Cholecystokinin Switches the Plasticity of GABA Synapses in the Dorsomedial Hypothalamus via Astrocytic ATP Release
Bibliographic record
Abstract
Whether synapses in appetite-regulatory brain regions undergo long-term changes in strength in response to satiety peptides is poorly understood. Here we show that following bursts of afferent activity, the neuromodulator and satiety peptide cholecystokinin (CCK) shifts the plasticity of GABA synapses in the dorsomedial nucleus of the hypothalamus of male Sprague Dawley rats from long-term depression to long-term potentiation (LTP). This LTP requires the activation of both type 2 CCK receptors and group 5 metabotropic glutamate receptors, resulting in a rise in astrocytic intracellular calcium and subsequent ATP release. ATP then acts on presynaptic P2X receptors to trigger a prolonged increase in GABA release. Our observations demonstrate a novel form of CCK-mediated plasticity that requires astrocytic ATP release, and could serve as a mechanism for appetite regulation. SIGNIFICANCE STATEMENT Satiety peptides, like cholecystokinin, play an important role in the central regulation of appetite, but their effect on synaptic plasticity is not well understood. The current data provide novel evidence that cholecystokinin shifts the plasticity from long-term depression to long-term potentiation at GABA synapses in the rat dorsomedial nucleus of the hypothalamus. We also demonstrate that this plasticity requires the concerted action of cholecystokinin and glutamate on astrocytes, triggering the release of the gliotransmitter ATP, which subsequently increases GABA release from neighboring inhibitory terminals. This research reveals a novel neuropeptide-induced switch in the direction of synaptic plasticity that requires astrocytes, and could represent a new mechanism by which cholecystokinin regulates appetite.
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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) | 0.000 | 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".