Chemical profiles and hypoglycemic activities of mulberry leaf extracts vary with ethanol concentration

Dual-target affinity analysis and separation of α-amylase and α-glucosidase inhibitors from Morus alba leaves using a magnetic bifunctional immobilized enzyme system

Morus alba leaves are a natural product with great antidiabetic potential. However, the therapeutic efficacy of natural products is usually achieved through the interaction of active compounds with specific targets. Among them, active compounds with multi-target therapeutic functions are more effective than single-target enzymes. In this study, a bienzyme system was constructed by co-immobilizing α-amylase and α-glucosidase onto Fe₃ O₄ for affinity screening of dual-target active components in the complex extract from M. alba leaves. As a result, a potential active compound was selectively screened by ligand fishing, separated by high-speed countercurrent chromatography using a solvent system of ethyl acetate-n-butanol-water (3:2:5, v/v), and identified as rutin. In addition, the result of molecular docking showed that rutin could interact with the active center of α-amylase and α-glucosidase through multiple hydrogen bonds, van der Waals forces, etc. to play an inhibitory role. These results demonstrate the effectiveness of the polydopamine magnetically immobilized bienzyme system for dual-target affinity screening of active substances. This study not only reveals the chemical basis of the antidiabetic activity of M. alba leaves from a dual-target perspective, but also promotes the progress of multitarget affinity screening.

A Narrative Review of Human Clinical Trials on the Impact of Phenolic-Rich Plant Extracts on Prediabetes and Its Subgroups

Phenolic-rich plant extracts have been demonstrated to improve glycemic control in individuals with prediabetes. However, there is increasing evidence that people with prediabetes are not a homogeneous group but exhibit different glycemic profiles leading to the existence of prediabetes subgroups. Prediabetes subgroups have been identified as: isolated impaired fasting glucose (IFG), isolated impaired glucose tolerance (IGT), and combined impaired fasting glucose and glucose intolerance (IFG/IGT). The present review investigates human clinical trials examining the hypoglycemic potential of phenolic-rich plant extracts in prediabetes and prediabetes subgroups. Artemisia princeps Pampanini, soy (Glycine max (L.) Merrill) leaf and Citrus junos Tanaka peel have been demonstrated to improve fasting glycemia and thus may be more useful for individuals with IFG with increasing hepatic insulin resistance. In contrast, white mulberry (Morus alba Linn.) leaf, persimmon (Diospyros kaki) leaf and Acacia. Mearnsii bark were shown to improve postprandial glycemia and hence may be preferably beneficial for individuals with IGT with increasing muscle insulin resistance. Elaeis guineensis leaf was observed to improve both fasting and postprandial glycemic measures depending on the dose. Current evidence remains scarce regarding the impact of the plant extracts on glycemic control in prediabetes subgroups and therefore warrants further study.

Investigation of Chemical Profiles of Different Parts of Morus alba Using a Combination of Molecular Networking Methods with Mass Spectral Data from Two Ionization Modes of LC/MS

Plants produce numerous secondary metabolites with diverse physicochemical properties. Because different parts of a single plant produce various components, several spectroscopic methods are necessary to inspect their chemical profiles. Mass spectral data are recognized as one of the most useful tools for analyzing components with a wide range of polarities. However, interpreting mass spectral data generated from positive and negative ionization modes is a challenging task because of the diverse chemical profiles of secondary metabolites. Herein, we combine and analyze mass spectral data generated in two ionization modes to detect as many metabolites as possible using the molecular networking approach. We selected different parts of a single plant, Morus alba (Moraceae), which are used in the functional food and medicinal herb industries. The mass spectral data generated from two ionization modes were combined and analyzed using various molecular networking workflows. We confirmed that our approach could be applied to simultaneously analyze the different types of secondary metabolites with different physicochemical properties.