Disease mutations in the N-terminal extension and α2’-helix of dyskerin do not abolish dimerization but disrupt binding to the telomerase RNA
Bibliographic record
Abstract
The H/ACA ribonucleoprotein complex component dyskerin is essential for the biogenesis of H/ACA RNAs, including the human telomerase RNA (hTR). The N-terminal extension and α2' helix of dyskerin are hotspots for disease-associated mutations linked to X-linked dyskeratosis congenita (X-DC), a premature aging disorder. Some of these mutations disrupt dyskerin-hTR interactions, leading to hTR destabilization and reduced telomerase activity. Cryo-EM structures of human telomerase have shown that the N-terminal extension and α2' helix participate in dyskerin dimerization. However, biochemical evidence for dyskerin dimerization is still lacking, and it remains unclear whether mutations in these regions impair hTR binding by disrupting dimerization. Here, we provide the first biochemical evidence that dyskerin undergoes dimerization. We further demonstrate that dimerization is RNA independent and not abolished by disease mutations in the N-terminal extension or α2' helix. Instead, these mutations impair hTR binding. Our findings offer new mechanistic insight into how mutations in the dyskerin N-terminal extension and α2' helix contribute to the pathogenesis of X-DC.
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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) | 0.004 | 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".