1
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Russell CA, Fouchier RAM, Ghaswalla P, Park Y, Vicic N, Ananworanich J, Nachbagauer R, Rudin D. Seasonal influenza vaccine performance and the potential benefits of mRNA vaccines. Hum Vaccin Immunother 2024; 20:2336357. [PMID: 38619079 PMCID: PMC11020595 DOI: 10.1080/21645515.2024.2336357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/26/2024] [Indexed: 04/16/2024] Open
Abstract
Influenza remains a public health threat, partly due to suboptimal effectiveness of vaccines. One factor impacting vaccine effectiveness is strain mismatch, occurring when vaccines no longer match circulating strains due to antigenic drift or the incorporation of inadvertent (eg, egg-adaptive) mutations during vaccine manufacturing. In this review, we summarize the evidence for antigenic drift of circulating viruses and/or egg-adaptive mutations occurring in vaccine strains during the 2011-2020 influenza seasons. Evidence suggests that antigenic drift led to vaccine mismatch during four seasons and that egg-adaptive mutations caused vaccine mismatch during six seasons. These findings highlight the need for alternative vaccine development platforms. Recently, vaccines based on mRNA technology have demonstrated efficacy against SARS-CoV-2 and respiratory syncytial virus and are under clinical evaluation for seasonal influenza. We discuss the potential for mRNA vaccines to address strain mismatch, as well as new multi-component strategies using the mRNA platform to improve vaccine effectiveness.
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Affiliation(s)
- Colin A. Russell
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Ron A. M. Fouchier
- Department of Viroscience, Erasmus Medical Center, Rotterdam, the Netherlands
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2
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Forna A, Weedop KB, Damodaran L, Hassell N, Kondor R, Bahl J, Drake JM, Rohani P. Sequence-based detection of emerging antigenically novel influenza A viruses. Proc Biol Sci 2024; 291:20240790. [PMID: 39140324 PMCID: PMC11323087 DOI: 10.1098/rspb.2024.0790] [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: 10/04/2023] [Revised: 05/21/2024] [Accepted: 07/11/2024] [Indexed: 08/15/2024] Open
Abstract
The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.
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Affiliation(s)
- Alpha Forna
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - K. Bodie Weedop
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
| | - Lambodhar Damodaran
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - Norman Hassell
- Centers for Disease Control and Prevention, Atlanta, GA30329, USA
| | - Rebecca Kondor
- Centers for Disease Control and Prevention, Atlanta, GA30329, USA
| | - Justin Bahl
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Center for Influenza Disease & Emergence Research (CIDER), Athens, GA30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Center for Influenza Disease & Emergence Research (CIDER), Athens, GA30602, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA30602, USA
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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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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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5
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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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6
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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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7
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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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8
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Park SY, Lindner MS, Brick K, Noll N, Ounit R, Noa LJ, Sabzwari R, Trible R, Sniffen JC, Roth P, Khan A, Rodriguez A, Sahra S, Davis MJ, Brar IS, Balasundaram G, Nolte FS, Blauwkamp TA, Perkins BA, Bercovici S. Detection of Mpox Virus Using Microbial Cell-Free DNA: The Potential of Pathogen-Agnostic Sequencing for Rapid Identification of Emerging Pathogens. J Infect Dis 2024; 229:S144-S155. [PMID: 37824825 DOI: 10.1093/infdis/jiad452] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/09/2023] [Accepted: 10/09/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND The 2022 global outbreak of Monkeypox virus (MPXV) highlighted challenges with polymerase chain reaction detection as divergent strains emerged and atypical presentations limited the applicability of swab sampling. Recommended testing in the United States requires a swab of lesions, which arise late in infection and may be unrecognized. We present MPXV detections using plasma microbial cell-free DNA (mcfDNA) sequencing. METHODS Fifteen plasma samples from 12 case-patients were characterized through mcfDNA sequencing. Assay performance was confirmed through in silico inclusivity and exclusivity assessments. MPXV isolates were genotyped using mcfDNA, and phylodynamic information was imputed using publicly available sequences. RESULTS MPXV mcfDNA was detected in 12 case-patients. Mpox was not suspected in 5, with 1 having documented resolution of mpox >6 months previously. Six had moderate to severe mpox, supported by high MPXV mcfDNA concentrations; 4 died. In 7 case-patients, mcfDNA sequencing detected coinfections. Genotyping by mcfDNA sequencing identified 22 MPXV mutations at 10 genomic loci in 9 case-patients. Consistent with variation observed in the 2022 outbreak, 21 of 22 variants were G > A/C > T. Phylogenetic analyses imputed isolates to sublineages arising at different time points and from different geographic locations. CONCLUSIONS We demonstrate the potential of plasma mcfDNA sequencing to detect, quantify, and, for acute infections with high sequencing coverage, subtype MPXV using a single noninvasive test. Sequencing plasma mcfDNA may augment existing mpox testing in vulnerable patient populations or in patients with atypical symptoms or unrecognized mpox. Strain type information may supplement disease surveillance and facilitate tracking emerging pathogens.
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Affiliation(s)
- Sarah Y Park
- Medical Affairs, Karius, Inc, Redwood City, California
| | | | - Kevin Brick
- Analytics, Karius, Inc., Redwood City, California
| | | | - Rachid Ounit
- Analytics, Karius, Inc., Redwood City, California
| | - Luis J Noa
- Infectious Disease Section, AdventHealth Orlando, Florida
| | - Rabeeya Sabzwari
- Infectious Diseases, Edward Hines Jr Veterans Affairs Hospital, Hines, Illinois
| | | | | | - Prerana Roth
- Infectious Diseases, Prisma Health-Upstate, Greenville, South Carolina
| | - Amir Khan
- Infectious Diseases, Carle Foundation Hospital, Urbana, Illinois
| | | | - Syeda Sahra
- Department of Infectious Diseases, Oklahoma University Medical Center, Oklahoma City
| | - Michael J Davis
- Department of Infectious Diseases and International Medicine, University of Minnesota, Minneapolis, MN
| | - Inderjeet S Brar
- Infectious Diseases, Baptist Memorial Health Care, Memphis, Tennessee
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9
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Loes AN, Tarabi RAL, Huddleston J, Touyon L, Wong SS, Cheng SMS, Leung NHL, Hannon WW, Bedford T, Cobey S, Cowling BJ, Bloom JD. High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584176. [PMID: 38496577 PMCID: PMC10942427 DOI: 10.1101/2024.03.08.584176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we have developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes via a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately one month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals, and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers six months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. This study demonstrates the utility of high-throughput sequencing-based neutralization assays that enable titers to be simultaneously measured against many different viral strains. We provide a detailed experimental protocol (DOI: https://dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and a computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.
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Affiliation(s)
- Andrea N Loes
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Rosario Araceli L Tarabi
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - John Huddleston
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Lisa Touyon
- HKU-Pasteur Research Pole, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Sook San Wong
- HKU-Pasteur Research Pole, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Samuel M S Cheng
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Nancy H L Leung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - William W Hannon
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA 98109, USA
| | - Trevor Bedford
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Jesse D Bloom
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
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10
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Hodgson D, Sánchez-Ovando S, Carolan L, Liu Y, Hadiprodjo AJ, Fox A, Sullivan SG, Kucharski AJ. Quantifying the impact of pre-vaccination titre and vaccination history on influenza vaccine immunogenicity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.24.24301614. [PMID: 38343865 PMCID: PMC10854332 DOI: 10.1101/2024.01.24.24301614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Epidemiological studies suggest that heterogeneity in influenza vaccine antibody response is associated with host factors, including pre-vaccination immune status, age, gender, and vaccination history. However, the pattern of reported associations varies between studies. To better understand the underlying influences on antibody responses, we combined host factors and vaccine-induced in-host antibody kinetics from a cohort study conducted across multiple seasons with a unified analysis framework. We developed a flexible individual-level Bayesian model to estimate associations and interactions between host factors, including pre-vaccine HAI titre, age, sex, vaccination history and study setting, and vaccine-induced HAI titre antibody boosting and waning. We applied the model to derive population-level and individual effects of post-vaccine antibody kinetics for vaccinating and circulating strains for A(H1N1) and A(H3N2) influenza subtypes. We found that post-vaccine HAI titre dynamics were significantly influenced by pre-vaccination HAI titre and vaccination history and that lower pre-vaccination HAI titre results in longer durations of seroprotection (HAI titre equal to 1:40 or higher). Consequently, for A(H1N1), our inference finds that the expected duration of seroprotection post-vaccination was 171 (95% Posterior Predictive Interval[PPI] 128-220) and 159 (95% PPI 120-200) days longer for those who are infrequently vaccinated (<2 vaccines in last five years) compared to those who are frequently vaccinated (2 or more vaccines in the last five years) at pre-vaccination HAI titre values of 1:10 and 1:20 respectively. In addition, we found significant differences in the empirical distributions that describe the individual-level duration of seroprotection for A(H1N1) circulating strains. In future, studies that rely on serological endpoints should include the impact of pre-vaccine HAI titre and prior vaccination status on seropositivity and seroconversion estimates, as these significantly influence an individual's post-vaccination antibody kinetics.
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Affiliation(s)
- David Hodgson
- Center of Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Stephany Sánchez-Ovando
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Louise Carolan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Yi Liu
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - A. Jessica Hadiprodjo
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Annette Fox
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Sheena G. Sullivan
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Adam J. Kucharski
- Center of Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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11
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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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12
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Meijers M, Ruchnewitz D, Eberhardt J, Łuksza M, Lässig M. Population immunity predicts evolutionary trajectories of SARS-CoV-2. Cell 2023; 186:5151-5164.e13. [PMID: 37875109 PMCID: PMC10964984 DOI: 10.1016/j.cell.2023.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/26/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023]
Abstract
The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany.
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13
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Markin A, Wagle S, Grover S, Vincent Baker AL, Eulenstein O, Anderson TK. PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny. Syst Biol 2023; 72:1052-1063. [PMID: 37208300 PMCID: PMC10627562 DOI: 10.1093/sysbio/syad028] [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: 09/13/2022] [Revised: 04/26/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023] Open
Abstract
The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires objective subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than existing approaches. To demonstrate its utility, we applied parnas to 1) quantify SARS-CoV-2 genetic diversity over time, 2) select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data, and 3) identify gaps in H3N2 human influenza A virus vaccine coverage. We suggest that our method, through the objective selection of representatives in a phylogeny, provides criteria for quantifying genetic diversity that has application in the the rational design of multivalent vaccines and genomic epidemiology. PARNAS is available at https://github.com/flu-crew/parnas.
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Affiliation(s)
- Alexey Markin
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
| | - Sanket Wagle
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
| | - Siddhant Grover
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
| | - Amy L Vincent Baker
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
| | - Oliver Eulenstein
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
| | - Tavis K Anderson
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
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14
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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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15
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Puente-Massaguer E, Vasilev K, Beyer A, Loganathan M, Francis B, Scherm MJ, Arunkumar GA, González-Domínguez I, Zhu X, Wilson IA, Coughlan L, Sun W, Palese P, Krammer F. Chimeric hemagglutinin split vaccines elicit broadly cross-reactive antibodies and protection against group 2 influenza viruses in mice. SCIENCE ADVANCES 2023; 9:eadi4753. [PMID: 37703367 PMCID: PMC10499326 DOI: 10.1126/sciadv.adi4753] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/11/2023] [Indexed: 09/15/2023]
Abstract
Seasonal influenza virus vaccines are effective when they are well matched to circulating strains. Because of antigenic drift/change in the immunodominant hemagglutinin (HA) head domain, annual vaccine reformulations are necessary to maintain a match with circulating strains. In addition, seasonal vaccines provide little to no protection against newly emerging pandemic strains. Sequential vaccination with chimeric HA (cHA) constructs has been proven to direct the immune response toward the immunosubdominant but more conserved HA stalk domain. In this study, we show that immunization with group 2 cHA split vaccines in combination with the CpG 1018 adjuvant elicits broadly cross-reactive antibodies against all group 2 HAs, as well as systemic and local antigen-specific T cell responses. Antibodies elicited after sequential vaccination are directed to conserved regions of the HA such as the stalk and the trimer interface and also to the N2 neuraminidase (NA). Immunized mice were fully protected from challenge with a broad panel of influenza A viruses.
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Affiliation(s)
- Eduard Puente-Massaguer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kirill Vasilev
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Annika Beyer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Madhumathi Loganathan
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Benjamin Francis
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael J. Scherm
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | | | - Xueyong Zhu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Ian A. Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
- Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Lynda Coughlan
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Center for Vaccine Development and Global Health (CVD), University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Weina Sun
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Peter Palese
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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16
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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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17
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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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18
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Moulana A, Dupic T, Phillips AM, Desai MM. Genotype-phenotype landscapes for immune-pathogen coevolution. Trends Immunol 2023; 44:384-396. [PMID: 37024340 PMCID: PMC10147585 DOI: 10.1016/j.it.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 04/07/2023]
Abstract
Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Angela M Phillips
- Department of Microbiology and Immunology, University of California at San Francisco, San Francisco, CA 94143, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA; NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA; Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA.
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19
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Hood N, Flannery B, Gaglani M, Beeram M, Wernli K, Jackson ML, Martin ET, Monto AS, Zimmerman R, Raviotta J, Belongia EA, McLean HQ, Kim S, Patel MM, Chung JR. Influenza Vaccine Effectiveness Among Children: 2011-2020. Pediatrics 2023; 151:e2022059922. [PMID: 36960655 PMCID: PMC10071433 DOI: 10.1542/peds.2022-059922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Infants and children are at increased risk of severe influenza virus infection and its complications. Influenza vaccine effectiveness (VE) varies by age, influenza season, and influenza virus type/subtype. This study's objective was to examine the effectiveness of inactivated influenza vaccine against outpatient influenza illness in the pediatric population over 9 influenza seasons after the 2009 A(H1N1) pandemic. METHODS During the 2011-2012 through the 2019-2020 influenza seasons at outpatient clinics at 5 sites of the US Influenza Vaccine Effectiveness Network, children aged 6 months to 17 years with an acute respiratory illness were tested for influenza using real-time, reverse-transcriptase polymerase chain reaction. Vaccine effectiveness was estimated using a test-negative design. RESULTS Among 24 148 enrolled children, 28% overall tested positive for influenza, 3017 tested positive for influenza A(H3N2), 1459 for influenza A(H1N1)pdm09, and 2178 for influenza B. Among all enrollees, 39% overall were vaccinated, with 29% of influenza cases and 43% of influenza-negative controls vaccinated. Across all influenza seasons, the pooled VE for any influenza was 46% (95% confidence interval, 43-50). Overall and by type/subtype, VE against influenza illness was highest among children in the 6- to 59-month age group compared with older pediatric age groups. VE was lowest for influenza A(H3N2) virus infection. CONCLUSIONS Analysis of multiple seasons suggested substantial benefit against outpatient illness. Investigation of host-specific or virus-related mechanisms that may result in differences by age and virus type/subtype may help further efforts to promote increased vaccination coverage and other influenza-related preventative measures.
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Affiliation(s)
- Nicole Hood
- Influenza Division, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Brendan Flannery
- Influenza Division, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Manjusha Gaglani
- Texas A&M University Health Science Center College of Medicine, Temple, Texas
- Baylor Scott & White Health Research Institute, Temple, Texas
| | - Madhava Beeram
- Texas A&M University Health Science Center College of Medicine, Temple, Texas
- Baylor Scott & White Health Research Institute, Temple, Texas
| | - Karen Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Michael L. Jackson
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Emily T. Martin
- School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Arnold S. Monto
- School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Richard Zimmerman
- School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jonathan Raviotta
- School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | | | | | - Sara Kim
- Influenza Division, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Manish M. Patel
- Influenza Division, US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jessie R. Chung
- Influenza Division, US Centers for Disease Control and Prevention, Atlanta, Georgia
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20
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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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21
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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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22
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Li Y, Barton JP. Estimating linkage disequilibrium and selection from allele frequency trajectories. Genetics 2023; 223:iyac189. [PMID: 36610715 PMCID: PMC9991507 DOI: 10.1093/genetics/iyac189] [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: 10/14/2022] [Revised: 10/14/2022] [Accepted: 12/11/2022] [Indexed: 01/09/2023] Open
Abstract
Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.
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Affiliation(s)
- Yunxiao Li
- Department of Physics and Astronomy, University of California, Riverside, CA 92521, USA
| | - John P Barton
- Department of Physics and Astronomy, University of California, Riverside, CA 92521, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
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23
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Minimal Antigenic Evolution after a Decade of Norovirus GII.4 Sydney_2012 Circulation in Humans. J Virol 2023; 97:e0171622. [PMID: 36688654 PMCID: PMC9973034 DOI: 10.1128/jvi.01716-22] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Norovirus is a major human pathogen that can cause severe gastroenteritis in vulnerable populations. The extensive viral diversity presented by human noroviruses constitutes a major roadblock for the development of effective vaccines. In addition to the large number of genotypes, antigenically distinct variants of GII.4 noroviruses have chronologically emerged over the last 3 decades. The last variant to emerge, Sydney_2012, has been circulating at high incidence worldwide for over a decade. We analyzed 1449 capsid sequences from GII.4 Sydney_2012 viruses to determine genetic changes indicative of antigenic diversification. Phylogenetic analyses show that Sydney_2012 viruses scattered within the tree topology with no single cluster dominating during a given year or geographical location. Fourteen residues presented high variability, 7 of which mapped to 4 antigenic sites. Notably, ~52% of viruses presented mutations at 2 or more antigenic sites. Mutational patterns showed that residues 297 and 372, which map to antigenic site A, changed over time. Virus-like particles (VLPs) developed from wild-type Sydney_2012 viruses and engineered to display all mutations detected at antigenic sites were tested against polyclonal sera and monoclonal antibodies raised against Sydney_2012 and Farmington_Hills_2002 VLPs. Minimal changes in reactivity were detected with polyclonal sera and only 4 MAbs lost binding, with all mapping to antigenic site A. Notably, reversion of residues from Sydney_2012 reconstituted epitopes from ancestral GII.4 variants. Overall, this study demonstrates that, despite circulating for over a decade, Sydney_2012 viruses present minimal antigenic diversification and provides novel insights on the diversification of GII.4 noroviruses that could inform vaccine design. IMPORTANCE GII.4 noroviruses are the major cause of acute gastroenteritis in all age groups. This predominance has been attributed to the continued emergence of phylogenetically discrete variants that escape immune responses to previous infections. The last GII.4 variant to emerge, Sydney_2012, has been circulating at high incidence for over a decade, raising the question of whether this variant is undergoing antigenic diversification without presenting a major distinction at the phylogenetic level. Sequence analyses that include >1400 capsid sequences from GII.4 Sydney_2012 showed changes in 4 out of the 6 major antigenic sites. Notably, while changes were detected in one of the most immunodominant sites over time, these resulted in minimal changes in the antigenic profile of these viruses. This study provides new insights on the mechanism governing the antigenic diversification of GII.4 norovirus that could help in the development of cross-protective vaccines to human noroviruses.
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24
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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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25
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Yin R, Thwin NN, Zhuang P, Lin Z, Kwoh CK. IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3497-3506. [PMID: 34469306 DOI: 10.1109/tcbb.2021.3108971] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we apply a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combining the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.
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26
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Algorithmically-guided discovery of viral epitopes via linguistic parsing: Problem formulation and solving by soft computing. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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27
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Optimal sequence-based design for multi-antigen HIV-1 vaccines using minimally distant antigens. PLoS Comput Biol 2022; 18:e1010624. [PMID: 36315492 PMCID: PMC9621458 DOI: 10.1371/journal.pcbi.1010624] [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: 01/11/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
The immense global diversity of HIV-1 is a significant obstacle to developing a safe and effective vaccine. We recently showed that infections established with multiple founder variants are associated with the development of neutralization breadth years later. We propose a novel vaccine design strategy that integrates the variability observed in acute HIV-1 infections with multiple founder variants. We developed a probabilistic model to simulate this variability, yielding a set of sequences that present the minimal diversity seen in an infection with multiple founders. We applied this model to a subtype C consensus sequence for the Envelope (Env) (used as input) and showed that the simulated Env sequences mimic the mutational landscape of an infection with multiple founder variants, including diversity at antibody epitopes. The derived set of multi-founder-variant-like, minimally distant antigens is designed to be used as a vaccine cocktail specific to a HIV-1 subtype or circulating recombinant form and is expected to promote the development of broadly neutralizing antibodies. Diverse HIV-1 populations are generally thought to promote neutralizing responses. Current leading HIV-1 vaccine design strategies maximize the distance between antigens to attempt to cover global HIV-1 diversity or serialize immunizations to recapitulate the temporal evolution of HIV-1 during infection. To date, no vaccine has elicited broadly neutralizing antibodies. As we recently demonstrated that infection with multiple HIV-1 founder variants is predictive of neutralization breadth, we propose a novel strategy that endeavors to promote the development of broadly neutralizing antibodies by replicating the diversity of multi-founder variant acute infections. By training an HIV-1 Env consensus sequence on the diversity from acute infections with multiple founders, we derived in silico a set of minimally distant antigens that is representative of the diversity seen in a multi-founder acute infection. As the model is particular to the input sequence, it can produce antigens specific to any HIV-1 subtype or circulating recombinant form (CRF). We applied this to HIV-1 subtype C and obtained a set of minimally distant antigens that can be used as a vaccine cocktail.
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28
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Williams BJM, Ogbunugafor CB, Althouse BM, Hébert-Dufresne L. Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin. PNAS NEXUS 2022; 1:pgac143. [PMID: 36060623 PMCID: PMC9434636 DOI: 10.1093/pnasnexus/pgac143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Seasonal influenza kills hundreds of thousands every year, with multiple constantly changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity indicative of critical behavior. We argue that (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.
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Affiliation(s)
- Blake J M Williams
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
| | - C Brandon Ogbunugafor
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
- Department of Ecology and Evolutionary Biology, Yale University , New Haven, CT 06511, USA
- Santa Fe Institute , Santa Fe, NM 87501, USA
- Public Health Modeling Unit, Yale School of Public Health , New Haven, CT 06510, USA
| | - Benjamin M Althouse
- Institute for Disease Modeling, Global Health, Bill & Melinda Gates Foundation , Seattle, WA 98109, USA
- Information School, University of Washington , Seattle, WA 98195, USA
- Department of Biology, New Mexico State University , Las Cruces, NM 88003, USA
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont , Burlington VT 05405, USA
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29
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Deviatkin AA, Simonov RA, Trutneva KA, Maznina AA, Khavina EM, Volchkov PY. Universal Flu mRNA Vaccine: Promises, Prospects, and Problems. Vaccines (Basel) 2022; 10:vaccines10050709. [PMID: 35632465 PMCID: PMC9145388 DOI: 10.3390/vaccines10050709] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 02/05/2023] Open
Abstract
The seasonal flu vaccine is, essentially, the only known way to prevent influenza epidemics. However, this approach has limited efficacy due to the high diversity of influenza viruses. Several techniques could potentially overcome this obstacle. A recent first-in-human study of a chimeric hemagglutinin-based universal influenza virus vaccine demonstrated promising results. The coronavirus pandemic triggered the development of fundamentally new vaccine platforms that have demonstrated their effectiveness in humans. Currently, there are around a dozen messenger RNA and self-amplifying RNA flu vaccines in clinical or preclinical trials. However, the applicability of novel approaches for a universal influenza vaccine creation remains unclear. The current review aims to cover the current state of this problem and to suggest future directions for RNA-based flu vaccine development.
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Affiliation(s)
- Andrei A. Deviatkin
- The National Medical Research Center for Endocrinology, 117036 Moscow, Russia; (A.A.D.); (K.A.T.)
- Genome Engineering Lab, Life Sciences Research Center, Moscow Institute of Physics and Technology (National Research University), 141700 Dolgoprudniy, Russia; (R.A.S.); (A.A.M.); (E.M.K.)
| | - Ruslan A. Simonov
- Genome Engineering Lab, Life Sciences Research Center, Moscow Institute of Physics and Technology (National Research University), 141700 Dolgoprudniy, Russia; (R.A.S.); (A.A.M.); (E.M.K.)
| | - Kseniya A. Trutneva
- The National Medical Research Center for Endocrinology, 117036 Moscow, Russia; (A.A.D.); (K.A.T.)
- Genome Engineering Lab, Life Sciences Research Center, Moscow Institute of Physics and Technology (National Research University), 141700 Dolgoprudniy, Russia; (R.A.S.); (A.A.M.); (E.M.K.)
| | - Anna A. Maznina
- Genome Engineering Lab, Life Sciences Research Center, Moscow Institute of Physics and Technology (National Research University), 141700 Dolgoprudniy, Russia; (R.A.S.); (A.A.M.); (E.M.K.)
| | - Elena M. Khavina
- Genome Engineering Lab, Life Sciences Research Center, Moscow Institute of Physics and Technology (National Research University), 141700 Dolgoprudniy, Russia; (R.A.S.); (A.A.M.); (E.M.K.)
| | - Pavel Y. Volchkov
- The National Medical Research Center for Endocrinology, 117036 Moscow, Russia; (A.A.D.); (K.A.T.)
- Genome Engineering Lab, Life Sciences Research Center, Moscow Institute of Physics and Technology (National Research University), 141700 Dolgoprudniy, Russia; (R.A.S.); (A.A.M.); (E.M.K.)
- Correspondence:
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30
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Wang Y, Tang CY, Wan XF. Antigenic characterization of influenza and SARS-CoV-2 viruses. Anal Bioanal Chem 2022; 414:2841-2881. [PMID: 34905077 PMCID: PMC8669429 DOI: 10.1007/s00216-021-03806-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/21/2021] [Accepted: 11/24/2021] [Indexed: 12/24/2022]
Abstract
Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.
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Affiliation(s)
- Yang Wang
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Cynthia Y Tang
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Xiu-Feng Wan
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA.
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA.
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, USA.
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31
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Hu YF, Hu JC, Gong HR, Danchin A, Sun R, Chu H, Hung IFN, Yuen KY, To KKW, Zhang BZ, Yau T, Huang JD. Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants. Front Immunol 2022; 13:861050. [PMID: 35401572 PMCID: PMC8987580 DOI: 10.3389/fimmu.2022.861050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/25/2022] [Indexed: 11/13/2022] Open
Abstract
It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online.
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Affiliation(s)
- Ye-Fan Hu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing-Chu Hu
- Chinese Academy of Sciences Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hua-Rui Gong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Antoine Danchin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Kodikos Labs, Paris, France
| | - Ren Sun
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Hin Chu
- Department of Microbiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SARS, China
| | - Ivan Fan-Ngai Hung
- Department of Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Kwok Yung Yuen
- Department of Microbiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SARS, China
| | - Kelvin Kai-Wang To
- Department of Microbiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SARS, China
| | - Bao-Zhong Zhang
- Chinese Academy of Sciences Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Bao-Zhong Zhang, ; Thomas Yau, ; Jian-Dong Huang,
| | - Thomas Yau
- Department of Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
- *Correspondence: Bao-Zhong Zhang, ; Thomas Yau, ; Jian-Dong Huang,
| | - Jian-Dong Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Chinese Academy of Sciences Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen University, Guangzhou, China
- *Correspondence: Bao-Zhong Zhang, ; Thomas Yau, ; Jian-Dong Huang,
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Wraith S, Balmaseda A, Carrillo FAB, Kuan G, Huddleston J, Kubale J, Lopez R, Ojeda S, Schiller A, Lopez B, Sanchez N, Webby R, Nelson MI, Harris E, Gordon A. Homotypic protection against influenza in a pediatric cohort in Managua, Nicaragua. Nat Commun 2022; 13:1190. [PMID: 35246548 PMCID: PMC8897407 DOI: 10.1038/s41467-022-28858-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/16/2022] [Indexed: 12/02/2022] Open
Abstract
The period of protection from repeat infection following symptomatic influenza is not well established due to limited availability of longitudinal data. Using data from a pediatric cohort in Managua, Nicaragua, we examine the effects of natural influenza virus infection on subsequent infection with the same influenza virus subtype/lineage across multiple seasons, totaling 2,170 RT-PCR-confirmed symptomatic influenza infections. Logistic regression models assessed whether infection in the prior influenza season protected against homologous reinfection. We sequenced viruses from 2011-2019 identifying dominant clades and measuring antigenic distances between hemagglutinin clades. We observe homotypic protection from repeat infection in children infected with influenza A/H1N1pdm (OR 0.12, CI 0.02-0.88), A/H3N2 (OR 0.41, CI 0.24-0.73), and B/Victoria (OR 0.00, CI 0.00-0.14), but not with B/Yamagata viruses (OR 0.60, CI 0.09-2.10). Overall, protection wanes as time or antigenic distance increases. Individuals infected with one subtype or lineage of influenza virus have significantly lower odds of homologous reinfection for the following one to two years; after two years this protection wanes. This protection is demonstrated across multiple seasons, subtypes, and lineages among children.
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Affiliation(s)
- Steph Wraith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Angel Balmaseda
- Sustainable Sciences Institute, Managua, Nicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
| | - Fausto Andres Bustos Carrillo
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Guillermina Kuan
- Sustainable Sciences Institute, Managua, Nicaragua
- Centro de Salud Sócrates Flores Vivas, Ministry of Health, Managua, Nicaragua
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - John Kubale
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Roger Lopez
- Sustainable Sciences Institute, Managua, Nicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
| | - Sergio Ojeda
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Amy Schiller
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Brenda Lopez
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Nery Sanchez
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Richard Webby
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Eva Harris
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Aubree Gordon
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
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Brouwer AF, Balmaseda A, Gresh L, Patel M, Ojeda S, Schiller AJ, Lopez R, Webby RJ, Nelson MI, Kuan G, Gordon A. Birth cohort relative to an influenza A virus's antigenic cluster introduction drives patterns of children's antibody titers. PLoS Pathog 2022; 18:e1010317. [PMID: 35192673 PMCID: PMC8896668 DOI: 10.1371/journal.ppat.1010317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 03/04/2022] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
An individual's antibody titers to influenza A strains are a result of the complicated interplay between infection history, cross-reactivity, immune waning, and other factors. It has been challenging to disentangle how population-level patterns of humoral immunity change as a function of age, calendar year, and birth cohort from cross-sectional data alone. We analyzed 1,589 longitudinal sera samples from 260 children across three studies in Nicaragua, 2006-16. Hemagglutination inhibition (HAI) titers were determined against four H3N2 strains, one H1N1 strain, and two H1N1pdm strains. We assessed temporal patterns of HAI titers using an age-period-cohort modeling framework. We found that titers against a given virus depended on calendar year of serum collection and birth cohort but not on age. Titer cohort patterns were better described by participants' ages relative to year of likely introduction of the virus's antigenic cluster than by age relative to year of strain introduction or by year of birth. These cohort effects may be driven by a decreasing likelihood of early-life infection after cluster introduction and by more broadly reactive antibodies at a young age. H3N2 and H1N1 viruses had qualitatively distinct cohort patterns, with cohort patterns of titers to specific H3N2 strains reaching their peak in children born 3 years prior to that virus's antigenic cluster introduction and with titers to H1N1 and H1N1pdm strains peaking for children born 1-2 years prior to cluster introduction but not being dramatically lower for older children. Ultimately, specific patterns of strain circulation and antigenic cluster introduction may drive population-level antibody titer patterns in children.
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Affiliation(s)
- Andrew F. Brouwer
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (AFB); (AG)
| | - Angel Balmaseda
- Sócrates Flores Vivas Health Center, Ministry of Health, Managua, Nicaragua
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Lionel Gresh
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Mayuri Patel
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sergio Ojeda
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Amy J. Schiller
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Roger Lopez
- Sócrates Flores Vivas Health Center, Ministry of Health, Managua, Nicaragua
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Richard J. Webby
- Department of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, Tennessee, United States of America
| | - Martha I. Nelson
- Laboratory of Parasitic Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Guillermina Kuan
- Sustainable Sciences Institute, Managua, Nicaragua
- Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
| | - Aubree Gordon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (AFB); (AG)
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Kang M, Zanin M, Wong SS. Subtype H3N2 Influenza A Viruses: An Unmet Challenge in the Western Pacific. Vaccines (Basel) 2022; 10:vaccines10010112. [PMID: 35062773 PMCID: PMC8778411 DOI: 10.3390/vaccines10010112] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/07/2022] [Accepted: 01/07/2022] [Indexed: 02/04/2023] Open
Abstract
Subtype H3N2 influenza A viruses (A(H3N2)) have been the dominant strain in some countries in the Western Pacific region since the 2009 influenza A(H1N1) pandemic. Vaccination is the most effective way to prevent influenza; however, low vaccine effectiveness has been reported in some influenza seasons, especially for A(H3N2). Antigenic mismatch introduced by egg-adaptation during vaccine production between the vaccine and circulating viral stains is one of the reasons for low vaccine effectiveness. Here we review the extent of this phenomenon, the underlying molecular mechanisms and discuss recent strategies to ameliorate this, including new vaccine platforms that may provide better protection and should be considered to reduce the impact of A(H3N2) in the Western Pacific region.
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Affiliation(s)
- Min Kang
- School of Public Health, Southern Medical University, Guangzhou 510515, China;
- Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Mark Zanin
- State Key Laboratory for Respiratory Diseases and National Clinical Research Centre for Respiratory Disease, Guangzhou Medical University, 195 Dongfengxi Road, Guangzhou 511436, China;
- School of Public Health, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong, China
| | - Sook-San Wong
- State Key Laboratory for Respiratory Diseases and National Clinical Research Centre for Respiratory Disease, Guangzhou Medical University, 195 Dongfengxi Road, Guangzhou 511436, China;
- School of Public Health, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong, China
- Correspondence: ; Tel.: +86-178-2584-6078
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35
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Wang Y, Lei R, Nourmohammad A, Wu NC. Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing. eLife 2021; 10:e72516. [PMID: 34878407 PMCID: PMC8683081 DOI: 10.7554/elife.72516] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/07/2021] [Indexed: 11/13/2022] Open
Abstract
As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.
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Affiliation(s)
- Yiquan Wang
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
| | - Ruipeng Lei
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
| | - Armita Nourmohammad
- Department of Physics, University of WashingtonSeattleUnited States
- Max Planck Institute for Dynamics and Self-OrganizationGöttingenGermany
- Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Carle Illinois College of Medicine, University of Illinois at Urbana-ChampaignUrbanaUnited States
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McLeod DV, Wahl LM, Mideo N. Mosaic vaccination: How distributing different vaccines across a population could improve epidemic control. Evol Lett 2021; 5:458-471. [PMID: 34621533 PMCID: PMC8484727 DOI: 10.1002/evl3.252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/27/2021] [Indexed: 01/19/2023] Open
Abstract
Although vaccination has been remarkably effective against some pathogens, for others, rapid antigenic evolution results in vaccination conferring only weak and/or short‐lived protection. Consequently, considerable effort has been invested in developing more evolutionarily robust vaccines, either by targeting highly conserved components of the pathogen (universal vaccines) or by including multiple immunological targets within a single vaccine (multi‐epitope vaccines). An unexplored third possibility is to vaccinate individuals with one of a number of qualitatively different vaccines, creating a “mosaic” of individual immunity in the population. Here we explore whether a mosaic vaccination strategy can deliver superior epidemiological outcomes to “conventional” vaccination, in which all individuals receive the same vaccine. We suppose vaccine doses can be distributed between distinct vaccine “targets” (e.g., different surface proteins against which an immune response can be generated) and/or immunologically distinct variants at these targets (e.g., strains); the pathogen can undergo antigenic evolution at both targets. Using simple mathematical models, here we provide a proof‐of‐concept that mosaic vaccination often outperforms conventional vaccination, leading to fewer infected individuals, improved vaccine efficacy, and lower individual risks over the course of the epidemic.
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Affiliation(s)
- David V McLeod
- Centre D'Ecologie Fonctionnelle & Evolutive CNRS Montpellier 34090 France
| | - Lindi M Wahl
- Mathematics Western University London ON N6A 5B7 Canada
| | - Nicole Mideo
- Department of Ecology and Evolutionary Biology University of Toronto Toronto ON M5S 3B2 Canada
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Poor Vaccine Effectiveness against Influenza B-Related Severe Acute Respiratory Infection in a Temperate North Indian State (2019-2020): A Call for Further Data for Possible Vaccines with Closer Match. Vaccines (Basel) 2021; 9:vaccines9101094. [PMID: 34696202 PMCID: PMC8540586 DOI: 10.3390/vaccines9101094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/21/2021] [Accepted: 09/21/2021] [Indexed: 01/22/2023] Open
Abstract
Background: Influenza vaccine uptake in India is poor, and scant data exist regarding the effectiveness of influenza vaccine against hospitalization. Methods: From October 2019 to March 2020, vaccination status of 1219 patients (males n = 571, aged 5–107 years; median, 50 years) hospitalized with severe acute respiratory illness (SARI) was assessed. The patients were tested for influenza viruses and their subtypes by RT PCR. Sequencing of the HA gene was performed. Vaccine effectiveness (VE) against influenza subtypes was estimated by the test negative design. Results: A total of 336 (27.5%) patients were influenza-positive, with influenza B/Victoria accounting for 49.7% (n = 167), followed by influenza A/H1N1 (47.6%; n = 155) and influenza A/H3N2 (4.4%; n = 15). About 6.8% and 8.6% of the influenza-positive and influenza-negative patients, respectively, had been vaccinated. Adjusted VE for any influenza strain was 13% (95% CI −42 to 47), which for influenza B was 0%. HA sequencing revealed that influenza B samples mainly belonged to subclade V1A.3/133R with deletion of residues 163–165, as against the 2-aa deletion in influenza B/Colorado/06/2017 strain, contained in the vaccine. VE for influenza A/H1N1 was 55%. Conclusions: Poor VE due to a genetic mismatch between the circulating strain and the vaccine strain calls for efforts to reduce the mismatch.
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38
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Antigenic cartography reveals complexities of genetic determinants that lead to antigenic differences among pandemic GII.4 noroviruses. Proc Natl Acad Sci U S A 2021; 118:2015874118. [PMID: 33836574 PMCID: PMC7980451 DOI: 10.1073/pnas.2015874118] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Noroviruses are the predominant cause of acute gastroenteritis, with a single genotype (GII.4) responsible for the majority of infections. This prevalence is characterized by the periodic emergence of new variants that present substitutions at antigenic sites of the major structural protein (VP1), facilitating escape from herd immunity. Notably, the contribution of intravariant mutations to changes in antigenic properties is unknown. We performed a comprehensive antigenic analysis on a virus-like particle panel representing major chronological GII.4 variants to investigate diversification at the inter- and intravariant level. Immunoassays, neutralization data, and cartography analyses showed antigenic similarities between phylogenetically related variants, with major switches to antigenic properties observed over the evolution of GII.4 variants. Genetic analysis indicated that multiple coevolving amino acid changes-primarily at antigenic sites-are associated with the antigenic diversification of GII.4 variants. These data highlight complexities of the genetic determinants and provide a framework for the antigenic characterization of emerging GII.4 noroviruses.
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McLean HQ, Belongia EA. Influenza Vaccine Effectiveness: New Insights and Challenges. Cold Spring Harb Perspect Med 2021; 11:cshperspect.a038315. [PMID: 31988202 DOI: 10.1101/cshperspect.a038315] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Methods for assessing influenza vaccine efficacy and effectiveness have evolved over six decades. Randomized trials remain the gold standard for licensure, but observational studies are needed for annual assessment of vaccine effectiveness (VE). The test-negative design (TND) has become the de facto standard for these field studies. Patients who seek medical care with acute respiratory illness are tested for influenza, and VE is estimated from the odds of vaccination among influenza cases versus test-negative controls. VE varies across seasons, populations, age groups, and products, but VE estimates are consistently higher for A(H1N1)pdm09 and type B compared with A(H3N2). VE studies are increasingly used in combination with molecular epidemiology to understand the viral and immune system factors that drive clinical efficacy and effectiveness. The emerging field of immunoepidemiology offers the potential to understand complex host-virus interactions that affect vaccine protection, and this knowledge will contribute to universal vaccine development.
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Affiliation(s)
- Huong Q McLean
- Center for Clinical Epidemiology & Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin 54449, USA
| | - Edward A Belongia
- Center for Clinical Epidemiology & Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin 54449, USA
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40
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Yin R, Zhang Y, Zhou X, Kwoh CK. Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks. J Bioinform Comput Biol 2021; 18:2040002. [PMID: 32336247 DOI: 10.1142/s0219720020400028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the Pepitope<0.2 indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics.
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Affiliation(s)
- Rui Yin
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yu Zhang
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xinrui Zhou
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
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41
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Eguia RT, Crawford KHD, Stevens-Ayers T, Kelnhofer-Millevolte L, Greninger AL, Englund JA, Boeckh MJ, Bloom JD. A human coronavirus evolves antigenically to escape antibody immunity. PLoS Pathog 2021; 17:e1009453. [PMID: 33831132 PMCID: PMC8031418 DOI: 10.1371/journal.ppat.1009453] [Citation(s) in RCA: 147] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 03/04/2021] [Indexed: 12/31/2022] Open
Abstract
There is intense interest in antibody immunity to coronaviruses. However, it is unknown if coronaviruses evolve to escape such immunity, and if so, how rapidly. Here we address this question by characterizing the historical evolution of human coronavirus 229E. We identify human sera from the 1980s and 1990s that have neutralizing titers against contemporaneous 229E that are comparable to the anti-SARS-CoV-2 titers induced by SARS-CoV-2 infection or vaccination. We test these sera against 229E strains isolated after sera collection, and find that neutralizing titers are lower against these "future" viruses. In some cases, sera that neutralize contemporaneous 229E viral strains with titers >1:100 do not detectably neutralize strains isolated 8-17 years later. The decreased neutralization of "future" viruses is due to antigenic evolution of the viral spike, especially in the receptor-binding domain. If these results extrapolate to other coronaviruses, then it may be advisable to periodically update SARS-CoV-2 vaccines.
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Affiliation(s)
- Rachel T. Eguia
- Basic Sciences and Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Katharine H. D. Crawford
- Basic Sciences and Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America
| | - Terry Stevens-Ayers
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | | | - Alexander L. Greninger
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Janet A. Englund
- Seattle Children’s Research Institute, Seattle, Washington, United States of America
- Department of Pediatrics, University of Washington, Seattle, Washington, United States of America
| | - Michael J. Boeckh
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Jesse D. Bloom
- Basic Sciences and Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
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42
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Zeller MA, Gauger PC, Arendsee ZW, Souza CK, Vincent AL, Anderson TK. Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine. mSphere 2021; 6:e00920-20. [PMID: 33731472 PMCID: PMC8546707 DOI: 10.1128/msphere.00920-20] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 02/23/2021] [Indexed: 11/20/2022] Open
Abstract
The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of nonlinear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic data set of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Errors between predicted and measured distances of uncharacterized strains were 0.35, 0.61, 1.69, and 0.13 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine by using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen.IMPORTANCE Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impact food security, and are a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift. To overcome this, we developed nonlinear regression models that predict antigenic phenotype from genetic sequence data by training the model on hemagglutination inhibition assay results. We used these models to predict antigenic phenotype for previously uncharacterized IAV, ranked the importance of genetic features for antigenic phenotype, and experimentally validated our predictions. Our model predicted virus antigenic characteristics from genetic sequence data and provides a rapid and accurate method linking genetic sequence data to antigenic characteristics. This approach also provides support for public health by identifying viruses that are antigenically advanced from strains used as pandemic preparedness candidate vaccine viruses.
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Affiliation(s)
- Michael A Zeller
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Iowa, USA
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA
| | - Phillip C Gauger
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Iowa, USA
| | - Zebulun W Arendsee
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, Iowa, USA
| | - Carine K Souza
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, Iowa, USA
| | - Amy L Vincent
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, Iowa, USA
| | - Tavis K Anderson
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, Iowa, USA
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43
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Barrat-Charlaix P, Huddleston J, Bedford T, Neher RA. Limited Predictability of Amino Acid Substitutions in Seasonal Influenza Viruses. Mol Biol Evol 2021; 38:2767-2777. [PMID: 33749787 PMCID: PMC8233509 DOI: 10.1093/molbev/msab065] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Seasonal influenza viruses repeatedly infect humans in part because they rapidly change their antigenic properties and evade host immune responses, necessitating frequent updates of the vaccine composition. Accurate predictions of strains circulating in the future could therefore improve the vaccine match. Here, we studied the predictability of frequency dynamics and fixation of amino acid substitutions. Current frequency was the strongest predictor of eventual fixation, as expected in neutral evolution. Other properties, such as occurrence in previously characterized epitopes or high Local Branching Index (LBI) had little predictive power. Parallel evolution was found to be moderately predictive of fixation. Although the LBI had little power to predict frequency dynamics, it was still successful at picking strains representative of future populations. The latter is due to a tendency of the LBI to be high for consensus-like sequences that are closer to the future than the average sequence. Simulations of models of adapting populations, in contrast, show clear signals of predictability. This indicates that the evolution of influenza HA and NA, while driven by strong selection pressure to change, is poorly described by common models of directional selection such as traveling fitness waves.
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Affiliation(s)
- Pierre Barrat-Charlaix
- Biozentrum, Universität Basel, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - John Huddleston
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Trevor Bedford
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Richard A Neher
- Biozentrum, Universität Basel, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
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Identifying Potentially Beneficial Genetic Mutations Associated with Monophyletic Selective Sweep and a Proof-of-Concept Study with Viral Genetic Data. mSystems 2021; 6:6/1/e01151-20. [PMID: 33622855 PMCID: PMC8573955 DOI: 10.1128/msystems.01151-20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Genetic mutations play a central role in evolution. For a significantly beneficial mutation, a one-time mutation event suffices for the species to prosper and predominate through the process called “monophyletic selective sweep.” However, existing methods that rely on counting the number of mutation events to detect selection are unable to find such a mutation in selective sweep. We here introduce a method to detect mutations at the single amino acid/nucleotide level that could be responsible for monophyletic selective sweep evolution. The method identifies a genetic signature associated with selective sweep using the population genetic test statistic Tajima’s D. We applied the algorithm to ebolavirus, influenza A virus, and severe acute respiratory syndrome coronavirus 2 to identify known biologically significant mutations and unrecognized mutations associated with potential selective sweep. The method can detect beneficial mutations, possibly leading to discovery of previously unknown biological functions and mechanisms related to those mutations. IMPORTANCE In biology, research on evolution is important to understand the significance of genetic mutation. When there is a significantly beneficial mutation, a population of species with the mutation prospers and predominates, in a process called “selective sweep.” However, there are few methods that can find such a mutation causing selective sweep from genetic data. We here introduce a novel method to detect such mutations. Applying the method to the genomes of ebolavirus, influenza viruses, and the novel coronavirus, we detected known biologically significant mutations and identified mutations the importance of which is previously unrecognized. The method can deepen our understanding of molecular and evolutionary biology.
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45
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Abstract
Public health policymakers face increasingly complex questions and decisions and need to deal with an increasing quantity of data and information. For policy advisors to make use of scientific evidence and to assess available intervention options effectively and therefore indirectly for those deciding on and implementing public health policies, mathematical modeling has proven to be a useful tool. In some areas, the use of mathematical modeling for public health policy support has become standard practice at various levels of decision-making. To make use of this tool effectively within public health organizations, it is necessary to provide good infrastructure and ensure close collaboration between modelers and policymakers. Based on experience from a national public health institute, we discuss the strategic requirements for good modeling practice for public health. For modeling to be of maximal value for a public health institute, the organization and budgeting of mathematical modeling should be transparent, and a long-term strategy for how to position and develop mathematical modeling should be in place.
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46
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Lee EK, Tian H, Nakaya HI. Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks. Hum Vaccin Immunother 2020; 16:2690-2708. [PMID: 32750260 PMCID: PMC7734114 DOI: 10.1080/21645515.2020.1734397] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The rapid evolution of influenza A viruses poses a great challenge to vaccine development. Analytical and machine learning models have been applied to facilitate the process of antigenicity determination. In this study, we designed deep convolutional neural networks (CNNs) to predict Influenza antigenicity. Our model is the first that systematically analyzed 566 amino acid properties and 141 amino acid substitution matrices for their predictability. We then optimized the structure of the CNNs using particle swarm optimization. The optimal neural networks outperform other predictive models with a blind validation accuracy of 95.8%. Further, we applied our model to vaccine recommendations in the period 1997 to 2011 and contrasted the performance of previous vaccine recommendations using traditional experimental approaches. The results show that our model outperforms the WHO recommendation and other existing models and could potentially improve the vaccine recommendation process. Our results show that WHO often selects virus strains with small variation from year to year and learns slowly and recovers once coverage dips very low. In contrast, the influenza strains selected via our CNN model can differ quite drastically from year to year and exhibit consistently good coverage. In summary, we have designed a comprehensive computational pipeline for optimizing a CNN in the modeling of Influenza A antigenicity and vaccine recommendation. It is more cost and time-effective when compared to traditional hemagglutination inhibition assay analysis. The modeling framework is flexible and can be adopted to study other type of viruses.
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Affiliation(s)
- Eva K Lee
- Center for Operations Research in Medicine and Healthcare, Georgia Institute of Technology , Atlanta, GA, USA.,Center for Computational Biology and Bioinformatics, Georgia Institute of Technology , Atlanta, GA, USA.,School of Biological Sciences, Georgia Institute of Technology , Atlanta, GA, USA.,School of Industrial and Systems Engineering, Georgia Institute of Technology , Atlanta, GA, USA
| | - Haozheng Tian
- Center for Operations Research in Medicine and Healthcare, Georgia Institute of Technology , Atlanta, GA, USA.,Center for Computational Biology and Bioinformatics, Georgia Institute of Technology , Atlanta, GA, USA.,School of Biological Sciences, Georgia Institute of Technology , Atlanta, GA, USA
| | - Helder I Nakaya
- School of Pharmaceutical Sciences, University of Sao Paulo , Sao Paulo, Brazil
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47
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Ralph DK, Matsen FA. Using B cell receptor lineage structures to predict affinity. PLoS Comput Biol 2020; 16:e1008391. [PMID: 33175831 PMCID: PMC7682889 DOI: 10.1371/journal.pcbi.1008391] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 11/23/2020] [Accepted: 08/30/2020] [Indexed: 11/18/2022] Open
Abstract
We are frequently faced with a large collection of antibodies, and want to select those with highest affinity for their cognate antigen. When developing a first-line therapeutic for a novel pathogen, for instance, we might look for such antibodies in patients that have recovered. There exist effective experimental methods of accomplishing this, such as cell sorting and baiting; however they are time consuming and expensive. Next generation sequencing of B cell receptor (BCR) repertoires offers an additional source of sequences that could be tapped if we had a reliable method of selecting those coding for the best antibodies. In this paper we introduce a method that uses evolutionary information from the family of related sequences that share a naive ancestor to predict the affinity of each resulting antibody for its antigen. When combined with information on the identity of the antigen, this method should provide a source of effective new antibodies. We also introduce a method for a related task: given an antibody of interest and its inferred ancestral lineage, which branches in the tree are likely to harbor key affinity-increasing mutations? We evaluate the performance of these methods on a wide variety of simulated samples, as well as two real data samples. These methods are implemented as part of continuing development of the partis BCR inference package, available at https://github.com/psathyrella/partis. Comments Please post comments or questions on this paper as new issues at https://git.io/Jvxkn.
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Affiliation(s)
- Duncan K. Ralph
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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48
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Perofsky AC, Nelson MI. The challenges of vaccine strain selection. eLife 2020; 9:62955. [PMID: 33048046 PMCID: PMC7553772 DOI: 10.7554/elife.62955] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 10/09/2020] [Indexed: 12/23/2022] Open
Abstract
New measures of influenza virus fitness could improve vaccine strain selection through more accurate forecasts of the evolution of the virus.
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Affiliation(s)
- Amanda C Perofsky
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, United States
| | - Martha I Nelson
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, United States
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49
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van Boven M, Teirlinck AC, Meijer A, Hooiveld M, van Dorp CH, Reeves RM, Campbell H, van der Hoek W. Estimating Transmission Parameters for Respiratory Syncytial Virus and Predicting the Impact of Maternal and Pediatric Vaccination. J Infect Dis 2020; 222:S688-S694. [PMID: 32821916 PMCID: PMC7751153 DOI: 10.1093/infdis/jiaa424] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Respiratory syncytial virus (RSV) is a leading cause of respiratory tract illness in young children and a major cause of hospital admissions globally. METHODS Here we fit age-structured transmission models with immunity propagation to data from the Netherlands (2012-2017). Data included nationwide hospitalizations with confirmed RSV, general practitioner (GP) data on attendance for care from acute respiratory infection, and virological testing of acute respiratory infections at the GP. The transmission models, equipped with key parameter estimates, were used to predict the impact of maternal and pediatric vaccination. RESULTS Estimates of the basic reproduction number were generally high (R0 > 10 in scenarios with high statistical support), while susceptibility was estimated to be low in nonelderly adults (<10% in persons 20-64 years) and was higher in older adults (≥65 years). Scenario analyses predicted that maternal vaccination reduces the incidence of infection in vulnerable infants (<1 year) and shifts the age of first infection from infants to young children. CONCLUSIONS Pediatric vaccination is expected to reduce the incidence of infection in infants and young children (0-5 years), slightly increase incidence in 5 to 9-year-old children, and have minor indirect benefits.
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Affiliation(s)
- Michiel van Boven
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Anne C Teirlinck
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Adam Meijer
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
| | | | - Christiaan H van Dorp
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Rachel M Reeves
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Wim van der Hoek
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
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50
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Azarian T, Martinez PP, Arnold BJ, Qiu X, Grant LR, Corander J, Fraser C, Croucher NJ, Hammitt LL, Reid R, Santosham M, Weatherholtz RC, Bentley SD, O’Brien KL, Lipsitch M, Hanage WP. Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae. PLoS Biol 2020; 18:e3000878. [PMID: 33091022 PMCID: PMC7580979 DOI: 10.1371/journal.pbio.3000878] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 09/18/2020] [Indexed: 11/30/2022] Open
Abstract
Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.
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Affiliation(s)
- Taj Azarian
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, Florida, United States of America
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Pamela P. Martinez
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Brian J. Arnold
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Xueting Qiu
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Lindsay R. Grant
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Jukka Corander
- Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicholas J. Croucher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Laura L. Hammitt
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Raymond Reid
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Mathuram Santosham
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Robert C. Weatherholtz
- Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Stephen D. Bentley
- Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | | | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
- Department of Immunology and Infectious Diseases, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - William P. Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
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