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.

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.

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.

Systematic identification and quantification of phase variation in commensal and pathogenic Escherichia coli.

Bacteria have been shown to generate constant genetic variation in a process termed phase variation. We present a tool based on whole genome sequencing that allows detection and quantification of coexisting genotypes mediated by genomic inversions in bacterial cultures. We tested our method on widely used strains of Escherichia coli, and detected stable and reproducible phase variation in several invertible loci. These are shown here to be responsible for maintaining constant variation in populations grown from a single colony. Applying this tool on other bacterial strains can shed light on how pathogens adjust to hostile environments by diversifying their genomes.

Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations.

The great therapeutic achievements of antibiotics have been dramatically undercut by the evolution of bacterial strategies that overcome antibiotic stress. These strategies fall into two classes. 'Resistance' makes it possible for a microorganism to grow in the constant presence of the antibiotic, provided that the concentration of the antibiotic is not too high. 'Tolerance' allows a microorganism to survive antibiotic treatment, even at high antibiotic concentrations, as long as the duration of the treatment is limited. Although both resistance and tolerance are important reasons for the failure of antibiotic treatments, the evolution of resistance is much better understood than that of tolerance. Here we followed the evolution of bacterial populations under intermittent exposure to the high concentrations of antibiotics used in the clinic and characterized the evolved strains in terms of both resistance and tolerance. We found that all strains adapted by specific genetic mutations, which became fixed in the evolved populations. By monitoring the phenotypic changes at the population and single-cell levels, we found that the first adaptive change to antibiotic stress was the development of tolerance through a major adjustment in the single-cell lag-time distribution, without a change in resistance. Strikingly, we found that the lag time of bacteria before regrowth was optimized to match the duration of the antibiotic-exposure interval. Whole genome sequencing of the evolved strains and restoration of the wild-type alleles allowed us to identify target genes involved in this antibiotic-driven phenotype: 'tolerance by lag' (tbl). Better understanding of lag-time evolution as a key determinant of the survival of bacterial populations under high antibiotic concentrations could lead to new approaches to impeding the evolution of antibiotic resistance.

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.

HipA-mediated antibiotic persistence via phosphorylation of the glutamyl-tRNA-synthetase.

Bacterial persistence has been shown to be an underlying factor in the failure of antibiotic treatments. Although many pathways, among them the stringent response and toxin-antitoxin modules, have been linked to antibiotic persistence, a clear molecular mechanism for the growth arrest that characterizes persistent bacteria remained elusive. Here, we screened an expression library for putative targets of HipA, the first toxin linked to persistence, and a serine/threonine kinase. We found that the expression of GltX, the glutamyl-tRNA-synthetase, reverses the toxicity of HipA and prevents persister formation. We show that upon HipA expression, GltX undergoes phosphorylation at Ser239, its ATP-binding site. This phosphorylation leads to accumulation of uncharged tRNA(Glu) in the cell, which results in the activation of the stringent response. Our findings demonstrate a mechanism for persister formation by the hipBA toxin-antitoxin module and provide an explanation for the long-observed connection between persistence and the stringent response.

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.

Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence.

In the face of antibiotics, bacterial populations avoid extinction by harboring a subpopulation of dormant cells that are largely drug insensitive. This phenomenon, termed "persistence," is a major obstacle for the treatment of a number of infectious diseases. The mechanism that generates both actively growing as well as dormant cells within a genetically identical population is unknown. We present a detailed study of the toxin-antitoxin module implicated in antibiotic persistence of Escherichia coli. We find that bacterial cells become dormant if the toxin level is higher than a threshold, and that the amount by which the threshold is exceeded determines the duration of dormancy. Fluctuations in toxin levels above and below the threshold result in coexistence of dormant and growing cells. We conclude that toxin-antitoxin modules in general represent a mixed network motif that can serve to produce a subpopulation of dormant cells and to supply a mechanism for regulating the frequency and duration of growth arrest. Toxin-antitoxin modules thus provide a natural molecular design for implementing a bet-hedging strategy.