PAX6 MiniPromoters drive restricted expression from rAAV in the adult mouse retina
Bibliographic record
Abstract
Current gene therapies predominantly use small, strong, and readily available ubiquitous promoters. However, as the field matures, the availability of small, cell-specific promoters would be greatly beneficial. Here we design seven small promoters from the human paired box 6 (PAX6) gene and test them in the adult mouse retina using recombinant adeno-associated virus. We chose the retina due to previous successes in gene therapy for blindness, and the PAX6 gene since it is: well studied; known to be driven by discrete regulatory regions; expressed in therapeutically interesting retinal cell types; and mutated in the vision-loss disorder aniridia, which is in need of improved therapy. At the PAX6 locus, 31 regulatory regions were bioinformatically predicted, and nine regulatory regions were constructed into seven MiniPromoters. Driving Emerald GFP, these MiniPromoters were packaged into recombinant adeno-associated virus, and injected intravitreally into postnatal day 14 mice. Four MiniPromoters drove consistent retinal expression in the adult mouse, driving expression in combinations of cell-types that endogenously express Pax6: ganglion, amacrine, horizontal, and Müller glia. Two PAX6-MiniPromoters drive expression in three of the four cell types that express PAX6 in the adult mouse retina. Combined, they capture all four cell types, making them potential tools for research, and PAX6-gene therapy 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) | 0.000 | 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".