Positive Feedback Regulation of Human Inducible Nitric-oxide Synthase Expression by Ras Protein S-Nitrosylation
Bibliographic record
Abstract
The production of nitric oxide (NO) by inducible NO synthase (iNOS) regulates many aspects of physiology and pathology. The expression of iNOS needs to be tightly regulated to balance the broad ranging properties of NO. We have investigated the feedback regulation of cytokine-induced iNOS expression by NO in human cells. The pharmacological inhibition of iNOS activity reduced iNOS protein levels in response to cytokine stimulation in a human epithelial cell line (A549 cells) as well as in primary human astrocytes and bronchial epithelial cells. The addition of exogenous NO using a NO donor prevented the reduction in iNOS levels caused by blockade of iNOS activity. Examination of signaling pathways affected by iNOS indicated that NO S -nitrosylated Ras. Transfection of cells with a S -nitrosylation-resistant Ras mutant reduced iNOS protein levels, indicating a role for this Ras modification in the amplification of iNOS levels. Further, the induction of iNOS protein levels correlated with the late activation of the phosphatidylinositol 3-kinase/Akt and mammalian target of rapamycin (mTOR) pathways, and inhibition of these signaling molecules reduced iNOS levels. Altogether, our findings reveal a previously unknown regulatory pathway that amplifies iNOS expression in human cells. Background: Feedback regulatory processes controlling iNOS expression are incompletely defined. Results: Induction of iNOS expression was attenuated by 1) inhibition of iNOS activity, 2) prevention of Ras S -nitrosylation, and 3) inhibition of PI3K and mTOR activity. Conclusion: iNOS-derived NO amplifies iNOS expression through S -nitrosylation of Ras and activation of PI3K and mTOR. Significance: We have defined a previously unrecognized positive feedback pathway that amplifies human iNOS expression.
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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".