564PDevelopment of an antisense oligonucleotide therapy for oculopharyngeal muscular dystrophy using iPSC-derived myocytes
Bibliographic record
Abstract
Oculopharyngeal muscular dystrophy (OPMD) is an autosomal dominant disorder prevalent in French Canadians (1:1000). It causes progressive ptosis, dysphagia, and proximal weakness, with no disease-modifying therapy available. OPMD results from GCG expansions in the PABPN1 gene, with patients commonly having 13-15 repeats versus 10 in healthy individuals. We aim to develop a cellular OPMD model and evaluate antisense oligonucleotides (ASOs) as a therapy by 1) Generating CRISPR-edited induced pluripotent stem cells (iPSCs) with 13 GCN repeats in PABPN1, and 2) Differentiating these cells into myocytes for investigating candidate ASOs. The expanded GCG repeats will be introduced into KOLF2.1J iPSCs by using a pair of single guide RNAs combined with a Cas9 nickase. After validation, cells will be differentiated into myocytes using PiggyBac-Tet-On-MyoD1. Myocytes will be characterized by immunofluorescence for myogenic markers and PABPN1 pathology. We will test phosphorothioate-modified ASOs with LNA or 2′-MOE chemistry targeting expanded repeats to reduce mutant PABPN1 while preserving wild-type levels. Preliminary experiments showed that the designed guide RNAs created a staggered double-strand break in the PABPN1 gene, which is ideal to favor homology-directed repair while minimizing off-target effects. In addition, wild type iPSCs were successfully differentiated into homogeneous myotubes culture expressing myosin filaments. We anticipate creating a model displaying PABPN1 nuclear aggregates and identifying ASOs that selectively target the expanded allele while preserving wild-type PABPN1 function, essential for normal muscle physiology. This project establishes a foundation for developing a disease-modifying therapy for OPMD through a human cellular model and allele-specific ASOs, potentially leading to clinical translation for this untreatable condition.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".