Dantas PHLF, Uchoa GR, Mendes AF, Araújo Júnior WOD, Castellano LRC, Fernandes AIV, Goulart LR, Oliveira RADS, Assis PACD, Souza JRD, Morais CNLD. Differential epitope prediction across diverse circulating variants of SARS-COV-2 in Brazil. Comput Biol Chem 2024; 112:108139. [PMID: 38972100 DOI: 10.1016/j.compbiolchem.2024.108139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 06/09/2024] [Accepted: 06/22/2024] [Indexed: 07/09/2024]
Abstract
COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants.
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Affiliation(s)
| | | | | | | | | | | | | | - Ana Isabel Vieira Fernandes
- Health Promotion Department of the Medical Sciences Center and Division for Infectious and Parasitic Diseases, Lauro Wanderley University Hospital, Federal University of Paraiba, Brazil
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Zhang X, Li M, Zhang N, Li Y, Teng F, Li Y, Zhang X, Xu X, Li H, Zhu Y, Wang Y, Jia Y, Qin C, Wang B, Guo S, Wang Y, Yu X. SARS-CoV-2 Evolution: Immune Dynamics, Omicron Specificity, and Predictive Modeling in Vaccinated Populations. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2402639. [PMID: 39206813 PMCID: PMC11516136 DOI: 10.1002/advs.202402639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 07/25/2024] [Indexed: 09/04/2024]
Abstract
Host immunity is central to the virus's spread dynamics, which is significantly influenced by vaccination and prior infection experiences. In this work, we analyzed the co-evolution of SARS-CoV-2 mutation, angiotensin-converting enzyme 2 (ACE2) receptor binding, and neutralizing antibody (NAb) responses across various variants in 822 human and mice vaccinated with different non-Omicron and Omicron vaccines is analyzed. The link between vaccine efficacy and vaccine type, dosing, and post-vaccination duration is revealed. The classification of immune protection against non-Omicron and Omicron variants is co-evolved with genetic mutations and vaccination. Additionally, a model, the Prevalence Score (P-Score) is introduced, which surpasses previous algorithm-based models in predicting the potential prevalence of new variants in vaccinated populations. The hybrid vaccination combining the wild-type (WT) inactivated vaccine with the Omicron BA.4/5 mRNA vaccine may provide broad protection against both non-Omicron variants and Omicron variants, albeit with EG.5.1 still posing a risk. In conclusion, these findings enhance understanding of population immunity variations and provide valuable insights for future vaccine development and public health strategies.
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Affiliation(s)
- Xiaohan Zhang
- State Key Laboratory of Medical ProteomicsBeijing Proteome Research CenterNational Center for Protein Sciences‐Beijing (PHOENIX Center)Beijing Institute of LifeomicsBeijing102206China
- School of MedicineNanjing University of Chinese MedicineNanjing210023China
| | - Mansheng Li
- State Key Laboratory of Medical ProteomicsBeijing Proteome Research CenterNational Center for Protein Sciences‐Beijing (PHOENIX Center)Beijing Institute of LifeomicsBeijing102206China
| | - Nana Zhang
- Department of VirologyState Key Laboratory of Pathogen and BiosecurityBeijing Institute of Microbiology and EpidemiologyAcademy of Military Medical SciencesBeijing100071China
| | - Yunhui Li
- Department of Clinical LaboratoryBeijing Ditan HospitalCapital Medical UniversityBeijing100015China
| | - Fei Teng
- Emergency Medicine Clinical Research CenterBeijing Chao‐Yang HospitalCapital Medical UniversityBeijing Key Laboratory of Cardiopulmonary Cerebral ResuscitationBeijing100020China
| | - Yongzhe Li
- Department of Clinical LaboratoryPeking Union Medical College HospitalChinese Academy of Medical Science & Peking Union Medical CollegeBeijing100730China
| | - Xiaomei Zhang
- State Key Laboratory of Medical ProteomicsBeijing Proteome Research CenterNational Center for Protein Sciences‐Beijing (PHOENIX Center)Beijing Institute of LifeomicsBeijing102206China
| | - Xingming Xu
- State Key Laboratory of Medical ProteomicsBeijing Proteome Research CenterNational Center for Protein Sciences‐Beijing (PHOENIX Center)Beijing Institute of LifeomicsBeijing102206China
| | - Haolong Li
- Department of Clinical LaboratoryPeking Union Medical College HospitalChinese Academy of Medical Science & Peking Union Medical CollegeBeijing100730China
| | - Yunping Zhu
- State Key Laboratory of Medical ProteomicsBeijing Proteome Research CenterNational Center for Protein Sciences‐Beijing (PHOENIX Center)Beijing Institute of LifeomicsBeijing102206China
| | - Yumin Wang
- The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Yan Jia
- ProteomicsEra Medical Co. Ltd.Beijing102206China
| | - Chengfeng Qin
- Department of VirologyState Key Laboratory of Pathogen and BiosecurityBeijing Institute of Microbiology and EpidemiologyAcademy of Military Medical SciencesBeijing100071China
| | - Bingwei Wang
- School of MedicineNanjing University of Chinese MedicineNanjing210023China
| | - Shubin Guo
- Emergency Medicine Clinical Research CenterBeijing Chao‐Yang HospitalCapital Medical UniversityBeijing Key Laboratory of Cardiopulmonary Cerebral ResuscitationBeijing100020China
| | - Yajie Wang
- Department of Clinical LaboratoryBeijing Ditan HospitalCapital Medical UniversityBeijing100015China
| | - Xiaobo Yu
- State Key Laboratory of Medical ProteomicsBeijing Proteome Research CenterNational Center for Protein Sciences‐Beijing (PHOENIX Center)Beijing Institute of LifeomicsBeijing102206China
- The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
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Powers JM, Leist SR, Mallory ML, Yount BL, Gully KL, Zweigart MR, Bailey AB, Sheahan TP, Harkema JR, Baric RS. Divergent pathogenetic outcomes in BALB/c mice following Omicron subvariant infection. Virus Res 2024; 341:199319. [PMID: 38224840 PMCID: PMC10835285 DOI: 10.1016/j.virusres.2024.199319] [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: 09/30/2023] [Revised: 01/02/2024] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
Following the emergence of B.1.1.529 Omicron, the SARS-CoV-2 virus evolved into a significant number of sublineage variants that possessed numerous mutations throughout the genome, but particularly within the spike glycoprotein (S) gene. For example, the BQ.1.1 and the XBB.1 and XBB.1.5 subvariants contained 34 and 41 mutations in S, respectively. However, these variants elicited largely replication only or mild disease phenotypes in mice. To better model pathogenic outcomes and measure countermeasure performance, we developed mouse adapted versions (BQ.1.1 MA; XBB.1 MA; XBB.1.5 MA) that reflect more pathogenic acute phase pulmonary disease symptoms of SARS-CoV-2, as well as derivative strains expressing nano-luciferase (nLuc) in place of ORF7 (BQ.1.1 nLuc; XBB.1 nLuc; XBB.1.5 nLuc). Amongst the mouse adapted (MA) viruses, a wide range of disease outcomes were observed including mortality, weight loss, lung dysfunction, and tissue viral loads in the lung and nasal turbinates. Intriguingly, XBB.1 MA and XBB.1.5 MA strains, which contained identical mutations throughout except at position F486S/P in S, exhibited divergent disease outcomes in mice (Ao et al., 2023). XBB.1.5 MA infection was associated with significant weight loss and ∼45 % mortality across two independent studies, while XBB.1 MA infected animals suffered from mild weight loss and only 10 % mortality across the same two independent studies. Additionally, the development and use of nanoluciferase expressing strains provided moderate throughput for live virus neutralization assays. The availability of small animal models for the assessment of Omicron VOC disease potential will enable refined capacity to evaluate the efficacy of on market and pre-clinical therapeutics and interventions.
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Affiliation(s)
- John M Powers
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
| | - Sarah R Leist
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Michael L Mallory
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Boyd L Yount
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kendra L Gully
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Mark R Zweigart
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Alexis B Bailey
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Timothy P Sheahan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jack R Harkema
- Department of Pathobiology & Diagnostic Investigation, Michigan State University, East Lansing, MI, USA
| | - Ralph S Baric
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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