The endocannabinoid 2-arachidonoylglycerol inhibits long-term potentiation of glutamatergic synapses onto ventral tegmental area dopamine neurons in mice
Bibliographic record
Abstract
Drugs of abuse cause changes in the mesocorticolimbic dopamine (DA) system, such as a long-term potentiation (LTP)-like phenomenon at glutamatergic synapses onto ventral tegmental area (VTA) DA neurons. Abolishing this LTP interferes with drug-seeking behavior. Endocannabinoids (ECs) can be released by DA neurons in response to repetitive activation, which can inhibit glutamate release. Therefore, we hypothesized that ECs may act as negative regulators of LTP. Here we tested the induction of LTP in DA neurons of the VTA in mice expressing enhanced green fluorescent protein under the control of the tyrosine hydroxylase promoter. Immunohistochemistry showed colocalization of CB1 receptors with vesicular glutamate transporter (VGLUT)1 in terminals near DA neuron dendrites, with less extensive colocalization with VGLUT2. In addition, a CB1 receptor agonist, as well as trains of stimulation leading to EC production, decreased glutamate release onto DA neurons. We found that blocking CB1 receptors or synthesis of the EC 2-arachidonoylglycerol (2-AG) was without effect on basal excitatory postsynaptic potential amplitude; however, it facilitated the induction of LTP. As previously reported, antagonizing γ-aminobutyric acid (GABA)(A) transmission also facilitated LTP induction. Combining GABA(A) and CB1 receptor antagonists did not lead to larger LTP. LTP induced in the presence of CB1 receptor blockade was prevented by an N-methyl-d-aspartate receptor antagonist. Our observations argue in favor of the hypothesis that 2-AG acts as a negative regulator of LTP in the VTA. Understanding the factors that regulate long-term synaptic plasticity in this circuit is critical to aid our comprehension of drug addiction in humans.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".