Regulatory and metabolic networks for the adaptation of Pseudomonas aeruginosa biofilms to urinary tract-like conditions.

Biofilms of the Gram-negative bacterium Pseudomonas aeruginosa are one of the major causes of complicated urinary tract infections with detrimental outcome. To develop novel therapeutic strategies the molecular adaption strategies of P. aeruginosa biofilms to the conditions of the urinary tract were investigated thoroughly at the systems level using transcriptome, proteome, metabolome and enzyme activity analyses. For this purpose biofilms were grown anaerobically in artificial urine medium (AUM). Obtained data were integrated bioinformatically into gene regulatory and metabolic networks. The dominating response at the transcriptome and proteome level was the adaptation to iron limitation via the broad Fur regulon including 19 sigma factors and up to 80 regulated target genes or operons. In agreement, reduction of the iron cofactor-dependent nitrate respiratory metabolism was detected. An adaptation of the central metabolism to lactate, citrate and amino acid as carbon sources with the induction of the glyoxylate bypass was observed, while other components of AUM like urea and creatinine were not used. Amino acid utilization pathways were found induced, while fatty acid biosynthesis was reduced. The high amounts of phosphate found in AUM explain the reduction of phosphate assimilation systems. Increased quorum sensing activity with the parallel reduction of chemotaxis and flagellum assembly underscored the importance of the biofilm life style. However, reduced formation of the extracellular polysaccharide alginate, typical for P. aeruginosa biofilms in lungs, indicated a different biofilm type for urinary tract infections. Furthermore, the obtained quorum sensing response results in an increased production of virulence factors like the extracellular lipase LipA and protease LasB and AprA explaining the harmful cause of these infections.

Insulin up-regulates natriuretic peptide clearance receptor expression in the subcutaneous fat depot in obese subjects: a missing link between CVD risk and obesity?

CONTEXT: Natriuretic peptides (NP) regulate cardiovascular homeostasis and have multiple metabolic properties. Decreased levels of NP or "natriuretic handicap" are signs of insulin resistance such as central obesity. Increased expression of NP clearance receptor (NPRC) in sc adipose tissue (SAT) was observed in insulin-resistant subjects. OBJECTIVE: We hypothesized that insulin acutely regulates NP receptor expression in adipose tissue. DESIGN AND PARTICIPANTS: NPRA, NPRB, and NPRC mRNA expression was measured in paired samples of visceral adipose tissue (VAT) and SAT from 157 subjects (108 with type 2 diabetes). The effect of insulin on NPR gene expression in SAT was studied in euglycemic-hyperinsulinemic and hyperglycemic-hyperinsulinemic clamp experiments. Additionally, the effect of insulin and glucose on NPR expression in the culture of primary human monocytes and macrophages was tested. RESULTS: NPRA and NPRC gene expression was higher in VAT compared with SAT (P < 0.01), but only NPRC gene expression strongly correlated with fasting insulin levels (r = 0.65, P = 0.04 × 10(-3); and r = 0.54, P = 0.002, for VAT and SAT, respectively). NPRB expression was lower in VAT than in SAT in subjects with type 2 diabetes and was lower compared with nondiabetic subjects. NPRC gene expression was up-regulated in SAT during both euglycemic- and hyperglycemic-hyperinsulinemic clamps (P = 0.038 and P = 0.048, respectively), and was increased in high glucose and insulin treatment in monocytes (70.2%; P = 0.01), but not in mature macrophages. CONCLUSION: Insulin increased expression of NPRC in SAT independently of circulating glucose concentrations. Thus, insulin might suppress circulating NP via up-regulation of NPRC expression in obesity, providing a novel link between hyperinsulinemia and obesity.

GeneReporter--sequence-based document retrieval and annotation.

UNLABELLED: GeneReporter is a web tool that reports functional information and relevant literature on a protein-coding sequence of interest. Its purpose is to support both manual genome annotation and document retrieval. PubMed references corresponding to a sequence are detected by the extraction of query words from UniProt entries of homologous sequences. Data on protein families, domains, potential cofactors, structure, function, cellular localization, metabolic contribution and corresponding DNA binding sites complement the information on a given gene product of interest. AVAILABILITY AND IMPLEMENTATION: GeneReporter is available at http://www.genereporter.tu-bs.de. The web site integrates databases and analysis tools as SOAP-based web services from the EBI (European Bioinformatics Institute) and NCBI (National Center for Biotechnology Information).

Genotypic and phenotypic characterization of Pseudomonas aeruginosa isolates from urinary tract infections.

Pseudomonas aeruginosa is one of the most frequent agents of urinary tract infections especially in patients with indwelling urethral catheters. A total of 30 P. aeruginosa isolates from urinary tract infections was investigated for their genotypic and phenotypic characteristics. 'Single Nucleotide Polymorphism' chip typing experiments in combination with bioinformatical cluster analyses allowed genotypic grouping of the isolates. Some similarities to strains from lung infections but also to environmental strains were observed. Finally, several urinary tract-specific groups were identified indicating a strong heterogeneity of the urethral isolates. Pyoverdin, protease, and phospholipase A production in combination with quorum sensing activity and biofilm formation were common phenotypic characteristics of these strains. In contrast, swarming phenotypes, the production of pyocyanin, and the extracellular enzymes phospholipase C and elastase were rarely observed. Interestingly, strains isolated from catheter-associated infections showed significantly enhanced biofilm formation, decreased motility, and a slightly increased expression of virulence factors in relation to isolates from acute urinary tract infections.

PRODORIC (release 2009): a database and tool platform for the analysis of gene regulation in prokaryotes.

PRODORIC is a database that provides annotated information on the regulation of gene expression in prokaryotes. It integrates a large compilation of gene regulatory data including transcription factor binding sites, promoter structures and gene expression patterns. The whole dataset is manually curated and relies on published results extracted from the scientific literature. The current extended version of PRODORIC contains gene regulatory data for several new microorganisms. Major improvements were realized in the design of the web interface and the accessibility of the stored information. The database was further improved by the implementation of various new tools for the elucidation of gene regulatory interactions. Thus, the PRODORIC platform represents a framework for the interactive exploration, prediction and evaluation of gene regulatory networks in prokaryotes. PRODORIC is accessible at http://www.prodoric.de.

Strepto-DB, a database for comparative genomics of group A (GAS) and B (GBS) streptococci, implemented with the novel database platform 'Open Genome Resource' (OGeR).

Streptococci are the causative agent of many human infectious diseases including bacterial pneumonia and meningitis. Here, we present Strepto-DB, a database for the comparative genome analysis of group A (GAS) and group B (GBS) streptococci. The known genomes of various GAS and GBS contain a large fraction of distributed genes that were found absent in other strains or serotypes of the same species. Strepto-DB identifies the homologous proteins deduced from the genomes of interest. It allows for the elucidation of the GAS and GBS core- and pan-genomes via genome-wide comparisons. Moreover, an intergenic region analysis tool provides alignments and predictions for transcription factor binding sites in the non-coding sequences. An interactive genome browser visualizes functional annotations. Strepto-DB (http://oger.tu-bs.de/strepto_db) was created by the use of OGeR, the Open Genome Resource for comparative analysis of prokaryotic genomes. OGeR is a newly developed open source database and tool platform for the web-based storage, distribution, visualization and comparison of prokaryotic genome data. The system automatically creates the dedicated relational database and web interface and imports an arbitrary number of genomes derived from standardized genome files. OGeR can be downloaded at http://oger.tu-bs.de.

An emergent self-organizing map based analysis pipeline for comparative metabolome studies.

Modern high-throughput techniques allow for the identification and quantification of hundreds of metabolites ofa biological system which cover central parts of the metabolome. Due to the amount and complexity of obtained data there is an increasing need for the development of appropriate computational interpretation methods. A novel data analysis pipeline designed for high-throughput determined metabolomic data is presented. The combination of principal component analysis (PCA) with emergent self-organizing maps (ESOM) and hierarchical cluster analysis (HCA)algorithms is used to unravel the structure underlying metabolomic data sets, including the detection of outliers. Observed differences between various analyzed metabolomes are automatically mapped and visualized using KEGG metabolic pathway maps. This way typical metabolic biomarker for data sets from various analyzed growth conditions and genetic backgrounds become visible. In order to validate the described methods we analyzed time resolved metabolomic datasets obtained for Corynebacterium glutamicum cells grown on various carbon sources consisting of 126 different metabolic patterns. The analysis pipeline was implemented in the user-friendly Java software eSOMet. The software was successfully used for the clustering of the metabolome data mentioned above. Metabolic biomarkers typical for the utilized carbon sources and analyzed growth phases were identified.

ROSY--a flexible and universal database and bioinformatics tool platform for Roseobacter related species.

Systems biology approaches to bacteria require an integrated database and a bioinformatics tool platform to enable automated and manual annotation, regulatory and metabolic network deduction, and the storage of related experimental as well as predicted data. In this context ROSY--the Roseobacter SYstems biology database--was developed for completed and draft genomes of representatives of the marine Roseobacter clade, which constitutes one of the most abundant bacterial clades in the ocean. ROSY provides an integrative view on comprehensive data collections such as KEGG, GenBank, RoseoBase, BRENDA, and PRODORIC as well as mediates the use of connected tools for promoter analysis (Virtual Footprint), genome and pathway visualization (CGView, PathCompare), and prediction of signal peptides (PrediSi). Moreover, metabolome, transcriptome, and proteome data can be stored in ROSY, supplying an integrated platform for comparative genomics and systems biology. This entire database system along with the data retrieval, comparative analysis, and website presentation tools (http://rosy.tu-bs.de) can be easily adopted for the systems biological analysis of other bacterial groups.

SYSTOMONAS--an integrated database for systems biology analysis of Pseudomonas.

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at http://www.systomonas.de.