This study aimed to characterise the milk metabolome in Irish seasonal pasture-based dairy systems using ¹H-NMR spectroscopy. Seasonal, lactational, and milk source-related variations in milk metabolites were investigated across the production year using 533 milk samples collected over 41 weeks. The work sought to identify metabolites and metabolic pathways associated with physiological adaptations, energy balance, metabolic stress, and nitrogen use efficiency in dairy cows. Potential biomarker metabolites linked to seasonality were also evaluated, which may contribute improving sustainability, metabolic resilience, and management strategies in pasture-based dairy production systems.
Here you can explore the metabolites identified and quantified in Irish milk across lactation, including their concentrations by season. By clicking on each metabolite, you will access detailed individual “MetaBó-Cards” with expanded information on trends across the seasons and months as well as, biochemical description and links for further information.
In addition, the database includes visualisations showing metabolite variation by season, month, and milk source, providing a comprehensive overview of changes in the milk metabolome throughout lactation.
Seasonality of the Irish Milk Metabolome
Partial Least Squares Discriminant Analysis (PLS-DA) score plots differentiating between Farm and Shop milk metabolome profiles. PLS-DA accounts for a total of 49.2% of variation (Component 1: 19.6%, Component 2: 29.6%). Metabolites contributing most to the separation are indicated as loading vectors. The permutation test (n= 2000) resulted in a p-value of 0.087, indicating that the observed separation between groups is not statistically significant.
Partial Least Squares Discriminant Analysis (PLS-DA) score plots differentiating milk metabolome profiles by season (A). PLS-DA accounts for a total of 56.3% of variation (Component 1: 29.6%, Component 2: 26.7%). Metabolites contributing most to the separation are indicated as loading vectors. To characterise these discriminant metabolites, variable importance in projection (VIP) scores differentiating milk metabolome profiles by season (B) were calculated. Metabolites with VIP scores >1 are considered relevant for discrimination. Heatmap colours in the VIP indicate metabolite concentrations, where red indicates higher concentrations and blue indicates lower concentrations across seasons. Calendar Season criteria: Winter = February; Spring = March, April, May; Summer = June, July, August; Autumn = September, October, November.
Partial Least Squares Discriminant Analysis (PLS-DA) score plots differentiating milk metabolome profiles by month (A). PLS-DA accounts for a total of 56.4% of variation (Component 1: 32.1%, Component 2: 24.3%). Metabolites contributing most to the separation are indicated as loading vectors. To characterise these discriminant metabolites, variable importance in projection (VIP) scores differentiating milk metabolome profiles by month (B) were calculated. Metabolites with VIP scores >1 are considered relevant for discrimination. Heatmap colours in the VIP indicate metabolite concentrations, where red indicates higher concentrations and blue indicates lower concentrations across seasons.
Paula Rojas-Gómez, Raghunath Pariyani, Michael Dineen, Denis Lynch, Lorraine Bateman, Eoghan Roche, Anita R. Maguire, Noel A. McCarthy, Thomas Brendan Murphy, James A. O’Mahony, Tom F. O’Callaghan
This study aims to characterise the milk metabolome from a seasonal pasture-based dairy system using 1H-NMR spectroscopy. Over 41 weeks, ten dairy farms were visited weekly for the collection of raw bulk tank milk samples (n = 410) and three commercial pasteurised skimmed milks were also purchased weekly (n = 123). In total, 38 milk metabolites were quantified, 30 of which exhibited significant seasonal variation. Multivariate analysis identified several key compounds associated with seasonal metabolic changes. Winter-Feb milk, corresponding to early-lactation period, was enriched in ketone bodies, O-phosphocholine, creatinine, and glucose-1-phosphate, reflecting increased metabolic stress and negative energy balance following parturition. In contrast, autumn milk, corresponding to late-lactation, contained higher concentrations of choline and urea, indicative of improved energy status but reduced nitrogen use efficiency. These findings highlight the potential of milk metabolomics as a valuable tool for monitoring physiological status and guiding interventions to enhance sustainability in dairy systems.