Disruption of neurofilament network with aggregation of light neurofilament protein: a common pathway leading to motor neuron degeneration due to Charcot–Marie–Tooth disease-linked mutations in NFL and HSPB1
Bibliographic record
Abstract
Mutations in neurofilament light (NFL) subunit and small heat-shock protein B1 (HSPB1) cause autosomal-dominant axonal Charcot-Marie-Tooth disease type 2E (CMT2E) and type 2F (CMT2F). Previous studies have shown that CMT mutations in NFL and HSPB1 disrupt NF assembly and cause aggregation of NFL protein. In this study, we investigate the role of aggregation of NFL protein in the neurotoxicity of CMT mutant NFL and CMT mutant HSPB1 in motor neurons. We find that expression of CMT mutant NFL leads to progressive degeneration and loss of neuronal viability of cultured motor neurons. Degenerating motor neurons show fragmentation and loss of neuritic processes associated with disruption of NF network and aggregation of NFL protein. Co-expression of wild-type HSPB1 diminishes aggregation of CMT mutant NFL, induces reversal of CMT mutant NFL aggregates and reduces CMT mutant NFL-induced loss of motor neuron viability. Like CMT mutant NFL, expression of S135F CMT mutant HSPB1 also leads to progressive degeneration of motor neurons with disruption of NF network and aggregation of NFL protein. Further studies show that wild-type and S135F mutant HSPB1 associate with wild-type and CMT mutant NFL and that S135F mutant HSPB1 has dominant effect on disruption of NF assembly and aggregation of NFL protein. Finally, we show that deletion of NFL markedly reduces degeneration and loss of motor neuron viability induced by S135F mutant HSPB1. Together, our data support the view that disruption of NF network with aggregation of NFL is a common triggering event of motor neuron degeneration in CMT2E and CMT2F disease.
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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 | 0.000 | 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 | 0.000 |
| 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 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".