New MiniPromoter Ple345 ( <i>NEFL</i> ) Drives Strong and Specific Expression in Retinal Ganglion Cells of Mouse and Primate Retina
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Bibliographic record
Abstract
Retinal gene therapy is leading the neurological gene therapy field, with 32 ongoing clinical trials of recombinant adeno-associated virus (rAAV)-based therapies. Importantly, over 50% of those trials are using restricted promoters from human genes. Promoters that restrict expression have demonstrated increased efficacy and can limit the therapeutic to the target cells thereby reducing unwanted off-target effects. Retinal ganglion cells are a critical target in ocular gene therapy; they are involved in common diseases such as glaucoma, rare diseases such as Leber's hereditary optic neuropathy, and in revolutionary optogenetic treatments. Here, we used computational biology and mined the human genome for the best genes from which to develop a novel minimal promoter element(s) designed for expression in restricted cell types (MiniPromoter) to improve the safety and efficacy of retinal ganglion cell gene therapy. Gene selection included the use of the first available droplet-based single-cell RNA sequencing (Drop-seq) dataset, and promoter design was bioinformatically driven and informed by a wide range of genomics datasets. We tested seven promoter designs from four genes in rAAV for specificity and quantified expression strength in retinal ganglion cells in mouse, and then the single best in nonhuman primate retina. Thus, we developed a new human-DNA MiniPromoter, Ple345 (NEFL), which in combination with intravitreal delivery in rAAV9 showed specific and robust expression in the retinal ganglion cells of the nonhuman-primate rhesus macaque retina. In mouse, we also developed MiniPromoters expressing in retinal ganglion cells, the hippocampus of the brain, a pan neuronal pattern in the brain, and peripheral nerves. As single-cell transcriptomics such as Drop-seq become available for other cell types, many new opportunities for additional novel restricted MiniPromoters will present.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it