129 Maternal plane of nutrition during mid-gestation affects the skeletal muscle transcriptome in beef cattle progeny
Bibliographic record
Abstract
The objective was to determine the effect of maternal nutrition management during mid-gestation on the skeletal muscle transcriptome in progeny using 3 dietary treatments. Spring-calving, multiparous cows (n = 25) were limit-fed a common diet at three levels of intake to achieve 70% NRC requirement (70%REQ; 5.2 kg DMI), 100% NRC energy and protein requirements (REQ; 7.4 kg DMI), and 130% NRC requirements (130%REQ; 9.7 kg DMI). Diet composition included soy hulls, corn silage, and alfalfa haylage. Treatment diets were fed during mid-gestation (d 88 to 171 of gestation), and a common diet formulated to meet 100% NRC requirements was fed during the remainder of gestation. After calving, all cows and calves were managed similarly as a single contemporary group. Longissimus muscle biopsies were taken on 99 and 392 d of age. Extracted RNA was used in paired-end RNA sequencing on the Illumina HiSeq 2500 platform to analyze the transcriptome at d 99 and 392. Transcriptomic data were mapped to the UMD3.1.1 bovine reference genome and analyzed using a mixed model approach within the R-package limma. Over 7,000 expressed genes were included in weighted gene co-expression network analysis (WGCNA), which clustered genes into 11 modules including 4 that correlated to phenotypic measures. The largest module included over 2,000 co-expressed genes and was downregulated (P < 0.01) in progeny born to 130%REQ-fed compared with REQ-fed dams. The Dynamic Impact Approach annotated genes within this module to KEGG pathways. The pathways most impacted by maternal plane of nutrition were mainly lipid-associated including steroid and steroid hormone biosynthesis, sulfur metabolism, retinol metabolism, ketone synthesis and degradation, fat digestion and absorption, and PPAR signaling pathways. Module 7 (342 genes) was correlated positively with Warner-Bratzler shear force and negatively with marbling score. Major pathways in module 7 centered on glycolysis/gluconeogenesis, energy metabolism, and calcium signaling. These genes and associated pathways support increased glycolytic muscle fibers (type 2x) in progeny born to 130%REQ-fed compared with 70%REQ-fed dams. Results indicate the skeletal muscle transcriptome and associated metabolic functions prior to slaughter are affected by mid-gestation maternal plane of nutrition and may be regulated by epigenetic factors.
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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".