Prostaglandin E2 Suppresses Chemokine Production in Human Macrophages through the EP4 Receptor
Bibliographic record
Abstract
Pro-inflammatory pathways participate in the pathogenesis of atherosclerosis. However, the role of endogenous anti-inflammatory pathways in atheroma has received much less attention. Therefore, using cDNA microarrays, we screened for genes regulated by prostaglandin E(2) (PGE(2)), a potential endogenous anti-inflammatory mediator, in lipopolysaccharide (LPS)-treated human macrophages (MPhi). PGE(2) (50 nm) attenuated LPS-induced mRNA and protein expression of chemokines including monocyte chemoattractant protein-1, interleukin-8, macrophage inflammatory protein-1alpha and -1beta, and interferon-inducible protein-10. PGE(2) also inhibited the tumor necrosis factor-alpha-, interferon-gamma-, and interleukin-1beta-mediated expression of these chemokines. In contrast to the case of MPhi, PGE(2) did not suppress chemokine expression in human endothelial and smooth muscle cells (SMC) treated with LPS and pro-inflammatory cytokines. To assess the potential paracrine effect of endogenous PGE(2) on macrophage-derived chemokine production, we co-cultured MPhi with SMC in the presence of LPS. In these co-cultures, cyclooxygenase-2-dependent PGE(2) production exceeded that in the mono-cultures, and MIP-1beta declined significantly compared with MPhi cultured without SMC. We further documented prominent expression of the PGE(2) receptor EP4 in MPhi in both culture and human atheroma. Moreover, a selective EP4 antagonist completely reversed PGE(2)-mediated suppression of chemokine production. Thus, endogenous PGE(2) may modulate inflammation during atherogenesis and other inflammatory diseases by suppressing macrophage-derived chemokine production via the EP4 receptor.
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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) | 0.002 | 0.001 |
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".