Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics

Proteome partitioning constraints in long-term laboratory evolution

Adaptive laboratory evolution experiments provide a controlled context in which the dynamics of selection and adaptation can be followed in real-time at the single-nucleotide level. And yet this precision introduces hundreds of degrees-of-freedom as genetic changes accrue in parallel lineages over generations. On short timescales, physiological constraints have been leveraged to provide a coarse-grained view of bacterial gene expression characterized by a small set of phenomenological parameters. Here, we ask whether this same framework, operating at a level between genotype and fitness, informs physiological changes that occur on evolutionary timescales. Using a strain adapted to growth in glucose minimal medium, we find that the proteome is substantially remodeled over 40 000 generations. The most striking change is an apparent increase in enzyme efficiency, particularly in the enzymes of lower-glycolysis. We propose that deletion of metabolic flux-sensing regulation early in the adaptation results in increased enzyme saturation and can account for the observed proteome remodeling.

Characterization of clumpy adhesion of Escherichia coli to human cells and associated factors influencing antibiotic sensitivity

Escherichia coli intestinal infection pathotypes are characterized by distinct adhesion patterns, including the recently described clumpy adhesion phenotype. Here, we identify and characterize the genetic factors contributing to the clumpy adhesion of E. coli strain 4972. In this strain, the transcriptome and proteome of adhered bacteria were found to be distinct from planktonic bacteria in the supernatant. A total of 622 genes in the transcriptome were differentially expressed in bacteria present in clumps relative to the planktonic bacteria. Seven genes targeted for disruption had variable distribution in different pathotypes and nonpathogenic E. coli, with the pilV and spnT genes being the least frequent or absent from most groups. Deletion (Δ) of five differentially expressed genes, flgH, ffp, pilV, spnT, and yggT, affected motility, adhesion, or antibiotic stress. ΔflgH exhibited 80% decrease and ΔyggT depicted 184% increase in adhesion, and upon complementation, adhesion was significantly reduced to 13%. ΔflgH lost motility and was regenerated when complemented, whereas Δffp had significantly increased motility, and reintroduction of the same gene reduced it to the wild-type level. The clumps produced by Δffp and ΔspnT were more resistant and protected the bacteria, with ΔspnT showing the best clump formation in terms of ampicillin stress protection. ΔyggT had the lowest tolerance to gentamicin, where the antibiotic stress completely eliminated the bacteria. Overall, we were able to investigate the influence of clump formation on cell surface adhesion and antimicrobial tolerance, with the contribution of several factors crucial to clump formation on susceptibility to the selected antibiotics.

IMPORTANCE

The study explores a biofilm-like clumpy adhesion phenotype in Escherichia coli, along with various factors and implications for antibiotic susceptibility. The phenotype permitted the bacteria to survive the onslaught of high antibiotic concentrations. Profiles of the transcriptome and proteome allowed the differentiation between adhered bacteria in clumps and planktonic bacteria in the supernatant. The deletion mutants of genes differentially expressed between adhered and planktonic bacteria, i.e., flgH, ffp, pilV, spnT, and yggT, and respective complementations in trans cemented their roles in multiple capacities. ffp, an uncharacterized gene, is involved in motility and resistance to ampicillin in a clumpy state. The work also affirms for the first time the role of the yggT gene in adhesion and its involvement in susceptibility against another aminoglycoside antibiotic, i.e., gentamicin. Overall, the study contributes to the mechanisms of biofilm-like adhesion phenotype and understanding of the antimicrobial therapy failures and infections of E. coli.

Interplay between environmental yielding and dynamic forcing modulates bacterial growth

Many bacterial habitats-ranging from gels and tissues in the body to cell-secreted exopolysaccharides in biofilms-are rheologically complex, undergo dynamic external forcing, and have unevenly distributed nutrients. How do these features jointly influence how the resident cells grow and proliferate? Here, we address this question by studying the growth of Escherichia coli dispersed in granular hydrogel matrices with defined and highly tunable structural and rheological properties, under different amounts of external forcing imposed by mechanical shaking, and in both aerobic and anaerobic conditions. Our experiments establish a general principle: that the balance between the yield stress of the environment that the cells inhabit, σy, and the external stress imposed on the environment, σ, modulates bacterial growth by altering transport of essential nutrients to the cells. In particular, when σy<σ, the environment is easily fluidized and mixed over large scales, providing nutrients to the cells and sustaining complete cellular growth. By contrast, when σy>σ, the elasticity of the environment suppresses large-scale fluid mixing, limiting nutrient availability and arresting cellular growth. Our work thus reveals a new mechanism, beyond effects that change cellular behavior via local forcing, by which the rheology of the environment may modulate microbial physiology in diverse natural and industrial settings.

Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Bigger problems from smaller colonies: emergence of antibiotic-tolerant small colony variants of Mycobacterium avium complex in MAC-pulmonary disease patients

BACKGROUND

Mycobacterium avium complex (MAC) is a group of slow-growing mycobacteria that includes Mycobacterium avium and Mycobacterium intracellulare. MAC pulmonary disease (MAC-PD) poses a threat to immunocompromised individuals and those with structural pulmonary diseases worldwide. The standard treatment regimen for MAC-PD includes a macrolide in combination with rifampicin and ethambutol. However, the treatment failure and disease recurrence rates after successful treatment remain high.

RESULTS

In the present study, we investigated the unique characteristics of small colony variants (SCVs) isolated from patients with MAC-PD. Furthermore, revertant (RVT) phenotype, emerged from the SCVs after prolonged incubation on 7H10 agar. We observed that SCVs exhibited slower growth rates than wild-type (WT) strains but had higher minimum inhibitory concentrations (MICs) against multiple antibiotics. However, some antibiotics showed low MICs for the WT, SCVs, and RVT phenotypes. Additionally, the genotypes were identical among SCVs, WT, and RVT. Based on the MIC data, we conducted time-kill kinetic experiments using various antibiotic combinations. The response to antibiotics varied among the phenotypes, with RVT being the most susceptible, WT showing intermediate susceptibility, and SCVs displaying the lowest susceptibility.

CONCLUSIONS

In conclusion, the emergence of the SCVs phenotype represents a survival strategy adopted by MAC to adapt to hostile environments and persist during infection within the host. Additionally, combining the current drugs in the treatment regimen with additional drugs that promote the conversion of SCVs to RVT may offer a promising strategy to improve the clinical outcomes of patients with refractory MAC-PD.

Metabolic network and proteomic expression perturbed by cyclosporine A to model microbe Escherichia coli

Cyclosporine A (CsA) is a model drug that has caused great concern due to its widespread use and abuse in the environment. However, the potential harm of CsA to organisms also remains largely unknown, and this issue is exceptionally important for the health risk assessment of antibiotics. To address this concern, the crosstalk between CsA stress and cellular metabolism at the proteomic level in Escherichia coli was investigated and dissected in this study. The results showed that CsA inhibited E. coli growth in a time-dependent manner. CsA induced reactive oxygen species (ROS) overproduction in a dose- and time-dependent manner, leading to membrane depolarization followed by cell apoptosis. In addition, translation, the citric acid cycle, amino acid biosynthesis, glycolysis and responses to oxidative stress and heat were the central metabolic pathways induced by CsA stress. The upregulated proteins, including PotD, PotF and PotG, controlled cell growth. The downregulated proteins, including SspA, SspB, CstA and DpS, were regulators of self-feedback during the starvation process. And the up- and downregulated proteins, including AtpD, Adk, GroS, GroL and DnaK, controlled energy production. These results provide an important reference for the environmental health risk assessment of CsA.

Gene expression tradeoffs determine bacterial survival and adaptation to antibiotic stress

To optimize their fitness, cells face the crucial task of efficiently responding to various stresses. This necessitates striking a balance between conserving resources for survival and allocating resources for growth and division. The fundamental principles governing these tradeoffs is an outstanding challenge in the physics of living systems. In this study, we introduce a coarse-grained theoretical framework for bacterial physiology that establishes a connection between the physiological state of cells and their survival outcomes in dynamic environments, particularly in the context of antibiotic exposure. Predicting bacterial survival responses to varying antibiotic doses proves challenging due to the profound influence of the physiological state on critical parameters, such as the Minimum Inhibitory Concentration (MIC) and killing rates, even within an isogenic cell population. Our proposed theoretical model bridges the gap by linking extracellular antibiotic concentration and nutrient quality to intracellular damage accumulation and gene expression. This framework allows us to predict and explain the control of cellular growth rate, death rate, MIC and survival fraction in a wide range of time-varying environments. Surprisingly, our model reveals that cell death is rarely due to antibiotic levels being above the maximum physiological limit, but instead survival is limited by the inability to alter gene expression sufficiently quickly to transition to a less susceptible physiological state. Moreover, bacteria tend to overexpress stress response genes at the expense of reduced growth, conferring greater protection against further antibiotic exposure. This strategy is in contrast to those employed in different nutrient environments, in which bacteria allocate resources to maximize growth rate. This highlights an important tradeoff between the cellular capacity for growth and the ability to survive antibiotic exposure.

Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing

Antibiotic effectiveness depends on a variety of factors. While many mechanistic details of antibiotic action are known, the connection between death rate and bacterial physiology is poorly understood. A common observation is that death rate in antibiotics rises linearly with growth rate; however, it remains unclear how other factors, such as environmental conditions and whole-cell physiological properties, affect bactericidal activity. To address this, we developed a high-throughput assay to precisely measure antibiotic-mediated death. We found that death rate is linear in growth rate, but the slope depends on environmental conditions. Growth under stress lowers death rate compared to nonstressed environments with similar growth rate. To understand stress's role, we developed a mathematical model of bacterial death based on resource allocation that includes a stress-response sector; we identify this sector using RNA-seq. Our model accurately predicts the minimal inhibitory concentration (MIC) with zero free parameters across a wide range of growth conditions. The model also quantitatively predicts death and MIC when sectors are experimentally modulated using cyclic adenosine monophosphate (cAMP), including protection from death at very low cAMP levels. The present study shows that different conditions with equal growth rate can have different death rates and establishes a quantitative relation between growth, death, and MIC that suggests approaches to improve antibiotic efficacy.

Quantitative Modelling in Stem Cell Biology and Beyond: How to Make Best Use of It

Purpose of Review

This article gives a broad overview of quantitative modelling approaches in biology and provides guidance on how to employ them to boost stem cell research, by helping to answer biological questions and to predict the outcome of biological processes.

Recent Findings

The twenty-first century has seen a steady increase in the proportion of cell biology publications employing mathematical modelling to aid experimental research. However, quantitative modelling is often used as a rather decorative element to confirm experimental findings, an approach which often yields only marginal added value, and is in many cases scientifically questionable.

Summary

Quantitative modelling can boost biological research in manifold ways, but one has to take some careful considerations before embarking on a modelling campaign, in order to maximise its added value, to avoid pitfalls that may lead to wrong results, and to be aware of its fundamental limitations, imposed by the risks of over-fitting and "universality".

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Multi-integrated approach for unraveling small open reading frames potentially associated with secondary metabolism in Streptomyces

IMPORTANCE

Due to their small size and special chemical features, small open reading frame (smORF)-encoding peptides (SEPs) are often neglected. However, they may play critical roles in regulating gene expression, enzyme activity, and metabolite production. Studies on bacterial microproteins have mainly focused on pathogenic bacteria, which are importance to systematically investigate SEPs in streptomycetes and are rich sources of bioactive secondary metabolites. Our study is the first to perform a global identification of smORFs in streptomycetes. We established a peptidogenomic workflow for non-model microbial strains and identified multiple novel smORFs that are potentially linked to secondary metabolism in streptomycetes. Our multi-integrated approach in this study is meaningful to improve the quality and quantity of the detected smORFs. Ultimately, the workflow we established could be extended to other organisms and would benefit the genome mining of microproteins with critical functions for regulation and engineering useful microorganisms.

Fitness Costs of Antibiotic Resistance Impede the Evolution of Resistance to Other Antibiotics

Antibiotic resistance is a major threat to global health, claiming the lives of millions every year. With a nearly dry antibiotic development pipeline, novel strategies are urgently needed to combat resistant pathogens. One emerging strategy is the use of sequential antibiotic therapy, postulated to reduce the rate at which antibiotic resistance evolves. Here, we use the soft agar gradient evolution (SAGE) system to carry out high-throughput in vitro bacterial evolution against antibiotic pressure. We find that evolution of resistance to the antibiotic chloramphenicol (CHL) severely affects bacterial fitness, slowing the rate at which resistance to the antibiotics nitrofurantoin and streptomycin emerges. In vitro acquisition of compensatory mutations in the CHL-resistant cells markedly improves fitness and nitrofurantoin adaptation rates but fails to restore rates to wild-type levels against streptomycin. Genome sequencing reveals distinct evolutionary paths to resistance in fitness-impaired populations, suggesting resistance trade-offs in favor of mitigation of fitness costs. We show that the speed of bacterial fronts in SAGE plates is a reliable indicator of adaptation rates and evolutionary trajectories to resistance. Identification of antibiotics whose mutational resistance mechanisms confer stable impairments may help clinicians prescribe sequential antibiotic therapies that are less prone to resistance evolution.

Dynamical model of antibiotic responses linking expression of resistance to metabolism explains emergence of heterogeneity during drug exposures

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistance tet operon in E. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Potential selection and maintenance of manure-originated multi-drug resistant plasmids at sub-clinical concentrations for tetracycline family antibiotics

The goal of this study was to (a) determine the minimum selection concentrations of tetracycline family antibiotics necessary to maintain plasmids carrying tetracycline-resistant genes and (b) correlate these results to environmental hotspot concentrations reported in previous studies. This study used two plasmids (pT295A and pT413A) originating from dairy manure in a surrogate Escherichia coli host CV601. The minimum selection concentrations of antibiotics tested in nutrient-rich medium were determined as follows: 0.1 mg/L for oxytetracycline, 0.45 mg/L for chlortetracycline, and 0.13-0.25 mg/L for tetracycline. Mixing oxytetracycline and chlortetracycline had minimum selection concentration values increased 2-fold compared to those in single antibiotic tests. Minimum selection concentrations found in this study were lower than reported environmental hotspot concentrations, suggesting that tetracycline family antibiotics were likely to be the driver for the selection and maintenance of these plasmids. Relatively high plasmid loss rates (>90%) were observed when culturing a strain carrying a tetracycline-resistant plasmid in antibiotic-free nutrient-rich and nutrient-defined media. Overall, results suggested that these plasmids can be maintained at concentrations environmentally relevant in wastewater treatment plants, sewage, manure, and manured soil; however, they are unstable and easily lost in the absence of antibiotics.

Light-Based Anti-Biofilm and Antibacterial Strategies

Biofilm formation and antimicrobial resistance pose significant challenges not only in clinical settings (i.e., implant-associated infections, endocarditis, and urinary tract infections) but also in industrial settings and in the environment, where the spreading of antibiotic-resistant bacteria is on the rise. Indeed, developing effective strategies to prevent biofilm formation and treat infections will be one of the major global challenges in the next few years. As traditional pharmacological treatments are becoming inadequate to curb this problem, a constant commitment to the exploration of novel therapeutic strategies is necessary. Light-triggered therapies have emerged as promising alternatives to traditional approaches due to their non-invasive nature, precise spatial and temporal control, and potential multifunctional properties. Here, we provide a comprehensive overview of the different biofilm formation stages and the molecular mechanism of biofilm disruption, with a major focus on the quorum sensing machinery. Moreover, we highlight the principal guidelines for the development of light-responsive materials and photosensitive compounds. The synergistic effects of combining light-triggered therapies with conventional treatments are also discussed. Through elegant molecular and material design solutions, remarkable results have been achieved in the fight against biofilm formation and antibacterial resistance. However, further research and development in this field are essential to optimize therapeutic strategies and translate them into clinical and industrial applications, ultimately addressing the global challenges posed by biofilm and antimicrobial resistance.

Estimating the optimal efflux inhibitor concentration of carvacrol as a function of the bacterial physiological state

Our aim was to find the optimal efflux inhibitor concentration of a natural component, carvacrol, as a function of the physiological state of Escherichia coli. Using fluorescence-based measurements with two strains of E. coli, the effect of carvacrol was assessed at 17 sub-inhibitory concentrations, at which the bacterial efflux mechanism was compromised. The efficacy of carvacrol, as an efflux inhibitor, was compared to synthetic inhibitors and we found carvacrol the most efficient one. We considered the accumulation of Ethidium Bromide (EtBr) as a proxy for drugs spreading in the cell, thus measuring the efflux activity indirectly. The change in membrane integrity caused by the exposure to carvacrol was monitored using the LIVE/DEAD BacLight Bacterial Viability kit. To find the optimal inhibitory concentration of carvacrol, we used predictive microbiology methods. This optimum varied with the bacterial physiological state, as non-growing cultures were less susceptible to the effect of carvacrol than growing cultures were. Moreover, we point out, for the first time, that the efflux-mediated resistance of untreated cultures was also stronger in the non-growing than in the growing phase at population level.

Cellular resource allocation strategies for cell size and shape control in bacteria

Bacteria are highly adaptive microorganisms that thrive in a wide range of growth conditions via changes in cell morphologies and macromolecular composition. How bacterial morphologies are regulated in diverse environmental conditions is a long-standing question. Regulation of cell size and shape implies control mechanisms that couple the growth and division of bacteria to their cellular environment and macromolecular composition. In the past decade, simple quantitative laws have emerged that connect cell growth to proteomic composition and the nutrient availability. However, the relationships between cell size, shape, and growth physiology remain challenging to disentangle and unifying models are lacking. In this review, we focus on regulatory models of cell size control that reveal the connections between bacterial cell morphology and growth physiology. In particular, we discuss how changes in nutrient conditions and translational perturbations regulate the cell size, growth rate, and proteome composition. Integrating quantitative models with experimental data, we identify the physiological principles of bacterial size regulation, and discuss the optimization strategies of cellular resource allocation for size control.

Effects of antibiotics on bacterial cell morphology and their physiological origins

Characterizing the physiological response of bacterial cells to antibiotic treatment is crucial for the design of antibacterial therapies and for understanding the mechanisms of antibiotic resistance. While the effects of antibiotics are commonly characterized by their minimum inhibitory concentrations or the minimum bactericidal concentrations, the effects of antibiotics on cell morphology and physiology are less well characterized. Recent technological advances in single-cell studies of bacterial physiology have revealed how different antibiotic drugs affect the physiological state of the cell, including growth rate, cell size and shape, and macromolecular composition. Here, we review recent quantitative studies on bacterial physiology that characterize the effects of antibiotics on bacterial cell morphology and physiological parameters. In particular, we present quantitative data on how different antibiotic targets modulate cellular shape metrics including surface area, volume, surface-to-volume ratio, and the aspect ratio. Using recently developed quantitative models, we relate cell shape changes to alterations in the physiological state of the cell, characterized by changes in the rates of cell growth, protein synthesis and proteome composition. Our analysis suggests that antibiotics induce distinct morphological changes depending on their cellular targets, which may have important implications for the regulation of cellular fitness under stress.

Tolerance and resistance of microbial biofilms

Chronic infections caused by microbial biofilms represent an important clinical challenge. The recalcitrance of microbial biofilms to antimicrobials and to the immune system is a major cause of persistence and clinical recurrence of these infections. In this Review, we present the extent of the clinical problem, and the mechanisms underlying the tolerance of biofilms to antibiotics and to host responses. We also explore the role of biofilms in the development of antimicrobial resistance mechanisms.

Growth-mediated negative feedback shapes quantitative antibiotic response

Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole‐cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth‐mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth‐mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Computational models, databases and tools for antibiotic combinations

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

Fast bacterial growth reduces antibiotic accumulation and efficacy

Phenotypic variations between individual microbial cells play a key role in the resistance of microbial pathogens to pharmacotherapies. Nevertheless, little is known about cell individuality in antibiotic accumulation. Here, we hypothesise that phenotypic diversification can be driven by fundamental cell-to-cell differences in drug transport rates. To test this hypothesis, we employed microfluidics-based single-cell microscopy, libraries of fluorescent antibiotic probes and mathematical modelling. This approach allowed us to rapidly identify phenotypic variants that avoid antibiotic accumulation within populations of Escherichia coli, Pseudomonas aeruginosa, Burkholderia cenocepacia, and Staphylococcus aureus. Crucially, we found that fast growing phenotypic variants avoid macrolide accumulation and survive treatment without genetic mutations. These findings are in contrast with the current consensus that cellular dormancy and slow metabolism underlie bacterial survival to antibiotics. Our results also show that fast growing variants display significantly higher expression of ribosomal promoters before drug treatment compared to slow growing variants. Drug-free active ribosomes facilitate essential cellular processes in these fast-growing variants, including efflux that can reduce macrolide accumulation. We used this new knowledge to eradicate variants that displayed low antibiotic accumulation through the chemical manipulation of their outer membrane inspiring new avenues to overcome current antibiotic treatment failures.

Antibiotic Resistance via Bacterial Cell Shape-Shifting

Bacteria have evolved to develop multiple strategies for antibiotic resistance by effectively reducing intracellular antibiotic concentrations or antibiotic binding affinities, but the role of cell morphology in antibiotic resistance remains poorly understood. By analyzing cell morphological data for different bacterial species under antibiotic stress, we find that bacteria increase or decrease the cell surface-to-volume ratio depending on the antibiotic target. Using quantitative modeling, we show that by reducing the surface-to-volume ratio, bacteria can effectively reduce the intracellular antibiotic concentration by decreasing antibiotic influx. The model further predicts that bacteria can increase the surface-to-volume ratio to induce the dilution of membrane-targeting antibiotics, in agreement with experimental data. Using a whole-cell model for the regulation of cell shape and growth by antibiotics, we predict shape transformations that bacteria can utilize to increase their fitness in the presence of antibiotics. We conclude by discussing additional pathways for antibiotic resistance that may act in synergy with shape-induced resistance.

Growth-dependent heterogeneity in the DNA damage response in Escherichia coli

In natural environments, bacteria are frequently exposed to sub-lethal levels of DNA damage, which leads to the induction of a stress response (the SOS response in Escherichia coli). Natural environments also vary in nutrient availability, resulting in distinct physiological changes in bacteria, which may have direct implications on their capacity to repair their chromosomes. Here, we evaluated the impact of varying the nutrient availability on the expression of the SOS response induced by chronic sub-lethal DNA damage in E. coli. We found heterogeneous expression of the SOS regulon at the single-cell level in all growth conditions. Surprisingly, we observed a larger fraction of high SOS-induced cells in slow growth as compared with fast growth, despite a higher rate of SOS induction in fast growth. The result can be explained by the dynamic balance between the rate of SOS induction and the division rates of cells exposed to DNA damage. Taken together, our data illustrate how cell division and physiology come together to produce growth-dependent heterogeneity in the DNA damage response.

Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.

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.

Study of the contribution of active defense mechanisms to ciprofloxacin tolerance in Escherichia coli growing at different rates

Using rpoS, tolC, ompF, and recA knockouts, we investigated their effect on the physiological response and lethality of ciprofloxacin in E. coli growing at different rates on glucose, succinate or acetate. We have shown that, regardless of the strain, the degree of changes in respiration, membrane potential, NAD⁺/NADH ratio, ATP and glutathione (GSH) strongly depends on the initial growth rate and the degree of its inhibition. The deletion of the regulator of the general stress response RpoS, although it influenced the expression of antioxidant genes, did not significantly affect the tolerance to ciprofloxacin at all growth rates. The mutant lacking TolC, which is a component of many E. coli efflux pumps, showed the same sensitivity to ciprofloxacin as the parent. The absence of porin OmpF slowed down the entry of ciprofloxacin into cells, prolonged growth and shifted the optimal bactericidal concentration towards higher values. Deficiency of RecA, a regulator of the SOS response, dramatically altered the late phase of the SOS response (SOS-dependent cell death), preventing respiratory inhibition and a drop in membrane potential. The recA mutation inverted GSH fluxes across the membrane and abolished ciprofloxacin-induced H₂S production. All studied mutants showed an inverse linear relationship between logCFU ml^-1 and the specific growth rate. Mutations shifted the plot of this dependence relative to the parental strain according to their significance for ciprofloxacin tolerance. The crucial role of the SOS system is confirmed by dramatic shift down of this plot in the recA mutant.

Shedding light on the bacterial resistance to toxic UV filters: a comparative genomic study

UV filters are toxic to marine bacteria that dominate the marine biomass. Ecotoxicology often studies the organism response but rarely integrates the toxicity mechanisms at the molecular level. In this study, in silico comparative genomics between UV filters sensitive and resistant bacteria were conducted in order to unravel the genes responsible for a resistance phenotype. The genomes of two environmentally relevant Bacteroidetes and three Firmicutes species were compared through pairwise comparison. Larger genomes were carried by bacteria exhibiting a resistant phenotype, favoring their ability to adapt to environmental stresses. While the antitoxin and CRISPR systems were the only distinctive features in resistant Bacteroidetes, Firmicutes displayed multiple unique genes that could support the difference between sensitive and resistant phenotypes. Several genes involved in ROS response, vitamin biosynthesis, xenobiotic degradation, multidrug resistance, and lipophilic compound permeability were shown to be exclusive to resistant species. Our investigation contributes to a better understanding of UV filters resistance phenotypes, by identifying pivotal genes involved in key pathways.

Effects of Incubation Time and Inoculation Level on the Stabilities of Bacteriostatic and Bactericidal Antibiotics against Salmonella Typhimurium

This study was designed to evaluate the stability of chloramphenicol, erythromycin, tetracycline, cephalothin, ciprofloxacin, and tobramycin against antibiotic-sensitive Salmonella Typhimurium (ASST) and antibiotic-resistant S. Typhimurium (ARST) during the broth microdilution assay. The antimicrobial activity in association with antibiotic stability was measured by using antibiotic susceptibility, time-delayed inoculation, time-extended incubation, and inoculum effect assays. The loss of the antimicrobial activity of cephalothin against ASST exposed to 1 MIC was observed for the 10 h delayed inoculation. The antimicrobial activities of tetracycline and ciprofloxacin against ASST and ARST exposed to ½ MIC were significantly decreased after the 10 h delayed inoculation. All antibiotics used in this study, except for ciprofloxacin, showed the considerable losses of antimicrobial activities against ASST and ARST after 40 h of incubation at 37 °C when compared to the 20 h of incubation during AST. Compared to the standard inoculum level (6 log CFU/mL), the MIC0.1 values of bactericidal antibiotics, ciprofloxacin and tobramycin against ASST were increased by more than 4-fold at the high inoculum level of 9 log CFU/mL. This would provide practical information for better understanding the clinical efficacy of the currently used antibiotics by considering the antibiotic stability during incubation time at different inoculum levels.

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.

IMPORTANCEEscherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

RexAB promotes the survival of Staphylococcus aureus exposed to multiple classes of antibiotics

Antibiotics inhibit essential bacterial processes, resulting in arrest of growth and, in some cases, cell death. Many antibiotics are also reported to trigger endogenous production of reactive oxygen species (ROS), which damage DNA, leading to induction of the mutagenic SOS response associated with the emergence of drug resistance. However, the type of DNA damage that arises and how this triggers the SOS response are largely unclear. We found that several different classes of antibiotic triggered dose-dependent induction of the SOS response in Staphylococcus aureus, indicative of DNA damage, including some bacteriostatic drugs. The SOS response was heterogenous and varied in magnitude between strains and antibiotics. However, in many cases, full induction of the SOS response was dependent upon the RexAB helicase/nuclease complex, which processes DNA double-strand breaks to produce single-stranded DNA and facilitate RecA nucleoprotein filament formation. The importance of RexAB in repair of DNA was confirmed by measuring bacterial survival during antibiotic exposure, with most drugs having significantly greater bactericidal activity against rexB mutants than against wild-type strains. For some, but not all, antibiotics there was no difference in bactericidal activity between wild type and rexB mutant under anaerobic conditions, indicative of a role for reactive oxygen species in mediating DNA damage. Taken together, this work confirms previous observations that several classes of antibiotics cause DNA damage in S. aureus and extends them by showing that processing of DNA double-strand breaks by RexAB is a major trigger of the mutagenic SOS response and promotes bacterial survival.

Antibiotics Shift the Temperature Response Curve of Escherichia coli Growth

Temperature variation-through time and across climatic gradients-affects individuals, populations, and communities. Yet how the thermal response of biological systems is altered by environmental stressors is poorly understood. Here, we quantify two key features-optimal temperature and temperature breadth-to investigate how temperature responses vary in the presence of antibiotics. We use high-throughput screening to measure growth of Escherichia coli under single and pairwise combinations of 12 antibiotics across seven temperatures that range from 22°C to 46°C. We find that antibiotic stress often results in considerable changes in the optimal temperature for growth and a narrower temperature breadth. The direction of the optimal temperature shifts can be explained by the similarities between antibiotic-induced and temperature-induced damage to the physiology of the bacterium. We also find that the effects of pairs of stressors in the temperature response can often be explained by just one antibiotic out of the pair. Our study has implications for a general understanding of how ecological systems adapt and evolve to environmental changes. IMPORTANCE The growth of living organisms varies with temperature. This dependence is described by a temperature response curve that is described by an optimal temperature where growth is maximized and a temperature range (termed breadth) across which the organism can grow. Because an organism's temperature response evolves or acclimates to its environment, it is often assumed to change over only evolutionary or developmental timescales. Counter to this, we show here that antibiotics can quickly (over hours) change the optimal growth temperature and temperature breadth for the bacterium Escherichia coli. Moreover, our results suggest a shared-damage hypothesis: when an antibiotic damages similar cellular components as hot (or cold) temperatures do, this shared damage will combine and compound to more greatly reduce growth when that antibiotic is administered at hot (or cold) temperatures. This hypothesis could potentially also explain how temperature responses are modified by stressors other than antibiotics.

Antibiotic-Resistant Bacteria in Aquaculture and Climate Change: A Challenge for Health in the Mediterranean Area

Aquaculture is the productive activity that will play a crucial role in the challenges of the millennium, such as the need for proteins that support humans and the respect for the environment. Aquaculture is an important economic activity in the Mediterranean basin. A great impact is presented, however, by aquaculture practices as they involve the use of antibiotics for treatment and prophylaxis. As a consequence of the use of antibiotics in aquaculture, antibiotic resistance is induced in the surrounding bacteria in the column water, sediment, and fish-associated bacterial strains. Through horizontal gene transfer, bacteria can diffuse antibiotic-resistance genes and mobile resistance genes further spreading genetic determinants. Once triggered, antibiotic resistance easily spreads among aquatic microbial communities and, from there, can reach human pathogenic bacteria, making vain the use of antibiotics for human health. Climate change claims a significant role in this context, as rising temperatures can affect cell physiology in bacteria in the same way as antibiotics, causing antibiotic resistance to begin with. The Mediterranean Sea represents a 'hot spot' in terms of climate change and aspects of antibiotic resistance in aquaculture in this area can be significantly amplified, thus increasing threats to human health. Practices must be adopted to counteract negative impacts on human health, with a reduction in the use of antibiotics as a pivotal point. In the meantime, it is necessary to act against climate change by reducing anthropogenic impacts, for example by reducing CO2 emissions into the atmosphere. The One Health type approach, which involves the intervention of different skills, such as veterinary, ecology, and medicine in compliance with the principles of sustainability, is necessary and strongly recommended to face these important challenges for human and animal health, and for environmental safety in the Mediterranean area.

Metabolic fitness landscapes predict the evolution of antibiotic resistance

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here, we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. The model maps metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of growth rates and resistance levels, which characterize Pareto resistance mutations emerging at different drug dosages. We also predict the prevalent resistance mechanism depending on drug and nutrient levels: low-dosage drug defence is mounted by regulation, evolution of distinct metabolic sectors sets in at successive threshold dosages. Evolutionary resistance mechanisms include membrane permeability changes and drug target mutations. These predictions are confirmed by empirical growth inhibition curves and genomic data of Escherichia coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

Minimal biophysical model of combined antibiotic action

Phenomenological relations such as Ohm's or Fourier's law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial "growth laws," which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.

Oxygen and Metabolism: Digesting Determinants of Antibiotic Susceptibility in the Gut

Microbial metabolism is a major determinant of antibiotic susceptibility. Environmental conditions that modify metabolism, notably oxygen availability and redox potential, can directly fine-tune susceptibility to antibiotics. Despite this, relatively few studies have discussed these modifications within the gastrointestinal tract and their implication on in vivo drug activity and the off-target effects of antibiotics in the gut. In this review, we discuss the environmental and biogeographical complexity of the gastrointestinal tract in regard to oxygen availability and redox potential, addressing how the heterogeneity of gut microhabitats may modify antibiotic activity in vivo. We contextualize the current literature surrounding oxygen availability and antibiotic efficacy and discuss empirical treatments. We end by discussing predicted patterns of antibiotic activity in prominent microbiome taxa, given gut heterogeneity, oxygen availability, and polymicrobial interactions. We also propose additional work required to fully elucidate the role of oxygen metabolism on antibiotic susceptibility in the context of the gut.

Pathogenic Escherichia coli Possess Elevated Growth Rates under Exposure to Sub-Inhibitory Concentrations of Azithromycin

Antimicrobial resistance (AMR) has been identified by the World Health Organization (WHO) as one of the ten major threats to global health. Advances in technology, including whole-genome sequencing, have provided new insights into the origin and mechanisms of AMR. However, our understanding of the short-term impact of antimicrobial pressure and resistance on the physiology of bacterial populations is limited. We aimed to investigate morphological and physiological responses of clinical isolates of E. coli under short-term exposure to key antimicrobials. We performed whole-genome sequencing on twenty-seven E. coli isolates isolated from children with sepsis to evaluate their AMR gene content. We assessed their antimicrobial susceptibility profile and measured their growth dynamics and morphological characteristics under exposure to varying concentrations of ciprofloxacin, ceftriaxone, tetracycline, gentamicin, and azithromycin. AMR was common, with all organisms resistant to at least one antimicrobial; a total of 81.5% were multi-drug-resistant (MDR). We observed an association between resistance profile and morphological characteristics of the E. coli over a three-hour exposure to antimicrobials. Growth dynamics experiments demonstrated that resistance to tetracycline promoted the growth of E. coli under antimicrobial-free conditions, while resistance to the other antimicrobials incurred a fitness cost. Notably, antimicrobial exposure heterogeneously suppressed bacterial growth, but sub-MIC concentrations of azithromycin increased the maximum growth rate of the clinical isolates. Our results outline complex interactions between organism and antimicrobials and raise clinical concerns regarding exposure of sub-MIC concentrations of specific antimicrobials.

Cytosolic Crowding Drives the Dynamics of Both Genome and Cytosol in Escherichia coli Challenged with Sub-lethal Antibiotic Treatments

In contrast to their molecular mode of action, the system-level effect of antibiotics on cells is only beginning to be quantified. Molecular crowding is expected to be a relevant global regulator, which we explore here through the dynamic response phenotypes in Escherichia coli, at single-cell resolution, under sub-lethal regimes of different classes of clinically relevant antibiotics, acting at very different levels in the cell. We measure chromosomal mobility through tracking of fast (<15 s timescale) fluctuations of fluorescently tagged chromosomal loci, and we probe the fluidity of the cytoplasm by tracking cytosolic aggregates. Measuring cellular density, we show how the overall levels of macromolecular crowding affect both quantities, regardless of antibiotic-specific effects. The dominant trend is a strong correlation between the effects in different parts of the chromosome and between the chromosome and cytosol, supporting the concept of an overall global role of molecular crowding in cellular physiology.

The Open Challenge of in vitro Modeling Complex and Multi-Microbial Communities in Three-Dimensional Niches

The comprehension of the underlying mechanisms of the interactions within microbial communities represents a major challenge to be faced to control their outcome. Joint efforts of in vitro, in vivo and ecological models are crucial to controlling human health, including chronic infections. In a broader perspective, considering that polymicrobial communities are ubiquitous in nature, the understanding of these mechanisms is the groundwork to control and modulate bacterial response to any environmental condition. The reduction of the complex nature of communities of microorganisms to a single bacterial strain could not suffice to recapitulate the in vivo situation observed in mammals. Furthermore, some bacteria can adapt to various physiological or arduous environments embedding themselves in three-dimensional matrices, secluding from the external environment. Considering the increasing awareness that dynamic complex and dynamic population of microorganisms (microbiota), inhabiting different apparatuses, regulate different health states and protect against pathogen infections in a fragile and dynamic equilibrium, we underline the need to produce models to mimic the three-dimensional niches in which bacteria, and microorganisms in general, self-organize within a microbial consortium, strive and compete. This review mainly focuses, as a case study, to lung pathology-related dysbiosis and life-threatening diseases such as cystic fibrosis and bronchiectasis, where the co-presence of different bacteria and the altered 3D-environment, can be considered as worst-cases for chronic polymicrobial infections. We illustrate the state-of-art strategies used to study biofilms and bacterial niches in chronic infections, and multispecies ecological competition. Although far from the rendering of the 3D-environments and the polymicrobial nature of the infections, they represent the starting point to face their complexity. The increase of knowledge respect to the above aspects could positively affect the actual healthcare scenario. Indeed, infections are becoming a serious threat, due to the increasing bacterial resistance and the slow release of novel antibiotics on the market.

A Decrease in Transcription Capacity Limits Growth Rate upon Translation Inhibition

Exposure of bacteria to sublethal concentrations of antibiotics can lead to bacterial adaptation and survival at higher doses of inhibitors, which in turn can lead to the emergence of antibiotic resistance. The presence of sublethal concentrations of antibiotics targeting translation results in an increase in the amount of ribosomes per cell but nonetheless a decrease in the cells’ growth rate. In this work, we have found that inhibition of ribosome activity can result in a decrease in the amount of free RNA polymerase available for transcription, thus limiting the protein expression rate via a different pathway than what was expected. This result can be explained by our observation that long genes, such as those coding for RNA polymerase subunits, have a higher probability of premature translation termination in the presence of ribosome inhibitors, while expression of short ribosomal genes is affected less, consistent with their increased concentration.

In bacterial cells, inhibition of ribosomes by sublethal concentrations of antibiotics leads to a decrease in the growth rate despite an increase in ribosome content. The limitation of ribosomal activity results in an increase in the level of expression from ribosomal promoters; this can deplete the pool of RNA polymerase (RNAP) that is available for the expression of nonribosomal genes. However, the magnitude of this effect remains to be quantified. Here, we use the change in the activity of constitutive promoters with different affinities for RNAP to quantify the change in the concentration of free RNAP. The data are consistent with a significant decrease in the amount of RNAP available for transcription of both ribosomal and nonribosomal genes. Results obtained with different reporter genes reveal an mRNA length dependence on the amount of full-length translated protein, consistent with the decrease in ribosome processivity affecting more strongly the translation of longer genes. The genes coding for the β and β' subunits of RNAP are among the longest genes in the Escherichia coli genome, while the genes coding for ribosomal proteins are among the shortest genes. This can explain the observed decrease in transcription capacity that favors the expression of genes whose promoters have a high affinity for RNAP, such as ribosomal promoters.

IMPORTANCE Exposure of bacteria to sublethal concentrations of antibiotics can lead to bacterial adaptation and survival at higher doses of inhibitors, which in turn can lead to the emergence of antibiotic resistance. The presence of sublethal concentrations of antibiotics targeting translation results in an increase in the amount of ribosomes per cell but nonetheless a decrease in the cells’ growth rate. In this work, we have found that inhibition of ribosome activity can result in a decrease in the amount of free RNA polymerase available for transcription, thus limiting the protein expression rate via a different pathway than what was expected. This result can be explained by our observation that long genes, such as those coding for RNA polymerase subunits, have a higher probability of premature translation termination in the presence of ribosome inhibitors, while expression of short ribosomal genes is affected less, consistent with their increased concentration.

Antifungal resistance-modifying multiplexing action of Momordica charantia protein and phosphorylated derivatives on the basis of growth-dependent gene coregulation in Candida albicans

Fungal growth-dependent gene coregulation is strongly implicated in alteration of gene-encoding target proteases ruling with an antifungal resistance niche and biology of resistant mutants. On the basis of multi-alterative processes in this platform, the resistance-modifying strategy is designed in ketoconazole resistant Candida albicans and evaluated with less selective Momordica charantia protein and allosterically phosphorylated derivatives at the Thr102, Thr24 and Thr255 sites, respectively. We demonstrate absolutely chemo-sensitizing efficacy regarding stepwise-modifying resistance in sensitivity, by a load of only 26.23-40.00 μg/l agents in Sabouraud's dextrose broth. Five successive modifying-steps realize the decreasing of ketoconazole E-test MIC50 from 11.10 to a lower level than 0.10 mg/l. With the ketoconazole resistance-modifying, colony undergoes a high-frequency morphological switch between high ploidy (opaque) and small budding haploid (white). A cellular event in the first modifying-step associates with relatively slow exponential growth (ie, a 4-h delay)-dependent action, mediated by agents adsorption. Moreover, multiple molecular roles are coupled with intracellularly and extracellularly binding to ATP-dependent RNA helicase dbp6; the 0.08-2.45 fold upregulation of TATA-box-binding protein, rRNA-processing protein and translation initiation factor 5A; and the 7.52-55.33% decrease of cytochrome P450 lanosterol 14α-demethylase, glucan 1, 3-β glucosidase, candidapepsin-1 and 1-acylglycerol-3-phosphate O-acyltransferase. Spatial and temporal gene coregulation, in the transcription and translation initiation stages with rRNA-processing, is a new coprocessing platform enabling target protease attenuations for resistance-impairing. An updated resistance-modifying measure of these agents in the low-dose antifungal strategic design may provide opportunities to a virtually safe therapy that is in high dose-dependency.

LAY SUMMARY

A new platform to modify resistance is fungal growth-dependent gene coregulation. MAP30 and phosphorylated derivatives are candidate resistance-modifying agents. Low-dose stepwise treatment absolutely modifies azole resistance in model fungus.

A Roadblock-and-Kill Mechanism of Action Model for the DNA-Targeting Antibiotic Ciprofloxacin

Fluoroquinolones, antibiotics that cause DNA damage by inhibiting DNA topoisomerases, are clinically important, but their mechanism of action is not yet fully understood. In particular, the dynamical response of bacterial cells to fluoroquinolone exposure has hardly been investigated, although the SOS response, triggered by DNA damage, is often thought to play a key role. Here, we investigated the growth inhibition of the bacterium Escherichia coli by the fluoroquinolone ciprofloxacin at low concentrations. We measured the long-term and short-term dynamical response of the growth rate and DNA production rate to ciprofloxacin at both the population and single-cell levels. We show that, despite the molecular complexity of DNA metabolism, a simple roadblock-and-kill model focusing on replication fork blockage and DNA damage by ciprofloxacin-poisoned DNA topoisomerase II (gyrase) quantitatively reproduces long-term growth rates in the presence of ciprofloxacin. The model also predicts dynamical changes in the DNA production rate in wild-type E. coli and in a recombination-deficient mutant following a step-up of ciprofloxacin. Our work highlights that bacterial cells show a delayed growth rate response following fluoroquinolone exposure. Most importantly, our model explains why the response is delayed: it takes many doubling times to fragment the DNA sufficiently to inhibit gene expression. We also show that the dynamical response is controlled by the timescale of DNA replication and gyrase binding/unbinding to the DNA rather than by the SOS response, challenging the accepted view. Our work highlights the importance of including detailed biophysical processes in biochemical-systems models to quantitatively predict the bacterial response to antibiotics.

Mechanisms of drug interactions between translation-inhibiting antibiotics

Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by "translation bottlenecks": points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of "continuous epistasis" in bacterial physiology.

Membrane voltage dysregulation driven by metabolic dysfunction underlies bactericidal activity of aminoglycosides

Aminoglycosides are broad-spectrum antibiotics whose mechanism of action is under debate. It is widely accepted that membrane voltage potentiates aminoglycoside activity, which is ascribed to voltage-dependent drug uptake. In this paper, we measured the response of Escherichia coli treated with aminoglycosides and discovered that the bactericidal action arises not from the downstream effects of voltage-dependent drug uptake, but rather directly from dysregulated membrane potential. In the absence of voltage, aminoglycosides are taken into cells and exert bacteriostatic effects by inhibiting translation. However, cell killing was immediate upon re-polarization. The hyperpolarization arose from altered ATP flux, which induced a reversal of the F1Fo-ATPase to hydrolyze ATP and generated the deleterious voltage. Heterologous expression of an ATPase inhibitor completely eliminated bactericidal activity, while loss of the F-ATPase reduced the electrophysiological response to aminoglycosides. Our data support a model of voltage-induced death, and separates aminoglycoside bacteriostasis and bactericide in E. coli.

Toxicity of UV filters on marine bacteria: Combined effects with damaging solar radiation

Organic UV filters are of emerging concern due to their occurrence and persistence in coastal ecosystems. Because marine bacteria are crucial in the major biogeochemical cycles, there is an urgent need to understand to what extent these microorganisms are affected by those chemicals. This study deciphers the impact of five common sunscreen UV filters on twenty-seven marine bacteria, combining both photobiology and toxicity analysis on environmentally relevant species. Seven bacteria were sensitive to different organic UV filters at 1000 μg L^-1, including octinoxate and oxybenzone. This is the first report demonstrating inhibition of bacterial growth from 100 μg L^-1. None of the UV filters showed any toxicity at 1000 μg L^-1 on stationary phase cells, demonstrating that physiological state was found to be a key parameter in the bacterial response to UV-filters. Indeed, non-growing bacteria were resistant to UV filters whereas growing cells exhibited UV filter dependent sensitivity. Octinoxate was the most toxic chemical at 1000 μg L^-1 on growing cells. Interestingly, photobiology experiments revealed that the toxicity of octinoxate and homosalate decreased after light exposure while the other compounds were not affected. In terms of environmental risk characterization, our results revealed that the increasing use of sun blockers could have detrimental impacts on bacterioplanktonic communities in coastal areas. Our findings contribute to a better understanding of the impact of the most common UV filters on bacterial species and corroborate the importance to consider environmental parameters such as solar radiation in ecotoxicology studies.

A Physiological Basis for Nonheritable Antibiotic Resistance

Antibiotics constitute one of the cornerstones of modern medicine. However, individuals may succumb to a bacterial infection if a pathogen survives exposure to antibiotics. The ability of bacteria to survive bactericidal antibiotics results from genetic changes in the preexisting bacterial genome, from the acquisition of genes from other organisms, and from nonheritable phenomena that give rise to antibiotic tolerance. Nonheritable antibiotic tolerance can be exhibited by a large fraction of the bacterial population or by a small subpopulation referred to as persisters. Nonheritable resistance to antibiotics has been ascribed to the activity of toxins that are part of toxin-antitoxin modules, to the universal energy currency ATP, and to the signaling molecule guanosine (penta) tetraphosphate. However, these molecules are dispensable for nonheritable resistance to antibiotics in many organisms. By contrast, nutrient limitation, treatment with bacteriostatic antibiotics, or expression of genes that slow bacterial growth invariably promote nonheritable resistance. We posit that antibiotic persistence results from conditions promoting feedback inhibition among core cellular processes, resulting phenotypically in a slowdown or halt in bacterial growth.

Theoretical investigation of stochastic clearance of bacteria: first-passage analysis

Understanding mechanisms of bacterial eradication is critically important for overcoming failures of antibiotic treatments. Current studies suggest that the clearance of large bacterial populations proceeds deterministically, while for smaller populations, the stochastic effects become more relevant. Here, we develop a theoretical approach to investigate the bacterial population dynamics under the effect of antibiotic drugs using a method of first-passage processes. It allows us to explicitly evaluate the most important characteristics of bacterial clearance dynamics such as extinction probabilities and extinction times. The new meaning of minimal inhibitory concentrations for stochastic clearance of bacterial populations is also discussed. In addition, we investigate the effect of fluctuations in population growth rates on the dynamics of bacterial eradication. It is found that extinction probabilities and extinction times generally do not correlate with each other when random fluctuations in the growth rates are taking place. Unexpectedly, for a significant range of parameters, the extinction times increase due to these fluctuations, indicating a slowing in the bacterial clearance dynamics. It is argued that this might be one of the initial steps in the pathway for the development of antibiotic resistance. Furthermore, it is suggested that extinction times is a convenient measure of bacterial tolerance.

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.