Neurofilament depletion improves microtubule dynamics via modulation of Stat3/stathmin signaling
Bibliographic record
Abstract
In neurons, microtubules form a dense array within axons, and the stability and function of this microtubule network is modulated by neurofilaments. Accumulation of neurofilaments has been observed in several forms of neurodegenerative diseases, but the mechanisms how elevated neurofilament levels destabilize axons are unknown so far. Here, we show that increased neurofilament expression in motor nerves of pmn mutant mice, a model of motoneuron disease, causes disturbed microtubule dynamics. The disease is caused by a point mutation in the tubulin-specific chaperone E (Tbce) gene, leading to an exchange of the most C-terminal amino acid tryptophan to glycine. As a consequence, the TBCE protein becomes instable which then results in destabilization of axonal microtubules and defects in axonal transport, in particular in motoneurons. Depletion of neurofilament increases the number and regrowth of microtubules in pmn mutant motoneurons and restores axon elongation. This effect is mediated by interaction of neurofilament with the stathmin complex. Accumulating neurofilaments associate with stathmin in axons of pmn mutant motoneurons. Depletion of neurofilament by Nefl knockout increases Stat3-stathmin interaction and stabilizes the microtubules in pmn mutant motoneurons. Consequently, counteracting enhanced neurofilament expression improves axonal maintenance and prolongs survival of pmn mutant mice. We propose that this mechanism could also be relevant for other neurodegenerative diseases in which neurofilament accumulation and loss of microtubules are prominent features.
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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".