Bone morphogenetic protein 6–mediated crosstalk between endothelial cells and hepatocytes recapitulates the iron-sensing pathway in vitro
Bibliographic record
Abstract
Liver sinusoidal endothelial cell-derived bone morphogenetic protein 6 (BMP6) and the BMP6-small mothers against decapentaplegic homolog (SMAD) signaling pathway are essential for the expression of hepcidin, the secretion of which is considered the systemic master switch of iron homeostasis. However, there are continued controversies related to the strong and direct suppressive effect of iron on hepatocellular hepcidin in vitro in contrast to in vivo conditions. Here, we directly studied the crosstalk between endothelial cells (ECs) and hepatocytes using in vitro coculture models that mimic hepcidin signaling in vivo. Huh7 cells were directly cocultured with ECs, and EC conditioned media (CM) were also used to culture Huh7 cells and primary mouse hepatocytes. To explore the reactions of ECs to surrounding iron, they were grown in the presence of ferric ammonium citrate and heme, two iron-containing molecules. We found that both direct coculture with ECs and EC-CM significantly increased hepcidin expression in Huh7 cells. The upstream SMAD pathway, including phosphorylated SMAD1/5/8, SMAD1, and inhibitor of DNA binding 1, was induced by EC-CM, promoting hepcidin expression. Efficient blockage of this EC-mediated hepcidin upregulation by an inhibitor of the BMP6 receptor ALK receptor tyrosine kinase 2/3 or BMP6 siRNA identified BMP6 as a major hepcidin regulator in this coculture system, which highly fits the model of hepcidin regulation by iron in vivo. In addition, EC-derived BMP6 and hepcidin were highly sensitive to levels of not only ferric iron but also heme as low as 500 nM. We here establish a hepatocyte-endothelial coculture system to fully recapitulate iron regulation by hepcidin using EC-derived BMP6.
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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) | 0.001 | 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".