Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections

Spatial population dynamics of bacterial colonies with social antibiotic resistance

Bacteria frequently inhabit surface-attached communities where rich "social" interactions can significantly alter their population-level behavior, including their response to antibiotics. Understanding these collective effects in spatially heterogeneous communities is an ongoing challenge. Here, we investigated the spatial organization that emerges from antibiotic exposure in initially randomly distributed communities containing antibiotic-resistant and -sensitive strains of Enterococcus faecalis, an opportunistic pathogen. We identified that a range of complex spatial structures emerged in the population homeland-the inoculated region that microbes inhabit prior to range expansion-which depended on initial colony composition and antibiotic concentration. We found that these arrangements were explained by cooperative interactions between resistant and sensitive subpopulations with a variable spatial scale, the result of dynamic zones of protection afforded to sensitive cells by growing populations of enzyme-producing resistant neighbors. Using a combination of experiments and mathematical models, we explored the complex spatiotemporal interaction dynamics that create these patterns, and predicted spatial arrangements of sensitive and resistant subpopulations under new conditions. We illustrated how spatial population dynamics in the homeland affect subsequent range expansion, both because they modulate the composition of the initial expanding front, and through long-range cooperation between the homeland and the expanding region. Finally, we showed that these spatial constraints resulted in populations whose size and composition differed markedly from matched populations in well-stirred (planktonic) cultures. These findings underscore the importance of spatial structure and cooperation, long-studied features in theoretical ecology, for determining the fate of bacterial communities under antibiotic exposure.

An eco-evolutionary perspective on antimicrobial resistance in the context of One Health

The One Health approach musters growing concerns about antimicrobial resistance due to the increased use of antibiotics in healthcare and agriculture, with all of its consequences for human, livestock, and environmental health. In this perspective, we explore the current knowledge on how interactions at different levels of biological organization, from genetic to ecological interactions, affect the evolution of antimicrobial resistance. We discuss their role in different contexts, from natural systems with weak selection, to human-influenced environments that impose a strong pressure toward antimicrobial resistance evolution. We emphasize the need for an eco-evolutionary approach within the One Health framework and highlight the importance of horizontal gene transfer and microbiome interactions for increased understanding of the emergence and spread of antimicrobial resistance.

Antibiotic Resistance Dissemination and Mapping in the Environment Through Surveillance of Wastewater

Antibiotic resistance is one of the major health threat for humans, animals, and the environment, according to the World Health Organization (WHO) and the Global Antibiotic-Resistance Surveillance System (GLASS). In the last several years, wastewater/sewage has been identified as potential hotspots for the dissemination of antibiotic resistance and transfer of resistance genes. However, systematic approaches for mapping the antibiotic resistance situation in sewage are limited and underdeveloped. The present review has highlighted all possible perspectives by which the dynamics of ARBs/ARGs in the environment may be tracked, quantified and assessed spatio-temporally through surveillance of wastewater. Moreover, application of advanced methods like wastewater metagenomics for determining the community distribution of resistance at large has appeared to be promising. In addition, monitoring wastewater for antibiotic pollution at various levels, may serve as an early warning system and enable policymakers to take timely measures and build infrastructure to mitigate health crises. Thus, by understanding the alarming presence of antibiotic resistance in wastewater, effective action plans may be developed to address this global health challenge and its associated environmental risks.

Spatial population dynamics of bacterial colonies with social antibiotic resistance

Bacteria frequently inhabit surface-attached communities where rich "social" interactions can significantly alter their population-level behavior, including their response to antibiotics. Understanding these collective effects in spatially heterogeneous communities is an ongoing challenge. Here, we investigated the spatial organization that emerges from antibiotic exposure in initially randomly distributed communities containing antibiotic-resistant and -sensitive strains of E. faecalis , an opportunistic pathogen. We identified that a range of complex spatial structures emerged in the population homeland-the inoculated region that microbes inhabit prior to range expansion-, which depended on initial colony composition and antibiotic concentration. We found that these arrangements were explained by cooperative interactions between resistant and sensitive subpopulations with a variable spatial scale, the result of dynamic zones of protection afforded to sensitive cells by growing populations of enzyme-producing resistant neighbors. Using a combination of experiments and mathematical models, we explored the complex spatiotemporal interaction dynamics that create these patterns, and predicted spatial arrangements of sensitive and resistant subpopulations under new conditions. We illustrated how spatial population dynamics in the homeland affect subsequent range expansion, both because they modulate the composition of the initial expanding front, and through long-range cooperation between the homeland and the expanding region. Finally, we showed that these spatial constraints resulted in populations whose size and composition differed markedly from matched populations in well-stirred (planktonic) cultures. These findings underscore the importance of spatial structure and cooperation, long-studied features in theoretical ecology, for determining the fate of bacterial communities under antibiotic exposure.

Significance

Interactions between bacteria are common, particularly in the crowded surface-associated communities that occur anywhere from natural ecosystems to the human body to medical devices. Antibiotic resistance can be influenced by these "social" interactions, making it difficult to predict how spatial communities respond to antibiotic. Here, we show that complex spatial arrangements emerge when initially randomly distributed populations of antibiotic-resistant and -sensitive E. faecalis , a microbial pathogen, are exposed to antibiotic. Using mathematical models and experiments, we show how local competition and dynamic-range cross-protection drive pattern formation. As a result, these spatially structured populations respond differently to antibiotics than well-mixed communities. Our findings elucidate how "social" antibiotic resistance affects spatially structured bacterial communities, a step towards predicting and controlling resistance.

Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment

Diffusion and migration play pivotal roles in microbial communities - shaping, for example, colonization in new environments and the maintenance of spatial structures of biodiversity. While previous research has extensively studied free diffusion, such as range expansion, there remains a gap in understanding the effects of biologically or physically deleterious confined environments. In this study, we examine the interplay between migration and spatial drug heterogeneity within an experimental meta-community of E. faecalis, a Gram-positive opportunistic pathogen. When the community is confined to spatially-extended habitats ('islands') bordered by deleterious conditions, we find that the population level response depends on the trade-off between the growth rate within the island and the rate of transfer into regions with harsher conditions, a phenomenon we explore by modulating antibiotic concentration within the island. In heterogeneous islands, composed of spatially patterned patches that support varying levels of growth, the population's fate depends critically on the specific spatial arrangement of these patches - the same spatially averaged growth rate leads to diverging responses. These results are qualitatively captured by simple simulations, and analytical expressions which we derive using first-order perturbation approximations to reaction-diffusion models with explicit spatial dependence. Among all possible spatial arrangements, our theoretical and experimental findings reveal that the arrangement with the highest growth rates at the center most effectively mitigates population decline, while the arrangement with the lowest growth rates at the center is the least effective. Extending this approach to more complex experimental communities with varied spatial structures, such as a ring-structured community, further validates the impact of spatial drug arrangement. Our findings suggest new approaches to interpreting diverging clinical outcomes when applying identical drug doses and inform the possible optimization of spatially-explicit dosing strategies.

From Petri Dishes to Patients to Populations: Scales and Evolutionary Mechanisms Driving Antibiotic Resistance

Tackling the challenge created by antibiotic resistance requires understanding the mechanisms behind its evolution. Like any evolutionary process, the evolution of antimicrobial resistance (AMR) is driven by the underlying variation in a bacterial population and the selective pressures acting upon it. Importantly, both selection and variation will depend on the scale at which resistance evolution is considered (from evolution within a single patient to the host population level). While laboratory experiments have generated fundamental insights into the mechanisms underlying antibiotic resistance evolution, the technological advances in whole genome sequencing now allow us to probe antibiotic resistance evolution beyond the lab and directly record it in individual patients and host populations. Here we review the evolutionary forces driving antibiotic resistance at each of these scales, highlight gaps in our current understanding of AMR evolution, and discuss future steps toward evolution-guided interventions.

Differential Selection for Survival and for Growth in Adaptive Laboratory Evolution Experiments With Benzalkonium Chloride

Biocides are used to control microorganisms across different applications, but emerging resistance may pose risks for those applications. Resistance to biocides has commonly been studied using adaptive laboratory evolution (ALE) experiments with growth at subinhibitory concentrations linked to serial subculturing. It has been shown recently that Escherichia coli adapts to repeated lethal stress imposed by the biocide benzalkonium chloride (BAC) by increased survival (i.e., tolerance) and not by evolving the ability to grow at increased concentrations (i.e., resistance). Here, we investigate the contributions of evolution for tolerance as opposed to resistance for the outcome of ALE experiments with E. coli exposed to BAC. We find that BAC concentrations close to the half maximal effective concentration (EC50, 4.36 μg mL-1) show initial killing (~40%) before the population resumes growth. This indicates that cells face a two-fold selection pressure: for increased survival and for increased growth. To disentangle the effects of both selection pressures, we conducted two ALE experiments: (i) one with initial killing and continued stress close to the EC50 during growth and (ii) another with initial killing and no stress during growth. Phenotypic characterization of adapted populations showed that growth at higher BAC concentrations was only selected for when BAC was present during growth. Whole genome sequencing revealed distinct differences in mutated genes across treatments. Treatments selecting for survival-only led to mutations in genes for metabolic regulation (cyaA) and cellular structure (flagella fliJ), while treatments selecting for growth and survival led to mutations in genes related to stress response (hslO and tufA). Our results demonstrate that serial subculture ALE experiments with an antimicrobial at subinhibitory concentrations can select for increased growth and survival. This finding has implications for the design of ALE experiments to assess resistance risks of antimicrobials in different scenarios such as disinfection, preservation, and environmental pollution.

Whole-Genome Sequencing and Phenotypic Analysis of Streptococcus equi subsp. zooepidemicus Sequence Type 147 Isolated from China

Streptococcus equi subsp. zooepidemicus (S. zooepidemicus) is one of the important zoonotic and opportunistic pathogens. In recent years, there has been growing evidence that supports the potential role of S. zooepidemicus in severe diseases in horses and other animals, including humans. Furthermore, the clinical isolation and drug resistance rates of S. zooepidemicus have been increasing yearly, leading to interest in its in-depth genomic analysis. In order to deepen the understanding of the S. zooepidemicus characteristics and genomic features, we investigated the genomic islands, mobile genetic elements, virulence and resistance genes, and phenotype of S. zooepidemicus strain ZHZ 211 (ST147), isolated from an equine farm in China. We obtained a 2.18 Mb, high-quality chromosome and found eight genomic islands. According to a comparative genomic investigation with other reference strains, ZHZ 211 has more virulence factors, like an iron uptake system, adherence, exoenzymes, and antiphagocytosis. More interestingly, ZHZ 211 has acquired a mobile genetic element (MGE), prophage Ph01, which was found to be in the chromosome of this strain and included two hyaluronidase (hyl) genes, important virulence factors of the strain. Moreover, two transposons and two virulence (virD4) genes were found to be located in the same genome island of ZHZ 211. In vitro phenotypic results showed that ZHZ 211 grows faster and is resistant to clarithromycin, enrofloxacin, and sulfonamides. The higher biofilm-forming capabilities of ZHZ 211 may provide a competitive advantage for survival in its niche. The results expand our understanding of the genomic, pathogenicity, and resistance characterization of Streptococcus zooepidemicus and facilitate further exploration of its molecular pathogenic mechanism.

Revealing discrepancies and drivers in the impact of lomefloxacin on groundwater denitrification throughout microbial community growth and succession

The coexistence of antibiotics and nitrates has raised great concern about antibiotic's impact on denitrification. However, conflicting results in these studies are very puzzling, possibly due to differences in microbial succession stages. This study investigated the effects of the high-priority urgent antibiotic, lomefloxacin (LOM), on groundwater denitrification throughout microbial growth and succession. The results demonstrated that LOM's impact on denitrification varied significantly across three successional stages, with the most pronounced effects exhibited in the initial stage (53.8% promotion at 100 ng/L-LOM, 84.6% inhibition at 100 μg/L-LOM), followed by the decline stage (13.3-18.2% inhibition), while no effect in the stable stage. Hence, a distinct pattern encompassing susceptibility, insusceptibility, and sub-susceptibility in LOM's impact on denitrification was discovered. Microbial metabolism and environment variation drove the pattern, with bacterial numbers and antibiotic resistance as primary influencers (22.5% and 15.3%, p < 0.01), followed by carbon metabolism and microbial community (5.0% and 3.68%, p < 0.01). The structural equation model confirmed results reliability. Bacterial numbers and resistance influenced susceptibility by regulating compensation and bacteriostasis, while carbon metabolism and microbial community impacted energy, electron transfer, and gene composition. These findings provide valuable insights into the complex interplay between antibiotics and denitrification patterns in groundwater.

Interaction with lipopolysaccharide is key to efficacy of tryptophan- and arginine-rich α-melanocyte-stimulating hormone analogs against Gram-negative bacteria

Aim: In order to search for novel antibacterial therapeutics against Gram-negative bacteria, the antibacterial efficacies and mechanism of action of tryptophan- and arginine-rich α-melanocyte-stimulating hormone analogs were investigated. Materials & methods: We performed a killing assay to determine their efficacy; fluorescence, microscopic studies were used to understand their mechanism and peptide-lipopolysaccharide interaction. A checkerboard assay was used to find the effective combination of peptide and antibiotics. Results: Ana-peptides displayed good killing activity against Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. Their strong interaction with lipopolysaccharide damaged the bacterial membranes and led to their subsequent death. Ana-5, the highest cationic and hydrophobic analog, emerged as the most potent peptide, showing synergistic action with rifampicin and erythromycin. Conclusion: Ana-5 can be presented as an important therapeutic candidate against bacterial infections.

Comparison of the inoculum effect of in vitro antibacterial activity of Imipenem/relebactam and Ceftazidime/avibactam against ESBL-, KPC- and AmpC-producing Escherichia coli and Klebsiella pneumoniae

OBJECTIVE

To evaluate effect of inoculum size of extended-spectrum β-Lactamase (ESBL)-producing-, AmpC-producing-, and KPC-producing Escherichia coli and Klebsiella pneumoniae on the in vitro antibacterial effects of imipenem/relebactam (IMR) and ceftazidime/avibactam (CZA).

METHODS

We compared the impact of inoculum size on IMR and CZA of sixteen clinical isolates and three standard isolates through antimicrobial susceptibility tests, time-kill assays and in vitro PK/PD studies.

RESULTS

When inoculum size increased from 105 to 107 CFU/mL, an inoculum effect was observed for 26.3% (5/19) and 52.6% (10/19) of IMR and CZA, respectively; time-kill assays revealed that the concentration of CZA increased from ≥ 4 × MIC to 16 × MIC to reach 99.9% killing rate against K. pneumoniae ATCC-BAA 1705 (KPC-2-, OXA-9- and SHV-182-producing) and 60,700 (SHV-27- and DHA-1-producing). While for IMR, a concentration from 1 × MIC to 4 × MIC killed 99.9% of the four strains. When the inoculum size increased to 109 CFU/mL, neither IMR nor CZA showed a detectable antibacterial effect, even at a high concentration. An in vitro PK/PD study revealed a clear bactericidal effect when IMR administered as 1.25 g q6h when inoculum size increased.

CONCLUSION

An inoculum effect on CZA was observed more frequent than that on IMR. Among the β-lactamase-producing strains, the inoculum effect was most common for SHV-producing and KPC-producing strains.

Antibiotic resistance in bacterial communities

Bacteria are single-celled organisms, but the survival of microbial communities relies on complex dynamics at the molecular, cellular, and ecosystem scales. Antibiotic resistance, in particular, is not just a property of individual bacteria or even single-strain populations, but depends heavily on the community context. Collective community dynamics can lead to counterintuitive eco-evolutionary effects like survival of less resistant bacterial populations, slowing of resistance evolution, or population collapse, yet these surprising behaviors are often captured by simple mathematical models. In this review, we highlight recent progress - in many cases, advances driven by elegant combinations of quantitative experiments and theoretical models - in understanding how interactions between bacteria and with the environment affect antibiotic resistance, from single-species populations to multispecies communities embedded in an ecosystem.

Inferring density-dependent population dynamics mechanisms through rate disambiguation for logistic birth-death processes

Density dependence is important in the ecology and evolution of microbial and cancer cells. Typically, we can only measure net growth rates, but the underlying density-dependent mechanisms that give rise to the observed dynamics can manifest in birth processes, death processes, or both. Therefore, we utilize the mean and variance of cell number fluctuations to separately identify birth and death rates from time series that follow stochastic birth-death processes with logistic growth. Our nonparametric method provides a novel perspective on stochastic parameter identifiability, which we validate by analyzing the accuracy in terms of the discretization bin size. We apply our method to the scenario where a homogeneous cell population goes through three stages: (1) grows naturally to its carrying capacity, (2) is treated with a drug that reduces its carrying capacity, and (3) overcomes the drug effect to restore its original carrying capacity. In each stage, we disambiguate whether the dynamics occur through the birth process, death process, or some combination of the two, which contributes to understanding drug resistance mechanisms. In the case of limited sample sizes, we provide an alternative method based on maximum likelihood and solve a constrained nonlinear optimization problem to identify the most likely density dependence parameter for a given cell number time series. Our methods can be applied to other biological systems at different scales to disambiguate density-dependent mechanisms underlying the same net growth rate.

Measuring competitive exclusion in non-small cell lung cancer

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

Neglected resistance risks: Cooperative resistance of antibiotic resistant bacteria influenced by primary soil components

Various antibiotic resistant bacteria (ARB) can thrive in soil and resist such environmental pressures as antibiotics through cooperative resistance, thereby promoting ARB retention and antibiotic resistance genes transmission. However, there has been finite knowledge in regard to the mechanisms and potential ecological risks of cooperative resistance in soil microbiome. In this study, soil minerals and organic matters were designed to treat a mixture of two Escherichia coli strains with different antibiotic resistance (E. coli DH5α/pUC19 and E. coli XL2-Blue) to determine how soil components affected cooperative resistance, and Luria-Bertani plates containing two antibiotics were used to observe dual-drug resistant bacteria (DRB) developed via cooperative resistance. Results showed quartz, humic acid, and biochar promoted E. coli XL2-Blue with high fitness costs, whereas kaolin, montmorillonite, and soot inhibited both strains. Using fluorescence microscope and PCR, it was speculated DRB could resist the antibiotic pressure via E. coli XL2-Blue coating E. coli DH5α/pUC19. E. coli DH5α/pUC19 dominated cooperative resistance. Correlation analysis and scanning electron microscope images indicated soil components influenced cooperative resistance. Biochar promoted cooperative resistance by increasing intracellular reactive oxygen species, thereby reducing the dominant strain concentration required for DRB development. Kaolin inhibited cooperative resistance the most, followed by soot and montmorillonite.

Inter-species interactions alter antibiotic efficacy in bacterial communities

The efficacy of antibiotic treatments targeting polymicrobial communities is not well predicted by conventional in vitro susceptibility testing based on determining minimum inhibitory concentration (MIC) in monocultures. One reason for this is that inter-species interactions can alter the community members' susceptibility to antibiotics. Here we quantify, and identify mechanisms for, community-modulated changes of efficacy for clinically relevant antibiotics against the pathogen Pseudomonas aeruginosa in model cystic fibrosis (CF) lung communities derived from clinical samples. We demonstrate that multi-drug resistant Stenotrophomonas maltophilia can provide high levels of antibiotic protection to otherwise sensitive P. aeruginosa. Exposure protection to imipenem was provided by chromosomally encoded metallo-β-lactamase that detoxified the environment; protection was dependent upon S. maltophilia cell density and was provided by S. maltophilia strains isolated from CF sputum, increasing the MIC of P. aeruginosa by up to 16-fold. In contrast, the presence of S. maltophilia provided no protection against meropenem, another routinely used carbapenem. Mathematical ordinary differential equation modelling shows that the level of exposure protection provided against different carbapenems can be explained by differences in antibiotic efficacy and inactivation rate. Together, these findings reveal that exploitation of pre-occurring antimicrobial resistance, and inter-specific competition, can have large impacts on pathogen antibiotic susceptibility, highlighting the importance of microbial ecology for designing successful antibiotic treatments for multispecies communities.

Cellular Growth Arrest and Efflux Pumps Are Associated With Antibiotic Persisters in Streptococcus pyogenes Induced in Biofilm-Like Environments

Streptococcus pyogenes (group A Streptococcus-GAS) is an important pathogen for humans. GAS has been associated with severe and invasive diseases. Despite the fact that these bacteria remain universally susceptible to penicillin, therapeutic failures have been reported in some GAS infections. Many hypotheses have been proposed to explain these antibiotic-unresponsive infections; however, none of them have fully elucidated this phenomenon. In this study, we show that GAS strains have the ability to form antimicrobial persisters when inoculated on abiotic surfaces to form a film of bacterial agglomerates (biofilm-like environment). Our data suggest that efflux pumps were possibly involved in this phenomenon. In fact, gene expression assays by real-time qRT-PCR showed upregulation of some genes associated with efflux pumps in persisters arising in the presence of penicillin. Phenotypic reversion assay and whole-genome sequencing indicated that this event was due to non-inherited resistance mechanisms. The persister cells showed downregulation of genes associated with protein biosynthesis and cell growth, as demonstrated by gene expression assays. Moreover, the proteomic analysis revealed that susceptible cells express higher levels of ribosome proteins. It is remarkable that previous studies have reported the recovery of S. pyogenes viable cells from tissue biopsies of patients presented with GAS invasive infections and submitted to therapy with antibiotics. The persistence phenomenon described herein brings new insights into the origin of therapeutic failures in S. pyogenes infections. Multifactorial mechanisms involving protein synthesis inhibition, cell growth impairment and efflux pumps seem to play roles in the formation of antimicrobial persisters in S. pyogenes.

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.

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.

Inoculum effect of antimicrobial peptides

The activity of many antibiotics depends on the initial density of cells used in bacterial growth inhibition assays. This phenomenon, termed the inoculum effect, can have important consequences for the therapeutic efficacy of the drugs, because bacterial loads vary by several orders of magnitude in clinically relevant infections. Antimicrobial peptides are a promising class of molecules in the fight against drug-resistant bacteria because they act mainly by perturbing the cell membranes rather than by inhibiting intracellular targets. Here, we report a systematic characterization of the inoculum effect for this class of antibacterial compounds. Minimum inhibitory concentration values were measured for 13 peptides (including all-D enantiomers) and peptidomimetics, covering more than seven orders of magnitude in inoculated cell density. In most cases, the inoculum effect was significant for cell densities above the standard inoculum of 5 × 105 cells/mL, while for lower densities the active concentrations remained essentially constant, with values in the micromolar range. In the case of membrane-active peptides, these data can be rationalized by considering a simple model, taking into account peptide-cell association, and hypothesizing that a threshold number of cell-bound peptide molecules is required in order to cause bacterial killing. The observed effect questions the clinical utility of activity and selectivity determinations performed at a fixed, standardized cell density. A routine evaluation of the dependence of the activity of antimicrobial peptides and peptidomimetics on the inoculum should be considered.

Assessment of cooperative antibiotic resistance of Salmonella Typhimurium within heterogeneous population

This study was designed to investigate the cooperative resistance in the mixed culture of antibiotic-sensitive and antibiotic-resistant Salmonella Typhimurium. Strains of S. Typhimurium ATCC 19585 (ST^S) and clinically isolated antibiotic-resistant S. Typhimurium CCARM 8009 (ST^R) grown in single and mixture with 1 × MIC ceftriaxone (CEF) were used to determine the viability, β-lactamase activity, and gene expression. The MIC₅₀ values of ST^R to CEF was increased by more than 5-fold with increasing inoculum densities from 10² to 10⁷ CFU/mL. ST^S was resistant to 1 × MIC CEF in the mixed culture of ST^S and ST^R, showing the more than 10⁸ CFU/mL after 20 h of incubation at 37 °C. The highest β-lactamase activity was 18 μmol/min/mL in the mixed culture, corresponding to the highest relative expression of β-lactamase-related genes (bla_TEM). These results shed new light on the cooperative resistance of antibiotic-sensitive bacteria within a heterogeneous population including β-lactamase-producing bacteria.

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.

An Automated Tabletop Continuous Culturing System with Multicolor Fluorescence Monitoring for Microbial Gene Expression and Long-Term Population Dynamics

Real-time monitoring of gene expression dynamics and population levels in a multispecies microbial community could enable the study of the role of changing gene expression patterns on eco-evolutionary outcomes. Here we report the design and validation of a unique experimental platform with an in situ fluorescence measurement system that has high dynamic range and temporal resolution and is capable of monitoring multiple fluorophores for long-term gene expression and population dynamics experiments. We demonstrate the capability of our system to capture gene expression dynamics in response to external perturbations in two synthetic genetic systems: a simple inducible genetic circuit and a bistable toggle switch. Finally, in exploring the population dynamics of a two species microbial community, we show that our system can capture the switch between competitive exclusion and long-term coexistence in response to different nutrient conditions.

Ecology and evolution of antimicrobial resistance in bacterial communities

Accumulating evidence suggests that the response of bacteria to antibiotics is significantly affected by the presence of other interacting microbes. These interactions are not typically accounted for when determining pathogen sensitivity to antibiotics. In this perspective, we argue that resistance and evolutionary responses to antibiotic treatments should not be considered only a trait of an individual bacteria species but also an emergent property of the microbial community in which pathogens are embedded. We outline how interspecies interactions can affect the responses of individual species and communities to antibiotic treatment, and how these responses could affect the strength of selection, potentially changing the trajectory of resistance evolution. Finally, we identify key areas of future research which will allow for a more complete understanding of antibiotic resistance in bacterial communities. We emphasise that acknowledging the ecological context, i.e. the interactions that occur between pathogens and within communities, could help the development of more efficient and effective antibiotic treatments.

Bistable Bacterial Growth Dynamics in the Presence of Antimicrobial Agents

The outcome of an antibiotic treatment on the growth capacity of bacteria is largely dependent on the initial population size (Inoculum Effect). We characterized and built a model of this effect in E. coli cultures using a large variety of antimicrobials, including conventional antibiotics, and for the first time, cationic antimicrobial peptides (CAMPs). Our results show that all classes of antimicrobial drugs induce an inoculum effect, which, as we explain, implies that the dynamic is bistable: For a range of anti-microbial densities, a very small inoculum decays whereas a larger inoculum grows, and the threshold inoculum depends on the drug concentration. We characterized three distinct classes of drug-induced bistable growth dynamics and demonstrate that in rich medium, CAMPs correspond to the simplest class, bacteriostatic antibiotics to the second class, and all other traditional antibiotics to the third, more complex class. These findings provide a unifying universal framework for describing the dynamics of the inoculum effect induced by antimicrobials with inherently different killing mechanisms.

Quorum sensing: a new prospect for the management of antimicrobial-resistant infectious diseases

INTRODUCTION

Quorum-sensing (QS) is a microbial cell-to-cell communication system that utilizes small signaling molecules to mediates interactions between cross-kingdom microorganisms, including Gram-positive and -negative microbes. QS molecules include N-acyl-homoserine-lactones (AHLs), furanosyl borate, hydroxyl-palmitic acid methylester, and methyl-dodecanoic acid. These signaling molecules maintain the symbiotic relationship between a host and the healthy microbial flora and also control various microbial virulence factors. This manuscript has been developed based on published scientific papers.

AREAS COVERED

Furanones, glycosylated chemicals, heavy metals, and nanomaterials are considered QS inhibitors (QSIs) and are therefore capable of inhibiting the microbial QS system. QSIs are currently being considered as antimicrobial therapeutic options. Currently, the low speed at which new antimicrobial agents are being developed impairs the treatment of drug-resistant infections. Therefore, QSIs are currently being studied as potential interventions targeting QS-signaling molecules and quorum quenching (QQ) enzymes to reduce microbial virulence.

EXPERT OPINION

QSIs represent a novel opportunity to combat antimicrobial resistance (AMR). However, no clinical trials have been conducted thus far assessing their efficacy. With the recent advancements in technology and the development of well-designed clinical trials aimed at targeting various components of the, QS system, these agents will undoubtedly provide a useful alternative to treat infectious diseases.

Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen.

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding coculture. Using our previously described weakest-link hypothesis, we hypothesized antibiotic interactions in coculture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in coculture. We tested the interactions between 10 previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict coculture patterns based solely on the weakest-link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.

Mathematical modeling of the 'inoculum effect': six applicable models and the MIC advancement point concept

Antimicrobial treatment regimens against bacterial pathogens are designed using the drug's minimum inhibitory concentration (MIC) measured at a bacterial density of 5.7 log₁₀(colony-forming units (CFU)/mL) in vitro. However, MIC changes with pathogen density, which varies among infectious diseases and during treatment. Incorporating this into treatment design requires realistic mathematical models of the relationships. We compared the MIC–density relationships for Gram-negative Escherichia coli and non-typhoidal Salmonella enterica subsp. enterica and Gram-positive Staphylococcus aureus and Streptococcus pneumonia (for n = 4 drug-susceptible strains per (sub)species and 1–8 log₁₀(CFU/mL) densities), for antimicrobial classes with bactericidal activity against the (sub)species: β-lactams (ceftriaxone and oxacillin), fluoroquinolones (ciprofloxacin), aminoglycosides (gentamicin), glycopeptides (vancomycin) and oxazolidinones (linezolid). Fitting six candidate mathematical models to the log₂(MIC) vs. log₁₀(CFU/mL) curves did not identify one model best capturing the relationships across the pathogen–antimicrobial combinations. Gompertz and logistic models (rather than a previously proposed Michaelis–Menten model) fitted best most often. Importantly, the bacterial density after which the MIC sharply increases (an MIC advancement-point density) and that density's intra-(sub)species range evidently depended on the antimicrobial mechanism of action. Capturing these dependencies for the disease–pathogen–antimicrobial combination could help determine the MICs for which bacterial densities are most informative for treatment regimen design.

Shaping an Endospore: Architectural Transformations During Bacillus subtilis Sporulation

Endospore formation in Bacillus subtilis provides an ideal model system for studying development in bacteria. Sporulation studies have contributed a wealth of information about the mechanisms of cell-specific gene expression, chromosome dynamics, protein localization, and membrane remodeling, while helping to dispel the early view that bacteria lack internal organization and interesting cell biological phenomena. In this review, we focus on the architectural transformations that lead to a profound reorganization of the cellular landscape during sporulation, from two cells that lie side by side to the endospore, the unique cell within a cell structure that is a hallmark of sporulation in B. subtilis and other spore-forming Firmicutes. We discuss new insights into the mechanisms that drive morphogenesis, with special emphasis on polar septation, chromosome translocation, and the phagocytosis-like process of engulfment, and also the key experimental advances that have proven valuable in revealing the inner workings of bacterial cells.

Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times

Mass production and use of antibiotics has led to the rise of resistant bacteria, a problem possibly exacerbated by inappropriate and non-optimal application. Antibiotic treatment often follows fixed-dose regimens, with a standard dose of antibiotic administered equally spaced in time. But are such fixed-dose regimens optimal or can alternative regimens be designed to increase efficacy? Yet, few mathematical models have aimed to identify optimal treatments based on biological data of infections inside a living host. In addition, assumptions to make the mathematical models analytically tractable limit the search space of possible treatment regimens (e.g. to fixed-dose treatments). Here, we aimed to address these limitations by using experiments in a Galleria mellonella (insect) model of bacterial infection to create a fully parametrised mathematical model of a systemic Vibrio infection. We successfully validated this model with biological experiments, including treatments unseen by the mathematical model. Then, by applying artificial intelligence, this model was used to determine optimal antibiotic dosage regimens to treat the host to maximise survival while minimising total antibiotic used. As expected, host survival increased as total quantity of antibiotic applied during the course of treatment increased. However, many of the optimal regimens tended to follow a large initial ‘loading’ dose followed by doses of incremental reductions in antibiotic quantity (dose ‘tapering’). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.

Research into optimal antibiotic use to improve efficacy is far behind that of cancer care, where personalised treatment is common. The integration of mathematical models with biological observations offers hope to optimise antibiotic use, and in this present study an in vivo insect model of systemic Vibrio infection was used for the first time to determine critical model parameters for optimal antibiotic treatment. By this approach, the optimal regimens tended to result from a large initial ‘loading’ dose followed by subsequent doses of incremental reductions in antibiotic quantity (dose ‘tapering’). The approach and findings of this study opens a new avenue towards optimal application of our precious antibiotic arsenal and may lead to more effective treatment regimens for patients, thus reducing the health and economic burdens associated with bacterial infections. Importantly, it can be argued that until we understand how to use a single antibiotic optimally, it is unlikely we will identify optimal ways to use multiple antibiotics simultaneously.

Temporal encoding of bacterial identity and traits in growth dynamics

In biology, it is often critical to determine the identity of an organism and phenotypic traits of interest. Whole-genome sequencing can be useful for this but has limited power for trait prediction. However, we can take advantage of the inherent information content of phenotypes to bypass these limitations. We demonstrate, in clinical and environmental bacterial isolates, that growth dynamics in standardized conditions can differentiate between genotypes, even among strains from the same species. We find that for pairs of isolates, there is little correlation between genetic distance, according to phylogenetic analysis, and phenotypic distance, as determined by growth dynamics. This absence of correlation underscores the challenge in using genomics to infer phenotypes and vice versa. Bypassing this complexity, we show that growth dynamics alone can robustly predict antibiotic responses. These findings are a foundation for a method to identify traits not easily traced to a genetic mechanism.

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.

Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. In obligate mutualisms, the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners. This sets the community antibiotic sensitivity at that of the 'weakest link' species. In this study, we tested the hypothesis that weakest link dynamics in an obligate cross-feeding relationship would limit the extent and mechanisms of antibiotic resistance evolution. We experimentally evolved an obligate co-culture and monoculture controls along gradients of two different antibiotics. We measured the rate at which each treatment increased antibiotic resistance, and sequenced terminal populations to question whether mutations differed between mono- and co-cultures. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.

Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, a containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here, we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant Escherichia coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

Fluorescent reporter plasmids for single-cell and bulk-level composition assays in E. faecalis

Fluorescent reporters are an important tool for monitoring dynamics of bacterial populations at the single cell and community level. While there are a large range of reporter constructs available–particularly for common model organisms like E. coli–fewer options exist for other species, including E. faecalis, a gram-positive opportunistic pathogen. To expand the potential toolkit available for E. faecalis, we exchanged the original fluorescent reporter in a previously developed plasmid (pBSU101) with one of eight fluorescent reporters and confirmed that all constructs exhibited detectable fluorescence in single E. faecalis cells and mixed biofilm communities. To identify promising constructs for bulk-level experiments, we then measured the fluorescence spectra from E. faecalis populations in microwell plate (liquid) cultures during different phases of aerobic growth. Cultures showed density- and reporter-specific variations in fluorescent signal, though spectral signatures of all reporters become clear in late-exponential and stationary-phase populations. Based on these results, we identified six pairs of reporters that can be combined with simple spectral unmixing to accurately estimate population composition in 2-strain mixtures at or near stationary phase. This approach offers a simple and scalable method for selection and competition experiments in simple two-species populations under aerobic growth conditions. Finally, we incorporated codon-optimized variants of blue (BFP) and red (RFP) reporters and show that they lead to increased fluorescence in exponentially growing cells. As a whole, the results inform the scope of application of different reporters and identify both single reporters and reporter pairs that are promising for fluorescence-based assays at bulk and single-cell levels in E. faecalis.

Deep reinforcement learning for the control of microbial co-cultures in bioreactors

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures within continuous bioreactors. We confirm that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.

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.

Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance

Predicting antibiotic efficacy within microbial communities remains highly challenging. Interspecies interactions can impact antibiotic activity through many mechanisms, including alterations to bacterial physiology. Here, we studied synthetic communities constructed from the core members of the fruit fly gut microbiota. Co-culturing of Lactobacillus plantarum with Acetobacter species altered its tolerance to the transcriptional inhibitor rifampin. By measuring key metabolites and environmental pH, we determined that Acetobacter species counter the acidification driven by L. plantarum production of lactate. Shifts in pH were sufficient to modulate L. plantarum tolerance to rifampin and the translational inhibitor erythromycin. A reduction in lag time exiting stationary phase was linked to L. plantarum tolerance to rifampicin, opposite to a previously identified mode of tolerance to ampicillin in E. coli. This mechanistic understanding of the coupling among interspecies interactions, environmental pH, and antibiotic tolerance enables future predictions of growth and the effects of antibiotics in more complex communities.

Microbial Ecology: Complex Bacterial Communities Reduce Selection for Antibiotic Resistance

Competition between antibiotic-resistant and -susceptible bacteria is well studied in single-species communities, but less is known about selection for resistance in more complex ecologies. A new experiment shows natural microbial communities can hinder selection by increasing the fitness costs of resistance or by offering protection to drug-sensitive strains.

Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

Evolved resistance to one antibiotic may be associated with "collateral" sensitivity to other drugs. Here, we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict because independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. Although phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in a four-drug uniform cycle.

Heterogeneous absorption of antimicrobial peptide LL37 in Escherichia coli cells enhances population survivability

Antimicrobial peptides (AMPs) are broad spectrum antibiotics that selectively target bacteria. Here we investigate the activity of human AMP LL37 against Escherichia coli by integrating quantitative, population and single-cell level experiments with theoretical modeling. We observe an unexpected, rapid absorption and retention of a large number of LL37 peptides by E. coli cells upon the inhibition of their growth, which increases population survivability. This transition occurs more likely in the late stage of cell division cycles. Cultures with high cell density exhibit two distinct subpopulations: a non-growing population that absorb peptides and a growing population that survive owing to the sequestration of the AMPs by others. A mathematical model based on this binary picture reproduces the rather surprising observations, including the increase of the minimum inhibitory concentration with cell density (even in dilute cultures) and the extensive lag in growth introduced by sub-lethal dosages of LL37 peptides.

Migration alters oscillatory dynamics and promotes survival in connected bacterial populations

Migration influences population dynamics on networks, thereby playing a vital role in scenarios ranging from species extinction to epidemic propagation. While low migration rates prevent local populations from becoming extinct, high migration rates enhance the risk of global extinction by synchronizing the dynamics of connected populations. Here, we investigate this trade-off using two mutualistic strains of E. coli that exhibit population oscillations when co-cultured. In experiments, as well as in simulations using a mechanistic model, we observe that high migration rates lead to synchronization whereas intermediate migration rates perturb the oscillations and change their period. Further, our simulations predict, and experiments show, that connected populations subjected to more challenging antibiotic concentrations have the highest probability of survival at intermediate migration rates. Finally, we identify altered population dynamics, rather than recolonization, as the primary cause of extended survival.

Drug treatment efficiency depends on the initial state of activation in nonlinear pathways

An accurate prediction of the outcome of a given drug treatment requires quantitative values for all parameters and concentrations involved as well as a detailed characterization of the network of interactions where the target molecule is embedded. Here, we present a high-throughput in silico screening of all potential networks of three interacting nodes to study the effect of the initial conditions of the network in the efficiency of drug inhibition. Our study shows that most network topologies can induce multiple dose-response curves, where the treatment has an enhanced, reduced or even no effect depending on the initial conditions. The type of dual response observed depends on how the potential bistable regimes interplay with the inhibition of one of the nodes inside a nonlinear pathway architecture. We propose that this dependence of the strength of the drug on the initial state of activation of the pathway may be affecting the outcome and the reproducibility of drug studies and clinical trials.

Optimization of E. coli Inactivation by Benzalkonium Chloride Reveals the Importance of Quantifying the Inoculum Effect on Chemical Disinfection

Optimal disinfection protocols are fundamental to minimize bacterial resistance to the compound applied, or cross-resistance to other antimicrobials such as antibiotics. The objective is twofold: guarantee safe levels of pathogens and minimize the excess of disinfectant after a treatment. In this work, the disinfectant dose is optimized based on a mathematical model. The model explains and predicts the interplay between disinfectant and pathogen at different initial microbial densities (inocula) and dose concentrations. The study focuses on the disinfection of Escherichia coli with benzalkonium chloride, the most common quaternary ammonium compound. Interestingly, the specific benzalkonium chloride uptake (mean uptake per cell) decreases exponentially when the inoculum concentration increases. As a consequence, the optimal disinfectant dose increases exponentially with the initial bacterial concentration.

Extracellular pH and lung infections in cystic fibrosis

Cystic fibrosis (CF) is an autosomal recessive disease caused by CFTR mutations. It is characterized by high NaCl concentration in sweat and the production of a thick and sticky mucus, occluding secretory ducts, intestine and airways, accompanied by chronic inflammation and infections of the lungs. This causes a progressive and lethal decline in lung function. Therefore, finding the mechanisms driving the high susceptibility to lung infections has been a key issue. For decades the prevalent hypothesis was that a reduced airway surface liquid (ASL) volume and composition, and the consequent increased mucus concentration (dehydration), create an environment favoring infections. However, a few years ago, in a pig model of CF, the Na⁺/K⁺ concentrations and the ASL volume were found intact. Immediately a different hypothesis arose, postulating a reduced ASL pH as the cause for the increased susceptibility to infections, due to a diminished bicarbonate secretion through CFTR. Noteworthy, a recent report found normal ASL pH values in CF children and in cultured primary airway cells, challenging the ASL pH hypothesis. On the other hand, recent evidences revitalized the hypothesis of a reduced ASL secretion. Thus, the role of the ASL pH in the CF is still a controversial matter. In this review we discuss the basis that sustain the role of CFTR in modulating the extracellular pH, and the recent results sustaining the different points of view. Finding the mechanisms of CFTR signaling that determine the susceptibility to infections is crucial to understand the pathophysiology of CF and related lung diseases.

Robust, linear correlations between growth rates and β-lactam-mediated lysis rates

It is widely acknowledged that faster-growing bacteria are killed faster by β-lactam antibiotics. This notion serves as the foundation for the concept of bacterial persistence: dormant bacterial cells that do not grow are phenotypically tolerant against β-lactam treatment. Such correlation has often been invoked in the mathematical modeling of bacterial responses to antibiotics. Due to the lack of thorough quantification, however, it is unclear whether and to what extent the bacterial growth rate can predict the lysis rate upon β-lactam treatment under diverse conditions. Enabled by experimental automation, here we measured >1,000 growth/killing curves for eight combinations of antibiotics and bacterial species and strains, including clinical isolates of bacterial pathogens. We found that the lysis rate of a bacterial population linearly depends on the instantaneous growth rate of the population, regardless of how the latter is modulated. We further demonstrate that this predictive power at the population level can be explained by accounting for bacterial responses to the antibiotic treatment by single cells. This linear dependence of the lysis rate on the growth rate represents a dynamic signature associated with each bacterium-antibiotic pair and serves as the quantitative foundation for designing combination antibiotic therapy and predicting the population-structure change in a population with mixed phenotypes.

Antibiotic-induced population fluctuations and stochastic clearance of bacteria

Effective antibiotic use that minimizes treatment failures remains a challenge. A better understanding of how bacterial populations respond to antibiotics is necessary. Previous studies of large bacterial populations established the deterministic framework of pharmacodynamics. Here, characterizing the dynamics of population extinction, we demonstrated the stochastic nature of eradicating bacteria with antibiotics. Antibiotics known to kill bacteria (bactericidal) induced population fluctuations. Thus, at high antibiotic concentrations, the dynamics of bacterial clearance were heterogeneous. At low concentrations, clearance still occurred with a non-zero probability. These striking outcomes of population fluctuations were well captured by our probabilistic model. Our model further suggested a strategy to facilitate eradication by increasing extinction probability. We experimentally tested this prediction for antibiotic-susceptible and clinically-isolated resistant bacteria. This new knowledge exposes fundamental limits in our ability to predict bacterial eradication. Additionally, it demonstrates the potential of using antibiotic concentrations that were previously deemed inefficacious to eradicate bacteria.