Altered Distribution of SNARE Proteins in Primary Neurons Exposed to Different Alpha-Synuclein Proteoforms
Bibliographic record
Abstract
Growing evidence indicates that the pathological alpha-synuclein (α-syn) aggregation in Parkinson's disease (PD) and dementia with Lewy bodies (DLB) starts at the synapses. Physiologic α-syn is involved in regulating neurotransmitter release by binding to the SNARE complex protein VAMP-2 on synaptic vesicles. However, in which way the SNARE complex formation is affected by α-syn pathology remains unclear. In this study, primary cortical neurons were exposed to either α-syn monomers or preformed fibrils (PFFs) for different time points and the effect on SNARE protein distribution was analyzed with a novel proximity ligation assay (PLA). Short-term exposure to monomers or PFFs for 24 h increased the co-localization of VAMP-2 and syntaxin-1, but reduced the co-localization of SNAP-25 and syntaxin-1, indicating a direct effect of the added α-syn on SNARE protein distribution. Long-term exposure to α-syn PFFs for 7 d reduced VAMP-2 and SNAP-25 co-localization, although there was only a modest induction of ser129 phosphorylated (pS129) α-syn. Similarly, exposure to extracellular vesicles collected from astrocytes treated with α-syn PFFs for 7 d influenced VAMP-2 and SNAP-25 co-localization despite only low levels of pS129 α-syn being formed. Taken together, our results demonstrate that different α-syn proteoforms have the potential to alter the distribution of SNARE proteins at the synapse.
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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".