Membrane Cholesterol But Not Putative Receptors Mediates Anandamide–Induced Hepatocyte Apoptosis
Bibliographic record
Abstract
The endogenous cannabinoid anandamide, a lipid mediator, induces various physiologic events such as vascular relaxation, inhibition of gap-junctions formation, tumor proliferation, neurologic analgesia, and apoptosis. Although increased concentration of anandamide in plasma has been implicated in pathophysiologic states including endotoxin-induced hypotension, the effects of anandamide on hepatocytes still remain unclear. In this study, we present evidence that plasma anandamide concentration is highly increased in severe hepatitis and cirrhosis patients. In addition, concentrations of anandamide within the pathophysiologic range potently induced apoptosis of hepatoma cell line (Hep G2) and primary hepatocytes, suggesting a possible link between increased anandamide level and hepatocyte damage. Anandamide-induced cell death was preceded by G0/G1 cell-cycle arrest, activation of proapoptotic signaling (i.e., p38 MAPK and JNK), and inhibition of antiapoptotic signaling (i.e., PKB/Akt) pathways. Moreover, anandamide increased susceptibility to oxidative stress-induced hepatocyte damage. In this context, methyl-beta-cyclodextrin (MCD), a membrane cholesterol depletor, or mevastatin, an HMG-CoA reductase inhibitor, or N-acetyl cysteine, an antioxidant, potently inhibited the anandamide-induced proapoptotic events and cell death, whereas putative cannabinoid receptor antagonists did not exhibit an inhibitory effect on anandamide-induced cell death. Furthermore, binding assay using polymyxin beads revealed that anandamide could interact with cholesterol. In conclusion, our data suggest that cholesterol present in the cell membrane determines the fate of hepatocytes exposed to anandamide, possibly functioning as an anandamide receptor.
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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 | 0.001 |
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".