Informed switching strongly decreases the prevalence of antibiotic resistance in hospital wards

Antibiotic resistant nosocomial infections are an important cause of mortality and morbidity in hospitals. Antibiotic cycling has been proposed to contain this spread by a coordinated use of different antibiotics. Theoretical work, however, suggests that often the random deployment of drugs ("mixing") might be the better strategy. We use an epidemiological model for a single hospital ward in order to assess the performance of cycling strategies which take into account the frequency of antibiotic resistance in the hospital ward. We assume that information on resistance frequencies stems from microbiological tests, which are performed in order to optimize individual therapy. Thus the strategy proposed here represents an optimization at population-level, which comes as a free byproduct of optimizing treatment at the individual level. We find that in most cases such an informed switching strategy outperforms both periodic cycling and mixing, despite the fact that information on the frequency of resistance is derived only from a small sub-population of patients. Furthermore we show that the success of this strategy is essentially a stochastic phenomenon taking advantage of the small population sizes in hospital wards. We find that the performance of an informed switching strategy can be improved substantially if information on resistance tests is integrated over a period of one to two weeks. Finally we argue that our findings are robust against a (moderate) preexistence of doubly resistant strains and against transmission via environmental reservoirs. Overall, our results suggest that switching between different antibiotics might be a valuable strategy in small patient populations, if the switching strategies take the frequencies of resistance alleles into account.     

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.     

Source-sink dynamics shape the evolution of antibiotic resistance and its pleiotropic fitness cost

Understanding the conditions that favour the evolution and maintenance of antibiotic resistance is the central goal of epidemiology. A crucial feature explaining the adaptation to harsh, or 'sink', environments is the supply of beneficial mutations via migration from a 'source' population. Given that antibiotic resistance is frequently associated with antagonistic pleiotropic fitness costs, increased migration rate is predicted not only to increase the rate of resistance evolution but also to increase the probability of fixation of resistance mutations with minimal fitness costs. Here we report in vitro experiments using the nosocomial pathogenic bacterium Pseudomonas aeruginosa that support these predictions: increasing rate of migration into environments containing antibiotics increased the rate of resistance evolution and decreased the associated costs of resistance. Consistent with previous theoretical work, we found that resistance evolution arose more rapidly in the presence of a single antibiotic than two. Evolution of resistance was also more rapid when bacteria were subjected to sequential exposure with two antibiotics (cycling therapy) compared with simultaneous exposure (bi-therapy). Furthermore, pleiotropic fitness costs of resistance to two antibiotics were higher than for one antibiotic, and were also higher under bi-therapy than cycling therapy, although the cost of resistance depended on the order of the antibiotics through time. These results may be relevant to the clinical setting where immigration is known to be important between chemotherapeutically treated patients, and demonstrate the importance of ecological and evolutionary dynamics in the control of antibiotic resistance.     

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.     

抗生素的使用情况调查及细菌耐药性分析

目的:分析我院的抗生素的使用频率以及细菌耐药率的变化,为规范临床用药提供参考资料。方法:采用回顾性分析的方法对我院2009年3月-2013年3月收治的8000例住院患者的抗生素使用情况进行调查,并对我院临床上常见革兰阴性菌和阳性菌的耐药率变化进行比较,分析抗生素的使用频率与细菌耐药率变化之间的关系。结果:临床上抗生素的使用频率最大的是β-内酰胺酶抑制剂以及头孢菌素类。金葡菌对环丙沙星的耐药率与青霉素类抗生素的DDDs呈正相关,大肠埃希菌对亚胺培南的耐药率与头孢菌素类抗生素的DDDs呈负相关。结论:抗生素的用药频率与病原菌对抗生素的耐药率有相关性,并且,单一的抗生素并不能引起病原菌的耐药性,而会同时影响其他类型的抗生素的耐药情况。     

Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.     

Combating bacteria and drug resistance by inhibiting mechanisms of persistence and adaptation

Antibiotics have revolutionized the treatment of infectious disease but have also rapidly selected for the emergence of resistant pathogens. Traditional methods of antibiotic discovery have failed to keep pace with the evolution of this resistance, which suggests that new strategies to combat bacterial infections may be required. An improved understanding of bacterial stress responses and evolution suggests that in some circumstances, the ability of bacteria to survive antibiotic therapy either by transiently tolerating antibiotics or by evolving resistance requires specific biochemical processes that may themselves be subject to intervention. Inhibiting these processes may prolong the efficacy of current antibiotics and provide an alternative to escalating the current arms race between antibiotics and bacterial resistance. Though these approaches are not clinically validated and will certainly face their own set of challenges, their potential to protect our ever-shrinking arsenal of antibiotics merits their investigation. This Review summarizes the early efforts toward this goal.     

When the Most Potent Combination of Antibiotics Selects for the Greatest Bacterial Load: The Smile-Frown Transition

Conventional wisdom holds that the best way to treat infection with antibiotics is to ‘hit early and hit hard’. A favoured strategy is to deploy two antibiotics that produce a stronger effect in combination than if either drug were used alone. But are such synergistic combinations necessarily optimal? We combine mathematical modelling, evolution experiments, whole genome sequencing and genetic manipulation of a resistance mechanism to demonstrate that deploying synergistic antibiotics can, in practice, be the worst strategy if bacterial clearance is not achieved after the first treatment phase. As treatment proceeds, it is only to be expected that the strength of antibiotic synergy will diminish as the frequency of drug-resistant bacteria increases. Indeed, antibiotic efficacy decays exponentially in our five-day evolution experiments. However, as the theory of competitive release predicts, drug-resistant bacteria replicate fastest when their drug-susceptible competitors are eliminated by overly-aggressive treatment. Here, synergy exerts such strong selection for resistance that an antagonism consistently emerges by day 1 and the initially most aggressive treatment produces the greatest bacterial load, a fortiori greater than if just one drug were given. Whole genome sequencing reveals that such rapid evolution is the result of the amplification of a genomic region containing four drug-resistance mechanisms, including the acrAB efflux operon. When this operon is deleted in genetically manipulated mutants and the evolution experiment repeated, antagonism fails to emerge in five days and antibiotic synergy is maintained for longer. We therefore conclude that unless super-inhibitory doses are achieved and maintained until the pathogen is successfully cleared, synergistic antibiotics can have the opposite effect to that intended by helping to increase pathogen load where, and when, the drugs are found at sub-inhibitory concentrations.     

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.     

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.     

Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids

Inactivation of β ‐lactam antibiotics by resistant bacteria is a ‘cooperative’ behavior that may allow sensitive bacteria to survive antibiotic treatment. However, the factors that determine the fraction of resistant cells in the bacterial population remain unclear, indicating a fundamental gap in our understanding of how antibiotic resistance evolves. Here, we experimentally track the spread of a plasmid that encodes a β ‐lactamase enzyme through the bacterial population. We find that independent of the initial fraction of resistant cells, the population settles to an equilibrium fraction proportional to the antibiotic concentration divided by the cell density. A simple model explains this behavior, successfully predicting a data collapse over two orders of magnitude in antibiotic concentration. This model also successfully predicts that adding a commonly used β ‐lactamase inhibitor will lead to the spread of resistance, highlighting the need to incorporate social dynamics into the study of antibiotic resistance.     

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.     

Infectious plasmid resistance and efflux pump mediated resistance

Various bacterial plasmids can be eliminated from bacterial species cultured as pure or mixed bacterial cultures by non-mutagenic heterocyclic compounds at subinhibitory concentrations. For plasmid curing, the replication should be inhibited at three different levels simultaneously: the intracellular replication of plasmid DNA, partition and intercellular transconjugal transfer. The antiplasmid action of the compounds depends on the chemical structure. The targets for antiplasmid compounds were analysed in detail. It was found that amplified extrachromosomal DNA in the superhelical state binds more drug molecules than does the linear or open-circular form of the plasmid or the chromosome, without stereospecificity which leads to functional inactivation of the extrachromosomal genetic code. Plasmid elimination also occurs in ecosystems containing numerous bacterial species simultaneously, but the elimination of antibiotic resistance-encoding plasmids from all individual cells of the population is never complete. The medical significance of plasmid elimination in vitro is, it provides a method to isolate plasmid-free bacteria for biotechnology without any risk of mutations, and it opens up a new perspective in rational drug design against bacterial plasmids. Hypothetically, the combination of antiplasmid drugs and antibiotics may improve the effectivity of antibiotics against resistant bacteria; therefore, the results cannot be exploited until the curing efficiency reaches 100%. Inhibition of the conjugational transfer of antibiotic resistance plasmids can be exploited to reduce the spreading of these plasmids in ecosystems.     

畜禽养殖废物中抗生素和重金属抗性基因的产生机制和控制方法研究进展

动物饲料中常混有抗生素和重金属,导致外排的动物粪便中携带有抗生素和重金属,引发细菌产生耐药性和重金属抗性,继而产生抗生素抗性基因和重金属抗性基因。抗生素和重金属抗性基因污染已成为威胁人类身体健康及破坏生态环境的重大问题。本文从细菌进化的角度,明确了细菌的抗生素和重金属长期进化试验对抗性机制研究的重要性;抗生素抗性基因与重金属抗性基因间存在复杂的协同选择抗性,两者间相互影响,共同决定着细菌环境行为;抗性基因的水平转移增加了细菌在环境中的可变性,可移动遗传元件在抗性基因水平转移中发挥着重要作用。在抗性基因污染控制方面,高级氧化技术具有很好的抗性基因去除效果,尤其是UV/TiO₂氧化技术,能使抗生素抗性基因丰度减少4.7～5.8 log,减少率大于99.99%。其他的控制策略,如抗生素替代品博落回提取物以及噬菌体与抗生素结合使用,对于抗性基因的控制也具有重要意义。     

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.     

Alternatives to antibiotics for the control of bacterial disease in aquaculture

The wide and frequent use of antibiotics in aquaculture has resulted in the development and spread of antibiotic resistance. Because of the health risks associated with the use of antibiotics in animal production, there is a growing awareness that antibiotics should be used with more care. This is reflected in the recent implementation of more strict regulations on the prophylactic use of antibiotics and the presence of antibiotic residues in aquaculture products. For a sustainable further development of the aquaculture industry, novel strategies to control bacterial infections are needed. This review evaluates several alternative biocontrol measures that have emerged recently. Most of these methods are still in research phase; few have been tested in real aquaculture settings. It is important to further develop different strategies that could be combined or used in rotation in order to maximise the chance of successfully protecting the animals and to prevent resistance development.     

Antimicrobial use and bacterial resistance

The current epidemic of bacterial resistance is attributed, in part, to the overuse of antibiotics. Recent studies have documented increases in resistance with over-use of particular antibiotics and improvements in susceptibility when antibiotic use is controlled. The most effective means of improving use of antibiotics is unknown. Comprehensive management programs directed by multi-disciplinary teams, computer-assisted decision-making, and antibiotic cycling have been beneficial in controlling antibiotic use, decreasing costs without impacting patient outcomes, and possibly decreasing resistance.     

Evaluating the efficacy of antimicrobial cycling programmes and patient isolation on dual resistance in hospitals

Antibiotic-resistant bacteria cause a number of infections in hospitals and are considered a threat to public health. A strategy suggested to curb the development of resistant hospital-acquired infections is antimicrobial cycling, in which antibiotic classes are alternated over time. This can be compared with a mixing programme in which, when given two drugs, half of the physicians prescribe one drug over the other. A mathematical model of antimicrobial cycling in a hospital population setting is developed to evaluate the efficacy of a cycling programme with an emphasis on reducing the emergence and significance of dual resistance. The model also considers the effects of physician compliance and isolating patients harbouring dual-resistant bacteria. Simulation results show that the optimal antimicrobial drug usage programme in hospital populations depends upon the type of resistance being targeted for treatment; a cycling programme is more effective against dual resistance compared with mixing. Patient isolation and high compliance to a cycling programme is also shown to dramatically decrease dual resistance in hospitalized populations. Ultimately, the exclusive use of antimicrobials in fighting nosocomial infection does not solve the problem but just slows down what appears to be a losing battle against drug resistance. We hope that this paper serves to instigate discussion on the many dimensions of the complex problem of drug resistance in hospital settings.     

What are the missing pieces needed to stop antibiotic resistance?

As recognized by several international agencies, antibiotic resistance is nowadays one of the most relevant problems for human health. While this problem was alleviated with the introduction of new antibiotics into the market in the golden age of antimicrobial discovery, nowadays few antibiotics are in the pipeline. Under these circumstances, a deep understanding on the mechanisms of emergence, evolution and transmission of antibiotic resistance, as well as on the consequences for the bacterial physiology of acquiring resistance is needed to implement novel strategies, beyond the development of new antibiotics or the restriction in the use of current ones, to more efficiently treat infections. There are still several aspects in the field of antibiotic resistance that are not fully understood. In the current article, we make a non-exhaustive critical review of some of them that we consider of special relevance, in the aim of presenting a snapshot of the studies that still need to be done to tackle antibiotic resistance.     

Fast drug rotation reduces bacterial resistance evolution in a microcosm experiment

Drug rotation (cycling), in which multiple drugs are administrated alternatively, has the potential for limiting resistance evolution in pathogens. The frequency of drug alternation could be a major factor to determine the effectiveness of drug rotation. Drug rotation practices often have low frequency of drug alternation, with an expectation of resistance reversion. Here we, based on evolutionary rescue and compensatory evolution theories, suggest that fast drug rotation can limit resistance evolution in the first place. This is because fast drug rotation would give little time for the evolutionarily rescued populations to recover in population size and genetic diversity, and thus decrease the chance of future evolutionary rescue under alternate environmental stresses. We experimentally tested this hypothesis using the bacterium Pseudomonas fluorescens and two antibiotics (chloramphenicol and rifampin). Increasing drug rotation frequency reduced the chance of evolutionary rescue, and most of the finally surviving bacterial populations were resistant to both drugs. Drug resistance incurred significant fitness costs, which did not differ among the drug treatment histories. A link between population sizes during the early stages of drug treatment and the end-point fates of populations (extinction vs survival) suggested that population size recovery and compensatory evolution before drug shift increase the chance of population survival. Our results therefore advocate fast drug rotation as a promising approach to reduce bacterial resistance evolution, which in particular could be a substitute for drug combination when the latter has safety risks.