FlrA-independent production of flagellar proteins is required for proper flagellation in Shewanella putrefaciens.

Flagella are multiprotein complexes whose assembly and positioning require complex spatiotemporal control. Flagellar assembly is thought to be controlled by several transcriptional tiers, which are mediated through various master regulators. Here, we revisited the regulation of flagellar genes in polarly flagellated gammaproteobacteria by the regulators FlrA, RpoN (σ54 ) and FliA (σ28 ) in Shewanella putrefaciens CN-32 at the transcript and protein level. We found that a number of regulatory and structural proteins were present in the absence of the main regulators, suggesting that initiation of flagella assembly and motor activation relies on the abundance control of only a few structural key components that are required for the formation of the MS- and C-ring and the flagellar type III secretion system. We identified FlrA-independent promoters driving expression of the regulators of flagellar number and positioning, FlhF and FlhG. Reduction of the gene expression levels from these promoters resulted in the emergence of hyperflagellation. This finding indicates that basal expression is required to adjust the flagellar counter in Shewanella. This is adding a deeper layer to the regulation of flagellar synthesis and assembly.

Groundwater, soil and compost, as possible sources of virulent and antibiotic-resistant Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a major public health concern all around the world. In the frame of this work, a set of diverse environmental P. aeruginosa isolates with various antibiotic resistance profiles were examined in a Galleria mellonella virulence model. Motility, serotypes, virulence factors and biofilm-forming ability were also examined. Molecular types were determined by pulsed-field gel electrophoresis (PFGE). Based on our results, the majority of environmental isolates were virulent in the G. mellonella test and twitching showed a positive correlation with mortality. Resistance against several antibiotic agents such as Imipenem correlated with a lower virulence in the applied G. mellonella model. PFGE revealed that five examined environmental isolates were closely related to clinically detected pulsed-field types. Our study demonstrated that industrial wastewater effluents, composts, and hydrocarbon-contaminated sites should be considered as hot spots of high-risk clones of P. aeruginosa.

Single-Nucleotide Polymorphism-Based Genetic Diversity Analysis of Clinical Pseudomonas aeruginosa Isolates.

Extensive use of next-generation sequencing has the potential to transform our knowledge on how genomic variation within bacterial species impacts phenotypic versatility. Because different environments have unique selection pressures, they drive divergent evolution. However, there is also parallel or convergent evolution of traits in independent bacterial isolates inhabiting similar environments. The application of tools to describe population-wide genomic diversity provides an opportunity to measure the predictability of genetic changes underlying adaptation. Here, we describe patterns of sequence variations in the core genome among 99 individual Pseudomonas aeruginosa clinical isolates and identified single-nucleotide polymorphisms that are the basis for branching of the phylogenetic tree. We also identified single-nucleotide polymorphisms that were acquired independently, in separate lineages, and not through inheritance from a common ancestor. Although our results demonstrate that the Pseudomonas aeruginosa core genome is highly conserved and in general, not subject to adaptive evolution, instances of parallel evolution will provide an opportunity to uncover genetic changes that underlie phenotypic diversity.

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

BACTOME-a reference database to explore the sequence- and gene expression-variation landscape of Pseudomonas aeruginosa clinical isolates.

Extensive use of next-generation sequencing (NGS) for pathogen profiling has the potential to transform our understanding of how genomic plasticity contributes to phenotypic versatility. However, the storage of large amounts of NGS data and visualization tools need to evolve to offer the scientific community fast and convenient access to these data. We introduce BACTOME as a database system that links aligned DNA- and RNA-sequencing reads of clinical Pseudomonas aeruginosa isolates with clinically relevant pathogen phenotypes. The database allows data extraction for any single isolate, gene or phenotype as well as data filtering and phenotypic grouping for specific research questions. With the integration of statistical tools we illustrate the usefulness of a relational database structure for the identification of phenotype-genotype correlations as an essential part of the discovery pipeline in genomic research. Furthermore, the database provides a compilation of DNA sequences and gene expression values of a plethora of clinical isolates to give a consensus DNA sequence and consensus gene expression signature. Deviations from the consensus thereby describe the genomic landscape and the transcriptional plasticity of the species P. aeruginosa. The database is available at https://bactome.helmholtz-hzi.de.

Environment-driven changes of mRNA and protein levels in Pseudomonas aeruginosa.

Systems biology approaches address the challenge of translating sequence information into function. In this study, we described the Pseudomonas aeruginosa PA14 proteomic landscape and quantified environment-driven changes in protein levels by the use of LC-MS techniques. Previously recorded mRNA data allowed a comparison of RNA to protein ratios for each individual gene and, thus, to explore the relationship between an mRNA being differentially expressed between environmental conditions and the mRNA-protein correlation for that gene. We developed a Random Forest-based predictor for protein levels and found that the mRNA to protein correlation was higher for genes/proteins that undergo dynamic changes. One example of a discrepancy between protein and predicted protein levels was observed for a phage-related gene cluster, which was translated into low protein levels under standard growth conditions. However, under SOS-inducing conditions more protein was produced and the prediction of protein levels based on mRNA abundancy became more accurate. In conclusion, our systems biology approach sheds light on complex mRNA to protein level relationships and uncovered condition-dependent post-transcriptional regulatory events.

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa.

Emerging resistance to antimicrobials and the lack of new antibiotic drug candidates underscore the need for optimization of current diagnostics and therapies to diminish the evolution and spread of multidrug resistance. As the antibiotic resistance status of a bacterial pathogen is defined by its genome, resistance profiling by applying next-generation sequencing (NGS) technologies may in the future accomplish pathogen identification, prompt initiation of targeted individualized treatment, and the implementation of optimized infection control measures. In this study, qualitative RNA sequencing was used to identify key genetic determinants of antibiotic resistance in 135 clinical Pseudomonas aeruginosa isolates from diverse geographic and infection site origins. By applying transcriptome-wide association studies, adaptive variations associated with resistance to the antibiotic classes fluoroquinolones, aminoglycosides, and ß-lactams were identified. Besides potential novel biomarkers with a direct correlation to resistance, global patterns of phenotype-associated gene expression and sequence variations were identified by predictive machine learning approaches. Our research serves to establish genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way for faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential for resistance evolution.

The virulence factor LecB varies in clinical isolates: consequences for ligand binding and drug discovery.

P. aeruginosa causes a substantial number of nosocomial infections and is the leading cause of death of cystic fibrosis patients. This Gram-negative bacterium is highly resistant against antibiotics and further protects itself by forming a biofilm. Moreover, a high genomic variability among clinical isolates complicates therapy. Its lectin LecB is a virulence factor and necessary for adhesion and biofilm formation. We analyzed the sequence of LecB variants in a library of clinical isolates and demonstrate that it can serve as a marker for strain family classification. LecB from the highly virulent model strain PA14 presents 13% sequence divergence with LecB from the well characterized PAO1 strain. These differences might result in differing ligand binding specificities and ultimately in reduced efficacy of drugs directed towards LecB. Despite several amino acid variations at the carbohydrate binding site, glycan array analysis showed a comparable binding pattern for both variants. A common high affinity ligand could be identified and after its chemoenzymatic synthesis verified in a competitive binding assay: an N-glycan presenting two blood group O epitopes (H-type 2 antigen). Molecular modeling of the complex suggests a bivalent interaction of the ligand with the LecB tetramer by bridging two separate binding sites. This binding rationalizes the strong avidity (35 nM) of LecBPA14 to this human fucosylated N-glycan. Biochemical evaluation of a panel of glycan ligands revealed that LecBPA14 demonstrated higher glycan affinity compared to LecBPAO1 including the extraordinarily potent affinity of 70 nM towards the monovalent human antigen Lewisa. The structural basis of this unusual high affinity ligand binding for lectins was rationalized by solving the protein crystal structures of LecBPA14 with several glycans.

The Pseudomonas aeruginosa Transcriptional Landscape Is Shaped by Environmental Heterogeneity and Genetic Variation.

UNLABELLED: Phenotypic variability among bacteria depends on gene expression in response to different environments, and it also reflects differences in genomic structure. In this study, we analyzed transcriptome sequencing (RNA-seq) profiles of 151 Pseudomonas aeruginosa clinical isolates under standard laboratory conditions and of one P. aeruginosa type strain under 14 different environmental conditions. Our approach allowed dissection of the impact of the genetic background versus environmental cues on P. aeruginosa gene expression profiles and revealed that phenotypic variation was larger in response to changing environments than between genomically different isolates. We demonstrate that mutations within the global regulator LasR affect more than one trait (pleiotropy) and that the interaction between mutations (epistasis) shapes the P. aeruginosa phenotypic plasticity landscape. Because of pleiotropic and epistatic effects, average genotype and phenotype measures appeared to be uncorrelated in P. aeruginosa. IMPORTANCE: This work links experimental data of unprecedented complexity with evolution theory and delineates the transcriptional landscape of the opportunistic pathogen Pseudomonas aeruginosa. We found that gene expression profiles are most strongly influenced by environmental cues, while at the same time the transcriptional profiles were also shaped considerably by genetic variation within global regulators. The comprehensive set of transcriptomic and genomic data of more than 150 clinical P. aeruginosa isolates will be made publically accessible to all researchers via a dedicated web interface. Both Pseudomonas specialists interested in expression and regulation of specific genes and researchers from other fields with more global interest in the phenotypic and genotypic variation of this important model species can access all information on various levels of detail.

The extensive set of accessory Pseudomonas aeruginosa genomic components.

Up to 20% of the chromosomal Pseudomonas aeruginosa DNA belong to the so-called accessory genome. Its elements are specific for subgroups or even single strains and are likely acquired by horizontal gene transfer (HGT). Similarities of the accessory genomic elements to DNA from other bacterial species, mainly the DNA of γ- and ß-proteobacteria, indicate a role of interspecies HGT. In this study, we analysed the expression of the accessory genome in 150 clinical P. aeruginosa isolates as uncovered by transcriptome sequencing and the presence of accessory genes in eleven additional isolates. Remarkably, despite the large number of P. aeruginosa strains that have been sequenced to date, we found new strain-specific compositions of accessory genomic elements and a high portion (10-20%) of genes without P. aeruginosa homologues. Although some genes were detected to be expressed/present in several isolates, individual patterns regarding the genes, their functions and the possible origin of the DNA were widespread among the tested strains. Our results demonstrate the unaltered potential to discover new traits within the P. aeruginosa population and underline that the P. aeruginosa pangenome is likely to increase with increasing sequence information.