Systems Biology and Pangenome of Salmonella O-Antigens.

O-antigens are glycopolymers in lipopolysaccharides expressed on the cell surface of Gram-negative bacteria. Variability in the O-antigen structure constitutes the basis for the establishment of the serotyping schema. We pursued a two-pronged approach to define the basis for O-antigen structural diversity. First, we developed a bottom-up systems biology approach to O-antigen metabolism by building a reconstruction of Salmonella O-antigen biosynthesis and used it to (i) update 410 existing Salmonella strain-specific metabolic models, (ii) predict a strain's serogroup and its O-antigen glycan synthesis capability (yielding 98% agreement with experimental data), and (iii) extend our workflow to more than 1,400 Gram-negative strains. Second, we used a top-down pangenome analysis to elucidate the genetic basis for intraserogroup O-antigen structural variations. We assembled a database of O-antigen gene islands from over 11,000 sequenced Salmonella strains, revealing (i) that gene duplication, pseudogene formation, gene deletion, and bacteriophage insertion elements occur ubiquitously across serogroups; (ii) novel serotypes in the group O:4 B2 variant, as well as an additional genotype variant for group O:4, and (iii) two novel O-antigen gene islands in understudied subspecies. We thus comprehensively defined the genetic basis for O-antigen diversity.IMPORTANCE Lipopolysaccharides are a major component of the outer membrane in Gram-negative bacteria. They are composed of a conserved lipid structure that is embedded in the outer leaflet of the outer membrane and a polysaccharide known as the O-antigen. O-antigens are highly variable in structure across strains of a species and are crucial to a bacterium's interactions with its environment. They constitute the first line of defense against both the immune system and bacteriophage infections and have been shown to mediate antimicrobial resistance. The significance of our research is in identifying the metabolic and genetic differences within and across O-antigen groups in Salmonella strains. Our effort constitutes a first step toward characterizing the O-antigen metabolic network across Gram-negative organisms and a comprehensive overview of genetic variations in Salmonella

A Systematic Framework to Derive N-glycan Biosynthesis Process and the Automated Construction of Glycosylation Networks

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Development and Analysis of Web Resources using Glycoinformatics

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Correlation Index-Based Responsible-Enzyme Gene Screening (CIRES), a Novel DNA Microarray-Based Method for Enzyme Gene Involved in Glycan Biosynthesis

BACKGROUND: Glycan biosynthesis occurs though a multi-step process that requires a variety of enzymes ranging from glycosyltransferases to those involved in cytosolic sugar metabolism. In many cases, glycan biosynthesis follows a glycan-specific, linear pathway. As glycosyltransferases are generally regulated at the level of transcription, assessing the overall transcriptional profile for glycan biosynthesis genes seems warranted. However, a systematic approach for assessing the correlation between glycan expression and glycan-related gene expression has not been reported previously. METHODOLOGY: To facilitate genetic analysis of glycan biosynthesis, we sought to correlate the expression of genes involved in cell-surface glycan formation with the expression of the glycans, as detected by glycan-recognizing probes. We performed cross-sample comparisons of gene expression profiles using a newly developed, glycan-focused cDNA microarray. Cell-surface glycan expression profiles were obtained using flow cytometry of cells stained with plant lectins. Pearson's correlation coefficients were calculated for these profiles and were used to identify enzyme genes correlated with glycan biosynthesis. CONCLUSIONS: This method, designated correlation index-based responsible-enzyme gene screening (CIRES), successfully identified genes already known to be involved in the biosynthesis of certain glycans. Our evaluation of CIRES indicates that it is useful for identifying genes involved in the biosynthesis of glycan chains that can be probed with lectins using flow cytometry

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

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