Development of an unbiased antigen-mining approach to identify novel vaccine antigens and diagnostic reagents for bovine tuberculosis

Previous experiments for the identification of novel diagnostic or vaccine candidates for bovine tuberculosis have followed a targeted approach, wherein specific groups of proteins suspected to contain likely candidates are prioritized for immunological assessment (for example, with in silico approaches). However, a disadvantage of this approach is that the sets of proteins analyzed are restricted by the initial selection criteria. In this paper, we describe a series of experiments to evaluate a nonbiased approach to antigen mining by utilizing a Gateway clone set for Mycobacterium tuberculosis, which constitutes a library of clones expressing 3,294 M. tuberculosis proteins. Although whole-blood culture experiments using Mycobacterium bovis-infected animals and M. bovis BCG-vaccinated controls did not reveal proteins capable of differential diagnosis, several novel immunogenic proteins were identified and prioritized for efficacy studies in a murine vaccination/challenge model. These results demonstrate that Rv3329-immunized mice had lower bacterial cell counts in their spleens following challenge with M. bovis. In conclusion, we demonstrate that this nonbiased approach to antigen mining is a useful tool for identifying and prioritizing novel proteins for further assessment as vaccine antigens.