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/.

Analysis of protein-protein interaction network in chronic obstructive pulmonary disease

“…To explore the pathogenic mechanisms of COPD in the PPI network, we used a bioinformatic method in disease treatment (Zhang et al, 2012). As expected, several significantly enriched GO terms in COPD were discovered.…”

Probing the Protein Interaction Network of Pseudomonas aeruginosa Cells by Chemical Cross-Linking Mass Spectrometry

“…Different edge colors indicate that the identified PPI is included in a computationally predicted PAO1 interactome database ( edge color: Red ) and/or is a homologous PPI in E.coli , previously reported in the EcID database ( edge color: Blue ) and/or is identified from PIR cross-linked E.coli cells in our lab ( edge color: Green ; (Weisbrod et al, 2013a; Zheng et al, 2011, and additional efforts http://brucelab.gs.washington.edu/xlinkdb/dataRetriever.php?tablename=ecoli_total&dataset=&privateFlag=1), or is a previously unknown PPI ( edge color: Black ). An experimentally validated protein interaction database for PA does not currently exist; however, the cross-linking-derived binary protein interaction network was compared to a predicted PAO1 interactome database (Zhang et al, 2012)(Red edges). Only 7 PPIs between the cross-linked proteins that share an operon (SecD-SecF; SucC-SucD; LptE-LptF) or a subcellular location (i.e., membrane proteins: OprI-OprL, OprI-OprN or cytoplasmic proteins: SucA-rpsG and rplB-fusA) were found in the predicted PAO1 interactome database.…”

“…Fig. S2d–ii) were predicted to interact (Zhang et al, 2012). Our unbiased in vivo cross-linking data provide the first direct evidence of their interaction in living bacterial cells, along with interaction with major lipoprotein OprI.…”

“…against a compiled reference dataset created from known PPIs in three closely related Gram-negative bacteria (Zhang et al, 2012). Such databases provide a key first step in defining relevant PPIs in bacterial pathogenicity and drug resistance, and can be greatly accelerated by experimental measurements.…”

Human Disease and Drug Pharmacology, Complex as Real Life

“…to such perturbations. Alternatively, these costly and time-and effortconsuming large scale genomics, proteomics and metabolomics experimental methodologies can be replaced by computational methods that have been developed for predicting protein-protein interactions and constructing interactome models [49][50][51][52][53].…”