Pseudomonas aeruginosa post-translational responses to elevated c-di-GMP levels.

C-di-GMP signaling can directly influence bacterial behavior by affecting the functionality of c-di-GMP-binding proteins. In addition, c-di-GMP can exert a global effect on gene transcription or translation, for example, via riboswitches or by binding to transcription factors. In this study, we investigated the effects of changes in intracellular c-di-GMP levels on gene expression and protein production in the opportunistic pathogen Pseudomonas aeruginosa. We induced c-di-GMP production via an ectopically introduced diguanylate cyclase and recorded the transcriptional, translational as well as proteomic profile of the cells. We demonstrate that rising levels of c-di-GMP under growth conditions otherwise characterized by low c-di-GMP levels caused a switch to a non-motile, auto-aggregative P. aeruginosa phenotype. This phenotypic switch became apparent before any c-di-GMP-dependent role on transcription, translation, or protein abundance was observed. Our results suggest that rising global c-di-GMP pools first affects the motility phenotype of P. aeruginosa by altering protein functionality and only then global gene transcription.

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.