Phenotypic heterogeneity can confer clonal groups of organisms with new functionality. A paradigmatic example is the bistable expression of virulence genes in Salmonella typhimurium, which leads to phenotypically virulent and phenotypically avirulent subpopulations. The two subpopulations have been shown to divide labor during S. typhimurium infections. Here, we show that heterogeneous virulence gene expression in this organism also promotes survival against exposure to antibiotics through a bet-hedging mechanism. Using microfluidic devices in combination with fluorescence time-lapse microscopy and quantitative image analysis, we analyzed the expression of virulence genes at the single cell level and related it to survival when exposed to antibiotics. We found that, across different types of antibiotics and under concentrations that are clinically relevant, the subpopulation of bacterial cells that express virulence genes shows increased survival after exposure to antibiotics. Intriguingly, there is an interplay between the two consequences of phenotypic heterogeneity. The bet-hedging effect that arises through heterogeneity in virulence gene expression can protect clonal populations against avirulent mutants that exploit and subvert the division of labor within these populations. We conclude that bet-hedging and the division of labor can arise through variation in a single trait and interact with each other. This reveals a new degree of functional complexity of phenotypic heterogeneity. In addition, our results suggest a general principle of how pathogens can evade antibiotics: Expression of virulence factors often entails metabolic costs and the resulting growth retardation could generally increase tolerance against antibiotics and thus compromise treatment.

Scientists have recently realized that nature and nurture are not the only determinants of an individual's traits; some organisms also use random molecular processes to generate phenotypic variation among genetically identical individuals. This raises the question of whether such phenotypic variation could be beneficial and what such possible benefits might be. Working with pathogenic Salmonella bacteria, we discovered that phenotypic variation in one single trait—the expression of virulence genes—provides this pathogen with two critical benefits. First, it leads to the division of labor between different phenotypic variants that allows for effective host colonization, and second, it provides tolerance to antibiotics through a “bet-hedging” mechanism. Our results provide a new perspective on how phenotypic differences between individuals can provide benefits to clonal groups of organisms. At the same time, this study contributes to explaining why some pathogens can evade treatment, and could help to find new and better ways for controlling infectious disease.

Here, we present a case where phenotypic heterogeneity in a single trait—virulence gene expression in Salmonella typhimurium—shows characteristics of both strategies, the division of labor and bet-hedging. In S. typhimurium, expression of the type three secretion system 1 (ttss-1) is bistable [14] – [16] . S. typhimurium uses ttss-1 for injecting effector proteins into host cells, promoting penetration of the host tissue. It is, therefore, an important determinant of virulence in this pathogen [17] . It has been shown that the bistable expression of ttss-1 leads to the division of labor among the members of a population. One subpopulation expresses ttss-1 (T1 + cells) and a fraction of those cells invade host tissue and evoke an inflammatory response that is beneficial for the S. typhimurium cells that do not invade [18] , [19] . This is thus a special case of “cooperative virulence” where the cooperative behavior is only expressed by a fraction of the population. Recently, it has also been shown that members of the T1 + subpopulation have low cellular growth rates [9] , [20] . Slow growth has been associated with tolerance to environmental stresses such as exposure to antibiotics [11] , [21] – [25] , and the formation of a slow-growing and persistent subpopulation has been interpreted as a typical example for bet-hedging in other organisms [11] , [26] . This raised the question of whether the slowly growing T1 + subpopulation is more tolerant to antibiotic exposure than the faster growing T1 − subpopulation, so that the formation of these two subpopulations could promote bet-hedging during exposure to antibiotics. Although this question does not imply that exposure to antibiotics was the selective force that might have promoted phenotypic heterogeneity in virulence gene expression, it is interesting to ask whether a bet-hedging benefit under exposure to antibiotics is a potentially very relevant consequence of this heterogeneity.

Two possible types of benefits have been proposed. First, heterogeneous gene expression can enable a population to hedge its bets in an unpredictable and fluctuating environment [3] , [5] – [7] . In this bet-hedging scenario, one part of the population expresses a phenotype optimized for the current environment, allowing it to survive and reproduce at a high rate. Another part of the population expresses a phenotype less well suited to the current environment, yet it is adapted to a state the environment might change into. Second, phenotypic heterogeneity can promote the division of labor in groups of genetically identical individuals [8] – [10] . This allows a population to perform different functions simultaneously that would be costly or impossible to combine within a single individual. Bet-hedging and division of labor are two fundamentally different adaptive strategies: The benefit of bet-hedging only manifests in fluctuating environments over time; the benefit of division of labor does not require environmental fluctuations to manifest, and the payoff to each subpopulation depends on the interaction with the other subpopulation. Both strategies have been shown independently to play important roles in microbial populations [8] – [13] . Whether heterogeneity in a single trait can promote both functions simultaneously, and how these functions can interact, is an open question. Our aim is to address this question and thereby to gain new insights into the functional complexity of phenotypic heterogeneity.

Genetically identical bacterial cells can exhibit remarkable phenotypic differences even when grown in homogeneous environments [1] , [2] . These differences can arise from stochastic fluctuations in the expression of individual genes [3] . Although there is evidence that the majority of genes are under selection for tight control of expression [4] , some genes are expressed heterogeneously. This raises the question of whether phenotypic heterogeneity can provide benefits and what those benefits might be.

Results and Discussion

To investigate whether the two subpopulations—T1+ and T1−—exhibit a difference in susceptibility to antibiotics, we grew clonal populations of S. typhimurium in a microfluidic device that allows single cell observation over extended periods of time under precisely controlled conditions, and quantifying cellular parameters of large numbers of individual cells [27] (Figure 1A shows a temporal montage of two microfluidic channels, and Figure S1 shows a schematic drawing of the microfluidic device). During exponential growth in LB medium, the majority of cells are T1−, which is consistent with previous results [20]. We use filtered medium from late exponential phase cultures grown in LB (“spent LB”) to induce the expression of ttss-1 [20], which, by virtue of its bistable expression, leads to the emergence of two phenotypic subpopulations. The first subpopulation remains T1−, whereas the second induces the expression of ttss-1 (as observed based on a reporter for sicA promoter activity; the sicA promoter controls expression of the sicAsipBCDA operon, encoding key parts of the ttss-1 virulence system). Importantly, and as we will show in more detail below, the T1+ subpopulation pays a cost for the expression of ttss-1, and grows and divides at a slower rate, in line with previously published observations [20]. To test for differential susceptibility of the two subpopulations, we then added 0.05 µg/ml ciprofloxacin. After 3 h of ciprofloxacin exposure, the medium was changed to fresh LB to wash out the antibiotic and to allow growth of surviving cells. Survival of ciprofloxacin treatment showed a positive correlation with single cell GFP intensities (Figure 1B and Figure 1C) and a negative correlation with single cell elongation rates (Figure 1D), and thus with the expression of ttss-1 (Movie S1); T1+ cells, having a growth deficit in the absence of antibiotics, were more likely to survive ciprofloxacin exposure than T1− cells. The bistable expression of ttss-1 can therefore have two functional consequences: In addition to promoting division of labor between two phenotypes, it can also promote persistence of the genotype in the face of fluctuating exposure to antibiotics through a bet-hedging mechanism.

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larger image TIFF original image Download: Figure 1. Expression of ttss-1 is associated with tolerance to antibiotic exposure. (A and B) We used time-lapse analysis of single cells to study tolerance to antibiotics. Bacteria were first grown in LB medium (marked blue), shifted to spent LB at 160 min (yellow), shifted to spent LB containing 0.05 µg/ml ciprofloxacin at 290 min (red), and shifted back to LB at 435 min (blue). (A) Bacterial cells were grown in microfluidic devices in dead-ended channels, with medium flowing through a bigger main channel orthogonal to them. The top row shows a temporal montage of images of a channel in which the bottom cell started expressing ttss-1 and resumed division after exposure to 0.05 µg/ml ciprofloxacin (“Ch 1”), and the bottom row shows a temporal montage of images of a channel in which the bottom cell did not express ttss-1 and did not resume division after exposure to antibiotic (“Ch 2”). For each channel, five sets of still images from different phases of the experiment are shown. Each set consists of 12 images recorded at 5-min time intervals. (B) Quantitative analysis of all 149 cells from this experiment. Every horizontal line represents data for an individual cell over 975 min. Dots mark the time points at which this cell divided. Cells are sorted according to ttss-1 expression during antibiotic exposure, measured as mean GFP fluorescence during that time interval. The cell index indicates the rank of a cell according to its mean GFP expression during antibiotic exposure; a lower cell index indicates lower GFP expression, and a higher cell index indicates higher GFP expression. The color of the lines indicates real-time GFP intensity. Shading indicates the data corresponding to the cells shown in (A). Three independent experiments were performed, all of them showing significant positive correlations of survival with ttss-1 expression (logistic regression with ANOVA, p = 8.8×10−14, 2.4×10−5, 1.3×10−8; N = 149, 137, and 144), indicating that T1+ cells preferentially survive antibiotic exposure. (C) Histogram of cells in different GFP categories. Color-coding of the columns denotes the probabilities to survive exposure to 0.05 µg/ml ciprofloxacin. The columns were assigned visually to two categories according to GFP intensity (“GFP on,” “GFP off”), and the percentage of cells surviving in the different categories was calculated. (D) Analysis of the single cell elongation rates 75 min before and 25 min after addition of antibiotic and survival. The black curve indicates the survival probability depending on the cell elongation rate as determined by a logistic regression model. The histograms show how many cells were in the respective ranges of elongation rates, and whether they survive antibiotic treatment (full bars, top) or die (empty bars, bottom). For all three experiments, survival was negatively correlated with single cell elongation rates (logistic regression with ANOVA, p = 7.8×10−4, 3.2×10−4, 1.0×10−4, N = 151, 137, and 114). https://doi.org/10.1371/journal.pbio.1001928.g001

We then carried out two important control experiments. First, we subjected a strain that is genetically avirulent (ΔhilD) to the same experimental conditions as used in Figure 1. HilD is a positive regulator of ttss-1, and deletion of hilD yields a population of fast growing T1− individuals [20]. None of the observed ΔhilD cells resumed division after exposure to 0.05 µg/ml ciprofloxacin (Figure S2), showing that the function of HilD is required to survive antibiotic exposure. Second, we tested whether the tolerance observed in the T1+ subpopulation is due to the acquisition of genetic resistance. We subjected cells that had survived exposure to 0.05 µg/ml ciprofloxacin to treatment with the same antibiotic a second time, without inducing expression of ttss-1 through growth in spent LB. None of the observed cells resumed division after the second antibiotic exposure (Figure S3, Movie S2), indicating that antibiotic tolerance is a phenotypic trait, rather than the result of a resistance mutation.

Next, we tested if our results were specific to antibiotic class and concentration. To test whether the differential killing of the two subpopulations is also observed with an antibiotic from a different class, we treated S. typhimurium cells with 16 µg/ml kanamycin, otherwise using the same experimental conditions as in Figure 1. Kanamycin is an aminoglycoside and has a fundamentally different mechanism of action from ciprofloxacin, a fluoroquinolone. Again, survival of antibiotic treatment was positively correlated with GFP fluorescence and negatively correlated with single cell elongation rates, and thus to expression of ttss-1 (Figure S4, Movie S3). Second, we tested whether the tolerance of T1+ cells plays a role at antibiotic concentrations that are in the range of those measured in patients treated with ciprofloxacin [28] and kanamycin [29], respectively. Using the same experimental setup as above, we subjected S. typhimurium to a ciprofloxacin concentration of 10 µg/ml and a kanamycin concentration of 50 µg/ml. These concentrations are higher than the ones we have used before, and correspond to 200 times the minimal inhibitory concentration (MIC) measured for ciprofloxacin, and 6.25 times the MIC measured for kanamycin (Figure S5). Although more cells died in total when subjected to those higher antibiotic concentrations, survival of T1+ cells was again significantly higher compared to T1− cells for both antibiotics tested (Figure 2 and Movie S4 for ciprofloxacin treatment, and Figure S6 for kanamycin treatment). This partial tolerance of T1+ cells against clinically relevant concentrations of antibiotics could potentially explain the observation of relapsing S. typhimurium infections after treatment [30].

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larger image TIFF original image Download: Figure 2. Tolerance of T1+ cells is also observed at a clinically relevant antibiotic concentration. Pooled results of three independent experiments analogous to the one shown in Figure 1, except that cells were exposed to a higher ciprofloxacin concentration, 10 µg/ml. We determined the ttss-1 expression levels of a total of 3,337 cells (measured as GFP fluorescence intensity at the last time point during antibiotic exposure) and recorded their fate during exposure to antibiotics. The histogram shows the number of cells in different GFP intensity categories, indicating ttss-1 expression levels. Background fluorescence intensity (measured in areas of the image not containing cells) was subtracted from the measured GFP values in order to allow pooling of different experiments. Color-coding denotes the probabilities to survive exposure to 10 µg/ml ciprofloxacin for each GFP intensity category. In three independent experiments, cells that express ttss-1 have a significantly higher survival probability (logistic regression with ANOVA, p = 0.03, 6.2×10−8, 4.2×10−3; N = 653, 1,208, and 1,476). In addition, columns were assigned visually to two categories according to their GFP expression (“GFP on,” “GFP off”), and the percentage of cells surviving in the different categories was calculated. https://doi.org/10.1371/journal.pbio.1001928.g002

Our results raise the question of whether the growth difference between the T1+ and T1− subpopulations can explain the difference in survival. We tested this in two different ways. First, we grew genetically avirulent ΔhilD cells in chemostats at the two different growth rates observed for the T1− and the T1+ subpopulations, respectively. These growth rates were determined in an experiment where wild-type S. typhimurium cells were grown for an extended period of time in spent LB, and single cell growth rates of T1− and T1+ cells were determined to be 0.96 and 0.26 doublings per hour, respectively (see Materials and Methods for details). The ΔhilD strain allowed us to test the effect of growth rate in a phenotypically uniform population. When treated with 0.05 µg/ml ciprofloxacin, viability counts in the chemostat populations remained largely stable for the slow growth condition (corresponding to the rate at which the T1+ subpopulation grows, 0.26 doublings per hour) over a period of 5 h, whereas viability counts for the fast growth condition (corresponding to the rate at which the T1− subpopulation grows, 0.96 doublings per hour) dropped sharply during the first 3 h and then remained stable at around 1 in 105 cells of the initial population (Figure S7). As a second way to reduce cell growth, we manipulated ΔhilD cells into overexpressing LacZ, a gratuitous protein under the growth conditions used, using an IPTG-inducible promoter on a high copy plasmid [31]. Again, we observed a strong negative correlation between single cell growth rate and survival (Figure S8, Movie S5). Cells lacking the plasmid do not survive when exposed to the same IPTG concentrations (unpublished data). We therefore conclude that the growth rate difference between the T1+ and T1− subpopulations can explain a substantial part of the difference in antibiotic susceptibility, and that the expression of abundant protein upon ttss-1 induction is a plausible reason for this growth deficiency.

The link between virulence gene expression and tolerance against antibiotics that we observe has potential consequences for within-host evolution of virulence. In experimental model systems of cooperative virulence [9],[32]–[34] as well as in a clinical setting [35] it has been shown that genetically avirulent mutants can rise in frequency during infection, leading to improved host condition. If exposure to antibiotics kills phenotypically avirulent cells preferentially, one would expect selection against the emergence of such genetically avirulent mutants. In order to test this, we competed the genetically avirulent S. typhimurium mutant ΔhilD against wild-type S. typhimurium cells in the presence and absence of ciprofloxacin in vitro (Figure 3). In the absence of antibiotic, we saw an increase in prevalence of the genetically avirulent ΔhilD strain, as reported previously [20]. If antibiotic was added to the culture, overall population viability counts dropped (Figure S9) and we observed the opposite effect: The wild-type increased relative to the avirulent ΔhilD mutant (Figure 3). The observation that exposure to antibiotics can lead to selection against genetically avirulent mutants in vitro raises the question of whether antibiotic treatment could contribute to the maintenance of virulence in a clinical context. These findings also suggest that the two established functions of phenotypic heterogeneity—division of labor and bet-hedging—can interact. Variation in virulence expression in clonal populations can translate into differential susceptibility to antibiotics and lead to a bet-hedging benefit, which could in turn protect clonal populations against the invasion of avirulent mutants [9],[32]–[35] that exploit and subvert the division of labor within these populations [8],[9]. This reveals a new level of complexity in the functional consequences of phenotpyic heterogeneity.

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larger image TIFF original image Download: Figure 3. Selection for genetically avirulent mutants is reversed when exposed to antibiotic. Dynamics of the fraction of ΔhilD cells in mixed cultures with wild-type cells over time. Cultures were inoculated with approximately 1∶1 ratios of the two strains. A kanamycin resistance marker was used to distinguish between strains. Without antibiotic challenge, ΔhilD cells increase in frequency relative to wild-type cells (blue boxes), whereas exposure to 0.05 µg/ml ciprofloxacin reverses the trend, and ΔhilD cells decrease in frequency relative to wild type (red boxes). Boxes span the range between upper and lower quartile; thick lines denote the median; whiskers denote the highest and lowest values still within 1.5 interquartile ranges of the upper and lower quartiles, respectively; empty circles represent data points that are outside this range. The addition of ciprofloxacin has a significant influence on the outcome of competition (two way ANOVA, Time×Treatment interaction, p<2×10−16, N = 20). https://doi.org/10.1371/journal.pbio.1001928.g003