Etude des interactions ARN-protéines de la télomérase chez Tetrahymena et chez l'humain
Bibliographic record
Abstract
Telomerase is the major mechanism that compensates for telomere loss during conventional DNA replication in eukaryotic cells. This enzyme is a ribonucleoprotein reverse transcriptase, composed of an RNA subunit (TR) containing a template that dictates the telomeric DNA sequence, a catalytic subunit (TERT), and others associated proteins. In order to characterize the telomerase RNA-proteins interactions in two different organisms, Tetrahymena thermophila and human, we developed two electrophoretic RNA mobility shift assays. In Tetrahymena, we identified a specific RNA-protein complex by using whole cell extracts partially purified and in vitro transcribed and radiolabeled Tetrahymena telomerase RNA (159 nt). Certain Tetrahymena telomerase RNA mutants tested in competition with wild type RNA by shift assays and containing deletions of structures and sequences previously predicted to be involved in protein binding were unable to competitively inhibit complex formation, suggesting a role in protein binding for the deleted residues or structures. In human, we identified a specific telomerase RNA-protein complex by using 293 human cell extracts partially purified, and in vitro transcribed and radiolabeled hTR (451 nt). Using different hTR mutants in competition with wild type hTR, we identified nucleotides 1--424 of hTR as a minimal region that is able to reconstitute a stable RNA-protein complex.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".