Effects of prior exposure to antibiotics on bacterial adaptation to phages

Understanding adaptation to complex environments requires information about how exposure to one selection pressure affects adaptation to others. For bacteria, antibiotics and viral parasites (phages) are two of the most common selection pressures and are both relevant for treatment of bacterial infections: increasing antibiotic resistance is generating significant interest in using phages in addition or as an alternative to antibiotics. However, we lack knowledge of how exposure to antibiotics affects bacterial responses to phages. Specifically, it is unclear how the negative effects of antibiotics on bacterial population growth combine with any possible mutagenic effects or physiological responses to influence adaptation to other stressors such as phages, and how this net effect varies with antibiotic concentration. Here, we experimentally addressed the effect of pre‐exposure to a wide range of antibiotic concentrations on bacterial responses to phages. Across 10 antibiotics, we found a strong association between their effects on bacterial population size and subsequent population growth in the presence of phages (which in these conditions indicates phage‐resistance evolution). We detected some evidence of mutagenesis among populations treated with fluoroquinolones and β‐lactams at sublethal doses, but these effects were small and not consistent across phage treatments. These results show that, although stressors such as antibiotics can boost adaptation to other stressors at low concentrations, these effects are weak compared to the effect of reduced population growth at inhibitory concentrations, which in our experiments strongly reduced the likelihood of subsequent phage‐resistance evolution.     

Persistence and resistance as complementary bacterial adaptations to antibiotics

Bacterial persistence represents a simple of phenotypic heterogeneity, whereby a proportion of cells in an isogenic bacterial population can survive exposure to lethal stresses such as antibiotics. In contrast, genetically based antibiotic resistance allows for continued growth in the presence of antibiotics. It is unclear, however, whether resistance and persistence are complementary or alternative evolutionary adaptations to antibiotics. Here, we investigate the co‐evolution of resistance and persistence across the genus Pseudomonas using comparative methods that correct for phylogenetic nonindependence. We find that strains of Pseudomonas vary extensively in both their intrinsic resistance to antibiotics (ciprofloxacin and rifampicin) and persistence following exposure to these antibiotics. Crucially, we find that persistence correlates positively to antibiotic resistance across strains. However, we find that different genes control resistance and persistence implying that they are independent traits. Specifically, we find that the number of type II toxin–antitoxin systems (TAs) in the genome of a strain is correlated to persistence, but not resistance. Our study shows that persistence and antibiotic resistance are complementary, but independent, evolutionary adaptations to stress and it highlights the key role played by TAs in the evolution of persistence.     

Isolated cell behavior drives the evolution of antibiotic resistance

Bacterial antibiotic resistance is typically quantified by the minimum inhibitory concentration (MIC), which is defined as the minimal concentration of antibiotic that inhibits bacterial growth starting from a standard cell density. However, when antibiotic resistance is mediated by degradation, the collective inactivation of antibiotic by the bacterial population can cause the measured MIC to depend strongly on the initial cell density. In cases where this inoculum effect is strong, the relationship between MIC and bacterial fitness in the antibiotic is not well defined. Here, we demonstrate that the resistance of a single, isolated cell—which we call the single‐cell MIC (scMIC)—provides a superior metric for quantifying antibiotic resistance. Unlike the MIC, we find that the scMIC predicts the direction of selection and also specifies the antibiotic concentration at which selection begins to favor new mutants. Understanding the cooperative nature of bacterial growth in antibiotics is therefore essential in predicting the evolution of antibiotic resistance.     

A discrete-time model of the acquisition of antibiotic-resistant infections in hospitalized patients

Antibiotic use is thought to promote bacterial antibiotic resistance by selectively inhibiting the growth of sensitive strains. This study investigates the relation between antibiotic use and the propagation of antibiotic-resistant hospital-acquired infections due to gram-negative bacteria in a population of hospitalized patients. It treats infection spread and hospital mortality as a Markov process, in which the transition probabilities are logistic functions of a set of personal and hospital characteristics. Data from a university hospital are used to derive the parameters of the model.     

Influence of tetracycline exposure on tetracycline resistance and the carriage of tetracycline resistance genes within commensal Escherichia coli populations

AIMS: To assess the influence of incremental tetracycline exposure on the genetic basis of tetracycline resistance within faecal Escherichia coli. METHODS AND RESULTS: Through the adoption of a novel combination of multiple breakpoint selection, phenotypic characterization and the application of a polymerase chain reaction based gene identification system it proved possible to monitor the influence of antibiotic exposure on resistance gene possession. Using tetracycline as a case study a clear hierarchy was revealed between tet genes, strongly influenced by host antimicrobial exposure history. CONCLUSIONS: The antimicrobial exposure regime under which an animal is produced affects both the identity and magnitude of resistance gene possession of a selected bacterial population within its enteric microflora. Among the ramifications associated with such resistance gene selection is the degree of resistance conferred and the carriage of linked resistance determinants. This selection is applied by exposure to antibiotic concentrations well below recognized minimum inhibitory tetracycline concentration breakpoints widely adopted to characterize bacterial 'susceptibility'. SIGNIFICANCE AND IMPACT OF THE STUDY: This study confirms the ability of minimal antibiotic exposure to select for the continued persistence of resistance genes within the enteric microflora. It is clearly demonstrated that different antimicrobial regimes select for different resistance genes, the implications of which are discussed.     

Effects of antibiotic resistance alleles on bacterial evolutionary responses to viral parasites

Antibiotic resistance has wide-ranging effects on bacterial phenotypes and evolution. However, the influence of antibiotic resistance on bacterial responses to parasitic viruses remains unclear, despite the ubiquity of such viruses in nature and current interest in therapeutic applications. We experimentally investigated this by exposing various Escherichia coli genotypes, including eight antibiotic-resistant genotypes and a mutator, to different viruses (lytic bacteriophages). Across 960 populations, we measured changes in population density and sensitivity to viruses, and tested whether variation among bacterial genotypes was explained by their relative growth in the absence of parasites, or mutation rate towards phage resistance measured by fluctuation tests for each phage. We found that antibiotic resistance had relatively weak effects on adaptation to phages, although some antibiotic-resistance alleles impeded the evolution of resistance to phages via growth costs. By contrast, a mutator allele, often found in antibiotic-resistant lineages in pathogenic populations, had a relatively large positive effect on phage-resistance evolution and population density under parasitism. This suggests costs of antibiotic resistance may modify the outcome of phage therapy against pathogenic populations previously exposed to antibiotics, but the effects of any co-occurring mutator alleles are likely to be stronger.     

Rule-based modelling of conjugative plasmid transfer and incompatibility

COSMIC-rules, an individual-based model for bacterial adaptation and evolution, has been used to study virtual transmission of plasmids within bacterial populations, in an environment varying between supportive and inhibitory. The simulations demonstrate spread of antibiotic resistance (R) plasmids, both compatible and incompatible, by the bacterial gene transfer process of conjugation. This paper describes the behaviour of virtual plasmids, their modes of exchange within bacterial populations and the impact of antibiotics, together with the rules governing plasmid transfer. Three case studies are examined: transfer of an R plasmid within an antibiotic-susceptible population, transfer of two incompatible R plasmids and transfer of two compatible R plasmids. R plasmid transfer confers antibiotic resistance on recipients. For incompatible plasmids, one or other plasmid could be maintained in bacterial cells and only that portion of the population acquiring the appropriate plasmid-encoded resistance survives exposure to the antibiotics. By contrast, the compatible plasmids transfer and mix freely within the bacterial population that survives in its entirety in the presence of the antibiotics. These studies are intended to inform models for examining adaptive evolution in bacteria. They provide proof of principle in simple systems as a platform for predicting the behaviour of bacterial populations in more complex situations, for example in response to changing environments or in multi-species bacterial assemblages.     

A Window of Opportunity to Control the Bacterial Pathogen Pseudomonas aeruginosa Combining Antibiotics and Phages

The evolution of antibiotic resistance in bacteria is a global concern and the use of bacteriophages alone or in combined therapies is attracting increasing attention as an alternative. Evolutionary theory predicts that the probability of bacterial resistance to both phages and antibiotics will be lower than to either separately, due for example to fitness costs or to trade-offs between phage resistance mechanisms and bacterial growth. In this study, we assess the population impacts of either individual or combined treatments of a bacteriophage and streptomycin on the nosocomial pathogen Pseudomonas aeruginosa. We show that combining phage and antibiotics substantially increases bacterial control compared to either separately, and that there is a specific time delay in antibiotic introduction independent of antibiotic dose, that minimizes both bacterial density and resistance to either antibiotics or phage. These results have implications for optimal combined therapeutic approaches.     

A Population Model Evaluating the Consequences of the Evolution of Double-Resistance and Tradeoffs on the Benefits of Two-Drug Antibiotic Treatments

The evolution of antibiotic resistance in microbes poses one of the greatest challenges to the management of human health. Because addressing the problem experimentally has been difficult, research on strategies to slow the evolution of resistance through the rational use of antibiotics has resorted to mathematical and computational models. However, despite many advances, several questions remain unsettled. Here we present a population model for rational antibiotic usage by adding three key features that have been overlooked: 1) the maximization of the frequency of uninfected patients in the human population rather than the minimization of antibiotic resistance in the bacterial population, 2) the use of cocktails containing antibiotic pairs, and 3) the imposition of tradeoff constraints on bacterial resistance to multiple drugs. Because of tradeoffs, bacterial resistance does not evolve directionally and the system reaches an equilibrium state. When considering the equilibrium frequency of uninfected patients, both cycling and mixing improve upon single-drug treatment strategies. Mixing outperforms optimal cycling regimens. Cocktails further improve upon aforementioned strategies. Moreover, conditions that increase the population frequency of uninfected patients also increase the recovery rate of infected individual patients. Thus, a rational strategy does not necessarily result in a tragedy of the commons because benefits to the individual patient and general public are not in conflict. Our identification of cocktails as the best strategy when tradeoffs between multiple-resistance are operating could also be extended to other host-pathogen systems. Cocktails or other multiple-drug treatments are additionally attractive because they allow re-using antibiotics whose utility has been negated by the evolution of single resistance.     

Dynamics of antibiotic resistant Mycobacterium tuberculosis during long-term infection and antibiotic treatment

For an infecting bacterium the human body provides several potential ecological niches with both internally (e.g. host immunity) and externally (e.g. antibiotic use) imposed growth restrictions that are expected to drive adaptive evolution in the bacterium, including the development of antibiotic resistance. To determine the extent and pattern of heterogeneity generated in a bacterial population during long-term antibiotic treatment, we examined in a monoclonal Mycobacterium tuberculosis infection antibiotic resistant mutants isolated from one patient during a 9-years period. There was a progressive accumulation of resistance mutations in the infecting clone. Furthermore, apparent clonal sweeps as well as co-existence of different resistant mutants were observed during this time, demonstrating that during treatment there is a high degree of dynamics in the bacterial population. These findings have important implications for diagnostics and treatment of drug resistant tuberculosis infections.     

Antibody‐mediated crosslinking of gut bacteria hinders the spread of antibiotic resistance

The body is home to a diverse microbiota, mainly in the gut. Resistant bacteria are selected by antibiotic treatments, and once resistance becomes widespread in a population of hosts, antibiotics become useless. Here, we develop a multiscale model of the interaction between antibiotic use and resistance spread in a host population, focusing on an important aspect of within‐host immunity. Antibodies secreted in the gut enchain bacteria upon division, yielding clonal clusters of bacteria. We demonstrate that immunity‐driven bacteria clustering can hinder the spread of a novel resistant bacterial strain in a host population. We quantify this effect both in the case where resistance preexists and in the case where acquiring a new resistance mutation is necessary for the bacteria to spread. We further show that the reduction of spread by clustering can be countered when immune hosts are silent carriers, and are less likely to get treated, and/or have more contacts. We demonstrate the robustness of our findings to including stochastic within‐host bacterial growth, a fitness cost of resistance, and its compensation. Our results highlight the importance of interactions between immunity and the spread of antibiotic resistance, and argue in the favor of vaccine‐based strategies to combat antibiotic resistance.     

基于基因组数据分析的细菌耐药基因识别与表型预测

利用基因组数据和生物信息学分析方法,快速鉴定耐药基因并预测耐药表型,为细菌耐药状况监测提供了有力辅助手段。目前,已有的数十个耐药数据库及其相关分析工具这些资源为细菌耐药基因的识别以及耐药表型的预测提供了数据信息和技术手段。随着细菌基因组数据的持续增加以及耐药表型数据的不断积累,大数据和机器学习能够更好地建立耐药表型与基因组信息之间的相关性,因此,构建高效的耐药表型预测模型成为研究热点。本文围绕细菌耐药基因的识别和耐药表型的预测,针对耐药相关数据库、耐药特征识别理论与方法、耐药数据的机器学习与表型预测等方面展开讨论,以期为细菌耐药的相关研究提供手段和思路。     

Modeling antibiotic resistance in the microbiota using multi-level Petri Nets

Background

The unregulated use of antibiotics not only in clinical practice but also in farm animals breeding is causing a unprecedented growth of antibiotic resistant bacterial strains. This problem can be analyzed at different levels, from the antibiotic resistance spreading dynamics at the host population level down to the molecular mechanisms at the bacteria level. In fact, antibiotic administration policies and practices affect the societal system where individuals developing resistance interact with each other and with the environment. Each individual can be seen as a meta-organism together with its associated microbiota, which proves to have a prominent role in the resistance spreading dynamics. Eventually, in each microbiota, bacterial population dynamics and vertical or horizontal gene transfer events activate cellular and molecular mechanisms for resistance spreading that can also be possible targets for its prevention.

Results

In this work we show how to use the Nets-Within-Nets formalism to model the dynamics between different antibiotic administration protocols and antibiotic resistance, both at the individuals population and at the single microbiota level. Three application examples are presented to show the flexibility of this approach in integrating heterogeneous information in the same model, a fundamental property when creating computational models complex biological systems. Simulations allow to explicitly take into account timing and stochastic events.

Conclusions

This work demonstrates how the NWN formalism can be used to efficiently model antibiotic resistance population dynamics at different levels of detail. The proposed modeling approach not only provides a valuable tool for investigating causal, quantitative relations between different events and mechanisms, but can be also used as a valid support for decision making processes and protocol development.

     

Increased antibiotic resistance of E. coli exposed to static magnetic fields

The present study demonstrates that exposure of bacteria to medium strength static magnetic fields can significantly alter antibiotic sensitivity. Cultures of Escherichia coli were exposed to fields produced by permanent magnets. Samples of bacterial cultures continuously growing in the presence and in the absence of static magnetic fields were left untreated or were treated with an antibiotic and measured at 45 min intervals for cell growth and survival. It was found that exposure of E. coli to the static fields significantly increased antibiotic resistance. Bioelectromagnetics 22:129-137, 2001. Published 2001 Wiley-Liss, Inc.     

Impact of ciprofloxacin impurities on bacterial growth,antibiotic resistance development and content assays

In addition to active pharmaceutical ingredient (API), antibiotics may contain small amounts of excipients and impurities and be prone to accumulation of degradation products. There has been limited work characterizing how these substances impact bacterial growth and antibiotic resistance development. We investigated how two ciprofloxacin (CIP) impurities, fluoroquinolonic acid (FQA) and ciprofloxacin ethylenediamine analogue (CEA), impact growth and antibiotic resistance in Escherichia coli. Additionally, we investigated how these impurities impact a frequently used API content assay. Both impurities displayed modest antimicrobial activity compared to the CIP API. The effective antimicrobial activity of a medicine containing increased impurity levels may permit bacterial growth and resistance development. Our results also suggest that increasing exposure concentration and duration to CEA and FQA, independent of CIP, can promote antibiotic resistance development. However, at concentrations of 100% and below the MIC of the API, impurities had limited contributions to resistance development compared to the CIP API. From a methodological standpoint, we found that UV spectrophotometry may be inadequate to account for antibiotic impurities or degradation products. This can lead to incorrect estimations of API content and we propose additional multi-wavelength measures when using UV spectrophotometry to help identify impurities or degradation.     

Simulating Genetic Circuits in Bacterial Populations with Growth Heterogeneity

We computationally study genetic circuits in bacterial populations with heterogeneities in the growth rate. To that end, we present a stochastic simulation method for gene circuits in populations of cells and propose an efficient implementation that we call the “Next Family Method”. Within this approach, we implement different population setups, specifically Chemostat-type growth and growth in an ideal Mother Machine and show that the population structure and its statistics are different for the different setups whenever there is growth heterogeneity. Such dependence on the population setup is demonstrated, in the case of bistable systems with different growth rates in the stable states, to have distinctive signatures on quantities including the distributions of protein concentration and growth rates, and hysteresis curves. Applying this method to a bistable antibiotic resistance circuit, we find that as a result of the different statistics in different population setups, the estimated minimal inhibitory concentration of the antibiotic becomes dependent on the population setup in which it is measured.     

Antibiotic resistance and persistence—Implications for human health and treatment perspectives

Antimicrobial resistance (AMR) and persistence are associated with an elevated risk of treatment failure and relapsing infections. They are thus important drivers of increased morbidity and mortality rates resulting in growing healthcare costs. Antibiotic resistance is readily identifiable with standard microbiological assays, and the threat imposed by antibiotic resistance has been well recognized. Measures aiming to reduce resistance development and spreading of resistant bacteria are being enforced. However, the phenomenon of bacteria surviving antibiotic exposure despite being fully susceptible, so‐called antibiotic persistence, is still largely underestimated. In contrast to antibiotic resistance, antibiotic persistence is difficult to measure and therefore often missed, potentially leading to treatment failures. In this review, we focus on bacterial mechanisms allowing evasion of antibiotic killing and discuss their implications on human health. We describe the relationship between antibiotic persistence and bacterial heterogeneity and discuss recent studies that link bacterial persistence and tolerance with the evolution of antibiotic resistance. Finally, we review persister detection methods, novel strategies aiming at eradicating bacterial persisters and the latest advances in the development of new antibiotics.     

Long-term shifts in patterns of antibiotic resistance in enteric bacteria

Several mechanisms are responsible for the ability of microorganisms to tolerate antibiotics, and the incidence of resistance to these compounds within bacterial species has increased since the commercial use of antibiotics became widespread. To establish the extent of and changes in the diversity of antibiotic resistance patterns in natural populations, we determined the MICs of five antibiotics for collections of enteric bacteria isolated from diverse hosts and geographic locations and during periods before and after commercial application of antibiotics began. All of the pre-antibiotic era strains were susceptible to high levels of these antibiotics, whereas 20% of strains from contemporary populations of Escherichia coli and Salmonella enterica displayed high-level resistance to at least one of the antibiotics. In addition to the increase in the frequency of high-level resistance, background levels, conferred by genes providing nonspecific low-level resistance to multiple antibiotics, were significantly higher among contemporary strains. Changes in the incidence and levels of antibiotic resistance are not confined to particular segments of the bacterial population and reflect responses to the increased exposure of bacteria to antimicrobial compounds over the past several decades.     

Antibiotic and insecticide resistance modeling--is it time to start talking?

Mathematical models have played an important part in understanding both antibiotic and insecticide resistance. However, there has been little, if any, interdisciplinary work between these two areas of active research. One primary reason for this is that bacterial population genetics differ substantially from the population genetics of diploid organisms. This article examines these differences and their effect on resistance. It explores what efforts have gone into modeling resistance mathematically in both arenas, and offers suggestions on how the two groups could work together to gain a more comprehensive understanding of the resistance phenomenon     

Static recipient cells as reservoirs of antibiotic resistance during antibiotic therapy

How does taking the full course of antibiotics prevent antibiotic resistant bacteria establishing in patients? We address this question by testing the possibility that horizontal/lateral gene transfer (HGT) is critical for the accumulation of the antibiotic-resistance phenotype while bacteria are under antibiotic stress. Most antibiotics prevent bacterial reproduction, some by preventing de novo gene expression. Nevertheless, in some cases and at some concentrations, the effects of most antibiotics on gene expression may not be irreversible. If the stress is removed before the bacteria are cleared from the patients by normal turnover, gene expression restarts, converting the residual population to phenotypic resistance. Using mathematical models we investigate how static recipients of resistance genes carried by plasmids accumulate resistance genes, and how specifically an environment cycling between presence and absence of the antibiotic uniquely favors the evolution of horizontally mobile resistance genes. We found that the presence of static recipients can substantially increase the persistence of the plasmid and that this effect is most pronounced when the cost of carriage of the plasmid decreases the cell's growth rate by as much as a half or more. In addition, plasmid persistence can be enhanced even when conjugation rates are as low as half the rate required for the plasmid to persist as a parasite on its own.