Allele-specific CRISPR/Cas9 genome editing of the single-base P23H mutation for rhodopsin associated dominant retinitis pigmentosa
Bibliographic record
Abstract
Abstract Treatment strategies for dominantly inherited disorders typically involve silencing or ablating the pathogenic allele. CRISPR/Cas nucleases have shown promise in allele-specific knockout approaches when the dominant allele creates unique protospacer adjacent motifs (PAMs) that can lead to allele restricted targeting. Here, we present a spacer-mediated allele-specific knockout approach that utilizes both SpCas9 variants and truncated single guide RNAs (trusgRNAs) to achieve efficient discrimination of a single-nucleotide mutation in rhodopsin ( Rho )-P23H mice, a model of dominant retinitis pigmentosa (RP). We found that approximately 45% of the mutant P23H allele was edited at DNA level, and that the relative RNA expression of wild-type Rho was about 2.8 times more than that of mutant Rho in treated retinas. Furthermore, the progression of photoreceptor cell degeneration in outer nuclear layer was significantly delayed in treated regions of the Rho -P23H retinas at five weeks of age. Our proof-of-concept study therefore outlines a general strategy that could potentially be expanded to examine the therapeutic benefit of allele-specific gene editing approach to treat human P23H patient. Our study also extends allele-specific editing strategies beyond discrimination within the PAM sites, with potentially broad applicability to other dominant diseases.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".