Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning

Abstract Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Alexandre Maciel-Guerra, Necati Esener, Katharina Giebel, Daniel Lea, Martin J. Green, Andrew J. Bradley, Tania Dottorini
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/42f3ad06393f4ed2ba628b81430e4b11
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:42f3ad06393f4ed2ba628b81430e4b11
record_format	dspace
spelling	oai:doaj.org-article:42f3ad06393f4ed2ba628b81430e4b112021-12-02T14:37:14ZPrediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning10.1038/s41598-021-87300-02045-2322https://doaj.org/article/42f3ad06393f4ed2ba628b81430e4b112021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87300-0https://doaj.org/toc/2045-2322Abstract Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen’s kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen’s kappa to 92.2% and 84.1% respectively. A computational framework integrating protein–protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.Alexandre Maciel-GuerraNecati EsenerKatharina GiebelDaniel LeaMartin J. GreenAndrew J. BradleyTania DottoriniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexandre Maciel-Guerra Necati Esener Katharina Giebel Daniel Lea Martin J. Green Andrew J. Bradley Tania Dottorini Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning
description	Abstract Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen’s kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen’s kappa to 92.2% and 84.1% respectively. A computational framework integrating protein–protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.
format	article
author	Alexandre Maciel-Guerra Necati Esener Katharina Giebel Daniel Lea Martin J. Green Andrew J. Bradley Tania Dottorini
author_facet	Alexandre Maciel-Guerra Necati Esener Katharina Giebel Daniel Lea Martin J. Green Andrew J. Bradley Tania Dottorini
author_sort	Alexandre Maciel-Guerra
title	Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning
title_short	Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning
title_full	Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning
title_fullStr	Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning
title_full_unstemmed	Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning
title_sort	prediction of streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/42f3ad06393f4ed2ba628b81430e4b11
work_keys_str_mv	AT alexandremacielguerra predictionofstreptococcusuberisclinicalmastitistreatmentsuccessindairyherdsbymeansofmassspectrometryandmachinelearning AT necatiesener predictionofstreptococcusuberisclinicalmastitistreatmentsuccessindairyherdsbymeansofmassspectrometryandmachinelearning AT katharinagiebel predictionofstreptococcusuberisclinicalmastitistreatmentsuccessindairyherdsbymeansofmassspectrometryandmachinelearning AT daniellea predictionofstreptococcusuberisclinicalmastitistreatmentsuccessindairyherdsbymeansofmassspectrometryandmachinelearning AT martinjgreen predictionofstreptococcusuberisclinicalmastitistreatmentsuccessindairyherdsbymeansofmassspectrometryandmachinelearning AT andrewjbradley predictionofstreptococcusuberisclinicalmastitistreatmentsuccessindairyherdsbymeansofmassspectrometryandmachinelearning AT taniadottorini predictionofstreptococcusuberisclinicalmastitistreatmentsuccessindairyherdsbymeansofmassspectrometryandmachinelearning
_version_	1718391002212859904

Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning

Ejemplares similares