New Results

Multisite phosphorylation regulates phenotypic variability in antibiotic tolerance

Elizabeth A. Libby, Shlomi Reuveni, Jonathan Dworkin
doi: https://doi.org/10.1101/315085

Abstract

Isogenic populations of cells exhibit phenotypic variability that has specific physiological consequences. For example, individual bacteria within a population can differ in their sensitivity to an antibiotic, but whether this variability can be regulated or is generally an unavoidable consequence of stochastic fluctuations is unclear. We observed that a bacterial stress response gene, the (p)ppGpp synthetase sasA, exhibits high levels of extrinsic noise in expression, suggestive of a regulatory process. We traced this variability to the convergence of two signaling systems that together control the multisite phosphorylation of a transcription factor, an event largely unexplored in bacteria, This regulatory intersection between a Ser/Thr kinase and a prototypical two component system is crucial for controlling the appearance of outliers, rare cells with unusually high levels of sasA expression. Additionally, by examining the full distributions of gene expression we calculated the contribution of the additional Ser/Thr kinase-dependent phosphorylation in setting the relative abundance of cells with a given a level of SasA. We then created a predictive model for the probability of a given cell surviving antibiotic treatment as a function of sasA expression. Therefore, our data show that multisite phosphorylation can be used to strongly regulate variations in phenotypes across a bacterial population.

Introduction

Many bacterial phenotypes, including antibiotic tolerance and virulence, often reflect the phenotype of a subset of the population rather than the average behavior 1, 2. Subpopulations of bacteria can arise through purely stochastic processes as well as by regulatory and signaling pathways 3. Theoretically, one way to create phenotypic diversity via a signaling pathway is multisite phosphorylation, in which each successive phosphorylation changes the activity of a protein 4, 5. However, it has not been experimentally shown in bacterial populations that multisite phosphorylation regulates variation in gene expression between cells, and subsequently, the emergence of phenotypic diversity. Recently, multisite phosphorylation of transcription factors have been observed in pathways involved in antibiotic tolerance and virulence 6, suggesting that dynamics of multisite phosphorylation could have particular physiological relevance.

Bacterial signaling is often characterized in the context of two-component signal transduction systems (TCS) that generally consist of a histidine kinase that phosphorylates a response regulator on a single residue, which then acts as a transcription factor 7. The stimulus-dependent response of this type of signaling system architecture has been analyzed theoretically 8, 9 and experimentally 10, 11, with little cell-to-cell variability observed (as quantified by CV), regardless of inducer level. This suggests that extensive cell-to-cell variability is not a general feature of bacterial TCS. However, some notable exceptions have been found for two-component systems with more complex architectures, such as the broad distribution of gene expression in the E. coli TorS/TorR regulon 12 which has recently been shown to be an important factor for cell survival during oxygen depletion 13. The network architecture of bacterial signal transduction systems may therefore play an underappreciated role in the dynamics and survival of bacterial populations.

In addition to TCS, bacteria also have eukaryotic-like (also called Hank’s type) Ser/Thr kinase – phosphatase pairs with homology to eukaryotic kinase systems that perform reversible phosphorylation on Ser and Thr residues 14. One particular subfamily of these systems appears to be universally conserved across Gram-positive bacteria and plays key roles in growth and virulence for many clinically important pathogens including the streptococci, S. aureus, M. tuberculosis, E. faecalis, and others 6, 15. Genetic and proteomic studies indicate that these Ser/Thr kinases can perform transcriptional regulation of key cellular processes involved in antibiotic tolerance and persistence through multisite phosphorylation of transcription factors. However, to date, the consequences of multisite phosphorylation for gene regulation at the single-cell-level has not been quantified. In this context the model gram-positive bacterium B. subtilis presents a comparatively straightforward system to quantify the contribution of the additional Ser/Thr phosphorylation in vivo: the homologous kinase-phosphatase pair is PrkC/PrpC, and it has been verified to regulate gene expression through additional phosphorylation of a response regulator.

It has been apparent for over 60 years that bacterial populations contain rare cells that display increased phenotypic resistance to antibiotics 16. These cells, presumed to be quiescent, have been implicated in antibiotic treatment failure in genetically susceptible bacterial infections 17. To date, it remains unclear to what extent the appearance of these rare cells, is subject to regulation. Emerging evidence strongly implicates elevated levels of the nucleotide second messenger (p)ppGpp as a causative agent of quiescence in many bacterial species 18-21. (p)ppGpp downregulates essential cellular processes such as transcription, translation, and DNA replication 22. Although the precise mechanism of (p)ppGpp synthesis and its direct cellular targets vary between bacterial species, highly elevated levels of (p)ppGpp confer a quiescent state to the bacterial cell. As many antibiotics target active cellular processes, the resulting cells exhibit increased antibiotic tolerance, suggesting that cell-to-cell variability in (p)ppGpp may be involved in phenotypic resistance to antibiotics 21, 23-25.

The mechanistic origin of cell-to-cell variability in (p)ppGpp levels across bacterial populations remains a major open question. To date, this has been best studied in E. coli, which has the RelA (p)ppGpp synthetase and the SpoT hydrolase 26. In contrast, other bacterial species often possess dedicated (p)ppGpp synthetases, termed small alarmone synthetases (SAS), in addition to bi-functional synthetase-hydrolases 27. These SAS proteins can be activated transcriptionally 22, suggesting that cell-to-cell variability in (p)ppGpp levels could originate in the transcriptional regulation of the synthetases themselves. In the Gram-positive bacterium B. subtilis, (p)ppGpp synthesis is regulated by three distinct proteins: RelA, SasA, and SasB 28. B. subtilis RelA is a bi-functional (p)ppGpp synthetase-hydrolase, and both SasA and SasB are dedicated synthetases. Although relA and sasB transcripts are both readily detectable during log phase growth, sasA (formerly ywaC) transcripts are found at considerably lower levels. However, sasA is inducible by certain classes of cell-wall-active antibiotics 29, 30, and its induction by alkaline shock increases the cellular levels of ppGpp 28. Since sasA expression stops growth 31, SasA-mediated (p)ppGpp synthesis provides a mechanism to induce cellular quiescence in response to environmental stresses. To date, SasA is only known to be regulated transcriptionally, so significant cell-to-cell variability in sasA expression could produce physiologically relevant cell-to-cell variability in (p)ppGpp levels. The pre-existing distribution of sasA expression may therefore be critical in predicting the relative survival of cells under conditions that do not specifically induce sasA.

In this work, we demonstrate that sasA expression displays physiologically relevant amounts of extrinsic noise, although the average level of sasA expression is very low during growth under non-inducing conditions. Furthermore, we find that both the distribution of sasA expression and the frequency of outliers are strongly regulated by the activity of a highly conserved eukaryotic-like Ser/Thr kinase system and its subsequent multisite phosphorylation of a transcription factor. Using quantitative analysis of the full distributions of sasA expression, we find that multisite phosphorylation is responsible for exponentially regulating the abundance of cells with a given level of SasA and generate a predictive model for sasA-expression-dependent antibiotic tolerance.

Results

The (p)ppGpp synthetase sasA exhibits high levels of extrinsic noise in expression

While the population average level of sasA expression during growth is extremely low 29, the average behavior may mask important phenotypic variation between cells. We therefore generated a transcriptional reporter for sasA (PsasA-yfp) to study the population at the single-cell level. Surprisingly, there was considerable cell-to-cell variability in PsasA-yfp (coefficient of variation, CV~4.95 ± 0.42, mean ± SEM), with most cells having very low expression, and rare cells showing significantly higher levels of expression (Fig. 1A). Quantification of YFP fluorescence revealed that a small fraction of the population had much higher (>~10x) levels of fluorescence than the mean, and rare cells had ~100x. Note that a typical bacterial gene has a CV in the range 0.1-1 32-34.

Figure 1:
Figure 1: sasA exhibits cell-to-cell variability in expression.

A) PsasA-yfp expression in log phase culture. Left: YFP image. Color scale indicates the fold change relative to the population average. Right: Phase contrast. Scale bar indicates 10

μm.

B) Schematic of dual-color transcriptional reporter strain to characterize the noise in sasA expression. Two copies of the sasA promoter driving different reporters (PsasA-yfp, PsasA-mcherry) are inserted at separate ectopic loci.

C) Evidence for extrinsic noise in sasA expression. Images of single cells from the dual reporter strain with significant fluorescence shown in mCherry (Left), YFP (Center), and phase contrast (Right) channels. Scale bar indicates 5 μm.

D) Quantification of cellular fluorescence intensities for single cells in the experiment described in B, C. Values were normalized relative to the population mean value for each channel. Data shown was measured on ~ 580 cells with detectable fluorescence in both channels. The fluorescence of the two reporters have a Pearson’s correlation coefficient of r ~ 0.90±0.08 (mean ± SEM, 4 experiments). For each reporter, the CV ~ 4.95±0.42 (mean ± SEM, 4 experiments).

The high levels of cell-to-cell variability in sasA expression could be caused by intrinsic noise from the promoter itself, or by extrinsic noise originating in an upstream process 35. To differentiate between these mechanisms, we used a strain with dual fluorescent reporters, PsasA-yfp and PsasA-mcherry (Fig. 1B). Expression of the dual reporters in individual cells was highly correlated (Pearson’s correlation coefficient, r~0.90±0.08, mean ± SEM), demonstrating that the noise was largely extrinsic to the promoter (Fig. 1C, D).

To determine if variation in YFP levels were simply caused by global changes in the population that result in the accumulation of large amounts of fluorescent protein 36, PsasA-yfp was compared to a presumably unrelated promoter known to be constitutively active during log phase growth, Pveg-mcherry 37 (Fig. S1). We found that YFP and mCherry levels were not highly correlated, suggesting that the high levels of variability in sasA expression are largely caused by a sasA-specific pathway. We then tested a previously characterized sasA regulator, the sigma factor σM (SigM) 38, that is required for sasA expression 30 (Fig. S2A, B). However, sasA expression also did not correlate strongly with sigM expression (Fig. S2C, D) demonstrating that sigM levels alone do not predict variability in sasA expression.

sasA is repressed by the Ser/Thr kinase PrkC through the response regulator WalR

Another potential regulator of sasA is the WalR transcription factor observed to bind the sasA promoter in a genome-wide screen 39. WalR is the response regulator of the essential WalRK two-component system and is activated by phosphorylation of Asp-53 by WalK 40. Once phosphorylated, WalR can either activate or repress genes in its regulon. A reversible second phosphorylation on WalR Thr-101 by the eukaryotic-like Ser/Thr kinase-phosphatase pair PrkC/PrpC 41 further increases WalR activity at both activating and repressing sites 42. In rich media (LB), the multisite phosphorylation of WalR affects gene expression (e.g., enhanced activation of yocH) specifically in post-log phase 42. However, in commonly used defined minimal media (S7-glucose), there is a consistent PrkC-dependent effect on the population average level of yocH expression throughout log phase (Fig. S3). sasA is known to be activated by antibiotics such as bacitracin 29 through σM activation. We first tested whether the PrkC/PrpC – WalR system regulates sasA at the population level to determine if WalR activates or represses sasA. We found that PrkC activity represses sasA expression through WalR Thr101~P during bacitracin treatment (Fig. S4). Based on these bulk measurements, we developed a model for sasA regulation (Fig. 2A) in which PrkC activity further potentiates WalR-repressing activity at sasA through a second phosphorylation of WalR at Thr-101. However, it remained unclear to what extent multisite phosphorylation of WalR affects pre-existing cell-to-cell variability in sasA expression under non-inducing conditions.

Figure 2:
Figure 2: The Ser/Thr kinase PrkC and phosphatase PrpC regulate cell-to-cell variability in sasA

A) Model for PrkC-dependent regulation of sasA. WalR binding to the sasA promoter represses sasA. WalR activity is primarily controlled by phosphorylation on Asp-53 by its cognate histidine kinase WalK, and secondarily by phosphorylation on Thr-101 by the Ser/Thr kinase PrkC. WalR Thr-101~P can be dephosphorylated by the phosphatase PrpC. Thr-101~P further enhances the repressing activity of WalR Asp-53~P, resulting in lower expression of sasA under conditions with high Thr-101~P (e.g., ΔprpC). Under conditions lacking Thr-101~P (e.g., ΔprkC), the increased repression of sasA is relieved.

B) Histograms demonstrating PrkC-dependent cell-to-cell variability in sasA. Top: PsasA-yfp reporter activity was quantified by flow cytometry in wild type (WT, gray), ΔprpC (orange), ΔprkC (green), and Δ(prpC-prkC) (blue) backgrounds. Shaded region of each plot indicates the range of cellular autofluorescence observed. Each histogram was computed from data on ~3.0*104 events. Bottom: Smoothed, overlaid histograms of the data shown above for comparison.

C) Percentage of outliers in each genetic background. At least 4 independent experiments, similar to and including the representative one shown in B, were performed in walR WT (Left) and walR T101A (Right) backgrounds. Each experiment was normalized to a control and outliers were defined as cells above a fixed threshold level of normalized fluorescence (~1250 A.U.). Dots represent the percentage of each population that is above the threshold; bars and lines represent the mean and SEM, respectively.

D) Summary of each genotype, its effect on the maximal occupancy of the two known WalR phosphosites, and the expected effect on WalR activity relative to WT. For each genotype the mean percentage of outliers in sasA expression (shown in C) are also summarized. Note that in the ΔprpC background, the Thr-101 phosphorylation is stabilized (denoted as T101*).

PrkC regulates noise in sasA expression through WalR Thr-101 phosphorylation

Cell-to-cell variability in gene expression can be tuned by changing repressor-binding affinities 43, 44, suggesting that multisite phosphorylation of WalR may play a critical role in setting the observed distribution of sasA expression across the population. To test this, we measured the distribution of sasA expression in wild type (WT) cells and compared it to genetic backgrounds that alter the phosphorylation state of WalR: ΔprpC (no phosphatase, high levels of T101~P), and ΔprkC (no kinase, no detectable T101~P) (Fig. 2B). Qualitatively, in the ΔprpC background, the frequency of cells with high levels of sasA expression was strongly reduced, whereas it was strongly increased in the ΔprkC background. The PrpC-dependent effect on sasA expression requires PrkC, since the distribution of sasA expression in a strain lacking both the kinase and phosphatase (Δ(prpC-prkC)) is very similar to a strain lacking just the kinase.

We first sought to quantify the effect of WalR multisite phosphorylation on the frequency of “outliers”: cells with a level of sasA expression above a fixed threshold in each population. We therefore compared independent measurements of the distribution of sasA expression in WT, ΔprpC, and Δ(prpC-prkC) backgrounds (Fig. 2C, left) and found that PrkC significantly affects the mean frequency of outliers >8 fold by this measure (walR WT, ΔprpC vs Δ(prpC-prkC): **p-value~0.004, Kolmogorov-Smirnov test). We repeated the measurements in a walR T101A background (Fig. 2C, right) and found that PrkC no longer has a significant effect on the mean frequency of outliers in the phosphosite mutant background (walR T101A, ΔprpC vs Δ(prpC-prkC): p-value~0.56, ns, Kolmogorov-Smirnov test). These results are consistent with increased WalR activity by Thr-101 phosphorylation causing increased repression of sasA, and thereby regulating the frequency of sasA outliers (Fig. 2D). Furthermore, heterologous expression of PrkC was sufficient to reduce the frequency of outliers observed in the ΔprkC background (Fig. S5A). Heterologous expression of PrkC was also able to further reduce the variability to below that observed in the ΔprpC background, approaching the level of cellular autofluorescence (Fig. S5B). This suggests that at least some of the remaining variability in the ΔprpC background arises due to incomplete saturation of WalR T101~P.

This definition of outliers, however, relies on the definition of a cutoff threshold and therefore does not fully address how multisite phosphorylation affects the entire distribution of sasA expression across the population. To quantify the effect of PrkC on the distribution of cell-to-cell variability in sasA expression, we deconvolved the measured data from the cellular autofluorescence (SI). This resulted in autofluorescence-free distributions of sasA expression, allowing better quantitative comparison of expression between genetic backgrounds (Fig. 3A). This deconvolution method uses only the first two moments (i.e., the mean and variance) of the observed distributions of fluorescence. As such, the auto-fluorescence free distributions are relatively insensitive to the observed “outliers” in each distribution, but makes a statistical prediction for those frequencies. To verify the accuracy of the predictions, these calculated distributions were re-convolved with the cellular autofluorescence and the reconstructed data set compared to the original data (Fig. S6). Calculation of the relative enrichment of cells with a given level of sasA in each genetic background revealed that maximal T101~P (ΔprpC) compared to the absence of T101~P (ΔprkC) results in exponential changes in the relative abundance of cells at a given level of sasA (Fig. 3B).

Figure 3:
Figure 3: Multisite phosphorylation causes exponential changes in abundance of cells with a given level of sasA expression

A) Functional fits of autofluorescence-free distributions of sasA expression. A deconvolution algorithm was used to remove the contribution of autofluorescence from the measured distributions of sasA expression shown in Figure 2B. Validation of the fits are shown in Fig. S6.

B) Effect of PrkC on the distribution of sasA expression. For each genotype in A the frequency of cells with a given level of sasA expression was normalized against the equivalent frequency in the WT population. On this semi-logarithmic plot, straight lines indicate that the enrichment factor changes ~exponentially with fluorescence.

C) PrkC-dependent regulation of cell-to-cell variability in sasA is WalR Thr-101-dependent. PsasA-yfp reporter activity was quantified by flow cytometry in a ΔprpC walR T101A (yellow, solid) background and compared to ΔprpC (orange), Δ(prpC-prkC) (blue), and Δ(prpC-prkC) walR T101A (yellow, dashed) backgrounds in the same experiment. Plots were generated by applying the deconvolution method described in A to the measured data in Fig. S7A.

D) Gradual inhibition of PrkC results in progressive enrichment of cells with elevated levels of sasA. PsasA-yfp reporter activity was quantified by flow cytometry during treatment with increasing concentrations of staurosporine: 0 (solvent only; black), 50 nM, 100 nM, and 1 μM (shades of magenta), in otherwise WT populations. For reference, a Δ(prpC-prkC) (blue) population treated with solvent only is shown; note that the effect of staurosporine saturates at this level. Plots were generated by applying the deconvolution method described in A to the measured data in Fig. S7B, C. For each condition the frequency of cells with a given level of sasA expression was normalized against the equivalent frequency in the WT population. As in B, straight lines indicate that enrichment increases ~exponentially with fluorescence. Enrichment becomes more pronounced as the concentration of staurosporine increases and saturates at the level of complete inhibition ~Δ(prpC-prkC).

Together, the model and the outlier analysis in Fig. 2 suggest that PrkC-dependent regulation of the distribution of sasA expression requires the second WalR phosphosite at Thr-101. To test this, we repeated the deconvolution procedure for a strain expressing a WalR mutant that lacks the Thr-101 phosphosite, walR T101A, and found that the PrkC-dependent effect on sasA expression is indeed WalR Thr-101-dependent (Fig. 3C, Fig. S7A). Thus, multisite phosphorylation is responsible for the exponential depletion of cells with medium to high levels of sasA expression in the ΔprpC background (Fig. 3B).

We then measured how intermediate levels of multisite phosphorylation regulate the distribution of sasA expression using the kinase inhibitor staurosporine to progressively inhibit PrkC activity 45 (Figs. S7B; S8). The distributions of sasA expression were again deconvolved (Fig. S7C), and we calculated the relative enrichment of cells with a given level of sasA fluorescence at increasing concentrations of staurosporine (Fig. 3D). Titration of PrkC activity resulted in exponential enrichment of cells with a given level of sasA. Therefore, even small changes in PrkC activity result in large changes in the abundance of “outliers”, cells with unusually high levels of sasA.

sasA expression level continuously predicts the probability of surviving antibiotic treatment

Cell-to-cell variability in (p)ppGpp production has been proposed to result in cell-to-cell variability in antibiotic survival 47,48, 49. However, a direct and quantitative relationship between the expression of a transcriptionally regulated (p)ppGpp synthetase and the probability of survival for an individual cell has not been demonstrated. We therefore sought to determine if cells with pre-existing high levels of sasA preferentially survive antibiotic exposure, and if so, provide a model for how the level of sasA expression influences the probability of survival for a given cell.

We used ciprofloxacin, a DNA gyrase inhibitor that does not significantly increase the population average level of sasA expression 29. We measured (Fig. S9) and deconvolved (Fig. 4A, B) the distributions of sasA expression (PsasA-yfp) both pre-and post-ciprofloxacin treatment that results in ~99% killing in both WT and ΔsasA backgrounds. We note that, importantly, the starting distributions of PsasA-yfp are very similar in both genetic backgrounds, allowing a direct comparison. Using these distributions, we calculated the relative enrichment of cells with a given level of sasA following antibiotic treatment (Fig. 4C), yielding a simple model for the effect of antibiotic treatment on the distribution of sasA expression (SI). Because survival after antibiotic treatments can be affected by many processes, we separated out the size of the sasA-dependent effect by using a ΔsasA mutant as a control. WT populations exhibited a significant increase in the fraction of cells with elevated levels of sasA (Fig. 4C). This effect is strongly reduced in the ΔsasA background, demonstrating that sasA has a significant contribution to survival after ciprofloxacin treatment.

Figure 4:
Figure 4: Cells with elevated sasA expression preferentially survive antibiotic treatment

A) Autofluorescence-free distributions of sasA expression before (dark gray) and after (light gray) ciprofloxacin treatment in an otherwise WT background based on the data shown in Fig. S9A.

B) Autofluorescence-free distributions of sasA expression before (dark red) and after (light red) ciprofloxacin treatment in a ΔsasA background based on the data shown in Fig. S9B.

C) Enrichment plot following antibiotic treatment for WT and ΔsasA populations. For each genotype in A and B the frequency of cells with a given level of PsasA-yfp after antibiotic treatment was normalized against the corresponding frequency prior to antibiotic treatment. Dashed lines and dots indicate the average values used in D and E.

D) Histograms of PsasA-yfp fluorescence in WT (black) and ΔsasA (red) backgrounds measured by flow cytometry prior to sorting into “low” (4.1 ± 1.8) or “high” (779.5 ± 134.9) expression groups (relative mean ± SEM, 3 experiments). Each histogram is comprised of data obtained from ~3.0*104 events and is from a representative experiment used in E.

E) Relative survival of the “high” and “low” expression populations in D after treatment with ciprofloxacin for ~3.5 h as in A, B. Bars indicate the mean survival ratio of the “high” to “low” expressing populations. The data shown represents 3 independent experiments (paired t-test, p-value ~ 0.009, **). The red dashed line represents a survival ratio of 1, indicating no advantage.

We then sought to determine whether our enrichment model reflects the probability of survival for cells as a function of sasA expression. Since SasA is a (p)ppGpp synthetase, the sasA-dependent enrichment we observe post-ciprofloxacin treatment could be due to an expression-dependent probability of surviving antibiotic treatment. Alternately, since the measured increase in mean fluorescence was relatively small (~2 fold), it is also possible that ciprofloxacin acts in a complex, expression-dependent, manner to generate the observed post-treatment distribution of sasA expression but does not affect survival. To differentiate between these hypotheses, we used FACS to sort bacteria prior to ciprofloxacin treatment from both WT and ΔsasA populations into “high” (upper ~1%) and “low” (~average) PsasA-yfp expression groups (Fig. 4D). Following ciprofloxacin treatment (as in Figs. 4A, B, S9), the relative survival of “high” and “low” expression cells, or the survival ratio, was assayed by plating for CFUs (Fig. 4E). The average fluorescence cutoff values used in the FACS experiments, low: 4.1±1.8 and high: 779.5±134.9 (mean ± SEM, 3 experiments), were then used as inputs for a model where the enrichment of cells with increased levels of sasA (Fig. 4C) is caused by increased survival (SI). The model yielded good agreement with the results of the FACS experiments: it predicted relative survival ratios of ~9 for wild-type, and ~2 for ΔsasA, respectively, compared to the measured values of ~9.5 ± 0.6 (WT) and 1.8 ± 0.7 (ΔsasA) (Fig. 4E). Therefore, the enrichment of cells with elevated levels of sasA post-ciprofloxacin treatment can be largely attributed to the increased survival probability of pre-existing cells in the population with elevated sasA expression.

Taken together, our results demonstrate that an important consequence of PrkC-dependent multisite phosphorylation of WalR is the regulation of cell-to-cell variability, or noise, in the WalR regulon gene sasA. By comparing the full distributions of gene expression, we demonstrate that this effect is not just confined to the regulation of outliers in gene expression above an arbitrary threshold within the population, but has an exponential effect on the relative abundance of cells with a given level of expression. By analyzing the full distributions of expression, we are also able to demonstrate that sasA expression also continuously affects the antibiotic tolerance of individual cells: specifically, the survival probability during a fixed course antibiotic treatment. This model (SI) is consistent with cell sorting experiments that explicitly demonstrate that the observed distributions are a consequence of survival probability.

Discussion

Antibiotic tolerance is believed to be an important factor in the failure of antibiotic treatments and a key step toward the development of antibiotic resistance 50. We therefore sought to trace the origin of the cell-to-cell variability in expression of sasA and determine if it can be regulated by genetic or chemical means. Noise in gene expression can be conceptually separated into intrinsic and extrinsic noise. Although it is difficult to design strategies to specifically target events generated by intrinsic noise, extrinsic noise may have upstream regulatory pathways that can be modulated. Therefore, it is significant that the cell-to-cell variability in sasA was dominated by extrinsic noise at high levels of expression (Fig. 1) that have the strongest effect on antibiotic tolerance (Fig. 4). Furthermore, since multisite phosphorylation is responsible for setting the observed distribution of cell-to-cell variability (Figs. 2, 3), this regulatory pathway could be a novel antibiotic target.

Multisite phosphorylation can expand the range of a protein’s function, generating both switch-like 51, 52, and graded 53, 54 changes in average activity. In contrast, here we observed only minimal changes in the average levels of sasA expression as a function of PrkC activity, but measured up to a ~100-fold effect on the frequency of “outliers”, cells with particularly high levels of expression (Fig. 3B). This response was shown to be graded, rather than switch-like, likely arising as a consequence of the integration of signals from two distinct signaling systems. A single phosphorylation at WalR Asp-53 strongly, but imperfectly, represses the sasA promoter. The addition of the second phosphorylation at Thr-101 by a distinct signaling system then acts as a second input to further regulate WalR. Interestingly, even small changes in activity of the second system result in marked changes in the frequency of outliers. Heterologous expression of PrkC is capable of reducing the variability observed to close to cellular autofluorescence, but does not eliminate it completely. This remaining variability in sasA may be due to PrkC overexpression still being unable to completely saturate WalR phosphorylation, intrinsic noise at the promoter, or as yet unidentified sources.

Transcriptional regulation of outliers in eukaryotes has been shown to be predictive of which cancer cells survive drug treatment 55. Here we found that transcriptional regulation by multisite phosphorylation is also critical for setting the pre-existing distribution of survival probabilities for cells within a bacterial population. Distinct from bacterial persistence, which is characterized by bi-phasic killing, these survival probabilities reflect antibiotic tolerance or the killing kinetics during a relatively short, fixed time-course, antibiotic treatment 56. In the ΔsasA background, we observed a much weaker dependence of antibiotic tolerance on sasA expression. This residual dependence is consistent with previous results that have implicated many global processes in antibiotic tolerance including heterogeneity in growth state 24, 57, 58 and enhanced expression of drug efflux pumps 59, 60. This is also consistent with the relatively weak correlation in expression between sasA and the constitutive promoter veg (Fig. S2). Indeed, it remains to be seen precisely how cellular physiology changes in a sasA-expression dependent manner. SasA has been shown to be important for ribosome assembly in B. subtilis 31 and for survival during envelope stress in S. aureus 61. More generally, various cellular processes are known to be directly and indirectly affected by rising (p)ppGpp levels including inhibition of DNA primase activity 62, and reduction in intracellular GTP pools 63 thereby downregulating rRNA transcription 64. As our results show that antibiotic survival increases continuously with sasA expression, it suggests that SasA exerts a continuous effect proportional to its level on physiological processes that mediate ciprofloxacin killing. Therefore, multisite phosphorylation may provide a “bet-hedging” strategy to regulate the phenotypic diversity of a bacterial population, serving as a broadly useful mechanism to tune the frequency of rare phenotypes that facilitate survival under adverse conditions.

Acknowledgements

We thank Eric Brown for the strain EB1385, Isaac Plant (Silver lab) for pIP384 and IP563, and Amir Figueroa at the Microbiology & Immunology Core Facility at Columbia University for assistance with flow cytometry and cell sorting. This work was supported by NIH GM114213 and a BWF Investigators in the Pathogenesis of Infectious Disease award to JD, a grant from the Department of Systems Biology at Harvard Medical School to EAL, and SR was supported by NIH GM095784 and gratefully acknowledges support from the Azrieli Foundation. Author Contributions: Conceived and designed the experiments EAL and JD. EAL performed the experiments. EAL and SR analyzed data. Contributed reagents/materials/analysis tools: EAL and SR. Wrote the paper: EAL, SR and JD.

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Multisite phosphorylation regulates phenotypic variability in antibiotic tolerance
Elizabeth A. Libby, Shlomi Reuveni, Jonathan Dworkin
bioRxiv 315085; doi: https://doi.org/10.1101/315085
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