Skip to main content
  • ASM
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Eukaryotic Cell
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems
  • Log in
  • My alerts
  • My Cart

Main menu

  • Home
  • Articles
    • Current Issue
    • Accepted Manuscripts
    • COVID-19 Special Collection
    • Archive
    • Minireviews
  • For Authors
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics Resources and Policies
  • About the Journal
    • About AAC
    • Editor in Chief
    • Editorial Board
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • AAC Podcast
    • RSS
    • FAQ
  • Subscribe
    • Members
    • Institutions
  • ASM
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Eukaryotic Cell
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems

User menu

  • Log in
  • My alerts
  • My Cart

Search

  • Advanced search
Antimicrobial Agents and Chemotherapy
publisher-logosite-logo

Advanced Search

  • Home
  • Articles
    • Current Issue
    • Accepted Manuscripts
    • COVID-19 Special Collection
    • Archive
    • Minireviews
  • For Authors
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics Resources and Policies
  • About the Journal
    • About AAC
    • Editor in Chief
    • Editorial Board
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • AAC Podcast
    • RSS
    • FAQ
  • Subscribe
    • Members
    • Institutions
Mechanisms of Resistance

A Large-Scale Whole-Genome Comparison Shows that Experimental Evolution in Response to Antibiotics Predicts Changes in Naturally Evolved Clinical Pseudomonas aeruginosa

Samuel J. T. Wardell, Attika Rehman, Lois W. Martin, Craig Winstanley, Wayne M. Patrick, Iain L. Lamont
Samuel J. T. Wardell
aDepartment of Biochemistry, University of Otago, Dunedin, New Zealand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Samuel J. T. Wardell
Attika Rehman
aDepartment of Biochemistry, University of Otago, Dunedin, New Zealand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lois W. Martin
aDepartment of Biochemistry, University of Otago, Dunedin, New Zealand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Craig Winstanley
bInstitute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Craig Winstanley
Wayne M. Patrick
aDepartment of Biochemistry, University of Otago, Dunedin, New Zealand
cSchool of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Wayne M. Patrick
Iain L. Lamont
aDepartment of Biochemistry, University of Otago, Dunedin, New Zealand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Iain L. Lamont
DOI: 10.1128/AAC.01619-19
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

ABSTRACT

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.

INTRODUCTION

Pseudomonas aeruginosa is an opportunistic pathogen responsible for a wide range of acute and chronic infections. It is a frequent cause of hospital-acquired infections and chronically infects the lungs of most adults with cystic fibrosis (CF), bronchiectasis, or chronic obstructive pulmonary disease (1, 2). Although a number of antibiotics are available for treating P. aeruginosa infections, many isolates are now resistant to one or more of these (3). The failure of antibiotic therapy to eradicate P. aeruginosa infections is a contributing factor to the development of resistance, as bacteria are likely to experience subinhibitory concentrations of antibiotics with selection for more highly resistant mutants (4). Treatment of infections with antibiotics that are ineffective due to resistance increases the length of hospital stays, increases patient morbidity, and can also increase the emergence of resistance (5, 6). Consequently, there is a need for improved tools for determining optimal antibiotic treatment. One attractive option is the use of whole-genome sequencing of infecting bacteria to determine which antibiotics are likely to be effective, an approach that has been developed successfully for other species (7, 8). This approach requires a thorough understanding of the genetic basis of antibiotic resistance.

The basis of antibiotic resistance in P. aeruginosa has been investigated using genetic and biochemical approaches and is multifactorial. Mechanisms of resistance can be classified into the following four groups: reduced antibiotic uptake, enhanced antibiotic efflux, reduced affinity of antibiotics to their cellular targets, and inactivation of antibiotics (9, 10). Resistance mainly occurs through mutations, although genes obtained via horizontal gene transfer also confer a resistant phenotype.

The emergence of whole-genome sequencing (WGS) technologies has provided new approaches to understanding the molecular mechanisms driving antibiotic resistance (11). Experimental evolution of antibiotic-resistant mutants from sensitive parent strains, followed by whole-genome sequencing to identify resistance-conferring mutations, is an approach that has been applied to a number of species, including P. aeruginosa (12–18). Studies to date have confirmed the involvement in resistance of genes previously proposed to be associated with resistance in P. aeruginosa. These include the fusA1 gene that encodes the EFG-1 protein and is associated with aminoglycoside resistance (19) and the ftsI gene that encodes a penicillin binding protein and is associated with carbapenem resistance (20). This approach also has a high potential for identifying previously unknown antibiotic resistance-associated mutations and genes, but some key issues remain to be addressed. Experimental evolution of small numbers of mutants may overlook mutations that can contribute to resistance but do not always arise, making it more difficult to draw robust conclusions. The mutations obtained may be influenced by the selection method used—for example, continuous exposure to increasing amounts of antibiotic in broth culture may give different outcomes to intermittent antibiotic exposure, as occurs during infection in patients. Last and perhaps most importantly, a rigorous comparison of experimentally evolved bacteria and those that have evolved naturally during infection is lacking despite the possibility of resistance mutations in experimentally evolved bacteria differing from those that arise in bacteria during infection. For example, mutations that increase resistance in the laboratory setting may not be tolerated in the complex environment of an infection. It is also of clinical importance to determine whether experimentally evolved mutants have cross-resistance or increased susceptibility (i.e., collateral sensitivity) to other antibiotics. The overall aim of this research was to address these issues and to determine the relevance of mutations arising during experimental evolution to the evolution of antibiotic resistance during infection.

RESULTS

Antibiotic resistance of experimentally evolved mutants.Our research strategy for identifying mutations causing antibiotic resistance is illustrated in Fig. S1 in the supplemental material. First, highly resistant mutants of P. aeruginosa PAO1 were evolved in 13 parallel experiments for each of 3 antibiotics, namely, tobramycin, meropenem, and ciprofloxacin. Each mutant was selected through serial passage on antibiotic gradient agar plates containing increasing amounts of antibiotic, interspersed with growth in antibiotic-free broth. Mutants were considered to have reached maximum resistance when selection failed to give rise to any further increase in resistance (typically between 6 and 8 serial passages). A control culture was passaged 6 times in the absence of antibiotic.

Figure 1 shows the MIC values for each of the 39 experimentally evolved mutants when tested against 6 different antibiotics belonging to 3 different classes. Compared with the parental strain P. aeruginosa PAO1, the evolved mutants had a minimum of 64-fold and a maximum of 2,048-fold increase in their MIC values for the selecting antibiotic. All mutants had similarly increased resistance to a second antibiotic of the same class (levofloxacin, imipenem, and gentamicin for ciprofloxacin-, meropenem-, and tobramycin-selected mutants, respectively).

FIG 1
  • Open in new tab
  • Download powerpoint
FIG 1

MICs of experimentally evolved antibiotic-resistant mutants to fluoroquinolones, carbapenems, and aminoglycosides. MIC values are shown for the parental strain P. aeruginosa PAO1 and for mutants selected for resistance to ciprofloxacin (C1 to C13), meropenem (M1 to M13), or tobramycin (T1 to T13). MIC values are shown in mg/liter and colored based on log2-fold change from strain PAO1, ranging from −4 (gray) to 11 (red). Clinical breakpoints as defined by EUCAST (www.eucast.org) in mg/liter are ≥0.5 (ciprofloxacin [Cip]), ≥1 (levofloxacin [Lev]), ≥8 (meropenem [Mer]), ≥8 (imipenem [Imi]), ≥4 (tobramycin [Tob]), and ≥4 (gentamicin [Gen]). A control culture that underwent six serial passages in the absence of any antibiotic selection had the same MIC for all antibiotics as the parental P. aeruginosa PAO1 strain.

The mutants were also tested for cross-resistance to antibiotics of different classes. Most of the meropenem-selected mutants had increased resistance to fluoroquinolone antibiotics and were at or above the EUCAST clinical breakpoint for resistance in many cases (Fig. 1). Similarly, most of the tobramycin-selected mutants had increased resistance to the carbapenems tested, in particular imipenem. Conversely, most of the ciprofloxacin-selected mutants had increased (collateral) sensitivity to at least one carbapenem or aminoglycoside, and some of the meropenem-selected mutants also had increased aminoglycoside sensitivity.

Growth of antibiotic-resistant mutants.To assess the impact of antibiotic resistance on bacterial growth, we undertook growth analysis for each of the experimentally evolved mutants (Fig. 2; see Fig. S2 and S3 in the supplemental material). Growth was measured as area under the growth curve (AUC) in order to capture differences in both growth rate and bacterial cell density in stationary phase. In the absence of antibiotics, mutants in all three resistance groups grew significantly less well than the parental strain P. aeruginosa PAO1, indicating a reduction in fitness. Tobramycin-selected mutants showing the greatest reduction in growth (median values of 1,147.5, 747.5, 610.3, and 468.0 AUC units for strain PAO1 and for ciprofloxacin-, meropenem-, and tobramycin-resistant mutants, respectively). However, there was a large degree of heterogeneity in growth within each group, with some mutants in each group having at least twice the growth of others in the same group.

FIG 2
  • Open in new tab
  • Download powerpoint
FIG 2

Growth analysis of experimentally evolved mutants in the absence of antibiotics. Growth of each experimentally evolved mutant was measured during 18 h of incubation at 37°C and area under the concentration-time curve (AUC) values were determined. At least 3 biological replicates were carried out for each mutant, with mean AUC values shown. A one-way ANOVA with post hoc Dunnett’s test was carried out on each antibiotic selection using PAO1 as a comparison (n = 11). Bonferroni-corrected P values for Cip-, Mer-, and Tob-evolved mutants are 3.98 × 10−6, 3.36 × 10−7, and 2.11 × 10−10, respectively. Cip, ciprofloxacin; Mer, meropenem; Tob, tobramycin.

Identification of antibiotic resistance-associated mutations.Whole-genome sequencing (WGS) and variant calling of the 39 antibiotic-resistant mutants showed that each mutant contained between 2 and 8 mutations. Mutations were present across 78 genes; 27 genes were mutated in 2 or more mutants (Table 1; see Table S1 in the supplemental material). In addition, 4 intergenic mutations were present in meropenem-resistant mutants and 5 of the 13 meropenem-resistant mutants contained large deletions ranging in size from 225 to 479 kb (Table S1; see Fig. S4 in the supplemental material). Putative large duplications up to 600 kb were identified in 2 tobramycin-resistant isolates and 1 meropenem-resistant isolate (see Fig. S5 in the supplemental material). No mutations were present in bacteria serially passaged in the absence of antibiotic selection.

View this table:
  • View inline
  • View popup
  • Download powerpoint
TABLE 1

Genes mutated in more than one experimentally evolved antibiotic-resistant mutant

Ciprofloxacin-selected mutants.Twenty-nine mutated genes were identified following WGS of the 13 ciprofloxacin-evolved mutants. All of the mutants had a mutation in the gyrA gene and 9 of the mutants also had mutations in the parC or parE genes that encode DNA topoisomerase, known targets of ciprofloxacin (21). Twelve of the mutants also had mutations in nfxB that encodes an efflux pump regulator, with mutations in nfxB being known contributors to ciprofloxacin resistance (21). Ten of the mutants had mutations in pilin-encoding pil genes (Table 1; Table S1). The relationship between pil mutations and ciprofloxacin resistance is not clear, although very recently, other researchers also reported an association between pil mutations and ciprofloxacin resistance (22). Eleven of the mutants had a mutation in the gene PA3491 that, to the best of our knowledge, has not previously been associated with antibiotic resistance.

Meropenem-selected mutants.A total of 26 genes were mutated in the 13 meropenem-selected mutants, with 8 genes mutated in more than 1 mutant (Table 1; Table S1). Many of the mutations were in genes previously associated with meropenem resistance. Eleven of the mutants contained mutations in the porin-encoding oprD gene, with mutations in this gene representing a primary mechanism for carbapenem resistance (13, 23–25). Ten of the mutants had mutations in nalC, nalD, or mexR, with mutations in these genes causing upregulation of the efflux pumps and being associated with β-lactam resistance (26–29). Mutations in ftsI, which encodes the meropenem-binding protein PBP3, were present in 3 mutants, consistent with the known role of such mutations in resistance (13, 16). Mutations were also present in genes not commonly associated with meropenem resistance. Three of the evolved mutants contained mutations within the aroB gene that encodes an enzyme, dehydroquinate synthase, required for the synthesis of aromatic amino acids. Five of the mutants had mutations in genes encoding tRNA ligases, with a different gene being mutated in each case.

Five of the 13 mutants had large deletions ranging in size from 225 kb up to 480 kb. These deletions all overlapped, with the genome region between PA2022 and PA2208 being deleted in all the five mutants. A smaller deletion (1,986 bp) spanning PA2022 to PA2024 was present in one mutant. The deleted region contains PA2023 (galU). Insertion mutations in galU lead to an increase in resistance to meropenem, cephalosporin, and aminoglycosides (24, 25, 30).

Tobramycin-selected mutants.A total of 24 individual genes were mutated across the 13 tobramycin-selected mutants, with 10 genes mutated in more than 1 mutant (Table 1; Table S1). All mutants had at least one mutation in fusA1 that encodes elongation factor G. Mutations in fusA1 have recently been reported to confer tobramycin resistance in laboratory-evolved mutants (19). Nine mutants also had mutations in the wbpL or PA5001 genes that are associated with lipopolysaccharide synthesis, and 10 mutants had mutations in genes directly involved in oxidative phosphorylation and generation of proton motive force (cco, nuoG, PA1549, and PA4429). Mutations affecting either of these processes increased aminoglycoside resistance in a whole-genome screen for resistance genes (31). Several other genes were mutated in a smaller number of tobramycin-selected mutants (Table S1). Many of these, including PA1767, mexY, amgS, and nalC have previously been implicated in aminoglycoside resistance (10, 31–33).

Frequencies of mutations in clinical isolates of P. aeruginosa.It is common for mutations that confer antibiotic resistance in P. aeruginosa to arise during infection in cystic fibrosis patients, with the rate of mutation being increased in some patients by the presence of hypermutator strains (34, 35). We, therefore, addressed the following question: do mutations that arose in the experimental evolution experiments reflect those that have arisen in clinical isolates of P. aeruginosa? To do so, we analyzed the genomes of 558 P. aeruginosa clinical isolates. For comparison, we analyzed the genomes of 172 P. aeruginosa isolates obtained from the general environment and which are, therefore, unlikely to have had antibiotic exposure. All genes that were mutated in two or more of our experimentally evolved mutants were analyzed in each genome to determine whether likely resistance-associated mutations had occurred. The absence of genome sequences of ancestral strains prevented the direct identification of mutations. Instead, genetic variants that are likely to alter protein function by contributing to antibiotic resistance were inferred using PROVEAN, a widely used tool for predicting the likelihood that amino acid differences affect protein function (36). The results are summarized in Table 2.

View this table:
  • View inline
  • View popup
  • Download powerpoint
TABLE 2

Frequencies of predicted change-of-function mutations in clinical and environmental isolates of P. aeruginosa, for genes identified through experimental evolutiona

Many of the genes that were mutated in our experimentally evolved mutants had higher frequencies of predicted function-altering variants in clinical isolates than in environmental isolates. For example, the antibiotic target genes gyrA (ciprofloxacin resistance), ftsI (meropenem resistance), and fusA1 (tobramycin resistance) all had predicted function-altering variants in over 15% of the clinical isolates. In many cases, the variations in the clinical isolates were identical to those in experimentally evolved mutants. For example, the variant T83I in GyrA was present in 96 (51.3% of isolates containing predicted functional variants) clinical isolates. Predicted function-altering variants in genes encoding regulatory proteins that are associated with antibiotic resistance, such as nalD, mexR, and amgS, were also common in the clinical isolates. Predicted differences in mexY, which encodes an efflux pump component known to be associated with tobramycin resistance (32), were also frequent in the clinical isolates. Furthermore, the aroB gene, which does not have a characterized role in antibiotic resistance, was identified through whole-genome sequencing of the experimentally evolved meropenem-resistant mutants and was predicted to have function-affecting differences in over 10% of the clinical isolates. Some genes (pilT, pilF, nalC, and PA1767) that were mutated in our experimentally evolved mutants had only low (<5%) frequencies of predicted function-altering differences in the clinical isolates, implying that mutations in these genes may not be advantageous during infection. However, overall, predicted change-of-function variants in the genes analyzed had significantly less impact on the genomes of the environmental isolates than on the genomes of clinical isolates (P = 2.0 × 10−16) (Fig. 3).

FIG 3
  • Open in new tab
  • Download powerpoint
FIG 3

Comparison of change-of-function variants in clinical and environmental isolates. PROVEAN values were determined for clinical and environmental (ENV) isolates for all variants in genes that were mutated in at least two experimentally evolved antibiotic-resistant mutants. More negative values indicate variants that are more likely to affect function. The dashed line represents a cutoff value of −2.5, with variants below this value being highly likely to affect protein function. The medians and first and third quartiles are shown in the overlaid boxplots (whiskers showing 1.5 times the interquartile range above the 75th percentile and below the 25th percentile). An ANOVA test using Tukey honestly significant difference (HSD) correction showed that there was a significant difference in the PROVEAN values in the clinical group compared with the ENV group (P = 2 × 10−16).

In addition to point mutations, five of the experimentally evolved meropenem-resistant mutants had large (over 200 kb) deletions. Large deletions of comparable size were also present in 87 of the clinical isolates and only 1 of the environmental isolates (see Fig. S6 in the supplemental material; Table 2).

DISCUSSION

In this study, we have extended the understanding of the genetics of antibiotic resistance in P. aeruginosa. In contrast to previous experimental evolution studies, we used an agar-based selection method interspersed with growth in antibiotic-free broth. This methodology, in conjunction with an analysis of a large number of resistant mutants for each of three clinically relevant antibiotics, identified a number of resistance-associated genes not found in other studies. Importantly, comparison with the genomes of P. aeruginosa isolated from chronically infected patients as well as isolates from the general environment has confirmed that many mutations in experimentally evolved mutants are clinically relevant, while indicating that some appear to be restricted to the laboratory situation.

Our approach of carrying out antibiotic selection on agar plates interspersed with periods of antibiotic-free growth has some parallels with the conditions faced by P. aeruginosa when it colonizes the lungs of cystic fibrosis patients. In both circumstances, bacterial growth is on a semisolid surface with intermittent exposure to antibiotics and so selects for mutations that are stably inherited in the absence of antibiotics. Many of the mutations identified here were in genes that were identified in other experimental evolution studies. For example, mutations altering the target site proteins GyrA and ParC as well as the efflux pump regulator NfxB are well-established contributors to fluoroquinolone resistance (21). Our selection protocol and the large number of experimentally evolved mutants that we analyzed also allowed us to identify some genes that were known to contribute to clinical resistance but were not identified in previous experimental evolution studies. These included parE (ciprofloxacin resistance) (37) and amgS and wbpL (tobramycin resistance) (38).

Importantly, experimentally evolved mutants in this study also had mutations in genes not usually associated with resistance. These include PA3491 and pilin-encoding genes in ciprofloxacin-selected mutants; aroB and tRNA ligases in meropenem-selected mutants (16, 25); and PA1767, cco, and wbpL in tobramycin-selected mutants. The occurrence of mutations in these genes in multiple independently evolved lines (Table 1; Table S1) strongly suggests that the mutations increase antibiotic tolerance in our selection system. Entry of tobramycin into P. aeruginosa can be affected by changes to the cell surface or to membrane potential (31, 39), and mutations in cytochrome c oxidase or wbpL may result in such changes. Cytochrome mutations may also reduce the formation of radical oxygen species that have been proposed to contribute to antibiotic-induced cell death (40). How the other genes may affect resistance is not clear. Further research will be required to determine how the mutations in these genes increase resistance as well as their role (if any) in the resistance of clinical isolates.

Another noteworthy observation was the prevalence of large deletions (up to 8% of the genome) in mutants selected for resistance to meropenem, something that has also been observed previously (13). All the deletions overlapped, with PA2023 (galU) that encodes an enzyme required for LPS core synthesis being deleted in all cases. In a genome-wide screen, a mutation in galU increased meropenem tolerance (24, 25). Deletion of galU may, therefore, contribute to increased tolerance in the deletion-containing mutants obtained here, perhaps in conjunction with other deleted genes.

We used PROVEAN to assess the frequency of likely change-of-function mutations in isolates of P. aeruginosa from patients with CF. For comparison, we analyzed a panel of isolates from the general environment (Table 2). A significant proportion of isolates from CF patients are antibiotic resistant (2, 3), whereas environmental isolates are typically sensitive to antibiotics (41). A high proportion of clinical isolates contained likely function-altering differences in genes that are established as contributing to clinical resistance to fluoroquinolones (gyrA and nfxB), carbapenems (mexR), and/or aminoglycosides (mexY, amgS) (Table 2) (9, 10). These genes contained few or no predicted function-altering differences in isolates from the general environment, emphasizing their clinical relevance as well as validating our approach. Three genes, namely, fusA1, ftsI and aroB, that have only recently been identified as affecting antibiotic resistance (aroB in this study) also had predicted function-altering differences in clinical isolates but few or no environmental isolates (13, 19). These findings demonstrate the utility of experimental evolution for identifying clinically relevant genes associated with antibiotic resistance.

The oprD gene that is well established as being associated with carbapenem resistance had a high frequency of predicted function-altering differences in clinical isolates, as expected. It also had a high frequency of such differences in environmental isolates of P. aeruginosa consistent with earlier findings (23), a finding that was related to its role in the environmental adaptation of P. aeruginosa. Nonetheless the mean PROVEAN scores for OprD were lower in the clinical isolates (−2.8 clinical, −0.8 environmental), likely reflecting the need for more severe loss-of-function changes to OprD in contributing to carbapenem resistance.

Conversely, the parE, parC, and nalC genes that were previously shown to influence the resistance phenotype of clinical isolates (26, 37, 42) and in experimental evolution studies (Table 2) (12, 13) had only low frequencies of predicted function-altering differences in the clinical isolates in this study. This may indicate that mutations in these genes are associated with higher levels of antibiotic resistance than that of the isolates in our study. Mutations in these genes may also have a high fitness cost in the clinical environment or their effects may be influenced by epistatic interactions with other genes (43).

A number of genes that were mutated in multiple experimentally evolved mutants are not commonly associated with antibiotic resistance. Many of these, such as pil genes, tpiA, and PA1767, had only low frequencies of predicted function-changing differences in the clinical isolates. This finding suggests that mutations in these genes contribute to increased antibiotic tolerance in laboratory culture but likely do not do so during infection and further emphasizes the importance of comparing experimentally evolved mutants with clinical isolates. In contrast, mutations in genes such as mexZ are well-characterized in clinical isolates (44–48) but were not identified in our experiments or in other experimental evolution studies, indicating that experimental evolution studies alone are not necessarily sufficient to identify all resistance-associated mutations.

The experimentally evolved mutants had increased tolerance to antibiotics of the same class (Fig. 1), as found previously (14). Many of the mutants also had altered MICs for antibiotics of different classes, and some of these changes could be related to the mutations that were present. For example, many of the meropenem-selected mutants had mutations in nalC, and such mutations lead to the overexpression of mexAB-oprM (29), which may reduce susceptibility to fluoroquinolones. Conversely, several of the ciprofloxacin-selected mutants had increased susceptibility to aminoglycosides and to imipenem, likely due to the presence of nfxB mutations (49). The occurrence of collateral changes in antibiotic sensitivity as a result of mutations selected in response to antibiotic exposure has also been observed in isolates of P. aeruginosa from patients (50–53). This has important clinical implications because such outcomes during infection may affect treatment options for patients with CF or other chronic infections.

In the absence of antibiotics, the majority of the experimentally evolved mutants showed significant reductions in growth relative to the wild-type control, consistent with previous studies (Fig. 2) (13, 54–56). It is noteworthy that isolates of P. aeruginosa from patients are often slow growing (57), although the extent to which this phenotype is related to antibiotic resistance is not clear.

The analysis of whole-genome sequences of infecting bacteria, in order to predict which antibiotics will be effective in treatment, has the potential to significantly improve the quality of patient care, and this approach is advanced for some species of bacteria (7, 8). The genetic complexity of antibiotic resistance in P. aeruginosa makes it more challenging to develop accurate tools for the prediction of resistance phenotypes from genome sequences. However, cataloguing the gene set that contributes to resistance phenotypes through experimental evolution, coupled to our analysis of clinical isolates, will advance efforts to develop reliable genome-based prediction tools.

In conclusion, our study demonstrates the power of experimentally evolving multiple mutants for identifying genes that, when mutated, contribute to increased antibiotic resistance. The evolution of multiple independent mutants reveals the frequencies at which mutations arise in individual genes, which is likely to be related to the extent to which they contribute to increased resistance in our selection system. Use of an agar-based method instead of the broth-based methods of earlier studies identified genes not previously associated with resistance and showed that the spectrum of resistance mutations is influenced by the selection protocol. Crucially, the analysis of resistance-associated genes in clinical isolates of P. aeruginosa validated the clinical relevance of genes identified only through experimental evolution approaches, while demonstrating that some genes that are mutated in laboratory-based experiments are unlikely to be relevant to resistance during infection. The extension of our approach to other antibiotics and, indeed, other bacterial species will greatly strengthen our understanding of the genetic basis of antibiotic resistance in infectious bacteria.

MATERIALS AND METHODS

In vitro evolution of antibiotic-resistant mutants.Antibiotic gradient plates were prepared, according to the methodology described by Bryson and Szybalski (58), to evolve meropenem- (Penembact, Venus Remedies Limited), tobramycin- (Mylan New Zealand ltd), and ciprofloxacin- (Cipflox, Mylan New Zealand Ltd.) resistant P. aeruginosa mutants. Briefly, 15 ml aliquots of molten Difco Muller-Hinton (MH) agar were poured into a petri dish tilted at an approximate incline of 15° to ensure a slanted slope of media. When cooled, the petri dish was placed on a flat surface, and an additional 15 ml of molten MH agar, supplemented with the antibiotic treatment, was poured onto the slanted MH agar to create an antibiotic gradient across the plate. Overnight broth culture prepared from a single colony subculture of reference strain P. aeruginosa PAO1 was adjusted to an optical density at 600 nm (OD600) of 0.01 (1.5 × 106 CFU/ml) and a 2-ml aliquot poured onto the antibiotic gradient plate. Excess liquid was removed by pipette following a 10-minute incubation at room temperature. Inoculated plates were incubated under aerobic conditions for 24 hours at 37°C. An isolated single colony, growing furthest up the antibiotic concentration gradient, was selected and cultured overnight in Luria broth at 37°C/200 rpm. This inoculum was then adjusted as described above and used to inoculate a subsequent gradient plate containing antibiotics of one doubling concentration greater than the previous plate. This technique was repeated with increasing antibiotic concentrations until it was no longer possible to identify mutants resistant to higher amounts of antibiotic. Thirteen replicate experiments were carried out for each antibiotic, with one mutant from experiment being used for further study. As a control, strain PAO1 was passaged six times on agar without antibiotic. An overview of methods used is shown in Fig. S6.

MIC determination.MIC determination was carried out in accordance with the protocol described by Wiegand and colleagues (59). Briefly, overnight broth cultures of the evolved mutants and the laboratory strain PAO1 were adjusted to OD600 of 0.01 (1.5 × 106 CFU/ml), and 5-μl aliquots were spread onto MH agar plates containing doubling antibiotic concentrations. Control plates, MH agar without antibiotic supplementation, were used as a growth comparison. All MIC plates were incubated in aerobic conditions at 37°C for 24 hours. The MIC for each isolate was determined as the lowest concentration that inhibited visible growth, excluding single colonies or faint haze. Antibiotics selected for testing included each of the target antibiotics along with one other antibiotic from the same class (aminoglycosides [gentamicin], carbapenems [imipenem], and fluoroquinolones [levofloxacin]). The EUCAST guidelines (www.eucast.org) were used to interpret antimicrobial susceptibility patterns.

Growth analysis.Overnight broth cultures of the antibiotic-resistant mutants and laboratory strain PAO1 were adjusted to OD600 of 0.01 (1.5 × 106 CFU/ml), and 200-μl aliquots were dispensed into JETbiofil 96-well tissue culture plates. The microtiter plates were incubated in a BMG FLUOstar Omega microplate reader at 37°C/200 rpm for 18 hours. Optical density (OD600) was recorded every 30 minutes to measure the growth of the isolates. Area under the growth curve (AUC) was used to give a measure of growth that included lag phase, rate of growth during log phase, and final cell density. Growth dynamics were calculated by using the R package GrowthCurver version 0.2.1 (60). Logistic AUC was used as the metric for quantifying growth.

Clinical and environmental isolates of P. aeruginosa.A cohort of 558 clinical and 172 environmental genome assemblies of P. aeruginosa isolates from multiple countries were used in this study (see Table S2 in the supplemental material) (61, 62). Clinical isolates were from patients with cystic fibrosis, bronchiectasis, or chronic obstructive pulmonary disease. Six isolates from patients with cystic fibrosis were unique to this study.

Statistical analysis.All statistical analyses were carried out using R version 3.5.0 (63). Post hoc Dunnett tests were done using R package multcomp version 1.4-8 (64). Plots were created using R package ggplot2 (65).

Whole-genome sequencing.Genome assemblies of clinical and environmental isolates have been described previously (61, 62) (Table S2). For experimentally evolved mutants and clinical isolates sequenced in this study, genomic DNA was extracted from overnight cultures of endpoint-resistant mutants using the MoBio UltraClean microbial DNA isolation kit. Library preparation and sequencing were carried out by New Zealand Genomics Limited using the Illumina HiSeq 2000 and Illumina MiSeq platforms.

Analysis of genomic data and mutation detection.Raw sequencing reads were examined for quality pre- and posttrimming using FastQC version 0.11.5. Trimming of raw reads was done using Trimmomatic version 0.36 (66). Draft genomes of clinical isolates sequenced in this study were assembled using SPAdes version 3.12.0 (67). The genome sequence of the P. aeruginosa PAO1 used in this study (PAO1-Otago) was assembled using a combination of in-house code and GDtools part of the BreSeq package (68), with mapping to the P. aeruginosa PAO1 genome sequence at https://www.pseudomonas.com (69). A full list of differences between the genomes is shown in Table S3 in the supplemental material. Mutations in the experimentally evolved resistant isolates were identified through comparison to PAO1-Otago using BreSeq version 0.30.0 (68).

Prediction of duplications.For the prediction of duplicated regions within the experimentally evolved mutants, CNOGpro (70) was used. Hits files were generated from bam file output in the breseq analysis and parsed into CNOGpro, G+C content was normalized, and 1,000 bootstrap replicates were performed using quartiles of 0.025 and 0.975. Hidden Markov model (HMM) correction was performed, allowing for a maximum of 3 possible states and allowing for an error rate of 0.01. Tables were output containing the predicted copy number for each gene and intergenic region. Regions were called as putative duplications when there was a sustained increase in predicted copy number across more than 5 genes. Known multicopy operons were removed after comparison to the wild-type P. aeruginosa PAO1-Otago coverage.

Mutant comparisons to clinical and environmental P. aeruginosa isolates.Protein sequences of genes found to be mutated within this study were obtained from https://www.pseudomonas.com (69). These were used as query sequences for a tblastn search against compiled databases containing either clinical or environmental P. aeruginosa isolates (Table S2). BLAST outputs were aligned to the reference PAO1 sequence using Clustal-Omega version 1.2.4 (71). Polymorphic sites were called using a modified version of snp-sites version 2.3.2 (72). Nonsynonymous polymorphic sites (variants) were extracted from vcf files. The effects of variants were predicted using PROVEAN version 1.1 (36) and compiled using BLAST version 2.2.28+ (73), psiBLAST version 2.2.28+ (74), cd-hit version 4.7 (75), and the NCBI nonredundant database (version retrieved 1 May 2019) (76). Variations with a score of −2.5 or below were considered being likely to affect the biological functions of proteins (36).

Data availability.Raw sequence reads (fastq format) of experimentally evolved mutants are available under BioProject accession number PRJNA542028 in the NCBI Sequence Read Archive (SRA). Accession numbers for the genomes of other isolates of P. aeruginosa are listed in Table S2.

ACKNOWLEDGMENTS

This research was supported by research grants to I.L.L. and W.M.P. from the University of Otago and the New Zealand Health Research Council (17/372). C.W. gratefully acknowledges support from the Cystic Fibrosis Trust (UK). S.J.T.W. was supported by a Postgraduate Scholarship from the University of Otago and A.R. by a New Zealand International Doctoral Research Scholarship.

We are very grateful to Roger Levesque and coworkers for making genome assemblies available prior to publication. Additionally, we are grateful to all members of the International Pseudomonas Consortium Database (IPCD), who donated strains utilized in this study. We are grateful to Kay Ramsay and Scott Beatson for their comments on an earlier version of the manuscript.

FOOTNOTES

    • Received 11 August 2019.
    • Returned for modification 9 September 2019.
    • Accepted 23 September 2019.
    • Accepted manuscript posted online 30 September 2019.
  • Supplemental material for this article may be found at https://doi.org/10.1128/AAC.01619-19.

  • Copyright © 2019 American Society for Microbiology.

All Rights Reserved.

REFERENCES

  1. 1.↵
    1. de Bentzmann S,
    2. Plésiat P
    . 2011. The Pseudomonas aeruginosa opportunistic pathogen and human infections. Environ Microbiol 13:1655–1665. doi:10.1111/j.1462-2920.2011.02469.x.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Talwalkar JS,
    2. Murray TS
    . 2016. The approach to Pseudomonas aeruginosa in cystic fibrosis. Clin Chest Med 37:69–81. doi:10.1016/j.ccm.2015.10.004.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Pendleton JN,
    2. Gorman SP,
    3. Gilmore BF
    . 2013. Clinical relevance of the ESKAPE pathogens. Expert Rev Anti Infect Ther 11:297–308. doi:10.1586/eri.13.12.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Winstanley C,
    2. O’Brien S,
    3. Brockhurst MA
    . 2016. Pseudomonas aeruginosa evolutionary adaptation and diversification in cystic fibrosis chronic lung infections. Trends Microbiol 24:327–337. doi:10.1016/j.tim.2016.01.008.
    OpenUrlCrossRefPubMed
  5. 5.↵
    1. Hirsch EB,
    2. Tam VH
    . 2010. Impact of multidrug-resistant Pseudomonas aeruginosa infection on patient outcomes. Expert Rev Pharmacoecon Outcomes Res 10:441–451. doi:10.1586/erp.10.49.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Nathwani D,
    2. Raman G,
    3. Sulham K,
    4. Gavaghan M,
    5. Menon V
    . 2014. Clinical and economic consequences of hospital-acquired resistant and multidrug-resistant Pseudomonas aeruginosa infections: a systematic review and meta-analysis. Antimicrob Resist Infect Control 3:32. doi:10.1186/2047-2994-3-32.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Bradley P,
    2. Gordon NC,
    3. Walker TM,
    4. Dunn L,
    5. Heys S,
    6. Huang B,
    7. Earle S,
    8. Pankhurst LJ,
    9. Anson L,
    10. de Cesare M,
    11. Piazza P,
    12. Votintseva AA,
    13. Golubchik T,
    14. Wilson DJ,
    15. Wyllie DH,
    16. Diel R,
    17. Niemann S,
    18. Feuerriegel S,
    19. Kohl TA,
    20. Ismail N,
    21. Omar SV,
    22. Smith EG,
    23. Buck D,
    24. McVean G,
    25. Walker AS,
    26. Peto TE,
    27. Crook DW,
    28. Iqbal Z
    . 2015. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun 6:10063. doi:10.1038/ncomms10063.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Moradigaravand D,
    2. Palm M,
    3. Farewell A,
    4. Mustonen V,
    5. Warringer J,
    6. Parts L
    . 2018. Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data. PLoS Comput Biol 14:e1006258. doi:10.1371/journal.pcbi.1006258.
    OpenUrlCrossRef
  9. 9.↵
    1. Breidenstein EBM,
    2. de la Fuente-Núñez C,
    3. Hancock REW
    . 2011. Pseudomonas aeruginosa: all roads lead to resistance. Trends Microbiol 19:419–426. doi:10.1016/j.tim.2011.04.005.
    OpenUrlCrossRefPubMedWeb of Science
  10. 10.↵
    1. Poole K
    . 2011. Pseudomonas aeruginosa: resistance to the max. Front Microbiol 2:65. doi:10.3389/fmicb.2011.00065.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Klemm E,
    2. Dougan G
    . 2016. Advances in understanding bacterial pathogenesis gained from whole-genome sequencing and phylogenetics. Cell Host Microbe 19:599–610. doi:10.1016/j.chom.2016.04.015.
    OpenUrlCrossRef
  12. 12.↵
    1. Jorgensen KM,
    2. Wassermann T,
    3. Jensen PO,
    4. Hengzuang W,
    5. Molin S,
    6. Hoiby N,
    7. Ciofu O
    . 2013. Sublethal ciprofloxacin treatment leads to rapid development of high-level ciprofloxacin resistance during long-term experimental evolution of Pseudomonas aeruginosa. Antimicrob Agents Chemother 57:4215–4221. doi:10.1128/AAC.00493-13.
    OpenUrlAbstract/FREE Full Text
  13. 13.↵
    1. Cabot G,
    2. Zamorano L,
    3. Moya B,
    4. Juan C,
    5. Navas A,
    6. Blazquez J,
    7. Oliver A
    . 2016. Evolution of Pseudomonas aeruginosa antimicrobial resistance and fitness under low and high mutation rates. Antimicrob Agents Chemother 60:1767–1778. doi:10.1128/AAC.02676-15.
    OpenUrlAbstract/FREE Full Text
  14. 14.↵
    1. Feng Y,
    2. Jonker MJ,
    3. Moustakas I,
    4. Brul S,
    5. Ter Kuile BH
    . 2016. Dynamics of mutations during development of resistance by Pseudomonas aeruginosa against five antibiotics. Antimicrob Agents Chemother 60:4229–4236. doi:10.1128/AAC.00434-16.
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    1. Lopez-Causape C,
    2. Rubio R,
    3. Cabot G,
    4. Oliver A
    . 2018. Evolution of the Pseudomonas aeruginosa aminoglycoside mutational resistome in vitro and in the cystic fibrosis setting. Antimicrob Agents Chemother 62:e02583-17. doi:10.1128/AAC.02583-17.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Jorth P,
    2. McLean K,
    3. Ratjen A,
    4. Secor PR,
    5. Bautista GE,
    6. Ravishankar S,
    7. Rezayat A,
    8. Garudathri J,
    9. Harrison JJ,
    10. Harwood RA,
    11. Penewit K,
    12. Waalkes A,
    13. Singh PK,
    14. Salipante SJ
    . 2017. Evolved aztreonam resistance is multifactorial and can produce hypervirulence in Pseudomonas aeruginosa. mBio 8:e00517-17. doi:10.1128/mBio.00517-17.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. Barbosa C,
    2. Trebosc V,
    3. Kemmer C,
    4. Rosenstiel P,
    5. Beardmore R,
    6. Schulenburg H,
    7. Jansen G
    . 2017. Alternative evolutionary paths to bacterial antibiotic resistance cause distinct collateral effects. Mol Biol Evol 34:2229–2244. doi:10.1093/molbev/msx158.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Jochumsen N,
    2. Marvig RL,
    3. Damkiær S,
    4. Jensen RL,
    5. Paulander W,
    6. Molin S,
    7. Jelsbak L,
    8. Folkesson A
    . 2016. The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is shaped by strong epistatic interactions. Nat Commun 7:13002. doi:10.1038/ncomms13002.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Bolard A,
    2. Plesiat P,
    3. Jeannot K
    . 2017. Mutations in gene fusA1 as a novel mechanism of aminoglycoside resistance in clinical strains of Pseudomonas aeruginosa. Antimicrob Agents Chemother 62:e01835-17. doi:10.1128/AAC.01835-17.
    OpenUrlAbstract/FREE Full Text
  20. 20.↵
    1. Clark ST,
    2. Sinha U,
    3. Zhang Y,
    4. Wang PW,
    5. Donaldson SL,
    6. Coburn B,
    7. Waters VJ,
    8. Yau YCW,
    9. Tullis DE,
    10. Guttman DS,
    11. Hwang DM
    . 2019. Penicillin binding protein 3 is a common adaptive target among Pseudomonas aeruginosa isolates from adult cystic fibrosis patients treated with beta-lactams. Int J Antimicrob Agents 53:620–628. doi:10.1016/j.ijantimicag.2019.01.009.
    OpenUrlCrossRef
  21. 21.↵
    1. Rehman A,
    2. Patrick WM,
    3. Lamont IL
    . 2019. Mechanisms of ciprofloxacin resistance in Pseudomonas aeruginosa: new approaches to an old problem. J Med Microbiol 68:1–10. doi:10.1099/jmm.0.000873.
    OpenUrlCrossRef
  22. 22.↵
    1. Ahmed MN,
    2. Porse A,
    3. Sommer MOA,
    4. Hoiby N,
    5. Ciofu O
    . 2018. Evolution of antibiotic resistance in biofilm and planktonic Pseudomonas aeruginosa populations exposed to subinhibitory levels of ciprofloxacin. Antimicrob Agents Chemother 62:e00320-18. doi:10.1128/AAC.00320-18.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    1. Pirnay JP,
    2. De Vos D,
    3. Mossialos D,
    4. Vanderkelen A,
    5. Cornelis P,
    6. Zizi M
    . 2002. Analysis of the Pseudomonas aeruginosa oprD gene from clinical and environmental isolates. Environ Microbiol 4:872–882. doi:10.1046/j.1462-2920.2002.00281.x.
    OpenUrlCrossRefPubMedWeb of Science
  24. 24.↵
    1. Dotsch A,
    2. Becker T,
    3. Pommerenke C,
    4. Magnowska Z,
    5. Jansch L,
    6. Haussler S
    . 2009. Genomewide identification of genetic determinants of antimicrobial drug resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother 53:2522–2531. doi:10.1128/AAC.00035-09.
    OpenUrlAbstract/FREE Full Text
  25. 25.↵
    1. Alvarez-Ortega C,
    2. Wiegand I,
    3. Olivares J,
    4. Hancock REW,
    5. Martínez JL
    . 2010. Genetic determinants involved in the susceptibility of Pseudomonas aeruginosa to beta-lactam antibiotics. Antimicrob Agents Chemother 54:4159–4167. doi:10.1128/AAC.00257-10.
    OpenUrlAbstract/FREE Full Text
  26. 26.↵
    1. Quale J,
    2. Bratu S,
    3. Gupta J,
    4. Landman D
    . 2006. Interplay of efflux system, ampC, and oprD expression in carbapenem resistance of Pseudomonas aeruginosa clinical isolates. Antimicrob Agents Chemother 50:1633–1641. doi:10.1128/AAC.50.5.1633-1641.2006.
    OpenUrlAbstract/FREE Full Text
  27. 27.↵
    1. Tomás M,
    2. Doumith M,
    3. Warner M,
    4. Turton JF,
    5. Beceiro A,
    6. Bou G,
    7. Livermore DM,
    8. Woodford N
    . 2010. Efflux pumps, OprD porin, AmpC beta-lactamase, and multiresistance in Pseudomonas aeruginosa isolates from cystic fibrosis patients. Antimicrob Agents Chemother 54:2219–2224. doi:10.1128/AAC.00816-09.
    OpenUrlAbstract/FREE Full Text
  28. 28.↵
    1. Braz VS,
    2. Furlan JP,
    3. Fernandes AF,
    4. Stehling EG
    . 2016. Mutations in NalC induce MexAB-OprM overexpression resulting in high level of aztreonam resistance in environmental isolates of Pseudomonas aeruginosa. FEMS Microbiol Lett 363:fnw166. doi:10.1093/femsle/fnw166.
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. López-Causapé C,
    2. Cabot G,
    3. Del Barrio-Tofiño E,
    4. Oliver A
    . 2018. The versatile mutational resistome of Pseudomonas aeruginosa. Front Microbiol 9:685. doi:10.3389/fmicb.2018.00685.
    OpenUrlCrossRef
  30. 30.↵
    1. El’Garch F,
    2. Jeannot K,
    3. Hocquet D,
    4. Llanes-Barakat C,
    5. Plesiat P
    . 2007. Cumulative effects of several nonenzymatic mechanisms on the resistance of Pseudomonas aeruginosa to aminoglycosides. Antimicrob Agents Chemother 51:1016–1021. doi:10.1128/AAC.00704-06.
    OpenUrlAbstract/FREE Full Text
  31. 31.↵
    1. Schurek KN,
    2. Marr AK,
    3. Taylor PK,
    4. Wiegand I,
    5. Semenec L,
    6. Khaira BK,
    7. Hancock RE
    . 2008. Novel genetic determinants of low-level aminoglycoside resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother 52:4213–4219. doi:10.1128/AAC.00507-08.
    OpenUrlAbstract/FREE Full Text
  32. 32.↵
    1. Lau CH,
    2. Hughes D,
    3. Poole K
    . 2014. MexY-promoted aminoglycoside resistance in Pseudomonas aeruginosa: involvement of a putative proximal binding pocket in aminoglycoside recognition. mBio 5:e01068. doi:10.1128/mBio.01068-14.
    OpenUrlCrossRefPubMed
  33. 33.↵
    1. Lau CH-F,
    2. Fraud S,
    3. Jones M,
    4. Peterson SN,
    5. Poole K
    . 2013. Mutational activation of the AmgRS two-component system in aminoglycoside-resistant Pseudomonas aeruginosa. Antimicrob Agents Chemother 57:2243–2251. doi:10.1128/AAC.00170-13.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    1. Sriramulu D
    . 2013. Evolution and impact of bacterial drug resistance in the context of cystic fibrosis disease and nosocomial settings. Microbiol Insights 6:29–36. doi:10.4137/MBI.S10792.
    OpenUrlCrossRef
  35. 35.↵
    1. Oliver A,
    2. Canton R,
    3. Campo P,
    4. Baquero F,
    5. Blazquez J
    . 2000. High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288:1251–1254. doi:10.1126/science.288.5469.1251.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Choi Y,
    2. Sims GE,
    3. Murphy S,
    4. Miller JR,
    5. Chan AP
    . 2012. Predicting the functional effect of amino acid substitutions and indels. PLoS One 7:e46688. doi:10.1371/journal.pone.0046688.
    OpenUrlCrossRefPubMed
  37. 37.↵
    1. Lee JK,
    2. Lee YS,
    3. Park YK,
    4. Kim BS
    . 2005. Alterations in the GyrA and GyrB subunits of topoisomerase II and the ParC and ParE subunits of topoisomerase IV in ciprofloxacin-resistant clinical isolates of Pseudomonas aeruginosa. Int J Antimicrob Agents 25:290–295. doi:10.1016/j.ijantimicag.2004.11.012.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Lau CH,
    2. Krahn T,
    3. Gilmour C,
    4. Mullen E,
    5. Poole K
    . 2015. AmgRS-mediated envelope stress-inducible expression of the mexXY multidrug efflux operon of Pseudomonas aeruginosa. Microbiologyopen 4:121–135. doi:10.1002/mbo3.226.
    OpenUrlCrossRefPubMed
  39. 39.↵
    1. Bryan LE,
    2. Kwan S
    . 1983. Roles of ribosomal binding, membrane potential, and electron transport in bacterial uptake of streptomycin and gentamicin. Antimicrob Agents Chemother 23:835–845. doi:10.1128/aac.23.6.835.
    OpenUrlAbstract/FREE Full Text
  40. 40.↵
    1. Kohanski MA,
    2. Dwyer DJ,
    3. Collins JJ
    . 2010. How antibiotics kill bacteria: from targets to networks. Nat Rev Microbiol 8:423–435. doi:10.1038/nrmicro2333.
    OpenUrlCrossRefPubMedWeb of Science
  41. 41.↵
    1. Ramsay KA,
    2. Wardell SJT,
    3. Patrick WM,
    4. Brockway B,
    5. Reid DW,
    6. Winstanley C,
    7. Bell SC,
    8. Lamont IL
    . 2019. Genomic and phenotypic comparison of environmental and patient-derived isolates of Pseudomonas aeruginosa suggests that antimicrobial resistance is rare within the environment. J Medical Microbiology doi:10.1099/jmm.0.001085.
    OpenUrlCrossRef
  42. 42.↵
    1. Yang X,
    2. Xing B,
    3. Liang C,
    4. Ye Z,
    5. Zhang Y
    . 2015. Prevalence and fluoroquinolone resistance of Pseudomonas aeruginosa in a hospital of South China. Int J Clin Exp Med 8:1386–1390.
    OpenUrl
  43. 43.↵
    1. Vogwill T,
    2. Kojadinovic M,
    3. MacLean RC
    . 2016. Epistasis between antibiotic resistance mutations and genetic background shape the fitness effect of resistance across species of Pseudomonas. Proc Biol Sci 283:20160151. doi:10.1098/rspb.2016.0151.
    OpenUrlCrossRefPubMed
  44. 44.↵
    1. Llanes C,
    2. Hocquet D,
    3. Vogne C,
    4. Benali-Baitich D,
    5. Neuwirth C,
    6. Plésiat P
    . 2004. Clinical strains of Pseudomonas aeruginosa overproducing MexAB-OprM and MexXY efflux pumps simultaneously. Antimicrob Agents Chemother 48:1797–1802. doi:10.1128/aac.48.5.1797-1802.2004.
    OpenUrlAbstract/FREE Full Text
  45. 45.↵
    1. Jahandideh S
    . 2013. Diversity in structural consequences of MexZ mutations in Pseudomonas aeruginosa. Chem Biol Drug Des 81:600–606. doi:10.1111/cbdd.12104.
    OpenUrlCrossRefPubMed
  46. 46.↵
    1. López-Causapé C,
    2. Sommer LM,
    3. Cabot G,
    4. Rubio R,
    5. Ocampo-Sosa AA,
    6. Johansen HK,
    7. Figuerola J,
    8. Cantón R,
    9. Kidd TJ,
    10. Molin S,
    11. Oliver A
    . 2017. Evolution of the Pseudomonas aeruginosa mutational resistome in an international cystic fibrosis clone. Sci Rep 7:5555. doi:10.1038/s41598-017-05621-5.
    OpenUrlCrossRef
  47. 47.↵
    1. Prickett MH,
    2. Hauser AR,
    3. McColley SA,
    4. Cullina J,
    5. Potter E,
    6. Powers C,
    7. Jain M
    . 2017. Aminoglycoside resistance of Pseudomonas aeruginosa in cystic fibrosis results from convergent evolution in the mexZ gene. Thorax 72:40–47. doi:10.1136/thoraxjnl-2015-208027.
    OpenUrlAbstract/FREE Full Text
  48. 48.↵
    1. Frimodt-Møller J,
    2. Rossi E,
    3. Haagensen JAJ,
    4. Falcone M,
    5. Molin S,
    6. Johansen HK
    . 2018. Mutations causing low level antibiotic resistance ensure bacterial survival in antibiotic-treated hosts. Sci Rep 8:12512. doi:10.1038/s41598-018-30972-y.
    OpenUrlCrossRef
  49. 49.↵
    1. Imamovic L,
    2. Ellabaan MMH,
    3. Dantas Machado AM,
    4. Citterio L,
    5. Wulff T,
    6. Molin S,
    7. Krogh Johansen H,
    8. Sommer M
    . 2018. Drug-driven phenotypic convergence supports rational treatment strategies of chronic infections. Cell 172:121–134.e14. doi:10.1016/j.cell.2017.12.012.
    OpenUrlCrossRef
  50. 50.↵
    1. Masuda N,
    2. Sakagawa E,
    3. Ohya S,
    4. Gotoh N,
    5. Nishino T
    . 2001. Hypersusceptibility of the Pseudomonas aeruginosa nfxB mutant to beta-lactams due to reduced expression of the ampC beta-lactamase. Antimicrob Agents Chemother 45:1284–1286. doi:10.1128/AAC.45.4.1284-1286.2001.
    OpenUrlAbstract/FREE Full Text
  51. 51.↵
    1. Vettoretti L,
    2. Plesiat P,
    3. Muller C,
    4. El Garch F,
    5. Phan G,
    6. Attree I,
    7. Ducruix A,
    8. Llanes C
    . 2009. Efflux unbalance in Pseudomonas aeruginosa isolates from cystic fibrosis patients. Antimicrob Agents Chemother 53:1987–1997. doi:10.1128/AAC.01024-08.
    OpenUrlAbstract/FREE Full Text
  52. 52.↵
    1. Imamovic L,
    2. Sommer MO
    . 2013. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci Transl Med 5:204ra132. doi:10.1126/scitranslmed.3006609.
    OpenUrlAbstract/FREE Full Text
  53. 53.↵
    1. Pál C,
    2. Papp B,
    3. Lázár V
    . 2015. Collateral sensitivity of antibiotic-resistant microbes. Trends Microbiol 23:401–407. doi:10.1016/j.tim.2015.02.009.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Wong A,
    2. Kassen R
    . 2011. Parallel evolution and local differentiation in quinolone resistance in Pseudomonas aeruginosa. Microbiology 157:937–944. doi:10.1099/mic.0.046870-0.
    OpenUrlCrossRefPubMedWeb of Science
  55. 55.↵
    1. Gifford DR,
    2. MacLean RC
    . 2013. Evolutionary reversals of antibiotic resistance in experimental populations of Pseudomonas aeruginosa. Evolution 67:2973–2981. doi:10.1111/evo.12158.
    OpenUrlCrossRefPubMedWeb of Science
  56. 56.↵
    1. Melnyk AH,
    2. Wong A,
    3. Kassen R
    . 2015. The fitness costs of antibiotic resistance mutations. Evol Appl 8:273–283. doi:10.1111/eva.12196.
    OpenUrlCrossRefPubMed
  57. 57.↵
    1. Malone JG
    . 2015. Role of small colony variants in persistence of Pseudomonas aeruginosa infections in cystic fibrosis lungs. Infect Drug Resist 8:237–247. doi:10.2147/IDR.S68214.
    OpenUrlCrossRef
  58. 58.↵
    1. Bryson V,
    2. Szybalski W
    . 1952. Microbial Selection. Science 116:45–51. doi:10.1126/science.116.3003.45.
    OpenUrlFREE Full Text
  59. 59.↵
    1. Wiegand I,
    2. Hilpert K,
    3. Hancock RE
    . 2008. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat Protoc 3:163–175. doi:10.1038/nprot.2007.521.
    OpenUrlCrossRefPubMedWeb of Science
  60. 60.↵
    1. Sprouffske K,
    2. Wagner A
    . 2016. Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinformatics 17:172. doi:10.1186/s12859-016-1016-7.
    OpenUrlCrossRef
  61. 61.↵
    1. Freschi L,
    2. Vincent AT,
    3. Jeukens J,
    4. Emond-Rheault JG,
    5. Kukavica-Ibrulj I,
    6. Dupont MJ,
    7. Charette SJ,
    8. Boyle B,
    9. Levesque RC
    . 2019. The Pseudomonas aeruginosa pan-genome provides new insights on its population structure, horizontal gene transfer, and pathogenicity. Genome Biol Evol 11:109–120. doi:10.1093/gbe/evy259.
    OpenUrlCrossRef
  62. 62.↵
    1. Hilliam Y,
    2. Moore MP,
    3. Lamont IL,
    4. Bilton D,
    5. Haworth CS,
    6. Foweraker J,
    7. Walshaw MJ,
    8. Williams D,
    9. Fothergill JL,
    10. De Soyza A,
    11. Winstanley C
    . 2017. Pseudomonas aeruginosa adaptation and diversification in the non-cystic fibrosis bronchiectasis lung. Eur Respir J 49:1602108. doi:10.1183/13993003.02108-2016.
    OpenUrlAbstract/FREE Full Text
  63. 63.↵
    R Development Core Team. 2017. R: a language and environment for statistical computing, v3.4.3. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
  64. 64.↵
    1. Hothorn T,
    2. Bretz F,
    3. Westfall P
    . 2008. Simultaneous inference in general parametric models. Biom J 50:346–363. doi:10.1002/bimj.200810425.
    OpenUrlCrossRefPubMedWeb of Science
  65. 65.↵
    1. Wickham H
    . 2016. ggplot2: elegant graphics for data analysis. Springer-Verlag, New York, NY.
  66. 66.↵
    1. Bolger AM,
    2. Lohse M,
    3. Usadel B
    . 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. doi:10.1093/bioinformatics/btu170.
    OpenUrlCrossRefPubMedWeb of Science
  67. 67.↵
    1. Bankevich A,
    2. Nurk S,
    3. Antipov D,
    4. Gurevich AA,
    5. Dvorkin M,
    6. Kulikov AS,
    7. Lesin VM,
    8. Nikolenko SI,
    9. Pham S,
    10. Prjibelski AD,
    11. Pyshkin AV,
    12. Sirotkin AV,
    13. Vyahhi N,
    14. Tesler G,
    15. Alekseyev MA,
    16. Pevzner PA
    . 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477. doi:10.1089/cmb.2012.0021.
    OpenUrlCrossRefPubMed
  68. 68.↵
    1. Deatherage DE,
    2. Barrick JE
    . 2014. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol Biol 1151:165–188. doi:10.1007/978-1-4939-0554-6_12.
    OpenUrlCrossRefPubMedWeb of Science
  69. 69.↵
    1. Winsor GL,
    2. Griffiths EJ,
    3. Lo R,
    4. Dhillon BK,
    5. Shay JA,
    6. Brinkman FS
    . 2016. Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database. Nucleic Acids Res 44:D646–D653. doi:10.1093/nar/gkv1227.
    OpenUrlCrossRefPubMed
  70. 70.↵
    1. Brynildsrud O,
    2. Snipen LG,
    3. Bohlin J
    . 2015. CNOGpro: detection and quantification of CNVs in prokaryotic whole-genome sequencing data. Bioinformatics 31:1708–1715. doi:10.1093/bioinformatics/btv070.
    OpenUrlCrossRefPubMed
  71. 71.↵
    1. Sievers F,
    2. Wilm A,
    3. Dineen D,
    4. Gibson TJ,
    5. Karplus K,
    6. Li W,
    7. Lopez R,
    8. McWilliam H,
    9. Remmert M,
    10. Soding J,
    11. Thompson JD,
    12. Higgins DG
    . 2011. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539. doi:10.1038/msb.2011.75.
    OpenUrlAbstract/FREE Full Text
  72. 72.↵
    1. Page AJ,
    2. Taylor B,
    3. Delaney AJ,
    4. Soares J,
    5. Seemann T,
    6. Keane JA,
    7. Harris SR
    . 2016. SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments. Microb Genom 2:e000056. doi:10.1099/mgen.0.000056.
    OpenUrlCrossRefPubMed
  73. 73.↵
    1. Camacho C,
    2. Coulouris G,
    3. Avagyan V,
    4. Ma N,
    5. Papadopoulos J,
    6. Bealer K,
    7. Madden TL
    . 2009. BLAST+: architecture and applications. BMC Bioinformatics 10:421. doi:10.1186/1471-2105-10-421.
    OpenUrlCrossRefPubMed
  74. 74.↵
    1. Altschul SF,
    2. Madden TL,
    3. Schaffer AA,
    4. Zhang J,
    5. Zhang Z,
    6. Miller W,
    7. Lipman DJ
    . 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402. doi:10.1093/nar/25.17.3389.
    OpenUrlCrossRefPubMedWeb of Science
  75. 75.↵
    1. Li W,
    2. Godzik A
    . 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659. doi:10.1093/bioinformatics/btl158.
    OpenUrlCrossRefPubMedWeb of Science
  76. 76.↵
    1. Pruitt KD,
    2. Tatusova T,
    3. Maglott DR
    . 2007. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35:D61–D65. doi:10.1093/nar/gkl842.
    OpenUrlCrossRefPubMedWeb of Science
  77. 77.
    1. King JD,
    2. Kocincova D,
    3. Westman EL,
    4. Lam JS
    . 2009. Review: lipopolysaccharide biosynthesis in Pseudomonas aeruginosa. Innate Immun 15:261–312. doi:10.1177/1753425909106436.
    OpenUrlCrossRefPubMedWeb of Science
PreviousNext
Back to top
Download PDF
Citation Tools
A Large-Scale Whole-Genome Comparison Shows that Experimental Evolution in Response to Antibiotics Predicts Changes in Naturally Evolved Clinical Pseudomonas aeruginosa
Samuel J. T. Wardell, Attika Rehman, Lois W. Martin, Craig Winstanley, Wayne M. Patrick, Iain L. Lamont
Antimicrobial Agents and Chemotherapy Nov 2019, 63 (12) e01619-19; DOI: 10.1128/AAC.01619-19

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Print

Alerts
Sign In to Email Alerts with your Email Address
Email

Thank you for sharing this Antimicrobial Agents and Chemotherapy article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
A Large-Scale Whole-Genome Comparison Shows that Experimental Evolution in Response to Antibiotics Predicts Changes in Naturally Evolved Clinical Pseudomonas aeruginosa
(Your Name) has forwarded a page to you from Antimicrobial Agents and Chemotherapy
(Your Name) thought you would be interested in this article in Antimicrobial Agents and Chemotherapy.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A Large-Scale Whole-Genome Comparison Shows that Experimental Evolution in Response to Antibiotics Predicts Changes in Naturally Evolved Clinical Pseudomonas aeruginosa
Samuel J. T. Wardell, Attika Rehman, Lois W. Martin, Craig Winstanley, Wayne M. Patrick, Iain L. Lamont
Antimicrobial Agents and Chemotherapy Nov 2019, 63 (12) e01619-19; DOI: 10.1128/AAC.01619-19
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Top
  • Article
    • ABSTRACT
    • INTRODUCTION
    • RESULTS
    • DISCUSSION
    • MATERIALS AND METHODS
    • ACKNOWLEDGMENTS
    • FOOTNOTES
    • REFERENCES
  • Figures & Data
  • Info & Metrics
  • PDF

KEYWORDS

Pseudomonas aeruginosa
adaptive mutations
antibiotic resistance
ciprofloxacin
cross-resistance
cystic fibrosis
drug resistance evolution
experimental evolution
meropenem
tobramycin

Related Articles

Cited By...

About

  • About AAC
  • Editor in Chief
  • Editorial Board
  • Policies
  • For Reviewers
  • For the Media
  • For Librarians
  • For Advertisers
  • Alerts
  • AAC Podcast
  • RSS
  • FAQ
  • Permissions
  • Journal Announcements

Authors

  • ASM Author Center
  • Submit a Manuscript
  • Article Types
  • Ethics
  • Contact Us

Follow #AACJournal

@ASMicrobiology

       

ASM Journals

ASM journals are the most prominent publications in the field, delivering up-to-date and authoritative coverage of both basic and clinical microbiology.

About ASM | Contact Us | Press Room

 

ASM is a member of

Scientific Society Publisher Alliance

 

American Society for Microbiology
1752 N St. NW
Washington, DC 20036
Phone: (202) 737-3600

Copyright © 2021 American Society for Microbiology | Privacy Policy | Website feedback

Print ISSN: 0066-4804; Online ISSN: 1098-6596