Multidrug-therapy and evolution of antibiotic resistance: When order

Multidrug-therapy and evolution of antibiotic

resistance: When order matters

Running title: Sequential Therapy and Evolution of Drug Resistance

Gabriel G. Perron*1, 2, Sergey Kryazhimskiy*1, Daniel P.

Rice1 & Angus Buckling2, 3

1FAS Center for Systems Biology & Organismic and Evolutionary Biology, Harvard University,

Cambridge, MA, 02138, USA. 2Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK.

3Biosciences, University of Exeter, Tremough, Cornwall TR10 9EZ, UK

Corresponding author:

Gabriel G. Perron

FAS Center for Systems Biology

Harvard University

52 Oxford Street, Cambridge, MA 02138, USA

Email: [email protected]

*These authors contributed equally to this work

Copyright © 2012, American Society for Microbiology. All Rights Reserved.Appl. Environ. Microbiol. doi:10.1128/AEM.01078-12 AEM Accepts, published online ahead of print on 22 June 2012

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Sequential Therapy and Evolution of Drug Resistance

2

ABSTRACT 1

The evolution of drug-resistance among pathogenic bacteria has led 2

public health workers to rely increasingly on multidrug therapy to treat 3

infections. Here, we compare the efficacy of combination therapy (i.e. using two 4

antibiotics simultaneously) and sequential therapy (i.e. switching two antibiotics) 5

in minimizing the evolution of multidrug-resistance. Using in vitro experiments, 6

we show that the sequential use of two antibiotics against Pseudomonas 7

aeruginosa can slow down the evolution of multiple-drug resistance when the two 8

antibiotics are used in a specific order. A simple population dynamics model 9

reveals that using an antibiotic associated with high costs of resistance first 10

minimizes the chance of multidrug-resistance evolution during sequential 11

therapy under limited mutation supply rate. As well as presenting a novel 12

approach to multidrug therapy, this work shows that costs of resistance not only 13

influences the persistence of antibiotic resistant bacteria, but also play an 14

important role in the emergence of resistance. 15

16

INTRODUCTION 17

With the rapid emergence of microorganisms resistant to one or many 18

antibiotics (24), clinicians worldwide have increasingly relied on multidrug therapy to 19

fight bacterial infections (21, 22). Although multidrug treatments proved successful in 20

reducing the prevalence of severe infections such as Mycobacterium tuberculosis (10, 21

14), the pervasive use of antibiotics has resulted in the evolution of multidrug-22

resistance (MDR) in many species of bacteria (1, 26). MDR is now frequent in many 23

healthcare-associated bacterial infections such as Clostridium difficile, 24


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Staphylococcus aureus and Pseudomonas aeruginosa (19, 21, 25), raising serious 25

challenges regarding the optimal use of multidrug therapy (15). 26

Two key objectives of multidrug therapy are to minimize the rate of evolution 27

of multidrug-resistant bacteria and to limit the total amount of antibiotic used in 28

hospitals (17). These problems can be approached at two levels: hospital-wide level 29

and single patient level. At the hospital-wide level, two therapeutic approaches are 30

typically employed: mixing therapy (i.e. two or more drugs are used simultaneously in 31

the hospital where each patient receives a single drug) and periodic hospital-wide 32

rotation of antibiotics (i.e. two or more drugs can be alternated periodically within a 33

hospital). While clinical trials for different bacterial infections produced mixed results 34

(4, 8, 33, 34, 40), theoretical results typically suggest that a mixing strategy minimizes 35

the evolution of MDR compared to hospital-wide rotation (9, 11, 42). The mixing 36

strategy is believed to increase the rate at which bacteria are exposed to different 37

antibiotics relative to rotation, therefore minimizing the opportunity for resistance 38

evolution. Although recent theoretical results suggest that it should be possible to find 39

an optimal rotation strategy that minimize resistance evolution (6), the range of 40

optimal parameters is a matter of debate (5, 11, 20). 41

Despite the use of hospital-wide strategies, pathogenic bacteria such as P. 42

aeruginosa and M. tuberculosis often evolve resistance in the course of a single-host 43

treatment (16, 33). Therefore, clinicians also face decisions on how to best administer 44

one or many antibiotics to a single patient (18). For example, antibiotics can be used 45

simultaneously (i.e. combination therapy), or sequentially (i.e. sequential therapy), 46

where two or more different antibiotics are used one after the other. While 47

combination therapy has been used successfully, for example, to treat Helicobacter 48

pylori, the etiological agent of peptic ulcers, combination therapy can be associated 49


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with uncomfortable side effects (13) or its effectiveness maybe limited as a result of 50

poorly-studied drug interactions (41). 51

Unlike hospital-wide cycling, sequential therapy within a single host exposes 52

bacterial infections to a rapid change in antibiotics. While the cycling of antibiotic 53

within a hospital system can take months to years to implement, it is possible to 54

switch antibiotics within a single host over a matter of days. Provided that the 55

antibiotics chosen for sequential therapy elicit no cross-resistance, mutants resistant 56

against one antibiotic are unlikely to reach high frequencies within the host before a 57

second antibiotic is applied. Furthermore, pleiotropic fitness costs associated with 58

resistance mutations are believed to affect the persistence of antibiotic-resistant 59

bacteria and to slow the spread of resistance (2). Therefore, a rapid switch in 60

antibiotic use has the potential to minimize multi-drug resistance while mitigating 61

negative clinical consequences of combination therapy. 62

Given that the rate at which resistance mutations are generated varies among 63

antibiotics (22) and that fitness costs associated with resistance mutations vary both 64

within and among individual antibiotics (2, 28), the success of sequential therapy is 65

likely to depend on both the antimicrobial activity of the antibiotics and the order in 66

which they are used. Building from the work presented in Perron et al. [2007], we 67

investigate the importance of antibiotic treatment order in determining the efficacy of 68

multidrug therapy using experimental and theoretical results. First, we compared the 69

effect of combination therapy and sequential therapy on MDR evolution in 70

experimental populations of P. aeruginosa maintained under different mutation 71

supply rate regimes. We show that using two antibiotics one after the other early in 72

the treatment can give results similar to using the two drugs simultaneously; but the 73

effect depends on which antibiotic is used first and on the mutation supply rate of 74


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resistant mutations in the population. Second, we introduce a simple population 75

dynamics model identifying pleiotropic fitness costs associated with resistance 76

mutations as a plausible mechanism responsible for the order effect observed in our 77

experiment. 78

79

MATERIALS AND METHODS 80

Study organism and growth conditions. In this experiment, we cultured a total of 81

seventy-two experimental populations of Pseudomonas aeruginosa strain PAO1 (37). 82

P. aeruginosa is a non-recombinogenic Gram-negative bacterium that is an important 83

opportunistic pathogen of human. Each population was initially inoculated with 84

approximately 104 cells and was incubated at 37 ºC in 150 μL of M9KB media (per 85

litre: 20g proteose peptone #3, 12.8g Na2HPO4⋅7H2O, 10g glycerol, 3g KH2PO4, 0.5g 86

NaCl and 1g NH4Cl). Every 24 hours, each culture was mixed thoroughly by pipetting 87

50 μL in and out repeatedly before 1% of the overnight culture (approximately 106 88

bacterial cells) was transferred to a fresh microcosm. Growth was monitored daily for 89

ten days and was measured as optical density (OD600) using a universal microplate 90

reader (BioTek, Winooski, VT). Three antibiotic treatments were implemented as 91

described below. 92

93

Mutation supply rate. We looked at the effect of increasing the mutation supply rate 94

by manipulating the immigration rate of susceptible bacteria sampled from an 95

isogenic population of P. aeruginosa grown overnight. For each antibiotic treatment, 96

we grew twelve replicate populations with of the following mutation supply rates: no 97


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immigration, 0.1% (approx. 1000 cells), 1.0% (approx. 10,000 cells) and 10% 98

(approx. 100,000 cells) immigration. 99

100

Antibiotic treatments. The antibiotics rifampicin and streptomycin were used in this 101

experiment. Rifampicin acts by lodging itself into the DNA/RNA tunnel of the 102

polymerase, and sterically blocking elongation of nascent mRNA molecules (12). 103

Streptomycin targets the S16 rRNA protein of the 30S ribosomal subunit, interfering 104

with the binding of tRNA, and therefore initiation of protein synthesis (36). Previous 105

studies of rifampicin- and streptomycin-resistant mutants of P. aeruginosa showed 106

that cross-resistance between the two drugs is unlikely in similar experimental 107

conditions (28, 39). 108

The antibiotics were supplemented to the media as follows: rifampicin (62.5 109

μg⋅ml-1) and streptomycin (16 μg⋅ml-1). These concentrations of antibiotic were 110

shown to completely inhibit the growth of P. aeruginosa in the absence of 111

immigration (28). Also, we established that rifampicin was more effective than 112

streptomycin to inhibit the growth of P. aeruginosa and that resistance to rifampicin 113

incurred a higher fitness cost (28). For each mutation supply rate treatment, three 114

different combinations of the antibiotics were used to treat populations of P. 115

aeruginosa: 1) the combination of streptomycin and rifampicin; 2) sequential therapy 116

with rifampicin used first; and 3) sequential therapy with streptomycin used first. 117

During sequential therapy, antibiotics were switched at every transfer. 118

119

Pleiotropic fitness costs of resistance. At the end of the experiment, the populations 120

were grown for three transfers in unsupplemented KB to test the heritability of 121

resistance and to estimate the pleiotropic cost of resistance of each population. The 122


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cost of resistance was measured as the optical density (OD600) in unsupplemented KB 123

and was compared to that of PAO1 strains cultivated in parallel for the length of the 124

experiment to control for adaptation to normal laboratory conditions. 125

126

Statistical analyses. To analyze the evolution of antibiotic resistance during the 127

course of the experiment, we modeled the temporal dynamics of bacterial growth 128

using a hierarchical linear mixed model (lme function of the nlme package of the R 129

2.11.1 software), sometimes referred to repeated measure anova. We used the optical 130

density data (OD600) as the response. Time (number of transfers following the 131

beginning of the experiment) was considered as a random variable whilst antibiotic 132

treatment (3 levels) and the rate of immigration (4 levels) were considered fixed 133

effects. We also accounted for the non-linear growth dynamics over the bacteria 134

populations by computing the quadratic term for time. Because all replicates were 135

started under similar conditions we constrained the model to a unique intercept. 136

Replicates were taken to be random effects and were nested within treatments. We 137

began by fitting the full model that included all fixed effects and their interactions, 138

and then simplified it by sequential backward selection. We used an F-test to compare 139

the fit of different models. A variance function (varIdent of nlme library) that permits 140

different variances for each level of a stratification variable (here treatment) was used 141

to model heteroscedasticity when necessary. We also used the corAR1 function to 142

model the autocorrelation structure in the time series. Significance of fixed effects 143

was tested with F-tests. Differences between treatments were tested with pairwise 144

comparisons using log likelihood ratio tests. Model parameters and confidence 145

intervals were estimated with restricted maximum likelihood methods (31). 146


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We also modeled the effect of antibiotic treatments and mutation supply rate 147

on the average growth (OD600) of all selection lines over the length of the experiment. 148

The model was fit in a Linear General Model using antibiotic treatments (3 levels) as 149

a fixed factor and mutation supply rate as continuous factor (this to control for the 150

non-normality of the data). Finally, we looked at the effect of antibiotic treatment (3 151

levels) and mutation supply rate (4 levels) on pleiotropic fitness costs of resistance 152

fitting the carrying capacity of each population in the absence of antibiotic as a 153

dependent variable. All analyses and model assumptions were performed and verified 154

using R 2.10.1 software (http://www.r-project.org). 155

156

RESULTS 157

Experimental evolution of multidrug-resistance under multidrug therapy 158

We investigated the efficacy of multidrug therapy and sequential therapy by 159

treating experimental populations of the bacterium P. aeruginosa with one of 160

following three treatments: 1) the combination of streptomycin and rifampicin; 161

sequential therapy of rifampicin and streptomycin with 2) rifampicin used as the first 162

antibiotic; and 3) streptomycin used as the first antibiotic. We observe that the three 163

treatments have a significantly different effect on MDR evolution and that this effect 164

depends on the mutation supply rate of the treated population (treatment × 165

immigration × time × time: F(6, 624) = 5.266; P < 0.0001; Figure 1). Under normal 166

growth conditions, we found that MDR evolved only in populations first exposed to 167

streptomycin during sequential therapy; all population treated with both antibiotics 168

went extinct after 24 hours while populations treated with rifampicin first went extinct 169

after being subsequently exposed to streptomycin (Figure 1a). Given that P. 170

aeruginosa can evolve resistance to both rifampicin and streptomycin when used 171


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individually (28), our results confirm that, given a certain order, sequential therapy 172

can significantly decrease resistance evolution. 173

Bacterial populations rarely evolve in isolation (33). In previous studies, we 174

showed that immigration of susceptible bacteria cells from a larger source population 175

could favor adaptation to antibiotic environments by increasing the supply of 176

resistance mutations in local populations of bacteria (27-29). For this reason, we 177

simulated the effect of immigration by supplementing each experimental population 178

treated with antibiotics with susceptible bacterial cells at each transfer. As predicted, 179

immigration had a positive effect on MDR evolution (immigration: F(3, 60) = 44.306; P 180

< 0.0001): while small amounts of immigration (i.e. 1000 susceptible cells per 181

transfer) favored MRD evolution under sequential therapy (Figure 1b), higher level of 182

immigration (i.e. 100,000 cells per transfer), allowed MDR evolution in all treatments 183

(Figure 1c). 184

185

Pleiotropic fitness costs of resistance 186

To assess whether the type of multidrug therapy used impacted the pleiotropic 187

fitness costs associated with MDR, we measured the growth of each MDR population 188

in the absence of antibiotics. We found that the choice of therapy and the mutation 189

supply rate regime significantly affected the evolution of costs of resistance 190

(treatment × immigration: F(1,66) = 24.578; P < 0.001; Figure 2). While fitness cost of 191

resistance decreased as immigration rate increased, fitness costs were generally higher 192

under combination therapy than under both sequential treatments. At lower 193

immigration rates, costs of resistance were generally lower in sequential therapy 194

initiated with streptomycin than in sequential therapy initiated with rifampicin. As 195

immigration increased, both sequential treatments generated little or no cost of 196


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resistance. These results indicate that the combination and the order in which two 197

antibiotics are used over the course of a treatment plays an important role in the 198

evolution of resistance costs in addition to resistance evolution. 199

200

Modeling the antibiotic order effect in sequential therapy 201

To understand the effect of antibiotic treatment order on the evolution of 202

resistance under sequential therapy, we considered a simple population dynamics 203

model of the evolution of MDR in a bacterial population that is sequentially exposed 204

to two antibiotics. Because most extinction events in our experiment happened within 205

the first two antibiotic switches, we focused on the population dynamic involved in 206

the first two transfers. In this model, we assume that bacteria acquire resistance 207

mutations against the first and the second antibiotics with rates μ1 and μ2 respectively 208

and that an antibiotic-resistant allele confers a cost of resistance in the absence of that 209

antibiotic. In particular, mutations that confer resistance against the first antibiotic are 210

deleterious with selection coefficient s1 in the absence of this antibiotic, while the 211

fitness cost of mutation conferring resistance to the second antibiotic is s2. We also 212

assume that si >> μj, i,j = 1,2, which reflects our knowledge of typical mutation rates 213

and costs of resistance (22). 214

In the first phase of our experiment, the population grows in the absence of 215

antibiotics and reaches population size N0. We assume that the fraction of mutants 216

resistant to the first antibiotic by the end of this phase reaches the mutation-selection 217

balance, μ1/s1, and that there are no MDR mutants. Thus, by the time the first 218

antibiotic treatment is applied, there are on average N0μ1/s1 bacterial cells that harbor 219

resistance to the first antibiotic. Starting with this (relatively small) number of 220

resistant cells, the population then grows for time T in the presence of the first 221


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antibiotic, until the second antibiotic is applied and the first antibiotic is withdrawn. 222

We consider that MDR evolves in our experiment if on average there is at least one 223

cell harboring resistance to both antibiotics at the time the second antibiotic is applied. 224

Mathematically, if N12(t) is the expected number of MDR cells at time t during the 225

second growth phase, the condition for MDR evolution is 226

(1) N12 (T ) ≥1. 227

To express condition (1) in terms of evolutionary parameters μ1, μ2, s1 and s2, 228

we assume that in the presence of the first antibiotic the population stays below its 229

carrying capacity and grows exponentially for time T. If N1(t) is the expected number 230

of bacteria resistant against the first antibiotic but not against the second antibiotic at 231

time t, the average population dynamics is described by differential equations 232

(2) N1 = r1N1 − μ2N1

N 12 = (r1 − s2 )N12 + μ2N1

233

with the initial condition at the beginning of the second growth phase 234

(3) N1(0) = N0μ1 / s1

N12 (0) = 0 , 235

where r1 is the intrinsic growth rate of single-drug resistant bacteria in the presence of 236

the first antibiotic. The solution to system (2), (3) is 237

N1(t) = N0μ1 / s1( )e(r1−μ2 )t

N12 (t) =N0μ1 / s1( )μ2

μ2 − s2

e(r1−s2 )t − e(r1−μ2 )t( ). 238

Condition (1) then becomes 239

N0μ1μ2e

(r1−μ2 )T

s1

1− e−(s2−μ2 )T

s2 − μ2

≥1, 240

which we can rewrite as 241

(4) )( 21 sAfs ≤ , 242


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where A = N0μ1μ2e(r1 −μ 2 )TT and f (s) =

1− e−(s−μ 2 )T

(s − μ 2)T. Notice that function f(s) is 243

positive (because, as mentioned above, in practice s2>>μ2) and is monotonically 244

decreasing. 245

We now ask which antibiotic should be applied first, given that the costs of 246

resistance to each are different. Consider two antibiotics, X and Y, for which 247

resistance incurs the costs sX and sY, respectively. Without loss of generality, we can 248

assume that sX > sY . We then say that antibiotic X is in this sense “strong” and 249

antibiotic Y is “weak”. From equation (4), the condition of MDR evolution in case X 250

is applied before Y (“strong-weak” treatment), is 251

(5) ( )YX sAfs ≤ , 252

and, vice versa, in case Y is applied before X (“weak-strong” treatment), MDR 253

evolves if 254

(6) )( XY sAfs ≤ . 255

As illustrated in Figure 3, inequalities (5) and (6), divide the (sX, sY) semi-256

plane into three regions. As we mentioned above, we only consider the area under the 257

diagonal (where sX > sY is respected). In region I, both conditions (5) and (6) are 258

satisfied implying that MDR is expected to evolve regardless of the order in which 259

antibiotics are applied. In region II, condition (6) is satisfied but condition (5) is not, 260

which implies that MDR is expected to evolve only under the weak-strong treatment. 261

Finally, in region III, both conditions (5) and (6) are violated, and MDR is not 262

expected to evolve under either treatment. In this analysis, the parameter region where 263

MDR evolves under the weak-strong treatment (regions I and II) is strictly larger than 264

the parameter region where MDR evolves under the strong-weak treatment (region I). 265

In other words, our model predicts that MDR is less likely to evolve under sequential 266


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therapy when the cost of resistance to the first antibiotic used is higher than that of the 267

second antibiotic used. 268

Predictions of our simple model are consistent with our experimental 269

observations. As shown previously in Perron et al. (2007), resistance mutations 270

against rifampicin generally confer a larger cost on fitness than resistance mutations 271

against streptomycin. In the above terminology, rifampicin is the strong antibiotic and 272

streptomycin is the weak antibiotic. The fact that the MDR evolves under the weak-273

strong treatment (streptomycin first) but not under the strong-weak treatment 274

(rifampicin first) suggests that the costs of resistance against these antibiotics fall into 275

region II of the parameter space of our experiment (Figure 3). 276

277

DISCUSSION 278

In this study, we show that the order in which two antibiotics are used during 279

sequential therapy of a single bacterial “infection” has a significant effect on MDR 280

evolution and its fitness costs. When we grew our experimental populations in 281

isolation, without the influx in mutation provided by the immigration of cells from a 282

source population, the use of rifampicin before streptomycin precluded the evolution 283

of MDR just as successfully as the combination of the two antibiotics. Using a simple 284

population dynamics model, we demonstrate that the fitness cost associated with 285

mutations conferring resistance to the first antibiotic predominantly determines this 286

order effect. Specifically, the chance of MDR evolution is reduced by first using the 287

antibiotic for which resistant mutation confers the highest fitness cost. Our results 288

demonstrate that costs of resistance play an important role in reducing the frequency 289

of resistant mutants in bacteria populations and therefore minimizing the rate of MDR 290

evolution. Given that our model captures the fundamental population parameters 291


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governing resistance evolution, we expect that our results are not be limited to the 292

specific antibiotics used, but instead may be general to many antibiotic combinations. 293

Our model provides a simple explanation for why antibiotics’ order in 294

sequential treatment can affect the evolution of resistance. Because of severe 295

competition in large bacterial populations within the host prior to the application of 296

any antibiotics, the frequency of resistant mutants is limited by the rate at which they 297

arise and the pleiotropic fitness costs they carry. Resistance mutations that arise at a 298

low probability and resistance mutations that incur a large cost on fitness will be less 299

frequent in a population that is not treated with antibiotics; a phenomenon known as 300

the mutation-selection balance (7). Importantly, at the moment the first antibiotic is 301

applied, (a) the frequency of a resistant mutant is inversely proportional to the cost of 302

resistance that it carries and (b) the probability that this mutant spreads in the 303

population after the antibiotic is applied is proportional to its initial frequency. This 304

implies that the cost of resistance against the first antibiotic directly modulates 305

bacterial population density during the antibiotic-free phase and, consequently, the 306

probability of MDR evolution. The application of the first antibiotic dramatically 307

reduces population density and, thus, competition between bacterial cells. With little 308

competition, the rate of resistance evolution against the second antibiotic is limited 309

primarily by the rate at which such resistance mutations arise; the cost of resistance 310

against the second antibiotic plays a relatively small role. Mathematically, this is 311

reflected in that the shape of function f(s2) in equation (4): it depends very weakly on 312

s2. Therefore, if resistance mutations against the two antibiotics incur different costs, 313

the order in which the antibiotics are applied critically determines the probability of 314

MDR evolution. 315


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Although mutation supply rate is important factors in the evolution of MDR, it 316

does not contribute to the effect of antibiotic treatment order in our model. This result 317

means that changes in mutation rates over time within a population, such as 318

hypertmutability arising (29) or one of the drugs inducing the bacterium’s SOS 319

response (32), will not affect our conclusions. The model provides a striking match 320

with our experimental results: MDR evolve under sequential therapy when 321

streptomycin was first applied but did not when rifampicin was the first antibiotic 322

used. Since resistance to rifampicin generally confer a larger cost on fitness than 323

resistance to streptomycin (28), the order effect observed in this study is consistent 324

with our theoretical model and is likely to be independent of the target and the 325

function of the antibiotics. 326

Two important assumptions reflecting the settings of our experimental work 327

are crucial for our theoretical results to hold. First, the time scale between the use of 328

the two antibiotics determines the frequency of resistance mutations found at each 329

phase. While, the pre-antibiotic phase must be long enough for the population to reach 330

the mutation-selection balance with respect for resistance mutations, the population 331

size post-treatment must remain low relative to carrying capacity; otherwise the 332

resistance cost against the second antibiotic would become important. Although it 333

can be difficult to control the exact concentration of antibiotics that reach the site of 334

infection (3), it is relatively simple to switch between different antibiotics over a short 335

period of time when treating individual patients. 336

Second, we make the assumption that there is no cross-resistance, no 337

recombination between the resistance mutations, no possibility of compensatory 338

mutations, and no epistasis between resistance mutations. Cross-resistance between 339

the two antibiotics would cancel the order effect since evolution of resistance to one 340


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antibiotic would confer resistance to the second antibiotic. With the accumulation of 341

data on resistance and cross-resistance for an increasing number of antibiotics, 342

however, it should be possible for clinicians to avoid the use of two drugs for which 343

cross-resistance is known. Horizontal gene transfer can bring together resistance 344

genes that evolved in different bacteria lineages (23) and can therefore promote MDR 345

evolution (30). The compensation of fitness costs associated with resistance could 346

also contribute to increasing the frequency of antibiotic-resistant mutants in natural 347

populations of bacteria by alleviating the competitive disadvantage of resistant 348

mutants (29, 35). Finally, epistatic interactions among different resistance mutations 349

could either promote or inhibit the evolution of MDR (38). 350

Given the availability of minimal information pertaining to fitness costs 351

associated with resistance to specific antibiotics, our results provide a simple 352

guideline for sequential multidrug-therapy: one ought to use antibiotic incurring the 353

stronger cost of resistance as a first line-antibiotic in order to minimize the change of 354

MDR evolution. Because of the different pharmacodynamics characteristics 355

associated with different antibiotics and the complex nature of bacteria infections in 356

the human body, it is practically impossible to accurately model the evolution of 357

antibiotic resistance within a human host. Still, our simple model captures the key 358

aspects of such evolution and makes a strong qualitative prediction: MDR is less 359

likely to evolve if the antibiotic that incurs larger resistance costs is applied first. 360

Although costs of resistance to specific antibiotics can change substantially between 361

bacterial species and between different environments (2, 27, 28), it usually follows 362

predictable distributions readily identified in laboratory experiments (22). 363

As the use of multidrug therapy is increasingly prevalent in the treatment of 364

healthcare-associated infections such as P. aeruginosa (21), it is crucial to understand 365


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the full evolutionary consequences associated with drug deployments. Our results not 366

only demonstrate the influence cost of resistance has on the evolution of MDR, but 367

suggest a simple approach that could potentially improve multidrug-therapy. 368

369

370

ACKNOWLEDGMENTS 371

The authors would like to thank Sam P. Brown, R. Fredrick Inglis, and Luiz-Miguel 372

Chevin for their comments on earlier versions of this manuscript. This work was 373

funded by the European Research Council (ERC) and the Leverhulme Trust. G.G.P. 374

was funded during this work by the Clarendon Funds of the University of Oxford and 375

the National Science Engineering and Research Council of Canada (NSERC) and is 376

now funded by the Fond Québécois pour la Recherche sur la Nature et les 377

Technologies (FQRNT). The funders had no role in study design, data collection and 378

analysis, decision to publish, or preparation of the manuscript. 379

380

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FIGURE LEGENDS 517

518

Figure 1. Evolution of multidrug-resistance in experimental populations of 519

Pseudomonas aeruginosa treated with multidrug therapy. Multidrug treatments 520

were as follows: rifampicin and streptomycin simultaneously (filled circles); 521

sequential therapy with exposure to streptomycin first (filled triangles); and 522

sequential therapy with exposure to rifampicin first (filled squares). For sequential 523

therapy, the antibiotics were switched on a daily basis. Populations were grown in the 524

absence of immigration, or in 1% b) and 10% c) immigration. Resistance was 525

measured as average density (OD600) of the selection lines at the end of each transfer. 526

Error bars represent standard error of the mean. 527

528

Figure 2. Pleiotropic fitness costs of experimental populations of Pseudomonas 529

aeruginosa that have evolved resistance under three multidrug treatments and four 530

immigration rates. Drug treatments are as follow: rifampicin and streptomycin 531

simultaneously (filled circle); sequential therapy with exposure to streptomycin first 532

(filled triangles); and sequential therapy with exposure to rifampicin first (filled 533

squares). Relative fitness is measured as the growth (OD600) of the resistant 534

populations in the absence of drug compared to that of control populations grown in 535

the absence of drug for the duration of the experiment. 536

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Figure 3. Schematic illustration of regions where MDR is expected to evolve 540

under different treatments. Any pair of antibiotics X and Y with costs of resistance 541

sX and sY (such that sX > sY) represents a point in the lower semi-plane of the (sX, sY)-542

plane. The region to the left of the dashed line is the region where MDR is expected to 543

evolve under the strong-weak treatment (i.e., when antibiotic X is applied first). The 544

region below the solid line is the region where MDR is expected to evolve under the 545

weak-strong treatment (i.e., when antibiotic Y is applied first). The parameter region 546

where MDR evolves under the strong-weak treatment (region I) is strictly smaller 547

than the region where MDR evolves under weak-strong treatment (regions I and II). 548

Thus, the strong-weak treatment represents a safer bet against MDR evolution under 549

uncertainty. 550

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Figure 1 562

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B) 570

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Figure 2

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Figure 3 607

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Multidrug Therapy and Evolution of Antibiotic Resistance: WhenOrder Matters

Gabriel G. Perron, Sergey Kryazhimskiy, Daniel P. Rice, Angus Buckling

FAS Center for Systems Biology and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA; Department of Zoology,University of Oxford, Oxford, United Kingdom; Biosciences, University of Exeter, Tremough, Cornwall, United Kingdom

Volume 78, no. 17, p. 6137– 6142, 2012. The authors retract this paper because of unsatisfactory referencing of previously publishedexperimental data. Specifically, all data in Fig. 1 in this paper were presented in Fig. 1 in reference 28 in the original list of references(G. G. Perron, A. Gonzalez, and A. Buckling, Proc. Biol. Sci. 274:2351–2356, 2007), and all data points except for zero immigration ratein Fig. 2 were presented in Fig. 2 in the same reference. The authors maintain that all results and conclusions of the paper are correct.Sergey Kryazhimskiy and Daniel P. Rice were not involved in the reanalysis of previously published data.

Copyright © 2013, American Society for Microbiology. All Rights Reserved.

doi:10.1128/AEM.02554-13

RETRACTION

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