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Surasinghe S, Kabengele K, Turner PE, Ogbunugafor CB. Evolutionary Invasion Analysis of Modern Epidemics Highlights the Context-Dependence of Virulence Evolution. Bull Math Biol 2024; 86:88. [PMID: 38877355 PMCID: PMC11178639 DOI: 10.1007/s11538-024-01313-0] [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: 11/13/2023] [Accepted: 05/25/2024] [Indexed: 06/16/2024]
Abstract
Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis-a method used to identify the evolution of uninvadable strategies in dynamical systems-to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal peculiar patterns of virulence evolution between epidemics with different disease natural histories (SARS-CoV-2 and hepatitis C virus). We discuss the findings with regards to the public health implications of predicting virus evolution, and in broader theoretical canon involving virulence evolution in host-parasite systems.
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Affiliation(s)
- Sudam Surasinghe
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06520, USA
- Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Ketty Kabengele
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06520, USA
| | - Paul E Turner
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06520, USA
- Microbiology Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - C Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06520, USA.
- Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, 06510, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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2
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Garcia-Vozmediano A, Maurella C, Ceballos LA, Crescio E, Meo R, Martelli W, Pitti M, Lombardi D, Meloni D, Pasqualini C, Ru G. Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy. Vet Res 2024; 55:72. [PMID: 38840261 PMCID: PMC11154984 DOI: 10.1186/s13567-024-01323-9] [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: 03/03/2023] [Accepted: 04/15/2024] [Indexed: 06/07/2024] Open
Abstract
Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.
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Affiliation(s)
- Aitor Garcia-Vozmediano
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy.
| | - Cristiana Maurella
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Leonardo A Ceballos
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Elisabetta Crescio
- Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Tecnológico, 64849, Monterrey, N.L., México
| | - Rosa Meo
- Department of Computer Science, University of Turin, Corso Svizzera 185, 10149, Turin, Italy
| | - Walter Martelli
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Monica Pitti
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Daniela Lombardi
- Piedmont Regional Service for the Epidemiology of Infectious Diseases (SeREMI), Via Venezia 6, 15121, Alessandria, Italy
| | - Daniela Meloni
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Chiara Pasqualini
- Piedmont Regional Service for the Epidemiology of Infectious Diseases (SeREMI), Via Venezia 6, 15121, Alessandria, Italy
| | - Giuseppe Ru
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
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3
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Abousamra E, Figgins M, Bedford T. Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.30.23299240. [PMID: 38076866 PMCID: PMC10705624 DOI: 10.1101/2023.11.30.23299240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant R t . These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ~0.6% median absolute error and ~6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.
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Affiliation(s)
- Eslam Abousamra
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Marlin Figgins
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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4
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Shah SAW, Palomar DP, Barr I, Poon LLM, Quadeer AA, McKay MR. Seasonal antigenic prediction of influenza A H3N2 using machine learning. Nat Commun 2024; 15:3833. [PMID: 38714654 PMCID: PMC11076571 DOI: 10.1038/s41467-024-47862-9] [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: 05/22/2023] [Accepted: 04/10/2024] [Indexed: 05/10/2024] Open
Abstract
Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.
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Affiliation(s)
- Syed Awais W Shah
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Daniel P Palomar
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
- Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Ian Barr
- WHO Collaborating Centre for Reference and Research on Influenza, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, University of Melbourne, at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Leo L M Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong SAR, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia.
| | - Matthew R McKay
- Department of Microbiology and Immunology, University of Melbourne, at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia.
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5
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585703. [PMID: 38746108 PMCID: PMC11092427 DOI: 10.1101/2024.03.19.585703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .
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6
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. ARXIV 2024:arXiv:2403.12684v2. [PMID: 38745695 PMCID: PMC11092678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Malancha Karmakar
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
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7
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Exciting times for evolutionary biology. Nat Ecol Evol 2024; 8:593-594. [PMID: 38605230 DOI: 10.1038/s41559-024-02402-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
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8
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Lou J, Liang W, Cao L, Hu I, Zhao S, Chen Z, Chan RWY, Cheung PPH, Zheng H, Liu C, Li Q, Chong MKC, Zhang Y, Yeoh EK, Chan PKS, Zee BCY, Mok CKP, Wang MH. Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations. Nat Commun 2024; 15:2546. [PMID: 38514647 PMCID: PMC10958014 DOI: 10.1038/s41467-024-46918-0] [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: 12/06/2023] [Accepted: 03/12/2024] [Indexed: 03/23/2024] Open
Abstract
Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.
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Affiliation(s)
- Jingzhi Lou
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- Beth Bioinformatics Co. Ltd, Hong Kong SAR, China
| | - Weiwen Liang
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lirong Cao
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Inchi Hu
- Department of Statistics, George Mason University, Fairfax, VA, USA
| | - Shi Zhao
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- School of Public Health, Tianjin Medical University, Tianjin, China
| | - Zigui Chen
- Department of Microbiology, CUHK, Hong Kong SAR, China
| | - Renee Wan Yi Chan
- Department of Paediatrics, CUHK, Hong Kong SAR, China
- Hong Kong Hub of Paediatric Excellence, CUHK, Hong Kong SAR, China
| | | | - Hong Zheng
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
| | - Caiqi Liu
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
| | - Qi Li
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
| | - Marc Ka Chun Chong
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Yexian Zhang
- Beth Bioinformatics Co. Ltd, Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Eng-Kiong Yeoh
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- Centre for Health Systems and Policy Research, CUHK, Hong Kong SAR, China
| | - Paul Kay-Sheung Chan
- Department of Microbiology, CUHK, Hong Kong SAR, China
- Stanley Ho Centre for Emerging Infectious Diseases, CUHK, Hong Kong SAR, China
| | - Benny Chung Ying Zee
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Chris Ka Pun Mok
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China.
- Li Ka Shing Institute of Health Sciences, Faculty of Medicine, CUHK, Hong Kong SAR, China.
| | - Maggie Haitian Wang
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China.
- CUHK Shenzhen Research Institute, Shenzhen, China.
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9
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Loes AN, Tarabi RAL, Huddleston J, Touyon L, Wong SS, Cheng SMS, Leung NHL, Hannon WW, Bedford T, Cobey S, Cowling BJ, Bloom JD. High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584176. [PMID: 38496577 PMCID: PMC10942427 DOI: 10.1101/2024.03.08.584176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we have developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes via a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately one month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals, and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers six months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. This study demonstrates the utility of high-throughput sequencing-based neutralization assays that enable titers to be simultaneously measured against many different viral strains. We provide a detailed experimental protocol (DOI: https://dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and a computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.
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Affiliation(s)
- Andrea N Loes
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Rosario Araceli L Tarabi
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - John Huddleston
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Lisa Touyon
- HKU-Pasteur Research Pole, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Sook San Wong
- HKU-Pasteur Research Pole, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Samuel M S Cheng
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Nancy H L Leung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - William W Hannon
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA 98109, USA
| | - Trevor Bedford
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Jesse D Bloom
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
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10
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Lässig M, Mustonen V, Nourmohammad A. Steering and controlling evolution - from bioengineering to fighting pathogens. Nat Rev Genet 2023; 24:851-867. [PMID: 37400577 PMCID: PMC11137064 DOI: 10.1038/s41576-023-00623-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.
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Affiliation(s)
- Michael Lässig
- Institute for Biological Physics, University of Cologne, Cologne, Germany.
| | - Ville Mustonen
- Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
| | - Armita Nourmohammad
- Department of Physics, University of Washington, Seattle, WA, USA.
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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11
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Meijers M, Ruchnewitz D, Eberhardt J, Łuksza M, Lässig M. Population immunity predicts evolutionary trajectories of SARS-CoV-2. Cell 2023; 186:5151-5164.e13. [PMID: 37875109 PMCID: PMC10964984 DOI: 10.1016/j.cell.2023.09.022] [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: 12/02/2022] [Revised: 08/26/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023]
Abstract
The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany.
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12
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Chardès V, Mazzolini A, Mora T, Walczak AM. Evolutionary stability of antigenically escaping viruses. Proc Natl Acad Sci U S A 2023; 120:e2307712120. [PMID: 37871216 PMCID: PMC10622963 DOI: 10.1073/pnas.2307712120] [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: 05/08/2023] [Accepted: 08/24/2023] [Indexed: 10/25/2023] Open
Abstract
Antigenic variation is the main immune escape mechanism for RNA viruses like influenza or SARS-CoV-2. While high mutation rates promote antigenic escape, they also induce large mutational loads and reduced fitness. It remains unclear how this cost-benefit trade-off selects the mutation rate of viruses. Using a traveling wave model for the coevolution of viruses and host immune systems in a finite population, we investigate how immunity affects the evolution of the mutation rate and other nonantigenic traits, such as virulence. We first show that the nature of the wave depends on how cross-reactive immune systems are, reconciling previous approaches. The immune-virus system behaves like a Fisher wave at low cross-reactivities, and like a fitness wave at high cross-reactivities. These regimes predict different outcomes for the evolution of nonantigenic traits. At low cross-reactivities, the evolutionarily stable strategy is to maximize the speed of the wave, implying a higher mutation rate and increased virulence. At large cross-reactivities, where our estimates place H3N2 influenza, the stable strategy is to increase the basic reproductive number, keeping the mutation rate to a minimum and virulence low.
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Affiliation(s)
- Victor Chardès
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
- Center for Computational Biology, Flatiron Institute, New York, NY10010
| | - Andrea Mazzolini
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
| | - Thierry Mora
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
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13
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Garjani A, Chegini AM, Salehi M, Tabibzadeh A, Yousefi P, Razizadeh MH, Esghaei M, Esghaei M, Rohban MH. Forecasting influenza hemagglutinin mutations through the lens of anomaly detection. Sci Rep 2023; 13:14944. [PMID: 37696867 PMCID: PMC10495359 DOI: 10.1038/s41598-023-42089-y] [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: 07/16/2022] [Accepted: 09/05/2023] [Indexed: 09/13/2023] Open
Abstract
The influenza virus hemagglutinin is an important part of the virus attachment to the host cells. The hemagglutinin proteins are one of the genetic regions of the virus with a high potential for mutations. Due to the importance of predicting mutations in producing effective and low-cost vaccines, solutions that attempt to approach this problem have recently gained significant attention. A historical record of mutations has been used to train predictive models in such solutions. However, the imbalance between mutations and preserved proteins is a big challenge for the development of such models that need to be addressed. Here, we propose to tackle this challenge through anomaly detection (AD). AD is a well-established field in Machine Learning (ML) that tries to distinguish unseen anomalies from normal patterns using only normal training samples. By considering mutations as anomalous behavior, we could benefit existing rich solutions in this field that have emerged recently. Such methods also fit the problem setup of extreme imbalance between the number of unmutated vs. mutated training samples. Motivated by this formulation, our method tries to find a compact representation for unmutated samples while forcing anomalies to be separated from the normal ones. This helps the model to learn a shared unique representation between normal training samples as much as possible, which improves the discernibility and detectability of mutated samples from the unmutated ones at the test time. We conduct a large number of experiments on four publicly available datasets, consisting of three different hemagglutinin protein datasets, and one SARS-CoV-2 dataset, and show the effectiveness of our method through different standard criteria.
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Affiliation(s)
- Ali Garjani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | | | - Mohammadreza Salehi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Alireza Tabibzadeh
- Department of Virology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Parastoo Yousefi
- Department of Virology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | | | - Moein Esghaei
- Cognitive Neuroscience Laboratory, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany
| | - Maryam Esghaei
- Department of Virology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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14
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Einav T, Ma R. Using interpretable machine learning to extend heterogeneous antibody-virus datasets. CELL REPORTS METHODS 2023; 3:100540. [PMID: 37671020 PMCID: PMC10475791 DOI: 10.1016/j.crmeth.2023.100540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/14/2023] [Accepted: 06/30/2023] [Indexed: 09/07/2023]
Abstract
A central challenge in biology is to use existing measurements to predict the outcomes of future experiments. For the rapidly evolving influenza virus, variants examined in one study will often have little to no overlap with other studies, making it difficult to discern patterns or unify datasets. We develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We validate this method using hemagglutination inhibition data from seven studies and predict 2,000,000 new values ± uncertainties. Our analysis quantifies the transferability between vaccination and infection studies in humans and ferrets, shows that serum potency is negatively correlated with breadth, and provides a tool for pandemic preparedness. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."
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Affiliation(s)
- Tal Einav
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Rong Ma
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
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15
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Sominina A, Danilenko D, Komissarov AB, Pisareva M, Fadeev A, Konovalova N, Eropkin M, Petrova P, Zheltukhina A, Musaeva T, Eder V, Ivanova A, Komissarova K, Stolyarov K, Karpova L, Smorodintseva E, Dorosh A, Krivitskaya V, Kuznetzova E, Majorova V, Petrova E, Boyarintseva A, Ksenafontov A, Shtro A, Nikolaeva J, Bakaev M, Burtseva E, Lioznov D. Assessing the Intense Influenza A(H1N1)pdm09 Epidemic and Vaccine Effectiveness in the Post-COVID Season in the Russian Federation. Viruses 2023; 15:1780. [PMID: 37632122 PMCID: PMC10458445 DOI: 10.3390/v15081780] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/31/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
The COVID-19 pandemic had a profound impact on influenza activity worldwide. However, as the pandemic progressed, influenza activity resumed. Here, we describe the influenza epidemic of high intensity of the 2022-2023 season. The epidemic had an early start and peaked in week 51.2022. The extremely high intensity of the epidemic may have been due to a significant decrease in herd immunity. The results of PCR-testing of 220,067 clinical samples revealed that the influenza A(H1N1)pdm09 virus dominated, causing 56.4% of positive cases, while A(H3N2) influenza subtype accounted for only 0.6%, and influenza B of Victoria lineage-for 34.3%. The influenza vaccine was found to be highly effective, with an estimated effectiveness of 92.7% in preventing admission with laboratory-confirmed influenza severe acute respiratory illness (SARI) cases and 54.7% in preventing influenza-like illness/acute respiratory illness (ILI/ARI) cases due to antigenic matching of circulated viruses with influenza vaccine strains for the season. Full genome next-generation sequencing of 1723 influenza A(H1N1)pdm09 viruses showed that all of them fell within clade 6B.1A.5.a2; nine of them possessed H275Y substitution in the NA gene, a genetic marker of oseltamivir resistance. Influenza A(H3N2) viruses belonged to subclade 3C.2a1b.2a.2 with the genetic group 2b being dominant. All 433 influenza B viruses belonged to subclade V1A.3a.2 encoding HA1 substitutions A127T, P144L, and K203R, which could be further divided into two subgroups. None of the influenza A(H3N2) and B viruses sequenced had markers of resistance to NA inhibitors. Thus, despite the continuing circulation of Omicron descendant lineages, influenza activity has resumed in full force, raising concerns about the intensity of fore coming seasonal epidemics.
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Affiliation(s)
- Anna Sominina
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Daria Danilenko
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Andrey B. Komissarov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Maria Pisareva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Artem Fadeev
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Nadezhda Konovalova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Mikhail Eropkin
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Polina Petrova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Alyona Zheltukhina
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Tamila Musaeva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Veronika Eder
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anna Ivanova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Kseniya Komissarova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Kirill Stolyarov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Ludmila Karpova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Elizaveta Smorodintseva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anna Dorosh
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Vera Krivitskaya
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Elena Kuznetzova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Victoria Majorova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Ekaterina Petrova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anastassia Boyarintseva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Andrey Ksenafontov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anna Shtro
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Julia Nikolaeva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Mikhail Bakaev
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Elena Burtseva
- National Research Center for Epidemiology and Microbiology Named after N.F. Gamaleya, 123098 Moscow, Russia
| | - Dmitry Lioznov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
- Department of Infectious Diseases, First Pavlov State Medical University, 197022 Saint Petersburg, Russia
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16
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Ladner JT, Sahl JW. Towards a post-pandemic future for global pathogen genome sequencing. PLoS Biol 2023; 21:e3002225. [PMID: 37527248 PMCID: PMC10393143 DOI: 10.1371/journal.pbio.3002225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023] Open
Abstract
Pathogen genome sequencing has become a routine part of our response to active outbreaks of infectious disease and should be an important part of our preparations for future epidemics. In this Essay, we discuss the innovations that have enabled routine pathogen genome sequencing, as well as how genome sequences can be used to understand and control the spread of infectious disease. We also explore the impact of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic on the field of pathogen genomics and outline the challenges we must address to further improve the utility of pathogen genome sequencing in the future.
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Affiliation(s)
- Jason T Ladner
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Jason W Sahl
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, United States of America
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17
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Asatryan MN, Timofeev BI, Shmyr IS, Khachatryan KR, Shcherbinin DN, Timofeeva TA, Gerasimuk ER, Agasaryan VG, Ershov IF, Shashkova TI, Kardymon OL, Ivanisenko NV, Semenenko TA, Naroditsky BS, Logunov DY, Gintsburg AL. [Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype H 3N 2]. Vopr Virusol 2023; 68:252-264. [PMID: 37436416 DOI: 10.36233/0507-4088-179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 07/13/2023]
Abstract
INTRODUCTION The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data. MATERIALS AND METHODS In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create real-time databases according to the set tasks. RESULTS Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues. CONCLUSION The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.
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Affiliation(s)
- M N Asatryan
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - B I Timofeev
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - I S Shmyr
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | | | - D N Shcherbinin
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - T A Timofeeva
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | | | - V G Agasaryan
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - I F Ershov
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | | | | | | | - T A Semenenko
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - B S Naroditsky
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - D Y Logunov
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - A L Gintsburg
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
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18
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Hill V, Githinji G, Vogels CBF, Bento AI, Chaguza C, Carrington CVF, Grubaugh ND. Toward a global virus genomic surveillance network. Cell Host Microbe 2023; 31:861-873. [PMID: 36921604 PMCID: PMC9986120 DOI: 10.1016/j.chom.2023.03.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
The COVID-19 pandemic galvanized the field of virus genomic surveillance, demonstrating its utility for public health. Now, we must harness the momentum that led to increased infrastructure, training, and political will to build a sustainable global genomic surveillance network for other epidemic and endemic viruses. We suggest a generalizable modular sequencing framework wherein users can easily switch between virus targets to maximize cost-effectiveness and maintain readiness for new threats. We also highlight challenges associated with genomic surveillance and when global inequalities persist. We propose solutions to mitigate some of these issues, including training and multilateral partnerships. Exploring alternatives to clinical sequencing can also reduce the cost of surveillance programs. Finally, we discuss how establishing genomic surveillance would aid control programs and potentially provide a warning system for outbreaks, using a global respiratory virus (RSV), an arbovirus (dengue virus), and a regional zoonotic virus (Lassa virus) as examples.
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Affiliation(s)
- Verity Hill
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
| | - George Githinji
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya; Department of Biochemistry and Biotechnology, Pwani University, Kilifi, Kenya
| | - Chantal B F Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA; Yale Institute for Global Health, Yale University, New Haven, CT, USA
| | - Ana I Bento
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA; The Rockefeller Foundation, New York, NY, USA
| | - Chrispin Chaguza
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA; Yale Institute for Global Health, Yale University, New Haven, CT, USA
| | - Christine V F Carrington
- Department of Preclinical Sciences, The University of the West Indies, St. Augustine Campus, St. Augustine, Trinidad and Tobago
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA; Yale Institute for Global Health, Yale University, New Haven, CT, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA; Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
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19
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Moulana A, Dupic T, Phillips AM, Desai MM. Genotype-phenotype landscapes for immune-pathogen coevolution. Trends Immunol 2023; 44:384-396. [PMID: 37024340 PMCID: PMC10147585 DOI: 10.1016/j.it.2023.03.006] [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: 02/03/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 04/07/2023]
Abstract
Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Angela M Phillips
- Department of Microbiology and Immunology, University of California at San Francisco, San Francisco, CA 94143, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA; NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA; Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA.
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20
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Pillai TK, Johnson KE, Song T, Gregianini TS, Tatiana G. B, Wang G, Medina RA, Van Bakel H, García-Sastre A, Nelson MI, Ghedin E, Veiga ABG. Tracking the emergence of antigenic variants in influenza A virus epidemics in Brazil. Virus Evol 2023; 9:vead027. [PMID: 37207002 PMCID: PMC10191192 DOI: 10.1093/ve/vead027] [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: 02/09/2023] [Revised: 04/04/2023] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
Influenza A virus (IAV) circulation patterns differ in North America and South America, with influenza seasons often characterized by different subtypes and strains. However, South America is relatively undersampled considering the size of its population. To address this gap, we sequenced the complete genomes of 220 IAVs collected between 2009 and 2016 from hospitalized patients in southern Brazil. New genetic drift variants were introduced into southern Brazil each season from a global gene pool, including four H3N2 clades (3c, 3c2, 3c3, and 3c2a) and five H1N1pdm clades (clades 6, 7, 6b, 6c, and 6b1). In 2016, H1N1pdm viruses belonging to a new 6b1 clade caused a severe influenza epidemic in southern Brazil that arrived early and spread rapidly, peaking mid-autumn. Inhibition assays showed that the A/California/07/2009(H1N1) vaccine strain did not protect well against 6b1 viruses. Phylogenetically, most 6b1 sequences that circulated in southern Brazil belong to a single transmission cluster that rapidly diffused across susceptible populations, leading to the highest levels of influenza hospitalization and mortality seen since the 2009 pandemic. Continuous genomic surveillance is needed to monitor rapidly evolving IAVs for vaccine strain selection and understand their epidemiological impact in understudied regions.
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Affiliation(s)
- Tara K Pillai
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
| | - Katherine E Johnson
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
- Department of Biology, Center for Genomics & Systems Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Timothy Song
- Department of Biology, Center for Genomics & Systems Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Tatiana S Gregianini
- Laboratório Central de Saúde Pública, Centro Estadual de Vigilância em Saúde da Secretaria de Saúde do Estado do Rio Grande do Sul—LACEN/CEVS/SES‐RS, Av. Ipiranga, 5400, Porto Alegre, RS 90450-190, Brazil
| | - Baccin Tatiana G.
- Graduate Program in Pathology, Universidade Federal de Ciências da Saúde de Porto Alegre, Rua Sarmento Leite, 245, Rio Grande do Sul, RS 90050-170, Brazil
- Department of Pediatric Infectious Diseases and Immunology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Marcoleta 391, Santiago, RM 8330024, Chile
| | - Guojun Wang
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Rafael A Medina
- Department of Pediatric Infectious Diseases and Immunology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Marcoleta 391, Santiago, RM 8330024, Chile
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Department of Pathology and Experimental Medicine, School of Medicine, Emory University, 1462 Clifton Road, Office 429, Atlanta, GA 30322, USA
| | - Harm Van Bakel
- Laboratory of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Martha I Nelson
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Elodie Ghedin
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
- Department of Biology, Center for Genomics & Systems Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Ana B G Veiga
- Graduate Program in Pathology, Universidade Federal de Ciências da Saúde de Porto Alegre, Rua Sarmento Leite, 245, Rio Grande do Sul, RS 90050-170, Brazil
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
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21
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Harvey WT, Davies V, Daniels RS, Whittaker L, Gregory V, Hay AJ, Husmeier D, McCauley JW, Reeve R. A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. PLoS Comput Biol 2023; 19:e1010885. [PMID: 36972311 PMCID: PMC10079231 DOI: 10.1371/journal.pcbi.1010885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/06/2023] [Accepted: 01/20/2023] [Indexed: 03/29/2023] Open
Abstract
Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development, though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharacterised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.
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Affiliation(s)
- William T. Harvey
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- * E-mail: (WTH); (RR)
| | - Vinny Davies
- School of Computing, College of Science and Engineering, University of Glasgow, Glasgow, United Kingdom
- School of Mathematics and Statistics, College of Science and Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Rodney S. Daniels
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Lynne Whittaker
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Victoria Gregory
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Alan J. Hay
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Dirk Husmeier
- School of Mathematics and Statistics, College of Science and Engineering, University of Glasgow, Glasgow, United Kingdom
| | - John W. McCauley
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Richard Reeve
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- * E-mail: (WTH); (RR)
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22
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Lee J, Hadfield J, Black A, Sibley TR, Neher RA, Bedford T, Huddleston J. Joint visualization of seasonal influenza serology and phylogeny to inform vaccine composition. FRONTIERS IN BIOINFORMATICS 2023; 3:1069487. [PMID: 37035035 PMCID: PMC10073671 DOI: 10.3389/fbinf.2023.1069487] [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: 10/13/2022] [Accepted: 03/08/2023] [Indexed: 04/11/2023] Open
Abstract
Seasonal influenza vaccines must be updated regularly to account for mutations that allow influenza viruses to escape our existing immunity. A successful vaccine should represent the genetic diversity of recently circulating viruses and induce antibodies that effectively prevent infection by those recent viruses. Thus, linking the genetic composition of circulating viruses and the serological experimental results measuring antibody efficacy is crucial to the vaccine design decision. Historically, genetic and serological data have been presented separately in the form of static visualizations of phylogenetic trees and tabular serological results to identify vaccine candidates. To simplify this decision-making process, we have created an interactive tool for visualizing serological data that has been integrated into Nextstrain's real-time phylogenetic visualization framework, Auspice. We show how the combined interactive visualizations may be used by decision makers to explore the relationships between complex data sets for both prospective vaccine virus selection and retrospectively exploring the performance of vaccine viruses.
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Affiliation(s)
- Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Allison Black
- Chan Zuckerberg Initiative, San Francisco, CA, United States
| | - Thomas R. Sibley
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Richard A. Neher
- Biozentrum, Universität Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Howard Hughes Medical Institute, Seattle, WA, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
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23
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Braun KM, Haddock III LA, Crooks CM, Barry GL, Lalli J, Neumann G, Watanabe T, Imai M, Yamayoshi S, Ito M, Moncla LH, Koelle K, Kawaoka Y, Friedrich TC. Avian H7N9 influenza viruses are evolutionarily constrained by stochastic processes during replication and transmission in mammals. Virus Evol 2023; 9:vead004. [PMID: 36814938 PMCID: PMC9939568 DOI: 10.1093/ve/vead004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 01/05/2023] [Accepted: 01/17/2023] [Indexed: 01/20/2023] Open
Abstract
H7N9 avian influenza viruses (AIVs) have caused over 1,500 documented human infections since emerging in 2013. Although wild-type H7N9 AIVs can be transmitted by respiratory droplets in ferrets, they have not yet caused widespread outbreaks in humans. Previous studies have revealed molecular determinants of H7N9 AIV host switching, but little is known about potential evolutionary constraints on this process. Here, we compare patterns of sequence evolution for H7N9 AIV and mammalian H1N1 viruses during replication and transmission in ferrets. We show that three main factors-purifying selection, stochasticity, and very narrow transmission bottlenecks-combine to severely constrain the ability of H7N9 AIV to effectively adapt to mammalian hosts in isolated, acute spillover events. We find rare evidence of natural selection favoring new, potentially mammal-adapting mutations within ferrets but no evidence of natural selection acting during transmission. We conclude that human-adapted H7N9 viruses are unlikely to emerge during typical spillover infections. Our findings are instead consistent with a model in which the emergence of a human-transmissible virus would be a rare and unpredictable, though highly consequential, 'jackpot' event. Strategies to control the total number of spillover infections will limit opportunities for the virus to win this evolutionary lottery.
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Affiliation(s)
| | | | - Chelsea M Crooks
- AIDS Vaccine Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 585 Science Dr. Madison, WI 53711, USA
| | - Gabrielle L Barry
- AIDS Vaccine Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 585 Science Dr. Madison, WI 53711, USA
| | - Joseph Lalli
- Department of Genetics, University of Wisconsin-Madison, 425 Henry Mall Madison, WI 53706, US
| | - Gabriele Neumann
- Influenza Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 575 Science Dr. Madison, WI 53711, USA
| | - Tokiko Watanabe
- Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan,Department of Molecular Virology, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka Suita City, Osaka 565-0871, Japan,Center for Infectious Disease Education and Research (CiDER), Osaka University, 2-8 Yamadaoka Suita City, Osaka 565-0871, Japan
| | - Masaki Imai
- Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan,The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, 1 Chome-21-1 Toyama Shinjuku City, Tokyo 162-8655, Japan
| | | | - Mutsumi Ito
- Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan
| | | | | | - Yoshihiro Kawaoka
- Influenza Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 575 Science Dr. Madison, WI 53711, USA,Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan,The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, 1 Chome-21-1 Toyama Shinjuku City, Tokyo 162-8655, Japan
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24
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Verstraete MM, Heinkel F, Li J, Cao S, Tran A, Halverson EC, Gene R, Stangle E, Silva-Moreno B, Arrafi S, Bavananthasivam J, Fung M, Eji-Lasisi M, Masterman S, Xanthoudakis S, Dixit S, Babcook J, Clavette B, Fogg M, Escobar-Cabrera E. Multivalent IgM scaffold enhances the therapeutic potential of variant-agnostic ACE2 decoys against SARS-CoV-2. MAbs 2023; 15:2212415. [PMID: 37229608 DOI: 10.1080/19420862.2023.2212415] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/03/2023] [Accepted: 05/05/2023] [Indexed: 05/27/2023] Open
Abstract
As immunological selection for escape mutants continues to give rise to future SARS-CoV-2 variants, novel universal therapeutic strategies against ACE2-dependent viruses are needed. Here we present an IgM-based decavalent ACE2 decoy that has variant-agnostic efficacy. In immuno-, pseudovirus, and live virus assays, IgM ACE2 decoy had potency comparable or superior to leading SARS-CoV-2 IgG-based mAb therapeutics evaluated in the clinic, which were variant-sensitive in their potency. We found that increased ACE2 valency translated into increased apparent affinity for spike protein and superior potency in biological assays when decavalent IgM ACE2 was compared to tetravalent, bivalent, and monovalent ACE2 decoys. Furthermore, a single intranasal dose of IgM ACE2 decoy at 1 mg/kg conferred therapeutic benefit against SARS-CoV-2 Delta variant infection in a hamster model. Taken together, this engineered IgM ACE2 decoy represents a SARS-CoV-2 variant-agnostic therapeutic that leverages avidity to drive enhanced target binding, viral neutralization, and in vivo respiratory protection against SARS-CoV-2.
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Affiliation(s)
| | | | | | | | - Anh Tran
- Department of Human Health Therapeutics, National Research Council Canada, Ottawa, Canada
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25
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Wortel MT, Agashe D, Bailey SF, Bank C, Bisschop K, Blankers T, Cairns J, Colizzi ES, Cusseddu D, Desai MM, van Dijk B, Egas M, Ellers J, Groot AT, Heckel DG, Johnson ML, Kraaijeveld K, Krug J, Laan L, Lässig M, Lind PA, Meijer J, Noble LM, Okasha S, Rainey PB, Rozen DE, Shitut S, Tans SJ, Tenaillon O, Teotónio H, de Visser JAGM, Visser ME, Vroomans RMA, Werner GDA, Wertheim B, Pennings PS. Towards evolutionary predictions: Current promises and challenges. Evol Appl 2023; 16:3-21. [PMID: 36699126 PMCID: PMC9850016 DOI: 10.1111/eva.13513] [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: 02/09/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 12/14/2022] Open
Abstract
Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.
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Affiliation(s)
- Meike T. Wortel
- Swammerdam Institute for Life SciencesUniversity of AmsterdamAmsterdamThe Netherlands
| | - Deepa Agashe
- National Centre for Biological SciencesBangaloreIndia
| | | | - Claudia Bank
- Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Gulbenkian Science InstituteOeirasPortugal
| | - Karen Bisschop
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
- Laboratory of Aquatic Biology, KU Leuven KulakKortrijkBelgium
| | - Thomas Blankers
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
| | | | - Enrico Sandro Colizzi
- Origins CenterGroningenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | | | - Bram van Dijk
- Max Planck Institute for Evolutionary BiologyPlönGermany
| | - Martijn Egas
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | - Jacintha Ellers
- Department of Ecological ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Astrid T. Groot
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | | | | | - Ken Kraaijeveld
- Leiden Centre for Applied BioscienceUniversity of Applied Sciences LeidenLeidenThe Netherlands
| | - Joachim Krug
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Liedewij Laan
- Department of Bionanoscience, Kavli Institute of NanoscienceTU DelftDelftThe Netherlands
| | - Michael Lässig
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Peter A. Lind
- Department Molecular BiologyUmeå UniversityUmeåSweden
| | - Jeroen Meijer
- Theoretical Biology and Bioinformatics, Department of BiologyUtrecht UniversityUtrechtThe Netherlands
| | - Luke M. Noble
- Institute de Biologie, École Normale Supérieure, CNRS, InsermParisFrance
| | | | - Paul B. Rainey
- Department of Microbial Population BiologyMax Planck Institute for Evolutionary BiologyPlönGermany
- Laboratoire Biophysique et Évolution, CBI, ESPCI Paris, Université PSL, CNRSParisFrance
| | - Daniel E. Rozen
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | - Shraddha Shitut
- Origins CenterGroningenThe Netherlands
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | | | | | | | | | - Marcel E. Visser
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)WageningenThe Netherlands
| | - Renske M. A. Vroomans
- Origins CenterGroningenThe Netherlands
- Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands
| | | | - Bregje Wertheim
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
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26
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Piantham C, Ito K. Predicting the Trajectory of Replacements of SARS-CoV-2 Variants Using Relative Reproduction Numbers. Viruses 2022; 14:v14112556. [PMID: 36423165 PMCID: PMC9697243 DOI: 10.3390/v14112556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
New variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high effective reproduction numbers are continuously being selected by natural selection. To establish effective control measures for new variants, it is crucial to know their transmissibility and replacement trajectory in advance. In this paper, we conduct retrospective prediction tests for the variant replacement from Alpha to Delta in England, using the relative reproduction numbers of Delta with respect to Alpha estimated from partial observations. We found that once Delta's relative frequency reached 0.15, the date when the relative frequency of Delta would reach 0.90 was predicted with maximum absolute prediction errors of three days. This means that the time course of the variant replacement could be accurately predicted from early observations. Together with the estimated relative reproduction number of a new variant with respect to old variants, the predicted replacement timing will be crucial information for planning control strategies against the new variant.
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Affiliation(s)
- Chayada Piantham
- Graduate School of Infectious Diseases, Hokkaido University, Hokkaido 060-0818, Japan
| | - Kimihito Ito
- International Institute for Zoonosis Control, Hokkaido University, Hokkaido 001-0020, Japan
- Correspondence:
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27
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Hoyos D, Greenbaum BD. Perfecting antigen prediction. J Exp Med 2022; 219:e20220846. [PMID: 35972475 PMCID: PMC9386507 DOI: 10.1084/jem.20220846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a threefold approach to improving antigen predictions: further defining the geometry of the antigen landscape, incorporating the coupling of antigen recognition to other cellular processes, and diversifying the training sets used for models that predict immunogenicity.
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Affiliation(s)
- David Hoyos
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Benjamin D. Greenbaum
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
- Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY
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28
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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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29
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Otunuga OM. Analysis of multi-strain infection of vaccinated and recovered population through epidemic model: Application to COVID-19. PLoS One 2022; 17:e0271446. [PMID: 35905113 PMCID: PMC9337708 DOI: 10.1371/journal.pone.0271446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/30/2022] [Indexed: 11/19/2022] Open
Abstract
In this work, an innovative multi-strain SV EAIR epidemic model is developed for the study of the spread of a multi-strain infectious disease in a population infected by mutations of the disease. The population is assumed to be completely susceptible to n different variants of the disease, and those who are vaccinated and recovered from a specific strain k (k ≤ n) are immune to previous and present strains j = 1, 2, ⋯, k, but can still be infected by newer emerging strains j = k + 1, k + 2, ⋯, n. The model is designed to simulate the emergence and dissemination of viral strains. All the equilibrium points of the system are calculated and the conditions for existence and global stability of these points are investigated and used to answer the question as to whether it is possible for the population to have an endemic with more than one strain. An interesting result that shows that a strain with a reproduction number greater than one can still die out on the long run if a newer emerging strain has a greater reproduction number is verified numerically. The effect of vaccines on the population is also analyzed and a bound for the herd immunity threshold is calculated. The validity of the work done is verified through numerical simulations by applying the proposed model and strategy to analyze the multi-strains of the COVID-19 virus, in particular, the Delta and the Omicron variants, in the United State.
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30
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Cohen LE, Spiro DJ, Viboud C. Projecting the SARS-CoV-2 transition from pandemicity to endemicity: Epidemiological and immunological considerations. PLoS Pathog 2022; 18:e1010591. [PMID: 35771775 PMCID: PMC9246171 DOI: 10.1371/journal.ppat.1010591] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
In this review, we discuss the epidemiological dynamics of different viral infections to project how the transition from a pandemic to endemic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) might take shape. Drawing from theories of disease invasion and transmission dynamics, waning immunity in the face of viral evolution and antigenic drift, and empirical data from influenza, dengue, and seasonal coronaviruses, we discuss the putative periodicity, severity, and age dynamics of SARS-CoV-2 as it becomes endemic. We review recent studies on SARS-CoV-2 epidemiology, immunology, and evolution that are particularly useful in projecting the transition to endemicity and highlight gaps that warrant further research.
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Affiliation(s)
- Lily E. Cohen
- Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - David J. Spiro
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
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31
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Wang Y, Tang CY, Wan XF. Antigenic characterization of influenza and SARS-CoV-2 viruses. Anal Bioanal Chem 2022; 414:2841-2881. [PMID: 34905077 PMCID: PMC8669429 DOI: 10.1007/s00216-021-03806-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/21/2021] [Accepted: 11/24/2021] [Indexed: 12/24/2022]
Abstract
Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.
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Affiliation(s)
- Yang Wang
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Cynthia Y Tang
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Xiu-Feng Wan
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA.
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA.
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, USA.
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32
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McLeod DV, Wahl LM, Mideo N. Mosaic vaccination: How distributing different vaccines across a population could improve epidemic control. Evol Lett 2021; 5:458-471. [PMID: 34621533 PMCID: PMC8484727 DOI: 10.1002/evl3.252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/27/2021] [Indexed: 01/19/2023] Open
Abstract
Although vaccination has been remarkably effective against some pathogens, for others, rapid antigenic evolution results in vaccination conferring only weak and/or short‐lived protection. Consequently, considerable effort has been invested in developing more evolutionarily robust vaccines, either by targeting highly conserved components of the pathogen (universal vaccines) or by including multiple immunological targets within a single vaccine (multi‐epitope vaccines). An unexplored third possibility is to vaccinate individuals with one of a number of qualitatively different vaccines, creating a “mosaic” of individual immunity in the population. Here we explore whether a mosaic vaccination strategy can deliver superior epidemiological outcomes to “conventional” vaccination, in which all individuals receive the same vaccine. We suppose vaccine doses can be distributed between distinct vaccine “targets” (e.g., different surface proteins against which an immune response can be generated) and/or immunologically distinct variants at these targets (e.g., strains); the pathogen can undergo antigenic evolution at both targets. Using simple mathematical models, here we provide a proof‐of‐concept that mosaic vaccination often outperforms conventional vaccination, leading to fewer infected individuals, improved vaccine efficacy, and lower individual risks over the course of the epidemic.
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Affiliation(s)
- David V McLeod
- Centre D'Ecologie Fonctionnelle & Evolutive CNRS Montpellier 34090 France
| | - Lindi M Wahl
- Mathematics Western University London ON N6A 5B7 Canada
| | - Nicole Mideo
- Department of Ecology and Evolutionary Biology University of Toronto Toronto ON M5S 3B2 Canada
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33
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Poor Vaccine Effectiveness against Influenza B-Related Severe Acute Respiratory Infection in a Temperate North Indian State (2019-2020): A Call for Further Data for Possible Vaccines with Closer Match. Vaccines (Basel) 2021; 9:vaccines9101094. [PMID: 34696202 PMCID: PMC8540586 DOI: 10.3390/vaccines9101094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/21/2021] [Accepted: 09/21/2021] [Indexed: 01/22/2023] Open
Abstract
Background: Influenza vaccine uptake in India is poor, and scant data exist regarding the effectiveness of influenza vaccine against hospitalization. Methods: From October 2019 to March 2020, vaccination status of 1219 patients (males n = 571, aged 5–107 years; median, 50 years) hospitalized with severe acute respiratory illness (SARI) was assessed. The patients were tested for influenza viruses and their subtypes by RT PCR. Sequencing of the HA gene was performed. Vaccine effectiveness (VE) against influenza subtypes was estimated by the test negative design. Results: A total of 336 (27.5%) patients were influenza-positive, with influenza B/Victoria accounting for 49.7% (n = 167), followed by influenza A/H1N1 (47.6%; n = 155) and influenza A/H3N2 (4.4%; n = 15). About 6.8% and 8.6% of the influenza-positive and influenza-negative patients, respectively, had been vaccinated. Adjusted VE for any influenza strain was 13% (95% CI −42 to 47), which for influenza B was 0%. HA sequencing revealed that influenza B samples mainly belonged to subclade V1A.3/133R with deletion of residues 163–165, as against the 2-aa deletion in influenza B/Colorado/06/2017 strain, contained in the vaccine. VE for influenza A/H1N1 was 55%. Conclusions: Poor VE due to a genetic mismatch between the circulating strain and the vaccine strain calls for efforts to reduce the mismatch.
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34
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Harvey WT, Carabelli AM, Jackson B, Gupta RK, Thomson EC, Harrison EM, Ludden C, Reeve R, Rambaut A, Peacock SJ, Robertson DL. SARS-CoV-2 variants, spike mutations and immune escape. Nat Rev Microbiol 2021; 19:409-424. [PMID: 34075212 PMCID: PMC8167834 DOI: 10.1038/s41579-021-00573-0] [Citation(s) in RCA: 2104] [Impact Index Per Article: 701.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2021] [Indexed: 02/07/2023]
Abstract
Although most mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome are expected to be either deleterious and swiftly purged or relatively neutral, a small proportion will affect functional properties and may alter infectivity, disease severity or interactions with host immunity. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis lasting about 11 months. Since late 2020, however, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations, in the context of 'variants of concern', that impact virus characteristics, including transmissibility and antigenicity, probably in response to the changing immune profile of the human population. There is emerging evidence of reduced neutralization of some SARS-CoV-2 variants by postvaccination serum; however, a greater understanding of correlates of protection is required to evaluate how this may impact vaccine effectiveness. Nonetheless, manufacturers are preparing platforms for a possible update of vaccine sequences, and it is crucial that surveillance of genetic and antigenic changes in the global virus population is done alongside experiments to elucidate the phenotypic impacts of mutations. In this Review, we summarize the literature on mutations of the SARS-CoV-2 spike protein, the primary antigen, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets.
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Affiliation(s)
- William T Harvey
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | | | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Ravindra K Gupta
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
| | - Emma C Thomson
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Ewan M Harrison
- Department of Medicine, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Richard Reeve
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
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35
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Harvey WT, Carabelli AM, Jackson B, Gupta RK, Thomson EC, Harrison EM, Ludden C, Reeve R, Rambaut A, Peacock SJ, Robertson DL. SARS-CoV-2 variants, spike mutations and immune escape. Nat Rev Microbiol 2021; 19:409-424. [PMID: 34075212 DOI: 10.1038/s41579-021-00573-02] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2021] [Indexed: 05/26/2023]
Abstract
Although most mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome are expected to be either deleterious and swiftly purged or relatively neutral, a small proportion will affect functional properties and may alter infectivity, disease severity or interactions with host immunity. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis lasting about 11 months. Since late 2020, however, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations, in the context of 'variants of concern', that impact virus characteristics, including transmissibility and antigenicity, probably in response to the changing immune profile of the human population. There is emerging evidence of reduced neutralization of some SARS-CoV-2 variants by postvaccination serum; however, a greater understanding of correlates of protection is required to evaluate how this may impact vaccine effectiveness. Nonetheless, manufacturers are preparing platforms for a possible update of vaccine sequences, and it is crucial that surveillance of genetic and antigenic changes in the global virus population is done alongside experiments to elucidate the phenotypic impacts of mutations. In this Review, we summarize the literature on mutations of the SARS-CoV-2 spike protein, the primary antigen, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets.
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Affiliation(s)
- William T Harvey
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | | | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Ravindra K Gupta
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
| | - Emma C Thomson
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Ewan M Harrison
- Department of Medicine, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Richard Reeve
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
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36
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Abstract
The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even farther away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of coevolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune coevolution generates an emergent timescale, the persistence time of the wave's direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen-host coevolution.
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37
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McLean HQ, Belongia EA. Influenza Vaccine Effectiveness: New Insights and Challenges. Cold Spring Harb Perspect Med 2021; 11:cshperspect.a038315. [PMID: 31988202 DOI: 10.1101/cshperspect.a038315] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Methods for assessing influenza vaccine efficacy and effectiveness have evolved over six decades. Randomized trials remain the gold standard for licensure, but observational studies are needed for annual assessment of vaccine effectiveness (VE). The test-negative design (TND) has become the de facto standard for these field studies. Patients who seek medical care with acute respiratory illness are tested for influenza, and VE is estimated from the odds of vaccination among influenza cases versus test-negative controls. VE varies across seasons, populations, age groups, and products, but VE estimates are consistently higher for A(H1N1)pdm09 and type B compared with A(H3N2). VE studies are increasingly used in combination with molecular epidemiology to understand the viral and immune system factors that drive clinical efficacy and effectiveness. The emerging field of immunoepidemiology offers the potential to understand complex host-virus interactions that affect vaccine protection, and this knowledge will contribute to universal vaccine development.
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Affiliation(s)
- Huong Q McLean
- Center for Clinical Epidemiology & Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin 54449, USA
| | - Edward A Belongia
- Center for Clinical Epidemiology & Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin 54449, USA
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38
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Piantham C, Ito K. Modeling the selective advantage of new amino acids on the hemagglutinin of H1N1 influenza viruses using their patient age distributions. Virus Evol 2021; 7:veab049. [PMID: 34285812 PMCID: PMC8286795 DOI: 10.1093/ve/veab049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
In 2009, a new strain of H1N1 influenza A virus caused a pandemic, and its descendant strains are causing seasonal epidemics worldwide. Given the high mutation rate of influenza viruses, variant strains having different amino acids on hemagglutinin (HA) continuously emerge. To prepare vaccine strains for the next influenza seasons, it is an urgent task to predict which variants will be selected in the viral population. An analysis of 24,681 pairs of an amino acid sequence of HA of H1N1pdm2009 viruses and its patient age showed that the empirical fixation probability of new amino acids on HA significantly differed depending on their frequencies in the population, patient age distributions, and epitope flags. The selective advantage of a variant strain having a new amino acid was modeled by linear combinations of patients age distributions and epitope flags, and then the fixation probability of the new amino acid was modeled using Kimura’s formula for advantageous selection. The parameters of models were estimated from the sequence data and models were tested with four-fold cross validations. The frequency of new amino acids alone can achieve high sensitivity, specificity, and precision in predicting the fixation of a new amino acid of which frequency is more than 0.11. The estimated parameter suggested that viruses with a new amino acid having a frequency in the population higher than 0.11 have a significantly higher selective advantage compared to viruses with the old amino acid at the same position. The model considering the Z-value of patient age rank-sums of new amino acids predicted amino acid substitutions on HA with a sensitivity of 0.78, specificity of 0.86, and precision of 0.83, showing significant improvement compared to the constant selective advantage model, which used only the frequency of the amino acid. These results suggested that H1N1 viruses tend to be selected in the adult population, and frequency of viruses having new amino acids and their patient ages are useful to predict amino acid substitutions on HA.
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Affiliation(s)
- Chayada Piantham
- Division of Bioinformatics, Graduate School of Infectious Diseases, Hokkaido University, Sapporo 0600818, Japan
| | - Kimihito Ito
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo 0010020, Japan
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39
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Thompson CL, Alberti M, Barve S, Battistuzzi FU, Drake JL, Goncalves GC, Govaert L, Partridge C, Yang Y. Back to the future: Reintegrating biology to understand how past eco-evolutionary change can predict future outcomes. Integr Comp Biol 2021; 61:2218-2232. [PMID: 33964141 DOI: 10.1093/icb/icab068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: 1) utilizing knowledge of biological systems to better inform eco-evolutionary models, 2) generating models with more accurate predictions, and 3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.
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Affiliation(s)
| | - Marina Alberti
- Department of Urban Design and Planning, University of Washington,
| | - Sahas Barve
- Smithsonian National Museum of Natural History,
| | | | - Jeana L Drake
- Department of Earth, Planetary, and Space Sciences, University of California Los Angeles,
| | | | - Lynn Govaert
- Department of Evolutionary Biology and Environmental Studies, University of Zurich; Department of Aquatic Ecology, Swiss Federal Institute of Aquatic Science and Technology, URPP Global Change and Biodiversity, University of Zurich,
| | | | - Ya Yang
- Department of Plant and Microbial Biology, University of Minnesota,
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40
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Barrat-Charlaix P, Huddleston J, Bedford T, Neher RA. Limited Predictability of Amino Acid Substitutions in Seasonal Influenza Viruses. Mol Biol Evol 2021; 38:2767-2777. [PMID: 33749787 PMCID: PMC8233509 DOI: 10.1093/molbev/msab065] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Seasonal influenza viruses repeatedly infect humans in part because they rapidly change their antigenic properties and evade host immune responses, necessitating frequent updates of the vaccine composition. Accurate predictions of strains circulating in the future could therefore improve the vaccine match. Here, we studied the predictability of frequency dynamics and fixation of amino acid substitutions. Current frequency was the strongest predictor of eventual fixation, as expected in neutral evolution. Other properties, such as occurrence in previously characterized epitopes or high Local Branching Index (LBI) had little predictive power. Parallel evolution was found to be moderately predictive of fixation. Although the LBI had little power to predict frequency dynamics, it was still successful at picking strains representative of future populations. The latter is due to a tendency of the LBI to be high for consensus-like sequences that are closer to the future than the average sequence. Simulations of models of adapting populations, in contrast, show clear signals of predictability. This indicates that the evolution of influenza HA and NA, while driven by strong selection pressure to change, is poorly described by common models of directional selection such as traveling fitness waves.
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Affiliation(s)
- Pierre Barrat-Charlaix
- Biozentrum, Universität Basel, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - John Huddleston
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Trevor Bedford
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Richard A Neher
- Biozentrum, Universität Basel, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
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41
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Azarian T, Martinez PP, Arnold BJ, Qiu X, Grant LR, Corander J, Fraser C, Croucher NJ, Hammitt LL, Reid R, Santosham M, Weatherholtz RC, Bentley SD, O’Brien KL, Lipsitch M, Hanage WP. Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae. PLoS Biol 2020; 18:e3000878. [PMID: 33091022 PMCID: PMC7580979 DOI: 10.1371/journal.pbio.3000878] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 09/18/2020] [Indexed: 11/30/2022] Open
Abstract
Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.
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Affiliation(s)
- Taj Azarian
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, Florida, United States of America
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Pamela P. Martinez
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Brian J. Arnold
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Xueting Qiu
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Lindsay R. Grant
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Jukka Corander
- Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicholas J. Croucher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Laura L. Hammitt
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Raymond Reid
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Mathuram Santosham
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Robert C. Weatherholtz
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Stephen D. Bentley
- Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | | | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
- Department of Immunology and Infectious Diseases, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - William P. Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
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42
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Abstract
Seasonal influenza vaccines prevent influenza-related illnesses, hospitalizations, and deaths. However, these vaccines are not as effective as other viral vaccines, and there is clearly room for improvement. Here, we review the history of seasonal influenza vaccines, describe challenges associated with producing influenza vaccine antigens, and discuss the inherent difficulties of updating influenza vaccine strains each influenza season. We argue that seasonal influenza vaccines can be dramatically improved by modernizing antigen production processes and developing models that are better at predicting viral evolution. Resources should be specifically dedicated to improving seasonal influenza vaccines while developing entirely new vaccine platforms.
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Affiliation(s)
- Sigrid Gouma
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; , ,
| | - Elizabeth M Anderson
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; , ,
| | - Scott E Hensley
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; , ,
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43
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Viboud C, Gostic K, Nelson MI, Price GE, Perofsky A, Sun K, Sequeira Trovão N, Cowling BJ, Epstein SL, Spiro DJ. Beyond clinical trials: Evolutionary and epidemiological considerations for development of a universal influenza vaccine. PLoS Pathog 2020; 16:e1008583. [PMID: 32970783 PMCID: PMC7514029 DOI: 10.1371/journal.ppat.1008583] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The prospect of universal influenza vaccines is generating much interest and research at the intersection of immunology, epidemiology, and viral evolution. While the current focus is on developing a vaccine that elicits a broadly cross-reactive immune response in clinical trials, there are important downstream questions about global deployment of a universal influenza vaccine that should be explored to minimize unintended consequences and maximize benefits. Here, we review and synthesize the questions most relevant to predicting the population benefits of universal influenza vaccines and discuss how existing information could be mined to begin to address these questions. We review three research topics where computational modeling could bring valuable evidence: immune imprinting, viral evolution, and transmission. We address the positive and negative consequences of imprinting, in which early childhood exposure to influenza shapes and limits immune responses to future infections via memory of conserved influenza antigens. However, the mechanisms at play, their effectiveness, breadth of protection, and the ability to "reprogram" already imprinted individuals, remains heavily debated. We describe instances of rapid influenza evolution that illustrate the plasticity of the influenza virus in the face of drug pressure and discuss how novel vaccines could introduce new selective pressures on the evolution of the virus. We examine the possible unintended consequences of broadly protective (but infection-permissive) vaccines on the dynamics of epidemic and pandemic influenza, compared to conventional vaccines that have been shown to provide herd immunity benefits. In conclusion, computational modeling offers a valuable tool to anticipate the benefits of ambitious universal influenza vaccine programs, while balancing the risks from endemic influenza strains and unpredictable pandemic viruses. Moving forward, it will be important to mine the vast amount of data generated in clinical studies of universal influenza vaccines to ensure that the benefits and consequences of these vaccine programs have been carefully modeled and explored.
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Affiliation(s)
- Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
- * E-mail:
| | - Katelyn Gostic
- Dept. of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States
- Dept. of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States
| | - Martha I. Nelson
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Graeme E. Price
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States
| | - Amanda Perofsky
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Nídia Sequeira Trovão
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Suzanne L. Epstein
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States
| | - David J. Spiro
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
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44
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Huddleston J, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Whittaker L, Ermetal B, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Barr I, Subbarao K, Barrat-Charlaix P, Neher RA, Bedford T. Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution. eLife 2020; 9:e60067. [PMID: 32876050 PMCID: PMC7553778 DOI: 10.7554/elife.60067] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 08/24/2020] [Indexed: 12/17/2022] Open
Abstract
Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth.
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Affiliation(s)
- John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Molecular and Cell Biology Program, University of WashingtonSeattleUnited States
| | - John R Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Ian Barr
- The WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Kanta Subbarao
- The WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Pierre Barrat-Charlaix
- Biozentrum, University of BaselBaselSwitzerland
- Swiss Institute of BioinformaticsBaselSwitzerland
| | - Richard A Neher
- Biozentrum, University of BaselBaselSwitzerland
- Swiss Institute of BioinformaticsBaselSwitzerland
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research CenterSeattleUnited States
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45
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Abstract
Vaccinations and therapies targeting evolving pathogens aim to curb the pathogen and to steer it toward a controlled evolutionary state. Control is leveraged against the pathogen’s intrinsic evolutionary forces, which in turn, can drive an escape from control. Here, we analyze a simple model of control, in which a host produces antibodies that bind the pathogen. We show that the leverages of host (or external intervention) and pathogen are often highly imbalanced: an error threshold separates parameter regions of efficient control from regions of compromised control, where the pathogen retains the upper hand. Because control efficiency can be predicted from few measurable fitness parameters, our results establish a proof of principle how control theory can guide interventions against evolving pathogens. Control can alter the eco-evolutionary dynamics of a target pathogen in two ways, by changing its population size and by directed evolution of new functions. Here, we develop a payoff model of eco-evolutionary control based on strategies of evolution, regulation, and computational forecasting. We apply this model to pathogen control by molecular antibody–antigen binding with a tunable dosage of antibodies. By analytical solution, we obtain optimal dosage protocols and establish a phase diagram with an error threshold delineating parameter regimes of successful and compromised control. The solution identifies few independently measurable fitness parameters that predict the outcome of control. Our analysis shows how optimal control strategies depend on mutation rate and population size of the pathogen, and how monitoring and computational forecasting affect protocols and efficiency of control. We argue that these results carry over to more general systems and are elements of an emerging eco-evolutionary control theory.
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Skarlupka AL, Handel A, Ross TM. Influenza hemagglutinin antigenic distance measures capture trends in HAI differences and infection outcomes, but are not suitable predictive tools. Vaccine 2020; 38:5822-5830. [PMID: 32682618 DOI: 10.1016/j.vaccine.2020.06.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 05/28/2020] [Accepted: 06/16/2020] [Indexed: 01/24/2023]
Abstract
Vaccination is the most effective method to combat influenza. Vaccine effectiveness is influenced by the antigenic distance between the vaccine strain and the actual circulating virus. Amino acid sequence based methods of quantifying the antigenic distance were designed to predict influenza vaccine effectiveness in humans. The use of these antigenic distance measures has been proposed as an additive method for seasonal vaccine selection. In this report, several antigenic distance measures were evaluated as predictors of hemagglutination inhibition titer differences and clinical outcomes following influenza vaccination or infection in mice or ferrets. The antigenic distance measures described the increasing trend in the change of HAI titer, lung viral titer and percent weight loss in mice and ferrets. However, the variability of outcome variables produced wide prediction intervals for any given antigenic distance value. The amino acid substitution based antigenic distance measures were no better predictors of viral load and weight loss than HAI titer differences, the current predictive measure of immunological correlate of protection for clinical signs after challenge.
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Affiliation(s)
- Amanda L Skarlupka
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA
| | - Andreas Handel
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA, USA
| | - Ted M Ross
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA; Department of Infectious Diseases, University of Georgia, Athens, GA, USA.
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47
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Ho BS, Chao KM. On the influenza vaccination policy through mathematical modeling. Int J Infect Dis 2020; 98:71-79. [PMID: 32561427 DOI: 10.1016/j.ijid.2020.06.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/11/2020] [Accepted: 06/12/2020] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES Aimed at mitigating influenza transmission, this study assessed the timing of the vaccination program and took vaccine capacity, strain mismatch and priority group into consideration. METHODS An age-structured dynamic transmission model was fitted to the laboratory data of the national influenza surveillance system to reconstruct a baseline scenario with which the vaccination scenarios of interest could be compared. Outcome measures were defined as the impacts on the seasonal epidemic: decompression of the epidemic peak, reduction of the epidemic burden and change of the epidemic peak time. RESULTS It was found that vaccine capacity building, although indispensable, could not guarantee substantial impact on the seasonal influenza epidemic. Vaccine mismatch might greatly offset vaccine capacity building. Notably, advance vaccine distribution could compensate for some vaccine underperformance. In the case of a well-matched vaccine, advance vaccine distribution could even exploit its utility. CONCLUSIONS This study indicated that timely vaccine distribution should be put high on the agenda of seasonal influenza control policies. It provided a tangible platform for the policymakers to evaluate health policy impacts and to enhance risk communication with the public through mathematical modeling.
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Affiliation(s)
- Bin-Shenq Ho
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC; Department of Family Medicine, National Taiwan University Hospital, Taipei, Taiwan, ROC
| | - Kun-Mao Chao
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, ROC.
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48
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Mina MJ, Metcalf CJE, McDermott AB, Douek DC, Farrar J, Grenfell BT. A Global lmmunological Observatory to meet a time of pandemics. eLife 2020; 9:e58989. [PMID: 32510329 PMCID: PMC7292646 DOI: 10.7554/elife.58989] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 06/05/2020] [Indexed: 01/22/2023] Open
Abstract
SARS-CoV-2 presents an unprecedented international challenge, but it will not be the last such threat. Here, we argue that the world needs to be much better prepared to rapidly detect, define and defeat future pandemics. We propose that a Global Immunological Observatory and associated developments in systems immunology, therapeutics and vaccine design should be at the heart of this enterprise.
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Affiliation(s)
- Michael J Mina
- Center for Communicable Disease Dynamics, Department of Epidemiology, and Department of Immunology and Infectious Diseases, Harvard School of Public HealthBostonUnited States
- Department of Pathology, Brigham and Women’s HospitalBostonUnited States
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
- Woodrow Wilson School, Princeton UniversityPrincetonUnited States
| | - Adrian B McDermott
- Vaccine Research Center, National Institutes of HealthBethesdaUnited States
| | - Daniel C Douek
- Vaccine Research Center, National Institutes of HealthBethesdaUnited States
| | | | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
- Woodrow Wilson School, Princeton UniversityPrincetonUnited States
- Fogarty International Center, National Institutes of HealthBethesdaUnited States
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49
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Hayati M, Biller P, Colijn C. Predicting the short-term success of human influenza virus variants with machine learning. Proc Biol Sci 2020; 287:20200319. [PMID: 32259469 PMCID: PMC7209065 DOI: 10.1098/rspb.2020.0319] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 03/16/2020] [Indexed: 12/13/2022] Open
Abstract
Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines' benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier 'area under the curve' 0.75-0.9.
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Affiliation(s)
- Maryam Hayati
- Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, CanadaV5A 1S6
| | - Priscila Biller
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, CanadaV5A 1S6
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, CanadaV5A 1S6
- Department of Mathematics, Imperial College London, London SW7 2BU, UK
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50
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Early prediction of antigenic transitions for influenza A/H3N2. PLoS Comput Biol 2020; 16:e1007683. [PMID: 32069282 PMCID: PMC7048310 DOI: 10.1371/journal.pcbi.1007683] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 02/28/2020] [Accepted: 01/26/2020] [Indexed: 11/20/2022] Open
Abstract
Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection. The efficacy of annual seasonal influenza vaccines depends on selecting the strain that best matches circulating viruses. This selection takes place 9–12 months prior to the influenza season. To advise this decision, we used an influenza A/H3N2 phylodynamic simulation to explore how reliably and how far in advance can we identify strains that will dominate future influenza seasons? What data should we collect to accelerate and improve the accuracy of such forecasts? And importantly, what is the gap between the theoretical limit of prediction and prediction based on current influenza surveillance? Our results suggest that even with detailed virological information, the tight race between the antigenic turnover dynamics and the vaccine development timeline limits early detection of emerging viruses. Predictions based on current influenza surveillance do not achieve the theoretical limit and thus our results provide impetus for denser sampling and the development of rapid methods for estimating viral fitness.
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