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

Maciel-Guerra, Alexandre; Esener, Necati; Giebel, Katharina; Lea, Daniel; Green, Martin J.; Bradley, Andrew J.; Dottorini, Tania

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


Authors

Alexandre Maciel-Guerra

Necati Esener

Katharina Giebel

Daniel Lea

MARTIN GREEN martin.green@nottingham.ac.uk
Professor of Cattle Health & Epidemiology



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.

Citation

Maciel-Guerra, A., Esener, N., Giebel, K., Lea, D., Green, M. J., Bradley, A. J., & Dottorini, T. (2021). Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning. Scientific Reports, 11(1), Article 7736. https://doi.org/10.1038/s41598-021-87300-0

Journal Article Type Article
Acceptance Date Mar 26, 2021
Online Publication Date Apr 8, 2021
Publication Date Apr 8, 2021
Deposit Date Apr 12, 2021
Publicly Available Date Mar 29, 2024
Journal Scientific Reports
Print ISSN 2045-2322
Electronic ISSN 2045-2322
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 11
Issue 1
Article Number 7736
DOI https://doi.org/10.1038/s41598-021-87300-0
Keywords Multidisciplinary
Public URL https://nottingham-repository.worktribe.com/output/5461059
Publisher URL https://www.nature.com/articles/s41598-021-87300-0

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