Development of an unbiased antigen-mining approach to identify novel vaccine antigens and diagnostic reagents for bovine tuberculosis.
CLINICAL AND VACCINE IMMUNOLOGY(2013)
摘要
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.
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