<i>Tetrahymena</i> proteins p80 and p95 are not core telomerase components
Bibliographic record
Abstract
Telomeres provide stability to eukaryotic chromosomes and consist of tandem DNA repeat sequences. Telomeric repeats are synthesized and maintained by a specialized reverse transcriptase, termed telomerase. Tetrahymena thermophila telomerase contains two essential components: Tetrahymena telomerase reverse transcriptase (tTERT), the catalytic protein component, and telomerase RNA that provides the template for telomere repeat synthesis. In addition to these two components, two proteins, p80 and p95, were previously found to copurify with telomerase activity and to interact with tTERT and telomerase RNA. To investigate the role of p80 and p95 in the telomerase enzyme, we tested the interaction of p80, p95, and tTERT in several different recombinant expression systems and in Tetrahymena extracts. Immunoprecipitation of recombinant proteins showed that although p80 and p95 associated with each other, they did not associate with tTERT. In in vitro transcription and translation lysates, tTERT was associated with telomerase activity, but p80 and p95 were not. p80 bound telomerase RNA as well as several other unrelated RNAs, suggesting p80 has a general affinity for RNA. Immunoprecipitations from Tetrahymena extracts also showed no evidence for an interaction between the core tTERT/telomerase RNA complex and the p80 and p95 proteins. These data suggest that p80 and p95 are not associated with the bulk of active telomerase in Tetrahymena.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".