Dual CRALBP isoforms unveiled: iPSC-derived retinal modeling and AAV2/5-RLBP1 gene transfer raise considerations for effective therapy
Bibliographic record
Abstract
Inherited retinal diseases (IRDs) are characterized by progressive vision loss. There are over 270 causative IRD genes, and variants within the same gene can cause clinically distinct disorders. One example is RLBP1 , which encodes CRALBP. CRALBP is an essential protein in the rod and cone visual cycles that take place primarily in the retinal pigment epithelium (RPE) but also in Müller cells of the neuroretina. RLBP1 variants lead to three clinical subtypes: Bothnia dystrophy, retinitis punctata albescens, and Newfoundland rod-cone dystrophy. We modeled RLBP1 -IRD subtypes using patient-specific induced pluripotent stem cell (iPSC)-derived RPE and identified pathophysiological markers that served as pertinent therapeutic read-outs. We developed an AAV2/5-mediated gene-supplementation strategy and performed a proof-of-concept study in the human models, which was validated in vivo in an Rlbp1 −/− murine model. Most importantly, we identified a previously unsuspected smaller CRALBP isoform that is naturally and differentially expressed both in the human and murine retina. This previously unidentified isoform is produced from an alternative methionine initiation site. This work provides further insights into CRALBP expression and RLBP1 -associated pathophysiology and raises important considerations for successful gene-supplementation 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".