Type 1 Cannabinoid Receptor Ligands Display Functional Selectivity in a Cell Culture Model of Striatal Medium Spiny Projection Neurons
Bibliographic record
Abstract
Modulation of type 1 cannabinoid receptor (CB1) activity has been touted as a potential means of treating addiction, anxiety, depression, and neurodegeneration. Different agonists of CB1 are known to evoke varied responses in vivo. Functional selectivity is the ligand-specific activation of certain signal transduction pathways at a receptor that can signal through multiple pathways. To understand cannabinoid-specific functional selectivity, different groups have examined the effect of individual cannabinoids on various signaling pathways in heterologous expression systems. In the current study, we compared the functional selectivity of six cannabinoids, including two endocannabinoids (2-arachidonyl glycerol (2-AG) and anandamide (AEA)), two synthetic cannabinoids (WIN55,212-2 and CP55,940), and two phytocannabinoids (cannabidiol (CBD) and Δ(9)-tetrahydrocannabinol (THC)) on arrestin2-, Gα(i/o)-, Gβγ-, Gα(s)-, and Gα(q)-mediated intracellular signaling in the mouse STHdh(Q7/Q7) cell culture model of striatal medium spiny projection neurons that endogenously express CB1. In this system, 2-AG, THC, and CP55,940 were more potent mediators of arrestin2 recruitment than other cannabinoids tested. 2-AG, AEA, and WIN55,212-2, enhanced Gα(i/o) and Gβγ signaling, with 2-AG and AEA treatment leading to increased total CB1 levels. 2-AG, AEA, THC, and WIN55,212-2 also activated Gα(q)-dependent pathways. CP55,940 and CBD both signaled through Gα(s). CP55,940, but not CBD, activated downstream Gα(s) pathways via CB1 targets. THC and CP55,940 promoted CB1 internalization and decreased CB1 protein levels over an 18-h period. These data demonstrate that individual cannabinoids display functional selectivity at CB1 leading to activation of distinct signaling pathways. To effectively match cannabinoids with therapeutic goals, these compounds must be screened for their signaling bias.
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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".