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 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".