α-Synuclein acts as a cholesteryl-ester sensor on lipid droplets regulating organelle size and abundance
Bibliographic record
Abstract
Abstract While aggregated alpha-Synuclein (αSyn) is commonly associated with Parkinson’s disease, its physiological function as a membrane-binding protein is poorly understood. Here, we show that endogenous αSyn binds lipid droplets (LDs) in multiple human cell lines and in stem cell-derived dopaminergic neurons. LD-binding encompasses αSyn residues 1-100, which masks their detection by immunofluorescence microscopy, probably explaining the scarcity of similar observations in earlier studies. αSyn-LD interactions are highly temperature-sensitive and selective for cholesteryl-ester-rich LDs. They promote the formation of αSyn multimers that dissociate from LDs at non-permissive temperatures. αSyn remains LD-bound throughout starvation-induced lipolysis, whereas siRNA-knockdown diminishes LD abundance and compromises cell viability upon nutrient depletion, without affecting LD biosynthesis. Reciprocally, excess αSyn stimulates LD accumulation in dependence of lipid availability, restricts organelle size and ensures intracellular LD organization, which strictly depends on functional membrane-binding. Supporting a general role of αSyn in cellular lipid and cholesterol metabolism, our results point to additional loss-of-function similarities between Parkinson’s, Alzheimer’s and Gaucher’s 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) | 0.002 | 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".