Towards a microRNA-based Johne's disease diagnostic predictive system: Preliminary results.

Background: Johne's disease, caused by Mycobacterium avium subspecies paratuberculosis (MAP), is a chronic enteritis that adversely affects welfare and productivity in cattle. Screening and subsequent removal of affected animals is a common approach for disease management, but efforts are hindered by low diagnostic sensitivity. Expression levels of small non-coding RNA molecules involved in gene regulation (microRNAs), which may be altered during mycobacterial infection, may present an alternative diagnostic method.

Methods: The expression levels of 24 microRNAs affected by mycobacterial infection were measured in sera from MAP-positive (n = 66) and MAP-negative cattle (n = 65). They were then used within a machine learning approach to build an optimal classifier for MAP diagnosis.

Results: The method provided 72% accuracy, 73% sensitivity and 71% specificity on average, with an area under the curve of 78%.

Limitations: Although control samples were collected from farms nominally MAP-free, the low sensitivity of current diagnostics means some animals may have been misclassified.

Conclusion: MicroRNA profiling combined with advanced predictive modelling enables rapid and accurate diagnosis of Johne's disease in cattle.