Observation of universal ageing dynamics in antibiotic persistence

Antibiotic heteroresistance and persistence: an additional aid in hospital acquired infections by Enterococcus spp.?

Enterococcus, particularly E. faecium and E. faecalis, are responsible for many hospital-acquired infections. With their intrinsic antibiotic resistance and ability to form biofilms, enterococcal infections are already challenging to manage. However, when heterogenous populations are present, such as those exhibiting heteroresistance and persistence, the complexity of these infections increases exponentially not only due to their treatment but also due to their difficult diagnosis. In this study, we provide a summary of the current understanding of both heteroresistance and persistence in terms of mechanisms, diagnosis and treatment and subsequently review recent literature pertaining to these susceptibility types specifically in enterococci.

Cancer drug-tolerant persister cells: from biological questions to clinical opportunities

The emergence of drug resistance is the most substantial challenge to the effectiveness of anticancer therapies. Orthogonal approaches have revealed that a subset of cells, known as drug-tolerant 'persister' (DTP) cells, have a prominent role in drug resistance. Although long recognized in bacterial populations which have acquired resistance to antibiotics, the presence of DTPs in various cancer types has come to light only in the past two decades, yet several aspects of their biology remain enigmatic. Here, we delve into the biological characteristics of DTPs and explore potential strategies for tracking and targeting them. Recent findings suggest that DTPs exhibit remarkable plasticity, being capable of transitioning between different cellular states, resulting in distinct DTP phenotypes within a single tumour. However, defining the biological features of DTPs has been challenging, partly due to the complex interplay between clonal dynamics and tissue-specific factors influencing their phenotype. Moreover, the interactions between DTPs and the tumour microenvironment, including their potential to evade immune surveillance, remain to be discovered. Finally, the mechanisms underlying DTP-derived drug resistance and their correlation with clinical outcomes remain poorly understood. This Roadmap aims to provide a comprehensive overview of the field of DTPs, encompassing past achievements and current endeavours in elucidating their biology. We also discuss the prospect of future advancements in technologies in helping to unveil the features of DTPs and propose novel therapeutic strategies that could lead to their eradication.

Bacterial Persister Cells and Development of Antibiotic Resistance in Chronic Infections: An Update

The global issue of antimicrobial resistance poses significant challenges to public health. The World Health Organization (WHO) has highlighted it as a major global health threat, causing an estimated 700,000 deaths worldwide. Understanding the multifaceted nature of antibiotic resistance is crucial for developing effective strategies. Several physiological and biochemical mechanisms are involved in the development of antibiotic resistance. Bacterial cells may escape the bactericidal actions of the drugs by entering a physiologically dormant state known as bacterial persistence. Recent findings in this field suggest that bacterial persistence can be one of the main sources of chronic infections. The antibiotic tolerance developed by the persister cells could tolerate high levels of antibiotics and may give rise to persister offspring. These persister offspring could be attributed to antibiotic resistance mechanisms, especially in chronic infections. This review attempts to shed light on persister-induced antibiotic resistance and the current therapeutic strategies.

Singular effect of linkage on long-term genetic gain in Fisher's infinitesimal model

During the founding of the field of quantitative genetics, Fisher formulated in 1918 his "infinitesimal model" that provided a novel mathematical framework to describe the Mendelian transmission of quantitative traits. If the infinitely many genes in that model are assumed to segregate independently during reproduction, corresponding to having no linkage, directional selection asymptotically leads to a constant genetic gain at each generation. In reality, genes are subject to strong linkage because they lie on chromosomes and thus segregate in a correlated way. Various approximations have been used in the past to study that more realistic case of the infinitesimal model with the expectation that the asymptotic gain per generation is modestly decreased. To treat this system even in the strong linkage limit, we take the genes to lie on continuous chromosomes. Surprisingly, the consequences of genetic linkage are in fact rather singular, changing the nature of the long-term gain per generation: the asymptotic gain vanishes rather than being simply decreased. Nevertheless, the per-generation gain tends to zero sufficiently slowly for the total gain, accumulated over generations, to be unbounded.

Phenotypic resistant single-cell characteristics under recurring ampicillin antibiotic exposure in Escherichia coli

Non-heritable, phenotypic drug resistance toward antibiotics challenges antibiotic therapies. Characteristics of such phenotypic resistance have implications for the evolution of heritable resistance. Diverse forms of phenotypic resistance have been described, but phenotypic resistance characteristics remain less explored than genetic resistance. Here, we add novel combinations of single-cell characteristics of phenotypic resistant E. coli cells and compare those to characteristics of susceptible cells of the parental population by exposure to different levels of recurrent ampicillin antibiotic. Contrasting expectations, we did not find commonly described characteristics of phenotypic resistant cells that arrest growth or near growth. We find that under ampicillin exposure, phenotypic resistant cells reduced their growth rate by about 50% compared to growth rates prior to antibiotic exposure. The growth reduction is a delayed alteration to antibiotic exposure, suggesting an induced response and not a stochastic switch or caused by a predetermined state as frequently described. Phenotypic resistant cells exhibiting constant slowed growth survived best under ampicillin exposure and, contrary to expectations, not only fast-growing cells suffered high mortality triggered by ampicillin but also growth-arrested cells. Our findings support diverse modes of phenotypic resistance, and we revealed resistant cell characteristics that have been associated with enhanced genetically fixed resistance evolution, which supports claims of an underappreciated role of phenotypic resistant cells toward genetic resistance evolution. A better understanding of phenotypic resistance will benefit combatting genetic resistance by developing and engulfing effective anti-phenotypic resistance strategies.

IMPORTANCE

Antibiotic resistance is a major challenge for modern medicine. Aside from genetic resistance to antibiotics, phenotypic resistance that is not heritable might play a crucial role for the evolution of antibiotic resistance. Using a highly controlled microfluidic system, we characterize single cells under recurrent exposure to antibiotics. Fluctuating antibiotic exposure is likely experienced under common antibiotic therapies. These phenotypic resistant cell characteristics differ from previously described phenotypic resistance, highlighting the diversity of modes of resistance. The phenotypic characteristics of resistant cells we identify also imply that such cells might provide a stepping stone toward genetic resistance, thereby causing treatment failure.

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.

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

Microbial life in slow and stopped lanes

Microbes in nature often lack nutrients and face extreme or widely fluctuating temperatures, unlike microbes in growth-optimized settings in laboratories that much of the literature examines. Slowed or suspended lives are the norm for microbes. Studying them is important for understanding the consequences of climate change and for addressing fundamental questions about life: are there limits to how slowly a cell's life can progress, and how long cells can remain viable without self-replicating? Recent studies began addressing these questions with single-cell-level measurements and mathematical models. Emerging principles that govern slowed or suspended lives of cells - including lives of dormant spores and microbes at extreme temperatures - are re-defining discrete cellular states as continuums and revealing intracellular dynamics at new timescales. Nearly inactive, lifeless-appearing microbes are transforming our understanding of life.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Implications of lytic phage infections inducing persistence

Phage therapy holds much promise as an alternative to antibiotics for fighting infection. However, this approach is no panacea as recent results show that a small fraction of cells survives lytic phage infection due to both dormancy (i.e. formation of persister cells) and resistance (genetic change). In this brief review, we summarize evidence suggesting phages induce the persister state. Therefore, it is predicted that phage cocktails should be combined with antipersister compounds to eradicate bacterial infections.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Grow now, pay later: When should a bacterium go into debt?

Microbes grow in a wide variety of environments and must balance growth and stress resistance. Despite the prevalence of such trade-offs, understanding of their role in nonsteady environments is limited. In this study, we introduce a mathematical model of "growth debt," where microbes grow rapidly initially, paying later with slower growth or heightened mortality. We first compare our model to a classical chemostat experiment, validating our proposed dynamics and quantifying Escherichia coli's stress resistance dynamics. Extending the chemostat theory to include serial-dilution cultures, we derive phase diagrams for the persistence of "debtor" microbes. We find that debtors cannot coexist with nondebtors if "payment" is increased mortality but can coexist if it lowers enzyme affinity. Surprisingly, weak noise considerably extends the persistence of resistance elements, pertinent for antibiotic resistance management. Our microbial debt theory, broadly applicable across many environments, bridges the gap between chemostat and serial dilution systems.

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.

The macrophage-bacterium mismatch in persister formation

Many pathogens are hard to eradicate, even in the absence of genetically detectable antimicrobial resistance mechanisms and despite proven antibiotic susceptibility. The fraction of clonal bacteria that temporarily elude effective antibiotic treatments is commonly known as 'antibiotic persisters.' Over the past decade, there has been a growing body of research highlighting the pivotal role played by the cellular host in the development of persisters. In parallel, this research has also sought to elucidate the molecular mechanisms underlying the formation of intracellular antibiotic persisters and has demonstrated a prominent role for the bacterial stress response. However, questions remain regarding the conditions leading to the formation of stress-induced persisters among a clonal population of intracellular bacteria and despite an ostensibly uniform environment. In this opinion, following a brief review of the current state of knowledge regarding intracellular antibiotic persisters, we explore the ways in which macrophage functional heterogeneity and bacterial phenotypic heterogeneity may contribute to the emergence of these persisters. We propose that the degree of mismatch between the macrophage permissiveness and the bacterial preparedness to invade and thrive intracellularly may explain the formation of stress-induced nonreplicating intracellular persisters.

A stochastic approach for modelling the in-vitro effect of osmotic stress on growth dynamics and persistent cell formation in Listeria monocytogenes

Persistent bacteria (or persisters) can be defined as a microbial subpopulation that, exposed to bactericidal treatment, is killed more slowly than the rest of the population they are part of. They stochastically originate in response to environmental stressors or spontaneously without external signals. When transferred into a fresh medium, persisters can resume active replication although they spend more time adapting to the new conditions remaining in the lag phase longer. They were studied for decades for their ability to survive antibiotic treatments while studies on their formation in food and potential impact on their safety are lacking. The most common food preservation techniques may act as stressors that trigger the formation of persistent bacteria able to survive bactericidal treatments and grow later in foods during storage. This study aimed to investigate a possible relationship between exposure to different salt concentrations (osmotic stress) and the amount of persisters triggered in a strain of Listeria monocytogenes. Furthermore, we described this phenomenon from a mathematical perspective through predictive microbiology models commonly used in the food field. The lag time distribution of a L. monocytogenes ATCC 7644 strain grown in broth with additional 2 %, 4 % and 6 % NaCl was evaluated using the software ScanLag. It uses office scanners to automatically record the colony growth on agar plates and evaluate the frequency distribution of their appearance times (lag phase) by automated image analysis. The same broth cultures were diluted to equalize salt concentration and transferred into a fresh broth to evaluate how the previous salt exposure impacted their growth kinetics. The observed growth curves were reproduced using predictive models in which the mean duration of the lag phase of the whole population took into account the occurrence of persisters with a longer lag phase. The models were solved first using a deterministic approach and then a stochastic one introducing a stochastic term that mimics the variability of lag phase duration due to the persisters occurrence. Results showed that the growth of L. monocytogenes in broth with additional NaCl might trigger the formation of persistent cells whose number increased consistently with salt concentrations. The proposed predictive approach reproduced the observed real curves in strong agreement, especially through the stochastic resolution of the models. Persistence is currently a neglected bacterial defence strategy in the food sector but the persisters' formation during production cannot be excluded; therefore, further insights on the topic are certainly desirable.

Polar accumulation of pyoverdin and exit from stationary phase

Pyoverdin is a water-soluble metal-chelator synthesized by members of the genus Pseudomonas and used for the acquisition of insoluble ferric iron. Although freely diffusible in aqueous environments, preferential dissemination of pyoverdin among adjacent cells, fine-tuning of intracellular siderophore concentrations, and fitness advantages to pyoverdin-producing versus nonproducing cells, indicate control of location and release. Here, using time-lapse fluorescence microscopy to track single cells in growing microcolonies of Pseudomonas fluorescens SBW25, we show accumulation of pyoverdin at cell poles. Accumulation occurs on cessation of cell growth, is achieved by cross-feeding in pyoverdin-nonproducing mutants and is reversible. Moreover, accumulation coincides with localization of a fluorescent periplasmic reporter, suggesting that pyoverdin accumulation at cell poles is part of the general cellular response to starvation. Compatible with this conclusion is absence of non-accumulating phenotypes in a range of pyoverdin mutants. Analysis of the performance of pyoverdin-producing and nonproducing cells under conditions promoting polar accumulation shows an advantage to accumulation on resumption of growth after stress. Examination of pyoverdin polar accumulation in a multispecies community and in a range of laboratory and natural species of Pseudomonas, including P. aeruginosa PAO1 and P. putida KT2440, confirms that the phenotype is characteristic of Pseudomonas.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

Slow closure of Earth's carbon cycle

Carbon near the Earth's surface cycles between the production and consumption of organic carbon; the former sequesters carbon dioxide while the latter releases it. Microbes attempt to close the loop, but the longer organic matter survives, the slower microbial degradation becomes. This aging effect leaves observable quantitative signatures: Organic matter decays at rates that are inversely proportional to its age, while microbial populations and concentrations of organic carbon in ocean sediments decrease at distinct powers of age. Yet mechanisms that predict this collective organization remain unknown. Here, I show that these and other observations follow from the assumption that the decay of organic matter is limited by progressively rare extreme fluctuations in the energy available to microbes for decomposition. The theory successfully predicts not only observed scaling exponents but also a previously unobserved scaling regime that emerges when microbes subsist on the minimum energy flux required for survival. The resulting picture suggests that the carbon cycle's age-dependent dynamics are analogous to the slow approach to equilibrium in disordered systems. The impact of these slow dynamics is profound: They preclude complete oxidation of organic carbon in sediments, thereby freeing molecular oxygen to accumulate in the atmosphere.

Emergent antibiotic persistence in a spatially structured synthetic microbial mutualism

Antibiotic persistence (heterotolerance) allows a subpopulation of bacteria to survive antibiotic-induced killing and contributes to the evolution of antibiotic resistance. Although bacteria typically live in microbial communities with complex ecological interactions, little is known about how microbial ecology affects antibiotic persistence. Here, we demonstrated within a synthetic two-species microbial mutualism of Escherichia coli and Salmonella enterica that the combination of cross-feeding and community spatial structure can emergently cause high antibiotic persistence in bacteria by increasing the cell-to-cell heterogeneity. Tracking ampicillin-induced death for bacteria on agar surfaces, we found that E. coli forms up to 55 times more antibiotic persisters in the cross-feeding coculture than in monoculture. This high persistence could not be explained solely by the presence of S. enterica, the presence of cross-feeding, average nutrient starvation, or spontaneous resistant mutations. Time-series fluorescent microscopy revealed increased cell-to-cell variation in E. coli lag time in the mutualistic co-culture. Furthermore, we discovered that an E. coli cell can survive antibiotic killing if the nearby S. enterica cells on which it relies die first. In conclusion, we showed that the high antibiotic persistence phenotype can be an emergent phenomenon caused by a combination of cross-feeding and spatial structure. Our work highlights the importance of considering spatially structured interactions during antibiotic treatment and understanding microbial community resilience more broadly.

Understanding Staphylococcus aureus internalisation and induction of antimicrobial tolerance

INTRODUCTION

Staphylococcus aureus, a human commensal, is also one of the most common and serious pathogens for humans. In recent years, its capacity to survive and replicate in phagocytic and non-phagocytic cells has been largely demonstrated. In these intracellular niches, bacteria are shielded from the immune response and antibiotics, turning host cells into long-term infectious reservoirs. Moreover, neutrophils carry intracellular bacteria in the bloodstream, leading to systemic spreading of the disease. Despite the serious threat posed by intracellular S. aureus to human health, the molecular mechanisms behind its intracellular survival and subsequent antibiotic treatment failure remain elusive.

AREA COVERED

We give an overview of the killing mechanisms of phagocytes and of the impressive arsenal of virulence factors, toxins and stress responses deployed by S. aureus as a response. We then discuss the different barriers to antibiotic activity in this intracellular niche and finally describe innovative strategies to target intracellular persisting reservoirs.

EXPERT OPINION

Intracellular niches represent a challenge in terms of diagnostic and treatment. Further research using ad-hoc in-vivo models and single cell approaches are needed to better understand the molecular mechanisms underlying intracellular survival and tolerance to antibiotics in order to identify strategies to eliminate these persistent bacteria.

Molecular and functional profiling of chemotolerant cells unveils nucleoside metabolism-dependent vulnerabilities in medulloblastoma

Chemotherapy resistance is considered one of the main causes of tumor relapse, still challenging researchers for the identification of the molecular mechanisms sustaining its emergence. Here, we setup and characterized chemotherapy-resistant models of Medulloblastoma (MB), one of the most lethal pediatric brain tumors, to uncover targetable vulnerabilities associated to their resistant phenotype. Integration of proteomic, transcriptomic and kinomic data revealed a significant deregulation of several pathways in resistant MB cells, converging to cell metabolism, RNA/protein homeostasis, and immune response, eventually impacting on patient outcome. Moreover, resistant MB cell response to a large library of compounds through a high-throughput screening (HTS), highlighted nucleoside metabolism as a relevant vulnerability of chemotolerant cells, with peculiar antimetabolites demonstrating increased efficacy against them and even synergism with conventional chemotherapeutics. Our results suggest that drug-resistant cells significantly rewire multiple cellular processes, allowing their adaptation to a chemotoxic environment, nevertheless exposing alternative actionable susceptibilities for their specific targeting.

Escherichia coli cells are primed for survival before lethal antibiotic stress

Non-genetic factors can cause significant fluctuations in gene expression levels. Regardless of growing in a stable environment, this fluctuation leads to cell-to-cell variability in an isogenic population. This phenotypic heterogeneity allows a tiny subset of bacterial cells in a population called persister cells to tolerate long-term lethal antibiotic effects by entering into a non-dividing, metabolically repressed state. We occasionally noticed a high variation in persister levels, and to explore this, we tested clonal populations starting from a single cell using a modified Luria-Delbrück fluctuation test. Although we kept the conditions same, the diversity in persistence level among clones was relatively consistent: varying from ~60- to 100- and ~40- to 70-fold for ampicillin and apramycin, respectively. Then, we divided and diluted each clone to observe whether the same clone had comparable persister levels for more than one generation. Replicates had similar persister levels even when clones were divided, diluted by 1:20, and allowed to grow for approximately five generations. This result explicitly shows a cellular memory passed on for generations and eventually lost when cells are diluted to 1:100 and regrown (>seven generations). Our result demonstrates (1) the existence of a small population prepared for stress ("primed cells") resulting in higher persister numbers; (2) the primed memory state is reproducible and transient, passed down for generations but eventually lost; and (3) a heterogeneous persister population is a result of a transiently primed reversible cell state and not due to a pre-existing genetic mutation. IMPORTANCE Antibiotics have been highly effective in treating lethal infectious diseases for almost a century. However, the increasing threat of antibiotic resistance is again causing these diseases to become life-threatening. The longer a bacteria can survive antibiotics, the more likely it is to develop resistance. Complicating matters is that non-genetic factors can allow bacterial cells with identical DNA to gain transient resistance (also known as persistence). Here, we show that a small fraction of the bacterial population called primed cells can pass down non-genetic information ("memory") to their offspring, enabling them to survive lethal antibiotics for a long time. However, this memory is eventually lost. These results demonstrate how bacteria can leverage differences among genetically identical cells formed through non-genetic factors to form primed cells with a selective advantage to survive antibiotics.

Bacterial memory in antibiotic resistance evolution and nanotechnology in evolutionary biology

Bacterial memory refers to the phenomenon in which past experiences influence current behaviors in response to changing environments. It serves as a crucial process that enables adaptation and evolution. We first summarize the state-of-art approaches regarding history-dependent behaviors that impact growth dynamics and underlying mechanisms. Then, the phenotypic and genotypic origins of memory and how encoded memory modulates drug tolerance/resistance are reviewed. We also provide a summary of possible memory effects induced by antimicrobial nanoparticles. The regulatory networks and genetic underpinnings responsible for memory building partially overlap with nanoparticle and drug exposures, which may raise concerns about the impact of nanotechnology on adaptation. Finally, we provide a perspective on the use of nanotechnology to harness bacterial memory based on its unique mode of actions on information processing and transmission in bacteria. Exploring bacterial memory mechanisms provides valuable insights into acclimation, evolution, and the potential applications of nanotechnology in harnessing memory.

Environmental, mechanistic and evolutionary landscape of antibiotic persistence

Recalcitrant infections pose a serious challenge by prolonging antibiotic therapies and contributing to the spread of antibiotic resistance, thereby threatening the successful treatment of bacterial infections. One potential contributing factor in persistent infections is antibiotic persistence, which involves the survival of transiently tolerant subpopulations of bacteria. This review summarizes the current understanding of antibiotic persistence, including its clinical significance and the environmental and evolutionary factors at play. Additionally, we discuss the emerging concept of persister regrowth and potential strategies to combat persister cells. Recent advances highlight the multifaceted nature of persistence, which is controlled by deterministic and stochastic elements and shaped by genetic and environmental factors. To translate in vitro findings to in vivo settings, it is crucial to include the heterogeneity and complexity of bacterial populations in natural environments. As researchers continue to gain a more holistic understanding of this phenomenon and develop effective treatments for persistent bacterial infections, the study of antibiotic persistence is likely to become increasingly complex.

Predictive Population Dynamics of Escherichia coli O157:H7 and Salmonella enterica on Plants: a Mechanistic Mathematical Model Based on Weather Parameters and Bacterial State

Weather affects key aspects of bacterial behavior on plants but has not been extensively investigated as a tool to assess risk of crop contamination with human foodborne pathogens. A novel mechanistic model informed by weather factors and bacterial state was developed to predict population dynamics on leafy vegetables and tested against published data tracking Escherichia coli O157:H7 (EcO157) and Salmonella enterica populations on lettuce and cilantro plants. The model utilizes temperature, radiation, and dew point depression to characterize pathogen growth and decay rates. Additionally, the model incorporates the population level effect of bacterial physiological state dynamics in the phyllosphere in terms of the duration and frequency of specific weather parameters. The model accurately predicted EcO157 and S. enterica population sizes on lettuce and cilantro leaves in the laboratory under various conditions of temperature, relative humidity, light intensity, and cycles of leaf wetness and dryness. Importantly, the model successfully predicted EcO157 population dynamics on 4-week-old romaine lettuce plants under variable weather conditions in nearly all field trials. Prediction of initial EcO157 population decay rates after inoculation of 6-week-old romaine plants in the same field study was better than that of long-term survival. This suggests that future augmentation of the model should consider plant age and species morphology by including additional physical parameters. Our results highlight the potential of a comprehensive weather-based model in predicting contamination risk in the field. Such a modeling approach would additionally be valuable for timing field sampling in quality control to ensure the microbial safety of produce. IMPORTANCE Fruits and vegetables are important sources of foodborne disease. Novel approaches to improve the microbial safety of produce are greatly lacking. Given that bacterial behavior on plant surfaces is highly dependent on weather factors, risk assessment informed by meteorological data may be an effective tool to integrate into strategies to prevent crop contamination. A mathematical model was developed to predict the population trends of pathogenic E. coli and S. enterica, two major causal agents of foodborne disease associated with produce, on leaves. Our model is based on weather parameters and rates of switching between the active (growing) and inactive (nongrowing) bacterial state resulting from prevailing environmental conditions on leaf surfaces. We demonstrate that the model has the ability to accurately predict dynamics of enteric pathogens on leaves and, notably, sizes of populations of pathogenic E. coli over time after inoculation onto the leaves of young lettuce plants in the field.

Single-cell microfluidics enabled dynamic evaluation of drug combinations on antibiotic resistance bacteria

The rapid spread of antibiotic resistance has become a significant threat to global health, yet the development of new antibiotics is outpaced by emerging new resistance. To treat multidrug-resistant bacteria and prolong the lifetime of existing antibiotics, a productive strategy is to use combinations of antibiotics and/or adjuvants. However, evaluating drug combinations is primarily based on end-point checkerboard measurements, which provide limited information to study the mechanism of action and the discrepancies in the clinical outcomes. Here, single-cell microfluidics is used for rapid evaluation of the efficacy and mode of action of antibiotic combinations within 3 h. Focusing on multidrug-resistant Acinetobacter baumannii, the combination between berberine hydrochloride (BBH, as an adjuvant) and carbapenems (meropenem, MEM) or β-lactam antibiotic is evaluated. Real-time tracking of individual cells to programmable delivered antibiotics reveals multiple phenotypes (i.e., susceptible, resistant, and persistent cells) with fidelity. Our study discovers that BBH facilitates the accumulation of antibiotics within cells, indicating synergistic effects (FICI = 0.5). For example, the combination of 256 mg/L BBH and 16 mg/L MEM has a similar killing effect (i.e., the inhibition rates >90%) as the MIC of MEM (64 mg/L). Importantly, the synergistic effect of a combination can diminish if the bacteria are pre-stressed with any single drug. Such information is vital for understanding the underlying mechanisms of combinational treatments. Overall, our platform provides a promising approach to evaluate the dynamic and heterogenous response of a bacterial population to antibiotics, which will facilitate new drug discovery and reduce emerging antibiotic resistance.

Decline in nitrosative stress drives antibiotic persister regrowth during infection

Internalization of pathogenic bacteria by macrophages results in formation of antibiotic-tolerant persisters. These cells are maintained in a non-growing state for extended periods of time, and it is assumed that their growth resumption causes infection relapse after cessation of antibiotic treatment. Despite this clinical relevance, the signals and conditions that drive persister regrowth during infection are not yet understood. Here, we found that after persister formation in macrophages, host reactive nitrogen species (RNS) produced in response to Salmonella infection lock persisters in growth arrest by intoxicating their TCA cycle, lowering cellular respiration and ATP production. Intracellular persisters resume growth when macrophage RNS production subsides and functionality of their TCA cycle is regained. Persister growth resumption within macrophages is slow and heterogeneous, dramatically extending the time the persister reservoir feeds infection relapse. Using an inhibitor of RNS production, we can force recalcitrant bacteria to regrow during antibiotic treatment, thereby facilitating their eradication.

Antibiotic tolerance: targeting bacterial survival

Antimicrobial susceptibility testing is the cornerstone of antibiotic treatments. Yet, active drugs are frequently unsuccessful in vivo and most clinical trials investigating antibiotics fail. So far, bacterial survival strategies, other than drug resistance, have been largely ignored. As such, drug tolerance and persisters, allowing bacterial populations to survive during antibiotic treatments, could fill a gap in antibiotic susceptibility testing. Therefore, it remains critical to establish robust and scalable bacterial viability measures and to define the clinical relevance of bacterial survivors across various bacterial infections. If successful, these tools could improve drug design and development to prevent tolerance formation or target bacterial survivors, to ultimately reduce treatment failures and curb resistance evolution.

Shaping bacterial gene expression by physiological and proteome allocation constraints

Networks of molecular regulators are often the primary objects of focus in the study of gene regulation, with the machinery of protein synthesis tacitly relegated to the background. Shifting focus to the constraints imposed by the allocation of protein synthesis flux reveals surprising ways in which the actions of molecular regulators are shaped by physiological demands. Using carbon catabolite repression as a case study, we describe how physiological constraints are sensed through metabolic fluxes and how flux-controlled regulation gives rise to simple empirical relations between protein levels and the rate of cell growth.

Absorption/Attenuation Spectral Description of ESKAPEE Bacteria: Application to Seeder-Free Culture Monitoring, Mammalian T-Cell and Bacteria Mixture Analysis and Contamination Description

Despite numerous innovations, measuring bacteria concentrations on a routine basis is still time consuming and ensuring accurate measurements requires careful handling. Furthermore, it often requires sampling small volumes of bacteria suspensions which might be poorly representative of the real bacteria concentration. In this paper, we propose a spectroscopy measurement method based on a description of the absorption/attenuation spectra of ESKAPEE bacteria. Concentrations were measured with accuracies less than 2%. In addition, mixing the mathematical description of the absorption/attenuation spectra of mammalian T-cells and bacteria allows for the simultaneous measurements of both species' concentrations. This method allows real-time, sampling-free and seeder-free measurement and can be easily integrated into a closed-system environment.

Damage dynamics and the role of chance in the timing of E. coli cell death

Genetically identical cells in the same stressful condition die at different times. The origin of this stochasticity is unclear; it may arise from different initial conditions that affect the time of demise, or from a stochastic damage accumulation mechanism that erases the initial conditions and instead amplifies noise to generate different lifespans. To address this requires measuring damage dynamics in individual cells over the lifespan, but this has rarely been achieved. Here, we used a microfluidic device to measure membrane damage in 635 carbon-starved Escherichia coli cells at high temporal resolution. We find that initial conditions of damage, size or cell-cycle phase do not explain most of the lifespan variation. Instead, the data points to a stochastic mechanism in which noise is amplified by a rising production of damage that saturates its own removal. Surprisingly, the relative variation in damage drops with age: cells become more similar to each other in terms of relative damage, indicating increasing determinism with age. Thus, chance erases initial conditions and then gives way to increasingly deterministic dynamics that dominate the lifespan distribution.

Effects of osmotic stress on Listeria monocytogenes ATCC 7644: persistent cells and heat resistance

Persistent bacteria are a microbial subpopulation that, exposed to bactericidal treatment, is killed at a slower rate than the rest of the population they are part of. They can be triggered either following stress or stochastically without external signals. The hallmark of persistent bacteria is the biphasic killing curve, a sign that, within a microbial population, two subpopulations are inactivated at a different rate. Furthermore, when plated into a fresh medium and in the absence of stressors, persistent bacteria typically remain in the lag phase longer before resuming active replication. This study aims to evaluate in vitro whether the formation of persistent cells in a strain of Listeria monocytogenes can be triggered by exposure to osmotic stress and if this phenomenon can increase heat resistance in the bacterial population. In a first experiment, the lag time distribution of a L. monocytogenes strain grown in a 6% NaCl broth was evaluated using the software ScanLag. A stationary phase broth culture was inoculated on agar plates placed on an office scanner inside an incubator at 37°C. The plates were scanned every 20' for 4 days and the acquired images were automatically elaborated with the aid of MatLab software in order to evaluate the appearance times of every single colony. The experiment was also carried out on a control culture obtained by growing the strain in the broth without salt. In a second experiment, the same broth cultures, after proper dilutions to rebalance NaCl concentration, were subjected to a heat treatment at 51°C and the death curves obtained were parameterized using the GinaFit system. Results showed that the lag phase of 31.40% of the salt culture colonies was long enough to suppose the formation of persistent bacteria. Analyses of the thermal survival curves showed that the shoulder and tail model was the one that best represented the inactivation trend of the salt culture, unlike the control culture, whose trend was essentially linear. Results of the present study show how exposure to salt could induce the formation of persistent bacteria in a L. monocytogenes strain. The last raises concerns as persistent cells may not only be undetected with common analytical techniques but they even show a greater heat resistance.

Resuscitation dynamics reveal persister partitioning after antibiotic treatment

Bacteria can survive antibiotics by forming dormant, drug-tolerant persisters. Persisters can resuscitate from dormancy after treatment and prolong infections. Resuscitation is thought to occur stochastically, but its transient, single-cell nature makes it difficult to investigate. We tracked the resuscitation of individual persisters by microscopy after ampicillin treatment and, by characterizing their dynamics, discovered that Escherichia coli and Salmonella enterica persisters resuscitate exponentially rather than stochastically. We demonstrated that the key parameters controlling resuscitation map to the ampicillin concentration during treatment and efflux during resuscitation. Consistently, we observed many persister progeny have structural defects and transcriptional responses indicative of cellular damage, for both β-lactam and quinolone antibiotics. During resuscitation, damaged persisters partition unevenly, generating both healthy daughter cells and defective ones. This persister partitioning phenomenon was observed in S. enterica, Klebsiella pneumoniae, Pseudomonas aeruginosa, and an E. coli urinary tract infection (UTI) isolate. It was also observed in the standard persister assay and after in situ treatment of a clinical UTI sample. This study reveals novel properties of resuscitation and indicates that persister partitioning may be a survival strategy in bacteria that lack genetic resistance.

Single Cell Killing Kinetics Differentiate Phenotypic Bacterial Responses to Different Antibacterial Classes

With the spread of multidrug-resistant bacteria, there has been an increasing focus on molecular classes that have not yet yielded an antibiotic. A key capability for assessing and prescribing new antibacterial treatments is to compare the effects antibacterial agents have on bacterial growth at a phenotypic, single-cell level. Here, we combined time-lapse microscopy with microfluidics to investigate the concentration-dependent killing kinetics of stationary-phase Escherichia coli cells. We used antibacterial agents from three different molecular classes, β-lactams and fluoroquinolones, with the known antibiotics ampicillin and ciprofloxacin, respectively, and a new experimental class, protein Ψ-capsids. We found that bacterial cells elongated when treated with ampicillin and ciprofloxacin used at their minimum inhibitory concentration (MIC). This was in contrast to Ψ-capsids, which arrested bacterial elongation within the first two hours of treatment. At concentrations exceeding the MIC, all the antibacterial agents tested arrested bacterial growth within the first 2 h of treatment. Further, our single-cell experiments revealed differences in the modes of action of three different agents. At the MIC, ampicillin and ciprofloxacin caused the lysis of bacterial cells, whereas at higher concentrations, the mode of action shifted toward membrane disruption. The Ψ-capsids killed cells by disrupting their membranes at all concentrations tested. Finally, at increasing concentrations, ampicillin and Ψ-capsids reduced the fraction of the population that survived treatment in a viable but nonculturable state, whereas ciprofloxacin increased this fraction. This study introduces an effective capability to differentiate the killing kinetics of antibacterial agents from different molecular classes and offers a high content analysis of antibacterial mechanisms at the single-cell level. IMPORTANCE Antibiotics act against bacterial pathogens by inhibiting their growth or killing them directly. Different modes of action determine different antibacterial responses, whereas phenotypic differences in bacteria can challenge the efficacy of antibiotics. Therefore, it is important to be able to differentiate the concentration-dependent killing kinetics of antibacterial agents at a single-cell level, in particular for molecular classes which have not yielded an antibiotic before. Here, we measured single-cell responses using microfluidics-enabled imaging, revealing that a novel class of antibacterial agents, protein Ψ-capsids, arrests bacterial elongation at the onset of treatment, whereas elongation continues for cells treated with β-lactam and fluoroquinolone antibiotics. The study advances our current understanding of antibacterial function and offers an effective strategy for the comparative design of new antibacterial therapies, as well as clinical antibiotic susceptibility testing.

Resonant learning in scale-free networks

Large networks of interconnected components, such as genes or machines, can coordinate complex behavioral dynamics. One outstanding question has been to identify the design principles that allow such networks to learn new behaviors. Here, we use Boolean networks as prototypes to demonstrate how periodic activation of network hubs provides a network-level advantage in evolutionary learning. Surprisingly, we find that a network can simultaneously learn distinct target functions upon distinct hub oscillations. We term this emergent property resonant learning, as the new selected dynamical behaviors depend on the choice of the period of the hub oscillations. Furthermore, this procedure accelerates the learning of new behaviors by an order of magnitude faster than without oscillations. While it is well-established that modular network architecture can be selected through evolutionary learning to produce different network behaviors, forced hub oscillations emerge as an alternative evolutionary learning strategy for which network modularity is not necessarily required.

Advances in linking single-cell bacterial stress response to population-level survival

Stress response mechanisms can allow bacteria to survive a myriad of challenges, including nutrient changes, antibiotic encounters, and antagonistic interactions with other microbes. Expression of these stress response pathways, in addition to other cell features such as growth rate and metabolic state, can be heterogeneous across cells and over time. Collectively, these single-cell-level phenotypes contribute to an overall population-level response to stress. These include diversifying actions, which can be used to enable bet-hedging, and coordinated actions, such as biofilm production, horizontal gene transfer, and cross-feeding. Here, we highlight recent results and emerging technologies focused on both single-cell and population-level responses to stressors, and we draw connections about the combined impact of these effects on survival of bacterial communities.

Studying antibiotic persistence in vivo using the model organism Salmonella Typhimurium

Antibiotic persistence permits a subpopulation of susceptible bacteria to survive lethal concentrations of bactericidal antibiotics. This prolongs antibiotic therapy, promotes the evolution of antibiotic-resistant pathogen strains and can select for pathogen virulence within infected hosts. Here, we review the literature exploring antibiotic persistence in vivo, and describe the consequences of recalcitrant subpopulations, with a focus on studies using the model pathogen Salmonella Typhimurium. In vitro studies have established a concise set of features distinguishing true persisters from other forms of bacterial recalcitrance to bactericidal antibiotics. We discuss how animal infection models are useful for exploring these features in vivo, and describe how technical challenges can sometimes prevent the conclusive identification of true antibiotic persistence within infected hosts. We propose using two complementary working definitions for studying antibiotic persistence in vivo: the strict definition for studying the mechanisms of persister formation, and an operative definition for functional studies assessing the links between invasive virulence and persistence as well as the consequences for horizontal gene transfer, or the emergence of antibiotic-resistant mutants. This operative definition will enable further study of how antibiotic persisters arise in vivo, and of how surviving populations contribute to diverse downstream effects such as pathogen transmission, horizontal gene transfer and the evolution of virulence and antibiotic resistance. Ultimately, such studies will help to improve therapeutic control of antibiotic- recalcitrant populations.

Anti-infective therapy using species-specific activators of Staphylococcus aureus ClpP

The emergence of methicillin-resistant Staphylococcus aureus isolates highlights the urgent need to develop more antibiotics. ClpP is a highly conserved protease regulated by ATPases in bacteria and in mitochondria. Aberrant activation of  bacterial ClpP is an alternative method of discovering antibiotics, while it remains difficult to develop selective  Staphylococcus aureus ClpP activators that can avoid disturbing Homo sapiens ClpP functions. Here, we use a structure-based design to identify (R)- and (S)-ZG197 as highly selective Staphylococcus aureus ClpP activators. The key structural elements in Homo sapiens ClpP, particularly W146 and its joint action with the C-terminal motif, significantly contribute to the discrimination of the activators. Our selective activators display wide antibiotic properties towards an array of multidrug-resistant staphylococcal strains in vitro, and demonstrate promising antibiotic efficacy in zebrafish and murine skin infection models. Our findings indicate that the species-specific activators of Staphylococcus aureus ClpP are exciting therapeutic agents to treat staphylococcal infections.

Bacteria grow swiftly and live thriftily

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

A Shift in Perspective: A Role for the Type I Toxin TisB as Persistence-Stabilizing Factor

Bacterial persistence is a phenomenon that is founded by the existence of a subpopulation of multidrug-tolerant cells. These so-called persister cells endure otherwise lethal stress situations and enable restoration of bacterial populations upon return to favorable conditions. Persisters are especially notorious for their ability to survive antibiotic treatments without conventional resistance genes and to cause infection relapse. The persister state is typically correlated with reduction or inhibition of cellular activity. Early on, chromosomal toxin-antitoxin (TA) systems were suspected to induce the persister state in response to environmental stress. However, this idea has been challenged during the last years. Especially the involvement of toxins from type II TA systems in persister formation is put into question. For toxins from type I TA systems the debate has just started. Here, we would like to summarize recent knowledge gained for the type I TA system tisB/istR-1 from Escherichia coli. TisB is a small, membrane-targeting toxin, which disrupts the proton motive force (PMF), leading to membrane depolarization. Based on experimental data, we hypothesize that TisB primarily stabilizes the persister state through depolarization and further, secondary effects. We will present a simple model that will provide a framework for future directions.