Decision letter: Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa (2019)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics 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). Figure 1 Download asset Open asset 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. Figure 1—source data 1 Mean optical density and CI95 values obtained after 12 hr of growth in minimal media and different antibiotics as reported in Figure 1. The populations tested here include the PA14 wild type, and four resistant populations. Each value is the average of 8 technical replicates per bacterial population. https://cdn.elifesciences.org/articles/51481/elife-51481-fig1-data1-v2.txt Download elife-51481-fig1-data1-v2.txt 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. Figure 2 Download asset Open asset 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. Figure 2—source data 1 Count data of extinction events as reported in Figure 2. Extinct populations were determined by their inability to grow in rich media after 24 hr of incubation at 37 °C. https://cdn.elifesciences.org/articles/51481/elife-51481-fig2-data1-v2.txt Download elife-51481-fig2-data1-v2.txt 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 Download asset Open asset 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. Figure 3—source data 1 Evolutionary dynamics summarized by the area under the curve (AUC) across experimental seasons relative to the reference treatment with no drugs. Data is shown for all following experiments: CARR>GEN, GENR>CAR, PITR>STR and STRR>PIT, as reported in Figure 3a and b and Figure 3—figure supplement 1a and b. https://cdn.elifesciences.org/articles/51481/elife-51481-fig3-data1-v2.txt Download elife-51481-fig3-data1-v2.txt Figure 3—source data 2 Dose-response curves data of surviving populations challenged with CAR, GEN, STR and PIT. Data is shown in Figure 3c and d, and Figure 3—figure supplement 1c and d. Optical density values were recorded after 12 hr of incubation at 37 °C. https://cdn.elifesciences.org/articles/51481/elife-51481-fig3-data2-v2.txt Download elife-51481-fig3-data2-v2.txt Figure 3—source data 3 Growth rate estimates of the surviving population challenged with CAR or GEN, as reported in Figure 3. Growth rate was calculated as specified in the Materials and methods Download Drug order determined re-sensitization or emergence of multidrug resistance in the surviving populations of the CAR/GEN pair caused multidrug resistance in the suboptimal but re-sensitization to similar levels to of the PA14 (Figure 3—figure supplement 2, Figure 3—figure supplement 2—source 1 and in the alternative switch (Figure 3c and d, Figure 3—source data 2). In all surviving populations significantly increased resistance against the second drug change between two and that of the starting Figure 3c and d, Supplementary file 1–Figure 3–supplementary tables 2 and Figure 3—source data – in with the recorded biomass In the suboptimal 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 in combination under unconstrained we observed cases of significant re-sensitization to the first drug. We conclude that drug order can determine treatment or multidrug resistance, and, in to a re-sensitization towards the first drug in the surviving populations, as for applicability of collateral sensitivity cycling and Sommer, 2013). We that the contrasting evolutionary in constrained versus unconstrained treatments of the GEN>CAR switch were caused by an additional in this case between drug resistance and growth We obtained a for such a general which is across the distinct antibiotic treatments, by maximum exponential growth rate under drug-free conditions and it against the growth rate for the PA14 though measured in drug-free environments, a possible in growth rates still indicate a general growth or adaptation which is also relevant under other conditions (i.e., under antibiotic The starting clones for the second evolution experiment showed significantly growth rates under drug-free conditions, with to relative to the (Barbosa et al., 2017). As a selection have favored for which both trade-offs (i.e., the collateral sensitivity and also the general adaptation were during evolution. For the GEN>CAR we found a significant increase in growth rate relative to the wild type PA14 in the unconstrained treatments (Figure Figure 3—source data 3). populations for this particular switch still showed a significantly growth rate relative to with significantly values relative to the starting population (Figure Figure 3—source data 3). The alternative switch not show similar due to the that the of the population were not as high (Figure Figure 3—source data 3). this data that selection under the GEN>CAR unconstrained treatments the advantage of two previously evolutionary trade-offs, hypersensitivity to a second drug and increases in growth re-sensitization could have favored over multidrug resistance because of the adaptation trade-off that can treatment collateral sensitivity switches. identified possible targets of antibiotic selection We used population genomic analysis to functional changes that were likely by antibiotic selection and allowed populations to survive the second evolution experiment for the CAR/GEN pair (Figure Figure data 1). In we of the resistant starting clones from the and 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 by the sequential of mutations under unconstrained conditions, in (Figure Figure data 1), a main of the of and other factors et al., under constrained conditions, we found mutations in the genes or (Figure Figure data 1), which are known to and resistance against et al., Figure Download asset Open asset of experimental evolution for the CAR/GEN drug (a) relevant genomic changes in populations selected with GEN, and (b) populations selected with CAR. to indicate resistance mutations from Barbosa et al., 2017), and indicate The these indicate frequencies by the of in population and to the found among the populations from the treatment. The evolved resistance is by (see in (c) relative to PA14 against CAR and (d) GEN, of and double The and horizontal lines indicate the as by of the The following supplementary source data is available for Figure Figure data 1; Figure data 2. Figure data 1 changes to Pseudomonas aeruginosa PA14 wild type strain as determined by was performed with The data is shown in Figure are with antibiotic to which they are antibiotic to which the clone collateral well in the during experimental evolution. Download Figure data 2 values for several against CAR and GEN, as reported in Figure Download For the more switch GEN>CAR, multidrug resistance in the constrained treatments with mutations in and an independent of et al., and in resistance and and response et al., The re-sensitization towards the first drug in the unconstrained GEN>CAR treatments was with two main of changes at high frequencies across