Intrinsic Antimicrobial Resistance Determinants in the Superbug Pseudomonas aeruginosa.
Antimicrobial-resistant bacteria pose a serious threat in the clinic. This is particularly true for opportunistic pathogens that possess high intrinsic resistance. Though many studies have focused on understanding the acquisition of bacterial resistance upon exposure to antimicrobials, the mechanisms controlling intrinsic resistance are not well understood. In this study, we subjected the model opportunistic superbug Pseudomonas aeruginosa to 14 antimicrobials under highly controlled conditions and assessed its response using expression- and fitness-based genomic approaches. Our results reveal that gene expression changes and mutant fitness in response to sub-MIC antimicrobials do not correlate on a genomewide scale, indicating that gene expression is not a good predictor of fitness determinants. In general, fewer fitness determinants were identified for antiseptics and disinfectants than for antibiotics. Analysis of gene expression and fitness data together allowed the prediction of antagonistic interactions between antimicrobials and insight into the molecular mechanisms controlling these interactions.


OBJECTIVE
Infections involving multidrug-resistant pathogens are difficult to treat because the therapeutic options are limited. These infections impose a significant financial burden on infected patients and on health care systems. Despite years of antimicrobial resistance research, we lack a comprehensive understanding of the intrinsic mechanisms controlling antimicrobial resistance. This work uses two fine-scale genomic approaches to identify genetic loci important for antimicrobial resistance of the opportunistic pathogen Pseudomonas aeruginosa. Our results reveal that antibiotics have more resistance determinants than antiseptics/disinfectants and that gene expression upon exposure to antimicrobials is not a good predictor of these resistance determinants. In addition, we show that when used together, genomewide gene expression and fitness profiling can provide mechanistic insights into multidrug resistance mechanisms.
Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA Center for Infectious Disease, The University of Texas at Austin, Austin, Texas, USA.
Justine L
JL
Murray
Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA Institute of Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, USA Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, USA Department of Biomedical Engineering, School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Taejoon
T
Kwon
Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA Institute of Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, USA Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, USA.
Edward M
EM
Marcotte
Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA Institute of Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, USA Center for Infectious Disease, The University of Texas at Austin, Austin, Texas, USA mwhiteley@austin.utexas.edu.
Marvin
M
Whiteley
D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D024901:Drug Resistance, Multiple, Bacterial; D020869:Gene Expression Profiling; D008826:Microbial Sensitivity Tests; D009154:Mutation; D011550:Pseudomonas aeruginosa / Q000187:drug effects* / Q000235:genetics* / Q000254:growth & development
D000900:Anti-Bacterial Agents