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Open Access Research article

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

Aminael Sánchez-Rodríguez16, Hanne LP Tytgat12, Joris Winderickx3, Jos Vanderleyden1, Sarah Lebeer12* and Kathleen Marchal145*

Author Affiliations

1 Department of Microbial and Molecular Systems, KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, box 2460, Leuven B-3001, Belgium

2 Department of Bioscience Engineering, University of Antwerp, Groenenborgerlaan 171, Antwerp B-2020, Belgium

3 Department of Biology, Functional Biology, KU Leuven, Kasteelpark Arenberg 31, box 2433, Leuven B-3001, Belgium

4 Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, Ghent B-9052, Belgium

5 Department of Information Technology, Ghent University, IMinds, Gent 9052, Belgium

6 Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, San Cayetano Alto s/n Loja, Ecuador

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BMC Genomics 2014, 15:349  doi:10.1186/1471-2164-15-349

Published: 8 May 2014

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; Sequence-based prediction; Bacterial glycosylation; Glycosyltransferases; Lactobacillus rhamnosus GG; Campylobacter jejuni