α-Synuclein Membrane Interactions and Lipid Specificity
Bibliographic record
Abstract
With the discovery of missense mutations (A53T and A30P) in alpha-synuclein (alpha-Syn) in several families with early onset familial Parkinson's disease, alpha-Syn aggregation and fibril formation have been thought to play a role in the pathogenesis of alpha-synucleinopathies, such as Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. As previous reports have suggested that alpha-Syn plays a role in lipid transport and synaptic membrane biogenesis, we investigated whether alpha-Syn binds to a specific lipid ligand using thin layer chromatography overlay and examined the changes in its secondary structure using circular dichroism spectroscopy. alpha-Syn was found to bind to acidic phospholipid vesicles and this binding was significantly augmented by the presence of phosphatidylethanolamine, a neutral phospholipid. We further examined the interaction of alpha-Syn with lipids by in situ atomic force microscopy. The association of soluble wild-type alpha-Syn with planar lipid bilayers resulted in extensive bilayer disruption and the formation of amorphous aggregates and small fibrils. The A53T mutant alpha-Syn disrupted the lipid bilayers in a similar fashion but at a slower rate. These results suggest that alpha-Syn membrane interactions are physiologically important and the lipid composition of the cellular membranes may affect these interactions in vivo.
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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) | 0.001 | 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".