Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) resear...

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Autores principales:	Richard Reeve, Belinda Blignaut, Jan J Esterhuysen, Pamela Opperman, Louise Matthews, Elizabeth E Fry, Tjaart A P de Beer, Jacques Theron, Elizabeth Rieder, Wilna Vosloo, Hester G O'Neill, Daniel T Haydon, Francois F Maree
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/7df684d164f5422b9d01823f9093a3f6
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spelling	oai:doaj.org-article:7df684d164f5422b9d01823f9093a3f62021-11-18T05:50:50ZSequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.1553-734X1553-735810.1371/journal.pcbi.1001027https://doaj.org/article/7df684d164f5422b9d01823f9093a3f62010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21151576/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence--by controlling for phylogenetic structure--for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease.Richard ReeveBelinda BlignautJan J EsterhuysenPamela OppermanLouise MatthewsElizabeth E FryTjaart A P de BeerJacques TheronElizabeth RiederWilna VoslooHester G O'NeillDaniel T HaydonFrancois F MareePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 12, p e1001027 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Richard Reeve Belinda Blignaut Jan J Esterhuysen Pamela Opperman Louise Matthews Elizabeth E Fry Tjaart A P de Beer Jacques Theron Elizabeth Rieder Wilna Vosloo Hester G O'Neill Daniel T Haydon Francois F Maree Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.
description	Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence--by controlling for phylogenetic structure--for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease.
format	article
author	Richard Reeve Belinda Blignaut Jan J Esterhuysen Pamela Opperman Louise Matthews Elizabeth E Fry Tjaart A P de Beer Jacques Theron Elizabeth Rieder Wilna Vosloo Hester G O'Neill Daniel T Haydon Francois F Maree
author_facet	Richard Reeve Belinda Blignaut Jan J Esterhuysen Pamela Opperman Louise Matthews Elizabeth E Fry Tjaart A P de Beer Jacques Theron Elizabeth Rieder Wilna Vosloo Hester G O'Neill Daniel T Haydon Francois F Maree
author_sort	Richard Reeve
title	Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.
title_short	Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.
title_full	Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.
title_fullStr	Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.
title_full_unstemmed	Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.
title_sort	sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/7df684d164f5422b9d01823f9093a3f6
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Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus.

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