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. 2016 Jun 14;2016(1):182-94.
doi: 10.1093/emph/eow016. Print 2016.

Association Between Clinical Antibiotic Resistance and Susceptibility of Pseudomonas in the Cystic Fibrosis Lung

Free PMC article

Association Between Clinical Antibiotic Resistance and Susceptibility of Pseudomonas in the Cystic Fibrosis Lung

Gunther Jansen et al. Evol Med Public Health. .
Free PMC article


Background and objectives: Cystic fibrosis patients suffer from chronic lung infections that require long-term antibiotic therapy. Pseudomonas readily evolve resistance, rendering antibiotics ineffective. In vitro experiments suggest that resistant bacteria may be treated by exploiting their collateral sensitivity to other antibiotics. Here, we investigate correlations of sensitivity and resistance profiles of Pseudomonas aeruginosa that naturally adapted to antibiotics in the cystic fibrosis lung.

Methodology: Resistance profiles for 13 antibiotics were obtained using broth dilution, E-test and VITEK mass spectroscopy. Genetic variants were determined from whole-genome sequences and interrelationships among isolates were analyzed using 13 MLST loci.

Result: Our study focused on 45 isolates from 13 patients under documented treatment with antibiotics. Forty percent of these were clinically resistant and 15% multi-drug resistant. Colistin resistance was found once, despite continuous colistin treatment and even though colistin resistance can readily evolve experimentally in the laboratory. Patients typically harbored multiple genetically and phenotypically distinct clones. However, genetically similar clones often had dissimilar resistance profiles. Isolates showed mutations in genes encoding cell wall synthesis, alginate production, efflux pumps and antibiotic modifying enzymes. Cross-resistance was commonly observed within antibiotic classes and between aminoglycosides and β-lactam antibiotics. No evidence was found for consistent phenotypic resistance to one antibiotic and sensitivity to another within one genotype.

Conclusions and implications: Evidence supporting potential collateral sensitivity in clinical P. aeruginosa isolates remains equivocal. However, cross-resistance within antibiotic classes is common. Colistin therapy is promising since resistance to it was rare despite its intensive use in the studied patients.

Keywords: Pseudomonas aeruginosa; antibiotics; clinical sampling depth; collateral sensitivity; cross-resistance; cystic fibrosis; multi-drug resistance.


Figure 1.
Figure 1.
Resistance profiles of clinical isolates compared with EUCAST resistance breakpoints based on (a) broth dilution; (b) E-test and (c) VITEK. Heat maps represent categories resistant (red), intermediately resistant (white), sensitive (blue) or not measured (grey). Asterisks indicate COL-TOB cycling. Abbreviations on the left describe previous treatment of patients. See Table 1 for abbreviations of antibiotics
Figure 2.
Figure 2.
Pairwise correlations of resistances among antibiotics based on EUCAST resistance data using Pearson’s correlation coefficient ρ. Only statistically significant correlations (P < 0.05) and corresponding values of ρ are shown. Purple circles, sensitivity; green circles, resistance. Circle sizes graphically depict strengths of correlations
Figure 3.
Figure 3.
Number of SNPs and indels for clinical isolates compared with PA14. Stars indicate truncated bars. Strains with high numbers of differences to PA14 are shown in the inlay (note increased range of Y axis)
Figure 4.
Figure 4.
Phylogenetic interrelationships among clinical isolates based on 13 MLST genes. Bayesian phylogram based on a GTR + Γ + I model of molecular evolution, 6 million generations of MCMCMC with 25% burn-in. Nodes are labeled with posterior probabilities. The phylogeny is annotated with the EUCAST E-test resistance profiles as in Fig. 1b. Yellow and magenta boxes indicate isolates from patients treated with colistin and tobramycin, respectively. M, mucoid; N, non-mucoid. Numbers in corresponding colours express order in which isolates were taken from the same patient at consecutive time points

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    1. Jansen G, Aktipis CA. Resistance is mobile: the accelerating evolution of mobile genetic elements encoding resistance. J Evol Med 2014; 2:1–3.
    1. Talbot GH, Bradley J, Edwards JE. et al. Bad bugs need drugs: an update on the development pipeline from the antimicrobial availability task force of the infectious diseases society of America. Clin Infect Dis 2006; 42:657–68. - PubMed
    1. Read AF, Day T, Huijben S. The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc Natl Acad Sci U S A 2011; 108:10871–7. - PMC - PubMed
    1. Imamovic L, Sommer MOA. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci Transl Med 2013; 5:204ra132-204ra132. - PubMed
    1. Szybalski W, Bryson V. Genetic studies on microbial cross resistance to toxic agents I. Cross resistance of Escherichia coli to fifteen antibiotics. J Bacteriol 1952; 64:489–99. - PMC - PubMed