Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures

• Published in: Journal of natural products (Washington, D.C.) Vol. 82; no. 3; pp. 469 - 484
• Main Authors: Caesar, Lindsay K, Kellogg, Joshua J, Kvalheim, Olav M, Cech, Nadja B
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society and American Society of Pharmacognosy 22.03.2019
• Subjects: Angelica - chemistry; Anti-Infective Agents - pharmacology; Biological Products - chemistry; Biological Products - pharmacology; Chromatography, Liquid - methods; Complex Mixtures - chemistry; Mass Spectrometry - methods; Metabolomics; Microbial Sensitivity Tests; Plant Roots - chemistry
• ISSN: 0163-3864; 1520-6025; 1520-6025
• DOI: 10.1021/acs.jnatprod.9b00176

Compounds derived from natural sources represent the majority of small-molecule drugs utilized today. Plants, owing to their complex biosynthetic pathways, are poised to synthesize diverse secondary metabolites that selectively target biological macromolecules. Despite the vast chemical landscape of botanicals, drug discovery programs from these sources have diminished due to the costly and time-consuming nature of standard practices and high rates of compound rediscovery. Untargeted metabolomics approaches that integrate biological and chemical data sets potentially enable the prediction of active constituents early in the fractionation process. However, data acquisition and data processing parameters may have major impacts on the success of models produced. Using an inactive botanical mixture spiked with known antimicrobial compounds, untargeted mass spectrometry-based metabolomics data were combined with bioactivity data to produce selectivity ratio models subjected to a variety of data acquisition and data processing parameters. Selectivity ratio models were used to identify active constituents that were intentionally added to the mixture, along with an additional antimicrobial compound, randainal (5), which was masked by the presence of antagonists in the mixture. These studies found that data-processing approaches, particularly data transformation and model simplification tools using a variance cutoff, had significant impacts on the models produced, either masking or enhancing the ability to detect active constituents in samples. The current study highlights the importance of the data processing step for obtaining reliable information from metabolomics models and demonstrates the strengths and limitations of selectivity ratio analysis to comprehensively assess complex botanical mixtures.