The O-specific polysaccharide structure and biosynthetic gene cluster of Yersinia pseudotuberculosis serotype O:11

In the Yersinia pseudotuberculosis serotyping scheme, 21 serotypes are present originating from about 30 different O-factors distributed within the species. With regard to the chemical structures of lipopolysaccharides (LPSs) and the genetic basis of their biosynthesis, a number, but not all, of Y. pseudotuberculosis strains representing different serotypes have been investigated. In order to present an overall picture of the relationship between genetics and structures, we have been working on the genetics and structures of various Y. pseudotuberculosis O-specific polysaccharides (OPSs). Here, we present a structural and genetic analysis of the Y. pseudotuberculosis serotype O:11 OPS. Our results showed that this OPS structure has the same backbone as that of Y. pseudotuberculosis O:1b, but with a 6d-l-Altf side-branch instead of Parf. The 3′ end of the gene cluster is the same as that for O:1b and has the genes for synthesis of the backbone and for processing the completed repeat unit. The 5′ end has genes for synthesis of 6d-l-Altf and its transfer to the repeating unit backbone. The pathway for the synthesis of the 6d-l-Altf appears to be different from that for 6d-l-Altp in Y. enterocolitica O:3. The chemical structure of the O:11 repeating unit is [Figure]

Yersinia high pathogenicity island genes modify the Escherichia coli primary metabolome independently of siderophore production

Bacterial siderophores may enhance pathogenicity by scavenging iron, but their expression has been proposed to exert a substantial metabolic cost. Here we describe a combined metabolomic-genetic approach to determine how mutations affecting the virulence-associated siderophore yersiniabactin affect the Escherichia coli primary metabolome. Contrary to expectations, we did not find yersiniabactin biosynthesis to correspond to consistent metabolomic shifts. Instead, we found that targeted deletion of ybtU or ybtA, dissimilar genes with similar roles in regulating yersiniabactin expression, were associated with a specific shift in arginine pathway metabolites during growth in minimal media. This interaction was associated with high arginine levels in the model uropathogen Escherichia coli UTI89 compared to its ybtU and ybtA mutants and the K12 strain MG1655, which lacks yersiniabactin-associated genes. Because arginine is not a direct yersiniabactin biosynthetic substrate, these findings show that virulence-associated secondary metabolite systems may shape bacterial primary metabolism independently of substrate consumption

The genetics and structure of the O-specific polysaccharide of Yersinia pseudotuberculosis serotype O:10 and its relationship with Escherichia coli O111 and Salmonella enterica O35

The O-specific polysaccharide (OPS) is a variable constituent of the lipopolysaccharide of Gram-negative bacteria. The polymorphic nature of OPSs within a species is usually first defined serologically, and the current serotyping scheme for Yersinia pseudotuberculosis consists of 21 O serotypes of which 15 have been characterized genetically and structurally. Here, we present the structure and DNA sequence of Y. pseudotuberculosis O:10 OPS. The O unit consists of one residue each of d-galactopyranose, N-acetyl-d-galactosamine (2-amino-2-deoxy-d-galactopyranose) and d-glucopyranose in the backbone, with two colitose (3,6-dideoxy-l-xylo-hexopyranose) side-branch residues. This structure is very similar to that shared by Escherichia coli O111 and Salmonella enterica O35. The gene cluster sequences of these serotypes, however, have only low levels of similarity to that of Y. pseudotuberculosis O:10, although there is significant conservation of gene order. Within Y. pseudotuberculosis, the O10 structure is most closely related to the O:6 and O:7 structures.

Genetic characterisation and structural analysis of the O-specific polysaccharide of Yersinia pseudotuberculosis serotype O:1c

Many, but not all, of the current 21 serotypes of Yersinia pseudotuberculosis have been investigated with regard to the chemical structures of their O-specific polysaccharide (OPS) and the genetic basis of their biosynthesis. Completion of the genetics and structures of the remaining serotypes will enhance our understanding of the emerging relationship between genetics and structures within this species. Here, we present a structural and genetic analysis of the Y. pseudotuberculosis serotype O:1c OPS. Our results showed that this OPS has the same backbone as Y. pseudotuberculosis O:2b, but with a 3,6-dideoxy-D-ribo-hexofuranose (paratofuranose, Parf) side-branch instead of a 3,6-dideoxy-D-xylo-hexopyranose (abequopyranose, Abep). The 3'-end of the gene cluster is the same as for O:2b and has the genes for synthesis of the backbone and for processing the completed repeat unit. The 5'-end of the cluster consists of the same genes as O:1b for synthesis of Parf and a related gene for its transfer to the repeating unit backbone.

The O-specific polysaccharide structure and gene cluster of serotype O:12 of the Yersinia pseudotuberculosis complex, and the identification of a novel L-quinovose biosynthesis gene

A major virulence factor for Yersinia pseudotuberculosis is lipopolysaccharide, including O-polysaccharide (OPS). Currently, the OPS based serotyping scheme for Y. pseudotuberculosis includes 21 known O-serotypes, with genetic and structural data available for 17 of them. The completion of the OPS structures and genetics of this species will enable the visualization of relationships between O-serotypes and allow for analysis of the evolutionary processes within the species that give rise to new serotypes. Here we present the OPS structure and gene cluster of serotype O:12, thus adding one more to the set of completed serotypes, and show that this serotype is present in both Y. pseudotuberculosis and the newly identified Y. similis species. The O:12 structure is shown to include two rare sugars: 4-C[(R)-1-hydroxyethyl]-3,6-dideoxy-d-xylo-hexose (d-yersiniose) and 6-deoxy-l-glucopyranose (l-quinovose). We have identified a novel putative guanine diphosphate (GDP)-l-fucose 4-epimerase gene and propose a pathway for the synthesis of GDP-l-quinovose, which extends the known GDP-l-fucose pathway.

The Wzy O-antigen polymerase of Yersinia pseudotuberculosis O:2a has a dependence on the Wzz chain-length determinant for efficient polymerization

Lipopolysaccharide is a major immunogenic structure for the pathogen Yersinia pseudotuberculosis, which contains the O-specific polysaccharide (OPS) that is presented on the cell surface. The OPS contains many repeats of the oligosaccharide O-unit and exhibits a preferred modal chain length that has been shown to be crucial for cell protection in Yersinia. It is well established that the Wzz protein determines the preferred chain length of the OPS, and in its absence, the polymerization of O units by the Wzy polymerase is uncontrolled. However, for Y. pseudotuberculosis, a wzz mutation has never been described. In this study, we examine the effect of Wzz loss in Y. pseudotuberculosis serotype O:2a and compare the lipopolysaccharide chain-length profile to that of Escherichia coli serotype O111. In the absence of Wzz, the lipopolysaccharides of the two species showed significant differences in Wzy polymerization. Yersinia pseudotuberculosis O:2a exhibited only OPS with very short chain lengths, which is atypical of wzz-mutant phenotypes that have been observed for other species. We hypothesise that the Wzy polymerase of Y. pseudotuberculosis O:2a has a unique default activity in the absence of the Wzz, revealing the requirement of Wzz to drive O-unit polymerization to greater lengths.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Microbes, the gut and ankylosing spondylitis

It is increasingly clear that the interaction between host and microbiome profoundly affects health. There are 10 times more bacteria in and on our bodies than the total of our own cells, and the human intestine contains approximately 100 trillion bacteria. Interrogation of microbial communities by using classic microbiology techniques offers a very restricted view of these communities, allowing us to see only what we can grow in isolation. However, recent advances in sequencing technologies have greatly facilitated systematic and comprehensive studies of the role of the microbiome in human health and disease. Comprehensive understanding of our microbiome will enhance understanding of disease pathogenesis, which in turn may lead to rationally targeted therapy for a number of conditions, including autoimmunity.