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

A. Umanets, A. Dinkla, S. Vastenhouw, L. Ravesloot, A.P. Koets*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background 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. Results The results indicate that machine learning approaches applied to microbial composition can be used to classify cows into groups by severity of MAP shedding. The classification accuracy correlates with the age of the animals and use of samples from older individuals resulted in a higher classification precision. The classification model based on samples from the first 12 months of life showed an AUC between 0.78 and 0.79 (95% CI), while the model based on samples from animals older than 24 months showed an AUC between 0.91 and 0.92 (95% CI). Prediction for samples from animals between 12 and 24 month of age showed intermediate accuracy [AUC between 0.86 and 0.87 (95% CI)]. In addition, the results indicate that a limited number of microbial taxa were important for classification and could be considered as biomarkers. Conclusions The study provides evidence for the link between microbiota composition and severity of MAP infection and shedding, as well as lays ground for the development of predictive diagnostic tools based on the faecal microbiota composition.
Original languageEnglish
Article number78
Number of pages13
JournalAnimal Microbiome
Issue number1
Publication statusPublished - 14 Nov 2021


  • Mycobacterium avium subsp
  • paratuberculosis
  • Gut microbiota
  • Machine learning
  • Prediction
  • Pathogen shedding
  • Bovine
  • Random forest
  • DNA

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