Integration of Biochemometrics and Molecular Networking to Identify Antimicrobials in Angelica keiskei

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 towards 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 Koidzumi 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, 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.

Synergy and antagonism in natural product extracts: when 1 + 1 does not equal 2

“…159 This study found that data transformation, contaminant filtering, and model simplification tools had major impacts on the selectivity ratio models, emphasizing the importance of proper data processing approaches for extracting reliable information from biochemometric datasets. 159 In all selectivity ratio studies applied to identify bioactive natural products, 146,159,160 bioactive mixture constituents were identified early in the fractionation process, enabling chromatographic isolation efforts to be tailored to mixture constituents that were most likely to possess bioactivity.…”

“…(Apiaceae) and the molecular families to which they belonged. 160 Using this approach, a subset of chalcone analogs were targeted for isolation, yielding two known antimicrobial constituents and an additional, low-abundance compound not previously known to possess antimicrobial activity. 160 This concept was streamlined into a process called “bioactive molecular networking,” in which bioactivity predictions are directly visualized in molecular networks themselves, where the size of individual nodes correspond to the predicted bioactivity score for each ion (Fig.…”

Selection and characterization of botanical natural products for research studies: a NaPDI center recommended approach

“…[162][163][164] PLS modeling has been adapted to identify individual (or several) metabolites that are predicted to be responsible for the bioactivity of a complex natural product mixture. 77,165,166 For the NaPDI Center study of green tea, a biochemometric approach was used to predict which catechins were responsible for the in vitro inhibition of intestinal UDP-glucuronosyltransferases (UGTs). The selectivity ratio [167][168][169] was used as a metric to demonstrate the extent to which a given mixture constituent was associated with biological activity.…”

Autophagy induction by xanthoangelol exhibits anti‐metastatic activities in hepatocellular carcinoma

“…23 XAG, an active constituent isolated from the Japanese koidzumi (Ashitaba) herb, has recently attracted attention for its extensive pharmacological range, including anti-inflammatory, anti-microbials, anti-platelet, and antioxidant activities. [24][25][26][27] The anti-tumor effects of XAG have also been recognized against huamn maliganacies, and it mainly acts via the inhibition of cell proliferation, migration, invasion, and angiogenesis, as well as induction of apoptosis. [28][29][30] In contrast to the findings of Akihisa et al, 31 we found no cytotoxic effect of XAG on HCC cells.…”