Liraglutide stimulates the β-catenin signaling cascade in mouse epididymal fat tissue
Bibliographic record
Abstract
Although canonical Wnt signaling pathway activation was shown to negatively regulate adipogenesis, recent investigations suggest that Wnt pathway effectors TCF7L2 and β-catenin (β-cat) in adipose tissues are also involved in energy homeostasis during adulthood. In assessing the metabolic beneficial effect of GLP-1-based diabetes drugs in high-fat diet (HFD)-challenged mice, we observed that liraglutide treatment affected the expression of a battery of adipose tissue-specific genes, including those that encode adiponectin and leptin, mainly in epididymal white adipose tissue (eWAT). Fourteen-week HFD challenge repressed TCF7L2 and β-cat S675 phosphorylation in eWAT, while such repression was reversed by liraglutide treatment (150 µg/kg body weight daily) during weeks 10-14. In Glp1r-/-mice, liraglutide failed in stimulating TCF7L2 or β-cat in eWAT. We detected Glp1r expression in mouse eWAT and its level is enriched in its stromal vascular fraction (SVF). Mouse eWAT-SVF showed reduced expression of Tcf7l2 and its Tcf7l2 level could not be stimulated by liraglutide treatment; while following adipogenic differentiation, rat eWAT-SVF showed elevated Tcf7l2 expression. Direct in vitro liraglutide treatment in eWAT-SVF stimulated CREB S133, β-cat S675 phosphorylation, and cellular cAMP level. Thus, cAMP/β-cat signaling cascade can be stimulated by liraglutide in eWAT via GLP-1R expressed in eWAT-SVF.
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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 | 0.001 | 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) | 0.002 | 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".