Concentration‐dependent activation of dopamine receptors differentially modulates <scp>GABA</scp> release onto orexin neurons
Bibliographic record
Abstract
Dopamine (DA) and orexin neurons play important roles in reward and food intake. There are anatomical and functional connections between these two cell groups: orexin peptides stimulate DA neurons in the ventral tegmental area and DA inhibits orexin neurons in the hypothalamus. However, the cellular mechanisms underlying the action of DA on orexin neurons remain incompletely understood. Therefore, the effect of DA on inhibitory transmission to orexin neurons was investigated in rat brain slices using the whole-cell patch-clamp technique. We found that DA modulated the frequency of spontaneous and miniature IPSCs (mIPSCs) in a concentration-dependent bidirectional manner. Low (1 μM) and high (100 μM) concentrations of DA decreased and increased IPSC frequency, respectively. These effects did not accompany a change in mIPSC amplitude and persisted in the presence of G-protein signaling inhibitor GDPβS in the pipette, suggesting that DA acts presynaptically. The decrease in mIPSC frequency was mediated by D2 receptors whereas the increase required co-activation of D1 and D2 receptors and subsequent activation of phospholipase C. In summary, our results suggest that DA has complex effects on GABAergic transmission to orexin neurons, involving cooperation of multiple receptor subtypes. The direction of dopaminergic influence on orexin neurons is dependent on the level of DA in the hypothalamus. At low levels DA disinhibits orexin neurons whereas at high levels it facilitates GABA release, which may act as negative feedback to curb the excitatory orexinergic output to DA neurons. These mechanisms may have implications for consummatory and motivated behaviours.
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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 | 0.001 |
| 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".