Aggregation and neurotoxicity of recombinant α-synuclein aggregates initiated by dimerization
Bibliographic record
Abstract
BACKGROUND: Aggregation of the α-Synuclein (α-Syn) protein, amyloid fibril formation and progressive neurodegeneration are the neuropathological hallmarks of Parkinson's Disease (PD). However, a detailed mechanism of α-Syn aggregation/fibrillogenesis and the exact nature of toxic oligomeric species produced during amyloid formation process are still unknown. RESULTS: In this study, the rates of α-Syn aggregation were compared for the recombinant wild-type (WT) α-Syn and a structurally relevant chimeric homologous protein containing an inducible Fv dimerizing domain (α-SynFv), capable to form dimers in the presence of a divalent ligand (AP20187). In the presence of AP20187, we report a rapid random coil into β-sheet conformational transformation of α-SynFv within 24 h, whereas WT α-Syn showed 24 h delay to achieve β-sheet structure after 48 h. Fluorescence ANS and ThT binding experiments demonstrate an accelerated oligomer/amyloid formation of dimerized α-SynFv, compared to the slower oligomerization and amyloidogenesis of WT α-Syn or α-SynFv without dimerizer AP20187. Both α-SynFv and α-Syn pre-fibrillar aggregates internalized cells and induced neurotoxicity when injected into the hippocampus of wild-type mice. These recombinant toxic aggregates further converted into non-toxic amyloids which were successfully amplified by protein misfolding cyclic amplification method, providing the first evidence for the in vitro propagation of synthetic α-Syn aggregates. CONCLUSIONS: Together, we show that dimerization is important for α-Syn conformational transition and aggregation. In addition, α-Syn dimerization can accelerate the formation of neurotoxic aggregates and amyloid fibrils which can be amplified in vitro. A detailed characterization of the mechanism of α-Syn aggregation/amyloidogenesis and toxicity is crucial to comprehend Parkinson's disease pathology at the molecular level.
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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) | 0.000 | 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".