Alcohol Metabolism-mediated Oxidative Stress Down-regulates Hepcidin Transcription and Leads to Increased Duodenal Iron Transporter Expression
Bibliographic record
Abstract
Patients with alcoholic liver disease frequently exhibit iron overload in association with increased hepatic fibrosis. Even moderate alcohol consumption elevates body iron stores; however, the underlying molecular mechanisms are unknown. Hepcidin, a circulatory peptide synthesized in the liver, is a key mediator of iron metabolism. Ethanol metabolism significantly down-regulated both in vitro and in vivo hepcidin mRNA and protein expression. 4-Methylpyrazole, a specific inhibitor of the alcohol-metabolizing enzymes, abolished the effects of ethanol on hepcidin. However, ethanol did not alter the expression of transferrin receptor1 and ferritin or the activation of iron regulatory RNA-binding proteins, IRP1 and IRP2. Mice maintained on 10-20% ethanol for 7 days displayed down-regulation of liver hepcidin expression without changes in liver triglycerides or histology. This was accompanied by elevated duodenal divalent metal transporter1 and ferroportin protein expression. Injection of hepcidin peptide negated the effect of ethanol on duodenal iron transporters. Ethanol down-regulated hepcidin promoter activity and the DNA binding activity of CCAAT/enhancer-binding protein alpha (C/EBPalpha) but not beta. Interestingly, the antioxidants vitamin E and N-acetylcysteine abolished both the alcohol-mediated down-regulation of C/EBPalpha binding activity and hepcidin expression in the liver and the up-regulation of duodenal divalent metal transporter 1. Collectively, these findings indicate that alcohol metabolism-mediated oxidative stress regulates hepcidin transcription via C/EBPalpha, which in turn leads to increased duodenal iron transport.
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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) | 0.002 | 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".