Metabolic prediction of maturity at birth in pigs

Improving piglet survival is a key objective for breeders. Piglets that have not yet fully developed are more likely to die prematurely. Here, focus was to better characterize maturity at birth. Very immature piglets exhibit a distinctive head morphology with a reminiscent of a dolphin's, with prominent eyes. This study proposed integrating phenotyping data with blood sampling to develop a predictive metabolic signature of piglet maturity at birth. Following analysis of the head morphology, the study categorized 278 newborns (99 Landrace, 87 Large White, 92 LR×LW) according to their maturity level. Furthermore, a metabolomic analysis was also performed by 1H-NMR on blood samples (serum) collected on piglets in the hours following birth. The raw spectra were analyzed using the R package ASICS. The following statistics were based on 55 metabolites with non-zero variance. A subset of 14 metabolites was selected to develop a predictive model based on random Forests and GLM methods. The two models accurately predict 100\% of the severe immaturity status in both the training and test samples. Some piglets that are morphologically classified as mature may be metabolically immature. The 14-metabolite signature can qualify the maturity with a qualitative score as mature or not, and two quantitative scores, a mean predicted value and a stability of the prediction, which allow the confidence of the prediction to be assessed. The predictive model was applied to an independent dataset of blood collected on different farms and from piglets of different genetic origins. This allowed the relevance of the model to be evaluated, taking into account other phenotypes related to the status of birth piglets, such as birth weight, and body mass index. Genetic selection for survival at birth and growth is primarily based on the measurement of birth weight. As these traits are correlated, it is important to unravel these correlations to understand the underlying molecular mechanisms. The identification of a molecular signature could facilitate future experiments aimed at deciphering the genetic architecture of complex traits, such as maturity. Therefore, we have developed a minimally invasive blood sample that allows for low-cost, user-friendly metabolic analysis of serum. While maturity is typically defined at the biometric level, we propose a novel approach to define this complex trait at the metabolic level.