Membrane Repair Deficit in Facioscapulohumeral Muscular Dystrophy
Bibliographic record
Abstract
Deficits in plasma membrane repair have been identified in dysferlinopathy and Duchenne Muscular Dystrophy, and contribute to progressive myopathy. Although Facioscapulohumeral Muscular Dystrophy (FSHD) shares clinicopathological features with these muscular dystrophies, it is unknown if FSHD is characterized by plasma membrane repair deficits. Therefore, we exposed immortalized human FSHD myoblasts, immortalized myoblasts from unaffected siblings, and myofibers from a murine model of FSHD (FLExDUX4) to focal, pulsed laser ablation of the sarcolemma. Repair kinetics and success were determined from the accumulation of intracellular FM1-43 dye post-injury. We subsequently treated FSHD myoblasts with a DUX4-targeting antisense oligonucleotide (AON) to reduce DUX4 expression, and with the antioxidant Trolox to determine the role of DUX4 expression and oxidative stress in membrane repair. Compared to unaffected myoblasts, FSHD myoblasts demonstrate poor repair and a greater percentage of cells that failed to repair, which was mitigated by AON and Trolox treatments. Similar repair deficits were identified in FLExDUX4 myofibers. This is the first study to identify plasma membrane repair deficits in myoblasts from individuals with FSHD, and in myofibers from a murine model of FSHD. Our results suggest that DUX4 expression and oxidative stress may be important targets for future membrane-repair therapies.
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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 | 0.000 | 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".