Sustained Hydrogen Peroxide Induces Iron Uptake by Transferrin Receptor-1 Independent of the Iron Regulatory Protein/Iron-responsive Element Network
Bibliographic record
Abstract
Local and systemic inflammatory conditions are characterized by the intracellular deposition of excess iron, which may promote tissue damage via Fenton chemistry. Because the Fenton reactant H(2)O(2) is continuously released by inflammatory cells, a tight regulation of iron homeostasis is required. Here, we show that exposure of cultured cells to sustained low levels of H(2)O(2) that mimic its release by inflammatory cells leads to up-regulation of transferrin receptor 1 (TfR1), the major iron uptake protein. The increase in TfR1 results in increased transferrin-mediated iron uptake and cellular accumulation of the metal. Although iron regulatory protein 1 is transiently activated by H(2)O(2), this response is not sufficient to stabilize TfR1 mRNA and to repress the synthesis of the iron storage protein ferritin. The induction of TfR1 is also independent of transcriptional activation via hypoxia-inducible factor 1alpha or significant protein stabilization. In contrast, pulse experiments with (35)S-labeled methionine/cysteine revealed an increased rate of TfR1 synthesis in cells exposed to sustained low H(2)O(2) levels. Our results suggest a novel mechanism of iron accumulation by sustained H(2)O(2), based on the translational activation of TfR1, which could provide an important (patho) physiological link between iron metabolism and inflammation.
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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) | 0.001 | 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".