ArticleJDS communications2026
Applicability of targeted metabolomics for the prediction of feed efficiency groups and metrics in dairy sheep.
Article in JDS communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Identifying animals with high feed efficiency (FE) can significantly reduce production costs and environmental impact and improve profitability in livestock systems. Previous studies demonstrated that protein metabolism played a prominent role in predicting FE profiles in the Assaf breed. Consequently, a deeper investigation of the potential for protein-related metabolites to act as biomarkers for FE profile could help to determine a reduced subset of metabolites for FE prediction. In this study, 40 Assaf ewes were evaluated for FE using 2 metrics: residual feed intake (RFI) and feed efficiency index (FEI). Animals consistently classified at the extremes of both metrics were grouped as 6 lower (L-FE) and 6 higher (H-FE) FE individuals. Targeted plasma metabolomics, identified through ultra-performance liquid chromatography with multiple reaction monitoring tandem mass spectrometry with isotope-labeled target metabolites, was used to assess the potential of various metabolites related to protein metabolism to classify FE groups and predict FE metrics. Partial least squares discriminant analysis identified 9 metabolites with high variable importance in projection (>1.5) that separated L-FE and H-FE groups. In leave-one-out cross-validation, the model based on the first principal component (PC1) achieved Pearson correlations of 0.745 for RFI and 0.920 for FEI with observed values. Subsequently, the dimensionality of these data was reduced via principal component analysis, and PC1 was used in linear models to predict RFI and FEI. The functional profile of the selected metabolites reinforced the central role of protein metabolism, especially AA transport and utilization, in shaping FE phenotypes in dairy ewes. These findings enhance our understanding of protein metabolism signatures linked to FE and support the development of cost-effective tools (when compared with whole metabolomics) for selecting high-efficiency animals in ruminant production systems.
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