Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning.
Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning.
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DOI：
10.1038/s41598-021-87300-0
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发表时间：
2021-04-08
影响因子：
4.6
通讯作者：
Dottorini T
中科院分区：
文献类型：
--
作者：
Maciel-Guerra A;Esener N;Giebel K;Lea D;Green MJ;Bradley AJ;Dottorini T
乳房链球菌是全世界引起乳腺炎的主要病原体之一。鉴定对抗生素治疗无效的乳房链球菌菌株对于更好的决策和治疗选择至关重要。我们证明，监督机器学习和基质辅助激光解吸电离/飞行时间（MALDI-TOF）质谱法的结合可以区分引起临床乳腺炎的乳房链球菌菌株，这些菌株可能对治疗有反应或无反应。对来自 26 个不同农场的 90 个人进行训练的诊断预测系统在准确性和 Cohen kappa 方面分别达到了 86.2% 和 71.5%。通过在编码的 MALDI-TOF 光谱中添加元数据（胎次、先前哺乳的体细胞计数和阳性乳腺炎病例计数），性能进一步提高，这将准确性和 Cohen kappa 分别提高到 92.2% 和 84.1%。将蛋白质-蛋白质网络和结构蛋白质信息集成到机器学习结果中的计算框架揭示了响应和无响应表型背后的分子决定因素。
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.