Cannabinoid-induced tolerance is associated with a CB1 receptor G protein coupling switch that is prevented by ultra-low dose rimonabant
Bibliographic record
Abstract
The analgesic effect of opioids is enhanced, and tolerance is attenuated, by ultra-low doses (nanomolar to picomolar) of an opioid antagonist, an effect that is mediated by preventing the receptor from coupling to Gs proteins. Recently, we demonstrated a cannabinoid-opioid interaction at the ultra-low dose level, suggesting that the effect might not be specific to opioid receptors. The purpose of this study was to examine, both behaviorally and mechanistically, whether the cannabinoid CB1 receptor was also sensitive to ultra-low dose effects. Antinociception was tested in rats after an injection of either vehicle, the CB1 receptor agonist WIN 55 212-2 (WIN), an ultra-low dose of the CB1 receptor antagonist rimonabant (SR 141716), or a combination of WIN and the ultra-low-dose rimonabant. In the acute experiment, tail-flick latencies were recorded at 10-min intervals for 90 min; in the chronic experiment, tail-flick latencies were recorded 10 min after a daily injection over 7 days. Ultra-low dose rimonabant extended the duration of WIN-induced antinociception. WIN produced maximal tolerance by day 7, whereas WIN+ultra-low dose rimonabant continued to produce strong antinociception, demonstrating that ultra-low dose rimonabant prevented the development of WIN-induced tolerance. Animals chronically treated with WIN alone had CB1 receptors predominantly coupling to Gs receptors in the striatum, whereas the vehicle, ultra-low dose rimonabant, and WIN+ultra-low dose rimonabant groups had CB1 receptors predominantly coupling to Gi receptors. Cannabinoid-induced tolerance is thus associated with a G protein coupling switch from the inhibitory Gi protein to the excitatory Gs protein, an effect which is prevented by the ultra-low dose rimonabant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".