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A network-based approach to identify substrate classes of bacterial glycosyltransferases

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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Background: Bacterial interactions with the environment-and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity. Results: In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred. Conclusions: We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation.
Keywords
Network-based prediction, IBCN, Sequence-based prediction, Bacterial glycosylation, Glycosyltransferases, Lactobacillus rhamnosus GG, Campylobacter jejuni, LACTOBACILLUS-RHAMNOSUS GG, LINKED PROTEIN GLYCOSYLATION, UDP-N-ACETYLGLUCOSAMINE, GENOME SEQUENCE, O-GLYCOSYLATION, PRIMING GLYCOSYLTRANSFERASE, ESCHERICHIA-COLI, BINDING-PROTEIN, CELL-WALL, GLYCAN CHAINS

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Citation

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Chicago
Sánchez-Rodríguez, Aminael, Hanne LP Tytgat, Joris Winderickx, Jos Vanderleyden, Sarah Lebeer, and Kathleen Marchal. 2014. “A Network-based Approach to Identify Substrate Classes of Bacterial Glycosyltransferases.” Bmc Genomics 15.
APA
Sánchez-Rodríguez, A., Tytgat, H. L., Winderickx, J., Vanderleyden, J., Lebeer, S., & Marchal, K. (2014). A network-based approach to identify substrate classes of bacterial glycosyltransferases. BMC GENOMICS, 15.
Vancouver
1.
Sánchez-Rodríguez A, Tytgat HL, Winderickx J, Vanderleyden J, Lebeer S, Marchal K. A network-based approach to identify substrate classes of bacterial glycosyltransferases. BMC GENOMICS. 2014;15.
MLA
Sánchez-Rodríguez, Aminael, Hanne LP Tytgat, Joris Winderickx, et al. “A Network-based Approach to Identify Substrate Classes of Bacterial Glycosyltransferases.” BMC GENOMICS 15 (2014): n. pag. Print.
@article{4661374,
  abstract     = {Background: Bacterial interactions with the environment-and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity. 
Results: In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred. 
Conclusions: We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation.},
  articleno    = {349},
  author       = {S{\'a}nchez-Rodr{\'i}guez, Aminael and Tytgat, Hanne LP and Winderickx, Joris and Vanderleyden, Jos and Lebeer, Sarah and Marchal, Kathleen},
  issn         = {1471-2164},
  journal      = {BMC GENOMICS},
  keyword      = {Network-based prediction,IBCN,Sequence-based prediction,Bacterial glycosylation,Glycosyltransferases,Lactobacillus rhamnosus GG,Campylobacter jejuni,LACTOBACILLUS-RHAMNOSUS GG,LINKED PROTEIN GLYCOSYLATION,UDP-N-ACETYLGLUCOSAMINE,GENOME SEQUENCE,O-GLYCOSYLATION,PRIMING GLYCOSYLTRANSFERASE,ESCHERICHIA-COLI,BINDING-PROTEIN,CELL-WALL,GLYCAN CHAINS},
  language     = {eng},
  pages        = {21},
  title        = {A network-based approach to identify substrate classes of bacterial glycosyltransferases},
  url          = {http://dx.doi.org/10.1186/1471-2164-15-349},
  volume       = {15},
  year         = {2014},
}

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