Lipogenic transcription factor ChREBP mediates fructose-induced metabolic adaptations to prevent hepatotoxicity
Bibliographic record
Abstract
Epidemiologic and animal studies implicate overconsumption of fructose in the development of nonalcoholic fatty liver disease, but the molecular mechanisms underlying fructose-induced chronic liver diseases remain largely unknown. Here, we have presented evidence supporting the essential function of the lipogenic transcription factor carbohydrate response element-binding protein (ChREBP) in mediating adaptive responses to fructose and protecting against fructose-induced hepatotoxicity. In WT mice, a high-fructose diet (HFrD) activated hepatic lipogenesis in a ChREBP-dependent manner; however, in Chrebp-KO mice, a HFrD induced steatohepatitis. In Chrebp-KO mouse livers, a HFrD reduced levels of molecular chaperones and activated the C/EBP homologous protein-dependent (CHOP-dependent) unfolded protein response, whereas administration of a chemical chaperone or Chop shRNA rescued liver injury. Elevated expression levels of cholesterol biosynthesis genes in HFrD-fed Chrebp-KO livers were paralleled by an increased nuclear abundance of sterol regulatory element-binding protein 2 (SREBP2). Atorvastatin-mediated inhibition of hepatic cholesterol biosynthesis or depletion of hepatic Srebp2 reversed fructose-induced liver injury in Chrebp-KO mice. Mechanistically, we determined that ChREBP binds to nuclear SREBP2 to promote its ubiquitination and destabilization in cultured cells. Therefore, our findings demonstrate that ChREBP provides hepatoprotection against a HFrD by preventing overactivation of cholesterol biosynthesis and the subsequent CHOP-mediated, proapoptotic unfolded protein response. Our findings also identified a role for ChREBP in regulating SREBP2-dependent cholesterol metabolism.
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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) | 0.002 | 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".