Beneficial effects of cysteamine in Thy1-α-Syn mice and induced pluripotent stem cells with a SNCA gene triplication
Bibliographic record
Abstract
A number of publications have reported that cysteamine has significant therapeutic effects on several aspects of Parkinson's disease (PD)-related pathology but none of these studies have evaluated its impact on pathological forms of α-Synuclein (α-Syn), one of the main hallmarks of PD. We therefore tested the efficacy of cysteamine on the Thy1-α-Syn mouse model which over-expresses full-length human wild-type α-Syn. Two-month (early stage disease) and 6-month old (late stage disease) mice and littermate controls were treated daily with cysteamine (20 mg/kg, i.p.) to assess the protective and restorative properties of this compound. After 6 weeks of treatment, animals were tested using a battery of motor tests. Cysteamine-treated transgenic mice displayed significant improvements in motor performance as compared to saline-treated transgenic littermates. Post-mortem readouts revealed a reduction in fibrillation, phosphorylation and total levels of overexpresed human α-Syn. To determine if such outcomes extended to human cells, the benefits of cysteamine were additionally tested using 6-hydroxydopamine (6-OHDA) treated neurons differentiated from induced pluripotent stem cells (iPSCs) derived from a PD patient harbouring a triplication of the SNCA gene. SNCA neurons treated with cysteamine exhibited significantly more intact/healthy neurites than cells treated with 6-OHDA alone. Additionally, SNCA neurons treated with cysteamine in the absence of 6-OHDA showed a trend towards lower total α-Syn levels. Overall, our in vivo and in vitro findings suggest that cysteamine can act as a disease-modifying molecule by enhancing -the survival of dopaminergic neurons and reducing pathological forms of α-Syn.
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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 | 0.001 | 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".