Bacteria grow swiftly and live thriftily.

Bacteria have evolved numerous strategies to use resources efficiently. However, bacterial economies depend on both the physiological context of the organisms as well as their growth state - whether they are growing, non-growing or reinitiating growth. In this essay, we discuss some of the features that make bacteria efficient under these different conditions and during the transitions between them. We also highlight the many outstanding questions regarding the physiology of non-growing bacterial cells. Lastly, we examine how efficiency is apparent in both the mode and tempo of bacterial evolution.

Prevalence of Antibiotic Tolerance and Risk for Reinfection Among Escherichia coli Bloodstream Isolates: A Prospective Cohort Study.

BACKGROUND: Tolerance is the ability of bacteria to survive transient exposure to high concentrations of a bactericidal antibiotic without a change in the minimal inhibitory concentration, thereby limiting the efficacy of antimicrobials. The study sought to determine the prevalence of tolerance in a prospective cohort of E. coli bloodstream infection and to explore the association of tolerance with reinfection risk. METHODS: Tolerance, determined by the Tolerance Disk Test (TDtest), was tested in a prospective cohort of consecutive patient-unique E. coli bloodstream isolates and a collection of strains from patients who had recurrent blood cultures with E. coli (cohorts 1 and 2, respectively). Selected isolates were further analyzed using time-dependent killing and typed using whole-genome sequencing. Covariate data were retrieved from electronic medical records. The association between tolerance and reinfection was assessed by the Cox proportional-hazards regression and a Poisson regression models. RESULTS: In cohort 1, 8/94 isolates (8.5%) were tolerant. Using multivariate analysis, it was determined that the risk for reinfection in the patients with tolerant index bacteremia was significantly higher than for patients with a nontolerant strain, hazard ratio, 3.98 (95% confidence interval, 1.32-12.01). The prevalence of tolerance among cohort 2 was higher than in cohort 1, 6/21(28.6%) vs 8/94 (8.5%), respectively (P = .02). CONCLUSIONS: Tolerant E. coli are frequently encountered among bloodstream isolates and are associated with an increased risk of reinfection. The TDtest appears to be a practicable approach for tolerance detection and could improve future patient management.

Observation of universal ageing dynamics in antibiotic persistence.

Stress responses allow cells to adapt to changes in external conditions by activating specific pathways1. Here we investigate the dynamics of single cells that were subjected to acute stress that is too strong for a regulated response but not lethal. We show that when the growth of bacteria is arrested by acute transient exposure to strong inhibitors, the statistics of their regrowth dynamics can be predicted by a model for the cellular network that ignores most of the details of the underlying molecular interactions. We observed that the same stress, applied either abruptly or gradually, can lead to totally different recovery dynamics. By measuring the regrowth dynamics after stress exposure on thousands of cells, we show that the model can predict the outcome of antibiotic persistence measurements. Our results may account for the ubiquitous antibiotic persistence phenotype2, as well as for the difficulty in attempts to link it to specific genes3. More generally, our approach suggests that two different cellular states can be observed under stress: a regulated state, which prepares cells for fast recovery, and a disrupted cellular state due to acute stress, with slow and heterogeneous recovery dynamics. The disrupted state may be described by general properties of large random networks rather than by specific pathway activation. Better understanding of the disrupted state could shed new light on the survival and evolution of cells under stress.

Quantitative biology of survival under antibiotic treatments.

The mathematical formulation for the dynamics of growth reduction and/or killing under antibiotic treatments has a long history. Even before the extensive use of antibiotics, attempts to model the killing dynamics of biocides were made [1]. Here, we review relatively simple quantitative formulations of the two main modes of survival under antibiotics, resistance and tolerance, as well as their heterogeneity in bacterial populations. We focus on the two main types of heterogeneity that have been described: heteroresistance and antibiotic persistence, each linked to the variation in a different parameter of the antibiotic response dynamics. Finally, we review the effects on survival of combining resistance and tolerance mutations as well as on the mode and tempo of evolution under antibiotic treatments.

Interaction Tolerance Detection Test for Understanding the Killing Efficacy of Directional Antibiotic Combinations.

Combination treatments are commonly prescribed for enhancing drug efficacy, as well as for preventing the evolution of resistance. The interaction between drugs is typically evaluated near the MIC, using growth rate as a measure of treatment efficacy. However, for infections in which the killing activity of the treatment is important, measurements far above the MIC are needed. In this regime, the killing rate often becomes weakly concentration dependent, and a different metric is needed to characterize drug interactions. We evaluate the interaction metric on killing using an easy visual assay, the interaction tolerance detection test (iTDtest), that estimates the survival of bacteria under antibiotic combinations. We identify antibiotic combinations that enable the eradication of tolerant bacteria. Furthermore, the visualization of the antibiotic interactions reveals directional drug interactions and enables predicting high-order combination outcomes, therefore facilitating the determination of optimal treatments. IMPORTANCE The killing efficacy of antibiotic combinations is rarely measured in the clinical setting. However, in cases where the treatment is required to kill the infecting organism and not merely arrest its growth, the information on the killing efficacy is important, especially when tolerant strains are implicated. Here, we report on an easy method for the determination of the killing efficacy of antibiotic combinations which enabled to reveal combinations effective against tolerant bacteria. The results could be generally used to guide antimicrobial therapy in life-threatening infections.

Antibiotic resistance: turning evolutionary principles into clinical reality.

Antibiotic resistance is one of the major challenges facing modern medicine worldwide. The past few decades have witnessed rapid progress in our understanding of the multiple factors that affect the emergence and spread of antibiotic resistance at the population level and the level of the individual patient. However, the process of translating this progress into health policy and clinical practice has been slow. Here, we attempt to consolidate current knowledge about the evolution and ecology of antibiotic resistance into a roadmap for future research as well as clinical and environmental control of antibiotic resistance. At the population level, we examine emergence, transmission and dissemination of antibiotic resistance, and at the patient level, we examine adaptation involving bacterial physiology and host resilience. Finally, we describe new approaches and technologies for improving diagnosis and treatment and minimizing the spread of resistance.

Effect of tolerance on the evolution of antibiotic resistance under drug combinations.

Drug combinations are widely used in clinical practice to prevent the evolution of resistance. However, little is known about the effect of tolerance, a different mode of survival, on the efficacy of drug combinations for preventing the evolution of resistance. In this work, we monitored Staphylococcus aureus strains evolving in patients under treatment. We detected the rapid emergence of tolerance mutations, followed by the emergence of resistance, despite the combination treatment. Evolution experiments on the clinical strains in vitro revealed a new way by which tolerance promotes the evolution of resistance under combination treatments. Further experiments under different antibiotic classes reveal the generality of the effect. We conclude that tolerance is an important factor to consider in designing combination treatments that prevent the evolution of resistance.

Epistasis between antibiotic tolerance, persistence, and resistance mutations.

Understanding the evolution of microorganisms under antibiotic treatments is a burning issue. Typically, several resistance mutations can accumulate under antibiotic treatment, and the way in which resistance mutations interact, i.e., epistasis, has been extensively studied. We recently showed that the evolution of antibiotic resistance in Escherichia coli is facilitated by the early appearance of tolerance mutations. In contrast to resistance, which reduces the effectiveness of the drug concentration, tolerance increases resilience to antibiotic treatment duration in a nonspecific way, for example when bacteria transiently arrest their growth. Both result in increased survival under antibiotics, but the interaction between resistance and tolerance mutations has not been studied. Here, we extend our analysis to include the evolution of a different type of tolerance and a different antibiotic class and measure experimentally the epistasis between tolerance and resistance mutations. We derive the expected model for the effect of tolerance and resistance mutations on the dynamics of survival under antibiotic treatment. We find that the interaction between resistance and tolerance mutations is synergistic in strains evolved under intermittent antibiotic treatment. We extend our analysis to mutations that result in antibiotic persistence, i.e., to tolerance that is conferred only on a subpopulation of cells. We show that even when this population heterogeneity is included in our analysis, a synergistic interaction between antibiotic persistence and resistance mutations remains. We expect our general framework for the epistasis in killing conditions to be relevant for other systems as well, such as bacteria exposed to phages or cancer cells under treatment.

Publisher Correction: Definitions and guidelines for research on antibiotic persistence.

In Figure 2b, the minimal duration for killing (MDK) 99% of tolerant cells was erroneously labelled as MDK99.99 instead of MDK99. This has now been corrected in all versions of the Review. The publisher apologizes to the authors and to readers for this error.

Definitions and guidelines for research on antibiotic persistence.

Increasing concerns about the rising rates of antibiotic therapy failure and advances in single-cell analyses have inspired a surge of research into antibiotic persistence. Bacterial persister cells represent a subpopulation of cells that can survive intensive antibiotic treatment without being resistant. Several approaches have emerged to define and measure persistence, and it is now time to agree on the basic definition of persistence and its relation to the other mechanisms by which bacteria survive exposure to bactericidal antibiotic treatments, such as antibiotic resistance, heteroresistance or tolerance. In this Consensus Statement, we provide definitions of persistence phenomena, distinguish between triggered and spontaneous persistence and provide a guide to measuring persistence. Antibiotic persistence is not only an interesting example of non-genetic single-cell heterogeneity, it may also have a role in the failure of antibiotic treatments. Therefore, it is our hope that the guidelines outlined in this article will pave the way for better characterization of antibiotic persistence and for understanding its relevance to clinical outcomes.

Learning from Noise: How Observing Stochasticity May Aid Microbiology.

For many decades, the wedding of quantitative data with mathematical modeling has been fruitful, leading to important biological insights. Here, we review some of the ongoing efforts to gain insights into problems in microbiology - and, in particular, cell-cycle progression and its regulation - through observation and quantitative analysis of the natural fluctuations in the system. We first illustrate this idea by reviewing a classic example in microbiology - the Luria-Delbrück experiment - and discussing how, in that case, useful information was obtained by looking beyond the mean outcome of the experiment, but instead paying attention to the variability between replicates of the experiment. We then switch gears to the contemporary problem of cell cycle progression and discuss in more detail how insights into cell size regulation and, when relevant, coupling between the cell cycle and the circadian clock, can be gained by studying the natural fluctuations in the system and their statistical properties. We end with a more general discussion of how (in this context) the correct level of phenomenological model should be chosen, as well as some of the pitfalls associated with this type of analysis. Throughout this review the emphasis is not on providing details of the experimental setups or technical details of the models used, but rather, in fleshing out the conceptual structure of this particular approach to the problem. For this reason, we choose to illustrate the framework on a rather broad range of problems, and on organisms from all domains of life, to emphasize the commonality of the ideas and analysis used (as well as their differences).

Tackling Antibiotic Resistance with Systems-Level Perspective.

Quantitative dissection of regulatory motifs could lead to new ways to fight antibiotic resistance.

An Experimental Framework for Quantifying Bacterial Tolerance.

Antibiotic tolerance and persistence are often associated with treatment failure and relapse, yet are poorly characterized. In distinction from resistance, which is measured using the minimum inhibitory concentration metric, tolerance and persistence values are not currently evaluated in the clinical setting, and so are overlooked when a course of treatment is prescribed. In this article, we introduce a metric and an automated experimental framework for measuring tolerance and persistence. The tolerance metric is the minimum duration for killing 99% of the population, MDK99, which can be evaluated by a statistical analysis of measurements performed manually or using a robotic system. We demonstrate the technique on strains of Escherichia coli with various tolerance levels. We hope that this, to our knowledge, new approach will be used, along with the existing minimum inhibitory concentration, as a standard for the in vitro characterization of sensitivity to antimicrobials. Quantification of tolerance and persistence may provide vital information in healthcare, and aid research in the field.

TDtest: easy detection of bacterial tolerance and persistence in clinical isolates by a modified disk-diffusion assay.

Antibiotic tolerance - the ability for prolonged survival under bactericidal treatments - is a potentially clinically significant phenomenon that is commonly overlooked in the clinical microbiology laboratory. Recent in vitro experiments show that high tolerance can evolve under intermittent antibiotic treatments in as little as eight exposures to high doses of antibiotics, suggesting that tolerance may evolve also in patients. However, tests for antibiotic susceptibilities, such as the disk-diffusion assay, evaluate only the concentration at which a bacterial strain stops growing, namely resistance level. High tolerance strains will not be detected using these tests. We present a simple modification of the standard disk-diffusion assay that allows the semi-quantitative evaluation of tolerance levels. This novel method, the "TDtest", enabled the detection of tolerant and persistent bacteria by promoting the growth of the surviving bacteria in the inhibition zone, once the antibiotic has diffused away. Using the TDtest, we were able to detect different levels of antibiotic tolerance in clinical isolates of E. coli. The TDtest also identified antibiotics that effectively eliminate tolerant bacteria. The additional information on drug susceptibility provided by the TDtest should enable tailoring better treatment regimens for pathogenic bacteria.

Antibiotic tolerance facilitates the evolution of resistance.

Controlled experimental evolution during antibiotic treatment can help to explain the processes leading to antibiotic resistance in bacteria. Recently, intermittent antibiotic exposures have been shown to lead rapidly to the evolution of tolerance-that is, the ability to survive under treatment without developing resistance. However, whether tolerance delays or promotes the eventual emergence of resistance is unclear. Here we used in vitro evolution experiments to explore this question. We found that in all cases, tolerance preceded resistance. A mathematical population-genetics model showed how tolerance boosts the chances for resistance mutations to spread in the population. Thus, tolerance mutations pave the way for the rapid subsequent evolution of resistance. Preventing the evolution of tolerance may offer a new strategy for delaying the emergence of resistance.

A long-term epigenetic memory switch controls bacterial virulence bimodality.

When pathogens enter the host, sensing of environmental cues activates the expression of virulence genes. Opposite transition of pathogens from activating to non-activating conditions is poorly understood. Interestingly, variability in the expression of virulence genes upon infection enhances colonization. In order to systematically detect the role of phenotypic variability in enteropathogenic E. coli (EPEC), an important human pathogen, both in virulence activating and non-activating conditions, we employed the ScanLag methodology. The analysis revealed a bimodal growth rate. Mathematical modeling combined with experimental analysis showed that this bimodality is mediated by a hysteretic memory-switch that results in the stable co-existence of non-virulent and hyper-virulent subpopulations, even after many generations of growth in non-activating conditions. We identified the per operon as the key component of the hysteretic switch. This unique hysteretic memory switch may result in persistent infection and enhanced host-to-host spreading.

Persistence to anti-cancer treatments in the stationary to proliferating transition.

The heterogeneous responses of clonal cancer cells to treatment is understood to be caused by several factors, including stochasticity, cell-cycle dynamics, and different micro-environments. In a tumor, cancer cells may encounter fluctuating conditions and transit from a stationary culture to a proliferating state, for example this may occur following treatment. Here, we undertake a quantitative evaluation of the response of single cancerous lymphoblasts (L1210 cells) to various treatments administered during this transition. Additionally, we developed an experimental system, a "Mammalian Mother Machine," that tracks the fate of thousands of mammalian cells over several generations under transient exposure to chemotherapeutic drugs. Using our developed system, we were able to follow the same cell under repeated treatments and continuously track many generations. We found that the dynamics of the transition between stationary and proliferative states are highly variable and affect the response to drug treatment. Using cell-cycle markers, we were able to isolate a subpopulation of persister cells with distinctly higher than average survival probability. The higher survival rate encountered with cell-cycle phase specific drugs was associated with a significantly longer time-till-division, and was reduced by a non cell-cycle specific drug. Our results suggest that the variability of transition times from the stationary to the proliferating state may be an obstacle hampering the effectiveness of drugs and should be taken into account when designing treatment regimens.

Distinguishing between resistance, tolerance and persistence to antibiotic treatment.

Antibiotic tolerance is associated with the failure of antibiotic treatment and the relapse of many bacterial infections. However, unlike resistance, which is commonly measured using the minimum inhibitory concentration (MIC) metric, tolerance is poorly characterized, owing to the lack of a similar quantitative indicator. This may lead to the misclassification of tolerant strains as resistant, or vice versa, and result in ineffective treatments. In this Opinion article, we describe recent studies of tolerance, resistance and persistence, outlining how a clear and distinct definition for each phenotype can be developed from these findings. We propose a framework for classifying the drug response of bacterial strains according to these definitions that is based on the measurement of the MIC together with a recently defined quantitative indicator of tolerance, the minimum duration for killing (MDK). Finally, we discuss genes that are associated with increased tolerance - the 'tolerome' - as targets for treating tolerant bacterial strains.

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability.

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues.

Quantitative Measurements of Type I and Type II Persisters Using ScanLag.

The present method quantifies the number of slow-growing bacteria leading to antibiotic persistence in a clonal population. First, it enables discriminating between slow growers that are generated by exposure to a stress signal (Type I persisters) and slow growers that are continuously generated during exponential growth (Type II persisters). Second, the method enables determining the amount of slow growers in a culture.