PSIII-23 Maternal metabolizable protein level and rumen-protected methionine impacts on beef steer progeny gene expression in muscle and liver
Bibliographic record
Abstract
Abstract This experiment evaluated how maternal metabolizable protein level and rumen-protected methionine supplementation during late gestation impacts expression of genes associated with fetal programming in beef steer offspring. In a 3 x 2 factorial arrangement, 138 Angus crossbred cows were randomly assigned one of three metabolizable protein (MP) treatments (fed to meet 90, 100, or 110% of MP requirements), with(without) 9 g/d of rumen-protected methionine (MET; Smartamine®M, Adisseo Inc.). The isocaloric diets were fed for the last 8 weeks of gestation. Steer progeny (n = 51) were assigned to one of seven pens by weight and received a common corn-based grower diet (58% corn silage, 26% alfalfa haylage, 15% soybean meal) for 47 days, followed by a finisher diet (78% high moisture corn, 12% alfalfa haylage, 8% soybean meal) for 115 ±31.5 days until slaughter. At slaughter, samples of Sternomandibularis muscle and liver were collected and snap-frozen in liquid nitrogen for RNA isolation and analysis of gene expression for growth, myogenic, and adipogenic genes. Data were analyzed using PROC GLIMMIX in SAS, with maternal MP level and MET supplementation as fixed effects, and pen as a random effect. Exceeding maternal MP requirements increased expression of myogenic regulatory factor, myogenin (MYOG; 90% MP: 0.72, 100% MP: 0.94, and 110% MP: 1.00; SEM = 0.083, respectively; P = 0.02) in muscle from steer progeny at slaughter. Methionine supplementation decreased expression of pyruvate kinase (PKM; MET: 0.81, No MET: 1.16; SEM = 0.092; P = 0.02) in muscle, which is associated with lean tissue growth. Maternal nutritional treatment did not influence hepatic gene expression (P ≥ 0.06). These data suggest that maternal nutrition may impact progeny muscle development, while maintaining metabolic function of the liver.
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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 | 0.000 | 0.000 |
| 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".