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United States Department of Agriculture

Agricultural Research Service

Research Project: FOOT-AND-MOUTH DISEASE VIRUS (FMDV) COUNTERMEASURES DISCOVERY Title: Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus

Authors
item Reeve, R -
item Blignaut, B -
item Esterhuysen, J -
item Opperman, P -
item Matthews, L -
item Fry, E -
item DE Beer, T -
item Theron, J -
item Rieder, Aida
item Vosloo, W -
item O'Neill, H -
item Haydon, D -
item Maree, F -

Submitted to: PLoS Computational Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 9, 2010
Publication Date: December 9, 2010
Citation: Reeve, R., Blignaut, B., Esterhuysen, J.J., Opperman, P., Matthews, L., Fry, E., De Beer, T.A., Theron, J., Rieder, A.E., Vosloo, W., O'Neill, H.G., Haydon, D.T., Maree, F.F. 2010. Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus. PLoS Computational Biology. DOI: 10.1371/journal.pcbi.1001027.

Interpretive Summary: The aim of this study was to develop an in silico tool to predict vaccine efficacy using sequence data, neutralizing titres and structural information. We have obtained a broad spectrum of South African Territories serotype 1 (SAT1) and South African Territories serotype 2 (SAT2) viruses which were sequenced and have generated sera from representative protective strains. We have used these to generate an extensive serological dataset from virus neutralization tests. The techniques developed here can be used directly for any Foot-and-Mouth Disease Virus serotype where cross-reactivity experiments have been carried out, both to predict vaccine match for new isolates and to estimate efficacy of new candidate seed strains against a panel of circulating virus isolates where no appropriate vaccine exists.

Technical Abstract: 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 intensity 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 use linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identify those substitutions in genes encoding surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allow successful 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 contain sites that are known epitopes on other serotypes. Furthermore, for one serotype, for which epitopes have never previously been identified, we provide strong evidence – by controlling for phylogenetic structure – for the presence of three epitopes (a conformational epitope previously identified as Site 3 on the A10 virus, the VP1 G-H loop, which is the major antigenic site for FMDV, and a site on the VP3 G-H loop, previously identified as Site 3 on A12) 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 experiments 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.

Last Modified: 12/19/2014
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