Milk transcriptome biomarker identification to enhance feed efficiency and reduce nutritional costs in dairy ewes
[EN] In recent years, rising prices for high-quality protein-based feeds have significantly increased nutrition costs. Consequently, investigating strategies to reduce these expenses and improve feed efficiency (FE) have become increasingly important for the dairy sheep industry. This research inves...
| Autores: | , , , , , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Universidad de León |
| Repositório: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/24289 |
| Acesso em linha: | https://www.sciencedirect.com/science/article/pii/S1751731124001812?via%3Dihub https://hdl.handle.net/10612/24289 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Producción animal Veterinaria Biomarkers Dairy sheep Feed Efficiency Machine Learning Transcriptomics 3109 Ciencias Veterinarias 3104 Producción Animal |
| Resumo: | [EN] In recent years, rising prices for high-quality protein-based feeds have significantly increased nutrition costs. Consequently, investigating strategies to reduce these expenses and improve feed efficiency (FE) have become increasingly important for the dairy sheep industry. This research investigates the impact of nutritional protein restriction (NPR) during prepuberty and FE on the milk transcriptome of dairy Assaf ewes (sampled during the first lactation). To this end, we first compared transcriptomic differences between NPR and control ewes. Subsequently, we evaluated gene expression differences between ewes with divergent FE, using feed conversion ratio (FCR), residual feed intake (RFI), and consensus classifications of high− and low-FE animals for both indices. Lastly, we assess milk gene expression as a predictor of FE phenotype using random forest. No effect was found for the prepubertal NPR on milk performance or FE. Moreover, at the milk transcriptome level, only one gene, HBB, was differentially expressed between the NPR (n = 14) and the control group (n = 14). Further, the transcriptomic analysis between divergent FE sheep revealed 114 differentially expressed genes (DEGs) for RFI index (high-FERFI = 10 vs low-FERFI = 10), 244 for FCR (high-FEFCR = 10 vs low-FEFCR = 10), and 1 016 DEGs between divergent consensus ewes for both indices (high-FEconsensus = 8 vs low-FEconsensus = 8). These results underscore the critical role of selected FE indices for RNA-Seq analyses, revealing that consensus divergent animals for both indices maximise differences in transcriptomic responses. Genes overexpressed in high-FEconsensus ewes were associated with milk production and mammary gland development, while low-FEconsensus genes were linked to higher metabolic expenditure for tissue organisation and repair. The best prediction accuracy for FE phenotype using random forest was obtained for a set of 44 genes consistently differentially expressed across lactations, with Spearman correlations of 0.37 and 0.22 for FCR and RFI, respectively. These findings provide insights into potential sustainability strategies for dairy sheep, highlighting the utility of transcriptomic markers as FE proxies |
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