Pharmacodynamics, population dynamics, and the evolution of persistence in Staphylococcus aureus

Inhibition of stationary phase respiration impairs persister formation in E. coli

Bacterial persisters are rare phenotypic variants that temporarily tolerate high antibiotic concentrations. Persisters have been hypothesized to underlie the recalcitrance of biofilm infections, and strategies to eliminate these cells have the potential to improve treatment outcomes for many hospital-treated infections. Here we investigate the role of stationary phase metabolism in generation of type I persisters in Escherichia coli, which are those that are formed by passage through stationary phase. We find that persisters are unlikely to derive from bacteria with low redox activity, and that inhibition of respiration during stationary phase reduces persister levels by up to ∼1,000-fold. Loss of stationary phase respiratory activity prevents digestion of endogenous proteins and RNA, which yields bacteria that are more capable of translation, replication and concomitantly cell death when exposed to antibiotics. These findings establish bacterial respiration as a prime target for reducing the number of persisters formed in nutrient-depleted, non-growing populations.

A few bacterial cells within a genetically homogeneous population can become ‘persisters', or temporarily tolerant to antibiotics. Here Orman and Brynildsen show that development of persisters among growth-arrested E. coli cells can be prevented by inhibiting bacterial respiration.

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 novel point mutation promotes growth phase-dependent daptomycin tolerance in Staphylococcus aureus

Recalcitrance of genetically susceptible bacteria to antibiotic killing is a hallmark of bacterial drug tolerance. This phenomenon is prevalent in biofilms, persisters, and also planktonic cells and is associated with chronic or relapsing infections with pathogens such as Staphylococcus aureus. Here we report the in vitro evolution of an S. aureus strain that exhibits a high degree of nonsusceptibility to daptomycin as a result of cyclic challenges with bactericidal concentrations of the drug. This phenotype was attributed to stationary growth phase-dependent drug tolerance and was clearly distinguished from resistance. The underlying genetic basis was revealed to be an adaptive point mutation in the putative inorganic phosphate (Pi) transporter gene pitA. Drug tolerance caused by this allele, termed pitA6, was abrogated when the upstream gene pitR was inactivated. Enhanced tolerance toward daptomycin, as well as the acyldepsipeptide antibiotic ADEP4 and various combinations of other drugs, was accompanied by elevated intracellular concentrations of Pi and polyphosphate, which may reversibly interfere with critical cellular functions. The evolved strain displayed increased rates of survival within human endothelial cells, demonstrating the correlation of intracellular persistence and drug tolerance. These findings will be useful for further investigations of S. aureus drug tolerance, toward the development of additional antipersister compounds and strategies.

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.

Antibiotic resistance is a growing problem that the World Health Organization describes as “one of the top three threats to global health.” To date, bacteria have developed resistance to all antibiotics used in clinical settings. Unfortunately, the evolution of antibiotic resistant bacteria is accelerating, as antibiotics continue to be misused and overused. As the antibiotic pipeline is drying up, it becomes increasingly critical to utilize the antibiotics already on the market more effectively. The key to designing better regimens lies in the ability to predict how bacteria will respond to a particular antibiotic treatment. Because of this, we need a simple metric that characterizes this pathogen-antibiotic interaction that can be easily measured and used to design dosing protocols that will effectively clear an infection. To help guide the design of effective protocols, we use quantitative modeling to develop a metric that is easy to measure and quantifies the pathogen-antibiotic interaction. Through optimized antibiotic regimens, our strategy could extend the use of first-line antibiotics, improve treatment outcome, and preserve last-resort antibiotics.

Biofilm-related infections: bridging the gap between clinical management and fundamental aspects of recalcitrance toward antibiotics

Surface-associated microbial communities, called biofilms, are present in all environments. Although biofilms play an important positive role in a variety of ecosystems, they also have many negative effects, including biofilm-related infections in medical settings. The ability of pathogenic biofilms to survive in the presence of high concentrations of antibiotics is called "recalcitrance" and is a characteristic property of the biofilm lifestyle, leading to treatment failure and infection recurrence. This review presents our current understanding of the molecular mechanisms of biofilm recalcitrance toward antibiotics and describes how recent progress has improved our capacity to design original and efficient strategies to prevent or eradicate biofilm-related infections.

Collective antibiotic tolerance: mechanisms, dynamics and intervention

Bacteria have developed resistance against every antibiotic at a rate that is alarming considering the timescale at which new antibiotics are developed. Thus, there is a critical need to use antibiotics more effectively, extend the shelf life of existing antibiotics and minimize their side effects. This requires understanding the mechanisms underlying bacterial drug responses. Past studies have focused on survival in the presence of antibiotics by individual cells, as genetic mutants or persisters. Also important, however, is the fact that a population of bacterial cells can collectively survive antibiotic treatments lethal to individual cells. This tolerance can arise by diverse mechanisms, including resistance-conferring enzyme production, titration-mediated bistable growth inhibition, swarming and interpopulation interactions. These strategies can enable rapid population recovery after antibiotic treatment and provide a time window during which otherwise susceptible bacteria can acquire inheritable genetic resistance. Here, we emphasize the potential for targeting collective antibiotic tolerance behaviors as an antibacterial treatment strategy.

Staphylococcus aureus adapts to oxidative stress by producing H2O2-resistant small-colony variants via the SOS response

The development of chronic and recurrent Staphylococcus aureus infections is associated with the emergence of slow-growing mutants known as small-colony variants (SCVs), which are highly tolerant of antibiotics and can survive inside host cells. However, the host and bacterial factors which underpin SCV emergence during infection are poorly understood. Here, we demonstrate that exposure of S. aureus to sublethal concentrations of H₂O₂ leads to a specific, dose-dependent increase in the population frequency of gentamicin-resistant SCVs. Time course analyses revealed that H₂O₂ exposure caused bacteriostasis in wild-type cells during which time SCVs appeared spontaneously within the S. aureus population. This occurred via a mutagenic DNA repair pathway that included DNA double-strand break repair proteins RexAB, recombinase A, and polymerase V. In addition to triggering SCV emergence by increasing the mutation rate, H₂O₂ also selected for the SCV phenotype, leading to increased phenotypic stability and further enhancing the size of the SCV subpopulation by reducing the rate of SCV reversion to the wild type. Subsequent analyses revealed that SCVs were significantly more resistant to the toxic effects of H₂O₂ than wild-type bacteria. With the exception of heme auxotrophs, gentamicin-resistant SCVs displayed greater catalase activity than wild-type bacteria, which contributed to their resistance to H₂O₂. Taken together, these data reveal a mechanism by which S. aureus adapts to oxidative stress via the production of a subpopulation of H₂O₂-resistant SCVs with enhanced catalase production.

Modelling mechanical and chemical treatment of biofilms with two phenotypic resistance mechanisms

Bacterial biofilms are notoriously difficult to eradicate owing to a number of tolerance mechanisms including physiological, physical, genotypic and phenotypic variations. Recent focus has shifted to phenotypic tolerance which is apparently the main defence mechanism that protects biofilms against long-term disinfection. Previous mathematical models have addressed phenotypic dynamics by considering adaptive response and persister formation separately. The aim of this manuscript is to consider a combined model to understand the interplay between these two defence mechanisms. We find that each mechanism protects the biofilm differently and hence responds differently to antibiotic challenge. We focus on on-off dosing that has been shown to eradicate each subpopulation alone. Our results indicate that the combined resistance exhibits qualitatively similar behavior to persister formation for short dosing times, and similar behavior to adaptive resistance for long dosing times. To further contrast the behavior of the model under different parameter regimes, we explore two classes of combination treatment that include mechanical and chemical treatments. The examples focus on different applications - pipe clearance and dental carrie prevention - and demonstrate the underlying conclusion that adaptive and persister mechanism provide protection for different challenges and are thus not redundant systems and each may require specific treatment plans.

Lack of intracellular trehalose affects formation of Escherichia coli persister cells

Persisters are dormant antibiotic-tolerant cells that usually compose a small fraction of bacterial populations. In this work, we focused on the role of trehalose in persister formation. We found that the ΔotsA mutant, which is unable to synthesize trehalose, produced increased levels of persisters in the early stationary phase and under heat stress conditions. The lack of trehalose in the ΔotsA mutant resulted in oxidative stress, manifested by increased membrane lipid peroxidation after heat shock. Stationary ΔotsA cells additionally exhibited increased levels of oxidized proteins and apurinic/apyrimidinic sites in DNA as compared to WT cells. Oxidative stress caused by the lack of trehalose was accompanied by the overproduction of extracellular indole, a signal molecule that has been shown to stimulate persister formation. Our major conclusion is that intracellular trehalose protects E. coli cells against oxidative stress and limits indole synthesis, which in turn inhibits the formation of persisters.

Persistence: a copacetic and parsimonious hypothesis for the existence of non-inherited resistance to antibiotics

We postulate that phenotypic resistance to antibiotics, persistence, is not an evolved (selected-for) character but rather like mutation, an inadvertent product of different kinds of errors and glitches. The rate of generation of these errors is augmented by exposure to these drugs. The genes that have been identified as contributing to the production of persisters are analogous to the so-called mutator genes; they modulate the rate at which these errors occur and/or are corrected. In theory, these phenotypically resistant bacteria can retard the rate of microbiological cure by antibiotic treatment.

Cellular noise and information transmission

The technological revolution in biological research, and in particular the use of molecular fluorescent labels, has allowed investigation of heterogeneity of cellular responses to stimuli on the single cell level. Computational, theoretical, and synthetic biology advances have allowed predicting and manipulating this heterogeneity with an exquisite precision previously reserved only for physical sciences. Functionally, this cell-to-cell variability can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. And yet quantification of the functional importance of this response heterogeneity remained elusive. Recently the mathematical language of information theory has been proposed to address this problem. This opinion reviews the recent advances and discusses the broader implications of using information-theoretic tools to characterize heterogeneity of cellular behaviors.

Metabolic and transcriptional activities of Staphylococcus aureus challenged with high-doses of daptomycin

Treatment of stationary growth phase Staphylococcus aureus SA113 with 100-fold of the MIC of the lipopeptide antibiotic daptomycin leaves alive a small fraction of drug tolerant albeit genetically susceptible bacteria. This study shows that cells of this subpopulation exhibit active metabolism even hours after the onset of the drug challenge. Isotopologue profiling using fully (13)C-labeled glucose revealed de novo biosynthesis of the amino acids Ala, Asp, Glu, Ser, Gly and His. The isotopologue composition in Asp and Glu suggested an increased activity of the TCA cycle under daptomycin treatment compared to unaffected stationary growth phase cells. Microarray analysis showed differential expression of specific genes 10 min and 3 h after addition of the drug. Besides factors involved in drug response, a number of metabolic genes appear to shape the signature of daptomycin-tolerant S. aureus cells. These observations will be useful toward the development of new strategies against persisters and related forms of bacterial cells with downshifted physiology.

Development of oxacillin resistance in a patient with recurrent Staphylococcus aureus bacteremia

Whole-genome sequencing was used to compare longitudinal isolates of Staphylococcus aureus that developed resistance to oxacillin (MIC up to 16 μg/ml). The mecA gene was absent. A novel 5-bp TATCC frameshift insertion in a gene encoding an ABC transporter similar to that of the teichoic acid translocation ATP-binding protein TagH and a 3-bp GCT nonframeshift insertion in the pdhA pyruvate dehydrogenase gene were detected in the oxacillin-resistant isolates.

Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections

The successful treatment of bacterial infections is the product of a collaboration between antibiotics and the host's immune defenses. Nevertheless, in the design of antibiotic treatment regimens, few studies have explored the combined action of antibiotics and the immune response to clearing infections. Here, we use mathematical models to examine the collective contribution of antibiotics and the immune response to the treatment of acute, self-limiting bacterial infections. Our models incorporate the pharmacokinetics and pharmacodynamics of the antibiotics, the innate and adaptive immune responses, and the population and evolutionary dynamics of the target bacteria. We consider two extremes for the antibiotic-immune relationship: one in which the efficacy of the immune response in clearing infections is directly proportional to the density of the pathogen; the other in which its action is largely independent of this density. We explore the effect of antibiotic dose, dosing frequency, and term of treatment on the time before clearance of the infection and the likelihood of antibiotic-resistant bacteria emerging and ascending. Our results suggest that, under most conditions, high dose, full-term therapy is more effective than more moderate dosing in promoting the clearance of the infection and decreasing the likelihood of emergence of antibiotic resistance. Our results also indicate that the clinical and evolutionary benefits of increasing antibiotic dose are not indefinite. We discuss the current status of data in support of and in opposition to the predictions of this study, consider those elements that require additional testing, and suggest how they can be tested.

Characterization of multi-drug tolerant persister cells in Streptococcus suis

Background

Persister cells constitute a subpopulation of dormant cells within a microbial population which are genetically identical but phenotypically different to regular cells. Notably, persister cells show an elevated tolerance to antimicrobial agents. Thus, they are considered to represent a microbial ‘bet-hedging’ strategy and are of particular importance in pathogenic bacteria.

Results

We studied the ability of the zoonotic pathogen Streptococcus (S.) suis to form multi-drug tolerant variants and identified persister cells dependent on the initial bacterial growth phase. We observed lower numbers of persisters in exponential phase cultures than in stationary growth phase populations. S. suis persister cells showed a high tolerance to a variety of antibiotics, and the phenotype was not inherited as tested with four passages of S. suis populations. Furthermore, we provide evidence that the persister phenotype is related to expression of genes involved in general metabolic pathways since we found higher numbers of persister cells in a mutant strain defective in the catabolic arginine deiminase system as compared to its parental wild type strain. Finally, we observed persister cell formation also in other S. suis strains and pathogenic streptococcal species.

Conclusions

Taken together, this is the first study that reports multi-drug tolerant persister cells in the zoonotic pathogen S. suis.

Bacterial persisters: formation, eradication, and experimental systems

Persisters are multidrug-tolerant bacteria that could account for the relapse of infections. For a long time, persisters have been assumed to be nonreplicating dormant bacteria, but the growth status of these recalcitrant cells is still debated. Toxin-antitoxin (TA) modules have an important role in the formation of persisters and several studies show that they can form in response to different triggers. These findings, together with the invention of new tools to study persisters, could have important implications for the development of novel therapeutics to eradicate persisting subpopulations.

Clonal expansion during Staphylococcus aureus infection dynamics reveals the effect of antibiotic intervention

To slow the inexorable rise of antibiotic resistance we must understand how drugs impact on pathogenesis and influence the selection of resistant clones. Staphylococcus aureus is an important human pathogen with populations of antibiotic-resistant bacteria in hospitals and the community. Host phagocytes play a crucial role in controlling S. aureus infection, which can lead to a population "bottleneck" whereby clonal expansion of a small fraction of the initial inoculum founds a systemic infection. Such population dynamics may have important consequences on the effect of antibiotic intervention. Low doses of antibiotics have been shown to affect in vitro growth and the generation of resistant mutants over the long term, however whether this has any in vivo relevance is unknown. In this work, the population dynamics of S. aureus pathogenesis were studied in vivo using antibiotic-resistant strains constructed in an isogenic background, coupled with systemic models of infection in both the mouse and zebrafish embryo. Murine experiments revealed unexpected and complex bacterial population kinetics arising from clonal expansion during infection in particular organs. We subsequently elucidated the effect of antibiotic intervention within the host using mixed inocula of resistant and sensitive bacteria. Sub-curative tetracycline doses support the preferential expansion of resistant microorganisms, importantly unrelated to effects on growth rate or de novo resistance acquisition. This novel phenomenon is generic, occurring with methicillin-resistant S. aureus (MRSA) in the presence of β-lactams and with the unrelated human pathogen Pseudomonas aeruginosa. The selection of resistant clones at low antibiotic levels can result in a rapid increase in their prevalence under conditions that would previously not be thought to favor them. Our results have key implications for the design of effective treatment regimes to limit the spread of antimicrobial resistance, where inappropriate usage leading to resistance may reduce the efficacy of life-saving drugs.

Mathematical modeling on bacterial resistance to multiple antibiotics caused by spontaneous mutations

We formulate a mathematical model that describes the population dynamics of bacteria exposed to multiple antibiotics simultaneously, assuming that acquisition of resistance is through mutations due to antibiotic exposure. Qualitative analysis reveals the existence of a free-bacteria equilibrium, resistant-bacteria equilibrium and an endemic equilibrium where both bacteria coexist.

Persisters and beyond: mechanisms of phenotypic drug resistance and drug tolerance in bacteria

One of the challenges in clinical infectious diseases is the problem of chronic infections, which can require long durations of antibiotic treatment and often recur. An emerging explanation for the refractoriness of some infections to treatment is the existence of subpopulations of drug tolerant cells. While typically discussed as "persister" cells, it is becoming increasingly clear that there is significant heterogeneity in drug responses within a bacterial population and that multiple mechanisms underlie the emergence of drug tolerant and drug-resistant subpopulations. Many of these parallel mechanisms have been shown to affect drug susceptibility at the level of a whole population. Here we review mechanisms of phenotypic drug tolerance and resistance in bacteria with the goal of providing a framework for understanding the similarities and differences in these cells.

Activated ClpP kills persisters and eradicates a chronic biofilm infection

Chronic infections are difficult to treat with antibiotics but are caused primarily by drug-sensitive pathogens. Dormant persister cells that are tolerant to killing by antibiotics are responsible for this apparent paradox. Persisters are phenotypic variants of normal cells and pathways leading to dormancy are redundant, making it challenging to develop anti-persister compounds. Biofilms shield persisters from the immune system, suggesting that an antibiotic for treating a chronic infection should be able to eradicate the infection on its own. We reasoned that a compound capable of corrupting a target in dormant cells will kill persisters. The acyldepsipeptide antibiotic (ADEP4) has been shown to activate the ClpP protease, resulting in death of growing cells. Here we show that ADEP4-activated ClpP becomes a fairly nonspecific protease and kills persisters by degrading over 400 proteins, forcing cells to self-digest. Null mutants of clpP arise with high probability, but combining ADEP4 with rifampicin produced complete eradication of Staphylococcus aureus biofilms in vitro and in a mouse model of a chronic infection. Our findings indicate a general principle for killing dormant cells-activation and corruption of a target, rather than conventional inhibition. Eradication of a biofilm in an animal model by activating a protease suggests a realistic path towards developing therapies to treat chronic infections.

The pharmaco -, population and evolutionary dynamics of multi-drug therapy: experiments with S. aureus and E. coli and computer simulations

There are both pharmacodynamic and evolutionary reasons to use multiple rather than single antibiotics to treat bacterial infections; in combination antibiotics can be more effective in killing target bacteria as well as in preventing the emergence of resistance. Nevertheless, with few exceptions like tuberculosis, combination therapy is rarely used for bacterial infections. One reason for this is a relative dearth of the pharmaco-, population- and evolutionary dynamic information needed for the rational design of multi-drug treatment protocols. Here, we use in vitro pharmacodynamic experiments, mathematical models and computer simulations to explore the relative efficacies of different two-drug regimens in clearing bacterial infections and the conditions under which multi-drug therapy will prevent the ascent of resistance. We estimate the parameters and explore the fit of Hill functions to compare the pharmacodynamics of antibiotics of four different classes individually and in pairs during cidal experiments with pathogenic strains of Staphylococcus aureus and Escherichia coli. We also consider the relative efficacy of these antibiotics and antibiotic pairs in reducing the level of phenotypically resistant but genetically susceptible, persister, subpopulations. Our results provide compelling support for the proposition that the nature and form of the interactions between drugs of different classes, synergy, antagonism, suppression and additivity, has to be determined empirically and cannot be inferred from what is known about the pharmacodynamics or mode of action of these drugs individually. Monte Carlo simulations of within-host treatment incorporating these pharmacodynamic results and clinically relevant refuge subpopulations of bacteria indicate that: (i) the form of drug-drug interactions can profoundly affect the rate at which infections are cleared, (ii) two-drug therapy can prevent treatment failure even when bacteria resistant to single drugs are present at the onset of therapy, and (iii) this evolutionary virtue of two-drug therapy is manifest even when the antibiotics suppress each other's activity.

In this study, we combine pharmacodynamic experiments using pathogenic strains of E. coli and S. aureus with mathematical and computer simulation models to explore the relative efficacies of two-drug antibiotic combinations in clearing infections and preventing the emergence of resistance. We develop a pharmacodynamic method that provides a convenient way to determine whether drug combinations will interact synergistically, antagonistically, additively or suppressively. We find that it is not possible to predict the nature and form of drug interactions based on what is known about the mode of action of individual drugs, thus illustrating the necessity of assessing the efficacy of drug combinations empirically. Our simulations of the within-host population and evolutionary dynamics of bacteria undergoing multi-drug treatment indicate that the form of the interaction between drugs observed experimentally can substantially affect the rate of clearance of the infection. On the other hand, the form of these interactions plays a minimal role in the emergence of resistance. Even when antibiotics are suppressive, two-drug therapy can prevent the ascent of bacteria resistant to single drugs that are present at the start of therapy and/or generated during the course of the infection.

Quantitative analysis of persister fractions suggests different mechanisms of formation among environmental isolates of E. coli

BACKGROUND

Bacterial persistence describes a phenomenon wherein a small subpopulation of cells is able to survive a challenge with high doses of an antibiotic (or other stressor) better than the majority of the population. Previous work has shown that cells that are in a dormant or slow-growing state are persistent to antibiotic treatment and that populations with higher fractions of dormant cells exhibit higher levels of persistence. These data suggest that a major determinant of the fraction of persisters within a population is the rate at which cells enter and exit from dormancy. However, it is not known whether there are physiological changes in addition to dormancy that influence persistence. Here, we use quantitative measurements of persister fractions in a set of environmental isolates of E. coli together with a mathematical model of persister formation to test whether a single general physiological change, such as cell dormancy, can explain the differences in persister phenotypes observed in different strains.

RESULTS

If a single physiological change (e.g. cell dormancy) underlies most persister phenotypes, then strains should exhibit characteristic fractions of persister cells: some strains will consistently have high fractions of persisters (dormant cells), whereas others will have low fractions. Although we found substantial variation in the fraction of persisters between different environmental isolates of E. coli, these fractions were not correlated across antibiotics. Some strains exhibited high persister fractions in one antibiotic, but low persister fractions in a second antibiotic. Surprisingly, no correlation in persister fractions was observed between any two drugs, even for antibiotics with nearly identical modes of action (ciprofloxacin and nalidixic acid).

CONCLUSIONS

These data support the hypothesis that there is no single physiological change that determines the persistence level in a population of cells. Instead, the fraction of cells that survive antibiotic treatment (persist) depends critically on the specific antibiotic that is used, suggesting that physiological changes in addition to dormancy can underlie persister phenotypes.