Functional interaction between telomere protein TPP1 and telomerase
Bibliographic record
Abstract
Human chromosome end-capping and telomerase regulation require POT1 (Protection of Telomeres 1) and TPP1 proteins, which bind to the 3' ssDNA extension of human telomeres. POT1-TPP1 binding to telomeric DNA activates telomerase repeat addition processivity. We now provide evidence that this POT1-TPP1 activation requires specific interactions with telomerase, rather than it being a DNA substrate-specific effect. First, telomerase from the fish medaka, which extends the same telomeric DNA primer as human telomerase, was not activated by human POT1-TPP1. Second, mutation of a conserved glycine, Gly100 in the TEN (telomerase essential N-terminal) domain of TERT, abolished the enhancement of telomerase processivity by POT1-TPP1, in contrast to other single amino acid mutations. Chimeric human-fish telomerases that contained the human TEN domain were active but not stimulated by POT1-TPP1, showing that additional determinants of processivity lie outside the TEN domain. Finally, primers bound to mouse POT1A and human TPP1 were activated for extension by human telomerase, whereas mPOT1A-mTPP1 was most active with mouse telomerase, indicating that these mammalian telomerases have specificity for their respective TPP1 proteins. We suggest that a sequence-specific interaction between TPP1 in the TPP1-POT1-telomeric DNA complex and the G100 region of the TEN domain of TERT is necessary for high-processivity telomerase action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".