A network-based approach to identify substrate classes of bacterial glycosyltransferases
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 pbkp_rediction of the substrate specificity.Results: In this work, we developed an analysis flow that uses sequence-based strategies to pbkp_redict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these pbkp_redicted 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 pbkp_redict 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 pbkp_rediction 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 pbkp_redict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation. © 2014 Sánchez-Rodríguez et al.; licensee BioMed Central Ltd.
Año de publicación:
2014
Keywords:
- Lactobacillus rhamnosus GG
- Bacterial glycosylation
- Campylobacter jejuni
- Glycosyltransferases
- Network-based pbkp_rediction
- Sequence-based pbkp_rediction
Fuente:
Tipo de documento:
Article
Estado:
Acceso abierto
Áreas de conocimiento:
- Bioquímica
Áreas temáticas:
- Ciencias de la computación