Multiple Mechanisms Contribute to the Cell Growth Defects Imparted by Human Telomerase Insertion in Fingers Domain Mutations Associated with Premature Aging Diseases
Bibliographic record
Abstract
Normal human stem cells rely on low levels of active telomerase to sustain their high replicative requirements. Deficiency in telomere maintenance mechanisms leads to the development of premature aging diseases, such as dyskeratosis congenita and aplastic anemia. Mutations in the unique "insertion in fingers domain" (IFD) in the human telomerase reverse transcriptase catalytic subunit (hTERT) have previously been identified and shown to be associated with dyskeratosis congenita and aplastic anemia. However, little is known about the molecular mechanisms impacted by these IFD mutations. We performed comparative functional analyses of disease-associated IFD variants at the molecular and cellular levels. We report that hTERT-P721R- and hTERT-R811C-expressing cells exhibited growth defects likely due to impaired TPP1-mediated recruitment of these variant enzymes to telomeres. We showed that activity and processivity of hTERT-T726M failed to be stimulated by TPP1-POT1 overexpression and that dGTP usage by this variant was less efficient compared with the wild-type enzyme. hTERT-P785L-expressing cells did not show growth defects, and this variant likely confers cell survival through increased DNA synthesis and robust activity stimulation by TPP1-POT1. Altogether, our data suggest that multiple mechanisms contribute to cell growth defects conferred by the IFD variants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".