Abstract
Clinical mastitis (CM), the most prevalent and costly disease in dairy cows, is diagnosed most commonly shortly after calving. Current indicators do not satisfactorily predict CM. This study aimed to develop a robust and comprehensive mass spectrometry-based metabolomic and lipidomic workflow using untargeted ultra-performance liquid chromatography high-resolution mass spectrometry for predictive biomarker detection. Using a nested case-control design, we measured weekly during the prepartal transition period differences in serum metabolites, lipids, inflammation markers, and minerals between clinically healthy Holstein dairy cows diagnosed with mastitis postcalving (CMP; n = 8; CM diagnosis d 1 = 3 cows, d 2 = 2 cows, d 4 = 1 cow; d 25 = 1 cow, and d 43 = 1 cow that had subclinical mastitis since d 3) or not (control; n = 9). The largest fold differences between CMP and control cows during the prepartal transition period were observed for 3'-sialyllactose in serum. Seven metabolites (N-methylethanolamine phosphate, choline, phosphorylcholine, free carnitine, trimethyl lysine, tyrosine, and proline) and 3 metabolite groups (carnitines, AA metabolites, and water-soluble phospholipid metabolites) could correctly classify cows for their future CM status at both 21 and 14 d before calving. Biochemical analysis using lipid and metabolite-specific commercial diagnostic kits supported our mass spectrometry-based omics results and additionally showed elevated inflammatory markers (serum amyloid A and visfatin) in CMP cows. In conclusion, metabolic phenotypes (i.e., metabotype) with elevated protein and lipid metabolism and inflammation may precede CM in prepartal transition dairy cows. The discovered serum metabolites and lipids may assist in predictive diagnostics, prevention strategies, and early treatment intervention against CM, and thereby improve cow health and welfare.