Parkinson's disease pathology is directly correlated to SIRT3 in human subjects and animal models: Implications for AAV.SIRT3-myc as a disease-modifying therapy
Bibliographic record
Abstract
In Parkinson's disease (PD), post-mortem studies in affected brain regions have demonstrated a decline in mitochondrial number and function. This combined with many studies in cell and animal models suggest that mitochondrial dysfunction is central to PD pathology. We and others have shown that the mitochondrial protein deacetylase, SIRT3 has neurorestorative effects in PD models. In this study, to determine whether there is a link between PD pathology and SIRT3, we analysed SIRT3 levels in human subjects with PD, and compared to age-matched controls. In the SNc of PD subjects, SIRT3 was reduced by 56.8 ± 15.5% compared to control, regardless of age (p < 0.05, R = 0.6539). Given that age is the primary risk factor for PD, this finding suggests that reduced SIRT3 may contribute to PD pathology. Next, we measured whether there was a correlation between α-synuclein and SIRT3. In a parallel study, we assessed the disease-modifying potential of SIRT3 over-expression in a seeding model of α-synuclein. In PFF rats, infusion of rAAV1.SIRT3-myc reduced abundance of α-synuclein inclusions by 30.1 ± 18.5%. This was not observed when deacetylation deficient SIRT3H248Y was transduced, demonstrating the importance of SIRT3 deacetylation in reducing α-synuclein aggregation. These studies confirm show the that there is a clear difference in SIRT3 levels in subjects with Parkinson's disease compared age-matched controls, suggesting a link between SIRT3 and the progression of PD. We also demonstrate that over-expression of SIRT3 reduces α-synuclein aggregation, further validating AAV.SIRT3-myc as a potential disease-modifying solution for 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 | 0.001 | 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 | 0.001 | 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".