The physiology and genetics of bacterial responses to antibiotic combinations

Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.

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.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

Multidrug Resistance Pumps as a Keystone of Bacterial Resistance

Antibiotic resistance is a global problem of modern medicine. A harbinger of the onset of the postantibiotic era is the complexity and high cost of developing new antibiotics as well as their inefficiency due to the rapidly developing resistance of bacteria. Multidrug resistance (MDR) pumps, involved in the formation of resistance to xenobiotics, the export of toxins, the maintenance of cellular homeostasis, and the formation of biofilms and persistent cells, are the keystone of bacterial protection against antibiotics. MDR pumps are the basis for the nonspecific protection of bacteria, while modification of the drug target, inactivation of the drug, and switching of the target or sequestration of the target is the second specific line of their protection. Thus, the nonspecific protection of bacteria formed by MDR pumps is a barrier that prevents the penetration of antibacterial substances into the cell, which is the main factor determining the resistance of bacteria. Understanding the mechanisms of MDR pumps and a balanced assessment of their contribution to total resistance, as well as to antibiotic sensitivity, will either seriously delay the onset of the postantibiotic era or prevent its onset in the foreseeable future.

Tissue compartmentalization enables Salmonella persistence during chemotherapy

Antimicrobial chemotherapy can fail to eradicate the pathogen, even in the absence of antimicrobial resistance. Persisting pathogens can subsequently cause relapsing diseases. In vitro studies suggest various mechanisms of antibiotic persistence, but their in vivo relevance remains unclear because of the difficulty of studying scarce pathogen survivors in complex host tissues. Here, we localized and characterized rare surviving Salmonella in mouse spleen using high-resolution whole-organ tomography. Chemotherapy cleared >99.5% of the Salmonella but was inefficient against a small Salmonella subset in the white pulp. Previous models could not explain these findings: drug exposure was adequate, Salmonella continued to replicate, and host stresses induced only limited Salmonella drug tolerance. Instead, antimicrobial clearance required support of Salmonella-killing neutrophils and monocytes, and the density of such cells was lower in the white pulp than in other spleen compartments containing higher Salmonella loads. Neutrophil densities declined further during treatment in response to receding Salmonella loads, resulting in insufficient support for Salmonella clearance from the white pulp and eradication failure. However, adjunctive therapies sustaining inflammatory support enabled effective clearance. These results identify uneven Salmonella tissue colonization and spatiotemporal inflammation dynamics as main causes of Salmonella persistence and establish a powerful approach to investigate scarce but impactful pathogen subsets in complex host environments.

Quantification and surface localization of the hemolysin A type 1 secretion system at the endogenous level and under conditions of overexpression

Secretion systems are essential for Gram-negative bacteria as these nanomachineries allow a communication with the outside world by exporting proteins into the extracellular space or directly into the cytosol of a host cell. For example, type one secretion systems (T1SS) secrete a broad range of substrates across both membranes into the extracellular space. One well-known example is the hemolysin A (HlyA) T1SS from Escherichia coli (E. coli), which consists of an ABC transporter (HlyB), a membrane fusion protein (HlyD), the outer membrane protein TolC and the substrate HlyA, a member of the family of RTX (repeats in toxins) toxins. Here, we determined the amount of TolC at the endogenous level (parental strain, UTI89) and under conditions of overexpression (T7 expression system, BL21(DE3)-BD). The overall amount of TolC was not influenced by the overexpression of the HlyBD complex. Moving one step further, we determined the localization of the HlyA T1SS by super-resolution microscopy. In contrast to other bacterial secretion systems, no polarization was observed with respect to endogenous or overexpression levels. Additionally, the cell growth and division cycle did not influence the polarization. Most importantly, the size of the observed T1SS clusters did not correlate with the recently proposed outer membrane islands. These data indicate that T1SS cluster at the outer membrane generating domains of so far not described identity. Importance Uropathogenic Escherichia coli (UPEC) strains cause about 110 million urinary tract infections each year worldwide representing a global burden to the healthcare system. UPEC secrete many virulence factors among these the TX toxin hemolysin A via a cognate T1SS into the extracellular space. In this study, we determined the endogenous copy number of the HlyA T1SS in UTI89 and analyzed the surface localization in BL21(DE3)-BD and UTI89, respectively. With approximately 800 copies of the T1SS in UTI89, this is one of the highest expressed bacterial secretion systems. Furthermore and in clear contrast to other secretion systems, no polarized surface localization was detected. Finally, quantitative analysis of the super-resolution data revealed that clusters of the HlyA T1SS are not related to the recently identified outer membrane protein islands. These data provide insights into the quantitative molecular architecture of the HlyA T1SS.

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.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

Quantitative biology of survival under antibiotic treatments

The mathematical formulation for the dynamics of growth reduction and/or killing under antibiotic treatments has a long history. Even before the extensive use of antibiotics, attempts to model the killing dynamics of biocides were made [1]. Here, we review relatively simple quantitative formulations of the two main modes of survival under antibiotics, resistance and tolerance, as well as their heterogeneity in bacterial populations. We focus on the two main types of heterogeneity that have been described: heteroresistance and antibiotic persistence, each linked to the variation in a different parameter of the antibiotic response dynamics. Finally, we review the effects on survival of combining resistance and tolerance mutations as well as on the mode and tempo of evolution under antibiotic treatments.

Efflux Impacts Intracellular Accumulation Only in Actively Growing Bacterial Cells

For antibiotics with intracellular targets, effective treatment of bacterial infections requires the drug to accumulate to a high concentration inside cells. Bacteria produce a complex cell envelope and possess drug export efflux pumps to limit drug accumulation inside cells. Decreasing cell envelope permeability and increasing efflux pump activity can reduce intracellular accumulation of antibiotics and are commonly seen in antibiotic-resistant strains. Here, we show that the balance between influx and efflux differs depending on bacterial growth phase in Gram-negative bacteria. Accumulation of the fluorescent compound ethidium bromide (EtBr) was measured in Salmonella enterica serovar Typhimurium SL1344 (wild type) and efflux deficient (ΔacrB) strains during growth. In SL1344, EtBr accumulation remained low, regardless of growth phase, and did not correlate with acrAB transcription. EtBr accumulation in the ΔacrB strains was high in exponential phase but dropped sharply later in growth, with no significant difference from that in SL1344 in stationary phase. Low EtBr accumulation in stationary phase was not due to the upregulation of other efflux pumps but instead was due to decreased permeability of the envelope in stationary phase. Transcriptome sequencing (RNA-seq) identified changes in expression of several pathways that remodel the envelope in stationary phase, leading to lower permeability.

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

While being genetically the same, a population of cells can show phenotypic variability even under homogeneous environments. Often advantageous under heterogeneous environments, this phenotypic heterogeneity is highly relevant in the studies of antibiotic resistance evolution and cancer resurgence. Numerous theoretical models exist applying a simple model of phenotypic switching. Experimental measurements on phenotypic heterogeneity have increased in precision over the past decade, and the simple models are inadequate to explain the new observations. In this paper, we explore the role of cellular memory as a crucial component of phenotypic switching. We see that memory helps account for the hitherto unexplained observations and fundamentally extend our understanding of phenotypic heterogeneity.

Nonrandom segregation of sister chromosomes by Escherichia coli MukBEF

Structural maintenance of chromosomes (SMC) complexes contribute to chromosome organization in all domains of life. In Escherichia coli, MukBEF, the functional SMC homolog, promotes spatiotemporal chromosome organization and faithful chromosome segregation. Here, we address the relative contributions of MukBEF and the replication terminus (ter) binding protein, MatP, to chromosome organization-segregation. We show that MukBEF, but not MatP, is required for the normal localization of the origin of replication to midcell and for the establishment of translational symmetry between newly replicated sister chromosomes. Overall, chromosome orientation is normally maintained through division from one generation to the next. Analysis of loci flanking the replication termination region (ter), which demark the ends of the linearly organized portion of the nucleoid, demonstrates that MatP is required for maintenance of chromosome orientation. We show that DNA-bound β2-processivity clamps, which mark the lagging strands at DNA replication forks, localize to the cell center, independent of replisome location but dependent on MukBEF action, and consistent with translational symmetry of sister chromosomes. Finally, we directly show that the older ("immortal") template DNA strand, propagated from previous generations, is preferentially inherited by the cell forming at the old pole, dependent on MukBEF and MatP. The work further implicates MukBEF and MatP as central players in chromosome organization, segregation, and nonrandom inheritance of genetic material and suggests a general framework for understanding how chromosome conformation and dynamics shape subcellular organization.

Multiform antimicrobial resistance from a metabolic mutation

A critical challenge for microbiology and medicine is how to cure infections by bacteria that survive antibiotic treatment by persistence or tolerance. Seeking mechanisms behind such high survival, we developed a forward-genetic method for efficient isolation of high-survival mutants in any culturable bacterial species. We found that perturbation of an essential biosynthetic pathway (arginine biosynthesis) in a mycobacterium generated three distinct forms of resistance to diverse antibiotics, each mediated by induction of WhiB7: high persistence and tolerance to kanamycin, high survival upon exposure to rifampicin, and minimum inhibitory concentration-shifted resistance to clarithromycin. As little as one base change in a gene that encodes, a metabolic pathway component conferred multiple forms of resistance to multiple antibiotics with different targets. This extraordinary resilience may help explain how substerilizing exposure to one antibiotic in a regimen can induce resistance to others and invites development of drugs targeting the mediator of multiform resistance, WhiB7.

Stochastic response of bacterial cells to antibiotics: its mechanisms and implications for population and evolutionary dynamics

The effectiveness of antibiotics against bacterial infections has been declining due to the emergence of resistance. Precisely understanding the response of bacteria to antibiotics is critical to maximizing antibiotic-induced bacterial eradication while minimizing the emergence of antibiotic resistance. Cell-to-cell heterogeneity in antibiotic susceptibility is observed across various bacterial species for a wide range of antibiotics. Heterogeneity in antibiotic susceptibility is not always due to the genetic differences. Rather, it can be caused by non-genetic mechanisms such as stochastic gene expression and biased partitioning upon cell division. Heterogeneous susceptibility leads to the stochastic growth and death of individual cells and stochastic fluctuations in population size. These fluctuations have important implications for the eradication of bacterial populations and the emergence of genotypic resistance.

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

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

The AcrAB-TolC Efflux Pump Impacts Persistence and Resistance Development in Stationary-Phase Escherichia coli following Delafloxacin Treatment

Bacteria have a repertoire of strategies to overcome antibiotics in clinical use, complicating our ability to treat and cure infectious diseases. In addition to evolving resistance, bacteria within genetically clonal cultures can undergo transient phenotypic changes and tolerate high doses of antibiotics. These cells, termed persisters, exhibit heterogeneous phenotypes; the strategies that a bacterial population deploys to overcome one class of antibiotics can be distinct from those needed to survive treatment with drugs with another mode of action. It was previously reported that fluoroquinolones, which target DNA topoisomerases, retain the capacity to kill nongrowing bacteria that tolerate other classes of antibiotics. Here, we show that in Escherichia coli stationary-phase cultures and colony biofilms, persisters that survive treatment with the anionic fluoroquinolone delafloxacin depend on the AcrAB-TolC efflux pump. In contrast, we did not detect this dependence on AcrAB-TolC in E. coli persisters that survive treatment with three other fluoroquinolone compounds. We found that the loss of AcrAB-TolC activity via genetic mutations or chemical inhibition not only reduces delafloxacin persistence in nongrowing E. coli MG1655 or EDL933 (an E. coli O157:H7 strain), but it limits resistance development in progenies derived from delafloxacin persisters that were given the opportunity to recover in nutritive medium following antibiotic treatment. Our findings highlight the heterogeneity in defense mechanisms that persisters use to overcome different compounds within the same class of antibiotics. They further indicate that efflux pump inhibitors can potentiate the activity of delafloxacin against stationary-phase E. coli and block resistance development in delafloxacin persister progenies.

Unraveling the Novel Effect of Patchouli Alcohol Against the Antibiotic Resistance of Helicobacter pylori

The long-term colonization of Helicobacter pylori can cause various gastrointestinal diseases, and its high genetic variability is prone to antibiotic resistance and leads to failure of clinical treatment. Intracellular survival also contributes to the drug tolerance of H. pylori. Patchouli alcohol (PA) shows a highly efficient activity against H. pylori in vitro and in vivo. And this study aims to explore whether PA can reduce the resistance of H. pylori and determine the underlying mechanism. Checkerboard and time-kill bactericidal curve assay reveal that the combination of PA and clarithromycin (CLR) promoted the inhibition and bactericidal effect against H. pylori. Stimulation of CLR leads to the internalization of H. pylori, but PA can effectively inhibit the invasion induced by CLR. Compared with antibiotics, PA remarkably eradicated the intracellular H. pylori, and this intracellular sterilized ability was further improved in combination with antibiotics (CLR and metronidazole). The expression of H. pylori efflux pump genes (hp0605, hp1327, and hp1489) was dose-dependently downregulated by PA. Digital droplet PCR indicated that the H. pylori mutant of A2143G can be inhibited by PA. Cellular uptake and transport assays showed that PA is rapidly absorbed, which promotes its activity against intracellular bacteria. Therefore, PA can act synergistically with CLR as a candidate treatment against drug-resistant H. pylori.

Probing bacterial cell wall growth by tracing wall-anchored protein complexes

The dynamic assembly of the cell wall is key to the maintenance of cell shape during bacterial growth. Here, we present a method for the analysis of Escherichia coli cell wall growth at high spatial and temporal resolution, which is achieved by tracing the movement of fluorescently labeled cell wall-anchored flagellar motors. Using this method, we clearly identify the active and inert zones of cell wall growth during bacterial elongation. Within the active zone, the insertion of newly synthesized peptidoglycan occurs homogeneously in the axial direction without twisting of the cell body. Based on the measured parameters, we formulate a Bernoulli shift map model to predict the partitioning of cell wall-anchored proteins following cell division.

Microfluidic Single-Cell Phenotyping of the Activity of Peptide-Based Antimicrobials

Antibiotic resistance is a major challenge for modern medicine, and there is a dire need to refresh the antibiotic development pipeline to treat infections that are resistant to currently available drugs. Peptide-based antimicrobials represent a promising source of novel anti-infectives, but their development is severely impeded due to the lack of suitable techniques to accurately quantify their antimicrobial efficacy. A major problem involves the heterogeneity of cellular phenotypes in response to these peptides, even within a clonal population of bacteria. There is thus a need to develop single-cell resolution assays to quantify drug efficacy for these novel therapeutics. We present here a detailed microfluidics-microscopy protocol for testing the efficacy of peptide-based antimicrobials on hundreds to thousands of individual bacteria in well-defined microenvironments. This enables the study of cell-to-cell differences in drug response within a clonal population. It is a highly versatile tool, which can be used to quantify drug efficacy, including the number of individual survivors at defined drug doses; it even enables the potential exploration of the molecular mechanisms of action of the drug, which are often unknown in the early stages of drug development. We present here protocols for working with Escherichia coli, but organisms of different geometric shapes and sizes may also be tested with suitable modifications of the microfluidic device.

Nongenetic individuality, changeability, and inheritance in bacterial behavior

Isogenic microbial populations in constant and homogeneous environments can display remarkable levels of phenotypic diversity. Quantitative understanding of how such diversity is generated and maintained in populations is, however, experimentally and theoretically challenging. We focus on the swimming behavior of Escherichia coli as a model system of phenotypic diversity and show that, despite temporal changes in behavior that each individual undergoes, significant differences between individuals persist throughout most of their lifetimes. While the behavior of even closely related bacteria can be remarkably different, the behavioral variations produced by nongenetic mechanisms are inherited across generations. The general experimental and theoretical framework developed here can be applied to study quantitative aspects of phenotypic diversity in many biological systems.

Isogenic populations often display remarkable levels of phenotypic diversity even in constant, homogeneous environments. Such diversity results from differences between individuals (“nongenetic individuality”) as well as changes during individuals’ lifetimes (“changeability”). Yet, studies that capture and quantify both sources of diversity are scarce. Here we measure the swimming behavior of hundreds of Escherichia coli bacteria continuously over two generations and use a model-independent method for quantifying behavior to show that the behavioral space of E. coli is low-dimensional, with variations occurring mainly along two independent and interpretable behavioral traits. By statistically decomposing the diversity in these two traits, we find that individuality is the main source of diversity, while changeability makes a smaller but significant contribution. Finally, we show that even though traits of closely related individuals can be remarkably different, they exhibit positive correlations across generations that imply nongenetic inheritance. The model-independent experimental and theoretical framework developed here paves the way for more general studies of microbial behavioral diversity.

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.

The Antibiotic Dosage of Fastest Resistance Evolution: gene amplifications underpinning the inverted-U

To determine the dosage at which antibiotic resistance evolution is most rapid, we treated Escherichia coli in vitro, deploying the antibiotic erythromycin at dosages ranging from zero to high. Adaptation was fastest just below erythromycin’s minimal inhibitory concentration (MIC) and genotype-phenotype correlations determined from whole genome sequencing revealed the molecular basis: simultaneous selection for copy number variation in three resistance mechanisms which exhibited an “inverted-U” pattern of dose-dependence, as did several insertion sequences and an integron. Many genes did not conform to this pattern, however, reflecting changes in selection as dose increased: putative media adaptation polymorphisms at zero antibiotic dosage gave way to drug target (ribosomal RNA operon) amplification at mid dosages whereas prophage-mediated drug efflux amplifications dominated at the highest dosages. All treatments exhibited E. coli increases in the copy number of efflux operons acrAB and emrE at rates that correlated with increases in population density. For strains where the inverted-U was no longer observed following the genetic manipulation of acrAB, it could be recovered by prolonging the antibiotic treatment at subMIC dosages.

Drug repurposing for next-generation combination therapies against multidrug-resistant bacteria

Antimicrobial resistance has been a global health challenge that threatens our ability to control and treat life-threatening bacterial infections. Despite ongoing efforts to identify new drugs or alternatives to antibiotics, no new classes of antibiotic or their alternatives have been clinically approved in the last three decades. A combination of antibiotics and non-antibiotic compounds that could inhibit bacterial resistance determinants or enhance antibiotic activity offers a sustainable and effective strategy to confront multidrug-resistant bacteria. In this review, we provide a brief overview of the co-evolution of antibiotic discovery and the development of bacterial resistance. We summarize drug-drug interactions and uncover the art of repurposing non-antibiotic drugs as potential antibiotic adjuvants, including discussing classification and mechanisms of action, as well as reporting novel screening platforms. A pathogen-by-pathogen approach is then proposed to highlight the critical value of drug repurposing and its therapeutic potential. Finally, general advantages, challenges and development trends of drug combination strategy are discussed.

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.

The social network: Impact of host and microbial interactions on bacterial antibiotic tolerance and persistence

Antibiotics have vastly improved our quality of life since their discovery and introduction into modern medicine. Yet, widespread use and misuse have compromised the efficacy of these compounds and put our ability to cure infectious diseases in jeopardy. To defend themselves against antibiotics, bacteria have evolved an arsenal of survival strategies. In addition to acquiring mutations and genetic determinants that confer antibiotic resistance, bacteria can respond to environmental cues and adopt reversible phenotypic changes that transiently enhance their ability to survive adverse conditions, including those brought on by antibiotics. These antibiotic tolerant and persistent bacteria, which are prevalent in biofilms and can survive antimicrobial therapy without inheriting resistance, are thought to underlie treatment failure and infection relapse. At infection sites, bacteria encounter a range of signals originating from host immunity and the local microbiota that can induce transcriptomic and metabolic reprogramming. In this review, we will focus on the impact of host factors and microbial interactions on antibiotic tolerance and persistence. We will also outline current efforts in leveraging the knowledge of host-microbe and microbe-microbe interactions in designing therapies that potentiate antibiotic activity and reduce the burden caused by recurrent infections.

Combating Antibiotic-Resistant Gram-Negative Bacteria Strains with Tetracycline-Conjugated Carbon Nanoparticles

Nontoxic carbon nanoparticle samples prepared by both bottom-up and top-down approaches do not inhibit Gram-negative bacterial growth, indicating excellent biocompatibilities. However, cell growth inhibitory efficacies increase considerably when the carbon nanoparticles are conjugated with the antibiotic tetracycline. In tetracycline-resistant bacteria, these efficacies can approach tenfold higher activities when compared to tetracycline alone. No structural abnormality such as membrane disruptions is evident in the tested bacterial strains; this is in contrast with other nanocarbon systems such as graphene oxides, carbon nanotubes, and amine-functionalized carbon nanoparticles which do exhibit membrane disruptions. In comparison, the tetracycline-conjugated carbon nanoparticles induce membrane perturbations (but not membrane disruptions), inhibiting bacterial efflux mechanisms. It is proposed that when tetracycline is conjugated to the surface of carbon nanoparticles, it functions to direct the nanoparticles to membrane-associated tetracycline efflux pumps, thereby blocking and subsequently inhibiting their function. The conjugation between biocompatible carbon nanoparticles and subtherapeutic but well-established antibiotic molecules may provide hybrid antibiotic assembly strategies resulting in effective multidrug efflux inhibition for combating antibiotic resistance.

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.

Single-cell microfluidics facilitates the rapid quantification of antibiotic accumulation in Gram-negative bacteria

The double-membrane cell envelope of Gram-negative bacteria is a formidable barrier to intracellular antibiotic accumulation. A quantitative understanding of antibiotic transport in these cells is crucial for drug development, but this has proved elusive due to a dearth of suitable investigative techniques. Here we combine microfluidics and time-lapse auto-fluorescence microscopy to rapidly quantify antibiotic accumulation in hundreds of individual Escherichia coli cells. By serially manipulating the microfluidic environment, we demonstrated that stationary phase Escherichia coli, traditionally more refractory to antibiotics than growing cells, display reduced accumulation of the antibiotic ofloxacin compared to actively growing cells. Our novel microfluidic method facilitates the quantitative comparison of the role of the microenvironment versus that of the absence of key membrane transport pathways in cellular drug accumulation. Unlike traditional techniques, our assay is rapid, studying accumulation as the cells are dosed with the drug. This platform provides a powerful new tool for studying antibiotic accumulation in bacteria, which will be critical for the rational development of the next generation of antibiotics.

Direct visualisation of drug-efflux in liveEscherichia colicells

Drug-efflux by pump proteins is one of the major mechanisms of antibiotic resistance in bacteria. Here, we use quantitative fluorescence microscopy to investigate the real-time dynamics of drug accumulation and efflux in live E. coli cells. We visualize simultaneously the intrinsically fluorescent protein-synthesis inhibitor tetracycline (Tc) and the fluorescently labelled Tc-specific efflux pump, TetA. We show that Tc penetrates the cells within minutes and accumulates to stable intracellular concentration after ∼20 min. The final level of drug accumulation reflects the balance between Tc-uptake by the cells and Tc-efflux by pump proteins. In wild-type Tc-sensitive cells, drug accumulation is significantly limited by the activity of the multidrug efflux pump, AcrAB-TolC. Tc-resistance wild-type cells carrying a plasmid-borne Tn10 transposon contain variable amounts of TetA protein, produced under steady-state repression by the TetR repressor. TetA content heterogeneity determines the cells’ initial ability to efflux Tc. Yet, efflux remains partial until the synthesis of additional TetA pumps allows for Tc-efflux activity to surpass Tc-uptake. Cells overproducing TetA no longer accumulate Tc and become resistant to high concentrations of the drug. This work uncovers the dynamic balance between drug entry, protein-synthesis inhibition, efflux-pump production, drug-efflux activity and drug-resistance levels.

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.

Infectious Plant Diseases: Etiology, Current Status, Problems and Prospects in Plant Protection

In recent years, there has been an increase in the number of diseases caused by bacterial, fungal, and viral infections. Infections affect plants at different stages of agricultural production. Depending on weather conditions and the phytosanitary condition of crops, the prevalence of diseases can reach 70-80% of the total plant population, and the yield can decrease in some cases down to 80-98%. Plants have innate cellular immunity, but specific phytopathogens have an ability to evade that immunity. This article examined phytopathogens of viral, fungal, and bacterial nature and explored the concepts of modern plant protection, methods of chemical, biological, and agrotechnical control, as well as modern methods used for identifying phytopathogens.

Quantifying plasmid dynamics using single-cell microfluidics and image bioinformatics

Multicopy plasmids play an important role in bacterial ecology and evolution by accelerating the rate of adaptation and providing a platform for rapid gene amplification and evolutionary rescue. Despite the relevance of plasmids in bacterial evolutionary dynamics, evaluating the population-level consequences of randomly segregating and replicating plasmids in individual cells remains a challenging problem, both in theory and experimentally. In recent years, technological advances in fluorescence microscopy and microfluidics have allowed studying temporal changes in gene expression by quantifying the fluorescent intensity of individual cells under controlled environmental conditions. In this paper, we will describe the manufacture, experimental setup, and data analysis pipeline of different microfluidic systems that can be used to study plasmid dynamics, both in single-cells and in populations. To illustrate the benefits and limitations of microfluidics to study multicopy plasmid dynamics, we will use an experimental model system consisting on Escherichia coli K12 carrying non-conjugative, multicopy plasmids (19 copies per cell, in average) encoding different fluorescent markers and β-lactam resistance genes. First, we will use an image-based flow cytometer to estimate changes in the allele distribution of a heterogeneous population under different selection regimes. Then we will use a mothermachine microfluidic device to obtain time-series of fluorescent intensity of individual cells to argue that plasmid segregation and replication dynamics are inherently stochastic processes. Finally, using a microchemostat, we track thousands of cells in time to reconstruct bacterial lineages and evaluate the allele frequency distributions that emerge in response to a range of selective pressures.

Interactions of a Bacterial RND Transporter with a Transmembrane Small Protein in a Lipid Environment

The small protein AcrZ in Escherichia coli interacts with the transmembrane portion of the multidrug efflux pump AcrB and increases resistance of the bacterium to a subset of the antibiotic substrates of that transporter. It is not clear how the physical association of the two proteins selectively changes activity of the pump for defined substrates. Here, we report cryo-EM structures of AcrB and the AcrBZ complex in lipid environments, and comparisons suggest that conformational changes occur in the drug-binding pocket as a result of AcrZ binding. Simulations indicate that cardiolipin preferentially interacts with the AcrBZ complex, due to increased contact surface, and we observe that chloramphenicol sensitivity of bacteria lacking AcrZ is exacerbated when combined with cardiolipin deficiency. Taken together, the data suggest that AcrZ and lipid cooperate to allosterically modulate AcrB activity. This mode of regulation by a small protein and lipid may occur for other membrane proteins.

Allocation of gene products to daughter cells is determined by the age of the mother in single Escherichia coli cells

Gene expression and growth rate are highly stochastic in Escherichia coli. Some of the growth rate variations result from the deterministic and asymmetric partitioning of damage by the mother to its daughters. One daughter, denoted the old daughter, receives more damage, grows more slowly and ages. To determine if expressed gene products are also allocated asymmetrically, we compared the levels of expressed green fluorescence protein in growing daughters descending from the same mother. Our results show that old daughters were less fluorescent than new daughters. Moreover, old mothers, which were born as old daughters, produced daughters that were more asymmetric when compared to new mothers. Thus, variation in gene products in a clonal E. coli population also has a deterministic component. Because fluorescence levels and growth rates were positively correlated, the aging of old daughters appears to result from both the presence of both more damage and fewer expressed gene products.

Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications

Phenotypic delay-the time delay between genetic mutation and expression of the corresponding phenotype-is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.

Chemical, Metabolic, and Cellular Characterization of a FtsZ Inhibitor Effective Against Burkholderia cenocepacia

There is an urgent need for new antimicrobials to treat the opportunistic Gram-negative Burkholderia cenocepacia, which represents a problematic challenge for cystic fibrosis patients. Recently, a benzothiadiazole derivative, C109, was shown to be effective against the infections caused by B. cenocepacia and other Gram-negative and-positive bacteria. C109 has a promising cellular target, the cell division protein FtsZ, and a recently developed PEGylated formulation make it an attractive molecule to counteract Burkholderia infections. However, the ability of efflux pumps to extrude it out of the cell represents a limitation for its use. Here, more than 50 derivatives of C109 were synthesized and tested against Gram-negative species and the Gram-positive Staphylococcus aureus. In addition, their activity was evaluated on the purified FtsZ protein. The chemical, metabolic and cellular stability of C109 has been assayed using different biological systems, including quantitative single-cell imaging. However, no further improvement on C109 was achieved, and the role of efflux in resistance was further confirmed. Also, a novel nitroreductase that can inactivate the compound was characterized, but it does not appear to play a role in natural resistance. All these data allowed a deep characterization of the compound, which will contribute to a further improvement of its properties.

Droplet-based digital antibiotic susceptibility screen reveals single-cell clonal heteroresistance in an isogenic bacterial population

Since antibiotic resistance is a major threat to global health, recent observations that the traditional test of minimum inhibitory concentration (MIC) is not informative enough to guide effective antibiotic treatment are alarming. Bacterial heteroresistance, in which seemingly susceptible isogenic bacterial populations contain resistant sub-populations, underlies much of this challenge. To close this gap, here we developed a droplet-based digital MIC screen that constitutes a practical analytical platform for quantifying the single-cell distribution of phenotypic responses to antibiotics, as well as for measuring inoculum effect with high accuracy. We found that antibiotic efficacy is determined by the amount of antibiotic used per bacterial colony forming unit (CFU), not by the absolute antibiotic concentration, as shown by the treatment of beta-lactamase-carrying Escherichia coli with cefotaxime. We also noted that cells exhibited a pronounced clustering phenotype when exposed to near-inhibitory amounts of cefotaxime. Overall, our method facilitates research into the interplay between heteroresistance and antibiotic efficacy, as well as research into the origin and stimulation of heterogeneity by exposure to antibiotics. Due to the absolute bacteria quantification in this digital assay, our method provides a platform for developing reference MIC assays that are robust against inoculum-density variations.

Protein aggregation in bacteria

Protein aggregation occurs as a consequence of perturbations in protein homeostasis that can be triggered by environmental and cellular stresses. The accumulation of protein aggregates has been associated with aging and other pathologies in eukaryotes, and in bacteria with changes in growth rate, stress resistance and virulence. Numerous past studies, mostly performed in Escherichia coli, have led to a detailed understanding of the functions of the bacterial protein quality control machinery in preventing and reversing protein aggregation. However, more recent research points toward unexpected diversity in how phylogenetically different bacteria utilize components of this machinery to cope with protein aggregation. Furthermore, how persistent protein aggregates localize and are passed on to progeny during cell division and how their presence impacts reproduction and the fitness of bacterial populations remains a controversial field of research. Finally, although protein aggregation is generally seen as a symptom of stress, recent work suggests that aggregation of specific proteins under certain conditions can regulate gene expression and cellular resource allocation. This review discusses recent advances in understanding the consequences of protein aggregation and how this process is dealt with in bacteria, with focus on highlighting the differences and similarities observed between phylogenetically different groups of bacteria.

Stochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations

Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzyme's catalytic rate alone may contribute to resistance if drug binding is not rate limiting. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.

Role of AcrAB-TolC multidrug efflux pump in drug-resistance acquisition by plasmid transfer

Drug-resistance dissemination by horizontal gene transfer remains poorly understood at the cellular scale. Using live-cell microscopy, we reveal the dynamics of resistance acquisition by transfer of the Escherichia coli fertility factor-conjugation plasmid encoding the tetracycline-efflux pump TetA. The entry of the single-stranded DNA plasmid into the recipient cell is rapidly followed by complementary-strand synthesis, plasmid-gene expression, and production of TetA. In the presence of translation-inhibiting antibiotics, resistance acquisition depends on the AcrAB-TolC multidrug efflux pump, because it reduces tetracycline concentrations in the cell. Protein synthesis can thus persist and TetA expression can be initiated immediately after plasmid acquisition. AcrAB-TolC efflux activity can also preserve resistance acquisition by plasmid transfer in the presence of antibiotics with other modes of action.

Frontiers in Microfluidics, a Teaching Resource Review

This is a literature teaching resource review for biologically inspired microfluidics courses or exploring the diverse applications of microfluidics. The structure is around key papers and model organisms. While courses gradually change over time, a focus remains on understanding how microfluidics has developed as well as what it can and cannot do for researchers. As a primary starting point, we cover micro-fluid mechanics principles and microfabrication of devices. A variety of applications are discussed using model prokaryotic and eukaryotic organisms from the set of bacteria (Escherichia coli), trypanosomes (Trypanosoma brucei), yeast (Saccharomyces cerevisiae), slime molds (Physarum polycephalum), worms (Caenorhabditis elegans), flies (Drosophila melangoster), plants (Arabidopsis thaliana), and mouse immune cells (Mus musculus). Other engineering and biochemical methods discussed include biomimetics, organ on a chip, inkjet, droplet microfluidics, biotic games, and diagnostics. While we have not yet reached the end-all lab on a chip, microfluidics can still be used effectively for specific applications.

Isolating live cells after high-throughput, long-term, time-lapse microscopy

Single-cell genetic screens can be incredibly powerful, but current high-throughput platforms do not track dynamic processes, and even for non-dynamic properties they struggle to separate mutants of interest from phenotypic outliers of the wild-type population. Here we introduce SIFT, single-cell isolation following time-lapse imaging, to address these limitations. After imaging and tracking individual bacteria for tens of consecutive generations under tightly controlled growth conditions, cells of interest are isolated and propagated for downstream analysis, free of contamination and without genetic or physiological perturbations. This platform can characterize tens of thousands of cell lineages per day, making it possible to accurately screen complex phenotypes without the need for barcoding or genetic modifications. We applied SIFT to identify a set of ultraprecise synthetic gene oscillators, with circuit variants spanning a 30-fold range of average periods. This revealed novel design principles in synthetic biology and demonstrated the power of SIFT to reliably screen diverse dynamic phenotypes.

Bacterial ageing in the absence of external stressors

Evidence of ageing in the bacterium Escherichia coli was a landmark finding in senescence research, as it suggested that even organisms with morphologically symmetrical fission may have evolved strategies to permit damage accumulation. However, recent work has suggested that ageing is only detectable in this organism in the presence of extrinsic stressors, such as the fluorescent proteins and strong light sources typically used to excite them. Here we combine microfluidics with brightfield microscopy to provide evidence of ageing in E. coli in the absence of these stressors. We report (i) that the doubling time of the lineage of cells that consistently inherits the 'maternal old pole' progressively increases with successive rounds of cell division until it reaches an apparent asymptote, and (ii) that the parental cell divides asymmetrically, with the old pole daughter showing a longer doubling time and slower glucose accumulation than the new pole daughter. Notably, these patterns arise without the progressive accumulation or asymmetric partitioning of observable misfolded-protein aggregates, phenomena previously hypothesized to cause the ageing phenotype. Our findings suggest that ageing is part of the naturally occurring ecologically-relevant phenotype of this bacterium and highlight the importance of alternative mechanisms of damage accumulation in this context. This article is part of a discussion meeting issue 'Single cell ecology'.

Preexisting variation in DNA damage response predicts the fate of single mycobacteria under stress

Clonal microbial populations are inherently heterogeneous, and this diversification is often considered as an adaptation strategy. In clinical infections, phenotypic diversity is found to be associated with drug tolerance, which in turn could evolve into genetic resistance. Mycobacterium tuberculosis, which ranks among the top ten causes of mortality with high incidence of drug-resistant infections, exhibits considerable phenotypic diversity. In this study, we quantitatively analyze the cellular dynamics of DNA damage responses in mycobacteria using microfluidics and live-cell fluorescence imaging. We show that individual cells growing under optimal conditions experience sporadic DNA-damaging events manifested by RecA expression pulses. Single-cell responses to these events occur as transient pulses of fluorescence expression, which are dependent on the gene-network structure but are triggered by extrinsic signals. We demonstrate that preexisting subpopulations, with discrete levels of DNA damage response, are associated with differential susceptibility to fluoroquinolones. Our findings reveal that the extent of DNA integrity prior to drug exposure impacts the drug activity against mycobacteria, with conceivable therapeutic implications.

Antibiotic chemotherapy against heterogeneous pathogen populations in complex host tissues

Antibiotic chemotherapy effectively cures many infections caused by susceptible bacterial pathogens. However, in some cases, even extended treatment duration does not completely eradicate the pathogenic bacteria from host tissues. A common model for underlying mechanisms assumes the stochastic formation of bacterial persisters similar to observations in laboratory cultures. However, alternative explanations related to the complexity of infected host tissues could also be relevant. We discuss several of these aspects and emphasize the need for integrated analysis as a basis for new control strategies.

Single cell ecology

Cells are the building blocks of life, from single-celled microbes through to multi-cellular organisms. To understand a multitude of biological processes we need to understand how cells behave, how they interact with each other and how they respond to their environment. The use of new methodologies is changing the way we study cells allowing us to study them on minute scales and in unprecedented detail. These same methods are allowing researchers to begin to sample the vast diversity of microbes that dominate natural environments. The aim of this special issue is to bring together research and perspectives on the application of new approaches to understand the biological properties of cells, including how they interact with other biological entities. This article is part of a discussion meeting issue 'Single cell ecology'.