Nuclear Stabilization of β-Catenin and Inactivation of Glycogen Synthase Kinase-3β by Gonadotropin-Releasing Hormone: Targeting Wnt Signaling in the Pituitary Gonadotrope
Bibliographic record
Abstract
The GnRH receptor is a G protein-coupled receptor (GPCR), and its ligand GnRH is the central regulator of the reproductive system. GnRH receptors are known to target a wide variety of signal transduction pathways. Several recent studies have shown that activation of GPCRs can impact on beta-catenin signaling. beta-Catenin is the main effecter of the Wnt signaling pathway where it acts with the transcription factors T cell factor/lymphoid enhancer factor to mediate the transcription of Wnt target genes. We show that GnRH treatment promotes the nuclear accumulation of beta-catenin, activation of T cell factor-dependent transcription, and up-regulation of Wnt target genes, c-Jun, Fra-1, and c-Myc. These results are observed in human embryonic kidney 293/GnRH receptor-expressing cells and have been recapitulated in LbetaT2 and alphaT3-1 mouse gonadotrope cells. In addition to these findings, we show that GnRH treatment mediates the inactivation of glycogen synthase kinase-3, a protein serine/threonine kinase that regulates beta-catenin degradation within the Wnt signaling pathway. Our findings extend the number of GPCRs that can target beta-catenin signaling through diverse pathways. Furthermore, this is the first demonstration of the targeting of Wnt/beta-catenin signaling by a peptide hormone GPCR.
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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".