Presence of ethanol‐sensitive and ethanol‐insensitive glycine receptors in the ventral tegmental area and prefrontal cortex in mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Glycine receptors composed of α1 and β subunits are primarily found in the spinal cord and brainstem and are potentiated by ethanol (10-100 mM). However, much less is known about the presence, composition and ethanol sensitivity of these receptors in higher CNS regions. Here, we examined two regions of the brain reward system, the ventral tegmental area (VTA) and the prefrontal cortex (PFC), to determine their glycine receptor subunit composition and sensitivity to ethanol. EXPERIMENTAL APPROACH: We used Western blot, immunohistochemistry and electrophysiological techniques in three different models: wild-type C57BL/6, glycine receptor subunit α1 knock-in and glycine receptor subunit α2 knockout mice. KEY RESULTS: Similar levels of α and β receptor subunits were detected in both brain regions, and electrophysiological recordings demonstrated the presence of glycine-activated currents in both areas. Sensitivity of glycine receptors to glycine was lower in the PFC compared with VTA. Picrotoxin only partly blocked the glycine-activated current in the PFC and VTA, indicating that both regions express heteromeric αβ receptors. Glycine receptors in VTA neurons, but not in PFC neurons, were potentiated by ethanol. CONCLUSION AND IMPLICATIONS: Glycine receptors in VTA neurons from WT and α2 KO mice were potentiated by ethanol, but not in neurons from the α1 KI mice, supporting the conclusion that α1 glycine receptors are predominantly expressed in the VTA. By contrast, glycine receptors in PFC neurons were not potentiated in any of the mouse models studied, suggesting the presence of α2/α3/α4, rather than α1 glycine receptor subunits.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".