Liver-Specific Expression of Dominant-Negative Transcription Factor 7-Like 2 Causes Progressive Impairment in Glucose Homeostasis
Bibliographic record
Abstract
Investigations on the metabolic role of the Wnt signaling pathway and hepatic transcription factor 7-like 2 (TCF7L2) have generated opposing views. While some studies demonstrated a repressive effect of TCF7L2 on hepatic gluconeogenesis, a recent study using liver-specific Tcf7l2(-/-) mice suggested the opposite. As a consequence of redundant and bidirectional actions of transcription factor (TCF) molecules and other complexities of the Wnt pathway, knockout of a single Wnt pathway component may not effectively reveal a complete metabolic picture of this pathway. To address this, we generated the liver-specific dominant-negative (DN) TCF7L2 (TCF7L2DN) transgenic mouse model LTCFDN. These mice exhibited progressive impairment in response to pyruvate challenge. Importantly, LTCFDN hepatocytes displayed elevated gluconeogenic gene expression, gluconeogenesis, and loss of Wnt-3a-mediated repression of gluconeogenesis. In C57BL/6 hepatocytes, adenovirus-mediated expression of TCF7L2DN, but not wild-type TCF7L2, increased gluconeogenesis and gluconeogenic gene expression. Our further mechanistic exploration suggests that TCF7L2DN-mediated inhibition of Wnt signaling causes preferential interaction of β-catenin (β-cat) with FoxO1 and increased binding of β-cat/FoxO1 to the Pck1 FoxO binding site, resulting in the stimulation of Pck1 expression and increased gluconeogenesis. Together, our results using TCF7L2DN as a unique tool revealed that the Wnt signaling pathway and its effector β-cat/TCF serve a beneficial role in suppressing hepatic gluconeogenesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".