Axonal Transport Defect in Gigaxonin Deficiency Rescued by Tubastatin A
Bibliographic record
Abstract
Giant axonal neuropathy (GAN) is a disease caused by a deficiency of gigaxonin, a mediator of the degradation of intermediate filament (IF) proteins. A lack of gigaxonin alters the turnover of IF proteins, provoking accumulation and disorganization of neurofilaments (NFs) in neurons, a hallmark of the disease. However, the effects of IF disorganization on neuronal function remain unknown. Here, we report that cultured embryonic dorsal root ganglia (DRG) neurons derived from Gan−/− mice exhibit accumulations of IF proteins and defects in fast axonal transport of organelles. Kymographs generated by time-lapse microscopy revealed substantial reduction of anterograde movements of mitochondria and lysosomes in axons of Gan−/− DRG neurons. Treatment of Gan−/− DRG neurons with Tubastatin A (TubA) increased the levels of acetylated tubulin and it restored the normal axonal transport of these organelles. Furthermore, we tested the effects of TubA in a new mouse model of GAN consisting of Gan−/− mice with overexpression of peripherin (Prph) transgene. Treatment of 12-month-old Gan−/−;TgPer mice with TubA led to a slight amelioration of motor function, especially a significant improvement of gait performance as measured by footprint analyses. Moreover, TubA treatment reduced the abnormal accumulations of Prph and NF proteins in spinal neurons and it boosted the levels of Prph transported into peripheral nerve axons. These results suggest that drug inhibitors of histone deacetylase aiming to enhance axonal transport should be considered as a potential treatment for GAN disease.
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 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) | 0.000 | 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 | 0.001 |
| 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".