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

Multiplicity of type 6 secretion system toxins limits the evolution of resistance

The bacterial type 6 secretion system (T6SS) is a toxin-injecting nanoweapon that mediates competition in plant- and animal-associated microbial communities. Bacteria can evolve de novo resistance against T6SS attacks, but resistance is far from universal in natural communities, suggesting key features of T6SS weaponry may act to limit its evolution. Here, we combine ecoevolutionary modeling and experimental evolution to examine how toxin type and multiplicity in Acinetobacter baylyi attackers shape resistance evolution in susceptible Escherichia coli competitors. In both our models and experiments, we find that combinations of multiple distinct toxins limit resistance evolution by creating genetic bottlenecks, driving resistant lineages extinct before they can reach high frequency. We also show that, paradoxically, single-toxin attackers can drive the evolution of cross-resistance, protecting bacteria against unfamiliar toxin combinations, even though such evolutionary pathways were inaccessible against multitoxin attackers. Our findings indicate that, comparable to antimicrobial and anticancer combination therapies, multitoxin T6SS arsenals function to limit resistance evolution in competing microbes. This helps us to understand why T6SSs remain widespread and effective weapons in microbial communities, and why many T6SS-armed bacteria encode functionally diverse anticompetitor toxins.

Predicting the evolution of antibiotic resistance

Predicting the evolution of antibiotic resistance is critical for realizing precision antibiotic therapies. How exactly to achieve such predictions is a theoretical challenge. Insights from mathematical models that reflect future behavior of microbes under antibiotic stress can inform intervention protocols. However, this requires going beyond heuristic approaches by modeling ecological and evolutionary responses linked to metabolic pathways and cellular functions. Developing such models is now becoming possible due to increasing data availability from systematic experiments with microbial systems. Here, I review recent theoretical advances promising building blocks to piece together a predictive theory of antibiotic resistance evolution. I focus on the conceptual framework of eco-evolutionary response models grounded on quantitative laws of bacterial physiology. These forward-looking models can predict previously unknown behavior of bacteria upon antibiotic exposure. With current developments covering mostly the case of ribosome-targeting antibiotics, I write this Opinion piece as an invitation to generalize the principles discussed here to a broader range of drugs and context dependencies.

Ecological and evolutionary mechanisms driving within-patient emergence of antimicrobial resistance

The ecological and evolutionary mechanisms of antimicrobial resistance (AMR) emergence within patients and how these vary across bacterial infections are poorly understood. Increasingly widespread use of pathogen genome sequencing in the clinic enables a deeper understanding of these processes. In this Review, we explore the clinical evidence to support four major mechanisms of within-patient AMR emergence in bacteria: spontaneous resistance mutations; in situ horizontal gene transfer of resistance genes; selection of pre-existing resistance; and immigration of resistant lineages. Within-patient AMR emergence occurs across a wide range of host niches and bacterial species, but the importance of each mechanism varies between bacterial species and infection sites within the body. We identify potential drivers of such differences and discuss how ecological and evolutionary analysis could be embedded within clinical trials of antimicrobials, which are powerful but underused tools for understanding why these mechanisms vary between pathogens, infections and individuals. Ultimately, improving understanding of how host niche, bacterial species and antibiotic mode of action combine to govern the ecological and evolutionary mechanism of AMR emergence in patients will enable more predictive and personalized diagnosis and antimicrobial therapies.

Mating Assay: Plating Below a Cell Density Threshold is Required for Unbiased Estimation of Plasmid Conjugation Frequency of RP4 Transfer Between E. coli Strains

Mating assays are common laboratory experiments for measuring the conjugation frequency, i.e. efficiency at which a plasmid transfers from a population of donor cells to a population of recipient cells. Selective plating remains a widely used quantification method to enumerate transconjugants at the end of such assays. However, conjugation frequencies may be inaccurately estimated because plasmid transfer can occur on transconjugant-selective plates rather than only during the intended mating duration. We investigated the influence of cell density on this phenomenon. We conducted mating experiments with IncPα plasmid RP4 harbored in Escherichia coli at a fixed cell density and mating conditions, inoculated a serial dilution of the mating mixture on transconjugant-selective plates or in transconjugant-selective broth, and compared the results to a model of cell-to-cell distance distribution. Our findings suggest that irrespective of the mating mode (liquid vs solid), the enumeration of transconjugants becomes significantly biased if the plated cell density exceeds 28 Colony Forming Unit (CFU)/mm2 (or 1.68•105 CFU/standard 9 cm Petri dish). This threshold is determined with a 95% confidence interval of ± 4 CFU/mm2 (± 2.46•104 CFU/standard 9 cm Petri dish). Liquid mating assays were more sensitive to this bias because the conjugation frequency of RP4 is several orders of magnitude lower in suspension compared to surface mating. Therefore, if selective plating is used, we recommend to plate at this density threshold and that negative controls are performed where donors and recipients are briefly mixed before plating at the same dilutions as for the actual mating assay. As an alternative, a liquid enumeration method can be utilized to increase the signal-to-noise ratio and allow for more accurate enumeration of transconjugants.

Persistent glucose consumption under antibiotic treatment protects bacterial community

Antibiotics typically induce major physiological changes in bacteria. However, their effect on nutrient consumption remains unclear. Here we found that Escherichia coli communities can sustain normal levels of glucose consumption under a broad range of antibiotics. The community-living resulted in a low membrane potential in the bacteria, allowing slow antibiotic accumulation on treatment and better adaptation. Through multi-omics analysis, we identified a prevalent adaptive response characterized by the upregulation of lipid synthesis, which substantially contributes to sustained glucose consumption. The consumption was maintained by the periphery region of the community, thereby restricting glucose penetration into the community interior. The resulting spatial heterogeneity in glucose availability protected the interior from antibiotic accumulation in a membrane potential-dependent manner, ensuring rapid recovery of the community postantibiotic treatment. Our findings unveiled a community-level antibiotic response through spatial regulation of metabolism and suggested new strategies for antibiotic therapies.

The effect of antibiotic selection on collateral effects and evolvability of uropathogenic Escherichia coli

Trimethoprim is recommended as a first-line treatment of urinary tract infections (UTIs) in the UK. In 2018, 31.4% of Escherichia coli isolated from UTIs in England were trimethoprim-resistant, leading to overreliance on other first and second-line antibiotics. Here, we assessed whether, in principle, prior selection with trimethoprim results in collateral effects to other antibiotics recommended for the treatment of UTIs. As collateral effects, we considered changes in susceptibility, mutation-selection window and population establishment probability. We selected 10 trimethoprim-resistant derivatives from three clinical isolates of uropathogenic Escherichia coli. We found that mutations conferring trimethoprim resistance did not have any collateral effects on fosfomycin. In contrast, resistance to trimethoprim resulted in decreased susceptibility (collateral resistance) to nitrofurantoin, below the clinical breakpoint and narrowed the mutation-selection window, thereby reducing the maximum concentration for selection of nitrofurantoin resistance mutations. Our analyses demonstrate that multiple collateral responses should be accounted for when predicting and optimising antibiotic use, limiting future antimicrobial resistance emergence.

Biological Synthesis of Silver Nanoparticles from Lavandula mairei Humbert: Antibacterial and Antioxidant Activities

Hospital-acquired infections involving carbapenem-resistant Acinetobacter baumannii (A. baumannii) and extended spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae pose significant challenges in the intensive care units. The lack of novel antimicrobial drugs amplifies the urgency to explore innovative management strategies. Nanotechnology, with its ability to generate nanoparticles possessing specific properties beneficial in drug delivery and nanomedicine, stands as a pivotal research domain. The objective of this study was to synthesize, for the first time, biologically silver nanoparticles (Ag-NPs) from Lavandula mairei Humbert (L. mairei) plant. The biosynthesized Ag-NPs were characterized by UV-visible spectral analysis, X-Ray diffraction Analysis, Fourier transform infrared spectroscopy analysis, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy. Subsequently, the antibacterial and antioxidant activities of Ag-NPs were assessed using the micro-dilution method, DPPH test and FRAP assay, respectively. The green-synthesized Ag-NPs exhibited high antibacterial activity against ESBL-producing multidrug-resistant (MDR) strains and against carbapenem-resistant and non-carbapenem-resistant strains of A. baumannii, as well as a very interesting antioxidant activity. The present study suggests that these results hold very promising for the potential application of biologically synthesized Ag-NPs from L. mairei (Ag-LM-NPs) in the invention of novel antibacterial and antioxidant agents.

Targeting the Pseudomonas aeruginosa quorum sensing system to inhibit virulence factors and eradicate biofilm formation using AHL-analogue phytochemicals

Quorum sensing plays a major role in the expression of virulence and development of biofilm in the human pathogen Pseudomonas aeruginosa. Natural compounds are well-known for their antibacterial characteristics by blocking various metabolic pathways. The goal of this study is to find natural compounds that mimic AHL (Acyl homoserine lactone) and suppress virulence in P. aeruginosa, which is triggered by quorum sensing-dependent pathways as an alternative drug development strategy. To support this rationale, functional network analysis and in silico investigations were carried out to find natural AHL analogues, followed by molecular docking studies. Out of the 16 top-hit AHL analogues derived from phytochemicals, seven ligands were found to bind to the quorum sensing activator proteins. Cassialactone, an AHL analogue, exhibited the highest binding affinity for RhlI, RhlR, and PqsE of P. aeruginosa, with a docking score of -9.4, -8.9, and -8.7 kcal/mol, respectively. 2(5H)-Furanone, a well-known inhibitor, was also docked to compare the docking score and intermolecular interactions between the ligand and the target protein. Furthermore, molecular dynamics simulations and binding free energy calculations were performed to determine the stability of the docked complexes. Additionally, the ADME properties of the analogues were also analyzed to evaluate the pharmacological parameters. Functional network analysis further showed that the interconnectedness of proteins such as RhlI, RhlR, LasI, and PqsE with the virulence and biofilm phenotype of the pathogen could offer potential as a therapeutic target.Communicated by Ramaswamy H. Sarma.

Cross-feeding affects the target of resistance evolution to an antifungal drug

Pathogenic fungi are a cause of growing concern. Developing an efficient and safe antifungal is challenging because of the similar biological properties of fungal and host cells. Consequently, there is an urgent need to better understand the mechanisms underlying antifungal resistance to prolong the efficacy of current molecules. A major step in this direction would be to be able to predict or even prevent the acquisition of resistance. We leverage the power of experimental evolution to quantify the diversity of paths to resistance to the antifungal 5-fluorocytosine (5-FC), commercially known as flucytosine. We generated hundreds of independent 5-FC resistant mutants derived from two genetic backgrounds from wild isolates of Saccharomyces cerevisiae. Through automated pin-spotting, whole-genome and amplicon sequencing, we identified the most likely causes of resistance for most strains. Approximately a third of all resistant mutants evolved resistance through a pleiotropic drug response, a potentially novel mechanism in response to 5-FC, marked by cross-resistance to fluconazole. These cross-resistant mutants are characterized by a loss of respiration and a strong tradeoff in drug-free media. For the majority of the remaining two thirds, resistance was acquired through loss-of-function mutations in FUR1, which encodes an important enzyme in the metabolism of 5-FC. We describe conditions in which mutations affecting this particular step of the metabolic pathway are favored over known resistance mutations affecting a step upstream, such as the well-known target cytosine deaminase encoded by FCY1. This observation suggests that ecological interactions may dictate the identity of resistance hotspots.

Propensity of selecting mutant parasites for the antimalarial drug cabamiquine

We report an analysis of the propensity of the antimalarial agent cabamiquine, a Plasmodium-specific eukaryotic elongation factor 2 inhibitor, to select for resistant Plasmodium falciparum parasites. Through in vitro studies of laboratory strains and clinical isolates, a humanized mouse model, and volunteer infection studies, we identified resistance-associated mutations at 11 amino acid positions. Of these, six (55%) were present in more than one infection model, indicating translatability across models. Mathematical modelling suggested that resistant mutants were likely pre-existent at the time of drug exposure across studies. Here, we estimated a wide range of frequencies of resistant mutants across the different infection models, much of which can be attributed to stochastic differences resulting from experimental design choices. Structural modelling implicates binding of cabamiquine to a shallow mRNA binding site adjacent to two of the most frequently identified resistance mutations.

Quantifying stochastic establishment of mutants in microbial adaptation

Studies of microbial evolution, especially in applied contexts, have focused on the role of selection in shaping predictable, adaptive responses to the environment. However, chance events - the appearance of novel genetic variants and their establishment, i.e. outgrowth from a single cell to a sizeable population - also play critical initiating roles in adaptation. Stochasticity in establishment has received little attention in microbiology, potentially due to lack of awareness as well as practical challenges in quantification. However, methods for high-replicate culturing, mutant labelling and detection, and statistical inference now make it feasible to experimentally quantify the establishment probability of specific adaptive genotypes. I review methods that have emerged over the past decade, including experimental design and mathematical formulas to estimate establishment probability from data. Quantifying establishment in further biological settings and comparing empirical estimates to theoretical predictions represent exciting future directions. More broadly, recognition that adaptive genotypes may be stochastically lost while rare is significant both for interpreting common lab assays and for designing interventions to promote or inhibit microbial evolution.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Mutators can drive the evolution of multi-resistance to antibiotics

Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with 'mutators', organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic-under conditions where purely wild-type populations cannot. In populations of Escherichia coli subjected to combination treatment, we detected a diverse array of acquired mutations, including multiple alleles in the canonical targets of resistance for the two drugs, as well as mutations in multi-drug efflux pumps and genes involved in DNA replication and repair. Unexpectedly, mutators not only allowed multi-resistance to evolve under combination treatment where it was favoured, but also under single-drug treatments. Using simulations, we show that the increase in mutation rate of the two canonical resistance targets is sufficient to permit multi-resistance evolution in both single-drug and combination treatments. Under both conditions, the mutator allele swept to fixation through hitch-hiking with single-drug resistance, enabling subsequent resistance mutations to emerge. Ultimately, our results suggest that mutators may hinder the utility of combination therapy when mutators are present. Additionally, by raising the rates of genetic mutation, selection for multi-resistance may have the unwanted side-effect of increasing the potential to evolve resistance to future antibiotic treatments.

Guidelines for the estimation and reporting of plasmid conjugation rates

Conjugation is a central characteristic of plasmid biology and an important mechanism of horizontal gene transfer in bacteria. However, there is little consensus on how to accurately estimate and report plasmid conjugation rates, in part due to the wide range of available methods. Given the similarity between approaches, we propose general reporting guidelines for plasmid conjugation experiments. These constitute best practices based on recent literature about plasmid conjugation and methods to measure conjugation rates. In addition to the general guidelines, we discuss common theoretical assumptions underlying existing methods to estimate conjugation rates and provide recommendations on how to avoid violating these assumptions. We hope this will aid the implementation and evaluation of conjugation rate measurements, and initiate a broader discussion regarding the practice of quantifying plasmid conjugation rates.

Bacterial defences: mechanisms, evolution and antimicrobial resistance

Throughout their evolutionary history, bacteria have faced diverse threats from other microorganisms, including competing bacteria, bacteriophages and predators. In response to these threats, they have evolved sophisticated defence mechanisms that today also protect bacteria against antibiotics and other therapies. In this Review, we explore the protective strategies of bacteria, including the mechanisms, evolution and clinical implications of these ancient defences. We also review the countermeasures that attackers have evolved to overcome bacterial defences. We argue that understanding how bacteria defend themselves in nature is important for the development of new therapies and for minimizing resistance evolution.

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.

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

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

An optogenetic toolkit for light-inducible antibiotic resistance

Antibiotics are a key control mechanism for synthetic biology and microbiology. Resistance genes are used to select desired cells and regulate bacterial populations, however their use to-date has been largely static. Precise spatiotemporal control of antibiotic resistance could enable a wide variety of applications that require dynamic control of susceptibility and survival. Here, we use light-inducible Cre recombinase to activate expression of drug resistance genes in Escherichia coli. We demonstrate light-activated resistance to four antibiotics: carbenicillin, kanamycin, chloramphenicol, and tetracycline. Cells exposed to blue light survive in the presence of lethal antibiotic concentrations, while those kept in the dark do not. To optimize resistance induction, we vary promoter, ribosome binding site, and enzyme variant strength using chromosome and plasmid-based constructs. We then link inducible resistance to expression of a heterologous fatty acid enzyme to increase production of octanoic acid. These optogenetic resistance tools pave the way for spatiotemporal control of cell survival.

Factors affecting antimicrobial resistance in Streptococcus pneumoniae following vaccination introduction

Streptococcus pneumoniae is a major cause of pneumonia, meningitis, and septicaemia worldwide. Pneumococcal antimicrobial resistance (AMR) has been highlighted by the WHO as an important public health concern, with emerging serotypes showing resistance to multiple antibiotics. Indeed, although the introduction of pneumococcal conjugate vaccines (PCVs) has been associated with an overall decline in pneumococcal AMR, there have been increases in prevalence of potentially disease-causing AMR serotypes not targeted by vaccination. Here, we discuss a variety of evolutionary mechanisms at the host, pathogen, and environmental levels that may contribute to changes in the prevalence of pneumococcal AMR in the post-vaccination era. The relative importance of these factors may vary by population, pneumococcal lineage, geography, and time, leading to the complex relationship between vaccination, antibiotic use, and AMR.

Predicting the re-distribution of antibiotic molecules caused by inter-species interactions in microbial communities

Microbes associate in nature forming complex communities, but they are often studied in purified form. Here I show that neighbouring species enforce the re-distribution of carbon and antimicrobial molecules, predictably changing drug efficacy with respect to standard laboratory assays. A simple mathematical model, validated experimentally using pairwise competition assays, suggests that differences in drug sensitivity between the competing species causes the re-distribution of drug molecules without affecting carbon uptake. The re-distribution of drug is even when species have similar drug sensitivity, reducing drug efficacy. But when their sensitivities differ the re-distribution is uneven: The most sensitive species accumulates more drug molecules, increasing efficacy against it. Drug efficacy tests relying on samples with multiple species are considered unreliable and unpredictable, but study demonstrates that efficacy in these cases can be qualitatively predicted. It also suggests that living in communities can be beneficial even when all species compete for a single carbon source, as the relationship between cell density and drug required to inhibit their growth may be more complex than previously thought.

Cheater suppression and stochastic clearance through quorum sensing

The evolutionary consequences of quorum sensing in regulating bacterial cooperation are not fully understood. In this study, we reveal unexpected effects of regulating public good production through quorum sensing on bacterial population dynamics, showing that quorum sensing can be a collectively harmful alternative to unregulated production. We analyze a birth-death model of bacterial population dynamics accounting for public good production and the presence of non-producing cheaters. Our model demonstrates that when demographic noise is a factor, the consequences of controlling public good production according to quorum sensing depend on the cost of public good production and the growth rate of populations in the absence of public goods. When public good production is inexpensive, quorum sensing is a destructive alternative to unconditional production, in terms of the mean population extinction time. When costs are higher, quorum sensing becomes a constructive strategy for the producing strain, both stabilizing cooperation and decreasing the risk of population extinction.

Quorum sensing is a process through which bacteria can regulate gene expression according to their population density. The reasons for why bacteria use quorum sensing to regulate production of “public goods”, biochemical products that benefit nearby bacteria, are not entirely clear. We use mathematical modeling to explore how quorum sensing compares to other strategies for controlling production of public goods, namely unconditional production independent on population density, in small populations of bacteria where the random nature of growth is significant. Our model captures both how likely “cheater” strains, which do not produce public goods but benefit from them, are to take over a population and how long on average the population will last before going extinct. We find that depending on how expensive public good production is and how critical public goods are for growth, quorum sensing can decrease or increase the mean time to extinction compared with unconditional production, while always reducing the likelihood of cheaters taking over. Our results could have important implications for the growth of bacterial infections, for example Pseudomonas aeruginosa infections of the lungs of cystic fibrosis patients.

Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis

To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria–Delbrück method (“LDM”), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude.

Plasmid transfer can often spread resistance between important clinical pathogens. This study shows that widely used methods can lead to biased estimates of plasmid transfer rate by several orders of magnitude, and presents a new approach, inspired by the classic Luria-Delbrück approach, for accurately assessing this fundamental rate parameter

Droplet-based methods for tackling antimicrobial resistance

Application of droplet-based methods enables (i) faster detection, (ii) increased sensitivity, (iii) characterization of the level of heterogeneity in response to antibiotics by bacterial populations, and (iv) expanded screening of the effectiveness of antibiotic combinations. Hereby, we discuss the key steps and parameters of droplet-based experiments to investigate antimicrobial resistance. We also review recent findings accomplished with these methods and highlight their advantages and capacity to yield new insights into the problem of antimicrobial resistance.

Stochastic establishment of β-lactam-resistant Escherichia coli mutants reveals conditions for collective resistance

For antibiotic resistance to arise, new resistant mutants must establish in a bacterial population before they can spread via natural selection. Comprehending the stochastic factors that influence mutant establishment is crucial for a quantitative understanding of antibiotic resistance emergence. Here, we quantify the single-cell establishment probability of four Escherichia coli strains expressing β-lactamase alleles with different activity against the antibiotic cefotaxime, as a function of antibiotic concentration in both unstructured (liquid) and structured (agar) environments. We show that concentrations well below the minimum inhibitory concentration (MIC) can substantially hamper establishment, particularly for highly resistant mutants. While the pattern of establishment suppression is comparable in both tested environments, we find greater variability in establishment probability on agar. Using a simple branching model, we investigate possible sources of this stochasticity, including environment-dependent lineage variability, but cannot reject other possible causes. Lastly, we use the single-cell establishment probability to predict each strain's MIC in the absence of social interactions. We observe substantially higher measured than predicted MIC values, particularly for highly resistant strains, which indicates cooperative effects among resistant cells at large cell numbers, such as in standard MIC assays.

Influence of Sub-Inhibitory Dosage of Cefotaxime on Multidrug Resistant Staphylococcus haemolyticus Isolated from Sick Neonatal Care Unit

Staphylococcus haemolyticus has emerged to be a frequently encountered late-onset sepsis pathogen among newborn infants. Critical care of neonates involves substantial usage of antibiotics and these pathogens are often exposed to sub-optimal doses of antibiotics which can augment maintenance of selection determinants and a range of physiological effects, prime among them being biofilm formation. Therefore, in this study, the outcome of a sub-inhibitory dosage of a commonly prescribed third-generation antibiotic, cefotaxime (CTX), on multidrug resistant (MDR) S. haemolyticus, was investigated. A total of 19 CTX-resistant, MDR and 5 CTX-susceptible strains isolated from neonates were included. Biofilm-forming abilities of S. haemolyticus isolates in the presence of sub-optimal CTX (30 μg/mL) were determined by crystal violet assays and extracellular DNA (eDNA) quantitation. CTX was found to significantly enhance biofilm production among the non-susceptible isolates (p-valueWilcoxintest—0.000008) with an increase in eDNA levels (p-valueWilcoxintest—0.000004). Further, in the absence of antibiotic selection in vitro, populations of MDR isolates, JNM56C1 and JNM60C2 remained antibiotic non-susceptible after >500 generations of growth. These findings demonstrate that sub-optimal concentration of CTX induces biofilm formation and short-term non-exposure to antibiotics does not alter non-susceptibility among S. haemolyticus isolates under the tested conditions.

From the soil to the clinic: the impact of microbial secondary metabolites on antibiotic tolerance and resistance

Secondary metabolites profoundly affect microbial physiology, metabolism and stress responses. Increasing evidence suggests that these molecules can modulate microbial susceptibility to commonly used antibiotics; however, secondary metabolites are typically excluded from standard antimicrobial susceptibility assays. This may in part account for why infections by diverse opportunistic bacteria that produce secondary metabolites often exhibit discrepancies between clinical antimicrobial susceptibility testing results and clinical treatment outcomes. In this Review, we explore which types of secondary metabolite alter antimicrobial susceptibility, as well as how and why this phenomenon occurs. We discuss examples of molecules that opportunistic and enteric pathogens either generate themselves or are exposed to from their neighbours, and the nuanced impacts these molecules can have on tolerance and resistance to certain antibiotics.

Effect of methylpyrazoles and coumarin association on the growth of Gram-negative bacteria

One approach to overcome the antimicrobial resistance of many pathogens is to associate compounds with antimicrobial properties and obtain combinations superior compared to the effect of each compound. To identify a possible potentiating effect, we tested and analyzed the inhibitory effect of the combination of coumarin with two pyrazole derivatives, 1,1'-methandiylbis (3,5-dimethyl-1H-pyrazole (AM4) and 3,5-dimethyl-1H-pyrazol-1-yl) methanol 3,5-dimethyl-1-hydroxymethylpyrazol (SAM4). A clear synergistic effect was recorded when coumarin was associated with SAM4, in which case the Fractional Inhibitory Concentration Index (FICI) had a value equal to 0.468 for Citrobacter freundii, Proteus mirabilis, and E. coli. In the other cases, however, both the association between coumarin and AM4 and coumarin SAM4 had only an additive effect (FICI = 0.937-1.00). The bactericidal effect of the coumarin-pyrazole combination over time was better in all cases compared to the effect of the compounds used separately. The viability of the bacterial cells at sub-inhibitory concentrations of the tested compounds was variable, depending on both the type of compound and the bacterial strain.

Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, Cantón R, Coque TM. Evolutionary Pathways and Trajectories in Antibiotic Resistance

Evolution is the hallmark of life. Descriptions of the evolution of microorganisms have provided a wealth of information, but knowledge regarding "what happened" has precluded a deeper understanding of "how" evolution has proceeded, as in the case of antimicrobial resistance. The difficulty in answering the "how" question lies in the multihierarchical dimensions of evolutionary processes, nested in complex networks, encompassing all units of selection, from genes to communities and ecosystems. At the simplest ontological level (as resistance genes), evolution proceeds by random (mutation and drift) and directional (natural selection) processes; however, sequential pathways of adaptive variation can occasionally be observed, and under fixed circumstances (particular fitness landscapes), evolution is predictable. At the highest level (such as that of plasmids, clones, species, microbiotas), the systems' degrees of freedom increase dramatically, related to the variable dispersal, fragmentation, relatedness, or coalescence of bacterial populations, depending on heterogeneous and changing niches and selective gradients in complex environments. Evolutionary trajectories of antibiotic resistance find their way in these changing landscapes subjected to random variations, becoming highly entropic and therefore unpredictable. However, experimental, phylogenetic, and ecogenetic analyses reveal preferential frequented paths (highways) where antibiotic resistance flows and propagates, allowing some understanding of evolutionary dynamics, modeling and designing interventions. Studies on antibiotic resistance have an applied aspect in improving individual health, One Health, and Global Health, as well as an academic value for understanding evolution. Most importantly, they have a heuristic significance as a model to reduce the negative influence of anthropogenic effects on the environment.

Pharmacological properties of biogenically synthesized silver nanoparticles using endophyte Bacillus cereus extract of Berberis lyceum against oxidative stress and pathogenic multidrug-resistant bacteria

The emergence of multidrug resistance in pathogenic bacteria limits the utilization of available antibiotics. The development of alternate options to treat infectious diseases is the need of the day.The present study was aimed to synthesize, characterize and evaluate the bioactive properties of silver nanoparticles. Endophytic bacterium Bacillus cereus (MT193718) isolated from Berberis lycium was used to synthesize biocompatible silver nanoparticles. Antibacterial properties of AgNPs were evaluated against clinically isolated multidrug-resistant strains of Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae. AgNPs indicated significant antibacterial activity against S. aureus and K. pneumoniae fwith a zone of inhibition of 17 and 18 mm at a concentration of 1000 µg/ mL with minimum inhibitory concentration of 15.6 and 62.5 µg/mL respectively. Significant antioxidant activity with an IC50 value of 9.5 µg/mL was recorded. Biosynthesized AgNPs were found compatible with red blood cells at a concentration of 31.5 µg/ml with no clumping of erythrocytes. The study suggested that AgNPs synthesized by the endophytic bacterium Bacillus cereus are biologically active and can be used as antioxidant and antibacterial agents against drug-resistant bacteria.

Drug-induced resistance evolution necessitates less aggressive treatment

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

Antibiotic Breakdown by Susceptible Bacteria Enhances the Establishment of β-Lactam Resistant Mutants

For a better understanding of the evolution of antibiotic resistance, it is imperative to study the factors that determine the initial establishment of mutant resistance alleles. In addition to the antibiotic concentration, the establishment of resistance alleles may be affected by interactions with the surrounding susceptible cells from which they derive, for instance via the release of nutrients or removal of the antibiotic. Here, we investigate the effects of social interactions with surrounding susceptible cells on the establishment of Escherichia coli mutants with increasing β-lactamase activity (i.e., the capacity to hydrolyze β-lactam antibiotics) from single cells under the exposure of the antibiotic cefotaxime (CTX) on agar plates. We find that relatively susceptible cells, expressing a β-lactamase with very low antibiotic-hydrolyzing activity, increase the probability of mutant cells to survive and outgrow into colonies due to the active breakdown of the antibiotic. However, the rate of breakdown by the susceptible strain is much higher than expected based on its low enzymatic activity. A detailed theoretical model suggests that this observation may be explained by cell filamentation causing delayed lysis. While susceptible cells may hamper the spread of higher-resistant β-lactamase mutants at relatively high frequencies, our findings show that they promote their initial establishment.

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.

Atypical Genetic Basis of Pyrazinamide Resistance in Monoresistant Mycobacterium tuberculosis

Pyrazinamide (PZA) is a widely used antitubercular chemotherapeutic. Typically, PZA resistance (PZA-R) emerges in Mycobacterium tuberculosis strains with existing resistance to isoniazid and rifampin (i.e., multidrug resistance [MDR]) and is conferred by loss-of-function pncA mutations that inhibit conversion to its active form, pyrazinoic acid (POA). PZA-R departing from this canonical scenario is poorly understood. Here, we genotyped pncA and purported alternative PZA-R genes (panD, rpsA, and clpC1) with long-read sequencing of 19 phenotypically PZA-monoresistant isolates collected in Sweden and compared their phylogenetic and genomic characteristics to a large set of MDR PZA-R (MDR_PZA-R) isolates. We report the first association of ClpC1 mutations with PZA-R in clinical isolates, in the ClpC1 promoter (clpC1p₋₁₃₈) and the N terminus of ClpC1 (ClpC1_Val63Ala). Mutations have emerged in both these regions under POA selection in vitro, and the N-terminal region of ClpC1 has been implicated further, through its POA-dependent efficacy in PanD proteolysis. ClpC1_Val63Ala mutants spanned 4 Indo-Oceanic sublineages. Indo-Oceanic isolates invariably harbored ClpC1_Val63Ala and were starkly overrepresented (odds ratio [OR] = 22.2, P < 0.00001) among PZA-monoresistant isolates (11/19) compared to MDR_PZA-R isolates (5/80). The genetic basis of Indo-Oceanic isolates’ overrepresentation in PZA-monoresistant tuberculosis (TB) remains undetermined, but substantial circumstantial evidence suggests that ClpC1_Val63Ala confers low-level PZA resistance. Our findings highlight ClpC1 as potentially clinically relevant for PZA-R and reinforce the importance of genetic background in the trajectory of resistance development.

Rapid evolution and host immunity drive the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection

It is well established that antibiotic treatment selects for resistance, but the dynamics of this process during infections are poorly understood. Here we map the responses of Pseudomonas aeruginosa to treatment in high definition during a lung infection of a single ICU patient. Host immunity and antibiotic therapy with meropenem suppressed P. aeruginosa, but a second wave of infection emerged due to the growth of oprD and wbpM meropenem resistant mutants that evolved in situ. Selection then led to a loss of resistance by decreasing the prevalence of low fitness oprD mutants, increasing the frequency of high fitness mutants lacking the MexAB-OprM efflux pump, and decreasing the copy number of a multidrug resistance plasmid. Ultimately, host immunity suppressed wbpM mutants with high meropenem resistance and fitness. Our study highlights how natural selection and host immunity interact to drive both the rapid rise, and fall, of resistance during infection.

Coexistence of fluorescent Escherichia coli strains in millifluidic droplet reactors

Understanding competition and cooperation within microbiota is of high fundamental and clinical importance, helping to comprehend species' evolution and biodiversity. We co-encapsulated and cultured two isogenic Escherichia coli strains expressing blue (BFP) and yellow (YFP) fluorescent proteins into numerous emulsion droplets and quantified their growth by employing fluorescence measurements. To characterize and compare the bacterial growth kinetics and behavior in mono and co-culture, we compared the experimental observations with predictions from a simple growth model. Varying the initial ratio (R0) of both cell types injected, we observed a broad landscape from competition to cooperation between both strains in their confined microenvironments depending on start frequency: from a nearly symmetric situation at R0 = 1, up to the domination of one subpopulation when R0 ≫ 1 (or R0 ≪ 1). Due to competition between the strains, their doubling times and final biomass ratios (R1) continuously deviate from the monoculture behavior. The correlation map of the two strains' doubling times reveals that the R0 is one of the critical parameters affecting the competitive interaction between isogenic bacterial strains. Thanks to this strategy, different species of bacteria can be monitored simultaneously in real-time. Further advantages include high statistical output, unaffected bacteria growth, and long-time measurements in a well-mixed environment. We expect that the millifluidic droplet-based reactor can be utilized for practical clinical applications, such as bacterial antibiotic resistance and enzyme reaction kinetics studies.

Using ecological coexistence theory to understand antibiotic resistance and microbial competition

Tackling antibiotic resistance necessitates deep understanding of how resource competition within and between species modulates the fitness of resistant microbes. Recent advances in ecological coexistence theory offer a powerful framework to probe the mechanisms regulating intra- and interspecific competition, but the significance of this body of theory to the problem of antibiotic resistance has been largely overlooked. In this Perspective, we draw on emerging ecological theory to illustrate how changes in resource niche overlap can be equally important as changes in competitive ability for understanding costs of resistance and the persistence of resistant pathogens in microbial communities. We then show how different temporal patterns of resource and antibiotic supply, alongside trade-offs in competitive ability at high and low resource concentrations, can have diametrically opposing consequences for the coexistence and exclusion of resistant and susceptible strains. These insights highlight numerous opportunities for innovative experimental and theoretical research into the ecological dimensions of antibiotic resistance.

Mutability of demographic noise in microbial range expansions

Demographic noise, the change in the composition of a population due to random birth and death events, is an important driving force in evolution because it reduces the efficacy of natural selection. Demographic noise is typically thought to be set by the population size and the environment, but recent experiments with microbial range expansions have revealed substantial strain-level differences in demographic noise under the same growth conditions. Many genetic and phenotypic differences exist between strains; to what extent do single mutations change the strength of demographic noise? To investigate this question, we developed a high-throughput method for measuring demographic noise in colonies without the need for genetic manipulation. By applying this method to 191 randomly-selected single gene deletion strains from the E. coli Keio collection, we find that a typical single gene deletion mutation decreases demographic noise by 8% (maximal decrease: 81%). We find that the strength of demographic noise is an emergent trait at the population level that can be predicted by colony-level traits but not cell-level traits. The observed differences in demographic noise from single gene deletions can increase the establishment probability of beneficial mutations by almost an order of magnitude (compared to in the wild type). Our results show that single mutations can substantially alter adaptation through their effects on demographic noise and suggest that demographic noise can be an evolvable trait of a population.

Bacterial defenses against a natural antibiotic promote collateral resilience to clinical antibiotics

Bacterial opportunistic human pathogens frequently exhibit intrinsic antibiotic tolerance and resistance, resulting in infections that can be nearly impossible to eradicate. We asked whether this recalcitrance could be driven by these organisms' evolutionary history as environmental microbes that engage in chemical warfare. Using Pseudomonas aeruginosa as a model, we demonstrate that the self-produced antibiotic pyocyanin (PYO) activates defenses that confer collateral tolerance specifically to structurally similar synthetic clinical antibiotics. Non-PYO-producing opportunistic pathogens, such as members of the Burkholderia cepacia complex, likewise display elevated antibiotic tolerance when cocultured with PYO-producing strains. Furthermore, by widening the population bottleneck that occurs during antibiotic selection and promoting the establishment of a more diverse range of mutant lineages, PYO increases apparent rates of mutation to antibiotic resistance to a degree that can rival clinically relevant hypermutator strains. Together, these results reveal an overlooked mechanism by which opportunistic pathogens that produce natural toxins can dramatically modulate the efficacy of clinical antibiotics and the evolution of antibiotic resistance, both for themselves and other members of clinically relevant polymicrobial communities.

Isolation and Characterization of Multidrug-Resistant Escherichia coli and Salmonella spp. from Healthy and Diseased Turkeys

Diseases caused by Escherichia coli (E. coli) and Salmonella spp. can negatively impact turkey farming. The aim of this study was to isolate and characterize multidrug-resistant (MDR) E. coli and Salmonella spp. in healthy and diseased turkeys. A total of 30 fecal samples from healthy turkeys and 25 intestinal samples from diseased turkeys that died of enteritis were collected. Bacterial isolation and identification were based on biochemical properties and polymerase chain reaction (PCR). Antibiogram profiles were determined by disk diffusion. The tetracycline-resistance gene tetA was detected by PCR. All samples were positive for E. coli. Only 11 samples (11/30; 36.67%) were positive for Salmonella spp. from healthy turkeys, whereas 16 (16/25; 64%) samples were positive for Salmonella spp. from diseased turkeys. E. coli isolated from diseased turkeys showed higher resistance to levofloxacin, gentamicin, chloramphenicol, ciprofloxacin, streptomycin, and tetracycline. Salmonella spp. isolated from healthy turkeys exhibited higher resistance to gentamicin, chloramphenicol, ciprofloxacin, streptomycin, imipenem, and meropenem. All E. coli and Salmonella spp. from both healthy and diseased turkeys were resistant to erythromycin. Salmonella spp. from both healthy and diseased turkeys were resistant to tetracycline. Multidrug resistance was observed in both E. coli and Salmonella spp. from diseased turkeys. Finally, the tetA gene was detected in 93.1% of the E. coli isolates and in 92.59% of the Salmonella spp. isolates. To the best of our knowledge, this is the first study to isolate and characterize tetA-gene-containing MDR E. coli and Salmonella spp. from healthy and diseased turkeys in Bangladesh. Both microorganisms are of zoonotic significance and represent a significant public health challenge.

Efflux pump activity potentiates the evolution of antibiotic resistance across S. aureus isolates

The rise of antibiotic resistance in many bacterial pathogens has been driven by the spread of a few successful strains, suggesting that some bacteria are genetically pre-disposed to evolving resistance. Here, we test this hypothesis by challenging a diverse set of 222 isolates of Staphylococcus aureus with the antibiotic ciprofloxacin in a large-scale evolution experiment. We find that a single efflux pump, norA, causes widespread variation in evolvability across isolates. Elevated norA expression potentiates evolution by increasing the fitness benefit provided by DNA topoisomerase mutations under ciprofloxacin treatment. Amplification of norA provides a further mechanism of rapid evolution in isolates from the CC398 lineage. Crucially, chemical inhibition of NorA effectively prevents the evolution of resistance in all isolates. Our study shows that pre-existing genetic diversity plays a key role in shaping resistance evolution, and it may be possible to predict which strains are likely to evolve resistance and to optimize inhibitor use to prevent this outcome.