Correction of pseudoexon splicing caused by a novel intronic dysferlin mutation
Bibliographic record
Abstract
OBJECTIVE: Dysferlin is a large transmembrane protein that functions in critical processes of membrane repair and vesicle fusion. Dysferlin-deficiency due to mutations in the dysferlin gene leads to muscular dystrophy (Miyoshi myopathy (MM), limb girdle muscular dystrophy type 2B (LGMD2B), distal myopathy with anterior tibial onset (DMAT)), typically with early adult onset. At least 416 pathogenic dysferlin mutations are known, but for approximately 17% of patients, one or both of their pathogenic variants remain undefined following standard exon sequencing methods that interrogate exons and nearby flanking intronic regions but not the majority of intronic regions. METHODS: We sequenced RNA from myogenic cells to identify a novel dysferlin pathogenic variant in two affected siblings that previously had only one disease-causing variant identified. We designed antisense oligonucleotides (AONs) to bypass the effects of this mutation on RNA splicing. RESULTS: We identified a new pathogenic point mutation deep within dysferlin intron 50i. This intronic variant causes aberrant mRNA splicing and inclusion of an additional pseudoexon (PE, we term PE50.1) within the mature dysferlin mRNA. PE50.1 inclusion alters the protein sequence, causing premature translation termination. We identified this mutation in 23 dysferlinopathy patients (seventeen families), revealing it to be one of the more prevalent dysferlin mutations. We used AON-mediated exon skipping to correct the aberrant PE50.1 splicing events in vitro, which increased normal mRNA production and significantly restored dysferlin protein expression. INTERPRETATION: Deep intronic mutations can be a common underlying cause of dysferlinopathy, and importantly, could be treatable with AON-based exon-skipping strategies.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".