Dataset Information

Identification and Characterization of Cannabimovone, a Cannabinoid from Cannabis sativa, as a Novel PPAR? Agonist via a Combined Computational and Functional Study.

ABSTRACT: Phytocannabinoids (pCBs) are a large family of meroterpenoids isolated from the plant Cannabis sativa. ?9-Tetrahydrocannabinol (THC) and cannabidiol (CBD) are the best investigated phytocannabinoids due to their relative abundance and interesting bioactivity profiles. In addition to various targets, THC and CBD are also well-known agonists of peroxisome proliferator-activated receptor gamma (PPAR?), a nuclear receptor involved in energy homeostasis and lipid metabolism. In the search of new pCBs potentially acting as PPAR? agonists, we identified cannabimovone (CBM), a structurally unique abeo-menthane pCB, as a novel PPAR? modulator via a combined computational and experimental approach. The ability of CBM to act as dual PPAR?/? agonist was also evaluated. Computational studies suggested a different binding mode toward the two isoforms, with the compound able to recapitulate the pattern of H-bonds of a canonical agonist only in the case of PPAR?. Luciferase assays confirmed the computational results, showing a selective activation of PPAR? by CBM in the low micromolar range. CBM promoted the expression of PPAR? target genes regulating the adipocyte differentiation and prevented palmitate-induced insulin signaling impairment. Altogether, these results candidate CBM as a novel bioactive compound potentially useful for the treatment of insulin resistance-related disorders.

SUBMITTER: Iannotti FA

PROVIDER: S-EPMC7179127 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and Characterization of Cannabimovone, a Cannabinoid from Cannabis sativa, as a Novel PPARγ Agonist via a Combined Computational and Functional Study.

Iannotti Fabio Arturo FA De Maio Fabrizia F Panza Elisabetta E Appendino Giovanni G Taglialatela-Scafati Orazio O De Petrocellis Luciano L Amodeo Pietro P Vitale Rosa Maria RM

Molecules (Basel, Switzerland) 20200303 5

Phytocannabinoids (pCBs) are a large family of meroterpenoids isolated from the plant Cannabis sativa. Δ9-Tetrahydrocannabinol (THC) and cannabidiol (CBD) are the best investigated phytocannabinoids due to their relative abundance and interesting bioactivity profiles. In addition to various targets, THC and CBD are also well-known agonists of peroxisome proliferator-activated receptor gamma (PPARγ), a nuclear receptor involved in energy homeostasis and lipid metabolism. In the ...[more]

PMID: 32138197

Similar Datasets

Project description:Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

Dataset Information

Identification and Characterization of Cannabimovone, a Cannabinoid from Cannabis sativa, as a Novel PPAR? Agonist via a Combined Computational and Functional Study.

Publications

Identification and Characterization of Cannabimovone, a Cannabinoid from <i>Cannabis sativa</i>, as a Novel PPARγ Agonist via a Combined Computational and Functional Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets