Hypoferremic Response to Chronic Inflammation Is Controlled via the Hemojuvelin/Hepcidin/Ferroportin Axis and Does Not Involve Hepcidin‐Independent Regulation of <i>Fpn</i><scp>mRNA</scp>
Bibliographic record
Abstract
ABSTRACT The iron regulatory hormone hepcidin contributes to the pathogenesis of anemia of inflammation (AI) by inhibiting the iron exporter ferroportin in target cells, causing hypoferremia. Under acute inflammation, hepcidin induction requires hemojuvelin (Hjv), a bone morphogenetic protein co‐receptor, while Fpn mRNA is also suppressed in a hepcidin‐independent manner. However, it is unclear whether, during chronic inflammation, Hjv and hepcidin‐independent Fpn mRNA regulation are critical for hypoferremia and AI. To address these questions, wild type and Hjv−/− mice, a model of hemochromatosis, were fed for 8 weeks an adenine‐rich diet to develop chronic kidney disease (CKD). Renal inflammation, accessed by increased Il6 mRNA expression, did not differ among genotypes. Hjv disruption did not mitigate the severity of kidney injury but suppressed the inflammatory induction of liver hepcidin. CKD triggered hypoferremia and mild anemia in wild type mice; however, Hjv−/− littermates maintained high serum iron and normal hemoglobin, consistent with a protective effect of Hjv/hepcidin deficiency. Notably, tissue Fpn mRNA levels were not affected by the inflammatory milieu of CKD. Following injection of wild type or Hjv−/− mice with heat‐killed Brucella abortus, Fpn mRNA was suppressed during the acute phase of inflammation but quickly recovered and persisted in the chronic phase. We conclude that Hjv deficiency reduces hepcidin levels and mitigates anemia in the CKD model, providing further support for pharmacological targeting of Hjv for the treatment of AI. Moreover, our data demonstrate that Fpn mRNA suppression only occurs under acute but not chronic inflammatory conditions and therefore cannot substantially contribute to AI pathogenesis.
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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".