The constitutive production of the endocannabinoid 2‐arachidonoylglycerol participates in oligodendrocyte differentiation
Bibliographic record
Abstract
Endocannabinoids have recently emerged as instructive cues in the developing central nervous system, and, based on the expression of their receptors, we identified oligodendrocytes as potential targets of these molecules. Here, we show that the enzymes responsible for the synthesis of the endocannabinoid 2-arachidonoylglycerol (2-AG), diacylglycerol lipase alpha (DAGLα) and beta (DAGLβ), and degradation, monoacylglycerol lipase (MAGL), can be found in oligodendrocytes at different developmental stages. Moreover, cultured oligodendrocyte progenitor cells (OPCs) express DAGLα and β abundantly, resulting in the stronger production of 2-AG than in differentiated oligodendrocytes. The opposite is observed with MAGL. CB1 and CB2 receptor antagonists (SR141716 and AM630) impaired OPC differentiation into mature oligodendrocytes and likewise, inhibiting DAGL activity with RHC-80267 or tetrahydrolipstatin also blocked oligodendrocyte maturation, an effect reversed by the addition of exogenous 2-AG. Likewise, 2-AG synthesis disruption using specific siRNAs against DAGLα and DAGLβ significantly reduced myelin protein expression in vitro, whereas a pharmacological gain-of-function approach by using cannabinoid agonists or MAGL inhibition had the opposite effects. ERK/MAPK pathway is implicated in oligodendrocyte differentiation because PD98059, an inhibitor of MEK1, abrogated oligodendrocyte maturation. The cannabinoid receptor antagonists and RHC-80267 all diminished basal ERK1/2 phosphorylation, effects that were partially reversed by the addition of 2-AG. Overall, our data suggest a novel role of endocannabinoids in oligodendrocyte differentiation such that constitutive release of 2-AG activates cannabinoid receptors in an autocrine/paracrine way in OPCs, stimulating the ERK/MAPK signaling pathway.
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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) | 0.001 | 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".