In Vitro Signaling Properties of Cannabinoid and Orexin Receptors: How Orexin Receptors Influence Cannabinoid Receptor‐Mediated Signaling
Bibliographic record
Abstract
ABSTRACT The co‐expression of different types of G protein‐coupled receptors (GPCRs) in the same cells can have implications for receptor signaling and receptor cross‐talk, potentially altering the apparent potency or efficacy of ligands targeting each receptor. The endocannabinoid and orexinergic systems, consisting of class A GPCRs and their endogenous ligands, are highly complex and regulate processes such as appetite, sleep, nociception, and energy homeostasis. The shared anatomical distribution of cannabinoid and orexin receptors in various regions of the central nervous system (CNS), coupled with data from previous studies exploring physical and functional interactions between these receptors, suggests that the endocannabinoid and orexinergic systems engage in crosstalk. In this study, we explored how orexin receptors (OX1, OX2) altered the in vitro signaling of cannabinoid receptors (CB1, CB2) in Chinese hamster ovary (CHO)‐K1 cells by quantifying cyclic adenosine monophosphate (cAMP) inhibition and βarrestin2 recruitment. Our results suggest that orexin receptors alter agonist‐dependent signaling at the cannabinoid receptors by enhancing cannabinoid receptor‐mediated cAMP inhibition while increasing or decreasing cannabinoid receptor‐mediated βarrestin2 recruitment. These initial results are important for understanding the effects associated with cannabinoid ligands and may provide novel insights for therapeutics targeting physiological processes modulated by both systems.
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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) | 0.002 | 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".