Glucagon Like Peptide-1 Promotes Adipocyte Differentiation via the Wnt4 Mediated Sequestering of Beta-Catenin
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) plays a role in the regulation of adipogenesis; however, the precise underlying molecular mechanism has not been fully defined. Wnt was recently identified as an important regulator of adipogenesis. This study aimed to investigate the involvement of the Wnt signaling pathway in the effects of GLP-1 on adipocyte differentiation. 3T3-L1 cells were induced to differentiate. The changes in the expression levels of adipogenic transcription factors and Wnts and the phosphorylation level and subcellular localization of β-catenin were quantified after GLP-1 treatment. GLP-1 stimulated adipocyte differentiation and lipid accumulation, which were accompanied by the expression of adipocyte marker genes. The expression of Wnt4 was upregulated in the process of adipocyte differentiation, which was further enhanced by treatment with GLP-1. β-catenin, an important mediator of the Wnt pathway, was immediately dephosphorylated and translocated from cytoplasm to nucleus when differentiation was induced. In the presence of GLP-1, however, β-catenin was redirected to the cell plasma membrane leading to its decreased accumulation in the nucleus. Knockdown of Wnt4 blocked the effect of GLP-1 on the cellular localization of β-catenin and expression level of adipogenic transcription factors. Our findings showed that GLP-1 promoted adipogenesis through the modulation of the Wnt4/β-catenin signaling pathway, suggesting that the GLP-1-Wntβ-catenin system might be a new target for the treatment of metabolic disease.
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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 | 0.001 |
| 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".