Induction of hTERT Expression and Telomerase Activity by Estrogens in Human Ovary Epithelium Cells
Bibliographic record
Abstract
In mammals, molecular mechanisms and factors involved in the tight regulation of telomerase expression and activity are still largely undefined. In this study, we provide evidence for a role of estrogens and their receptors in the transcriptional regulation of hTERT, the catalytic subunit of human telomerase and, consequently, in the activation of the enzyme. Through a computer analysis of the hTERT 5'-flanking sequences, we identified a putative estrogen response element (ERE) which was capable of binding in vitro human estrogen receptor alpha (ERalpha). In vivo DNA footprinting revealed specific modifications of the ERE region in ERalpha-positive but not ERalpha-negative cells upon treatment with 17beta-estradiol (E2), indicative of estrogen-dependent chromatin remodelling. In the presence of E2, transient expression of ERalpha but not ERbeta remarkably increased hTERT promoter activity, and mutation of the ERE significantly reduced this effect. No telomerase activity was detected in human ovary epithelial cells grown in the absence of E2, but the addition of the hormone induced the enzyme within 3 h of treatment. The expression of hTERT mRNA and protein was induced in parallel with enzymatic activity. This prompt estrogen modulation of telomerase activity substantiates estrogen-dependent transcriptional regulation of the hTERT gene. The identification of hTERT as a target of estrogens represents a novel finding which advances the understanding of telomerase regulation in hormone-dependent cells and has implications for a potential role of hormones in their senescence and malignant conversion.
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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".