Iron Drives Anabolic Metabolism Through Active Histone Demethylation and mTORC1
Bibliographic record
Abstract
Abstract All living cells require a minimal iron threshold to sustain anabolic metabolism. However, the mechanisms by which cells sense iron to regulate anabolic processes are unclear. Here, we report a universal eukaryotic pathway for iron sensing in which molecular iron is required to sustain active histone demethylation and maintain the expression of critical components of the pro-anabolic mTORC1 pathway. Specifically, we identify the iron-binding histone-demethylase KDM3B as an intrinsic iron sensor that regulates mTORC1 activity by demethylating H3K9me2 at enhancers of genes encoding high-affinity leucine transporters and RAPTOR. By directly suppressing leucine availability and RAPTOR levels, iron deficiency (ID) supersedes other nutrient inputs into mTORC1. This process occurs in vivo, and is not an indirect effect by canonical iron-utilizing pathways. Elevated expression of KDM3B targets is associated with reduced survival in a subset of human cancers and ID represses mTORC1 in patient-derived tumor cells and sensitizes cancer cells to chemotherapy. These data demonstrate a novel mechanism of eukaryotic iron sensing through dynamic chromatin remodeling and repression of mTORC1 mediated anabolism. Due to ancestral eukaryotes sharing homologues of KDMs and mTORC1 core components, this pathway likely predated the emergence of the other kingdom-specific nutrient sensors for mTORC1.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".