A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1

Abstract

ABSTRACT Hard-wired within the genome of a pathogen is the information regarding factors responsible for its pathogenicity. Advances in functional genomics, particularly gene expression analysis, have made possible genome-wide interrogation of a pathogen to decipher pathogenicity-associated genes. Standard protocols assess differential expression of genes during pathogenesis; however, often a conservative approach is taken, which requires expression fold change above an arbitrarily defined threshold and a statistical significance test to infer a gene as differentially expressing. This renders a high-confidence set of differentially expressed genes in pathogenesis, however, at the cost of numerous false negatives. To circumvent this problem and comprehensively catalog pathogenicity-associated genes, we have developed a novel pipeline that uses standard protocol in combination with gene co-expression network of a pathogen constructed using publicly available RNA-Seq data sets. We assessed the efficacy of this pipeline on Pseudomonas aeruginosa PAO1, a model bacterial pathogen, highlighting the power of our network-based approach in discovering novel genes or pathways associated with the pathogenesis, or antibiotic resistance of this strain. Complementing standard protocol with a gene network-based method thus elevated the ability to identify pathogenicity-associated genes in P . aeruginosa PAO1. IMPORTANCE We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.