A Novel Prodrug of Epigallocatechin-3-gallate: Differential Epigenetic <i>hTERT</i> Repression in Human Breast Cancer Cells
Why this work is in the frame
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Bibliographic record
Abstract
Epigallocatechin-3-gallate (EGCG), a major component of green tea polyphenols (GTP), has been reported to downregulate telomerase activity in breast cancer cells thereby increasing cellular apoptosis and inhibiting cellular proliferation. However, the major concerns with GTPs are their bioavailability and stability under physiologic conditions. In the present study, we show that treatments with EGCG and a novel prodrug of EGCG (pro-EGCG or pEGCG) dose- and time-dependently inhibited the proliferation of human breast cancer MCF-7 and MDA-MB-231 cells but not normal control MCF10A cells. Furthermore, both EGCG and pro-EGCG inhibited the transcription of hTERT (human telomerase reverse transcriptase), the catalytic subunit of telomerase, through epigenetic mechanisms in estrogen receptor (ER)-positive MCF-7 and ER-negative MDA-MB-231 cells. The downregulation of hTERT expression was found to be because of hTERT promoter hypomethylation and histone deacetylations, mediated at least partially through inhibition of DNA methyltransferase and histone acetyltransferase activities, respectively. In addition, we also observed that EGCG and pEGCG can remodel chromatin structures of the hTERT promoter by decreasing the level of acetyl-H3, acetyl-H3K9, and acetyl-H4 to the hTERT promoter. EGCG and pEGCG induced chromatin alterations that facilitated the binding of many hTERT repressors such as MAD1 and E2F-1 to the hTERT regulatory region. Depletion of E2F-1 and MAD1 by using siRNA reversed the pEGCG downregulated hTERT expression and associated cellular apoptosis differently in ER-positive and ER-negative breast cancer cells. Collectively, our data provide new insights into breast cancer prevention through epigenetic modulation of telomerase by using pro-EGCG, a more stable form of EGCG, as a novel chemopreventive compound.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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