Ethanol Facilitates Glutamatergic Transmission to Dopamine Neurons in the Ventral Tegmental Area
Bibliographic record
Abstract
The cellular mechanisms underlying alcohol addiction are poorly understood. In several brain areas, ethanol depresses glutamatergic excitatory transmission, but how it affects excitatory synapses on dopamine neurons of the ventral tegmental area (VTA), a crucial site for the development of drug addiction, is not known. We report here that in midbrain slices from rats, clinically relevant concentrations of ethanol (10-80 mM) increase the amplitude of evoked EPSCs and reduce their paired-pulse ratio in dopamine neurons in the VTA. The EPSCs were mediated by glutamate alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptors. In addition, ethanol increases the frequency but not the amplitude of spontaneous EPSCs. Furthermore, ethanol increases extracellular glutamate levels in the VTA of midbrain slices. The effects of ethanol are mimicked by SKF 38393, a dopamine D(1) receptor agonist, and by GBR 12935, a dopamine reuptake inhibitor, and they are blocked by SKF 83566, a D(1) antagonist, or by reserpine, which depletes dopamine stores. The enhancement of sEPSC frequency reaches a peak with 40 mM ethanol and declines with concentrations >or=80 mM ethanol, which is quite likely a result of D(2) receptor activation as raclopride, a D(2) receptor blocker, significantly enhanced 80 mM ethanol-induced enhancement of sEPSCs. Finally, 6, 7-dinitroquinoxaline-2, 3-dione (DNQX), an AMPA receptor antagonist, attenuates ethanol-induced excitation of VTA DA neurons. We therefore conclude that, acting via presynaptic D(1) receptors, ethanol at low concentrations increases glutamate release in the VTA, thus raising somatodendritic dopamine release, which further activates the presynaptic D(1) receptors. Enhancement of this positive feedback loop may significantly contribute to the development of alcohol addiction.
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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".