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Structural and Functional Interaction of Δ9-Tetrahydrocannabinol with Liver Fatty Acid Binding Protein (FABP1).

Huan HuangAvery L McIntoshGregory G MartinLawrence J DangottAnn B KierFriedhelm Schroeder
Published in: Biochemistry (2018)
Although serum Δ9-tetrahydrocannabinol (Δ9-THC) undergoes rapid hepatic clearance and metabolism, almost nothing is known regarding the mechanism(s) whereby this highly lipophilic phytocannabinoid is transported for metabolism/excretion. A novel NBD-arachidonoylethanolamide (NBD-AEA) fluorescence displacement assay showed that liver fatty acid binding protein (FABP1), the major hepatic endocannabinoid (EC) binding protein, binds the first major metabolite of Δ9-THC (Δ9-THC-OH) as well as Δ9-THC itself. Circular dichroism (CD) confirmed that not only Δ9-THC and Δ9-THC-OH but also downstream metabolites Δ9-THC-COOH and Δ9-THC-CO-glucuronide directly interact with FABP1. Δ9-THC and metabolite interaction differentially altered the FABP1 secondary structure, increasing total α-helix (all), decreasing total β-sheet (Δ9-THC-COOH, Δ9-THC-CO-glucuronide), increasing turns (Δ9-THC-OH, Δ9-THC-COOH, Δ9-THC-CO-glucuronide), and decreasing unordered structure (Δ9-THC, Δ9-THC-OH). Cultured primary hepatocytes from wild-type (WT) mice took up and converted Δ9-THC to the above metabolites. Fabp1 gene ablation (LKO) dramatically increased hepatocyte accumulation of Δ9-THC and even more so its primary metabolites Δ9-THC-OH and Δ9-THC-COOH. Concomitantly, rtPCR and Western blotting indicated that LKO significantly increased Δ9-THC's ability to regulate downstream nuclear receptor transcription of genes important in both EC ( Napepld > Daglb > Dagla, Naaa, Cnr1) and lipid ( Cpt1A > Fasn, FATP4) metabolism. Taken together, the data indicated that FABP1 may play important roles in Δ9-THC uptake and elimination as well as Δ9-THC induction of genes regulating hepatic EC levels and downstream targets in lipid metabolism.
Keyphrases
  • binding protein
  • fatty acid
  • ms ms
  • type diabetes
  • genome wide
  • machine learning
  • dna methylation
  • endothelial cells
  • quantum dots
  • insulin resistance
  • artificial intelligence