Integration of biochemometrics and molecular networking to identify antimicrobials in Angelica keiskei
- UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
- Nadja B. Cech, Patricia A. Sullivan Distinguished Professor of Chemistry (Creator)
- Joshua J. Kellogg, NIH Postdoctoral Research Fellow (Creator)
- Institution
- The University of North Carolina at Greensboro (UNCG )
- Web Site: http://library.uncg.edu/
Abstract: Botanical medicines have been utilized for centuries. but it remains challenging to identify bioactive constituents from complex botanical extracts. Bioassay-guided fractionation is often biased toward abundant or easily isolatable compounds. To comprehensively evaluate active botanical mixtures, methods that allow for the prioritization of active compounds are needed. To this end, a method integrating bioassay-guided fractionation, biochemometric selectivity ratio analysis, and molecular networking was devised and applied to Angelica keiskei to comprehensively evaluate its antimicrobial activity against Staphylococcus aureus. This approach enabled the identification of putative active constituents early in the fractionation process and provided structural information for these compounds. A subset of chalcone analogs were prioritized for isolation. yielding 4-hydroxyderricin (1, minimal inhibitory concentration [MIC] = 4.6 µM, IC50 = 2.0 µM), xanthoangelol (2, MIC = 4.0 µM, IC50 = 2.3) and xanthoangelol K (4, IC50 = 168 µM). This approach allowed for the identification of a low-abundance compound (xanthoangelol K) that has not been previously reported to possess antimicrobial activity and facilitated a more comprehensive understanding of the compounds responsible for A. keiskei's antimicrobial activity.
Integration of biochemometrics and molecular networking to identify antimicrobials in Angelica keiskei
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Created on 4/11/2019
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Additional Information
- Publication
- Planta Medica 84(09/10), 721-728
- Language: English
- Date: 2018
- Keywords
- biochemometrics, chalcones, selectivity ratio, molecular networking, mass spectrometry, Angelica keiskei, Apiaceae