Hepcidin-mediated hypoferremic response to acute inflammation requires a threshold of Bmp6/Hjv/Smad signaling
Bibliographic record
Abstract
Abstract Systemic iron balance is controlled by hepcidin, a liver hormone that limits iron efflux to the bloodstream by promoting degradation of the iron exporter ferroportin in target cells. Iron-dependent hepcidin induction requires hemojuvelin (HJV), a bone morphogenetic protein (BMP) coreceptor that is disrupted in juvenile hemochromatosis, causing dramatic hepcidin deficiency and tissue iron overload. Hjv−/− mice recapitulate phenotypic hallmarks of hemochromatosis but exhibit blunted hepcidin induction following lipopolysaccharide (LPS) administration. We show that Hjv−/− mice fail to mount an appropriate hypoferremic response to acute inflammation caused by LPS, the lipopeptide FSL1, or Escherichia coli infection because residual hepcidin does not suffice to drastically decrease macrophage ferroportin levels. Hfe−/− mice, a model of milder hemochromatosis, exhibit almost wild-type inflammatory hepcidin expression and associated effects, whereas double Hjv−/−Hfe−/− mice phenocopy single Hjv−/− counterparts. In primary murine hepatocytes, Hjv deficiency does not affect interleukin-6 (IL-6)/Stat, and only slightly inhibits BMP2/Smad signaling to hepcidin; however, it severely impairs BMP6/Smad signaling and thereby abolishes synergism with the IL-6/Stat pathway. Inflammatory induction of hepcidin is suppressed in iron-deficient wild-type mice and recovers after the animals are provided overnight access to an iron-rich diet. We conclude that Hjv is required for inflammatory induction of hepcidin and controls the acute hypoferremic response by maintaining a threshold of Bmp6/Smad signaling. Our data highlight Hjv as a potential pharmacological target against anemia of inflammation.
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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) | 0.003 | 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".