Using Sequence Data To Infer the Antigenicity of Influenza Virus

ABSTRACT The efficacy of current influenza vaccines requires a close antigenic match between circulating and vaccine strains. As such, timely identification of emerging influenza virus antigenic variants is central to the success of influenza vaccination programs. Empirical methods to determine infl...

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Autores principales:	Hailiang Sun, Jialiang Yang, Tong Zhang, Li-Ping Long, Kun Jia, Guohua Yang, Richard J. Webby, Xiu-Feng Wan
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2013
Materias:	Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/6690a1e8d4a1400fa12168d175bc3252
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spelling	oai:doaj.org-article:6690a1e8d4a1400fa12168d175bc32522021-11-15T15:43:09ZUsing Sequence Data To Infer the Antigenicity of Influenza Virus10.1128/mBio.00230-132150-7511https://doaj.org/article/6690a1e8d4a1400fa12168d175bc32522013-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.00230-13https://doaj.org/toc/2150-7511ABSTRACT The efficacy of current influenza vaccines requires a close antigenic match between circulating and vaccine strains. As such, timely identification of emerging influenza virus antigenic variants is central to the success of influenza vaccination programs. Empirical methods to determine influenza virus antigenic properties are time-consuming and mid-throughput and require live viruses. Here, we present a novel, experimentally validated, computational method for determining influenza virus antigenicity on the basis of hemagglutinin (HA) sequence. This method integrates a bootstrapped ridge regression with antigenic mapping to quantify antigenic distances by using influenza HA1 sequences. Our method was applied to H3N2 seasonal influenza viruses and identified the 13 previously recognized H3N2 antigenic clusters and the antigenic drift event of 2009 that led to a change of the H3N2 vaccine strain. IMPORTANCE This report supplies a novel method for quantifying antigenic distance and identifying antigenic variants using sequences alone. This method will be useful in influenza vaccine strain selection by significantly reducing the human labor efforts for serological characterization and will increase the likelihood of correct influenza vaccine candidate selection.Hailiang SunJialiang YangTong ZhangLi-Ping LongKun JiaGuohua YangRichard J. WebbyXiu-Feng WanAmerican Society for MicrobiologyarticleMicrobiologyQR1-502ENmBio, Vol 4, Iss 4 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbiology QR1-502
spellingShingle	Microbiology QR1-502 Hailiang Sun Jialiang Yang Tong Zhang Li-Ping Long Kun Jia Guohua Yang Richard J. Webby Xiu-Feng Wan Using Sequence Data To Infer the Antigenicity of Influenza Virus
description	ABSTRACT The efficacy of current influenza vaccines requires a close antigenic match between circulating and vaccine strains. As such, timely identification of emerging influenza virus antigenic variants is central to the success of influenza vaccination programs. Empirical methods to determine influenza virus antigenic properties are time-consuming and mid-throughput and require live viruses. Here, we present a novel, experimentally validated, computational method for determining influenza virus antigenicity on the basis of hemagglutinin (HA) sequence. This method integrates a bootstrapped ridge regression with antigenic mapping to quantify antigenic distances by using influenza HA1 sequences. Our method was applied to H3N2 seasonal influenza viruses and identified the 13 previously recognized H3N2 antigenic clusters and the antigenic drift event of 2009 that led to a change of the H3N2 vaccine strain. IMPORTANCE This report supplies a novel method for quantifying antigenic distance and identifying antigenic variants using sequences alone. This method will be useful in influenza vaccine strain selection by significantly reducing the human labor efforts for serological characterization and will increase the likelihood of correct influenza vaccine candidate selection.
format	article
author	Hailiang Sun Jialiang Yang Tong Zhang Li-Ping Long Kun Jia Guohua Yang Richard J. Webby Xiu-Feng Wan
author_facet	Hailiang Sun Jialiang Yang Tong Zhang Li-Ping Long Kun Jia Guohua Yang Richard J. Webby Xiu-Feng Wan
author_sort	Hailiang Sun
title	Using Sequence Data To Infer the Antigenicity of Influenza Virus
title_short	Using Sequence Data To Infer the Antigenicity of Influenza Virus
title_full	Using Sequence Data To Infer the Antigenicity of Influenza Virus
title_fullStr	Using Sequence Data To Infer the Antigenicity of Influenza Virus
title_full_unstemmed	Using Sequence Data To Infer the Antigenicity of Influenza Virus
title_sort	using sequence data to infer the antigenicity of influenza virus
publisher	American Society for Microbiology
publishDate	2013
url	https://doaj.org/article/6690a1e8d4a1400fa12168d175bc3252
work_keys_str_mv	AT hailiangsun usingsequencedatatoinfertheantigenicityofinfluenzavirus AT jialiangyang usingsequencedatatoinfertheantigenicityofinfluenzavirus AT tongzhang usingsequencedatatoinfertheantigenicityofinfluenzavirus AT lipinglong usingsequencedatatoinfertheantigenicityofinfluenzavirus AT kunjia usingsequencedatatoinfertheantigenicityofinfluenzavirus AT guohuayang usingsequencedatatoinfertheantigenicityofinfluenzavirus AT richardjwebby usingsequencedatatoinfertheantigenicityofinfluenzavirus AT xiufengwan usingsequencedatatoinfertheantigenicityofinfluenzavirus
_version_	1718427588947345408

Using Sequence Data To Infer the Antigenicity of Influenza Virus

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