Inducible nitric oxide synthase modulates lipolysis in adipocytes
Bibliographic record
Abstract
The role of inducible nitric oxide synthase (iNOS) in the modulation of adipocyte lipolysis was investigated. Treatment of white and brown adipose cell lines and mouse adipose explants with a mixture of tumor necrosis factor-alpha, interferon-gamma, and lipopolysaccharide (LPS) doubled the lipolytic rate, and this was associated with marked induction of iNOS expression and nitric oxide (NO) production. iNOS inhibition by 1400W, aminoguanidine, or L-NIL pretreatment further increased the cytokine/LPS-mediated lipolysis by 30% (P < 0.05) in cultured adipocytes and in adipose explants. However, this potentiating effect of iNOS inhibition was abolished in adipose explants isolated from iNOS knockout mice. Pharmacological inhibitors of adenylyl cyclase or protein kinase A reduced cytokine/LPS-induced lipolysis and also blunted the potentiating effect of iNOS inhibition on the lipolytic rate. Furthermore, addition of the antioxidants l-cystine and l-glutathione to cytokine/LPS-stimulated adipocytes mimicked the lipolytic effect of iNOS inhibition. In conclusion, inhibition of iNOS activity in adipocytes potentiates cytokine/LPS-induced lipolysis. This effect was fully reversed by adenylyl cyclase and protein kinase A inhibitors but was mimicked by cellular antioxidants. These data suggest that iNOS-mediated NO production counteracts cytokine/LPS-mediated lipolysis in adipocytes and that this feedback mechanism involves an oxidative process upstream of cAMP production in the signaling pathway.
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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) | 0.000 | 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 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".