Monoacylglycerol lipase inhibitors produce pro- or antidepressant responses via hippocampal CA1 GABAergic synapses
Bibliographic record
Abstract
The probability of suffering the mood disorder depression is up to 30% in women and 15% in men during their life span. Pharmacological options for depression are limited: conventional antidepressants have low efficacy and a delayed onset of action (several weeks). Here we investigate the antidepressant actions of inhibitors of monoacylglycerol lipase (MAGL), the major degradative enzyme of the endocannabinoid 2-arachidonoylglycerol. A low-dose of MAGL inhibitors produces antidepressant effects on acute stress-exposed mice, through glutamatergic synaptic long-term depression (LTD), without significant effects on chronic corticosterone-exposed mice. In contrast, a high-dose of MAGL inhibitors produces pro- or antidepressant effects on acute stress- or chronic corticosterone-exposed mice, respectively, through GABAergic synaptic disinhibition. In the hippocampus, in vivo inhibition of MAGL induces a CB 1 cannabinoid receptor (CB 1 R)-dependent suppression of inhibitory GABAergic synapses and an in vivo LTD of excitatory glutamatergic synapses. LTD induction requires CB 1 R in astroglial cells (but not in GABAergic or glutamatergic neurons) and postsynaptic glutamate receptors. The conventional antidepressant fluoxetine produces rapid or delayed antidepressant effects in acute stress- or chronic corticosterone-exposed mice, respectively. We propose that depression-like behavior of animals in response to acute stress is the normal behavioral response, and thus, MAGL inhibitors, which produce antidepressant effects in chronic corticosterone-exposed animals through GABAergic synaptic disinhibition, represent a new class of rapidly-acting and long-lasting antidepressants.
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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 | 0.001 |
| 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".