Classification and prediction of Mycobacterium Avium subsp. Paratuberculosis (MAP) shedding severity in cattle based on young stock heifer faecal microbiota composition using random forest algorithms

Bovine paratuberculosis is a devastating infectious disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). The development of the paratuberculosis in cattle can take up to a few years and vastly differs between individuals in severity of the clinical symptoms and shedding of the pathogen. Timely identification of high shedding animals is essential for paratuberculosis control and minimization of economic losses. Widely used methods for detection and quantification of MAP, such as culturing and PCR based techniques rely on direct presence of the pathogen in a sample and have little to no predictive value concerning the disease development. In the current study, we investigated the possibility of predicting MAP shedding severity in cattle based on the faecal microbiota composition. Twenty calves were experimentally infected with MAP and faecal samples were collected biweekly up to four years of age. All collected samples were subjected to culturing on selective media to obtain data about shedding severity. Faecal microbiota was profiled in a subset of samples (n = 264). Using faecal microbiota composition and shedding intensity data a random forest classifier was built for prediction of the shedding status of the individual animals.