Human iPSC-derived RPE and retinal organoids reveal impaired alternative splicing of genes involved in pre-mRNA splicing in PRPF31 autosomal dominant retinitis pigmentosa
Bibliographic record
Abstract
Mutations in pre-mRNA processing factors (PRPFs) cause 40% of autosomal dominant retinitis pigmentosa (RP), but it is unclear why mutations in ubiquitously expressed PRPFs cause retinal disease. To understand the molecular basis of this phenotype, we have generated RP type 11 (PRPF31-mutated) patient-specific retinal organoids and retinal pigment epithelium (RPE) from induced pluripotent stem cells (iPSC). Impaired alternative splicing of genes encoding pre-mRNA splicing proteins occurred in patient-specific retinal cells and Prpf31+/- mouse retinae, but not fibroblasts and iPSCs, providing mechanistic insights into retinal-specific phenotypes of PRPFs. RPE was the most affected, characterised by loss of apical-basal polarity, reduced trans-epithelial resistance, phagocytic capacity, microvilli, and cilia length and incidence. Disrupted cilia morphology was observed in patient-derived-photoreceptors that displayed progressive features associated with degeneration and cell stress. In situ gene-editing of a pathogenic mutation rescued key structural and functional phenotypes in RPE and photoreceptors, providing proof-of-concept for future therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".