Modulation of Cellular Iron Metabolism by Hydrogen Peroxide
Bibliographic record
Abstract
Cellular iron uptake and storage are coordinately controlled by binding of iron-regulatory proteins (IRP), IRP1 and IRP2, to iron-responsive elements (IREs) within the mRNAs encoding transferrin receptor (TfR) and ferritin. Under conditions of iron starvation, both IRP1 and IRP2 bind with high affinity to cognate IREs, thus stabilizing TfR and inhibiting translation of ferritin mRNAs. The IRE/IRP regulatory system receives additional input by oxidative stress in the form of H(2)O(2) that leads to rapid activation of IRP1. Here we show that treating murine B6 fibroblasts with a pulse of 100 microm H(2)O(2) for 1 h is sufficient to alter critical parameters of iron homeostasis in a time-dependent manner. First, this stimulus inhibits ferritin synthesis for at least 8 h, leading to a significant (50%) reduction of cellular ferritin content. Second, treatment with H(2)O(2) induces a approximately 4-fold increase in TfR mRNA levels within 2-6 h, and subsequent accumulation of newly synthesized protein after 4 h. This is associated with a profound increase in the cell surface expression of TfR, enhanced binding to fluorescein-tagged transferrin, and stimulation of transferrin-mediated iron uptake into cells. Under these conditions, no significant alterations are observed in the levels of mitochondrial aconitase and the Divalent Metal Transporter DMT1, although both are encoded by two as yet lesser characterized IRE-containing mRNAs. Finally, H(2)O(2)-treated cells display an increased capacity to sequester (59)Fe in ferritin, despite a reduction in the ferritin pool, which results in a rearrangement of (59)Fe intracellular distribution. Our data suggest that H(2)O(2) regulates cellular iron acquisition and intracellular iron distribution by both IRP1-dependent and -independent mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".