Herniaria hirsuta L. (Caryophyllaceae) is used for treatment of urinary stones and as a diuretic. Little is known about the active compounds and the mechanism of action. The phytochemical composition of H. hirsuta was comprehensively characterized using UHPLC-UV-HRMS (Ultrahigh-Performance Liquid Chromatography-Ultraviolet-High Resolution Mass Spectrometry) data. An in vitro gastrointestinal model was used to simulate biotransformation, which allowed the monitoring of the relative abundances of individual compounds over time. To analyze the longitudinal multiclass LC–MS data, XCMS, a platform that enables online metabolomics data processing and interpretation, and EDGE, a statistical method for time series data, were used to extract significant differential profiles from the raw data. An interactive Shiny app in R was used to rate the quality of the resulting features. These ratings were used to train a random forest model. The most abundant aglycone after gastrointestinal biotransformation was subjected to hepatic biotransformation using human S9 fractions. A diversity of compounds was detected, mainly saponins and flavonoids. Besides the known saponins, 15 new saponins were tentatively identified as glycosides of medicagenic acid, acetylated medicagenic acid and zanhic acid. It is suggested that metabolites of phytochemicals present in H. hirsuta, most likely saponins, are responsible for the pharmaceutical effects. It was observed that the relative abundance of saponin aglycones increased, indicating loss of sugar moieties during colonic biotransformation, with medicagenic acid as the most abundant aglycone. Hepatic biotransformation of this aglycone resulted in different metabolites formed by phase I and II reactions.

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

(1) Background: Filipendula ulmaria (L.) Maxim. (Rosaceae) (meadowsweet) is widely used in phytotherapy against inflammatory diseases. However, its active constituents are not exactly known. Moreover, it contains many constituents, such as flavonoid glycosides, which are not absorbed, but metabolized in the colon by gut microbiota, producing potentially active metabolites that can be absorbed. The aim of this study was to characterize the active constituents or metabolites. (2) Methods: A F. ulmaria extract was processed in an in vitro gastrointestinal biotransformation model, and the metabolites were characterized using UHPLC-ESI-QTOF-MS analysis. In vitro anti-inflammatory activity was evaluated by testing the inhibition of NF-κB activation, COX-1 and COX-2 enzyme inhibition. (3) Results: The simulation of gastrointestinal biotransformation showed a decrease in the relative abundance of glycosylated flavonoids such as rutin, spiraeoside and isoquercitrin in the colon compartment, and an increase in aglycons such as quercetin, apigenin, naringenin and kaempferol. The genuine as well as the metabolized extract showed a better inhibition of the COX-1 enzyme as compared to COX-2. A mix of aglycons present after biotransformation showed a significant inhibition of COX-1. (4) Conclusions: The anti-inflammatory activity of F. ulmaria may be explained by an additive or synergistic effect of genuine constituents and metabolites.