Alternative Splicing of Human Telomerase Reverse Transcriptase May Not Be Involved in Telomerase Regulation During all-trans-Retinoic Acid-Induced HL-60 Cell Differentiation
Bibliographic record
Abstract
Alternative splicing of the human telomerase reverse transcriptase subunit (hTERT) suppresses telomerase activity during the development of human fetal kidney cells into mature cells. Tumor cell differentiation is the process of turning abnormal tumor cells into 'normal' cells accompanied by down-regulation of telomerase activity. However, the precise mechanism of the regulation of telomerase activity in differentiated cells is not fully understood. In this study, we observed the role of alternative splicing of hTERT in the regulation of telomerase activity in all-trans-retinoic acid (ATRA)-induced, differentiated HL-60 cells. ATRA-induced down-regulation of telomerase activity in differentiated HL-60 cells was associated with a decrease in hTERT and an increase in human telomerase-associated protein-1 (hTP1) transcription. Expression of full length variant hTERT alpha+ beta+ mRNA decreased in a dose- and time-dependent manner. The drop of hTERT beta- mRNA was time-dependent. hTERT alpha- and hTERT alpha- beta- mRNA were reduced dramatically after ATRA treatment. In the dose-effect study, hTERT alpha+ beta+ and hTERT beta- maintained a relatively stable ratio when telomerase activity decreased largely from treatment with 1 to 5 microM ATRA. Although the splicing pattern of hTERT mRNA was altered in time-effect research, the change was not related to the ATRA-treated decline of telomerase activity. The expression of alternative splicing variants of hTERT also decreased at the protein level. All these results suggested that alternative splicing of hTERT mRNA may not contribute to the suppression of telomerase activity during ATRA-induced HL-60 leukemia cell differentiation.
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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".