Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa

Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.