Experimental evolution as an efficient tool to dissect adaptive paths to antibiotic resistance

Accepted Manuscript Title: Experimental Evolution as an Efficient Tool to Dissect Adaptive Paths to Antibiotic Resistance Author: Gunther Jansen Camilo Barbosa Hinrich Schulenburg PII: DOI: Reference:

S1368-7646(14)00004-1 http://dx.doi.org/doi:10.1016/j.drup.2014.02.002 YDRUP 529

To appear in:

Drug Resistance Updates

Please cite this article as: Gunther JansenCamilo BarbosaHinrich Schulenburg Experimental Evolution as an Efficient Tool to Dissect Adaptive Paths to Antibiotic Resistance (2014), http://dx.doi.org/10.1016/j.drup.2014.02.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Experimental Evolution as an Efficient Tool to Dissect Adaptive Paths to Antibiotic Resistance Gunther Jansen*, Camilo Barbosa, Hinrich Schulenburg 5

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Department of Evolutionary Ecology and Genetics Zoological Institute Christian-Albrechts University of Kiel Germany

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*Corresponding author: Gunther Jansen, PhD Department of Evolutionary Ecology and Genetics Zoological Institute Christian-Albrechts University of Kiel Am Botanischen Garten 1-9 24118 Kiel Germany Tel.: +49 431 880 4148 Fax: +49 431 880 2403 E-mail: [email protected]; [email protected]

Running title: in vitro antibiotic resistance evolution 45

Keywords: antibiotics, resistance, experimental evolution, MIC, horizontal gene transfer

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Abstract Antibiotic treatments increasingly fail due to rapid dissemination of drug resistance. Comparative genomics of clinical isolates highlights the role of de novo adaptive mutations and horizontal gene transfer (HGT) in the acquisition of resistance. Yet it cannot fully describe the selective pressures and evolutionary trajectories that yielded today’s problematic strains. Experimental evolution offers

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a compelling addition to such studies because the combination of replicated experiments under tightly controlled conditions with genomics of intermediate time points allows real-time

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reconstruction of evolutionary trajectories. Recent studies thus established causal links between antibiotic deployment therapies and the course and timing of mutations, the cost of resistance and the likelihood of compensating mutations. They particularly underscored the importance of long-

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term effects. Similar investigations incorporating horizontal gene transfer (HGT) are wanting, likely

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because of difficulties associated with its integration into experiments. In this review, we describe current advances in experimental evolution of antibiotic resistance and reflect on ways to

systematic and highly controlled dissection of evolutionary paths to antibiotic resistance that needs

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incorporate horizontal gene transfer into the approach. We contend it provides a powerful tool for

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to be taken into account for the development of sustainable anti-bacterial treatment strategies.

Introduction

The alarming spread of antibiotic resistance across the globe is rendering hitherto treatable bacterial 70

infections virtually impossible to eradicate (Goldberg et al., 2012; Ho et al., 2010; Tzouvelekis et al., 2012; Corbett et al., 2003; Levy and Marshall, 2004). The gravity of the situation is exemplified by the surge of multi (MDR) and extensively drug resistant (XDR) Mycobacterium tuberculosis (500 000 cases yearly; WHO, 2012), which now resist nearly all antibiotics previously deemed effective (Raviglione and Smith, 2007; Borrell and Gagneux, 2009). We are therefore in urgent need

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of innovative solutions to combat or delay the spread of antibiotic resistance. Traditionally, the 2 Page 2 of 39

answer is to circumvent molecular mechanisms of resistance using chemically altered or newly discovered compounds with a different mode of action (Koul et al., 2011; Fischbach and Walsh, 2009). Unfortunately, their clinical introduction is often promptly met with a rise of resistance (Fischbach and Walsh, 2009; Davies and Davies, 2010). With antimicrobial discovery becoming slower and increasingly challenging, the outlook for keeping pace with fast emerging antibiotic

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resistance is grim indeed.

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Why do antibiotics fail eventually? Paradoxically, antimicrobial treatments aimed at killing

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bacteria inevitably select for microbe survival (O’Brien, 2002; Read et al., 2011) —resistance is an evolutionary response. Strategies aimed at minimizing resistance therefore need to explicitly take evolution into account. This important realization represents one of the core challenges for

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evolutionary medicine (Stearns, 2012) and requires in-depth understanding of the exact

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evolutionary dynamics and genetic mechanisms underlying the rise and spread of resistance. The evolutionary steps leading from drug-sensitivity to clinical resistance are usually

and/or during antibiotic treatment (Mwangi et al., 2007; Howden et al., 2011; Song et al., 2013).

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disentangled using a posteriori comparative genomics of clones isolated from patients before, after

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The resulting correlations between sets of mutations and clinical resistance, however, fall short of unequivocally determining the underlying evolutionary trajectories, due to several limitations. First, clinical strains can only be isolated after discovery, which implies they already obtained all evolutionary changes necessary for resistance. Second, reconstruction of evolutionary trajectories 95

that led to highly successful, clinically resistant genomes is challenged by (i) lack of information of past selection conditions relevant for resistance evolution, (ii) complications introduced by confounding selective pressures that similarly influence pathogen evolution irrespective of selection for antibiotic resistance (e.g., nutrient or metabolic constraints and/or competition with other microbes), and (iii) obfuscation of relevant intermediate adaptive mutations by subsequent selective

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events. The a posteriori approach thus casts an incomplete, potentially misleading light on the

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genetics and evolution of drug resistant genomes. A complementary approach is experimental evolution (Box 1), because it permits dissection of pathogen adaptation to antibiotics during the evolutionary process in real-time and under highly controlled laboratory conditions. This review examines the potential role of experimental evolution as a tool for the in-depth investigation of bacterial adaptation to antibiotic deployment. The focus

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lies on antibiotic choice and dosage —the most central and challenging aspect of any antibiotic

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treatment strategy. Currently, clinical best practices ensure that treatments cure patients quickly,

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avoid side effects, prevent transmission of pathogens to other patients, and minimise resistance. We are concerned here with recommendations for minimising resistance, which are rooted in short-term in vitro evaluations. So they underestimate evolution. Evolution experiments, by definition, provide

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a longer-term perspective, and may therefore yield novel yet essential information for the design of

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treatment protocols optimally reducing the likelihood of resistance while maximising infection clearance. Despite the limited availability of such studies, important insights are emerging that

Nevertheless, evolution experiments have so far only tested a very limited number of specific

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challenge some of the most accepted concepts in the prevention of antibiotic resistance.

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scenarios where resistance may arise. One central factor in resistance evolution that did not receive sufficient attention in these studies is horizontal gene transfer (HGT). Despite the challenges associated with incorporating HGT into experimental evolution protocols, its consideration could yield highly valuable insights into pathogen evolutionary dynamics. 120

Optimal antibiotic dosage is determined using short time analyses Before antibiotics can be used in clinical trials or as therapy, their effects on bacterial growth need to be determined. The effectiveness of antibiotics is measured in vitro as inhibitory concentration (IC), the percent inhibition relative to growth without antibiotics within a 24h time frame. The 125

minimum inhibitory concentration (MIC) is of particular pharmacological interest because it is

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thought to suppress bacterial growth entirely. Not surprisingly, MICs have been determined for a wide range of bacteria-antibiotic combinations (Zhao and Drlica, 2002; Lu et al., 2003) and are considered one of the most important properties for antibiotic application. Indeed, because sublethal doses below the MIC cannot clear all susceptible cells, physicians traditionally administer drugs at concentrations well above the MIC.

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This approach, however, neglects low-frequency resistant mutants occurring spontaneously

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in any bacterial population. Such mutants tremendously complicate measurements of antibiotic

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efficacy. As shown in Figure 1, selection on rare resistant mutants gradually grows stronger as sublethal antibiotic concentrations approach the MIC. Above the MIC, in the harsh conditions of the mutant selection window (MSW; Zhao and Drlica, 2001; Drlica and Zhao, 2007), mutants benefit

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from the complete suppression of wild type cells that previously outcompeted them. Growth of

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resistant cells is only halted at the even higher mutant prevention window (MPC, Dong et al., 1999; Drlica and Zhao, 2007). The insight that resistance is particularly favored above the MIC suggests

the same time, minimizing selection of rare mutants.

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that optimal antibiotic concentrations should achieve maximal bacterial growth inhibition while, at

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Bacterial inhibition is further complicated by pharmacokinetic and -dynamic effects, which may push initially effective antibiotic concentrations above the MPC into the MSW or even below the MIC. For example, MRSA-infected patients injected with vancomycin show highly variable drug concentrations in lungs, brain or blood serum due to variation in tissue penetration, inoculum 145

size and protein binding effects (reviewed in Rybak, 2006). Local antibiotic concentrations decrease even further as they are gradually cleared by the body. Also chemical degradation challenges optimal antibiotic dosage (Palmer et al., 2010a). Tetracycline, for example, strongly selects for resistant mutants, but quickly decays into a more stable mixture of anhydro-, epianhydro- and epitetracycline. These compounds paradoxically select against resistance. When tetracycline

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diffusion remains sufficiently low, bacteria are exposed to these stable products longer than to

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tetracycline, which may result in a selective advantage for drug-susceptible cells (Palmer et al., 2010a). Although it is becoming widely accepted that therapeutic concentrations close to and above the MIC may strongly select for resistance, the picture remains less clear for lower, sublethal antibiotic concentrations. Such concentrations are commonly reached during therapy due to

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pharmacodynamic and -kinetic effects, but can also be measured in the environment due to human

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pollution and production by natural producers (Kummerer, 2009; Thiele-Bruhn, 2003; Forsberg et

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al., 2012). According to the classical MSW hypothesis, selection for resistant mutants is only relevant within the MSW (Drlica and Zhao, 2007). Below the MIC, the hypothesis assumes that competition between slow-growing wild type cells and resistant mutants keeps selection sufficiently

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weak to prevent enrichment of resistant mutations. However, several studies have demonstrated that

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even subtle differences in susceptibility between resistant and susceptible cells can lead to selective enrichment of resistant genotypes at low antibiotic concentrations (Negri et al., 2000; Baquero et

mutations with no or low costs may be selected below the MIC (Gullberg et al., 2011).

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al., 1998; Liu et al., 2011). Some authors additionally suggest that a different (smaller) set of

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Nevertheless, low-dose treatment with ciprofloxacin yielded resistant mutants with a hundred-fold increased MIC, conveyed through the same gyrA and gyrB mutations as found during above-MIC treatments (Jørgensen et al., 2013). The extension of the MSW to concentrations far below the MIC has led to the introduction of yet another term, the Minimal Selective Concentration (MSC), below 170

which no resistant mutants can be detected, not even with ultra-sensitive bio-essays (Liu et al., 2011). Intriguingly, at very low concentrations (below the MSC), antibiotics seem to adopt entirely different functions as signaling molecules that alter transcription levels, affecting traits as diverse as virulence, motility, stress tolerance and biofilm formation (Goh et al., 2002; Yim et al., 2006; 2007; for more extensive reviews of the natural roles of antibiotics, see Davies et al., 2006 and Sengupta

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et al., 2013).

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In summary, short-term in vitro experiments are commonly used to determine antibiotic treatment concentrations that minimize or even prevent bacterial growth. In light of likely complications in an in vivo clinical setting, several authors recommended either very high drug concentrations and/or combination therapy (Cokol et al., 2011; Holm, 1986). Some antibiotics can be cleverly combined to achieve near-complete inhibition at lower individual drug doses, and may

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theoretically have the important advantage that resistance would require two or more mutations.

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The likelihood of one cell simultaneously obtaining two mutations conferring resistance to two

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antibiotics with different modes of action was considered highly unlikely (Mouton, 1999; Drlica and Zhao, 2007). In clinical practice combinations are therefore often chosen as the empirical therapy of choice for bacterial infections ranging from urinary tract infections to community

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acquired pneumonia and tuberculosis (Thiem et al., 2011; Mitchison, 2004), although its efficacy

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remains subject to considerable debate (Chow and Yu, 1999; Yeh et al., 2009; Tamma et al., 2011).

Although traditional therapies attempt to achieve high inhibitory activity while maintaining

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Evolution experiments add a longer-term perspective

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narrow mutant selection windows (Walsch, 2003), resistant mutants continue to arise at an alarming rate. Underestimation of rates of resistance evolution may result from the discrepancy between typical clinical therapies lasting several days (Kumar, 2009) up to, in the case of tuberculosis, months or even years (WHO, 2010), and in vitro studies evaluating antibiotic effects over short 195

periods of bacterial growth (i.e., 24h or less). During such short periods, de novo evolution of resistance is far less likely than selection on existing low-frequency resistant mutants. Moreover, continuous exposure to antibiotics allows investigation of multi-step mutations, which are difficult to capture using traditional plate assays. It has been shown that multi-step resistance evolution is relevant in the clinical setting (Farhat et al., 2013; Safi et al., 2013; Zhang et al., 2013). In vitro

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studies lasting several bacterial growth cycles therefore permit more complete evaluations of the

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importance of de novo evolution, allowing for multiple evolutionary mechanisms and the investigations of the temporal dynamics of mutation and selection. A typical experimental evolution study is explained in Figure 2, while Table 1 summarizes the outcome of recent evolution experiments. Several of these confirm that prolonged exposure to single antibiotics leads to dramatic (ten

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to thousand-fold) increases in MIC, and thus resistance. This was shown for e.g. E. coli and

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Pseudomonas exposed to ciprofloxacin (Wong et al., 2013; Zhang et al., 2011), even at relative low

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doses (Jørgensen et al., 2013) or E. coli subjected continuously to chloramphenicol, doxycycline or trimethoprim treatment (Toprak et al., 2012). Evolution may thus push 24h MICs to levels unanticipated by short-term in vitro experiments, rendering these treatments highly ineffective

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within days.

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Comparisons of combination and sequential multi drug treatments over longer periods of time have yielded additional insights in resistance evolution. Clinicians usually prefer synergistic

combinations. Such combinations are highly effective at suppressing bacteria in the short term, but

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combinations —antibiotics that amplify each other’s effects —over monotherapies and antagonistic

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also create severe selective pressures for resistance. This may lead to substantially increased evolutionary rates compared to antagonistic drug pairs (Hegreness et al., 2008) and effectively favor the evolution of resistance (Michel et al., 2008). The underlying rationale is that resistance mutations to one of a pair of synergistic drugs may yield cross-resistance to the other drug, whereas 220

resistance to one of an antagonistic pair may reveal the inhibition normally blocked by the other (Yeh et al., 2009). These ideas were explored further in a five day serial dilution experiment comparing doxycycline-erythromycin combination treatments to either monotherapy (Pena-Miller et al., 2013). After 36 hours of antibiotic treatment, the synergistic combination initially most effective at suppressing growth yielded the highest bacterial load and thus the worst possible

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therapy. Surprisingly, bacterial load was even higher for the maximally synergistic combination

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than for the corresponding single drug treatments. A different conclusion was reached when combination treatments with rifampicin (marketed in the US as rifampin) and streptomycin were evaluated against monotherapies and sequential treatments that switch between either drugs in daily intervals (Perron et al., 2007). Here, the combination yielded the lowest bacterial densities and thus lowest resistance in ten days.

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Interestingly, multi drug resistance was highest for sequential therapies starting with streptomycin,

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but not when rifampicin was deployed first. The authors concluded that multi drug resistance is less

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likely to evolve if the antibiotic incurring the largest fitness cost is used first. Moreover, immigration from populations not exposed to antibiotics led to increased MICs and accelerated resistance evolution in all treatments (Perron et al., 2007), indicating the potentially exacerbating

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effects of bacterial migration from antibiotic-free reservoirs.

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Evolution experiments not only evaluate outcomes of antibiotic treatments, but also permit detailed molecular dissection of evolutionary trajectories. Although there is a vast and quickly

analyses that permit inference of evolutionary trajectories to a particular resistance phenotype. For

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growing body of literature on the genetics of antibiotic resistance, we here focus exclusively on

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example, E. coli quickly developed high resistance to ciprofloxacin through a binding site modifying mutation in gyrA (the target of ciprofloxacin), a mis-sense mutation in a ribose ABC transporter and loss of function of the marR repressor, causing constitutive activation of a central multi drug resistance program (Zhang et al., 2011). Resistance against trimethoprim was narrowly 245

constrained, requiring the non-randomly ordered sequential fixation of four DHFR mutations, including one of two particular promotor mutations and one specific final mutation (Toprak et al., 2012). Similar mutational constraints to evolution have been documented for ß-lactamases, enzymes that hydrolyse the ß-lactam ring of penicillin, cephalosporins and their derivatives (for a review of all clinically and experimentally investigated mutations in the enzyme and their effects on

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resistance, see Salverda et al., 2010). Only 3.4% of all possible first-order base-pair substitutions in

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the TEM-1 ß-lactamase proved beneficial for cefotaxime resistance (Schenk et al., 2012). Moreover, step-wise evolution of the five mutations found in a clinically resistant ß-lactamase was only possible via 15% of the 120 theoretical evolutionary trajectories (Weinreich et al., 2006). A comparable investigation of evolutionary trajectories within two sets of four resistance mutations revealed that combinations of more than two led to significantly lowered resistance and were thus

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unlikely to evolve (Schenck et al., 2013). More generally, information on clinically and

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experimentally identified mutations and their co-occurrence frequencies may be exploited to build

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network models, which can predict the most likely adaptive paths to high-level resistance (Guthrie et al., 2001).

The availability of relatively few evolutionary pathways to high-level, multi-mutational

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resistance may be explained by epistasis, evolutionary potentiation and/or pleiotropy. Interactions

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between mutations can lead to stronger (positive epistasis) or weaker (negative epistasis) effects on the phenotype than expected from single mutations alone. Sign epistasis is particularly interesting,

resistance to the the wild type) detrimental or neutral in another, for example after fixation of a first-

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because it renders mutations that are beneficial in one genetic background (say, conferring increased

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order mutation (Weinrich et al. 2006, Salverda et al., 2011). Epistatic interactions may thus effectively block evolutionary paths from passing through certain mutations, while forcing it through others, possibly contingent on particular allelic backgrounds. Potentiation is a consequence of epistasis, making mutations accessible that would have had negative effects in the wild type (Hall 270

et al., 2010, Woods et al., 2011). This means that such mutations could only evolve after the previous one has been fixed in the population, which may force evolutionary paths into a constrained set of early mutations. Mutations may additionally incur pleiotropic effects on other genes, which can lead to unchanged (Kurland et al., 1996; Tubulekas and Hughes, 1993; Sander et al., 2002), increased (Luo et al., 2005; Paulander et al., 2009; Marcusson et al., 2009) or decreased

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(Gagneux et al., 2006; O'Regan et al., 2010) fitness in the absence of antibiotics (see Andersson and

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Hughes 2010 for an overview of the costs of resistance). Compensatory mutations may in turn alleviate fitness disadvantages of resistance mutations. This makes them essential steps within evolutionary paths to high-level resistance, although they do not directly contribute to the resistance phenotype. Interestingly, few cases have been documented where removal of antibiotics has successfully reduced incidence of resistant strains (Marcusson et al., 2009; Seppälä et al., 1997),

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which suggests evolution to compensation is more likely than evolutionary reversal to drug

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sensitivity (Levin et al., 2000; Andersson and Hughes, 2010 but see Gifford and MacLean, 2013).

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This was confirmed in a recent evolution experiment, where initial fitness costs of artificially introduced resistance mutations in an experimentally evolved rifampicin resistant E. coli genotype were quickly reduced during subsequent experimental adaptation to antibiotic-free medium (Angst

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and Hall, 2013). Finally, pleiotropy can also have surprising effects. E. coli evolved rifampicin

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resistance in an environment lacking antibiotics (Rodríguez-Verdugo et al., 2013). Here, the mutations in rpoB that were highly beneficial for growth at high temperatures in glucose-limited

understanding constraints on the likelihood and evolutionary paths to resistance requires

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medium also pleiotropically conferred resistance to rifampicin at no apparent cost. Thus,

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assessments of the underlying mutational fitness landscapes. Nevertheless, many resistance mutations do not occur along a narrowly defined evolutionary

trajectory. Resistance to chloramphenicol or doxycycline could be reached through multiple evolutionary paths requiring few mutations and/or amplifications in a diverse set of genes encoding 295

membrane transporters or regulators of transcription or translation (Toprak et al., 2012). This evolutionary adaptation could be realized in multiple ways, and always conferred cross-resistance to the other drug, although bacteria were only exposed to one of the two. A less anticipated mechanism was identified in a comparison between combination and monotherapies. While resistance to the latter evolved through point mutations in regulatory genes such as mar or acr, the stronger selection

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exerted by drug combinations caused 315kb duplications of a region containing several resistance

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genes, including those coding for a multi-drug pump acr (Pena-Miller et al., 2013). These results question the main argument for combination therapy, which states that resistance to simultaneously deployed antibiotics is less likely because it requires multiple independent mutations. Taken together, experimental evolution studies challenge the traditional, static idea of MIC. Instead, they emphasize the inherently dynamic nature of this concept, as it is highly dependent on

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the immediate adaptive responses of bacterial populations. Systematic explorations of alternative

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treatments additionally demonstrated longer-term effects of antibiotic exposure. Most surprisingly,

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initially highly effective treatments such as synergistic combinations select most strongly for resistances and are thus likely the worst option in the long term. Genomic analysis of the mechanistic basis of extremely rapid adaptation further suggest that bacteria possess innate fast-

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response options to cope with antibiotic burden. Interestingly, resistance to some antibiotics requires

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a relatively limited set of mutations, whereas for others multiple evolutionary realisations exist. The available results thus emphasize that optimal treatment strategies must take into account the

to date, the paucity of studies capitalizing on its advantages for studying resistance evolution, we

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underlying evolutionary dynamics. Considering the potential power of evolution experiments and,

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argue for further exploitation of this tool to improve drug treatment strategies.

Horizontal gene transfer: an unexplored theme in evolution experiments Evolution experiments so far focused mainly on evaluating relatively simple scenarios of

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clonal adaptation through de novo evolution. These results are pivotal for understanding pathogens such as Mycobacterium tuberculosis, which generate a substantial number of de novo mutants even within one host (Cambau et al., 1994; Heym et al., 1999; Somoskovi et al., 2001; Hirano et al., 1997; Lemaitre et al., 1999; Sreevatsan et al., 1996, 1997; Gillespie, 2002; Mariam et al., 2011; Sun et al., 2012). However, many clinical strains become resistant at much higher rates than can be

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explained by vertical evolution (Barlow, 2009). Indeed, clinical isolates often carry resistance genes

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organised in genomic islands or plasmids (Laverde Gomez et al., 2010; Hussain et al., 2012; Kumar et al., 2011; Ohnishi et al., 2009; Palmer et al., 2010b; Ruiz-Martinez et al., 2011; Traglia et al., 2012), which are obtained from the clinical environment or from other resistant strains. The archetype of nosocomial infections, methicillin-resistant Staphylococcus aureus (MRSA), sports no less than seven distinct SCCmec genomic islands carrying varying batteries of resistance genes and

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virulence factors (Shukla et al., 2012; Baba et al., 2008; Enright et al., 2000; Fitzgerald et al., 2001;

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Robinson and Enright, 2003; Fitzgerald et al., 2003), see Figure 3). The striking nucleotide-level

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similarities of genomic islands found across very different species strongly suggest that promiscuous sharing of resistance genes is common. Moreover, many resistance genes probably did not evolve de novo in response to human antibiotic intervention, but likely already originated

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millions of years ago (D’Costa et al., 2012). Antibiotic use may therefore selectively enrich strains

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carrying these ancient genes and promote their transfer to sensitive strains (O’Brien, 2002). This

observe.

Acquisition of foreign resistance genes may occur via one of at least five distinct

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implies that study of vertical evolution cannot suffice to fully understand the resistance patterns we

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mechanisms of horizontal gene transfer (Figure 4 and Box 2). The three most studied mechanisms are bacterial acquisition of DNA from the environment (transformation), procurement of new genes via phage-mediated transduction and intra- or interspecific DNA exchange via genetically encoded donor-recipient systems (conjugation) (Griffith, 1928; Lederberg and Tatum, 1946; Lorenz and 345

Wackernagel, 1994; Mazodier and Davies, 1991; Smillie et al., 2012; Chen et al., 2005). More recent discoveries also describe exchange of (possibly non-hereditary) cytoplasmic molecules and/or plasmids via non-specific nanotubes and serial transduction by way of phage-like gene transfer agents (GTAs) (Dubey and Ben-Yehuda, 2011; Mashburn-Warren and Whiteley, 2006; Chiura et al., 2011). Transposons additionally play a potentiating role in accidental transfer of

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resistance genes (reviewed in Chen et al., 2005; Clewell and Gawron-Burke, 1986; Mazel, 2006).

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The redistribution of available gene cassettes likely leads to entirely distinct evolutionary dynamics compared to those ensuing from de novo origins of resistance (Levin and Cornejo, 2009; Baltrus et al., 2008; Cooper, 2007), because it bestows both unique benefits and costs on the receiving bacterial cell. HGT of resistance genes is beneficial because it allows immediate expansion into new

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environments such as clinics or farms (Paul et al., 2013). In contrast, de novo adaptation usually

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requires a set of smaller changes (most commonly point mutations) providing their boon more

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gradually. Second, mobile genetic elements often contain more than one gene (often including toxin and virulence genes; Barlow, 2009; Wiedenbeck and Cohan, 2011; Gomez-Lus, 1998). Therefore cross-resistance and multi drug resistance may be more common in resistant strains that evolved

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through HGT than through mutation and selection. Third, HGT in combination with recombination

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may create additional, often large-scale genomic variability. This in turn may accelerate adaptation independently of the transferred resistance genes. Fourth, HGT may bestow drug resistance to

strongly density-dependent and may not occur when donors and/or receptors are rare (Levin and

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multiple species in a community, where evolution only involves a single clone. Finally, HGT is

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Cornejo, 2009), whereas de novo mutations occur at a fixed rate. All of these advantages may thus result in distinct selective pressures.

Despite these advantages, HGT can also come with costs for the receiving cell (Andersson

and Levin, 1999; Andersson and Hughes, 2010; Lenski, 1998; Ender et al., 2004). Disruption of 370

genome integrity may result from e.g. inactivation of genes through unfortunate foreign DNA insertion or through epistatic interactions between the genomic background and the newly introduced genes (Cohan et al., 1994; Nogueira et al., 2009). These detrimental effects may in turn reduce growth rate, alter metabolic capacities and curtail the competitive abilities of the receptor strain much more dramatically than de novo point mutations or indels (Andersson and Hughes,

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2010; Lenski, 1998). More specifically, transduction is risky for bacteria because it may involve

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infection with parasites that replicate at the expense of lysed host cells. Conjugation is an energyintensive process (Zatyka and Thomas, 1998) that introduces selfish plasmids, and transformation risks the inadvertent uptake of toxic gene fragments or DNA that has degraded in the environment.

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of DNA uptake contingent on each of the possible mechanisms of HGT.

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A full analysis of resistance evolution thus requires a balanced assessment of the costs and benefits

Calculations of benefits and costs of resistance are mostly based on a combination of

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comparative genomics and in vitro studies. The former yield correlations between clinical resistance

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and horizontally acquired genes. However, the observed molecular patterns often disallow inference of the mechanisms they originated from (Laverde Gomez et al., 2010; Kumar et al., 2011; Ohnishi et al., 2009; Ruiz-Martinez et al., 2011). In vitro tests, on the other hand, do specifically document

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fitness losses or gains immediately after uptake for each of the HGT mechanisms (Starikova et al.,

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2013; Albarracin Orio et al., 2011; Marciano et al., 2007; Trindade et al., 2009), but a longer-term perspective may additionally highlight how disadvantages may be rapidly mitigated by the

cases within 200 (Starikova et al., 2013) to 420 generations (Dionisio et al., 2005). For example,

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evolution of compensating mutations (Andersson and Levin, 1999; Cohan et al., 1994), in some

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52% of the studied interactions between antibiotic resistance mutations and conjugative plasmids carrying resistance genes were shown to exhibit epistasis, 77% of which were instances of sign epistasis (Silva et al., 2011). This implies that a single, costly resistance mutation may be ameliorated by acquisition of a resistance plasmid carrying even more resistance genes. In other 395

words, HGT may play an active role in the evolution of (multi-drug) resistance that reaches beyond the direct benefits bestowed by the genes it carries. Systematic evaluations of cost-benefit ratios of HGT can thus be analyzed better using longer-term evolution experiments. Experimental control over the mechanisms of HGT, however, poses a particular challenge to experimental manipulation, whereby each HGT type requires

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different solutions. In particular, transduction depends on the presence of a prophage in the host

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population. But its activation leads to host cell lysis, which is an evolutionary disadvantage for evolving bacterial cells. To prevent immediate extinction, conditions for prophage activation need to be carefully considered and the density of initial prophage-carrying host genomes chosen wisely. For experiments involving transformation, choice of bacterial species is pivotal. Most appealing would be experiments with clinically important and naturally competent (see Box 2) bacterial

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strains such as Staphylococcus aureus or Acinetobacter baumanii, where the HGT of resistance

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genes has been empirically observed. For example, experimental addition of extracellular DNA

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carrying rifampicin resistance genes accelerated adaptive evolution in Acinetobacter. This effect was not seen when a non-competent strain was used, nor when WT DNA or water was added, suggesting resistance evolved through transformation (Perron et al., 2012). However, without

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genetic confirmation of cellular integration of resistance genes it could not be shown unequivocally

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that extracellular DNA was taken up and expressed by the bacteria. The major challenge for experiments with conjugation is to ensure selection for receptor strains that acquired resistance

prevent competitive exclusion of non-resistant receptor cells by introducing auxotrophic donor

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through HGT, while preventing the spread of already resistant donor cells. One solution may be to

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cells, such that only receptor cells may grow after acquiring resistance. In one study, this problem was solved with spontaneous mutants resistant to either nalidixic acid and mecillinam or to rifampicin and fosfomycin (Dionisio et al., 2002). Subsequently, one of the two mutants (the later donor) was transformed with a plasmid carrying resistance to six different antibiotics. Donor and 420

receptor cells could thus be easily distinguished based on their final resistance profiles. Once these challenges have been addressed, the influence of each process of HGT on the evolutionary dynamics of resistance can be investigated through comparisons of populations adapting to antibiotics in the presence and the absence of prophages, conjugant donors or external DNA, respectively. The spread of horizontally transferred elements through these populations may be

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tracked genetically, phenotypically and/or microscopically in time using GFP- or otherwise labeled

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HGT resistance genes. This may ultimately allow evaluation of rates of resistance evolution in the absence or presence of HGT, and, in combination with genomic analyses, the reconstruction of evolutionary trajectories for specific antibiotic treatment conditions. A serial passage experiment showed that a small proportion of plasmid-carrying donor cells in heterogeneous bacterial communities accelerated the spread of the plasmid and thus resistance by several orders of

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magnitude (Dionisio et al., 2002).

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In summary, experimental evolution has, with rare exceptions (Dionisio et al., 2002; Perron

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et al., 2012), so far focused on simple vertical clonal evolution. Comparative genomic studies, however, repeatedly highlight the prevalence of horizontally transferred genes in clinically resistant strains. Horizontal gene transfer very likely accelerates resistance evolution by providing

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“prefabricated”, fully functional resistance cassettes, potentially to a whole community instead of to

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single clones, that often yield high levels of cross resistance. Genetic end-point analyses of resistant clones usually fall short of capturing the factors influencing and facilitating initial introduction and

transformation, transduction and conjugation each represent vastly different evolutionary routes to

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subsequent spread of horizontally transferred elements. Particularly the consideration that

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resistance, each associated with its own set of ecological parameters, costs and likelihoods of compensating mutations, has not received sufficient attention from experimentalists. Therefore evolution experiments should aim at evaluating the relative contribution of each mechanism of HGT to resistance evolution under controlled conditions, and should subsequently contrast each 445

with the dynamics of de novo evolution. Experimental designs including HGT face particular challenges which can be addressed e.g. through the use of GFP and other genetic markers scored over time. Combined with whole genome analysis of different time points, a detailed dissection of antibiotic adaptation can be obtained. This may yield a full appreciation of more realistic, complex dynamics of resistance evolution as it has been observed in clinics during the last decades.

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Conclusion Experimentally evolving bacteria provide exciting insights into the course and timing of adaptation to antibiotics, fitness costs associated with resistance, the likelihood and frequency of compensating mutations, fixation probabilities and the predictability of evolution. Using powerful experimental designs, these changes can be causally linked to antibiotic concentrations, types of therapies or even

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environments such as the cystic fibrosis lung (Wong et al., 2013). Here, we argue that experimental evolution is an indispensable tool for the investigation of antibiotic resistance evolution and for

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testing optimal treatment schemes. Despite their low number, experimental evolution studies have already challenged some of the most traditional concepts in antibiotic therapy, highlighting the roles of evolutionary dynamics, bacterial innate stress responses and genetics. Particularly, they suggest

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that evolutionary trajectories may be more or less constrained and thus more or less predictable,

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depending on the antibiotics used. Although HGT clearly plays an important role in the fast spread of resistance, it has not received much attention in experimental evolution, possibly due to the

antibiotics with genetically labeled mobile elements so they can be tracked microscopically and

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challenges it poses to experimental design. We suggest to mix bacterial populations adapting to

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genetically. Complemented with analyses of de novo evolution in the bacterial chromosome, such a potentially powerful in vitro system may allow integrated analyses of resistance evolution under controlled conditions where causal links can be reliably established. A major challenge for the future will be to devise series of evolution experiments tailored to inform clinical practice, for 470

example by providing information on fundamental biological properties of pathogens during patient infections, predicting the course of infections, or evaluating treatment options before clinical implementation.

Box 1. Experimental evolution of antibiotic resistance Experimental evolution aims at investigating the influence of environmental and intrinsic factors on 18 Page 18 of 39

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evolutionary change and adaptation. It consists of exposing replicate populations of the focal organism to highly controlled laboratory conditions, whereby the tested factors are manipulated experimentally and the resulting changes are scored in real-time. This approach thus provides a powerful tool to precisely test cause-effect relationships during evolutionary adaptation and find out

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which environmental and/or genetic factors are responsible for a specific phenotypic change.

Evolution experiments have repeatedly been used to assess the causes and dynamics of antibiotic

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resistance evolution in bacteria. Two general protocols can be applied. (i) The chemostat approach

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maintains a bacterial population continuously in exponential phase by regular re-introduction of nutrients and, at the same time, removal of bacteria. (ii) The serial dilution protocol includes

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culturing of bacterial populations for a defined time period, usually 24h and usually covering exponential to stationary phase, after which a small bacterial inoculum is transferred to new culture

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medium and the entire cycle starts afresh. A typical serial dilution experiment may include 5- 10 transfers, although Lenski's seminal experiments, not considering resistance evolution, have been

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following the growth of E. coli for over 50,000 generations (Wiser et al., 2013). Both chemostat and

For example, the latter protocol may reflect patient-to-patient transfer or spread across tissues

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serial dilution approaches may cover certain situations in the patient and/or clinical environment.

within a single patient, whereby the transmitted inoculum is usually very small and can then grow to higher densities after successful invasion. The chemostat would rather model infection within single tissues or systemic bacteremia. In both cases, antibiotics are applied at sub-lethal doses, in order to allow the bacteria to evolve at all. A comparatively novel approach is based on microfluidic 495

“lab-on-a-chip” technology, which constrains bacterial cells to extremely small, usually twodimensional environments, thus allowing analysis of bacterial evolution at the single cell level (e.g., (Zhang et al., 2011)). This approach has the particular advantage that growth conditions as well as environmental structuring can be more precisely controlled than with the standard chemostat or serial dilution protocols. Irrespective of the specific approach used, evolution experiments generally

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offer several unique advantages over the end-point analysis clinical pathogen isolates:

1.

Under laboratory conditions important variables such as resource availability or antibiotic

concentration can be easily manipulated which permits separation of cause and effect (e.g., Zhang

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2.

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et al., 2011);

Highly replicated experiments using independent biological populations provide crucial

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estimates of the amount of parallel evolution, and therefore the predictability of bacterial adaptation

3.

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to antibiotics (e.g., Toprak et al., 2012);

Cryopreservation of samples from different time points creates a living fossil record which

Toprak et al., 2012); 4.

Genomic regions under selection can be identified and also manipulated owing to well

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facilitates ancestor-descendant comparisons and reconstruction of evolutionary trajectories (e.g.,

characterized bacterial genomes, and thus genetic changes can be correlated to phenotypic

Box 2. Horizontal Gene Transfer

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properties such as fitness and evolvability (e.g., Pena-Miller et al., 2013; Walkiewicz et al., 2012).

Bacteria have adapted to an immense variety of environments. Horizontal gene transfer

(HGT), the process by which bacteria acquire genes, sets of genes or even whole genomes from an extracellular source, may play a key role in adaptation, particularly under intense selective pressures (Gogarten and Townsend, 2005). Prokaryotes can horizontally transfer genetic material by 520

transformation, conjugation, transduction, serial transduction (GTAs) or nanotubes, each of which will be discussed in detail below and schematically depicted in Figure 2. Transformation involves DNA uptake from the environment, usually from neighboring lysed or DNA secreting cells. DNA is subsequently incorporated into the genome through mechanisms such as homologous recombination, transposition, integrons or other recombinase-dependent

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mechanisms (see Domingues et al., 20120; Ploy et al., 2000). Only few bacterial species are known to be naturally competent, including Bacillus subtilis, Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae and Neisseria gonorrhoeae. Acquisition of antibiotic resistance through transformation is thought to be clinically relevant particularly for the last three (Barlow,

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2009), but direct evidence remains scarce (Jeon et al., 2008; Vegge et al., 2012; de Boer et al., 2002).

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Conjugation requires direct contact between a receiving cell and a donor cell that possesses

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conjugative plasmids or transposons. These conjugative elements contain genes required for conjugation, i.e. for mediating contact between donor and recipient cells, mating bridge formation,

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conjugative rolling circle replication and DNA recircularisation in the recipient cell. Conjugative transfer is considered the major mechanism for dissemination of resistance between a large set of

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clinically resistant Gram negative and positive bacteria (Waters, 1999), and may even be even stimulated by the presence of antibiotics (Davies, 1994)!

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Transduction occurs when bacteriophages inject their DNA into new bacterial host cells.

When the prophage is activated by e.g. host cell stress or UV exposure, it excises from the host

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During the lysogenic cycle, the phage is incorporated into the host genome, forming a prophage.

genome. The subsequent packaging of viral DNA into the viral capsule may accidentally include parts of the host genome. After lysis of the host cell, the newly produced viruses may thus inadvertently transfer host genes to newly infected bacteria. Recent studies suggest that bacteriophages may form important reservoirs of antibiotic resistance genes in the environment 545

(Colomer-Lluch et al., 2011; Modi et al., 2013), including the cystic fibrosis lung (Rolain et al., 2011; Fancello et al., 2011). Serial transduction though gene transfer agents (GTAs) was first observed in the photosynthetic bacterium Rhodopseudomonas capsulata (Marrs, 1974) and later found in all completely sequenced genomes of alphaproteobacteria, in the delta-proteobacterium Desulfovibrio

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desulfuricans, in the spirochaete Brachyspira hyodysenteriae and the archaeon Methanococcus voltae (see Lang et al., 2012 and references therein). The genetic mechanism is similar to transduction, although the phage-like particles are smaller than any other transducing phage. Some studies suggest they may in fact be defective dsDNA tailed phages (Lang and Beatty, 2007, 2000;

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Stanton et al., 2009). GTA genes embedded in the host genome have never been observed to excise from the genome. Rather, they encode GTA particles which subsequently package random

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fragments of the host genome, occasionally including some of the GTA genes. Strikingly, GTAs are

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too short to contain all genes coding for the structural proteins of the particle (Lang et al., 2012), and thus, unlike phages, cannot transfer GTA-forming capabilities to other cells. The finding of

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endolysins in several GTAs suggests that GTA release requires host cell lysis (Hynes et al., 2012). Intriguingly, when GTAs were added to oceanic microbial communities, rates of HGT of kanamycin

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and streptomycin resistance genes were increased compared to natural transformation and transduction in the absence of GTAs (McDaniel et al., 2010). This suggests GTAs may play an as

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yet unknown role in resistance evolution.

and/or non- conjugative plasmids using intercellular nanotubes, a mechanism distinct from

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Bacteria growing on solid surfaces may exchange non-heritable cytoplasmic molecules

conjugation (Dubey and Ben-Yehuda, 2011). Bacteria can exchange non-conjugative plasmids via nanotubes in a constitutive way, independent of donor-recipient pairing, albeit in a much lower frequency than conjugation and much less specifically. Nanotube formation has been observed between very diverse strains of bacteria, including between Gram-positives and Gram-negatives. 570

Their potential role in HGT and antibiotic resistance, however, has not been investigated.

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Acknowledgments The authors thank Robert Beardmore, Anette Friedrichs, Tal Dagan, Philip Rosenstiel and Roderich 575

Römhild for helpful discussions. The manuscript was improved with the helpful comments from

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four anonymous reviewers. This work was funded by the University of Kiel. CB receives funding

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by the International Max-Planck Research School (IMPRS) for Evolutionary Biology at Kiel

University. GJ thanks the Wissenschaftskolleg zu Berlin (Institute for Advanced Study) for hosting

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his sabbatical while writing this manuscript.

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23 Page 23 of 39

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Figure 1

Schematic antibiotic dose response curve. At sublethal drug concentrations, higher antibiotic concentrations result in a decreasing number of growing bacterial cells. At the Minimum Inhibitory 1020

Concentration (MIC), all susceptible cells are suppressed by the antibiotic. Spontaneous resistant mutants are always present in the population at low frequencies. At high antibiotic concentrations close to and above the MIC, they benefit from increasing selection for resistance and reduced competition from susceptible cells. In the Mutant Selection Window (MSW), resistant mutants are 32 Page 32 of 39

the only cells able to grow. However, at the Mutant Prevention Concentration (MPC), also highly 1025

resistant cells are inhibited completely. Because several distinct resistant mutants can exist in any population, each with their own MIC, the growth of resistant mutants is predicted to decline in a

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step-wise fashion.

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1030

Figure 2

Illustration of experimental evolution of antibiotic resistance supported with genomic analysis of evolutionary pathways. Five populations of E. coli were continuously exposed to either 1035

chloramphenicol (CHL, a ribosome inhibitor) or thrimethoprim (TMP, inhibits folic acid biosynthesis) in a morbidostat that maintained constant inhibition (i.e., increasing growth rates due to resistance were met with higher antibiotic concentrations). a,b) IC50 levels (and thus resistance) increased over time for all replicates in all treatments. a) TMP adaptation evolved in a step-wise fashion; the final IC50 was 1680-fold increased compared to that of the ancestor. b) CHL-resistance

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1040

increased smoothly over time with a 870-fold increase of the IC50. c) Genomic analyses of evolved populations yielded SNPs in coding (block arrows) or non-coding regions (rectangles) of genes involved in folic acid synthesis, membrane function, transcription or translation. Blue squares represent SNPs found in TMP-adapted populations, red circles in the CHL-adapted populations.

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Colour intensity corresponds to the number of independent SNPs found in each gene. Populations adapting to CHL had independent SNPs in various genes involved in transcription, translation or

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membrane transport, suggesting there are multiple ways of obtaining resistance to CHL. In contrast,

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TMP-adaptation required a sequence of mutations occurring in a non-random order in a very small set of genes always involving the dehydrofolate reductase (DHFR) gene. pDHFR and pcmr :

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promotors of DHFR and cmr genes, respectively. Modified from Toprak et al., 2012.

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Figure 3

Comparison of SCCmec pathogenicity islands in Staphylococcus aureus. Orange = genes encoding 1055

methycillin resistance; red = regulators of mecA expression; brown = streptomycin/spectinomycin resistance; yellow = erythromycin resistance; dark purple = tetracycline resistance; grey = heavy metal resistance; green = insertion sequences; light and blue = parts of ccr-mediated recombination operon; violet = restriction modification system. These mobile genetic islands combine different sets of resistance genes such as complete (Class A SCCmec) or disrupted (Class B and C SCCmec)

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methicillin resistance operons (mec1-mecR1-mecA). Tn554 encodes erythromycin (ermA) and streptomycin/spectinomycin resistance (aad9 or spc); copA encodes a putative copper-transport

34 Page 34 of 39

ATPase; hsdR, hsdM, and hsdS encode a partial restriction-modification system (RM) type I; Tn4001 encodes an aminoglycoside resistance operon (aacA-aphD); plasmid pT181 encodes tetracycline resistance (tet); WTn554 encodes cadmium resistance (cadB, cadC ); and plasmid 1065

pUB110 encodes bleomycin (ble) and tobramycin resistance (ant40 ). pls Plasmin-sensitive surface

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protein. The ccr gene complex mediates recombination targeted at insertion sites. Modified from

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Malachowa and DeLeo, 2010.

1070

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Figure 4

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Overview of mechanisms of horizontal gene transfer. For more details, refer to Box 2.

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i cr us

Factor(s) addressed

Main findings

Alternative drug treatments

Low antibiotic concentration

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drugs

Synergistic combinations select more strongly for

Bacterium

Reference

E. coli, S. aureus

Hegreness et al. 2008; Michel

resistance than single drugs or antagonistic

et al. 2008; Pena-Miller et

combinations

al. 2013

ed

Antibiotic combination vs single

an

Table 1. Recent examples of evolution experiments on antibiotic resistance

Low drug concentrations lead to increased multi-drug

E. coli

Kohanski et al. 2010

E. coli

Toprak et al. 2012

P. aeruginosa

Perron et al. 2007

E. coli,

Perron et al. 2006

concentrations

Ac

Sequential vs. combination treatment

Resistance evolution is based on drug-dependent

ce

Temporally increasing drug

pt

resistance due to elevated mutation rates

Antimicrobial peptide (AMP) as

distinct evolutionary trajectories

Sequential treatment may minimize resistance evolution depending on drug order AMPs do not prevent resistance evolution

alternative drug Antibiotic-phage combination

P. fluorescens Antibiotic-phage combination can minimize drug resistance evolution

P. fluorescens

Zhang et al. 2012 Evol Appl; Escobar-Páramo et al. 2012 Evol Appl 37

Page 37 of 39

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Population characteristics

Bacterial immigration from source population enhances

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Immigration

P. aeruginosa

Perron et al. 2007, 2008

Acinetobacter

Perron et al. 2012 PRS

resistance evolution

Horizontal gene transfer via transformation increases

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Horizontal gene transfer

resistance evolution

Antibiotic gradients and microhabitat structures

ed

Antibiotic gradient and

Altruism among bacteria

E. coli

Zhang et al. 2011 Science

E. coli

Lee et al. 2010

P. aeruginosa

Wong et al. 2012 PLoS Genet

P. aeruginosa

MacLean et al. 2010

Bacteroides

Walkiewicz et al. 2012 PNAS

promote rapid resistance evolution Resistance evolution is enhanced in populations with

pt

environmental structuring

baylyi

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altruistic cells

Resistance evolution in cystic

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fibrosis-like environment

High level of parallel evolution, incl. costly resistance mutations coinciding with compensatory fitnessenhancing changes

Genetics of resistance

Evolutionary trajectories of distinct genotypes

Selection leads to fixation of beneficial mutations with diminishing returns in the course of drug resistance evolution

Alternative mutations in a

Mutations with small functional effects can have large

38 Page 38 of 39

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fitness effects during resistance evolution

Alternative resistance mutations

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resistance enzyme

Costs of resistance mutations, compensatory mutations

/ E. coli Hall et al. 2010 PRS; Hall et

E. coli, P. aeruginosa

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and genotype-environment interactions influence

thetaiotaomicron

bacterial adaptation

Gene amplification and subsequent divergence enhance

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Gene amplification

JEB Salmonella

Näsvall et al. 2012 Science

enterica

pt

resistance evolution

al. 2011 Genetics; Hall 2013

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39 Page 39 of 39