New Results

Escherichia coli cells are primed for survival before lethal antibiotic stress

Tahmina Hossain, Abhyudai Singh, Nicholas C. Butzin
doi: https://doi.org/10.1101/2022.11.11.516214

Abstract

Non-genetic factors can cause significant fluctuations in gene expression levels. Regardless of growing in a stable environment, this fluctuation leads to cell-to-cell variability in an isogenic population. This phenotypic heterogeneity allows a tiny subset of bacterial cells in a population, referred to as persister cells, to tolerate long-term lethal antibiotic effects by entering into a non-dividing, metabolically altered state. One fundamental question is whether this heterogeneous persister population is due to a pre-existing genetic mutation or a result of a transiently-primed reversible cell state. To explore this, we tested clonal populations starting from a single cell using a modified Luria–Delbrück fluctuation test. Through we kept the conditions the same, the diversity in persistence level among clones was relatively consistent: varying from ∼60-100 and ∼40-70 fold for ampicillin (Amp) and apramycin (Apr), respectively. Then we divided and diluted each clone to observe whether the same clone had comparable persister levels for more than one generation. Replicates had similar persister levels even when clones were divided, diluted by 1:20, and allowed to grow for ∼5 generations. This result explicitly shows a cellular memory passed on for generations and eventually lost when cells are diluted to 1:100 and regrown (>7 generations). Our result demonstrates 1) the existence of a small population prepared for stress (“primed cells”) resulting in higher persister numbers, 2) the primed memory state is reproducible and transient, passed down for generations but eventually lost, and 3) a heterogeneous persister population is a result of a transiently-primed reversible cell state and not due to a pre-existing genetic mutation.

Introduction

Clonal populations often exhibit phenotypic heterogeneity, leading to specific physiological effects that distinguish some cells from others1-3. Variability can slightly reduce fitness in a common environment but, in return, maximize it during environmental perturbations2, 4. For example, bacterial persister cells, a phenotypic variant, can endure prolonged lethal antibiotic treatment by entering a metabolically altered state5. This small subpopulation can reestablish infection after treatment and necessitate repeated long-term antibiotic therapy. They also have a high mutation rate that increases the likelihood of evolving antibiotic resistance, a pressing public health concern. A recent study provides evidence that among infectious diseases, antibiotic resistance may be the leading cause of death worldwide (more than HIV or malaria)6. Global death projections are now estimated at 100M/year by 20507, 8 unless new technologies are developed to combat them, and we have a better understanding of persister survival. Here, we specifically focus on how heterogeneity in a population drives persister numbers.

Noise or fluctuation in gene expression levels drives phenotypic variation that could be an inherent survival strategy of a clonal bacterial population2, 9. Studies reported that stress-response genes are generally more variable than housekeeping genes10, 11. But it is uncertain whether this variability is controlled or an effect of inevitable stochastic fluctuations in gene expression12. We formalize this notion and hypothesize that specific cells are prepared for stress through a transiently inherited cell state.

In this work, we take advantage of the Luria & Delbrück fluctuation test (FT), which was recently used to identify genes related to cancer persistence against cancer drugs13. Cancer persisters are similar in phenotype to bacterial persistence but biochemically unrelated. We used a similar approach to probe bacterial persistence. The FT was first pioneered ∼80 years ago when Luria & Delbrück demonstrated that genetic mutations arise randomly in the absence of selection but not in response to the selection14. They were studying phage (bacterial virus) infections. During that time, it was debated whether mutations leading to resistance were directly induced by the virus (Lamarckian theory) or if they developed randomly in the population before viral infection (Darwinian theory). They designed an elegant experiment where single cells were isolated and grown into clones and then infected by a phage (Fig. 1a). The number of resistant bacteria was counted across clones. Suppose mutations are virus-induced (i.e., no heritable genetic component to resistance), where each cell has a small and independent probability of acquiring resistance. In this case, clone-to-clone fluctuations in the number of resistant cells should follow Poisson statistics (a memoryless process). In contrast, if mutations occur randomly before viral exposure (spontaneous mutation), the quantity of surviving bacteria will vary considerably across clones depending on when the mutation arose in the clone expansion. The data clearly showed a non-Poissonian skewed distribution for the number of resistant bacteria, validating the Darwinian theory of evolution14 (Fig. 1a). This work led to a Nobel Prize, and the FT remains the most commonly used method to measure mutation rates in microbes15. While Luria-Delbrück focused on irreversible genetic alterations driving phage resistance, here we use the FT to elucidate transient cell states that originate via reversible non-genetic mechanisms13, 16-18. Notably, this generalized FT can be applied when single cells reversibly switch between drug-sensitive and drug-tolerant states even before treatment. Clone-to-clone fluctuations can be exploited to quantify these switching rates rigorously19. This approach has been key in deciphering drug-tolerant cancer cells that arise stochastically even before drug exposure. Thus cancer cells have a bet-hedging mechanism to survive sudden hostile extracellular environmental changes13, 16, 17. Here, we are exploring ‘primed cells’ (cells prepared for stress and arise by a rare, transiently inherited cell state) present before the treatment and prolonging survival once treatment begins (Fig. 1b-e).

Fig. 1.
Fig. 1.

a. The Luria-Delbruck fluctuation test (FT). Each clone starts from a single cell and is then infected by a phage (virus). If resistance mutations are virus-induced, the number of resistant cells would follow a Poisson distribution across clones. In contrast, if mutant cells arise spontaneously prior to viral exposure, there will be considerable clone-to-clone fluctuations in the number of surviving cells, including mutations that happen early in the lineage expansion causing many cells to be resistant. b. FT to determine if some cells are primed for stress. This design follows the same experimental setup as a., but it uses antibiotic stress and measures variation in persister cells (cells in an altered cellular activity state by having transient gene expression change) across the clones. If persisters are antibiotic-induced, then the number of persister cells would follow a Poisson distribution across clones. In contrast, if there are primed cells with a unique transient gene expression profile (caused by non-genetic factors) that allows them to prepare for stress before antibiotic exposure, there will be considerable clone-to-clone fluctuations in the number of surviving cells. Transient gene expression changes can be heritable and happen early in the lineage expansion, causing many cells to become primed for stress with a transient memory. c. Persister variation check in a single population, referred to as NC: Noise control. Cultures were grown to mid-log phase, ∼1E+8 CFU/ml, separated into 48 wells, treated with an antibiotic, 0.1 mg/mL Amp or Apr, and plated before and after antibiotic treatment to get % Survival. d. Persister variation check in clonal populations started from a single cell, referred to as FT. Cultures were diluted to 0.5 cell/well and treated similarly to a. e. A model for competing hypotheses; Induced persistence: persisters are induced due to stress, and no difference will be observed in persister variation between clonal populations and noise control. Primed persistence: some cells are primed prior to stress; thus, some clonal populations will have more primed cells than others, and the persister variation will be higher in the clonal populations than in the Noise control (NC).

In this study, we applied the FT to indicate some cells are prepared for stress and have an inherent transient memory. We applied this approach in conjunction with mathematical modeling to elucidate stochastic phenotype-switching in response to antibiotic stress, an inherent survival strategy that gives flexibility to a clonal population. Our work demonstrates how heritable transient cell state changes can lead to variation in persister numbers. Exploring this phenomenon can shed light on how bacteria endure stress, a key question in persister research20.

Result

Primed cells are prepared for stress before antibiotic treatment

Bacterial populations are heterogeneous, even in log growth phase in a well-controlled lab environment, and especially in natural systems21, 22. The quantification of persister numbers often have huge variations with large error bars (SD or SEM), sometimes with 100s of fold differences23-25. We used defined media (MMB+26, 27), which contains only chemically known components, to minimize variations between experiments. We optimized and standardized our experimental design, and reduced the internal error rate to below 2-fold. Much of this error comes from the compounding imprecisions from multiple pipetting (all pipets have an error rate, and these experiments require serial dilutions). Despite reducing the error rate, we noticed that there would occasionally be huge outliers (up to 100-fold higher or lower than the average).

We determined the typical persister variation in an E. coli population (Noise control: NC) is higher than our internal error. We grew a culture to mid-log phase and then divided it into 48 wells. We then treated it with ampicillin (Amp) or apramycin (Apr) for 3 h, followed by a persister assay (Fig. 1c). This resulted in a variation of ∼6-fold for either antibiotic. These results do not explain the 100-fold changes we occasionally observed. Unable to explain the variation at the population level, we wondered if a “memory” was passed down over several generations and skewed our data. We set out to test for variations from a single cell using a FT13, 16, 17. Cultures were diluted to about 0.5 cell/well (Limiting Dilution assay28, 29) in a 96-well microplate; on average, 48 wells had growth, and 48 wells did not (Fig. 1d). Cells were then grown to an OD of 0.4-0.6 (log phase) and treated with Amp for 3 h. Most (but not all wells) reach the desired OD range simultaneously. We use two 96-well plates and pick 48 clones within the OD range to deal with this. After treatment, the cells were plated on Petri dishes, grown, and colonies were counted to get CFU/mL. We first tested lethal Amp dosages for 3 h, and the persister range was vast, ∼60-100-fold. We tested this several times (Fig. 2a; 8 separate experiments with ∼48 clones/experiment), and there is consistently an extensive range of persister variation among the clones. We wondered if this was specific to Amp, so we tested another class of antibiotic, Apr. Amp targets the cell wall30, while Apr targets the 30S ribosomal subunit31. Again, the persister range was vast, ∼40-70-fold range with Apr (Fig. 2a). We hypothesize that the extensive range in persistence is due to either (1) mutation or (2) some cells are prepared for stress (“primed cells”), and these primed cells exhibited specific characteristics which allow them to prepare prior to the stress. To assess if a mutation causes this extensive persister range, we tested for antibiotic resistance by streaking clones on antibiotic plates, and no resistant colonies grew (Fig. S1a). To further test for mutations, we diluted the high persister clone (Hp clone) into 0.5 cell/well and repeated the fluctuation test. If a high persister clone was mutated, the average persister percentage would increase, and the range would decrease. However, they had the same average persister percentage and similar range (∼60-100 fold) in both fluctuation experiments (Fig. 2b i). If the cells developed resistance, their minimum inhibitory concentration (MIC, the minimal antibiotic required to hinder growth) would change. But it remained the same (Fig. 2b ii). These results undoubtedly rule out mutations as the cause and led us to test hypothesis 2, that some cells are primed prior to stress.

Fig. 2.
Fig. 2. Some cells are prepared and primed for stress.

a. Results support primed persistence hypothesis: Population-level variation for Amp and Apr: ∼6 fold (experimental setup described in Fig. 1d). FT from single cell level (experimental setup described in Fig. 1e): treatment with Amp or Apr shows 60 to 100-fold and 40 to 70-fold variation, respectively. b. Mutations do not cause persister level variation among the clones. i. FT with wild-type clonal populations and FT with clonal populations started from a high persister clone have a similar average persister level and persister range among 48 clones. ii. MIC test showing no change in MIC level in high persister clones. ***P<0.001.

Fig. 3.
Fig. 3. Cells are primed for persistence, not for short-term tolerance. i.

A simplified model of population decay indicates a biphasic death curve where susceptible cells or short-term tolerant cells die quicker than persisters. ii. Compare each clone’s short-term tolerance levels (1 h Amp) to their persister level (3 h Amp). iii. Compare each clone’s persister level at 3 h Amp to 4 h Amp or 5 h Amp (long-term persister level). About 48 clones were grown from a single cell and treated with Amp (0.1 mg/mL), and the % survival was determined at different time points. r: Pearson’s correlation coefficient; ns: not significant.

Cell density is not the primary factor for variation in persister number

Some bacterial persistence levels are controlled (in part) via quorum sensing32, which is a density-dependent response. Thus, before testing hypothesis 2, we wanted to know how much cell density could skew our results by testing the overall persister range with different cell densities using FTs. For FTs in Fig 2a, cells were harvested at OD 0.4-0.6 since the persister levels are remarkably similar at these ODs. We detected no correlation between cell density and % Survival in 15 FTs (7 Amp and 8 Apr) and a weak correlation in FT3 with Amp treatment (Fig. S1b i). Thus, to further determine how much cell density affects the persister levels, we tested persister levels from OD 0.3 to 0.7 in ∼0.1 OD intervals (cells are in log phase and cell density ranges from ∼2E07 to 2E08 CFU/mL) in a general population at 3 h Amp or Apr. No appreciable correlation was observed with either antibiotic (Fig. S1b ii).

Cells are primed for persistence, not short-term tolerance

Short-term tolerance can mask persistence, and experimental evidence has shown that the phenotypes are distinct from each other33. Short-term tolerant cells are dividing and likely have different survival mechanisms than persisters. In the initial stage of antibiotic treatment, there are far more short-term tolerant cells than persister cells (Fig. 3 i). We did a similar FT with clones grown from a single cell and treated them with a lethal Amp concentration (0.1 mg/mL). % Survival was determined for 1 h and 3 h of treatment using persister assays26, 27, 34, and short-term tolerance at 1 h does not indicate the level of persistence at 3 h with lethal Amp; no correlation at 1 h vs 3 h population (r2 = 0.02). If the primed cells are advantageous for long-term survival, we expect the high persister populations observed at 3 h treatment to stay high with more prolonged antibiotic exposure. As expected, % Survival at 3 h highly correlates with % Survival at 4 h (r2 = 0.85) and 5 h (r2 = 0.78) Amp treated population (Fig. 3). Therefore, our results demonstrate that cells are primed for persistence and not for short-term tolerance.

Primed cells have transient memory

Next, we determined whether high persister clones arise randomly due to noise in gene expression levels or in rare events where the gene expression level (memory) is passed down for several generations. We hypothesize that there is a transient memory at the transcriptomic level. The null hypothesis is that there is no memory and the variation range in persistence is random and solely due to noise. To test this, we divided and diluted the culture between 1:1 to 1:100 into separate microplates and allowed them to grow (Fig. 4a). If the null hypothesis is correct, there should be no correlation between divided cultures. However, persister levels were strongly correlated until the 1:20 dilution, and the memory is completely lost after a 1:100 dilution, supporting our transient memory hypothesis (Fig. 4b). We further confirmed the transient memory hypothesis with Apr (Fig. 4c). These results also add additional support that primed cells are not mutants, because 1:100 dilution led to no long-term (genetic) survival phenotype (in several different clones), as a resistance mutation would allow.

Fig. 4.
Fig. 4. Primed cells have transient memory.

a. Fluctuation test (FT) setup to check primed cell’s memory: cultures were diluted to 0.5 cells/well, grown to mid-log phase (∼1E+8 CFU/mL), each clone is divided and diluted into 2 replicates, grown to mid-log, and then tested for persistence. b & c. Primed cells have transient memory for several generations before 3 h Amp and 3 h Apr treatment. The persister levels correlate even with 1:20 dilutions between Replicate 1 and 2, suggesting a strong memory within the primed subpopulation. The memory is eventually lost with a 1:100 dilution. d. Primed cells show a weak memory when split and tested with Amp vs. Apr. *Antibiotic: Amp vs. Amp (replicate 1 & 2 both treated with Amp) or Apr vs. Apr (replicate 1 & 2 both treated with Apr) or Amp vs. Apr (replicates 1 & 2 treated with Amp and Apr, respectively). Axes are % Survival. r: Pearson’s correlation coefficient; ns: not significant. e. Plot representing the fraction of persisters Embedded Image as predicted by the differential equation model (see Methods), assuming that initially 10% of cells where persisters and the steady-state persister level was fs = 1%. The five lines are plotted assuming the persister proliferation rate, kp = kd, 0.9kd, 0.8kd, 0.5kd, 0 where kd = 1 and k2 = kp/10. The black dashed lines show the numbers of generations it takes for Embedded Image, when k = 0.9kd.

We hypothesize that the same primed cells will allow higher persister levels when using antibiotics from classes that target distinct cellular processes, e.g. Amp and Apr. If both Amp and Apr primed cells use an akin mechanism, we expect a reasonable correlation between their persister numbers/well. If they do not correlate, diverse types of primed cells likely exist. To understand this, we did experiments similar to Fig. 4a, but we tested Replica 1 with Amp and Replica 2 with Apr. In this case, both replicas had a transient memory, and the memory was lost by 1:20 dilutions (Fig. 4d).

Primed cells are not spontaneous persisters

Previous researchers proposed spontaneous persisters formation; persisters that are generated stochastically at a constant rate during exponential phase growth and switch to a dormant or a protected state (distinct slower growth rate than other cells, and this slowed growth rate is maintained for several generations and turn into persisters in the presence of stress)5, 20, 35. In addition, they proposed that during exponential growth, these phenotypic variants (e.g. persister formation) could also be induced by stress5. However, several research groups criticized the concept of spontaneous persister formation36, 37, questioning if it exists or is a proper terminology. In the original paper, where persistence was proposed in 1944, persisters were defined as non-dividing cells38. However, spontaneous persisters were defined as dividing cells39. Here we use the original definition of persisters; they do not divide. In addition, if persister formation is only induced by stress, all cells in the population should turn into persisters instead of a small percentage of the population. Also, induced persister formation (or sense-and-respond strategies) could be costly for the cells since it necessities constitutive expression of essential molecular machinery40.

On the contrary, primed cells (already prepared for stress in a population through heterogeneity) could provide a simple mechanism for adaptation to stresses they might or might not encounter. A key phenotype of spontaneous persisters is that they grow slowly. However, we did not observe any significant growth rate changes among the clones, and recent results demonstrate that persisters are not slow-growing before antibiotic treatment41. To further explore whether primed cells are reliant on slow growth, we constructed a simple mathematical model (explained in the Methods), where we plotted the fraction of persister cells Embedded Image as a function of time for persister proliferation being 100%, 90%, 80%, 50%, and 0% of the proliferation rate of the drug-sensitive cells. We observed when persister cells do not proliferate (kp = 0), their fraction rapidly dilutes back to the steady-state level in a few generations (Fig. 4e). These results show that a high correlation in persister maintenance, as seen in Fig 4b for several generations, requires persister proliferation. For example, one requires kp = 0.9kd for it to take roughly 4 generations for the fraction of persisters to fall to 50% of its initial levels, similar to the drop in the correlation between replicates to 0.5 in Fig. 4b. Our evidence clearly shows that primed cells are not persisters (non-dividing cells) before antibiotic treatment since they grow and maintain a transient memory.

Discussion

Phenotypic heterogeneity is a fact of life and exists in both unicellular and multicellular organism1, 2, 4. This latent variation in phenotypic plasticity reveals in unfavorable environmental condition4. Noise in gene expression can drive this heterogeneity, and heterogeneity is hypothesized to be a key player that regulates bet-hedging strategies to endure harsh environmental fluctuation, such as bacterial persistence2, 42. Persisters have reduced efficacy to antibiotic treatment and are a key contributor to the rise in antibiotic resistance. Unraveling the underlying molecular mechanisms of heterogeneity is therefore crucial to comprehend bacterial persistence.

In this study, we used a powerful fluctuation test framework to infer transient cell states that arise via reversible and non-genetic mechanisms, recently employed in probing cancer persistence13, 16, 17, to find hidden features of bacterial persistence. Using the FTs, we tested the variation between clonal populations that originated from an identical clone and showed that a subset of the population, primed cells, have a “memory” that leads to high numbers of persisters. Our results demonstrate that under the same conditions, phenotypic heterogeneity often occurs in a range of ∼60-100 and ∼40-70 fold for Amp and Apr, respectively, despite (likely) being driven by a stochastic noise in gene expression. The relative consistency in primed cell numbers suggests that changes in specific genes result in primed cells, though we did not experimentally explore the mechanisms of primed cells in this work. Having high numbers of primed cells is a selective advantage that offers phenotypic plasticity to a bacterial population experiencing frequent harsh environmental stress. This heritable transient state might be favored in the course of evolution as a survival tactic compared to DNA mutation because it requires no long-term commitment. A recent paper demonstrated a transient cellular memory in E. coli, and that inheritance of non-genetic elements can help maintain cellular memory43. Thus, reducing variation among new cells for a few generations43. They found that some inherited elements, cell size, and the time required for cell division were maintained for nearly ten generations43.

We did not explore experimentally the regulatory mechanisms behind primed cells in this study, but we offered a mathematical model that supports and gives clues to the regulation of the transient memory. However, a few possible mechanisms that bacteria employ are already known. DNA methylation is a common method of epigenetic regulation in organisms, and many forms of methylation have been identified in bacteria44-46. Some are related to antibiotic resistance or persistence47, 48. For example, the deletion of dam, which is responsible for adenine methylation, can lead to lower persister numbers in pathogenic E. coli strains49. Another recent study showed multisite phosphorylation may regulate the phenotypic variability in a bacterial population. A gene encoding ppGpp Synthetase, sasA, is regulated by multisite phosphorylation of WalR and exhibits elevated levels of extrinsic noise in gene expression. Due to this noise, cells having elevated levels of sasA expression have increased short-term antibiotic tolerance12. Another possible mechanism could be ploidy. The Brynildsen group showed that ploidy or chromosome abundance produces phenotypic heterogeneity that affects persister numbers. Stationary-phase E. coli cells with two chromosomes had ∼40-fold more persisters than cells with one chromosome against fluoroquinolone antibiotic50.

In this study, we have not dealt with viable but nonculturable (VBNC) cells. Several studies showed VBNCs and persisters share similar phenotypes, and the major difference between them is after stress (e.g. antibiotics), persisters can grow on Petri plates while VBNCs can only grow in liquid. Currently, there is a furious debate if VBNCs are actually persisters, simply dying cells, or if VBNCs even exist51-55. A recent study called in to question many of the indicators that are currently used to classify bacterial cells as alive or dead56.

We tested if VBNCs could be resurrected using the 0.5 cell/well technique. The 0.5 cell/well is based on cell counts on Petri dishes (CFU/ml). Often, VBNCs are cited as being at equal concentrations (using a hemocytometer and microscope) as CFUs57, 58, but we did not observe this. We observed a ∼20% count difference (cell/mL) between hemocytometer and agar plate count, which is within the hemocytometer’s manufacture error rate. However, still if VBNCs are present and grow only in liquid, we expect 1 cell/well in a 96-wells plate when using CFU/mL from Petri dishes. Instead, we consistently get ∼48 wells had growth. We also consistently get growth in ∼24, ∼12, and ∼6 wells when we use 0.25, 0.125, and 0.0625 cells/well, respectively, with an r2 of 0.97, when plotting the wells with growth vs. cells/well based on CFU/ml. Supplementing media with pyruvate may resurrect VBNCs quicker59, 60. We tested the addition of pyruvate after antibiotic therapy with two carbon sources: glycerol and glucose. VBNCs did not resurrect; only ∼48 wells had growth.

Recognizing that the previous assays do not account for all the nuances of VBNCs, we tested whether VBNCs would alter our overall results using 2 cells/well (based on CFU/ml). Again, we see a high variation with Amp treatment, ∼78-fold (Fig. S1c.). This shows that if there are VBNCs, they are not skewing our results and that we still observe primed cells even with 2 cells in each well. It also demonstrates that the FT does not require 1 cell/well, which is an important finding because it is unrealistic to assume that every well we test will always have 1 cell when diluted to 0.5 cells/well.

Overall, our findings show that a subset of the population has a transient memory allowing them to prepare for antibiotic stress (we refer to them as ‘primed cells’). However, further exploration is needed to determine the regulatory mechanism(s) behind this cellular memory and the contribution of non-genetic factors in phenotypic heterogeneity.

Methods

Microbial strains and media

Escherichia coli DH5αZ1 having the p24KmNB82 plasmid was used in this study. We used this strain because DH5αZ1 was a derivative of E. coli K12 strain, and it has been used in our previous persistence studies26, 27. The cultures were grown in the defined media MMB+26, 27 with thiamine (10 μg/mL), 0.5% glycerol (or glucose), Km (25 μg/mL), and amino acids (40 μg/mL) or on Miller’s lysogeny broth (LB) agar plates +Km (25 μg/mL). For pyruvate assays, MMB+ media were supplemented with 2 mM sodium pyruvate. All cultures were incubated at 37°C and shaken at 300 rpm.

Population-level variation check (Noise control: NC)

We used two different types of antibiotics, Ampicillin (Amp, target cell wall30) and Apramycin (Apr, target 30S ribosome31), for all fluctuation tests. We started with a mid-log phase E. coli culture having ∼1E+8 CFU/mL (∼OD 0.5), subsequently divided the culture into 48 well (48 replicate) of 96-Well Optical-Bottom Plate with Polymer Base (ThermoFisher) and treated them with Amp (0.1 mg/mL) or Apr (0.1 mg/mL) for 3 h at 300 rpm, 37°C in a FLUOstar Omega microplate reader. Persister percentage was calculated by comparing CFUs per milliliter (CFU/mL) before antibiotic treatment to CFU/mL after antibiotic treatment. Plates were incubated at 37ºC for 42-48 h, then scanned using a flatbed scanner. Custom scripts were used to identify and count bacterial colonies61, 62.

Fluctuation test (FT) from a single cell level

We started with a log phase E. coli culture with ∼1E+8 CFU/mL (∼OD 0.5) and subsequently diluted the culture into 0.5 cells per well in a 96-Well Optical-Bottom Plate and grown in a FLUOstar Omega microplate reader at 37°C, 300 rpm. Each dilution experiment was done twice for each single-cell experiment to confirm the dilution, and cultures were only picked when 50-60% of the well had growth (48-55 wells out of 96 wells). The single cell was proliferated to mid-log phase ∼1E+8 CFU/mL (∼OD 0.5) and treated with Amp (0.1 mg/mL) or Apr (0.1 mg/mL) for 3 h, 300 rpm at 37°C. The persister percentage was calculated in the same manner described in the above section. The splitting and dilution test was done exactly as above, except when the single cell proliferated into mid-log phase, the cultures were separated and diluted into 2 plates with pre-warmed MMB+ media. Then, the cultures were grown to exponential phase, and persister assays were performed.

Antibiotic resistance assay

After each fluctuation test, clones were diluted 1:100 in 1 mL LB media and were grown for 12 h. The cultures were then centrifuged for 3 min at 16,000 x g. After that, the supernatant was removed, and the pellet was streaked with and without antibiotic containing LB agar plate and incubated at 37°C.

Minimal inhibitory concentration (MIC) test

Clones, including high persister clones from fluctuation tests, were diluted 1:100 in 1 mL LB media and grown for 12 h. First, 0.2 mL of culture was spread on an LB agar plate, then a MIC strip was placed on top of it and incubated at 37°C.

Mathematical model

To understand primed cell proliferation rate, we considered a model of persister formation where drug-sensitive cells switch to a persister state with a rate k1, and persister cells revert back to the drug-sensitive state with a rate k2. We assumed the rate of cellular proliferation in the drug-sensitive and persisters states to be kd and kp, respectively. In an expanding cell colony, the number of persister cells can be captured by the following system of ordinary differential equations, Embedded Image where x(t) and y(t) are the total number of cells and the number of persister cells, respectively, at time t. By setting, Embedded Image ensures that the steady-state persister fraction Embedded Image. We considered a clone that initially had a high fraction of persister cells. We used the model to explore the relaxation of persister numbers back to steady-state levels, and how this time scale particularly depends on the persister proliferation rate kp. We then plotted the fraction of persister cells Embedded Image as a function of time for persister proliferation being 100%, 90%, 80%, 50% and 0% of the proliferation rate of the drug-sensitive cells.

Cell counting

Initially, using the common protocol for a hemocytometer, microscopic counting, and live/dead dye, we saw about equal “VBNCs” per non-VBNCs in log and stationary phase, and with lethal antibiotics. This is consistent with the literature57, 58. However, we adjusted our counting protocol to match the manufacturer’s recommendation. As a result, we no longer see 2X the VBNCs compared to the CFU/ml; instead, the microscope count and Petri plate count only varied by ∼20 - 25% in log phase. We attempted to use several live-dead dyes to calculate viability after antibiotic treatment, but we found these assays to be quite unreliable, as recently cited in the literature63-67. So, we took the other approaches we described.

Author contributions

T.H. wrote the manuscript, performed the experiments, and ran data analysis. A.S. helped to formalize the research concept and design the experiments, as well as constructed the mathematical modeling. N.C.B. planned and directed the project. All authors contributed to discussing and editing the manuscript.

Supplement Figure

Supplement Fig. S1.
Supplement Fig. S1.

a. Streaking plate shows mutation is not the reason for persister level variability among clones. b. Cell density is not the reason for persister level variation among clones. i. Each FT tests treated with Amp or Apr show no correlation between CFU/mL vs. % Survival. ii. General populations treated with Amp or Apr show no correlation between CFU/mL vs. % Survival. c. FT started with 2-cells/well, and showed ∼78-fold variation among clones.

Acknowledgements

This work is supported by the National Science Foundation award Numbers 1922542 and 1849206, and by a USDA National Institute of Food and Agriculture Hatch project grant number SD00H653-18/project accession no. 1015687. We would also like to thank KM Taufiqur Rahman for sharing his counting results using hemocytometer and petri plate assays.

Footnotes

  • A few figures were called out wrong in the text. This was fixed.

References

  1. 1.↵
    Elowitz, M. B.; Levine, A. J.; Siggia, E. D.; Swain, P. S., Stochastic gene expression in a single cell. Science 2002, 297 (5584), 1183–1186.
    OpenUrlAbstract/FREE Full Text
  2. 2.↵
    Symmons, O.; Raj, A., What’s luck got to do with it: single cells, multiple fates, and biological nondeterminism. Molecular cell 2016, 62 (5), 788–802.
    OpenUrlCrossRefPubMed
  3. 3.↵
    Sampaio, N. M. V.; Dunlop, M. J., Functional roles of microbial cell-to-cell heterogeneity and emerging technologies for analysis and control. Current opinion in microbiology 2020, 57, 87–94.
    OpenUrlCrossRefPubMed
  4. 4.↵
    Engl, C., Noise in bacterial gene expression. Biochemical Society Transactions 2019, 47 (1), 209–217.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    Balaban, N. Q.; Merrin, J.; Chait, R.; Kowalik, L.; Leibler, S., Bacterial persistence as a phenotypic switch. Science 2004, 305 (5690), 1622–1625.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    Murray, C. J.; Ikuta, K. S.; Sharara, F.; Swetschinski, L.; Aguilar, G. R.; Gray, A.; Han, C.; Bisignano, C.; Rao, P.; Wool, E., Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet 2022, 399 (10325), 629–655.
    OpenUrl
  7. 7.↵
    Huemer, M.; Mairpady Shambat, S.; Brugger, S. D.; Zinkernagel, A. S., Antibiotic resistance and persistence—Implications for human health and treatment perspectives. EMBO reports 2020, 21 (12), e51034.
    OpenUrl
  8. 8.↵
    Control, C. f. D.; Prevention, Antibiotic resistance threats in the United States, 2019. US Department of Health and Human Services, Centres for Disease Control and …: 2019.
  9. 9.↵
    El Meouche, I.; Siu, Y.; Dunlop, M. J., Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Scientific reports 2016, 6 (1), 1–9.
    OpenUrl
  10. 10.↵
    Bar-Even, A.; Paulsson, J.; Maheshri, N.; Carmi, M.; O’Shea, E.; Pilpel, Y.; Barkai, N., Noise in protein expression scales with natural protein abundance. Nature genetics 2006, 38 (6), 636–643.
    OpenUrlCrossRefPubMedWeb of Science
  11. 11.↵
    Newman, J. R.; Ghaemmaghami, S.; Ihmels, J.; Breslow, D. K.; Noble, M.; DeRisi, J. L.; Weissman, J. S., Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 2006, 441 (7095), 840–846.
    OpenUrlCrossRefPubMedWeb of Science
  12. 12.↵
    Libby, E. A.; Reuveni, S.; Dworkin, J., Multisite phosphorylation drives phenotypic variation in (p) ppGpp synthetase-dependent antibiotic tolerance. Nature communications 2019, 10 (1), 1–10.
    OpenUrl
  13. 13.↵
    Shaffer, S. M.; Emert, B. L.; Reyes Hueros, R. A.; Cote, C.; Harmange, G.; Schaff, D. L.; Sizemore, A. E.; Gupte, R.; Torre, E.; Singh, A.; Bassett, D. S.; Raj, A., Memory Sequencing Reveals Heritable Single-Cell Gene Expression Programs Associated with Distinct Cellular Behaviors. Cell 2020, 182 (4), 947–959 e17.
    OpenUrl
  14. 14.↵
    Luria, S. E.; Delbruck, M., Mutations of Bacteria from Virus Sensitivity to Virus Resistance. Genetics 1943, 28 (6), 491–511.
    OpenUrlFREE Full Text
  15. 15.↵
    Lang, G. I., Measuring Mutation Rates Using the Luria-Delbruck Fluctuation Assay. Methods Mol Biol 2018, 1672, 21–31.
    OpenUrl
  16. 16.↵
    Shaffer, S. M.; Dunagin, M. C.; Torborg, S. R.; Torre, E. A.; Emert, B.; Krepler, C.; Beqiri, M.; Sproesser, K.; Brafford, P. A.; Xiao, M.; Eggan, E.; Anastopoulos, I. N.; Vargas-Garcia, C. A.; Singh, A.; Nathanson, K. L.; Herlyn, M.; Raj, A., Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 2017, 546 (7658), 431–435.
    OpenUrlCrossRefPubMed
  17. 17.↵
    Chang, C. A.; Jen, J.; Jiang, S.; Sayad, A.; Mer, A. S.; Brown, K. R.; Nixon, A. M. L.; Dhabaria, A.; Tang, K. H.; Venet, D.; Sotiriou, C.; Deng, J.; Wong, K. K.; Adams, S.; Meyn, P.; Heguy, A.; Skok, J. A.; Tsirigos, A.; Ueberheide, B.; Moffat, J.; Singh, A.; Haibe-Kains, B.; Khodadadi-Jamayran, A.; Neel, B. G., Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer. Cancer Discov 2021.
  18. 18.↵
    Bokes, P.; Singh, A., A modified fluctuation test for elucidating drug resistance in microbial and cancer cells. European Journal of Control 2021, 62, 130–135.
    OpenUrl
  19. 19.↵
    Saint-Antoine, M. M.; Grima, R.; Singh, A., A fluctuation-based approach to infer kinetics and topology of cell-state switching. bioRxiv 2022.
  20. 20.↵
    Cohen, N. R.; Lobritz, M. A.; Collins, J. J., Microbial persistence and the road to drug resistance. Cell host & microbe 2013, 13 (6), 632–642.
    OpenUrlCrossRefPubMed
  21. 21.↵
    Long, A. M.; Hou, S.; Ignacio-Espinoza, J. C.; Fuhrman, J. A., Benchmarking microbial growth rate predictions from metagenomes. ISME J 2021, 15 (1), 183–195.
    OpenUrlCrossRef
  22. 22.↵
    Kearns, D. B.; Losick, R., Cell population heterogeneity during growth of Bacillus subtilis. Genes & development 2005, 19 (24), 3083–94.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    Lang, M.; Krin, E.; Korlowski, C.; Sismeiro, O.; Varet, H.; Coppee, J. Y.; Mazel, D.; Baharoglu, Z., Sleeping ribosomes: Bacterial signaling triggers RaiA mediated persistence to aminoglycosides. iScience 2021, 24 (10), 103128.
    OpenUrl
  24. 24.
    Salcedo-Sora, J. E.; Kell, D. B., A Quantitative Survey of Bacterial Persistence in the Presence of Antibiotics: Towards Antipersister Antimicrobial Discovery. Antibiotics (Basel) 2020, 9 (8).
  25. 25.↵
    Mohiuddin, S. G.; Hoang, T.; Saba, A.; Karki, P.; Orman, M. A., Identifying Metabolic Inhibitors to Reduce Bacterial Persistence. Frontiers in microbiology 2020, 11, 472.
    OpenUrl
  26. 26.↵
    Deter, H. S.; Hossain, T.; Butzin, N. C., Antibiotic tolerance is associated with a broad and complex transcriptional response in E. coli. Sci Rep 2021, 11 (1), 6112.
    OpenUrl
  27. 27.↵
    Deter, H. S.; Abualrahi, A. H.; Jadhav, P.; Schweer, E. K.; Ogle, C. T.; Butzin, N. C., Proteolytic Queues at ClpXP Increase Antibiotic Tolerance. ACS synthetic biology 2020, 9 (1), 95–103.
    OpenUrl
  28. 28.↵
    Ye, M.; Wilhelm, M.; Gentschev, I.; Szalay, A., A Modified Limiting Dilution Method for Monoclonal Stable Cell Line Selection Using a Real-Time Fluorescence Imaging System: A Practical Workflow and Advanced Applications. Methods Protoc 2021, 4 (1).
  29. 29.↵
    Zitzmann, J.; Schreiber, C.; Eichmann, J.; Bilz, R. O.; Salzig, D.; Weidner, T.; Czermak, P., Single-cell cloning enables the selection of more productive Drosophila melanogaster S2 cells for recombinant protein expression. Biotechnol Rep (Amst) 2018, 19, e00272.
    OpenUrl
  30. 30.↵
    Rafailidis, P. I.; Ioannidou, E. N.; Falagas, M. E., Ampicillin/sulbactam. Drugs 2007, 67 (13), 1829–1849.
    OpenUrlCrossRefPubMedWeb of Science
  31. 31.↵
    O’Connor, S.; Lam, L.; Jones, N. D.; Chaney, M. O., Apramycin, a unique aminocyclitol antibiotic. The Journal of organic chemistry 1976, 41 (12), 2087–2092.
    OpenUrlCrossRefPubMed
  32. 32.↵
    Maisonneuve, E.; Gerdes, K., Molecular mechanisms underlying bacterial persisters. Cell 2014, 157 (3), 539–48.
    OpenUrlCrossRefPubMedWeb of Science
  33. 33.↵
    Ronneau, S.; Hill, P. W.; Helaine, S., Antibiotic persistence and tolerance: not just one and the same. Curr Opin Microbiol 2021, 64, 76–81.
    OpenUrl
  34. 34.↵
    Hossain, T.; Deter, H. S.; Peters, E. J.; Butzin, N. C., Antibiotic tolerance, persistence, and resistance of the evolved minimal cell, Mycoplasma mycoides JCVI-Syn3B. iScience (Cell Press, Elsevier) 2021, 24 (5), 102391.
    OpenUrl
  35. 35.↵
    Balaban, N. Q.; Helaine, S.; Lewis, K.; Ackermann, M.; Aldridge, B.; Andersson, D. I.; Brynildsen, M. P.; Bumann, D.; Camilli, A.; Collins, J. J., Definitions and guidelines for research on antibiotic persistence. Nature Reviews Microbiology 2019, 17 (7), 441–448.
    OpenUrlCrossRef
  36. 36.↵
    Kim, J. S.; Wood, T. K., Persistent Persister Misperceptions. Frontiers in microbiology 2016, 7, 2134.
    OpenUrl
  37. 37.↵
    Keren, I.; Kaldalu, N.; Spoering, A.; Wang, Y.; Lewis, K., Persister cells and tolerance to antimicrobials. FEMS microbiology letters 2004, 230 (1), 13–18.
    OpenUrlCrossRefPubMedWeb of Science
  38. 38.↵
    Bigger, J., TREATMENT OF STAPHYLOCOCCAL INFECTIONS WITH PENICILLIN BY INTERMITTENT STERILISATION. The Lancet 1944, 244 (6320), 497–500.
    OpenUrlCrossRef
  39. 39.↵
    Balaban, N. Q.; Merrin, J.; Chait, R.; Kowalik, L.; Leibler, S., Bacterial persistence as a phenotypic switch. Science 2004, 305 (5690), 1622–5.
    OpenUrlAbstract/FREE Full Text
  40. 40.↵
    Wakamoto, Y.; Dhar, N.; Chait, R.; Schneider, K.; Signorino-Gelo, F.; Leibler, S.; McKinney, J. D., Dynamic persistence of antibiotic-stressed mycobacteria. Science 2013, 339 (6115), 91–95.
    OpenUrlAbstract/FREE Full Text
  41. 41.↵
    Goormaghtigh, F.; Van Melderen, L., Single-cell imaging and characterization of Escherichia coli persister cells to ofloxacin in exponential cultures. Sci Adv 2019, 5 (6), eaav9462.
    OpenUrlFREE Full Text
  42. 42.↵
    Lyu, Z.; Yang, A.; Villanueva, P.; Singh, A.; Ling, J., Heterogeneous flagellar expression in single salmonella cells promotes diversity in antibiotic tolerance. MBio 2021, 12 (5), e02374–21.
    OpenUrl
  43. 43.↵
    Vashistha, H.; Kohram, M.; Salman, H., Non-genetic inheritance restraint of cell-to-cell variation. Elife 2021, 10.
  44. 44.↵
    Beaulaurier, J.; Schadt, E. E.; Fang, G., Deciphering bacterial epigenomes using modern sequencing technologies. Nat Rev Genet 2019, 20 (3), 157–172.
    OpenUrlCrossRefPubMed
  45. 45.
    Adhikari, S.; Curtis, P. D., DNA methyltransferases and epigenetic regulation in bacteria. FEMS microbiology reviews 2016, 40 (5), 575–91.
    OpenUrlCrossRefPubMed
  46. 46.↵
    Casadesus, J.; Low, D., Epigenetic gene regulation in the bacterial world. Microbiol Mol Biol Rev 2006, 70 (3), 830–56.
    OpenUrlAbstract/FREE Full Text
  47. 47.↵
    Masuda, I.; Matsubara, R.; Christian, T.; Rojas, E. R.; Yadavalli, S. S.; Zhang, L.; Goulian, M.; Foster, L. J.; Huang, K. C.; Hou, Y. M., tRNA Methylation Is a Global Determinant of Bacterial Multi-drug Resistance. Cell Syst 2019, 8 (4), 302–314 e8.
    OpenUrl
  48. 48.↵
    Cohen, N. R.; Ross, C. A.; Jain, S.; Shapiro, R. S.; Gutierrez, A.; Belenky, P.; Li, H.; Collins, J. J., A role for the bacterial GATC methylome in antibiotic stress survival. Nature genetics 2016, 48 (5), 581–6.
    OpenUrlCrossRef
  49. 49.↵
    Xu, Y.; Liu, S.; Zhang, Y.; Zhang, W., DNA adenine methylation is involved in persister formation in E. coli. Microbiol Res 2021, 246, 126709.
    OpenUrl
  50. 50.↵
    Murawski, A. M.; Brynildsen, M. P., Ploidy is an important determinant of fluoroquinolone persister survival. Current biology : CB 2021, 31 (10), 2039–2050 e7.
    OpenUrl
  51. 51.↵
    Song, S.; Wood, T. K., ‘Viable but non-culturable cells’ are dead. Environmental microbiology 2021, 23 (5), 2335–2338.
    OpenUrl
  52. 52.
    Kirschner, A. K. T.; Vierheilig, J.; Flemming, H. C.; Wingender, J.; Farnleitner, A. H., How dead is dead? Viable but non-culturable versus persister cells. Environ Microbiol Rep 2021.
  53. 53.
    Nystrom, T., Nonculturable bacteria: programmed survival forms or cells at death’s door? Bioessays 2003, 25 (3), 204–11.
    OpenUrlCrossRefPubMedWeb of Science
  54. 54.
    McDougald, D.; Rice, S. A.; Weichart, D.; Kjelleberg, S., Nonculturability: adaptation or debilitation? FEMS microbiology ecology 1998, 25 (1), 1–9.
    OpenUrlCrossRefWeb of Science
  55. 55.↵
    Nystrom, T., Not quite dead enough: on bacterial life, culturability, senescence, and death. Archives of microbiology 2001, 176 (3), 159–64.
    OpenUrlCrossRefPubMedWeb of Science
  56. 56.↵
    Barros, A. C.; Melo, L. F.; Pereira, A., Pseudomonas fluorescens Cells’ Recovery after Exposure to BAC and DBNPA Biocides. Antibiotics 2022, 11 (8), 1042.
    OpenUrl
  57. 57.↵
    Orman, M. A.; Brynildsen, M. P., Establishment of a method to rapidly assay bacterial persister metabolism. Antimicrobial agents and chemotherapy 2013, 57 (9), 4398–4409.
    OpenUrlAbstract/FREE Full Text
  58. 58.↵
    Ayrapetyan, M.; Williams, T.; Oliver, J. D., Relationship between the viable but nonculturable state and antibiotic persister cells. Journal of bacteriology 2018, 200 (20).
  59. 59.↵
    Vilhena, C.; Kaganovitch, E.; Grunberger, A.; Motz, M.; Forne, I.; Kohlheyer, D.; Jung, K., Importance of Pyruvate Sensing and Transport for the Resuscitation of Viable but Nonculturable Escherichia coli K-12. Journal of bacteriology 2019, 201 (3).
  60. 60.↵
    Morishige, Y.; Fujimori, K.; Amano, F., Differential resuscitative effect of pyruvate and its analogues on VBNC (viable but non-culturable) Salmonella. Microbes Environ 2013, 28 (2), 180–6.
    OpenUrlCrossRefPubMed
  61. 61.↵
    Deter, H. S.; Dies, M.; Cameron, C. C.; Butzin, N. C.; Buceta, J., A Cell Segmentation/Tracking Tool Based on Machine Learning. In Computer Optimized Microscopy, Springer: 2019; pp 399–422.
  62. 62.↵
    Datla, U. S.; Mather, W. H.; Chen, S.; Shoultz, I. W.; Täuber, U. C.; Jones, C. N.; Butzin, N. C., The spatiotemporal system dynamics of acquired resistance in an engineered microecology. Scientific reports 2017, 7 (1), 1–9.
    OpenUrl
  63. 63.↵
    Rosenberg, M.; Azevedo, N. F.; Ivask, A., Propidium iodide staining underestimates viability of adherent bacterial cells. Scientific reports 2019, 9 (1), 1–12.
    OpenUrl
  64. 64.
    Song, S.; Wood, T. K., ‘Viable but non-culturable cells’ are dead. Environmental microbiology 2021, 23 (5), 2335–2338.
    OpenUrl
  65. 65.
    Stiefel, P.; Schmidt-Emrich, S.; Maniura-Weber, K.; Ren, Q., Critical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide. BMC microbiology 2015, 15 (1), 1–9.
    OpenUrlCrossRefPubMed
  66. 66.
    Kirchhoff, C.; Cypionka, H., Propidium ion enters viable cells with high membrane potential during live-dead staining. Journal of microbiological methods 2017, 142, 79–82.
    OpenUrlCrossRef
  67. 67.↵
    Davey, H.; Hexley, P., Red but not dead? Membranes of stressed Saccharomyces cerevisiae are permeable to propidium iodide. Environmental microbiology 2011, 13 (1), 163–171.
    OpenUrlCrossRefPubMedWeb of Science
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Posted November 16, 2022.
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Escherichia coli cells are primed for survival before lethal antibiotic stress
Tahmina Hossain, Abhyudai Singh, Nicholas C. Butzin
bioRxiv 2022.11.11.516214; doi: https://doi.org/10.1101/2022.11.11.516214
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