NADPH Oxidase–Derived Superoxide Destabilizes Lipopolysaccharide-Induced Interleukin 8 mRNA Via p38, Extracellular Signal–Regulated Kinase Mitogen-Activated Protein Kinase, and the Destabilizing Factor Tristetraprolin
Bibliographic record
Abstract
Expression of inflammatory cytokines is regulated by transcriptional and posttranscriptional mechanisms. We previously showed that NADPH oxidase-derived superoxide induces inflammatory mediators in response to tumor necrosis factor α (TNF-α) and lipopolysaccharide (LPS). In this study, we examined the role of endothelial NADPH oxidase in the regulation of mRNA stability of three inflammatory mediators: interleukin (IL) 8, IL-6, and intercellular adhesion molecule 1 (ICAM-1). Tumor necrosis factor α increased mRNA stability of ICAM-1, IL-8, and IL-6 by a p38 mitogen-activated protein kinase (MAPK)-dependent mechanism, but this did not involve NADPH oxidase. Surprisingly, whereas LPS treatment alone did not alter stability of these molecules, the antioxidant N-acetyl-L-cysteine; the flavine inhibitor diphenylene iodonium; short interfering RNA against Nox2, Nox4; and the p22(phox) subunit of NADPH oxidase all enhanced IL-8 mRNA stability in LPS-treated cells, indicating that LPS induced destabilization through NADPH oxidase. This occurred by a mechanism that involved extracellular signal-regulated kinase 1/2, p38 MAPK, and the mRNA-destabilizing factor tristetraprolin. On the other hand, N-acetyl-L-cysteine decreased mRNA stability of ICAM-1 and IL-6 in LPS-treated cells and IL-6 and ICAM-1 in TNF-α-treated cells. In conclusion, NADPH oxidase contributes to destabilization of IL-8 mRNA stability and propose a model for the complex underlying mechanism, which is dependent upon agonist (LPS vs. TNF-α) and target molecule (IL-8 vs. IL-6 and ICAM-1) and involves tristetraprolin, p38, and extracellular signal-regulated kinase 1/2 MAPK.
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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 | 0.001 |
| 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".