Discovery of Lipid Peroxidation Inhibitors from Bacopa Species Prioritized through Multivariate Data Analysis and Multi-Informative Molecular Networking

Saesong Saesong,Queiroz Queiroz,Wolfender Wolfender,Allard Allard,Khorana Khorana,Nuengchamnong Nuengchamnong,Temkitthawon Temkitthawon,Ingkaninan Ingkaninan,Marcourt Marcourt

doi:10.3390/molecules24162989

Abstract

A major goal in the discovery of bioactive natural products is to rapidly identify active compound(s) and dereplicate known molecules from complex biological extracts. The conventional bioassay-guided fractionation process can be time consuming and often requires multi-step procedures. Herein, we apply a metabolomic strategy merging multivariate data analysis and multi-informative molecular maps to rapidly prioritize bioactive molecules directly from crude plant extracts. The strategy was applied to 59 extracts of three Bacopa species (B. monnieri, B. caroliniana and B. floribunda), which were profiled by UHPLC-HRMS2 and screened for anti-lipid peroxidation activity. Using this approach, six lipid peroxidation inhibitors 1–6 of three Bacopa spp. were discovered, three of them being new compounds: monnieraside IV (4), monnieraside V (5) and monnieraside VI (6). The results demonstrate that this combined approach could efficiently guide the discovery of new bioactive natural products. Furthermore, the approach allowed to evidence that main semi-quantitative changes in composition linked to the anti-lipid peroxidation activity were also correlated to seasonal effects notably for B. monnieri.

Highlights

Natural products (NPs) play an important role as a source of various pharmaceuticals and biologically active substances
Three unprioritized compounds 9–11 were isolated and their anti-lipid peroxidation activity assayed. We found that these three compounds displayed very low inhibition of lipid peroxidation with the IC50 values > 1000 μM, indicating that the employed strategy was effective to highlight bioactive compounds from complex mixtures of NPs prior to isolation
The integration of multivariate data analysis (MVA) and multi-informative Molecular network analysis (MN) based on LC–HRMS2 metabolite profiling with bioactivity data was proven to be an efficient way to identify bioactive constituents in closely related plant extracts

Summary

Introduction

Natural products (NPs) play an important role as a source of various pharmaceuticals and biologically active substances. The discovery of new bioactive NPs is challenging because of the inherent complex composition of crude natural extracts. Such extracts contain hundreds, if not thousands, of chemical constituents and the purification and identification of bioactive NPs by conventional methods is a time consuming multi-step procedure. Bioactive substances can be lost during purification and effort can be wasted in the unnecessary re-isolation of known. NPs. it is important to pinpoint bioactive candidates and recognize known metabolites (dereplication) early in the purification process in order to avoid the redundant isolation of known molecules [1,2].

Methods

Discussion

Conclusion