PSVI-2 Multigenerational effects of maternal rate of body weight gain on fetal liver miRNA expression
Bibliographic record
Abstract
Abstract Maternal rate of gain is critical to heifer development, pregnancy outcomes, and cow longevity. Multigenerational epigenetic effects in offspring resulting from differing levels of maternal body weight (BW) gain in heifers have not been explored previously in beef cattle. The objective of this study was to evaluate the effects of early gestational BW gain in beef heifers on epigenetic mechanisms regulating gene expression in the fetal liver of the F1 generation and to determine their persistence into the F2 generation. Therefore, we hypothesized that the heifer rate of BW gain during early pregnancy would alter the hepatic micro-RNA (miRNA) expression profile of the F1 generation and that these effects would persist into the F2 offspring. Angus cross-bred heifers (n = 132; ~13 mo of age) bred through artificial insemination were assigned to treatments targeting different rates of BW gain (LG; low BW gain targeting 0.28 kg/d, n = 66; or MG; moderate BW gain targeting 0.79 kg/d, n = 66). Fifteen F0 pregnant heifers (LG = 7, MG = 8) were slaughtered on d 83 of gestation, and F1 fetal liver samples were collected. The remaining pregnant heifers (LG, n = 23; MG, n = 25) were maintained on pasture and managed as one group through calving and weaning, with no dietary treatments. The F1 heifers were then bred and slaughtered to collect the fetal liver tissues of the F2 generation from 16 pregnant heifers (LG = 8, MG = 8) on d 83 of pregnancy. The fetal hepatic miRNA profiles of F1 and F2 generations were determined using miRNA-Seq. The data were analyzed using the mirDeep2 pipeline, and differentially expressed miRNAs were identified (FDR < 0.05). Bta-miR-206 was the only differentially expressed miRNA in both the F1 and F2 generations for the MG vs. LG comparison. Based on gene target predictions, 809 genes are targeted by this miRNA. Functionally over-represented pathways (FDR < 0.05) of targeted genes included key energy metabolic pathways like mTOR, Wnt, MAPK, and PI3K-Akt signaling pathways, along with thyroid hormone signaling and longevity-regulating pathways. The results indicated that the effects of the F0 maternal rate of BW gain persisted into the F1 and F2 generations, and the maternal rate of BW gain during early pregnancy altered the hepatic miRNA expression profile to potentially regulate energy metabolism in beef heifers.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".