Induction of α-Synuclein Aggregation by Intracellular Nitrative Insult
Bibliographic record
Abstract
Brain lesions containing filamentous and aggregated alpha-synuclein are hallmarks of neurodegenerative synucleinopathies. Oxidative stress has been implicated in the formation of these lesions. Using HEK 293 cells stably transfected with wild-type and mutant alpha-synuclein, we demonstrated that intracellular generation of nitrating agents results in the formation of alpha-synuclein aggregates. Cells were exposed simultaneously to nitric oxide- and superoxide-generating compounds, and the intracellular formation of peroxynitrite was demonstrated by monitoring the oxidation of dihydrorhodamine 123 and the nitration of alpha-synuclein. Light microscopy using antibodies against alpha-synuclein and electron microscopy revealed the presence of perinuclear aggregates under conditions in which peroxynitrite was generated but not when cells were exposed to nitric oxide- or superoxide-generating compounds separately. alpha-Synuclein aggregates were observed in 20-30% of cells expressing wild-type or A53T mutant alpha-synuclein and in 5% of cells expressing A30P mutant alpha-synuclein. No evidence of synuclein aggregation was observed in untransfected cells or cells expressing beta-synuclein. In contrast, selective inhibition of the proteasome resulted in the formation of aggregates detected with antibodies to ubiquitin in the majority of the untransfected cells and cells expressing alpha-synuclein. However, alpha-synuclein did not colocalize with these aggregates, indicating that inhibition of the proteasome does not promote alpha-synuclein aggregation. In addition, proteasome inhibition did not alter the steady-state levels of alpha-synuclein, but addition of the lysosomotropic agent ammonium chloride significantly increased the amount of alpha-synuclein, indicating that lysosomes are involved in degradation of alpha-synuclein. Our data indicate that nitrative and oxidative insult may initiate pathogenesis of alpha-synuclein aggregates.
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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) | 0.000 | 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) | 0.001 | 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".