Modulation of SLP-2 expression protects against alpha-synuclein neuropathology by mitigating mitochondrial dysfunction
Bibliographic record
Abstract
Abstract Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuron loss and the accumulation of alpha-synuclein (αSyn)-rich aggregates known as Lewy bodies. Mitochondrial dysfunction is a key contributor to PD pathology, and mitochondrial defects are part of the pathogenic mechanisms induced by αSyn. Stomatin-Like protein 2 (SLP-2) is a mitochondrial scaffold protein that regulates mitochondrial integrity and function. Here, we investigated whether SLP-2 induction can counteract αSyn-induced mitochondrial dysfunction and neurodegeneration. We found that SLP-2 levels were reduced in human PD brains and an A53T αSyn mouse model. Mild overexpression of SLP-2 improved mitochondrial function, reduced oxidative stress, and prevented αSyn-mitochondria interactions in human iPSC-derived neurons. In vivo , SLP-2 overexpression protected dopaminergic neurons and motor function, while its depletion exacerbated degeneration and motor deficits in both mouse and Drosophila models. These findings suggest SLP-2 as a key regulator of mitochondrial resilience and a potential therapeutic target for PD and alpha-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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".