Interplay between Sumoylation and Phosphorylation for Protection against α-Synuclein Inclusions
Bibliographic record
Abstract
Parkinson disease is associated with the progressive loss of dopaminergic neurons from the substantia nigra. The pathological hallmark of the disease is the accumulation of intracytoplasmic inclusions known as Lewy bodies that consist mainly of post-translationally modified forms of α-synuclein. Whereas phosphorylation is one of the major modifications of α-synuclein in Lewy bodies, sumoylation has recently been described. The interplay between α-synuclein phosphorylation and sumoylation is poorly understood. Here, we examined the interplay between these modifications as well as their impact on cell growth and inclusion formation in yeast. We found that α-synuclein is sumoylated in vivo at the same sites in yeast as in human cells. Impaired sumoylation resulted in reduced yeast growth combined with an increased number of cells with inclusions, suggesting that this modification plays a protective role. In addition, inhibition of sumoylation prevented autophagy-mediated aggregate clearance. A defect in α-synuclein sumoylation could be suppressed by serine 129 phosphorylation by the human G protein-coupled receptor kinase 5 (GRK5) in yeast. Phosphorylation reduced foci formation, alleviated yeast growth inhibition, and partially rescued autophagic α-synuclein degradation along with the promotion of proteasomal degradation, resulting in aggregate clearance in the absence of a small ubiquitin-like modifier. These findings suggest a complex interplay between sumoylation and phosphorylation in α-synuclein aggregate clearance, which may open new horizons for the development of therapeutic strategies for Parkinson disease.
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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".