Polymerization Defects within Human Telomerase Are Distinct from Telomerase RNA and TEP1 Binding
Bibliographic record
Abstract
The minimal, active core of human telomerase is postulated to contain two components, the telomerase RNA hTER and the telomerase reverse transcriptase hTERT. The reconstitution of human telomerase activity in vitro has facilitated the identification of sequences within the telomerase RNA and the RT motifs of hTERT that are essential for telomerase activity. However, the precise role of residues outside the RT domain of hTERT is unknown. Here we have delineated several regions within hTERT that are important for telomerase catalysis, primer use, and interaction with the telomerase RNA and the telomerase-associated protein TEP1. In particular, certain deletions of the amino and carboxy terminus of hTERT that retained an interaction with telomerase RNA and TEP1 were nonetheless completely inactive in vitro and in vivo. Furthermore, hTERT truncations lacking the amino terminus that were competent to bind the telomerase RNA were severely compromised for the ability to elongate telomeric and nontelomeric primers. These results suggest that the interaction of telomerase RNA with hTERT can be functionally uncoupled from polymerization, and that there are regions outside the RT domain of hTERT that are critical for telomerase activity and primer use. These results establish that the human telomerase RT possesses unique polymerization determinants that distinguish it from other RTs.
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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".