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.