Liver sinusoidal endothelial cells induce BMP6 expression in response to non–transferrin-bound iron
Bibliographic record
Abstract
Homeostatic adaptation to systemic iron overload involves transcriptional induction of bone morphogenetic protein 6 (BMP6) in liver sinusoidal endothelial cells (LSECs). BMP6 is then secreted to activate signaling of the iron hormone hepcidin (HAMP) in neighboring hepatocytes. To explore the mechanism of iron sensing by LSECs, we generated TfrcTek-Cre mice with endothelial cell-specific ablation of transferrin receptor 1 (Tfr1). We also used control Tfrcfl/fl mice to characterize the LSEC-specific molecular responses to iron using single-cell transcriptomics. TfrcTek-Cre animals tended to have modestly increased liver iron content (LIC) compared with Tfrcfl/fl controls but expressed physiological Bmp6 and Hamp messenger RNA (mRNA). Despite a transient inability to upregulate Bmp6, they eventually respond to iron challenges with Bmp6 and Hamp induction, yet occasionally to levels slightly lower relative to LIC. High dietary iron intake triggered the accumulation of serum nontransferrin bound iron (NTBI), which significantly correlated with liver Bmp6 and Hamp mRNA levels and elicited more profound alterations in the LSEC transcriptome than holo-transferrin injection. This culminated in the robust induction of Bmp6 and other nuclear factor erythroid 2-related factor 2 (Nrf2) target genes, as well as Myc target genes involved in ribosomal biogenesis and protein synthesis. LSECs and midzonal hepatocytes were the most responsive liver cells to iron challenges and exhibited the highest expression of Bmp6 and Hamp mRNAs, respectively. Our data suggest that during systemic iron overload, LSECs internalize NTBI, which promotes oxidative stress and thereby transcriptionally induces Bmp6 via Nrf2. Tfr1 appears to contribute to iron sensing by LSECs, mostly under low iron conditions.
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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 | 0.001 |
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".