Transferrin receptor 2 (Tfr2) is necessary for hepcidin induction in mice with hepatocyte-specific transferrin receptor 1 (Tfr1) deficiency
Bibliographic record
Abstract
Abstract In hepatocytes, transferrin receptor 1 (Tfr1) does not have a major role in iron acquisition but rather controls signaling to the iron hormone hepcidin via its interaction with the hemochromatosis protein Hfe. Hepatocellular Tfr2, a Tfr1 homologue, operates as an iron sensor and direct regulator of hepcidin. We generated TfrcAlb-Cre;Tfr2Alb-Cre mice with hepatocyte-specific ablation of both Tfr1 and Tfr2 to study implications in iron homeostasis. These animals are viable and phenocopy Tfr2Alb-Cre mice by developing systemic iron overload. Only Tfr1-proficient primary hepatocytes from Tfrcfl/fl;Tfr2fl/fl and Tfr2Alb-Cre mice could internalize fluorescent AF647-transferrin, arguing against a significant contribution of Tfr2 in uptake of transferrin-bound iron. After prolonged (12 weeks) dietary iron restriction, Tfr2-deficient livers from TfrcAlb-Cre;Tfr2Alb-Cre and Tfr2Alb-Cre mice had indistinguishably low Hamp mRNA levels and iron content, even though Tfr1 was robustly upregulated in the latter. Under these conditions, Tfr2-proficient livers from TfrcAlb-Cre mice had significantly higher Hamp mRNA levels, presumably because Tfr1 disruption “liberates” Hfe. Following short-term (6 h) exposure of iron-deficient animals to a high-iron diet, iron-dependent Hamp mRNA induction was evident in TfrcAlb-Cre but not in TfrcAlb-Cre;Tfr2Alb-Cre mice. These findings suggest that “liberated” active Hfe can only induce hepcidin in the presence of Tfr2. Our data demonstrate that transferrin receptors are dispensable for hepatocellular iron supply, while Tfr2 is essential for iron signaling to hepcidin in Tfr1-deficient hepatocytes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".