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

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: Apr 12, 2021
• Journal: Scientific Reports
• 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