A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach.

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model. Using microarray datasets from the Gene Expression Omnibus (GEO), we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN K-Nearest neighbor and RF Random Forest). Gene expression patterns and topological features at the systems level were included to describe the perturbome elements. We were able to select and describe 46 core response genes associated with multiple perturbations in P. aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels, and topological features in molecular networks revealed biological functions of biosynthesis, binding, and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including data partitioning, classification approaches, and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.

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.