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

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Authors

Jeong, Eunah; Kim, Wonyong; Son, Seungju; Yang, Sungyeon; Gwon, Dasom; Hong, Jihee; Cho, Yoonhee; Jang, Chang-Young; Steinegger, Martin; Lim, Young Woon; Kang, Kyo Bin

Issue Date
2023-07
Publisher
National Academy of Sciences
Citation
Proceedings of the National Academy of Sciences of the United States of America, Vol.120 No.28, p. e2301007120
Abstract
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.
ISSN
0027-8424
URI
https://hdl.handle.net/10371/202505
DOI
https://doi.org/10.1073/pnas.2301007120
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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