ScanLag: high-throughput quantification of colony growth and lag time

Bacterial persisters: molecular mechanisms and therapeutic development

Persisters refer to genetically drug susceptible quiescent (non-growing or slow growing) bacteria that survive in stress environments such as antibiotic exposure, acidic and starvation conditions. These cells can regrow after stress removal and remain susceptible to the same stress. Persisters are underlying the problems of treating chronic and persistent infections and relapse infections after treatment, drug resistance development, and biofilm infections, and pose significant challenges for effective treatments. Understanding the characteristics and the exact mechanisms of persister formation, especially the key molecules that affect the formation and survival of the persisters is critical to more effective treatment of chronic and persistent infections. Currently, genes related to persister formation and survival are being discovered and confirmed, but the mechanisms by which bacteria form persisters are very complex, and there are still many unanswered questions. This article comprehensively summarizes the historical background of bacterial persisters, details their complex characteristics and their relationship with antibiotic tolerant and resistant bacteria, systematically elucidates the interplay between various bacterial biological processes and the formation of persister cells, as well as consolidates the diverse anti-persister compounds and treatments. We hope to provide theoretical background for in-depth research on mechanisms of persisters and suggest new ideas for choosing strategies for more effective treatment of persistent infections.

The metabolic slowdown caused by the deletion of pspA accelerates protein aggregation during stationary phase facilitating antibiotic persistence

Entering a dormant state is a prevailing mechanism used by bacterial cells to transiently evade antibiotic attacks and become persisters. The dynamic progression of bacterial dormancy depths driven by protein aggregation has been found to be critical for antibiotic persistence in recent years. However, our current understanding of the endogenous genes that affects dormancy depth remains limited. Here, we discovered a novel role of phage shock protein A (pspA) gene in modulating bacterial dormancy depth. Deletion of pspA of Escherichia coli resulted in increased bacterial dormancy depths and prolonged lag times for resuscitation during the stationary phase. ∆pspA exhibited a higher persister ratio compared to the wild type when challenged with various antibiotics. Microscopic images revealed that ∆pspA showed accelerated formation of protein aggresomes, which were collections of endogenous protein aggregates. Time-lapse imaging established the positive correlation between protein aggregation and antibiotic persistence of ∆pspA at the single-cell level. To investigate the molecular mechanism underlying accelerated protein aggregation, we performed transcriptome profiling and found the increased abundance of chaperons and a general metabolic slowdown in the absence of pspA. Consistent with the transcriptomic results, the ∆pspA strain showed a decreased cellular ATP level, which could be rescued by glucose supplementation. Then, we verified that replenishment of cellular ATP levels by adding glucose could inhibit protein aggregation and reduce persister formation in ∆pspA. This study highlights the novel role of pspA in maintaining proteostasis, regulating dormancy depth, and affecting antibiotic persistence during stationary phase.

Toward standardized solid medium cultivations: Online microbial monitoring based on respiration activity

Cultivating microorganisms on solid agar media is a fundamental technique in microbiology and other related disciplines. For the evaluation, most often, a subjective visual examination is performed. Crucial information, such as metabolic activity, is not assessed. Thus, time-resolved monitoring of the respiration activity in agar cultivations is presented to provide additional insightful data on the metabolism. A modified version of the Respiration Activity MOnitoring System (RAMOS) was used to determine area-specific oxygen and carbon dioxide transfer rates and the resulting respiratory quotients of agar cultivations. Therewith, information on growth, substrate consumption, and product formation was obtained. The validity of the presented method was tested for different prokaryotic and eukaryotic organisms on agar, such as Escherichia coli BL21, Pseudomonas putida KT2440, Streptomyces coelicolor A3(2), Saccharomyces cerevisiae WT, Pichia pastoris WT, and Trichoderma reesei RUT-C30. Furthermore, it is showcased that several potential applications, including the determination of colony forming units, antibiotic diffusion tests, quality control for spore production or for pre-cultures and media optimization, can be quantitatively evaluated by interpretation of the respiration activity.

Antifungal Tolerance and Resistance Emerge at Distinct Drug Concentrations and Rely upon Different Aneuploid Chromosomes

Antifungal drug tolerance is a response distinct from resistance, in which cells grow slowly above the MIC. Here, we found that the majority (69.2%) of 133 Candida albicans clinical isolates, including standard lab strain SC5314, exhibited temperature-enhanced tolerance at 37°C and 39°C, and were not tolerant at 30°C. Other isolates were either always tolerant (23.3%) or never tolerant (7.5%) at these three temperatures, suggesting that tolerance requires different physiological processes in different isolates. At supra-MIC fluconazole concentrations (8 to 128 μg/mL), tolerant colonies emerged rapidly at a frequency of ~10^-3. In liquid passages over a broader range of fluconazole concentrations (0.25 to 128 μg/mL), tolerance emerged rapidly (within one passage) at supra-MICs. In contrast, resistance appeared at sub-MICs after 5 or more passages. Of 155 adaptors that evolved higher tolerance, all carried one of several recurrent aneuploid chromosomes, often including chromosome R, alone or in combination with other chromosomes. Furthermore, loss of these recurrent aneuploidies was associated with a loss of acquired tolerance, indicating that specific aneuploidies confer fluconazole tolerance. Thus, genetic background and physiology and the degree of drug stress (above or below the MIC) influence the evolutionary trajectories and dynamics with which antifungal drug resistance or tolerance emerges. IMPORTANCE Antifungal drug tolerance differs from drug resistance: tolerant cells grow slowly in drug, while resistant cells usually grow well, due to mutations in a few known genes. More than half of Candida albicans clinical isolates have higher tolerance at body temperature than they do at the lower temperatures used for most lab experiments. This implies that different isolates achieve drug tolerance via several cellular processes. When we evolved different strains at a range of high drug concentrations above inhibitory levels, tolerance emerged rapidly and at high frequency (one in 1,000 cells) while resistance appeared only later at very low drug concentrations. An extra copy of all or part of chromosome R was associated with tolerance, while point mutations or different aneuploidies were seen with resistance. Thus, genetic background and physiology, temperature, and drug concentration all influence how drug tolerance or resistance evolves.

Genetic, physiological, and cellular heterogeneities of bacterial pathogens in food matrices: Consequences for food safety

In complex food systems, bacteria live in heterogeneous microstructures, and the population displays phenotypic heterogeneities at the single-cell level. This review provides an overview of spatiotemporal drivers of phenotypic heterogeneity of bacterial pathogens in food matrices at three levels. The first level is the genotypic heterogeneity due to the possibility for various strains of a given species to contaminate food, each of them having specific genetic features. Then, physiological heterogeneities are induced within the same strain, due to specific microenvironments and heterogeneous adaptative responses to the food microstructure. The third level of phenotypic heterogeneity is related to cellular heterogeneity of the same strain in a specific microenvironment. Finally, we consider how these phenotypic heterogeneities at the single-cell level could be implemented in mathematical models to predict bacterial behavior and help ensure microbiological food safety.

A Role for the RNA Polymerase Gene Specificity Factor σ54 in the Uniform Colony Growth of Uropathogenic Escherichia coli

The canonical function of a bacterial sigma (σ) factor is to determine the gene specificity of the RNA polymerase (RNAP). In several diverse bacterial species, the σ⁵⁴ factor uniquely confers distinct functional and regulatory properties on the RNAP. A hallmark feature of the σ⁵⁴-RNAP is the obligatory requirement for an activator ATPase to allow transcription initiation. Different activator ATPases couple diverse environmental cues to the σ⁵⁴-RNAP to mediate adaptive changes in gene expression. Hence, the genes that rely upon σ⁵⁴ for their transcription have a wide range of different functions suggesting that the repertoire of functions performed by genes, directly or indirectly affected by σ⁵⁴, is not yet exhaustive. By comparing the growth patterns of prototypical enteropathogenic, uropathogenic, and nonpathogenic Escherichia coli strains devoid of σ⁵⁴, we uncovered that the absence of σ⁵⁴ results in two differently sized colonies that appear at different times specifically in the uropathogenic E. coli (UPEC) strain. Notably, UPEC bacteria devoid of individual activator ATPases of the σ⁵⁴-RNAP do not phenocopy the σ⁵⁴ mutant strain. Thus, it seems that σ⁵⁴’s role as a determinant of uniform colony appearance in UPEC bacteria represents a putative non-canonical function of σ⁵⁴ in regulating genetic information flow.

IMPORTANCE RNA synthesis is the first step of gene expression. The multisubunit RNA polymerase (RNAP) is the central enzyme responsible for RNA synthesis in bacteria. The dissociable sigma (σ) factor subunit directs the RNAP to different sets of genes to allow their expression in response to various cellular needs. Of the seven σ factors in Escherichia coli and related bacteria, σ⁵⁴ exists in a class of its own. This study has uncovered that σ⁵⁴ is a determinant of the uniform growth of uropathogenic E. coli on solid media. This finding suggests a role for this σ⁵⁴ in gene regulation that extends beyond its known function as an RNAP gene specificity factor.

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

Visualization and Quantification of Genetically Adapted Microbial Cells During Preculture

As culture history is known to affect the length of the lag phase and microbial cell growth, precultures are often grown in the same medium as the main culture for physiological adaptation and to reduce a prolonged lag time in some microbial cells. To understand the adaptation process of microbial cells during transfer from Luria-Bertani medium to minimal medium, we used the growth of Escherichia coli BL21(DE3) in succinate minimal medium as a model system. We observed that only one or two sequential transfers from minimal medium to fresh minimal medium accelerated the growth rate of BL21(DE3) cells. In addition, the number of large colonies (diameter ≥0.1 cm) on succinate agar increased with the number of transfers. Genome and transcript analyses showed that the C-to-T point mutation in large colony cells converted the inactive promoter of kgtP (known to encode α-ketoglutarate permease) to the active form, allowing efficient uptake of exogenous succinate. Moreover, we visualized the occurrence of genetically adapted cells with better fitness in real time and quantified the number of those cells in the microbial population during transfer to the same medium. Fluorescence microscopy showed the occurrence and increase of adapted mutant cells, which contain intracellular KgtP-fused green fluorescent proteins, as a result of the C-to-T mutation in the promoter of a fused kgtP-sfgfp during transfer to fresh medium. Flow cytometry revealed that the proportion of mutant cells increased from 1.75% (first transfer) to 12.16% (second transfer) and finally 70.79% (third transfer), explaining the shortened lag time and accelerated growth rate of BL21(DE3) cells during adaptation to the minimal medium. This study provides new insights into the genetic heterogeneity of microbial populations that aids microbial adaptability in new environments.

Inexpensive Apparatus for High-Quality Imaging of Microbial Growth on Agar Plates

The ability to capture images of results or processes is an important tool in the biologist’s tool kit. In microbiology, capturing high-quality images of microbial growth on agar plates is difficult due to the reflective surface of the plates and limitations in common photography techniques. Equipment is available to overcome these challenges, but acquisition costs are high. We have developed and tested an inexpensive and efficient apparatus for high-quality imaging of microbial colonies. The imaging box, as we have named the apparatus, is designed to eliminate glare and reduce reflections on the surface of the plate while providing uniform, diffuse light from all sides. The imaging box was used to capture hundreds of images in research and teaching lab settings.

Clinically relevant mutations in core metabolic genes confer antibiotic resistance

Although metabolism plays an active role in antibiotic lethality, antibiotic resistance is generally associated with drug target modification, enzymatic inactivation, and/or transport rather than metabolic processes. Evolution experiments of Escherichia coli rely on growth-dependent selection, which may provide a limited view of the antibiotic resistance landscape. We sequenced and analyzed E. coli adapted to representative antibiotics at increasingly heightened metabolic states. This revealed various underappreciated noncanonical genes, such as those related to central carbon and energy metabolism, which are implicated in antibiotic resistance. These metabolic alterations lead to lower basal respiration, which prevents antibiotic-mediated induction of tricarboxylic acid cycle activity, thus avoiding metabolic toxicity and minimizing drug lethality. Several of the identified metabolism-specific mutations are overrepresented in the genomes of >3500 clinical E. coli pathogens, indicating clinical relevance.

LI Detector: a framework for sensitive colony-based screens regardless of the distribution of fitness effects

Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: (1) that fitness effects are normally distributed, and (2) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive toward increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

Tackling microbial threats in agriculture with integrative imaging and computational approaches

Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.

Antibiotic resistance and persistence-Implications for human health and treatment perspectives

Antimicrobial resistance (AMR) and persistence are associated with an elevated risk of treatment failure and relapsing infections. They are thus important drivers of increased morbidity and mortality rates resulting in growing healthcare costs. Antibiotic resistance is readily identifiable with standard microbiological assays, and the threat imposed by antibiotic resistance has been well recognized. Measures aiming to reduce resistance development and spreading of resistant bacteria are being enforced. However, the phenomenon of bacteria surviving antibiotic exposure despite being fully susceptible, so-called antibiotic persistence, is still largely underestimated. In contrast to antibiotic resistance, antibiotic persistence is difficult to measure and therefore often missed, potentially leading to treatment failures. In this review, we focus on bacterial mechanisms allowing evasion of antibiotic killing and discuss their implications on human health. We describe the relationship between antibiotic persistence and bacterial heterogeneity and discuss recent studies that link bacterial persistence and tolerance with the evolution of antibiotic resistance. Finally, we review persister detection methods, novel strategies aiming at eradicating bacterial persisters and the latest advances in the development of new antibiotics.

PerSort Facilitates Characterization and Elimination of Persister Subpopulation in Mycobacteria

Mycobacterium tuberculosis (MTB) persists and survives antibiotic treatments by generating phenotypically heterogeneous drug-tolerant subpopulations. The surviving cells, persisters, are a major barrier to the relapse-free treatment of tuberculosis (TB), which is already killing >1.8 million people every year and becoming deadlier with the emergence of multidrug-resistant strains.

Mycobacterium tuberculosis (MTB) generates phenotypic diversity to persist and survive the harsh conditions encountered during infection. MTB avoids immune effectors and antibacterial killing by entering into distinct physiological states. The surviving cells, persisters, are a major barrier to the timely and relapse-free treatment of tuberculosis (TB). We present for the first time, PerSort, a method to isolate and characterize persisters in the absence of antibiotic or other pressure. We demonstrate the value of PerSort to isolate translationally dormant cells that preexisted in small numbers within Mycobacterium species cultures growing under optimal conditions but that dramatically increased in proportion under stress conditions. The translationally dormant subpopulation exhibited multidrug tolerance and regrowth properties consistent with those of persister cells. Furthermore, PerSort enabled single-cell transcriptional profiling that provided evidence that the translationally dormant persisters were generated through a variety of mechanisms, including vapC30, mazF, and relA/spoT overexpression. Finally, we demonstrate that notwithstanding the varied mechanisms by which the persister cells were generated, they converge on a similar low-oxygen metabolic state that was reversed through activation of respiration to rapidly eliminate persisters fostered under host-relevant stress conditions. We conclude that PerSort provides a new tool to study MTB persisters, enabling targeted strategies to improve and shorten the treatment of TB.

IMPORTANCEMycobacterium tuberculosis (MTB) persists and survives antibiotic treatments by generating phenotypically heterogeneous drug-tolerant subpopulations. The surviving cells, persisters, are a major barrier to the relapse-free treatment of tuberculosis (TB), which is already killing >1.8 million people every year and becoming deadlier with the emergence of multidrug-resistant strains. This study describes PerSort, a cell sorting method to isolate and characterize, without antibiotic treatment, translationally dormant persisters that preexist in small numbers within Mycobacterium cultures. Characterization of this subpopulation has discovered multiple mechanisms by which mycobacterial persisters emerge and unveiled the physiological basis for their dormant and multidrug-tolerant physiological state. This analysis has discovered that activating oxygen respiratory physiology using l-cysteine eliminates preexisting persister subpopulations, potentiating rapid antibiotic killing of mycobacteria under host-relevant stress. PerSort serves as a new tool to study MTB persisters for enabling targeted strategies to improve and shorten the treatment of TB.

Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

Populations of genetically identical bacteria are phenotypically heterogeneous, giving rise to population functionalities that would not be possible in homogeneous populations. For instance, a proportion of non-dividing bacteria could persist through antibiotic challenges and secure population survival. This heterogeneity can be studied in complex environmental or clinical samples by spreading the bacteria on agar plates and monitoring time to growth resumption in order to infer their metabolic state distribution. We present ColTapp, the Colony Time-lapse application for bacterial colony growth quantification. Its intuitive graphical user interface allows users to analyze time-lapse images of agar plates to monitor size, color and morphology of colonies. Additionally, images at isolated timepoints can be used to estimate lag time. Using ColTapp, we analyze a dataset of Staphylococcus aureus time-lapse images including populations with heterogeneous lag time. Colonies on dense plates reach saturation early, leading to overestimation of lag time from isolated images. We show that this bias can be corrected by taking into account the area available to each colony on the plate. We envision that in clinical settings, improved analysis of colony growth dynamics may help treatment decisions oriented towards personalized antibiotic therapies.

The Adaptive Response to Long-Term Nitrogen Starvation in Escherichia coli Requires the Breakdown of Allantoin

Bacteria initially respond to nutrient starvation by eliciting large-scale transcriptional changes. The accompanying changes in gene expression and metabolism allow the bacterial cells to effectively adapt to the nutrient-starved state. How the transcriptome subsequently changes as nutrient starvation ensues is not well understood. We used nitrogen (N) starvation as a model nutrient starvation condition to study the transcriptional changes in Escherichia coli experiencing long-term N starvation. The results reveal that the transcriptome of N-starved E. coli undergoes changes that are required to maximize chances of viability and to effectively recover growth when N starvation conditions become alleviated. We further reveal that, over time, N-starved E. coli cells rely on the degradation of allantoin for optimal growth recovery when N becomes replenished. This study provides insights into the temporally coordinated adaptive responses that occur in E. coli experiencing sustained N starvation.IMPORTANCE Bacteria in their natural environments seldom encounter conditions that support continuous growth. Hence, many bacteria spend the majority of their time in states of little or no growth due to starvation of essential nutrients. To cope with prolonged periods of nutrient starvation, bacteria have evolved several strategies, primarily manifesting themselves through changes in how the information in their genes is accessed. How these coping strategies change over time under nutrient starvation is not well understood, and this knowledge is important not only to broaden our understanding of bacterial cell function but also to potentially find ways to manage harmful bacteria. This study provides insights into how nitrogen-starved Escherichia coli bacteria rely on different genes during long-term nitrogen starvation.

Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.

Drug resistance and tolerance in fungi

Systemic fungal infections pose a serious clinical problem. Treatment options are limited, and antifungal drug resistance is increasing. In addition, a substantial proportion of patients do not respond to therapy despite being infected with fungi that are susceptible to the drug. The discordance between overall treatment outcome and low levels of clinical resistance may be attributable to antifungal drug tolerance. In this Review, we define and distinguish resistance and tolerance and discuss the current understanding of the molecular, genetic and physiological mechanisms that contribute to those phenomena. Distinguishing tolerance from resistance might provide important insights into the reasons for treatment failure in some settings.

Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations

While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with a cell's minimum tolerance level ranging between 0 mM and 8 mM. Tolerant cells have a distinct gene expression profile from non-tolerant cells. This form of heterogeneity is continuous in terms of threshold (the formaldehyde concentration where growth ceases), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell time-lapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin.

Scientists tend to appreciate microbes for their simplicity and predictability: a population of genetically identical cells inhabiting a uniform environment is expected to behave in a uniform way. However, counter-examples to this assumption are frequently being discovered, forcing a re-examination of the relationship between genotype and phenotype. In most such examples, bacterial cells are found to split into two discrete populations, for instance growing and non-growing. Here, we report the discovery of a novel example of microbial phenotypic heterogeneity in which cells are distributed along a gradient of phenotypes, ranging from low to high tolerance of a toxic chemical. Furthermore, we demonstrate that the distribution of phenotypes changes in different growth conditions, and we use mathematical modeling to show that cells may change their phenotype either randomly or in a particular direction in response to the environment. Our work expands our understanding of how a bacterial cell's genome, family history, and environment all contribute to its behavior, with implications for the diverse situations in which we care to understand the growth of any single-celled populations.

Type I toxin-dependent generation of superoxide affects the persister life cycle of Escherichia coli

Induction of growth stasis by bacterial toxins from chromosomal toxin-antitoxin systems is suspected to favor formation of multidrug-tolerant cells, named persisters. Recurrent infections are often attributed to resuscitation and regrowth of persisters upon termination of antibiotic therapy. Several lines of evidence point to oxidative stress as a crucial factor during the persister life cycle. Here, we demonstrate that the membrane-depolarizing type I toxins TisB, DinQ, and HokB have the potential to provoke reactive oxygen species formation in Escherichia coli. More detailed work with TisB revealed that mainly superoxide is formed, leading to activation of the SoxRS regulon. Deletion of the genes encoding the cytoplasmic superoxide dismutases SodA and SodB caused both a decline in TisB-dependent persisters and a delay in persister recovery upon termination of antibiotic treatment. We hypothesize that expression of depolarizing toxins during the persister formation process inflicts an oxidative challenge. The ability to counteract oxidative stress might determine whether cells will survive and how much time they need to recover from dormancy.

High-Throughput Proteomics Identifies Proteins With Importance to Postantibiotic Recovery in Depolarized Persister Cells

Bacterial populations produce phenotypic variants called persisters to survive harmful conditions. Persisters are highly tolerant to antibiotics and repopulate environments after the stress has vanished. In order to resume growth, persisters have to recover from the persistent state, but the processes behind recovery remain mostly elusive. Deciphering these processes is an essential step toward understanding the persister phenomenon in its entirety. High-throughput proteomics by mass spectrometry is a valuable tool to assess persister physiology during any stage of the persister life cycle, and is expected to considerably contribute to our understanding of the recovery process. In the present study, an Escherichia coli strain, that overproduces the membrane-depolarizing toxin TisB, was established as a model for persistence by the use of high-throughput proteomics. Labeling of TisB persisters with stable isotope-containing amino acids (pulsed-SILAC) revealed an active translational response to ampicillin, including several RpoS-dependent proteins. Subsequent investigation of the persister proteome during postantibiotic recovery by label-free quantitative proteomics identified proteins with importance to the recovery process. Among them, AhpF, a component of alkyl hydroperoxide reductase, and the outer membrane porin OmpF were found to affect the persistence time of TisB persisters. Assessing the role of AhpF and OmpF in TisB-independent persisters demonstrated that the importance of a particular protein for the recovery process strongly depends on the physiological condition of a persister cell. Our study provides important insights into persister physiology and the processes behind recovery of depolarized cells.

Antifungal tolerance is a subpopulation effect distinct from resistance and is associated with persistent candidemia

Tolerance to antifungal drug concentrations above the minimal inhibitory concentration (MIC) is rarely quantified, and current clinical recommendations suggest it should be ignored. Here, we quantify antifungal tolerance in Candida albicans isolates as the fraction of growth above the MIC, and find that it is distinct from susceptibility/resistance. Instead, tolerance is due to the slow growth of subpopulations of cells that overcome drug stress more efficiently than the rest of the population, and correlates inversely with intracellular drug accumulation. Many adjuvant drugs used in combination with fluconazole, a widely used fungistatic drug, reduce tolerance without affecting resistance. Accordingly, in an invertebrate infection model, adjuvant combination therapy is more effective than fluconazole in treating infections with highly tolerant isolates and does not affect infections with low tolerance isolates. Furthermore, isolates recovered from immunocompetent patients with persistent candidemia display higher tolerance than isolates readily cleared by fluconazole. Thus, tolerance correlates with, and may help predict, patient responses to fluconazole therapy.

The authors show that antifungal tolerance, defined as the fraction of growth of a fungal pathogen above the minimal inhibitory concentration, is due to the slow growth of subpopulations of cells that overcome drug stress, and that high tolerance is often associated with persistent infections.

The spatiotemporal system dynamics of acquired resistance in an engineered microecology

Great strides have been made in the understanding of complex networks; however, our understanding of natural microecologies is limited. Modelling of complex natural ecological systems has allowed for new findings, but these models typically ignore the constant evolution of species. Due to the complexity of natural systems, unanticipated interactions may lead to erroneous conclusions concerning the role of specific molecular components. To address this, we use a synthetic system to understand the spatiotemporal dynamics of growth and to study acquired resistance in vivo. Our system differs from earlier synthetic systems in that it focuses on the evolution of a microecology from a killer-prey relationship to coexistence using two different non-motile Escherichia coli strains. Using empirical data, we developed the first ecological model emphasising the concept of the constant evolution of species, where the survival of the prey species is dependent on location (distance from the killer) or the evolution of resistance. Our simple model, when expanded to complex microecological association studies under varied spatial and nutrient backgrounds may help to understand the complex relationships between multiple species in intricate natural ecological networks. This type of microecological study has become increasingly important, especially with the emergence of antibiotic-resistant pathogens.

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.

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.

A review of the current state of digital plate reading of cultures in clinical microbiology

Digital plate reading (DPR) is increasingly being adopted as a means to facilitate the analysis and improve the quality and efficiency within the clinical microbiology laboratory. This review discusses the role of DPR in the context of total laboratory automation and explores some of the platforms currently available or in development for digital image capturing of microbial growth on media. The review focuses on the advantages and challenges of DPR. Peer-reviewed studies describing the utility and quality of these novel DPR systems are largely lacking, and professional guidelines for DPR implementation and quality management are needed. Further development and more widespread adoption of DPR is anticipated.