Multi-omics analysis reveals the effects of prenatal nutrition on carcass-related tissues in beef cattle
Bibliographic record
Abstract
This study evaluated the long-term metabolic effects of prenatal nutrition in Nellore bulls. Pregnant cows (n = 126) received mineral supplementation only (NP), protein-energy supplementation during the last trimester (PP), or supplementation throughout pregnancy (FP). At slaughter, longissimus (muscle and meat) and subcutaneous fat samples from the offspring were collected for transcriptomics and metabolomics analyses. Data were reduced using Weighted Gene Co-expression Network Analysis, followed by functional enrichment, and then integrated via Spearman's correlations and holistic pathway analysis. Distinct molecular patterns emerged across prenatal nutrition treatments, although all groups influenced energy metabolism and cellular processes. The NP group was strongly associated with protein and lipid metabolism, highlighted by PPAR and sphingolipid signaling pathways, and key hub components including CNOT4 and tryptophan. In contrast, PP and FP groups were more closely linked to immune function, stress resilience, with enrichment of NF-kB signaling, cortisol synthesis, and hub components including TIE1, YWHAZ, carnitine, and glutaconylcarnitine. Shared transcriptome-metabolome modules between groups displayed inverse correlations, suggesting potential antagonistic effects driven by maternal diet. Overall, these results indicate that prenatal nutrition shapes key metabolic processes in muscle, meat, and fat, offering insights to enhance meat quality and production through maternal feeding strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".