Early stages of aggregation of engineered α-synuclein monomers and oligomers in solution
Bibliographic record
Abstract
α-Synuclein is a protein that aggregates as amyloid fibrils in the brains of patients with Parkinson's disease and dementia with Lewy bodies. Small oligomers of α-synuclein are neurotoxic and are thought to be closely associated with disease. Whereas α-synuclein fibrillization and fibril morphologies have been studied extensively with various methods, the earliest stages of aggregation and the properties of oligomeric intermediates are less well understood because few methods are able to detect and characterize early-stage aggregates. We used fluorescence spectroscopy to investigate the early stages of aggregation by studying pairwise interactions between α-synuclein monomers, as well as between engineered tandem oligomers of various sizes (dimers, tetramers, and octamers). The hydrodynamic radii of these engineered α-synuclein species were first determined by fluorescence correlation spectroscopy and dynamic light scattering. The rate of pairwise aggregation between different species was then monitored using dual-color fluorescence cross-correlation spectroscopy, measuring the extent of association between species labelled with different dyes at various time points during the early aggregation process. The aggregation rate and extent increased with tandem oligomer size. Self-association of the tandem oligomers was found to be the preferred pathway to form larger aggregates: interactions between oligomers occurred faster and to a greater extent than interactions between oligomers and monomers, indicating that the oligomers were not as efficient in seeding further aggregation by addition of monomers. These results suggest that oligomer-oligomer interactions may play an important role in driving aggregation during its early stages.
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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 | 0.001 |
| 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".