Metabolic alterations in dairy cattle with lameness revealed by untargeted metabolomics of dried milk spots using direct infusion-tandem mass spectrometry and the triangulation of multiple machine learning models

He, Wenshi; Cardoso, Ana S.; Hyde, Robert M.; Green, Martin J.; Scurr, David J; Griffiths, Rian; Randall, Laura V.; Kim, Dong-Hyun

Abstract

Lameness is a major challenge in the dairy cattle industry in terms of animal welfare and economic implications. Better understanding of metabolic alteration associated with lameness could lead to early diagnosis and effective treatment{,} there-fore reducing its prevalence. To determine whether metabolic signatures associated with lameness could be discovered with untargeted metabolomics{,} we developed a novel workflow using direct infusion-tandem mass spectrometry to rapidly analyse (2 min/sample) dried milk spots (DMS) that were stored on commercially available Whatman® FTA® DMPK cards for a prolonged period (8 and 16 days). An orthogonal partial least squares-discriminant analysis (OPLS-DA) method validated by triangulation of multiple machine learning (ML) models and stability selection was employed to reliably identify important discriminative metabolites. With this approach{,} we were able to differentiate between lame and healthy cows based on a set of lipid molecules and several small metabolites. Among the discriminative molecules{,} we identified phosphatidylglycerol (PG 35:4) as the strongest and most sensitive lameness indicator based on stability selection. Overall{,} this untargeted metabolomics workflow is found to be a fast{,} robust{,} and discriminating method for determining lameness in DMS samples. The DMS cards can be potentially used as a convenient and cost-effective sample matrix for larger scale research and future routine screening for lameness.

Citation

He, W., Cardoso, A. S., Hyde, R. M., Green, M. J., Scurr, D. J., Griffiths, R., Randall, L. V., & Kim, D.-H. (2022). Metabolic alterations in dairy cattle with lameness revealed by untargeted metabolomics of dried milk spots using direct infusion-tandem mass spectrometry and the triangulation of multiple machine learning models. Analyst, 147(23), 5537-5545. https://doi.org/10.1039/d2an01520j

• Journal Article Type: Article
• Acceptance Date: Oct 18, 2022
• Online Publication Date: Oct 26, 2022
• Publication Date: Dec 7, 2022
• Deposit Date: Oct 27, 2022
• Publicly Available Date: Oct 27, 2022
• Journal: Analyst
• Print ISSN: 0003-2654
• Electronic ISSN: 1364-5528
• Publisher: Royal Society of Chemistry
• Peer Reviewed: Peer Reviewed
• Volume: 147
• Issue: 23
• Pages: 5537-5545
• DOI: https://doi.org/10.1039/d2an01520j
• Keywords: Electrochemistry; Spectroscopy; Environmental Chemistry; Biochemistry; Analytical Chemistry
• Public URL: https://nottingham-repository.worktribe.com/output/12898034
• Publisher URL: https://pubs.rsc.org/en/Content/ArticleLanding/2022/AN/D2AN01520J