The Endocannabinoid Metabolite Prostaglandin E2 (PGE2)-Glycerol Inhibits Human Neutrophil Functions: Involvement of Its Hydrolysis into PGE2 and EP Receptors
Bibliographic record
Abstract
Abstract The endocannabinoids 2-arachidonoyl-glycerol and N-arachidonoyl-ethanolamine mediate an array of pro- and anti-inflammatory effects. These effects are related, in part, to their metabolism by eicosanoid biosynthetic enzymes. For example, N-arachidonoyl-ethanolamine and 2-arachidonoyl-glycerol can be metabolized by cyclooxygenase-2 into PG-ethanolamide (PG-EA) and PG-glycerol (PG-G), respectively. Although PGE2 is a recognized suppressor of neutrophil functions, the impact of cyclooxygenase-derived endocannabinoids such as PGE2-EA or PGE2-G on neutrophils is unknown. This study’s aim was to define the effects of these mediators on neutrophil functions and the underlying cellular mechanisms involved. We show that PGE2-G, but not PGE2-EA, inhibits leukotriene B4 biosynthesis, superoxide production, migration, and antimicrobial peptide release. The effects of PGE2-G were prevented by EP1/EP2 receptor antagonist AH-6809 but not the EP4 antagonist ONO-AE2-227. The effects of PGE2-G required its hydrolysis into PGE2, were not observed with the non-hydrolyzable PGE2-serinol amide, and were completely prevented by methyl-arachidonoyl-fluorophosphate and palmostatin B, and partially prevented by JZL184 and WWL113. Although we could detect six of the documented PG-G hydrolases in neutrophils by quantitative PCR, only ABHD12 and ABHD16A were detected by immunoblot. Our pharmacological data, combined with our protein expression data, did not allow us to pinpoint one PGE2-G lipase, and rather support the involvement of an uncharacterized lipase and/or of multiple hydrolases. In conclusion, we show that PGE2-G inhibits human neutrophil functions through its hydrolysis into PGE2, and by activating the EP2 receptor. This also indicates that neutrophils could regulate inflammation by altering the balance between PG-G and PG levels in vivo.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".