Accurate and robust classification of Mycobacterium bovis-infected cattle using peripheral blood RNA-seq data

The zoonotic bacterium, Mycobacterium bovis, causes bovine tuberculosis (bTB) and is closely related to Mycobacterium tuberculosis, the primary cause of human tuberculosis (hTB). Bovine TB remains recalcitrant to eradication in endemic countries where current diagnostics fail to identify all infected animals. While blood-based RNA biomarkers identified through machine learning have shown accurate discrimination of hTB-positive and hTB-negative individuals, similar approaches have not been explored for bTB. Here, we use RNA-seq and machine learning to investigate the utility of peripheral blood mRNA as a host-response biomarker for bTB using data from Ireland, the UK and the US. We identify a 30-gene signature and a 273-gene elastic net classifier that differentiate bTB-positive from bTB-negative cattle, achieving area under the curve (AUC) values of 0.986/0.900 for the former and 0.968/0.938 for the latter in training and testing, respectively. These two classifiers produced high sensitivity and specificity values ([≥] 0.853 for both metrics) in the testing set. Additionally, we show that they robustly distinguish bTB+ animals from those infected with other bacterial or viral pathogens (AUC [≥] 0.819). These RNA-based classifiers accurately diagnose bTB and differentiate bTB from other diseases, representing a promising tool for augmenting current diagnostics to advance bTB eradication efforts in endemic regions.