ASPA 25th Congress Book of Abstract

Adequate fat deposition in the legs is essential for high-quality dry-cured ham production, as fat plays a critical role in flavor development, texture, and the curing process of dry-cured ham. For the Italian Large White breed, this is indirectly assessed by measuring traits like backfat thickness (BFT), to ensure pigs have sufficient subcutaneous fat, lean cut (LC), used to estimate fat-to-lean ratios and ham weight (HW), as heavier hams typically come from pigs with higher fat deposition. Recent advancements in omics technologies suggest that plasma untargeted metabolomics can be a promising source of biomarkers to improve accuracy in evaluation and selection. In this study, we used untargeted metabolomics to characterize the plasma of more than 700 Italian Large White pigs, categorised based on their breeding values for BFT, LC and HW traits. The pigs were grouped into three distinct pools based on the trait of interest: one for BFT, one for LC, and one for HW. Each pool consisted of animals with extreme and divergent breeding values (i.e. high and low) for their respective traits. Discriminant metabolites of high vs low within pools were identified using the machine learning Boruta algorithm. Among the 722 metabolites profiled, 23were discriminant for the BFT pool, 23 for the LC pool and 26 for the HW pool, with approximately 20% of metabolites shared by two or more pools. These discriminant metabolites mainly belong to lipid-related pathways such as glycosyl ceramides and mevalonate metabolism and have a heritability that, for two lipids, was higher than 0.5. The findings underscore the biological uniqueness of the three traits considered and the potential of metabolomics to uncover novel biochemical markers relevant to fat deposition. These metabolites may represent promising candidates to enhance the accuracy of selection and evaluation processes in breeding programs aimed at optimising fat deposition for high-quality dry-cured ham production. Research funded by the European Union – NextGenerationEU under the National Recovery and Resilience Plan (PNRR) – FEEDTHEPIG, proposal code P2022FZMJ9 – CUP J53D23018310001 and the European Union’s Horizon Europe research and innovation programme under the grant agreement No. 101059609 (Re-Livestock)