1
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Liu T, Reiser WK, Tan TJC, Lv H, Rivera-Cardona J, Heimburger K, Wu NC, Brooke CB. Natural variation in neuraminidase activity influences the evolutionary potential of the seasonal H1N1 lineage hemagglutinin. Virus Evol 2024; 10:veae046. [PMID: 38915760 PMCID: PMC11196192 DOI: 10.1093/ve/veae046] [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: 03/19/2024] [Revised: 05/30/2024] [Accepted: 06/12/2024] [Indexed: 06/26/2024] Open
Abstract
The antigenic evolution of the influenza A virus hemagglutinin (HA) gene poses a major challenge for the development of vaccines capable of eliciting long-term protection. Prior efforts to understand the mechanisms that govern viral antigenic evolution mainly focus on HA in isolation, ignoring the fact that HA must act in concert with the viral neuraminidase (NA) during replication and spread. Numerous studies have demonstrated that the degree to which the receptor-binding avidity of HA and receptor-cleaving activity of NA are balanced with each other influences overall viral fitness. We recently showed that changes in NA activity can significantly alter the mutational fitness landscape of HA in the context of a lab-adapted virus strain. Here, we test whether natural variation in relative NA activity can influence the evolutionary potential of HA in the context of the seasonal H1N1 lineage (pdmH1N1) that has circulated in humans since the 2009 pandemic. We observed substantial variation in the relative activities of NA proteins encoded by a panel of H1N1 vaccine strains isolated between 2009 and 2019. We comprehensively assessed the effect of NA background on the HA mutational fitness landscape in the circulating pdmH1N1 lineage using deep mutational scanning and observed pronounced epistasis between NA and residues in or near the receptor-binding site of HA. To determine whether NA variation could influence the antigenic evolution of HA, we performed neutralizing antibody selection experiments using a panel of monoclonal antibodies targeting different HA epitopes. We found that the specific antibody escape profiles of HA were highly contingent upon NA background. Overall, our results indicate that natural variation in NA activity plays a significant role in governing the evolutionary potential of HA in the currently circulating pdmH1N1 lineage.
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Affiliation(s)
- Tongyu Liu
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - William K Reiser
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Timothy J C Tan
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huibin Lv
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Joel Rivera-Cardona
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Kyle Heimburger
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Nicholas C Wu
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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2
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Edler P, Schwab LSU, Aban M, Wille M, Spirason N, Deng YM, Carlock MA, Ross TM, Juno JA, Rockman S, Wheatley AK, Kent SJ, Barr IG, Price DJ, Koutsakos M. Immune imprinting in early life shapes cross-reactivity to influenza B virus haemagglutinin. Nat Microbiol 2024:10.1038/s41564-024-01732-8. [PMID: 38890491 DOI: 10.1038/s41564-024-01732-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 05/15/2024] [Indexed: 06/20/2024]
Abstract
Influenza exposures early in life are believed to shape future susceptibility to influenza infections by imprinting immunological biases that affect cross-reactivity to future influenza viruses. However, direct serological evidence linked to susceptibility is limited. Here we analysed haemagglutination-inhibition titres in 1,451 cross-sectional samples collected between 1992 and 2020, from individuals born between 1917 and 2008, against influenza B virus (IBV) isolates from 1940 to 2021. We included testing of 'future' isolates that circulated after sample collection. We show that immunological biases are conferred by early life IBV infection and result in lineage-specific cross-reactivity of a birth cohort towards future IBV isolates. This translates into differential estimates of susceptibility between birth cohorts towards the B/Yamagata and B/Victoria lineages, predicting lineage-specific birth-cohort distributions of observed medically attended IBV infections. Our data suggest that immunological measurements of imprinting could be important in modelling and predicting virus epidemiology.
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Affiliation(s)
- Peta Edler
- Department of Infectious Diseases, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Lara S U Schwab
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Malet Aban
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Michelle Wille
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Centre for Pathogen Genomics, University of Melbourne, Melbourne, Victoria, Australia
| | - Natalie Spirason
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Yi-Mo Deng
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Michael A Carlock
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA, USA
- Florida Research and Innovation Centre, Cleveland Clinic, Port Saint Lucie, FL, 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
- Florida Research and Innovation Centre, Cleveland Clinic, Port Saint Lucie, FL, USA
- Department of Infection Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jennifer A Juno
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Steve Rockman
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Vaccine Product Development, CSL Seqirus Ltd, Parkville, Victoria, Australia
| | - Adam K Wheatley
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Stephen J Kent
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Melbourne Sexual Health Centre and Department of Infectious Diseases, Alfred Health, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Ian G Barr
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - David J Price
- Department of Infectious Diseases, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Centre for Epidemiology & Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Marios Koutsakos
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia.
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3
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Martin MA, Berg N, Koelle K. Influenza A genomic diversity during human infections underscores the strength of genetic drift and the existence of tight transmission bottlenecks. Virus Evol 2024; 10:veae042. [PMID: 38883977 PMCID: PMC11179161 DOI: 10.1093/ve/veae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 05/06/2024] [Accepted: 05/21/2024] [Indexed: 06/18/2024] Open
Abstract
Influenza infections result in considerable public health and economic impacts each year. One of the contributing factors to the high annual incidence of human influenza is the virus's ability to evade acquired immunity through continual antigenic evolution. Understanding the evolutionary forces that act within and between hosts is therefore critical to interpreting past trends in influenza virus evolution and in predicting future ones. Several studies have analyzed longitudinal patterns of influenza A virus genetic diversity in natural human infections to assess the relative contributions of selection and genetic drift on within-host evolution. However, in these natural infections, within-host viral populations harbor very few single-nucleotide variants, limiting our resolution in understanding the forces acting on these populations in vivo. Furthermore, low levels of within-host viral genetic diversity limit the ability to infer the extent of drift across transmission events. Here, we propose to use influenza virus genomic diversity as an alternative signal to better understand within- and between-host patterns of viral evolution. Specifically, we focus on the dynamics of defective viral genomes (DVGs), which harbor large internal deletions in one or more of influenza virus's eight gene segments. Our longitudinal analyses of DVGs show that influenza A virus populations are highly dynamic within hosts, corroborating previous findings based on viral genetic diversity that point toward the importance of genetic drift in driving within-host viral evolution. Furthermore, our analysis of DVG populations across transmission pairs indicates that DVGs rarely appeared to be shared, indicating the presence of tight transmission bottlenecks. Our analyses demonstrate that viral genomic diversity can be used to complement analyses based on viral genetic diversity to reveal processes that drive viral evolution within and between hosts.
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Affiliation(s)
- Michael A Martin
- Department of Pathology, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21287, USA
- Graduate Program in Population Biology, Ecology, and Evolution, Emory University, 1462 Clifton Road NE, Atlanta, GA 30322, USA
- Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, GA 30322, USA
| | - Nick Berg
- Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, GA 30322, USA
- Department of Biochemistry, Brandeis University, 415 South Street, Waltham, MA 02453, USA
- National Institute of Allergy and Infectious Diseases Laboratory of Viral Disease, National Institutes of Health, 33 North Drive, Bethesda, MD 20814, USA
| | - Katia Koelle
- Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, GA 30322, USA
- Emory Center of Excellence for Influenza Research and Response (Emory-CEIRR), 1510 Clifton Road NE, Atlanta, GA 30322, USA
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4
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Catani JPP, Smet A, Ysenbaert T, Vuylsteke M, Bottu G, Mathys J, Botzki A, Cortes-Garcia G, Strugnell T, Gomila R, Hamberger J, Catalan J, Ustyugova IV, Farrell T, Stegalkina S, Ray S, LaRue L, Saelens X, Vogel TU. The antigenic landscape of human influenza N2 neuraminidases from 2009 until 2017. eLife 2024; 12:RP90782. [PMID: 38805550 PMCID: PMC11132685 DOI: 10.7554/elife.90782] [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] [Indexed: 05/30/2024] Open
Abstract
Human H3N2 influenza viruses are subject to rapid antigenic evolution which translates into frequent updates of the composition of seasonal influenza vaccines. Despite these updates, the effectiveness of influenza vaccines against H3N2-associated disease is suboptimal. Seasonal influenza vaccines primarily induce hemagglutinin-specific antibody responses. However, antibodies directed against influenza neuraminidase (NA) also contribute to protection. Here, we analysed the antigenic diversity of a panel of N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. The antigenic breadth of these NAs was determined based on the NA inhibition (NAI) of a broad panel of ferret and mouse immune sera that were raised by infection and recombinant N2 NA immunisation. This assessment allowed us to distinguish at least four antigenic groups in the N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. Computational analysis further revealed that the amino acid residues in N2 NA that have a major impact on susceptibility to NAI by immune sera are in proximity of the catalytic site. Finally, a machine learning method was developed that allowed to accurately predict the impact of mutations that are present in our N2 NA panel on NAI. These findings have important implications for the renewed interest to develop improved influenza vaccines based on the inclusion of a protective NA antigen formulation.
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Affiliation(s)
- João Paulo Portela Catani
- VIB-UGent Center for Medical BiotechnologyGhentBelgium
- Department of Biochemistry and Microbiology, Ghent UniversityGhentBelgium
| | - Anouk Smet
- VIB-UGent Center for Medical BiotechnologyGhentBelgium
- Department of Biochemistry and Microbiology, Ghent UniversityGhentBelgium
| | - Tine Ysenbaert
- VIB-UGent Center for Medical BiotechnologyGhentBelgium
- Department of Biochemistry and Microbiology, Ghent UniversityGhentBelgium
| | | | | | | | | | | | - Tod Strugnell
- Sanofi, Research North AmericaCambridgeUnited States
| | - Raul Gomila
- Sanofi, Research North AmericaCambridgeUnited States
| | | | - John Catalan
- Sanofi, Research North AmericaCambridgeUnited States
| | | | | | | | - Satyajit Ray
- Sanofi, Research North AmericaCambridgeUnited States
| | - Lauren LaRue
- Sanofi, Research North AmericaCambridgeUnited States
| | - Xavier Saelens
- VIB-UGent Center for Medical BiotechnologyGhentBelgium
- Department of Biochemistry and Microbiology, Ghent UniversityGhentBelgium
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5
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Perofsky AC, Huddleston J, Hansen C, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Lewis N, Whittaker L, Ermetal B, Harvey R, Galiano M, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Sullivan SG, Barr IG, Subbarao K, Krammer F, Bedford T, Viboud C. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.02.23296453. [PMID: 37873362 PMCID: PMC10593063 DOI: 10.1101/2023.10.02.23296453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.
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Affiliation(s)
- Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
| | - Chelsea Hansen
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United 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), United 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), United 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), United 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), United 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), United States
| | - Nicola Lewis
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Ruth Harvey
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Monica Galiano
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Sheena G Sullivan
- 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 Immunity, Australia
| | - Ian G Barr
- 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 Immunity, Australia
| | - Kanta Subbarao
- 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 Immunity, Australia
| | - Florian Krammer
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, United States
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, United States
| | - Trevor Bedford
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
- Department of Genome Sciences, University of Washington, United States
- Howard Hughes Medical Institute, Seattle, United States
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, United States
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6
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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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7
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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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8
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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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9
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Wang L, Huang AT, Katzelnick LC, Lefrancq N, Escoto AC, Duret L, Chowdhury N, Jarman R, Conte MA, Berry IM, Fernandez S, Klungthong C, Thaisomboonsuk B, Suntarattiwong P, Vandepitte W, Whitehead SS, Cauchemez S, Cummings DAT, Salje H. Antigenic distance between primary and secondary dengue infections correlates with disease risk. Sci Transl Med 2024; 16:eadk3259. [PMID: 38657027 DOI: 10.1126/scitranslmed.adk3259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024]
Abstract
Many pathogens continuously change their protein structure in response to immune-driven selection, resulting in weakened protection even in previously exposed individuals. In addition, for some pathogens, such as dengue virus, poorly targeted immunity is associated with increased risk of severe disease through a mechanism known as antibody-dependent enhancement. However, it remains unclear whether the antigenic distances between an individual's first infection and subsequent exposures dictate disease risk, explaining the observed large-scale differences in dengue hospitalizations across years. Here, we develop a framework that combines detailed antigenic and genetic characterization of viruses with details on hospitalized cases from 21 years of dengue surveillance in Bangkok, Thailand, to identify the role of the antigenic profile of circulating viruses in determining disease risk. We found that the risk of hospitalization depended on both the specific order of infecting serotypes and the antigenic distance between an individual's primary and secondary infections, with risk maximized at intermediate antigenic distances. These findings suggest that immune imprinting helps determine dengue disease risk and provide a pathway to monitor the changing risk profile of populations and to quantifying risk profiles of candidate vaccines.
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Affiliation(s)
- Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 1TN, UK
| | - Angkana T Huang
- Department of Genetics, University of Cambridge, Cambridge CB2 1TN, UK
| | - Leah C Katzelnick
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Noémie Lefrancq
- Department of Genetics, University of Cambridge, Cambridge CB2 1TN, UK
| | - Ana Coello Escoto
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Loréna Duret
- Department of Genetics, University of Cambridge, Cambridge CB2 1TN, UK
| | - Nayeem Chowdhury
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Richard Jarman
- Coalition for Epidemic Preparedness Initiative, Washington, DC 20006, USA
| | - Matthew A Conte
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Irina Maljkovic Berry
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Stefan Fernandez
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok 10400, Thailand
| | - Chonticha Klungthong
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok 10400, Thailand
| | - Butsaya Thaisomboonsuk
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok 10400, Thailand
| | | | - Warunee Vandepitte
- Queen Sirikit National Institute of Child Health, Bangkok 10400, Thailand
| | - Stephen S Whitehead
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris 75015, France
| | - Derek A T Cummings
- Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge CB2 1TN, UK
- Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA
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10
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Hay JA, Zhu H, Jiang CQ, Kwok KO, Shen R, Kucharski A, Yang B, Read JM, Lessler J, Cummings DAT, Riley S. Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.18.24304371. [PMID: 38562868 PMCID: PMC10984066 DOI: 10.1101/2024.03.18.24304371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.
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Affiliation(s)
- James A. Hay
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Imperial College London
| | - Huachen Zhu
- Guangdong-Hong Kong Joint Laboratory of Emerging Infectious Diseases/MOE Joint Laboratory for International Collaboration in Virology and Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou University, Shantou, China
- State Key Laboratory of Emerging Infectious Diseases / World Health Organization Influenza Reference Laboratory, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- 5EKIH (Gewuzhikang) Pathogen Research Institute, Guangdong, China
| | | | - Kin On Kwok
- The Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ruiyin Shen
- Guangzhou No.12 Hospital, Guangzhou, Guangdong, China
| | - Adam Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, United Kingdom
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jonathan M. Read
- Centre for Health Informatics Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, United States
- UNC Carolina Population Center, Chapel Hill, United States
| | - Derek A. T. Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Imperial College London
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11
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Maurer DP, Vu M, Schmidt AG. Antigenic drift expands viral escape pathways from imprinted host humoral immunity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585891. [PMID: 38562862 PMCID: PMC10983950 DOI: 10.1101/2024.03.20.585891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
An initial virus exposure can imprint antibodies such that future responses to antigenically drifted strains are dependent on the identity of the imprinting strain. Subsequent exposure to antigenically distinct strains followed by affinity maturation can guide immune responses toward generation of cross-reactive antibodies. How viruses evolve in turn to escape these imprinted broad antibody responses is unclear. Here, we used clonal antibody lineages from two human donors recognizing conserved influenza virus hemagglutinin (HA) epitopes to assess viral escape potential using deep mutational scanning. We show that even though antibody affinity maturation does restrict the number of potential escape routes in the imprinting strain through repositioning the antibody variable domains, escape is still readily observed in drifted strains and attributed to epistatic networks within HA. These data explain how influenza virus continues to evolve in the human population by escaping even broad antibody responses.
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12
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Liu T, Reiser WK, Tan TJC, Lv H, Rivera-Cardona J, Heimburger K, Wu NC, Brooke CB. Natural variation in neuraminidase activity influences the evolutionary potential of the seasonal H1N1 lineage hemagglutinin. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585603. [PMID: 38562808 PMCID: PMC10983940 DOI: 10.1101/2024.03.18.585603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The antigenic evolution of the influenza A virus hemagglutinin (HA) gene poses a major challenge for the development of vaccines capable of eliciting long-term protection. Prior efforts to understand the mechanisms that govern viral antigenic evolution mainly focus on HA in isolation, ignoring the fact that HA must act in concert with the viral neuraminidase (NA) during replication and spread. Numerous studies have demonstrated that the degree to which the receptor binding avidity of HA and receptor cleaving activity of NA are balanced with each other influences overall viral fitness. We recently showed that changes in NA activity can significantly alter the mutational fitness landscape of HA in the context of a lab-adapted virus strain. Here, we test whether natural variation in relative NA activity can influence the evolutionary potential of HA in the context of the seasonal H1N1 lineage (pdmH1N1) that has circulated in humans since the 2009 pandemic. We observed substantial variation in the relative activities of NA proteins encoded by a panel of H1N1 vaccine strains isolated between 2009 and 2019. We comprehensively assessed the effect of NA background on the HA mutational fitness landscape in the circulating pdmH1N1 lineage using deep mutational scanning and observed pronounced epistasis between NA and residues in or near the receptor binding site of HA. To determine whether NA variation could influence the antigenic evolution of HA, we performed neutralizing antibody selection experiments using a panel of monoclonal antibodies targeting different HA epitopes. We found that the specific antibody escape profiles of HA were highly contingent upon NA background. Overall, our results indicate that natural variation in NA activity plays a significant role in governing the evolutionary potential of HA in the currently circulating pdmH1N1 lineage.
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13
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Gao C, Wen F, Guan M, Hatuwal B, Li L, Praena B, Tang CY, Zhang J, Luo F, Xie H, Webby R, Tao YJ, Wan XF. MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production. Nat Commun 2024; 15:1128. [PMID: 38321021 PMCID: PMC10847134 DOI: 10.1038/s41467-024-45145-x] [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: 05/30/2023] [Accepted: 01/15/2024] [Indexed: 02/08/2024] Open
Abstract
Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.
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Affiliation(s)
- Cheng Gao
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Feng Wen
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS, 39762, USA
| | - Minhui Guan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Bijaya Hatuwal
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Lei Li
- Department of Chemistry, Georgia State University, Atlanta, GA, 30303, USA
- Center for Diagnostics & Therapeutics, Georgia State University, Atlanta, GA, 30303, USA
| | - Beatriz Praena
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Cynthia Y Tang
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
| | - Jieze Zhang
- Department of Bioengineering, Rice University, Houston, TX, 77030, USA
| | - Feng Luo
- University School of Computing, Clemson University, Clemson, SC, 29634, USA
| | - Hang Xie
- Laboratory of Respiratory Viral Diseases, Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Richard Webby
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, TN, 63141, USA
| | - Yizhi Jane Tao
- Department of BioSciences, Rice University, Houston, TX, 77251, USA
| | - Xiu-Feng Wan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA.
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, 65211, USA.
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA.
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS, 39762, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA.
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14
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Meng J, Liu J, Song W, Li H, Wang J, Zhang L, Peng Y, Wu A, Jiang T. PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network. Brief Bioinform 2024; 25:bbae033. [PMID: 38343322 PMCID: PMC10859661 DOI: 10.1093/bib/bbae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 01/13/2024] [Accepted: 01/18/2024] [Indexed: 02/15/2024] Open
Abstract
Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.
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Affiliation(s)
- Jing Meng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Jingze Liu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Honglei Li
- Beijing Cloudna Technology Company, Limited, Beijing 100029, China
| | | | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Yousong Peng
- College of Biology, Hunan University, Changsha 410082, China
| | - Aiping Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Taijiao Jiang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
- Guangzhou National Laboratory, Guangzhou 510005, China
- State Key Laboratory of Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510120, China
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15
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de Jong SPJ, Felix Garza ZC, Gibson JC, van Leeuwen S, de Vries RP, Boons GJ, van Hoesel M, de Haan K, van Groeningen LE, Hulme KD, van Willigen HDG, Wynberg E, de Bree GJ, Matser A, Bakker M, van der Hoek L, Prins M, Kootstra NA, Eggink D, Nichols BE, Han AX, de Jong MD, Russell CA. Determinants of epidemic size and the impacts of lulls in seasonal influenza virus circulation. Nat Commun 2024; 15:591. [PMID: 38238318 PMCID: PMC10796432 DOI: 10.1038/s41467-023-44668-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 12/21/2023] [Indexed: 01/22/2024] Open
Abstract
During the COVID-19 pandemic, levels of seasonal influenza virus circulation were unprecedentedly low, leading to concerns that a lack of exposure to influenza viruses, combined with waning antibody titres, could result in larger and/or more severe post-pandemic seasonal influenza epidemics. However, in most countries the first post-pandemic influenza season was not unusually large and/or severe. Here, based on an analysis of historical influenza virus epidemic patterns from 2002 to 2019, we show that historic lulls in influenza virus circulation had relatively minor impacts on subsequent epidemic size and that epidemic size was more substantially impacted by season-specific effects unrelated to the magnitude of circulation in prior seasons. From measurements of antibody levels from serum samples collected each year from 2017 to 2021, we show that the rate of waning of antibody titres against influenza virus during the pandemic was smaller than assumed in predictive models. Taken together, these results partially explain why the re-emergence of seasonal influenza virus epidemics was less dramatic than anticipated and suggest that influenza virus epidemic dynamics are not currently amenable to multi-season prediction.
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Affiliation(s)
- Simon P J de Jong
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Zandra C Felix Garza
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Joseph C Gibson
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Sarah van Leeuwen
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Robert P de Vries
- Department of Chemical Biology and Drug Discovery, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Geert-Jan Boons
- Department of Chemical Biology and Drug Discovery, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
- Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands
- Department of Chemistry, University of Georgia, Athens, GA, USA
| | - Marliek van Hoesel
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Karen de Haan
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Laura E van Groeningen
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Katina D Hulme
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Hugo D G van Willigen
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Elke Wynberg
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Department of Infectious Diseases, Public Health Service of Amsterdam, Amsterdam, The Netherlands
| | - Godelieve J de Bree
- Department of Infectious Diseases, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Amy Matser
- Department of Infectious Diseases, Public Health Service of Amsterdam, Amsterdam, The Netherlands
| | - Margreet Bakker
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Lia van der Hoek
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Maria Prins
- Department of Infectious Diseases, Public Health Service of Amsterdam, Amsterdam, The Netherlands
- Department of Infectious Diseases, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Neeltje A Kootstra
- Department of Experimental Immunology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Dirk Eggink
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Brooke E Nichols
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Department of Global Health, School of Public Health, Boston University, Boston, MA, USA
| | - Alvin X Han
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Menno D de Jong
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Colin A Russell
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.
- Department of Global Health, School of Public Health, Boston University, Boston, MA, USA.
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16
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Trovão NS, Khan SM, Lemey P, Nelson MI, Cherry JL. Comparative evolution of influenza A virus H1 and H3 head and stalk domains across host species. mBio 2024; 15:e0264923. [PMID: 38078770 PMCID: PMC10886446 DOI: 10.1128/mbio.02649-23] [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: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 01/17/2024] Open
Abstract
IMPORTANCE For decades, researchers have studied the rapid evolution of influenza A viruses for vaccine design and as a useful model system for the study of host/parasite evolution. By performing an exhaustive analysis of hemagglutinin protein (HA) sequences from 49 lineages independently evolving in birds, swine, canines, equines, and humans over the last century, our work uncovers surprising features of HA evolution. In particular, the canine H3 stalk, unlike human H3 and H1 stalk domains, is not evolving slowly, suggesting that evolution in the stalk domain is not universally constrained across all host species. Therefore, a broader multi-host perspective on HA evolution may be useful during the evaluation and design of stalk-targeted vaccine candidates.
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Affiliation(s)
- Nidia S Trovão
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Sairah M Khan
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Martha I Nelson
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Joshua L Cherry
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
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17
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Do THT, Wheatley AK, Kent SJ, Koutsakos M. Influenza B virus neuraminidase: a potential target for next-generation vaccines? Expert Rev Vaccines 2024; 23:39-48. [PMID: 38037386 DOI: 10.1080/14760584.2023.2290691] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/29/2023] [Indexed: 12/02/2023]
Abstract
INTRODUCTION Influenza B viruses (IBV) cause a significant health and economic burden annually. Due to lower antigenic drift rate, less extensive antigenic diversity, and lack of animal reservoirs, the development of highly effective universal vaccines against IBV might be in reach. Current seasonal influenza vaccines are formulated to induce antibodies against the Hemagglutinin (HA) protein, but their effectiveness is reduced by mismatch between vaccine and circulating strains. AREAS COVERED Given antibodies against the Neuraminidase (NA) have been associated with protection during influenza infection, there is considerable interest in the development of NA-based influenza vaccines. This review summarizes insights into the role of NA-based immunity against IBV and highlights knowledge gaps that should be addressed to inform the design of next-generation influenza B vaccines. We discuss how antibodies recognize broadly cross-reactive epitopes on the NA and the lack of understanding of IBV NA antigenic evolution which would benefit vaccine development in the future. EXPERT OPINION Demonstrating NA antibodies as correlates of protection for IBV in humans would be paramount. Determining the extent of IBV NA antigenic evolution will be informative. Finally, it will be critical to determine optimal strategies for incorporating the appropriate NA antigens in existing clinically approved vaccine formulations.
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Affiliation(s)
- Thi Hoai Thu Do
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute of Infection and Immunity, Melbourne, Australia
| | - Adam K Wheatley
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute of Infection and Immunity, Melbourne, Australia
| | - Stephen J Kent
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute of Infection and Immunity, Melbourne, Australia
- Melbourne Sexual Health Centre and Department of Infectious Diseases, Alfred Hospital and Central Clinical School, Monash University, Melbourne, Australia
| | - Marios Koutsakos
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute of Infection and Immunity, Melbourne, Australia
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18
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Dosey A, Ellis D, Boyoglu-Barnum S, Syeda H, Saunders M, Watson MJ, Kraft JC, Pham MN, Guttman M, Lee KK, Kanekiyo M, King NP. Combinatorial immune refocusing within the influenza hemagglutinin RBD improves cross-neutralizing antibody responses. Cell Rep 2023; 42:113553. [PMID: 38096052 PMCID: PMC10801708 DOI: 10.1016/j.celrep.2023.113553] [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: 06/20/2023] [Revised: 09/28/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
The receptor-binding domain (RBD) of influenza virus hemagglutinin (HA) elicits potently neutralizing yet mostly strain-specific antibodies. Here, we evaluate the ability of several immunofocusing techniques to enhance the functional breadth of vaccine-elicited immune responses against the HA RBD. We present a series of "trihead" nanoparticle immunogens that display native-like closed trimeric RBDs from the HAs of several H1N1 influenza viruses. The series includes hyperglycosylated and hypervariable variants that incorporate natural and designed sequence diversity at key positions in the receptor-binding site periphery. Nanoparticle immunogens displaying triheads or hyperglycosylated triheads elicit higher hemagglutination inhibition (HAI) and neutralizing activity than the corresponding immunogens lacking either trimer-stabilizing mutations or hyperglycosylation. By contrast, mosaic nanoparticle display and antigen hypervariation do not significantly alter the magnitude or breadth of vaccine-elicited antibodies. Our results yield important insights into antibody responses against the RBD and the ability of several structure-based immunofocusing techniques to influence vaccine-elicited antibody responses.
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Affiliation(s)
- Annie Dosey
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Daniel Ellis
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Seyhan Boyoglu-Barnum
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hubza Syeda
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mason Saunders
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Michael J Watson
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - John C Kraft
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Minh N Pham
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Miklos Guttman
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Kelly K Lee
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Masaru Kanekiyo
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Neil P King
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA.
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19
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Welsh FC, Eguia RT, Lee JM, Haddox HK, Galloway J, Chau NVV, Loes AN, Huddleston J, Yu TC, Le MQ, Nhat NTD, Thanh NTL, Greninger AL, Chu HY, Englund JA, Bedford T, Matsen FA, Boni MF, Bloom JD. Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571235. [PMID: 38168237 PMCID: PMC10760046 DOI: 10.1101/2023.12.12.571235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population, and how this heterogeneity affects virus evolution. Here we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of the A/Hong Kong/45/2019 (H3N2) and A/Perth/16/2009 (H3N2) strains affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that fixed in influenza variants after 2020 cause the greatest escape from sera from younger individuals. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups, and suggest approaches to understand how this heterogeneous selection shapes viral evolution.
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Affiliation(s)
- Frances C Welsh
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA, 98109, USA
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Rachel T Eguia
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Juhye M Lee
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Hugh K Haddox
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jared Galloway
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Nguyen Van Vinh Chau
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Andrea N Loes
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Timothy C Yu
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA, 98109, USA
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Mai Quynh Le
- National Institutes for Hygiene and Epidemiology, Hanoi, Vietnam
| | - Nguyen T D Nhat
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Nguyen Thi Le Thanh
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Alexander L Greninger
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Helen Y Chu
- Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Janet A Englund
- Seattle Children's Research Institute, Seattle, WA, 98109, USA
| | - Trevor Bedford
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Frederick A Matsen
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Maciej F Boni
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
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20
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Kistler KE, Bedford T. An atlas of continuous adaptive evolution in endemic human viruses. Cell Host Microbe 2023; 31:1898-1909.e3. [PMID: 37883977 DOI: 10.1016/j.chom.2023.09.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/25/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
Through antigenic evolution, viruses such as seasonal influenza evade recognition by neutralizing antibodies. This means that a person with antibodies well tuned to an initial infection will not be protected against the same virus years later and that vaccine-mediated protection will decay. To expand our understanding of which endemic human viruses evolve in this fashion, we assess adaptive evolution across the genome of 28 endemic viruses spanning a wide range of viral families and transmission modes. Surface proteins consistently show the highest rates of adaptation, and ten viruses in this panel are estimated to undergo antigenic evolution to selectively fix mutations that enable the escape of prior immunity. Thus, antibody evasion is not an uncommon evolutionary strategy among human viruses, and monitoring this evolution will inform future vaccine efforts. Additionally, by comparing overall amino acid substitution rates, we show that SARS-CoV-2 is accumulating protein-coding changes at substantially faster rates than endemic viruses.
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Affiliation(s)
- Kathryn E Kistler
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA, USA.
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA, USA
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21
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Fox A. Drift and shape-new insights into human immunity against influenza virus neuraminidase. mBio 2023; 14:e0165423. [PMID: 37933976 PMCID: PMC10746272 DOI: 10.1128/mbio.01654-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
Influenza virus hemagglutinin mediates infection by binding sialic acids, whereas neuraminidase cleaves sialic acids to release progeny virions. Both are targets of protective antibodies, but influenza vaccine strain selection and antigen dose are based on hemagglutinin alone. Virus characterization using first infection ferret sera indicates that escape from hemagglutination inhibiting (HI) antibodies occurs more frequently and is not coordinated with escape from neuraminidase inhibiting (NI) antibodies. A key question addressed by Daulagala et al. (P. Daulagala, B. R. Mann, K. Leung, E. H. Y. Lau, et al., mBio 14:e00084-23, 2023, https://doi.org/10.1128/mbio.00084-23) is how this translates to humans who encounter multiple influenza viruses throughout life. Their cross-sectional study, using sera from a wide age range of participants and H1N1 viruses spanning 1977-2015, indicates that NI antibodies are more broadly cross-reactive than HI antibodies. Both HI and NI titers were highest against strains encountered in childhood indicating that both are shaped by priming exposures. The study further supports the development of NA-optimized vaccines.
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Affiliation(s)
- Annette Fox
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
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22
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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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23
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Wang L, Huang AT, Katzelnick LC, Lefrancq N, Escoto AC, Duret L, Chowdhury N, Jarman R, Conte MA, Berry IM, Fernandez S, Klungthong C, Thaisomboonsuk B, Suntarattiwong P, Vandepitte W, Whitehead S, Cauchemez S, Cummings DA, Salje H. Antigenic diversity and dengue disease risk. RESEARCH SQUARE 2023:rs.3.rs-3214507. [PMID: 37577717 PMCID: PMC10418532 DOI: 10.21203/rs.3.rs-3214507/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Many pathogens continuously change their protein structure in response to immune-driven selection, resulting in weakened protection. In addition, for some pathogens such as dengue virus, poorly targeted immunity is associated with increased risk of severe disease, through a mechanism known as antibody-dependent enhancement. However, it remains a mystery whether the antigenic distance between an individual's first infection and subsequent exposures dictate disease risk, explaining the observed large-scale differences in dengue hospitalisations across years. Here we develop an inferential framework that combines detailed antigenic and genetic characterisation of viruses, and hospitalised cases from 21 years of surveillance in Bangkok, Thailand to identify the role of the antigenic profile of circulating viruses in determining disease risk. We find that the risk of hospitalisation depends on both the specific order of infecting serotypes and the antigenic distance between an individual's primary and secondary infections, with risk maximised at intermediate antigenic distances. These findings suggest immune imprinting helps determine dengue disease risk, and provides a pathway to monitor the changing risk profile of populations and to quantifying risk profiles of candidate vaccines.
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Affiliation(s)
- Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Angkana T. Huang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Leah C. Katzelnick
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Noémie Lefrancq
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Ana Coello Escoto
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Loréna Duret
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Nayeem Chowdhury
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Richard Jarman
- Coalition for Epidemic Preparedness Initiative, Washington DC, USA
| | - Matthew A. Conte
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Irina Maljkovic Berry
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Stefan Fernandez
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Chonticha Klungthong
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Butsaya Thaisomboonsuk
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | | | | | - Stephen Whitehead
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France
| | - Derek A.T. Cummings
- Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
- Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA
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24
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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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25
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Li YQ, Ghafari M, Holbrook AJ, Boonen I, Amor N, Catalano S, Webster JP, Li YY, Li HT, Vergote V, Maes P, Chong YL, Laudisoit A, Baelo P, Ngoy S, Mbalitini SG, Gembu GC, Musaba AP, Goüy de Bellocq J, Leirs H, Verheyen E, Pybus OG, Katzourakis A, Alagaili AN, Gryseels S, Li YC, Suchard MA, Bletsa M, Lemey P. The evolutionary history of hepaciviruses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.30.547218. [PMID: 37425679 PMCID: PMC10327235 DOI: 10.1101/2023.06.30.547218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
In the search for natural reservoirs of hepatitis C virus (HCV), a broad diversity of non-human viruses within the Hepacivirus genus has been uncovered. However, the evolutionary dynamics that shaped the diversity and timescale of hepaciviruses evolution remain elusive. To gain further insights into the origins and evolution of this genus, we screened a large dataset of wild mammal samples (n = 1,672) from Africa and Asia, and generated 34 full-length hepacivirus genomes. Phylogenetic analysis of these data together with publicly available genomes emphasizes the importance of rodents as hepacivirus hosts and we identify 13 rodent species and 3 rodent genera (in Cricetidae and Muridae families) as novel hosts of hepaciviruses. Through co-phylogenetic analyses, we demonstrate that hepacivirus diversity has been affected by cross-species transmission events against the backdrop of detectable signal of virus-host co-divergence in the deep evolutionary history. Using a Bayesian phylogenetic multidimensional scaling approach, we explore the extent to which host relatedness and geographic distances have structured present-day hepacivirus diversity. Our results provide evidence for a substantial structuring of mammalian hepacivirus diversity by host as well as geography, with a somewhat more irregular diffusion process in geographic space. Finally, using a mechanistic model that accounts for substitution saturation, we provide the first formal estimates of the timescale of hepacivirus evolution and estimate the origin of the genus to be about 22 million years ago. Our results offer a comprehensive overview of the micro- and macroevolutionary processes that have shaped hepacivirus diversity and enhance our understanding of the long-term evolution of the Hepacivirus genus.
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Affiliation(s)
- YQ Li
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, KU Leuven, Leuven, 3000, Belgium
| | - M Ghafari
- Department of Biology, University of Oxford, Oxford, OX1, UK
| | - AJ Holbrook
- Department of Biostatistics, University of California, Los Angeles, CA 90095, USA
| | - I Boonen
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, KU Leuven, Leuven, 3000, Belgium
| | - N Amor
- Laboratory of Biodiversity, Parasitology, and Ecology of Aquatic Ecosystems, Department of Biology - Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, 2092, Tunisia
| | - S Catalano
- School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK
- Department of Pathobiology and Population Sciences, the Royal Veterinary College, University of London, Herts, AL9 7TA, UK
| | - JP Webster
- Department of Pathobiology and Population Sciences, the Royal Veterinary College, University of London, Herts, AL9 7TA, UK
| | - YY Li
- College of Life Sciences, Linyi University, Linyi, 276000, China
- Marine College, Shandong University (Weihai), Weihai, 264209, China
| | - HT Li
- College of Life Sciences, Liaocheng University, Liaocheng, 252000, China
- Marine College, Shandong University (Weihai), Weihai, 264209, China
| | - V Vergote
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, KU Leuven, Leuven, 3000, Belgium
| | - P Maes
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, KU Leuven, Leuven, 3000, Belgium
| | - YL Chong
- Animal Resource Science and Management Group, Faculty of Resource Science and Technology, Universiti Malaysia Sarawak (UNIMAS), 94300, Malaysia
- Department of Science and Environmental Studies, The Education University of Hong Kong, Hong Kong, 999077, China
| | - A Laudisoit
- EcoHealth Alliance, New York, NY 10018, USA
- Evolutionary Ecology group (EVECO), Department of Biology, University of Antwerp, Antwerp, 2020, Belgium
| | - P Baelo
- Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo
| | - S Ngoy
- Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo
| | - SG Mbalitini
- Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo
| | - GC Gembu
- Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo
| | - Akawa P Musaba
- Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo
| | - J Goüy de Bellocq
- Institute of Vertebrate Biology, The Czech Academy of Sciences, Květná 8, 603 65 Brno, Czech Republic
| | - H Leirs
- Evolutionary Ecology group (EVECO), Department of Biology, University of Antwerp, Antwerp, 2020, Belgium
| | - E Verheyen
- Evolutionary Ecology group (EVECO), Department of Biology, University of Antwerp, Antwerp, 2020, Belgium
| | - OG Pybus
- Department of Biology, University of Oxford, Oxford, OX1, UK
- Department of Pathobiology and Population Sciences, the Royal Veterinary College, University of London, Herts, AL9 7TA, UK
| | - A Katzourakis
- Department of Biology, University of Oxford, Oxford, OX1, UK
| | - AN Alagaili
- Laboratory of Biodiversity, Parasitology, and Ecology of Aquatic Ecosystems, Department of Biology - Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, 2092, Tunisia
| | - S Gryseels
- Evolutionary Ecology group (EVECO), Department of Biology, University of Antwerp, Antwerp, 2020, Belgium
| | - YC Li
- Marine College, Shandong University (Weihai), Weihai, 264209, China
| | - MA Suchard
- Department of Biostatistics, University of California, Los Angeles, CA 90095, USA
| | - M Bletsa
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, KU Leuven, Leuven, 3000, Belgium
- Department of Hygiene Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, 11527, Greece
| | - P Lemey
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, KU Leuven, Leuven, 3000, Belgium
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26
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Peng F, Xia Y, Li W. Prediction of Antigenic Distance in Influenza A Using Attribute Network Embedding. Viruses 2023; 15:1478. [PMID: 37515165 PMCID: PMC10385503 DOI: 10.3390/v15071478] [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: 04/28/2023] [Revised: 06/23/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
Owing to the rapid changes in the antigenicity of influenza viruses, it is difficult for humans to obtain lasting immunity through antiviral therapy. Hence, tracking the dynamic changes in the antigenicity of influenza viruses can provide a basis for vaccines and drug treatments to cope with the spread of influenza viruses. In this paper, we developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. An antigenic network is built to model and combine the genetic and antigenic characteristics of the influenza A virus H3N2, using the continuous distributed representation of the virus strain protein sequence (ProtVec) as a node attribute and the antigenic distance between virus strains as an edge weight. The results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy. Further analysis indicates that our prediction model can comprehensively and accurately track the differences in antigenic distances between vaccines and influenza virus strains, and it outperforms existing methods in predicting antigenic distances between strains.
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Affiliation(s)
- Fujun Peng
- School of Information Science and Engineering, Yunnan University, Kunming 650500, China
| | - Yuanling Xia
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650500, China
| | - Weihua Li
- School of Information Science and Engineering, Yunnan University, Kunming 650500, China
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27
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Rouzine IM, Rozhnova G. Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies. COMMUNICATIONS MEDICINE 2023; 3:86. [PMID: 37336956 DOI: 10.1038/s43856-023-00320-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/07/2023] [Indexed: 06/21/2023] Open
Abstract
Once the first SARS-CoV-2 vaccine became available, mass vaccination was the main pillar of the public health response to the COVID-19 pandemic. It was very effective in reducing hospitalizations and deaths. Here, we discuss the possibility that mass vaccination might accelerate SARS-CoV-2 evolution in antibody-binding regions compared to natural infection at the population level. Using the evidence of strong genetic variation in antibody-binding regions and taking advantage of the similarity between the envelope proteins of SARS-CoV-2 and influenza, we assume that immune selection pressure acting on these regions of the two viruses is similar. We discuss the consequences of this assumption for SARS-CoV-2 evolution in light of mathematical models developed previously for influenza. We further outline the implications of this phenomenon, if our assumptions are confirmed, for the future design of SARS-CoV-2 vaccination strategies.
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Affiliation(s)
- Igor M Rouzine
- Immunogenetics, Sechenov Institute of Evolutionary Physiology and Biochemistry of Russian Academy of Sciences, Saint-Petersburg, Russia.
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands.
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28
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Martinez MR, Gao J, Wan H, Kang H, Klenow L, Daniels R. Inactivated influenza virions are a flexible vaccine platform for eliciting protective antibody responses against neuraminidase. Vaccine 2023:S0264-410X(23)00629-1. [PMID: 37301705 DOI: 10.1016/j.vaccine.2023.05.068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
Most seasonal influenza vaccines are produced using hemagglutinin (HA) surface antigens from inactivated virions. However, virions are thought to be a suboptimal source for the less abundant neuraminidase (NA) surface antigen, which is also protective against severe disease. Here, we demonstrate that inactivated influenza virions are compatible with two modern approaches for improving protective antibody responses against NA. Using a DBA/2J mouse model, we show that the strong infection-induced NA inhibitory (NAI) antibody responses are only achieved by high dose immunizations of inactivated virions, likely due to the low viral NA content. Based on this observation, we first produced virions with higher NA content by using reverse genetics to exchange the viral internal gene segments. Single immunizations with these inactivated virions showed enhanced NAI antibody responses and improved NA-based protection from a lethal viral challenge while also allowing for the development of natural immunity to the heterotypic challenge virus HA. Second, we combined inactivated virions with recombinant NA protein antigens. These combination vaccines increased NA-based protection following viral challenge and elicited stronger antibody responses against NA than either component alone, especially when the NAs possessed similar antigenicity. Together, these results indicate that inactivated virions are a flexible platform that can be easily combined with protein-based vaccines to improve protective antibody responses against influenza antigens.
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Affiliation(s)
- Mira Rakic Martinez
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Jin Gao
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Hongquan Wan
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Hyeog Kang
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Laura Klenow
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Robert Daniels
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA.
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29
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Fourment M, Swanepoel CJ, Galloway JG, Ji X, Gangavarapu K, Suchard MA, Matsen IV FA. Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation. Genome Biol Evol 2023; 15:evad099. [PMID: 37265233 PMCID: PMC10282121 DOI: 10.1093/gbe/evad099] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/03/2023] Open
Abstract
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implemented in general-purpose machine-learning libraries such as TensorFlow and PyTorch. Although these libraries are highly optimized, it is not clear if their general-purpose nature will limit their algorithmic complexity or implementation speed for the phylogenetic case compared to phylogenetics-specific code. In this paper, we compare six gradient implementations of the phylogenetic likelihood functions, in isolation and also as part of a variational inference procedure. We find that although automatic differentiation can scale approximately linearly in tree size, it is much slower than the carefully implemented gradient calculation for tree likelihood and ratio transformation operations. We conclude that a mixed approach combining phylogenetic libraries with machine learning libraries will provide the optimal combination of speed and model flexibility moving forward.
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Affiliation(s)
- Mathieu Fourment
- Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo, NSW, Australia
| | - Christiaan J Swanepoel
- Centre for Computational Evolution, The University of Auckland, Auckland, New Zealand
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Jared G Galloway
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, Louisiana, USA
| | - Karthik Gangavarapu
- Department of Human Genetics, University of California, Los Angeles, California, USA
| | - Marc A Suchard
- Department of Human Genetics, University of California, Los Angeles, California, USA
- Department of Computational Medicine, University of California, Los Angeles, California, USA
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Frederick A Matsen IV
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Statistics, University of Washington, Seattle, Washington, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
- Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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30
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Dosey A, Ellis D, Boyoglu-Barnum S, Syeda H, Saunders M, Watson M, Kraft JC, Pham MN, Guttman M, Lee KK, Kanekiyo M, King NP. Combinatorial immune refocusing within the influenza hemagglutinin head elicits cross-neutralizing antibody responses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.23.541996. [PMID: 37292967 PMCID: PMC10245820 DOI: 10.1101/2023.05.23.541996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The head domain of influenza hemagglutinin (HA) elicits potently neutralizing yet mostly strain-specific antibodies during infection and vaccination. Here we evaluated a series of immunogens that combined several immunofocusing techniques for their ability to enhance the functional breadth of vaccine-elicited immune responses. We designed a series of "trihead" nanoparticle immunogens that display native-like closed trimeric heads from the HAs of several H1N1 influenza viruses, including hyperglycosylated variants and hypervariable variants that incorporate natural and designed sequence diversity at key positions in the periphery of the receptor binding site (RBS). Nanoparticle immunogens displaying triheads or hyperglycosylated triheads elicited higher HAI and neutralizing activity against vaccine-matched and -mismatched H1 viruses than corresponding immunogens lacking either trimer-stabilizing mutations or hyperglycosylation, indicating that both of these engineering strategies contributed to improved immunogenicity. By contrast, mosaic nanoparticle display and antigen hypervariation did not significantly alter the magnitude or breadth of vaccine-elicited antibodies. Serum competition assays and electron microscopy polyclonal epitope mapping revealed that the trihead immunogens, especially when hyperglycosylated, elicited a high proportion of antibodies targeting the RBS, as well as cross-reactive antibodies targeting a conserved epitope on the side of the head. Our results yield important insights into antibody responses against the HA head and the ability of several structure-based immunofocusing techniques to influence vaccine-elicited antibody responses.
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Affiliation(s)
- Annie Dosey
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Daniel Ellis
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Seyhan Boyoglu-Barnum
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hubza Syeda
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mason Saunders
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Michael Watson
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - John C. Kraft
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Minh N. Pham
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Miklos Guttman
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Kelly K. Lee
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Masaru Kanekiyo
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Neil P. King
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
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31
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Einav T, Khoo Y, Singer A. Quantitatively Visualizing Bipartite Datasets. PHYSICAL REVIEW. X 2023; 13:021002. [PMID: 38831998 PMCID: PMC11146982 DOI: 10.1103/physrevx.13.021002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
As experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily interpretable form. Often, each measurement conveys only the relationship between a pair of entries, and it is difficult to integrate these local interactions across a dataset to form a cohesive global picture. The classic localization problem tackles this question, transforming local measurements into a global map that reveals the underlying structure of a system. Here, we examine the more challenging bipartite localization problem, where pairwise distances are available only for bipartite data comprising two classes of entries (such as antibody-virus interactions, drug-cell potency, or user-rating profiles). We modify previous algorithms to solve bipartite localization and examine how each method behaves in the presence of noise, outliers, and partially observed data. As a proof of concept, we apply these algorithms to antibody-virus neutralization measurements to create a basis set of antibody behaviors, formalize how potently inhibiting some viruses necessitates weakly inhibiting other viruses, and quantify how often combinations of antibodies exhibit degenerate behavior.
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Affiliation(s)
- Tal Einav
- Divisions of Computational Biology and Basic Sciences, Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA
| | - Yuehaw Khoo
- Department of Statistics, University of Chicago, Chicago, Illinois 60637, USA
| | - Amit Singer
- Department of Mathematics and PACM, Princeton University, Princeton, New Jersey 08540, USA
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32
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Langer J, Welch VL, Moran MM, Cane A, Lopez SMC, Srivastava A, Enstone AL, Sears A, Markus KJ, Heuser M, Kewley RM, Whittle IJ. High Clinical Burden of Influenza Disease in Adults Aged ≥ 65 Years: Can We Do Better? A Systematic Literature Review. Adv Ther 2023; 40:1601-1627. [PMID: 36790682 PMCID: PMC9930064 DOI: 10.1007/s12325-023-02432-1] [Citation(s) in RCA: 7] [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: 11/22/2022] [Accepted: 01/09/2023] [Indexed: 02/16/2023]
Abstract
INTRODUCTION Influenza is a respiratory infection associated with a significant clinical burden globally. Adults aged ≥ 65 years are at increased risk of severe influenza-related symptoms and complications due to chronic comorbidity and immunosenescence. Annual influenza vaccination is recommended; however, current influenza vaccines confer suboptimal protection, in part due to antigen mismatch and poor durability. This systematic literature review characterizes the global clinical burden of seasonal influenza among adults aged ≥ 65 years. METHODS An electronic database search was conducted and supplemented with a conference abstract search. Included studies described clinical outcomes in the ≥ 65 years population across several global regions and were published in English between January 1, 2012 and February 9, 2022. RESULTS Ninety-nine publications were included (accounting for > 156,198,287 total participants globally). Clinical burden was evident across regions, with most studies conducted in the USA and Europe. Risk of influenza-associated hospitalization increased with age, particularly in those aged ≥ 65 years living in long-term care facilities, with underlying comorbidities, and infected with A(H3N2) strains. Seasons dominated by circulating A(H3N2) strains saw increased risk of influenza-associated hospitalization, intensive care unit admission, and mortality within the ≥ 65 years population. Seasonal differences in clinical burden were linked to differences in circulating strains. CONCLUSIONS Influenza exerts a considerable burden on adults aged ≥ 65 years and healthcare systems, with high incidence of hospitalization and mortality. Substantial influenza-associated clinical burden persists despite increasing vaccination coverage among adults aged ≥ 65 years across regions included in this review, which suggests limited effectiveness of currently available seasonal influenza vaccines. To reduce influenza-associated clinical burden, influenza vaccine effectiveness must be improved. Next generation vaccine production using mRNA technology has demonstrated high effectiveness against another respiratory virus-SARS-CoV-2-and may overcome the practical limitations associated with traditional influenza vaccine production.
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Affiliation(s)
- Jakob Langer
- Pfizer Patient & Health Impact, Pfizer Portugal, Lagoas Park, Edifício 10, 2740-271, Porto Salvo, Portugal.
| | - Verna L Welch
- Pfizer Vaccines Medical & Scientific Affairs, Collegeville, PA, USA
| | - Mary M Moran
- Pfizer Vaccines Medical & Scientific Affairs, Collegeville, PA, USA
| | - Alejandro Cane
- Pfizer Vaccines Medical & Scientific Affairs, Collegeville, PA, USA
| | | | - Amit Srivastava
- Pfizer Emerging Markets, Vaccines Medical & Scientific Affairs, Cambridge, MA, USA
| | | | - Amy Sears
- Adelphi Values PROVE, Bollington, SK10 5JB, UK
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33
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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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34
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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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35
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Makau DN, Prieto C, Martínez-Lobo FJ, Paploski IAD, VanderWaal K. Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning. Microbiol Spectr 2023; 11:e0408522. [PMID: 36511691 PMCID: PMC9927307 DOI: 10.1128/spectrum.04085-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 11/18/2022] [Indexed: 12/15/2022] Open
Abstract
The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance in silico, based on genetic sequence data, and identify differences in specific amino acid sites associated with antigenic differences between viruses. First, we obtained antigenic distance estimates derived from serum neutralization assays cross-reacting PRRSV monospecific antisera with virus isolates from 27 PRRSV1 viruses circulating in Europe. Antigenic distances were weakly to moderately associated with ectodomain amino acid distance for open reading frames (ORFs) 2 to 4 (ρ < 0.2) and ORF5 (ρ = 0.3), respectively. Dividing the antigenic distance values at the median, we then categorized the sera-virus pairs into two levels: low and high antigenic distance (dissimilarity). In the machine learning models, we used amino acid distances in the ectodomains of ORFs 2 to 5 and site-wise amino acid differences between the viruses as potential predictors of antigenic dissimilarity. Using mixed-effect gradient boosting models, we estimated the antigenic distance (high versus low) between serum-virus pairs with an accuracy of 81% (95% confidence interval, 76 to 85%); sensitivity and specificity were 86% and 75%, respectively. We demonstrate that using sequence data we can estimate antigenic distance and potential cross-protection between PRRSV1 strains. IMPORTANCE Understanding cross-protection between cocirculating PRRSV1 strains is crucial to reducing losses associated with PRRS outbreaks on farms. While experimental studies to determine cross-protection are instrumental, these in vivo studies are not always practical or timely for the many cocirculating and emerging PRRSV strains. In this study, we demonstrate the ability to rapidly estimate potential immunologic cross-reaction between different PRRSV1 strains in silico using sequence data routinely collected by production systems. These models can provide fast turn-around information crucial for improving PRRS management decisions such as selecting vaccines/live virus inoculation to be used on farms and assessing the risk of outbreaks by emerging strains on farms previously exposed to certain PRRSV strains and vaccine development among others.
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Affiliation(s)
- Dennis N. Makau
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Minneapolis, USA
| | - Cinta Prieto
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid, Spain
| | | | - I. A. D. Paploski
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Minneapolis, USA
| | - Kimberly VanderWaal
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Minneapolis, USA
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36
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Einav T, Creanga A, Andrews SF, McDermott AB, Kanekiyo M. Harnessing low dimensionality to visualize the antibody-virus landscape for influenza. NATURE COMPUTATIONAL SCIENCE 2023; 3:164-173. [PMID: 38177625 PMCID: PMC10766546 DOI: 10.1038/s43588-022-00375-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 11/11/2022] [Indexed: 01/06/2024]
Abstract
Antibodies constitute a key line of defense against the diverse pathogens we encounter in our lives. Although the interactions between a single antibody and a single virus are routinely characterized in exquisite detail, the inherent tradeoffs between attributes such as potency and breadth remain unclear. Moreover, there is a wide gap between the discrete interactions of single antibodies and the collective behavior of antibody mixtures. Here we develop a form of antigenic cartography called a 'neutralization landscape' that visualizes and quantifies antibody-virus interactions for antibodies targeting the influenza hemagglutinin stem. This landscape transforms the potency-breadth tradeoff into a readily solvable geometry problem. With it, we decompose the collective neutralization from multiple antibodies to characterize the composition and functional properties of the stem antibodies within. Looking forward, this framework can leverage the serological assays routinely performed for influenza surveillance to analyze how an individual's antibody repertoire evolves after vaccination or infection.
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Affiliation(s)
- Tal Einav
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
| | - Adrian Creanga
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Sarah F Andrews
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Adrian B McDermott
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Masaru Kanekiyo
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
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37
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Phillips AM, Maurer DP, Brooks C, Dupic T, Schmidt AG, Desai MM. Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody. eLife 2023; 12:83628. [PMID: 36625542 PMCID: PMC9995116 DOI: 10.7554/elife.83628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the VH-VL interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
| | - Daniel P Maurer
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Caelan Brooks
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Aaron G Schmidt
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
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38
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Jones-Gray E, Robinson EJ, Kucharski AJ, Fox A, Sullivan SG. Does repeated influenza vaccination attenuate effectiveness? A systematic review and meta-analysis. THE LANCET. RESPIRATORY MEDICINE 2023; 11:27-44. [PMID: 36152673 PMCID: PMC9780123 DOI: 10.1016/s2213-2600(22)00266-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/13/2022] [Accepted: 07/13/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Influenza vaccines require annual readministration; however, several reports have suggested that repeated vaccination might attenuate the vaccine's effectiveness. We aimed to estimate the reduction in vaccine effectiveness associated with repeated influenza vaccination. METHODS In this systematic review and meta-analysis, we searched MEDLINE, EMBASE, and CINAHL Complete databases for articles published from Jan 1, 2016, to June 13, 2022, and Web of Science for studies published from database inception to June 13, 2022. For studies published before Jan 1, 2016, we consulted published systematic reviews. Two reviewers (EJ-G and EJR) independently screened, extracted data using a data collection form, assessed studies' risk of bias using the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) and evaluated the weight of evidence by Grading of Recommendations Assessment, Development, and Evaluation (GRADE). We included observational studies and randomised controlled trials that reported vaccine effectiveness against influenza A(H1N1)pdm09, influenza A(H3N2), or influenza B using four vaccination groups: current season; previous season; current and previous seasons; and neither season (reference). For each study, we calculated the absolute difference in vaccine effectiveness (ΔVE) for current season only and previous season only versus current and previous season vaccination to estimate attenuation associated with repeated vaccination. Pooled vaccine effectiveness and ∆VE were calculated by season, age group, and overall. This study is registered with PROSPERO, CRD42021260242. FINDINGS We identified 4979 publications, selected 681 for full review, and included 83 in the systematic review and 41 in meta-analyses. ΔVE for vaccination in both seasons compared with the current season was -9% (95% CI -16 to -1, I2=0%; low certainty) for influenza A(H1N1)pdm09, -18% (-26 to -11, I2=7%; low certainty) for influenza A(H3N2), and -7% (-14 to 0, I2=0%; low certainty) for influenza B, indicating lower protection with consecutive vaccination. However, for all types, A subtypes and B lineages, vaccination in both seasons afforded better protection than not being vaccinated. INTERPRETATION Our estimates suggest that, although vaccination in the previous year attenuates vaccine effectiveness, vaccination in two consecutive years provides better protection than does no vaccination. The estimated effects of vaccination in the previous year are concerning and warrant additional investigation, but are not consistent or severe enough to support an alternative vaccination regimen at this time. FUNDING WHO and the US National Institutes of Health.
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Affiliation(s)
- Elenor Jones-Gray
- Department of Infectious Diseases, University of Melbourne, Melbourne, VIC, Australia
| | - Elizabeth J Robinson
- Department of Infectious Diseases, University of Melbourne, Melbourne, VIC, Australia
| | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene and Tropical Medicine, London, UK
| | - Annette Fox
- Department of Infectious Diseases, University of Melbourne, Melbourne, VIC, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Sheena G Sullivan
- Department of Infectious Diseases, University of Melbourne, Melbourne, VIC, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia; Department of Epidemiology, University of California, Los Angeles, CA, USA.
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Chen J, Tan S, Avadhanula V, Moise L, Piedra PA, De Groot AS, Bahl J. Diversity and evolution of computationally predicted T cell epitopes against human respiratory syncytial virus. PLoS Comput Biol 2023; 19:e1010360. [PMID: 36626370 PMCID: PMC9870173 DOI: 10.1371/journal.pcbi.1010360] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/23/2023] [Accepted: 12/07/2022] [Indexed: 01/11/2023] Open
Abstract
Human respiratory syncytial virus (RSV) is a major cause of lower respiratory infection. Despite more than 60 years of research, there is no licensed vaccine. While B cell response is a major focus for vaccine design, the T cell epitope profile of RSV is also important for vaccine development. Here, we computationally predicted putative T cell epitopes in the Fusion protein (F) and Glycoprotein (G) of RSV wild circulating strains by predicting Major Histocompatibility Complex (MHC) class I and class II binding affinity. We limited our inferences to conserved epitopes in both F and G proteins that have been experimentally validated. We applied multidimensional scaling (MDS) to construct T cell epitope landscapes to investigate the diversity and evolution of T cell profiles across different RSV strains. We find the RSV strains are clustered into three RSV-A groups and two RSV-B groups on this T epitope landscape. These clusters represent divergent RSV strains with potentially different immunogenic profiles. In addition, our results show a greater proportion of F protein T cell epitope content conservation among recent epidemic strains, whereas the G protein T cell epitope content was decreased. Importantly, our results suggest that RSV-A and RSV-B have different patterns of epitope drift and replacement and that RSV-B vaccines may need more frequent updates. Our study provides a novel framework to study RSV T cell epitope evolution. Understanding the patterns of T cell epitope conservation and change may be valuable for vaccine design and assessment.
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Affiliation(s)
- Jiani Chen
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease and Emergence Response, University of Georgia, Athens, Georgia, United States of America
| | - Swan Tan
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease and Emergence Response, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Vasanthi Avadhanula
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Leonard Moise
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- EpiVax Inc., Providence, Rhode Island, United States of America
| | - Pedro A. Piedra
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Anne S. De Groot
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- EpiVax Inc., Providence, Rhode Island, United States of America
| | - Justin Bahl
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease and Emergence Response, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, United States of America
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Venkatesh D, Anderson TK, Kimble JB, Chang J, Lopes S, Souza CK, Pekosz A, Shaw-Saliba K, Rothman RE, Chen KF, Lewis NS, Vincent Baker AL. Antigenic Characterization and Pandemic Risk Assessment of North American H1 Influenza A Viruses Circulating in Swine. Microbiol Spectr 2022; 10:e0178122. [PMID: 36318009 PMCID: PMC9769642 DOI: 10.1128/spectrum.01781-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 10/07/2022] [Indexed: 12/23/2022] Open
Abstract
The first pandemic of the 21st century was caused by an H1N1 influenza A virus (IAV) introduced from pigs into humans, highlighting the importance of swine as reservoirs for pandemic viruses. Two major lineages of swine H1 circulate in North America: the 1A classical swine lineage (including that of the 2009 H1N1 pandemic) and the 1B human seasonal-like lineage. Here, we investigated the evolution of these H1 IAV lineages in North American swine and their potential pandemic risk. We assessed the antigenic distance between the HA of representative swine H1 and human seasonal vaccine strains (1978 to 2015) in hemagglutination inhibition (HI) assays using a panel of monovalent antisera raised in pigs. Antigenic cross-reactivity varied by strain but was associated with genetic distance. Generally, the swine 1A lineage viruses that seeded the 2009 H1 pandemic were antigenically most similar to the H1 pandemic vaccine strains, with the exception of viruses in the genetic clade 1A.1.1.3, which had a two-amino acid deletion mutation near the receptor-binding site, which dramatically reduced antibody recognition. The swine 1B lineage strains, which arose from previously circulating (pre-2009 pandemic) human seasonal viruses, were more antigenically similar to pre-2009 human seasonal H1 vaccine viruses than post-2009 strains. Human population immunity was measured by cross-reactivity in HI assays to representative swine H1 strains. There was a broad range of titers against each swine strain that was not associated with age, sex, or location. However, there was almost no cross-reactivity in human sera to the 1A.1.1.3 and 1B.2.1 genetic clades of swine viruses, and the 1A.1.1.3 and 1B.2.1 clades were also the most antigenically distant to the human vaccine strains. Our data demonstrate that the antigenic distances of representative swine strains from human vaccine strains represent an important part of the rational assessment of swine IAV for zoonotic risk research and pandemic preparedness prioritization. IMPORTANCE Human H1 influenza A viruses (IAV) spread to pigs in North America, resulting in a sustained circulation of two major groups of H1 viruses in swine. We quantified the genetic diversity of H1 in swine and measured antigenic phenotypes. We demonstrated that the swine H1 lineages were significantly different from the human vaccine strains and that this antigenic dissimilarity increased over time as the viruses evolved in swine. Pandemic preparedness vaccine strains for human vaccines also demonstrated a loss in similarity with contemporary swine strains. Human sera revealed a range of responses to swine IAV, including two groups of viruses with little to no immunity. The surveillance and risk assessment of IAV diversity in pig populations are essential to detect strains with reduced immunity in humans and provide critical information for pandemic preparedness.
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Affiliation(s)
| | | | | | - Jennifer Chang
- National Animal Disease Center, USDA-ARS, Ames, Iowa, USA
| | - Sara Lopes
- Royal Veterinary College, London, United Kingdom
| | | | - Andrew Pekosz
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Kathryn Shaw-Saliba
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard E. Rothman
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kuan-Fu Chen
- Department of Emergency Medicine of Chang Gung Memorial Hospital at Keelung, Keelung City, Taiwan
| | - Nicola S. Lewis
- Royal Veterinary College, London, United Kingdom
- OIE/FAO International Reference Laboratory for Avian Influenza, Swine Influenza and Newcastle Disease, Animal and Plant Health Agency (APHA), Weybridge, Addlestone, Surrey, United Kingdom
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Park S, Kim JY, Kwon HC, Jang DS, Song YJ. Antiviral Activities of Ethyl Pheophorbides a and b Isolated from Aster pseudoglehnii against Influenza Viruses. MOLECULES (BASEL, SWITZERLAND) 2022; 28:molecules28010041. [PMID: 36615236 PMCID: PMC9822050 DOI: 10.3390/molecules28010041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Screening of the antiviral and virucidal activities of ethanol extracts from plants endemic to the Republic of Korea revealed the inhibitory activity of a 70% ethanol extract of the whole plant of A. pseudoglehnii (APE) against influenza virus infection. Two chlorophyll derivatives, ethyl pheophorbides a and b, isolated as active components of APE, exerted virucidal effects with no evident cytotoxicity. These compounds were effective only under conditions of direct incubation with the virus, and exerted no effects on the influenza A virus (IAV) surface glycoproteins hemagglutinin (HA) and neuraminidase (NA). Interestingly, virucidal activities of ethyl pheophorbides a and b were observed against enveloped but not non-enveloped viruses, suggesting that these compounds act by affecting the integrity of the viral membrane and reducing infectivity.
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Affiliation(s)
- Subin Park
- Department of Life Science, Gachon University, Seongnam 13120, Republic of Korea
| | - Ji-Young Kim
- Department of Biomedical and Pharmaceutical Sciences, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Hak Cheol Kwon
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung Institute, Gangneung 25451, Republic of Korea
| | - Dae Sik Jang
- Department of Biomedical and Pharmaceutical Sciences, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
- Correspondence: (D.S.J.); (Y.-J.S.); Tel.: +82-2-961-0719 (D.S.J.); +82-31-750-8731 (Y.-J.S.)
| | - Yoon-Jae Song
- Department of Life Science, Gachon University, Seongnam 13120, Republic of Korea
- Correspondence: (D.S.J.); (Y.-J.S.); Tel.: +82-2-961-0719 (D.S.J.); +82-31-750-8731 (Y.-J.S.)
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Yu TC, Thornton ZT, Hannon WW, DeWitt WS, Radford CE, Matsen FA, Bloom JD. A biophysical model of viral escape from polyclonal antibodies. Virus Evol 2022; 8:veac110. [PMID: 36582502 PMCID: PMC9793855 DOI: 10.1093/ve/veac110] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/12/2022] [Accepted: 11/29/2022] [Indexed: 12/14/2022] Open
Abstract
A challenge in studying viral immune escape is determining how mutations combine to escape polyclonal antibodies, which can potentially target multiple distinct viral epitopes. Here we introduce a biophysical model of this process that partitions the total polyclonal antibody activity by epitope and then quantifies how each viral mutation affects the antibody activity against each epitope. We develop software that can use deep mutational scanning data to infer these properties for polyclonal antibody mixtures. We validate this software using a computationally simulated deep mutational scanning experiment and demonstrate that it enables the prediction of escape by arbitrary combinations of mutations. The software described in this paper is available at https://jbloomlab.github.io/polyclonal.
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Affiliation(s)
- Timothy C Yu
- Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, 1959 NE Pacifc Street, Seattle, WA 98195, USA
| | - Zorian T Thornton
- Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, USA
| | - William W Hannon
- Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, 1959 NE Pacifc Street, Seattle, WA 98195, USA
| | - William S DeWitt
- Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, USA
| | - Caelan E Radford
- Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, 1959 NE Pacifc Street, Seattle, WA 98195, USA
| | - Frederick A Matsen
- Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, 1100 Fairview Ave N, Seattle, WA 98109, USA
| | - Jesse D Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, 1100 Fairview Ave N, Seattle, WA 98109, USA
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Yang B, García-Carreras B, Lessler J, Read JM, Zhu H, Metcalf CJE, Hay JA, Kwok KO, Shen R, Jiang CQ, Guan Y, Riley S, Cummings DA. Long term intrinsic cycling in human life course antibody responses to influenza A(H3N2): an observational and modeling study. eLife 2022; 11:81457. [PMID: 36458815 PMCID: PMC9757834 DOI: 10.7554/elife.81457] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/01/2022] [Indexed: 12/05/2022] Open
Abstract
Background Over a life course, human adaptive immunity to antigenically mutable pathogens exhibits competitive and facilitative interactions. We hypothesize that such interactions may lead to cyclic dynamics in immune responses over a lifetime. Methods To investigate the cyclic behavior, we analyzed hemagglutination inhibition titers against 21 historical influenza A(H3N2) strains spanning 47 years from a cohort in Guangzhou, China, and applied Fourier spectrum analysis. To investigate possible biological mechanisms, we simulated individual antibody profiles encompassing known feedbacks and interactions due to generally recognized immunological mechanisms. Results We demonstrated a long-term periodicity (about 24 years) in individual antibody responses. The reported cycles were robust to analytic and sampling approaches. Simulations suggested that individual-level cross-reaction between antigenically similar strains likely explains the reported cycle. We showed that the reported cycles are predictable at both individual and birth cohort level and that cohorts show a diversity of phases of these cycles. Phase of cycle was associated with the risk of seroconversion to circulating strains, after accounting for age and pre-existing titers of the circulating strains. Conclusions Our findings reveal the existence of long-term periodicities in individual antibody responses to A(H3N2). We hypothesize that these cycles are driven by preexisting antibody responses blunting responses to antigenically similar pathogens (by preventing infection and/or robust antibody responses upon infection), leading to reductions in antigen-specific responses over time until individual's increasing risk leads to an infection with an antigenically distant enough virus to generate a robust immune response. These findings could help disentangle cohort effects from individual-level exposure histories, improve our understanding of observed heterogeneous antibody responses to immunizations, and inform targeted vaccine strategy. Funding This study was supported by grants from the NIH R56AG048075 (DATC, JL), NIH R01AI114703 (DATC, BY), the Wellcome Trust 200861/Z/16/Z (SR), and 200187/Z/15/Z (SR). This work was also supported by research grants from Guangdong Government HZQB-KCZYZ-2021014 and 2019B121205009 (YG and HZ). DATC, JMR and SR acknowledge support from the National Institutes of Health Fogarty Institute (R01TW0008246). JMR acknowledges support from the Medical Research Council (MR/S004793/1) and the Engineering and Physical Sciences Research Council (EP/N014499/1). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Affiliation(s)
- Bingyi Yang
- Department of Biology, University of FloridaGainesvilleUnited States
- Emerging Pathogens Institute, University of FloridaGainesvilleUnited States
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong KongHong KongChina
| | - Bernardo García-Carreras
- Department of Biology, University of FloridaGainesvilleUnited States
- Emerging Pathogens Institute, University of FloridaGainesvilleUnited States
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public HealthBaltimoreUnited States
- Department of Epidemiology, UNC Gillings School of Global Public HealthChapel HillUnited States
- UNC Carolina Population CenterChapel HillUnited States
| | - Jonathan M Read
- Centre for Health Informatics Computing and Statistics, Lancaster UniversityLancasterUnited Kingdom
| | - Huachen Zhu
- Guangdong‐Hong Kong Joint Laboratory of Emerging Infectious Diseases/MOE Joint Laboratory for International Collaboration in Virology and Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou UniversityShantouChina
- State Key Laboratory of Emerging Infectious Diseases / World Health Organization Influenza Reference Laboratory, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong KongHong KongChina
- EKIH (Gewuzhikang) Pathogen Research InstituteGuangdongChina
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | - James A Hay
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College LondonLondonUnited Kingdom
- Center for Communicable Disease Dynamics, Harvard TH Chan School of Public HealthBostonUnited States
| | - Kin O Kwok
- The Jockey Club School of Public Health and Primary Care, Chinese University of Hong KongHong KongChina
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong KongHong KongChina
- Shenzhen Research Institute of The Chinese University of Hong KongGuangdongChina
| | - Ruiyun Shen
- Guangzhou No.12 Hospital, GuangzhouGuangdongChina
| | - Chao Q Jiang
- Guangzhou No.12 Hospital, GuangzhouGuangdongChina
| | - Yi Guan
- Guangdong‐Hong Kong Joint Laboratory of Emerging Infectious Diseases/MOE Joint Laboratory for International Collaboration in Virology and Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou UniversityShantouChina
- State Key Laboratory of Emerging Infectious Diseases / World Health Organization Influenza Reference Laboratory, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong KongHong KongChina
- EKIH (Gewuzhikang) Pathogen Research InstituteGuangdongChina
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College LondonLondonUnited Kingdom
| | - Derek A Cummings
- Department of Biology, University of FloridaGainesvilleUnited States
- Emerging Pathogens Institute, University of FloridaGainesvilleUnited States
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de Jong SPJ, Felix Garza ZC, Gibson JC, Han AX, van Leeuwen S, de Vries RP, Boons GJ, van Hoesel M, de Haan K, van Groeningen LE, Hulme KD, van Willigen HDG, Wynberg E, de Bree GJ, Matser A, Bakker M, van der Hoek L, Prins M, Kootstra NA, Eggink D, Nichols BE, de Jong MD, Russell CA. Potential impacts of prolonged absence of influenza virus circulation on subsequent epidemics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.02.05.22270494. [PMID: 36415458 PMCID: PMC9681055 DOI: 10.1101/2022.02.05.22270494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Background During the first two years of the COVID-19 pandemic, the circulation of seasonal influenza viruses was unprecedentedly low. This led to concerns that the lack of immune stimulation to influenza viruses combined with waning antibody titres could lead to increased susceptibility to influenza in subsequent seasons, resulting in larger and more severe epidemics. Methods We analyzed historical influenza virus epidemiological data from 2003-2019 to assess the historical frequency of near-absence of seasonal influenza virus circulation and its impact on the size and severity of subsequent epidemics. Additionally, we measured haemagglutination inhibition-based antibody titres against seasonal influenza viruses using longitudinal serum samples from 165 healthy adults, collected before and during the COVID-19 pandemic, and estimated how antibody titres against seasonal influenza waned during the first two years of the pandemic. Findings Low country-level prevalence of influenza virus (sub)types over one or more years occurred frequently before the COVID-19 pandemic and had relatively small impacts on subsequent epidemic size and severity. Additionally, antibody titres against seasonal influenza viruses waned negligibly during the first two years of the pandemic. Interpretation The commonly held notion that lulls in influenza virus circulation, as observed during the COVID-19 pandemic, will lead to larger and/or more severe subsequent epidemics might not be fully warranted, and it is likely that post-lull seasons will be similar in size and severity to pre-lull seasons. Funding European Research Council, Netherlands Organization for Scientific Research, Royal Dutch Academy of Sciences, Public Health Service of Amsterdam. Research in context Evidence before this study: During the first years of the COVID-19 pandemic, the incidence of seasonal influenza was unusually low, leading to widespread concerns of exceptionally large and/or severe influenza epidemics in the coming years. We searched PubMed and Google Scholar using a combination of search terms (i.e., "seasonal influenza", "SARS-CoV-2", "COVID-19", "low incidence", "waning rates", "immune protection") and critically considered published articles and preprints that studied or reviewed the low incidence of seasonal influenza viruses since the start of the COVID-19 pandemic and its potential impact on future seasonal influenza epidemics. We found a substantial body of work describing how influenza virus circulation was reduced during the COVID-19 pandemic, and a number of studies projecting the size of future epidemics, each positing that post-pandemic epidemics are likely to be larger than those observed pre-pandemic. However, it remains unclear to what extent the assumed relationship between accumulated susceptibility and subsequent epidemic size holds, and it remains unknown to what extent antibody levels have waned during the COVID-19 pandemic. Both are potentially crucial for accurate prediction of post-pandemic epidemic sizes.Added value of this study: We find that the relationship between epidemic size and severity and the magnitude of circulation in the preceding season(s) is decidedly more complex than assumed, with the magnitude of influenza circulation in preceding seasons having only limited effects on subsequent epidemic size and severity. Rather, epidemic size and severity are dominated by season-specific effects unrelated to the magnitude of circulation in the preceding season(s). Similarly, we find that antibody levels waned only modestly during the COVID-19 pandemic.Implications of all the available evidence: The lack of changes observed in the patterns of measured antibody titres against seasonal influenza viruses in adults and nearly two decades of epidemiological data suggest that post-pandemic epidemic sizes will likely be similar to those observed pre-pandemic, and challenge the commonly held notion that the widespread concern that the near-absence of seasonal influenza virus circulation during the COVID-19 pandemic, or potential future lulls, are likely to result in larger influenza epidemics in subsequent years.
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Caradonna TM, Ronsard L, Yousif AS, Windsor IW, Hecht R, Bracamonte-Moreno T, Roffler AA, Maron MJ, Maurer DP, Feldman J, Marchiori E, Barnes RM, Rohrer D, Lonberg N, Oguin TH, Sempowski GD, Kepler TB, Kuraoka M, Lingwood D, Schmidt AG. An epitope-enriched immunogen expands responses to a conserved viral site. Cell Rep 2022; 41:111628. [PMID: 36351401 PMCID: PMC9883670 DOI: 10.1016/j.celrep.2022.111628] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 08/22/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
Pathogens evade host humoral responses by accumulating mutations in surface antigens. While variable, there are conserved regions that cannot mutate without compromising fitness. Antibodies targeting these conserved epitopes are often broadly protective but remain minor components of the repertoire. Rational immunogen design leverages a structural understanding of viral antigens to modulate humoral responses to favor these responses. Here, we report an epitope-enriched immunogen presenting a higher copy number of the influenza hemagglutinin (HA) receptor-binding site (RBS) epitope relative to other B cell epitopes. Immunization in a partially humanized murine model imprinted with an H1 influenza shows H1-specific serum and >99% H1-specific B cells being RBS-directed. Single B cell analyses show a genetically restricted response that structural analysis defines as RBS-directed antibodies engaging the RBS with germline-encoded contacts. These data show how epitope enrichment expands B cell responses toward conserved epitopes and advances immunogen design approaches for next-generation viral vaccines.
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Affiliation(s)
| | - Larance Ronsard
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Ashraf S Yousif
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | | | - Rachel Hecht
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | | | - Anne A Roffler
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Max J Maron
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Daniel P Maurer
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Jared Feldman
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Elisa Marchiori
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Ralston M Barnes
- Bristol-Myers Squibb, 700 Bay Road, Redwood City, CA 94063-2478, USA
| | - Daniel Rohrer
- Bristol-Myers Squibb, 700 Bay Road, Redwood City, CA 94063-2478, USA
| | - Nils Lonberg
- Bristol-Myers Squibb, 700 Bay Road, Redwood City, CA 94063-2478, USA
| | - Thomas H Oguin
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham NC 27703, USA
| | - Gregory D Sempowski
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham NC 27703, USA
| | - Thomas B Kepler
- Department of Microbiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Masayuki Kuraoka
- Department of Immunology, Duke University, Durham, NC 27710, USA
| | - Daniel Lingwood
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.
| | - Aaron G Schmidt
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA; Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA.
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46
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Rosu ME, Lexmond P, Bestebroer TM, Hauser BM, Smith DJ, Herfst S, Fouchier RAM. Substitutions near the HA receptor binding site explain the origin and major antigenic change of the B/Victoria and B/Yamagata lineages. Proc Natl Acad Sci U S A 2022; 119:e2211616119. [PMID: 36215486 PMCID: PMC9586307 DOI: 10.1073/pnas.2211616119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/13/2022] [Indexed: 11/18/2022] Open
Abstract
Influenza B virus primarily infects humans, causing seasonal epidemics globally. Two antigenic variants-Victoria-like and Yamagata-like-were detected in the 1980s, of which the molecular basis of emergence is still incompletely understood. Here, the antigenic properties of a unique collection of historical virus isolates, sampled from 1962 to 2000 and passaged exclusively in mammalian cells to preserve antigenic properties, were determined with the hemagglutination inhibition assay and an antigenic map was built to quantify and visualize the divergence of the lineages. The antigenic map revealed only three distinct antigenic clusters-Early, Victoria, and Yamagata-with relatively little antigenic diversity in each cluster until 2000. Viruses with Victoria-like antigenic properties emerged around 1972 and diversified subsequently into two genetic lineages. Viruses with Yamagata-like antigenic properties evolved from one lineage and became clearly antigenically distinct from the Victoria-like viruses around 1988. Recombinant mutant viruses were tested to show that insertions and deletions (indels), as observed frequently in influenza B virus hemagglutinin, had little effect on antigenic properties. In contrast, amino-acid substitutions at positions 148, 149, 150, and 203, adjacent to the hemagglutinin receptor binding site, determined the main antigenic differences between the Early, Victoria-like, and Yamagata-like viruses. Surprisingly, substitutions at two of the four positions reverted in recent viruses of the Victoria lineage, resulting in antigenic properties similar to viruses circulating ∼50 y earlier. These data shed light on the antigenic diversification of influenza viruses and suggest there may be limits to the antigenic evolution of influenza B virus.
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Affiliation(s)
- Miruna E. Rosu
- Department of Viroscience, Erasmus Medical Centre, Rotterdam 3015 CE, The Netherlands
| | - Pascal Lexmond
- Department of Viroscience, Erasmus Medical Centre, Rotterdam 3015 CE, The Netherlands
| | - Theo M. Bestebroer
- Department of Viroscience, Erasmus Medical Centre, Rotterdam 3015 CE, The Netherlands
| | - Blake M. Hauser
- Center for Pathogen Evolution, Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Derek J. Smith
- Center for Pathogen Evolution, Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Sander Herfst
- Department of Viroscience, Erasmus Medical Centre, Rotterdam 3015 CE, The Netherlands
| | - Ron A. M. Fouchier
- Department of Viroscience, Erasmus Medical Centre, Rotterdam 3015 CE, The Netherlands
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47
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Makau DN, Lycett S, Michalska-Smith M, Paploski IAD, Cheeran MCJ, Craft ME, Kao RR, Schroeder DC, Doeschl-Wilson A, VanderWaal K. Ecological and evolutionary dynamics of multi-strain RNA viruses. Nat Ecol Evol 2022; 6:1414-1422. [PMID: 36138206 DOI: 10.1038/s41559-022-01860-6] [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: 12/21/2021] [Accepted: 07/28/2022] [Indexed: 11/09/2022]
Abstract
Potential interactions among co-circulating viral strains in host populations are often overlooked in the study of virus transmission. However, these interactions probably shape transmission dynamics by influencing host immune responses or altering the relative fitness among co-circulating strains. In this Review, we describe multi-strain dynamics from ecological and evolutionary perspectives, outline scales in which multi-strain dynamics occur and summarize important immunological, phylogenetic and mathematical modelling approaches used to quantify interactions among strains. We also discuss how host-pathogen interactions influence the co-circulation of pathogens. Finally, we highlight outstanding questions and knowledge gaps in the current theory and study of ecological and evolutionary dynamics of multi-strain viruses.
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Affiliation(s)
- Dennis N Makau
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
| | | | | | - Igor A D Paploski
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
| | - Maxim C-J Cheeran
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
| | - Meggan E Craft
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, USA
| | - Rowland R Kao
- Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Declan C Schroeder
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
- School of Biological Sciences, University of Reading, Reading, UK
| | | | - Kimberly VanderWaal
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA.
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48
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Rahman S, Hasan M, Alam MS, Uddin KMM, Moni S, Rahman M. The evolutionary footprint of influenza A subtype H3N2 strains in Bangladesh: implication of vaccine strain selection. Sci Rep 2022; 12:16186. [PMID: 36171388 PMCID: PMC9519982 DOI: 10.1038/s41598-022-20179-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
In February each year, World Health Organization (WHO) recommends candidate vaccine viruses for the forthcoming northern hemisphere (NH) season; however, the influenza season in the temperate zone of NH begins in October. During egg- or cell culture-propagation, the vaccine viruses become too old to confer the highest match with the latest strains, impacting vaccine effectiveness. Therefore, an alternative strategy like mRNA-based vaccine using the most recent strains should be considered. We analyzed influenza A subtype H3N2 strains circulating in NH during the last 10 years (2009-2020). Phylogenetic analysis revealed multiple clades of influenza strains circulating every season, which had substantial mismatches with WHO-recommended vaccine strains. The clustering pattern suggests that influenza A subtype H3N2 strains are not fixed to the specific geographical region but circulate globally in the same season. By analyzing 39 seasons from eight NH countries with the highest vaccine coverage, we also provide evidence that the influenza A, subtype H3N2 strains from South and Southeast Asia, including Bangladesh, had the highest genetic proximity to the NH strains. Furthermore, insilico analysis showed minimal effect on the Bangladeshi HA protein structure, indicating the stability of Bangladeshi strains. Therefore, we propose that Bangladeshi influenza strains represent genetic makeup that may better fit and serve as the most suitable candidate vaccine viruses for the forthcoming NH season.
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Affiliation(s)
- Sezanur Rahman
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Mehedi Hasan
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Md Shaheen Alam
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - K M Main Uddin
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Sayra Moni
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Mustafizur Rahman
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh. .,Genomics Centre, icddr,b, Mohakhali, Dhaka, 1212, Bangladesh.
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49
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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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50
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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