GSK‐3β regulates the synaptic expression of NMDA receptors via phosphorylation of phosphatidylinositol 4 kinase type IIα
Bibliographic record
Abstract
Deregulation of GSK-3β is strongly implicated in a variety of serious brain conditions, such as Alzheimer disease, bipolar disorder and schizophrenia. To understand how GSK-3β becomes dysregulated in these conditions, it is important to understand its physiological functions in the central nervous system. In this context, GSK-3β plays a role in the induction of NMDA receptor-dependent long-term depression (LTD) and several substrates for GSK-3β have been identified in this form of synaptic plasticity, including KLC-2, PSD-95 and tau. Stabilization of NMDA receptors at synapses has also been shown to involve GSK-3β, but the substrates involved are currently unknown. Recent work has identified phosphatidylinositol 4 kinase type IIα (PI4KIIα) as a neuronal GSK-3β substrate that can potentially regulate the surface expression of AMPA receptors. In the present study, we investigated the synaptic role of PI4KIIα in organotypic rat hippocampal slices. We found that knockdown of PI4KIIα has no effect on synaptic AMPA receptor-mediated synaptic transmission but substantially reduces NMDA receptor-mediated synaptic transmission. Furthermore, the ability of the selective GSK-3 inhibitor, CT99021, to reduce the amplitude of NMDA receptor-mediated currents was occluded in shRNA-PI4KIIα transfected neurons. The effects of knocking down PI4KIIα were fully rescued by a shRNA-resistant wild-type construct, but not by a mutant construct that cannot be phosphorylated by GSK-3β. These data suggest that GSK-3β phosphorylates PI4KIIα to stabilize NMDA receptors at the synapse.
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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) | 0.000 | 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) | 0.001 | 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".