Unveiling long-term prenatal nutrition biomarkers in beef cattle via multi-tissue and multi-OMICs analysis.

INTRODUCTION: Maternal nutrition during gestation plays a crucial role in shaping offspring development, metabolism, and long-term health, yet the underlying molecular mechanisms remain poorly understood. OBJECTIVES: This study investigated potential biomarkers through multi-OMICs and multi-tissue analyses in offspring of beef cows subjected to different gestational nutrition regimes. METHODS: A total of 126 cows were allocated to three groups: NP (control, mineral supplementation only), PP (protein-energy supplementation in the last trimester), and FP (protein-energy supplementation throughout gestation). Post-finishing phase, samples (blood, feces, ruminal fluid, fat, liver, and longissimus muscle/meat) were collected from 63 male offspring. RNA sequencing was performed on muscle and liver, metabolomics on plasma, fat, liver, and meat, and 16S rRNA sequencing on feces and ruminal fluid. Data were analyzed via DIABLO (mixOmics, R). RESULTS: The muscle transcriptome showed strong cross-block correlations (|r| > 0.7), highlighting its sensitivity to maternal nutrition. Plasma glycerophospholipids (PC ae C30:0, PC ae C38:1, lysoPC a C28:0) were key biomarkers, particularly for FP. The PP group exhibited liver-associated markers (IL4I1 gene, butyrylcarnitine), reflecting late-gestation effects, while NP had reduced ruminal Clostridia (ASV151, ASV241), suggesting impaired microbial energy metabolism. CONCLUSIONS: This integrative multi-OMICs approach provided deeper insights than single-layer analyses, distinguishing nutritional groups and revealing tissue- and OMIC-specific patterns. These findings demonstrate the value of combining transcriptomic, metabolomic, and microbiome data to identify biomarkers linked to maternal nutrition in beef cattle.