Mass spectrometry-based ginsenoside profiling: Recent applications, limitations, and perspectives.

Ginseng, the roots of Panax species, is an important medicinal herb used as a tonic. As ginsenosides are key bioactive components of ginseng, holistic chemical profiling of them has provided many insights into understanding ginseng. Mass spectrometry has been a major methodology for profiling, which has been applied to realize numerous goals in ginseng research, such as the discrimination of different species, geographical origins, and ages, and the monitoring of processing and biotransformation. This review summarizes the various applications of ginsenoside profiling in ginseng research over the last three decades that have contributed to expanding our understanding of ginseng. However, we also note that most of the studies overlooked a crucial factor that influences the levels of ginsenosides: genetic variation. To highlight the effects of genetic variation on the chemical contents, we present our results of untargeted and targeted ginsenoside profiling of different genotypes cultivated under identical conditions, in addition to data regarding genome-level genetic diversity. Additionally, we analyze the other limitations of previous studies, such as imperfect variable control, deficient metadata, and lack of additional effort to validate causation. We conclude that the values of ginsenoside profiling studies can be enhanced by overcoming such limitations, as well as by integrating with other -omics techniques.

The Depside Derivative Pericodepside Inhibits Cancer Cell Metastasis and Proliferation by Suppressing Epithelial-Mesenchymal Transition.

A depside derivative, named pericodepside (2), along with the known depside proatranorin III (1), was isolated from the solid cultivation of an Ascochyta rabiei strain that heterologously expresses atr1 and atr2 that are involved in the biosynthesis of atranorin in a fruticose lichen, Stereocaulon alpinum. The structure of 2 was determined by 1D and 2D NMR and MS spectroscopic data. The structure of 2 consisted of a depside-pericosine conjugate, with the depside moiety being identical to that found in 1, suggesting that 1 acted as an intermediate during the formation of 2 through the esterification process. Pericodepside (2) strongly suppressed cell invasion and proliferation by inhibiting epithelial-mesenchymal transition and the transcriptional activities of ß-catenin, STAT, and NF-κB in U87 (glioma cancer), MCF-7 (breast cancer), and PC3 (prostate cancer) cell lines.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Correction: Genetic and chemical markers for authentication of three Artemisia species: A. capillaris, A. gmelinii, and A. fukudo.

[This corrects the article DOI: 10.1371/journal.pone.0264576.].

A fatty acid-rich fraction of an endolichenic fungus Phoma sp. suppresses immune checkpoint markers via AhR/ARNT and ESR1.

Lung cancer has the highest mortality rates worldwide. The disease is caused by environmental pollutants, smoking, and many other factors. Recent treatments include immunotherapeutics, which have shown some success; however, the search for new therapeutics is ongoing. Endolichenic fungi produce a whale of a lot of secondary metabolites, the therapeutic effects of which are being evaluated. Here, we used a crude extract and subfractions of the endolichenic fungus, Phoma sp. (EL006848), isolated from the Pseudevernia furfuracea. It was identified the fatty acid components, palmitic acid, stearic acid, and oleic acid, exist in subfractions E1 and E2. In addition, EL006848 and its fatty acids fractions suppressed benzo[a]pyrene (an AhR ligand)- induced expression of PD-L1 to inhibit the activity of multiple immune checkpoints. E2 subfraction, which had a higher fatty acid content than E1, downregulated expression of AhR/ARNT and several human transcription factors related to ESR1. Moreover, E2 showed a strong inhibitory effect on STAT3 expression and mild effect on NF-kB activity. These results suggest that fatty acids extracted from an endolichenic fungus can exert strong immunotherapeutic effects.

Discovery of Naphthol Tetramers from Endolichenic Fungus Daldinia childiae 047219 Based on MS/MS Molecular Networking.

Feature-based molecular networking analysis suggested the presence of naphthol tetramers in Daldinia childae 047219, the same species but a different strain from one used previously for the discovery of naphthol trimers promoting adiponectin synthesis. The new tetramers were composed of 5-methoxy-4-naphthol, each of which was connected to one another in various positions. Targeted isolation afforded six previously unreported naphthol tetramers (1-6) together with 13 known polyketides (7-19) including naphthol monomers, dimers, and trimers. Structures of the isolated compounds were established by using NMR and mass spectroscopic analysis. Nodulisporin A (13), nodulisporin B (14), and 1,1',3',3″-ternaphthalene-5,5',5″-trimethoxy-4,4',4″-triol (16) demonstrated anti-inflammatory activities against NO production, but the new compounds were less active.

Qualitative metabolomics-based characterization of a phenolic UDP-xylosyltransferase with a broad substrate spectrum from Lentinus brumalis.

Wood-decaying fungi are the major decomposers of plant litter. Heavy sequencing efforts on genomes of wood-decaying fungi have recently been made due to the interest in their lignocellulolytic enzymes; however, most parts of their proteomes remain uncharted. We hypothesized that wood-decaying fungi would possess promiscuous enzymes for detoxifying antifungal phytochemicals remaining in the dead plant bodies, which can be useful biocatalysts. We designed a computational mass spectrometry-based untargeted metabolomics pipeline for the phenotyping of biotransformation and applied it to 264 fungal cultures supplemented with antifungal plant phenolics. The analysis identified the occurrence of diverse reactivities by the tested fungal species. Among those, we focused on O-xylosylation of multiple phenolics by one of the species tested, Lentinus brumalis. By integrating the metabolic phenotyping results with publicly available genome sequences and transcriptome analysis, a UDP-glycosyltransferase designated UGT66A1 was identified and validated as an enzyme catalyzing O-xylosylation with broad substrate specificity. We anticipate that our analytical workflow will accelerate the further characterization of fungal enzymes as promising biocatalysts.

Phthalides Isolated from the Endolichenic Arthrinium sp. EL000127 Exhibits Antiangiogenic Activity.

Endolichenic fungi (ELF) produce specialized metabolites that have various medicinal properties. Inhibition of tumor angiogenesis efficaciously suppresses many types of cancer. This study aimed to discover novel antiangiogenic agents from specialized metabolite extracts of ELF strains isolated from Korean lichens. The EtOAc extracts of 51 ELF strains were subjected to a screening pipeline consisting of cell viability, scratch wound healing, and Transwell migration assays. The EtOAc extract of Arthrinium sp. EL000127 showed the most potent inhibitory activity against the chemotactic migration of human umbilical vein endothelial cells (HUVEC). Targeted isolation on the major LC-MS peaks exhibited a previously known phthalide, 3-O-methylcyclopolic acid (1), and two unknown analogues of 1, 3-O-phenylethylcyclopolic acid (2) and 3-O-p-hydroxyphenylethylcyclopolic acid (3). The structures were characterized by MS and NMR analyses. All the isolates were acquired and applied to bioassays as racemates due to spontaneous racemization. Among the isolates, compound 3 effectively inhibits HUVEC motility by suppressing mRNA expressions of genes regulating epithelial cell survival and motility, which suggested that compound 3 is a potent antiangiogenic agent suitable for further exploration as a potential novel therapeutic against cancers.

Comparative transcriptome and metabolome analyses of four Panax species explore the dynamics of metabolite biosynthesis.

Background: The genus Panax in the Araliaceae family has been used as traditional medicinal plants worldwide and is known to biosynthesize ginsenosides and phytosterols. However, genetic variation between Panax species has influenced their biosynthetic pathways is not fully understood. Methods: Simultaneous analysis of transcriptomes and metabolomes obtained from adventitious roots of two tetraploid species (Panax ginseng and P. quinquefolius) and two diploid species (P. notoginseng and P. vietnamensis) revealed the diversity of their metabolites and related gene expression profiles. Results: The transcriptome analysis showed that 2,3-OXIDOSQUALENE CYCLASEs (OSCs) involved in phytosterol biosynthesis are upregulated in the diploid species, while the expression of OSCs contributing to ginsenoside biosynthesis is higher in the tetraploid species. In agreement with these results, the contents of dammarenediol-type ginsenosides were higher in the tetraploid species relative to the diploid species. Conclusion: These results suggest that a whole-genome duplication event has influenced the triterpene biosynthesis pathway in tetraploid Panax species during their evolution or ecological adaptation. This study provides a basis for further efforts to explore the genetic variation of the Panax genus.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

An acetonic extract and secondary metabolites from the endolichenic fungus Nemania sp. EL006872 exhibit immune checkpoint inhibitory activity in lung cancer cell.

Background: Endolichenic fungi (ELF), which live the inside the lichen thallus, contain many secondary metabolites that show various biological activities. Recent studies show that lichen and ELF secondary metabolites have antioxidant, antibacterial, antifungal, cytotoxic, and anticancer activities. Purpose: Here, the effects of an ELF extract and its bioactive compounds were investigated on the H1975 cell line focusing on immune checkpoint marker inhibition. Methods: An ELF was isolated from the host lichen Bryoria fuscescens (Gyelnik) Brodo and D. Hawksw and identified the species as Nemania sp. EL006872. The fungus was cultured on agar medium and acetonic extracts were obtained. Secondary metabolites radianspenes C and D, and dahliane D, were isolated from the crude extract. The biological effects of both the crude extract and the isolated secondary metabolites were evaluated in cell viability, qRT-PCR assays, flow cytometry analysis and western blotting. Results: The cell viability assay revealed that extracts from Nemania sp. EL006872 and the isolated secondary compounds had low cytotoxicity. The crude extract, radianspenes C and D, and dahliane D, suppressed expression of mRNA encoding PD-L1 and aromatic hydrocarbon receptor (AhR), and surface expression of PD-L1 protein by cells exposed to benzo[a] pyrene. Radianspenes C and D, and dahliane D, reduced expression of AhR, PD-L1, ICOSL, and GITRL proteins by H1975 lung cancer cells, as well as exerting anti-proliferative effects. Conclusion: Radianspenes C and D, and dahliane D, bioactive compounds isolated from Nemania sp. EL006872 ELF, have the potential for use as immunotherapy and immunoncology treatments.

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.

Identification of Antibacterial Sterols from Korean Wild Mushroom Daedaleopsis confragosa via Bioactivity- and LC-MS/MS Profile-Guided Fractionation.

As part of an ongoing natural product chemical research for the discovery of bioactive secondary metabolites with novel structures, wild fruiting bodies of Daedaleopsis confragosa were collected and subjected to chemical and biological analyses. We subjected the fractions derived from the methanol extract of the fruiting bodies of D. confragosa to bioactivity-guided fractionation because the methanol extract of D. confragosa showed antibacterial activity against Helicobacter pylori strain 51, according to our bioactivity screening. The n-hexane and dichloromethane fractions showed moderate to weak antibacterial activity against H. pylori strain 51, and the active fractions were analyzed for the isolation of antibacterial compounds. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis revealed that the n-hexane fraction contains several compounds which are absent in the other fractions, so the fraction was prioritized for further fractionation. Through chemical analysis of the active n-hexane and dichloromethane fractions, we isolated five ergosterol derivatives (1-5), and their chemical structures were determined to be demethylincisterol A3 (1), (20S,22E,24R)-ergosta-7,22-dien-3ß,5α,6ß-triol (2), (24S)-ergosta-7-ene-3ß,5α,6ß-triol (3), 5α,6α-epoxy-(22E,24R)-ergosta-7,22-dien-3ß-ol (4), and 5α,6α-epoxy-(24R)-ergosta-7-en-3ß-ol (5) by NMR spectroscopic analysis. This is the first report on the presence of ergosterol derivatives (1-5) in D. confragosa. Compound 1 showed the most potent anti-H. pylori activity with 33.9% inhibition, rendering it more potent than quercetin, a positive control. Compound 3 showed inhibitory activity comparable to that of quercetin. Distribution analysis of compound 1 revealed a wide presence of compound 1 in the kingdom Fungi. These findings indicate that demethylincisterol A3 (1) is a natural antibiotic that may be used in the development of novel antibiotics against H. pylori.

Genetic and chemical markers for authentication of three Artemisia species: A. capillaris, A. gmelinii, and A. fukudo.

The genus Artemisia is an important source of medicines in both traditional and modern pharmaceutics, particularly in East Asia. Despite the great benefits of herbal medicine, quality assessment methods for these medicinal herbs are lacking. The young leaves from Artemisia species are generally used, and most of the species have similar morphology, which often leads to adulteration and misuse. This study assembled five complete chloroplast genomes of three Artemisia species, two accessions of A. gmelinii and A. capillaris, and one A. fukudo. Through comparative analysis, we revealed genomic variations and phylogenetic relationships between these species and developed seven InDel-based barcode markers which discriminated the tested species from each other. Additionally, we analyzed specialized metabolites from the species using LC-MS and suggested chemical markers for the identification and authentication of these herbs. We expect that this integrated and complementary authentication method would aid in reducing the misuse of Artemisia species.

Cyclohumulanoid Sesquiterpenes Induced by the Noncompetitive Coculture of Phellinus orientoasiaticus and Xylodon flaviporus.

Microbial cocultivation has been applied as a strategy to induce the biosynthesis of specialized metabolites. However, most previous studies have focused on competitive interactions between test strains. During our LC-MS-based chemical screening of randomized cocultures of Basidiomycetous fungi, we discovered that the coculture of Phellinus orientoasiaticus (Hymenochaetaceae) and Xylodon flaviporus (Schizoporaceae) induces multiple metabolites, although they did not show any competitive morphology. Targeted isolation yielded three new sesquiterpenes (1-3) along with five known analogues (4-8). The structures of the isolates were determined by MS and NMR experiments as well as electronic circular dichroism analysis. LC-MS analysis suggested that cyclohumulanoids of illudane-, sterpurane-, and tremulane-type scaffolds (1-7) were produced by P. orientoasiaticus, whereas a drimane-type sesquiterpene (8) was produced by X. flaviporus. None of the isolates exhibited antifungal activity or cytotoxicity, and compounds 1-7 exhibited NO production of LPS-treated RAW276.4 cells in a range of 15.9% to 38.0% at 100 µM.

Tandem Mass Spectrometry Molecular Networking as a Powerful and Efficient Tool for Drug Metabolism Studies.

Molecular networking (MN) has become a popular data analysis method for untargeted mass spectrometry (MS)/MS-based metabolomics. Recently, MN has been suggested as a powerful tool for drug metabolite identification, but its effectiveness for drug metabolism studies has not yet been benchmarked against existing strategies. In this study, we compared the performance of MN, mass defect filtering, Agilent MassHunter Metabolite ID, and Agilent Mass Profiler Professional workflows to annotate metabolites of sildenafil generated in an in vitro liver microsome-based metabolism study. Totally, 28 previously known metabolites with 15 additional unknown isomers and 25 unknown metabolites were found in this study. The comparison demonstrated that MN exhibited performances comparable or superior to those of the existing tools in terms of the number of detected metabolites (27 known metabolites and 22 unknown metabolites), ratio of false positives, and the amount of time and effort required for human labor-based postprocessing, which provided evidence of the efficiency of MN as a drug metabolite identification tool.

Antioxidant and Anti-Inflammatory Effects of 3-Dehydroxyceanothetric Acid 2-Methyl Ester Isolated from Ziziphus jujuba Mill. against Cisplatin-Induced Kidney Epithelial Cell Death.

Cisplatin is a platinum-based chemotherapeutic agent for treating solid tumors; however, it presents a risk factor for nephropathy. In the present study, we investigated the antioxidant and anti-inflammatory effects of 3-dehydroxyceanothetric acid 2-methyl ester (3DC2ME) isolated from Ziziphus jujuba Mill. in LLC-PK1 cells following cisplatin-induced cytotoxicity. These cells were exposed to 3DC2ME for 2 h, followed by treatment with cisplatin for 24 h. The treated cells were subjected to cell viability analysis using the Ez-Cytox assay. Reactive oxygen species (ROS) were detected via 2', 7'- dichlorodihydrofluorescein diacetate (DCFH-DA) staining. In addition, western blotting and fluorescent immunostaining were performed to evaluate protein expressions related to oxidative stress and inflammation pathways. Pretreatment with 3DC2ME protected LLC-PK1 cells from cisplatin-induced cytotoxicity and oxidative stress. In addition, pretreatment with 3DC2ME upregulated heme oxygenase 1 (HO-1) via the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway in the cisplatin-treated LLC-PK1 cells. Furthermore, the increase in the expressions of IκB kinase α/ß (IKKα/ß), inhibitor of kappa B alpha (IκBα), nuclear factor kappa B (NF-κB), inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) in these cells was inhibited. These results provide basic scientific evidence for understanding the antioxidant and anti-inflammatory effects of 3DC2ME isolated from Z. jujuba against cisplatin-induced kidney epithelial cell death.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.