Although some herbal remedies have been used for decades, little is known about the active compounds and the mechanism of action. Many natural products, such as glycosides, can be considered as prodrugs, which become active after biotransformation. To optimize the workflow of in vitro biotransformation followed by automated data analysis, hederacoside C was used as a model compound for saponins. Hederacoside C was subjected to gastrointestinal enzymes and fecal microflora. Samples were analyzed with UHPLC-PDA-HRMS before, during and after in vitro biotransformation, which allowed the monitoring of the relative abundances of the compound and its metabolites. The data-analysis workflow was optimized to render as much information as possible from the longitudinal LCMS data. XCMS was used to convert the raw data into features via peak-picking, followed by grouping, and EDGE was used for the extraction of significant differential profiles. To evaluate if the workflow was suitable for dynamic multiclass metabolomics data, an interactive Shiny web app was developed in R to rate the quality of the resulting features. These ratings were used to train a random forest model for predicting experts response. A performance analysis revealed that the random forest model was capable of correctly predicting the reviewers response in most cases (AUC 0.926 with 10 fold cross validation). The automated data analysis workflow was used for unbiased screening for metabolites and revealed the biotransformation of hederacoside C. As expected, a decrease in relative abundance of hederacoside C was observed over time. Additionally, the relative abundance of metabolites increased, illustrating the biotransformation of hederacoside C, especially in the colon phase, where microbial fermentation takes place. Stepwise progressive elimination of sugar moieties was the major metabolic pathway.
Keywords: Biotransformation; Data-analysis; Hederacoside C; Metabolomics.
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