Abstract

Natural products have been fundamental materials in drug discovery. Traditional strategies for observing natural products with novel structure and/or biological activity are challenging due to large cost and time consumption. Implementation of the MS/MS-based molecular networking strategy with the in silico annotation tool is expected to expedite the dereplication of secondary metabolites. In this study, using this tool, two new dilignans with a 2-phenyl-3-chromanol motif, obovatolins A (1) and B (2), were discovered from the stem barks of Magnolia obovata Thunb. along with six known compounds (3–8), expanding chemical diversity of lignan skeletons in this natural source. Their structures and configurations were elucidated using spectroscopic data. All isolates were evaluated for their PCSK9 mRNA expression inhibitory activity. Obovatolins A (1) and B (2), and magnolol (3) showed potent lipid controlling activities. To identify transcriptionally controlled genes by 1 along with downregulation of PCSK9, using small set of genes (42 genes) related to lipid metabolism selected from the database, focused bioinformatic analysis was carried out. As a result, it showed the correlations between gene expression under presence of 1, which led to detailed insight of the lipid metabolism caused by 1.

Highlights

  • Natural products have been considered important sources for drug discovery and development [1]

  • This tool has been increasingly used in natural product discovery, which enables to find candidate active molecules directly from fractionated bioactive extracts [8], portray the taxonomic relationship between different plant species [9], or depict

  • The n-hexane fraction was further fractionated by a silica gel column chromatography to yield 11 subfractions, and these subfractions were analyzed by UHPLC-Q-ToF-MS/MS

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Summary

Introduction

Natural products have been considered important sources for drug discovery and development [1]. To overcome one of these drawbacks, various dereplication and structure identification methods have been developed [2] Among these methods, liquid chromatography tandem mass spectrometry (LC-MS/MS) is the common technology for comprehensive profiling of natural products and helps facilitate structural characterization of constituents in complex mixtures [3,4,5]. Molecular networking (MN) is one of the recent bioinformatics approaches that estimate structural similarity by comparing MS/MS data [6,7]. This tool has been increasingly used in natural product discovery, which enables to find candidate active molecules directly from fractionated bioactive extracts [8], portray the taxonomic relationship between different plant species [9], or depict

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