The pregnant myometrium is epigenetically activated at contractility-driving gene loci prior to the onset of labor in mice
Bibliographic record
Abstract
Abstract During gestation, uterine smooth muscle cells transition from a state of quiescence to one of contractility, but the molecular mechanisms underlying this transition at a genomic level are not well-known. To better understand these events, we evaluated the epigenetic landscape of the mouse myometrium during pregnancy, labor and post-partum. We established gestational timepoint-specific enrichment profiles involving histone H3K27 acetylation (H3K27ac), H3K4 tri-methylation (H3K4me3), and RNA polymerase II (RNAPII) occupancy by chromatin immunoprecipitation sequencing (ChIP-seq), as well as gene expression profiles by total RNA-sequencing (RNA-seq). Our findings reveal that 533 genes, including known contractility-driving genes ( Gja1, Fos, Oxtr, Ptgs2 ), are upregulated during active labor due to an increase in transcription at gene bodies. Their promoters and putative intergenic enhancers, however, are epigenetically activated by H3K27ac as early as day 15, four days prior to the onset of active labor on day 19. In fact, we find that the majority of genome-wide H3K27ac or H3K4me3 peaks identified during active labor are present in the myometrium on day 15. Despite the early presence of H3K27ac at labor-associated genes, both an increase in non-coding enhancer RNA (eRNA) production, and in recruitment of RNAPII to corresponding genes occur during active labor, at labor upregulated gene loci. Our findings indicate that epigenetic activation of the myometrial genome precedes active labor by at least four days in the mouse model, suggesting the myometrium is poised for rapid activation of contraction-associated genes in order to exit the state of quiescence.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".