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Pseudomonas aeruginosa is an opportunistic pathogen that causes a wide range of acute and chronic infections. An increasing number of isolates have mutations that make them antibiotic resistant, making treatment difficult. To identify resistance-associated mutations, we experimentally evolved the antibiotic-sensitive strain P. aeruginosa PAO1 to become resistant to three widely used antipseudomonal antibiotics, namely, ciprofloxacin, meropenem, and tobramycin. Mutants could tolerate up to 2,048-fold higher concentrations of antibiotics than strain PAO1. Genome sequences were determined for 13 mutants for each antibiotic. Each mutant had between 2 and 8 mutations. For each antibiotic, at least 8 genes were mutated in multiple mutants, demonstrating the genetic complexity of resistance. For all three antibiotics, mutations arose in genes known to be associated with resistance but also in genes not previously associated with resistance. To determine the clinical relevance of mutations uncovered in this study, we analyzed the corresponding genes in 558 isolates of P. aeruginosa from patients with chronic lung disease and in 172 isolates from the general environment. Many genes identified through experimental evolution had predicted function-altering changes in clinical isolates but not in environmental isolates, showing that mutated genes in experimentally evolved bacteria can predict those that undergo mutation during infection. Additionally, large deletions of up to 479 kb arose in experimentally evolved meropenem-resistant mutants, and large deletions were present in 87 of the clinical isolates. These findings significantly advance understanding of antibiotic resistance in P. aeruginosa and demonstrate the validity of experimental evolution in identifying clinically relevant resistance-associated mutations.

Ciprofloxacin is one of the most widely used antibiotics for treating Pseudomonas aeruginosa infections. However P. aeruginosa acquires mutations that confer ciprofloxacin resistance, making treatment more difficult. Resistance is multifactorial, with mutations in multiple genes influencing the resistance phenotype. However the contributions of individual mutations and mutation combinations to the amounts of ciprofloxacin that P. aeruginosa can tolerate are not well understood. Engineering P. aeruginosa strain PAO1 to contain mutations in any one of the resistance-associated genes gyrA, nfxB, rnfC, parC and parE showed that only gyrA mutations increased the Minimum Inhibitory Concentration (MIC) for ciprofloxacin. Mutations in parC and parE increased the MIC of a gyrA mutant, making the bacteria ciprofloxacin resistant. Mutations in nfxB and rnfC increased the MIC, conferring resistance, only if both were mutated in a gyrA background. Mutations in all of gyrA, nfxB, rnfC and parC/E further increased the MIC. These findings reveal an epistatic network of gene-gene interactions in ciprofloxacin resistance. We used this information to predict ciprofloxacin resistance/susceptibility for 274 isolates of P. aeruginosa from their genome sequences. Antibiotic susceptibility profiles were predicted correctly for 84% of the isolates. The majority of isolates for which prediction was unsuccessful were ciprofloxacin resistant, demonstrating the involvement of additional as yet unidentified genes and mutations in resistance. Our data show that gene-gene interactions can play an important role in antibiotic resistance and can be successfully incorporated into models predicting resistance phenotype.

Pseudomonas aeruginosa is an opportunistic pathogen that causes a wide range of acute and chronic infections. An increasing number of isolates have acquired mutations that make them antibiotic resistant, making treatment more difficult. To identify resistance-associated mutations we experimentally evolved the antibiotic sensitive strain P. aeruginosa PAO1 to become resistant to three widely used anti-pseudomonal antibiotics, ciprofloxacin, meropenem and tobramycin. Mutants were able to tolerate up to 2048-fold higher concentrations of antibiotic than strain PAO1. Genome sequences were determined for thirteen mutants for each antibiotic. Each mutant had between 2 and 8 mutations. There were at least 8 genes mutated in more than one mutant per antibiotic, demonstrating the complexity of the genetic basis of resistance. Additionally, large deletions of up to 479kb arose in multiple meropenem resistant mutants. For all three antibiotics mutations arose in genes known to be associated with resistance, but also in genes not previously associated with resistance. To determine the clinical relevance of mutations uncovered in experimentallyevolved mutants we analysed the corresponding genes in 457 isolates of P. aeruginosa from patients with cystic fibrosis or bronchiectasis as well as 172 isolates from the general environment. Many of the genes identified through experimental evolution had changes predicted to be function-altering in clinical isolates but not in isolates from the general environment, showing that mutated genes in experimentally evolved bacteria can predict those that undergo mutation during infection. These findings expand understanding of the genetic basis of antibiotic resistance in P. aeruginosa as well as demonstrating the validity of experimental evolution in identifying clinically-relevant resistance-associated mutations.' ImportanceThe rise in antibiotic resistant bacteria represents an impending global health crisis. As such, understanding the genetic mechanisms underpinning this resistance can be a crucial piece of the puzzle to combatting it. The importance of this research is that by experimentally evolving P. aeruginosa to three clinically relevant antibiotics, we have generated a catalogue of genes that can contribute to resistance in vitro. We show that many (but not all) of these genes are clinically relevant, by identifying variants in clinical isolates of P. aeruginosa. This research furthers our understanding of the genetics leading to resistance in P. aeruginosa and provides tangible evidence that these genes can play a role clinically, potentially leading to new druggable targets or inform therapies.

Pseudomonas aeruginosa causes a wide range of severe infections. Ceftazidime, a cephalosporin, is a key antibiotic for treating infections but a significant proportion of isolates are ceftazidime-resistant. The aim of this research was to identify mutations that contribute to resistance, and to quantify the impacts of individual mutations and mutation combinations. Thirty-five mutants with reduced susceptibility to ceftazidime were evolved from two antibiotic-sensitive P. aeruginosa reference strains PAO1 and PA14. Mutations were identified by whole genome sequencing. The evolved mutants tolerated ceftazidime at concentrations between 4 and 1000 times that of the parental bacteria, with most mutants being ceftazidime resistant (minimum inhibitory concentration [MIC] ≥ 32 mg/L). Many mutants were also resistant to meropenem, a carbapenem antibiotic. Twenty-eight genes were mutated in multiple mutants, with dacB and mpl being the most frequently mutated. Mutations in six key genes were engineered into the genome of strain PAO1 individually and in combinations. A dacB mutation by itself increased the ceftazidime MIC by 16-fold although the mutant bacteria remained ceftazidime sensitive (MIC < 32 mg/L). Mutations in ampC, mexR, nalC or nalD increased the MIC by 2- to 4-fold. The MIC of a dacB mutant was increased when combined with a mutation in ampC, rendering the bacteria resistant, whereas other mutation combinations did not increase the MIC above those of single mutants. To determine the clinical relevance of mutations identified through experimental evolution, 173 ceftazidime-resistant and 166 sensitive clinical isolates were analysed for the presence of sequence variants that likely alter function of resistance-associated genes. dacB and ampC sequence variants occur most frequently in both resistant and sensitive clinical isolates. Our findings quantify the individual and combinatorial effects of mutations in different genes on ceftazidime susceptibility and demonstrate that the genetic basis of ceftazidime resistance is complex and multifactorial.