High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds

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 the Boruta algorithm, which is a Random Forest wrapper, and 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 eight 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.

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Journal
animal
Published
2024-12-04
DOI
https://doi.org/10.1016/j.animal.2024.101393
Citations
4
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.94
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article

High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds

Anisa Ribani, Samuele Bovo, Luca Fontanesi, Giuseppina Schiavo et al.
4 citations
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Metabolomics and Mass Spectrometry Studies
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article

High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds

Anisa Ribani, Samuele Bovo, Luca Fontanesi, Giuseppina Schiavo, Francesca Bertolini, Paolo Zambonelli, Matteo Bolner, Stefania Dall’Olio, M. Gallo, G. Galimberti
article en
4 citations

Abstract

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 the Boruta algorithm, which is a Random Forest wrapper, and 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 eight 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.

animalVol. 19(1)
ANT Foundation Italy Onlus (IT), University of Bologna (IT)
Zero hunger
Openalex Percentile: Top 24%
Metabolomics and Mass Spectrometry Studies
0.94
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