Combining dynamin 2 myopathy and neuropathy mutations rescues both phenotypes
Bibliographic record
Abstract
Mutations within a single gene can lead to diverse human genetic diseases affecting highly specialized tissues. Notably, dominant mutations in the DNM2 gene, encoding the mechanoenzyme dynamin, lead to distinct neuromuscular disorders: centronuclear myopathy (CNM) and Charcot-Marie-Tooth neuropathy (CMT). CNM is characterized by myofiber structural anomalies while CMT presents peripheral nerve defects, both culminating in muscle weakness and atrophy. Despite their shared genetic origin, the mechanisms driving these diseases remain elusive, and no cure is available. Here, we present in vitro assays underlining opposing effects of DNM2 mutations, gain-of-function in CNM and loss-of-function in CMT. In vivo, we explored the potential compensatory effects of CNM and CMT mutations by breeding Dnm2S619L/+ CNM with Dnm2K562E/+ CMT mouse models. Dnm2S619L/K562E offspring exhibit strongly improved motor coordination and muscle strength and mass, compared to single-mutant littermates. Dnm2S619L/K562E mice present normalized muscle structure and nerve fiber organization. This study reveals that two distinct disease-causing mutations within the DNM2 gene compensate each other in vivo, leading to corrections of most individual phenotypes. The inverse modulation of DNM2 activity emerges as a promising therapeutic strategy to address CNM and CMT diseases. Heterozygous mutations in the mechanoenzyme dynamin (DNM2) manifest as either a myopathy or a peripheral neuropathy. Here, the authors show antagonistic effects of these mutations and combining them, in mice, mitigates the phenotypic manifestations observed in individual mutants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".