Complex interaction network revealed by mutation of human telomerase ‘insertion in fingers’ and essential N-terminal domains and the telomere protein TPP1
Bibliographic record
Abstract
In the majority of human cancer cells, cellular immortalization depends on the maintenance of telomere length by telomerase. An essential step required for telomerase function is its recruitment to telomeres, which is regulated by the interaction of the telomere protein, TPP1, with the telomerase essential N-terminal (TEN) domain of the human telomerase reverse transcriptase, hTERT. We previously reported that the hTERT 'insertion in fingers domain' (IFD) recruits telomerase to telomeres in a TPP1-dependent manner. Here, we use hTERT truncations and the IFD domain containing mutations in conserved residues or premature aging disease-associated mutations to map the interactions between the IFD and TPP1. We find that the hTERT-IFD domain can interact with TPP1. However, deletion of the IFD motif in hTERT lacking the N-terminus and the C-terminal extension does not abolish interaction with TPP1, suggesting the IFD is not essential for hTERT interaction with TPP1. Several conserved residues in the central IFD-TRAP region that we reported regulate telomerase recruitment to telomeres, and cell immortalization compromise interaction of the hTERT-IFD domain with TPP1 when mutated. Using a similar approach, we find that the IFD domain interacts with the TEN domain but is not essential for intramolecular hTERT interactions with the TEN domain. IFD-TEN interactions are not disrupted by multiple amino acid changes in the IFD or TEN, thus highlighting a complex regulation of IFD-TEN interactions as suggested by recent cryo-EM structures of human telomerase.
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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".