DOI: 10.5281/zenodo.14044560
title: High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds

description: Content

Dataset of the study: "High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds.
description: Metabolomics can describe the molecular phenome and may contribute to dissecting the biological processes linked to economically relevant traits in livestock species. Comparative analyses of metabolomic profiles in purebred pigs can provide insights into the basic biological mechanisms that may explain differences in production performances. Following this concept, this study was designed to compare on a large scale, the plasma metabolomic profiles of two Italian heavy pig breeds (Italian Duroc and Italian Large White) to indirectly evaluate the impact of their different genetic backgrounds on the breed metabolomes. We utilised a high-throughput untargeted metabolomics approach in a total of 962 pigs that allowed us to detect and relatively quantify 722 metabolites from various biological classes. The molecular data were analysed using a bioinformatics pipeline specifically designed for identifying differentially abundant metabolites between the two breeds in a robust and statistically significant manner, including Boruta algorithm (a Random Forest wrapper) and the sparse Partial Least Squares Discriminant Analysis (sPLS-DA) for feature selection. After thoroughly evaluating the impact of random components on missing value imputation, 100 discriminant metabolites were selected by Boruta and 17 discriminant metabolites (all included within the previous list) were identified with sPLS-DA. About half of the 100 discriminant metabolites had a higher concentration in one or the other breed (48 in Italian Large White pigs, with a prevalence of amino acids and peptides; 52 in Italian Duroc pigs, with a prevalence of lipids). These metabolites were from seven distinct super pathways and had an absolute mean value of percentage difference between the two breeds (|Δ|%) of 39.2±32.4. Six of these metabolites had |Δ|%>100. A general correlation network analysis based on Boruta identified metabolites consisted of 31 singletons and 69 metabolites connected by 141 edges, with two large clusters (>15 nodes), three medium clusters (3-6 nodes) and 8 additional pairs, with most metabolites belonging to the same super pathway. The major cluster representing the lipids super-pathway included 24 metabolites, primarily sphingomyelins. Overall, this study identified metabolomic differences between Italian Duroc and Italian Large White pigs explained by the specific genetic background of the two breeds. These biomarkers can explain the biological differences between these two breeds and can have potential practical applications in pig breeding and husbandry.
description: Financial support statement

This study was supported by: (1) PRIN2017 PigPhenomics that has received funding from the Italian MUR (for the production metabolomic data); (2) the Horizon Europe Re-Livestock project that has received funding from the European Union's Horizon Europe research and innovation programme under the grant agreement No. 01059609 (for the elaboration and analysis of the data); (3) the Italian PRIN2022 project FEEDTHEPIG, Funded by the European Union - NextGenerationEU under the National Recovery and Resilience Plan (PNRR) - Mission 4 Education and research - Component 2 From research to business - Investment 1.1 Notice PRIN 2022 PNRR (DD N. 1409 del 14/09/2022), proposal code P2022FZMJ9 - CUP J53D23018310001 for the analysis of part of the data; (4) the Italian PRIN2022 HamCapture, funded by the European Union - NextGenerationEU under the National Recovery and Resilience Plan (PNRR) - Mission 4 Education and research - Component 2 From research to business - Investment 1.1 Notice Prin 2022 - DD N. 104 del 2/2/2022, proposal code 202238NP9N – CUP J53D23009570001 (for the analysis of part of the data); (5) and was carried out within the Agritech National Research Center (Agritech Spoke 1) and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022) - This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.