Targeting multiple targets in Pseudomonas aeruginosa PAO1 using flux balance analysis of a reconstructed genome-scale metabolic network

Abstract

Constraint-based flux balance analysis (FBA) is a powerful tool for predicting target genes that can be engineered by analyzing the redistribution of metabolic fluxes on specific gene modifications. Specifically, the effects of metabolic gene deletions on flux distribution can be examined by forcing the fluxes of different reactions catalyzed by the corresponding gene product to zero. However, the target enzyme needs to be essential for survival of the organism to ensure that efficient chemical inhibition results in cell stasis or death. Here, we investigate the essentiality of enzymes in iMO1056 metabolic model of nosocomial pathogen Pseudomonas aeruginosa by performing in silico enzyme deletions using FBA. We identified 116/113 essential enzymes in rich medium in P. aeruginosa. These were then compared with human metabolic model to identify nonhomologous enzymes that could be possible drug targets. Here, we present a refined list of 41 novel potential targets for P. aeruginosa. These targets were then matched with the enzymes belonging to 97 correlated clusters through which we propose the concept of “one target per cluster.” Our approach relates to the “single drug multiple target (SDMT)” concept and has potential in efficient drug target discovery.

Keywords::

• Flux balance analysis (FBA)
• graph theory
• UP-UC enzymes
• Pseudomonas aeruginosa
• drug targets

Acknowledgements

The authors thank three anonymous reviewers for their comments that helped improve the manuscript significantly. A.S. would like to thank Sanjay Jain for discussions and the Max Planck Institute for Mathematics in the Sciences for a postdoctoral fellowship.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.