The Wnt signaling pathway effector TCF7L2 is upregulated by insulin and represses hepatic gluconeogenesis
Bibliographic record
Abstract
Certain single nucleotide polymorphisms (SNPs) in transcription factor 7-like 2 (TCF7L2) are strongly associated with the risk of type 2 diabetes. TCF7L2 and β-catenin (β-cat) form the bipartite transcription factor cat/TCF in stimulating Wnt target gene expression. cat/TCF may also mediate the effect of other signaling cascades, including that of cAMP and insulin in cell-type specific manners. As carriers of TCF7L2 type 2 diabetes risk SNPs demonstrated increased hepatic glucose production, we aimed to determine whether TCF7L2 expression is regulated by nutrient availability and whether TCF7L2 and Wnt regulate hepatic gluconeogenesis. We examined hepatic Wnt activity in the TOPGAL transgenic mouse, assessed hepatic TCF7L2 expression in mice upon feeding, determined the effect of insulin on TCF7L2 expression and β-cat Ser⁶⁷⁵ phosphorylation, and investigated the effect of Wnt activation and TCF7L2 knockdown on gluconeogenic gene expression and glucose production in hepatocytes. Wnt activity was observed in pericentral hepatocytes in the TOPGAL mouse, whereas TCF7L2 expression was detected in human and mouse hepatocytes. Insulin and feeding stimulated hepatic TCF7L2 expression in vitro and in vivo, respectively. In addition, insulin activated β-cat Ser⁶⁷⁵ phosphorylation. Wnt activation by intraperitoneal lithium injection repressed hepatic gluconeogenic gene expression in vivo, whereas lithium or Wnt-3a reduced gluconeogenic gene expression and glucose production in hepatic cells in vitro. Small interfering RNA-mediated TCF7L2 knockdown increased glucose production and gluconeogenic gene expression in cultured hepatocytes. These observations suggest that Wnt signaling and TCF7L2 are negative regulators of hepatic gluconeogenesis, and TCF7L2 is among the downstream effectors of insulin in hepatocytes.
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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".