Ghrelin Recruits the Endocannabinoid System to Modulate Food Reward
Bibliographic record
Abstract
Ghrelin enhances feeding by activating the growth hormone secretagogue receptor (GHSR). In the brain, GHSRs are expressed in regions responsible for regulating food motivation including the ventral tegmental area (VTA). Endogenous cannabinoids also promote food-seeking behaviors through the cannabinoid receptor-1 (CB-1Rs) in brain regions including the VTA. It is not known, however, if ghrelin and endocannabinoids interact in the VTA to produce these effects. We therefore examined if GHSR and CB-1R interact within the VTA to enhance food motivation. Results show that GHSR and CB-1R mRNA are expressed in the VTA cells in male and female rats and mice, with the GHSR being expressed in dopamine cells and the CB-1R being expressed primarily in nondopaminergic cells with no obvious sex differences. Ghrelin directly activated and increased excitatory tone onto dopamine cells of male and female mice. Male rats lacking fully functional GHSR signaling showed disrupted gene expression of transcripts important for regulating the synthesis, release, and degradation of endocannabinoids and lowered the levels of 2-arachidonoylglycerol (2-AG) within the VTA. Moreover, pharmacological antagonism of VTA CB-1Rs attenuates the orexigenic and appetitive effects of intra-VTA ghrelin in rats and blocks the ability of ghrelin to promote excitatory drive to VTA dopamine neurons. Finally, blocking the breakdown of cannabinoids in the VTA enhances the effects of ghrelin on food motivation. Together, our data show that ghrelin stimulates VTA dopamine cells and ultimately food motivation in part through a mechanism that involves endocannabinoid signaling at the CB-1R.
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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".