Optogenetic-induced α-synuclein accumulation reveals early synaptic dysfunction in experimental models of Parkinson’s disease
Bibliographic record
Abstract
Presynaptic accumulation of misfolded α-synuclein (α-syn) and altered synaptic transmission are considered early events in the pathogenesis of Parkinson's disease (PD), suggesting a potential causal link between these two events. However, the mechanisms by which α-syn aggregation induces synaptic dysfunction and the subsequent progressive neurodegeneration remain elusive. In the present study we leveraged the high temporal resolution of the Light-Inducible Protein Aggregation (LIPA) system in vivo and in human dopaminergic neurons to explore the early sequence of α-syn-induced pathological events leading to synaptopathy. We observed that nigrostriatal axonal transport and presynaptic accumulation of α-syn aggregates altered the activity of different neuronal populations in the mouse striatum. The results of histological and metabolite analyses show that presynaptic accumulation of α-syn induced a shift in the activation pattern of D1- and D2-expressing striatal medium spiny neurons, caused an increase in the size and density of dopaminergic synapses, and disrupted striatal dopamine signaling. Altogether, our findings reveal that the accumulation of α-syn in dopaminergic terminals triggered early presynaptic impairments, which subsequently altered striatal neuronal activity. Our study provides new insights into the molecular mechanisms underlying early synaptopathy in PD.
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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 | 0.001 | 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) | 0.001 | 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".