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Sun Y, Wang M, Lin W, Dong W, Xu J. "Mutation blacklist" and "mutation whitelist" of SARS-CoV-2. JOURNAL OF BIOSAFETY AND BIOSECURITY 2022; 4:114-120. [PMID: 35845149 PMCID: PMC9273572 DOI: 10.1016/j.jobb.2022.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/21/2022] [Accepted: 06/27/2022] [Indexed: 01/26/2023] Open
Abstract
Over the past two years, scientists throughout the world have completed more than 6 million SARS-CoV-2 genome sequences. Today, the number of SARS-CoV-2 genomes exceeds the total number of all other viral genomes. These genomes are a record of the evolution of SARS-CoV-2 in the human host, and provide information on the emergence of mutations. In this study, analysis of these sequenced genomes identified 296,728 de novo mutations (DNMs), and found that six types of base substitutions reached saturation in the sequenced genome population. Based on this analysis, a "mutation blacklist" of SARS-CoV-2 was compiled. The loci on the "mutation blacklist" are highly conserved, and these mutations likely have detrimental effects on virus survival, replication, and transmission. This information is valuable for SARS-CoV-2 research on gene function, vaccine design, and drug development. Through association analysis of DNMs and viral transmission rates, we identified 185 DNMs that positively correlated with the SARS-CoV-2 transmission rate, and these DNMs where classified as the "mutation whitelist" of SARS-CoV-2. The mutations on the "mutation whitelist" are beneficial for SARS-CoV-2 transmission and could therefore be used to evaluate the transmissibility of new variants. The occurrence of mutations and the evolution of viruses are dynamic processes. To more effectively monitor the mutations and variants of SARS-CoV-2, we built a SARS-CoV-2 mutation and variant monitoring and pre-warning system (MVMPS), which can monitor the occurrence and development of mutations and variants of SARS-CoV-2, as well as provide pre-warning for the prevention and control of SARS-CoV-2 (https://www.omicx.cn/). Additionally, this system could be used in real-time to update the "mutation whitelist" and "mutation blacklist" of SARS-CoV-2.
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Affiliation(s)
- Yamin Sun
- Research Institute of Public Health, Nankai University, Tianjin, PR China
- Research Center for Functional Genomics and Biochip, Tianjin, PR China
| | - Min Wang
- Research Center for Functional Genomics and Biochip, Tianjin, PR China
- TEDA Institute of Biological Sciences and Biotechnology, Nankai University, PR China
| | - Wenchao Lin
- Research Center for Functional Genomics and Biochip, Tianjin, PR China
| | - Wei Dong
- Research Center for Functional Genomics and Biochip, Tianjin, PR China
| | - Jianguo Xu
- Research Institute of Public Health, Nankai University, Tianjin, PR China
- State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 202206, PR China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing 100730, PR China
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2
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Williams BJM, Ogbunugafor CB, Althouse BM, Hébert-Dufresne L. Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin. PNAS NEXUS 2022; 1:pgac143. [PMID: 36060623 PMCID: PMC9434636 DOI: 10.1093/pnasnexus/pgac143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Seasonal influenza kills hundreds of thousands every year, with multiple constantly changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity indicative of critical behavior. We argue that (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.
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Affiliation(s)
- Blake J M Williams
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
| | - C Brandon Ogbunugafor
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
- Department of Ecology and Evolutionary Biology, Yale University , New Haven, CT 06511, USA
- Santa Fe Institute , Santa Fe, NM 87501, USA
- Public Health Modeling Unit, Yale School of Public Health , New Haven, CT 06510, USA
| | - Benjamin M Althouse
- Institute for Disease Modeling, Global Health, Bill & Melinda Gates Foundation , Seattle, WA 98109, USA
- Information School, University of Washington , Seattle, WA 98195, USA
- Department of Biology, New Mexico State University , Las Cruces, NM 88003, USA
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont , Burlington VT 05405, USA
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3
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Bull MB, Gu H, Ma FNL, Perera LP, Poon LLM, Valkenburg SA. Next-generation T cell-activating vaccination increases influenza virus mutation prevalence. SCIENCE ADVANCES 2022; 8:eabl5209. [PMID: 35385318 PMCID: PMC8986104 DOI: 10.1126/sciadv.abl5209] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
To determine the potential for viral adaptation to T cell responses, we probed the full influenza virus genome by next-generation sequencing directly ex vivo from infected mice, in the context of an experimental T cell-based vaccine, an H5N1-based viral vectored vaccinia vaccine Wyeth/IL-15/5Flu, versus the current standard-of-care, seasonal inactivated influenza vaccine (IIV) and unvaccinated conditions. Wyeth/IL-15/5Flu vaccination was coincident with increased mutation incidence and frequency across the influenza genome; however, mutations were not enriched within T cell epitope regions, but high allele frequency mutations within conserved hemagglutinin stem regions and PB2 mammalian adaptive mutations arose. Depletion of CD4+ and CD8+ T cell subsets led to reduced frequency of mutants in vaccinated mice; therefore, vaccine-mediated T cell responses were important drivers of virus diversification. Our findings suggest that Wyeth/IL-15/5Flu does not generate T cell escape mutants but increases stochastic events for virus adaptation by stringent bottlenecks.
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Affiliation(s)
- Maireid B. Bull
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Haogao Gu
- Division of Public Health Laboratory Sciences, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Fionn N. L. Ma
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Liyanage P. Perera
- Metabolism Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-1374, USA
| | - Leo L. M. Poon
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Division of Public Health Laboratory Sciences, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Sophie A. Valkenburg
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Department of Microbiology and Immunology, at The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
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4
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Lyons DM, Lauring AS. Mutation and Epistasis in Influenza Virus Evolution. Viruses 2018; 10:E407. [PMID: 30081492 PMCID: PMC6115771 DOI: 10.3390/v10080407] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 07/30/2018] [Accepted: 07/30/2018] [Indexed: 12/25/2022] Open
Abstract
Influenza remains a persistent public health challenge, because the rapid evolution of influenza viruses has led to marginal vaccine efficacy, antiviral resistance, and the annual emergence of novel strains. This evolvability is driven, in part, by the virus's capacity to generate diversity through mutation and reassortment. Because many new traits require multiple mutations and mutations are frequently combined by reassortment, epistatic interactions between mutations play an important role in influenza virus evolution. While mutation and epistasis are fundamental to the adaptability of influenza viruses, they also constrain the evolutionary process in important ways. Here, we review recent work on mutational effects and epistasis in influenza viruses.
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Affiliation(s)
- Daniel M Lyons
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Adam S Lauring
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA.
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Zheng J, Perlman S. Immune responses in influenza A virus and human coronavirus infections: an ongoing battle between the virus and host. Curr Opin Virol 2018; 28:43-52. [PMID: 29172107 PMCID: PMC5835172 DOI: 10.1016/j.coviro.2017.11.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 11/02/2017] [Indexed: 12/25/2022]
Abstract
Respiratory viruses, especially influenza A viruses and coronaviruses such as MERS-CoV, represent continuing global threats to human health. Despite significant advances, much needs to be learned. Recent studies in virology and immunology have improved our understanding of the role of the immune system in protection and in the pathogenesis of these infections and of co-evolution of viruses and their hosts. These findings, together with sophisticated molecular structure analyses, omics tools and computer-based models, have helped delineate the interaction between respiratory viruses and the host immune system, which will facilitate the development of novel treatment strategies and vaccines with enhanced efficacy.
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Affiliation(s)
- Jian Zheng
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, IA 52242, United States
| | - Stanley Perlman
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, IA 52242, United States.
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Morris DH, Gostic KM, Pompei S, Bedford T, Łuksza M, Neher RA, Grenfell BT, Lässig M, McCauley JW. Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology. Trends Microbiol 2018; 26:102-118. [PMID: 29097090 PMCID: PMC5830126 DOI: 10.1016/j.tim.2017.09.004] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/06/2017] [Accepted: 09/19/2017] [Indexed: 01/16/2023]
Abstract
Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.
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Affiliation(s)
- Dylan H Morris
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Katelyn M Gostic
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA
| | - Simone Pompei
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Marta Łuksza
- Institute for Advanced Study, Princeton, NJ, USA
| | - Richard A Neher
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Michael Lässig
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - John W McCauley
- Worldwide Influenza Centre, Francis Crick Institute, London, UK
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7
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Akand EH, Downard KM. Identification of epistatic mutations and insights into the evolution of the influenza virus using a mass-based protein phylogenetic approach. Mol Phylogenet Evol 2018; 121:132-138. [PMID: 29337273 DOI: 10.1016/j.ympev.2018.01.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 12/11/2017] [Accepted: 01/10/2018] [Indexed: 12/27/2022]
Abstract
A mass-based protein phylogenetic approach developed in this laboratory has been applied to study mutation trends and identify consecutive or near-consecutive mutations typically associated with positive epistasis. While epistasis is thought to occur commonly during the evolution of viruses, the extent of epistasis in influenza, and its role in the evolution of immune escape and drug resistant mutants, remains to be systematically investigated. Here putative epistatic mutations within H3 hemagglutinin in type A influenza are identified where leading parent mutations were found to predominate within reported antigenic sites of the protein. Frequent subsequent mutations resided exclusively in different antigenic regions, providing the virus with a possible immune escape mechanism, or at other remote sites that drive beneficial protein structural and functional change. The results also enable a "small steps" evolutionary model to be proposed where the more frequent consecutive, or near-consecutive, non-conservative mutations exhibited less structural, and thus functional, change. This favours the evolutionary survival of the virus over mutations associated with more substantive change that may cause or risk its own extinction.
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Affiliation(s)
- Elma H Akand
- Infectious Disease Responses Laboratory, University of New South Wales, Sydney, Australia
| | - Kevin M Downard
- Infectious Disease Responses Laboratory, University of New South Wales, Sydney, Australia.
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Uekermann F, Sneppen K. A cross-immunization model for the extinction of old influenza strains. Sci Rep 2016; 6:25907. [PMID: 27174658 PMCID: PMC4865727 DOI: 10.1038/srep25907] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 04/22/2016] [Indexed: 11/25/2022] Open
Abstract
Given the frequent mutation of antigenic features, the constancy of genetic and antigenic diversity of influenza within a subtype is surprising. While the emergence of new strains and antigenic features is commonly attributed to selection by the human immune system, the mechanism that ensures the extinction of older strains remains controversial. To replicate this dynamics of replacement current models utilize mechanisms such as short-lived strain-transcending immunity, a direct competition for hosts, stochastic extinction or constrained antigenic evolution. Building on the idea of short-lived immunity we introduce a minimal model that exhibits the aforementioned dynamics of replacement. Our model relies only on competition due to an antigen specific immune-response in an unconstrained antigenic space. Furthermore the model explains the size of typical influenza epidemics as well as the tendency that new epidemics are associated with mutations of old antigens.
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Affiliation(s)
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, Denmark
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