Discovery of T cell epitopes of the intracellular parasite Babesia bovis using immunoproteomic and immunoinformatic strategies

Project summary

Bovine babesiosis is a tick-borne disease caused by parasites Babesia bovis and B. bigemina. The disease affects cattle industry in tropical and subtropical regions of the world where bovine ticks are present. In bovines, Babesia bovis causes the most acute disease, characterized by anemia, neurological and renal damage and high mortality rates in adult animals.

Current control methods for babesiosis include vaccination with live attenuated strains of both parasites. Although these live vaccines are highly effective, they have some undesirable characteristics such as short shelf-life and the possibility of transmitting other diseases. For these reasons, there is an urgent need to produce new effective vaccines capable of inducing a strong protective immune response without these drawbacks.

Our proposal represents an important step towards addressing this need. We will identify some key protein fragments called T-cell epitopes of B. bovis that are specifically involved in the bovine immune response against the parasite. We will achieve this goal using cutting-edge mass spectrometry technologies combined with advanced immunoinformatics machine-learning to identify T-cell epitopes that remained unknown using previous techniques. This strategy will allow obtaining a set of novel vaccine candidates that could be included in new generation vaccines against bovine babesiosis.

Project outcomes

In this project, we have pursued the rational discovery of the parasite-derived immunopeptidome of Babesia bovis (B. bovis) in macrophages exposed to infected erythrocytes using high-performance liquid chromatography mass spectrometry (LC-MS). Using immunoinformatics, we utilised the obtained data not only for the discovery of previously unknown parasite antigens, but also for resolving accurate peptide sequence rules for antigen binding to bovine leukocyte antigen (BoLA) class II molecules.

The main outcome of the project was a set of 131 B. bovis peptides that are naturally presented by BoLA class II molecules in bovine macrophages in the context of exposure to B. bovis infected cells. Many of the 131 peptides were overlapping spanning “hotspots” in parasite proteins. After a bioinformatic clustering of these overlapping peptides, 28 novel parasite antigenic sequence candidates were shortlisted and elected for further validation of their antigenicity in vitro. Ongoing experiments include the measurement of secreted gamma interferon from lymphocytes derived from B. bovis-infected cattle stimulated with pools of the shortlisted 28 peptides using intracellular cytokine staining and flow cytometry.

We have also taken advantage of the information of the two BoLA alleles of the bovine used to obtain the macrophages to identify two distinct peptide binding motifs by bioinformatics. This information will contribute to the training of bioinformatic methods for the prediction of BoLA antigen presentation and application of such methods for the identification of new T cell epitopes for different pathogens.