RNA Based Gene Therapy for Dominantly Inherited Diseases
Bibliographic record
Abstract
There are numerous examples in the literature of gene therapy applications for recessive disorders. There are precious few instances, however, of studies conducted to treat dominantly inherited pathologies. The reasons are simple: there are fewer cases of dominantly inherited diseases on one hand, but mostly it is far easier to correct recessive mutations than dominant ones. Typically recessive mutations cause a loss of (or reduced) gene function which can be compensated for by introduction of a replacement allele into the cell. In contrast, dominant negative mutations not only display impaired function, but also exhibit a novel one that is pathologic to the cell. Treating these conditions by gene therapy implies silencing the dominant allele without altering the expression of the wild-type gene. We describe here different strategies aimed at silencing dominant mutations through mRNA destruction and provide examples of their application to known autosomal dominant diseases. An overview of the most common molecular tools (antisense DNA and RNA, ribozymes and RNA interference) suitable to utilize these strategies is also presented and we discuss the relevant aspects involved in the choice of a particular approach in a gene therapy experiment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".