Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes.

TitleMultivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes.
Publication TypeJournal Article
Year of Publication2023
AuthorsIthurbide, M, Wang, H, Fassier, T, Zheyuan, L, Pires, J, Larsen, T, Cao, J, Rupp, R, Friggens, NC
JournalJ Dairy Sci
Date Published2023 May 31

In a context of growing interest in breeding more resilient animals, a non-invasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity, i.e., productive longevity corrected for milk yield (60 low longevity line goats (Low_LGV), and 78 high longevity line goats (High_LGV)), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during pre-challenge, challenge and recovery periods. Functional PCA summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year x facility effect on the metabolites concentrations was pre-corrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher BOHB, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters (P = 0.009). These results suggest that multivariate analysis of non-invasive milk measures show potential for deriving new resilience phenotypes.

Alternate JournalJ Dairy Sci
PubMed ID37268569