Effects of Interferon-γ and Lipopolysaccharide on Macrophage Iron Metabolism Are Mediated by Nitric Oxide-induced Degradation of Iron Regulatory Protein 2
Bibliographic record
Abstract
Iron regulatory proteins (IRP-1 and IRP-2) control the synthesis of transferrin receptors (TfR) and ferritin by binding to iron-responsive elements, which are located in the 3'-untranslated region and the 5'-untranslated region of their respective mRNAs. Cellular iron levels affect binding of IRPs to iron-responsive elements and consequently expression of TfR and ferritin. Moreover, NO(*), a redox species of nitric oxide that interacts primarily with iron, can activate IRP-1 RNA binding activity resulting in an increase in TfR mRNA levels. Recently we found that treatment of RAW 264.7 cells (a murine macrophage cell line) with NO(+) (nitrosonium ion, which causes S-nitrosylation of thiol groups) resulted in a rapid decrease in RNA binding of IRP-2 followed by IRP-2 degradation, and these changes were associated with a decrease in TfR mRNA levels (Kim, S., and Ponka, P. (1999) J. Biol. Chem. 274, 33035-33042). In this study, we demonstrated that stimulation of RAW 264.7 cells with lipopolysaccharide (LPS) and interferon-gamma (IFN-gamma) increased IRP-1 binding activity, whereas RNA binding of IRP-2 decreased and was followed by a degradation of this protein. Moreover, the decrease of IRP-2 binding/protein levels was associated with a decrease in TfR mRNA levels in LPS/IFN-gamma-treated cells, and these changes were prevented by inhibitors of inducible nitric oxide synthase. Furthermore, LPS/IFN-gamma-stimulated RAW 264.7 cells showed increased rates of ferritin synthesis. These results suggest that NO(+)-mediated degradation of IRP-2 plays a major role in iron metabolism during inflammation.
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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) | 0.001 | 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".