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Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation

BACKGROUND: The ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to r...

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Autores principales: Anderson, Christopher S., McCall, Patrick R., Stern, Harry A., Yang, Hongmei, Topham, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809904/
https://www.ncbi.nlm.nih.gov/pubmed/29433425
http://dx.doi.org/10.1186/s12859-018-2042-4
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author Anderson, Christopher S.
McCall, Patrick R.
Stern, Harry A.
Yang, Hongmei
Topham, David J.
author_facet Anderson, Christopher S.
McCall, Patrick R.
Stern, Harry A.
Yang, Hongmei
Topham, David J.
author_sort Anderson, Christopher S.
collection PubMed
description BACKGROUND: The ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists. RESULTS: Here we develop a method of mapping antigenic relationships between influenza virus stains using a sequence-based antigenic distance approach (SBM). We used a modified version of the p-all-epitope sequence-based antigenic distance calculation, which determines the antigenic relatedness between strains using influenza hemagglutinin (HA) genetic coding sequence data and provide experimental validation of the p-all-epitope calculation. We calculated the antigenic distance between 4838 H1N1 viruses isolated from infected humans between 1918 and 2016. We demonstrate, for the first time, that sequence-based antigenic distances of H1N1 Influenza viruses can be accurately represented in 2-dimenstional antigenic cartography using classic multidimensional scaling. Additionally, the model correctly predicted decreases in cross-reactive antibody levels with 87% accuracy and was highly reproducible with even when small numbers of sequences were used. CONCLUSION: This work provides a highly accurate and precise bioinformatics tool that can be used to assess immune risk as well as design optimized vaccination strategies. SBM accurately estimated the antigenic relationship between strains using HA sequence data. Antigenic maps of H1N1 virus strains reveal that strains cluster antigenically similar to what has been reported for H3N2 viruses. Furthermore, we demonstrated that genetic variation differs across antigenic sites and discuss the implications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2042-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-58099042018-02-16 Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation Anderson, Christopher S. McCall, Patrick R. Stern, Harry A. Yang, Hongmei Topham, David J. BMC Bioinformatics Research Article BACKGROUND: The ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists. RESULTS: Here we develop a method of mapping antigenic relationships between influenza virus stains using a sequence-based antigenic distance approach (SBM). We used a modified version of the p-all-epitope sequence-based antigenic distance calculation, which determines the antigenic relatedness between strains using influenza hemagglutinin (HA) genetic coding sequence data and provide experimental validation of the p-all-epitope calculation. We calculated the antigenic distance between 4838 H1N1 viruses isolated from infected humans between 1918 and 2016. We demonstrate, for the first time, that sequence-based antigenic distances of H1N1 Influenza viruses can be accurately represented in 2-dimenstional antigenic cartography using classic multidimensional scaling. Additionally, the model correctly predicted decreases in cross-reactive antibody levels with 87% accuracy and was highly reproducible with even when small numbers of sequences were used. CONCLUSION: This work provides a highly accurate and precise bioinformatics tool that can be used to assess immune risk as well as design optimized vaccination strategies. SBM accurately estimated the antigenic relationship between strains using HA sequence data. Antigenic maps of H1N1 virus strains reveal that strains cluster antigenically similar to what has been reported for H3N2 viruses. Furthermore, we demonstrated that genetic variation differs across antigenic sites and discuss the implications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2042-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-12 /pmc/articles/PMC5809904/ /pubmed/29433425 http://dx.doi.org/10.1186/s12859-018-2042-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Anderson, Christopher S.
McCall, Patrick R.
Stern, Harry A.
Yang, Hongmei
Topham, David J.
Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation
title Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation
title_full Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation
title_fullStr Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation
title_full_unstemmed Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation
title_short Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation
title_sort antigenic cartography of h1n1 influenza viruses using sequence-based antigenic distance calculation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809904/
https://www.ncbi.nlm.nih.gov/pubmed/29433425
http://dx.doi.org/10.1186/s12859-018-2042-4
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