Computational and Systems Biology, Biozentrum, University of Basel, Basel, Switzerland

Abstract

Background

Bacterial persistence describes a phenomenon wherein a small subpopulation of cells is able to survive a challenge with high doses of an antibiotic (or other stressor) better than the majority of the population. Previous work has shown that cells that are in a dormant or slow-growing state are persistent to antibiotic treatment and that populations with higher fractions of dormant cells exhibit higher levels of persistence. These data suggest that a major determinant of the fraction of persisters within a population is the rate at which cells enter and exit from dormancy. However, it is not known whether there are physiological changes in addition to dormancy that influence persistence. Here, we use quantitative measurements of persister fractions in a set of environmental isolates of

Results

If a single physiological change (e.g. cell dormancy) underlies most persister phenotypes, then strains should exhibit characteristic fractions of persister cells: some strains will consistently have high fractions of persisters (dormant cells), whereas others will have low fractions. Although we found substantial variation in the fraction of persisters between different environmental isolates of

Conclusions

These data support the hypothesis that there is no single physiological change that determines the persistence level in a population of cells. Instead, the fraction of cells that survive antibiotic treatment (persist) depends critically on the specific antibiotic that is used, suggesting that physiological changes in addition to dormancy can underlie persister phenotypes.

Background

Bacterial persistence is a form of phenotypic heterogeneity in which a subset of cells within an isogenic population is able to survive challenges with antibiotics or other stressors better than the bulk of the population

Recent work has suggested that persisters become drug tolerant because they enter a dormant or slow-growing state

Genetic studies in

Other than

Most studies on persister formation have focused on strains harboring mutations that increase or decrease persister frequency. However, one recent study

Here, using a collection of environmental isolates of

Together, these data imply that the ability of cells to persist in the face of antibiotic treatment depends on the specific mechanism by which the persister phenotype is generated, and the precise manner in which the antibiotic acts: cells that persist in one antibiotic may not persist in a second antibiotic, even if that antibiotic has a very similar mode of action. These data contrast strongly with data from experimental studies on lab strains of

Results

Consistent quantification of persister fractions

A critical issue when studying bacterial persistence is the precise definition of the persister fraction. Previous studies have defined persister cells as the surviving fraction after antibiotic exposure for an arbitrary amount of time, ranging from hours

To quantify the fraction of persisters in a consistent manner, we use a model motivated by observations of persister cell dynamics first reported by Balaban et al.

Here we apply this simple two-state model assuming simplified type II persister dynamics. In this model, cells exist in two states, normal and persister. During antibiotic treatment, normal cells die at a rate μ and switch to a persister state at rate α. Persister cells do not die or grow, and switch to a normal state at rate β (see Additional file

**Appendix.**

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Persister fractions differ between environmental isolates

We selected 11

We selected the subset of 11 environmental isolates on the basis of their differential levels of survival in ampicillin after 24 hours of treatment (using CFU counts; see Methods). In doing so, we aimed to find strains that differed to the greatest extent in the fraction of persisters that were formed in ampicillin, such that we would have the greatest power to discern whether these differences were paralleled in other antibiotics. In addition to these isolates, we used the standard laboratory strain

For each of these strains, we first determined the MIC for ampicillin (see Methods), and found that the MICs for these strains differed by less than two-fold (Additional file

Minimum inhibitory antibiotic concentrations for each strain. The MICs ranged between 15-22.5 μg/ml for ampicillin, between 0.008-0.030 μg/ml for ciprofloxacin and 3-7.5 μg/ml for nalidixic acid. This variation in MICs was considerably smaller than the variation in persister fractions exhibited by the selected strains and moreover, the fraction of persisters and their corresponding MICs showed no correlation, suggesting that the variation in MICs does not account for the one observed in the level of persister cells. No resistance to the three used antibiotics was evident for any of the examined.

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We then quantified, for each strain, survival curves over 48 hours during treatment with 100 mg/ml of ampicillin (Figure

Environmental isolates exhibit substantial variation in persister fractions after treatment with 100ug ampicillin

**Environmental isolates exhibit substantial variation in persister fractions after treatment with 100 ug ampicillin.** The kill curves are characterized by biphasic behavior, implying that there are at least two distinct populations of cells with differing death rates. The plot shows the killing data of six replicate cultures for three strains (SC552, SC649 and MG1655); the lines indicate the best-fit models for each replicate.

Using this method, we found that the fraction of persisters differed significantly between strains, from less than 0.001% to more than 10% (Figures

Environmental isolates exhibit different fractions of persisters after treatment with ciprofloxacin or nalidixic acid

**Environmental isolates exhibit different fractions of persisters after treatment with ciprofloxacin or nalidixic acid.** The plots show six replicates for each of the three strains shown in Figure **A**: Killing dynamics during 48 hours of treatment with ciprofloxacin. Biphasic dynamics, similar to those observed in Figure **B**: Killing dynamics during 48 hours of treatment with nalidixic acid. There are large differences in persister fractions between the two antibiotics, with strain SC649 exhibiting a low fraction of persisters in ciprofloxacin, but a high fraction in nalidixic acid.

Estimated death rates and switching rates for all strains in the three antibiotics (ampicillin, ciprofloxacin, and nalidixic acid). The parameters are explained in the Additional file 1. Electronic supplementary material.

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Persister fractions in different antibiotics are uncorrelated

To infer persister fractions, we also measured kill curves for each strain in two additional antibiotics, ciprofloxacin and nalidixic acid, both belonging to the quinolone class of antibiotics

Our hypothesis is that for each strain, persisters are generated through a single general mechanism, such as cell dormancy, and that this mechanism confers a multi-drug tolerance. If this is true, then strains should exhibit characteristic persister fractions: we expect that for some isolates this subset of cells will be large, and thus these isolates will have high fractions of persisters across all antibiotics, while for other isolates, this subset of cells will be small, resulting in a small fraction of persisters across all antibiotics. This pattern has been shown previously for the

We tested this hypothesis by looking for positive correlations in the fraction of persisters in the three antibiotics (ampicillin, ciprofloxacin, and nalidixic acid). However, despite the considerable variation in the persister fractions found among isolates (Figure

No correlation is observed between persister fractions in different antibiotics

**No correlation is observed between persister fractions in different antibiotics.** We found that although the calculated persister fractions are repeatable, there is no consistent correlation between the fractions of persisters in any two antibiotics. The plots show the correlations in persister fractions. **A**: ampicillin and ciprofloxacin; **B**: ampicillin and nalidixic acid; and **C**: ciprofloxacin and nalidixic acid. Only one strain exhibits a very high fraction of persisters in two antibiotics; however, these antibiotics are ciprofloxacin and ampicillin, members of two different classes. The error bars indicate standard errors for the biological replicates. The values of Spearman’s rho and the corresponding p-value are shown in each plot.

Evidence that a subset of persister cells is multidrug tolerant

We selected two strains on the basis of the persister fractions that they exhibited in single antibiotics, requiring that the strains exhibit a high level of persistence in at least one antibiotic. For these two strains we re-measured the persister fractions in single antibiotics, as well as in all pairwise combinations of the three antibiotics. We found that the killing dynamics were qualitatively similar to those when using a single antibiotic: all kill curves exhibited biphasic behavior, indicating that at least two subpopulations of cells were present (Figure

Kill curves in combinations of antibiotics are biphasic and vary between treatments

**Kill curves in combinations of antibiotics are biphasic and vary between treatments.** We used combinations of antibiotics to examine the dynamics of cell killing. These dynamics are similar to those observed in single antibiotics. **A–C**: Killing dynamics of all replicate cultures upon treatment of strains SC552 with all pairwise combinations of the three antibiotics. **D-F**: Killing dynamics of strain SC649.

The precise dynamics of this killing in combinations of antibiotics may yield additional insight into how persisters are formed. We briefly outline three general possibilities. (1) No cells persist when a population is simultaneously treated with antibiotics. This implies that the mechanisms underlying persistence to the two antibiotics are exclusive, and cannot occur within the same cell. (2) The fraction of persistent cells under the combination of antibiotics is approximately multiplicative relative to the fraction in the two single antibiotics. Although this observation would be consistent with several explanations, the simplest is that the mechanisms of persister formation are independently induced, and occur randomly within the same cell. (3) The fraction of persistent cells under a combination of antibiotics is similar to the fraction observed under treatment with the more lethal antibiotic. Again, although several explanations would be consistent with this, the simplest is that cells that are persistent to the more lethal antibiotic are also persistent to the second. We refer to these three hypotheses as exclusive, independent, and coincident, respectively.

We found that for these two strains, there were no cases in which persister fractions were exclusive. Instead, the persister populations were largely coincident, with the fraction of cells in combinations of antibiotics being similar to the fraction observed in the more lethal antibiotic (Figures

A subset of persister cells is multidrug tolerant

**A subset of persister cells is multidrug tolerant.** The persister fractions estimated from the killing dynamics are shown for single or combinations of antibiotics. **A**: strain SC552; **B**: SC649. For both strains, there is a subset of persisters that appear to be resistant to both antibiotics.

Toxin-antitoxin pairs are frequently gained and lost in

One possible explanation for the differences in persister formation of environmental isolates is that the activation of different toxin-antitoxin pairs results in different antibiotic susceptibilities. To further examine this hypothesis, we looked at the presence of TA loci that are known to affect persister formation in 15

Known persister loci are rapidly gained and/or lost within the

**Known persister loci are rapidly gained and/or lost within the ****clade.** Grey boxes indicate the presence of the orthologue in the indicated genome; white indicates absence. The data suggests that toxin – antitoxin loci undergo rapid loss and/or gain within the

The rate of switching from normal to persister state is the primary determinant of persister fractions

In the analyses above, we have used information from cell-killing dynamics to infer the proportion of persister cells that were present at the start of antibiotic killing. These persisters are formed during exponential growth, and the fraction that is present is determined largely by two independent parameters, the rates of switching to and from the persister cell state. To gain additional insight into the mechanistic underpinnings of persister formation, we examined the relationship between the persister fraction and these two parameters. We find strong evidence that the primary determinant of the persister fraction is the rate at which persister cells are formed from normal cells: these two variables are strongly correlated across both strains and antibiotics (Figure

The primary determinant of the persister fraction is the rate of switching to the persister state

**The primary determinant of the persister fraction is the rate of switching to the persister state. A**: The rate of switching from the normal cellular state to the persister state is strongly correlated with the fraction of persisters in the population. **B**: There is little to no correlation between the rate of switching from the persister state to the normal state and the fraction of persisters. **C**: No correlation exists between the rate of death of normal cells and the persister fraction.

Discussion

In generating antibiotic kill curves from CFU data, we have shown that these curves differ substantially between environmental isolates of

We note that one complicating factor in this interpretation is that these different persister populations may have different propensities to form colonies, and that this might explain some of the differences in the shapes of the kill curves that we observed. However, given the range of persister fractions that we observed (over four orders of magnitude), we do not think that this mechanism can fully explain the patterns that we find.

It is also possible that although the isolates that we studied have similar MIC values, they differ in their pharmacodynamics

Evidence of two different types of persister cells has been shown previously by Balaban et al.

Gefen et al.

We note that the set of environment isolates that we have used are not known to be pathogenic, suggesting that many of them have had less exposure to antibiotics and the concomitant selection for resistant or persister phenotypes that arises from such exposure. In previous studies of clinical isolates, selection has been shown to result in rapid changes in the frequency of genotypes differing in their ability to form persisters

Previous studies have indirectly implied that mechanisms of persister formation may differ between strains for different antibiotics. Keren et al.

To our knowledge, the effect of pairwise combinations of antibiotics has not been investigated with respect to bacterial persistence. We found that the killing dynamics under combinations was qualitatively similar to that observed under a single antibiotic, with biphasic kill curves. Furthermore, the observation of co-incident persister fractions provide evidence that there is a small number of persister cells that exhibit multidrug resistance, and are thus persistent to all combinations of antibiotics (Figure

Conclusions

The results of our study clearly show that the fraction of persisters within an isogenic culture is highly dependent on the antimicrobial compound and the bacterial strain. Importantly, differences in persister fractions exist even for antibiotics of the same class. This contrasts markedly with the majority of laboratory studies of

Methods

Strains

The

Media

M9 salts supplemented with 0.2% glucose was used as a growth medium in all experiments.

Determination of minimum inhibitory concentrations (MICs)

Single colonies were used to inoculate 200 μl of M9 salts supplemented with 0.2% glucose in 96-well plates. The plates were incubated overnight at 37°C with shaking at 400 rpm. These overnight cultures were diluted 1:100 into fresh medium and incubated for 2 h at 37°C with shaking at 400 rpm to ensure logarithmic growth. Approximately 5 × 10^{7} cells were then used to inoculate 150 μl of M9 containing different concentrations of antibiotics and all wells were covered with 50 μl mineral oil to avoid evaporation. Growth was assessed by measuring the optical density (OD) at a wavelength of 600 nm over 20 hours using a plate-reader system from BioTek. The lowest concentration of antibiotic that did not exceed an OD of 0.01was taken to be the MIC of that antibiotic for a particular strain.

Antibiotic kill curves

Single colonies were used to inoculate 200 μl M9 minimal medium supplemented with 0.2% Glucose. The plates were incubated overnight at 37°C with shaking at 400 rpm. The overnight culture was diluted 1:100 into 1.5 ml fresh medium in a 24-well plate and incubated at 37°C with shaking at 250 rpm for 4 h to ensure logarithmic growth of the cultures. After 4 h of incubation, antibiotics were added at the following concentrations: 100 μg/ml ampicillin, 0.1 μg/ml ciprofloxacin and 150 μg/ml nalidixic acid. In preliminary experiments using kanamycin, we found that regrowth frequently occurred, despite a secondary spiking of the culture with kanamycin. This suggested that resistance often arose

For a subset of cases, we repeated the kill curve measurements using colonies that survived 48 hours of antibiotic treatment. In all cases, we observed dynamics similar to those observed for the original culture (data not shown), showing that these cells are likely to differ only in a phenotypic, and not genotypic, manner. In addition, we spiked the cultures with additional antibiotic after 24 hours, and found that this had no significant effect on the killing dynamics, showing that the dynamics we observe are not due to degradation of the antibiotic. Assays using combinations of antibiotics were performed similarly to those outlined above, with the antibiotics added at the same concentrations as they were in the single-drug assays. In this case, the experiments were performed in duplicate.

Quantification of persister fractions

The fraction of persisters, death rates and switching rates between persister and normal states were calculated using a model motivated by Balaban et al.

We used maximum likelihood to fit the CFU count data, under the assumption that the error in the CFU counts results primarily from Poisson sampling, using the likelihood function:

in which _{
t
} is the number of CFUs observed at time point _{
t
} is the dilution at time point

**R code for maximum likelihood fitting.** The R code used to perform the fits of the data is provided.

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Likelihood maximization was done using _{0} (the initial fraction of persisters) were determined independently for each replicate, and we calculated confidence intervals assuming normally distributed error. Because the values of a, b, and m cannot be uniquely fit (see Additional file _{0}; in most cases, the uncertainty in F_{0} is very low, with most minimum and maximum values of F_{0} ranging between 0.99 and 1. Thus, this approximation has little effect on our data.

All other statistical analyses were performed using R

Authors’ contributions

NH participated in the experimental design, collected all experimental data, performed the data analysis, and drafted the manuscript. EvN participated in the experimental design, performed the analytical derivations, and edited the manuscript. OKS conceived and designed the project, performed the computational and bioinformatic analyses, and drafted the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We thank Mike Sadowsky for providing the