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.

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.

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.

Tackling Antibiotic Resistance with Systems-Level Perspective.

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

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.

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.

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.

Direct observation of single stationary-phase bacteria reveals a surprisingly long period of constant protein production activity.

Exponentially growing bacteria are rarely found in the wild, as microorganisms tend to spend most of their lifetime at stationary phase. Despite this general prevalence of stationary-phase bacteria, they are as yet poorly characterized. Our goal was to quantitatively study this phase by direct observation of single bacteria as they enter into stationary phase and by monitoring their activity over several days during growth arrest. For this purpose, we devised an experimental procedure for starving single Escherichia coli bacteria in microfluidic devices and measured their activity by monitoring the production rate of fluorescent proteins. When amino acids were the sole carbon source, the production rate decreased by an order of magnitude upon entry into stationary phase. We found that, even while growth-arrested, bacteria continued to produce proteins at a surprisingly constant rate over several days. Our identification of this newly observed period of constant activity in nongrowing cells, designated as constant activity stationary phase, makes possible the conduction of assays that require constant protein expression over time, and are therefore difficult to perform under exponential growth conditions. Moreover, we show that exogenous protein expression bears no fitness cost on the regrowth of the population when starvation ends. Further characterization of constant activity stationary phase-a phase where nongrowing bacteria can be quantitatively studied over several days in a reproducible manner-should contribute to a better understanding of this ubiquitous but overlooked physiological state of bacteria in nature.

Automated imaging with ScanLag reveals previously undetectable bacterial growth phenotypes.

We developed an automated system, ScanLag, that measures in parallel the delay in growth (lag time) and growth rate of thousands of cells. Using ScanLag, we detected small subpopulations of bacteria with dramatically increased lag time upon starvation. By screening a library of Escherichia coli deletion mutants, we achieved two-dimensional mapping of growth characteristics, which showed that ScanLag enables multidimensional screens for quantitative characterization and identification of rare phenotypic variants.

The importance of being persistent: heterogeneity of bacterial populations under antibiotic stress.

While the DNA sequence is largely responsible for transmitting phenotypic traits over evolutionary time, organisms are also considerably affected by phenotypic variations that persist for more than one generation, with no direct change in the organisms' DNA sequence. In contrast to genetic variation, which is passed on over many generations, the phenotypic variation generated by nongenetic mechanisms is difficult to study due to the inherently limited life time of states that are not encoded in the DNA sequence, but makes it possible for the 'memory' of past environments to influence future organisms. One striking example of phenotypic variation is the phenomenon of bacterial persistence, whereby genetically identical bacterial populations respond heterogeneously to antibiotic treatment. Our aim is to review several experimental and theoretical approaches to the study of persistence. We define persistence as a characteristic of a heterogeneous bacterial population that is taken as a generic example through which we illustrate the approach and study the dynamics of population variability. The clinical and evolutionary implications of persistence are discussed in light of the mathematical description. This approach should be of relevance to the study of other phenomena in which nongenetic variability is involved, such as cellular differentiation or the response of cancer cells to treatment.

The Moore's Law of microbiology - towards bacterial culture miniaturization with the micro-Petri chip.

A recent publication has reported the fabrication of a new microbial culture device containing an array of unprecedented density - a million growth chambers. This micro-Petri chip should impact on various fields, including biotechnology, ecology, food microbiology and drug development, by enabling high-throughput screens and therefore the detection of rare phenotypes within a large population of microorganisms.

Single-cell protein induction dynamics reveals a period of vulnerability to antibiotics in persister bacteria.

Phenotypic variability in populations of cells has been linked to evolutionary robustness to stressful conditions. A remarkable example of the importance of cell-to-cell variability is found in bacterial persistence, where subpopulations of dormant bacteria, termed persisters, were shown to be responsible for the persistence of the population to antibiotic treatments. Here, we use microfluidic devices to monitor the induction of fluorescent proteins under synthetic promoters and characterize the dormant state of single persister bacteria. Surprisingly, we observe that protein production does take place in supposedly dormant bacteria, over a narrow time window after the exit from stationary phase. Only thereafter does protein production stop, suggesting that differentiation into persisters fully develops over this time window and not during starvation, as previously believed. In effect, we observe that exposure of bacteria to antibiotics during this time window significantly reduces persistence. Our results point to new strategies to fight persistent bacterial infections. The quantitative measurement of single-cell induction presented in this study should shed light on the processes leading to the dormancy of subpopulations in different systems, such as in subpopulations of viable but nonculturable bacteria, or those of quiescent cancer cells.