The Endocannabinoid 2-Arachidonoyl-Glycerol Activates Human Neutrophils: Critical Role of Its Hydrolysis and De Novo Leukotriene B4 Biosynthesis
Bibliographic record
Abstract
Although endocannabinoids are important players in nociception and obesity, their roles as immunomodulators remain elusive. The main endocannabinoids described to date, namely 2-arachidonoyl-glycerol (2-AG) and arachidonyl-ethanolamide (AEA), induce an intriguing profile of pro- and anti-inflammatory effects. This could relate to cell-specific cannabinoid receptor expression and/or the action of endocannabinoid-derived metabolites. Importantly, 2-AG and AEA comprise a molecule of arachidonic acid (AA) in their structure and are hydrolyzed rapidly. We postulated the following: 1) the released AA from endocannabinoid hydrolysis would be metabolized into eicosanoids; and 2) these eicosanoids would mediate some of the effects of endocannabinoids. To confirm these hypotheses, experiments were performed in which freshly isolated human neutrophils were treated with endocannabinoids. Unlike AEA, 2-AG stimulated myeloperoxidase release, kinase activation, and calcium mobilization by neutrophils. Although 2-AG did not induce the migration of neutrophils, it induced the release of a migrating activity for neutrophils. 2-AG also rapidly (1 min) induced a robust biosynthesis of leukotrienes, similar to that observed with AA. The effects of 2-AG were not mimicked nor prevented by cannabinoid receptor agonists or antagonists, respectively. Finally, the blockade of either 2-AG hydrolysis, leukotriene (LT) B(4) biosynthesis, or LTB(4) receptor 1 activation prevented all the effects of 2-AG on neutrophil functions. In conclusion, we demonstrated that 2-AG potently activates human neutrophils. This is the consequence of 2-AG hydrolysis, de novo LTB(4) biosynthesis, and an autocrine activation loop involving LTB(4) receptor 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".