Abstract
Molecular networking (MN) analysis intends to provide chemical insight of untargeted mass spectrometry (MS) data to the user's underlying biological questions. Foodomics is the study of chemical compounds in food using advanced omics methods. In this study, an MS-MN-based foodomics approach is developed to investigate the composition and anti-obesity activity of cannabinoids in hemp oil. A total of 16 cannabinoids are determined in optimized microwave pretreatment of hemp oil using the developed approach. Untargeted metabolomics analysis reveals that cannabinoid extract (CE) and its major constituent (cannabidiol, CBD), can alleviate high glucose-induced increases in lipids and carbohydrates, and decreases in amino acid and nucleic acid. Moreover, CE and CBD are also found to suppress the expression levels of mdt-15, sbp-1, fat-5, fat-6, fat-7, daf-2, and elevate the expression level of daf-1, daf-7, daf-16, sod-3, gst-4, lipl-4, resulting in the decrease of lipid synthesis and the enhance of kinetism. Canonical correspondence analysis (CCA) uncovers strong associations between specific metabolic alterations and gene expression levels. These findings from this exploratory study offer a new insight into the roles of cannabinoids in the treatment of obesity and related complications.
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