Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells

Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival

Heteroresistance and persistence are examples of mechanisms that can allow otherwise drug-susceptible bacteria to survive and resume growth after antibiotic exposure. These temporary forms of antibiotic tolerance can be caused by the upregulation of stress response genes or a decrease in cell growth rate. However, it is not clear how the expression of multiple genes contributes to tolerance phenotypes. Using fluorescent reporters for stress-related genes, we conducted real-time measurements of expression prior to, during, and after antibiotic exposure. We first identified relationships between growth rate and reporter levels based on auto- and cross-correlation analysis, revealing consistent patterns where changes in growth rate were anticorrelated with fluorescence following a delay. We then used pairs of stress gene reporters and time-lapse fluorescence microscopy to measure the growth rate and reporter levels in cells that survived or died following antibiotic exposure. Using these data, we asked whether combined information about reporter expression and growth rate could improve our ability to predict whether a cell would survive or die following antibiotic exposure. We developed a Bayesian inference model to predict how the combination of dual reporter expression levels and growth rate impacts ciprofloxacin survival in Escherichia coli. We found clear evidence of the impact of growth rate and gadX promoter activity on survival. Unexpectedly, our results also revealed examples where additional information from multiple genes decreased prediction accuracy, highlighting an important and underappreciated effect that can occur when integrating data from multiple simultaneous measurements.IMPORTANCETransient increases in bacterial antibiotic tolerance can result in treatment failure despite an infection initially presenting as susceptible, presenting a significant challenge in antibiotic therapy. This phenomenon can also provide a window of opportunity for bacteria to acquire permanent genetic resistance mutations. Although understanding the underlying mechanisms of these antibiotic tolerance phenotypes is crucial for developing effective approaches to treatment, current approaches for studying these transient phenotypes have limitations. Here, we use fluorescent reporters to monitor the expression of genes involved in stress response over time, aiming to link expression with antibiotic survival outcomes. Our results reveal a counterintuitive finding: monitoring multiple gene reporters does not necessarily improve our ability to predict antibiotic survival outcomes compared to single gene reporters. This result emphasizes the need for a deeper mechanistic understanding of the relationship between stress response gene expression and antibiotic tolerance.

Lineage-dependent variations in single-cell antibiotic susceptibility reveal the selective inheritance of phenotypic resistance in bacteria

Genetically identical bacterial cells often exhibit heterogeneous responses to antibiotics - some survive, others die. Here, we show that this heterogeneity propagates across generations to give rise to phenotypic resistance. Using real-time single-cell tracking, we exposed Escherichia coli to the β-lactam cefsulodin at its clinical breakpoint concentration and analyzed cell fate within genealogical trees statistically. Cell survival was strongly correlated among family members, driving the selective enrichment of robust lineages within an otherwise susceptible population. Our genealogical population model identified heritable phenotypic resistance as a key factor underlying this enrichment, which was validated experimentally. Comparing enrichment dynamics between the wild-type and a tolC knock-out strain, deficient in multidrug efflux, uncovered nuanced changes that increased the intergenerational memory of phenotypic resistance. Our findings provide evidence for heritable phenotypic resistance and demonstrate how its propagation through cell-to-cell heterogeneity enables the survival of minority cells within isogenic populations.

Coordination of cell envelope biology by Escherichia coli MarA protein potentiates intrinsic antibiotic resistance

The multiple antibiotic resistance activator (MarA) protein is a transcription factor implicated in control of intrinsic antibiotic resistance in enteric bacterial pathogens. In this work, we screened the Escherichia coli genome computationally for MarA binding sites. By incorporating global maps of transcription initiation, and clustering predicted targets according to gene function, we were able to avoid widespread misidentification of MarA sites, which has hindered prior studies. Subsequent genetic and biochemical analyses identified direct activation of genes for lipopolysaccharide (LPS) biosynthesis and repression of a cell wall remodelling endopeptidase. Rewiring of the MarA regulon, by mutating subsets of MarA binding sites, reveals synergistic interactions between regulatory targets of MarA. Specifically, we show that uncoupling LPS production, or cell wall remodelling, from regulation by MarA, renders cells hypersensitive to mutations altering lipid trafficking by the MlaFEDCB system. Together, our findings demonstrate how MarA co-regulates different aspects of cell envelope biology to maximise antibiotic resistance.

Pulsatile basal gene expression as a fitness determinant in bacteria

Active regulation of gene expression, orchestrated by complex interactions of activators and repressors at promoters, controls the fate of organisms. In contrast, basal expression at uninduced promoters is considered to be a dynamically inert mode of nonfunctional "promoter leakiness," merely a byproduct of transcriptional regulation. Here, we investigate the basal expression mode of the mar operon, the main regulator of intrinsic multiple antibiotic resistance in Escherichia coli, and link its dynamic properties to the noncanonical, yet highly conserved start codon of marR across Enterobacteriaceae. Real-time, single-cell measurements across tens of generations reveal that basal expression consists of rare stochastic gene expression pulses, which maximize variability in wildtype and, surprisingly, transiently accelerate cellular elongation rates. Competition experiments show that basal expression confers fitness advantages to wildtype across several transitions between exponential and stationary growth by shortening lag times. The dynamically rich basal expression of the mar operon has likely been evolutionarily maintained for its role in growth homeostasis of Enterobacteria within the gut environment, thereby allowing other ancillary gene regulatory roles to evolve, e.g., control of costly-to-induce multidrug efflux pumps. Understanding the complex selection forces governing genetic systems involved in intrinsic multidrug resistance is crucial for effective public health measures.

Highly Drug-Resistant Escherichia coli from Hospital Wastewater with Several Evolutionary Mutations: An Integrated Insights from Molecular, Computational, and Biophysics

Many people around the world are still unable to get access to clean drinking water. Escherichia coli is a common waterborne pathogen that frequently results from insufficient hygiene measures and needs attention to address health problems. The present study aimed to evaluate antibiotic resistance of Escherichia coli isolated from wastewater and drinking water samples of hospital and non-hospital settings at Peshawar. Out of 462 samples collected, 111 tested positive for E. coli. The majority of isolates were resistant to many antibiotics including Ampicillin, Gentamicin, Tobramycin, Imipenem, Meropenem, Tetracycline, Cefepime, Amikacin, Piperacillin, Levofloxacin, Ciprofloxacin, Ceftriaxone, and Cefazolin. However, they showed susceptibility to Chloramphenicol, Fosfomycin 200 mg, Colistin, and Tigecycline. Genetic analysis revealed various antibiotic resistance genes within the isolates, i.e., marA(20%), marB(40%) marR(30%), rob(30%), and soxS(35%). Following PCR, the resulting products underwent next-generation sequencing. marA exhibited T10P and D101H mutations, while MarR showed substitutions at M1G, V142S, L143P, and P144C positions. In Rob, D2I, A4P, L10F, I12N, and L253P mutations were observed. The SoxS displayed alterations at H105P, R106A, and L107V positions. Asinex antibacterial library was used to study molecular docking based on virtual screening. SWISS ADME was used to in silico evaluate the pharmacokinetics of these substances. 100 ns molecular dynamics simulation was conducted to estimate free binding energies, confirmation, and stability of the binding mode of the identified compounds. Screening results revealed that LAS-52505571, LAS52171241, LAS52202332, and LAS22461675 compounds showed high affinity to MarA, MarR, SoxS, and Rob proteins, respectively, with the lowest binding energies across the library. In brief, the current study aimed at establishing potential chemical entities that could facilitate the evolution of silicon drugs against antibiotic-resistant E. coli strains.

Microbiota of Cheese Ecosystems: A Perspective on Cheesemaking

This review contributes to the knowledge on the complex and adaptive microbial ecosystems within cheese, emphasizing their critical role in determining cheese quality, flavor, and safety. This review synthesizes the current knowledge on the microbial interactions and the dynamics of lactic acid bacteria (LAB), encompassing both starter (SLAB) and non-starter (NSLAB) strains, which are pivotal to the curd fermentation and ripening processes. The adaptability of these microbial consortia to environmental and technological stressors is explored, highlighting their contributions to acidification, proteolysis, and the development of distinctive organoleptic characteristics. Historical and technological perspectives on cheesemaking are also discussed, detailing the impact of milk treatment, starter culture selection, and post-renneting procedures on microbial activity and biochemical transformations. This review underscores the importance of microbial diversity and cooperative interactions in fostering ecosystem resilience and metabolic functionality, and it addresses the challenges in mimicking the technological performance of natural starters using selected cultures. By understanding the ecological roles and interactions of cheese microbiota, this review aims to guide improvements in cheese production practices. Additionally, these insights could spark the development of innovative strategies for microbial community management.

Interplay between porin deficiency, fitness, and virulence in carbapenem-non-susceptible Pseudomonas aeruginosa and Enterobacteriaceae

The increasing resistance of Gram-negative bacteria to last resort antibiotics, such as carbapenems, is particularly of concern as it is a significant cause of global health threat. In this context, there is an urgent need for better understanding underlying mechanisms leading to antimicrobial resistance in order to limit its diffusion and develop new therapeutic strategies. In this review, we focus on the specific role of porins in carbapenem-resistance in Enterobacteriaceae and Pseudomonas aeruginosa, which are major human pathogens. Porins are outer membrane proteins, which play a key role in the bacterial permeability to allow nutrients to enter and toxic waste to leave. However, these channels are also "Achilles' heel" of bacteria as antibiotics can also pass through them to reach their target and kill the bacteria. After describing normal structures and pathways regulating the expression of porins, we discuss strategies implemented by bacteria to limit the access of carbapenems to their cytoplasmic target. We further examine the real impact of changes in porins on carbapenems susceptibility. Finally, we decipher what is the effect of such changes on bacterial fitness and virulence. Our goal is to integrate all these findings to give a global overview of how bacteria modify their porins to face antibiotic selective pressure trying to not induce fitness cost.

Anti-correlation of LacI association and dissociation rates observed in living cells

The rate at which transcription factors (TFs) bind their cognate sites has long been assumed to be limited by diffusion, and thus independent of binding site sequence. Here, we systematically test this assumption using cell-to-cell variability in gene expression as a window into the in vivo association and dissociation kinetics of the model transcription factor LacI. Using a stochastic model of the relationship between gene expression variability and binding kinetics, we performed single-cell gene expression measurements to infer association and dissociation rates for a set of 35 different LacI binding sites. We found that both association and dissociation rates differed significantly between binding sites, and moreover observed a clear anticorrelation between these rates across varying binding site strengths. These results contradict the long-standing hypothesis that TF binding site strength is primarily dictated by the dissociation rate, but may confer the evolutionary advantage that TFs do not get stuck in near-operator sequences while searching.

Single-cell transcriptomes reveal spatiotemporal heat stress response in maize roots

Plant roots perceive heat stress (HS) and adapt their architecture accordingly, which in turn influence the yield in crops. Investigating their heterogeneity and cell type-specific response to HS is essential for improving crop resilience. Here, we generate single-cell transcriptional landscape of maize (Zea mays) roots in response to HS. We characterize 15 cell clusters corresponding to 9 major cell types and identify cortex as the main root cell type responsive to HS with the most differentially expressed genes and its trajectory being preferentially affected upon HS. We find that cortex size strongly correlated with heat tolerance that is experimentally validated by using inbred lines and genetic mutation analysis of one candidate gene in maize, providing potential HS tolerance indicator and targets for crop improvement. Moreover, interspecies comparison reveals conserved root cell types and core markers in response to HS in plants, which are experimentally validated. These results provide a universal atlas for unraveling the transcriptional programs that specify and maintain the cell identity of maize roots in response to HS at a cell type-specific level.

Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival

Heteroresistance can allow otherwise drug-susceptible bacteria to survive and resume growth after antibiotic exposure. This temporary form of antibiotic tolerance can be caused by the upregulation of stress response genes or a decrease in cell growth rate. However, it is not clear how expression of multiple genes contributes to the tolerance phenotype. By using fluorescent reporters for stress related genes, we conducted real time measurements of expression prior to, during, and after antibiotic exposure. We first identified relationships between growth rate and reporter levels based on auto and cross correlation analysis, revealing consistent patterns where changes in growth rate were anticorrelated with fluorescence following a delay. We then used pairs of stress gene reporters and time lapse fluorescence microcopy to measure the growth rate and reporter levels in cells that survived or died following antibiotic exposure. Using these data, we asked whether combined information about reporter expression and growth rate could improve our ability to predict whether a cell would survive or die following antibiotic exposure. We developed a Bayesian inference model to predict how the combination of dual reporter expression levels and growth rate impact ciprofloxacin survival in Escherichia coli . We found clear evidence of the impact of growth rate and the gadX promoter activity on survival. Unexpectedly, our results also revealed examples where additional information from multiple genes decreased prediction accuracy, highlighting an important and underappreciated effect that can occur when integrating data from multiple simultaneous measurements.

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.

Insights on Pseudomonas aeruginosa Carbohydrate Binding from Profiles of Cystic Fibrosis Isolates Using Multivalent Fluorescent Glycopolymers Bearing Pendant Monosaccharides

Pseudomonas aeruginosa contributes to frequent, persistent, and, often, polymicrobial respiratory tract infections for individuals with cystic fibrosis (CF). Chronic CF infections lead to bronchiectasis and a shortened lifespan. P. aeruginosa expresses numerous adhesins, including lectins known to bind the epithelial cell and mucin glycoconjugates. Blocking carbohydrate-mediated host-pathogen and intra-biofilm interactions critical to the initiation and perpetuation of colonization offer promise as anti-infective treatment strategies. To inform anti-adhesion therapies, we profiled the monosaccharide binding of P. aeruginosa from CF and non-CF sources, and assessed whether specific bacterial phenotypic characteristics affected carbohydrate-binding patterns. Focusing at the cellular level, microscopic and spectrofluorometric tools permitted the solution-phase analysis of P. aeruginosa binding to a panel of fluorescent glycopolymers possessing distinct pendant monosaccharides. All P. aeruginosa demonstrated significant binding to glycopolymers specific for α-D-galactose, β-D-N-acetylgalactosamine, and β-D-galactose-3-sulfate. In each culture, a small subpopulation accounted for the binding. The carbohydrate anomeric configuration and sulfate ester presence markedly influenced binding. While this opportunistic pathogen from CF hosts presented with various colony morphologies and physiological activities, no phenotypic, physiological, or structural feature predicted enhanced or diminished monosaccharide binding. Important to anti-adhesive therapeutic strategies, these findings suggest that, regardless of phenotype or clinical source, P. aeruginosa maintain a small subpopulation that may readily associate with specific configurations of specific monosaccharides. This report provides insights into whole-cell P. aeruginosa carbohydrate-binding profiles and into the context within which successful anti-adhesive and/or anti-virulence anti-infective agents for CF must contend.

Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.

The Mar, Sox, and Rob Systems

Environments inhabited by Enterobacteriaceae are diverse and often stressful. This is particularly true for Escherichia coli and Salmonella during host association in the gastrointestinal systems of animals. There, E. coli and Salmonella must survive exposure to various antimicrobial compounds produced or ingested by their host. A myriad of changes to cellular physiology and metabolism are required to achieve this feat. A central regulatory network responsible for sensing and responding to intracellular chemical stressors like antibiotics are the Mar, Sox, and Rob systems found throughout the Enterobacteriaceae. Each of these distinct regulatory networks controls expression of an overlapping set of downstream genes whose collective effects result in increased resistance to a wide array of antimicrobial compounds. This collection of genes is known as the mar-sox-rob regulon. This review will provide an overview of the mar-sox-rob regulon and molecular architecture of the Mar, Sox, and Rob systems.

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.

Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

Mapping single‐cell responses to population‐level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters.

Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions

Genetically identical bacterial cells commonly display different phenotypes. This phenotypic heterogeneity is well known for stress responses, where it is often explained as bet hedging against unpredictable environmental threats. Here, we explore phenotypic heterogeneity in a major stress response of Escherichia coli and find it has a fundamentally different basis. We characterize the response of cells exposed to hydrogen peroxide (H2O2) stress in a microfluidic device under constant growth conditions. A machine-learning model reveals that phenotypic heterogeneity arises from a precise and rapid feedback between each cell and its immediate environment. Moreover, we find that the heterogeneity rests upon cell-cell interaction, whereby cells shield each other from H2O2 via their individual stress responses. Our work shows how phenotypic heterogeneity in bacterial stress responses can emerge from short-range cell-cell interactions and result in a collective phenotype that protects a large proportion of the population.

Inducible Mega-Mediated Macrolide Resistance Confers Heteroresistance in Streptococcus pneumoniae

In Streptococcus pneumoniae (Spn), the 5.4 to 5.5 kb Macrolide Genetic Assembly (Mega) encodes an efflux pump (Mef[E]) and a ribosomal protection protein (Mel) conferring antibiotic resistance to commonly used macrolides in clinical isolates. We found the macrolide-inducible Mega operon provides heteroresistance (more than 8-fold range in MICs) to 14- and 15-membered ring macrolides. Heteroresistance is commonly missed during traditional clinical resistance screens but is highly concerning as resistant subpopulations can persist despite treatment. Spn strains containing the Mega element were screened via Etesting and population analysis profiling (PAP). All Mega-containing Spn strains screened displayed heteroresistance by PAP. The heteroresistance phenotype was linked to the mRNA expression of the mef(E)/mel operon of the Mega element. Macrolide induction uniformly increased Mega operon mRNA expression across the population, and heteroresistance was eliminated. A deletion of the 5' regulatory region of the Mega operon results in a mutant deficient in induction as well as in heteroresistance. The mef(E)L leader peptide sequence of the 5' regulatory region was required for induction and heteroresistance. Treatment with a noninducing 16-membered ring macrolide antibiotic did not induce the mef(E)/mel operon or eliminate the heteroresistance phenotype. Thus, inducibility of the Mega element by 14- and 15-membered macrolides and heteroresistance are linked in Spn. The stochastic variation in mef(E)/mel expression in a Spn population containing Mega provides the basis for heteroresistance.

Aspergillus fumigatus Can Display Persistence to the Fungicidal Drug Voriconazole

Aspergillus fumigatus is a filamentous fungus that can infect the lungs of patients with immunosuppression and/or underlying lung diseases. The mortality associated with chronic and invasive aspergillosis infections remain very high, despite availability of antifungal treatments. In the last decade, there has been a worrisome emergence and spread of resistance to the first-line antifungals, the azoles. The mortality caused by resistant isolates is even higher, and patient management is complicated as the therapeutic options are reduced. Nevertheless, treatment failure is also common in patients infected with azole-susceptible isolates, which can be due to several non-mutually exclusive reasons, such as poor drug absorption. In addition, the phenomena of tolerance or persistence, where susceptible pathogens can survive the action of an antimicrobial for extended periods, have been associated with treatment failure in bacterial infections, and their occurrence in fungal infections already proposed. Here, we demonstrate that some isolates of A. fumigatus display persistence to voriconazole. A subpopulation of the persister isolates can survive for extended periods and even grow at low rates in the presence of supra-MIC of voriconazole and seemingly other azoles. Persistence cannot be eradicated with adjuvant drugs or antifungal combinations and seemed to reduce the efficacy of treatment for certain individuals in a Galleria mellonella model of infection. Furthermore, persistence implies a distinct transcriptional profile, demonstrating that it is an active response. We propose that azole persistence might be a relevant and underestimated factor that could influence the outcome of infection in human aspergillosis. IMPORTANCE The phenomena of antibacterial tolerance and persistence, where pathogenic microbes can survive for extended periods in the presence of cidal drug concentrations, have received significant attention in the last decade. Several mechanisms of action have been elucidated, and their relevance for treatment failure in bacterial infections demonstrated. In contrast, our knowledge of antifungal tolerance and, in particular, persistence is still very limited. In this study, we have characterized the response of the prominent fungal pathogen Aspergillus fumigatus to the first-line therapy antifungal voriconazole. We comprehensively show that some isolates display persistence to this fungicidal antifungal and propose various potential mechanisms of action. In addition, using an alternative model of infection, we provide initial evidence to suggest that persistence may cause treatment failure in some individuals. Therefore, we propose that azole persistence is an important factor to consider and further investigate in A. fumigatus.

Dissociation kinetics of small-molecule inhibitors in Escherichia coli is coupled to physiological state of cells

Bioactive small-molecule inhibitors represent a treasure chest for future drugs. In vitro high-throughput screening is a common approach to identify the small-molecule inhibitors that bind tightly to purified targets. Here, we investigate the inhibitor-target binding/unbinding kinetics in E. coli cells using a benzimidazole-derivative DNA inhibitor as a model system. We find that its unbinding rate is not constant but depends on cell growth rate. This dependence is mediated by the cellular activity, forming a feedback loop with the inhibitor's activity. In accordance with this feedback, we find cell-to-cell heterogeneity in inhibitor-target interaction, leading to co-existence of two distinct subpopulations: actively growing cells that dissociate the inhibitors from the targets and non-growing cells that do not. We find similar heterogeneity for other clinical DNA inhibitors. Our studies reveal a mechanism that couples inhibitor-target kinetics to cell physiology and demonstrate the significant effect of this coupling on drug efficacy.

Probing transient memory of cellular states using single-cell lineages

The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria-Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.

Resolving colistin resistance and heteroresistance in Enterobacter species

Species within the Enterobacter cloacae complex (ECC) include globally important nosocomial pathogens. A three-year study of ECC in Germany identified Enterobacter xiangfangensis as the most common species (65.5%) detected, a result replicated by examining a global pool of 3246 isolates. Antibiotic resistance profiling revealed widespread resistance and heteroresistance to the antibiotic colistin and detected the mobile colistin resistance (mcr)-9 gene in 19.2% of all isolates. We show that resistance and heteroresistance properties depend on the chromosomal arnBCADTEF gene cassette whose products catalyze transfer of L-Ara4N to lipid A. Using comparative genomics, mutational analysis, and quantitative lipid A profiling we demonstrate that intrinsic lipid A modification levels are genospecies-dependent and governed by allelic variations in phoPQ and mgrB, that encode a two-component sensor-activator system and specific inhibitor peptide. By generating phoPQ chimeras and combining them with mgrB alleles, we show that interactions at the pH-sensing interface of the sensory histidine kinase phoQ dictate arnBCADTEF expression levels. To minimize therapeutic failures, we developed an assay that accurately detects colistin resistance levels for any ECC isolate.

Antibiotic perseverance increases the risk of resistance development

The rise of antibiotic-resistant bacterial infections poses a global threat. Antibiotic resistance development is generally studied in batch cultures which conceals the heterogeneity in cellular responses. Using single-cell imaging, we studied the growth response of Escherichia coli to sub-inhibitory and inhibitory concentrations of nine antibiotics. We found that the heterogeneity in growth increases more than what is expected from growth rate reduction for three out of the nine antibiotics tested. For two antibiotics (rifampicin and nitrofurantoin), we found that sub-populations were able to maintain growth at lethal antibiotic concentrations for up to 10 generations. This perseverance of growth increased the population size and led to an up to 40-fold increase in the frequency of antibiotic resistance mutations in gram-negative and gram-positive species. We conclude that antibiotic perseverance is a common phenomenon that has the potential to impact antibiotic resistance development across pathogenic bacteria.

TCA and SSRI Antidepressants Exert Selection Pressure for Efflux-Dependent Antibiotic Resistance Mechanisms in Escherichia coli

Microbial diversity is reduced in the gut microbiota of animals and humans treated with selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs). The mechanisms driving the changes in microbial composition, while largely unknown, is critical to understand considering that the gut microbiota plays important roles in drug metabolism and brain function. Using Escherichia coli, we show that the SSRI fluoxetine and the TCA amitriptyline exert strong selection pressure for enhanced efflux activity of the AcrAB-TolC pump, a member of the resistance-nodulation-cell division (RND) superfamily of transporters. Sequencing spontaneous fluoxetine- and amitriptyline-resistant mutants revealed mutations in marR and lon, negative regulators of AcrAB-TolC expression. In line with the broad specificity of AcrAB-TolC pumps these mutants conferred resistance to several classes of antibiotics. We show that the converse also occurs, as spontaneous chloramphenicol-resistant mutants displayed cross-resistance to SSRIs and TCAs. Chemical-genomic screens identified deletions in marR and lon, confirming the results observed for the spontaneous resistant mutants. In addition, deletions in 35 genes with no known role in drug resistance were identified that conferred cross-resistance to antibiotics and several displayed enhanced efflux activities. These results indicate that combinations of specific antidepressants and antibiotics may have important effects when both are used simultaneously or successively as they can impose selection for common mechanisms of resistance. Our work suggests that selection for enhanced efflux activities is an important factor to consider in understanding the microbial diversity changes associated with antidepressant treatments. IMPORTANCE Antidepressants are prescribed broadly for psychiatric conditions to alter neuronal levels of synaptic neurotransmitters such as serotonin and norepinephrine. Two categories of antidepressants are selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs); both are among the most prescribed drugs in the United States. While it is well-established that antidepressants inhibit reuptake of neurotransmitters there is evidence that they also impact microbial diversity in the gastrointestinal tract. However, the mechanisms and therefore biological and clinical effects remain obscure. We demonstrate antidepressants may influence microbial diversity through strong selection for mutant bacteria with increased AcrAB-TolC activity, an efflux pump that removes antibiotics from cells. Furthermore, we identify a new group of genes that contribute to cross-resistance between antidepressants and antibiotics, several act by regulating efflux activity, underscoring overlapping mechanisms. Overall, this work provides new insights into bacterial responses to antidepressants important for understanding antidepressant treatment effects.

Unravelling microbial efflux through mathematical modelling

AbstractMathematical modelling is a useful tool that is increasingly used in the life sciences to understand and predict the behaviour of biological systems. This review looks at how this interdisciplinary approach has advanced our understanding of microbial efflux, the process by which microbes expel harmful substances. The discussion is largely in the context of antimicrobial resistance, but applications in synthetic biology are also touched upon. The goal of this paper is to spark further fruitful collaborations between modellers and experimentalists in the efflux community and beyond.

Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response

Individual bacteria that share identical genomes and growth environments can display substantial cell-to-cell differences in expression of stress-response genes and single-cell growth rates. This phenotypic heterogeneity can impact the survival of single cells facing sudden stress. However, the windows of time that cells spend in vulnerable or tolerant states are often unknown. We quantify the temporal expression of a suite of stress-response reporters, while simultaneously monitoring growth. We observe pulsatile expression across genes with a range of stress-response functions, finding that single-cell growth rates are often anticorrelated with reporter levels. These dynamic phenotypic differences have a concrete link to function, in which individual cells undergoing a pulse of elevated expression and slow growth are predisposed to survive antibiotic exposure.

Cell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is important because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress-response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found several examples of pulsatile expression. Single-cell growth rates were often anticorrelated with reporter levels, with changes in growth preceding changes in expression. These dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that fluctuations in both gene expression and growth dynamics in stress-response networks have direct consequences on survival.

Dynamic Boolean modelling reveals the influence of energy supply on bacterial efflux pump expression

Antimicrobial resistance (AMR) is a global health issue. One key factor contributing to AMR is the ability of bacteria to export drugs through efflux pumps, which relies on the ATP-dependent expression and interaction of several controlling genes. Recent studies have shown that significant cell-to-cell ATP variability exists within clonal bacterial populations, but the contribution of intrinsic cell-to-cell ATP heterogeneity is generally overlooked in understanding efflux pumps. Here, we consider how ATP variability influences gene regulatory networks controlling expression of efflux pump genes in two bacterial species. We develop and apply a generalizable Boolean modelling framework, developed to incorporate the dependence of gene expression dynamics on available cellular energy supply. Theoretical results show that differences in energy availability can cause pronounced downstream heterogeneity in efflux gene expression. Cells with higher energy availability have a superior response to stressors. Furthermore, in the absence of stress, model bacteria develop heterogeneous pulses of efflux pump gene expression which contribute to a sustained sub-population of cells with increased efflux expression activity, potentially conferring a continuous pool of intrinsically resistant bacteria. This modelling approach thus reveals an important source of heterogeneity in cell responses to antimicrobials and sheds light on potentially targetable aspects of efflux pump-related antimicrobial resistance.

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Persistence against benzalkonium chloride promotes rapid evolution of tolerance during periodic disinfection

Biocides used as disinfectants are important to prevent the transmission of pathogens, especially during the current antibiotic resistance crisis. This crisis is exacerbated by phenotypically tolerant persister subpopulations that can survive transient antibiotic treatment and facilitate resistance evolution. Here, we show that E. coli displays persistence against a widely used disinfectant, benzalkonium chloride (BAC). Periodic, persister-mediated failure of disinfection rapidly selects for BAC tolerance, which is associated with reduced cell surface charge and mutations in the lpxM locus, encoding an enzyme for lipid A biosynthesis. Moreover, the fitness cost incurred by BAC tolerance turns into a fitness benefit in the presence of antibiotics, suggesting a selective advantage of BAC-tolerant mutants in antibiotic environments. Our findings highlight the links between persistence to disinfectants and resistance evolution to antimicrobials.

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics

The responses of plants to their environment are often dependent on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, the lack of implementation of these technologies in plants has limited concomitant quantitative studies in this kingdom. Here, we applied the PP7 and MS2 RNA-labelling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we counted nascent RNA transcripts in real time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana. Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the proportion of cells that switch from an undetectable basal state to a high-transcription state, instead of modulating the rate of transcription across all cells in a graded fashion. This switch-like behaviour, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from stochasticity intrinsic to individual alleles instead of variability in cellular composition. Together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.

Tuberculosis: An Update on Pathophysiology, Molecular Mechanisms of Drug Resistance, Newer Anti-TB Drugs, Treatment Regimens and Host- Directed Therapies

Human tuberculosis (TB) is primarily caused by Mycobacterium tuberculosis (Mtb) that inhabits inside and amidst immune cells of the host with adapted physiology to regulate interdependent cellular functions with intact pathogenic potential. The complexity of this disease is attributed to various factors such as the reactivation of latent TB form after prolonged persistence, disease progression specifically in immunocompromised patients, advent of multi- and extensivelydrug resistant (MDR and XDR) Mtb strains, adverse effects of tailor-made regimens, and drug-drug interactions among anti-TB drugs and anti-HIV therapies. Thus, there is a compelling demand for newer anti-TB drugs or regimens to overcome these obstacles. Considerable multifaceted transformations in the current TB methodologies and molecular interventions underpinning hostpathogen interactions and drug resistance mechanisms may assist to overcome the emerging drug resistance. Evidently, recent scientific and clinical advances have revolutionised the diagnosis, prevention, and treatment of all forms of the disease. This review sheds light on the current understanding of the pathogenesis of TB disease, molecular mechanisms of drug-resistance, progress on the development of novel or repurposed anti-TB drugs and regimens, host-directed therapies, with particular emphasis on underlying knowledge gaps and prospective for futuristic TB control programs.

Waddington's Landscapes in the Bacterial World

Conrad Waddington’s epigenetic landscape, a visual metaphor for the development of multicellular organisms, is appropriate to depict the formation of phenotypic variants of bacterial cells. Examples of bacterial differentiation that result in morphological change have been known for decades. In addition, bacterial populations contain phenotypic cell variants that lack morphological change, and the advent of fluorescent protein technology and single-cell analysis has unveiled scores of examples. Cell-specific gene expression patterns can have a random origin or arise as a programmed event. When phenotypic cell-to-cell differences are heritable, bacterial lineages are formed. The mechanisms that transmit epigenetic states to daughter cells can have strikingly different levels of complexity, from the propagation of simple feedback loops to the formation of complex DNA methylation patterns. Game theory predicts that phenotypic heterogeneity can facilitate bacterial adaptation to hostile or unpredictable environments, serving either as a division of labor or as a bet hedging that anticipates future challenges. Experimental observation confirms the existence of both types of strategies in the bacterial world.

Yeast cell fate control by temporal redundancy modulation of transcription factor paralogs

Recent single-cell studies have revealed that yeast stress response involves transcription factors that are activated in pulses. However, it remains unclear whether and how these dynamic transcription factors temporally interact to regulate stress survival. Here we show that budding yeast cells can exploit the temporal relationship between paralogous general stress regulators, Msn2 and Msn4, during stress response. We find that individual pulses of Msn2 and Msn4 are largely redundant, and cells can enhance the expression of their shared targets by increasing their temporal divergence. Thus, functional redundancy between these two paralogs is modulated in a dynamic manner to confer fitness advantages for yeast cells, which might feed back to promote the preservation of their redundancy. This evolutionary implication is supported by evidence from Msn2/Msn4 orthologs and analyses of other transcription factor paralogs. Together, we show a cell fate control mechanism through temporal redundancy modulation in yeast, which may represent an evolutionarily important strategy for maintaining functional redundancy between gene duplicates.

Antibiotic tolerance is associated with a broad and complex transcriptional response in E. coli

Antibiotic treatment kills a large portion of a population, while a small, tolerant subpopulation survives. Tolerant bacteria disrupt antibiotic efficacy and increase the likelihood that a population gains antibiotic resistance, a growing health concern. We examined how E. coli transcriptional networks changed in response to lethal ampicillin concentrations. We are the first to apply transcriptional regulatory network (TRN) analysis to antibiotic tolerance by leveraging existing knowledge and our transcriptional data. TRN analysis shows that gene expression changes specific to ampicillin treatment are likely caused by specific sigma and transcription factors typically regulated by proteolysis. These results demonstrate that to survive lethal concentration of ampicillin specific regulatory proteins change activity and cause a coordinated transcriptional response that leverages multiple gene systems.

Non-responder phenotype reveals apparent microbiome-wide antibiotic tolerance in the murine gut

Broad spectrum antibiotics cause both transient and lasting damage to the ecology of the gut microbiome. Antibiotic-induced loss of gut bacterial diversity has been linked to susceptibility to enteric infections. Prior work on subtherapeutic antibiotic treatment in humans and non-human animals has suggested that entire gut communities may exhibit tolerance phenotypes. In this study, we validate the existence of these community tolerance phenotypes in the murine gut and explore how antibiotic treatment duration or a diet enriched in antimicrobial phytochemicals might influence the frequency of this phenotype. Almost a third of mice exhibited whole-community tolerance to a high dose of the β-lactam antibiotic cefoperazone, independent of antibiotic treatment duration or dietary phytochemical amendment. We observed few compositional differences between non-responder microbiota during antibiotic treatment and the untreated control microbiota. However, gene expression was vastly different between non-responder microbiota and controls during treatment, with non-responder communities showing an upregulation of antimicrobial tolerance genes, like efflux transporters, and a down-regulation of central metabolism. Future work should focus on what specific host- or microbiome-associated factors are responsible for tipping communities between responder and non-responder phenotypes so that we might learn to harness this phenomenon to protect our microbiota from routine antibiotic treatment.

Functional roles of microbial cell-to-cell heterogeneity and emerging technologies for analysis and control

Clonal cell populations often display significant cell-to-cell phenotypic heterogeneity, even when maintained under constant external conditions. This variability can result from the inherently stochastic nature of transcription and translation processes, which leads to varying numbers of transcripts and proteins per cell. Here, we showcase studies that reveal links between stochastic cellular events and biological functions in isogenic microbial populations. Then, we highlight emerging tools from engineering, computation, and synthetic and molecular biology that enable precise measurement, control, and analysis of gene expression noise in microorganisms. The capabilities offered by this sophisticated toolbox will shape future directions in the field and generate insight into the behavior of living systems at the single-cell level.

Heterogeneous resistance to colistin in Enterobacter cloacae complex due to a new small transmembrane protein

BACKGROUND

Enterobacter strains can display heterogeneous resistance (heteroresistance) to colistin but the mechanisms remain largely unknown. We investigated potential mechanisms of colistin heteroresistance in an Enterobacter clinical strain, WCHECl-1060, and found a new mechanism.

METHODS

Strain WCHECl-1060 was subjected to WGS to identify known colistin resistance mechanisms. Tn5 insertional mutagenesis, gene knockout and complementation and shotgun cloning were employed to investigate unknown colistin heteroresistance mechanisms. RNA sequencing was performed to link the newly identified mechanism with known ones.

RESULTS

We showed that the phoP gene [encoding part of the PhoP-PhoQ two-component system (TCS)], the dedA(Ecl) gene (encoding an inner membrane protein of the DedA family) and the tolC gene (encoding part of the AcrAB-TolC efflux pump) are required for colistin heteroresistance. We identified a new gene, ecr, encoding a 72 amino acid transmembrane protein, which was able to mediate colistin heteroresistance. We then performed RNA sequencing and transcriptome analysis and found that in the presence of ecr the expression of phoP and the arnBCADTEF operon, which synthesizes and transfers l-Ara4N to lipid A, was increased significantly.

CONCLUSIONS

The small protein encoded by ecr represents a new colistin heteroresistance mechanism and is likely to mediate colistin heteroresistance via the PhoP-PhoQ TCS to act on the arnBCADTEF operon.

Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells

A better understanding of how antibiotic exposure impacts the evolution of resistance in bacterial populations is crucial for designing more sustainable treatment strategies. The conventional approach to this question is to measure the range of concentrations over which resistant strain(s) are selectively favored over a sensitive strain. Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using two Pseudomonas aeruginosa strains carrying resistance plasmids, we show that single resistant cells have <5% probability of detectable outgrowth at antibiotic concentrations as low as one-eighth of the resistant strain's minimum inhibitory concentration (MIC). This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, well below the MIC of resistant strains, may effectively restrict de novo emergence of resistance even though they cannot clear already-large resistant populations.

Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics

Glioblastoma (GBM) is the most aggressive malignant primary brain tumour with a median overall survival of 15 months. To treat GBM, patients currently undergo a surgical resection followed by exposure to radiotherapy and concurrent and adjuvant temozolomide (TMZ) chemotherapy. However, this protocol often leads to treatment failure, with drug resistance being the main reason behind this. To date, many studies highlight the role of O-6-methylguanine-DNA methyltransferase (MGMT) in conferring drug resistance. The mechanism through which MGMT confers resistance is not well studied-particularly in terms of computational models. With only a few reasonable biological assumptions, we were able to show that even a minimal model of MGMT expression could robustly explain TMZ-mediated drug resistance. In particular, we showed that for a wide range of parameter values constrained by novel cell growth and viability assays, a model accounting for only stochastic gene expression of MGMT coupled with cell growth, division, partitioning and death was able to exhibit phenotypic selection of GBM cells expressing MGMT in response to TMZ. Furthermore, we found this selection allowed the cells to pass their acquired phenotypic resistance onto daughter cells in a stable manner (as long as TMZ is provided). This suggests that stochastic gene expression alone is enough to explain the development of chemotherapeutic resistance.

Dynamic motility selection drives population segregation in a bacterial swarm

Population expansion in space, or range expansion, is widespread in nature and in clinical settings. Space competition among heterogeneous subpopulations during range expansion is essential to population ecology, and it may involve the interplay of multiple factors, primarily growth and motility of individuals. Structured microbial communities provide model systems to study space competition during range expansion. Here we use bacterial swarms to investigate how single-cell motility contributes to space competition among heterogeneous bacterial populations during range expansion. Our results revealed that motility heterogeneity can promote the spatial segregation of subpopulations via a dynamic motility selection process. The dynamic motility selection is enabled by speed-dependent persistence time bias of single-cell motion, which presumably arises from physical interaction between cells in a densely packed swarm. We further showed that the dynamic motility selection may contribute to collective drug tolerance of swarming colonies by segregating subpopulations with transient drug tolerance to the colony edge. Our results illustrate that motility heterogeneity, or "motility fitness," can play a greater role than growth rate fitness in determining the short-term spatial structure of expanding populations.

The Escherichia coli MarA protein regulates the ycgZ-ymgABC operon to inhibit biofilm formation

The Escherichia coli marRAB operon is a paradigm for chromosomally encoded antibiotic resistance. The operon exerts its effect via an encoded transcription factor called MarA that modulates efflux pump and porin expression. In this work, we show that MarA is also a regulator of biofilm formation. Control is mediated by binding of MarA to the intergenic region upstream of the ycgZ-ymgABC operon. The operon, known to influence the formation of curli fibres and colanic acid, is usually expressed during periods of starvation. Hence, the ycgZ-ymgABC promoter is recognised by σ³⁸ (RpoS)-associated RNA polymerase (RNAP). Surprisingly, MarA does not influence σ³⁸ -dependent transcription. Instead, MarA drives transcription by the housekeeping σ⁷⁰ -associated RNAP. The effects of MarA on ycgZ-ymgABC expression are coupled with biofilm formation by the rcsCDB phosphorelay system, with YcgZ, YmgA and YmgB forming a complex that directly interacts with the histidine kinase domain of RcsC.

Adaptive Evolution of Escherichia coli to Ciprofloxacin in Controlled Stress Environments: Contrasting Patterns of Resistance in Spatially Varying versus Uniformly Mixed Concentration Conditions

A microfluidic gradient chamber (MGC) and a homogeneous batch culturing system were used to evaluate whether spatial concentration gradients of the antibiotic ciprofloxacin allow development of greater antibiotic resistance in Escherichia coli strain 307 (E. coli 307) compared to exclusively temporal concentration gradients, as indicated in an earlier study. A linear spatial gradient of ciprofloxacin and Luria-Bertani broth (LB) medium was established and maintained by diffusion over 5 days across a well array in the MGC, with relative concentrations along the gradient of 1.7-7.7× the original minimum inhibitory concentration (MIC_original). The E. coli biomass increased in wells with lower ciprofloxacin concentrations, and only a low level of resistance to ciprofloxacin was detected in the recovered cells (∼2× MIC_original). Homogeneous batch culture experiments were performed with the same temporal exposure history to ciprofloxacin concentration, the same and higher initial cell densities, and the same and higher nutrient (i.e., LB) concentrations as in the MGC. In all batch experiments, E. coli 307 developed higher ciprofloxacin resistance after exposure, ranging from 4 to 24× MIC_original in all replicates. Hence, these results suggest that the presence of spatial gradients appears to reduce the driving force for E. coli 307 adaptation to ciprofloxacin, which suggests that results from batch experiments may over predict the development of antibiotic resistance in natural environments.

Forecasting cell fate during antibiotic exposure using stochastic gene expression

Antibiotic killing does not occur at a single, precise time for all cells within a population. Variability in time to death can be caused by stochastic expression of genes, resulting in differences in endogenous stress-resistance levels between individual cells in a population. Here we investigate whether single-cell differences in gene expression prior to antibiotic exposure are related to cell survival times after antibiotic exposure for a range of genes of diverse function. We quantified the time to death of single cells under antibiotic exposure in combination with expression of reporters. For some reporters, including genes involved in stress response and cellular processes like metabolism, the time to cell death had a strong relationship with the initial expression level of the genes. Our results highlight the single-cell level non-uniformity of antibiotic killing and also provide examples of key genes where cell-to-cell variation in expression is strongly linked to extended durations of antibiotic survival.

Heterogeneity in efflux pump expression predisposes antibiotic-resistant cells to mutation

Antibiotic resistance is often the result of mutations that block drug activity; however, bacteria also evade antibiotics by transiently expressing genes such as multidrug efflux pumps. A crucial question is whether transient resistance can promote permanent genetic changes. Previous studies have established that antibiotic treatment can select tolerant cells that then mutate to achieve permanent resistance. Whether these mutations result from antibiotic stress or preexist within the population is unclear. To address this question, we focused on the multidrug pump AcrAB-TolC. Using time-lapse microscopy, we found that cells with higher acrAB expression have lower expression of the DNA mismatch repair gene mutS, lower growth rates, and higher mutation frequencies. Thus, transient antibiotic resistance from elevated acrAB expression can promote spontaneous mutations within single cells.