Serotonin Availability Shapes the Effects of Phenelzine on Inflammatory Response and Gene Expression in Macrophages
Bibliographic record
Abstract
Background Serotonin (5-HT) is a neurotransmitter with extensive physiological influence in the central nervous system (CNS) and various behavioral and biological functions, including immune regulation through 5-HT receptors (5-HTRs) expressed by immune cells. A variety of serotonin-modulating drugs have been developed to treat neurological disorders. Phenelzine, a drug indicated for the management of treatment-resistant depression, is a potent, non-selective inhibitor of monoamine oxidase (MAO), the enzyme that metabolizes serotonin to 5-hydroxyindole acetic acid (5-HIAA). Given the emerging evidence of a bidirectional link between depression and inflammation, as well as the potential therapeutic applications of serotonin-modulating drugs in autoimmune diseases and cancer, our study investigated the pro-inflammatory and anti-inflammatory factors influenced by serotonin and phenelzine. Methodology We conducted experiments on RAW264.7 macrophages, exposing them to various combinations and concentrations of serotonin, 5-HIAA, and phenelzine. We assessed the relative gene expression of monoamine oxidase-A (MAO-A), CYP1B1, cyclooxygenase-2 (COX-2), and inducible nitric oxide synthase (iNOS) using real-time PCR and measured the production of IL-6, TNF-α, and IL-10 cytokines using enzyme-linked immunosorbent assays (ELISA). Results Our findings revealed that phenelzine can downregulate genes associated with the production of reactive oxygen and nitrogen species, reduce aryl hydrocarbon receptor (AHR)-related gene expression induced by serotonin, and enhance the production of inflammatory cytokines. These effects were significantly influenced by the concentration of available serotonin. Conclusions Our study demonstrates that various mechanisms, including AHR activation, modulation of reactive oxygen and nitrogen species production, and others, in addition to the increased availability of serotonin due to phenelzine treatment, can significantly influence the inflammatory state.
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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".