Effective therapeutic strategies in a preclinical mouse model of Charcot–Marie–Tooth disease
Bibliographic record
Abstract
Charcot-Marie-Tooth (CMT) disease is a neuropathy that lacks effective therapy. CMT patients show degeneration of peripheral nerves, leading to muscle weakness and loss of proprioception. Loss of mitochondrial oxidative phosphorylation proteins and enzymes of the antioxidant response accompany degeneration of nerves in skin biopsies of CMT patients. Herein, we followed a drug-repurposing approach to find drugs in a Food and Drug Administration-approved library that could prevent development of CMT disease in the Gdap1-null mouse model. We found that the antibiotic florfenicol is a mitochondrial uncoupler that prevents the production of reactive oxygen species and activates respiration in human GDAP1-knockdown neuroblastoma cells and in dorsal root ganglion neurons of Gdap1-null mice. Treatment of CMT-affected Gdap1-null mice with florfenicol has no beneficial effect in the course of the disease. However, administration of florfenicol, or the antioxidant MitoQ, to pre-symptomatic GDAP1-null mice prevented weight gain and ameliorated the motor coordination deficiencies that developed in the Gdap1-null mice. Interestingly, both florfenicol and MitoQ halted the decay in mitochondrial and redox proteins in sciatic nerves of Gdap1-null mice, supporting that oxidative damage is implicated in the etiology of the neuropathy. These findings support the development of clinical trials for translation of these drugs for treatment of CMT patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".