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

Gareth J. Jones, Bhagwati L. Khatri, M. Carmen Garcia-Pelayo, Daryan A. Kaveh, Véronique S. Bachy, Philip J. Hogarth, Esen Wooff, Paul Golby, H. Martin Vordermeier

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1675-1682
Number of pages8
JournalClinical and Vaccine Immunology
Volume20
Issue number11
Early online date28 Aug 2013
DOIs
Publication statusPublished - 01 Nov 2013

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