Potential Diagnostic Value of the Peripheral Blood Mononuclear Cell Transcriptome From Cattle With Bovine Tuberculosis

peer reviewedBovine tuberculosis (bTB) is a chronic disease of cattle caused by Mycobacterium bovis. During early-stage infection, M. bovis-infected cattle shed mycobacteria through nasal secretions, which can be detected via nested-polymerase chain reaction (PCR) experiments. Little research has focused on immune responses in nested PCR-positive (bTB PCR-P) or nested PCR-negative (bTB PCR-N) M. bovis-infected cattle. Here, we investigated the transcriptomes of peripheral blood mononuclear cells (PBMCs), with or without stimulation by purified protein derivative of bovine tuberculin (PPD-B), among bTB PCR-P, bTB PCR-N, and healthy cattle using RNA-Seq. We also explored the potential value of PBMC transcripts as novel biomarkers for diagnosing bTB. Numerous differentially expressed genes were identified following pair-wise comparison of different groups, with or without PPD-B stimulation (adjusted p < 0.05). Compared with healthy cattle, bTB PCR-P, and bTB PCR-N cattle shared 5 significantly dysregulated biological pathways, including Cytokine-cytokine receptor interaction, NF-kappa B signaling pathway, Hematopoietic cell lineage, Osteoclast differentiation and HTLV-I infection. Notably, dysregulated biological pathways of bTB PCR-P and bTB PCR-N cattle were associated with cell death and phagocytosis, respectively. Lymphotoxin alpha and interleukin-8 could potentially differentiate M. bovis-infected and healthy cattle upon stimulation with PPD-B, with area-under-the-curve (AUC) values of 0.9991 and 0.9343, respectively. B cell lymphoma 2 and chitinase 3-like 1 might enable differentiation between bTB PCR-P and bTB PCR-N upon stimulation with PPD-B, with AUC values of 0.9100 and 0.8893, respectively. Thus, the PBMC transcriptome revealed the immune responses in M. bovis-infected cattle (bTB PCR-P and bTB PCR-N) and may provide a novel sight in bTB diagnosis. © Copyright © 2020 Fang, Lin, Jia, Gao, Sui, Guo, Hou, Jiang, Zhu, Zhu, Ding, Jiang and Xin