Tracking the Antigenic Evolution of Foot-and-Mouth Disease Virus.

Quantifying and predicting the antigenic characteristics of a virus is something of a holy grail for infectious disease research because of its central importance to the emergence of new strains, the severity of outbreaks, and vaccine selection. However, these characteristics are defined by a complex interplay of viral and host factors so that phylogenetic measures of viral similarity are often poorly correlated to antigenic relationships. Here, we generate antigenic phylogenies that track the phenotypic evolution of two serotypes of foot-and-mouth disease virus by combining host serology and viral sequence data to identify sites that are critical to their antigenic evolution. For serotype SAT1, we validate our antigenic phylogeny against monoclonal antibody escape mutants, which match all of the predicted antigenic sites. For serotype O, we validate it against known sites where available, and otherwise directly evaluate the impact on antigenic phenotype of substitutions in predicted sites using reverse genetics and serology. We also highlight a critical and poorly understood problem for vaccine selection by revealing qualitative differences between assays that are often used interchangeably to determine antigenic match between field viruses and vaccine strains. Our approach provides a tool to identify naturally occurring antigenic substitutions, allowing us to track the genetic diversification and associated antigenic evolution of the virus. Despite the hugely important role vaccines have played in enhancing human and animal health, vaccinology remains a conspicuously empirical science. This study advances the field by providing guidance for tuning vaccine strains via site-directed mutagenesis through this high-resolution tracking of antigenic evolution of the virus between rare major shifts in phenotype.

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) 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.