Phenotypic bistability in Escherichia coli's central carbon metabolism

Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity

A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.

Modelling the colony growth dynamics of Listeria monocytogenes single cells after exposure to peracetic acid and acidic conditions

Studies of classical microbiology rely on the average behaviour of large cell populations without considering that clonal bacterial populations may bifurcate into phenotypic distinct sub-populations by random switching mechanisms.Listeria monocytogenes exposure to sublethal stresses may induce different physiological states that co-exist (i.e., sublethal injury or dormancy) and present variable resuscitation capacity. Exposures to peracetic acid (PAA; 10-30 ppm; for 3 h), acetic acid and hydrochloric acid (AA and HCl; pH 3.0-2.5; for 5 h) at 20 °C were used to induce different physiological states in L. monocytogenes, Scott A strain. After stress exposure, colony growth of single cells was monitored, on Tryptic Soy Agar supplemented with 0.6 % Yeast Extract, using time-lapse microscopy, at 37 °C. Images were acquired every 5 min and were analyzed using BaSCA framework. Most of the obtained growth curves of the colonies were fitted to the model of Baranyi and Roberts for the estimation of lag time (λ) and maximum specific growth rate (μmax), except the ones obtained after exposure to AA pH 2.7 and 2.5 that were fitted to the Trilinear model. The data of λ and μmax that followed a multivariate normal distribution were used to predict growth variability using Monte Carlo simulations. Outgrowth kinetics after treatment with AA (pH 2.7 and 2.5; for 5 h at 20 °C), PAA (30 ppm; for 3 h at 20 °C) revealed that these stress conditions increase the skewness of the variability distributions to the right, meaning that the variability in lag times increases in favour of longer outgrowth. Exposures to AA pH 2.5 and 30 ppm PAA resulted in two distinct subpopulations per generation with different growth dynamics. This switching mechanism may have evolved as a survival strategy for L. monocytogenes cells, maximizing the chances of survival. Simulation of microbial growth showed that heterogeneity in growth dynamics is increased when cells are recovering from exposure to sublethal stresses (i.e. PAA and acidic conditions) that may induce injury or dormancy.

Decoding Bacterial Persistence: Mechanisms and Strategies for Effective Eradication

The ability of pathogenic bacteria to evade antibiotic treatment is an intricate and multifaceted phenomenon. Over the years, treatment failure among patients due to determinants of antimicrobial resistance (AMR) has been the focal point for the research and development of new therapeutic agents. However, the survival of bacteria by persisting under antibiotic stress has largely been overlooked. Bacterial persisters are a subpopulation of sensitive bacterial cells exhibiting a noninheritable drug-tolerant phenotype. They are linked to the recalcitrance of infections in healthcare settings, in turn giving rise to AMR variants. The importance of bacterial persistence in recurring infections has been firmly recognized. Fundamental work over the past decade has highlighted numerous unique tolerance factors contributing to the persister phenotype in many clinically relevant pathogens. This review summarizes contributing factors that could aid in developing new strategies against bacterial antibiotic persisters.

Quantum modeling simulates nutrient effect of bioplastic polyhydroxyalkanoate (PHA) production in Pseudomonas putida

Polyhydroxyalkanoates (PHAs) could be used to make sustainable, biodegradable plastics. However, the precise and accurate mechanistic modeling of PHA biosynthesis, especially medium-chain-length PHA (mcl-PHA), for yield improvement remains a challenge to biology. PHA biosynthesis is typically triggered by nitrogen limitation and tends to peak at an optimal carbon-to-nitrogen (C/N) ratio. Specifically, simulation of the underlying dynamic regulation mechanisms for PHA bioprocess is a bottleneck owing to surfeit model complexity and current modeling philosophies for uncertainty. To address this issue, we proposed a quantum-like decision-making model to encode gene expression and regulation events as hidden layers by the general transformation of a density matrix, which uses the interference of probability amplitudes to provide an empirical-level description for PHA biosynthesis. We implemented our framework modeling the biosynthesis of mcl-PHA in Pseudomonas putida with respect to external C/N ratios, showing its optimization production at maximum PHA production of 13.81% cell dry mass (CDM) at the C/N ratio of 40:1. The results also suggest the degree of P. putida's preference in channeling carbon towards PHA production as part of the bacterium's adaptative behavior to nutrient stress using quantum formalism. Generic parameters (kD, kN and theta θ) obtained based on such quantum formulation, representing P. putida's PHA biosynthesis with respect to external C/N ratios, was discussed. This work offers a new perspective on the use of quantum theory for PHA production, demonstrating its application potential for other bioprocesses.

Algal methylated compounds shorten the lag phase of Phaeobacter inhibens bacteria

The lag phase is key in resuming bacterial growth, but it remains underexplored particularly in environmental bacteria. Here we use transcriptomics and 13C-labelled metabolomics to show that the lag phase of the model marine bacterium Phaeobacter inhibens is shortened by methylated compounds produced by the microalgal partner, Emiliania huxleyi. Methylated compounds are abundantly produced and released by microalgae, and we show that their methyl groups can be collected by bacteria and assimilated through the methionine cycle. Our findings underscore the significance of methyl groups as a limiting factor during the lag phase and highlight the adjustability of this growth phase. In addition, we show that methylated compounds, typical of photosynthetic organisms, prompt diverse reductions in lag times in bacteria associated with algae and plants, potentially favouring early growth in some bacteria. These findings suggest ways to accelerate bacterial growth and underscore the significance of studying bacteria within an environmental context.

Pseudomonas aeruginosa breaches respiratory epithelia through goblet cell invasion in a microtissue model

Pseudomonas aeruginosa, a leading cause of severe hospital-acquired pneumonia, causes infections with up to 50% mortality rates in mechanically ventilated patients. Despite some knowledge of virulence factors involved, it remains unclear how P. aeruginosa disseminates on mucosal surfaces and invades the tissue barrier. Using infection of human respiratory epithelium organoids, here we observed that P. aeruginosa colonization of apical surfaces is promoted by cyclic di-GMP-dependent asymmetric division. Infection with mutant strains revealed that Type 6 Secretion System activities promote preferential invasion of goblet cells. Type 3 Secretion System activity by intracellular bacteria induced goblet cell death and expulsion, leading to epithelial rupture which increased bacterial translocation and dissemination to the basolateral epithelium. These findings show that under physiological conditions, P. aeruginosa uses coordinated activity of a specific combination of virulence factors and behaviours to invade goblet cells and breach the epithelial barrier from within, revealing mechanistic insight into lung infection dynamics.

Highly Multiplexed Spatial Transcriptomics in Bacteria

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial mRNA. To overcome this challenge, we combine 1000-fold volumetric expansion with multiplexed error robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH. This method enables high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissect the response of E. coli to carbon starvation, systematically map subcellular RNA organization, and chart the adaptation of a gut commensal B. thetaiotaomicron to micron-scale niches in the mammalian colon. We envision bacterial-MERFISH will be broadly applicable to the study of bacterial single-cell heterogeneity in diverse, spatially structured, and native environments.

Reducing the Bacterial Lag Phase Through Methylated Compounds: Insights from Algal-Bacterial Interactions

The bacterial lag phase is a key period for resuming growth. Despite its significance, the lag phase remains underexplored, particularly in environmental bacteria. Here, we explore the lag phase of the model marine bacterium Phaeobacter inhibens when it transitions from starvation to growth with a microalgal partner. Utilizing transcriptomics and 13 C-labeled metabolomics, our study reveals that methylated compounds, which are abundantly produced by microalgae, shorten the bacterial lag phase. Our findings underscore the significance of methyl groups as a limiting factor during the lag phase and demonstrate that methyl groups can be harvested from algal compounds and assimilated through the methionine cycle. Furthermore, we show that methylated compounds, characteristic of photosynthetic organisms, induce variable reductions in lag times among bacteria associated with algae and plants. These findings highlight the adjustability of the bacterial lag phase and emphasize the importance of studying bacteria in an environmental context.

One-Sentence Summary

Bacteria use algal compounds as a metabolic shortcut to transition from starvation to growth.

Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

Limosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri's TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri's adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri's response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production.

IMPORTANCE

We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.

Universal Transitions between Growth and Dormancy via Intermediate Complex Formation

A simple cell model consisting of a catalytic reaction network with intermediate complex formation is numerically studied. As nutrients are depleted, the transition from the exponential growth phase to the growth-arrested dormant phase occurs along with hysteresis and a lag time for growth recovery. This transition is caused by the accumulation of intermediate complexes, leading to the jamming of reactions and the diversification of components. These properties are generic in random reaction networks, as supported by dynamical systems analyses of corresponding mean-field models.

Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation-Inhibiting Antibiotics

Understanding the phenotypic heterogeneity of antibiotic-resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol-induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time-lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non-equilibrium landscape and flux theory, the real-time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non-equilibrium dynamics and thermodynamics at play. The results highlight the multi-dimensional heterogeneous behaviors of antibiotic-resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.

Laboratory Evolution of Antimicrobial Resistance in Bacteria to Develop Rational Treatment Strategies

Laboratory evolution studies, particularly with Escherichia coli, have yielded invaluable insights into the mechanisms of antimicrobial resistance (AMR). Recent investigations have illuminated that, with repetitive antibiotic exposures, bacterial populations will adapt and eventually become tolerant and resistant to the drugs. Through intensive analyses, these inquiries have unveiled instances of convergent evolution across diverse antibiotics, the pleiotropic effects of resistance mutations, and the role played by loss-of-function mutations in the evolutionary landscape. Moreover, a quantitative analysis of multidrug combinations has shed light on collateral sensitivity, revealing specific drug combinations capable of suppressing the acquisition of resistance. This review article introduces the methodologies employed in the laboratory evolution of AMR in bacteria and presents recent discoveries concerning AMR mechanisms derived from laboratory evolution. Additionally, the review outlines the application of laboratory evolution in endeavors to formulate rational treatment strategies.

Expanding the microbiologist toolbox via new far-red-emitting dyes suitable for bacterial imaging

IMPORTANCE

By harnessing the versatility of fluorescence microscopy and super-resolution imaging, bacteriologists explore critical aspects of bacterial physiology and resolve bacterial structures sized beyond the light diffraction limit. These techniques are based on fluorophores with profitable photochemical and tagging properties. The paucity of available far-red (FR)-emitting dyes for bacterial imaging strongly limits the multicolor choice of bacteriologists, hindering the possibility of labeling multiple structures in a single experiment. The set of FR fluorophores characterized in this study expands the palette of dyes useful for microbiologists, as they can be used for bacterial LIVE/DEAD staining and for tagging the membranes of viable Escherichia coli and Bacillus subtilis cells. The absence of toxicity makes these dyes suitable for live-cell imaging and allows monitoring of bacterial membrane biogenesis. Moreover, a newly synthesized FR-fluorophore can be employed for imaging bacterial membranes with stimulated emission depletion microscopy, a super-resolution technique capable of increasing the resolving power of conventional microscopes.

Minorities drive growth resumption in cross-feeding microbial communities

Microbial communities are fundamental to life on Earth. Different strains within these communities are often connected by a highly connected metabolic network, where the growth of one strain depends on the metabolic activities of other community members. While distributed metabolic functions allow microbes to reduce costs and optimize metabolic pathways, they make them metabolically dependent. Here, we hypothesize that such dependencies can be detrimental in situations where the external conditions change rapidly, as they often do in natural environments. After a shift in external conditions, microbes need to remodel their metabolism, but they can only resume growth once partners on which they depend have also adapted to the new conditions. It is currently not well understood how microbial communities resolve this dilemma and how metabolic interactions are reestablished after an environmental shift. To address this question, we investigated the dynamical responses to environmental perturbation by microbial consortia with distributed anabolic functions. By measuring the regrowth times at the single-cell level in spatially structured communities, we found that metabolic dependencies lead to a growth delay after an environmental shift. However, a minority of cells-those in the immediate neighborhood of their metabolic partners-can regrow quickly and come to numerically dominate the community after the shift. The spatial arrangement of a microbial community is thus a key factor in determining the communities' ability to maintain metabolic interactions and growth in fluctuating conditions. Our results suggest that environmental fluctuations can limit the emergence of metabolic dependencies between microorganisms.

Metabolomic rearrangement controls the intrinsic microbial response to temperature changes

Temperature is one of the key determinants of microbial behavior and survival, whose impact is typically studied under heat- or cold-shock conditions that elicit specific regulation to combat lethal stress. At intermediate temperatures, cellular growth rate varies according to the Arrhenius law of thermodynamics without stress responses, a behavior whose origins have not yet been elucidated. Using single-cell microscopy during temperature perturbations, we show that bacteria exhibit a highly conserved, gradual response to temperature upshifts with a time scale of ~1.5 doublings at the higher temperature, regardless of initial/final temperature or nutrient source. We find that this behavior is coupled to a temperature memory, which we rule out as being neither transcriptional, translational, nor membrane dependent. Instead, we demonstrate that an autocatalytic enzyme network incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, which encodes a temperature memory and successfully predicts alterations in the upshift response observed under simple-sugar, low-nutrient conditions, and in fungi. This model also provides a mechanistic framework for both Arrhenius-dependent growth and the classical Monod Equation through temperature-dependent metabolite flux.

A genetic switch controls Pseudomonas aeruginosa surface colonization

Efficient colonization of mucosal surfaces is essential for opportunistic pathogens like Pseudomonas aeruginosa, but how bacteria collectively and individually adapt to optimize adherence, virulence and dispersal is largely unclear. Here we identified a stochastic genetic switch, hecR-hecE, which is expressed bimodally and generates functionally distinct bacterial subpopulations to balance P. aeruginosa growth and dispersal on surfaces. HecE inhibits the phosphodiesterase BifA and stimulates the diguanylate cyclase WspR to increase c-di-GMP second messenger levels and promote surface colonization in a subpopulation of cells; low-level HecE-expressing cells disperse. The fraction of HecE+ cells is tuned by different stress factors and determines the balance between biofilm formation and long-range cell dispersal of surface-grown communities. We also demonstrate that the HecE pathway represents a druggable target to effectively counter P. aeruginosa surface colonization. Exposing such binary states opens up new ways to control mucosal infections by a major human pathogen.

Dynamic fluctuations in a bacterial metabolic network

The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.

Application of an Escherichia coli triple reporter strain for at-line monitoring of single-cell physiology during L-phenylalanine production

Biotechnological production processes are sustainable approaches for the production of biobased components such as amino acids for food and feed industry. Scale-up from ideal lab-scale bioreactors to large-scale processes is often accompanied by loss in productivity. This may be related to population heterogeneities of cells originating from isogenic cultures that arise due to dynamic non-ideal conditions in the bioreactor. To better understand this phenomenon, deeper insights into single-cell physiologies in bioprocesses are mandatory before scale-up. Here, a triple reporter strain (3RP) was developed by chromosomally integrating the fluorescent proteins mEmerald, CyOFP1, and mTagBFP2 into the L-phenylalanine producing Escherichia coli strain FUS4 (pF81kan) to allow monitoring of growth, oxygen availability, and general stress response of the single cells. Functionality of the 3RP was confirmed in well-mixed lab-scale fed-batch processes with glycerol as carbon source in comparison to the strain without fluorescent proteins, leading to no difference in process performance. Fluorescence levels could successfully reflect the course of related process state variables, revealed population heterogeneities during the transition between different process phases and potentially subpopulations that exhibit superior process performance. Furthermore, indications were found for noise in gene expression as regulation strategy against environmental perturbation.

Navigating Environmental Transitions: the Role of Phenotypic Variation in Bacterial Responses

The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.

Precursor prioritization for p-cymene production through synergistic integration of biology and chemistry

The strategy of synergistic application of biological and chemical catalysis is an important approach for efficiently converting renewable biomass into chemicals and fuels. In particular, the method of determining the appropriate intermediate between the two catalytic methods is critical. In this work, we demonstrate p-cymene production through the integration of biosynthesis and heterogenous catalysis and show how a preferred biologically derived precursor could be determined. On the biological side, we performed the limonene and 1,8-cineole production through the mevalonate pathway. Titers of 0.605 g/L and a 1.052 g/L were achieved, respectively. This difference is in agreement with the toxicity of these compounds toward the producing microorganisms, which has implications for subsequent development of the microbial platform. On the heterogeneous catalysis side, we performed the reaction with both biological precursors to allow for direct comparison. Using hydrogenation/dehydrogenation metals on supports with acid sites, both limonene and 1,8-cineole were converted to p-cymene with similar yields under equivalent reaction conditions. Thus, we could determine that the most promising strategy would be to target 1,8-cineole, the higher titer and lower toxicity bio-derived precursor with subsequent catalytic conversion to p-cymene. We further optimized the biological production of 1,8-cineole via fed-batch fermentation and reached the titer of 4.37 g/L which is the highest known 1,8-cineole titer from microbial production. This work provides a valuable paradigm for early stage considerations to determine the best route for the high-efficiency production of a target biobased molecule using an integration of biology and chemistry.

Intercellular interaction mechanisms promote diversity in intracellular ATP concentration in Escherichia coli populations

In fluctuating environments, many microorganisms acquire phenotypic heterogeneity as a survival tactic to increase the likelihood of survival of the overall population. One example of this interindividual heterogeneity is the diversity of ATP concentration among members of Escherichia coli populations under glucose deprivation. Despite the importance of such environmentally driven phenotypic heterogeneity, how the differences in intracellular ATP concentration emerge among individual E. coli organisms is unknown. In this study, we focused on the mechanism through which individual E. coli achieve high intracellular ATP concentrations. First, we measured the ATP retained by E. coli over time when cultured at low (0.1 mM) and control (22.2 mM) concentrations of glucose and obtained the chronological change in ATP concentrations. Then, by comparing these chronological change of ATP concentrations and analyzing whether stochastic state transitions, periodic oscillations, cellular age, and intercellular communication-which have been reported as molecular biological mechanisms for generating interindividual heterogeneity-are involved, we showed that the appearance of high ATP-holding individuals observed among E. coli can be explained only by intercellular transmission. By performing metabolomic analysis of post-culture medium, we revealed a significant increase in the ATP, especially at low glucose, and that the number of E. coli that retain significantly higher ATP can be controlled by adding large amounts of ATP to the medium, even in populations cultured under control glucose concentrations. These results reveal for the first time that ATP-mediated intercellular transmission enables some individuals in E. coli populations grown at low glucose to retain large amounts of ATP.

The top 100 cited studies on bacterial persisters: A bibliometric analysis

Background: Bacterial persisters are thought to be responsible for the recalcitrance and relapse of persistent infections, and they also lead to antibiotic treatment failure in clinics. In recent years, researches on bacterial persisters have attracted worldwide attention and the number of related publications is increasing. The purpose of this study was to better understand research trends on bacterial persisters by identifying and bibliometrics analyzing the top 100 cited publications in this field.

Methods: The Web of Science Core Collection was utilized to retrieve the highly cited publications on bacterial persisters, and these publications were cross-matched with Google Scholar and Scopus. The top 100 cited publications were identified after reviewing the full texts. The main information of each publication was extracted and analyzed using Excel, SPSS, and VOSviewer.

Results: The top 100 cited papers on bacterial persisters were published between 1997 and 2019. The citation frequency of each publication ranged from 147 to 1815 for the Web of Science Core Collection, 153 to 1883 for Scopus, and 207 to 2,986 for Google Scholar. Among the top 100 cited list, there were 64 original articles, 35 review articles, and 1 editorial material. These papers were published in 51 journals, and the Journal of Bacteriology was the most productive journal with 8 papers. A total of 14 countries made contributions to the top 100 cited publications, and 64 publications were from the United States. 15 institutions have published two or more papers and nearly 87% of them were from the United States. Kim Lewis from Northeastern University was the most influential author with 18 publications. Furthermore, keywords co-occurrence suggested that the main topics on bacterial persisters were mechanisms of persister formation or re-growth. Finally, “Microbiology” was the most frequent category in this field.

Conclusion: This study identified and analyzed the top 100 cited publications related to bacterial persisters. The results provided a general overview of bacterial persisters and might help researchers to better understand the classic studies, historical developments, and new findings in this field, thus providing ideas for further research.

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

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

Glucose feeds the tricarboxylic acid cycle via excreted ethanol in fermenting yeast

Ethanol and lactate are typical waste products of glucose fermentation. In mammals, glucose is catabolized by glycolysis into circulating lactate, which is broadly used throughout the body as a carbohydrate fuel. Individual cells can both uptake and excrete lactate, uncoupling glycolysis from glucose oxidation. Here we show that similar uncoupling occurs in budding yeast batch cultures of Saccharomyces cerevisiae and Issatchenkia orientalis. Even in fermenting S. cerevisiae that is net releasing ethanol, media ¹³C-ethanol rapidly enters and is oxidized to acetaldehyde and acetyl-CoA. This is evident in exogenous ethanol being a major source of both cytosolic and mitochondrial acetyl units. ²H-tracing reveals that ethanol is also a major source of both NADH and NADPH high-energy electrons, and this role is augmented under oxidative stress conditions. Thus, uncoupling of glycolysis from the oxidation of glucose-derived carbon via rapidly reversible reactions is a conserved feature of eukaryotic metabolism.

Single-cell stable isotope probing in microbial ecology

Environmental and host-associated microbiomes are typically diverse assemblages of organisms performing myriad activities and engaging in a network of interactions that play out in spatially structured contexts. As the sum of these activities and interactions give rise to overall microbiome function, with important consequences for environmental processes and human health, elucidating specific microbial activities within complex communities is a pressing challenge. Single-cell stable isotope probing (SC-SIP) encompasses multiple techniques that typically utilize Raman microspectroscopy or nanoscale secondary ion mass spectrometry (NanoSIMS) to enable spatially resolved tracking of isotope tracers in cells, cellular components, and metabolites. SC-SIP techniques are uniquely suited for illuminating single-cell activities in microbial communities and for testing hypotheses about cellular functions generated for example from meta-omics datasets. Here, we illustrate the insights enabled by SC-SIP techniques by reviewing selected applications in microbiology and offer a perspective on their potential for future research.

Gene regulation in Escherichia coli is commonly selected for both high plasticity and low noise

Bacteria often respond to dynamically changing environments by regulating gene expression. Despite this regulation being critically important for growth and survival, little is known about how selection shapes gene regulation in natural populations. To better understand the role natural selection plays in shaping bacterial gene regulation, here we compare differences in the regulatory behaviour of naturally segregating promoter variants from Escherichia coli (which have been subject to natural selection) to randomly mutated promoter variants (which have never been exposed to natural selection). We quantify gene expression phenotypes (expression level, plasticity and noise) for hundreds of promoter variants across multiple environments and show that segregating promoter variants are enriched for mutations with minimal effects on expression level. In many promoters, we infer that there is strong selection to maintain high levels of plasticity, and direct selection to decrease or increase cell-to-cell variability in expression. Taken together, these results expand our knowledge of how gene regulation is affected by natural selection and highlight the power of comparing naturally segregating polymorphisms to de novo random mutations to quantify the action of selection.

Diversity of bet-hedging strategies in microbial communities-Recent cases and insights

Microbial communities are continuously exposed to unpredictable changes in their environment. To thrive in such dynamic habitats, microorganisms have developed the ability to readily switch phenotypes, resulting in a number of differently adapted subpopulations expressing various traits. In evolutionary biology, a particular case of phenotypic heterogeneity that evolved in an unpredictably changing environment has been defined as bet‐hedging. Bet‐hedging is a risk‐spreading strategy where isogenic populations stochastically (randomly) diversify their phenotypes, often resulting in maladapted individuals that suffer lower reproductive success. This fitness trade‐off in a specific environment may have a selective advantage upon the sudden environmental shift. Thus, a bet‐hedging strategy allows populations to persist in very dynamic habitats, but with a particular fitness cost. In recent years, numerous examples of phenotypic heterogeneity in different microorganisms have been observed, some suggesting bet‐hedging. Here, we highlight the latest reports concerning bet‐hedging phenomena in various microorganisms to show how versatile this strategy is within the microbial realms.

This article is categorized under:

Infectious Diseases > Molecular and Cellular Physiology

Transient Antibiotic Tolerance Triggered by Nutrient Shifts From Gluconeogenic Carbon Sources to Fatty Acid

Nutrient shifts from glycolytic-to-gluconeogenic carbon sources can create large sub-populations of extremely antibiotic tolerant bacteria, called persisters. Positive feedback in Escherichia coli central metabolism was believed to play a key role in the formation of persister cells. To examine whether positive feedback in nutrient transport can also support high persistence to β-lactams, we performed nutrient shifts for E. coli from gluconeogenic carbon sources to fatty acid (FA). We observed tri-phasic antibiotic killing kinetics characterized by a transient period of high antibiotic tolerance, followed by rapid killing then a slower persister-killing phase. The duration of transient tolerance (3-44 h) varies with pre-shift carbon source and correlates strongly with the time needed to accumulate the FA degradation enzyme FadD after the shift. Additionally, FadD accumulation time and thus transient tolerance time can be reduced by induction of the glyoxylate bypass prior to switching, highlighting that two interacting feedback loops simultaneously control the length of transient tolerance. Our results demonstrate that nutrient switches along with positive feedback are not sufficient to trigger persistence in a majority of the population but instead triggers only a temporary tolerance. Additionally, our results demonstrate that the pre-shift metabolic state determines the duration of transient tolerance and that supplying glyoxylate can facilitate antibiotic killing of bacteria.

Multisite Enzymes as a Mechanism for Bistability in Reaction Networks

Here, we focus on a common class of enzymes that have multiple substrate binding sites (multisite enzymes) and analyze their capacity to generate bistable dynamics in the reaction networks that they are embedded in. These networks include both substrate-product-substrate cycles and substrate-to-product conversion with subsequent product consumption. Using mathematical techniques, we show that the inherent binding and catalysis reactions arising from multiple substrate-enzyme complexes create a potential for bistable dynamics in such reaction networks. We construct a generic model of an enzyme with n-substrate binding sites and derive an analytical solution for the steady-state concentration of all enzyme-substrate complexes. By studying these expressions, we obtain a mechanistic understanding of bistability, derive parameter combinations that guarantee bistability, and show how changing specific enzyme kinetic parameters and enzyme levels can lead to bistability in reaction networks involving multisite enzymes. Thus, the presented findings provide a biochemical and mathematical basis for predicting and engineering bistability in multisite enzymes.

Mutations in respiratory complex I promote antibiotic persistence through alterations in intracellular acidity and protein synthesis

Antibiotic persistence describes the presence of phenotypic variants within an isogenic bacterial population that are transiently tolerant to antibiotic treatment. Perturbations of metabolic homeostasis can promote antibiotic persistence, but the precise mechanisms are not well understood. Here, we use laboratory evolution, population-wide sequencing and biochemical characterizations to identify mutations in respiratory complex I and discover how they promote persistence in Escherichia coli. We show that persistence-inducing perturbations of metabolic homeostasis are associated with cytoplasmic acidification. Such cytoplasmic acidification is further strengthened by compromised proton pumping in the complex I mutants. While RpoS regulon activation induces persistence in the wild type, the aggravated cytoplasmic acidification in the complex I mutants leads to increased persistence via global shutdown of protein synthesis. Thus, we propose that cytoplasmic acidification, amplified by a compromised complex I, can act as a signaling hub for perturbed metabolic homeostasis in antibiotic persisters.

Feedback regulation and coordination of the main metabolism for bacterial growth and metabolic engineering for amino acid fermentation

Living organisms such as bacteria are often exposed to continuous changes in the nutrient availability in nature. Therefore, bacteria must constantly monitor the environmental condition, and adjust the metabolism quickly adapting to the change in the growth condition. For this, bacteria must orchestrate (coordinate and integrate) the complex and dynamically changing information on the environmental condition. In particular, the central carbon metabolism (CCM), monomer synthesis, and macromolecular synthesis must be coordinately regulated for the efficient growth. It is a grand challenge in bioscience, biotechnology, and synthetic biology to understand how living organisms coordinate the metabolic regulation systems. Here, we consider the integrated sensing of carbon sources by the phosphotransferase system (PTS), and the feed-forward/feedback regulation systems incorporated in the CCM in relation to the pool sizes of flux-sensing metabolites and αketoacids. We also consider the metabolic regulation of amino acid biosynthesis (as well as purine and pyrimidine biosyntheses) paying attention to the feedback control systems consisting of (fast) enzyme level regulation with (slow) transcriptional regulation. The metabolic engineering for the efficient amino acid production by bacteria such as Escherichia coli and Corynebacterium glutamicum is also discussed (in relation to the regulation mechanisms). The amino acid synthesis is important for determining the rate of ribosome biosynthesis. Thus, the growth rate control (growth law) is further discussed on the relationship between (p)ppGpp level and the ribosomal protein synthesis.

Suboptimal resource allocation in changing environments constrains response and growth in bacteria

To respond to fluctuating conditions, microbes typically need to synthesize novel proteins. As this synthesis relies on sufficient biosynthetic precursors, microbes must devise effective response strategies to manage depleting precursors. To better understand these strategies, we investigate the active response of Escherichia coli to changes in nutrient conditions, connecting transient gene expression to growth phenotypes. By synthetically modifying gene expression during changing conditions, we show how the competition by genes for the limited protein synthesis capacity constrains cellular response. Despite this constraint cells substantially express genes that are not required, trapping them in states where precursor levels are low and the genes needed to replenish the precursors are outcompeted. Contrary to common modeling assumptions, our findings highlight that cells do not optimize growth under changing environments but rather exhibit hardwired response strategies that may have evolved to promote fitness in their native environment. The constraint and the suboptimality of the cellular response uncovered provide a conceptual framework relevant for many research applications, from the prediction of evolution to the improvement of gene circuits in biotechnology.

How to Perform a Microfluidic Cultivation Experiment—A Guideline to Success

As a result of the steadily ongoing development of microfluidic cultivation (MC) devices, a plethora of setups is used in biological laboratories for the cultivation and analysis of different organisms. Because of their biocompatibility and ease of fabrication, polydimethylsiloxane (PDMS)-glass-based devices are most prominent. Especially the successful and reproducible cultivation of cells in microfluidic systems, ranging from bacteria over algae and fungi to mammalians, is a fundamental step for further quantitative biological analysis. In combination with live-cell imaging, MC devices allow the cultivation of small cell clusters (or even single cells) under defined environmental conditions and with high spatio-temporal resolution. Yet, most setups in use are custom made and only few standardised setups are available, making trouble-free application and inter-laboratory transfer tricky. Therefore, we provide a guideline to overcome the most frequently occurring challenges during a MC experiment to allow untrained users to learn the application of continuous-flow-based MC devices. By giving a concise overview of the respective workflow, we give the reader a general understanding of the whole procedure and its most common pitfalls. Additionally, we complement the listing of challenges with solutions to overcome these hurdles. On selected case studies, covering successful and reproducible growth of cells in MC devices, we demonstrate detailed solutions to solve occurring challenges as a blueprint for further troubleshooting. Since developer and end-user of MC devices are often different persons, we believe that our guideline will help to enhance a broader applicability of MC in the field of life science and eventually promote the ongoing advancement of MC.

Bacterial quorum sensing and phenotypic heterogeneity: how the collective shapes the individual

Bacteria communicate with each other through a plethora of small, diffusible organic molecules called autoinducers. This cell-density-dependent regulatory principle is termed quorum sensing, and in many cases the process indeed coordinates group behavior of bacterial populations. Yet, even clonal bacterial populations are not uniform entities; rather, they adopt phenotypic heterogeneity to cope with consecutive, rapid, and frequent environmental fluctuations (bet-hedging) or to concurrently interact with each other by exerting different, often complementary, functions (division of labor). Quorum sensing is mainly regarded as a coordinator of bacterial collective behavior. However, it can also be a driver or a target of individual phenotypic heterogeneity. Hence, quorum sensing increases the overall fitness of a bacterial community by orchestrating group behavior as well as individual traits.

Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution

Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

Bistability in fatty-acid oxidation resulting from substrate inhibition

In this study we demonstrated through analytic considerations and numerical studies that the mitochondrial fatty-acid β-oxidation can exhibit bistable-hysteresis behavior. In an experimentally validated computational model we identified a specific region in the parameter space in which two distinct stable and one unstable steady state could be attained with different fluxes. The two stable states were referred to as low-flux (disease) and high-flux (healthy) state. By a modular kinetic approach we traced the origin and causes of the bistability back to the distributive kinetics and the conservation of CoA, in particular in the last rounds of the β-oxidation. We then extended the model to investigate various interventions that may confer health benefits by activating the pathway, including (i) activation of the last enzyme MCKAT via its endogenous regulator p46-SHC protein, (ii) addition of a thioesterase (an acyl-CoA hydrolysing enzyme) as a safety valve, and (iii) concomitant activation of a number of upstream and downstream enzymes by short-chain fatty-acids (SCFA), metabolites that are produced from nutritional fibers in the gut. A high concentration of SCFAs, thioesterase activity, and inhibition of the p46Shc protein led to a disappearance of the bistability, leaving only the high-flux state. A better understanding of the switch behavior of the mitochondrial fatty-acid oxidation process between a low- and a high-flux state may lead to dietary and pharmacological intervention in the treatment or prevention of obesity and or non-alcoholic fatty-liver disease.

Persister Escherichia coli Cells Have a Lower Intracellular pH than Susceptible Cells but Maintain Their pH in Response to Antibiotic Treatment

Persister and viable but non-culturable (VBNC) cells are two clonal subpopulations that can survive multidrug exposure via a plethora of putative molecular mechanisms. Here, we combine microfluidics, time-lapse microscopy, and a plasmid-encoded fluorescent pH reporter to measure the dynamics of the intracellular pH of individual persister, VBNC, and susceptible Escherichia coli cells in response to ampicillin treatment. We found that even before antibiotic exposure, persisters have a lower intracellular pH than those of VBNC and susceptible cells. We then investigated the molecular mechanisms underlying the observed differential pH regulation in persister E. coli cells and found that this is linked to the activity of the enzyme tryptophanase, which is encoded by tnaA. In fact, in a ΔtnaA strain, we found no difference in intracellular pH between persister, VBNC, and susceptible E. coli cells. Whole-genome transcriptomic analysis revealed that, besides downregulating tryptophan metabolism, the ΔtnaA strain downregulated key pH homeostasis pathways, including the response to pH, oxidation reduction, and several carboxylic acid catabolism processes, compared to levels of expression in the parental strain. Our study sheds light on pH homeostasis, proving that the regulation of intracellular pH is not homogeneous within a clonal population, with a subset of cells displaying a differential pH regulation to perform dedicated functions, including survival after antibiotic treatment. IMPORTANCE Persister and VBNC cells can phenotypically survive environmental stressors, such as antibiotic treatment, limitation of nutrients, and acid stress, and have been linked to chronic infections and antimicrobial resistance. It has recently been suggested that pH regulation might play a role in an organism's phenotypic survival to antibiotics; however, this hypothesis remains to be tested. Here, we demonstrate that even before antibiotic treatment, cells that will become persisters have a more acidic intracellular pH than clonal cells that will be either susceptible or VBNC upon antibiotic treatment. Moreover, after antibiotic treatment, persisters become more alkaline than VBNC and susceptible E. coli cells. This newly found phenotypic feature is remarkable because it distinguishes persister and VBNC cells that have often been thought to display the same dormant phenotype. We then show that this differential pH regulation is abolished in the absence of the enzyme tryptophanase via a major remodeling of bacterial metabolism and pH homeostasis. These new whole-genome transcriptome data should be taken into account when modeling bacterial metabolism at the crucial transition from exponential to stationary phase. Overall, our findings indicate that the manipulation of the intracellular pH represents a bacterial strategy for surviving antibiotic treatment. In turn, this suggests a strategy for developing persister-targeting antibiotics by interfering with cellular components, such as tryptophanase, that play a major role in pH homeostasis.

Reducing phenotypic instabilities of a microbial population during continuous cultivation based on cell switching dynamics

Predicting the fate of individual cells among a microbial population (i.e., growth and gene expression) remains a challenge, especially when this population is exposed to very dynamic environmental conditions, such as those encountered during continuous cultivation. Indeed, the dynamic nature of a continuous cultivation process implies the potential diversification of the microbial population resulting in genotypic and phenotypic heterogeneity. The present work focused on the induction of the arabinose operon in Escherichia coli as a model system to study this diversification process in continuous cultivations. As a preliminary step, the green fluorescent protein (GFP) level triggered by an arabinose-inducible ParaBAD promoter was tracked by flow cytometry in chemostat cultivations with glucose-arabinose co-feeding. For a wide range of glucose-arabinose co-feeding concentrations in the chemostats, the simultaneous occurrence of GFP positive and negative subpopulation was observed. In the second set of experiments, continuous cultivation was performed by adding glucose continuously and arabinose based on the capability of individual cells to switch from low GFP to high GFP expression states, performed with a technology setup called segregostat. In the segregostat cultivation mode, on-line flow cytometry analysis was used for adjusting the arabinose/glucose transitions based on the phenotypic switching profiles of the microbial population. This strategy allowed finding an appropriate arabinose pulsing frequency, leading to prolonged maintenance of the induction level with a limited increase in the phenotypic diversity for more than 60 generations. The results suggest that the steady forcing of individual cells into a given phenotypic trajectory may not be the best strategy for controlling cell populations. Instead, allowing individual cells to switch periodically around a predefined threshold seems to be a more robust strategy leading to oscillations, but within a predictable cell population behavior range.

Heterologous Protein Expression Favors the Formation of Protein Aggregates in Persister and Viable but Nonculturable Bacteria

Environmental and intracellular stresses can perturb protein homeostasis and trigger the formation and accumulation of protein aggregates. It has been recently suggested that the level of protein aggregates accumulated in bacteria correlates with the frequency of persister and viable but nonculturable cells that transiently survive treatment with multiple antibiotics. However, these findings have often been obtained employing fluorescent reporter strains. This enforced heterologous protein expression facilitates the visualization of protein aggregates but could also trigger the formation and accumulation of protein aggregates. Using microfluidics-based single-cell microscopy and a library of green fluorescent protein reporter strains, we show that heterologous protein expression favors the formation of protein aggregates. We found that persister and viable but nonculturable bacteria surviving treatment with antibiotics are more likely to contain protein aggregates and downregulate the expression of heterologous proteins. Our data also suggest that such aggregates are more basic with respect to the rest of the cell. These findings provide evidence for a strong link between heterologous protein expression, protein aggregation, intracellular pH, and phenotypic survival to antibiotics, suggesting that antibiotic treatments against persister and viable but nonculturable cells could be developed by modulating protein aggregation and pH regulation.

Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli

Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.

Bacterial persisters are a stochastically formed subpopulation of low-energy cells

Persisters represent a small subpopulation of non- or slow-growing bacterial cells that are tolerant to killing by antibiotics. Despite their prominent role in the recalcitrance of chronic infections to antibiotic therapy, the mechanism of their formation has remained elusive. We show that sorted cells of Escherichia coli with low levels of energy-generating enzymes are better able to survive antibiotic killing. Using microfluidics time-lapse microscopy and a fluorescent reporter for in vivo ATP measurements, we find that a subpopulation of cells with a low level of ATP survives killing by ampicillin. We propose that these low ATP cells are formed stochastically as a result of fluctuations in the abundance of energy-generating components. These findings point to a general "low energy" mechanism of persister formation.

Post-transcriptional deregulation of the tisB/istR-1 toxin-antitoxin system promotes SOS-independent persister formation in Escherichia coli

Bacterial dormancy is a valuable strategy to endure unfavourable conditions. The term 'persister' has been coined for cells that tolerate antibiotic treatments due to reduced cellular activity. The type I toxin-antitoxin system tisB/istR-1 is linked to persistence in Escherichia coli, because toxin TisB depolarizes the inner membrane and causes ATP depletion. Transcription of tisB is induced upon activation of the SOS response by DNA-damaging drugs. However, translation is repressed both by a 5' structure within the tisB mRNA and by RNA antitoxin IstR-1. This tight regulation limits TisB production to SOS conditions. Deletion of both regulatory RNA elements produced a 'high persistence' mutant, which was previously assumed to depend on stochastic SOS induction and concomitant TisB production. Here, we demonstrate that the mutant generates a subpopulation of growth-retarded cells during late stationary phase, likely due to SOS-independent TisB accumulation. Cell sorting experiments revealed that the stationary phase-derived subpopulation contains most of the persister cells. Collectively our data show that deletion of the regulatory RNA elements uncouples the persister formation process from the intended stress situation and enables the formation of TisB-dependent persisters in an SOS-independent manner.

Initial cell density encodes proliferative potential in cancer cell populations

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells.

Evolution of Bacterial Tolerance Under Antibiotic Treatment and Its Implications on the Development of Resistance

Recent laboratory evolution studies have shown that upon repetitive antibiotic treatments, bacterial populations will adapt and eventually became tolerant and resistant to the drug. Drug tolerance rapidly evolves upon frequent, intermittent antibiotic treatments, and such emerging drug tolerance seems to be specific to the treatment conditions, complicating clinical practice. Moreover, it has been shown that tolerance often promotes the development of resistance, which further reinforces the need of clinical diagnostics for antibiotic tolerance to reduce the occurrence of acquired resistance. Here, we discuss the laboratory evolution studies that were performed to track the development of tolerance in bacterial populations, and highlight the urgency of developing a comprehensive knowledge base of various tolerance phenotypes and their detection in clinics. Finally, we propose future directions for basic research in this growing field.

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

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

Availability of the Molecular Switch XylR Controls Phenotypic Heterogeneity and Lag Duration during Escherichia coli Adaptation from Glucose to Xylose

The glucose-xylose metabolic transition is of growing interest as a model to explore cellular adaption since these molecules are the main substrates resulting from the deconstruction of lignocellulosic biomass. Here, we investigated the role of the XylR transcription factor in the length of the lag phases when the bacterium Escherichia coli needs to adapt from glucose- to xylose-based growth. First, a variety of lag times were observed when different strains of E. coli were switched from glucose to xylose. These lag times were shown to be controlled by XylR availability in the cells with no further effect on the growth rate on xylose. XylR titration provoked long lag times demonstrated to result from phenotypic heterogeneity during the switch from glucose to xylose, with a subpopulation unable to resume exponential growth, whereas the other subpopulation grew exponentially on xylose. A stochastic model was then constructed based on the assumption that XylR availability influences the probability of individual cells to switch to xylose growth. The model was used to understand how XylR behaves as a molecular switch determining the bistability set-up. This work shows that the length of lag phases in E. coli is controllable and reinforces the role of stochastic mechanism in cellular adaptation, paving the way for new strategies for the better use of sustainable carbon sources in bioeconomy.

Computation of Single-Cell Metabolite Distributions Using Mixture Models

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

Environmental fluctuations and their effects on microbial communities, populations and individuals

From the homeostasis of human health to the cycling of Earth's elements, microbial activities underlie environmental, medical and industrial processes. These activities occur in chemical and physical landscapes that are highly dynamic and experienced by bacteria as fluctuations. In this review, we first discuss how bacteria can experience both spatial and temporal heterogeneity in their environments as temporal fluctuations of various timescales (seconds to seasons) and types (nutrient, sunlight, fluid flow, etc.). We then focus primarily on nutrient fluctuations to discuss how bacterial communities, populations and single cells respond to environmental fluctuations. Overall, we find that environmental fluctuations are ubiquitous and diverse, and strongly shape microbial behavior, ecology and evolution when compared with environments in which conditions remain constant over time. We hope this review may serve as a guide toward understanding the significance of environmental fluctuations in microbial life, such that their contributions and implications can be better assessed and exploited.

Microbial metabolic noise

From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.