Genistein depletes telomerase activity through cross‐talk between genetic and epigenetic mechanisms
Why this work is in the frame
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Bibliographic record
Abstract
Genistein, a natural isoflavone found in soybean products, has been reported to down-regulate telomerase activity and that this prevents cancer and contributes to the apoptosis of cancer cells. However, the precise molecular mechanism by which genistein represses telomerase is not clear. Here, we show that genistein inhibits the transcription of hTERT (human telomerase reverse transcriptase), the catalytic subunit of the human telomerase enzyme, in breast MCF10AT benign cells and MCF-7 cancer cells in a time- and dose-dependent manner. Three major DNA methyltransferases (DNMT1, 3a and 3b) were also decreased in genistein-treated breast cancer cells suggesting that genistein may repress hTERT by impacting epigenetic pathways. Sequential depletion of the hTERT promoter revealed that the hTERT core promoter region is responsible for the genistein-induced repression of hTERT transcription. Using a newly developed technique of chromatin immunoprecipitation (ChIP)-related bifulfite sequencing analysis, we found an increased binding of E2F-1 to the hTERT promoter is due to the site-specific hypomethylation of the E2F-1 recognition site. In addition, we found that genistein can remodel chromatin structures of the hTERT promoter by increasing trimethyl-H3K9 but decreasing dimethyl-H3K4 in the hTERT promoter. The repression of hTERT was enhanced by combination with genistein and the DNMT inhibitor, 5-aza-2'-deoxycytidine (5-aza-dCyd). These findings collectively show that genistein is working, at least in part, through epigenetic mechanisms of telomerase inhibition in breast benign and cancer cells and may facilitate approaches to breast cancer prevention and treatment using an epigenetic modulator combined with genistein.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it