Biofilms are microbial communities that represent a highly abundant form of microbial life on Earth. Inside biofilms, phenotypic and genotypic variations occur in three-dimensional space and time; microscopy and quantitative image analysis are therefore crucial for elucidating their functions. Here, we present BiofilmQ—a comprehensive image cytometry software tool for the automated and high-throughput quantification, analysis and visualization of numerous biofilm-internal and whole-biofilm properties in three-dimensional space and time.

Studying parallel evolution of similar traits in independent within-species lineages provides an opportunity to address evolutionary predictability of molecular changes underlying adaptation. In this study, we monitored biofilm forming capabilities, motility, and virulence phenotypes of a plethora of phylogenetically diverse clinical isolates of the opportunistic pathogen Pseudomonas aeruginosa. We also recorded biofilm-specific and planktonic transcriptional responses. We found that P. aeruginosa isolates could be stratified based on the production of distinct organismal traits. Three major biofilm phenotypes, which shared motility and virulence phenotypes, were produced repeatedly in several isolates, indicating that the phenotypes evolved via parallel or convergent evolution. Of note, while we found a restricted general response to the biofilm environment, the individual groups of biofilm phenotypes reproduced biofilm transcriptional profiles that included the expression of well-known biofilm features, such as surface adhesive structures and extracellular matrix components. Our results provide insights into distinct ways to make a biofilm and indicate that genetic adaptations can modulate multiple pathways for biofilm development that are followed by several independent clinical isolates. Uncovering core regulatory pathways that drive biofilm-associated growth and tolerance towards environmental stressors promises to give clues to host and environmental interactions and could provide useful targets for new clinical interventions.

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.

The overall success of a pathogenic microbe depends on its ability to efficiently adapt to challenging conditions in the human host. Long-term evolution experiments track and predict adaptive trajectories and have contributed significantly to our understanding of the driving forces of bacterial adaptation. In this study, we conducted a cross-sectional study instead of long-term longitudinal evolution experiments. We analyzed the transcriptional profiles as well as genomic sequence variations of a large number of clinical Pseudomonas aeruginosa isolates that have been recovered from different infected human sites. Convergent changes in gene expression patterns were found in different groups of clinical isolates. The majority of repeatedly observed expression patterns could be attributed to a defective lasR gene, which encodes the major quorum-sensing regulator LasR. Strikingly, the gene expression pattern of the lasR-defective strains appeared to reflect a transcriptional response that evolves in a direction consistent with growth within a biofilm. In a process of genetic assimilation, lasR-deficient P. aeruginosa isolates appear to constitutively express a biofilm-adapted transcriptional profile and no longer require a respective environmental trigger. Our results demonstrate that profiling the functional consequences of pathoadaptive mutations in clinical isolates reveals long-term evolutionary pathways and may explain the success of lasR mutants in the opportunistic pathogen P. aeruginosa in a clinical context.

Pseudomonas aeruginosa is a facultative pathogen that can cause, inter alia, acute or chronic pneumonia in predisposed individuals. The gram-negative bacterium displays considerable genomic and phenotypic diversity that is also shaped by small molecule secondary metabolites. The discrimination of virulence phenotypes is highly relevant to the diagnosis and prognosis of P. aeruginosa infections. In order to discover small molecule metabolites that distinguish different virulence phenotypes of P. aeruginosa, 35 clinical strains were cultivated under standard conditions, characterized in terms of virulence and biofilm phenotype, and their metabolomes were investigated by untargeted liquid chromatography—mass spectrometry. The data was both mined for individual candidate markers as well as used to construct statistical models to infer the virulence phenotype from metabolomics data. We found that clinical strains that differed in their virulence and biofilm phenotype also had pronounced divergence in their metabolomes, as underlined by 332 features that were significantly differentially abundant with fold changes greater than 1.5 in both directions. Important virulence-associated secondary metabolites like rhamnolipids, alkyl quinolones or phenazines were found to be strongly upregulated in virulent strains. In contrast, we observed little change in primary metabolism. A hitherto novel cationic metabolite with a sum formula of C₁₂H₁₅N₂ could be identified as a candidate biomarker. A random forest model was able to classify strains according to their virulence and biofilm phenotype with an area under the Receiver Operation Characteristics curve of 0.84. These findings demonstrate that untargeted metabolomics is a valuable tool to characterize P. aeruginosa virulence, and to explore interrelations between clinically important phenotypic traits and the bacterial metabolome.

Comparative genomics has greatly facilitated the identification of shared as well as unique features among individual cells or tissues, and thus offers the potential to find disease markers. While proteomics is recognized for its potential to generate quantitative maps of protein expression, comparative proteomics in bacteria has been largely restricted to the comparison of single cell lines or mutant strains. In this study, we used a data independent acquisition (DIA) technique, which enables global protein quantification of large sample cohorts, to record the proteome profiles of overall 27 whole genome sequenced and transcriptionally profiled clinical isolates of the opportunistic pathogen Pseudomonas aeruginosa. Analysis of the proteome profiles across the 27 clinical isolates grown under planktonic and biofilm growth conditions led to the identification of a core biofilm-associated protein profile. Furthermore, we found that protein-to-mRNA ratios between different P. aeruginosa strains are well correlated, indicating conserved patterns of post-transcriptional regulation. Uncovering core regulatory pathways, which drive biofilm formation and associated antibiotic tolerance in bacterial pathogens, promise to give clues to interactions between bacterial species and their environment and could provide useful targets for new clinical interventions to combat biofilm-associated infections.