Serotonin Activates the Hypothalamic-Pituitary-Adrenal Axis via Serotonin 2C Receptor Stimulation
Bibliographic record
Abstract
The dynamic interplay between serotonin [5-hydroxytryptamine (5-HT)] neurotransmission and the hypothalamic-pituitary-adrenal (HPA) axis has been extensively studied over the past 30 years, but the underlying mechanism of this interaction has not been defined. A possibility receiving little attention is that 5-HT regulates upstream corticotropin-releasing hormone (CRH) signaling systems via activation of serotonin 2C receptors (5-HT(2C)Rs) in the paraventricular nucleus of the hypothalamus (PVH). Through complementary approaches in wild-type rodents and 5-HT(2C)R-deficient mice, we determined that 5-HT(2C)Rs are necessary for 5-HT-induced HPA axis activation. We used laser-capture PVH microdissection followed by microarray analysis to compare the expression of 13 5-HTRs. Only 5-HT(2C)R and 5-HT(1D)R transcripts were consistently identified as present in the PVH, and of these, the 5-HT(2C)R was expressed at a substantially higher level. The abundant expression of 5-HT(2C)Rs in the PVH was confirmed with in situ hybridization histochemistry. Dual-neurohistochemical labeling revealed that approximately one-half of PVH CRH-containing neurons coexpressed 5-HT(2C)R mRNA. We observed that PVH CRH neurons consistently depolarized in the presence of a high-affinity 5-HT(2C)R agonist, an effect blocked by a 5-HT(2C)R antagonist. Supporting the importance of 5-HT(2C)Rs in CRH neuronal activity, genetic inactivation of 5-HT(2C)Rs produced a downregulation of CRH mRNA and blunted CRH and corticosterone release after 5-HT compound administration. These findings thus provide a mechanistic explanation for the longstanding observation of HPA axis stimulation in response to 5-HT and thereby give insight into the neural circuitry mediating the complex neuroendocrine responses to stress.
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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".