Stochastic Misfolding Drives the Emergence of Distinct α-Synuclein Strains
Bibliographic record
Abstract
Abstract The existence of α-synuclein conformational strains provides a potential explanation for the clinical and pathological differences among synucleinopathies such as Parkinson’s disease and multiple system atrophy. However, how distinct α-synuclein strains are formed in vivo remains unknown. Here, we examined whether unique strains of self-propagating α-synuclein aggregates can arise within a consistent molecular environment. Unexpectedly, we observed conformational heterogeneity between individual preparations of α-synuclein pre-formed fibrils (PFFs) generated by polymerizing recombinant wild-type or A53T-mutant human α-synuclein under identical conditions. Moreover, we found that α-synuclein aggregates formed spontaneously in the brains of a transgenic synucleinopathy mouse model were conformationally diverse, leading to the identification of three distinct disease subtypes. Propagation of putative PFF- and brain-derived α-synuclein strains in mice initiated several distinct synucleinopathies, characterized by differences in disease onset times, cerebral α-synuclein deposition patterns, and the conformational attributes of α-synuclein aggregates. The conformational diversity of α-synuclein aggregates across PFF preparations and between the brains of individual transgenic mice demonstrates that α-synuclein can spontaneously form multiple self-propagating strains within an identical environment both in vitro and in vivo . This suggests that stochastic misfolding into distinct aggregate structures drives the emergence of α-synuclein strains and implies that the intrinsic variability of common synucleinopathy research tools must be considered when designing and interpreting experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".