Dual <i>ABCA4</i> -AAV Vector Treatment Reduces Pathogenic Retinal A2E Accumulation in a Mouse Model of Autosomal Recessive Stargardt Disease
Bibliographic record
Abstract
Autosomal recessive Stargardt disease is the most common inherited macular degeneration in humans. It is caused by mutations in the retina-specific ATP binding cassette transporter A4 (ABCA4) that is essential for the clearance of all-trans-retinal from photoreceptor cells. Loss of this function results in the accumulation of toxic bisretinoids in the outer segment disk membranes and their subsequent transfer into adjacent retinal pigment epithelium (RPE) cells. This ultimately leads to the Stargardt disease phenotype of increased retinal autofluorescence and progressive RPE and photoreceptor cell loss. Adeno-associated virus (AAV) vectors have been widely used in gene therapeutic applications, but their limited cDNA packaging capacity of ∼4.5 kb has impeded their use for transgenes exceeding this limit. AAV dual vectors were developed to overcome this size restriction. In this study, we have evaluated the in vitro expression of ABCA4 using three options: overlap, transplicing, and hybrid ABCA4 dual vector systems. The hybrid system was the most efficient of these dual vector alternatives and used to express the full-length ABCA4 in Abca4−/− mice. The full-length ABCA4 protein correctly localized to photoreceptor outer segments. Moreover, treatment of Abca4−/− mice with this ABCA4 hybrid dual vector system resulted in a reduced accumulation of the lipofuscin/N-retinylidene-N-retinylethanolamine (A2E) autofluorescence in vivo, and retinal A2E quantification supported these findings. These results show that the hybrid AAV dual vector option is both safe and therapeutic in mice, and the delivered ABCA4 transgene is functional and has a significant effect on reducing A2E accumulation in the Abca4−/− mouse model of Stargardt disease.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".