A pipeline for identifying guide RNA sequences that promote RNA editing of nonsense mutations that cause inherited retinal diseases
Bibliographic record
Abstract
Adenosine deaminases acting on RNA (ADARs) are endogenous enzymes catalyzing the deamination of adenosines to inosines, which are then read as guanosines during translation. This ability to recode makes ADAR an attractive therapeutic tool to edit genetic mutations and reprogram genetic information at the mRNA level. Using the endogenous ADARs and guiding them to a selected target has promising therapeutic potential. Indeed, different studies have reported several site-directed RNA-editing approaches for making targeted base changes in RNA molecules. The basic strategy has been to use guide RNAs (gRNAs) that hybridize and form a double-stranded RNA (dsRNA) structure with the desired RNA target because of ADAR activity in regions of dsRNA formation. Here we report on a novel pipeline for identifying disease-causing variants as candidates for RNA editing, using a yeast-based screening system to select efficient gRNAs for editing of nonsense mutations, and test them in a human cell line reporter system. We have used this pipeline to modify the sequence of transcripts carrying nonsense mutations that cause inherited retinal diseases in the FAM161A , KIZ , TRPM1 , and USH2A genes. Our approach can serve as a basis for gene therapy intervention in knockin mouse models and ultimately in human patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".