Catabolism of Nucleic Acids by a Cystic Fibrosis Pseudomonas aeruginosa Isolate: An Adaptive Pathway to Cystic Fibrosis Sputum Environment

Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis

Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to clear the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation.

A Shaving Proteomic Approach to Unveil Surface Proteins Modulation of Multi-Drug Resistant Pseudomonas aeruginosa Strains Isolated From Cystic Fibrosis Patients

Cystic fibrosis (CF) is the most common rare disease caused by a mutation of the CF transmembrane conductance regulator gene encoding a channel protein of the apical membrane of epithelial cells leading to alteration of Na⁺ and K⁺ transport, hence inducing accumulation of dense and sticky mucus and promoting recurrent airway infections. The most detected bacterium in CF patients is Pseudomonas aeruginosa (PA) which causes chronic colonization, requiring stringent antibiotic therapies that, in turn induces multi-drug resistance. Despite eradication attempts at the first infection, the bacterium is able to utilize several adaptation mechanisms to survive in hostile environments such as the CF lung. Its adaptive machinery includes modulation of surface molecules such as efflux pumps, flagellum, pili and other virulence factors. In the present study we compared surface protein expression of PA multi- and pan-drug resistant strains to wild-type antibiotic-sensitive strains, isolated from the airways of CF patients with chronic colonization and recent infection, respectively. After shaving with trypsin, microbial peptides were analyzed by tandem-mass spectrometry on a high-resolution platform that allowed the identification of 174 differentially modulated proteins localized in the region from extracellular space to cytoplasmic membrane. Biofilm assay was performed to characterize all 26 PA strains in term of biofilm production. Among the differentially expressed proteins, 17 were associated to the virulome (e.g., Tse2, Tse5, Tsi1, PilF, FliY, B-type flagellin, FliM, PyoS5), six to the resistome (e.g., OprJ, LptD) and five to the biofilm reservoir (e.g., AlgF, PlsD). The biofilm assay characterized chronic antibiotic-resistant isolates as weaker biofilm producers than wild-type strains. Our results suggest the loss of PA early virulence factors (e.g., pili and flagella) and later expression of virulence traits (e.g., secretion systems proteins) as an indicator of PA adaptation and persistence in the CF lung environment. To our knowledge, this is the first study that, applying a shaving proteomic approach, describes adaptation processes of a large collection of PA clinical strains isolated from CF patients in early and chronic infection phases.

Access to highly specialized growth substrates and production of epithelial immunomodulatory metabolites determine survival of Haemophilus influenzae in human airway epithelial cells

Haemophilus influenzae (Hi) infections are associated with recurring acute exacerbations of chronic respiratory diseases in children and adults including otitis media, pneumonia, chronic obstructive pulmonary disease and asthma. Here, we show that persistence and recurrence of Hi infections are closely linked to Hi metabolic properties, where preferred growth substrates are aligned to the metabolome of human airway epithelial surfaces and include lactate, pentoses, and nucleosides, but not glucose that is typically used for studies of Hi growth in vitro. Enzymatic and physiological investigations revealed that utilization of lactate, the preferred Hi carbon source, required the LldD L-lactate dehydrogenase (conservation: 98.8% of strains), but not the two redox-balancing D-lactate dehydrogenases Dld and LdhA. Utilization of preferred substrates was directly linked to Hi infection and persistence. When unable to utilize L-lactate or forced to rely on salvaged guanine, Hi showed reduced extra- and intra-cellular persistence in a murine model of lung infection and in primary normal human nasal epithelia, with up to 3000-fold attenuation observed in competitive infections. In contrast, D-lactate dehydrogenase mutants only showed a very slight reduction compared to the wild-type strain. Interestingly, acetate, the major Hi metabolic end-product, had anti-inflammatory effects on cultured human tissue cells in the presence of live but not heat-killed Hi, suggesting that metabolic endproducts also influence HI-host interactions. Our work provides significant new insights into the critical role of metabolism for Hi persistence in contact with host cells and reveals for the first time the immunomodulatory potential of Hi metabolites.

Structure, Function and Regulation of a Second Pyruvate Kinase Isozyme in Pseudomonas aeruginosa

Pseudomonas aeruginosa (PA) depends on the Entner-Doudoroff pathway (EDP) for glycolysis. The main enzymatic regulator in the lower half of the EDP is pyruvate kinase. PA contains genes that encode two isoforms of pyruvate kinase, denoted PykAPA and PykFPA. In other well-characterized organisms containing two pyruvate kinase isoforms (such as Escherichia coli) each isozyme is differentially regulated. The structure, function and regulation of PykAPA has been previously characterized in detail, so in this work, we set out to assess the biochemical and structural properties of the PykFPA isozyme. We show that pykF PA expression is induced in the presence of the diureide, allantoin. In spite of their relatively low amino acid sequence identity, PykAPA and PykFPA display broadly comparable kinetic parameters, and are allosterically regulated by a very similar set of metabolites. However, the x-ray crystal structure of PykFPA revealed significant differences compared with PykAPA. Notably, although the main allosteric regulator binding-site of PykFPA was empty, the "ring loop" covering the site adopted a partially closed conformation. Site-directed mutation of the proline residues flanking the ring loop yielded apparent "locked on" and "locked off" allosteric activation phenotypes, depending on the residue mutated. Analysis of PykFPA inter-protomer interactions supports a model in which the conformational transition(s) accompanying allosteric activation involve re-orientation of the A and B domains of the enzyme and subsequent closure of the active site.

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.

Pseudomonas aeruginosa: An Audacious Pathogen with an Adaptable Arsenal of Virulence Factors

Pseudomonas aeruginosa is a dominant pathogen in people with cystic fibrosis (CF) contributing to morbidity and mortality. Its tremendous ability to adapt greatly facilitates its capacity to cause chronic infections. The adaptability and flexibility of the pathogen are afforded by the extensive number of virulence factors it has at its disposal, providing P. aeruginosa with the facility to tailor its response against the different stressors in the environment. A deep understanding of these virulence mechanisms is crucial for the design of therapeutic strategies and vaccines against this multi-resistant pathogen. Therefore, this review describes the main virulence factors of P. aeruginosa and the adaptations it undergoes to persist in hostile environments such as the CF respiratory tract. The very large P. aeruginosa genome (5 to 7 MB) contributes considerably to its adaptive capacity; consequently, genomic studies have provided significant insights into elucidating P. aeruginosa evolution and its interactions with the host throughout the course of infection.

From genotype to phenotype: adaptations of Pseudomonas aeruginosa to the cystic fibrosis environment

Pseudomonas aeruginosa is one of the main microbial species colonizing the lungs of cystic fibrosis patients and is responsible for the decline in respiratory function. Despite the hostile pulmonary environment, P. aeruginosa is able to establish chronic infections thanks to its strong adaptive capacity. Various longitudinal studies have attempted to compare the strains of early infection with the adapted strains of chronic infection. Thanks to new '-omics' techniques, convergent genetic mutations, as well as transcriptomic and proteomic dysregulations have been identified. As a consequence of this evolution, the adapted strains of P. aeruginosa have particular phenotypes that promote persistent infection.

Molecular Determinants of Antibiotic Resistance in the Costa Rican Pseudomonas aeruginosa AG1 by a Multi-omics Approach: A Review of 10 Years of Study

Pseudomonas aeruginosa AG1 (PaeAG1) is a Costa Rican strain that was isolated in 2010 in a major Hospital. This strain has resistance to multiple antibiotics such as β-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. PaeAG1 is considered critical (Priority 1) due to its resistance to carbapenems, and it was the first report of a P. aeruginosa isolate carrying both VIM-2 and IMP-18 genes encoding for metallo-β-lactamases (MBL) enzymes (both with carbapenemase activity). Owing to these traits, we have studied this model for 10 years using diverse approaches including multi-omics. In this review, we summarize the main points of the different steps that we have studied in PaeAG1: preliminary analyses of this strain at the genomic and phenomic levels revealed that this microorganism has particular features of antibiotic resistance. In the multi-omics approach, the genome assembly was the initial step to identify the genomic determinants of this strain, including virulence factors, antibiotic resistance genes, as well as a complex accessory genome. Second, a comparative genomic approach was implemented to define and update the phylogenetic relationship among complete P. aeruginosa genomes, the genomic island content in other strains, and the architecture of the two MBL-carrying integrons. Third, the proteomic profile of PaeAG1 was studied after exposure to antibiotics using 2-dimensional gel electrophoresis (2D-GE). Fourth, to study the central response to multiple perturbations in P. aeruginosa, i.e., the core perturbome, a machine learning approach was used. The analysis revealed biological functions and determinants that are shared by different disturbances. Finally, to evaluate the effects of ciprofloxacin (CIP) on PaeAG1, a growth curve comparison, differential expression analysis (RNA-Seq), and network analysis were performed. Using the results of the core perturbome (pathways that also were found in this perturbation with CIP), it was possible to identify the “exclusive” response and determinants of PaeAG1 after exposure to CIP. Altogether, after a decade of study using a multi-omics approach (at genomics, comparative genomics, perturbomics, transcriptomics, proteomics, and phenomics levels), we have provided new insights about the genomic and transcriptomic determinants associated with antibiotic resistance in PaeAG1. These results not only partially explain the high-risk condition of this strain that enables it to conquer nosocomial environments and its multi-resistance profile, but also this information may eventually be used as part of the strategies to fight this pathogen.

Evaluating the effect of antibiotics sub-inhibitory dose on Pseudomonas aeruginosa quorum sensing dependent virulence and its phenotypes

The opportunistic Pseudomonas aeruginosa virulence controlled by quorum sensing (QS) also identified as, cell-cell communication. QS system is organized by the LasI-LasR and the RhlI-RhlR components. Provided that QS tends to perform a key role in virulence gene expression and host defence function, QS inhibitors have been proposed as potential antipseudomonal therapies. Sub-inhibitory concentrations (sub-MIC) of antibiotics, although having biostatic effect on bacteria, but can interfere with bacterial QS system and virulence. This research aimed to examine the impact of sub-MIC of azithromycin, imipenem, cefepime and piperacillin/tazobactam on the QS-dependent virulence including pyocyanin and biofilm production, haemolysin, protease and DNase in P. aeruginosa wildtype and mutant strains; transcriptional-regulator (ΔLasR), autoinducer synthesis protein (ΔLasI), transcriptional-regulator (ΔRhlR), protease precursor (ΔLasA) and double regulators mutants (ΔLasR/RhlR). The growth of all strains showed similar pattern, however, in presence of antibiotics significant growth variation was observed among mutant strains when compared to wild type strain. Antimicrobial activity tested by agar diffusion method of all antibiotics on all strains were used to compare the zones of therapeutic and sub-MIC doses showing a significant difference in the inhibition zone. QS-dependant virulence as biofilm, pyocyanin, protease, haemolysin and DNase production showed significant variation on all strains compared to wild type in response to antibiotics used at sub-MIC doses. In conclusion well known antibiotics can be used in sub-MIC doses to decrease the virulence of P. aeruginosa in addition to overcoming the major side effect of the high doses and the occurrence of resistance.