1
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Huang X, Cheng Z, Lv Y, Li W, Liu X, Huang W, Zhao C. Neutralization potency of the 2023-24 seasonal influenza vaccine against circulating influenza H3N2 strains. Hum Vaccin Immunother 2024; 20:2380111. [PMID: 39205645 PMCID: PMC11364067 DOI: 10.1080/21645515.2024.2380111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/27/2024] [Accepted: 07/11/2024] [Indexed: 09/04/2024] Open
Abstract
Seasonal influenza is a severe disease that significantly impacts public health, causing millions of infections and hundreds of thousands of deaths each year. Seasonal influenza viruses, particularly the H3N2 subtype, exhibit high antigenic variability, often leading to mismatch between vaccine strains and circulating strains. Therefore, rapidly assessing the alignment between existing seasonal influenza vaccine and circulating strains is crucial for enhancing vaccine efficacy. This study, based on a pseudovirus platform, evaluated the match between current influenza H3N2 vaccine strains and circulating strains through cross-neutralization assays using clinical human immune sera against globally circulating influenza virus strains. The research results show that although mutations are present in the circulating strains, the current H3N2 vaccine strain still imparting effective protection, providing a scientific basis for encouraging influenza vaccination. This research methodology can be sustainably applied for the neutralization potency assessment of subsequent circulating strains, establishing a persistent methodological framework.
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Affiliation(s)
- Xiande Huang
- Division of HIV/AIDS and Sex-transmitted Virus Vaccines, Institute for Biological Product Control, National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Ziqi Cheng
- Division of HIV/AIDS and Sex-transmitted Virus Vaccines, Institute for Biological Product Control, National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Yake Lv
- Center of Vaccine Clinical Evaluation, Institute for Immunization Program, Shaanxi Provincial Centre for Disease Control and Prevention, Xi’an, Shaanxi Province, China
| | - Weixuan Li
- Center of Vaccine Clinical Evaluation, Institute for Immunization Program, Shaanxi Provincial Centre for Disease Control and Prevention, Xi’an, Shaanxi Province, China
| | - Xiaoyu Liu
- Center of Vaccine Clinical Evaluation, Institute for Immunization Program, Shaanxi Provincial Centre for Disease Control and Prevention, Xi’an, Shaanxi Province, China
| | - Weijin Huang
- Division of HIV/AIDS and Sex-transmitted Virus Vaccines, Institute for Biological Product Control, National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Chenyan Zhao
- Division of HIV/AIDS and Sex-transmitted Virus Vaccines, Institute for Biological Product Control, National Institutes for Food and Drug Control (NIFDC), Beijing, China
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2
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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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3
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Frazer SA, Baghbanzadeh M, Rahnavard A, Crandall KA, Oakley TH. Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD). Gigascience 2024; 13:giae073. [PMID: 39460934 PMCID: PMC11512451 DOI: 10.1093/gigascience/giae073] [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: 02/15/2024] [Revised: 06/25/2024] [Accepted: 09/01/2024] [Indexed: 10/28/2024] Open
Abstract
BACKGROUND Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families, including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax-the wavelength of maximum absorbance-which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype. RESULTS Here, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes that we call the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding λmax phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict λmax, account for nonadditive effects of mutations on function, and identify functionally critical amino acid sites. CONCLUSION The ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism's ecological niche, and may be used more broadly for de novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes.
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Affiliation(s)
- Seth A Frazer
- Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106, USA
| | - Mahdi Baghbanzadeh
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20012, USA
| | - Todd H Oakley
- Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106, USA
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4
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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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5
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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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6
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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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7
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Stadtmueller MN, Bhatia S, Kiran P, Hilsch M, Reiter-Scherer V, Adam L, Parshad B, Budt M, Klenk S, Sellrie K, Lauster D, Seeberger PH, Hackenberger CPR, Herrmann A, Haag R, Wolff T. Evaluation of Multivalent Sialylated Polyglycerols for Resistance Induction in and Broad Antiviral Activity against Influenza A Viruses. J Med Chem 2021; 64:12774-12789. [PMID: 34432457 DOI: 10.1021/acs.jmedchem.1c00794] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The development of multivalent sialic acid-based inhibitors active against a variety of influenza A virus (IAV) strains has been hampered by high genetic and structural variability of the targeted viral hemagglutinin (HA). Here, we addressed this challenge by employing sialylated polyglycerols (PGs). Efficacy of prototypic PGs was restricted to a narrow spectrum of IAV strains. To understand this restriction, we selected IAV mutants resistant to a prototypic multivalent sialylated PG by serial passaging. Resistance mutations mapped to the receptor binding site of HA, which was accompanied by altered receptor binding profiles of mutant viruses as detected by glycan array analysis. Specifying the inhibitor functionalization to 2,6-α-sialyllactose (SL) and adjusting the linker yielded a rationally designed inhibitor covering an extended spectrum of inhibited IAV strains. These results highlight the importance of integrating virological data with chemical synthesis and structural data for the development of sialylated PGs toward broad anti-influenza compounds.
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Affiliation(s)
- Marlena N Stadtmueller
- Unit 17, Influenza and Other Respiratory Viruses, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
| | - Sumati Bhatia
- Institut für Chemie und Biochemie, Freie Universität Berlin, Takustr. 3, 14195 Berlin, Germany
| | - Pallavi Kiran
- Institut für Chemie und Biochemie, Freie Universität Berlin, Takustr. 3, 14195 Berlin, Germany
| | - Malte Hilsch
- Institut für Biologie, Molekulare Biophysik, IRI Life Sciences, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany
| | - Valentin Reiter-Scherer
- Institut für Biologie, Molekulare Biophysik, IRI Life Sciences, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany
| | - Lutz Adam
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Robert-Roessle Strasse 10, 13125 Berlin, Germany.,Institut für Chemie, Humboldt Universität zu Berlin, Brook-Taylor Str. 2, 12489 Berlin, Germany
| | - Badri Parshad
- Institut für Chemie und Biochemie, Freie Universität Berlin, Takustr. 3, 14195 Berlin, Germany
| | - Matthias Budt
- Unit 17, Influenza and Other Respiratory Viruses, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
| | - Simon Klenk
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Robert-Roessle Strasse 10, 13125 Berlin, Germany.,Institut für Chemie, Humboldt Universität zu Berlin, Brook-Taylor Str. 2, 12489 Berlin, Germany
| | - Katrin Sellrie
- Department for Biomolecular Systems, Max Planck Institute for Colloids and Interfaces, Am Mühlenberg 1, 14476 Potsdam, Germany
| | - Daniel Lauster
- Institut für Chemie und Biochemie, Freie Universität Berlin, Takustr. 3, 14195 Berlin, Germany.,Institut für Biologie, Molekulare Biophysik, IRI Life Sciences, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany
| | - Peter H Seeberger
- Department for Biomolecular Systems, Max Planck Institute for Colloids and Interfaces, Am Mühlenberg 1, 14476 Potsdam, Germany
| | - Christian P R Hackenberger
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Robert-Roessle Strasse 10, 13125 Berlin, Germany
| | - Andreas Herrmann
- Institut für Biologie, Molekulare Biophysik, IRI Life Sciences, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany
| | - Rainer Haag
- Institut für Chemie und Biochemie, Freie Universität Berlin, Takustr. 3, 14195 Berlin, Germany
| | - Thorsten Wolff
- Unit 17, Influenza and Other Respiratory Viruses, Robert Koch-Institut, Seestraße 10, 13353 Berlin, Germany
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8
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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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9
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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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10
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Skarlupka AL, Handel A, Ross TM. Influenza hemagglutinin antigenic distance measures capture trends in HAI differences and infection outcomes, but are not suitable predictive tools. Vaccine 2020; 38:5822-5830. [PMID: 32682618 DOI: 10.1016/j.vaccine.2020.06.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 05/28/2020] [Accepted: 06/16/2020] [Indexed: 01/24/2023]
Abstract
Vaccination is the most effective method to combat influenza. Vaccine effectiveness is influenced by the antigenic distance between the vaccine strain and the actual circulating virus. Amino acid sequence based methods of quantifying the antigenic distance were designed to predict influenza vaccine effectiveness in humans. The use of these antigenic distance measures has been proposed as an additive method for seasonal vaccine selection. In this report, several antigenic distance measures were evaluated as predictors of hemagglutination inhibition titer differences and clinical outcomes following influenza vaccination or infection in mice or ferrets. The antigenic distance measures described the increasing trend in the change of HAI titer, lung viral titer and percent weight loss in mice and ferrets. However, the variability of outcome variables produced wide prediction intervals for any given antigenic distance value. The amino acid substitution based antigenic distance measures were no better predictors of viral load and weight loss than HAI titer differences, the current predictive measure of immunological correlate of protection for clinical signs after challenge.
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Affiliation(s)
- Amanda L Skarlupka
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA
| | - Andreas Handel
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA, USA
| | - Ted M Ross
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA; Department of Infectious Diseases, University of Georgia, Athens, GA, USA.
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11
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Allele-specific nonstationarity in evolution of influenza A virus surface proteins. Proc Natl Acad Sci U S A 2019; 116:21104-21112. [PMID: 31578251 DOI: 10.1073/pnas.1904246116] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Influenza A virus (IAV) is a major public health problem and a pandemic threat. Its evolution is largely driven by diversifying positive selection so that relative fitness of different amino acid variants changes with time due to changes in herd immunity or genomic context, and novel amino acid variants attain fitness advantage. Here, we hypothesize that diversifying selection also has another manifestation: the fitness associated with a particular amino acid variant should decline with time since its origin, as the herd immunity adapts to it. By tracing the evolution of antigenic sites at IAV surface proteins, we show that an amino acid variant becomes progressively more likely to become replaced by another variant with time since its origin-a phenomenon we call "senescence." Senescence is particularly pronounced at experimentally validated antigenic sites, implying that it is largely driven by host immunity. By contrast, at internal sites, existing variants become more favorable with time, probably due to arising contingent mutations at other epistatically interacting sites. Our findings reveal a previously undescribed facet of adaptive evolution and suggest approaches for prediction of evolutionary dynamics of pathogens.
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12
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Klingen TR, Loers J, Stanelle-Bertram S, Gabriel G, McHardy AC. Structures and functions linked to genome-wide adaptation of human influenza A viruses. Sci Rep 2019; 9:6267. [PMID: 31000776 PMCID: PMC6472403 DOI: 10.1038/s41598-019-42614-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 03/27/2019] [Indexed: 11/12/2022] Open
Abstract
Human influenza A viruses elicit short-term respiratory infections with considerable mortality and morbidity. While H3N2 viruses circulate for more than 50 years, the recent introduction of pH1N1 viruses presents an excellent opportunity for a comparative analysis of the genome-wide evolutionary forces acting on both subtypes. Here, we inferred patches of sites relevant for adaptation, i.e. being under positive selection, on eleven viral protein structures, from all available data since 1968 and correlated these with known functional properties. Overall, pH1N1 have more patches than H3N2 viruses, especially in the viral polymerase complex, while antigenic evolution is more apparent for H3N2 viruses. In both subtypes, NS1 has the highest patch and patch site frequency, indicating that NS1-mediated viral attenuation of host inflammatory responses is a continuously intensifying process, elevated even in the longtime-circulating subtype H3N2. We confirmed the resistance-causing effects of two pH1N1 changes against oseltamivir in NA activity assays, demonstrating the value of the resource for discovering functionally relevant changes. Our results represent an atlas of protein regions and sites with links to host adaptation, antiviral drug resistance and immune evasion for both subtypes for further study.
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MESH Headings
- Drug Resistance, Viral/genetics
- Evolution, Molecular
- Genome, Viral/genetics
- Humans
- Influenza A Virus, H1N1 Subtype/genetics
- Influenza A Virus, H1N1 Subtype/pathogenicity
- Influenza A Virus, H3N2 Subtype/genetics
- Influenza A Virus, H3N2 Subtype/pathogenicity
- Influenza, Human/genetics
- Influenza, Human/pathology
- Influenza, Human/virology
- Oseltamivir/therapeutic use
- Respiratory Tract Infections/genetics
- Respiratory Tract Infections/virology
- Viral Nonstructural Proteins/genetics
- Virus Replication/genetics
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Affiliation(s)
- Thorsten R Klingen
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
| | - Jens Loers
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
| | | | - Gülsah Gabriel
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
- University of Veterinary Medicine, Hannover, Germany
| | - Alice C McHardy
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany.
- German Center for Infection Research (DZIF), Braunschweig, Germany.
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13
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Quan L, Ji C, Ding X, Peng Y, Liu M, Sun J, Jiang T, Wu A. Cluster-Transition Determining Sites Underlying the Antigenic Evolution of Seasonal Influenza Viruses. Mol Biol Evol 2019; 36:1172-1186. [DOI: 10.1093/molbev/msz050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Lijun Quan
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou Institute of Systems Medicine, Suzhou, China
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Chengyang Ji
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou Institute of Systems Medicine, Suzhou, China
| | - Xiao Ding
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou Institute of Systems Medicine, Suzhou, China
| | - Yousong Peng
- College of Biology, Human University, Changsha, China
| | - Mi Liu
- Jiangsu Institute of Clinical Immunology & Jiangsu Key Laboratory of Clinical Immunology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jiya Sun
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou Institute of Systems Medicine, Suzhou, China
| | - Taijiao Jiang
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou Institute of Systems Medicine, Suzhou, China
| | - Aiping Wu
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou Institute of Systems Medicine, Suzhou, China
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14
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Ibrahim B, Arkhipova K, Andeweg AC, Posada-Céspedes S, Enault F, Gruber A, Koonin EV, Kupczok A, Lemey P, McHardy AC, McMahon DP, Pickett BE, Robertson DL, Scheuermann RH, Zhernakova A, Zwart MP, Schönhuth A, Dutilh BE, Marz M. Bioinformatics Meets Virology: The European Virus Bioinformatics Center's Second Annual Meeting. Viruses 2018; 10:E256. [PMID: 29757994 PMCID: PMC5977249 DOI: 10.3390/v10050256] [Citation(s) in RCA: 6] [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: 05/09/2018] [Revised: 05/11/2018] [Accepted: 05/11/2018] [Indexed: 11/16/2022] Open
Abstract
The Second Annual Meeting of the European Virus Bioinformatics Center (EVBC), held in Utrecht, Netherlands, focused on computational approaches in virology, with topics including (but not limited to) virus discovery, diagnostics, (meta-)genomics, modeling, epidemiology, molecular structure, evolution, and viral ecology. The goals of the Second Annual Meeting were threefold: (i) to bring together virologists and bioinformaticians from across the academic, industrial, professional, and training sectors to share best practice; (ii) to provide a meaningful and interactive scientific environment to promote discussion and collaboration between students, postdoctoral fellows, and both new and established investigators; (iii) to inspire and suggest new research directions and questions. Approximately 120 researchers from around the world attended the Second Annual Meeting of the EVBC this year, including 15 renowned international speakers. This report presents an overview of new developments and novel research findings that emerged during the meeting.
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Affiliation(s)
- Bashar Ibrahim
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 07743 Jena, Germany.
| | - Ksenia Arkhipova
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
| | - Arno C Andeweg
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Department of Viroscience, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands.
| | - Susana Posada-Céspedes
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland.
| | - François Enault
- Université Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France.
| | - Arthur Gruber
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, 05508-000 São Paulo, Brazil.
| | - Eugene V Koonin
- National Center for Biotechnology Information, NLM, National Institutes of Health, Bethesda, MD 20894, USA.
| | - Anne Kupczok
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Institute of General Microbiology, Kiel University, 24118 Kiel, Germany.
| | - Philippe Lemey
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Clinical and Epidemiological Virology, Rega Institute, KU Leuven, University of Leuven, 3000 Leuven, Belgium.
| | - Alice C McHardy
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, 38124 Braunschweig, Germany.
| | - Dino P McMahon
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Institute of Biology, Free University Berlin, Schwendenerstr. 1, 14195 Berlin, Germany.
- Department for Materials and Environment, BAM Federal Institute for Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany.
| | - Brett E Pickett
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- J. Craig Venter Institute, Rockville, MD 20850, USA.
| | - David L Robertson
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- MRC-University of Glasgow Centre for Virus Research, Garscube Campus, Glasgow G61 1QH, UK.
| | - Richard H Scheuermann
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- J. Craig Venter Institute, La Jolla, CA 92037, USA.
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, 9700 RB Groningen, The Netherlands.
| | - Mark P Zwart
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands.
| | - Alexander Schönhuth
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
| | - Bas E Dutilh
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
| | - Manja Marz
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 07743 Jena, Germany.
- Leibniz Institute for Age Research-Fritz Lipmann Institute, 07745 Jena, Germany.
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15
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Agor JK, Özaltın OY. Models for predicting the evolution of influenza to inform vaccine strain selection. Hum Vaccin Immunother 2018; 14:678-683. [PMID: 29337643 DOI: 10.1080/21645515.2017.1423152] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Influenza vaccine composition is reviewed before every flu season because influenza viruses constantly evolve through antigenic changes. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System monitor the antigenic phenotypes of circulating viruses all year round. Vaccine strains are selected in anticipation of the upcoming influenza season to allow adequate time for production. A mismatch between vaccine strains and predominant strains in the flu season can significantly reduce vaccine effectiveness. Models for predicting the evolution of influenza based on the relationship of genetic mutations and antigenic characteristics of circulating viruses may inform vaccine strain selection decisions. We review the literature on state-of-the-art tools and prediction methodologies utilized in modeling the evolution of influenza to inform vaccine strain selection. We then discuss areas that are open for improvement and need further research.
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Affiliation(s)
- Joseph K Agor
- a Operations Research, North Carolina State University , Raleigh , NC , USA
| | - Osman Y Özaltın
- b Edward P. Fitts Department of Industrial and Systems Engineering , North Carolina State University , Raleigh , NC , USA
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16
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Morris DH, Gostic KM, Pompei S, Bedford T, Łuksza M, Neher RA, Grenfell BT, Lässig M, McCauley JW. Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology. Trends Microbiol 2018; 26:102-118. [PMID: 29097090 PMCID: PMC5830126 DOI: 10.1016/j.tim.2017.09.004] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/06/2017] [Accepted: 09/19/2017] [Indexed: 01/16/2023]
Abstract
Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.
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Affiliation(s)
- Dylan H Morris
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Katelyn M Gostic
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA
| | - Simone Pompei
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Marta Łuksza
- Institute for Advanced Study, Princeton, NJ, USA
| | - Richard A Neher
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Michael Lässig
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - John W McCauley
- Worldwide Influenza Centre, Francis Crick Institute, London, UK
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17
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Klingen TR, Reimering S, Guzmán CA, McHardy AC. In Silico Vaccine Strain Prediction for Human Influenza Viruses. Trends Microbiol 2017; 26:119-131. [PMID: 29032900 DOI: 10.1016/j.tim.2017.09.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Revised: 07/21/2017] [Accepted: 09/06/2017] [Indexed: 02/02/2023]
Abstract
Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated.
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Affiliation(s)
- Thorsten R Klingen
- Department for Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany; Co-first authors
| | - Susanne Reimering
- Department for Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany; Co-first authors
| | - Carlos A Guzmán
- Department of Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany; German Centre for Infection Research (DZIF)
| | - Alice C McHardy
- Department for Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany; German Centre for Infection Research (DZIF).
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18
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Harvey WT, Benton DJ, Gregory V, Hall JPJ, Daniels RS, Bedford T, Haydon DT, Hay AJ, McCauley JW, Reeve R. Identification of Low- and High-Impact Hemagglutinin Amino Acid Substitutions That Drive Antigenic Drift of Influenza A(H1N1) Viruses. PLoS Pathog 2016; 12:e1005526. [PMID: 27057693 PMCID: PMC4825936 DOI: 10.1371/journal.ppat.1005526] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 03/04/2016] [Indexed: 12/20/2022] Open
Abstract
Determining phenotype from genetic data is a fundamental challenge. Identification of emerging antigenic variants among circulating influenza viruses is critical to the vaccine virus selection process, with vaccine effectiveness maximized when constituents are antigenically similar to circulating viruses. Hemagglutination inhibition (HI) assay data are commonly used to assess influenza antigenicity. Here, sequence and 3-D structural information of hemagglutinin (HA) glycoproteins were analyzed together with corresponding HI assay data for former seasonal influenza A(H1N1) virus isolates (1997–2009) and reference viruses. The models developed identify and quantify the impact of eighteen amino acid substitutions on the antigenicity of HA, two of which were responsible for major transitions in antigenic phenotype. We used reverse genetics to demonstrate the causal effect on antigenicity for a subset of these substitutions. Information on the impact of substitutions allowed us to predict antigenic phenotypes of emerging viruses directly from HA gene sequence data and accuracy was doubled by including all substitutions causing antigenic changes over a model incorporating only the substitutions with the largest impact. The ability to quantify the phenotypic impact of specific amino acid substitutions should help refine emerging techniques that predict the evolution of virus populations from one year to the next, leading to stronger theoretical foundations for selection of candidate vaccine viruses. These techniques have great potential to be extended to other antigenically variable pathogens. Influenza A viruses are characterized by rapid antigenic drift: structural changes in B-cell epitopes that facilitate escape from pre-existing immunity. Consequently, seasonal influenza continues to impose a major burden on human health. Accurate quantification of the antigenic impact of specific amino acid substitutions is a pre-requisite for predicting the fitness and evolutionary outcome of variant viruses. Using assays to attribute antigenic variation to amino acid sequence changes we identify substitutions that contribute to antigenic drift and quantify their impact. We show that substitutions identified as low-impact are a critical component of virus antigenic evolution and by including these, as well as the high-impact substitutions often focused on, the accuracy of predicting antigenic phenotypes of emerging viruses from genotype is doubled.
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Affiliation(s)
- William T. Harvey
- Boyd Orr Centre for Population and Ecosystem Health and Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Donald J. Benton
- The Crick Worldwide Influenza Centre, The Francis Crick Institute, Mill Hill Laboratory, The Ridgeway, Mill Hill, London, United Kingdom (formerly WHO Collaborating Centre for Reference and Research on Influenza, Division of Virology, MRC National Institute for Medical Research, The Ridgeway, Mill Hill, London, United Kingdom)
| | - Victoria Gregory
- The Crick Worldwide Influenza Centre, The Francis Crick Institute, Mill Hill Laboratory, The Ridgeway, Mill Hill, London, United Kingdom (formerly WHO Collaborating Centre for Reference and Research on Influenza, Division of Virology, MRC National Institute for Medical Research, The Ridgeway, Mill Hill, London, United Kingdom)
| | - James P. J. Hall
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Rodney S. Daniels
- The Crick Worldwide Influenza Centre, The Francis Crick Institute, Mill Hill Laboratory, The Ridgeway, Mill Hill, London, United Kingdom (formerly WHO Collaborating Centre for Reference and Research on Influenza, Division of Virology, MRC National Institute for Medical Research, The Ridgeway, Mill Hill, London, United Kingdom)
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Daniel T. Haydon
- Boyd Orr Centre for Population and Ecosystem Health and Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Alan J. Hay
- The Crick Worldwide Influenza Centre, The Francis Crick Institute, Mill Hill Laboratory, The Ridgeway, Mill Hill, London, United Kingdom (formerly WHO Collaborating Centre for Reference and Research on Influenza, Division of Virology, MRC National Institute for Medical Research, The Ridgeway, Mill Hill, London, United Kingdom)
| | - John W. McCauley
- The Crick Worldwide Influenza Centre, The Francis Crick Institute, Mill Hill Laboratory, The Ridgeway, Mill Hill, London, United Kingdom (formerly WHO Collaborating Centre for Reference and Research on Influenza, Division of Virology, MRC National Institute for Medical Research, The Ridgeway, Mill Hill, London, United Kingdom)
| | - Richard Reeve
- Boyd Orr Centre for Population and Ecosystem Health and Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- The Pirbright Institute, Pirbright, Woking, Surrey, United Kingdom
- * E-mail:
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19
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Kratsch C, Klingen TR, Mümken L, Steinbrück L, McHardy AC. Determination of antigenicity-altering patches on the major surface protein of human influenza A/H3N2 viruses. Virus Evol 2016; 2:vev025. [PMID: 27774294 PMCID: PMC4989879 DOI: 10.1093/ve/vev025] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Human influenza viruses are rapidly evolving RNA viruses that cause short-term respiratory infections with substantial morbidity and mortality in annual epidemics. Uncovering the general principles of viral coevolution with human hosts is important for pathogen surveillance and vaccine design. Protein regions are an appropriate model for the interactions between two macromolecules, but the currently used epitope definition for the major antigen of influenza viruses, namely hemagglutinin, is very broad. Here, we combined genetic, evolutionary, antigenic, and structural information to determine the most relevant regions of the hemagglutinin of human influenza A/H3N2 viruses for interaction with human immunoglobulins. We estimated the antigenic weights of amino acid changes at individual sites from hemagglutination inhibition data using antigenic tree inference followed by spatial clustering of antigenicity-altering protein sites on the protein structure. This approach determined six relevant areas (patches) for antigenic variation that had a key role in the past antigenic evolution of the viruses. Previous transitions between successive predominating antigenic types of H3N2 viruses always included amino acid changes in either the first or second antigenic patch. Interestingly, there was only partial overlap between the antigenic patches and the patches under strong positive selection. Therefore, besides alterations of antigenicity, other interactions with the host may shape the evolution of human influenza A/H3N2 viruses.
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Affiliation(s)
- Christina Kratsch
- Department for Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany and
| | - Thorsten R. Klingen
- Department for Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany and
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
| | - Linda Mümken
- Department for Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany and
| | - Lars Steinbrück
- Department for Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany and
| | - Alice C. McHardy
- Department for Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany and
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
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20
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Wedde M, Biere B, Wolff T, Schweiger B. Evolution of the hemagglutinin expressed by human influenza A(H1N1)pdm09 and A(H3N2) viruses circulating between 2008-2009 and 2013-2014 in Germany. Int J Med Microbiol 2015; 305:762-75. [PMID: 26416089 DOI: 10.1016/j.ijmm.2015.08.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
This report describes the evolution of the influenza A(H1N1)pdm09 and A(H3N2) viruses circulating in Germany between 2008-2009 and 2013-2014. The phylogenetic analysis of the hemagglutinin (HA) genes of both subtypes revealed similar evolution of the HA variants that were also seen worldwide with minor exceptions. The analysis showed seven distinct HA clades for A(H1N1)pdm09 and six HA clades for A(H3N2) viruses. Herald strains of both subtypes appeared sporadically since 2008-2009. Regarding A(H1N1)pdm09, herald strains of HA clade 3 and 4 were detected late in the 2009-2010 season. With respect to A(H3N2), we found herald strains of HA clade 3, 4 and 7 between 2009 and 2012. Those herald strains were predominantly seen for minor and not for major HA clades. Generally, amino acid substitutions were most frequently found in the globular domain, including substitutions near the antigenic sites or the receptor binding site. Differences between both influenza A subtypes were seen with respect to the position of the indicated substitutions in the HA. For A(H1N1)pdm09 viruses, we found more substitutions in the stem region than in the antigenic sites. In contrast, in A(H3N2) viruses most changes were identified in the major antigenic sites and five changes of potential glycosylation sites were identified in the head of the HA monomer. Interestingly, we found in seasons with less influenza activity a relatively high increase of substitutions in the head of the HA in both subtypes. This might be explained by the fact that mutations under negative selection are subsequently compensated by secondary mutations to restore important functions e.g. receptor binding properties. A better knowledge of basic evolution strategies of influenza viruses will contribute to the refinement of predictive mathematical models for identifying novel antigenic drift variants.
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Affiliation(s)
- Marianne Wedde
- Division of Influenza Viruses and other Respiratory Viruses, National Reference Centre for Influenza, Robert Koch-Institute, Seestrasse 10, 13353 Berlin, Germany
| | - Barbara Biere
- Division of Influenza Viruses and other Respiratory Viruses, National Reference Centre for Influenza, Robert Koch-Institute, Seestrasse 10, 13353 Berlin, Germany
| | - Thorsten Wolff
- Division of Influenza Viruses and other Respiratory Viruses, National Reference Centre for Influenza, Robert Koch-Institute, Seestrasse 10, 13353 Berlin, Germany
| | - Brunhilde Schweiger
- Division of Influenza Viruses and other Respiratory Viruses, National Reference Centre for Influenza, Robert Koch-Institute, Seestrasse 10, 13353 Berlin, Germany.
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21
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Neverov AD, Kryazhimskiy S, Plotkin JB, Bazykin GA. Coordinated Evolution of Influenza A Surface Proteins. PLoS Genet 2015; 11:e1005404. [PMID: 26247472 PMCID: PMC4527594 DOI: 10.1371/journal.pgen.1005404] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 06/30/2015] [Indexed: 11/18/2022] Open
Abstract
The surface proteins hemagglutinin (HA) and neuraminidase (NA) of human influenza A virus evolve under selection pressures to escape adaptive immune responses and antiviral drug treatments. In addition to these external selection pressures, some mutations in HA are known to affect the adaptive landscape of NA, and vice versa, because these two proteins are physiologically interlinked. However, the extent to which evolution of one protein affects the evolution of the other one is unknown. Here we develop a novel phylogenetic method for detecting the signatures of such genetic interactions between mutations in different genes – that is, inter-gene epistasis. Using this method, we show that influenza surface proteins evolve in a coordinated way, with mutations in HA affecting subsequent spread of mutations in NA and vice versa, at many sites. Of particular interest is our finding that the oseltamivir-resistance mutations in NA in subtype H1N1 were likely facilitated by prior mutations in HA. Our results illustrate that the adaptive landscape of a viral protein is remarkably sensitive to its genomic context and, more generally, that the evolution of any single protein must be understood within the context of the entire evolving genome. The fitness of an organism depends on the coordinated function of many genes. Thus, how a mutation in one gene affects fitness often depends on what mutations are present in other genes. This dependence is called “genetic interaction” or “epistasis”. The prevalence and type of such interactions are not well understood. Epistasis can be inferred from time-series sequencing data when a mutation in one gene is observed to facilitate the spread of a mutation in another gene. However, the situation is much more complicated when new combinations of genes are formed by processes such as recombination or reassortment. In such cases, deducing the time and order of genetic changes is difficult. Here, we devise a method to infer pairs of mutations in different genes which closely follow one another in the presence of reassortment. We apply it to evolution of two surface proteins of influenza A virus, hemagglutinin and neuraminidase, which are important targets for the human immune system and drugs. We show that mutations in one of these proteins are often facilitated by prior mutations, or compensated by subsequent mutations, in the other protein. In particular, drug-resistance mutations in neuraminidase were likely made possible by prior mutation in hemagglutinin. Knowledge of such interactions is necessary to fully understand and predict evolution.
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Affiliation(s)
| | - Sergey Kryazhimskiy
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
- FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Joshua B. Plotkin
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Georgii A. Bazykin
- Institute for Information Transmission Problems (Kharkevich Institute) of the Russian Academy of Sciences, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russia
- Pirogov Russian National Research Medical University, Moscow, Russia
- * E-mail:
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22
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Kratsch C, McHardy AC. RidgeRace: ridge regression for continuous ancestral character estimation on phylogenetic trees. Bioinformatics 2014; 30:i527-33. [PMID: 25161243 PMCID: PMC4147928 DOI: 10.1093/bioinformatics/btu477] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Ancestral character state reconstruction describes a set of techniques for estimating phenotypic or genetic features of species or related individuals that are the predecessors of those present today. Such reconstructions can reach into the distant past and can provide insights into the history of a population or a set of species when fossil data are not available, or they can be used to test evolutionary hypotheses, e.g. on the co-evolution of traits. Typical methods for ancestral character state reconstruction of continuous characters consider the phylogeny of the underlying data and estimate the ancestral process along the branches of the tree. They usually assume a Brownian motion model of character evolution or extensions thereof, requiring specific assumptions on the rate of phenotypic evolution. RESULTS We suggest using ridge regression to infer rates for each branch of the tree and the ancestral values at each inner node. We performed extensive simulations to evaluate the performance of this method and have shown that the accuracy of its reconstructed ancestral values is competitive to reconstructions using other state-of-the-art software. Using a hierarchical clustering of gene mutation profiles from an ovarian cancer dataset, we demonstrate the use of the method as a feature selection tool. AVAILABILITY AND IMPLEMENTATION The algorithm described here is implemented in C++ as a stand-alone program, and the source code is freely available at http://algbio.cs.uni-duesseldorf.de/software/RidgeRace.tar.gz. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christina Kratsch
- Department for Algorithmic Bioinformatics, Heinrich Heine University, Universitätsstr. 1, 40225 Düsseldorf, Germany
| | - Alice C McHardy
- Department for Algorithmic Bioinformatics, Heinrich Heine University, Universitätsstr. 1, 40225 Düsseldorf, Germany
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Computational prediction of vaccine strains for human influenza A (H3N2) viruses. J Virol 2014; 88:12123-32. [PMID: 25122778 DOI: 10.1128/jvi.01861-14] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. Importance: Human influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.
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