SIRT1/DNMT3B-mediated epigenetic gene silencing in response to phytoestrogens in mammary epithelial cells
Bibliographic record
Abstract
We performed an integrated analysis of genome-wide DNA methylation and expression datasets in normal cells and healthy animals exposed to polyphenols with estrogenic activity (i.e. phytoestrogens). We identified that phytoestrogens target genes linked to disrupted cellular homeostasis, e.g. genes limiting DNA break repair (RNF169) or promoting ribosomal biogenesis (rDNA). Existing evidence suggests that DNA methylation may be governed by sirtuin 1 (SIRT1) deacetylase via interactions with DNA methylating enzymes, specifically DNMT3B. Since SIRT1 was reported to be regulated by phytoestrogens, we test whether phytoestrogens suppress genes related to disrupted homeostasis via SIRT1/DNMT3B-mediated transcriptional silencing. Human MCF10A mammary epithelial cells were treated with phytoestrogens, pterostilbene (PTS) or genistein (GEN), followed by analysis of cell growth, DNA methylation, gene expression, and SIRT1/DNMT3B binding. SIRT1 occupancy at the selected phytoestrogen-target genes, RNF169 and rDNA, was accompanied by consistent promoter hypermethylation and gene downregulation in response to GEN, but not PTS. GEN-mediated hypermethylation and SIRT1 binding were linked to a robust DNMT3B enrichment at RNF169 and rDNA promoters. This was not observed in cells exposed to PTS, suggesting a distinct mechanism of action. Although both SIRT1 and DNMT3B bind to RNF169 and rDNA promoters upon GEN, the two proteins do not co-occupy the regions. Depletion of SIRT1 abolishes GEN-mediated decrease in rDNA expression, suggesting SIRT1-dependent epigenetic suppression of rDNA by GEN. These findings enhance our understanding of the role of SIRT1-DNMT3B interplay in epigenetic mechanisms mediating the impact of phytoestrogens on cell biology and cellular homeostasis.
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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".