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 expl...
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Elsevier
2025-01-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731124003306 |
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| author | S. Bovo M. Bolner G. Schiavo G. Galimberti F. Bertolini S. Dall’Olio A. Ribani P. Zambonelli M. Gallo L. Fontanesi |
| author_facet | S. Bovo M. Bolner G. Schiavo G. Galimberti F. Bertolini S. Dall’Olio A. Ribani P. Zambonelli M. Gallo L. Fontanesi |
| author_sort | S. Bovo |
| collection | DOAJ |
| 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 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. |
| format | Article |
| id | doaj-art-64c3136e96784518960ad38a9751b0b7 |
| institution | Kabale University |
| issn | 1751-7311 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-64c3136e96784518960ad38a9751b0b72025-01-19T06:24:50ZengElsevierAnimal1751-73112025-01-01191101393High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breedsS. Bovo0M. Bolner1G. Schiavo2G. Galimberti3F. Bertolini4S. Dall’Olio5A. Ribani6P. Zambonelli7M. Gallo8L. Fontanesi9Animal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, ItalyAnimal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, ItalyAnimal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, ItalyDepartment of Statistical Sciences “Paolo Fortunati”, University of Bologna, 40126 Bologna, ItalyAnimal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, ItalyAnimal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, ItalyAnimal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, ItalyAnimal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, ItalyAssociazione Nazionale Allevatori Suini, 00198 Roma, ItalyAnimal and Food Genomics Group, Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1751731124003306Machine learningMetaboliteMolecular phenomePlasmaSus scrofa |
| spellingShingle | S. Bovo M. Bolner G. Schiavo G. Galimberti F. Bertolini S. Dall’Olio A. Ribani P. Zambonelli M. Gallo L. Fontanesi High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds Animal Machine learning Metabolite Molecular phenome Plasma Sus scrofa |
| title | High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds |
| title_full | High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds |
| title_fullStr | High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds |
| title_full_unstemmed | High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds |
| title_short | High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds |
| title_sort | high throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds |
| topic | Machine learning Metabolite Molecular phenome Plasma Sus scrofa |
| url | http://www.sciencedirect.com/science/article/pii/S1751731124003306 |
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