1. Evolutionary Biology
  2. Microbiology and Infectious Disease
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Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa

  1. Camilo Barbosa
  2. Roderich Römhild
  3. Philip Rosenstiel
  4. Hinrich Schulenburg  Is a corresponding author
  1. University of Kiel, Germany
  2. Max Planck Institute for Evolutionary Biology, Germany
  3. UKSH, Germany
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Cite this article as: eLife 2019;8:e51481 doi: 10.7554/eLife.51481

Abstract

Evolution is at the core of the impending antibiotic crisis. Sustainable therapy must thus account for the adaptive potential of pathogens. One option is to exploit evolutionary trade-offs, like collateral sensitivity, where evolved resistance to one antibiotic causes hypersensitivity to another one. To date, the evolutionary stability and thus clinical utility of this trade-off is unclear. We performed a critical experimental test on this key requirement, using evolution experiments with Pseudomonas aeruginosa, and identified three main outcomes: (i) bacteria commonly failed to counter hypersensitivity and went extinct; (ii) hypersensitivity sometimes converted into multidrug resistance; and (iii) resistance gains frequently caused re-sensitization to the previous drug, thereby maintaining the trade-off. Drug order affected the evolutionary outcome, most likely due to variation in the effect size of collateral sensitivity, epistasis among adaptive mutations, and fitness costs. Our finding of robust genetic trade-offs and drug-order effects can guide design of evolution-informed antibiotic therapy.

eLife digest

Over time bacteria can undergo a number of genetic mutations that allow them to evolve in response to changes in their surrounding environment. This process of ‘bacterial evolution’ is one of the major causes of antibiotics resistance, whereby disease-causing microorganisms become resistant to multiple drugs and can no longer be destroyed using antibiotic treatment. However, when bacteria become resistant to a drug this can result in an evolutionary trade-off known as ‘collateral sensitivity’ – when evolving resistance to one drug causes bacteria to gain increased sensitivity to another.

Now, Barbosa, Roemhild et al. have investigated whether this evolutionary trade-off could be exploited to tackle the antibiotic crisis and prevent bacteria adapting to different treatments. If this evolutionary trade-off is to be used medically, it must be stable long enough for the bacteria population to either become extinct, or less able to evolve multi-drug resistance. To test how stable collateral sensitivity is over time, Barbosa, Roemhild et al. studied the bacterium Pseudomonas aeruginosa which is known to evolve collateral sensitivity to certain drug treatments.

P. aeruginosa were subjected to two rounds of evolution: first, bacteria were evolved to resist ‘Drug A’ and at the same time became more sensitive to another drug, ‘Drug B’. The bacteria were then allowed to adapt to Drug B either alone or in the presence of Drug A. These evolutionary experiments revealed that the following factors affected the stability of the trade-off: the molecular structure of the antibiotic bacteria evolved sensitivity to, the strength of the original evolutionary trade-off (i.e. how sensitive bacteria became), the order drugs were administrated, and whether resistance came at a large fitness cost (i.e. when the genetic mutations promoting resistance affect bacteria’s ability to replicate and survive in normal conditions).

According to the World Health Organization, P. aeruginosa is the second most problematic multi-drug resistant bacteria. The data collected in this study could therefore be used to develop a new antibiotic therapeutic strategy for fighting this bacterium, as well as other microbes which are resistant to multiple drugs.

Introduction

Treatment of infectious diseases and cancer often fail because of the rapid evolution of drug resistance (Bloemberg et al., 2015; Davies and Davies, 2010; Gottesman, 2002; Zaretsky et al., 2016). Optimal therapy should thus anticipate how resistance to treatment evolves and exploit this knowledge to improve therapy (Gatenby et al., 2009; Imamovic and Sommer, 2013). One promising strategy is based on evolved collateral sensitivity: the evolution of resistance against one drug A concomitantly causes hypersensitivity (i.e., collateral sensitivity) to a second drug B (Szybalski and Bryson, 1952). If evolved collateral sensitivity is reciprocal, it can – in theory – trap the bacteria in a double bind, thereby preventing the emergence of multidrug resistance during treatment (Baym et al., 2016; Pál et al., 2015; Roemhild and Schulenburg, 2019). Recent large-scale studies have demonstrated that evolved collateral sensitivity is pervasive in laboratory strains and clinical isolates of distinct bacterial species (Barbosa et al., 2017; Imamovic et al., 2018; Imamovic and Sommer, 2013; Jansen et al., 2016; Jiao et al., 2016; Lázár et al., 2014; Lázár et al., 2013; Oz et al., 2014; Podnecky et al., 2018) as well as cancer cells (Dhawan et al., 2017; Pluchino et al., 2012; Shaw et al., 2016; Zhao et al., 2016). More importantly, evolved collateral sensitivity can slow down resistance evolution during combination (Barbosa et al., 2018; Rodriguez de Evgrafov et al., 2015; Munck et al., 2014) and sequential therapy (Kim et al., 2014; Roemhild et al., 2015), and also limit the spread of plasmid-borne resistance genes (Rosenkilde et al., 2019).

Although collateral sensitivity appears to be pervasive, its utility for medical application is still dependent on several additional factors. Firstly, the evolution of collateral sensitivity should ideally be repeatable for a given set of conditions (Nichol et al., 2019). This means that independent populations selected with the same drug should produce identical collateral effects when exposed to a second one. Such high repeatability is not always observed. Recent work even identified evolution of contrasting collateral effects (i.e., some populations with evolved collateral sensitivity and others with cross-resistance) for different bacteria, including Pseudomonas aeruginosa (Barbosa et al., 2017), Escherichia coli (Nichol et al., 2019; Oz et al., 2014), Enterococcus faecalis (Maltas and Wood, 2019), and a BCR-ABL leukemeia cell line (Zhao et al., 2016). These patterns are likely due to the stochastic nature of mutations combined with alternative evolutionary paths to resistance against the first selective drug, subsequently causing distinct collateral effects against other drugs (Barbosa et al., 2017; Nichol et al., 2019). Secondly, the evolution of collateral sensitivity should ideally be repeatable across conditions, for example different population sizes. This is not always the case. For example, an antibiotic pair, which consistently produced collateral sensitivity in small Staphylococcus aureus populations (e.g., 106), instead produced complete cross-resistance in large populations (e.g., 109) and thus an escape from the evolutionary constraint, most likely due to the higher likelihood of advantageous rare mutations under these conditions (Jiao et al., 2016).

A third and largely unexplored factor is that evolved collateral sensitivity and, hence, the resistance trade-off should be stable across time. This implies that bacteria either cannot evolve to overcome collateral sensitivity and thus die out, or, if they achieve resistance to the new drug B, they should concomitantly be re-sensitized to the original drug A. Two recent studies, both with different main research objectives, yielded some insight into this topic. One example was focused on historical contingency during antibiotic resistance evolution of P. aeruginosa (Yen and Papin, 2017). As part of the results, the authors identified lineages with evolved resistance against ciprofloxacin that simultaneously showed increased sensitivity to piperacillin and tobramycin. The reverse pattern (i.e., evolved high resistance to either piperacillin or tobramycin and increased sensitivity to ciprofloxacin) was not observed and, thus, this case represents an example of uni-directional collateral sensitivity. The subsequent exposure of the ciprofloxacin-resistance lineages to either piperacillin or tobramycin led to the evolution of resistance against these two antibiotics and substantial (yet not complete) re-sensitization to ciprofloxacin. The second study focused on evolving E. coli populations in a morbidostat, in which the bacteria were exposed to repeated switches between two drugs (Yoshida et al., 2017). The evolution of multidrug resistance was only prevented in the two treatments with polymyxin B that were also characterized by evolved collateral sensitivity, although again only in one direction (Yoshida et al., 2017). To date, the general relevance of this third factor is still unclear, especially for conditions when collateral sensitivity is reciprocal and when the evolving populations are also allowed to go extinct (i.e., they cannot overcome the evolutionary trade-off).

Here, we specifically tested the potential of the model pathogen P. aeruginosa to escape reciprocal collateral sensitivity through de-novo evolution. We focused on the first switch between two drugs, because the evolutionary dynamics after this first switch will reveal the ability of the bacteria to adapt to the second drug, against which they evolved sensitivity, and, if so, whether this causes re-sensitization to the first drug. These two aspects are key criteria for applicability of a treatment strategy that exploits evolved collateral sensitivities. Our analysis is based on a two-step evolution experiment. Bacteria first evolved resistance against a first drug A and concomitant sensitivity against a second drug B. Thereafter, bacteria were subjected to a second evolution experiment, during which they were allowed to adapt to the second drug B, either alone or additionally in the presence of the first drug A. Phenotypic characterization of the evolved bacteria was combined with genomic and functional genetic analyses, in order to determine the exact targets of selection under these conditions. We finally validated the findings made by performing further, independent sets of similar two-step evolution experiments.

Results

The experimental design of our two-step evolution experiment is illustrated in Figure 1a. We took advantage of previously evolved, highly resistant P. aeruginosa populations, which we obtained from serial passage experiments with increasing concentrations of clinically relevant bactericidal antibiotics (drug A, Figure 1a) (Barbosa et al., 2017). From these, we identified two cases of reciprocal collateral sensitivity, including (i) piperacillin/tazobactam (PIT) and streptomycin (STR), or (ii) carbenicillin (CAR) and gentamicin (GEN). In the current study, we now re-assessed the reciprocity of collateral effects using dose-response analysis (Figure 1b and c, Supplementary file 1–Figure 1–supplementary table 1, Figure 1—source data 1). Thereafter, we isolated resistant colonies from these populations and switched treatment to the drug, against which collateral sensitivity was observed (drug B, Figure 1a). By starting this treatment step with clonal lineages, we here tested the ability of P. aeruginosa to overcome collateral sensitivity by de-novo mutation. The evolutionary challenge was initiated at sub-inhibitory concentrations of each drug (vertical black dashed lines in Figure 1b and c, Supplementary file 1–Figure 1–supplementary table 1, Figure 1—source data 1), followed by linear concentration increases at two different rates: mild or strong (vertical orange and blue dashed lines respectively, Figure 1b and c, Supplementary file 1–Figure 1–supplementary table 1, Figure 1—source data 1). We specifically chose linear increases, because our main objective was to better understand the evolutionary dynamics occurring during the first switch of a collateral sensitivity cycling strategy. Linear increases would, in this case, facilitate evolutionary rescue and provide ample opportunity to escape the double bind, thereby yielding a conservative measure for the applicability of collateral sensitivity as a treatment strategy. We additionally considered treatments where antibiotics were also switched to collateral sensitivity, but selection by drug A was continued in combination with drug B; hereby denoted as constrained environments. Overall, four selective conditions were run in parallel: mild or strong increases of the second drug B in either the presence (constrained) or absence (unconstrained) of the first drug A (Figure 1a). We further included control experiments without antibiotics. To determine treatment success, we monitored bacterial growth with continuous absorbance measurements, quantified frequencies of population extinction, and characterized changes in antibiotic resistance of the evolved bacteria as previously evaluated for P. aeruginosa and other bacteria (Barbosa et al., 2018; Barbosa et al., 2017; Hegreness et al., 2008).

Reciprocal collateral sensitivity and experimental design.

(a) Two-step experimental evolution: resistant populations of P. aeruginosa PA14 were previously experimentally evolved (Barbosa et al., 2017) with increasing concentrations of a particular drug (here labeled A), resulting in bacteria becoming hypersensitive to other drugs (here labeled B). In a second step, A-resistant clones were experimentally evolved in the presence of drug B, using four selection regimes: (i) strong dose increase of drug B in the presence of a constant high dose of drug A; (ii) mild dose increase of B in the presence of A; (iii) strong dose increases of B in the absence of A; and (iv) mild dose increase of B in the absence of A. Concentrations of B were increased using linear ramps starting at IC50 (dashed black lines) and ending at levels above the IC95 of the collaterally sensitive clone (mild increases, dashed orange line), or that of the PA14 wild type strain (strong increases, dashed black lines; detailed information on concentrations in Supplementary Table 1). (b) Validated reciprocity of collateral sensitivity for the isolated resistant clones and drug pair PIT/STR, and (c) CAR/GEN. Mean ±CI95, n = 8. Vertical dashed lines indicate the starting (black) and final doses of the mild (light orange) and strong drug increases (light blue). CAR, carbenicillin; GEN, gentamicin; STR, streptomycin; PIT, piperacillin with tazobactam; superscript R denotes resistance against the particular drug. The following supplementary table and source data are available for Figure 1b and c: Supplementary file 1-Figure 1-supplementary table 1 and Figure 1—source data 1.

Extinction rates were high even under mild selection regimes

The imposed antibiotic selection frequently caused population extinction (Figure 2, Figure 2—source data 1), even though sublethal drug concentrations were used. Extinction events occurred significantly more often when selection for the original resistance was maintained by the presence of both drugs (extinction in constrained treatments vs. only one drug, χ2 = 12.9, df = 1, p<0.0001; Figure 2). In treatments with only the second drug B, extinction occurred significantly more often under strong, but not mild concentration increases (strong vs. mild increases in unconstrained environments, χ2 = 5.5, df = 1, p=0.019). Drug switches with the antibiotic pair STR/PIT was particularly successful, with 33 extinction events (~51%, Figure 2). The results differed for the CAR/GEN pair, which produced only seven extinctions (~10%), all restricted to one drug order, GEN>CAR, suggesting asymmetry in the ability to counter collateral sensitivity. The observed differences in the extinction levels of the pairs considered here are unlikely to be the result of differences in combined synergy, since both combinations (PIT/STR and CAR/GEN) are synergistic against P. aeruginosa (Barbosa et al., 2018). From this, we conclude that strong genetic constraints against an evolutionary response to collateral sensitivity caused frequent population extinctions for STR/PIT switches, whereas evolutionary rescue was possible for the GEN/CAR pair, although influenced by drug identity and order.

Extinction events during second step of experimental evolution.

From left to right, extinction events for PITR-clones challenged with STR, STRR-clones challenged with PIT, CARR-clones challenged with GEN, and GENR-clones challenged with CAR. Superscript R indicates resistance against the particular antibiotic, as evolved during the first step of the evolution experiment. The following source data is available for Figure 2: Figure 2—source data 1.

Novel resistance evolved rapidly in many of the surviving populations

We subsequently focused our analysis of the evolutionary dynamics on the surviving populations of the CAR/GEN pair and identified rapid adaptive responses, especially when not constrained by the presence of the two drugs (Figure 3). We measured bacterial adaptation using relative biomass (see Materials and methods and Roemhild et al., 2018) and found it to have increased in all surviving populations (Figure 3a and b, Supplementary file 1–Figure 3–supplementary table 1, Figure 3—source data 1). For both drug orders, the increase was significantly slower in the constrained treatments, and, to a lesser extent, for the strong concentration increases (Figure 3a and b). Consistent with the asymmetry in extinction, the CAR>GEN switch (with no extinction) maintained a high relative biomass across time, while the reverse direction GEN>CAR (with extinction) produced lower relative biomass levels. These results indicate that P. aeruginosa can evolve resistance against a drug, to which it had previously shown hypersensitivity, and that such evolutionary rescue is favored for the suboptimal switch. For the STR/PIT pair, we generally obtained similar results (Figure 3—figure supplement 1a and b, Figure 3—source data 1); yet because of the few surviving populations and high variation among these rare survivors, the results remained inconclusive. We thus continued to focus on the evolved populations for the CAR/GEN pair and asked how the new adaptation influenced the original drug resistances.

Figure 3 with 2 supplements see all
Contrasting evolutionary stability of collateral sensitivity for CAR>GEN and GEN>CAR switches.

Evolutionary dynamics of surviving populations expressed as relative biomass for (a) CARR-populations during selection with GEN, and (b) GENR-populations during selection with CAR. The dotted horizontal line indicates growth equal to untreated controls. Mean ±CI95, number of biological replicates differs due to extinction (n = 2–8). Changes in antibiotic resistance at the end of the second-step evolution experiment for (c) CARR-populations after selection with GEN and (d) GENR-populations after selection with CAR. Resistance was tested either against the drug towards which bacteria initially showed resistance after the first evolution experiment (indicated as OLD), or the drug used during the second experiment (indicated as NEW). The change is measured by cumulative differences in dose-response before and after the second evolution experiment (i.e., the original antibiotic resistant clone versus its evolved descendants). Mean ±CI95, n = 2–8 biological replicates (differences due to extinction). Asterisks indicate significant changes in resistance (one-sample t-test, µ = 0, FDR-adjusted probabilities). Change of maximum exponential growth rate in drug-free conditions for the evolved lineages, relative to wild type PA14 for (e) CARR>GEN lineages and, (f) GENR>CAR lineages. This measure is used to explore the presence of a general adaptation trade-off. The evolved lineages were grouped by whether experimental evolution was performed under constrained conditions (i.e., presence of drug A and B; dark gray circles) or not (i.e., only presence of drug B; light gray circles). The dashed line in each panel then indicates relative growth rate of the starting resistant population. Asterisks show significant increases or decreases in growth rate relative to the wild type PA14 (One-sample t-test, µ = 1, p<0.004), while numerals indicate significant increases or decreases relative to the starting population (dashed lines in each panel, One-sample t-test, µ = GENR or CARR, p<0.004). Number of populations per group and experiment vary due to extinction (min = 10, max = 16). The following supplementary figures, tables and source data is available for Figure 3: Figure 3—figure supplement 1, Figure 3—figure supplement 2, Supplementary file 1-Figure 3-supplementary tables 1-3, and Figure 3—source data 1; Figure 3—source data 2; Figure 3—source data 3.

Drug order determined re-sensitization or emergence of multidrug resistance

Adaptation in the surviving populations of the CAR/GEN pair caused multidrug resistance in the suboptimal switch, but re-sensitization to similar levels to those of the PA14 ancestor (Figure 3—figure supplement 2, Figure 3—figure supplement 2—source datas 1 and 2) in the alternative switch (Figure 3c and d, Figure 3—source data 2). In detail, all surviving populations significantly increased resistance against the second drug (IC90-fold change between two and >64 times that of the starting populations; Figure 3c and d, Supplementary file 1–Figure 3–supplementary tables 2 and 3, Figure 3—source data 2) – in agreement with the recorded biomass dynamics. In the suboptimal switch, CAR>GEN, all populations maintained their original resistance, thereby yielding bacteria with multidrug resistance. This was different for the alternative direction GEN>CAR, where the original resistance was only maintained when both drugs were present in combination (constrained environments). Only under unconstrained evolution, we observed cases of significant re-sensitization to the first drug. We conclude that drug order can determine treatment efficacy, enhance or minimize multidrug resistance, and, in specific cases, lead to a re-sensitization towards the first drug in the surviving populations, as required for applicability of collateral sensitivity cycling (Imamovic and Sommer, 2013).

We hypothesized that the contrasting evolutionary outcomes in constrained versus unconstrained treatments of the GEN>CAR switch were caused by an additional trade-off, in this case between drug resistance and growth rate. We obtained a proxy for such a general trade-off, which is comparable across the distinct antibiotic treatments, by measuring maximum exponential growth rate under drug-free conditions and standardizing it against the corresponding growth rate for the ancestral PA14 strain. Even though measured in drug-free environments, a possible reduction in growth rates may still indicate a general growth constraint or adaptation trade-off, which is also relevant under other conditions (i.e., under antibiotic exposure). The starting clones for the second evolution experiment indeed showed significantly impaired growth rates under drug-free conditions, with up to 50% reductions relative to the ancestor (Barbosa et al., 2017). As a consequence, selection may have favored variants for which both trade-offs (i.e., the collateral sensitivity and also the general adaptation trade-offs) were ameliorated during evolution. For the GEN>CAR switch, we indeed found a significant increase in growth rate relative to the wild type PA14 in the unconstrained treatments (Figure 3f, Figure 3—source data 3). Constrained populations for this particular switch still showed a significantly reduced growth rate relative to PA14, with however significantly improved values relative to the starting population (Figure 3f, Figure 3—source data 3). The alternative switch did not show similar variations, mainly due to the fact that the costs of the initial population were not as high (Figure 3e, Figure 3—source data 3). Altogether, this data suggests that selection under the GEN>CAR unconstrained treatments provided the dual advantage of reversing two previously acquired evolutionary trade-offs, namely hypersensitivity to a second drug and increases in growth rate. Thus, re-sensitization could have been favored over multidrug resistance because of the associated adaptation trade-off that can ultimately influence treatment outcome upon collateral sensitivity switches.

Whole genome sequencing identified possible targets of antibiotic selection

We used population genomic analysis to characterize specific functional changes that were likely targeted by antibiotic selection and allowed populations to survive the second evolution experiment for the CAR/GEN pair (Figure 4, Figure 4—source data 1). In particular, we sequenced whole genomes of the resistant starting clones from the beginning and 21 surviving populations from the end of the second evolution experiment. Our results reveal that the evolution of multidrug resistance in the suboptimal switch CAR>GEN can be explained by the sequential fixation of mutations including, under unconstrained conditions, those in ptsP (Figure 4a, Figure 4—source data 1), a main component of the global regulatory system of ABC transporters and other virulence factors (Feinbaum et al., 2012). Similarly, under constrained conditions, we found mutations in the NADH-dehydrogenase genes nuoD or nuoG (Figure 4a, Figure 4—source data 1), which are known to influence proton motive force and resistance against aminoglycosides upon inactivation (El'Garch et al., 2007).

Genomics of experimental evolution for the CAR/GEN drug pair.

(a) Most relevant genomic changes in CAR-resistant populations selected with GEN, and (b) GEN-resistant populations selected with CAR. Square symbols next to gene names indicate ancestral resistance mutations (obtained from Barbosa et al., 2017), and circles indicate newly acquired mutations. The numbers inside these symbols indicate variant frequencies (as inferred by the percentage of reads in population genomics data) and correspond to the lowest frequency found among the sequenced populations from the respective treatment. The evolved resistance phenotype is highlighted by color shading (see legend in bottom left). (c) MIC relative to PA14 against CAR and (d) GEN, of single and double mutant strains. The cross and bold horizontal lines indicate the MIC as expected by addition of the individual effects. Blue arrows highlight epistatic effects. The following supplementary source data is available for Figure 4: Figure 4—source data 1; Figure 4—source data 2.

For the more effective switch GEN>CAR, multidrug resistance in the constrained treatments coincided with mutations in mexR, phoQ, and cpxS, an independent regulator of MexAB-OprM (Li et al., 2016) and two-component regulators involved in aminoglycoside resistance (Gooderham and Hancock, 2009) and envelope stress response (Roemhild et al., 2018), respectively. The re-sensitization towards the first drug in the unconstrained GEN>CAR treatments was associated with two main types of mutational changes at high frequencies across several replicates, including (i) mutations in nalC and nalD that upregulate the expression of the multidrug-efflux system MexAB-OprM in P. aeruginosa (Li et al., 2016); and (ii) large deletions in pmrB, which is part of a two-component regulatory system (Figure 4b, Figure 4—source data 1). Mutations in nalC were previously shown to mediate both resistance to CAR and hypersensitivity to GEN (Barbosa et al., 2017). Thus, re-sensitization to GEN may be caused by antagonistic pleiotropy of nalC mutations that override the resistance of the original pmrB mutation (Figure 4, Figure 4—source data 1). In addition, there may be epistasis between the two functional modules. A complementary mechanism for re-sensitization against GEN is the re-mutation of pmrB (Figure 4, Figure 4—source data 1). In three cases nalC mutations coincided with mutations in pmrB, including two deletions of 17 and 225 base pairs. Whilst the original SNP in pmrB altered gene function, the latter deletions may have suppressed the expression of the original SNP by pseudogenizing the gene. We conclude that mutations in the nalC or nalD regulators of the MexAB-OprM pump, sometimes in combination with follow up mutations in pmrB are likely to account for the re-sensitization towards the first drug GEN.

Functional genetic analysis revealed asymmetric epistasis among adaptive mutations

We next investigated whether epistasis between the two functional modules of efflux regulation (MexAB-OprM regulation by nalC or nalD) and surface charge modification (pmrB) may have contributed to re-sensitization using functional genetic analysis. The respective single and double mutations were re-constructed in the common ancestral background of PA14 (see Materials and methods for the specific mutations) and changes in resistance against CAR and GEN were measured using fold-change of minimal inhibitory concentrations (MIC, Figure 4c and d, Figure 4—source data 2). On CAR, the pmrB mutant had half of the MIC of PA14 (confirming collateral sensitivity), whilst nalC and nalD mutants had increased resistance to CAR. The double mutants had lower MIC on CAR than the nalC and nalD single mutants (Figure 4c, Figure 4—source data 2). The extent of MIC changes in the double mutants corresponded to the product of the individual effects in the respective single mutants, thus indicating an additive interaction among mutations on CAR. On GEN, however, the double mutants had substantially lower MICs than expected from the single mutants (Figure 4d, Figure 4—source data 2), strongly suggesting negative epistasis. In detail, GEN-resistance relative to PA14 was 0.4x for nalC (collateral sensitivity), 1x for nalD, and 57x for pmrB (Figure 4d). The pmrB, nalD double mutant had 3x lower MIC to GEN than expected from the individual effects. The pmrB, nalC double mutant had >30 x lower MIC to GEN than expected from the individual effects, resulting in greater sensitivity than PA14 (Figure 4d, Figure 4—source data 2). Altogether, we conclude that re-sensitization to GEN is mediated by antagonistic pleiotropy and negative epistasis.

Repetition of experimental evolution revealed an influence of drug type on population extinction and drug re-sensitization

To validate and generalize our findings, we repeated evolution experiments for 14 cases of collateral sensitivity, using a total of 38 distinct resistant populations (Figure 5aFigure 5—source data 1), obtained from independent biological replicates of our previous study (Barbosa et al., 2017). We initiated experiments with the available populations (Barbosa et al., 2017), rather than clones, and applied a less severe bottleneck of 107 cells. Therefore, any adaptative changes may not exclusively rely on de-novo mutations but could also result from available genetic diversity, thus reflecting a more general scenario for evolutionary adaptation to collateral sensitivity. The 14 cases of collateral sensitivity included the same four examples tested above and 10 additional cases, encompassing both reciprocal and also uni-directional collateral sensitivities. The effect size of collateral sensitivity, measured as fold-IC75, differed substantially among replicates (Figure 5a, Figure 5—figure supplement 1Figure 5—source data 1). In the current study, we now subjected the total of 38 resistant populations to either strong or strong+constrained increases of the second drug (Figure 5a) and assessed treatment outcome by measuring population extinction rates and changes in resistance profiles of the surviving populations (Supplementary file 1–Figure 5-supplementary tables 1-2, Figure 5—figure supplement 1—source data 1).

Figure 5 with 1 supplement see all
Evolutionary stability of collateral sensitivity in a larger set of drug switches.

(a) Fourteen collateral sensitivity treatments were tested using 38 distinct resistant populations as starting points. The resistant populations differed with respect to the effect size of collateral sensitivity. The left panel illustrates the treatment directions and effect size variation in different shades of purple, while the right panel shows a quantitative presentation of effect size variation. (b) Total extinction events, and endpoint resistance changes to drug A (here labeled OLD) and B (here labeled NEW) after treatment with drug B using strong concentration increases in the absence (strong) or the presence of drug A (strong + constrained). Mean ±CI95, n = 1–6 biological replicates (differences due to extinction). Asterisks indicate significant changes in resistance (one-sample t-test, µ = 0, FDR-adjusted probabilities). CS, collateral sensitivity; CAR, carbenicillin; GEN, gentamicin; PIT, piperacillin with tazobactam; STR, streptomycin; CIP, ciprofloxacin; CEF, cefsulodin; superscript R denotes resistance. The following supplementary figure, tables and source data are available for Figure 5: Figure 5—figure supplement 1, Supplementary file 1-Figure 5–supplementary tables 1-2, Figure 5—source data 1Figure 5—source data 2Figure 5—source data 3, and Figure 5—figure supplement 1—source data 1.

Our validation experiment now demonstrated that extinction events were frequent and occurred significantly more often when selection for the original resistance was maintained by the presence of both drugs (extinction in constrained vs. strong environments, χ2 = 16.204, df = 1, p<0.0001; Figure 5bFigure 5—source data 2). Extinction rates showed pronounced variation by drug identity and order. For example, CIP-resistant populations all survived when challenged with either of the aminoglycosides (STR, GEN), whereas more than 50% of these populations went extinct when exposed to β-lactams (CAR, PIT, Figure 5b). We specifically assessed the importance of drug target, collateral sensitivity effect size (fold-IC75), and relative growth rate under drug-free conditions, using a generalized linear model (GLM; Figure 6—source data 1). We extracted data on the relative exponential growth rate of the resistant populations in drug-free media from our previous publication (Barbosa et al., 2017). Our analysis revealed that variation in extinction was significantly associated with only the molecular target of the second antibiotic (GLM, combined extinction, n = 14 drug switches, F = 11.016, p=0.011; Supplementary file 1–Figure 6–supplementary table 1, Figure 6—source data 1), but not with growth rate, extent of collateral sensitivity, or target of the first antibiotic. Extinction frequencies were on average twice as high in the six treatments that switched to β-lactam antibiotics that target the cell wall, as compared to the eight treatments that switched to aminoglycoside antibiotics that target the ribosome (Figure 6a). It thus appears that extinctions in our experiments are stochastic events that are mainly influenced by the target of the current antibiotic, but not the preceding evolutionary conditions. Taken together, we conclude that switching from one to two drugs generally increases treatment potency and that this is influenced by drug order and identity. The preferred drug order is to use a β-lactam as the second antibiotic, which is consistent with the detailed dynamics on the clonal level.

Predictors of evolutionary stability of collateral sensitivity in a larger set of drug switches.

(a) Extinction was more likely when treatment was switched to β-lactam antibiotics that target the cell wall, as compared to aminoglycoside antibiotics that target ribosomes (box plot, n = 6 drug switches for ribosome, n = 8 treatments for the cell wall). Combined extinction events from both treatment groups are reported. (b) Surviving, evolved lineages showed stronger re-sensitization when the first antibiotic targeted the ribosome (box plot, n = 4 drug switches starting with ribosome inhibition, n = 4 drug switches starting with cell-wall inhibition, and n = 6 drug switches starting with DNA gyrase inhibition). (c) Evolution of new resistance was significantly positively associated with the average effect size of collateral sensitivity, as measured by fold-change of IC75. Values of sensitivity increase to the left. (d) Average initial growth rate were significantly associated with the resistance gains, with low resistance gains associated with large general adaptation trade-offs. CS, collateral sensitivity. Blue lines in c and d provide an illustration of the linear association between the respective variables. The following supplementary tables and source data are available for (Figure 6: Supplementary file 1-Figure 6-supplementary tables 1-2, and Figure 6—source data 1; Figure 6—source data 2).

Resistance gains to drug B were frequently associated with re-sensitization to the original drug A in the unconstrained treatments, while resistance to the new drug B increased in all 14 tested cases (leading to changes of 2 up to >64 times the IC90 of the starting populations; Supplementary file 1–Figure 5–supplementary tables 1 and 2). In detail, we observed significant antibiotic re-sensitization for 5 of the 14 cases (36%, Figure 5b, Figure 5—source data 3, Supplementary file 1–Figure 5–supplementary table 1, Figure 5—source data 1; Note that the single available replicate for STR>PIT showed a similar trend but could not be evaluated statistically). Within reciprocal collateral sensitivity, re-sensitization was not always observed in both directions, as illustrated by CAR/STR and STR/PIT. Re-sensitization did not occur in the constrained treatments. We noticed some differences between evolutionary outcomes of drug switches in clonal and population experiments. The resistance changes showed an overall categorical agreement of 75% (9/12 comparisons; no comparisons for STR>PIT, as this switch was tested with a single replicate). Yet, clonal evolution produced re-sensitization for only one sequential treatment direction for CAR/GEN, whereas population evolution showed re-sensitization for both directions. That re-sensitization occurred in both directions at the population level is expected if there was a residual frequency of cells lacking the collateral-sensitivity mutation in the inoculum. The population data further highlighted a lack of re-sensitization for PIT>STR, where we previously observed re-sensitization on the clonal level, albeit not at both concentration regimes. In addition, there is a mild significant increase of resistance to GEN in the strong constrained treatments in GEN>CAR, compared to no change. Overall our results indicate that re-sensitization is frequent for populations with collateral sensitivity, but not imperative (Figure 5b).

What are the drivers of the observed resistance gains and re-sensitization? Our statistical analysis revealed that the extent of re-sensitization in the unconstrained treatments was significantly associated to equal degrees with the target of drug A (GLM, resistance against drug A, n = 14, F = 15.27, p=0.0019; Supplementary file 1–Figure 6–supplementary table 2, Figure 6—source data 2), the effect size of collateral sensitivity (F = 15.91, p=0.004), and growth rate in drug-free media (F = 18.38, p=0.0027), while target of drug B did not have a significant influence. Re-sensitization was stronger in drug switches that started with an aminoglycoside, as compared to β-lactam, and fluoroquinolone (Figure 6b). This result is consistent with the drug-dependent negative epistasis (Figure 4), and validates the relative stability of collateral sensitivity switches from aminoglycosides to β -lactams. Growth rate in drug-free conditions and collateral sensitivity effect size showed small negative effects, indicating that they decelerate re-sensitization. In contrast, the extent of resistance gains in unconstrained treatments was not associated with drug targets of either drug A or B, but instead significantly associated with both the initial effect size of collateral sensitivity (GLM, resistance against drug B, n = 14, F = 6.71, p=0.0321; Supplementary file 1-Figure 6–supplementary table 2, Figure 6—source data 2), and the initial growth rate of the resistant population in drug-free media (GLM, resistance against drug B, n = 14, F = 7.90, p=0.0228). Collateral sensitivity effect size showed a negative association with the resistance gains to drug B (Figure 6c). High sensitivity thus appears to be prone to more rapid resistance gains, possibly because it opens a fitness space for rapid adaptation. The population resistance changes against drug B were generally larger than the initial collateral sensitivity (Figure 5b; Figure 5—source data 3); Supplementary file 1-Figure 5-supplementary tables 1-2; evolved lineages showed higher resistance than the PA14 wild type), indicating that they were not solely due to competition of pre-existing sub-populations, but involved new genetic adaptations. As observed above, the presence of a general adaptation trade-off (measured as reduction in growth rate relative to ancestral PA14 in drug-free environments) inhibited rapid adaptation (Figure 6d). Taken together, our results at the population level suggest that the stability of collateral sensitivity switching is shaped predominantly by drug targets, but additionally influenced by the effect size of collateral sensitivity and a general adaptation trade-off. The most stable collateral sensitivity was identified for the switch from aminoglycoside to β-lactam. Within a particular drug switch, the highest stability was achieved for low collateral sensitivity effect sizes. Large adaptation trade-offs tended to inhibit evolutionary change, that is both desirable re-sensitization, and new resistance. The conclusions are consistent with the data obtained from the clonal evolution experiments.

Discussion

Collateral sensitivity is a pervasive feature of resistance evolution, but its potential for medical application is currently debated (Nichol et al., 2019; Podnecky et al., 2018; Roemhild and Schulenburg, 2019). Its promise as a treatment focus is dependent on the premise that the exploited trade-off is evolutionarily stable and cannot be easily overcome. As a consequence, it should either drive bacterial populations to extinction or minimize the emergence of multidrug resistance by re-sensitization to one of the antibiotics. We here tested the validity of these key predictions with the help of evolution experiments and the model pathogen P. aeruginosa. We found that the effective exploitation of evolved collateral sensitivity in sequential therapy is contingent on drug order and combination, collateral sensitivity effect size, general adaptation trade-offs, and also epistatic genetic interactions.

Evolved reciprocal collateral sensitivity generally limited bacterial adaptation. Adaptation was particularly constrained in treatments that switched to a β-lactam, as reflected by the elevated extinction rates. The effect was strongest when the first antibiotic was maintained and a second was added. This finding may point to a promising, yet currently unexplored treatment strategy, namely single-drug therapy followed by combination therapy (for example the addition of a β-lactam), that can maximize exploitation of the evolutionary trade-off. Yet, extinction rates were still high under unconstrained conditions, when drugs were replaced, and in spite of a relatively mild selection intensity. In the detailed analysis with the CAR/GEN drug pair, we observed higher extinction and slower growth improvements in strong, compared to mild dose increases. This finding is generally consistent with previous studies, performed in different context, in which narrowed mutation space upon fast environmental deterioration increased extinction frequencies (Bell and Gonzalez, 2011; Lindsey et al., 2013). Interestingly, extinction rates are often not reported as an evolutionary outcome in related studies, possibly because of a different main focus of the study (Yen and Papin, 2017), or because extinction could not be recorded due to the particular experimental set-up (i.e., usage of a morbidostat; Yoshida et al., 2017). Considering that antimicrobial therapy usually aims at elimination of bacterial pathogens and extinction frequencies are known from previous evolution experiments to vary among treatment types (Barbosa et al., 2018; Hansen et al., 2017; Roemhild et al., 2018; Torella et al., 2010), their consideration should help us to refine our understanding of treatment efficacy.

In the treatments that replaced drugs, evolutionary stability of the resistance trade-off was determined by drug order. In both datasets (clonal and population-level evolution), we observed high relative stability for collateral sensitivity treatments switching from an aminoglycoside to a β-lactam. Our detailed analysis for the CAR/GEN pair at the clonal level demonstrates that this stability may be caused by slower adaptation (Figure 3a), and efficient re-sensitization (Figure 3c). Also, the slow adaptation rates may be the result of selection favoring the reversal of two evolutionary trade-offs (Figures 3e and 6d) together with a relative paucity of accessible resistance mutations. The latter is highlighted by several instances of re-mutation of pmrB, where the effect of the ancestral collateral-sensitivity SNP was countered by additional larger deletions, potentially leading to a loss of function of the same gene (Figure 4b). The efficient re-sensitization that was observed during switches from an aminoglycoside to a β-lactam may be explained by pleiotropy and drug-specific negative epistasis (Figure 4c and d). On the other hand, collateral sensitivity treatments that switch to aminoglycosides, tended to show lower stability, as reflected by the lower levels of extinction (Figure 6a) and the lack of re-sensitization (Figure 6b) at the population level. For instance, the collateral sensitivity treatments CAR>STR and CIP>STR were frequently countered by the evolving bacteria. These treatments hardly resulted in extinction but allowed for rapid loss of susceptibility to both drugs (Figure 5b). While our data cannot fully explain the high instability, it appears that small values for a general adaptation trade-off (i.e., indicated by reduced growth rates in drug-free environments), which are also frequently observed for CIP resistance mutations (Huseby et al., 2017), accelerated adaptation (Figure 6d). Overall, our finding of strong variation in the stability of collateral sensitivity treatments in pathogenic P. aeruginosa indicate the importance for a careful evaluation of new treatment options.

Our detailed analyses for GEN>CAR emphasize the importance of epistasis for the stability of collateral sensitivity. A recent publication confirmed that the expression of particular collateral sensitivity mutations strongly depended on the genetic background and could even cause opposite effects in the closely related species of E. coli and Salmonella enterica due to epistasis (Apjok et al., 2019). Our work on the GEN>CAR switch now shows, that epistasis is of high importance also for the temporal stability of antibiotic sensitivity. Here, drug re-sensitization in the unconstrained treatments was likely dependent on negative epistasis among pleiotropic resistance mutations. Mutations in pmrB and the efflux regulators nalC and nalD interacted negatively with each other and caused a complete re-sensitization of bacteria that were previously resistant against GEN. While re-sensitization reliably occurred for the GEN>CAR treatment, it did not occur in the reverse case. Similar examples of antibiotic re-sensitization were previously reported for E. coli and P. aeruginosa, but these relied on different mechanisms. For E. coli, repeated alternation between two antibiotics led to re-sensitization as a consequence of clonal interference between variants in two genes, secD and/or basB (Yoshida et al., 2017). The change between drugs prevented fixation of the competing variants, thus maintaining pleiotropic alleles and thereby the allele causing resistance to one drug and hypersensitivity to the other (Yoshida et al., 2017). In the previous example for P. aeruginosa, hypersensitivity to a β-lactam depended on an expression imbalance of the MexAB-OprM and the MexEF-OprN efflux systems after exposure to a fluoroquinolone (Maseda et al., 2004; Sobel et al., 2005; Yen and Papin, 2017). Interestingly, partial re-sensitization against the aminoglycoside tobramycin was dependent on inducible resistance, a phenomenon mediated by the MexXY-OprM efflux pump, whereby expression, and consequently resistance, is induced by the presence of the drug, but then reverted after its removal (Hocquet et al., 2003; Yen and Papin, 2017). We conclude that our finding of negative epistasis between pleiotropic resistance mutations is a previously unknown mechanism underlying re-sensitization. Whilst positive epistasis can substantially amplify resistance gains (Wistrand-Yuen et al., 2018), negative epistasis can limit evolutionary trajectories (Weinreich et al., 2006), thus possibly contributing to efficacy of treatment in our case.

The mutations observed in this study are commonly associated with variants observed in clinical isolates, particularly in those obtained from cystic fibrosis patients (Hancock and Speert, 2000; Jansen et al., 2016; Marvig et al., 2015; Tueffers et al., 2019). Both efflux regulators (including nalC and nalD) and two-component regulatory systems (mainly pmrAB and phoQF) were repeatedly reported to be associated with intermediate and highly resistant isolates of P. aeruginosa, E. coli, Acinetobacter baumannii and other pathogenic species against colistin and aminoglycosides (Cao et al., 2004; Gerson et al., 2019; Sato et al., 2018). This overlap suggests that the negative epistasis between the genes involved in resistance against β-lactam and aminoglycosides observed here could also be encountered and exploited in clinical settings.

We anticipate that the findings of our study could help to guide the design of sustainable antibiotic therapy that controls the infection, whilst reducing the emergence of multidrug resistance. In principle, the refined exploitation of collateral sensitivity could represent a promising addition to new evolution-informed treatment strategies, including as alternatives specific combination treatments (Barbosa et al., 2018; Chait et al., 2007; Rodriguez de Evgrafov et al., 2015; Gonzales et al., 2015; Munck et al., 2014), fast sequential therapy (Nichol et al., 2015; Yoshida et al., 2017), or treatments utilizing negative hysteresis (Roemhild et al., 2018). The success of this treatment strategy depends on several key factors. One critical prerequisite is that collateral sensitivity does evolve in response to treatment, which may vary depending on alternative evolutionary paths to resistance against the initially used drug A (Barbosa et al., 2017; Nichol et al., 2019). Our new data additionally suggest that treatment can be further optimized by switching from an aminoglycoside to a β-lactam and/or by focusing on drugs, for which resistance is associated with large general adaptation trade-offs, and/or by using drug combinations with small collateral sensitivity effect sizes. These new insights clearly warrant further evaluation, for example by using clinical isolates of P. aeruginosa and/or patient-related test conditions.

Materials and methods

Material

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All experiments were performed with P. aeruginosa UCBPP-PA14 (Rahme et al., 1995) and clones obtained from four antibiotic-resistant populations: CAR-10, GEN-4, PIT-1 and STR-2 (Barbosa et al., 2017). The resistant populations were previously selected for high levels of resistance against protein synthesis inhibitors from the aminoglycoside family, gentamicin (GEN; Carl Roth, Germany; Ref. HN09.1) and streptomycin (STR; Sigma-Aldrich, USA; Ref. S6501-5G), or alternatively cell-wall synthesis inhibitors from the β-lactam family, carbenicillin (CAR; Carl Roth, Germany; Ref. 6344.2) and piperacillin/tazobactam (PIT; Sigma-Aldrich, USA; Refs. P8396-1G and T2820-10MG). Resistant clones were isolated by streaking the resistant populations on LB agar plates supplemented with antibiotics and picking single colonies after an overnight growth at 37°C. Antibiotic stocks were prepared according to manufacturer instructions and frozen in aliquots for single use. Evolution experiments and resistance measurements were performed in liquid M9 minimal media supplemented with glucose (2 g/l), citrate (0.5 g/l) and casamino acids (1 g/l).

Measurements of reciprocal collateral sensitivity

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The previously reported collateral sensitivity trade-off (Barbosa et al., 2017) was confirmed for this study, by measuring sensitivity of the resistant populations CAR-10 to GEN, GEN-4 to CAR, PIT-1 to STR, and STR-2 to PIT, in comparison to PA14. Populations were grown to exponential phase, standardized by optical density at 600 nm (OD600 = 0.08), and inoculated into 96-well plates (100 µl volumes, 5 × 106 CFU/ml) containing linear concentrations of antibiotics (10 concentrations, eight replicates each). Antibiotic concentrations were spatially randomized. Plates were incubated at 37 °C for 12 hr, after which growth was measured by OD600 with a BioTek plate reader. Antibiotic susceptibility was quantified from dose-response curves using the lowest concentration required to inhibit growth by a defined value compared to wild type growth in drug free medium. IC95 (inhibitory concentration 95) refers to the smallest concentration required to inhibit growth by 95%. The metrics IC90 and IC75 refer to the concentrations that inhibit growth by 90% or 75%, respectively. Inhibitory concentrations were determined from the dose-response data using linear interpolation between the two closest OD600 values, as inferred with the approx function in the statistical environment R. In our case, the IC75 or IC90 metrics show higher accuracy and precision than the commonly used metric of the minimal inhibitory concentration (MIC, equivalent to IC100), because we inferred growth characteristics from OD600 measurements, which are subject to unfavorable signal-to-noise ratios close to OD values of zero and thus close to the IC100 condition. Please note that analysis of IC75 and IC90 values produced consistent results (e.g., Supplementary file 1-Figure 6-supplementary tables 1 and 2).

Experimental evolution initiated with resistant clones

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To test the evolutionary stability of reciprocal collateral sensitivity, we challenged clones from previously evolved resistant populations with increasing concentrations of new antibiotics against which the resistant populations showed hypersensitivity (so called collateral sensitivity): CAR-10 with GEN, GEN-4 with CAR, PIT-1 with STR, and STR-2 with PIT. Stability was assessed with 12 day evolution experiments using a serial transfer protocol (100 µl batch cultures, 2% serial transfers every 12 hr; the starting population size for the different populations was approx. 106 CFU/ml), as previously described (Roemhild et al., 2018). Each population was evaluated with eight replicate populations (4 clones x two technical replicates distributed in two plates: plate A and plate B) for each of 5 treatment groups: (i) untreated controls; linearly increasing concentration of hypersensitive antibiotic to a low level (ii) or high level (iii), without maintaining selection for previous resistance (unconstrained evolution); or linearly increasing concentration of hypersensitive antibiotic to a low level (iv) or high level (v), with simultaneous selection for previous resistance (constrained evolution). Concentration increases were started with defined initial inhibition levels of 50% (IC50) and concluded when concentrations were above the IC95 of the hypersensitive strain (mild increases) or IC95 of the wild type PA14 strain (strong increases), as specified in Supplementary file 1-Supplementary Table 1. Antibiotic selection was applied in 96-well plates and population growth was monitored throughout treatment by continuous measurements of OD600 in 15 min intervals (BioTek Instruments, USA; Ref. EON; 37 °C, 180 rpm double-orbital shaking). Extinction frequencies were determined at the end of the experiment by counting cases in which no growth was observed after an additional transfer to antibiotic-free media and 24 hr of incubation. Surviving evolved populations were frozen at −80 °C in 10% (v/v) DMSO, at the end of the experiment.

Relative biomass

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The continuous measurements of optical density during treatment provided a detailed growth trajectory that accurately describes the dynamics of resistance emergence. Relative biomass was defined as total optical growth relative to untreated control treatments, and was calculated by the ratio of the areas under the time-OD curves of treated compared to untreated controls that are passaged in parallel, as previously described (Roemhild et al., 2018).

Resistance of evolved populations

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Resistance of evolved populations was measured for the respective antibiotic pairs (GEN/CAR or STR/PIT), as described above, but using two-fold concentrations (1/4 to 8x the MIC of the starting clone). The respective starting clones of each evolved population served as controls and were measured in parallel. Resistance changes were quantified by subtracting the area under the dose-response curve of the evolved populations from that of the ancestral clones. Positive values indicate that the evolved lineages are more resistant than their ancestor, values close to zero indicate equivalent resistance levels, and negative values denote a loss of resistance. The cases of re-sensitization against GEN were validated by repeating the measurements, whereby the PA14 ancestor was included as an additional control (Figure 3—figure supplement 2).

Growth rate analyses

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Maximum exponential growth rates of evolved and ancestral populations were calculated from growth curves in drug-free media, using a sliding window approach. For measurements, sample cultures were diluted 50x from early stationary phase into 96-well plates (100 µl total volume) and growth was measured by OD600 every 15 min for 12 hr. Growth rate were calculated from log-transformed OD data for sliding windows of 1 hr, yielding two-peaked curves indicating initial growth on glucose and citrate. The reported values the maximum values of the first, larger peak. The values reported in Figure 3 are the changes of growth rate in evolved populations relative to their resistant ancestors. These values are taken as a proxy for a general adaptation trade-off, which is distinct to the collateral sensitivity trade-off.

Genomics

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We re-sequenced whole genomes of 5 starting clones (CAR-10 clone 2, GEN-2 clones 1–4), and 21 evolved populations (all descendants of these clones from plate B, including five untreated evolved control populations and 16 populations adapted to different treatment conditions) using samples from the end of the evolution experiments. Frozen material was thawed and grown in 10 ml of M9 minimal medium for 16–20 hr at 37 °C with constant shaking. Genomic DNA was extracted using a modified CTAB buffer protocol (von der Schulenburg et al., 2001) and sequenced at the Institute for Clinical Microbiology, Kiel University Hospital, using Illumina HiSeq paired-end technology with an insert size of 150 bp and 300x coverage. For the genomic analysis, we followed an established pipeline (Jansen et al., 2015). Briefly, reads were trimmed with Trimmomatic (Bolger et al., 2014), and mapped to the UCBPP-PA14 reference genome (available at http://pseudomonas.com/strain/download) using bwa and samtools (Li and Durbin, 2010; Li et al., 2009). We used MarkDuplicates in Picardtools to remove duplicated regions for single nucleotide polymorphisms (SNPs) and structural variants (SVs). To call SNPs and small SV we employed both heuristic and frequentist methods, only for variants above a threshold frequency of 0.1 and base quality above 20, using respectively VarScan and SNVer (Wei et al., 2011). Larger SVs were detected by Pindel and CNVnator (Abyzov et al., 2011; Ye et al., 2009; Ye et al., 2009). Variants were annotated using snpEFF (Cingolani et al., 2012), DAVID, and the Pseudomonas database (http://pseudomonas.com). Variants detected in the untreated evolved populations were removed from all other populations and analyses as these likely reflect adaptation to the lab media and not treatment. The fasta files of all sequenced populations here are available from NCBI under the BioProject number: PRJNA524114.

Genetic manipulation

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To understand re-sensitization, we analyzed candidate mutations from the GEN > CAR switch. The nalD mutation 1551588G > T (resulting in amino acid change p.T11N, as observed in replicate populations b24_G8, b24_D9, and b24_A9) was introduced into the PA14 genetic background using a scar-free recombination method (Trebosc et al., 2016). The same techniques were previously used to construct the mutants nalC (deletion 1391016–1391574) and pmrB (5637090T > A, resulting in amino acid change p.V136E) in the PA14 ancestor background (Barbosa et al., 2017). Based on these mutants and with the same techniques, we constructed the double mutants pmrB, nalD (pmrB p.V136E + nalD p.T11N), and pmrB, nalC (pmrB p.V136E + nalC deletion c.49–249, as observed in population b24_F7). Genetic manipulation and confirmation by sequencing was performed by BioVersys AG, Hochbergerstrasse 60 c, CH-4057 Basel, Switzerland.

Epistasis analysis

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Resistance of constructed mutant strains was measured in direct comparison to wildtype PA14, as described above. Relative fold-changes in MIC were calculated from dose-response curves. The expected relative resistance of the double mutants was calculated by multiplication of the mutation’s individual effects, as previously described (Wong, 2017). For example, if mutation A conferred a 2-fold increase in resistance and mutation B conferred a 4-fold increase of resistance, the expected resistance of the double mutant AB would be 2 × 4 = 8. A deviation from this null model indicates epistasis, which can be either positive (greater resistance than expected) or negative (lesser resistance than expected).

Validation of main findings through a repetition of evolution experiments using resistant populations as starting material

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The evolutionary stability of collateral sensitivity in genetically diverse populations was investigated by using the same general procedure as described above (section ‘Experimental evolution initiated with resistant clones’), but using an inoculum of roughly 107 cells instead of a single clone. This experiment was reduced to the treatments groups ‘strong’ and ‘strong+constrained’, and performed for a total of 38 (6 × 6 + 2) different resistant starting populations from our previous publication (Barbosa et al., 2017). Six replicate populations each from previous evolution for resistance to CAR, GEN, STR, PIT, ciprofloxacin (CIP) and cefsulodin (CEF), were challenged with increasing concentrations of antibiotic against which they showed collateral sensitivity. The population were each evolved against two new antibiotics. Due to variation in collateral sensitivity profiles among replicate populations, a seventh population had to be selected for CAR and PIT to assemble a set of 6 collaterally-sensitive populations for each of the switching directions. Only one STR-resistant population showed collateral sensitivity to PIT so that this test was conducted with a single replicate. CIP-resistant populations showed general collateral sensitivity and were tested against four new antibiotics. In total, we thus arrived at 79 (14 × 6 - 5) evolutionary switches to collateral sensitivity.

Statistical analysis for association with evolutionary stability

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To test for association of predictive factors with evolutionary stability, we used a generalized linear model (GLM) analysis, because it allows us to combine an evaluation of both categorical and continuous predictors and to assess the influence of each factor in consideration of the contributions of the other factors (which is not possible when using for example correlation analysis). For our analysis, we used the functions lm and anova in the statistical environment R and the main effects model: response ~target drug A + target drug B + collateral sensitivity effect size + drug-free relative growth rate.

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Decision letter

  1. Patricia J Wittkopp
    Senior Editor; University of Michigan, United States
  2. Csaba Pal
    Reviewing Editor; Biological Research Centre of the Hungarian Academy of Sciences, Hungary
  3. Balint Csorgo
    Reviewer
  4. Craig MacLean
    Reviewer; University of Oxford, United Kingdom
  5. Pal Johnsen
    Reviewer; UiT – The Arctic University of Norway, Norway

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Despite this significant progress in the study of collateral sensitivity over the recent years, there are several key questions about its clinical utility. This interesting work found that exploitation of evolved collateral sensitivity in sequential therapy is contingent on drug order, epistatic genetic interactions and consequent evolutionary trade-offs. These findings could contribute to the design of new antibiotic therapies with the aim of limiting the emergence of multidrug bacteria.

Decision letter after peer review:

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa" for consideration by eLife. Your article has been reviewed by a Senior Editor, a Reviewing Editor, and three reviewers. The following individuals involved in review of your submission have agreed to reveal their identity: Pal Johnsen (Reviewer #1); Balint Csorgo (Reviewer #2); Craig MacLean (Reviewer #3).

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife. As you can see from the reports, the reviewers found the topic of potential interest, but they raised serious concerns about the generality of the conclusions reached and clinical implications thereof. The reviewers noted that the scope of the study is rather limited, for two main reasons. Only a single source population for each selection treatment and only two collateral-sensitive drug pairs were studied. However, we would be willing to look at a substantially revised version should the authors decide to complete all the experiments suggested by the reviewers.

Reviewer #1:

This is a well written and very interesting manuscript addressing evolutionary stability of reciprocal collateral sensitivity (CS) in Pseudomonas aeruginosa. This is an important and currently un-explored aspect of CS networks and absolutely critical for potential future clinical application as a strategy to offset antibiotic resistance evolution.

The authors demonstrate that evolutionary stability of reciprocal CS phenotypes depend on not only pairs of drugs but also the order of administration during experimental evolution. They conclude that this is due to fitness cost variations and epistatic interactions between pleiotropic resistance mutations.

Essential revisions:

1) The authors conclude that for the first pair of drugs investigated (STR-PIT) frequent extinctions (51% of the populations) during evolution was due to strong genetic constraints against an evolutionary response to overcome CS. Beyond the initial phenotype and number of extinct populations I find this claim largely unsubstantiated. In full awareness of how difficult it would be to include a "non-CS control experiment" it would in my opinion benefit the paper to know what actually happened in the extinct versus non-extinct populations. Could not alternative hypotheses be that reduced susceptibility (STR-PIT) require accumulation of more mutations than for the CAR-GEN pair or if classical synergy between STR and PIT for the combination exposures led to more extinctions (aminoglycosides and β-lactams are often synergetic, including in Pseudomonas)?

2) The authors demonstrate elegantly an example of negative epistasis between pleiotropic resistance mutations and this is an important contribution to our mechanistic understanding of CS/hypersensitivity where very little mechanistic details exist. However, since new treatment strategies are frequently mentioned throughout the manuscript, I believe the manuscript would benefit from a link to clinical reality. Do the observed mutations also emerge during therapy or are they specific for the experimental conditions? How does the observed resistance levels relate to existing clinical breakpoints?

3) The authors argue, and partially conclude, that if the costs of resistance are high, it is more difficult to overcome CS. If I understand correctly, this is based on Figures 3E,F and Supplementary Figure 2, where relative growth rates are used as a proxy for relative fitness before and after evolution. First, these experiments are done in antibiotic free media and not in conditions experienced during evolution. Second, would not the proper comparisons be with the PIT-STR pairs, by analyzing data from the evolved populations (that did not go extinct) as well as doing relative fitness measurements with the pairs of genetically re-constructed mutants to avoid bias from environmental adaptation?

Reviewer #2:

In their work "Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa", Barbosa and colleagues explore the effects of adapting antibiotic resistant bacteria to other antibiotics against which they had previously developed hypersensitivity during their original adaptation. As the emergence of antibiotic resistant bacteria threatens to become an increasingly severe health crisis, multiple strategies are needed to combat this. One such strategy is the exploitation of collateral-sensitivity phenotypes of drug resistant bacteria, an approach which has picked up interest over the last 5 years or so. This work aims to study the long-term stability of these interactions, an aspect about which little is known about, therefore I think the general aim is timely and relevant.

Overall, I think the experiments are designed well, the paper is well written, and the general conclusions are supported by the experimental evidence presented. The authors found that attempting to exploit collateral sensitivity phenotypes through sequential antibiotic treatments had greatly varying effects, depending on which antibiotic was used, in what order antibiotic pairs were used, and whether combination therapy was employed in the second treatment or not. The PIT/STR antibiotic pair lead to high extinction rates while the CAR/GEN pair was not as effective, frequently resulting in the evolution of new resistance mechanisms that allowed either multidrug resistance, or less frequently, resensitization to the original drug. The likely molecular mechanisms responsible for these phenotypes were revealed through whole-genome sequencing. I appreciated their efforts in chromosomally reconstructing a small set of these mutations, which revealed an interesting case of negative epistasis between two resistance mutations that was responsible for the resensitization phenotype.

My questions are the following:

1) I found the scope of the work somewhat limited, as only two collateral-sensitive drug pairs were studied. It would be nice to see the same experimental setup on a greater number of reciprocal, collaterally-sensitive antibiotic pairs to get a better idea of the frequency of pairs that more frequently cause extinction and pairs that lead to resistance to the second antibiotic. From their previous work (Barbosa et al., 2017), this may be e.g. DOR/STR, CAR/PIT, CAR/STR; did the authors examine these strains?

2) On that note, it would have also been interesting to look at the resistance mechanisms of the survivors of the STR/PIT sequential treatments. The authors point out that this analysis was not possible because of the limited number of survivor populations that grew, however by my count, 31 out of 64 populations actually did survive from the various treatment regiments. It would be interesting to see if these populations are multidrug resistant or perhaps cases of resensitization can be observed here as well.

3) I found it interesting that the PIT/STR combination that seems to be the most effective in this study did not show reciprocal collateral sensitivity in the majority of cases in the authors' previous work (Barbosa et al., 2017). Only one out of 10 STR adapted lines was hypersensitive to PIT while cross-resistance arose in 5 of the cases. Can the fact that cross-resistance seems to be more common with this pair in the STR -> PIT direction mean that this combination would actually not be a prudent choice for treatment against P. aeruginosa PA14?

Reviewer #3:

In this manuscript, the authors use experimental evolution to investigate the consequences of collateral sensitivities that evolved when Pseudomonas aeruginosa evolved resistance to antibiotics in a previously published selection experiment. This paper investigates a hot topic in evolutionary biology (antibiotic resistance) using previously established methods, and this paper clearly makes a novel contribution to our understanding of trade-offs between resistance to alternative antibiotics (collateral sensitivity). The genome sequencing and mutant reconstruction provide good support for the experimental evolution component of the paper, and the use of this approach adds an interesting layer of detail to the paper. However, I have some reservations about the experimental design and data analysis for this paper, and I question the breadth of conclusions that can be drawn from this study.

1) Experimental design and possible pseudo-replication:

The authors setup their selection experiment with 2 replicate cultures of 4 clones from 4 different populations that were previously selected for resistance to CAR, GEN, STR and PIP. I have two criticisms of this design.

a) In their analysis of extinction rates and evolutionary dynamics (Figure 2 and Figure 3), the authors treat the 2 replicates of each clone as being independent of each other, even though they were derived from the same clone. It is difficult to evaluate the impact of this pseudo-replication on the results of the analysis, and this is an issue that the authors need to address.

b) By choosing multiple clones from a single source population, the authors limit the generality of their findings. For example, I do not think that it is a fair conclusion to say that PIT adapted populations tend to go extinct when they are challenged with STR (and vice versa), because all of the PIT adapted clones came from a single population that evolved PIT resistance.

c) Overall, I think that the study would have been stronger if the authors had instead started their selection experiments with 8 independently evolved clones, each from a different source population.

2) Measurements in resistance:

Clinical microbiologists measure antibiotic resistance using standardized techniques to estimate MIC. The authors measure changes in the resistance of evolved populations by measuring changes in the area under the inhibition curve (Figure 3). This data provides a more subtle and quantitative measure of resistance, but this metric lacks a unit and the authors should also measure changes in MIC so that their results can be placed more easily in the context of antibiotic resistance. For example, it is unclear to what extent adaptation to new antibiotics changed resistance to old antibiotics. This data will help clinicians to evaluate the extent to which evolutionary changes in resistance are likely to lead to different outcomes in the clinic.

3) Applicability of findings:

The authors conclude that the findings of their study could help to guide the design of sustainable antibiotic therapies in the clinic (Discussion section). I am not convinced that this is the case, and I think that the authors should be careful about overstating the applied significance of their work. Because the authors used only a single source population for each selection treatment, they cannot make any general conclusions about how different antibiotics collateral sensitivity (i.e. this study does not support the idea that PIT followed by STR will be a generally effective strategy for treating Pseudomonas infections). A further limitation to the applicability of this study comes from the fact that both collateral sensitivity combinations relied on using aminoglycoside antibiotics (STR and GEN); these antibiotics are rarely used to treat Pseudomonas infections (i.e. https://www.thoracic.org/statements/resources/tb-opi/hap-vap-guidelines-2016.pdf). I think that it is important to be careful about how researchers in this area interpret their findings in the context of clinical use of antibiotics.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for resubmitting your work entitled "Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa" for further consideration at eLife. Your revised article has been favorably evaluated by Patricia Wittkopp (Senior Editor), a Reviewing Editor, and three reviewers. The following individuals involved in review of your submission have agreed to reveal their identity: Balint Csorgo (Reviewer #1); Craig MacLean (Reviewer #2); Pal Johnsen (Reviewer #3).

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

Reviewer #3 has some remaining concerns that need to be addressed, including justification of the usage of GLM and description of the terms IC75, IC90 and IC95 and the way they were calculated.

Reviewer #1:

In their work studying the evolutionary stability of antibiotic collateral sensitivity interactions in P. aeruginosa, Barbosa and colleagues explored the effects of bacterial adaptation to antibiotic pairs in various combinations and treatment strategies. As a reviewer of a previous version of this manuscript, I found the topic of the paper as relevant and timely, the experiments well-designed and sufficiently novel, and the overall conclusions to be supported by the experimental data that was presented. My main criticism regarding the work in general was that I found the scope of the work somewhat limited based on the fact that the overwhelming majority of the data focused on a specific antibiotic pair and it was difficult to gauge how general these findings were when looking at a wider set of antibiotic pairs. In the revised version, the authors have clearly addressed these points by conducting an extensive set of additional experiments with other antibiotic pairs to get a much bigger picture regarding the general frequency of extinction, re-sensitization and multi-resistance with their experimental treatment. I think this adds value to their original findings and helps to put their original results with the CAR/GEN antibiotic pair into a clearer context.

Reviewer #2:

The authors have adequately addressed my concerns by including substantial new experimental work in their paper, and I think that this paper will make a good contribution to the growing literature on collateral sensitivity and antibiotic resistance.

Reviewer #3:

I have read "Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa" re-submitted to eLife following the initial round of peer-review by Barbosa and Roemhild et al. The authors have responded to all my initial comments and I largely find the responses and changes to the manuscript satisfactory.

Please find my comments to the added experiments below:

In my initial assessment I asked for more details concerning the extinct populations since the only evidence of "operating CS" was the initial susceptibility testing of ancestral clones founding these populations. As a response to this and other reviewers' comments the authors now included a new extensive set of additional experiments spanning 14 cases of reciprocal and non-reciprocal CS drug-pairs. The authors analyzed populations that did not go extinct and had developed reduced susceptibility to the second drug in the switch. They found that where single drug-treatment was followed by combinations of both drugs no re-sensitization was observed and thus lack of evolutionary stability. However, in mono-drug treatment the authors report 6 cases of re-sensitization towards the OLD drug suggesting that these drug-pairs displayed stable CS.

Data are now included from surviving populations that were excluded from the initial version and 2, potentially 3, cases of stable CS are reported from these populations which suggests that CS operates during evolution.

Six cases reported in text – only 5 are marked with asterisks in Figure 5 – STR/PIT combination significant too?

Using a generalized linear model, the authors then asked (1) which factors explained variation in extinction, and (2) drivers of resistance gains and re-sensitizations. They concluded from these analyses that (1) variation in extinction was significantly associated with the molecular target of the second drug, and (2) target of drug A, CS effect size, and growth rate all contributed to re-sensitization. Finally, the authors argue that resistance gains in unconstrained treatments was associated with initial effect size of CS and initial growth rates of resistant population.

There is no justification or description of the use of GLM in the text (this may be present in Figure 6—source data 1 frequently referred to).

Moreover, justification for factors affecting resistance gains seems to build on data presented in Figure 6C and 6D. In particular Figure 6D, the reporting tendency of co-variance seems unlikely to be significant?

Drug susceptibility are reported in terms of IC75, IC90, and IC95. I cannot see any description as to how these are estimated or the rationale for their use.

https://doi.org/10.7554/eLife.51481.sa1

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

Reviewer #1:

[…]

Essential revisions:

1) The authors conclude that for the first pair of drugs investigated (STR-PIT) frequent extinctions (51% of the populations) during evolution was due to strong genetic constraints against an evolutionary response to overcome CS. Beyond the initial phenotype and number of extinct populations I find this claim largely unsubstantiated. In full awareness of how difficult it would be to include a "non-CS control experiment" it would in my opinion benefit the paper to know what actually happened in the extinct versus non-extinct populations. Could not alternative hypotheses be that reduced susceptibility (STR-PIT) require accumulation of more mutations than for the CAR-GEN pair or if classical synergy between STR and PIT for the combination exposures led to more extinctions (aminoglycosides and β-lactams are often synergetic, including in Pseudomonas)?

Thank you very much for this comment. As emphasized by the reviewer, it would be (i) interesting to better understand what exactly happened in the extinct populations, yet (ii) impossible to study this directly because of the unavailability of these extinct lineages for subsequent experimental analysis.

In the revised manuscript, we now decided to take a different approach to assess in more detail the factors which contribute to population extinction during collateral sensitivity switches: we performed a new set of evolution experiment, for which we challenged 38 independent populations, covering 14 distinct cases of collateral sensitivity, to overcome their collateral sensitivity (see also response to reviewer #2). This new experiment now allows us to assess statistically which factors favor population extinction. Such statistical analysis was not really possible with the previous data, which only included 4 cases of collateral sensitivity. In detail, the new experiments covered cases with a range of initial values for fitness costs, collateral sensitivity effect sizes, and different drug targets for either first or second antibiotic. We then used the evolution experiments to assess which factor(s) were significantly associated with extinction and resistance evolution. Based on the new results, extinction seems to be determined primarily by the order of drug targets during treatment (Figure 6A). As such, the new data is consistent with our previous observation that extinction is maximized in directional switches from aminoglycoside to β-lactam.

Consequently, we now conclude that extinctions are stochastic events that are affected by drug identity and order (see subsection “Repetition of experimental evolution revealed an influence of drug type on population extinction and drug re-sensitization” in the revised manuscript).

Moreover, synergism between drugs is unlikely to explain higher extinction rates since combinations with high and low extinction rates (STR+PIT and CAR+GEN) both display strong synergy (as quantified in our previous paper (Barbosa et al., 2018) and now more clearly indicated in subsection “Extinction rates were high even under mild selection regimes”). Moreover, reciprocal cases of collateral sensitivity, which consist of synergistic drug combinations, vary in their ability to overcome collateral sensitivity (i.e., a strong order-effect on extinction rates, as indicated for example in Figure 6A).

2) The authors demonstrate elegantly an example of negative epistasis between pleiotropic resistance mutations and this is an important contribution to our mechanistic understanding of CS/hypersensitivity where very little mechanistic details exist. However, since new treatment strategies are frequently mentioned throughout the manuscript, I believe the manuscript would benefit from a link to clinical reality. Do the observed mutations also emerge during therapy or are they specific for the experimental conditions? How does the observed resistance levels relate to existing clinical breakpoints?

Many thanks for this comment. We agree that this information was missing in the previous version of the manuscript. We have now included a short paragraph to the Discussion section on the prevalence of the observed genetic changes in clinical isolates of our pathogen.

3) The authors argue, and partially conclude, that if the costs of resistance are high, it is more difficult to overcome CS. If I understand correctly, this is based on Figure 3E,F and Supplementary Figure 2, where relative growth rates are used as a proxy for relative fitness before and after evolution. First, these experiments are done in antibiotic free media and not in conditions experienced during evolution. Second, would not the proper comparisons be with the PIT-STR pairs, by analyzing data from the evolved populations (that did not go extinct) as well as doing relative fitness measurements with the pairs of genetically re-constructed mutants to avoid bias from environmental adaptation?

We thank the reviewer for this important comment. We agree that in our assay, the fitness cost is measured in drug-free environments and thus conditions not experienced by the evolving bacteria during experimental evolution. We still find this measure useful because of the following two reasons. (1) It is a measure that at least indicates that the bacteria experienced some kind of general trade-off when adapting to the antibiotic environment. (2) It is a measure that is comparable across the quite distinct antibiotic treatments, thereby allowing us to identify more general adaptation trade-offs (i.e. different from collateral sensitivity). In order to avoid any misunderstanding, we now explain the relevance of this measure in more detail (subsection “Drug order determined re-sensitization or emergence of multidrug resistance”) and also refer to it as an adaptation trade-off rather than a direct fitness cost (legends to Figure 3 and Figure 6). Please note that we do assess fitness under the relevant drug conditions with our measures of population extinction, relative biomass, and drug resistance (see Figure 2, Figure 3 and Figure 5). Moreover, we now emphasize that in certain cases, reversal of drug resistance also ameliorates this second evolutionary trade-off (See Figure 3E and 3F, subsection “Drug order determined re-sensitization or emergence of multidrug resistance”).

We also thank the reviewer for suggesting experiments to broaden the general insight from our study. As already explained above, we now performed an additional set of evolution experiments for this purpose (rather than analysis of reconstructed mutants, which we did in our analysis of epistasis effects, Figure 4). Most interestingly, the detailed statistical analysis of the new data now indicates that the adaptation constraint is significantly associated with the bacteria’s evolutionary responses, especially their ability to gain resistance to the new drug B (see Figure 6D, statistical results in the text). This new insight is based on a larger set of drug conditions and thus collateral sensitivity switch types, thereby allowing us to draw careful conclusions on the generality of our findings. We integrated a careful discussion of these results into the revised Discussion section.

Reviewer #2:

[…]

My questions are the following:

1) I found the scope of the work somewhat limited, as only two collateral-sensitive drug pairs were studied. It would be nice to see the same experimental setup on a greater number of reciprocal, collaterally-sensitive antibiotic pairs to get a better idea of the frequency of pairs that more frequently cause extinction and pairs that lead to resistance to the second antibiotic. From their previous work (Barbosa et al., 2017), this may be e.g. DOR/STR, CAR/PIT, CAR/STR; did the authors examine these strains?

We very much value this comment. As suggested, we have now performed a large validation experiment, in order to expand the scope of our study. In detail, we have performed a new set of evolution experiments, which cover a total of 14 distinct types of collateral sensitivity switches, which include several additional antibiotics, and which include 38 biologically independent replicate populations as starting material for the second step of experimental evolution. This new validation experiment now allows us to assess statistically which factors contribute significantly to evolutionary adaptation of the bacteria (for more details see subsection “Repetition of experimental evolution revealed an influence of drug type on population extinction and drug re-sensitization”). The new results are summarized in new Figure 5 and Figure 6. In general, we obtained similar results as with the clones. The importance of these findings are explained in the revised Discussion section.

2) On that note, it would have also been interesting to look at the resistance mechanisms of the survivors of the STR/PIT sequential treatments. The authors point out that this analysis was not possible because of the limited number of survivor populations that grew, however by my count, 31 out of 64 populations actually did survive from the various treatment regiments. It would be interesting to see if these populations are multidrug resistant or perhaps cases of resensitization can be observed here as well.

We appreciate this comment. We have now included equivalent data to Figures 3A-D for the STR/PIT treatment (Figure 3—figure supplement 1). We do see similar patterns of resensitization or the evolution of multidrug resistance. The patterns are less clear for this drug due to stronger between-replicate variation for the more potent switch (STR>PIT), where some replicates showed clear re-sensitization but others the opposite. Such dynamics could potentially be explained by a larger diversity of accessible adaptive mutations. However, its detailed analysis remains hampered by the fact that for certain treatment combinations, only few replicates are available for analysis. Therefore, we found it more reliable to focus the genomic analysis on the drug switch, for which a larger and statistically relevant number of replicates is still available and which may then serve as a test case to explore what kind of mechanisms may help the bacteria to overcome collateral sensitivity. Our genetic analysis did indeed allow us to identify and characterize such mechanisms, including new insights into the importance of epistatic interactions. We consider these findings novel and of relevance for our understanding of evolved collateral sensitivity.

In order to broaden our perspective, we then decided to rather focus on performance of a validation evolution experiment. This validation experiment allowed us to statistically assess the factors that minimize counteradaptations against collateral sensitivity, which was the main focus of our work.

3) I found it interesting that the PIT/STR combination that seems to be the most effective in this study did not show reciprocal collateral sensitivity in the majority of cases in the authors' previous work (Barbosa et al., 2017). Only one out of 10 STR adapted lines was hypersensitive to PIT while cross-resistance arose in 5 of the cases. Can the fact that cross-resistance seems to be more common with this pair in the STR -> PIT direction mean that this combination would actually not be a prudent choice for treatment against P. aeruginosa PA14?

We thank the reviewer for this important observation. We now attempted to address this comment with the help of our new validation experiment, which allowed us to statistically evaluate which factors and which drug properties favor desired treatment outcomes (e.g., extinction, reduced levels of multidrug resistance). Our analysis now suggests that, in general, starting with aminoglycoside antibiotics and switching to β-lactam will be more effective than the reverse switch or switches based on other drug types. See also above replies. We still agree with the reviewer that variation in the evolution of collateral sensitivity in response to treatment with the initially applied drug is similarly of key importance. To accommodate this point, we now re-wrote the final paragraph of the discussion. Here we now conclude that: “The success of this treatment strategy depends on several key factors. One critical prerequisite is that collateral sensitivity does evolve in response to treatment, which may vary depending on alternative evolutionary paths to resistance against the initially used drug A (Barbosa et al., 2017; Nichol et al., 2019). Our new data now suggests that treatment can be further optimized by switching from an aminoglycoside to a β-lactam and/or by focusing on drugs, for which resistance is associated with large general adaptation trade-offs, and/or by using drug combinations with small collateral sensitivity effect sizes.” See Discussion section.

Reviewer #3:

In this manuscript, the authors use experimental evolution to investigate the consequences of collateral sensitivities that evolved when Pseudomonas aeruginosa evolved resistance to antibiotics in a previously published selection experiment. This paper investigates a hot topic in evolutionary biology (antibiotic resistance) using previously established methods, and this paper clearly makes a novel contribution to our understanding of trade-offs between resistance to alternative antibiotics (collateral sensitivity). The genome sequencing and mutant reconstruction provide good support for the experimental evolution component of the paper, and the use of this approach adds an interesting layer of detail to the paper. However, I have some reservations about the experimental design and data analysis for this paper, and I question the breadth of conclusions that can be drawn from this study.

1) Experimental design and possible pseudo-replication:

The authors setup their selection experiment with 2 replicate cultures of 4 clones from 4 different populations that were previously selected for resistance to CAR, GEN, STR and PIP. I have two criticisms of this design.

a) In their analysis of extinction rates and evolutionary dynamics (Figure 2 and Figure 3), the authors treat the 2 replicates of each clone as being independent of each other, even though they were derived from the same clone. It is difficult to evaluate the impact of this pseudo-replication on the results of the analysis, and this is an issue that the authors need to address.

b) By choosing multiple clones from a single source population, the authors limit the generality of their findings. For example, I do not think that it is a fair conclusion to say that PIT adapted populations tend to go extinct when they are challenged with STR (and vice versa), because all of the PIT adapted clones came from a single population that evolved PIT resistance.

c) Overall, I think that the study would have been stronger if the authors had instead started their selection experiments with 8 independently evolved clones, each from a different source population.

Many thanks for comments 1a, b and c. In the revised manuscript, we now tackled the criticism of re-sampling from the same source population by performing a new, large-scale validation experiment that investigates the stability of collateral sensitivity starting with 6 independently evolved populations (Figure 5). As for possible pseudo-replication, we fully agree that it is more appropriate to treat the two experimental lineages that were derived from a single clone as technical replicates. This was already part of our previous GLM analysis. Many apologies if this was not clear.

2 Measurements in resistance:

Clinical microbiologists measure antibiotic resistance using standardized techniques to estimate MIC. The authors measure changes in the resistance of evolved populations by measuring changes in the area under the inhibition curve (Figure 3). This data provides a more subtle and quantitative measure of resistance, but this metric lacks a unit and the authors should also measure changes in MIC so that their results can be placed more easily in the context of antibiotic resistance. For example, it is unclear to what extent adaptation to new antibiotics changed resistance to old antibiotics. This data will help clinicians to evaluate the extent to which evolutionary changes in resistance are likely to lead to different outcomes in the clinic.

We thank the reviewer for this this helpful suggestion. We have now included the IC90 fold change for the two datasets in Supplementary file 1—figure 3—supplementary table 3 and Supplementary file 1—Figure 5—supplementary table 2. The results are consistent with our previous findings. Moreover, we also performed the statistical analysis for the new data using the different alternative measures and similarly obtained consistent results (see Supplementary file 1—Figure 6—supplementary table 1 and Supplementary file 1—Figure 6—supplementary table 2, Figure 6—source data 1 and Figure 6—source data 2 and subsection “Repetition of experimental evolution revealed an influence of drug type on population extinction and drug re-sensitization”).

3) Applicability of findings:

The authors conclude that the findings of their study could help to guide the design of sustainable antibiotic therapies in the clinic (Discussion section). I am not convinced that this is the case, and I think that the authors should be careful about overstating the applied significance of their work. Because the authors used only a single source population for each selection treatment, they cannot make any general conclusions about how different antibiotics collateral sensitivity (i.e. this study does not support the idea that PIT followed by STR will be a generally effective strategy for treating Pseudomonas infections). A further limitation to the applicability of this study comes from the fact that both collateral sensitivity combinations relied on using aminoglycoside antibiotics (STR and GEN); these antibiotics are rarely used to treat Pseudomonas infections (i.e. https://www.thoracic.org/statements/resources/tb-opi/hap-vap-guidelines-2016.pdf). This is a minor point, but I think that it is important to be careful about how researchers in this area interpret their findings in the context of clinical use of antibiotics.

We appreciate this comment. We admit that the conclusions in our previous manuscript were not sufficiently cautious. In response to this comment, we now rephrased the relevant parts of our discussion and tried to be more careful in our conclusions on the clinical applicability of our findings. Importantly, our results highlight that the concept of collateral sensitivity cannot be applied in a simple form to patient treatment. Instead, it appears that certain directions of collateral sensitivity (i.e., aminoglycoside followed by a βlactam) are more robust against bacterial counteradaptation. Here, it is important that this conclusion is strongly supported by our new validation experiment (Figure 6), in which we included (i) a larger number of different types of collateral sensitivity switches, (ii) a larger number of antibiotics, and (iii) a total of 38 independent replicate populations of evolved resistances. Considering that evolution of collateral sensitivity as the first step is not always predictable (see Barbosa et al., 2017), its use for treatment requires more detailed patient-specific evaluations. These are important, clinically relevant insights, which clearly warrant further exploration, as we now emphasize in our final conclusions (Discussion section). Moreover, we also agree that aminoglycosides are often not used to treat P. aeruginosa infections. However, sometimes, they still are, depending on country, exact infection type, patient history, hospital stewardship, and other factors. In our personal experience, we have repeatedly observed the use of the aminoglycoside tobramycin in the cystic fibrosis clinic in Kiel (see for example Tueffers et al., 2019). Therefore, in principle, our specific results on aminoglycosides can help those treatments, where these drugs are an option, and therefore we consider our conclusions to be valid and clinically relevant.

[Editors' note: the author responses to the re-review follow.]

Reviewer #3 has some remaining concerns that need to be addressed, including justification of the usage of GLM and description of the terms IC75, IC90 and IC95 and the way they were calculated.

Reviewer #3:

I have read "Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa" re-submitted to eLife following the initial round of peer-review by Barbosa and Roemhild et al. The authors have responded to all my initial comments and I largely find the responses and changes to the manuscript satisfactory.

Please find my comments to the added experiments below:

In my initial assessment I asked for more details concerning the extinct populations since the only evidence of "operating CS" was the initial susceptibility testing of ancestral clones founding these populations. As a response to this and other reviewers' comments the authors now included a new extensive set of additional experiments spanning 14 cases of reciprocal and non-reciprocal CS drug-pairs. The authors analyzed populations that did not go extinct and had developed reduced susceptibility to the second drug in the switch. They found that where single drug-treatment was followed by combinations of both drugs no re-sensitization was observed and thus lack of evolutionary stability. However, in mono-drug treatment the authors report 6 cases of re-sensitization towards the OLD drug suggesting that these drug-pairs displayed stable CS.

Data are now included from surviving populations that were excluded from the initial version and 2, potentially 3, cases of stable CS are reported from these populations which suggests that CS operates during evolution.

Six cases reported in text – only 5 are marked with asterisks in Figure 5 – STR/PIT combination significant too?

Many thanks for this observation. Our count of “6 of the 14 cases” includes the 5 cases marked by asterisks in Figure 5 for which a statistical evaluation was performed, and the case of STR>PIT. The latter only had a single replicate, and we therefore found a statistical evaluation inappropriate. However, this replicate showed very strong resensitization, such that we included it in the above count. Nevertheless, statistical significance could only be inferred for 5 cases. We have now clarified this point in subsection “Repetition of experimental evolution revealed an influence of drug type on population.

Using a generalized linear model, the authors then asked (1) which factors explained variation in extinction, and (2) drivers of resistance gains and re-sensitizations. They concluded from these analyses that (1) variation in extinction was significantly associated with the molecular target of the second drug, and (2) target of drug A, CS effect size, and growth rate all contributed to re-sensitization. Finally, the authors argue that resistance gains in unconstrained treatments was associated with initial effect size of CS and initial growth rates of resistant population.

There is no justification or description of the use of GLM in the text (this may be present in Figure 6—source data 1 frequently referred to).

Moreover, justification for factors affecting resistance gains seems to build on data presented in Figure 6C and 6D. In particular Figure 6D, the reporting tendency of co-variance seems unlikely to be significant?

Many thanks for these two comments. We have now revised the methods section and included a description and justification of the GLM analyses subsection, “Statistical analysis for association with evolutionary stability”. Please note that the association shown in Figure 6D was found to be significant, as inferred from GLM analysis reported in Supplementary file 1. In order to avoid misunderstandings, we now replaced “covariation”/“covaried” by the more correct terms “significant association”/“significantly associated” in the legend to Figure 6, because in a strict sense our GLM analysis only tested for associations. Moreover, we also removed any reference to correlation analysis in this context, because – as just emphasized – the associations were assessed using GLM analysis. See adjustments in subsection “Repetition of experimental evolution revealed an influence of drug type on population extinction and drug re-sensitization” and the legend to Figure 6.

Drug susceptibility are reported in terms of IC75, IC90, and IC95. I cannot see any description as to how these are estimated or the rationale for their use.

Many thanks for bringing this to our attention. In the revised manuscript, we now describe these terms and the methods of quantification in subsection “Measurements of reciprocal collateral sensitivity”. In general, the metrics IC75, IC90, and IC95 are very strongly correlated. IC95 is a close approximation of the MIC. High IC values are technically hard to estimate, when the highest concentrations do not result in total inhibition, especially when using OD measurements for their inference (as was the case in our study), which are usually subject to unfavorable signal-to-noise ratios close to the zero OD measurements. As such high levels of noise were observed for two samples in our validation data set, we instead chose to work with IC75 which could be accurately determined for all samples.

https://doi.org/10.7554/eLife.51481.sa2

Article and author information

Author details

  1. Camilo Barbosa

    Department of Evolutionary Ecology and Genetics, University of Kiel, Kiel, Germany
    Contribution
    Conceptualization, Investigation, Visualization, Writing—original draft, Writing—review and editing
    Contributed equally with
    Roderich Römhild
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0433-9311
  2. Roderich Römhild

    1. Department of Evolutionary Ecology and Genetics, University of Kiel, Kiel, Germany
    2. Max Planck Institute for Evolutionary Biology, Plön, Germany
    Present address
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
    Contribution
    Conceptualization, Investigation, Visualization, Writing—original draft, Writing—review and editing
    Contributed equally with
    Camilo Barbosa
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9480-5261
  3. Philip Rosenstiel

    Institute of Clinical Molecular Biology, UKSH, Kiel, Germany
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9692-8828
  4. Hinrich Schulenburg

    1. Department of Evolutionary Ecology and Genetics, University of Kiel, Kiel, Germany
    2. Max Planck Institute for Evolutionary Biology, Plön, Germany
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing
    For correspondence
    hschulenburg@zoologie.uni-kiel.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1413-913X

Funding

Deutsche Forschungsgemeinschaft (SCHU 1415/12-1)

  • Hinrich Schulenburg

Deutsche Forschungsgemeinschaft (EXC 22167-39088401)

  • Philip Rosenstiel
  • Hinrich Schulenburg

Leibniz-Gemeinschaft (EvoLUNG)

  • Camilo Barbosa
  • Hinrich Schulenburg

Max-Planck-Gesellschaft (IMPRS Evolutionary Biology)

  • Camilo Barbosa
  • Roderich Römhild

Max-Planck-Gesellschaft (Fellowship)

  • Hinrich Schulenburg

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank D I Andersson, R Kishony, C Kost, and V Lázár for feedback on the manuscript. Genome sequencing was performed by G Hemmrich-Stanisak and M Vollstedt from the Institute of Clinical Molecular Biology in Kiel, as supported by the DFG Cluster of Excellence EXC 306 ‘Inflammation at Interfaces’. This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) individual grant SCHU 1415/12 (to HS) and also under Germany`s Excellence Strategy – EXC 22167–39088401 (Excellence Cluster Precision Medicine in chronic Inflammation; HS, PR), the Leibniz Science Campus Evolutionary Medicine of the Lung (EvoLUNG; HS, CB), the International Max-Planck-Research School for Evolutionary Biology (CB, RR), and the Max-Planck Society (HS, RR).

Senior Editor

  1. Patricia J Wittkopp, University of Michigan, United States

Reviewing Editor

  1. Csaba Pal, Biological Research Centre of the Hungarian Academy of Sciences, Hungary

Reviewers

  1. Balint Csorgo
  2. Craig MacLean, University of Oxford, United Kingdom
  3. Pal Johnsen, UiT – The Arctic University of Norway, Norway

Publication history

  1. Received: August 30, 2019
  2. Accepted: October 21, 2019
  3. Accepted Manuscript published: October 29, 2019 (version 1)
  4. Version of Record published: November 27, 2019 (version 2)

Copyright

© 2019, Barbosa et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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