2-Arachidonoyl-glycerol- and arachidonic acid-stimulated neutrophils release antimicrobial effectors against<i>E. coli, S. aureus</i>, HSV-1, and RSV
Bibliographic record
Abstract
The endocannabinoid 2-AG is highly susceptible to its hydrolysis into AA, which activates neutrophils through de novo LTB(4) biosynthesis, independently of CB activation. In this study, we show that 2-AG and AA stimulate neutrophils to release antimicrobial effectors. Supernatants of neutrophils activated with nanomolar concentrations of 2-AG and AA indeed inhibited the infectivity of HSV-1 and RSV. Additionally, the supernatants of 2-AG- and AA-stimulated neutrophils strongly impaired the growth of Escherichia coli and Staphylococcus aureus. This correlated with the release of a large amount (micrograms) of α-defensins, as well as a limited amount (nanograms) of LL-37. All the effects of AA and 2-AG mentioned above were prevented by inhibiting LTB(4) biosynthesis or by blocking BLT(1). Importantly, neither CB(2) receptor agonists nor antagonists could mimic nor prevent the effects of 2-AG, respectively. In fact, qPCR data show that contaminating eosinophils express ∼100-fold more CB(2) receptor mRNA than purified neutrophils, suggesting that CB(2) receptor expression by human neutrophils is limited and that contaminating eosinophils are likely responsible for the previously documented CB(2) expression by freshly isolated human neutrophils. The rapid conversion of 2-AG to AA and their subsequent metabolism into LTB(4) promote 2-AG and AA as multifunctional activators of neutrophils, mainly exerting their effects by activating the BLT(1). Considering that nanomolar concentrations of AA or 2-AG were sufficient to impair viral infectivity, this suggests potential physiological roles for 2-AG and AA as regulators of host defense 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) | 0.001 | 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".