Immunoinformatic Analysis Reveals Antigenic Heterogeneity of Epstein-Barr Virus Is Immune-Driven

Abstract

Whole genome sequencing of Epstein-Barr virus (EBV) isolates from around the world has uncovered pervasive strain heterogeneity, but the forces driving strain diversification and the impact on immune recognition remained largely unknown. Using a data mining approach, we analyzed more than 300 T-cell epitopes in 168 published EBV strains. Polymorphisms were detected in approximately 65% of all CD8+ and 80% of all CD4+ T-cell epitopes and these numbers further increased when epitope flanking regions were included. Polymorphisms in CD8+ T-cell epitopes often involved MHC anchor residues and resulted in changes of the amino acid subgroup, suggesting that only a limited number of conserved T-cell epitopes may represent generic target antigens against different viral strains. Although considered the prototypic EBV strain, the rather low degree of overlap with most other viral strains implied that B95.8 may not represent the ideal reference strain for T-cell epitopes. Instead, a combinatorial library of consensus epitopes may provide better targets for diagnostic and therapeutic purposes when the infecting strain is unknown. Polymorphisms were significantly enriched in epitope versus non-epitope protein sequences, implicating immune selection in driving strain diversification. Remarkably, CD4+ T-cell epitopes in EBNA2, EBNA-LP, and the EBNA3 family appeared to be under negative selection pressure, hinting towards a beneficial role of immune responses against these latency type III antigens in virus biology. These findings validate this immunoinformatics approach for providing novel insight into immune targets and the intricate relationship of host defense and virus evolution that may also pertain to other pathogens.