Cholesterol is required for activity-dependent synaptic growth
Bibliographic record
Abstract
Changes in cholesterol content of neuronal membranes occur during development and brain aging. Little is known about whether synaptic activity regulates cholesterol levels in neuronal membranes and whether these changes affect neuronal development and function. We generated transgenic flies that express the cholesterol-binding D4H domain of perfringolysin O toxin and found increased levels of cholesterol in presynaptic terminals of Drosophila larval neuromuscular junctions following increased synaptic activity. Reduced cholesterol impaired synaptic growth and largely prevented activity-dependent synaptic growth. Presynaptic knockdown of adenylyl cyclase phenocopied the impaired synaptic growth caused by reducing cholesterol. Furthermore, the effects of knocking down adenylyl cyclase and reducing cholesterol were not additive, suggesting that they function in the same pathway. Increasing cAMP levels using a dunce mutant with reduced phosphodiesterase activity failed to rescue this impaired synaptic growth, suggesting that cholesterol functions downstream of cAMP. We used a protein kinase A (PKA) sensor to show that reducing cholesterol levels reduced presynaptic PKA activity. Collectively, our results demonstrate that enhanced synaptic activity increased cholesterol levels in presynaptic terminals and that these changes likely activate the cAMP-PKA pathway during activity-dependent growth.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".