Deoxyribonucleic Acid Methyltransferase 3B Promotes Epigenetic Silencing through Histone 3 Chromatin Modifications in Pituitary Cells
Bibliographic record
Abstract
CONTEXT AND OBJECTIVE: Epigenetic dysregulation is implicated in pituitary neoplasia as the cause of silencing of several tumor suppressor genes. However, the upstream mediators of such events remain unknown. DESIGN: We examined the three members of the DNA methyltransferase (DNMT) enzyme family in normal and neoplastic human and mouse pituitary cells. SETTING: This study was performed at a university-affiliated cancer research institute. MAIN OUTCOME MEASURES: Gene expression, promoter DNA methylation, histone modifications, and cell proliferation were determined. RESULTS: In contrast to DNMT1 and DNMT3a, DNMT3b was expressed at relatively higher levels in neoplastic pituitary cells. However, examination of the human DNMT3b 5' region showed uniformly low DNA methylation levels with little difference between normal and tumor samples. Through pharmacological methylation inhibition or histone deacetylation inhibition, we identified that DNMT3b gene expression is subject to histone modifications. Down-regulation of DNMT3b resulted in induction of retinoblastoma, p21, and p27, and reduction in cell proliferation. These targeted effects were associated with enhanced histone 3 acetylation and diminished histone methylation. CONCLUSION: Our findings identify DNMT3b as a putative mediator of epigenetic control through histone modifications of gene expression in pituitary cells.
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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".