Genomic context of the two integrons of ST-111 Pseudomonas aeruginosa AG1: A VIM-2-carrying old-acquaintance and a novel IMP-18-carrying integron.

Pseudomonas aeruginosa is an opportunist and versatile organism responsible for infections mainly in immunocompromised hosts. This pathogen has high intrinsic resistance to most antimicrobials. P. aeruginosa AG1 (PaeAG1) is a Costa Rican high-risk ST-111 strain with resistance to multiple antibiotics, including carbapenems, due to the activity of VIM-2 and IMP-18 metallo-ß-lactamases (MBLs). These genes are harbored in two class 1 integrons located inone out of the 57 PaeAG1 genomic islands. However, the genomic context associated to these determinants in PaeAG1 and other P. aeruginosa strains is unclear. Thus, we first assessed the transcriptional activity of VIM-2 and IMP-18 genes when exposed to imipenem (a carbapenem) by RT-qPCR. To select related genomes to PaeAG1, we implemented a pan-genome analysis to define and up-date the phylogenetic relationship among complete P. aeruginosa genomes. We also studied the PaeAG1 genomic islands content in the related strains and finally we described the architecture and possible evolutionary steps of the genomic regions around the VIM-2- and IMP-18-carrying integrons. Expression of VIM-2 and IMP-18 genes was demonstrated to be induced after imipenem exposure. In a subsequent comparative genomics analysis with 211 strains, the P. aeruginosa pan-genome revealed that complete genome sequences are able to separate clones by MLST profile, including a clear ST-111 cluster with PaeAG1. The PaeAG1 genomic islands were found to define a diverse presence/absence pattern among related genomes. Finally, landscape reconstruction of genomic regions showed that VIM-2-carrying integron (In59-like) is an old-acquaintance element harbored in the same known region found in other two ST-111 strains. Also, PaeAG1 has an exclusive genomic region containing a novel IMP-18-carrying integron (registered as In1666), with an arrangement never reported before. Altogether, we provide new insights about the genomic determinants associated with the resistance to carbapenems in this high-risk P. aeruginosa using comparative genomics.

Two-dimensional gel electrophoresis (2D-GE) image analysis based on CellProfiler: Pseudomonas aeruginosa AG1 as model.

Two-dimensional gel electrophoresis (2D-GE) is an indispensable technique for the study of proteomes of biological systems, providing an assessment of changes in protein abundance under various experimental conditions. However, due to the complexity of 2D-GE gels, there is no systematic, automatic, and reproducible protocol for image analysis and specific implementations are required for each context. In addition, practically all available solutions are commercial, which implies high cost and little flexibility to modulate the parameters of the algorithms. Using the bacterial strain, Pseudomonas aeruginosaAG1 as a model, we obtained images from 2D-GE of periplasmic protein profiles when the strain was exposed to multiple conditions, including antibiotics. Then, we proceeded to implement and evaluate an image analysis protocol with open-source software, CellProfiler. First, a preprocessing step included a bUnwarpJ-Image pipeline for aligning 2D-GE images. Then, using CellProfiler, we standardized two pipelines for spots identification. Total spots recognition was achieved using segmentation by intensity, whose performance was evaluated when compared with a reference protocol. In a second pipeline with the same program, differential identification of spots was addressed when comparing pairs of protein profiles. Due to the characteristics of the programs used, our workflow can automatically analyze a large number of images and it is parallelizable, which is an advantage with respect to other implementations. Finally, we compared six experimental conditions of bacterial strain in the presence or absence of antibiotics, determining protein profiles relationships by applying clustering algorithms PCA (Principal Components Analysis) and HC (Hierarchical Clustering).

Transcriptomic determinants of the response of ST-111 Pseudomonas aeruginosa AG1 to ciprofloxacin identified by a top-down systems biology approach.

Pseudomonas aeruginosa is an opportunistic pathogen that thrives in diverse environments and causes a variety of human infections. Pseudomonas aeruginosa AG1 (PaeAG1) is a high-risk sequence type 111 (ST-111) strain isolated from a Costa Rican hospital in 2010. PaeAG1 has both blaVIM-2 and blaIMP-18 genes encoding for metallo-ß-lactamases, and it is resistant to ß-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. Ciprofloxacin (CIP) is an antibiotic commonly used to treat P. aeruginosa infections, and it is known to produce DNA damage, triggering a complex molecular response. In order to evaluate the effects of a sub-inhibitory CIP concentration on PaeAG1, growth curves using increasing CIP concentrations were compared. We then measured gene expression using RNA-Seq at three time points (0, 2.5 and 5 h) after CIP exposure to identify the transcriptomic determinants of the response (i.e. hub genes, gene clusters and enriched pathways). Changes in expression were determined using differential expression analysis and network analysis using a top-down systems biology approach. A hybrid model using database-based and co-expression analysis approaches was implemented to predict gene-gene interactions. We observed a reduction of the growth curve rate as the sub-inhibitory CIP concentrations were increased. In the transcriptomic analysis, we detected that over time CIP treatment resulted in the differential expression of 518 genes, showing a complex impact at the molecular level. The transcriptomic determinants were 14 hub genes, multiple gene clusters at different levels (associated to hub genes or as co-expression modules) and 15 enriched pathways. Down-regulation of genes implicated in several metabolism pathways, virulence elements and ribosomal activity was observed. In contrast, amino acid catabolism, RpoS factor, proteases, and phenazines genes were up-regulated. Remarkably, > 80 resident-phage genes were up-regulated after CIP treatment, which was validated at phenomic level using a phage plaque assay. Thus, reduction of the growth curve rate and increasing phage induction was evidenced as the CIP concentrations were increased. In summary, transcriptomic and network analyses, as well as the growth curves and phage plaque assays provide evidence that PaeAG1 presents a complex, concentration-dependent response to sub-inhibitory CIP exposure, showing pleiotropic effects at the systems level. Manipulation of these determinants, such as phage genes, could be used to gain more insights about the regulation of responses in PaeAG1 as well as the identification of possible therapeutic targets. To our knowledge, this is the first report of the transcriptomic analysis of CIP response in a ST-111 high-risk P. aeruginosa strain, in particular using a top-down systems biology approach.