N-terminal acetylation reduces α-synuclein pathology in models of Parkinson’s disease
Bibliographic record
Abstract
Abstract The α-synuclein protein, encoded by SNCA gene, is a major constituent of pathological intracellular inclusions such as Lewy bodies found in the brains of patients with Parkinson’s disease and other synucleinopathies. Whereas α-synuclein phosphorylation has been much studied, comparatively less work has been devoted to other post-translational modifications such as acetylation, especially given that N-terminally acetylated α-synuclein is the most abundant endogenous form of the protein in the brain. In this study, using multiple in vitro and in vivo models, we sought to better understand the role of N-terminal acetylation in the pathogenesis of synucleinopathies. We found that N-terminal acetylation slowed aggregation of both α-synuclein monomers and pre-formed fibrils in vitro . Uptake of acetylated α-synuclein pre-formed fibrils into both immortalized cell lines and iPSC-derived dopamine neurons was also slowed compared non-acetylated fibrils. In addition, exposure to acetylated pre-formed fibrils induced less seeding of endogenous α-synuclein, as measured by the accumulation of Serine129-phosphorylated α-synuclein inclusions in both iPSC-derived dopamine neurons and mouse brain. Finally, mice injected with N-terminally acetylated α-synuclein pre-formed fibrils survived significantly longer than mice injected with non-acetylated fibrils. Taken together, our study indicates that N-terminal acetylation reduces α-synuclein aggregation, uptake into cells, seeding of endogenous α-synuclein, and toxicity in vivo , suggesting that this prevalent post-translational modification represents a potent, physiologically relevant protective mechanism, which has thus far largely not been taken into consideration in most experimental paradigms of Parkinson’s disease and synucleinopathies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".