Parkinson’s Disease-linked protein α-synuclein: do small-molecule inhibitors of protein aggregation also prevent its accumulation with lipids?
Bibliographic record
Abstract
Parkinson’s Disease (PD), a neurodegenerative disorder, is characterized by the pathological aggregation of α-synuclein (αS) into Lewy bodies, primarily composed of fibrillar and lipid/membrane-rich αS inclusions. Recent studies suggest that both fibrillar and lipid-rich αS aggregates may contribute to PD pathology. This study investigates how lipid-rich αS inclusions are affected by small molecules that had previously been shown to disaggregate fibrillar αS protein aggregation. Using a live-cellular assay based on YFP-tagged membrane - accumulating, engineered αS-3K (E35K+E46K+E61K) in M17D neuroblastoma cells, we assessed the effects of fasudil, Epigallocatechin gallate (EGCG), anle138b, and ethanol compared to the positive control trifluoperazine (TFP), which is known to disassemble lipid-rich αS aggregates. fasudil, EGCG, and anle138b had previously been shown to prevent protein aggregation of αS. In our experiments, fasudil and anle138b significantly decreased inclusion formation in a dose - dependent manner in both αS lipid-rich inclusion prevention and reduction assays, while EGCG and ethanol exhibited either negligible or inclusion-increasing effects. These findings indicate that fasudil and anle138b may effectively target lipid-rich αS inclusions, supporting further exploration of their therapeutic potential in PD. However, fasudil’s observed associated toxicity necessitates careful evaluation. This study underscores the therapeutic relevance of targeting diverse αS aggregate types in PD.
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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) | 0.001 | 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 | 0.001 | 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".