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Record W2975852739 · doi:10.1002/jlb.3a0919-049rrr

Human leukocytes differentially express endocannabinoid-glycerol lipases and hydrolyze 2-arachidonoyl-glycerol and its metabolites from the 15-lipoxygenase and cyclooxygenase pathways

2019· article· en· W2975852739 on OpenAlexafffund
Caroline Turcotte, Élizabeth Dumais, Anne‐Sophie Archambault, Cyril Martin, Marie‐Renée Blanchet, Élyse Y. Bissonnette, Louis‐Philippe Boulet, Michel Laviolette, Vincenzo Di Marzo, Nicolas Flamand

Bibliographic record

VenueJournal of Leukocyte Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEndocannabinoid systemLipoxygenaseCyclooxygenaseBiologyGlycerolEicosanoidEnzymeMonoacylglycerol lipaseBiochemistryHydrolysisArachidonic acidReceptor

Abstract

fetched live from OpenAlex

Abstract 2-Arachidonoyl-glycerol (2-AG) is an endocannabinoid with anti-inflammatory properties. Blocking 2-AG hydrolysis to enhance CB2 signaling has proven effective in mouse models of inflammation. However, the expression of 2-AG lipases has never been thoroughly investigated in human leukocytes. Herein, we investigated the expression of seven 2-AG hydrolases by human blood leukocytes and alveolar macrophages (AMs) and found the following protein expression pattern: monoacylglycerol (MAG lipase; eosinophils, AMs, monocytes), carboxylesterase (CES1; monocytes, AMs), palmitoyl-protein thioesterase (PPT1; AMs), α/β-hydrolase domain (ABHD6; mainly AMs), ABHD12 (all), ABHD16A (all), and LYPLA2 (lysophospholipase 2; monocytes, lymphocytes, AMs). We next found that all leukocytes could hydrolyze 2-AG and its metabolites derived from cyclooxygenase-2 (prostaglandin E2-glycerol [PGE2-G]) and the 15-lipoxygenase (15-hydroxy-eicosatetraenoyl-glycerol [15-HETE-G]). Neutrophils and eosinophils were consistently better at hydrolyzing 2-AG and its metabolites than monocytes and lymphocytes. Moreover, the efficacy of leukocytes to hydrolyze 2-AG and its metabolites was 2-AG ≥ 15-HETE-G >> PGE2-G for each leukocyte. Using the inhibitors methylarachidonoyl-fluorophosphonate (MAFP), 4-nitrophenyl-4-(dibenzo[d][1,3]dioxol-5-yl(hydroxy)methyl)piperidine-1-carboxylate (JZL184), Palmostatin B, 4′-carbamoylbiphenyl-4-yl methyl(3-(pyridin-4-yl)benzyl)carbamate, N-methyl-N-[[3-(4-pyridinyl)phenyl]methyl]-4′-(aminocarbonyl)[1,1′-biphenyl]-4-yl ester carbamic acid (WWL70), 4′-[[[methyl[[3-(4-pyridinyl)phenyl]methyl]amino]carbonyl]oxy]-[1,1′-biphenyl]-4-carboxylic acid, ethyl ester (WWL113), tetrahydrolipstatin, and ML349, we could not pinpoint a specific hydrolase responsible for the hydrolysis of 2-AG, PGE2-G, and 15-HETE-G by these leukocytes. Furthermore, JZL184, a selective MAG lipase inhibitor, blocked the hydrolysis of 2-AG, PGE2-G, and 15-HETE-G by neutrophils and the hydrolysis of PGE2-G and 15-HETE-G by lymphocytes, two cell types with limited/no MAG lipase. Using an activity-based protein profiling (ABPP) probe to label hydrolases in leukocytes, we found that they express many MAFP-sensitive hydrolases and an unknown JZL184-sensitive hydrolase of ∼52 kDa. Altogether, our results indicate that human leukocytes are experts at hydrolyzing 2-AG and its metabolites via multiple lipases and probably via a yet-to-be characterized 52 kDa hydrolase. Blocking 2-AG hydrolysis in humans will likely abrogate the ability of human leukocytes to degrade 2-AG and its metabolites and increase their anti-inflammatory effects in vivo.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.018
GPT teacher head0.274
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2019
Admission routes2
Has abstractyes

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