Dataset Information

Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.

ABSTRACT: Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.

SUBMITTER: Zhang M

PROVIDER: S-EPMC3404098 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.

Zhang Minlu M Su Shengchang S Bhatnagar Raj K RK Hassett Daniel J DJ Lu Long J LJ

PloS one 20120724 7

Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urg ...[more]

PMID: 22848443

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Project description:UnlabelledOxidative phosphorylation using multiple-component, membrane-associated protein complexes is the most effective way for a cell to generate energy. Here, we systematically investigated the multiple protein-protein interactions of the denitrification apparatus of the pathogenic bacterium Pseudomonas aeruginosa During denitrification, nitrate (Nar), nitrite (Nir), nitric oxide (Nor), and nitrous oxide (Nos) reductases catalyze the reaction cascade of NO(3-)? NO(2-)? NO ? N2O ? N2 Genetic experiments suggested that the nitric oxide reductase NorBC and the regulatory protein NosR are the nucleus of the denitrification protein network. We utilized membrane interactomics in combination with electron microscopy colocalization studies to elucidate the corresponding protein-protein interactions. The integral membrane proteins NorC, NorB, and NosR form the core assembly platform that binds the nitrate reductase NarGHI and the periplasmic nitrite reductase NirS via its maturation factor NirF. The periplasmic nitrous oxide reductase NosZ is linked via NosR. The nitrate transporter NarK2, the nitrate regulatory system NarXL, various nitrite reductase maturation proteins, NirEJMNQ, and the Nos assembly lipoproteins NosFL were also found to be attached. A number of proteins associated with energy generation, including electron-donating dehydrogenases, the complete ATP synthase, almost all enzymes of the tricarboxylic acid (TCA) cycle, and the Sec system of protein transport, among many other proteins, were found to interact with the denitrification proteins. This deduced nitrate respirasome is presumably only one part of an extensive cytoplasmic membrane-anchored protein network connecting cytoplasmic, inner membrane, and periplasmic proteins to mediate key activities occurring at the barrier/interface between the cytoplasm and the external environment.ImportanceThe processes of cellular energy generation are catalyzed by large multiprotein enzyme complexes. The molecular basis for the interaction of these complexes is poorly understood. We employed membrane interactomics and electron microscopy to determine the protein-protein interactions involved. The well-investigated enzyme complexes of denitrification of the pathogenic bacterium Pseudomonas aeruginosa served as a model. Denitrification is one essential step of the universal N cycle and provides the bacterium with an effective alternative to oxygen respiration. This process allows the bacterium to form biofilms, which create low-oxygen habitats and which are a key in the infection mechanism. Our results provide new insights into the molecular basis of respiration, as well as opening a new window into the infection strategies of this pathogen.

Project description:Antibiotic resistance evolves rapidly in response to drug selection, but it can also persist at appreciable levels even after the removal of the antibiotic. This suggests that many resistant strains can both be resistant and have high fitness in the absence of antibiotics. To explore the conditions under which high-fitness, resistant strains evolve and the genetic changes responsible, we used a combination of experimental evolution and whole-genome sequencing to track the acquisition of ciprofloxacin resistance in the opportunistic pathogen Pseudomonas aeruginosa under conditions of constant and fluctuating antibiotic delivery patterns. We found that high-fitness, resistant strains evolved readily under fluctuating but not constant antibiotic conditions and that their evolution was underlain by a trade-off between resistance and fitness. Whole-genome sequencing of evolved isolates revealed that resistance was gained through mutations in known resistance genes and that second-site mutations generally compensated for costs associated with resistance in the fluctuating treatment, leading to the evolution of cost-free resistance. Our results suggest that current therapies involving intermittent administration of antibiotics are contributing to the maintenance of antibiotic resistance at high levels in clinical settings. IMPORTANCE Antibiotic resistance is a global problem that greatly impacts human health. How resistance persists, even in the absence of antibiotic treatment, is thus a public health problem of utmost importance. In this study, we explored the antibiotic treatment conditions under which cost-free resistance arises, using experimental evolution of the bacterium Pseudomonas aeruginosa and the quinolone antibiotic ciprofloxacin. We found that intermittent antibiotic treatment led to the evolution of cost-free resistance and demonstrate that compensatory evolution is the mechanism responsible for cost-free resistance. Our results suggest that discontinuous administration of antibiotic may be contributing to the high levels of antibiotic resistance currently found worldwide.

Dataset Information

Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.

Publications

Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.

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