Monoamine Oxidase-B Inhibitor Reduction in Pro-Inflammatory Cytokines Mediated by Inhibition of cAMP-PKA/EPAC Signaling
Bibliographic record
Abstract
Mucosal epithelial cell integrity is an important component of innate immunity and it protects the host from an environment rich in microorganisms. Virulence factors from Gram-negative bacteria [e.g. lipopolysaccharide (LPS)] induce significant pro-inflammatory cytokine expression. Monoamine oxidase (MAO) inhibitors reduce cytokine expression in a variety of inflammatory models and may therefore have therapeutic potential for a number of inflammatory diseases. We tested the anti-inflammatory therapeutic potential of a recently developed reversible MAO-B inhibitor (RG0216) with reduced transport across the blood-brain barrier. In an epithelial cell culture model, RG0216 significantly decreased LPS-induced interleukin (IL)-6 and IL-1β gene and protein expression and was as effective as equimolar concentrations of deprenyl (an existing irreversible MAO-B inhibitor). Hydrogen peroxide and modulating dopamine receptor signaling had no effect on cytokine expression. We showed that LPS-induced expression of IL-6 and IL-1β was cAMP dependent, that IL-6 and IL-1β expression were induced by direct cAMP activation (forskolin) and that RG0216 and deprenyl effectively reduced cAMP-mediated cytokine expression. Targeted protein kinase A (PKA) and Exchange Protein Activated by cAMP (EPAC) activation regulated IL-6 and IL-1β expression, albeit in different ways, but both cytokines were effectively decreased with RG0216. RG0216 reduction of LPS-induced cytokine expression occurred by acting downstream of the cAMP-PKA/EPAC signaling cascade. This represents a novel mechanism by which MAO-B selective inhibitors regulate LPS-induced IL-6 and IL-1β 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) | 0.001 | 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) | 0.002 | 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".