On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient

Mutant fate in spatially structured populations on graphs: Connecting models to experiments

In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures on mutant fixation. However, the assumptions of evolutionary graph theory differ from the conditions of modern evolution experiments, making the comparison between theory and experiment challenging. Here, we aim to bridge this gap by using our new model of spatially structured populations. This model considers connected subpopulations that lie on the nodes of a graph, and allows asymmetric migrations. It can handle large populations, and explicitly models serial passage events with migrations, thus closely mimicking experimental conditions. We analyze recent experiments in light of this model. We suggest useful parameter regimes for future experiments, and we make quantitative predictions for these experiments. In particular, we propose experiments to directly test our recent prediction that the star graph with asymmetric migrations suppresses natural selection and can accelerate mutant fixation or extinction, compared to a well-mixed population.

A 'rich-get-richer' mechanism drives patchy dynamics and resistance evolution in antibiotic-treated bacteria

Bacteria in nature often form surface-attached communities that initially comprise distinct subpopulations, or patches. For pathogens, these patches can form at infection sites, persist during antibiotic treatment, and develop into mature biofilms. Evidence suggests that patches can emerge due to heterogeneity in the growth environment and bacterial seeding, as well as cell-cell signaling. However, it is unclear how these factors contribute to patch formation and how patch formation might affect bacterial survival and evolution. Here, we demonstrate that a 'rich-get-richer' mechanism drives patch formation in bacteria exhibiting collective survival (CS) during antibiotic treatment. Modeling predicts that the seeding heterogeneity of these bacteria is amplified by local CS and global resource competition, leading to patch formation. Increasing the dose of a non-eradicating antibiotic treatment increases the degree of patchiness. Experimentally, we first demonstrated the mechanism using engineered Escherichia coli and then demonstrated its applicability to a pathogen, Pseudomonas aeruginosa. We further showed that the formation of P. aeruginosa patches promoted the evolution of antibiotic resistance. Our work provides new insights into population dynamics and resistance evolution during surface-attached bacterial growth.

Microfluidic Ecology Unravels the Genetic and Ecological Drivers of T4r Bacteriophage Resistance in E. coli: Insights into Biofilm-Mediated Evolution

We use a microfluidic ecology which generates non-uniform phage concentration gradients and micro-ecological niches to reveal the importance of time, spatial population structure and collective population dynamics in the de novo evolution of T4r bacteriophage resistant motile E. coli. An insensitive bacterial population against T4r phage occurs within 20 hours in small interconnected population niches created by a gradient of phage virions, driven by evolution in transient biofilm patches. Sequencing of the resistant bacteria reveals mutations at the receptor site of bacteriophage T4r as expected but also in genes associated with biofilm formation and surface adhesion, supporting the hypothesis that evolution within transient biofilms drives de novo phage resistance.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

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.

Microfluidics for adaptation of microorganisms to stress: design and application

Microfluidic systems have fundamentally transformed the realm of adaptive laboratory evolution (ALE) for microorganisms by offering unparalleled control over environmental conditions, thereby optimizing mutant generation and desired trait selection. This review summarizes the substantial influence of microfluidic technologies and their design paradigms on microbial adaptation, with a primary focus on leveraging spatial stressor concentration gradients to enhance microbial growth in challenging environments. Specifically, microfluidic platforms tailored for scaled-down ALE processes not only enable highly autonomous and precise setups but also incorporate novel functionalities. These capabilities encompass fostering the growth of biofilms alongside planktonic cells, refining selection gradient profiles, and simulating adaptation dynamics akin to natural habitats. The integration of these aspects enables shaping phenotypes under pressure, presenting an unprecedented avenue for developing robust, stress-resistant strains, a feat not easily attainable using conventional ALE setups. The versatility of these microfluidic systems is not limited to fundamental research but also offers promising applications in various areas of stress resistance. As microfluidic technologies continue to evolve and merge with cutting-edge methodologies, they possess the potential not only to redefine the landscape of microbial adaptation studies but also to expedite advancements in various biotechnological areas. KEY POINTS: • Microfluidics enable precise microbial adaptation in controlled gradients. • Microfluidic ALE offers insights into stress resistance and distinguishes between resistance and persistence. • Integration of adaptation-influencing factors in microfluidic setups facilitates efficient generation of stress-resistant strains.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

Assisted percolation of slow-spreading mutants in heterogeneous environments

Environmental heterogeneity can drive genetic heterogeneity in expanding populations; mutant strains may emerge that trade overall growth rate for an improved ability to survive in patches that are hostile to the wild type. This evolutionary dynamic is of practical importance when seeking to prevent the emergence of damaging traits. We show that a subcritical slow-spreading mutant can attain dominance even when the density of patches is below their percolation threshold and predict this transition using geometrical arguments. This work demonstrates a phenomenon of "assisted percolation", where one subcritical process assists another to achieve supercriticality.

Bacterial motility can govern the dynamics of antibiotic resistance evolution

Spatial heterogeneity in antibiotic concentrations is thought to accelerate the evolution of antibiotic resistance, but current theory and experiments have overlooked the effect of cell motility on bacterial adaptation. Here, we study bacterial evolution in antibiotic landscapes with a quantitative model where bacteria evolve under the stochastic processes of proliferation, death, mutation and migration. Numerical and analytical results show that cell motility can both accelerate and decelerate bacterial adaptation by affecting the degree of genotypic mixing and ecological competition. Moreover, we find that for sufficiently high rates, cell motility can limit bacterial survival, and we derive conditions for all these regimes. Similar patterns are observed in more complex scenarios, namely where bacteria can bias their motion in chemical gradients (chemotaxis) or switch between motility phenotypes either stochastically or in a density-dependent manner. Overall, our work reveals limits to bacterial adaptation in antibiotic landscapes that are set by cell motility.

Multiple phase transitions shape biodiversity of a migrating population

In a wide variety of natural systems, closely related microbial strains coexist stably, resulting in high levels of fine-scale biodiversity. However, the mechanisms that stabilize this coexistence are not fully understood. Spatial heterogeneity is one common stabilizing mechanism, but the rate at which organisms disperse throughout the heterogeneous environment may strongly impact the stabilizing effect that heterogeneity can provide. An intriguing example is the gut microbiome, where active mechanisms affect the movement of microbes and potentially maintain diversity. We investigate how biodiversity is affected by migration rate using a simple evolutionary model with heterogeneous selection pressure. We find that the biodiversity-migration rate relationship is shaped by multiple phase transitions, including a reentrant phase transition to coexistence. At each transition, an ecotype goes extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the statistics of fluctuations due to demographic noise-this may provide an experimental means for detecting and altering impending extinction.

A coevolution experiment between Flavobacterium johnsoniae and Burkholderia thailandensis reveals parallel mutations that reduce antibiotic susceptibility

One interference mechanism of bacterial competition is the production of antibiotics. Bacteria exposed to antibiotics can resist antibiotic inhibition through intrinsic or acquired mechanisms. Here, we performed a coevolution experiment to understand the long-term consequences of antibiotic production and antibiotic susceptibility for two environmental bacterial strains. We grew five independent lines of the antibiotic-producing environmental strain, Burkholderia thailandensis E264, and the antibiotic-inhibited environmental strain, Flavobacterium johnsoniae UW101, together and separately on agar plates for 7.5 months (1.5 month incubations), transferring each line five times to new agar plates. We observed that the F. johnsoniae ancestor could tolerate the B. thailandensis-produced antibiotic through efflux mechanisms, but that the coevolved lines had reduced susceptibility. We then sequenced genomes from the coevolved and monoculture F. johnsoniae lines, and uncovered mutational ramifications for the long-term antibiotic exposure. The coevolved genomes from F. johnsoniae revealed four potential mutational signatures of reduced antibiotic susceptibility that were not observed in the evolved monoculture lines. Two mutations were found in tolC: one corresponding to a 33 bp deletion and the other corresponding to a nonsynonymous mutation. A third mutation was observed as a 1 bp insertion coding for a RagB/SusD nutrient uptake protein. The last mutation was a G83R nonsynonymous mutation in acetyl-coA carboxylayse carboxyltransferase subunit alpha (AccA). Deleting the 33 bp from tolC in the F. johnsoniae ancestor reduced antibiotic susceptibility, but not to the degree observed in coevolved lines. Furthermore, the accA mutation matched a previously described mutation conferring resistance to B. thailandensis-produced thailandamide. Analysis of B. thailandensis transposon mutants for thailandamide production revealed that thailandamide was bioactive against F. johnsoniae, but also suggested that additional B. thailandensis-produced antibiotics were involved in the inhibition of F. johnsoniae. This study reveals how multi-generational interspecies interactions, mediated through chemical exchange, can result in novel interaction-specific mutations, some of which may contribute to reductions in antibiotic susceptibility.

Antibiotic Cycling Affects Resistance Evolution Independently of Collateral Sensitivity

Antibiotic cycling has been proposed as a promising approach to slow down resistance evolution against currently employed antibiotics. It remains unclear, however, to which extent the decreased resistance evolution is the result of collateral sensitivity, an evolutionary trade-off where resistance to one antibiotic enhances the sensitivity to the second, or due to additional effects of the evolved genetic background, in which mutations accumulated during treatment with a first antibiotic alter the emergence and spread of resistance against a second antibiotic via other mechanisms. Also, the influence of antibiotic exposure patterns on the outcome of drug cycling is unknown. Here, we systematically assessed the effects of the evolved genetic background by focusing on the first switch between two antibiotics against Salmonella Typhimurium, with cefotaxime fixed as the first and a broad variety of other drugs as the second antibiotic. By normalizing the antibiotic concentrations to eliminate the effects of collateral sensitivity, we demonstrated a clear contribution of the evolved genetic background beyond collateral sensitivity, which either enhanced or reduced the adaptive potential depending on the specific drug combination. We further demonstrated that the gradient strength with which cefotaxime was applied affected both cefotaxime resistance evolution and adaptation to second antibiotics, an effect that was associated with higher levels of clonal interference and reduced cost of resistance in populations evolved under weaker cefotaxime gradients. Overall, our work highlights that drug cycling can affect resistance evolution independently of collateral sensitivity, in a manner that is contingent on the antibiotic exposure pattern.

Phenotypic convergence of bacterial adaption to sub-lethal antibiotic treatment

Microorganisms can adapt quickly to changes in their environment, leading to various phenotypes. The dynamic for phenotypic plasticity caused by environmental variations has not yet been fully investigated. In this study, we analyzed the time-series of phenotypic changes in Staphylococcus cells during adaptive process to antibiotics stresses using flow cytometry and Raman spectroscopy. The nine antibiotics with four different mode of actions were treated in bacterial cells at a sub-lethal concentration to give adaptable stress. Although the growth rate initially varied depending on the type of antibiotic, most samples reached the maximum growth comparable to the control through the short-term adaptation after 24 h. The phenotypic diversity, which showed remarkable changes depending on antibiotic treatment, converged identical to the control over time. In addition, the phenotype with cellular biomolecules converted into a bacterial cell that enhance tolerance to antibiotic stress with increases in cytochrome and lipid. Our findings demonstrated that the convergence into the phenotypes that enhance antibiotic tolerance in a short period when treated with sub-lethal concentrations, and highlight the feasibility of phenotypic approaches in the advanced antibiotic treatment.

The risk of drug resistance during long-acting antimicrobial therapy

The emergence of drug resistance during antimicrobial therapy is a major global health problem, especially for chronic infections like human immunodeficiency virus, hepatitis B and C, and tuberculosis. Sub-optimal adherence to long-term treatment is an important contributor to resistance risk. New long-acting drugs are being developed for weekly, monthly or less frequent dosing to improve adherence, but may lead to long-term exposure to intermediate drug levels. In this study, we analyse the effect of dosing frequency on the risk of resistance evolving during time-varying drug levels. We find that long-acting therapies can increase, decrease or have little effect on resistance, depending on the source (pre-existing or de novo) and degree of resistance, and rates of drug absorption and clearance. Long-acting therapies with rapid drug absorption, slow clearance and strong wild-type inhibition tend to reduce resistance caused by partially resistant strains in the early stages of treatment even if they do not improve adherence. However, if subpopulations of microbes persist and can reactivate during sub-optimal treatment, longer-acting therapies may substantially increase the resistance risk. Our results show that drug kinetics affect selection for resistance in a complicated manner, and that pathogen-specific models are needed to evaluate the benefits of new long-acting therapies.

Programmable self-regulated molecular buffers for precise sustained drug delivery

Unlike artificial nanosystems, biological systems are ideally engineered to respond to their environment. As such, natural molecular buffers ensure precise and quantitative delivery of specific molecules through self-regulated mechanisms based on Le Chatelier's principle. Here, we apply this principle to design self-regulated nucleic acid molecular buffers for the chemotherapeutic drug doxorubicin and the antimalarial agent quinine. We show that these aptamer-based buffers can be programmed to maintain any specific desired concentration of free drug both in vitro and in vivo and enable the optimization of the chemical stability, partition coefficient, pharmacokinetics and biodistribution of the drug. These programmable buffers can be built from any polymer and should improve patient therapeutic outcome by enhancing drug activity and minimizing adverse effects and dosage frequency.

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.

The gossip paradox: Why do bacteria share genes?

Bacteria, in contrast to eukaryotic cells, contain two types of genes: chromosomal genes that are fixed to the cell, and plasmids, smaller loops of DNA capable of being passed from one cell to another. The sharing of plasmid genes between individual bacteria and between bacterial lineages has contributed vastly to bacterial evolution, allowing specialized traits to 'jump ship' between one lineage or species and the next. The benefits of this generosity from the point of view of both recipient cell and plasmid are generally understood: plasmids receive new hosts and ride out selective sweeps across the population, recipient cells gain new traits (such as antibiotic resistance). Explaining this behavior from the point of view of donor cells is substantially more difficult. Donor cells pay a fitness cost in order to share plasmids, and run the risk of sharing advantageous genes with their competition and rendering their own lineage redundant, while seemingly receiving no benefit in return. Using both compartment based models and agent based simulations we demonstrate that 'secretive' genes which restrict horizontal gene transfer are favored over a wide range of models and parameter values, even when sharing carries no direct cost. 'Generous' chromosomal genes which are more permissive of plasmid transfer are found to have neutral fitness at best, and are generally disfavored by selection. Our findings lead to a peculiar paradox: given the obvious benefits of keeping secrets, why do bacteria share information so freely?

Emergence of Resistant Escherichia coli Mutants in Microfluidic On-Chip Antibiotic Gradients

Spatiotemporal structures and heterogeneities are common in natural habitats, yet their role in the evolution of antibiotic resistance is still to be uncovered. We applied a microfluidic gradient generator device to study the emergence of resistant bacteria in spatial ciprofloxacin gradients. We observed biofilm formation in regions with sub-inhibitory concentrations of antibiotics, which quickly expanded into the high antibiotic regions. In the absence of an explicit structure of the habitat, this multicellular formation led to a spatial structure of the population with local competition and limited migration. Therefore, such structures can function as amplifiers of selection and aid the spread of beneficial mutations. We found that the physical environment itself induces stress-related mutations that later prove beneficial when cells are exposed to antibiotics. This shift in function suggests that exaptation occurs in such experimental scenarios. The above two processes pave the way for the subsequent emergence of highly resistant specific mutations.

Biosynthetic Microcin J25 Exerts Strong Antibacterial, Anti-Inflammatory Activities, Low Cytotoxicity Without Increasing Drug-Resistance to Bacteria Target

Multidrug resistant (MDR) bacterial infection has emerged, raising concerns about untreatable infections, and posing the highest health risks. Antimicrobial peptides (AMPs) are thought to be the best remedy for this problem. Here, we showed biosynthetic microcin J25 (MccJ25) exhibited excellent bactericidal activity against standard and clinically relevant veterinary MDR strains with high stability, no cytotoxicity, and no increase in drug resistance. Analysis of antimicrobial mechanism possessed by sensitive enterotoxigenic Escherichia coli (ETEC) based on electron microscopy and Sytox Green methods was carried out. Results showed excellent activity against ETEC was due to permeabilizing bacterial membranes and strong affinity. MccJ25 exhibited high endotoxin-neutralizing activity in both in vivo and in vitro environments, and mice exposed to lipopolysaccharide (LPS) showed decreased plasma LPS levels and improved survival after administration of MccJ25. In an LPS-treated mouse septicemia model, MccJ25 treatment significantly alleviated inflammatory responses by inhibiting proinflammatory factor secretion and expression. In a mouse E. coli infection model, administration of MccJ25 effectively improved host defense against clinically source cocktail of multidrug-resistant E. coli strains induced intestinal inflammation and bacteria dissemination. Results of studies on anti-inflammatory mechanisms showed that MccJ25 downregulated nuclear factor kappa B kinase and mitogen-activated protein kinase, thereby reducing the production of toll-like receptor 4, myeloid differentiation factor 88 and decreasing the key proinflammatory cytokines. These findings clarify MccJ25 may be an ideal antibacterial/antiendotoxic drug candidate that has the potential to further guide the development of anti-inflammatory and/or antimicrobial agents in the war against MDR bacterial infection.

Gradients and consequences of heterogeneity in biofilms

Historically, appreciation for the roles of resource gradients in biology has fluctuated inversely to the popularity of genetic mechanisms. Nevertheless, in microbiology specifically, widespread recognition of the multicellular lifestyle has recently brought new emphasis to the importance of resource gradients. Most microorganisms grow in assemblages such as biofilms or spatially constrained communities with gradients that influence, and are influenced by, metabolism. In this Review, we discuss examples of gradient formation and physiological differentiation in microbial assemblages growing in diverse settings. We highlight consequences of physiological heterogeneity in microbial assemblages, including division of labour and increased resistance to stress. Our impressions of microbial behaviour in various ecosystems are not complete without complementary maps of the chemical and physical geographies that influence cellular activities. A holistic view, incorporating these geographies and the genetically encoded functions that operate within them, will be essential for understanding microbial assemblages in their many roles and potential applications.

Hydrodynamic flow and concentration gradients in the gut enhance neutral bacterial diversity

The gut microbiota features important genetic diversity, and the specific spatial features of the gut may shape evolution within this environment. We investigate the fixation probability of neutral bacterial mutants within a minimal model of the gut that includes hydrodynamic flow and resulting gradients of food and bacterial concentrations. We find that this fixation probability is substantially increased, compared with an equivalent well-mixed system, in the regime where the profiles of food and bacterial concentration are strongly spatially dependent. Fixation probability then becomes independent of total population size. We show that our results can be rationalized by introducing an active population, which consists of those bacteria that are actively consuming food and dividing. The active population size yields an effective population size for neutral mutant fixation probability in the gut.

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.

Probiotics as Therapeutic Tools against Pathogenic Biofilms: Have We Found the Perfect Weapon?

Bacterial populations inhabiting a variety of natural and human-associated niches have the ability to grow in the form of biofilms. A large part of pathological chronic conditions, and essentially all the bacterial infections associated with implanted medical devices or prosthetics, are caused by microorganisms embedded in a matrix made of polysaccharides, proteins, and nucleic acids. Biofilm infections are generally characterized by a slow onset, mild symptoms, tendency to chronicity, and refractory response to antibiotic therapy. Even though the molecular mechanisms responsible for resistance to antimicrobial agents and host defenses have been deeply clarified, effective means to fight biofilms are still required. Lactic acid bacteria (LAB), used as probiotics, are emerging as powerful weapons to prevent adhesion, biofilm formation, and control overgrowth of pathogens. Hence, using probiotics or their metabolites to quench and interrupt bacterial communication and aggregation, and to interfere with biofilm formation and stability, might represent a new frontier in clinical microbiology and a valid alternative to antibiotic therapies. This review summarizes the current knowledge on the experimental and therapeutic applications of LAB to interfere with biofilm formation or disrupt the stability of pathogenic biofilms.

Generation of flow and droplets with an ultra-long-range linear concentration gradient

In the chemical and biological fields, the creation of concentration gradient microenvironments is an important approach for many applications, such as crystal growth and drug screening. Although many concentration gradient generators have been demonstrated, current generators can hardly produce ultra-long linear concentration gradients. In this paper, we propose a concentration-gradient flow/droplet generator which consists of a microfluidic flow switch, a cavity array for stage-by-stage concentration dilution, and an optional T-junction for droplet formation. The generator can realize an ultra-long continuously-varying concentration gradient along the flow direction. Generation of a 38 mm concentration gradient was demonstrated. The length can be further extended by enlarging the capacity of the cavities and increasing the number of the stages. The concentration gradient showed high linearity in the range of 10% to 90%. Moreover, cyclic generation of a concentration gradient flow and droplets with different concentrations was realized by the generator. In a demonstration of drug screening, the generator was employed to produce paclitaxel in different concentrations. A negative correlation between the 4T1 cell viability and the paclitaxel concentration was observed after the treatment. We envision that the concentration gradient generator will be a promising candidate for various drug screening applications.

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

The role of chemotaxis and efflux pumps on nitrate reduction in the toxic regions of a ciprofloxacin concentration gradient

Spatial concentration gradients of antibiotics are prevalent in the natural environment. Yet, the microbial response in these heterogeneous systems remains poorly understood. We used a microfluidic reactor to create an artificial microscopic ecosystem that generates diffusive gradients of solutes across interconnected microenvironments. With this reactor, we showed that chemotaxis toward a soluble electron acceptor (nitrate) allowed Shewanella oneidensis MR-1 to inhabit and sustain metabolic activity in highly toxic regions of the antibiotic ciprofloxacin (>80× minimum inhibitory concentration, MIC). Acquired antibiotic resistance was not observed for cells extracted from the reactor, so we explored the role of transient adaptive resistance by probing multidrug resistance (MDR) efflux pumps, ancient elements that are important for bacterial physiology and virulence. Accordingly, we constructed an efflux pump deficient mutant (∆mexF) and used resistance-nodulation-division (RND) efflux pump inhibitors (EPIs). While batch results showed the importance of RND efflux pumps for microbial survival, microfluidic studies indicated that these pumps were not necessary for survival in antibiotic gradients. Our work contributes to an emerging body of knowledge deciphering the effects of antibiotic spatial heterogeneity on microorganisms and highlights differences of microbial response in these systems versus well-mixed batch conditions.

Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity

Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

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

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

Microfluidic Evolution-On-A-Chip Reveals New Mutations that Cause Antibiotic Resistance

Microfluidic devices can mimic naturally occurring microenvironments and create microbial population heterogeneities ranging from planktonic cells to biofilm states. The exposure of such populations to spatially organized stress gradients can promote their adaptation into complex phenotypes, which are otherwise difficult to achieve with conventional experimental setups. Here a microfluidic chip that employs precise chemical gradients in consecutive microcompartments to perform microbial adaptive laboratory evolution (ALE), a key tool to study evolution in fundamental and applied contexts is described. In the chip developed here, microbial cells can be exposed to a defined profile of stressors such as antibiotics. By modulating this profile, stress adaptation in the chip through resistance or persistence can be specifically controlled. Importantly, chip-based ALE leads to the discovery of previously unknown mutations in Escherichia coli that confer resistance to nalidixic acid. The microfluidic device presented here can enhance the occurrence of mutations employing defined micro-environmental conditions to generate data to better understand the parameters that influence the mechanisms of antibiotic resistance.

A novel nanohybrid antimicrobial based on chitosan nanoparticles and antimicrobial peptide microcin J25 with low toxicity

Bacterial resistance to antibiotics is a critical public health concern. Alternatives of antibiotics are needed urgently. Herein, we designed and engineered a new nano-antimicrobial, chitosan nanoparticles (CNs)-antimicrobial peptide microcin J25 (MccJ25) conjugates (CNMs). The engineered CNMs proved to be highly active against Gram-negative and Gram-positive bacteria, and the activity of CNMs and CNs was stable in various thermal and pH environments. Escherichia coli K88 strain treated with CNMs did not acquire resistance in serial passage assays over a period of 18 days. Risk assessment with cell lines showed that CNMs did not cause toxicity. Additionally, CNMs did not reduce the lifespan of C. elegans. In summary, this study demonstrated that CNMs can serve as an excellent novel antimicrobial agent against multi-drug resistance pathogens.

Evolution in alternating environments with tunable interlandscape correlations

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, although understanding the effects of varying selection pressures remains challenging. Here, we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the interlandscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anticorrelated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anticorrelated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.

The Moran process on 2-chromatic graphs

Resources are rarely distributed uniformly within a population. Heterogeneity in the concentration of a drug, the quality of breeding sites, or wealth can all affect evolutionary dynamics. In this study, we represent a collection of properties affecting the fitness at a given location using a color. A green node is rich in resources while a red node is poorer. More colors can represent a broader spectrum of resource qualities. For a population evolving according to the birth-death Moran model, the first question we address is which structures, identified by graph connectivity and graph coloring, are evolutionarily equivalent. We prove that all properly two-colored, undirected, regular graphs are evolutionarily equivalent (where “properly colored” means that no two neighbors have the same color). We then compare the effects of background heterogeneity on properly two-colored graphs to those with alternative schemes in which the colors are permuted. Finally, we discuss dynamic coloring as a model for spatiotemporal resource fluctuations, and we illustrate that random dynamic colorings often diminish the effects of background heterogeneity relative to a proper two-coloring.

Heterogeneity in environmental conditions can have profound effects on long-term evolutionary outcomes in structured populations. We consider a population evolving on a colored graph, wherein the color of a node represents the resources at that location. Using a combination of analytical and numerical methods, we quantify the effects of background heterogeneity on a population’s dynamics. In addition to considering the notion of an “optimal” coloring with respect to mutant invasion, we also study the effects of dynamic spatial redistribution of resources as the population evolves. Although the effects of static background heterogeneity can be quite striking, these effects are often attenuated by the movement (or “flow”) of the underlying resources.

Adapt or Perish: Evolutionary Rescue in a Gradually Deteriorating Environment

We investigate the evolutionary rescue of a microbial population in a gradually deteriorating environment, through a combination of analytical calculations and stochastic simulations. We consider a population destined for extinction in the absence of mutants, which can survive only if mutants sufficiently adapted to the new environment arise and fix. We show that mutants that appear later during the environment deterioration have a higher probability to fix. The rescue probability of the population increases with a sigmoidal shape when the product of the carrying capacity and of the mutation probability increases. Furthermore, we find that rescue becomes more likely for smaller population sizes and/or mutation probabilities if the environment degradation is slower, which illustrates the key impact of the rapidity of environment degradation on the fate of a population. We also show that our main conclusions are robust across various types of adaptive mutants, including specialist and generalist ones, as well as mutants modeling antimicrobial resistance evolution. We further express the average time of appearance of the mutants that do rescue the population and the average extinction time of those that do not. Our methods can be applied to other situations with continuously variable fitnesses and population sizes, and our analytical predictions are valid in the weak-to-moderate mutation regime.

Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

Antibiotic Resistance in Trinidad: A population-based survey of adults

Objectives: Antibiotic resistance (ABR) is a growing public health issue globally. This study aimed to ascertain the public’s knowledge of antibiotic resistance and to determine any associated demographic factors. Method: A cross-sectional survey was undertaken in Trinidad using a modified version of a World Health Organization questionnaire. Five hundred and fifty participants aged 16 years and older were interviewed. Data were analysed using SPSS Version-22. Results: The response rate was 91% (502/550). Most responders (53%) reported having taken antibiotics during the 6-month period prior to the survey. Almost 60% of responders believed that sore throat and colds/flu can be treated with antibiotics. Sixty-seven percent of responders believed that they had no individual role in combating the issue. Responders with a primary level education or older respondents were more likely than responders with a higher level of education or younger to agree that antibiotic resistance only affects those who use antibiotics frequently (p= 0.002 and p=0.017 respectively). Income level was also significantly associated with knowledge levels, the higher the income the more knowledgeable the responder. Conclusion: Our study found that public knowledge of antibiotic resistance is low. Age, income and educational levels were significantly associated with certain knowledge parameters. Much work needs to be done to change the attitudes of responders who believe they have no role to play in the fight against this national and global threat. These findings may be useful to health education professionals and government antimicrobial resistance programmes.

Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics

Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole-genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multidrug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and nonantibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and nondrug environments can be used to sensitize bacteria to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.

Role of a Concentration Gradient in Malaria Drug Resistance Evolution: A Combined within- and between-Hosts Modelling Approach

Resistance to antimalarial drugs is currently a growing public health problem, resulting in more cases with treatment failure. Although previous studies suggested that a concentration gradient facilitates the antibiotic resistance evolution in bacteria, no attempt has been made to investigate the roles of a concentration gradient in malaria drug resistance. Unlike the person-to-person mode of transmission of bacteria, the malaria parasites need to switch back and forth between the human and mosquito hosts to complete the life cycle and to spread the resistant alleles. Here we developed a stochastic combined within- and between-hosts evolutionary dynamics model specific to malaria parasites in order to investigate the influence of an antimalarial concentration gradient on the evolutionary dynamics of malaria drug resistance. Every stage of malaria development in both human and mosquito hosts are individually modelled using the tau-leaping algorithm. We found that the concentration gradient can accelerate antimalarial resistance evolution. The gain in resistance evolution was improved by the increase in the parasite mutation rate and the mosquito biting rate. In addition, even though the rate of resistance evolution is not sensitive to the changes in parasite reduction ratios (PRRs) of antimalarial drugs, the probability of finding the antimalarial drug resistant parasites decreases when the PRR increases.

Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial

The evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action.

Antibacterial Effect of Thymol Loaded SBA-15 Nanorods Incorporated in PCL Electrospun Fibers

For the effective management of infected chronic wounds, the incorporation of antimicrobial drugs into wound dressings can increase their local availability at the infection site. Mesoporous silicon dioxide SBA-15 is an excellent drug carrier with tunable drug release kinetics. In this work, synthesized SBA-15 loaded with the natural antimicrobial compound thymol (THY) was incorporated into polycaprolactone (PCL) electrospun nanofibers to obtain an advanced wound dressing. Rod-shaped particles with internal parallel channels oriented along the longitudinal axis (diameter: 138 ± 30 nm, length: 563 ± 100 nm) were loaded with 70.8 wt.% of THY. Fiber mats were prepared using these particles as nanofillers within polycaprolactone (PCL) electrospun fibers. The resulting mats contained 5.6 wt.% of THY and more than half of this loading was released in the first 7 h. This release would prevent an initial bacterial colonization and also inhibit or eliminate bacterial growth as in vitro shown against Staphylococcus aureus ATCC 25923. Minimal inhibitory concentration (MIC: 0.07 mg/mL) and minimal bactericidal concentration (MBC: 0.11 mg/mL) of released THY were lower than the amount of free THY required, demonstrating the benefit of drug encapsulation for a more efficient bactericidal capacity due to the direct contact between mats and bacteria.

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

The molecular underpinnings of antibiotic resistance are increasingly understood, but less is known about how these molecular events influence microbial dynamics on the population scale. Here, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where increasing population size or delaying drug exposure can promote population collapse. Specifically, we demonstrate how density-dependent feedback loops couple population growth and antibiotic efficacy when communities include drug-resistant subpopulations, leading to a wide range of behavior, including population survival, collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Finally, we demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx collapse.

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and drug resistance profiles. For example, selection in a dual β-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and β-lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations are nearly growth-optimized along a contour connecting profiles selected by the component drugs. Our results highlight trade-offs between drug interactions and resistance profiles during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

The effect of spatiotemporal antibiotic inhomogeneities on the evolution of resistance

Combating the evolution of widespread antibiotic resistance is one of the most pressing challenges facing modern medicine. Recent research has demonstrated that the evolution of pathogens with high levels of resistance can be accelerated by spatial and temporal inhomogeneities in antibiotic concentration, which frequently arise in patients and the environment. Strategies to predict and counteract the effects of such inhomogeneities will be critical in the fight against resistance. In this paper we develop a mechanistic framework for modelling the adaptive evolution of resistance in the presence of spatiotemporal antibiotic concentrations, which treats the adaptive process as an interaction between two mutually orthogonal forces; the first returns cells to their wild-type state in the absence of antibiotic selection, and the second selects for increased coping ability in the presence of an antibiotic. We apply our model to investigate laboratory adaptation experiments, and then extend it to consider the case in which multiple strategies for resistance undergo competitive evolution.

Diffusion-driven enhancement of the antibiotic resistance selection window

The current crisis of antimicrobial resistance in clinically relevant pathogens has highlighted our limited understanding of the ecological and evolutionary forces that drive drug resistance adaptation. For instance, although human tissues are highly heterogeneous, most of our mechanistic understanding about antibiotic resistance evolution is based on constant and well-mixed environmental conditions. A consequence of considering spatial heterogeneity is that, even if antibiotics are prescribed at high dosages, the penetration of drug molecules through tissues inevitably produces antibiotic gradients, exposing bacterial populations to a range of selective pressures and generating a dynamic fitness landscape that changes in space and time. In this paper, we will use a combination of mathematical modelling and computer simulations to study the population dynamics of susceptible and resistant strains competing for resources in a network of micro-environments with varying degrees of connectivity. Our main result is that highly connected environments increase diffusion of drug molecules, enabling resistant phenotypes to colonize a larger number of spatial locations. We validated this theoretical result by culturing fluorescently labelled Escherichia coli in 3D-printed devices that allow us to control the rate of diffusion of antibiotics between neighbouring compartments and quantify the spatio-temporal distribution of resistant and susceptible bacterial cells.

Growth-dependent drug susceptibility can prevent or enhance spatial expansion of a bacterial population

As a population wave expands, organisms at the tip typically experience plentiful nutrients while those behind the front become nutrient-depleted. If the environment also contains a gradient of some inhibitor (e.g. a toxic drug), a tradeoff exists: the nutrient-rich tip is more exposed to the inhibitor, while the nutrient-starved region behind the front is less exposed. Here we show that this can lead to complex dynamics when the organism's response to the inhibitory substance is coupled to nutrient availability. We model a bacterial population which expands in a spatial gradient of antibiotic, under conditions where either fast-growing bacteria at the wave's tip, or slow-growing, resource-limited bacteria behind the front are more susceptible to the antibiotic. We find that growth-rate dependent susceptibility can have strong effects on the dynamics of the expanding population. If slow-growing bacteria are more susceptible, the population wave advances far into the inhibitory zone, leaving a trail of dead bacteria in its wake. In contrast, if fast-growing bacteria are more susceptible, the wave is blocked at a much lower concentration of antibiotic, but a large population of live bacteria remains behind the front. Our results may contribute to understanding the efficacy of different antimicrobials for spatially structured microbial populations such as biofilms, as well as the dynamics of ecological population expansions more generally.

Competing evolutionary paths in growing populations with applications to multidrug resistance

Investigating the emergence of a particular cell type is a recurring theme in models of growing cellular populations. The evolution of resistance to therapy is a classic example. Common questions are: when does the cell type first occur, and via which sequence of steps is it most likely to emerge? For growing populations, these questions can be formulated in a general framework of branching processes spreading through a graph from a root to a target vertex. Cells have a particular fitness value on each vertex and can transition along edges at specific rates. Vertices represent cell states, say genotypes or physical locations, while possible transitions are acquiring a mutation or cell migration. We focus on the setting where cells at the root vertex have the highest fitness and transition rates are small. Simple formulas are derived for the time to reach the target vertex and for the probability that it is reached along a given path in the graph. We demonstrate our results on several scenarios relevant to the emergence of drug resistance, including: the orderings of resistance-conferring mutations in bacteria and the impact of imperfect drug penetration in cancer.

How long does it take for a treatment naive, growing bacterial colony to be able to survive exposure to a cocktail of antibiotics? En route to multidrug resistance, what order did the drugs become impotent in? Questions such as these that pertain to the emergence of a significant cell type in a growing population arise frequently. They are often investigated via mathematical modelling but biologically insightful results are challenging to obtain. Here we outline a general framework of a stochastically growing population spreading through a graph to study such questions and provide simple formulas as answers. The significant cell type appears upon the population reaching a target vertex. Due to their simplicity, the derived formulas are widely accessible and can be used to guide and develop intuition on a range of biological scenarios. We demonstrate this on several settings including: how a region where drugs cannot penetrate affects the emergence of resistance, and, the ordering of mutations that leads to drugs being ineffective.

Environmental heterogeneity can tip the population genetics of range expansions

The population genetics of most range expansions is thought to be shaped by the competition between Darwinian selection and random genetic drift at the range margins. Here, we show that the evolutionary dynamics during range expansions is highly sensitive to additional fluctuations induced by environmental heterogeneities. Tracking mutant clones with a tunable fitness effect in bacterial colonies grown on randomly patterned surfaces we found that environmental heterogeneity can dramatically reduce the efficacy of selection. Time-lapse microscopy and computer simulations suggest that this effect arises generically from a local 'pinning' of the expansion front, whereby stretches of the front are slowed down on a length scale that depends on the structure of the environmental heterogeneity. This pinning focuses the range expansion into a small number of 'lucky' individuals with access to expansion paths, altering the neutral evolutionary dynamics and increasing the importance of chance relative to selection.

Motility of Shewanella oneidensis MR-1 Allows for Nitrate Reduction in the Toxic Region of a Ciprofloxacin Concentration Gradient in a Microfluidic Reactor

Subsurface environments often contain mixtures of contaminants in which the microbial degradation of one pollutant may be inhibited by the toxicity of another. Agricultural settings exemplify these complex environments, where antimicrobial leachates may inhibit nitrate bioreduction, and are the motivation to address this fundamental ecological response. In this study, a microfluidic reactor was fabricated to create diffusion-controlled concentration gradients of nitrate and ciprofloxacin under anoxic conditions in order to evaluate the ability of Shewanella oneidenisis MR-1 to reduce the former in the presence of the latter. Results show a surprising ecological response, where swimming motility allow S. oneidensis MR-1 to accumulate and maintain metabolic activity for nitrate reduction in regions with toxic ciprofloxacin concentrations (i.e., 50× minimum inhibitory concentration, MIC), despite the lack of observed antibiotic resistance. Controls with limited nutrient flux and a nonmotile mutant (Δ flag) show that cells cannot colonize antibiotic rich microenvironments, and this results in minimal metabolic activity for nitrate reduction. These results demonstrate that under anoxic, nitrate-reducing conditions, motility can control microbial habitability and metabolic activity in spatially heterogeneous toxic environments.

Bacterial growth: a statistical physicist's guide

Bacterial growth presents many beautiful phenomena that pose new theoretical challenges to statistical physicists, and are also amenable to laboratory experimentation. This review provides some of the essential biological background, discusses recent applications of statistical physics in this field, and highlights the potential for future research.