Contribution of STAT3 and SMAD4 pathways to the regulation of hepcidin by opposing stimuli
Bibliographic record
Abstract
Hepcidin, a key regulator of iron metabolism, is a small antimicrobial peptide produced by the liver that regulates intestinal iron absorption and iron recycling by macrophages. Hepcidin is stimulated when iron stores increase and during inflammation and, conversely, is inhibited by hypoxia and augmented erythropoiesis. In many pathologic situations, such as in the anemia of chronic disease (ACD) and iron-loading anemias, several of these factors may be present concomitantly and may generate opposing signaling to regulate hepcidin expression. Here, we address the question of dominance among the regulators of hepcidin expression. We show that erythropoiesis drive, stimulated by erythropoietin but not hypoxia, down-regulates hepcidin in a dose-dependent manner, even in the presence of lipopolysaccharide (LPS) or dietary iron-loading, which may act additively. These effects are mediated through down-regulation of phosphorylation of Stat3 triggered by LPS and of Smad1/5/8 induced by iron. In conclusion, hepcidin expression levels in the presence of opposing signaling are determined by the strength of the individual stimuli rather than by an absolute hierarchy among signaling pathways. Our findings also suggest that erythropoietic drive can inhibit both inflammatory and iron-sensing pathways, at least in part, via the suppression of STAT3 and SMAD4 signaling in vivo.
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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".