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Drug Resistance Updates 15 (2012) 162– 172

Contents lists available at SciVerse ScienceDirect

Drug Resistance Updates

j o ur nal homep a ge: www.elsev ier .com/ locate /drup

volution of antibiotic resistance at non-lethal drug concentrations

an I. Andersson ∗, Diarmaid Hughesepartment of Medical Biochemistry and Microbiology, Box 582, SE-75123 Uppsala, Sweden

r t i c l e i n f o

rticle history:eceived 28 January 2012eceived in revised form 22 March 2012ccepted 26 March 2012

e would like to dedicate this paper torof. Fernando Baquero, who has pioneereddeas about the importance of low levels ofntibiotics for antibiotic resistance

a b s t r a c t

Human use of antimicrobials in the clinic, community and agricultural systems has driven selection forresistance in bacteria. Resistance can be selected at antibiotic concentrations that are either lethal ornon-lethal, and here we argue that selection and enrichment for antibiotic resistant bacteria is often aconsequence of weak, non-lethal selective pressures – caused by low levels of antibiotics – that operateson small differences in relative bacterial fitness. Such conditions may occur during antibiotic therapy orin anthropogenically drug-polluted natural environments. Non-lethal selection increases rates of mutantappearance and promotes enrichment of highly fit mutants and stable mutators.

© 2012 Elsevier Ltd. All rights reserved.

evelopment.

eywords:ntibiotic resistanceiocide

inimal inhibitory concentration

elective window

. Antibiotic resistance is an ecological problem

The extensive human use and misuse of antimicrobials in thelinic, community, animal husbandry and agriculture has resultedn strong selection pressures for the emergence, enrichment andpread of various resistance mechanisms in pathogenic bacteria.he temporal and spatial dynamics and driving forces of theserocesses are complex and we are only slowly beginning to under-tand how resistance genes, resistant bacteria and the selectivegents (antibiotics, biocides, environmental pollutants, etc.) moveetween different ecosystems and exert their effect. In this review,esistance is used in the broadest sense to indicate any reductionn susceptibility in comparison to the wild type population.

With regard to the dynamics of antibiotic resistance develop-ent it is essential to distinguish between (at least) three levels

f description, namely (i) where did the resistance mechanismtself originate, (ii) where did the resistant pathogenic bacteriaf relevance to human and animal infections emerge and (iii)here were these resistant pathogens enriched and transmitted?

n cases where the resistance is conferred by a mutational mecha-ism these three levels often converge on one particular bacterial

pecies and environment. For example, for Escherichia coli resistanto fluoroquinolones the de novo emergence of the resistance mech-nism and the subsequent enrichment might occur in the same

∗ Corresponding author. Tel.: +46 18 4714175.E-mail address: Dan.Andersson@imbim.uu.se (D.I. Andersson).

368-7646/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.drup.2012.03.005

environment and bacterial species (e.g. in E. coli bacteria presentin urine/feces in antibiotic-treated individuals) but for many othertypes of resistances, in particular for those that are horizontallytransferred, these three levels might be fully separated tempo-rally and spatially. Thus, the resistance mechanism could haveoriginated pre-therapeutic use of antibiotics in non-pathogenicenvironmental bacteria, and recent evidence strongly supports thenotion that many types of resistance mechanisms and resistant bac-teria were present long before human production, use and spread ofantibiotics expanded rapidly in the second half of the 1900s (Smith,1967; Hughes and Datta, 1983; Aminov and Mackie, 2007; Mindlinet al., 2008; Allen et al., 2009; D’Costa et al., 2011). The emergence ofresistant pathogens could have followed later when the resistancewas horizontally transferred from an environmental bacterium toa human/animal pathogen (Patel et al., 2000; Poirel et al., 2002,2005; Hong et al., 2004; Canton, 2009; Wright, 2010), possiblyin an environment where these bacteria only transiently residedtogether. Finally, these resistant human pathogens were enricheddue to selective pressures present in the clinic, community or agri-cultural setting. Often it is difficult to distinguish between theselevels but from the point of view of trying to restrict the emergenceand spread of resistance it is important that we attempt to do sosince the design and efficiency of our strategies will vary consider-ably depending on whether we try to prevent the initial emergence

of resistance, or try to prevent the enrichment of already existingresistant pathogens that have spread into clinical/community set-tings. For example, as suggested by increasing amounts of data,clinically relevant plasmid/ICE mediated resistances may emerge

D.I. Andersson, D. Hughes / Drug Resistance Updates 15 (2012) 162– 172 163

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where the biocide resistance conferring genes (e.g. qac, heavy metalresistance genes, etc.) are present on the same genetic element(e.g. a plasmid or ICE) as the antibiotic resistance genes, thereby

Table 1Fraction of antibiotic dose for different antibiotic classes that is excreted in activeform in human urine (Bryskier, 2005). The fractions will vary between differentcompounds from the same antibiotic class and these values should therefore betaken as approximate.

Antibiotic class Fraction of dose excreted inhuman urine in active form

Fluoroquinolones 40%Aminoglycosides 80–90%Tetracycline 40%

Fig. 1. Schematic representation of flows of resistant bacteria (blue

y HGT from environmental bacteria to pathogens in the out-ide environment (e.g. in waste water treatment facilities, manure,ludge, soils or similar environments) (Patel et al., 2000; Poirel et al.,002, 2005; Hong et al., 2004; Canton, 2009; Wright, 2010; Taylort al., 2011). With this knowledge in hand, an important controleasure to reduce the rate of resistance emergence might be active

tewardship in environmental resistance reservoirs (e.g. killing ofacteria and destruction/removal of antibiotic pollutants in wasteater, sludge, manure, etc.) in addition to clinical control programs

hat are aimed at reducing antibiotic use and selective pressuresear the patients. Conceivably it might be more efficient to preventhe initial emergence of resistant pathogens rather than to laterry to control their enrichment and spread in clinical and humanommunity settings by restrictive antibiotic use.

As outlined schematically in Fig. 1, resistance genes, resistantacteria and antibiotics will flow between different compartmentsnd environments, including humans, animals, soil and water,tc., creating situations where resistant bacteria may emerge,ecome enriched and spread between hosts and various naturalnvironments. Resistance might be selected in any of the abovenvironments but it is difficult to assess the relative contributionsf various environments and selections to the total problem. Forxample, it is commonly assumed that hospitals represent signifi-ant foci for resistance evolution because of the high use (per capita)f antibiotics that generate a strong local selection pressure, and theigh host population density that facilitates spread of the resistantacteria. This notion is most likely true for certain pathogens butith regard to the total selective pressure asserted globally due

o anthropogenic influences, hospital use of antibiotics probablyepresents only a few percent of the total volume of antibiotic usend it is conceivable that the weaker selective pressures presentn other environments are equally important breeding grounds forhe emergence of resistance (Baquero, 2001a,b), and possibly highntibiotic-use environments like hospitals mostly act as enrich-ent and transmission centers of already existing resistant clones.It is also notable that antibiotics exert their effect with widely

ifferent active concentrations during their movement througharious environments and ecosystems. When antibiotics are usedherapeutically in humans, animals and agriculture they generallyre present at high local concentrations and exert strong selection

ressures in these environments. However, about half of all antibi-tics given to humans and animals are excreted in an unchangedctive form mainly via urine (Table 1) into waste water, manure andun-off water, where they will ultimately end up in a diluted form in

antibiotics (red) between different environments and areas of use.

various aquatic environments and soil and exert a weaker selectivepressure. Even though antibiotic concentrations in these environ-ments are several orders lower than where the drugs were initiallyused they could still, as argued below, be of great importance forthe evolution of resistance.

2. Which are the significant selectors of resistance?

With regard to the selective pressures that enrich for resistance,it is probable that antibiotics represent the main selector. Thus,there exist convincing correlations between amount of antibioticused at the hospital, community and country level and the fre-quency of resistance observed (van de Sande-Bruinsma et al., 2008;Bergman et al., 2009; Goossens, 2009). However, it is likely thatother types of selective pressures are involved as well. For example,the amount (in metric tons) of human-made biocides that bacteriaare exposed to on a global scale is many orders of magnitude higherthan that for antibiotics (SCENIHR, 2009). As the increasing use ofantibiotics and biocides has been temporally and spatially linked itis difficult to entangle their relative contributions to the resistanceproblem but based on the wide use of biocides in numerous typesof environments (for example, in health care, household prod-ucts, textiles, food industry and many other environments) andfundamental evolutionary principles, it is expected that biocideswill exacerbate the problem (Maillard, 2007; Tumah, 2009). Bio-cides could contribute to the problem by co-selection mechanisms

Macrolides 20–30%Penicillins 50%Cephalosporins 70–90%Trimetoprim 50%

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64 D.I. Andersson, D. Hughes / Drug

ndirectly driving enrichment for antibiotic resistance (Laraki et al.,999; Bjorland et al., 2001; Sidhu et al., 2001, 2002; Cookson, 2005;oguchi et al., 2005; Sekiguchi et al., 2005, 2007; Dumitrescu et al.,007; Levings et al., 2007). Another mechanism might be by cross-esistance where the biocide and the antibiotic share a commonesistance mechanism, as has been shown for up-regulation offflux pumps (Paulsen et al., 1996; Brown et al., 1999; Putman et al.,000; Borges-Walmsley and Walmsley, 2001; Poole, 2001, 2002;evy, 2002; McKeegan et al., 2003; Piddock, 2006) alterations ofhe cell wall (Denyer and Maillard, 2002; Nikaido, 2003; Tkachenkot al., 2007) and modification of target enzymes (McMurry et al.,999; Heath et al., 2000; Parikh et al., 2000). Several studies havehown that cross-resistance to antibiotics can result from selectionor biocide resistance in the laboratory, strongly suggesting thatiocides in natural settings would have similar effects (Pomposiellot al., 2001; Braoudaki and Hilton, 2004; Codling et al., 2004;anchez et al., 2005; Karatzas et al., 2007; Randall et al., 2007; Fraudt al., 2008; Huet et al., 2008).

. Where are resistant bacteria selected?

It is commonly assumed that resistant bacteria are selected inntibiotic-treated humans or animals in response to the strongelective pressures that are generated when antibiotics are usedherapeutically and the concentrations reached are well above the

inimal inhibitory concentration (MIC) of the susceptible popu-ation of bacterial cells. This notion is certainly true for certainypes of infections, antibiotic regimens and resistance mechanisms.or example, in Mycobacterium tuberculosis examples exist whereutationally resistant bacilli were selected de novo from an ini-

ially susceptible population during inadequate treatment regimesBurman et al., 2006; Cox et al., 2007; Hu et al., 2011; Mariamt al., 2011; Skrahina et al., 2012). Similarly, mutational resistanceo fluoroquinolones has been shown to emerge during treatment of,or example, E. coli/Salmonella enterica (Cattoir et al., 2006; Corvect al., 2008; de Toro et al., 2010; Jeong et al., 2011) as well as fluoro-uinolone and �-lactam resistance in Pseudomonas aeruginosa (Lehomas et al., 2001; Leotard et al., 2001; Nakano et al., 2001). Otherxamples where resistance emerged de novo in treated patientss a result of horizontal gene transfer of resistance genes includehe appearance of vancomycin resistant MRSA (Chang et al., 2003;

eigel et al., 2003) and carbapenem resistant E. coli (Richter et al.,011) where the resistant donor bacteria were either co-infectingith resistant Enterococcus faecalis or Klebsiella pneumoniae.

On the other hand, many types of plasmid/ICE-mediated resis-ances (which represent the largest proportion of all resistance

echanisms) are unlikely to emerge de novo during treatment.hus, the patient/animal is either infected with the susceptibleacteria (and no resistance development occurs during treatment)r they are infected with the resistant strain and the antibi-tic treatment mainly causes an enrichment of a pre-existingutant. As described above, it is likely that for many of the

esistance mechanisms found today in human pathogens, theirrigin was in environmental bacteria and anthropogenic changesf the environment via the use of selective antimicrobials andcological opportunity for gene transfer allowed for transfer ofesistance genes into previously susceptible populations of bacte-ial pathogens.

. The power of small selective differences

In Section 1 we argued that the wider environment contains lev-ls of antibiotics and other agents with the potential to enrich forntibiotic-resistant pathogens. To understand quantitatively hownvironmental contamination influences resistance development

nce Updates 15 (2012) 162– 172

we must determine the lowest concentrations of different antibi-otics that selectively enrich for resistant variants. A complicationis that the interplay between the relative antibiotic susceptibilityand growth fitness of different bacterial variants will determinehow each is selectively enriched at different drug concentrations.At an antibiotic concentration that we term the minimum selectiveconcentration (MSC) the growth rate of the susceptible strain willbe reduced to a level where it equals the growth rate of the resis-tant strain, and by definition any antibiotic concentration higherthan the MSC will selectively enrich the resistant strain. Thus,paradoxically, relative to a particular susceptible strain, two dif-ferent resistant mutants with identical MICs but different fitnessvalues would have different MSCs. MSC will be lowest for strainswith the smallest fitness costs. Experiments have shown that ingeneral resistant strains have a lower fitness in drug-free envi-ronments than susceptible strains (Bjorkman et al., 1998; Nagaevet al., 2001), but the range of fitness values can extend up to a levelwhere it can prove very difficult to distinguish it experimentallyfrom that of an isogenic susceptible strain (Marcusson et al., 2009;Andersson and Hughes, 2010). In addition, the relative fitness dif-ference between isogenic resistant and susceptible strains is nota constant but instead changes continuously as a function of theantibiotic concentration to which they are exposed. The questionboils down to how small a selective effect can be, and still influencethe outcome of a competition?

We argue here that extremely small differences in relative fit-ness can be effectively selected in nature. An extreme example thatsupports this argument is the widespread evolution of the pref-erential use of some synonymous codons, so-called codon usagebias, in many free-living microorganisms. The particular set ofsynonymous codons that are preferentially used differs betweenmicroorganisms, showing that codon usage bias evolved indepen-dently in different genetic lineages. Codon usage bias is thoughtto be the result of selection for translationally optimal codons(Ikemura, 1985; Sharp et al., 2010). Based on this model the growthadvantage and selection coefficient for an optimal versus a non-optimal codon has been calculated to be in the range of 10−5

(Bulmer, 1991) to 10−8 (Sharp et al., 2010) per codon. The widerange in these values reflects different assumptions of effectivepopulation size (Ne) for bacterial species like E. coli (Berg, 1996)but the major conclusion from the widespread occurrence of biasin synonymous codon usage is that miniscule differences in fitness(≤10−5) can be effectively selected over evolutionary time scales.This example provides strong support for the idea that even anextremely weak selective advantage can drive the genome-wideevolution of DNA sequences in bacteria.

A relevant clinical example with respect to antibiotic expo-sure and selection is the situation that arises each time a patientis treated with an antibiotic. It is expected that, because of thecomplexity of the human body, the antibiotic will reach differ-ent concentrations in different body compartments, and that theseconcentrations will change over time (Baquero et al., 1997, 1998).Thus the exposure of different bacteria in the patient to the antibi-otic will vary temporally and spatially. If there are within thepatient sub-populations of less susceptible bacteria these couldthen be enriched in particular body compartments and may explainfor example, the apparent incremental step-wise evolution of �-lactamases (Baquero et al., 1998). Similar variation in antibioticselective pressures is also predicted to occur in the highly struc-tured environments of soil.

There are examples where small differences in fitness of antibi-otic resistant strains have been shown experimentally to be

selectable in the absence of the drug both in vitro and in vivo(Marcusson et al., 2009). The concept that small differences infitness could also be selected in a drug-concentration-dependentwindow was tested experimentally by competing isogenic strains

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D.I. Andersson, D. Hughes / Drug

arrying variant �-lactamases, TEM-1 and TEM-12 that differednly slightly in MIC for cefotaxime, 0.008 and 0.012 �g/mL, respec-ively (Negri et al., 2000). Isogenic strains carrying the variantEM genes were competed in vitro (laboratory liquid medium)nd in vivo (mouse thigh model) at a range of cefotaxime con-entrations. The TEM-12 variant was preferentially enriched in

selection lasting several hours in a narrow drug concentra-ion range, both in vitro and in vivo, supporting the concept of arug-concentration selective window (Negri et al., 2000) in whichpecific resistant mutants would be enriched. Interestingly, whenhe competition between the two TEM variants was continued for aonger period of several days and with higher drug concentrations,

variant with a secondary mutation in an outer membrane pro-ein, OmpF, was selected. The mutant selection window hypothesis,hat different drug concentrations will selectively enrich specific

utant sub-populations, has also been extensively explored andalidated in the context of the multi-step evolution of resistanceo fluoroquinolones (Dong et al., 1999; Marcusson et al., 2005;rlica and Zhao, 2007). The TEM-1, TEM-12 experiment describedbove (Negri et al., 2000) provides a model framework that poten-ially explains the step-wise evolution of novel �-lactamases inesponse to changing antibiotic selective pressures (Petrosino et al.,998; Salverda et al., 2010). However, because specific spontaneousutations are generally very infrequent (typical eubacterial muta-

ion rates are 10−9 to 10−11 per nucleotide per generation) thisarticular mode of resistance evolution is expected to be depen-ent on large bacterial populations to supply the specific mutationsequired for the selected phenotype. One way this limitation cane overcome is that several antibiotics increase mutation rates, at

east within a particular concentration range (Phillips et al., 1987;en et al., 1999; Perez-Capilla et al., 2005; Kohanski et al., 2010).nother way in which small bacterial populations can bypass theutation-supply bottleneck is by high frequency gene amplifica-

ion events, that alter the phenotype of the selected strain, andimultaneously amplify the genetic target for spontaneous muta-ions (Andersson and Hughes, 2009). The potential importance ofhis mechanism for �-lactamase evolution was shown experimen-ally when the TEM-1 gene was evolved in vitro to successivelyigher levels of resistance to a cephalosporin (cephalothin). Notehat TEM-1 is associated with a very small increase in MIC tohis antibiotic that provides a weak resistance phenotype thatan be amplified under selection. It was found that the mostrequent response to cephalosporin selection in independent lin-ages was amplification of the copy number of the TEM-1 geneSun et al., 2009). The amplification of the TEM-1 gene by itselfrovided a higher level of resistance (amplifying the very weakephalosporinase activity of TEM-1) but importantly it also allowedhe population of bacteria to survive and expand sufficiently to real-ze additional rare secondary mutations in the genome, including inmpF (Sun et al., 2009). These experiments illustrate the potential

nterplay between the diversity of bacterial genotypes present inarge populations, the possibility for small populations to undergoigh-frequency gene amplification, and the diversity of selectiveressures that can be produced along the antibiotic concentrationradients formed in human body compartments and other natu-al environments. These antibiotic gradients will potentially resultn differential growth rates of resistant bacterial variants (Baquerond Negri, 1997; Baquero et al., 1998; Baquero, 2001a,b), regardlessf the specific mechanisms by which the variants arise and evolve.

We can thus safely assume that antibiotic selection, especiallyf repeated over many generations, can amplify the effects ofxtremely small differences in fitness, resulting in an increased fre-

uency of resistant variants. In an experimental setting however, aractical concern is the resolution that can be achieved in measur-

ng small selective differences on a short time scale (if we assumehat we do not have millions of years at our disposal to complete

nce Updates 15 (2012) 162– 172 165

an experiment). Below we discuss different methods of measuringand selecting small differences in fitness and resistance.

5. Detection of selection

To get a quantitative understanding of the problems posed byantibiotics in the environment we need to determine the lowestlevels of antibiotics that confer a measurable selective advantageunder experimental conditions. Some standard measures of fitness,with a discriminatory power that is at best ±3% per generation, suchas growth rate measurements in the presence of different concen-trations of antibiotic, are too crude to be useful. In addition, suchmeasurements quantify only the exponential part of a bacterialgrowth cycle and may completely miss fitness parameters that areimportant in the presence of growth inhibiting antibiotics. A bet-ter approach is to compete isogenic strains differing only in someresistance determinant in mixed cultures where the competitioninvolves the complete growth cycle (Andersson and Hughes, 2010).A small difference in relative fitness can be amplified by cycling thecompeting mixture of strains, with successive rounds of inoculationinto fresh media, over several days. The change in the ratio of onestrain relative to the other can be monitored as a function of timeand the slope of the line generated can be used to calculate a selec-tion coefficient (Dean et al., 1988). We have used this method tomeasure relative fitness costs in isogenic strains marked with dif-ferent antibiotic resistance markers where the two populations arecounted on separate agar plates (Bjorkman et al., 1998). However,drawbacks include the tedium of counting colonies, the statisticaluncertainty introduced by the relatively small numbers counted,and the possibility that growth of colonies on different antibiotic-containing media introduces a systematic error into the ratio. Abetter method, which avoids using antibiotics to distinguish strains,is to compete isogenic strains where one is marked with a neutralmutation (for example araB), which allows the two populationsto be distinguished on the same agar plate based on differentialcolony colour (Marcusson et al., 2009). This has the advantagethat the competing populations are measured as colonies in thesame environment but a drawback is that the quantitative rangeover which the two populations can be measured is limited inpractice to about three orders of magnitude. Each of these com-petition methods discriminates fitness differences in the range of∼=1% per generation. An elegant chromogenic assay has been devel-oped that uses bacterial strains expressing either of the proteinsamilCP and amilGFP, and has the advantage that it permits a quali-tative discrimination between competing strains in liquid medium(Liu et al., 2011). Although the method is not designed for accu-rate quantification of small differences in selection coefficients, itmay prove useful for assaying environmental contamination byantibiotics.

Our aim was to measure small differences in selection coeffi-cients and so we developed an assay that is both sensitive andquantitative (Gullberg et al., 2011). The assay is based on growthcompetition between isogenic strains genetically tagged with vari-ants of the green fluorescent protein gene (yfp and cfp, encodingyellow- and cyan-fluorescent proteins, respectively). The compet-ing strains, susceptible and resistant, were competed for up to 80generations by serial passage in batch cultures. After each cycleof growth (10 generations per cycle) large populations of cells(approximately 105 cells) were counted by fluorescent activatedcell sorting (FACS), thereby significantly reducing any experimentalerrors associated with counting small cell populations. This exper-

imental set-up allows detection of growth rate differences of 0.2%per generation (Lind et al., 2010; Gullberg et al., 2011). This level ofdiscrimination approaches the limit of sensitivity set by the inter-ference caused by periodic selection events (Atwood et al., 1951;

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66 D.I. Andersson, D. Hughes / Drug

och, 1974; Berg, 1995) that are associated with mutational adap-ation to the growth media/conditions unrelated to the antibioticelective pressure. We have also begun using pre-adapted strainsor these assays; i.e. strains grown for at least 1000 generationsn the growth medium to be used for competition, before thentroduction of the fluorescent and resistance markers. The use ofre-adapted strains permits extended competition runs without

nterference from periodic selection, thus increasing the discrimi-atory power of the assay. This highly sensitive fluorescence-basedssay can be adapted for use in natural environments or in in vivonimal models, and we have recently initiated such experiments.n conclusion, we have developed a robust assay that can reliablynd quantitatively measure selective differences of ∼=10−3 per gen-ration, in a competition experiment that can be completed in 1–2eeks. We have used this experimental system to measure the MSC

f several different antibiotic classes in relation to several clinicallyelevant antibiotic resistance determinants (Gullberg et al., 2011).

. Sub-MIC selection

We measured, under controlled laboratory conditions, the MSCor three different classes of antibiotic: ciprofloxacin, a fluoro-uinolone; streptomycin, an aminoglycoside; and tetracycline,sing the growth competition method described in the previousection (Gullberg et al., 2011). The aim was to determine theowest antibiotic concentration that discriminated between com-eting isogenic wild type and mutant strains carrying any onef six different resistance determinants that are clinically rele-ant for resistance to these antibiotics. The mutations tested were:wo point mutations affecting the fluoroquinolone target DNAyrase (gyrA Ser83Leu, and Asp87Asn); two deletion mutations ofegulators of drug efflux (�marR, and �acrR); a point mutationn ribosomal protein S12 that causes resistance to streptomycinrpsL105 Lys42Arg); and the presence in the chromosome of aransposon causing resistance to tetracycline (Tn10). We made thexperiments as cyclic growth competitions in laboratory mediumsing two different bacterial species, E. coli and S. enterica serovaryphimurium. Each strain (wild-type and isogenic mutants) wasonstructed in two variant forms. One form was tagged with a cfpene inserted at the galK locus while the other form was taggedith a yfp gene inserted at the same locus. This allowed us to makearallel competition experiments in which either the wild typer the mutant strain was tagged with either of the fluorescence-roducing genes, to ensure that the tagging itself did not alter theutcome of the competition. Control experiments showed that themall difference in fitness costs between the cfp and yfp markersad a negligible impact on growth rates. For the competition exper-

ments wild-type and mutant cultures were mixed in a 1:1 rationd grown in liquid medium containing different antibiotic con-entrations, as well as in the absence of antibiotic, for up to 80enerations. After each cycle of growth (10 generations) the ratiof resistant to susceptible bacteria in the population was scoredy FACS analysis. Over this time scale (40–80 generations) peri-dic selection was not a significant problem. The data on changingatios were then plotted to calculate a selection coefficient for eachutant strain as a function of antibiotic concentration. For all six

f the competition experiments the data showed that the MSC ofhe mutant strain was significantly below the MIC of the isogenicild-type strain. The MSC values ranged from (1/4) MIC (strepto-ycin; rpsL105), through 1/10 MIC (ciprofloxacin; gyrA Asp87Asn,marR, and �acrR), to 1/100 MIC (tetracycline; Tn10). The lowest

SC was 1/230 MIC measured for gyrA Ser83Leu (ciprofloxacin),

mutation that is almost always found in clinical isolates of E.oli that are fluoroquinolone-resistant (Komp Lindgren et al., 2003;hristiansen et al., 2011; Nazir et al., 2011). There was also a

nce Updates 15 (2012) 162– 172

correlation between the magnitude of the reduced fitness of themutant strains in the absence of antibiotic and the magnitude ofthe measured MSC. Thus, the fitness deficits were smallest for thegyrA Ser83Leu mutation and the Tn10 insertion and this correlatedwith them having the smallest MSC values. Because each of theabove competition experiments had been made with a 1:1 ratio ofwild type and mutant we asked whether this experimental set-upcould have influenced the outcome of the competition. To test thiswe repeated a series of competition experiments (with wild-typeand isogenic Tn10 mutant) but this time used initial strain ratios of1:1, 10:1, 100:1, 1000:1 and 10,000:1 (wild type:mutant). The datawere collected and analyzed as before and for each of the competi-tions, regardless of the initial ratio, the calculated MSC was 1/100.This confirms that the measured MSC values are robust and are notsignificantly influenced by the initial ratio of wild type to mutant.The conclusion from these competition experiments is that in everycase tested the MSC is significantly below the MIC of the susceptiblewild type, and in some cases it is very far below MIC (1/100, 1/230).Results from an independent study agree qualitatively with thosefrom our study and show that antibiotic concentrations that are sig-nificantly below the wild-type MIC can selectively enrich resistantmutant populations (Liu et al., 2011). The MSC values measuredin our experiments correspond to absolute antibiotic concentra-tions of 1 �g/ml (streptomycin), 15 ng/ml (tetracycline), and from2.5 ng/ml down to 100 pg/ml (ciprofloxacin).

Having shown that very low concentrations of antibiotic couldselectively enrich resistant mutants in competition experimentswe next asked whether sub-MIC concentrations could also selectand enrich de novo mutants. For that experiment, 20 indepen-dent lineages each of the S. enterica and E. coli wild-types weregrown in batch cultures in the presence of sub-MIC concentra-tions of streptomycin (S. typhimurium, (1/4) MIC, 700 generations)or ciprofloxacin (E. coli, 1/10 MIC, 600 generations). Each lineagewas monitored periodically throughout the experiment to quan-tify the frequency of cells with increased levels of resistance. Ineach case we observed a significant and continuous increase in thefrequency of more resistant sub-populations within the lineages.For example, for streptomycin, after 400 generations all 20 lineageshad sub-populations (at least 1% frequency) that were 8 times thewild-type MIC, and after 600 generations 14 lineages had subpop-ulations that were 16 times MIC. Similarly for E. coli and sub-MICselection with ciprofloxacin: after 500 generations five of the lin-eages had sub-populations (≥1% frequency) with MICs from two-to six-fold higher than the wild-type (Gullberg et al., 2011). Thus,sub-MIC concentration of antibiotics can select for both low- andhigh-level resistance mutants de novo from a susceptible popula-tion. We draw two important conclusions from these evolutionexperiments. First, sub-MIC antibiotic concentrations can selectresistant mutants de novo from a susceptible bacterial population.Second, de novo-selected mutants often had MICs that were muchhigher than the selective concentration of antibiotic. Thus, bothhigh- and low-level antibiotic-resistant mutants could potentiallybe selected by exposure to low levels of antibiotics.

We also developed a mathematical model to calculate howrapidly a de novo resistance mutation could take over (at least 50%of the population) a susceptible population under selection at sub-MIC antibiotic concentrations. The calculations took into accountthe mutation rate (�), the population size (N), and the selectiveadvantage of the mutant (s) as a function of antibiotic concentra-tion based on the data from the competition experiments (Gullberget al., 2011). There are two terms that dominate the equations: thestochastic waiting time for the first mutation to appear, and the

subsequent time for the mutant to grow to 50% of the population.For populations where the mutant is already present (�N > 1) thetime to fixation can be rapid (100–1000 generations) for s valuesbetween 0.1 and 0.01. For populations where �N < 1 (for example

D.I. Andersson, D. Hughes / Drug Resistance Updates 15 (2012) 162– 172 167

Fig. 2. Growth rates as a function of antibiotic concentrations. The concentrationinterval where the susceptible strain (blue line) will outcompete the resistant strain(red line) is indicated in green. Sub-MIC selective window (orange) and traditionalmutant selective window (red) indicate concentration intervals where the resistantstrain will outcompete the susceptible strain. MICsusc = minimal inhibitory concen-tration of the susceptible strain, MICres = minimal inhibitory concentration of ther

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Table 2Differences in outcome for selection at lethal and non-lethal drug concentrations.

Lethal (>MIC) Non-lethal (<MIC)

Selection for rare mutations of bigeffects

Selection for commonmutations of smalleffect → Higher rates ofappearance of resistant mutants

Mutations need to pre-existselection

Mutations can form duringgrowth after selection isapplied → Mutation supplyincreased

Selective agent (i.e. antibiotic)cannot modulate the rate offormation of mutants sincebacteria die (or do not grow)

Selective agent can modulaterates of mutation,recombination, and horizontalgene transfer → Rates of mutantformation increased

The enrichment for mutators isweak for rare one-stepmutations of large effect

For step-wise selection ofmutations of small effect theenrichment for mutators isstrong → Enrichment formutators with increasedpropensity for further resistancedevelopment

Fitness of mutant less important Fitness of mutant importantsince only resistant mutantswith a fitness reduction lessthan that caused by theantibiotic in the susceptiblebacteria are selected → Mutantswith higher fitness selected

esistant strain and MSC = minimal selective concentration.

igure reproduced from PLoS Pathogens (Gullberg et al., 2011).

ery rare mutations, or small bacterial populations) the stochasticaiting time for a mutation to appear will dominate. However, it

s worth noting here that many antibiotics, including the fluoro-uinolones, have been shown to increase mutation rates (Kohanskit al., 2010) which could potentially reduce waiting times and shifthe balance toward �N > 1 where takeover is dominated by therowth selective advantage of the resistant mutant as a function ofntibiotic selective pressure. Thus, from the mathematical modele can infer that, especially in large populations where �N > 1,

nd at antibiotic concentrations where 0.01 < s < 1.0, that resistantutants will rapidly appear and take over the population in less

han 1000 generations of growth.

. Problems generated by non-lethal (sub-MIC) resistanceelection

Clearly resistant mutants can be selected by antibiotic concen-rations both in the traditional selective window and the sub-MICindow ranges (Fig. 2) and an important question is whether theseifferent concentration intervals will generate different endpointsith regard to which mutants are selected? As outlined below weould argue that non-lethal selections for resistance, where the

ntibiotics do not kill the susceptible bacteria but only slow downheir growth, are more problematic with regard to both the ratey which mutants emerge and the types of resistance mechanismshat are selected (Table 2). The reasons for this are several.

First, in a lethal selection (i.e. in the classical selective window),election only detects rare pre-existing mutations that provide suf-ciently high levels of resistance, and if they are not present whenhe selective pressure is applied the susceptible population willie and go extinct (e.g. the infection is cured). In contrast, in theub-MIC window selection is not lethal but only detects small dif-erences in growth rate between susceptible and resistant bacteriaFig. 2). A consequence is that under conditions when the mutationupply rate (i.e. population size times mutation rate) is limitinghe rate of resistance evolution, sub-MIC concentrations of antibi-tics will allow the population to grow until a (rare) resistanceutation appears. In effect, sub-MIC selections allow an increase

n population size and mutation supply.Second, as shown by recent studies certain antibiotic classes

an at sub-MIC increase the mutation rate by conferring a muta-enic effect. For example, fluoroquinolones, aminoglycosides and

beta-lactams can by induction of the SOS response (Ysern et al.,1990; Miller et al., 2004; Perez-Capilla et al., 2005; Cirz et al., 2007;Baharoglu and Mazel, 2011; Thi et al., 2011) and translational mis-reading (Ren et al., 1999; Balashov and Humayun, 2002) as well asby generation of mutagenic oxygen radicals (Kohanski et al., 2010)increase the mutation rate up to 15-fold, an effect that might berelevant under certain selective conditions (Cirz et al., 2005). Forexample, by experimentally increasing the mutation rate by as lit-tle as two-fold, the rate of evolution to fluoroquinolone-resistancein E. coli is significantly increased (Örlén and Hughes, 2006). In addi-tion, non-lethal concentrations of antibiotics also increase rates ofhomologous recombination and horizontal gene transfer, includ-ing in the biofilms present in many infections (Lopez et al., 2007;Couce and Blazquez, 2009; Canton and Morosini, 2011). Antibioticsat sub-MIC levels have been shown to induce the SOS responseand activate mobilization of genetic elements and increase ratesof recombination. For example, tetracycline stimulates by up to1000-fold horizontal transfer rates of both conjugative and inte-grative genetic elements in several significant human pathogens,including Staphylococcus aureus (Barr et al., 1986), E. faecalis andListeria monocytogenes (Doucet-Populaire et al., 1991; Bahl et al.,2004) and various SOS-inducing classes of antibiotics can increaseintegron recombination in Vibrio cholerae and E. coli (Guerin et al.,2009).

Third, another problematic aspect of exposure to sub-lethallevels of drug is the increased potential for enrichment of sta-ble genetic mutators. It has long been understood that antibioticsexert a second-order selection for mutator clones, i.e. clones of bac-teria with increased mutation rates, because increased mutationrates speed up the rate of adaptation to the selective conditions,especially when stepwise selection of different resistance muta-tions occurs (Mao et al., 1997; Taddei et al., 1997; Shaver et al.,2002). For example, among fluoroquinolone-resistant clinical iso-lates of E. coli there is a positive correlation between the number

of resistance-associated mutations and an increased mutation rate(Komp Lindgren et al., 2003). Similarly, natural isolates of E. coliwith intermediate mutator phenotypes have been found to be

168 D.I. Andersson, D. Hughes / Drug Resista

Fig. 3. Schematic representation of how selection at high and low antibiotic con-centrations influences the characteristics of resistant mutants with regard to theirfitness and level of resistance. The blue area indicates that high levels of antibi-otic select high-level resistant mutants, with either high or low fitness costs. Theyellow area indicates that low levels of antibiotic select low-fitness cost mutants,with either high- or low-level resistance. The overlapping box outlined in red indi-cates that mutants with high-level resistance and low fitness costs can potentiallybe selected at both high and low levels of antibiotic.

mTetctoacl

ibMfibetmtcntidtrMoctwctoebo2

3. Increased dosage of the antibiotic target molecule or an inef-ficient enzymatic antibiotic-detoxifying mechanism can also

ore likely to be multidrug resistant (Denamur et al., 2005).hese data strongly suggest that low levels of antibiotic in thenvironment could have significant effects on resistance evolu-ion by secondary selection of increased mutation rates. This is inontrast to selection at a high level of drug where typically resis-ance is conferred by acquiring in one step a specific mutationf large effect, e.g. rpsL and rpoB mutations causing streptomycinnd rifampicin resistance, respectively, or HGT of plasmid/ICE thatarries a specific resistance gene(s) and the enrichment effect isow.

Finally, one of the conclusions we can draw from the exper-ments that measure MSC is that there is a positive correlationetween small fitness costs in the absence of antibiotic, and lowSC values (Gullberg et al., 2011). The correlation between small

tness costs and low MSC is expected on theoretical groundsecause a fitness cost must first be overcome by the negativeffect of antibiotics on the susceptible strain before the resis-ant mutant can be selectively enriched. Thus, only resistance

echanisms with a fitness cost smaller than the growth reduc-ion caused by the antibiotic in the susceptible bacteria will beompetitive, and resistance mutations that confer a too high fit-ess costs will not be enriched at low levels of antibiotic (evenhough they might confer high level of resistance). An importantmplication from this reasoning is that non-lethal selective con-itions will enrich for resistant mutants with higher fitness, andhese mutants may express either a low level or a high level ofesistance (Fig. 3). In contrast, when the selection is lethal (aboveIC) it is only required that the acquired mechanism (mutation

r HGT) increases antibiotic resistance above the applied drugoncentration, and the effect of the resistance mechanism on bac-erial fitness is less important because even resistance mechanismsith a very high fitness cost will be selected as long as sus-

eptible competitor bacteria are eliminated. In conclusion, fromhe point of view of persistence and potential for reversibilityf resistance mechanisms that are selected at sub-MIC might bespecially problematic since the low fitness cost means that suchacteria will remain in the population longer when the antibi-

tic use and selective pressure is reduced (Andersson and Hughes,010).

nce Updates 15 (2012) 162– 172

8. Which resistance mechanisms are most likely to beselected at low antibiotic concentrations?

A priori one might think that selection at low antibiotic con-centrations preferentially select for mutations that confer lowresistance, but the key factor is that any mechanism that con-fers a sufficiently high increase in resistance and concomitantlydoes not decrease fitness too much can potentially be selected.However, it has been empirically observed that most studied, high-level resistances whether mutational or HGT based do confer asubstantial (i.e. >1%) fitness cost and at very low antibiotic concen-trations (i.e. well below the MIC) these mutants will accordinglynot be selected because the antibiotic selective effect is smallerthan the fitness cost. Thus, many of the presently known andwell-described resistance mechanisms are not expected to enrichunder sub-MIC conditions. One illustrative example of this is pro-vided by selection for streptomycin resistance in S. typhimuriumwhere at high streptomycin concentrations (>100 mg/L) the classi-cal rpsL mutations are almost exclusively isolated. These mutationsprovide a high level of resistance (>1024 mg/L) but they are alsogenerally associated with fitness costs ranging between 3 and27% (Bjorkman et al., 1998; Maisnier-Patin et al., 2002; Gullberget al., 2011). Instead, if selection occurs at sub-MIC of strepto-mycin for the susceptible wild type strain, rpsL mutations are notselected (because they are too costly) and instead a new class oflow cost mutations (in the gidB gene) and with moderate increasesin MIC is found (Okamoto et al., 2007). Note that this does notexclude the possibility that sub-MIC could select high-level resis-tance. The tendency will be that sub-MIC selects mutants withhigh fitness (regardless of resistance level), whereas greater-than-MIC selects mutants with high resistance (regardless of fitnesslevel).

Few studies have been aimed at identifying resistance mutationsat low antibiotic concentration regimes, especially under condi-tions of long-term exposure to sub-MIC levels. Several types ofmutants are known to provide low-level resistance but at presentwe cannot predict their occurrence, mainly because of limitedknowledge of which mutations confer no or very small fitness costs.Thus, as discussed above the likelihood that low-level resistancewill become enriched at sub-lethal concentrations of drug is largelydetermined by the magnitude of the fitness reduction as comparedto the strength of the selection. Below are outlined some examplesof potential mechanisms to be considered:

1. Resistance conferred by activation of efflux systems (either bymutation or regulation) can confer an unspecific and gener-ally low-level type of resistance. For example, activation of themar system in E. coli results in a several-fold increase in resis-tance to a number of different antibiotics (e.g. chloramphenicol,fluoroquinolones, tetracycline) and biocides (e.g. triclosan andquaternary ammonium compounds). Although these mutationsoccur at a high frequency (by inactivation of repression), theyoften have a considerable fitness cost and they are thereforeunlikely to be selected during prolonged exposure at very lowantibiotic concentrations (Gullberg et al., 2011).

2. Another class of mutations increasingly recognized as beingimportant for low-level resistance is mutation in porins, proteinsthat form water channels in the outer membrane of Gram-negative bacteria. Again these mutations typically only cause afew-fold increase in resistance to for example �-lactams, andtheir fitness costs are often quite low. Thus, at least certain typesof porin mutations are predicted to appear at sub-MIC selections.

provide low-level resistance. The latter type of mechanism havebeen observed for �-lactam resistance where successive gene

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amplification of the ampC/bla genes conferred increasing resis-tance from a very low basal level (Normark et al., 1977; Edlundand Normark, 1981; Sun et al., 2009). Interestingly, a change inthe opposite direction, by a mutation that reduced drug-targetenzyme expression, confers low-level resistance to quinolones(Ince and Hooper, 2003).

. Low-level resistance can also occur as a byproduct of a resis-tance mechanism that provides a high-level of resistance toa specific antibiotic but a low level of resistance to a relatedantibiotic of the same structural or chemical class. Examples ofthis includes certain gyrA mutations in E. coli that confer high-level resistance to nalidixic acid but only low level resistance tociprofloxacin (Yoshida et al., 1990; Gullberg et al., 2011), CTX-M, TEM and AmpC genes that confer high-level resistance topenicillins, but only miniscule increases in resistance to car-bapenems (Ardanuy et al., 1998; Martinez-Martinez et al., 1999;Mammeri et al., 2008; Girlich et al., 2009; Yang et al., 2009) andthe aminoglycoside modifying enzyme aminoglycoside (AG) 3′-phosphotransferase II that provide full resistance to kanamycinbut only low-level resistance to amikacin (Perlin and Lerner,1986).

. Furthermore, it is likely that there exist a number of resistancemechanisms that are yet to be identified. It is hard to predictwhich these functions might be but as shown by a recent screenfor low-level resistance conferred by over-expression of E. coligenes, increased dosage of 61 ORFs (from a library containingover 4000 genes) could confer partial resistance to 86 of 237antibiotic/toxin-containing environments. Of these ORFs, mostconferred small but significant increases in MIC against tetracy-clines, �-lactams, antifolates, aminoglycosides, and macrolides(Soo et al., 2011). This experiment demonstrates that there existsa considerable reservoir of genes in the so-called intrinsic resis-tome that could confer low-level resistance when increased indosage. It is also conceivable that amino acid changes in exist-ing ORFs could confer low resistance. A still unanswered keyquestion is how such genetic changes would affect fitness but ifthey are not too costly they could potentially be selected duringsub-MIC antibiotic exposure.

. Apart from genetic changes in the bacterium, low-level resis-tance can also be conferred by physiological and regulatoryresponses that activate intrinsic resistance mechanisms or gen-erate a physiological state where the bacteria are less susceptibleto the drugs. One example of the former is the activation of acryptic aminoglycoside resistance gene, aadA, which when acti-vated by certain growth conditions provides a moderate level ofresistance (Koskiniemi et al., 2011). Another well-studied exam-ple is the occurrence of tolerant cells that are refractory to drugs(Jayaraman, 2008; Lewis, 2008; Allison et al., 2011) or the for-mation of biofilms in which the bacteria are less susceptible(Anderson and O’Toole, 2008; Hoiby et al., 2010). In contrastto genetic mechanisms, these physiological states are rapidlyreversible if environmental conditions change but they could stillpotentially serve as stepping-stones that provide transient low-levels of resistance that allow bacteria to subsequently acquirestable, high-level resistance mechanisms.

. What are the drug concentrations in differentnvironments?

When examining the actual concentrations of antibiotics thatre found in different environments there is obviously an enor-

ous variation. Generally the concentrations observed in treated

umans, animals and in farming are relatively high (even thoughhere might exist compartments or time intervals where concen-rations are low, i.e. below MICs of the drugs against susceptible

nce Updates 15 (2012) 162– 172 169

bacteria) whereas levels in the outside environments are typi-cally substantially lower. Thus, antibiotic concentrations in rivers,lakes and soils are usually in the �g/L to ng/L range, i.e. theywould act as non-lethal selections (Thiele-Bruhn, 2003; Chanderet al., 2005; Kummerer, 2009). However, high antibiotic concen-trations can be observed in connection with wastewater outletsfrom, for example, pharmaceutical industries (Larsson et al., 2007;Fick et al., 2009) and hospitals (Kummerer and Henninger, 2003;Gomez et al., 2006; Thomas et al., 2007; Duong et al., 2008;Seifrtova et al., 2008; Verlicchi et al., 2010). High antibiotic con-centrations are also associated with wastewater from agriculturaland aquacultural activities (Pena et al., 2010; Thuy et al., 2011;Wei et al., 2011; Zou et al., 2011) and detectable levels are some-times found in tap water for human consumption (Fick et al., 2009;Yiruhan et al., 2010). In one extreme case the concentration ofthe fluoroquinolone ciprofloxacin found in river water (31 mg/L)downstream of a region with several antibiotic-producing indus-tries well exceeded the antibiotic concentration measured in theserum of ciprofloxacin-treated patients (about 2–3 mg/L), indicat-ing an extremely strong selective pressure (Larsson et al., 2007).

10. Approaches to reduce the presence of sub-lethalselective conditions

Even though antibiotic levels can be at sub-MIC in treatedhumans, animals and plants in clinical, community and agriculturalsettings, it is likely that the most common environments wherelow levels are consistently present over long time periods is inanimals treated with antibiotics for growth promotion and in vari-ous aquatic environments (Baquero et al., 2008; Kummerer, 2009;Martinez, 2009; Hoa et al., 2011) and in soils (Rooklidge, 2004;Pico and Andreu, 2007; Sukul and Spiteller, 2007; Martinez, 2008)where a big part of the therapeutically used drugs will ultimatelyend up if they have not been degraded. It is hard to obtain accurateestimates of global antibiotic use but it is clearly in excess of severalhundred thousands of tons per year and about half of these antibi-otics will enter natural environments in active form after they haveexerted their therapeutic or growth-promoting action in humansand animals.

Is there any reasonable way we may deal with these phar-maceutical pollutants and prevent them exerting selection forantimicrobial resistance in the environment? Obviously a generalreduction of antibiotic use will reduce antibiotic release into theenvironment. Thus, a more prudent therapeutic use of antibioticfor infections will have beneficial effects. Similarly, it is possibleto stop the use of antibiotics for growth promotion without majornegative effects on animal production, as shown by bans in Swedenand Denmark introduced in 1986 and 1997, respectively (Aarestrupet al., 2001; Bengtsson and Wierup, 2006; Grave et al., 2006) and therecent ban, applicable within all countries of the European Union,that was introduced by the European Parliament and the Council ofEurope in 2006 (2003; Cogliani et al., 2011). As the use of antibioticsfor growth promotion represents a substantial part of total antibi-otic consumption a global ban would drastically reduce the overallselective pressure. With regard to release of antibiotics into theenvironment one could either prevent antibiotic release into waterfrom humans (mainly urine) or inactivate the drugs downstreamfrom the release site. The latter is clearly achievable and severalmethods already exist that are technically feasible and economi-cally viable (Esplugas et al., 2007; MistraPharma, 2011; Wahlberget al., 2011). For example, ozone treatment of sewage water is an

effective and relatively cheap method for destruction of pharma-ceuticals, including antibiotics. An additional beneficial effect ofozone treatment is that essentially all types of pathogenic infec-tious agents will be efficiently inactivated. Thus, ozone treatment

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ill not only remove the selective agents but also break transmis-ion cycles of both susceptible and resistant microbes.

cknowledgements

This work was supported by grants from the Swedish Researchouncil Medicine, EU project Predicting Antibiotic Resistance (PAR,rant no. 241476), Swedish Strategic Research Foundation (SSF),wedish Innovation Agency (Vinnova) to DIA and DH. We thankhristina Greko and Linus Sandegren for helpful discussion andomments on the manuscript.

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