GSK-3β Contributes to Parkinsonian Dopaminergic Neuron Death: Evidence From Conditional Knockout Mice and Tideglusib
Bibliographic record
Abstract
Glycogen synthase kinase-3 (GSK-3) dysregulation has been implicated in nigral dopaminergic neurodegeneration, one of the main pathological features of Parkinson’s disease (PD). The two isoforms, GSK-3α and GSK-3β, have both been suggested to play a detrimental role in neuronal death. To date, several studies have focused on the role of GSK-3β on PD pathogenesis, while the role of GSK-3α has been largely overlooked. Here, we report in situ observations that both GSK-3α and GSK-3β are dephosphorylated at a negatively acting regulatory serine, indicating kinase activation, selectively in nigral dopaminergic neurons following exposure of mice to 1-methyl-4-pheny-1,2,3,6-tetrahydropyridine (MPTP). To identify whether GSK-3α and GSK-3β display functional redundancy in regulating parkinsonian dopaminergic cell death, we analysed dopaminergic neuron-specific Gsk3a null (Gsk3a△Dat) and Gsk3b null (Gsk3b△Dat) mice, respectively. We found that Gsk3b△Dat, but not Gsk3a△Dat, showed significant resistance to MPTP insult, revealing non-redundancy of GSK-3α and GSK-3β in PD pathogenesis. In addition, we tested the neuroprotective effect of tideglusib, the most clinically advanced inhibitor of GSK-3, in the MPTP model of PD. Administration of higher doses (200 mg/kg and 500 mg/kg) of tideglusib exhibited significant neuroprotection, whereas 50 mg/kg tideglusib failed to prevent dopaminergic neurodegeneration from MPTP toxicity. Administration of 200 mg/kg tideglusib improved motor symptoms of MPTP-treated mice. Together, these data demonstrate GSK-3β and not GSK-3α is critical for parkinsonian neurodegeneration. Our data support the view that GSK-3β acts as a potential therapeutic target in PD and tideglusib would be a candidate drug for PD neuroprotective therapy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".