Cite as: I. Levin-Reisman et al., Science 10.1126/science.aaj2191

(2017).

REPORTS

First release: 9 February 2017 www.sciencemag.org (Page numbers not final at time of first release) 1

Antibiotic-treatment failure is typically attributed to re-sistance. Many resistance mechanisms have been identified, including mutations that decrease the binding of the drug to its target and increased expression of efflux pumps (1). Resistance mutations result in a decrease in the effective concentration of the drug. The effect of such mutations is measured by the minimum inhibitory concentration (MIC), i.e., the lowest drug concentration needed to prevent the visible growth of the microorganism. However, it has long been realized that other mechanisms can help bacteria sur-vive antibiotic exposure (2). Non-growing or slow-growing bacteria can survive bactericidal antibiotics that require active growth for killing. This property is known as “toler-ance” (3). When the non-growing phenotype occurs in only part of a clonal population, for example in biofilms, this sub-population of “persisters” underlies treatment failure (4–6).

Recent studies (7–9) have shown that tolerance and per-sistence evolve rapidly under intermittent antibiotic expo-sure. When Escherichia coli populations were subjected to daily intermittent exposures to ampicillin, separated by in-tervals in fresh medium, the cultures became tolerant to ampicillin by acquiring mutations that extended their lag phase, i.e., the period before exponential growth is resumed after stationary phase, without any change in the MIC. The evolved mutants did not become resistant; they survived antibiotic treatment as long as they remained in the lag phase, but were efficiently killed by ampicillin once growth resumed (7).

Whether tolerant strains that can evolve rapidly, impede or accelerate the evolution of antibiotic resistance is the subject of debate (10–13). To understand the interplay be-tween resistance and tolerance, we evolved bacterial cul-tures using a slightly modified treatment protocol from ref.

(7) (fig. S1), with a lower dose of ampicillin (50 μg/ml), but still comparable to therapeutic doses and a fixed residual level during growth (14)(see (15)). We continued daily in-termittent exposures until resistance was established as de-fined by clinical standards (16). Starting with three different E.coli strains, including an enteropathogenic (EPEC) strain (table S1), we found that 11 of the 14 cultures reached an MIC at least 7-fold greater than the MIC of the ancestral strains (Fig. 1A). Further analysis of the resistant cultures using whole genome sequencing revealed that they all har-bored mutations in the promoter of ampC which codes for a beta-lactamase known to confer resistance to ampicillin when overexpressed (17–19) (Fig. 1B).

To dissect the path leading to resistance, we analyzed the dynamics of MIC increase in batch cultures at each cycle of antibiotic treatment. Simultaneously, we performed pheno-typic characterization using the ScanLag system (Fig. 2, A to C), which enables the measurement of the distribution of the lag period and growth of single colonies using an auto-mated scanner setup (20). We observed an increase in MIC after seven to 17 cycles (Fig. 2, D to F). Interestingly, after three to four cycles of antibiotic exposure, we observed that most bacteria showed delayed growth when plated on fresh medium (Fig. 2, G to I). As previously shown, delayed growth resulted from an extended lag time that conferred tolerance to the ampicillin treatment (7). Thus, the bacterial cultures seemed to have become tolerant several cycles be-fore the emergence of resistance.

The emergence of an extended lag time prior to the ap-pearance of resistance could imply that this is the order in which mutations accumulate and spread through the popu-lation (21) (Fig. 2J). Our analysis, however, was performed on batch cultures that contained different clones. The same sequence of events could therefore also be attributed to in-

Antibiotic tolerance facilitates the evolution of resistance Irit Levin-Reisman,1 Irine Ronin,1 Orit Gefen,1 Ilan Braniss,1 Noam Shoresh,2 Nathalie Q. Balaban1* 1Racah Institute of Physics and the Harvey M. Kruger Family Center for Nanoscience and Nanotechnology, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. 2Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA.

*Corresponding author: Email: nathalie.balaban@mail.huji.ac.il

Controlled experimental evolution during antibiotic treatment can shed light on the processes leading to antibiotic resistance in bacteria. Recently, intermittent antibiotic exposures have been shown to lead rapidly to the evolution of tolerance, i.e., the ability to survive under treatment without developing resistance. However, whether tolerance delays or promotes the eventual emergence of resistance is unclear. Here, we used in vitro evolution experiments to explore this question. We found that in all cases tolerance preceded resistance. A mathematical population-genetics model showed how tolerance boosts the chances for resistance mutations to spread in the population. Thus, tolerance mutations pave the way for the rapid subsequent evolution of resistance. Preventing the evolution of tolerance may offer a new strategy for delaying the emergence of resistance.

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dependent competing lineages (Fig. 2K). To determine whether the resistant mutations appeared on the ancestral background or on the background of a tolerant strain, we isolated the first resistant clone that established in each population. Mutations above a frequency of 80% in the whole genome sequencing of the batch cultures are listed in Table 1. We found that in addition to the resistance muta-tions in ampC, each clone bears an additional mutation in genes that had been previously mapped to the ‘tolerome’ (7, 22–24). Other mutations with putative association with the tolerome are listed in table S2.

Further analysis of previous cycles of antibiotic exposure for the batch cultures in which the tolerance+resistance mu-tants evolved revealed that the same tolerance mutations had been present prior the appearance of the ampC re-sistance mutations. Thus, resistance appeared as a second mutational event in addition to the tolerance mutation (Fig. 2, J to L). We note that in a tolerant clone (KLY (metGT)), only a sub-population became tolerant, a phenomenon called ’persistence’ (fig. S2) (4, 5, 22). The survival advantage of the persister sub-population was enough to enable the subsequent establishment of resistance.

Analysis of the phenotypic effect of the tolerance and persistence mutations, in clones that do not bear ampC re-sistance mutations, revealed that they increased survival by extending the lag time of the culture (Fig. 3, A to C). Restor-ing the wild type (wt) alleles of the tolerance mutations in the evolved tolerant+resistant clones abolished the extended lag period (Fig. 3D and fig. S3) without affecting the MIC (Fig. 3, D and E). These results enabled us to reconstruct the evolutionary trajectory that led to the establishment of these mutations in the population (Fig. 4A). The trajectory leading to increased survival first went through mutation endowing tolerance, which improved the survival of the ancestral strain by more than an order of magnitude (Fig. 4A; blue arrow). Survival was amplified by a subsequent resistant mutation in ampC that increased the MIC by a factor of 10 (Fig. 4A; red arrow and fig. S4). By initiating the evolution-ary protocol in parallel for tolerant strains as well as wt strains, we observed that the resistant mutations established faster on the tolerant background (Fig. 4B).

Why did resistance mutations in our experiments always occur on the background of tolerance or persistence muta-tions? In general, the probability for establishment of a mu-tation in the cyclic antibiotic exposure protocol, Pest, depends on two factors (25, 26): first, the probability of the occurrence of the mutation, and second, the probability that this mutation is not lost during the antibiotic exposure phase (see (15)).

( )1

2est min

02 1 (1 )

T tT

tR

tP N Sm

−−

=

= − −∑ (1)

Here Nmin2t is the population size at generation t, T is the

total number of generations, μ is mutation rate and SR is the survival rate of a resistant mutant. One reason for the rapid establishment of the tolerance mutations is that they occur more frequently than the resistance mutations (larger μ), owing to a larger target population size (22, 23). Indeed, mutations leading to high ampicillin resistance are mostly restricted to the ampC promoter, whereas tolerance muta-tions are observed in several genes and in different locations in those genes. Furthermore, we observe that the survival advantage conferred by resistance mutations under the high-level ampicillin treatment is comparable to that of the tolerance mutations (Fig. 4A). Despite the increase in MIC conferred by the ampC resistance mutations (16), and their classification as “Resistant” according to clinical standards (16), their MIC is still below the treatment exposure and results in partial resistance. Hence, tolerance mutations dominate the population after a few cycles (15).

As predicted in ref. (10), we observed that tolerance sup-ports the continued survival of the bacterial population. The reservoir of tolerant bacteria extends the window of oppor-tunity for rarer mutations to occur (11). In addition, we found that tolerance specifically enhances the establishment of resistance mutations by the epistasis between tolerance and partial resistance to the treatment. Using the experi-mental values (table S3 and figs. S5 and S6), we calculated the probability for the establishment of a resistant mutation if it occurs in a tolerant background as, Pest

R/tol, or in the wt background, Pest

R/wt. Our analysis shows that without toler-ance, the ampC mutations would most often be lost during antibiotic treatment and that more than 100 cycles of anti-biotic exposure would be needed for these partial resistance mutations to establish in the population (see (15), Fig. 4C). In other words, the tolerant background enables the estab-lishment of a resistance mutation by lowering its probability of getting lost during the antibiotic treatment (fig. S7). Sto-chastic simulations of the full experimental protocol con-firm this result (fig. S8). This analysis suggests that partial resistance mutations constantly arise in a population but are rapidly lost despite an increase in MIC, and that their chances of spreading in the population are significantly en-hanced on a tolerant background (fig. S9). We found that the effect of tolerance is particularly important at concen-trations of antibiotics high enough to prevent occurrence of full resistance, i.e., at concentrations that have been defined as the mutant prevention concentration (MPC) (27). At low-er concentrations, fully resistant single step mutants may appear and dominate the population, even in the absence of tolerance (Fig. 4D).

Our results indicate that tolerance plays a crucial role in the evolution of resistance in a bacterial population under cyclic exposures to high ampicillin concentrations. The key factors are that tolerance arises rapidly, as a result of the

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large number of possible mutations that lead to it, and that the combined effect of resistance and tolerance promotes the establishment of a partial resistance mutation on a tol-erant background. The initial mutations in the resistance gene that confer partial resistance, in this case in ampC, can lead to full resistance by additional mutations. It has been shown in many studies that several mutations are typically needed to confer high resistance (28, 29) and that partial resistance will lead rapidly to full resistance in vitro (30, 31) and in vivo (32). However, high resistance that requires sev-eral mutations is typically achieved by a gradual increase in antibiotic concentrations (29, 33, 34). Here, we show that even at concentrations above the MPC, the evolution of tol-erance can lead to the fixation of partial resistance muta-tions that significantly elevate the probability of full resistance. Indeed in one of our lines, full resistance was attained by the sequential accumulation of two mutational events in the promoter region of ampC (fig. S10). Co-evolution of tolerance and resistance factors, as suggested for FRD and ampC (35), may indicate how often tolerance has led to resistance.

Notably, two of the ampC promoter mutations that we mapped in our evolved strains were found in clinical iso-lates (18). However, the emergence of high resistance pre-ceded by the acquisition of tolerance would typically go undetected, as clinical isolates are routinely tested for MIC but not for tolerance. The detection of tolerance in clinical settings (36) may be crucial for determining the regime and type of antibiotics required to prevent the subsequent emer-gence of resistance. Analysis similar to ours for other anti-biotics and pathogens should shed light on the generality of the effect.

We have reported here enhancement by tolerance of the subsequent resistance evolution in bacterial populations under cyclic antibiotic treatments. The epistasis we describe between tolerance and resistance is quite generic, and should not depend on the mode of appearance of the muta-tion, whether evolved de novo or acquired horizontally, nor should it be specific to ampicillin, as tolerance increases survival to several classes of antibiotics. Finally, the im-portance of tolerance for the subsequent evolution of re-sistance indicates that new drugs (37, 38), or drug combinations, that decrease tolerance may impede the evo-lution of resistance.

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ACKNOWLEDGMENTS

We thank O. Fridman for discussion and technical help with Galaxy, A. Brauner for discussion, N. Dick for the work on ScanLag UI, and E. Winter for the BreSeq analysis. We thank Naama Barkai for comments on the manuscript. The work was supported by the European Research Council (Consolidator Grant no. 681819) and Israel Science Foundation (492/15). IL-R acknowledges support from the Dalia and Dan Maydan Fellowship. The accession number for the sequencing data is: BioSample accession: SAMN06227049. Additional files available at http://bio-site.phys.huji.ac.il/Materials

SUPPLEMENTARY MATERIALS www.sciencemag.org/cgi/content/full/science.aaj2191/DC1 Materials and Methods Figs. S1 to S10 Tables S1 to S5 References (39–53) 14 September 2016; accepted 16 January 2017 Published online 9 February 2017 10.1126/science.aaj2191

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Fig. 1. Cyclic antibiotic exposures lead to antibiotic resistance. (A) The MIC of evolved lines relative to the ancestral MIC. A representative line of each strain is shown. MICs of the batch cultures are shown, after each cycle during the evolution experiments. (B) Mutations detected in the resistant strains. Resistance is conferred in each strain by an insertion in the promoter of the ampC gene coding for a beta-lactamase. Similar insertions were shown to correct for the sub-optimal spacing in the ampC promoter and to result in ampC overexpression (17, 19) o

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Fig. 2. Monitoring the population dynamics of resistance and tolerance. (A to C) Detection of colony appearance and growth time using Scanlag on the evolved persistent strain KLY metGT ampCR . (A) Automatic colonies detection image. (B) Plot of the area of selected colonies over time. (C) Histogram of appearance time distribution for the evolved persistent strain. Note the long tail of late appearance, which indicates growth impairment despite the absence of antibiotics. Bacteria with a lag phase longer than the 4.5 hours ampicillin treatment would be able to survive under treatment. (D to F) MIC increase in evolving batch culture versus cycle number in KLY E7, EPEC E7 and MGY E7, respectively. (G to I) Appearance time box plots of colonies in evolving batch culture versus cycle number in, KLY E7, EPEC E7 and MGY E7, respectively. In all strains, the delay in the appearance of colonies due to extended lag time occurs several cycles before the MIC increase. (J) Schematic view of a possible scenario explaining the data presented in (D to I): tolerance mutants spread in the population and acquire a secondary resistant mutation on top of the tolerant background. (K) Alternative scenario: tolerance clones appear early in the culture and resistance clones appear independently later on. (L) Measured frequencies of the tolerant and resistant mutations in batch culture of the MGY-E7 line, cycles 0,5,6 and 11. Resistance is acquired in addition to the tolerant background, ruling out the scenario presented in (K).

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Fig. 3. The extended lag time underlies tolerance but does not affect resistance. (A) Survival fraction under 4.5 hours of ampicillin treatment when exposed during the lag phase for the ancestral (KLY), the Tolerant (KLYmetGT), the Tolerant+Resistant (KLYmetGT ampCR) and its restored metG wt allele (KLYmetGwt ampCR). (B) Quantification of the growth delay as the fraction of colonies that exited the lag phase after more than 4.5 hours for the strains shown in (A). The extended lag time of the tolerant mutants results in enhanced survival to treatment. The restored wt allele of metG restores the short wt lag but keeps the enhanced survival due to ampC. (C) Phase-contrast images of time-lapse microscopy of single cell lag time in ancestral, tolerant, Tolerant+Resistant and resistant only strain. The yellow arrows mark single bacteria with extended lag times characteristic of the tolerant strains. (D and E) Restoring the wt tolerant gene allele in the double mutants (Tolerant+Resistant) clones restores normal growth without decreasing the MIC: (D) ScanLag analysis of the appearance of colonies on plates presented as the fraction of colonies that were still undetected at designated time. As colonies start appearing, the fraction of undetected colonies decreases. Green: restoration of the tolerance mutation to the wt allele in the double mutants. (E) Disk diffusion assays for the strains shown in (D) (Disk diameter 6mm). Restoration of the wt alleles for the tolerance genes (prsA, metG) in the Tolerance+Resistance double mutants abolishes the growth delay without changing the MIC..

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Fig. 4. Understanding the evolutionary trajectory for the establishment of tolerance before resistance. (A) Experimental evolutionary trajectory first passed through the high tolerance peak (blue arrow) and then moved to resistance (red arrow). Height represents the survival under 4.5 hours of ampicillin at 50 μg/ml. Resistance is shown relative to ancestral MIC; Tolerance is shown as the percentage of colonies with a lag longer than 4.5 hour. (B) Evolution experiments with cultures started from a tolerant strain (dashed lines) evolved resistance earlier than the wt (solid lines). Top panel: KLY, KLYmetGT, middle panel: MGY, MGYprsT, bottom panel: KLY, KLYvapBT (without residual ampicillin (14)). (C) Enhancement of the probability to establish resistance in tolerant background. Simulation results for Pest

R for a wild type or a tolerant background. (D) Probability of establishment of typical tolerant mutations (top panel) or typical resistant mutation (middle panel) for different treatment durations and concentrations. The bottom panel shows the regions in which each mutation is more likely to establish. The dashed line in the middle panel marks the MPC.

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Table 1. Main mutations detected in WGS data, and verified by Sanger sequencing. Strain Seq ID Genomic

Position Mutation Amino Acid

Substitution Gene Annotation Phenotype

MGY E7

1261464 A>G F138L (TTC→CTC) Prs ← Ribose-phosphate pyrophosphokinase

Tolerance

MGY E7

4380612 IS2 (1330bp) ampC ← ampC promoter Resistance

KLY E1

CP008801.1 2171707-2171718

Δ12bp Δ89-92 ETIT metG → metG Methionyl-tRNA synthetase

Tolerance

KLY E1

4455876 +A ampC ← ampC promoter Resistance

EPEC E7

FM180568 2326669 G>A G649D (GGC→GAC)

metG → metG Methionyl-tRNA synthetase

Tolerance

EPEC E7

FM180568 4726849 +A ampC ← ampC promoter Resistance

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published online February 9, 2017and Nathalie Q. Balaban (February 9, 2017) Irit Levin-Reisman, Irine Ronin, Orit Gefen, Ilan Braniss, Noam ShoreshAntibiotic tolerance facilitates the evolution of resistance

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