Pre‐synaptic defects of NPC1‐deficient hippocampal neurons are not directly related to plasma membrane cholesterol
Bibliographic record
Abstract
Imbalances in brain cholesterol homeostasis have been observed in several neurodegenerative diseases. In Niemann-Pick Type C (NPC) disease, mutations in NPC1 or NPC2 lead to endosomal cholesterol accumulation, neuronal dysfunction and death. Cholesterol in synaptic plasma membranes influences membrane fluidity, curvature, and protein function, and its depletion may adversely affect synaptic vesicle cycling. We have investigated pre-synaptic function in primary hippocampal neurons with altered cholesterol distribution because of NPC1 deficiency or cyclodextrin treatment. In NPC1-deficient neurons grown in serum-free medium, plasma membrane cholesterol was reduced and total synaptic vesicle release during prolonged stimulation was attenuated. In NPC1-deficient neurons cultured in the presence of high-density lipoproteins, plasma membrane cholesterol markedly increased, but the defects in synaptic vesicle release in NPC1-deficient neurons were exacerbated. Treatment with 1 mM methyl-beta-cyclodextrin acutely depleted plasma membrane cholesterol in wild-type neurons to levels below those in NPC1 deficiency, but did not alter synaptic vesicle exo- or endocytosis. Defects only became apparent when higher methyl-beta-cyclodextrin concentrations were used. Our data indicate that synaptic vesicle release can tolerate some degree of plasma membrane cholesterol depletion and suggest that the pre-synaptic defects in NPC1-deficient neurons are not solely caused by a reduction of plasma membrane cholesterol.
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) | 0.000 | 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".