Intermediate filament protein accumulation in motor neurons derived from giant axonal neuropathy iPSCs rescued by restoration of gigaxonin
Bibliographic record
Abstract
Giant axonal neuropathy (GAN) is a progressive neurodegenerative disease caused by autosomal recessive mutations in the GAN gene resulting in a loss of a ubiquitously expressed protein, gigaxonin. Gene replacement therapy is a promising strategy for treatment of the disease; however, the effectiveness and safety of gigaxonin reintroduction have not been tested in human GAN nerve cells. Here we report the derivation of induced pluripotent stem cells (iPSCs) from three GAN patients with different GAN mutations. Motor neurons differentiated from GAN iPSCs exhibit accumulation of neurofilament (NF-L) and peripherin (PRPH) protein and formation of PRPH aggregates, the key pathological phenotypes observed in patients. Introduction of gigaxonin either using a lentiviral vector or as a stable transgene resulted in normalization of NEFL and PRPH levels in GAN neurons and disappearance of PRPH aggregates. Importantly, overexpression of gigaxonin had no adverse effect on survival of GAN neurons, supporting the feasibility of gene replacement therapy. Our findings demonstrate that GAN iPSCs provide a novel model for studying human GAN neuropathologies and for the development and testing of new therapies in relevant cell types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".