Pig fetal skeletal muscle development is associated with genome-wide DNA hypomethylation and corresponding alterations in transcript and microRNA expression
Bibliographic record
Abstract
Fetal myogenesis represents a critical period of porcine skeletal muscle development and requires coordinated expression of thousands of genes. Epigenetic mechanisms, including DNA methylation, drive transcriptional regulation during development; however, these processes are understudied in developing porcine tissues. We performed bisulfite sequencing to assess DNA methylation in pig longissimus dorsi muscle at 41- and 70-days gestation (dg), as well as RNA- and small RNA-sequencing to identify coordinated changes in methylation and expression between myogenic stages. We identified 45 739 differentially methylated regions (DMRs) between stages, and the majority ( N = 34 232) were hypomethylated at 70 versus 41 dg. Integration of methylation and transcriptomic data revealed strong associations between differential gene methylation and expression. Differential miRNA methylation was significantly negatively correlated with abundance, and dynamic expression of assayed miRNAs persisted postnatally. Motif analysis revealed significant enrichment of myogenic regulatory factor motifs among hypomethylated regions, suggesting that DNA hypomethylation may function to increase accessibility of muscle-specific transcription factors. We show that developmental DMRs are enriched for GWAS SNPs for muscle- and meat-related traits, demonstrating the potential for epigenetic processes to influence phenotypic diversity. Our results enhance understanding of DNA methylation dynamics of porcine myogenesis and reveal putative cis-regulatory elements governed by epigenetic processes.
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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".