Multivariate analysis as a method to evaluate antigenic relationships between BVDV vaccine and field strains

Bovine viral diarrhea virus (BVDV) is comprised of two species, BVDV-1 and BVDV-2, but given the genetic diversity among pestiviruses, at least 21 subgenotypes are described for BVDV-1 and 4 for BVDV-2. Genetic characterization can be achieved through complete or partial sequencing and phylogeny, but antigenic characterization can be difficult to determine due to the antigenic diversity and cross-neutralization that exists among isolates. The traditional method for evaluating antigenic relationships between pestivirus isolates is the virus neutralization (VN) assay, but interpretation of the data to determine antigenic difference can be unclear. Data from this study utilized a multivariate analysis for visualization of VN results to analyze the antigenic relationships between vaccine strains and multiple field isolates. Polyclonal sera were generated against 6 BVDV strains currently contained in vaccine formulations, and each serum was used in VN's to measure the neutralizing antibody titers against 15 BVDV field isolates characterized as prevalent and divergent subgenotypes in the USA. Principal component analysis (PCA) were performed on the VN assay datasets, and results were interpreted from PCA clustering within the PCA dendrogram and scatter plot. The results demonstrated clustering patterns among isolates suggestive of antigenic differences. While expected, the BVDV-1 and BVDV-2 isolates did not cluster together and had the greatest spatial distribution. In addition, other BVDV isolates had distinct spatial patterns suggesting antigenically divergent isolates. This analysis provides an alternative and more efficient means to analyze large VN datasets to visualize antigenic relationships between pestivirus isolates. This analysis could be beneficial for vaccine development and evaluation of efficacy, since most vaccines cannot fully protect animals from the broad range diversity of BVDV viruses.