The phenotypic characteristics of an animal are the result of complex molecular interactions between different layers of biological complexity, including the genetic background and intermediate phenotypes. These intermediate phenotypes describe the molecular phenome, which includes the transcriptomic, proteomic and metabolomic profiles. The final objective of this study was to identify novel breed-specific markers by exploring the pig plasma metabolome. We applied untargeted metabolomics to characterize the plasma metabolomic profile of 168 animals from three Italian heavy pig breeds: Italian Large White (ILW; n. 56), Italian Duroc (IDU; n. 56) and Italian Landrace (ILA; n. 56). These animals were sib-tested pigs, included in the selection programs of Italian heavy pigs run by the Italian Pig Breeders Association (ANAS). The pigs were raised in the same performance station and slaughtered at nine months of age (approx. 160 kg live weight). The metabolomic profiles, which included ~700 metabolites, underwent quality checks and were analyzed using a machine learning approach (Random Forest) to identify metabolites that could discriminate between pairs of breeds. In total, we identified 84, 19 and 10 metabolites for the ILW-IDU, ILW-ILA and IDU-ILA comparisons, respectively. These metabolites clearly separated the pig breeds when tested in an unsupervised manner, reflecting the different genetic backgrounds of the three breeds. Acknowledgments: This study has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 01059609 (Re-Livestock project).

Bovo, S., Bolner, M., Schiavo, G., Galimberti, G., Bertolini, F., Ribani, A., et al. (2025). Comparing metabolomic profiles among three Italian heavy pig breeds.

Comparing metabolomic profiles among three Italian heavy pig breeds

S. Bovo
;
M. Bolner;G. Schiavo;G. Galimberti;F. Bertolini;A. Ribani;S. Dall'Olio;P. Zambonelli;L. Fontanesi
2025

Abstract

The phenotypic characteristics of an animal are the result of complex molecular interactions between different layers of biological complexity, including the genetic background and intermediate phenotypes. These intermediate phenotypes describe the molecular phenome, which includes the transcriptomic, proteomic and metabolomic profiles. The final objective of this study was to identify novel breed-specific markers by exploring the pig plasma metabolome. We applied untargeted metabolomics to characterize the plasma metabolomic profile of 168 animals from three Italian heavy pig breeds: Italian Large White (ILW; n. 56), Italian Duroc (IDU; n. 56) and Italian Landrace (ILA; n. 56). These animals were sib-tested pigs, included in the selection programs of Italian heavy pigs run by the Italian Pig Breeders Association (ANAS). The pigs were raised in the same performance station and slaughtered at nine months of age (approx. 160 kg live weight). The metabolomic profiles, which included ~700 metabolites, underwent quality checks and were analyzed using a machine learning approach (Random Forest) to identify metabolites that could discriminate between pairs of breeds. In total, we identified 84, 19 and 10 metabolites for the ILW-IDU, ILW-ILA and IDU-ILA comparisons, respectively. These metabolites clearly separated the pig breeds when tested in an unsupervised manner, reflecting the different genetic backgrounds of the three breeds. Acknowledgments: This study has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 01059609 (Re-Livestock project).
2025
Book of Abstracts of the 76th Annual Meeting of the European Federation of Animal Science
395
395
Bovo, S., Bolner, M., Schiavo, G., Galimberti, G., Bertolini, F., Ribani, A., et al. (2025). Comparing metabolomic profiles among three Italian heavy pig breeds.
Bovo, S.; Bolner, M.; Schiavo, G.; Galimberti, G.; Bertolini, F.; Ribani, A.; Gallo, M.; Dall'Olio, S.; Zambonelli, P.; Fontanesi, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1022176
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