Trade-offs between drug toxicity and benefit in the multi-antibiotic resistance system underlie optimal growth of E. coli

Modulation of antibiotic effects on microbial communities by resource competition

Antibiotic treatment significantly impacts the human gut microbiota, but quantitative understanding of how antibiotics affect community diversity is lacking. Here, we build on classical ecological models of resource competition to investigate community responses to species-specific death rates, as induced by antibiotic activity or other growth-inhibiting factors such as bacteriophages. Our analyses highlight the complex dependence of species coexistence that can arise from the interplay of resource competition and antibiotic activity, independent of other biological mechanisms. In particular, we identify resource competition structures that cause richness to depend on the order of sequential application of antibiotics (non-transitivity), and the emergence of synergistic and antagonistic effects under simultaneous application of multiple antibiotics (non-additivity). These complex behaviors can be prevalent, especially when generalist consumers are targeted. Communities can be prone to either synergism or antagonism, but typically not both, and antagonism is more common. Furthermore, we identify a striking overlap in competition structures that lead to non-transitivity during antibiotic sequences and those that lead to non-additivity during antibiotic combination. In sum, our results establish a broadly applicable framework for predicting microbial community dynamics under deleterious perturbations.

Heterogeneous Distribution of Proton Motive Force in Nonheritable Antibiotic Resistance

Bacterial infections that are difficult to eradicate are often treated by sequentially exposing the bacteria to different antibiotics. Although effective, this approach can give rise to epigenetic or other phenomena that may help some cells adapt to and tolerate the antibiotics. Characteristics of such adapted cells are dormancy and low energy levels, which promote survival without lending long-term genetic resistance against antibiotics. In this work, we quantified motility in cells of Escherichia coli that adapted and survived sequential exposure to lethal doses of antibiotics. In populations that adapted to transcriptional inhibition by rifampicin, we observed that ~1 of 3 cells continued swimming for several hours in the presence of lethal concentrations of ampicillin. As motility is powered by proton motive force (PMF), our results suggested that many adapted cells retained a high PMF. Single-cell growth assays revealed that the high-PMF cells resuscitated and divided upon the removal of ampicillin, just as the low-PMF cells did, a behavior reminiscent of persister cells. Our results are consistent with the notion that cells in a clonal population may employ multiple different mechanisms to adapt to antibiotic stresses. Variable PMF is likely a feature of a bet-hedging strategy: a fraction of the adapted cell population lies dormant while the other fraction retains high PMF to be able to swim out of the deleterious environment. IMPORTANCE Bacterial cells with low PMF may survive antibiotic stress due to dormancy, which favors nonheritable resistance without genetic mutations or acquisitions. On the other hand, cells with high PMF are less tolerant, as PMF helps in the uptake of certain antibiotics. Here, we quantified flagellar motility as an indirect measure of the PMF in cells of Escherichia coli that had adapted to ampicillin. Despite the disadvantage of maintaining a high PMF in the presence of antibiotics, we observed high PMF in ~30% of the cells, as evidenced by their ability to swim rapidly for several hours. These and other results were consistent with the idea that antibiotic tolerance can arise via different mechanisms in a clonal population.

Decreased thermal niche breadth as a trade-off of antibiotic resistance

Evolutionary theory predicts that adaptations, including antibiotic resistance, should come with associated fitness costs; yet, many resistance mutations seemingly contradict this prediction by inducing no growth rate deficit. However, most growth assays comparing sensitive and resistant strains have been performed under a narrow range of environmental conditions, which do not reflect the variety of contexts that a pathogenic bacterium might encounter when causing infection. We hypothesized that reduced niche breadth, defined as diminished growth across a diversity of environments, can be a cost of antibiotic resistance. Specifically, we test whether chloramphenicol-resistant Escherichia coli incur disproportionate growth deficits in novel thermal conditions. Here we show that chloramphenicol-resistant bacteria have greater fitness costs at novel temperatures than their antibiotic-sensitive ancestors. In several cases, we observed no resistance cost in growth rate at the historic temperature but saw diminished growth at warmer and colder temperatures. These results were consistent across various genetic mechanisms of resistance. Thus, we propose that decreased thermal niche breadth is an under-documented fitness cost of antibiotic resistance. Furthermore, these results demonstrate that the cost of antibiotic resistance shifts rapidly as the environment changes; these context-dependent resistance costs should select for the rapid gain and loss of resistance as an evolutionary strategy.

From the soil to the clinic: the impact of microbial secondary metabolites on antibiotic tolerance and resistance

Secondary metabolites profoundly affect microbial physiology, metabolism and stress responses. Increasing evidence suggests that these molecules can modulate microbial susceptibility to commonly used antibiotics; however, secondary metabolites are typically excluded from standard antimicrobial susceptibility assays. This may in part account for why infections by diverse opportunistic bacteria that produce secondary metabolites often exhibit discrepancies between clinical antimicrobial susceptibility testing results and clinical treatment outcomes. In this Review, we explore which types of secondary metabolite alter antimicrobial susceptibility, as well as how and why this phenomenon occurs. We discuss examples of molecules that opportunistic and enteric pathogens either generate themselves or are exposed to from their neighbours, and the nuanced impacts these molecules can have on tolerance and resistance to certain antibiotics.

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Hidden suppressive interactions are common in higher-order drug combinations

The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.

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Most instances of suppressive interactions are missed by standard methods

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A majority (54%) of all antibiotic combinations tested contain hidden suppression

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Identifying hidden suppression can affect what combinations should be used

Extreme Antagonism Arising from Gene-Environment Interactions

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.

MarA, RamA, and SoxS as Mediators of the Stress Response: Survival at a Cost

To survive and adapt to changing environments, bacteria have evolved mechanisms to express appropriate genes at appropriate times. Exposure to antimicrobials triggers a global stress response in Enterobacteriaceae, underpinned by activation of a family of transcriptional regulators, including MarA, RamA, and SoxS. These control a program of altered gene expression allowing a rapid and measured response to improve fitness in the presence of toxic drugs. Increased expression of marA, ramA, and soxS up regulates efflux activity to allow detoxification of the cell. However, this also results in trade-offs in other phenotypes, such as impaired growth rates, biofilm formation and virulence. Here, we review the current knowledge regarding the trade-offs that exist between drug survival and other phenotypes that result from induction of marA, ramA, and soxS. Additionally, we present some new findings linking expression of these regulators and biofilm formation in Enterobacteriaceae, thereby demonstrating the interconnected nature of regulatory networks within the cell and explaining how trade-offs can exist between important phenotypes. This has important implications for our understanding of how bacterial virulence and biofilms can be influenced by exposure to antimicrobials.

MarA, RamA, and SoxS as Mediators of the Stress Response: Survival at a Cost

To survive and adapt to changing environments, bacteria have evolved mechanisms to express appropriate genes at appropriate times. Exposure to antimicrobials triggers a global stress response in Enterobacteriaceae, underpinned by activation of a family of transcriptional regulators, including MarA, RamA, and SoxS. These control a program of altered gene expression allowing a rapid and measured response to improve fitness in the presence of toxic drugs. Increased expression of marA, ramA, and soxS up regulates efflux activity to allow detoxification of the cell. However, this also results in trade-offs in other phenotypes, such as impaired growth rates, biofilm formation and virulence. Here, we review the current knowledge regarding the trade-offs that exist between drug survival and other phenotypes that result from induction of marA, ramA, and soxS. Additionally, we present some new findings linking expression of these regulators and biofilm formation in Enterobacteriaceae, thereby demonstrating the interconnected nature of regulatory networks within the cell and explaining how trade-offs can exist between important phenotypes. This has important implications for our understanding of how bacterial virulence and biofilms can be influenced by exposure to antimicrobials.

Persisters of Klebsiella pneumoniae and Proteus mirabilis: A Common Phenomenon and Different Behavior Profiles

Persisters of infectious agents are capable of surviving antibiotic treatment so the emergence of these subpopulations need to be overcome. In this study, we aimed to isolate, characterize and inhibit persister subpopulation in two clinical isolates Klebsiella pneumoniae and Proteus mirabilis. Different behavior profiles between the two isolates could be observed. The results of dose-dependent killing curve revealed that 2.3% (Klebsiella pneumoniae) versus 1.3% (Proteus mirabilis) persister cells could be recovered using 500 and 30 ug/ml ciprofloxacin, respectively. Upon resuscitation, persister cells exhibited only 65% versus 30% percentage growth and 5 versus 7 times cell elongation relative to Klebsiella pneumoniae and Proteus mirabilis, respectively. The levels of persister cells to ciprofloxacin of Klebsiella pneumoniae were dramatically decreased by about 79, 92, 97 and 83% in average by pre-exposure to hyperosmotic stress, temperature, different pHs, and hydrogen peroxide, respectively, while those of Proteus mirabilis were minimally decreased with corresponding reduction percentages of about 12%, 24 & 25%, and 0%. Regarding combating persisters, Klebsiella pneumoniae showed different response as compared to Proteus mirabilis. Among the tested sugars, the highest reduction of Klebsiella pneumoniae persister cells was obtained with pre-priming with sucrose while for Proteus mirabilis persister cells, the highest reduction was obtained with pre-priming with glucose. Using sodium salicylate with ciprofloxacin could eradicate persisters of Klebsiella pneumoniae at any tested concentration while for Proteus mirabilis it caused some reduction in persister cells at certain concentrations. Complete eradication of persisters was obtained by combining silver nitrate with ciprofloxacin for each test isolate.

Efflux Pumps in Chromobacterium Species Increase Antibiotic Resistance and Promote Survival in a Coculture Competition Model

Members of the Chromobacterium genus include opportunistic but often-fatal pathogens and soil saprophytes with highly versatile metabolic capabilities. In previous studies of Chromobacterium subtsugae (formerly C. violaceum) strain CV017, we identified a resistance nodulation division (RND)-family efflux pump (CdeAB-OprM) that confers resistance to several antibiotics, including the bactobolin antibiotic produced by the soil saprophyte Burkholderia thailandensis Here, we show the cdeAB-oprM genes increase C. subtsugae survival in a laboratory competition model with B. thailandensis We also demonstrate that adding sublethal bactobolin concentrations to the coculture increases C. subtsugae survival, but this effect is not through CdeAB-OprM. Instead, the increased survival requires a second, previously unreported pump we call CseAB-OprN. We show that in cells exposed to sublethal bactobolin concentrations, the cseAB-oprN genes are transcriptionally induced, and this corresponds to an increase in bactobolin resistance. Induction of this pump is highly specific and sensitive to bactobolin, while CdeAB-OprM appears to have a broader range of antibiotic recognition. We examine the distribution of cseAB-oprN and cdeAB-oprM gene clusters in members of the Chromobacterium genus and find the cseAB-oprN genes are limited to the nonpathogenic C. subtsugae strains, whereas the cdeAB-oprM genes are more widely distributed among members of the Chromobacterium genus. Our results provide new information on the antibiotic resistance mechanisms of Chromobacterium species and highlight the importance of efflux pumps for saprophytic bacteria existing in multispecies communities.IMPORTANCE Antibiotic efflux pumps are best known for increasing antibiotic resistance of pathogens; however, the role of these pumps in saprophytes is much less well defined. This study describes two predicted efflux pump gene clusters in the Chromobacterium genus, which is comprised of both nonpathogenic saprophytes and species that cause highly fatal human infections. One of the predicted efflux pump clusters is present in every member of the Chromobacterium genus and increases resistance to a broad range of antibiotics. The other gene cluster has more narrow antibiotic specificity and is found only in Chromobacterium subtsugae, a subset of entirely nonpathogenic species. We demonstrate the role of both pumps in increasing antibiotic resistance and demonstrate the importance of efflux-dependent resistance induction for C. subtsugae survival in a dual-species competition model. These results have implications for managing antibiotic-resistant Chromobacterium infections and for understanding the evolution of efflux pumps outside the host.

Salicylate Increases Fitness Cost Associated with MarA-Mediated Antibiotic Resistance

Antibiotic resistance is generally associated with a fitness deficit resulting from the burden of producing and maintaining resistance machinery. This additional cost suggests that resistant bacteria will be outcompeted by susceptible bacteria in conditions without antibiotics. However, in practice, this process is slow in part because of regulation that minimizes expression of these genes in the absence of antibiotics. This suggests that if it were possible to turn on their expression, the cost would increase, thereby accelerating removal of resistant strains. Experimental and theoretical studies have shown that environmental chemicals can change the fitness cost associated with resistance and therefore have a significant impact on population dynamics. The multiple antibiotic resistance activator (MarA) is a clinically important regulator in Escherichia coli that activates downstream genes to increase resistance against multiple classes of antibiotics. Salicylate is an inducer of MarA that can be found in the environment and derepresses marA's expression. In this study, we sought to unravel the interplay between salicylate and the fitness cost of MarA-mediated antibiotic resistance. Using salicylate as an inducer of MarA, we found that a wide spectrum of concentrations can increase burden in resistant strains compared to susceptible strains. Induction resulted in rapid exclusion of resistant bacteria from mixed populations of antibiotic-resistant and susceptible cells. A mathematical model captures the process and predicts its effect in various environmental conditions. Our work provides a quantitative understanding of salicylate exposure on the fitness of different MarA variants and suggests that salicylate can lead to selection against MarA-mediated resistant strains. More generally, our findings show that natural inducers may serve to bias population membership and could impact antibiotic resistance and other important phenotypes.

Heterogeneity in efflux pump expression predisposes antibiotic-resistant cells to mutation

Antibiotic resistance is often the result of mutations that block drug activity; however, bacteria also evade antibiotics by transiently expressing genes such as multidrug efflux pumps. A crucial question is whether transient resistance can promote permanent genetic changes. Previous studies have established that antibiotic treatment can select tolerant cells that then mutate to achieve permanent resistance. Whether these mutations result from antibiotic stress or preexist within the population is unclear. To address this question, we focused on the multidrug pump AcrAB-TolC. Using time-lapse microscopy, we found that cells with higher acrAB expression have lower expression of the DNA mismatch repair gene mutS, lower growth rates, and higher mutation frequencies. Thus, transient antibiotic resistance from elevated acrAB expression can promote spontaneous mutations within single cells.

A bargaining approach for resolving the tradeoff between beneficial and harmful drug responses

In medical treatments, a fundamental dilemma often arises: on the one hand, an increase in a drug's dose could lead to a stronger, therapeutic treatment response, but on the other hand this could also lead to increased toxicity risks. In this paper, we propose to solve this dilemma using a Nash bargaining approach. To do so, we reformulate the tradeoff problem in an equivalent form as a dilemma between a drug's beneficial response and the drug's safety, where the dilemma then becomes a two-objective problem with safety and response as the objectives. Using a general receptor response model, we show that the set of all feasible outcomes associated with a drug's treatment is characterized as a convex set, which allows the dilemma to be solved as a bargaining problem between the two objectives. The Nash bargaining solution (NBS) is found in closed form, and interesting properties associated with the solution are presented. In particular, it is shown that one can interpret the NBS as corresponding to a treatment giving the maximal expected safe treatment response associated with the drug. Moreover, the condition when the NBS coincides with the maxmin solution for the two objectives is derived. Two approaches to control the NBS solution are then presented: first, by means of assigning bargaining powers to objectives; second, by means of assigning an upper-bound on the drug's dosage. The obtained NBS are compared with the dosage returning maximal drug response efficiency.

Salicylate induces reactive oxygen species and reduces ultraviolet C susceptibility in Staphylococcus aureus

This study demonstrates that growth of Staphylococcus aureus in the presence of salicylate reduces ultraviolet C (UVC)-induced cell death and increases the generation of reactive oxygen species (ROS). In addition, compounds that scavenge ROS (N-acetylcysteine, glutathione, catalase and superoxide dismutase) reverse the increased UVC survival induced by growth in the presence of salicylate, while ROS donors (tert-butylhydroperoxide, H2O2 and NaClO) enhance survival of salicylate challenged cultures. Collectively, these findings suggest that ROS production induced by growth in the presence of salicylate protects S. aureus from UVC-induced cell death.

Expression of Heterologous Sigma Factor Expands the Searchable Space for Biofuel Tolerance Mechanisms

Microorganisms can produce hydrocarbons that can serve as replacements or additions to conventional liquid fuels for use in the transportation sector. However, a common problem in the microbial synthesis of biofuels is that these compounds often have toxic effects on the cell. In this study, we focused on mitigating the toxicity of the biojet fuel precursor pinene on Escherichia coli. We used genomic DNA from Pseudomonas putida KT2440, which has innate solvent-tolerance properties, to create transgenic libraries in an E. coli host. We exposed cells containing the library to pinene, selecting for genes that improved tolerance. Importantly, we found that expressing the sigma factor RpoD from P. putida greatly expanded the diversity of tolerance genes recovered. With low expression of rpoD_P.putida, we isolated a single pinene tolerance gene; with increased expression of the sigma factor our selection experiments returned multiple distinct tolerance mechanisms, including some that have been previously documented and also new mechanisms. Interestingly, high levels of rpoD_P.putida induction resulted in decreased diversity. We found that the tolerance levels provided by some genes are highly sensitive to the level of induction of rpoD_P.putida, while others provide tolerance across a wide range of rpoD_P.putida levels. This method for unlocking diversity in tolerance screening using heterologous sigma factor expression was applicable to both plasmid and fosmid-based transgenic libraries. These results suggest that by controlling the expression of appropriate heterologous sigma factors, we can greatly increase the searchable genomic space within transgenic libraries.

Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins

Populations of cells need to express proteins to survive the sudden appearance of stressors. However, these mechanisms may be taxing. Populations can introduce diversity, allowing individual cells to stochastically switch between fast-growing and stress-tolerant states. One way to achieve this is to use genetic networks coupled with noise to generate bimodal distributions with two distinct subpopulations, each adapted to a stress condition. Another survival strategy is to rely on random fluctuations in gene expression to produce continuous, unimodal distributions of the stress response protein. To quantify the environmental conditions where bimodal versus unimodal expression is beneficial, we used a differential evolution algorithm to evolve optimal distributions of stress response proteins given environments with sudden fluctuations between low and high stress. We found that bimodality evolved for a large range of environmental conditions. However, we asked whether these findings were an artifact of considering two well-defined stress environments (low and high stress). As noise in the environment increases, or when there is an intermediate environment (medium stress), the benefits of bimodality decrease. Our results indicate that under realistic conditions, a continuum of resistance phenotypes generated through a unimodal distribution is sufficient to ensure survival without a high cost to the population.

Bacterial persistence induced by salicylate via reactive oxygen species

Persisters are phenotypic variants of regular cells that exist in a dormant state with low metabolic activity, allowing them to exhibit high tolerance to antibiotics. Despite increasing recognition of their role in chronic and recalcitrant infections, the mechanisms that induce persister formation are not fully understood. In this study, we find that salicylate can induce persister formation in Escherichia coli via generation of reactive oxygen species (ROS). Salicylate-induced ROS cause a decrease in the membrane potential, reduce metabolism and lead to an increase in persistence. These effects can be recovered by culturing cells in the presence of a ROS quencher or in an anaerobic environment. Our findings reveal that salicylate-induced oxidative stress can lead to persistence, suggesting that ROS, and their subsequent impact on membrane potential and metabolism, may play a broad role in persister formation.

Benzoate- and Salicylate-Tolerant Strains of Escherichia coli K-12 Lose Antibiotic Resistance during Laboratory Evolution

Escherichia coli K-12 W3110 grows in the presence of membrane-permeant organic acids that can depress cytoplasmic pH and accumulate in the cytoplasm. We conducted experimental evolution by daily diluting cultures in increasing concentrations of benzoic acid (up to 20 mM) buffered at external pH 6.5, a pH at which permeant acids concentrate in the cytoplasm. By 2,000 generations, clones isolated from evolving populations showed increasing tolerance to benzoate but were sensitive to chloramphenicol and tetracycline. Sixteen clones grew to stationary phase in 20 mM benzoate, whereas the ancestral strain W3110 peaked and declined. Similar growth occurred in 10 mM salicylate. Benzoate-evolved strains grew like W3110 in the absence of benzoate, in media buffered at pH 4.8, pH 7.0, or pH 9.0, or in 20 mM acetate or sorbate at pH 6.5. Genomes of 16 strains revealed over 100 mutations, including single-nucleotide polymorphisms (SNPs), large deletions, and insertion knockouts. Most strains acquired deletions in the benzoate-induced multiple antibiotic resistance (Mar) regulon or in associated regulators such as rob and cpxA, as well as the multidrug resistance (MDR) efflux pumps emrA, emrY, and mdtA Strains also lost or downregulated the Gad acid fitness regulon. In 5 mM benzoate or in 2 mM salicylate (2-hydroxybenzoate), most strains showed increased sensitivity to the antibiotics chloramphenicol and tetracycline; some strains were more sensitive than a marA knockout strain. Thus, our benzoate-evolved strains may reveal additional unknown drug resistance components. Benzoate or salicylate selection pressure may cause general loss of MDR genes and regulators.

IMPORTANCE

Benzoate is a common food preservative, and salicylate is the primary active metabolite of aspirin. In the gut microbiome, genetic adaptation to salicylate may involve loss or downregulation of inducible multidrug resistance systems. This discovery implies that aspirin therapy may modulate the human gut microbiome to favor salicylate tolerance at the expense of drug resistance. Similar aspirin-associated loss of drug resistance might occur in bacterial pathogens found in arterial plaques.

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Current Trends in Multidrug Optimization

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells

Transient resistance can allow microorganisms to temporarily survive lethal concentrations of antibiotics. This can be accomplished through stochastic mechanisms, where individual cells within a population display diverse phenotypes to hedge against the appearance of an antibiotic. To date, research on transient stochastic resistance has focused primarily on mechanisms where a subpopulation of cells enters a dormant, drug-tolerant state. However, a fundamental question is whether stochastic gene expression can also generate variable resistance levels among growing cells in a population. We hypothesized that stochastic expression of antibiotic-inducible resistance mechanisms might play such a role. To investigate this, we focused on a prototypical example of such a system: the multiple antibiotic resistance activator MarA. Previous studies have shown that induction of MarA can lead to a multidrug resistant phenotype at the population level. We asked whether MarA expression also has a stochastic component, even when uninduced. Time lapse microscopy showed that isogenic cells express heterogeneous, dynamic levels of MarA, which were correlated with transient antibiotic survival. This finding has important clinical implications, as stochastic expression of resistance genes may be widespread, allowing populations to hedge against the sudden appearance of an antibiotic.

Trade-Offs in Improving Biofuel Tolerance Using Combinations of Efflux Pumps

Microbes can be engineered to produce next-generation biofuels; however, the accumulation of toxic biofuels can limit yields. Previous studies have shown that efflux pumps can increase biofuel tolerance and improve production. Here, we asked whether expressing multiple pumps in combination could further increase biofuel tolerance. Pump overexpression inhibits cell growth, suggesting a trade-off between biofuel and pump toxicity. With multiple pumps, it is unclear how the fitness landscape is impacted. To address this, we measured tolerance of Escherichia coli to the biojet fuel precursor α-pinene in one-pump and two-pump strains. To support our experiments, we developed a mathematical model describing toxicity due to biofuel and overexpression of pumps. We found that data from one-pump strains can accurately predict the performance of two-pump strains. This result suggests that it may be possible to dramatically reduce the number of experiments required for characterizing the effects of combined biofuel tolerance mechanisms.

Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms

To counter future uncertainty, cells can stochastically express stress response mechanisms to diversify their population and hedge against stress. This approach allows a small subset of the population to survive without the prohibitive cost of constantly expressing resistance machinery at the population level. However, expression of multiple genes in concert is often needed to ensure survival, requiring coordination of infrequent events across many downstream targets. This raises the question of how cells orchestrate the timing of multiple rare events without adding cost. To investigate this, we used a stochastic model to study regulation of downstream target genes by a transcription factor. We compared several upstream regulator profiles, including constant expression, pulsatile dynamics, and noisy expression. We found that pulsatile dynamics and noise are sufficient to coordinate expression of multiple downstream genes. Notably, this is true even when fluctuations in the upstream regulator are far below the dissociation constants of the regulated genes, as with infrequently activated genes. As an example, we simulated the dynamics of the multiple antibiotic resistance activator (MarA) and 40 diverse downstream genes it regulates, determining that low-level dynamics in MarA are sufficient to coordinate expression of resistance mechanisms. We also demonstrated that noise can play a similar coordinating role. Importantly, we found that these benefits are present without a corresponding increase in the population-level cost. Therefore, our model suggests that low-level dynamics or noise in a transcription factor can coordinate expression of multiple stress response mechanisms by engaging them simultaneously without adding to the overall cost.

Effect and evolution of gene expression noise on the fitness landscape

Gene expression is a stochastic process that affects cellular and population fitness. Noise in gene expression can enhance fitness by increasing cell to cell variability as well as the time cells spend in favorable expression states. Using a stochastic model of gene expression together with a fitness function that incorporates the costs and benefits of gene expression in a stressful environment, we show that the fitness landscape is shaped by gene expression noise in more complex ways than previously anticipated. We find that mutations modulating the properties of expression noise enable cell populations to optimize their position on the fitness landscape. Additionally, we find that low levels of expression noise evolve under conditions where the fitness benefits of expression exceed the fitness costs, and that high levels of expression noise evolve when the expression costs exceed the fitness benefits. The results presented in this study expand our understanding of the interplay between stochastic gene expression and fitness in selective environments.

Adaptive resistance in bacteria requires epigenetic inheritance, genetic noise, and cost of efflux pumps

Adaptive resistance emerges when populations of bacteria are subjected to gradual increases of antibiotics. It is characterized by a rapid emergence of resistance and fast reversibility to the non-resistant phenotype when the antibiotic is removed from the medium. Recent work shows that adaptive resistance requires epigenetic inheritance and heterogeneity of gene expression patterns that are, in particular, associated with the production of porins and efflux pumps. However, the precise mechanisms by which inheritance and variability govern adaptive resistance, and what processes cause its reversibility remain unclear. Here, using an efflux pump regulatory network (EPRN) model, we show that the following three mechanisms are essential to obtain adaptive resistance in a bacterial population: 1) intrinsic variability in the expression of the EPRN transcription factors; 2) epigenetic inheritance of the transcription rate of EPRN associated genes; and 3) energetic cost of the efflux pumps activity that slows down cell growth. While the first two mechanisms acting together are responsible for the emergence and gradual increase of the resistance, the third one accounts for its reversibility. In contrast with the standard assumption, our model predicts that adaptive resistance cannot be explained by increased mutation rates. Our results identify the molecular mechanism of epigenetic inheritance as the main target for therapeutic treatments against the emergence of adaptive resistance. Finally, our theoretical framework unifies known and newly identified determinants such as the burden of efflux pumps that underlie bacterial adaptive resistance to antibiotics.

Bacterial responses to antibiotics and their combinations

Antibiotics affect bacterial cell physiology at many levels. Rather than just compensating for the direct cellular defects caused by the drug, bacteria respond to antibiotics by changing their morphology, macromolecular composition, metabolism, gene expression and possibly even their mutation rate. Inevitably, these processes affect each other, resulting in a complex response with changes in the expression of numerous genes. Genome-wide approaches can thus help in gaining a comprehensive understanding of bacterial responses to antibiotics. In addition, a combination of experimental and theoretical approaches is needed for identifying general principles that underlie these responses. Here, we review recent progress in our understanding of bacterial responses to antibiotics and their combinations, focusing on effects at the levels of growth rate and gene expression. We concentrate on studies performed in controlled laboratory conditions, which combine promising experimental techniques with quantitative data analysis and mathematical modeling. While these basic research approaches are not immediately applicable in the clinic, uncovering the principles and mechanisms underlying bacterial responses to antibiotics may, in the long term, contribute to the development of new treatment strategies to cope with and prevent the rise of resistant pathogenic bacteria.

Uncovering scaling laws to infer multidrug response of resistant microbes and cancer cells

Drug resistance in bacterial infections and cancers constitutes a major threat to human health. Treatments often include several interacting drugs, but even potent therapies can become ineffective in resistant mutants. Here, we simplify the picture of drug resistance by identifying scaling laws that unify the multidrug responses of drug-sensitive and -resistant cells. On the basis of these scaling relationships, we are able to infer the two-drug response of resistant mutants in previously unsampled regions of dosage space in clinically relevant microbes such as E. coli, E. faecalis, S. aureus, and S. cerevisiae as well as human non-small-cell lung cancer, melanoma, and breast cancer stem cells. Importantly, we find that scaling relations also apply across evolutionarily close strains. Finally, scaling allows one to rapidly identify new drug combinations and predict potent dosage regimes for targeting resistant mutants without any prior mechanistic knowledge about the specific resistance mechanism.

Tunable stochastic pulsing in the Escherichia coli multiple antibiotic resistance network from interlinked positive and negative feedback loops

Cells live in uncertain, dynamic environments and have many mechanisms for sensing and responding to changes in their surroundings. However, sudden fluctuations in the environment can be catastrophic to a population if it relies solely on sensory responses, which have a delay associated with them. Cells can reconcile these effects by using a tunable stochastic response, where in the absence of a stressor they create phenotypic diversity within an isogenic population, but use a deterministic response when stressors are sensed. Here, we develop a stochastic model of the multiple antibiotic resistance network of Escherichia coli and show that it can produce tunable stochastic pulses in the activator MarA. In particular, we show that a combination of interlinked positive and negative feedback loops plays an important role in setting the dynamics of the stochastic pulses. Negative feedback produces a pulsatile response that is tunable, while positive feedback serves to amplify the effect. Our simulations show that the uninduced native network is in a parameter regime that is of low cost to the cell (taxing resistance mechanisms are expressed infrequently) and also elevated noise strength (phenotypic variability is high). The stochastic pulsing can be tuned by MarA induction such that variability is decreased once stresses are sensed, avoiding the detrimental effects of noise when an optimal MarA concentration is needed. We further show that variability in the expression of MarA can act as a bet hedging mechanism, allowing for survival in time-varying stress environments, however this effect is tunable to allow for a fully induced, deterministic response in the presence of a stressor.