- Discovery of novel glycoside hydrolases from C-glycoside-degrading bacteria using sequence similarity network analysis
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Bin Wei , Ya-Kun Wang , Jin-Biao Yu , Si-Jia Wang , Yan-Lei Yu , Xue-Wei Xu , Hong Wang
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J. Microbiol. 2021;59(10):931-940. Published online September 23, 2021
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DOI: https://doi.org/10.1007/s12275-021-1292-4
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 Abstract
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C-Glycosides are an important type of natural product with
significant bioactivities, and the C-glycosidic bonds of C-glycosides
can be cleaved by several intestinal bacteria, as exemplified
by the human faeces-derived puerarin-degrading bacterium
Dorea strain PUE. However, glycoside hydrolases in
these bacteria, which may be involved in the C-glycosidic bond
cleavage of C-glycosides, remain largely unknown. In this
study, the genomes of the closest phylogenetic neighbours of
five puerarin-degrading intestinal bacteria (including Dorea
strain PUE) were retrieved, and the protein-coding genes in
the genomes were subjected to sequence similarity network
(SSN) analysis. Only four clusters of genes were annotated as
glycoside hydrolases and observed in the genome of D. longicatena
DSM 13814T (the closest phylogenetic neighbour of
Dorea strain PUE); therefore, genes from D. longicatena DSM
13814T belonging to these clusters were selected to overexpress
recombinant proteins (CG1, CG2, CG3, and CG4) in
Escherichia coli BL21(DE3). In vitro assays indicated that
CG4 efficiently cleaved the O-glycosidic bond of daidzin and
showed moderate β-D-glucosidase and β-D-xylosidase activity.
CG2 showed weak activity in hydrolyzing daidzin and pNP-
β-D-fucopyranoside, while CG3 was identified as a highly
selective and efficient α-glycosidase. Interestingly, CG3 and
CG4 could be selectively inhibited by daidzein, explaining
their different performance in kinetic studies. Molecular docking
studies predicted the molecular determinants of CG2,
CG3, and CG4 in substrate selectivity and inhibition propensity.
The present study identified three novel and distinctive
glycoside hydrolases, highlighting the potential of SSN
in the discovery of novel enzymes from genomic data.
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Citations
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