Wirkstoffe

Indole-3-carbinol (I3C) is a major dietary component produced in Brassica vegetables from glucosinolates (GLS) upon herbivores’ attack. The compound is gaining increasing interest due to its anticancer activity. However, reports about improving its level in plants or other sources are still rare. Unfortunately, I3C is unstable in acidic media and tends to polymerize rendering its extraction and detection challenging. This review presents a multifaceted overview of I3C regarding its natural occurrence, biosynthesis, isolation, and extraction procedure from dietary sources, and optimization for the best recovery yield. Further, an overview is presented on its metabolism and biotransformation inside the body to account for its health benefits and factors to ensure the best metabolic yield. Compile of the different analytical approaches for I3C analysis in dietary sources is presented for the first time, together with approaches for its detection and its metabolism in body fluids for proof of efficacy. Lastly, the chemopreventive effects of I3C and the underlying action mechanisms are summarized. Optimizing the yield and methods for the detection of I3C will assist for its incorporation as a nutraceutical or adjuvant in cancer treatment programs. Highlighting the complete biosynthetic pathway and factors involved in I3C production will aid for its future biotechnological production.

Coffee is a worldwide beverage of increasing consumption, owing to its unique flavor and several health benefits. Metabolites of coffee are numerous and could be classified on various bases, of which some are endogenous to coffee seeds, i.e., alkaloids, diterpenes, sugars, and amino acids, while others are generated during coffee processing, for example during roasting and brewing, such as furans, pyrazines, and melanoidins. As a beverage, it provides various distinct flavors, i.e., sourness, bitterness, and an astringent taste attributed to the presence of carboxylic acids, alkaloids, and chlorogenic acids. To resolve such a complex chemical makeup and to relate chemical composition to coffee effects, large-scale metabolomics technologies are being increasingly reported in the literature for proof of coffee quality and efficacy. This review summarizes the applications of various mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based metabolomics technologies in determining the impact of coffee breeding, origin, roasting, and brewing on coffee chemical composition, and considers this in relation to quality control (QC) determination, for example, by classifying defected and non-defected seeds or detecting the adulteration of raw materials. Resolving the coffee metabolome can aid future attempts to yield coffee seeds of desirable traits and best flavor types.

Seeds of domesticated Vicia (vetch) species (family Fabaceae-Faboideae) are produced and consumed worldwide for their nutritional value. Seed accessions belonging to 16 different species of Vicia—both domesticated and wild taxa—were subjected to a chemotaxonomic study using ultraperformance liquid chromatography–mass spectrometry (UPLC-MS) analyzed by chemometrics. A total of 89 metabolites were observed in the examined Vicia accessions. Seventy-eight out of the 89 detected metabolites were annotated. Metabolites quantified belonged to several classes, viz., flavonoids, procyanidins, prodelphinidins, anthocyanins, stilbenes, dihydrochalcones, phenolic acids, coumarins, alkaloids, jasmonates, fatty acids, terpenoids, and cyanogenics, with flavonoids and fatty acids amounting to the major classes. Flavonoids, fatty acids, and anthocyanins showed up as potential chemotaxonomic markers in Vicia species discrimination. Fatty acids were more enriched in Vicia faba specimens, while the abundance of flavonoids was the highest in Vicia parviflora. Anthocyanins allowed for discrimination between Vicia hirsuta and Vicia sepium. To the best of our knowledge, this is the first report on employing UPLC-MS metabolomics to discern the diversity of metabolites at the intrageneric level among Vicia species.

Salvadora persica L. (toothbrush tree, Miswak) is well recognized in most Middle Eastern and African countries for its potential role in dental care, albeit the underlying mechanism for its effectiveness is still not fully understood. A comparative MS and NMR metabolomics approach was employed to investigate the major primary and secondary metabolites composition of S. persica in context of its organ type viz., root or stem to rationalize for its use as a tooth brush. NMR metabolomics revealed its enrichment in nitrogenous compounds including proline-betaines i.e., 4-hydroxy-stachydrine and stachydrine reported for the first time in S. persica. LC/MS metabolomics identified flavonoids (8), benzylurea derivatives (5), butanediamides (3), phenolic acids (8) and 5 sulfur compounds, with 21 constituents reported for the first time in S. persica. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) of either NMR or LC/MS dataset clearly separated stem from root specimens based on nitrogenous compounds abundance in roots and is justifying for its preference as toothbrush versus stems. The presence of betaines at high levels in S. persica (9−12 μg/mg dry weight) offers novel insights into its functioning as an osmoprotectant that maintains the hydration of oral mucosa. Additionally, the previously described anti-inflammatory activity of stachydrine along with the antimicrobial effects of sulfonated flavonoids, benzylisothiocynate and ellagic acid derivatives are likely contributors to S. persica oral hygiene health benefits. Among root samples, variation in sugars and organic acids levels were the main discriminatory criterion. This study provides the first standardization of S. persica extract using qNMR for further inclusion in nutraceuticals.

Legume sprouts are a fresh nutritive source of phytochemicals of increasing attention worldwide owing to their many health benefits. Nuclear magnetic resonance (NMR) was utilized for the metabolite fingerprinting of 4 major legume sprouts, belonging to family Fabaceae, to be exploited for quality control purposes. Thirty-two metabolites were identified belonging to different classes, i.e., fatty acids, sugars, amino acids, nucleobases, organic acids, sterols, alkaloids, and isoflavonoids. Quantitative NMR was employed for assessing the major identified metabolite levels and multivariate data analysis was utilized to assess metabolome heterogeneity among sprout samples. Isoflavones were detected exclusively in Cicer sprouts, whereas Trigonella was characterized by 4-hydroxyisoleucine. Vicia sprouts were distinguished from other legume sprouts by the presence of L-Dopa versus acetate abundance in Lens. A common alkaloid in all sprouts was trigonelline, detected at 8–25 µg/mg, suggesting its potential role in legume seeds’ germination. Trigonelline was found at highest levels in Trigonella sprouts. The aromatic NMR region data (δ 11.0–5.0 ppm) provided a better classification power than the full range (δ 11.0–0.0 ppm) as sprout variations mostly originated from secondary metabolites, which can serve as chemotaxonomic markers.

Carob is a legume tree of a considerable commercial importance for the flavor and sweet industry. In this context, it is cultivated mostly for its pods, which are known for their nutritive value and multiple health benefits. However, metabolite patterns, underlying these properties are still mostly uncharacterized. In this study, the role of geographical origin, ontogenetic changes and thermal processing on the Ceratonia siliqua pod metabolome was assessed by mass spectrometry (MS)-based metabolomics. Thereby, a total of 70 fruits primary metabolites, represented mainly by carbohydrates, organic and amino acids were detected. Analysis of secondary bioactive metabolites assessed by ultra-high-performance liquid chromatography-electrospray ionization high resolution mass spectrometry (UHPLC-ESI-HR-MS) revealed in total 83 signals. The major signals, most significantly contributing in discrimination of C. siliqua specimens were assigned to tannins and flavonoids. PCA models derived from either UHPLC-MS or GC-MS proved to be powerful tools for discrimination of C. siliqua specimens.