Liver-Specific Inhibition of ChREBP Improves Hepatic Steatosis and Insulin Resistance in <i>ob/ob</i> Mice
Bibliographic record
Abstract
Obesity is a metabolic disorder often associated with type 2 diabetes, insulin resistance, and hepatic steatosis. Leptin-deficient (ob/ob) mice are a well-characterized mouse model of obesity in which increased hepatic lipogenesis is thought to be responsible for the phenotype of insulin resistance. We have recently demonstrated that carbohydrate responsive element-binding protein (ChREBP) plays a key role in the control of lipogenesis through the transcriptional regulation of lipogenic genes, including acetyl-CoA carboxylase and fatty acid synthase. The present study reveals that ChREBP gene expression and ChREBP nuclear protein content are significantly increased in liver of ob/ob mice. To explore the involvement of ChREBP in the physiopathology of hepatic steatosis and insulin resistance, we have developed an adenovirus-mediated RNA interference technique in which short hairpin RNAs (shRNAs) were used to inhibit ChREBP expression in vivo. Liver-specific inhibition of ChREBP in ob/ob mice markedly improved hepatic steatosis by specifically decreasing lipogenic rates. Correction of hepatic steatosis also led to decreased levels of plasma triglycerides and nonesterified fatty acids. As a consequence, insulin signaling was improved in liver, skeletal muscles, and white adipose tissue, and overall glucose tolerance and insulin sensitivity were restored in ob/ob mice after a 7-day treatment with the recombinant adenovirus expressing shRNA against ChREBP. Taken together, our results demonstrate that ChREBP is central for the regulation of lipogenesis in vivo and plays a determinant role in the development of the hepatic steatosis and of insulin resistance in ob/ob mice.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".