A kinesin signaling complex mediates the ability of GSK-3β to affect mood-associated behaviors
Bibliographic record
Abstract
Lithium has been the gold standard in the treatment of bipolar disorder (BPD) for 60 y. Like lithium, glycogen synthase kinase 3 (GSK-3) inhibitors display both antimanic-like and antidepressant-like effects in some animal models. However, the molecular mechanisms of both lithium and GSK-3 inhibitors remain unclear. Here we show that the GSK-3 inhibitor AR-A014418 regulated alpha-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA)-induced GluR1 and GluR2 internalization via phosphorylation of kinesin light chain 2 (KLC2), the key molecule of the kinesin cargo delivery system. Specifically, AMPA stimulation triggered serine phosphorylation of KLC2 and, subsequently, the dissociation of the GluR1/KLC2 protein complex. This suggests that GSK-3 phosphorylation of KLC2 led to the dissociation of AMPA-containing vesicles from the kinesin cargo system. The peptide TAT-KLCpCDK, a specific inhibitor for KLC2 phosphorylation by GSK-3beta, reduced the formation of long-term depression. Furthermore, the TAT-KLCpCDK peptide showed antimanic-like effects similar to lithium's on amphetamine-induced hyperactivity, a frequently used animal model of mania. It also induced antidepressant-like effects in the tail suspension and forced swim tests, two commonly used animal models of depression. Taken together, the results demonstrated that KLC2 is a cellular target of GSK-3beta capable of regulating synaptic plasticity, particularly AMPA receptor trafficking, as well as mood-associated behaviors in animal models. The kinesin cargo system may provide valuable novel targets for the development of new therapeutics for mood disorders.
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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 | 0.000 |
| 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".