Sodium Nitroprusside Promotes IRP2 Degradation via an Increase in Intracellular Iron and in the Absence of S Nitrosylation at C178
Bibliographic record
Abstract
In iron-replete cells the posttranscriptional regulator IRP2 undergoes ubiquitination and proteasomal degradation. A similar response occurs in cells exposed to sodium nitroprusside (SNP), an NO-releasing drug. It has been proposed that nitroprusside ([Fe(CN)5NO]2-) fails to donate iron into cells and that it promotes IRP2 degradation via S nitrosylation at C178. This residue is located within a stretch of 73 amino acids, earlier proposed to define an iron-dependent degradation domain. Surprisingly, we show that IRP2 bearing a C178S mutation or a Delta73 deletion is sensitive to degradation not only by ferric ammonium citrate (FAC) but also by SNP. Moreover, FAC and SNP attenuate the RNA-binding activities of IRP2 and its homologue IRP1 with similar kinetics. Actinomycin D, cycloheximide, succinylacetone, and dimethyl-oxalylglycine antagonize IRP2 degradation in response to both FAC and SNP, suggesting a common mechanistic basis. IRP2 is not only sensitive to fresh, but also to photodegraded SNP and remains unaffected by S-nitrosoglutathione (GSNO), an established nitrosation agent. Importantly, both fresh and photodegraded SNP, but not GSNO, promote a >4-fold increase in the calcein-accessible labile iron pool. Collectively, these results suggest that IRP2 degradation by SNP does not require S nitrosylation but rather represents a response to iron loading.
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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".