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.

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/.

Structural Diversification of Pyrazinone Metabolites via Spontaneous Oxa-Michael Addition in Staphylococcus xylosus.

A family of pyrazinone metabolites (1-11) were characterized from Staphylococcus xylosus ATCC 29971. Six of them were hydroxylated or methoxylated, which were proposed to be produced by the rare noncatalytic oxa-Michael addition reaction with a water or methanol molecule. It was confirmed that isopropyl alcohol can also be the Michael donor of the reaction. 1-7 and the synthetic precursor 2a showed significant inhibition of breast cancer cell migration.

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

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.

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.

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.

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.

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.

FgPKS7 is an essential player in mating-type-mediated regulatory pathway required for completing sexual cycle in Fusarium graminearum.

Secondary metabolism is intimately linked to developmental processes in filamentous fungi. In a previous study, we revealed that several polyketide synthase (PKS) genes, including FgPKS7, are specifically induced during formation of the sexual fruiting body (perithecium) in the cereal pathogen Fusarium graminearum. The function of PKS7, which is essential for perithecial development and hyphal growth, is interchangeable between two phylogenetically related species, F. graminearum and F. asiaticum, but not conserved in the more distantly related species F. fujikuroi and F. neocosmosporiellum. FgPKS7 is under the control of global or upstream regulators including the mating-type (MAT) locus and regulates numerous downstream genes that are transcriptionally specific to and functionally essential for sexual development, several other PKS genes, and ABC transporter genes for azole resistance in F. graminearum. FgPKS7 is an essential element for proper sexual development and participates in a regulatory network controlled by the MAT locus. Although the chemical identity of FgPKS7 remains unclear, FgPKS7 is likely involved in chemical reaction(s) for synthesis of metabolite(s) that control or promote perithecial maturation in F. graminearum. This study provides in-depth insights into the direct role of secondary metabolites in sexual development of filamentous fungi.

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.

Species Prioritization Based on Spectral Dissimilarity: A Case Study of Polyporoid Fungal Species.

Biological species collections are critical for natural product drug discovery programs. However, prioritization of target species in massive collections remains difficult. Here, we introduce an untargeted metabolomics-based prioritization workflow that uses MS/MS molecular networking to estimate scaffold-level distribution. As a demonstration, we applied the workflow to 40 polyporoid fungal species. Nine species were prioritized as candidates based on the chemical structural and compositional similarity (CSCS) metric. Most of the selected species showed relatively higher richness and uniqueness of metabolites than those of the others. Cryptoporus volvatus, one of the prioritized species, was investigated further. The chemical profiles of the extracts of C. volvatus culture and fruiting bodies were compared, and it was shown that derivative-level diversity was higher in the fruiting bodies; meanwhile, scaffold-level diversity was similar. This showed that the compounds found from a cultured fungus can also be isolated in wild mushrooms. Targeted isolation of the fruiting body extract yielded three unknown (1-3) and six known (4-9) cryptoporic acid derivatives, which are drimane-type sesquiterpenes with isocitric acid moieties that have been reported in this species. Cryptoporic acid T (1) is a trimeric cryptoporic acid reported for the first time. Compounds 2 and 5 exhibited cytotoxicity against HCT-116 cell lines with IC50 values of 4.3 and 3.6 µM, respectively.

Comprehensive mass spectrometry-guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae.

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped the diversity of specialized metabolites, which then drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating the diversity of specialized metabolites in an evolutionary context have been limited by the impossibility of identifying chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites and apply it to over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5% based on spectral library matches alone to 42.6% of total MS/MS molecular features, extending annotations from well-known plant compound classes into dark plant metabolomics. To gain insights into substructural diversity within this plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations for the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.

Combined MS/MS-NMR Annotation Guided Discovery of Iris lactea var. chinensis Seed as a Source of Viral Neuraminidase Inhibitory Polyphenols.

In this study, the chemical diversity of polyphenols in Iris lactea var. chinensis seeds was identified by combined MS/MS-NMR analysis. Based on the annotated chemical profile, the isolation of stilbene oligomers was conducted, and consequently, stilbene oligomers (1-10) were characterized. Of these, compounds 1 and 2 are previously undescribed stilbene dimer glycoside (1) and tetramer glycoside (2), respectively. Besides, to evaluate this plant seed as a rich source of stilbene oligomers, we quantified three stilbene oligomers of I. lactea var. chinensis seeds. The contents of three major stilbene oligomers-trans-ε-viniferin (3), vitisin A (6), and vitisin B (9)-in I. lactea var. chinensis seeds were quantified as 2.32 (3), 4.95 (6), and 1.64 (9) mg/g dry weight (DW). All the isolated compounds were tested for their inhibitory activities against influenza neuraminidase. Compound 10 was found to be active with the half maximal inhibitory concentration (IC50) values at 4.76 µM. Taken together, it is concluded that I. lactea var. chinensis seed is a valuable source of stilbene oligomers with a human health benefit.

Molecular Networking Reveals the Chemical Diversity of Selaginellin Derivatives, Natural Phosphodiesterase-4 Inhibitors from Selaginella tamariscina.

Selaginellins are unique pigments found in the genus Selaginella, the largest genus of Lycopodiophyta. Recent studies reported that some selaginellin analogues have potent phosphodiesterase-4 (PDE4) inhibitory activity. In this study, the chemical diversity of natural selaginellin derivatives was revealed by an MS/MS molecular networking-based dereplication of the Selaginella tamariscina extract. It led to the prioritization of chromatographic peaks predicted as previously unknown selaginellin derivatives. Targeted isolation of these compounds afforded two unusual selaginellin analogues with a 1H,3H-dibenzo[de,h]isochromene skeleton, namely, selariscins A (1) and B (2), along with eight new diarylfluorene derivatives, selaginpulvilins M-T (3-10), and five known analogues, 11-15. The absolute configurations of 1, 2, and 8-10 were elucidated by spectroscopic data analyses including computational electronic circular dichroism data. Compounds 1 and 3-10 showed PDE4 inhibitory activity with IC50 values in the range of 2.8-33.8 µM, and their binding modes are suggested by a molecular docking study.

Identification and Semi-Synthesis of 3-O-Protocatechuoylceanothic Acid, a Novel and Natural GPR120 Agonist †.

The identification and three step synthesis of 3-O-protocatechuoylceanothic acid, a novel and natural GPR120 agonist, is described. This ceanothane-type triterpenoid was identified from the components of Ziziphus jujuba roots and was found to be a new GPR120 agonist with a novel structure. We synthetically converted ceanothic acid, which does not have GPR120 agonist activity, into 3-O-protocatechuoylceanothic acid in three steps. In addition, we present the corrected NMR spectrum of 3-O-protocatechuoylceanothic acid based on our synthesis.

Multiple Targets of 3-Dehydroxyceanothetric Acid 2-Methyl Ester to Protect Against Cisplatin-Induced Cytotoxicity in Kidney Epithelial LLC-PK1 Cells.

Chronic exposure to cisplatin, a potent anticancer drug, causes irreversible kidney damage. In this study, we investigated the protective effect and mechanism of nine lupane- and ceanothane-type triterpenoids isolated from jujube (Ziziphus jujuba Mill., Rhamnaceae) on cisplatin-induced damage to kidney epithelial LLC-PK1 cells via mitogen-activated protein kinase (MAPK) and apoptosis pathways. Cisplatin-induced LLC-PK1 cell death was most significantly reduced following treatment with 3-dehydroxyceanothetric acid 2-methyl ester (3DC2ME). Additionally, apoptotic cell death was significantly reduced. Expression of c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38 was markedly suppressed by 3DC2ME, indicating inhibition of the MAPK pathway. Treatment with 3DC2ME also significantly reduced expression of active caspase-8 and -3, Bcl-2-associated X protein (Bax), and B cell lymphoma 2 (Bcl-2), indicating the inhibition of apoptosis pathways in the kidneys. We also applied the network pharmacological analysis and identified multiple targets of 3DC2ME related to MAPK signaling pathway and apoptosis.

Genome and evolution of the shade-requiring medicinal herb Panax ginseng.

Panax ginseng C. A. Meyer, reputed as the king of medicinal herbs, has slow growth, long generation time, low seed production and complicated genome structure that hamper its study. Here, we unveil the genomic architecture of tetraploid P. ginseng by de novo genome assembly, representing 2.98 Gbp with 59 352 annotated genes. Resequencing data indicated that diploid Panax species diverged in association with global warming in Southern Asia, and two North American species evolved via two intercontinental migrations. Two whole genome duplications (WGD) occurred in the family Araliaceae (including Panax) after divergence with the Apiaceae, the more recent one contributing to the ability of P. ginseng to overwinter, enabling it to spread broadly through the Northern Hemisphere. Functional and evolutionary analyses suggest that production of pharmacologically important dammarane-type ginsenosides originated in Panax and are produced largely in shoot tissues and transported to roots; that newly evolved P. ginseng fatty acid desaturases increase freezing tolerance; and that unprecedented retention of chlorophyll a/b binding protein genes enables efficient photosynthesis under low light. A genome-scale metabolic network provides a holistic view of Panax ginsenoside biosynthesis. This study provides valuable resources for improving medicinal values of ginseng either through genomics-assisted breeding or metabolic engineering.

Targeted Isolation of Neuroprotective Dicoumaroyl Neolignans and Lignans from Sageretia theezans Using in Silico Molecular Network Annotation Propagation-Based Dereplication.

The integration of LC-MS/MS molecular networking and in silico MS/MS fragmentation is an emerging method for dereplication of natural products. In the present study, a targeted isolation of natural products using a new in silico-based annotation tool named Network Annotation Propagation (NAP) is described. NAP improves accuracy of in silico fragmentation analyses by reranking candidate structures based on the network topology from MS/MS-based molecular networking. Annotation for the MS/MS spectral network of the Sageratia theezans twig extract was performed using NAP, and most molecular families within the network, including the known triterpenoids 1-7, could be putatively annotated, without relying on any previous reports of molecules from this species. Based on the in silico dereplication results, molecules were prioritized for isolation. In total, six dicoumaroyl 8- O-4' neolignans (8-13) and three dicoumaroyl lignans (14-16) were isolated from the twigs of S. theezans and structurally characterized by spectroscopic analyses. Isolates were evaluated for their neuroprotective activity, and compounds 14-16 showed potent protective effects against glutamate-induced oxidative stress in mouse HT22 cells at a concentration of 12.5 µM.