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.

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.

plantMASST - Community-driven chemotaxonomic digitization of plants.

Understanding the distribution of hundreds of thousands of plant metabolites across the plant kingdom presents a challenge. To address this, we curated publicly available LC-MS/MS data from 19,075 plant extracts and developed the plantMASST reference database encompassing 246 botanical families, 1,469 genera, and 2,793 species. This taxonomically focused database facilitates the exploration of plant-derived molecules using tandem mass spectrometry (MS/MS) spectra. This tool will aid in drug discovery, biosynthesis, (chemo)taxonomy, and the evolutionary ecology of herbivore interactions.

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.