Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa

Cell Syst. 2019 Jan 23;8(1):3-14.e3. doi: 10.1016/j.cels.2018.12.002. Epub 2019 Jan 2.


Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To study this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. Our data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. A genome-scale metabolic network reconstruction of P. aeruginosa was paired with whole-genome sequencing data to predict genes contributing to observed changes in metabolism. We experimentally validated computational predictions to identify mutations in resistant P. aeruginosa affecting loss of catabolic function. Finally, we found a shared metabolic phenotype between lab-evolved P. aeruginosa and clinical isolates with similar mutational landscapes. Our results build upon previous knowledge of antibiotic-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens.

Keywords: Pseudomonas aeruginosa; antibiotic resistance; bacterial metabolism; genome-scale metabolic network reconstruction.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Computational Biology / methods*
  • Drug Resistance, Bacterial / genetics*
  • Genome, Bacterial / genetics*
  • Pseudomonas aeruginosa / drug effects*