Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms
Abstract Background There is a growing interest in uncovering the factors that shape microbiome composition due to its association with complex phenotypic traits in livestock. Host genetic variation is increasingly recognized as a major factor influencing the microbiome. The Iberian pig breed, known...
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| Format: | Article |
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BMC
2025-02-01
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| Series: | Animal Microbiome |
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| Online Access: | https://doi.org/10.1186/s42523-025-00378-z |
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| author | Lamiae Azouggagh Noelia Ibáñez-Escriche Marina Martínez-Álvaro Luis Varona Joaquim Casellas Sara Negro Cristina Casto-Rebollo |
| author_facet | Lamiae Azouggagh Noelia Ibáñez-Escriche Marina Martínez-Álvaro Luis Varona Joaquim Casellas Sara Negro Cristina Casto-Rebollo |
| author_sort | Lamiae Azouggagh |
| collection | DOAJ |
| description | Abstract Background There is a growing interest in uncovering the factors that shape microbiome composition due to its association with complex phenotypic traits in livestock. Host genetic variation is increasingly recognized as a major factor influencing the microbiome. The Iberian pig breed, known for its high-quality meat products, includes various strains with recognized genetic and phenotypic variability. However, despite the microbiome’s known impact on pigs’ productive phenotypes such as meat quality traits, comparative analyses of gut microbial composition across Iberian pig strains are lacking. This study aims to explore the gut microbiota of two Iberian pig strains, Entrepelado (n = 74) and Retinto (n = 63), and their reciprocal crosses (n = 100), using machine learning (ML) models to identify key microbial taxa relevant for distinguishing their genetic backgrounds, which holds potential application in the pig industry. Nine ML algorithms, including tree-based, kernel-based, probabilistic, and linear algorithms, were used. Results Beta diversity analysis on 16 S rRNA microbiome data revealed compositional divergence among genetic, age and batch groups. ML models exploring maternal, paternal and heterosis effects showed varying levels of classification performance, with the paternal effect scenario being the best, achieving a mean Area Under the ROC curve (AUROC) of 0.74 using the Catboost (CB) algorithm. However, the most genetically distant animals, the purebreds, were more easily discriminated using the ML models. The classification of the two Iberian strains reached the highest mean AUROC of 0.83 using Support Vector Machine (SVM) model. The most relevant genera in this classification performance were Acetitomaculum, Butyricicoccus and Limosilactobacillus. All of which exhibited a relevant differential abundance between purebred animals using a Bayesian linear model. Conclusions The study confirms variations in gut microbiota among Iberian pig strains and their crosses, influenced by genetic and non-genetic factors. ML models, particularly CB and RF, as well as SVM in certain scenarios, combined with a feature selection process, effectively classified genetic groups based on microbiome data and identified key microbial taxa. These taxa were linked to short-chain fatty acids production and lipid metabolism, suggesting microbial composition differences may contribute to variations in fat-related traits among Iberian genetic groups. |
| format | Article |
| id | doaj-art-185b709701964154b63a1ba57d605242 |
| institution | Kabale University |
| issn | 2524-4671 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Animal Microbiome |
| spelling | doaj-art-185b709701964154b63a1ba57d6052422025-02-09T12:56:13ZengBMCAnimal Microbiome2524-46712025-02-017111510.1186/s42523-025-00378-zCharacterization of microbiota signatures in Iberian pig strains using machine learning algorithmsLamiae Azouggagh0Noelia Ibáñez-Escriche1Marina Martínez-Álvaro2Luis Varona3Joaquim Casellas4Sara Negro5Cristina Casto-Rebollo6Institute for Animal Science and Technology, Universitat Politècnica de ValenciaInstitute for Animal Science and Technology, Universitat Politècnica de ValenciaInstitute for Animal Science and Technology, Universitat Politècnica de ValenciaInstituto Agroalimentario de Aragón (IA2), Universidad de ZaragozaDepartament de Ciència Animal i dels Aliments, Universitat Autònoma de BarcelonaInga FoodInstitute for Animal Science and Technology, Universitat Politècnica de ValenciaAbstract Background There is a growing interest in uncovering the factors that shape microbiome composition due to its association with complex phenotypic traits in livestock. Host genetic variation is increasingly recognized as a major factor influencing the microbiome. The Iberian pig breed, known for its high-quality meat products, includes various strains with recognized genetic and phenotypic variability. However, despite the microbiome’s known impact on pigs’ productive phenotypes such as meat quality traits, comparative analyses of gut microbial composition across Iberian pig strains are lacking. This study aims to explore the gut microbiota of two Iberian pig strains, Entrepelado (n = 74) and Retinto (n = 63), and their reciprocal crosses (n = 100), using machine learning (ML) models to identify key microbial taxa relevant for distinguishing their genetic backgrounds, which holds potential application in the pig industry. Nine ML algorithms, including tree-based, kernel-based, probabilistic, and linear algorithms, were used. Results Beta diversity analysis on 16 S rRNA microbiome data revealed compositional divergence among genetic, age and batch groups. ML models exploring maternal, paternal and heterosis effects showed varying levels of classification performance, with the paternal effect scenario being the best, achieving a mean Area Under the ROC curve (AUROC) of 0.74 using the Catboost (CB) algorithm. However, the most genetically distant animals, the purebreds, were more easily discriminated using the ML models. The classification of the two Iberian strains reached the highest mean AUROC of 0.83 using Support Vector Machine (SVM) model. The most relevant genera in this classification performance were Acetitomaculum, Butyricicoccus and Limosilactobacillus. All of which exhibited a relevant differential abundance between purebred animals using a Bayesian linear model. Conclusions The study confirms variations in gut microbiota among Iberian pig strains and their crosses, influenced by genetic and non-genetic factors. ML models, particularly CB and RF, as well as SVM in certain scenarios, combined with a feature selection process, effectively classified genetic groups based on microbiome data and identified key microbial taxa. These taxa were linked to short-chain fatty acids production and lipid metabolism, suggesting microbial composition differences may contribute to variations in fat-related traits among Iberian genetic groups.https://doi.org/10.1186/s42523-025-00378-zMicrobiomeIberian pig16S rRNAMachine learningClassificationDifferential abundance |
| spellingShingle | Lamiae Azouggagh Noelia Ibáñez-Escriche Marina Martínez-Álvaro Luis Varona Joaquim Casellas Sara Negro Cristina Casto-Rebollo Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms Animal Microbiome Microbiome Iberian pig 16S rRNA Machine learning Classification Differential abundance |
| title | Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms |
| title_full | Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms |
| title_fullStr | Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms |
| title_full_unstemmed | Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms |
| title_short | Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms |
| title_sort | characterization of microbiota signatures in iberian pig strains using machine learning algorithms |
| topic | Microbiome Iberian pig 16S rRNA Machine learning Classification Differential abundance |
| url | https://doi.org/10.1186/s42523-025-00378-z |
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