Telomerase regulation by the long non-coding RNA H19 in human acute promyelocytic leukemia cells
Bibliographic record
Abstract
BACKGROUND: Since tumor growth requires reactivation of telomerase (hTERT), this enzyme is a challenging target for drug development. Therefore, it is of great interest to identify telomerase expression and activity regulators. Retinoids are well-known inducers of granulocytic maturation associated with hTERT repression in acute promyelocytic leukemia (APL) blasts. In a maturation-resistant APL cell line, we have previously identified a new pathway of retinoid-induced hTERT transcriptional repression independent of differentiation. Furthermore, we reported the isolation of a cell variant resistant to this repression. Those cell lines could serve as unique tools to identify new telomerase regulators. METHODS: Using a microarray approach we identified the long non-coding RNA, H19 as a potential candidate playing a role in telomerase regulation. Expression of H19, hTERT, and hTR were examined by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Telomerase activity was quantified by quantitative telomeric repeats amplification protocol (qTRAP). In vitro and in vivo assays were performed to investigate H19 function on telomerase expression and activity. RESULTS: We showed both in retinoid-treated cell lines and in APL patient cells an inverse relationship between the expression of H19 and the expression and activity of hTERT. Exploring the mechanistic link between H19 and hTERT regulation, we showed that H19 is able to impede telomerase function by disruption of the hTERT-hTR interaction. CONCLUSIONS: This study identifies a new way of telomerase regulation through H19's involvement and thereby reveals a new function for this long non-coding RNA that can be targeted for therapeutic purpose.
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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".