The Effects of Glycogen Synthase Kinase-3beta in Serotonin Neurons
Bibliographic record
Abstract
Glycogen synthase kinase-3 (GSK3) is a constitutively active protein kinase in brain. Increasing evidence has shown that GSK3 acts as a modulator in the serotonin neurotransmission system, including direct interaction with serotonin 1B (5-HT1B) receptors in a highly selective manner and prominent modulating effect on 5-HT1B receptor activity. In this study, we utilized the serotonin neuron-selective GSK3β knockout (snGSK3β-KO) mice to test if GSK3β in serotonin neurons selectively modulates 5-HT1B autoreceptor activity and function. The snGSK3β-KO mice were generated by crossbreeding GSK3β-floxed mice and ePet1-Cre mice. These mice had normal growth and physiological characteristics, similar numbers of tryptophan hydroxylase-2 (TpH2)-expressing serotonin neurons, and the same brain serotonin content as in littermate wild type mice. However, the expression of GSK3β in snGSK3β-KO mice was diminished in TpH2-expressing serotonin neurons. Compared to littermate wild type mice, snGSK3β-KO mice had a reduced response to the 5-HT1B receptor agonist anpirtoline in the regulation of serotonergic neuron firing, cAMP production, and serotonin release, whereas these animals displayed a normal response to the 5-HT1A receptor agonist 8-OH-DPAT. The effect of anpirtoline on the horizontal, center, and vertical activities in the open field test was differentially affected by GSK3β depletion in serotonin neurons, wherein vertical activity, but not horizontal activity, was significantly altered in snGSK3β-KO mice. In addition, there was an enhanced anti-immobility response to anpirtoline in the tail suspension test in snGSK3β-KO mice. Therefore, results of this study demonstrated a serotonin neuron-targeting function of GSK3β by regulating 5-HT1B autoreceptors, which impacts serotonergic neuron firing, serotonin release, and serotonin-regulated behaviors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".