425 Altering methyl donors to beef heifers during the periconceptual period impacts fetal muscle transcript abundance
Bibliographic record
Abstract
Abstract New findings in developmental programming show an increased importance of methyl donor availability; however, effects of changing methyl donor concentration on the fetal transcriptome have yet to be identified. Differential gene expression (DEG) was used to determine the impact of maternal supplementation of methionine (MET), an obligate methyl donor, and guanidinoacetic acid (GAA), a methyl donor consumer, during the periconceptual period on bovine fetal development. MARC II heifers [n = 80; initial body weight (BW) = 346 ± 8.28 kg] receiving the same mixed ration and targeting the same gain (0.68 kg/d) were assigned to one of four treatments totaling 100 g/d of supplement with a ground corn carrier: MET (10 g/d), GAA (40 g/d), MET+GAA (10 g/d Met + 40 g/d GAA), and only ground corn carrier for control (CON). Supplementation began 63 d before breeding and concluded 63 d after breeding. Heifers pregnant with male offspring (CON, n = 10; MET, n = 8; GAA, n = 7; MET+GAA, n = 10) were slaughtered on d 63 of gestation. Transcript abundance was measured using RNA-Seq from extracted total RNA of fetal hindlimb samples (n = 35). The DEG analysis identified 227 upregulated and 121 downregulated genes from MET vs CON, 483 upregulated and 40 downregulated genes from GAA vs CON, and 672 upregulated and 34 downregulated genes from MET+GAA vs CON treated heifers (P ≤ 0.05). We identified 315 upregulated and 114 downregulated genes from MET vs GAA, 202 upregulated and 204 downregulated genes from GAA vs MET+GAA, and 35 upregulated and 133 downregulated genes from MET vs MET+GAA treated heifers (P ≤ 0.05). Over-representation analysis of DEGs highlighted immune response pathways that were found amongst the GAA vs CON, MET vs GAA, and GAA vs MET+GAA comparisons. Genes associated with cell inflammatory and immune response, including CD14, Toll-like receptor (TLR) 6, CD86, and TLR8, were upregulated in the GAA treatment. The TLR genes regulate expression of several pro-inflammatory cytokines, whereas CD genes are responsible for cell inflammatory response. Both are important for regulation of lipopolysaccharide (LPS)-binding protein, which plays a subsequent role in immune recognition when bound with LPS, resulting in genes from these families being recognized as markers for inflammatory response in beef cattle. Between MET vs CON and MET+GAA vs CON comparisons, 15 pathways including skeletal system development, alpha-amino acid catabolic process, and acylglycerol catabolic processes were shared. The FABP1 gene was upregulated in both aforementioned comparisons and has an integral role in transport and metabolism of fatty acids within cattle. Providing supplementation of methyl donors to the maternal environment results in over-representation of fetal muscle genes acting on immune response as well as fatty acid and amino acid metabolism. USDA is an equal opportunity provider and employer.
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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".