TY - JOUR
T1 - Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures
AU - Caesar, Lindsay K.
AU - Kellogg, Joshua J.
AU - Kvalheim, Olav M.
AU - Cech, Nadja B.
N1 - Funding Information:
This research was supported by the National Center for Complementary and Integrative Health of the National Institutes of Health under grant numbers 5 T32 AT008938 and 1R01 AT006860. Additionally, the authors would like to acknowledge R. Cech for his provision of plant material, Dr. A. Horswill for the provision of microbial strains, and S. Knowles for her assistance with NMR analysis. Mass spectrometry analyses were conducted in the Triad Mass Spectrometry Facility at the University of North Carolina at Greensboro (https://chem.uncg.edu/triadmslab/). This work was performed in part at the Joint School of Nanoscience and Nanoengineering, a member of the Southeastern Nanotechnology Infrastructure Corridor (SENIC) and National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant ECCS-1542174).
Publisher Copyright:
Copyright © 2019 American Chemical Society and American Society of Pharmacognosy.
PY - 2019/3/22
Y1 - 2019/3/22
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.jnatprod.9b00176
DO - 10.1021/acs.jnatprod.9b00176
M3 - Article
C2 - 30844279
AN - SCOPUS:85063322345
VL - 82
SP - 469
EP - 484
JO - Journal of Natural Products
JF - Journal of Natural Products
SN - 0163-3864
IS - 3
ER -