Germline CRISPR/Cas9-Mediated Gene Editing Prevents Vision Loss in a Novel Mouse Model of Aniridia
Bibliographic record
Abstract
Aniridia is a rare eye disorder, which is caused by mutations in the paired box 6 ( PAX6 ) gene and results in vision loss due to the lack of a long-term vision-saving therapy. One potential approach to treating aniridia is targeted CRISPR-based genome editing. To enable the Pax6 small eye ( Sey ) mouse model of aniridia, which carries the same mutation found in patients, for preclinical testing of CRISPR-based therapeutic approaches, we endogenously tagged the Sey allele, allowing for the differential detection of protein from each allele. We optimized a correction strategy in vitro then tested it in vivo in the germline of our new mouse to validate the causality of the Sey mutation. The genomic manipulations were analyzed by PCR, as well as by Sanger and next-generation sequencing. The mice were studied by slit lamp imaging, immunohistochemistry, and western blot analyses. We successfully achieved both in vitro and in vivo germline correction of the Sey mutation, with the former resulting in an average 34.8% ± 4.6% SD correction, and the latter in restoration of 3xFLAG-tagged PAX6 expression and normal eyes. Hence, in this study we have created a novel mouse model for aniridia, demonstrated that germline correction of the Sey mutation alone rescues the mutant phenotype, and developed an allele-distinguishing CRISPR-based strategy for aniridia.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".