The p75 Neurotrophin Receptor Can Induce Autophagy and Death of Cerebellar Purkinje Neurons
Bibliographic record
Abstract
The cellular mechanisms underlying Purkinje neuron death in various neurodegenerative disorders of the cerebellum are poorly understood. Here we investigate an in vitro model of cerebellar neuronal death. We report that cerebellar Purkinje neurons, deprived of trophic factors, die by a form of programmed cell death distinct from the apoptotic death of neighboring granule neurons. Purkinje neuron death was characterized by excessive autophagic-lysosomal vacuolation. Autophagy and death of Purkinje neurons were inhibited by nerve growth factor (NGF) and were activated by NGF-neutralizing antibodies. Although treatment with antisense oligonucleotides to the p75 neurotrophin receptor (p75ntr) decreased basal survival of cultured cerebellar neurons, p75ntr-antisense decreased autophagy and completely inhibited death of Purkinje neurons induced by trophic factor withdrawal. Moreover, adenoviral expression of a p75ntr mutant lacking the ligand-binding domain induced vacuolation and death of Purkinje neurons. These results suggest that p75ntr is required for Purkinje neuron survival in the presence of trophic support; however, during trophic factor withdrawal, p75ntr contributes to Purkinje neuron autophagy and death. The autophagic morphology resembles that found in neurodegenerative disorders, suggesting a potential role for this pathway in neurological 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) | 0.000 | 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 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".