Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

Combining the Strengths of MS and NMR in Biochemometrics: A Case Study on Buddleja officinalis

Biochemometrics has emerged as promising strategy for the targeted identification of bioactive constituents from natural sources. It is based on the correlation of bioactivity data with chemical data to reveal constituents contributing to activity. Providing complementary data and structural information, MS- and NMR-based biochemometric approaches have both been separately applied in the past. The herein presented study is dedicated to the evaluation of a combined MS- and NMR-based biochemometric workflow for the unambiguous identification of bioactives. As an example, a flower extract of Buddleja officinalis Maxim. was selected to unravel bioactive constituents in the context of dry eye disease pathology. While NMR-based biochemometrics relies on heterocovariance analysis (HetCA) of 1H NMR spectra using the previously established ELINA approach, a biochemometric molecular network was generated for the MS-based approach. Both analyses were performed in parallel and were ultimately combined to increase their power to identify the bioactive constituents from the complex mixture. As a result, phenylethanoid glycosides and triterpene saponins were discovered as main contributors for the antioxidant and cytotoxic effects of the extract. This article illustrates the advantages, opportunities, and limitations of MS and NMR in the context of biochemometrics.

Efficient constitution of a library of rotenoid analogs active against Trypanosoma cruzi from a digitalized plant extract collection

Natural products (NP) have proven to be a rich source of potentially bioactive compounds, and metabolomics is the current method of choice for characterizing natural extracts. To integrate the vast amount of data and information produced by modern metabolomics workflows, we recently developed a sample-centric approach for the semantic enrichment and alignment of metabolomics datasets. The resulting Experimental Natural Products Knowledge Graph (ENPKG) is queryable and integrates both newly acquired digitalized experimental data and information, and previously reported knowledge. It allows the highlighting of putative bioactive compounds at the extract level by comparing, for example, the occurrence of compounds of a given chemical class with bioactivity results. Using this approach, we recently described potent anti-Trypanosoma cruzi activity of two rotenoids, deguelin and rotenone. These compounds were identified in six active extracts from four plant species: Cnestis palala (Connaraceae), Chadsia grevei, Pachyrhizus erosus, and Desmodium heterophylum (Fabaceae). In this work, we present the results of the phytochemical investigation of four of these extracts and the establishment of a library of structural analogs for in vitro bioactivity testing. This work led to the isolation, characterization, and biological evaluation of the anti-T. cruzi potential of 41 compounds, including 11 rotenoids and seven compounds reported for the first time. Thanks to modern metabolite annotation and single-step isolation procedures, this work also demonstrates the possibility of considering natural extract libraries as a reservoir of rapidly accessible pure NPs. This perspective could increase the options for NP research and help accelerate NP drug discovery efforts.

: A trategy to nalyze lant Extracts Taste n epth. Application to the complex taste of Swertia chirayita (Roxb.) H.Karst

Analyzing bitterness is challenging because of the diverse range of bitter compounds, the variability in sensory perception, and its complex interaction with other tastes. To address this, we developed an untargeted approach to deconvolute the taste and molecular composition of complex plant extracts. We applied our methodology to an ethanolic extract of Swertia chirayita (Roxb.) H.Karst., a plant recognized for its distinctive bitterness. Chemical characterization was performed through nuclear magnetic resonance spectroscopy experiments together with untargeted liquid chromatography-high resolution tandem mass spectrometry analysis coupled to a charged aerosol detector. After clustering the fractions based on chemical similarity, we performed free sensory analysis and classical descriptive analysis on each cluster. Our results confirmed the attribution of bitterness to iridoids and highlighted the role of other important compounds in the overall taste. This method provides a systematic approach for analyzing and potentially enhancing the taste profiles of plant-based beverages.

Comprehensive mass spectrometric metabolomic profiling of a chemically diverse collection of plants of the Celastraceae family

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family.