Effect of Periodic Disinfection on Persisters in a One-Dimensional Biofilm Model

It is well known that disinfection methods that successfully kill suspended bacterial populations often fail to eliminate bacterial biofilms. Recent efforts to understand biofilm survival have focused on the existence of small, but very tolerant, subsets of the bacterial population termed persisters. In this investigation, we analyze a mathematical model of disinfection that consists of a susceptible-persister population system embedded within a growing domain. This system is coupled to a reaction-diffusion system governing the antibiotic and nutrient. We analyze the effect of periodic and continuous dosing protocols on persisters in a one-dimensional biofilm model, using both analytic and numerical method. We provide sufficient conditions for the existence of steady-state solutions and show that these solutions may not be unique. Our results also indicate that the dosing ratio (the ratio of dosing time to period) plays an important role. For long periods, large dosing ratios are more effective than similar ratios for short periods. We also compare periodic to continuous dosing and find that the results also depend on the method of distributing the antibiotic within the dosing cycle.     

细菌药物耐受

细菌药物耐受(Drug tolerance)是指在没有发生耐药突变的情况下细菌耐受抗生素杀菌的能力,表现为细菌群体难以或不能被杀菌型药物清除。细菌药物耐受的调控机制包括群体异质性和压力应答两种途径。药物耐受性的本质是细菌通过调控或遗传突变的方式改变生理代谢状态,从而抵制药物引起的细胞死亡途径。比如,处于缓慢生长或生长停滞生理状态的细菌往往能够抵抗药物的杀菌作用。临床研究发现细菌药物耐受是导致持续性感染疾病迁延难愈、复发率高的病原学机制之一。同时,研究证明耐受性的形成是细菌耐药性(Drug resistance)产生的进化途径之一。因此,揭示细菌药物耐受的机制将有助于人们深入了解抗生素的杀菌机理,以及细菌耐药性形成的适应性进化机制,并为新型杀菌药物以及药物增效剂靶标的发现和抗生素合理使用策略的开发奠定理论基础。     

Bacterial Temporal Dynamics Enable Optimal Design of Antibiotic Treatment

There is a critical need to better use existing antibiotics due to the urgent threat of antibiotic resistant bacteria coupled with the reduced effort in developing new antibiotics. β-lactam antibiotics represent one of the most commonly used classes of antibiotics to treat a broad spectrum of Gram-positive and -negative bacterial pathogens. However, the rise of extended spectrum β-lactamase (ESBL) producing bacteria has limited the use of β-lactams. Due to the concern of complex drug responses, many β-lactams are typically ruled out if ESBL-producing pathogens are detected, even if these pathogens test as susceptible to some β-lactams. Using quantitative modeling, we show that β-lactams could still effectively treat pathogens producing low or moderate levels of ESBLs when administered properly. We further develop a metric to guide the design of a dosing protocol to optimize treatment efficiency for any antibiotic-pathogen combination. Ultimately, optimized dosing protocols could allow reintroduction of a repertoire of first-line antibiotics with improved treatment outcomes and preserve last-resort antibiotics.     

Selection of resistant bacteria at very low antibiotic concentrations

The widespread use of antibiotics is selecting for a variety of resistance mechanisms that seriously challenge our ability to treat bacterial infections. Resistant bacteria can be selected at the high concentrations of antibiotics used therapeutically, but what role the much lower antibiotic concentrations present in many environments plays in selection remains largely unclear. Here we show using highly sensitive competition experiments that selection of resistant bacteria occurs at extremely low antibiotic concentrations. Thus, for three clinically important antibiotics, drug concentrations up to several hundred-fold below the minimal inhibitory concentration of susceptible bacteria could enrich for resistant bacteria, even when present at a very low initial fraction. We also show that de novo mutants can be selected at sub-MIC concentrations of antibiotics, and we provide a mathematical model predicting how rapidly such mutants would take over in a susceptible population. These results add another dimension to the evolution of resistance and suggest that the low antibiotic concentrations found in many natural environments are important for enrichment and maintenance of resistance in bacterial populations.     

Incorporating toxin hypothesis into a mathematical model of persister formation and dynamics

Biofilms are well known for their extreme tolerance to antibiotics. Recent experimental evidence has indicated the existence of a small fraction of specialized persister cells may be responsible for this tolerance. Although persister cells seem to exist in planktonic bacterial populations, within a biofilm the additional protection offered by the polymeric matrix allows persister cells to evade elimination and serve as a source for re-population. Whether persister cells develop through interactions with toxin/antitoxin modules or are senescent bacteria is an open question. In this investigation we contrast results of the analysis of a mathematical model of the toxin/antitoxin hypothesis for bacteria in a chemostat with results incorporating the senescence hypothesis. We find that the persister fraction of the population as a function of washout rate provides a viable distinction between the two hypotheses. We also give simulation results that indicate that a strategy of alternating dose/withdrawal disinfection can be effective in clearing the entire persister and susceptible populations of bacteria. This strategy was considered previously in analysis of a generic model of persister formation. We find that extending the model of persister formation to include the toxin/antitoxin interactions in a chemostat does not alter the qualitative results that success of the dosing strategy depends on the withdrawal time. While this treatment is restricted to planktonic bacterial populations, it serves as a framework for including persister cells in a spatially dependent biofilm model.     

Heterogeneous bacterial persisters and engineering approaches to eliminate them

Bacterial persistence is a state in which a subpopulation of cells (persisters) survives antibiotic treatment, and has been implicated in the tolerance of clinical infections and the recalcitrance of biofilms. There has been a renewed interest in the role of bacterial persisters in treatment failure in light of a wealth of recent findings. Here we review recent laboratory studies of bacterial persistence. Further, we pose the hypothesis that each bacterial population may contain a diverse collection of persisters and discuss engineering strategies for persister eradication.     

Shooting yourself in the foot: How immune cells induce antibiotic tolerance in microbial pathogens

Antibiotic treatment failure of infection is common and frequently occurs in the absence of genetically encoded antibiotic resistance mechanisms. In such scenarios, the ability of bacteria to enter a phenotypic state that renders them tolerant to the killing activity of multiple antibiotic classes is thought to contribute to antibiotic failure. Phagocytic cells, which specialize in engulfing and destroying invading pathogens, may paradoxically contribute to antibiotic tolerance and treatment failure. Macrophages act as reservoirs for some pathogens and impede penetration of certain classes of antibiotics. In addition, increasing evidence suggests that subpopulations of bacteria can survive inside these cells and are coerced into an antibiotic-tolerant state by host cell activity. Uncovering the mechanisms that drive immune-mediated antibiotic tolerance may present novel strategies to improving antibiotic therapy.     

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.

     

Stress-induced Antibiotic Susceptibility Testing on a Chip

We have developed a rapid microfluidic method for antibiotic susceptibility testing in a stress-based environment. Fluid is passed at high speeds over bacteria immobilized on the bottom of a microfluidic channel. In the presence of stress and antibiotic, susceptible strains of bacteria die rapidly. However, resistant bacteria survive these stressful conditions. The hypothesis behind this method is new: stress activation of biochemical pathways, which are targets of antibiotics, can accelerate antibiotic susceptibility testing. As compared to standard antibiotic susceptibility testing methods, the rate-limiting step - bacterial growth - is omitted during antibiotic application. The technical implementation of the method is in a combination of standard techniques and innovative approaches. The standard parts of the method include bacterial culture protocols, defining microfluidic channels in polydimethylsiloxane (PDMS), cell viability monitoring with fluorescence, and batch image processing for bacteria counting. Innovative parts of the method are in the use of culture media flow for mechanical stress application, use of enzymes to damage but not kill the bacteria, and use of microarray substrates for bacterial attachment. The developed platform can be used in antibiotic and nonantibiotic related drug development and testing. As compared to the standard bacterial suspension experiments, the effect of the drug can be turned on and off repeatedly over controlled time periods. Repetitive observation of the same bacterial population is possible over the course of the same experiment.     

Staphylococcus aureus in Continuous Culture: A Tool for the Rational Design of Antibiotic Treatment Protocols

In vitro measures of the pharmacodynamics of antibiotics that account for the factors anticipated for bacteria in infected patients are central to the rational design of antibiotic treatment protocols. We consider whether or not continuous culture devices are a way to obtain these measures. Staphylococcus aureus PS80 in high-density continuous cultures were exposed to oxacillin, ciprofloxacin, vancomycin, gentamicin, daptomycin and linezolid. Contrary to results from low density retentostats as well as to predictions of traditional PK/MIC ratios, daily dosing with up to 100× MIC did not clear these cultures. The densities of S. aureus in these cultures oscillated with constant amplitude and never fell below 10(5) CFU per ml. Save for daptomycin "treated" populations, the densities of bacteria in these cultures remained significantly below that of similar antibiotic-free cultures. Although these antibiotics varied in their pharmacodynamic properties there were only modest differences in their mean densities. Mathematical models and experiments suggest that the dominant factor preventing clearance was wall-adhering subpopulations reseeding the planktonic population which can be estimated and corrected for. Continuous cultures provide a way to evaluate the potential efficacy of antibiotic treatment regimes in vitro under conditions that are more clinically realistic and comprehensive than traditional in vitro PK/PD indices.     

How to break recombinant bacteria: does it matter?

Recombinant proteins and other materials of industrial interest produced in?Escherichia coli are usually retained within the bacterial cell, in the cytoplasmic?space, where they have been produced. Different protocols for cell disruption?have been implemented as an initial downstream step, which keeps the?biological and mechanical properties of the process products. Being necessarily?mild, these approaches often result in 95-99% cell disruption, what is more than?acceptable from the yield point of view. However, when the bacterial product?are nano or microparticulate entities that tend to co-sediment with entire?bacterial cells, the remaining undisrupted bacteria appear as abounding?contaminants, making the product not suitable for a spectrum of biomedical?applications. Since bacterial inclusion bodies are now seen as bacterial?materials valuable in different fields, we have developed an alternative cell?disruption protocol that permits obtaining fully bacterial free protein particles,?keeping the conformational status of the embedded proteins and the?mechanical properties of the full aggregates.     

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.     

When to wake up? The optimal waking-up strategies for starvation-induced persistence

Prolonged lag time can be induced by starvation contributing to the antibiotic tolerance of bacteria. We analyze the optimal lag time to survive and grow the iterative and stochastic application of antibiotics. A simple model shows that the optimal lag time can exhibit a discontinuous transition when the severeness of the antibiotic application, such as the probability to be exposed the antibiotic, the death rate under the exposure, and the duration of the exposure, is increased. This suggests the possibility of reducing tolerant bacteria by controlled usage of antibiotics application. When the bacterial populations are able to have two phenotypes with different lag times, the fraction of the second phenotype that has different lag time shows a continuous transition. We then present a generic framework to investigate the optimal lag time distribution for total population fitness for a given distribution of the antibiotic application duration. The obtained optimal distributions have multiple peaks for a wide range of the antibiotic application duration distributions, including the case where the latter is monotonically decreasing. The analysis supports the advantage in evolving multiple, possibly discrete phenotypes in lag time for bacterial long-term fitness.     

Modeling the mechanism of postantibiotic effect and determining implications for dosing regimens

A stochastic model is proposed to explain one possible underlying mechanism of the postantibiotic effect (PAE). This phenomenon, of continued inhibition of bacterial growth after removal of the antibiotic drug, is of high relevance in the context of optimizing dosing regimens. One clinical implication of long PAE lies in the possibility of increasing intervals between drug administrations. The model describes the dynamics of synthesis, saturation and removal of penicillin binding proteins (PBPs). High fractions of saturated PBPs are in the model associated with a lower growth capacity of bacteria. An analytical solution for the bivariate probability of saturated and unsaturated PBPs is used as a basis to explore optimal antibiotic dosing regimens. Our finding that longer PAEs do not necessarily promote for increased intervals between doses, might help for our understanding of data provided from earlier PAE studies and for the determination of the clinical relevance of PAE in future studies.      

“One-Size-Fits-All”? Optimizing Treatment Duration for Bacterial Infections

Historically, antibiotic treatment guidelines have aimed to maximize treatment efficacy and minimize toxicity, but have not considered the evolution of antibiotic resistance. Optimizing the duration and dosing of treatment to minimize the duration of symptomatic infection and selection pressure for resistance simultaneously has the potential to extend the useful therapeutic life of these valuable life-saving drugs without compromising the interests of individual patients.Here, using mathematical models, we explore the theoretical basis for shorter durations of treatment courses, including a range of ecological dynamics of bacteria that cause infections or colonize hosts as commensals. We find that immunity is an important mediating factor in determining the need for long duration of treatment. When immunity to infection is expected, shorter durations that reduce the selection for resistance without interfering with successful clinical outcome are likely to be supported. Adjusting drug treatment strategies to account for the impact of the differences in the ecological niche occupied by commensal flora relative to invasive bacteria could be effective in delaying the spread of bacterial resistance.     

Exploring the role of the immune response in preventing antibiotic resistance

For many bacterial infections, drug resistant mutants are likely present by the time antibiotic treatment starts. Nevertheless, such infections are often successfully cleared. It is commonly assumed that this is due to the combined action of drug and immune response, the latter facilitating clearance of the resistant population. However, most studies of drug resistance emergence during antibiotic treatment focus almost exclusively on the dynamics of bacteria and the drug and neglect the contribution of immune defenses. Here, we develop and analyze several mathematical models that explicitly include an immune response. We consider different types of immune responses and investigate how each impacts the emergence of resistance. We show that an immune response that retains its strength despite a strong drug-induced decline of bacteria numbers considerably reduces the emergence of resistance, narrows the mutant selection window, and mitigates the effects of non-adherence to treatment. Additionally, we show that compared to an immune response that kills bacteria at a constant rate, one that trades reduced killing at high bacterial load for increased killing at low bacterial load is sometimes preferable. We discuss the predictions and hypotheses derived from this study and how they can be tested experimentally.     

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.     

Isolation and Chemical Characterization of Lipid A from Gram-negative Bacteria

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS can be subdivided into three domains: the distal O-polysaccharide, a core oligosaccharide, and the lipid A domain consisting of a lipid A molecular species and 3-deoxy-D-manno-oct-2-ulosonic acid residues (Kdo). The lipid A domain is the only component essential for bacterial cell survival. Following its synthesis, lipid A is chemically modified in response to environmental stresses such as pH or temperature, to promote resistance to antibiotic compounds, and to evade recognition by mediators of the host innate immune response. The following protocol details the small- and large-scale isolation of lipid A from gram-negative bacteria. Isolated material is then chemically characterized by thin layer chromatography (TLC) or mass-spectrometry (MS). In addition to matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS, we also describe tandem MS protocols for analyzing lipid A molecular species using electrospray ionization (ESI) coupled to collision induced dissociation (CID) and newly employed ultraviolet photodissociation (UVPD) methods. Our MS protocols allow for unequivocal determination of chemical structure, paramount to characterization of lipid A molecules that contain unique or novel chemical modifications. We also describe the radioisotopic labeling, and subsequent isolation, of lipid A from bacterial cells for analysis by TLC. Relative to MS-based protocols, TLC provides a more economical and rapid characterization method, but cannot be used to unambiguously assign lipid A chemical structures without the use of standards of known chemical structure. Over the last two decades isolation and characterization of lipid A has led to numerous exciting discoveries that have improved our understanding of the physiology of gram-negative bacteria, mechanisms of antibiotic resistance, the human innate immune response, and have provided many new targets in the development of antibacterial compounds.     

Yersinia pseudotuberculosis doxycycline tolerance strategies include modulating expression of genes involved in cell permeability and tRNA modifications

Antibiotic tolerance is typically associated with a phenotypic change within a bacterial population, resulting in a transient decrease in antibiotic susceptibility that can contribute to treatment failure and recurrent infections. Although tolerant cells may emerge prior to treatment, the stress of prolonged antibiotic exposure can also promote tolerance. Here, we sought to determine how Yersinia pseudotuberculosis responds to doxycycline exposure, to then verify if these gene expression changes could promote doxycycline tolerance in culture and in our mouse model of infection. Only four genes were differentially regulated in response to a physiologically-relevant dose of doxycycline: osmB and ompF were upregulated, tusB and cnfy were downregulated; differential expression also occurred during doxycycline treatment in the mouse. ompF, tusB and cnfy were also differentially regulated in response to chloramphenicol, indicating these could be general responses to ribosomal inhibition. cnfy has previously been associated with persistence and was not a major focus here. We found deletion of the OmpF porin resulted in increased antibiotic accumulation, suggesting expression may promote diffusion of doxycycline out of the cell, while OsmB lipoprotein had a minor impact on antibiotic permeability. Overexpression of tusB significantly impaired bacterial survival in culture and in the mouse, suggesting that tRNA modification by tusB, and the resulting impacts on translational machinery, promotes survival during treatment with an antibiotic classically viewed as bacteriostatic. We believe this may be the first observation of bactericidal activity of doxycycline under physiological conditions, which was revealed by reversing tusB downregulation.