Targeted CNS delivery using human MiniPromoters and demonstrated compatibility with adeno-associated viral vectors
Bibliographic record
Abstract
Critical for human gene therapy is the availability of small promoters tools to drive gene expression in a highly specific and reproducible manner. We tackled this challenge by developing human DNA MiniPromoters (MiniPs) using computational biology and phylogenetic conservation. MiniPs were tested in mouse as single-copy knock-ins at the Hprt locus on the X chromosome and evaluated for lacZ reporter expression in central nervous system (CNS) and non–CNS tissue. Eighteen novel MiniPs driving expression in mouse brain were identified, 2 MiniPs for driving pan-neuronal expression and 17 MiniPs for the mouse eye. Key areas of therapeutic interest were represented in this set: the cerebral cortex, embryonic hypothalamus, spinal cord, bipolar and ganglion cells of the retina, and skeletal muscle. We also demonstrated that three retinal ganglion cell MiniPs exhibit similar cell type specificity when delivered via adeno-associated virus vectors intravitreally. We conclude that our methodology and characterization has resulted in desirable expression characteristics that are intrinsic to the MiniPromoter, not dictated by copy-number effects or genomic location, and results in constructs predisposed to success in adeno-associated virus. These MiniPs are immediately applicable for preclinical studies toward gene therapy in humans and are publicly available to facilitate basic and clinical research, and human gene therapy.
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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".