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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

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 Thumbnail


Authors

Wenshi He

Ana S. Cardoso

Robert M. Hyde

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

DAVID SCURR DAVID.SCURR@NOTTINGHAM.AC.UK
Principal Research Fellow

Laura V. Randall



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., …Kim, D. (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 (RSC)
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

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