Participation of nuclear factor (erythroid 2‐related), factor 2 in ameliorating lithocholic acid‐induced cholestatic liver injury in mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Lithocholic acid (LCA), the most toxic bile acid, induces cholestatic liver injury in rodents. We previously showed that LCA activates the oxidative stress-responsive nuclear factor (erythroid-2 like), factor 2 (Nrf2) in cultured liver cells, triggering adaptive responses that reduce cell injury. In this study, we determined whether Nrf2 protects the liver against LCA-induced toxicity in vivo. EXPERIMENTAL APPROACH: Nrf2 disrupted (Nrf2(-/-) ) and wild-type mice were treated with LCA (125 mg·kg(-1) body weight) to induce liver injury. Levels of mRNA, protein and function of important Nrf2 target genes coupled with liver histology and injury biomarkers of mice were examined. KEY RESULTS: In 4 day LCA treatments, we observed a significantly higher hepatic induction of Nrf2 target, cytoprotective genes including thioredoxin reductase 1, glutamate cysteine ligase subunits, glutathione S-transferases, haeme oxygenase-1 and multidrug resistance-associated proteins 3 and 4 in the wild type as compared with the Nrf2(-/-) mice. Moreover, basal and LCA-induced hepatic glutathione and activities of glutathione S-transferases and thioredoxin reductases were higher in wild-type than in Nrf2(-/-) mice. This reduced production of cytoprotective genes against LCA toxicity rendered Nrf2(-/-) mice more susceptible to severe liver damage with the presence of multifocal liver necrosis, inflamed bile ducts and elevation of lipid peroxidation and liver injury biomarkers, such as alanine aminotransferase and alkaline phosphatase. CONCLUSIONS AND IMPLICATIONS: Nrf2 plays a crucial cytoprotective role against LCA-induced liver injury by orchestrating adaptive responses. The pharmacological potential of targeting liver Nrf2 in the management of cholestatic liver diseases is proposed.
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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 | 0.000 |
| 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 | 0.001 |
| 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".