Genetic characterization indicates that a specific subpopulation of Pseudomonas aeruginosa is associated with keratitis infections.

Pseudomonas aeruginosa is a common opportunistic bacterial pathogen that causes a variety of infections in humans. Populations of P. aeruginosa are dominated by common clones that can be isolated from diverse clinical and environmental sources. To determine whether specific clones are associated with corneal infection, we used a portable genotyping microarray system to analyze a set of 63 P. aeruginosa isolates from patients with corneal ulcers (keratitis). We then used population analysis to compare the keratitis isolates to a wider collection of P. aeruginosa from various nonocular sources. We identified various markers in a subpopulation of P. aeruginosa associated with keratitis that were in strong disequilibrium with the wider P. aeruginosa population, including oriC, exoU, katN, unmodified flagellin, and the carriage of common genomic islands. The genome sequencing of a keratitis isolate (39016; representing the dominant serotype O11), which was associated with a prolonged clinical healing time, revealed several genomic islands and prophages within the accessory genome. The PCR amplification screening of all 63 keratitis isolates, however, provided little evidence for the shared carriage of specific prophages or genomic islands between serotypes. P. aeruginosa twitching motility, due to type IV pili, is implicated in corneal virulence. We demonstrated that 46% of the O11 keratitis isolates, including 39016, carry a distinctive pilA, encoding the pilin of type IV pili. Thus, the keratitis isolates were associated with specific characteristics, indicating that a subpopulation of P. aeruginosa is adapted to cause corneal infection.

How Pseudomonas aeruginosa adapts to various environments: a metabolomic approach.

In addition to transcriptome and proteome studies, metabolome analysis represents a third complementary approach to identify metabolic pathways and adaptation processes. In order to elucidate basic principles of metabolic versatility of Pseudomonas aeruginosa, we investigated the metabolome profiles of two genetically and morphologically divergent strains, the reference strain PAO1 and the mucoid clinical isolate TBCF10839 in exponential growth and stationary phase in six different carbon sources (cadaverine, casamino acids, citrate, glucose, succinate and tryptone). Both strains exhibited strong similarities in mode of growth; the metabolite patterns were mainly defined by the growth condition. Besides this adaptive response, a basic core metabolism shapes the P. aeruginosa metabolome, independent of growth phase, carbon source and genetic background. This core metabolism includes pathways related to the central energy and amino acid metabolism. These consistently utilized metabolic pathways are closely related to glutamate which represents a dominant metabolite in all conditions analysed. In nutrient-depleted media of stationary phase cultures, P. aeruginosa maintains a specific repertoire of metabolic pathways that are related to the carbon source formerly available. This specified adaptation strategy combined with the invariant basic core metabolism may represent a fundamental requirement for the metabolic versatility of this organism.

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.