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

Abstract 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 sh...

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Autores principales:	Alexander Umanets, Annemieke Dinkla, Stephanie Vastenhouw, Lars Ravesloot, Ad P. Koets
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Mycobacterium avium subsp. paratuberculosis Gut microbiota Machine learning Prediction Pathogen shedding Bovine Veterinary medicine SF600-1100 Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/4cb971ed0dfe4dbfb576f2e4691b642c
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spelling	oai:doaj.org-article:4cb971ed0dfe4dbfb576f2e4691b642c2021-11-21T12:29:01ZClassification and prediction of Mycobacterium Avium subsp. Paratuberculosis (MAP) shedding severity in cattle based on young stock heifer faecal microbiota composition using random forest algorithms10.1186/s42523-021-00143-y2524-4671https://doaj.org/article/4cb971ed0dfe4dbfb576f2e4691b642c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s42523-021-00143-yhttps://doaj.org/toc/2524-4671Abstract 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.Alexander UmanetsAnnemieke DinklaStephanie VastenhouwLars RaveslootAd P. KoetsBMCarticleMycobacterium avium subsp. paratuberculosisGut microbiotaMachine learningPredictionPathogen sheddingBovineVeterinary medicineSF600-1100MicrobiologyQR1-502ENAnimal Microbiome, Vol 3, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mycobacterium avium subsp. paratuberculosis Gut microbiota Machine learning Prediction Pathogen shedding Bovine Veterinary medicine SF600-1100 Microbiology QR1-502
spellingShingle	Mycobacterium avium subsp. paratuberculosis Gut microbiota Machine learning Prediction Pathogen shedding Bovine Veterinary medicine SF600-1100 Microbiology QR1-502 Alexander Umanets Annemieke Dinkla Stephanie Vastenhouw Lars Ravesloot Ad P. Koets 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
description	Abstract 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.
format	article
author	Alexander Umanets Annemieke Dinkla Stephanie Vastenhouw Lars Ravesloot Ad P. Koets
author_facet	Alexander Umanets Annemieke Dinkla Stephanie Vastenhouw Lars Ravesloot Ad P. Koets
author_sort	Alexander Umanets
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publisher	BMC
publishDate	2021
url	https://doaj.org/article/4cb971ed0dfe4dbfb576f2e4691b642c
work_keys_str_mv	AT alexanderumanets classificationandpredictionofmycobacteriumaviumsubspparatuberculosismapsheddingseverityincattlebasedonyoungstockheiferfaecalmicrobiotacompositionusingrandomforestalgorithms AT annemiekedinkla classificationandpredictionofmycobacteriumaviumsubspparatuberculosismapsheddingseverityincattlebasedonyoungstockheiferfaecalmicrobiotacompositionusingrandomforestalgorithms AT stephanievastenhouw classificationandpredictionofmycobacteriumaviumsubspparatuberculosismapsheddingseverityincattlebasedonyoungstockheiferfaecalmicrobiotacompositionusingrandomforestalgorithms AT larsravesloot classificationandpredictionofmycobacteriumaviumsubspparatuberculosismapsheddingseverityincattlebasedonyoungstockheiferfaecalmicrobiotacompositionusingrandomforestalgorithms AT adpkoets classificationandpredictionofmycobacteriumaviumsubspparatuberculosismapsheddingseverityincattlebasedonyoungstockheiferfaecalmicrobiotacompositionusingrandomforestalgorithms
_version_	1718418940327100416

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

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