BMC Genomics (Oct 2007)

Innate gene repression associated with <it>Mycobacterium bovis </it>infection in cattle: toward a gene signature of disease

  • O'Farrelly Cliona,
  • Fitzsimons Tara,
  • Doyle Mairéad B,
  • Gormley Eamonn,
  • Meade Kieran G,
  • Costello Eamon,
  • Keane Joseph,
  • Zhao Yingdong,
  • MacHugh David E

DOI
https://doi.org/10.1186/1471-2164-8-400
Journal volume & issue
Vol. 8, no. 1
p. 400

Abstract

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Abstract Background Bovine tuberculosis is an enduring disease of cattle that has significant repercussions for human health. The advent of high-throughput functional genomics technologies has facilitated large-scale analyses of the immune response to this disease that may ultimately lead to novel diagnostics and therapeutic targets. Analysis of mRNA abundance in peripheral blood mononuclear cells (PBMC) from six Mycobacterium bovis infected cattle and six non-infected controls was performed. A targeted immunospecific bovine cDNA microarray with duplicated spot features representing 1,391 genes was used to test the hypothesis that a distinct gene expression profile may exist in M. bovis infected animals in vivo. Results In total, 378 gene features were differentially expressed at the P ≤ 0.05 level in bovine tuberculosis (BTB)-infected and control animals, of which 244 were expressed at lower levels (65%) in the infected group. Lower relative expression of key innate immune genes, including the Toll-like receptor 2 (TLR2) and TLR4 genes, lack of differential expression of indicator adaptive immune gene transcripts (IFNG, IL2, IL4), and lower BOLA major histocompatibility complex – class I (BOLA) and class II (BOLA-DRA) gene expression was consistent with innate immune gene repression in the BTB-infected animals. Supervised hierarchical cluster analysis and class prediction validation identified a panel of 15 genes predictive of disease status and selected gene transcripts were validated (n = 8 per group) by real time quantitative reverse transcription PCR. Conclusion These results suggest that large-scale expression profiling can identify gene signatures of disease in peripheral blood that can be used to classify animals on the basis of in vivo infection, in the absence of exogenous antigenic stimulation.