Prostaglandin E<sub>2</sub> depresses <scp>GABA</scp> release onto parvocellular neuroendocrine neurones in the paraventricular nucleus of the hypothalamus via presynaptic receptors
Bibliographic record
Abstract
Inflammation‐induced activation of the hypothalamic‐pituitary‐adrenal (HPA) axis and the ensuing release of anti‐inflammatory glucocorticoids are critical for the fine‐tuning of the inflammatory response. This immune‐induced neuroendocrine response is in large part mediated by prostaglandin E2 (PGE2), the central actions of which ultimately translate into the excitation of parvocellular neuroendocrine cells (PNCs) in the hypothalamic paraventricular nucleus. However, the neuronal mechanisms by which PGE2 excites PNCs remain incompletely understood. In the present study, we report that PGE2 potently depresses GABAergic inhibitory synaptic transmission onto PNCs. Using whole‐cell patch clamp recordings obtained from PNCs in ex vivo hypothalamic slices from rats, we found that bath application of PGE2 (0.01‐100 μmol L‐1) concentration‐dependently decreased the amplitude of evoked inhibitory postsynaptic currents (eIPSCs) with maximum effects at 10 μmol L‐1. The PGE2‐mediated depression of eIPSCs had a rapid onset and was long‐lasting, and also was accompanied by an increase in paired pulse ratio. In addition, PGE2 decreased the frequency but not the amplitude of both spontaneous IPSCs and miniature IPSCs. These results collectively indicate that PGE2 acts at a presynaptic locus to decrease the probability of GABA release. Using pharmacological approaches, we also demonstrated that the EP3 subtype of the PGE2 receptor mediated the actions of PGE2 on GABA synapses. Taken together, our results show that PGE2, via actions of presynaptic EP3 receptors, potently depresses GABA release onto PNCs, providing a plausible mechanism for the disinhibition of HPA axis output during inflammation.
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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".