Growth feedback as a basis for persister bistability

Evidence for a Functional HipBA Toxin-Antitoxin System in Acidovorax citrulli

Bacterial fruit blotch (BFB) is a highly destructive seed-borne and seed-transmitted disease caused by the Gram-negative bacterium Acidovorax citrulli that has caused substantial economic losses for the cucurbit industry in China. Despite its potential for economic damage, little is known about the bacterium's molecular mechanisms of pathogenicity. Toxin-antitoxin (TA) systems are critical for the bacterial stress response. These systems are composed of two genes, toxin and antitoxin, that encode a stable toxin protein and a labile antitoxin protein, respectively. In this study, the genes for the putative HipBA TA system were identified in A. citrulli genomes through bioinformatic analysis. A series of molecular biology experiments have demonstrated that the HipBA TA system exists in A. citrulli Aac5. Furthermore, the transcription of hipA and hipB in A. citrulli Aac5 were induced by pH stress, chloramphenicol stress, and during plant infection. Overall, our results have revealed an active type II TA system, HipBA, in A. citrulli Aac5, and provided insights into its biological functions. These findings contribute to a better understanding of TA systems in plant pathogens.

Optimisation of gene expression noise for cellular persistence against lethal events

Bacterial cell persistence, crucial for survival under adverse conditions like antibiotic exposure, is intrinsically linked to stochastic fluctuations in gene expression. Certain genes, while inhibiting growth under normal circumstances, confer tolerance to antibiotics at elevated expression levels. The occurrence of antibiotic events lead to instantaneous cellular responses with varied survival probabilities correlated with gene expression levels. Notably, cells with lower protein concentrations face higher mortality rates. This study aims to elucidate an optimal strategy for protein expression conducive to cellular survival. Through comprehensive mathematical analysis, we determine the optimal burst size and frequency that maximise cell proliferation. Furthermore, we explore how the optimal expression distribution changes as the cost of protein expression to growth escalates. Our model reveals a hysteresis phenomenon, characterised by discontinuous transitions between deterministic and stochastic optima. Intriguingly, stochastic optima possess a noise floor, representing the minimal level of fluctuations essential for optimal cellular resilience.

Survival Resonances during Fractional Killing of Cell Populations

Fractional killing in response to drugs is a hallmark of nongenetic cellular heterogeneity. Yet how individual lineages evade drug treatment, as observed in bacteria and cancer cells, is not quantitatively understood. We study a stochastic population model with age-dependent division and death rates, allowing for persistence. In periodic drug environments, we discover peaks in the survival probabilities at division or death times that are multiples of the environment duration. The survival resonances are unseen in unstructured populations and are amplified by persistence.

Effects of growth feedback on adaptive gene circuits: A dynamical understanding

The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study more than four hundred topological structures and uncover various categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify several circuits that maintain optimal performance as designed, a feature important for applications.

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies

Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies.

Polymyxin B nonapeptide potentiates the eradication of Gram-negative bacterial persisters

Antibiotic-resistant Gram-negative bacteria remain a globally leading cause of bacterial infection-associated mortality, and it is imperative to identify novel therapeutic strategies. Recently, the advantage of using antibacterials selective against Gram-negative bacteria has been demonstrated with polymyxins that specifically target the lipopolysaccharides of Gram-negative bacteria. However, the severe cytotoxicity of polymyxins limits their clinical use. Here, we demonstrate that polymyxin B nonapeptide (PMBN), a polymyxin B derivative without the terminal amino acyl residue, can significantly enhance the effectiveness of commonly used antibiotics against only Gram-negative bacteria and their persister cells. We show that although PMBN itself does not exhibit antibacterial activity or cytotoxicity well above the 100-fold minimum inhibitory concentration of polymyxin B, PMBN can increase the potency of co-treated antibiotics. We also demonstrate that using PMBN in combination with other antibiotics significantly reduces the frequency of resistant mutant formation. Together, this work provides evidence of the utilities of PMBN as a novel potentiator for antibiotics against Gram-negative bacteria and insights for the eradication of bacterial persister cells during antibiotic treatment.

IMPORTANCE

The significance of our study lies in addressing the problem of antibiotic-resistant Gram-negative bacteria, which continue to be a global cause of mortality associated with bacterial infections. Therefore, identifying innovative therapeutic approaches is an urgent need. Recent research has highlighted the potential of selective antibacterials like polymyxins, which specifically target the lipopolysaccharides of Gram-negative bacteria. However, the clinical use of polymyxins is limited by their severe cytotoxicity. This study unveils the effectiveness of polymyxin B nonapeptide (PMBN) in significantly enhancing the eradication of persister cells in Gram-negative bacteria. Although PMBN itself does not exhibit antibacterial activity or cytotoxicity, it remarkably reduces persister cells during the treatment of antibiotics. Moreover, combining PMBN with other antibiotics reduces the emergence of resistant mutants. Our research emphasizes the utility of PMBN as a novel potentiator to decrease persister cells during antibiotic treatments for Gram-negative bacteria.

Growth Feedback Confers Cooperativity in Resource-Competing Synthetic Gene Circuits

Modularity is a key concept in designing synthetic gene circuits, as it allows for constructing complex molecular systems using well-characterized building blocks. One of the major challenges in this field is that these modular components often do not function as expected when assembled into larger circuits. One of the major issues is caused by resource competition, where multiple genes in the circuit compete for the same limited cellular resources, such as transcription factors and ribosomes. In addition, the mutual inhibition between synthetic gene circuits and cell growth results in growth feedback that significantly impacts its host-circuit dynamics. However, the complexity of the gene circuit dynamics under intertwined resource competition and growth feedback is not fully understood. This study developed a theoretical framework to examine the dynamics of synthetic gene circuits by considering both growth feedback and resource competition. Our results suggest a cooperative behavior between resource-competing gene circuits under growth feedback. Cooperation or competition is non-monotonically determined by the metabolic burden threshold. These two diverse effects could lead to the activation or deactivation of one circuit by the other. Lastly, the cooperativity mediated by growth feedback can attenuate the winner-takes-all resource competition. These findings show that coupling growth feedback and resource competition plays a crucial role in the dynamics of the host-circuit system, and understanding its effects helps control unexpected gene expression behaviors.

Probing transient memory of cellular states using single-cell lineages

The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria-Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.

Bacterial survivors: evaluating the mechanisms of antibiotic persistence

Bacteria withstand antibiotic onslaughts by employing a variety of strategies, one of which is persistence. Persistence occurs in a bacterial population where a subpopulation of cells (persisters) survives antibiotic treatment and can regrow in a drug-free environment. Persisters may cause the recalcitrance of infectious diseases and can be a stepping stone to antibiotic resistance, so understanding persistence mechanisms is critical for therapeutic applications. However, current understanding of persistence is pervaded by paradoxes that stymie research progress, and many aspects of this cellular state remain elusive. In this review, we summarize the putative persister mechanisms, including toxin-antitoxin modules, quorum sensing, indole signalling and epigenetics, as well as the reasons behind the inconsistent body of evidence. We highlight present limitations in the field and underscore a clinical context that is frequently neglected, in the hope of supporting future researchers in examining clinically important persister mechanisms.

Phenomenological Approach to Cancer Cell Persistence

Drug persistence is a phenomenon by which a small percentage of cancer cells survive the presentation of targeted therapy by transitioning to a quiescent state. Eventually some of these persister cells can transition back to an active growing state and give rise to resistant tumors. Here we introduce a quantitative genetics approach to drug-exposed populations of cancer cells in order to interpret recent experimental data regarding inheritance of persister probability. Our results indicate that alternating periods of drug treatment and drug removal may not be an effective strategy for eliminating persisters.

Integrative biology of persister cell formation: molecular circuitry, phenotypic diversification and fitness effects

Microbial populations often contain persister cells, which reduce the extinction risk upon sudden stresses. Persister cell formation is deeply intertwined with physiology. Due to this complexity, it cannot be satisfactorily understood by focusing only on mechanistic, physiological or evolutionary aspects. In this review, we take an integrative biology perspective to identify common principles of persister cell formation, which might be applicable across evolutionary-distinct microbes. Persister cells probably evolved to cope with a fundamental trade-off between cellular stress and growth tasks, as any biosynthetic resource investment in growth-supporting proteins is at the expense of stress tasks and vice versa. Natural selection probably favours persister cell subpopulation formation over a single-phenotype strategy, where each cell is prepared for growth and stress to a suboptimal extent, since persister cells can withstand harsher environments and their coexistence with growing cells leads to a higher fitness. The formation of coexisting phenotypes requires bistable molecular circuitry. Bistability probably emerges from growth-modulated, positive feedback loops in the cell's growth versus stress control network, involving interactions between sigma factors, guanosine pentaphosphate and toxin-antitoxin (TA) systems. We conclude that persister cell formation is most likely a response to a sudden reduction in growth rate, which can be achieved by antibiotic addition, nutrient starvation, sudden stresses, nutrient transitions or activation of a TA system.

An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control

Growth and environmental responses are essential for living organisms to survive and adapt to constantly changing environments. In order to simulate new conditions and capture dynamic responses to environmental shifts in a developing whole-cell model of E. coli, we incorporated additional regulation, including dynamics of the global regulator guanosine tetraphosphate (ppGpp), along with dynamics of amino acid biosynthesis and translation. With the model, we show that under perturbed ppGpp conditions, small molecule feedback inhibition pathways, in addition to regulation of expression, play a role in ppGpp regulation of growth. We also found that simulations with dysregulated amino acid synthesis pathways provide average amino acid concentration predictions that are comparable to experimental results but on the single-cell level, concentrations unexpectedly show regular fluctuations. Additionally, during both an upshift and downshift in nutrient availability, the simulated cell responds similarly with a transient increase in the mRNA:rRNA ratio. This additional simulation functionality should support a variety of new applications and expansions of the E. coli Whole-Cell Modeling Project.

A robust method for designing multistable systems by embedding bistable subsystems

Although multistability is an important dynamic property of a wide range of complex systems, it is still a challenge to develop mathematical models for realising high order multistability using realistic regulatory mechanisms. To address this issue, we propose a robust method to develop multistable mathematical models by embedding bistable models together. Using the GATA1-GATA2-PU.1 module in hematopoiesis as the test system, we first develop a tristable model based on two bistable models without any high cooperative coefficients, and then modify the tristable model based on experimentally determined mechanisms. The modified model successfully realises four stable steady states and accurately reflects a recent experimental observation showing four transcriptional states. In addition, we develop a stochastic model, and stochastic simulations successfully realise the experimental observations in single cells. These results suggest that the proposed method is a general approach to develop mathematical models for realising multistability and heterogeneity in complex systems.

Bacillus subtilis Histidine Kinase KinC Activates Biofilm Formation by Controlling Heterogeneity of Single-Cell Responses

In Bacillus subtilis, biofilm and sporulation pathways are both controlled by a master regulator, Spo0A, which is activated by phosphorylation via a phosphorelay-a cascade of phosphotransfer reactions commencing with autophosphorylation of histidine kinases KinA, KinB, KinC, KinD, and KinE. However, it is unclear how the kinases, despite acting via the same regulator, Spo0A, differentially regulate downstream pathways, i.e., how KinA mainly activates sporulation genes and KinC mainly activates biofilm genes. In this work, we found that KinC also downregulates sporulation genes, suggesting that KinC has a negative effect on Spo0A activity. To explain this effect, with a mathematical model of the phosphorelay, we revealed that unlike KinA, which always activates Spo0A, KinC has distinct effects on Spo0A at different growth stages: during fast growth, KinC acts as a phosphate source and activates Spo0A, whereas during slow growth, KinC becomes a phosphate sink and contributes to decreasing Spo0A activity. However, under these conditions, KinC can still increase the population-mean biofilm matrix production activity. In a population, individual cells grow at different rates, and KinC would increase the Spo0A activity in the fast-growing cells but reduce the Spo0A activity in the slow-growing cells. This mechanism reduces single-cell heterogeneity of Spo0A activity, thereby increasing the fraction of cells that activate biofilm matrix production. Thus, KinC activates biofilm formation by controlling the fraction of cells activating biofilm gene expression. IMPORTANCE In many bacterial and eukaryotic systems, multiple cell fate decisions are activated by a single master regulator. Typically, the activities of the regulators are controlled posttranslationally in response to different environmental stimuli. The mechanisms underlying the ability of these regulators to control multiple outcomes are not understood in many systems. By investigating the regulation of Bacillus subtilis master regulator Spo0A, we show that sensor kinases can use a novel mechanism to control cell fate decisions. By acting as a phosphate source or sink, kinases can interact with one another and provide accurate regulation of the phosphorylation level. Moreover, this mechanism affects the cell-to-cell heterogeneity of the transcription factor activity and eventually determines the fraction of different cell types in the population. These results demonstrate the importance of intercellular heterogeneity for understanding the effects of genetic perturbations on cell fate decisions. Such effects can be applicable to a wide range of cellular systems.

A Robust Method for Generating, Quantifying, and Testing Large Numbers of Escherichia coli Persisters

Bacteria can exhibit phenotypes that render them tolerant against antibiotics. However, often only a few cells of a bacterial population show the so-called persister phenotype, which makes it difficult to study this health-threatening phenotype. We recently found that certain abrupt nutrient shifts generate Escherichia coli populations that consist almost entirely of antibiotic-tolerant cells. These nearly homogeneous persister cell populations enable assessment with population-averaging experimental methods, such as high-throughput methods. In this chapter, we provide a detailed protocol for generating a large fraction of tolerant cells using the nutrient-switch approach. Furthermore, we describe how to determine the fraction of cells that enter the tolerant state upon a sudden nutrient shift and we provide a new way to assess antibiotic tolerance using flow cytometry. We envision that these methods will facilitate research into the important and exciting phenotype of bacterial persister cells.

Persistence as an Optimal Hedging Strategy

Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new, to our knowledge, theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest new opportunities for experimental investigation and design. Overall, we provide a new, to our knowledge, way of conceptualizing and modeling cellular decision making in volatile environments by explicitly unifying theory from mathematical biology and finance.

Topology-dependent interference of synthetic gene circuit function by growth feedback

Growth-mediated feedback between synthetic gene circuits and host organisms leads to diverse emerged behaviors, including growth bistability and enhanced ultrasensitivity. However, the range of possible impacts of growth feedback on gene circuits remains underexplored. Here, we mathematically and experimentally demonstrated that growth feedback affects the functions of memory circuits in a network topology-dependent way. Specifically, the memory of the self-activation switch is quickly lost due to the growth-mediated dilution of the circuit products. Decoupling of growth feedback reveals its memory, manifested by its hysteresis property across a broad range of inducer concentration. On the contrary, the toggle switch is more refractory to growth-mediated dilution and can retrieve its memory after the fast-growth phase. The underlying principle lies in the different dependence of active and repressive regulations in these circuits on the growth-mediated dilution. Our results unveil the topology-dependent mechanism on how growth-mediated feedback influences the behaviors of gene circuits.

Hypoionic Shock Facilitates Aminoglycoside Killing of Both Nutrient Shift- and Starvation-Induced Bacterial Persister Cells by Rapidly Enhancing Aminoglycoside Uptake

Bacterial persister cells are phenotypic variants that exhibit transient antibiotic tolerance and play a leading role in chronic infections and the development of antibiotic resistance. Determining the mechanism that underlies persister formation and developing anti-persister strategies, therefore, are clinically important goals. Here, we report that many gram-negative and gram-positive bacteria become highly tolerant to typical bactericidal antibiotics when the carbon source for their antibiotic-sensitive exponential growth phase is shifted to fumarate, suggesting a role for fumarate in persister induction. Nutrient shift-induced Escherichia coli but not Staphylococcus aureus persister cells can be killed by aminoglycosides upon hypoionic shock (i.e., the absence of ions), which is achieved by suspending the persisters in aminoglycoside-containing pure water for only 1 or 2 min. Such potentiation can be abolished by inhibitors of the electron transport chain (e.g., NaN₃) or proton motive force (e.g., CCCP). Additionally, we show that hypoionic shock facilitates the eradication of starvation-induced E. coli but not S. aureus persisters by aminoglycosides, and that such potentiation can be significantly suppressed by NaN₃ or CCCP. Mechanistically, hypoionic shock dramatically enhances aminoglycoside uptake by both nutrient shift- and starvation-induced E. coli persisters, whereas CCCP can diminish this uptake. Results of our study illustrate the general role of fumarate in bacterial persistence and may open new avenues for persister eradication and aminoglycoside use.

Excitable dynamics through toxin-induced mRNA cleavage in bacteria

Toxin-antitoxin (TA) systems in bacteria and archaea are small genetic elements consisting of the genes coding for an intracellular toxin and an antitoxin that can neutralize this toxin. In various cases, the toxins cleave the mRNA. In this theoretical work we use deterministic and stochastic modeling to explain how toxin-induced cleavage of mRNA in TA systems can lead to excitability, allowing large transient spikes in toxin levels to be triggered. By using a simplified network where secondary complex formation and transcriptional regulation are not included, we show that a two-dimensional, deterministic model captures the origin of such toxin excitations. Moreover, it allows to increase our understanding by examining the dynamics in the phase plane. By systematically comparing the deterministic results with Gillespie simulations we demonstrate that even though the real TA system is intrinsically stochastic, toxin excitations can be accurately described deterministically. A bifurcation analysis of the system shows that the excitable behavior is due to a nearby Hopf bifurcation in the parameter space, where the system becomes oscillatory. The influence of stress is modeled by varying the degradation rate of the antitoxin and the translation rate of the toxin. We find that stress increases the frequency of toxin excitations. The inclusion of secondary complex formation and transcriptional regulation does not fundamentally change the mechanism of toxin excitations. Finally, we show that including growth rate suppression and translational inhibition can lead to longer excitations, and even cause excitations in cases when the system would otherwise be non-excitable. To conclude, the deterministic model used in this work provides a simple and intuitive explanation of toxin excitations in TA systems.

Escherichia coli can survive stress by noisy growth modulation

Gene expression can be noisy, as can the growth of single cells. Such cell-to-cell variation has been implicated in survival strategies for bacterial populations. However, it remains unclear how single cells couple gene expression with growth to implement these strategies. Here, we show how noisy expression of a key stress-response regulator, RpoS, allows E. coli to modulate its growth dynamics to survive future adverse environments. We reveal a dynamic positive feedback loop between RpoS and growth rate that produces multi-generation RpoS pulses. We do so experimentally using single-cell, time-lapse microscopy and microfluidics and theoretically with a stochastic model. Next, we demonstrate that E. coli prepares for sudden stress by entering prolonged periods of slow growth mediated by RpoS. This dynamic phenotype is captured by the RpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability.

Noisy gene expression leading to phenotypic variability can help organisms to survive in changing environments. Here, Patange et al. show that noisy expression of a stress response regulator, RpoS, allows E. coli cells to modulate their growth rates to survive future adverse environments.

Persister Cells - a Plausible Outcome of Neutral Coevolutionary Drift

The phenomenon of bacterial persistence - a non-inherited antibiotic tolerance in a minute fraction of the bacterial population, was observed more than 70 years ago. Nowadays, it is suggested that "persister cells" undergo an alternative scenario of the cell cycle; however, pathways involved in its emergence are still not identified. We present a mathematically grounded scenario of such possibility. We have determined that population drift in the space of multiple neutrally coupled mutations, which we called "neutrally coupled co-evolution" (NCCE), leads to increased dynamic complexity of bacterial populations via appearance of cells capable of carrying out a single cell cycle in two or more alternative ways and that universal properties of the coupled transcription-translation system underlie this phenotypic multiplicity. According to our hypothesis, modern persister cells have derived from such cells and regulatory mechanisms that govern the consolidation of this phenomenon represented the trigger. We assume that the described type of neutrally coupled co-evolution could play an important role in the origin of extremophiles, both in bacteria and archaea.

Dynamics of Bacterial Gene Regulatory Networks

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

Phenotypic Plasticity, Bet-Hedging, and Androgen Independence in Prostate Cancer: Role of Non-Genetic Heterogeneity

It is well known that genetic mutations can drive drug resistance and lead to tumor relapse. Here, we focus on alternate mechanisms-those without mutations, such as phenotypic plasticity and stochastic cell-to-cell variability that can also evade drug attacks by giving rise to drug-tolerant persisters. The phenomenon of persistence has been well-studied in bacteria and has also recently garnered attention in cancer. We draw a parallel between bacterial persistence and resistance against androgen deprivation therapy in prostate cancer (PCa), the primary standard care for metastatic disease. We illustrate how phenotypic plasticity and consequent mutation-independent or non-genetic heterogeneity possibly driven by protein conformational dynamics can stochastically give rise to androgen independence in PCa, and suggest that dynamic phenotypic plasticity should be considered in devising therapeutic dosing strategies designed to treat and manage PCa.

Toxins, Targets, and Triggers: An Overview of Toxin-Antitoxin Biology

Bacterial toxin-antitoxin (TA) modules are abundant genetic elements that encode a toxin protein capable of inhibiting cell growth and an antitoxin that counteracts the toxin. The majority of toxins are enzymes that interfere with translation or DNA replication, but a wide variety of molecular activities and cellular targets have been described. Antitoxins are proteins or RNAs that often control their cognate toxins through direct interactions and, in conjunction with other signaling elements, through transcriptional and translational regulation of TA module expression. Three major biological functions of TA modules have been discovered, post-segregational killing ("plasmid addiction"), abortive infection (bacteriophage immunity through altruistic suicide), and persister formation (antibiotic tolerance through dormancy). In this review, we summarize the current state of the field and highlight how multiple levels of regulation shape the conditions of toxin activation to achieve the different biological functions of TA modules.

Characterization of YjjJ toxin of Escherichia coli

Reminiscent of eukaryotic apoptotic programmed cell death, bacteria also contain a large number of suicide genes, which are in general co-expressed with their cognate antitoxin genes. These systems called the toxin-antitoxin (TA) systems are associated with cellular dormancy, and play major roles in biofilm formation and persistent multidrug resistance of many human pathogens. In recent years, the study on TA system toxins has become a hot topic due to the health implications of these toxins by virtue of their role in bacterial pathogenicity. Here we report functional characterization of a hitherto uncharacterized Escherichia coli TA toxin, YjjJ. YjjJ exhibits several uncommon properties: (i) unlike the genes encoding most type II TA system toxins, the gene encoding YjjJ is present as a single gene and not in an operon, (ii) despite being a homolog of the well-characterized toxin HipA, YjjJ seems to have different cellular target(s), and (iii) HipB, the cognate antitoxin of HipA, also acts as an antitoxin for YjjJ. This forms a basis for an interesting next step in the study of TA systems with respect to cross-regulation between various TA systems and the evolutionary as well as clinical significance of these observations.

The Alternative Sigma Factors SigE and SigB Are Involved in Tolerance and Persistence to Antitubercular Drugs

The emergence and spread of drug-resistant Mycobacterium tuberculosis strains possibly threaten our ability to treat this disease in the future. Even though two new antitubercular drugs have recently been introduced, there is still the need to design new molecules whose mechanisms of action could reduce the length of treatment. We show that two alternative sigma factors of M. tuberculosis (SigE and SigB) have a major role in determining the level of basal resistance to several drugs and the amount of persisters surviving long-duration drug treatment. We also demonstrate that ethambutol, a bacteriostatic drug, is highly bactericidal for M. tuberculosis mutants missing either SigE or SigB. We suggest that molecules able to interfere with the activity of SigE or SigB not only could reduce M. tuberculosis virulence in vivo but also could boost the effect of other drugs by increasing the sensitivity of the organism and reducing the number of persisters able to escape killing.

Phenotypic Heterogeneity in Mycobacterium tuberculosis

The interaction between the host and the pathogen is extremely complex and is affected by anatomical, physiological, and immunological diversity in the microenvironments, leading to phenotypic diversity of the pathogen. Phenotypic heterogeneity, defined as nongenetic variation observed in individual members of a clonal population, can have beneficial consequences especially in fluctuating stressful environmental conditions. This is all the more relevant in infections caused by Mycobacterium tuberculosis wherein the pathogen is able to survive and often establish a lifelong persistent infection in the host. Recent studies in tuberculosis patients and in animal models have documented the heterogeneous and diverging trajectories of individual lesions within a single host. Since the fate of the individual lesions appears to be determined by the local tissue environment rather than systemic response of the host, studying this heterogeneity is very relevant to ensure better control and complete eradication of the pathogen from individual lesions. The heterogeneous microenvironments greatly enhance M. tuberculosis heterogeneity influencing the growth rates, metabolic potential, stress responses, drug susceptibility, and eventual lesion resolution. Single-cell approaches such as time-lapse microscopy using microfluidic devices allow us to address cell-to-cell variations that are often lost in population-average measurements. In this review, we focus on some of the factors that could be considered as drivers of phenotypic heterogeneity in M. tuberculosis as well as highlight some of the techniques that are useful in addressing this issue.

Relating switching rates between normal and persister cells to substrate and antibiotic concentrations: a mathematical modelling approach supported by experiments

We developed and compared two mathematical models of variable phenotypic switching rates between normal and persister cells that depend on substrate concentration and antibiotic presence. They could be used to simulate the formation of persisters in environments with concentration gradients such as biofilms. Our models are extensions of a previous model of the dynamics of normal and persistent cell populations developed by Balaban et al. (2004, Science 305: 1622). We calibrated the models’ parameters with experimental killing curves obtained after ciprofloxacin treatment of samples regularly harvested from planktonic batch cultures of Klebsiella pneumoniae. Our switching models accurately reproduced the dynamics of normal and persistent populations in planktonic batch cultures and under antibiotic treatment. Results showed that the models are valid for a large range of substrate concentrations and for zero or high doses of antibiotics.

Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments

Natural selection has shaped the strategies for survival and growth of microorganisms. The success of microorganisms depends not only on slow evolutionary tuning but also on the ability to adapt to unpredictable changes in their environment. In principle, adaptive strategies range from purely deterministic mechanisms to those that exploit the randomness intrinsic to many cellular and molecular processes. Depending on the environment and selective pressures, particular strategies can lie somewhere along this continuum. In recent years, non-genetic cell-to-cell differences have received a lot of attention, not least because of their potential impact on the ability of microbial populations to survive in dynamic environments. Using several examples, we describe the origins of spontaneous and induced mechanisms of phenotypic adaptation. We identify some of the commonalities of these examples and consider the potential role of chance and constraints in microbial phenotypic adaptation.

Bacterial persistence from a system-level perspective

In recent years, our understanding about bacterial persistence has significantly advanced: we comprehend the persister phenotype better, more triggers for persistence entry have been found, and more insights in the involvement and role of toxin-antitoxin systems and other molecular mechanisms have been unravelled. In this review, we attempt to put these findings into an integrated, system-level perspective. From this point of view, persistence can be seen as a response to a strong perturbation of metabolic homeostasis, either triggered environmentally, or by means of intracellular stochasticity. Metabolic-flux-regulated resource allocation ensures stress protection, and several feedback mechanisms stabilize the cells in this protected state. We hope that this novel view can advance our understanding about persistence.

Bacterial persistence is an active σS stress response to metabolic flux limitation

While persisters are a health threat due to their transient antibiotic tolerance, little is known about their phenotype and what actually causes persistence. Using a new method for persister generation and high‐throughput methods, we comprehensively mapped the molecular phenotype of Escherichia coli during the entry and in the state of persistence in nutrient‐rich conditions. The persister proteome is characterized by σ^S‐mediated stress response and a shift to catabolism, a proteome that starved cells tried to but could not reach due to absence of a carbon and energy source. Metabolism of persisters is geared toward energy production, with depleted metabolite pools. We developed and experimentally verified a model, in which persistence is established through a system‐level feedback: Strong perturbations of metabolic homeostasis cause metabolic fluxes to collapse, prohibiting adjustments toward restoring homeostasis. This vicious cycle is stabilized and modulated by high ppGpp levels, toxin/anti‐toxin systems, and the σ^S‐mediated stress response. Our system‐level model consistently integrates past findings with our new data, thereby providing an important basis for future research on persisters.

Time-kill curve analysis and pharmacodynamic modelling for in vitro evaluation of antimicrobials against Neisseria gonorrhoeae

BACKGROUND

Gonorrhoea is a sexually transmitted infection caused by the Gram-negative bacterium Neisseria gonorrhoeae. Resistance to first-line empirical monotherapy has emerged, so robust methods are needed to evaluate the activity of existing and novel antimicrobials against the bacterium. Pharmacodynamic models describing the relationship between the concentration of antimicrobials and the minimum growth rate of the bacteria provide more detailed information than the MIC only.

RESULTS

In this study, a novel standardised in vitro time-kill curve assay was developed. The assay was validated using five World Health Organization N. gonorrhoeae reference strains and a range of ciprofloxacin concentrations below and above the MIC. Then the activity of nine antimicrobials with different target mechanisms was examined against a highly antimicrobial susceptible clinical strain isolated in 1964. The experimental time-kill curves were analysed and quantified with a previously established pharmacodynamic model. First, the bacterial growth rates at each antimicrobial concentration were estimated with linear regression. Second, we fitted the model to the growth rates, resulting in four parameters that describe the pharmacodynamic properties of each antimicrobial. A gradual decrease of bactericidal effects from ciprofloxacin to spectinomycin and gentamicin was found. The beta-lactams ceftriaxone, cefixime and benzylpenicillin showed bactericidal and time-dependent properties. Chloramphenicol and tetracycline were purely bacteriostatic as they fully inhibited the growth but did not kill the bacteria. We also tested ciprofloxacin resistant strains and found higher pharmacodynamic MICs (zMIC) in the resistant strains and attenuated bactericidal effects at concentrations above the zMIC.

CONCLUSIONS

N. gonorrhoeae time-kill curve experiments analysed with a pharmacodynamic model have potential for in vitro evaluation of new and existing antimicrobials. The pharmacodynamic parameters based on a wide range of concentrations below and above the MIC provide information that could support improving future dosing strategies to treat gonorrhoea.

Antibiotic resistance: a physicist's view

The problem of antibiotic resistance poses challenges across many disciplines. One such challenge is to understand the fundamental science of how antibiotics work, and how resistance to them can emerge. This is an area where physicists can make important contributions. Here, we highlight cases where this is already happening, and suggest directions for further physics involvement in antimicrobial research.

The Chromosomal parDE2 Toxin-Antitoxin System of Mycobacterium tuberculosis H37Rv: Genetic and Functional Characterization

Mycobacterium tuberculosis H37Rv escapes host-generated stresses by entering a dormant persistent state. Activation of toxin-antitoxin modules is one of the mechanisms known to trigger such a state with low metabolic activity. M. tuberculosis harbors a large number of TA systems mostly located within discernible genomic islands. We have investigated the parDE2 operon of M. tuberculosis H37Rv encoding MParE2 toxin and MParD2 antitoxin proteins. The parDE2 locus was transcriptionally active from growth phase till late stationary phase in M. tuberculosis. A functional promoter located upstream of parD2 GTG start-site was identified by 5'-RACE and lacZ reporter assay. The MParD2 protein transcriptionally regulated the P parDE2 promoter by interacting through Arg16 and Ser15 residues located in the N-terminus. In Escherichia coli, ectopic expression of MParE2 inhibited growth in early stages, with a drastic reduction in colony forming units. Live-dead analysis revealed that the reduction was not due to cell death alone but due to formation of viable but non-culturable cells (VBNCs) also. The toxic activity of the protein, identified in the C-terminal residues Glu98 and Arg102, was neutralized by the antitoxin MParD2, both in vivo and in vitro. MParE2 inhibited mycobacterial DNA gyrase and interacted with the GyrB subunit without affecting its ATPase activity. Introduction of parE2 gene in the heterologous M. smegmatis host prevented growth and colony formation by the transformed cells. An M. smegmatis strain containing the parDE2 operon also switched to a non-culturable phenotype in response to oxidative stress. Loss in colony-forming ability of a major part of the MParE2 expressing cells suggests its potential role in dormancy, a cellular strategy for adaptation to environmental stresses. Our study has laid the foundation for future investigations to explore the physiological significance of parDE2 operon in mycobacterial pathogenesis.

Interpreting phenotypic antibiotic tolerance and persister cells as evolution via epigenetic inheritance

Epigenetic inheritance is the transmission of nongenetic material such as gene expression levels, RNA and other biomolecules from parents to offspring. There is a growing realization that such forms of inheritance can play an important role in evolution. Bacteria represent a prime example of epigenetic inheritance because a large array of cellular components is transmitted to offspring, in addition to genetic material. Interestingly, there is an extensive and growing empirical literature showing that many bacteria can form 'persister' cells that are phenotypically resistant or tolerant to antibiotics, but most of these results are not interpreted within the context of epigenetic inheritance. Instead, persister cells are usually viewed as a genetically encoded bet-hedging strategy that has evolved in response to a fluctuating environment. Here I show, using a relatively simple model, that many of these empirical findings can be more simply understood as arising from a combination of epigenetic inheritance and cellular noise. I therefore suggest that phenotypic drug tolerance in bacteria might represent one of the best-studied examples of evolution under epigenetic inheritance.

Classic reaction kinetics can explain complex patterns of antibiotic action

Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. We show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single-cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R(2) = 0.86), and makes several testable predictions, which we verified in new experiments and by analyzing published data from a clinical trial on tuberculosis therapy. Although a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model that considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial and error or expert opinion. Our "chemical reaction kinetics"-based approach may inform new strategies, which are based on rational design.

Computational Methods to Model Persistence

Bacterial persister cells are dormant cells, tolerant to multiple antibiotics, that are involved in several chronic infections. Toxin-antitoxin modules play a significant role in the generation of such persister cells. Toxin-antitoxin modules are small genetic elements, omnipresent in the genomes of bacteria, which code for an intracellular toxin and its neutralizing antitoxin. In the past decade, mathematical modeling has become an important tool to study the regulation of toxin-antitoxin modules and their relation to the emergence of persister cells. Here, we provide an overview of several numerical methods to simulate toxin-antitoxin modules. We cover both deterministic modeling using ordinary differential equations and stochastic modeling using stochastic differential equations and the Gillespie method. Several characteristics of toxin-antitoxin modules such as protein production and degradation, negative autoregulation through DNA binding, toxin-antitoxin complex formation and conditional cooperativity are gradually integrated in these models. Finally, by including growth rate modulation, we link toxin-antitoxin module expression to the generation of persister cells.

Dynamics of simple gene-network motifs subject to extrinsic fluctuations

Cellular processes do not follow deterministic rules; even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise (IN) of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise (EN) has been experimentally shown to dominate the variation. Here, we develop an analytical formalism that allows for calculation of the effect of EN on gene-expression motifs. We introduce a method for modeling bounded EN as an auxiliary species in the master equation. The method is fully generic and is not limited to systems with small EN magnitudes. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a nonregulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the EN properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of EN on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene-expression experiments.

Biofilm-related infections: bridging the gap between clinical management and fundamental aspects of recalcitrance toward antibiotics

Surface-associated microbial communities, called biofilms, are present in all environments. Although biofilms play an important positive role in a variety of ecosystems, they also have many negative effects, including biofilm-related infections in medical settings. The ability of pathogenic biofilms to survive in the presence of high concentrations of antibiotics is called "recalcitrance" and is a characteristic property of the biofilm lifestyle, leading to treatment failure and infection recurrence. This review presents our current understanding of the molecular mechanisms of biofilm recalcitrance toward antibiotics and describes how recent progress has improved our capacity to design original and efficient strategies to prevent or eradicate biofilm-related infections.

Resistance to chemotherapy: patient variability and cellular heterogeneity

The issue of resistance to targeted drug therapy is of pressing concern, as it constitutes a major barrier to progress in managing cancer. One important aspect is the role of stochasticity in determining the nature of the patient response. We examine two particular experiments. The first measured the maximal response of melanoma to targeted therapy before the resistance causes the tumor to progress. We analyze the data in the context of a Delbruck-Luria type scheme, wherein the continued growth of preexistent resistant cells are responsible for progression. We show that, aside from a finite fraction of resistant cell-free patients, the maximal response in such a scenario would be quite uniform. To achieve the measured variability, one is necessarily led to assume a wide variation from patient to patient of the sensitive cells' response to the therapy. The second experiment is an in vitro system of multiple myeloma cells. When subject to a spatial gradient of a chemotherapeutic agent, the cells in the middle of the system acquire resistance on a rapid (two-week) timescale. This finding points to the potential important role of cell-to-cell differences, due to differing local environments, in addition to the patient-to-patient differences encountered in the first part. See all articles in this Cancer Research section, "Physics in Cancer Research."

The bacterial antitoxin HipB establishes a ternary complex with operator DNA and phosphorylated toxin HipA to regulate bacterial persistence

Nearly all bacteria exhibit a type of phenotypic growth described as persistence that is thought to underlie antibiotic tolerance and recalcitrant chronic infections. The chromosomally encoded high-persistence (Hip) toxin–antitoxin proteins HipA_SO and HipB_SO from Shewanella oneidensis, a proteobacterium with unusual respiratory capacities, constitute a type II toxin–antitoxin protein module. Here we show that phosphorylated HipA_SO can engage in an unexpected ternary complex with HipB_SO and double-stranded operator DNA that is distinct from the prototypical counterpart complex from Escherichia coli. The structure of HipB_SO in complex with operator DNA reveals a flexible C-terminus that is sequestered by HipA_SO in the ternary complex, indicative of its role in binding HipA_SO to abolish its function in persistence. The structure of HipA_SO in complex with a non-hydrolyzable ATP analogue shows that HipA_SO autophosphorylation is coupled to an unusual conformational change of its phosphorylation loop. However, HipA_SO is unable to phosphorylate the translation factor Elongation factor Tu, contrary to previous reports, but in agreement with more recent findings. Our studies suggest that the phosphorylation state of HipA is an important factor in persistence and that the structural and mechanistic diversity of HipAB modules as regulatory factors in bacterial persistence is broader than previously thought.