Unveiling long-term prenatal nutrition biomarkers in beef cattle via multi-tissue and multi-OMICs analysis
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it