Integrated computational and experimental workflow identifies a novel T-cell epitope from Babesia bovis RON5 recognized by infected cattle.

Abstract

Babesia bovis is a tick-borne apicomplexan parasite that causes bovine babesiosis, a disease of major economic importance in cattle worldwide. The development of effective subunit vaccines against Babesia bovis requires the identification of T-cell epitopes capable of inducing protective cellular immune responses. This task remains challenging due to the limited characterization of binding specificity of bovine leukocyte antigen class II molecules, which are essential for accurate epitope prediction, as well as the scarce information available on the antigenic repertoire of B. bovis. Moreover, computational approaches must be complemented with experimental validation to confirm the immunogenicity of the predicted epitopes. In this study, we developed an integrated computational and experimental workflow to identify potential novel T-cell epitopes from a subset of B. bovis secreted proteins. Using a tailored epitope prediction algorithm based on the genome sequence of a pathogenic strain combined with available transcriptomic data, we identified and experimentally validated two peptides on their capacity to induce the release of IFN- γ by T CD4+ cells: one that overlaps with a previously reported epitope from the Rhoptry-Associated Protein 1 and a novel epitope derived from the Rhoptry Neck Protein 5. Our approach highlights the power of combining in silico prediction with in vitro validation to discover candidate antigens that may contribute to the development of effective vaccines or immunotherapies for bovine babesiosis.