Real‐time imaging reveals defects of fast axonal transport induced by disorganization of intermediate filaments
Bibliographic record
Abstract
Intermediate filament (IF) abnormalities frequently appear in neurodegenerative disorders, but how they may contribute to neuronal dysfunction remains unclear. Here, we examined the effects of IF disorganization on the fast axonal transport using time-lapse microscopy. We studied the axonal transport of mitochondria and lysosomes in cultured primary dorsal root ganglion (DRG) neurons derived from mice deficient for neurofilament light (NFL(-/-)), mice overexpressing peripherin (Per), and mice double transgenic Per;NFL(-/-). Unexpectedly, a net retrograde transport of mitochondria was detected in Per;NFL(-/-) neurons, opposite to the net anterograde transport of these organelles observed in wild-type (Wt), NFL(-/-), and Per neurons. A detailed analysis of the kinetic properties of mitochondrial movements revealed an increased frequency of retrograde movements and an increase of their velocity in Per;NFL(-/-) neurons compared to Wt, NFL(-/-), and Per neurons. We also noticed that the depletion of axonal neurofilaments (NFs) in NFL(-/-) and Per;NFL(-/-) neurons induced longer and more persistent movements of mitochondria and lysosomes in both directions, which suggests that the NF network hampers the traffic of these organelles. The finding that an up-regulation of peripherin in context of NFL deficiency can provoke a net retrograde transport of mitochondria is a phenomenon that may contribute to pathogenic changes in some neurodegenerative disorders with IF protein accumulations.
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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) | 0.000 | 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 | 0.001 |
| 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".