Iron‐triggered signaling via <scp>ETS1</scp> and the p38/<scp>JNK MAPK</scp> pathway regulates <i>Bmp6</i> expression
Bibliographic record
Abstract
BMP6 is an iron-sensing cytokine whose transcription in liver sinusoidal endothelial cells (LSECs) is enhanced by high iron levels, a step that precedes the induction of the iron-regulatory hormone hepcidin. While several reports suggested a cell-autonomous induction of Bmp6 by iron-triggered signals, likely via sensing of oxidative stress by the transcription factor NRF2, other studies proposed the dominant role of a paracrine yet unidentified signal released by iron-loaded hepatocytes. To further explore the mechanisms of Bmp6 transcriptional regulation, we used female mice aged 10-11 months, which are characterized by hepatocytic but not LSEC iron accumulation, and no evidence of systemic iron overload. We found that LSECs of aged mice exhibit increased Bmp6 mRNA levels as compared to young controls, but do not show a transcriptional signature characteristic of activated NFR2-mediated signaling in FACS-sorted LSECs. We further observed that primary murine LSECs derived from both wild-type and NRF2 knock-out mice induce Bmp6 expression in response to iron exposure. By analyzing transcriptomic data of FACS-sorted LSECs from aged versus young mice, as well as early after iron citrate injections, we identified ETS1 as a candidate transcription factor involved in Bmp6 transcriptional regulation. By performing siRNA-mediated knockdown, small-molecule treatments, and chromatin immunoprecipitation in primary LSECs, we show that Bmp6 transcription is regulated by iron via ETS1 and p38/JNK MAP kinase-mediated signaling, at least in part independently of NRF2. Thereby, these findings identify the new components of LSEC iron sensing machinery broadly associated with cellular stress responses.
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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 | 0.000 |
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".