A translocation-defective telomerase with low levels of activity and processivity stabilizes short telomeres and confers immortalization
Bibliographic record
Abstract
Short, repetitive, G-rich telomeric sequences are synthesized by telomerase, a ribonucleoprotein consisting of telomerase reverse transcriptase (TERT) and an integrally associated RNA. Human TERT (hTERT) can repetitively reverse transcribe its RNA template, acting processively to add multiple telomeric repeats onto the same substrate. We investigated whether certain threshold levels of telomerase activity and processivity are required to maintain telomere function and immortalize human cells with limited lifespan. We assessed hTERT variants with mutations in motifs implicated in processivity and interaction with DNA, namely the insertion in fingers domain (V791Y), and the E primer grip motif (W930F). hTERT-W930F and hTERT-V791Y reconstitute reduced levels of DNA synthesis and processivity compared with wild-type telomerase. Of interest, hTERT-W930F is more defective in translocation than hTERT-V791Y. Nonetheless, hTERT-W930F, but not hTERT-V791Y, immortalizes limited-lifespan human cells. Both hTERT-W930F- and hTERT-V791Y-expressing cells harbor short telomeres, measured as signal free ends (SFEs), yet SFEs persist only in hTERT-V791Y cells, which undergo apoptosis, likely as a consequence of a defect in recruitment of hTERT-V791Y to telomeres. Our study is the first to demonstrate that low levels of DNA synthesis--on the order of 20% of wild-type telomerase levels--and extension of as few as three telomeric repeats are sufficient to maintain functional telomeres and immortalize limited-lifespan human cells.
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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) | 0.002 | 0.001 |
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".