Identification of Predictive Biomarkers of Lameness in Transition Dairy Cows

Cardoso, Ana S.; Whitby, Alison; Green, Martin J.; Kim, Dong-Hyun; Randall, Laura V.

Abstract

The aim of this study was to identify with a high level of confidence metabolites previously identified as predictors of lameness and understand their biological relevance by carrying out pathway analyses. For the dairy cattle sector, lameness is a major challenge with a large impact on animal welfare and farm economics. Understanding metabolic alterations during the transition period associated with lameness before the appearance of clinical signs may allow its early detection and risk prevention. The annotation with high confidence of metabolite predictors of lameness and the understanding of interactions between metabolism and immunity are crucial for a better understanding of this condition. Using liquid chromatography–tandem mass spectrometry (LC-MS/MS) with authentic standards to increase confidence in the putative annotations of metabolites previously determined as predictive for lameness in transition dairy cows, it was possible to identify cresol, valproic acid, and gluconolactone as L1, L2, and L1, respectively which are the highest levels of confidence in identification. The metabolite set enrichment analysis of biological pathways in which predictors of lameness are involved identified six significant pathways (p < 0.05). In comparison, over-representation analysis and topology analysis identified two significant pathways (p < 0.05). Overall, our LC-MS/MS analysis proved to be adequate to confidently identify metabolites in urine samples previously found to be predictive of lameness, and understand their potential biological relevance, despite the challenges of metabolite identification and pathway analysis when performing untargeted metabolomics. This approach shows potential as a reliable method to identify biomarkers that can be used in the future to predict the risk of lameness before calving. Validation with a larger cohort is required to assess the generalization of these findings.

• Journal Article Type: Article
• Acceptance Date: Jul 7, 2024
• Online Publication Date: Jul 10, 2024
• Publication Date: Jul 10, 2024
• Deposit Date: Jul 15, 2024
• Publicly Available Date: Jul 16, 2024
• Journal: Animals
• Electronic ISSN: 2076-2615
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 14
• Article Number: 2030
• DOI: https://doi.org/10.3390/ani14142030
• Public URL: https://nottingham-repository.worktribe.com/output/37158448
• Publisher URL: https://www.mdpi.com/2076-2615/14/14/2030