Identification of novel dimers and chemical profiling of acid amide alkaloids in Piper nigrum.

Peppers (Piper nigrum L.) are distinguished by their pungent flavor and aroma. Piperine is a major acid-amide alkaloid with a piperidine ring that gives pepper its flavor and scent. In plant metabolomics research, the accessibility of the chemical standards is critical for scientific credibility. We isolated and identified 10 novel dimers of acid amide alkaloids (9-15 and 20-22), along with 12 known monomers (1-6) and dimers (7, 8, 16-19) from black pepper. Subsequently, we found the distribution of monomers and dimers of acid amide alkaloids in black and white peppers by twenty-two acid amide alkaloids which we obtained using the molecular networking technique and multivariate analysis to reveal the molecular relationships between the acid amide alkaloids in black and white peppers. Our research delved into the chemical diversity of acid amide alkaloids in black and white peppers, which could help inform future culinary and potential medicinal utilization of pepper.

Mass spectrometry data on specialized metabolome of medicinal plants used in East Asian traditional medicine.

Traditional East Asian medicine not only serves as a potential source of drug discovery, but also plays an important role in the healthcare systems of Korea, China, and Japan. Tandem mass spectrometry (MS/MS)-based untargeted metabolomics is a key methodology for high-throughput analysis of the complex chemical compositions of medicinal plants used in traditional East Asian medicine. This Data Descriptor documents the deposition to a public repository of a re-analyzable raw LC-MS/MS dataset of 337 medicinal plants listed in the Korean Pharmacopeia, in addition to a reference spectral library of 223 phytochemicals isolated from medicinal plants. Enhanced by recently developed repository-level data analysis pipelines, this information can serve as a reference dataset for MS/MS-based untargeted metabolomic analysis of plant specialized metabolites.

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.

α-Glucosidic hydroquinone derivatives from Viburnum erosum.

Six undescribed compounds (1-6) were isolated from the leaves of Viburnum erosum along with four known compounds 7-10. The structures were determined by NMR and MS spectroscopic analyses, and their absolute configurations were established by chemical and spectroscopic methods. Compounds 1-6 were α-glucosidic hydroquinone derivatives with different linear monoterpenoid structures. Compounds 1-10 were also evaluated for their tyrosinase inhibitory activities, and 10 showed potent inhibition of tyrosinase enzyme with IC50 value of 37.9 µM compared to 47.6 µM of the positive control (ß-arbutin).

Targeted Isolation of Cytotoxic Sesquiterpene Lactones from Eupatorium fortunei by the NMR Annotation Tool, SMART 2.0.

Small Molecular Accurate Recognition Technology (SMART 2.0) has recently been introduced as a NMR-based machine learning tool for the discovery and characterization of natural products. We attempted targeted isolation of sesquiterpene lactones from Eupatorium fortunei with the aid of structural annotation by SMART 2.0 and chemical profiling. Eight germacrene-type (1-7 and 10) and two eudesmane-type sesquiterpene lactones (8 and 9) were isolated from the whole plant of Eupatorium fortunei. With the guidance of the results of the subfractions from E. fortunei obtained by SMART 2.0, their cytotoxic activities were evaluated against five cancer cells (SKOV3, A549, PC3, HEp-2, and MCF-7). Compounds 4 and 8 exhibited IC50 values of 3.9 ± 1.2 and 3.9 ± 0.6 µM against prostate cancer cells, PC3, respectively. Compound 7 showed good cytotoxicity with IC50 values of 5.8 ± 0.1 µM against breast cancer cells, MCF-7. In the present study, the rapid annotation of the mixture of compounds in a fraction by the NMR-based machine learning tool helped the targeted isolation of bioactive compounds from natural products.

The Rapid Discrimination and Quality Assessment of Three Zanthoxylum Species Using 1H NMR Spectrometry.

The husks and fruits of Zanthoxylum species (Rutaceae) are the popular pungent and spicy ingredients of foods and the traditional medicines in many countries. Three Zanthoxylum species, Z. bungeanum, Z. schinifolium, and Z. piperitum, are distributed and intermixed with each other as "Zanthoxyli Pericarpium" in Korean markets. In the present study, we analyzed the ethyl acetate-soluble and nonpolar fractions of Zanthoxylum samples by 1H NMR spectrometry and performed a multivariate analysis for finding the discriminant markers between three species. Xanthoxylin was identified as the metabolic marker for the discrimination of Zanthoxylum species and quantified by the qNMR approach.

Exploring novel secondary metabolites from natural products using pre-processed mass spectral data.

Many natural product chemists are working to identify a wide variety of novel secondary metabolites from natural materials and are eager to avoid repeatedly discovering known compounds. Here, we developed liquid chromatography/mass spectrometry (LC/MS) data-processing protocols for assessing high-throughput spectral data from natural sources and scoring the novelty of unknown metabolites from natural products. This approach automatically produces representative MS spectra (RMSs) corresponding to single secondary metabolites in natural sources. In this study, we used the RMSs of Agrimonia pilosa roots and aerial parts as models to reveal the structural similarities of their secondary metabolites and identify novel compounds, as well as isolation of three types of nine new compounds including three pilosanidin- and four pilosanol-type molecules and two 3-hydroxy-3-methylglutaryl (HMG)-conjugated chromones. Furthermore, we devised a new scoring system, the Fresh Compound Index (FCI), which grades the novelty of single secondary metabolites from a natural material using an in-house database constructed from 466 representative medicinal plants from East Asian countries. We expect that the FCIs of RMSs in a sample will help natural product chemists to discover other compounds of interest with similar chemical scaffolds or novel compounds and will provide insights relevant to the structural diversity and novelty of secondary metabolites in natural products.