Disease-Associated Human Telomerase RNA Variants Show Loss of Function for Telomere Synthesis without Dominant-Negative Interference
Bibliographic record
Abstract
Telomerase adds simple-sequence repeats to chromosome ends to offset the terminal sequence loss inherent in each cycle of genome replication. Inherited mutations in genes encoding subunits of the human telomerase holoenzyme give rise to disease phenotypes including hematopoietic failure and pulmonary fibrosis. Disease-associated variants of the human telomerase RNA are expressed in heterozygous combination with wild-type telomerase RNA. Here, we exploit a sensitized human primary cell assay system to investigate the biological function of disease-linked telomerase RNA variants and their impact on the function of coexpressed wild-type telomerase RNA. We find that telomerase RNA variants discovered in patients with dyskeratosis congenita or aplastic anemia show loss of function without any indication of dominant-negative impact on telomere maintenance by the coexpressed wild-type RNA. To reconcile this result with contradictory findings from reconstitution assays in vitro, we demonstrate that the lack of dominant-negative impact on telomere maintenance correlates with physiological assembly of active human telomerase holoenzyme ribonucleoproteins harboring monomers rather than higher-order multimers of telomerase RNA and telomerase reverse transcriptase. These findings support loss of function of telomerase RNA as a general mechanism of human disease.
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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".