RNA Polymerase III Subunit Mutations in Genetic Diseases
Bibliographic record
Abstract
RNA polymerase (Pol) III transcribes small untranslated RNAs such as 5S ribosomal RNA, transfer RNAs, and U6 small nuclear RNA. Because of the functions of these RNAs, Pol III transcription is best known for its essential contribution to RNA maturation and translation. Surprisingly, it was discovered in the last decade that various inherited mutations in genes encoding nine distinct subunits of Pol III cause tissue-specific diseases rather than a general failure of all vital functions. Mutations in the POLR3A, POLR3C, POLR3E and POLR3F subunits are associated with susceptibility to varicella zoster virus-induced encephalitis and pneumonitis. In addition, an ever-increasing number of distinct mutations in the POLR3A, POLR3B, POLR1C and POLR3K subunits cause a spectrum of neurodegenerative diseases, which includes most notably hypomyelinating leukodystrophy. Furthermore, other rare diseases are also associated with mutations in genes encoding subunits of Pol III (POLR3H, POLR3GL) and the BRF1 component of the TFIIIB transcription initiation factor. Although the causal relationship between these mutations and disease development is widely accepted, the exact molecular mechanisms underlying disease pathogenesis remain enigmatic. Here, we review the current knowledge on the functional impact of specific mutations, possible Pol III-related disease-causing mechanisms, and animal models that may help to better understand the links between Pol III mutations and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.002 |
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".