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.
Capturing produced, consumed, or exchanged metabolites (metabolomics) and the result of gene expression (transcriptomics) require the extraction of metabolites and RNA. Multi-omics approaches and, notably, the combination of metabolomics and transcriptomic analyses are required for understanding the functional changes and adaptation of microorganisms to different physico-chemical and environmental conditions. A protocol was developed to extract total RNA and metabolites from less than 6 mg of a kind of phototrophic biofilm: oxygenic photogranules. These granules are aggregates of several hundred micrometers up to several millimeters. They harbor heterotrophic bacteria and phototrophs. After a common step for cell disruption by bead-beating, a part of the volume was recovered for RNA extraction, and the other half was used for the methanol- and dichloromethane-based extraction of metabolites. The solvents enabled the separation of two phases (aqueous and lipid) containing hydrophilic and lipophilic metabolites, respectively. The 1H nuclear magnetic resonance (NMR) analysis of these extracts produced spectra that contained over a hundred signals with a signal-to-noise ratio higher than 10. The quality of the spectra enabled the identification of dozens of metabolites per sample. Total RNA was purified using a commercially available kit, yielding sufficient concentration and quality for metatranscriptomic analysis. This novel method enables the co-extraction of RNA and metabolites from the same sample, as opposed to the parallel extraction from two samples. Using the same sample for both extractions is particularly advantageous when working with inherently heterogeneous complex biofilm. In heterogeneous systems, differences between samples may be substantial. The co-extraction will enable a holistic analysis of the metabolomics and metatranscriptomics data generated, minimizing experimental biases, including technical variations and, notably, biological variability. As a result, it will ensure more robust multi-omics analyses, particularly by improving the correlation between metabolic changes and transcript modifications.
