A New Mouse Model of Giant Axonal Neuropathy with Overt Phenotypes and Neurodegeneration Driven by Neurofilament Disorganization
Bibliographic record
Abstract
Research on pathogenic mechanisms underlying giant axonal neuropathy (GAN), a disease caused by a deficiency of gigaxonin, has been hindered by the lack of appropriate animal models exhibiting substantial symptoms and large neurofilament (NF) swellings, a hallmark of the human disease. It is well established that intermediate filament (IF) proteins are substrates for gigaxonin-mediated degradation. However, it has remained unknown to what extent NF accumulations contribute to GAN pathogenesis. Here, we report the generation of a new mouse model of GAN that is based on crossing transgenic mice overexpressing peripherin (Prph) with mice knockout for Gan . The Gan −/− ;TgPer mice developed early onset sensory-motor deficits along with IF accumulations made up of NF proteins and of Prph, causing swelling of spinal neurons at a young age. Abundant inclusion bodies composed of disorganized IFs were also detected in the brain of Gan −/− ;TgPer mice. At 12 months of age, the Gan −/− ;TgPer mice exhibited cognitive deficits as well as severe sensory and motor defects. The disease was associated with neuroinflammation and substantial loss of cortical neurons and spinal neurons. Giant axons (≥160 μm 2 ) enlarged by disorganized IFs, a hallmark of GAN disease, were also detected in dorsal and ventral roots of the Gan −/− ;TgPer mice. These results, obtained with both sexes, support the view that the disorganization of IFs can drive some neurodegenerative changes caused by gigaxonin deficiency. This new mouse model should be useful to investigate the pathogenic changes associated with GAN disease and for drug testing. SIGNIFICANCE STATEMENT Research on pathogenic mechanism and treatment of GAN has been hampered by the lack of animal models exhibiting overt phenotypes and substantial neurofilament disorganization, a hallmark of the disease. Moreover, it remains unknown whether neurologic defects associated with gigaxonin deficiency in GAN are because of neurofilament disorganization as gigaxonin may also act on other protein substrates to mediate their degradation. This study reports the generation of a new mouse model of GAN based on overexpression of Prph in the context of targeted disruption of gigaxonin gene. The results support the view that neurofilament disorganization may contribute to neurodegenerative changes in GAN disease. The Gan −/− ;TgPer mice provide a unique animal model of GAN for drug testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".