Bovine mastitis is a major health problem that affects dairy cows and has a negative impact on milk production. The presence of microRNAs in biofluids, such as blood and milk, could play a pivotal role in the detection of bovine mastitis. The purpose of the current study was to determine the levels of microRNA gene expression in milk, in combination with other reported mastitis indicators, as a biomarker of bovine mastitis. Milk samples (n = 171) were obtained from 113 dairy cows with known disease status (i.e., healthy; n = 23 cows, subclinical mastitis; n = 45 cows, or clinical mastitis; n = 45 cows) and analyzed for the presence of MIR24-2, MIR29B-2, MIR146A, MIR148A, MIR155, MIR181A1, MIR184, and MIR223 expression using the real-time PCR (qPCR) method. The expression data were then utilized in the creation of receiver operator characteristic curves (ROC) and further analyzed by the machine learning (ML) methods. MIR29B-2, MIR146A, MIR148A, and MIR155 expression levels differed significantly among the three groups. These potential microRNA biomarkers of mastitis exhibited high sensitivity and specificity. Next, we applied ML algorithm, specifically, a decision tree (DT) model to predict the status of milk based on MIR29B-2 and MIR146A expression levels. The results suggested that MIR29B-2, when used in combination with the California mastitis test (CMT) and days in milk (DIM) data, was applicable for screening and classification of milk samples from cows as healthy, subclinical mastitis, or mastitis. MIR29B-2 appears to have sufficient discriminatory power to enable it to be utilized as a biomarker in cases where the status of a milk sample cannot be determined based on CMT results.
Keywords: Bovine mastitis; Diagnostic tool; MicroRNA; Raw milk; Real-time PCR.
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