Prostaglandin E2 Represses Interleukin 1 Beta-Induced Inflammatory Mediator Output from Pregnant Human Myometrial Cells Through the EP2 and EP4 Receptors1
Bibliographic record
Abstract
Inflammatory mediators, including prostaglandins, cytokines, and chemokines, are strongly implicated in the mechanism of human labor, though their precise roles remain unknown. Here we demonstrate that interleukin 1 beta (IL-1beta) significantly increased the expression and release of interleukin-8 (CXCL8), monocyte chemotactic protein-1 (CCL2), and granulocyte macrophage colony-stimulating factor (CSF2) by primary human myometrial cells. However, this effect was repressed by prostaglandin E(2) (PGE(2)). As PGE(2) can activate four distinct PGE(2) receptors (EP(1), EP(2), EP(3), and EP(4)) to elicit various responses, we sought to define the EP receptor(s) responsible for this repression. Using selective EP receptor agonists and a selective EP(4) antagonist, we show that PGE(2) mediates the repression of IL-1beta-induced release of CXCL8, CCL2, and CSF2 via activation of the EP(2) and EP(4) receptors. The use of siRNA gene-specific knockdown further confirmed a role for both receptors. Real-time RT-PCR demonstrated that EP(2) was the most highly expressed of all four EP receptors at the mRNA level in human myometrial cells, and immunocytochemistry showed that EP(2) protein is abundantly present throughout the cells. Interestingly, PGE(2) does not appear to reduce mRNA expression of CXCL8, CCL2, and CSF2. Our results demonstrate that PGE(2) can elicit anti-inflammatory responses via activation of the EP(2) and EP(4) receptors in lower segment term pregnant human myometrial cells. Further elucidation of the EP receptor-mediated signaling pathways in the pregnant human uterus may be beneficial for optimizing the maintenance of pregnancy, induction of labor or indeed treatment of preterm labor.
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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".