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Antibiotic heteroresistance is a phenotype in which a bacterial isolate contains subpopulations of cells that show a substantial reduction in antibiotic susceptibility compared with the main population. Recent work indicates that heteroresistance is very common for several different bacterial species and antibiotic classes. The resistance phenotype is often unstable, and in the absence of antibiotic pressure it rapidly reverts to susceptibility. A common mechanistic explanation for the instability is the occurrence of genetically unstable tandem amplifications of genes that cause resistance. Due to their instability, low frequency and transient character, it is challenging to detect and study these subpopulations, which often leads to difficulties in unambiguously classifying bacteria as susceptible or resistant. Finally, in vitro experiments, mathematical modelling, animal infection models and clinical studies show that the resistant subpopulations can be enriched during antibiotic exposure, and increasing evidence suggests that heteroresistance can lead to treatment failure.

Key Points Bacteria are often exposed to non-lethal (that is, subinhibitory) levels of antibiotics in humans, animals and the environment. Subinhibitory levels of antibiotics accelerate the emergence and spread of antibiotic-resistant bacteria by selecting for resistance, generating genotypic and phenotypic variability and functioning as signalling molecules. Subinhibitory levels of antibiotics that are several hundred-fold below the minimal inhibitory concentration (MIC) can select for resistant bacteria. Subinhibitory levels of antibiotics can promote genetic variability by increasing rates of mutation, recombination and horizontal gene transfer. Antibiotics at subinhibitory concentrations can function as signalling molecules and cause alterations in bacterial virulence, biofilm formation, quorum sensing, gene expression and gene transfer. These changes may influence susceptibility to antibiotics. Bacteria are frequently exposed to subinhibitory concentrations of antibiotics, and recent evidence suggests that this is likely to select for resistance. In this Review, Andersson and Hughes discuss the ecology of antibiotics, the ability of subinhibitory concentrations of antibiotics to select for resistance and the effects of low-level drug exposure on bacterial physiology. The widespread use of antibiotics results in the generation of antibiotic concentration gradients in humans, livestock and the environment. Thus, bacteria are frequently exposed to non-lethal (that is, subinhibitory) concentrations of drugs, and recent evidence suggests that this is likely to have an important role in the evolution of antibiotic resistance. In this Review, we discuss the ecology of antibiotics and the ability of subinhibitory concentrations to select for bacterial resistance. We also consider the effects of low-level drug exposure on bacterial physiology, including the generation of genetic and phenotypic variability, as well as the ability of antibiotics to function as signalling molecules. Together, these effects accelerate the emergence and spread of antibiotic-resistant bacteria among humans and animals.

Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.Combining several antibiotics, either in mixtures or sequential order, is proposed to increase treatment efficacy and reduce resistance evolution. In this Review, Andersson and colleagues discuss the effects of antibiotic combinations, the directional effects of previous antibiotic treatments and the role of stress-response systems as well as the interactions between drugs and resistance mutations.

Broken arrows indicate reduced functions. https://doi.org/10.1371/journal.ppat.1009172.g001 The first sensitivity mechanism to be understood is the generally 2- to 4-fold lower MIC of aminoglycoside-resistant E. coli to several classes of antibiotics (beta-lactams, fluoroquinolones, chloramphenicol, doxycycline, tetracycline) that is caused by mutations in the ion transport protein trkH [3]. [...]recent work provides an example for mechanism 4 [5]. Since CS has several potentially useful effects, e.g., (1) inhibition of growth with lower concentrations of antibiotic and (2) faster and stronger killing of a resistant bacterium (compared to the susceptible), it could be applied to both increasing efficacy and/or reducing the rate of resistance evolution. [...]in cases when the infection is caused by a bacterium that carries a resistance to another drug that increases susceptibility to any of these toxic antibiotics (which is expected be quite rare), a lower concentration could be used for treatment.

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.

Abstract Antibiotic resistance can be acquired by mutation or horizontal transfer of a resistance gene, and generally an acquired mechanism results in a predictable increase in phenotypic resistance. However, recent findings suggest that the environment and/or the genetic context can modify the phenotypic expression of specific resistance genes/mutations. An important implication from these findings is that a given genotype does not always result in the expected phenotype. This dissociation of genotype and phenotype has important consequences for clinical bacteriology and for our ability to predict resistance phenotypes from genetics and DNA sequences. A related problem concerns the degree to which the genes/mutations currently identified in vitro can fully explain the in vivo resistance phenotype, or whether there is a significant additional amount of presently unknown mutations/genes (genetic ‘dark matter’) that could contribute to resistance in clinical isolates. Finally, a very important question is whether/how we can identify the genetic features that contribute to making a successful pathogen, and predict why some resistant clones are very successful and spread globally? In this review, we describe different environmental and genetic factors that influence phenotypic expression of antibiotic resistance genes/mutations and how this information is needed to understand why particular resistant clones spread worldwide and to what extent we can use DNA sequences to predict evolutionary success. The ability to predict resistance phenotypes from DNA sequence data, and to understand the success of problematic resistant clones, depends on having a good understanding of how environmental factors and genetic context modulate the expression of antibiotic resistance phenotypes and clonal success.

Key Points Most antibiotic resistance mechanisms are associated with a fitness cost, which is a key biological parameter that influences the development of resistance. The fitness cost is the main driver of resistance reversibility at the community level. Thus, the bigger the fitness cost, the faster the reversibility. The rate of reversibility is expected to be slow at the community level because of compensatory evolution, cost-free mutations and genetic co-selection. Knowledge about fitness costs and compensatory mutations can be used to reduce the likelihood of bacteria developing resistance, by enabling us to choose antibiotics for which the resistance mechanism confers a high fitness cost and the rate and extent of compensation mutations are low. It may be possible to exploit the detailed knowledge of the physiological basis of fitness costs in the choice and design of novel therapies that could target the physiological weaknesses associated with a particular resistance mechanism. An understanding of fitness costs and compensatory evolution should allow us to make better quantitative predictions about the rate and trajectory of the evolution of resistance to new and old drugs. The mutations that confer antibiotic resistance lead, in most cases, to a decrease in fitness. Here, Andersson and Hughes describe the various ways in which bacteria can minimize the fitness cost and how this may be exploited to reverse antibiotic resistance. Most antibiotic resistance mechanisms are associated with a fitness cost that is typically observed as a reduced bacterial growth rate. The magnitude of this cost is the main biological parameter that influences the rate of development of resistance, the stability of the resistance and the rate at which the resistance might decrease if antibiotic use were reduced. These findings suggest that the fitness costs of resistance will allow susceptible bacteria to outcompete resistant bacteria if the selective pressure from antibiotics is reduced. Unfortunately, the available data suggest that the rate of reversibility will be slow at the community level. Here, we review the factors that influence the fitness costs of antibiotic resistance, the ways by which bacteria can reduce these costs and the possibility of exploiting them.

The emergence and spread of antibiotic-resistant bacteria are aggravated by incorrect prescription and use of antibiotics. A core problem is that there is no sufficiently fast diagnostic test to guide correct antibiotic prescription at the point of care. Here, we investigate if it is possible to develop a point-of-care susceptibility test for urinary tract infection, a disease that 100 million women suffer from annually and that exhibits widespread antibiotic resistance. We capture bacterial cells directly from samples with low bacterial counts (10⁴ cfu/mL) using a custom-designed microfluidic chip and monitor their individual growth rates using microscopy. By averaging the growth rate response to an antibiotic over many individual cells, we can push the detection time to the biological response time of the bacteria. We find that it is possible to detect changes in growth rate in response to each of nine antibiotics that are used to treat urinary tract infections in minutes. In a test of 49 clinical uropathogenic Escherichia coli (UPEC) isolates, all were correctly classified as susceptible or resistant to ciprofloxacin in less than 10 min. The total time for antibiotic susceptibility testing, from loading of sample to diagnostic readout, is less than 30 min, which allows the development of a point-of-care test that can guide correct treatment of urinary tract infection.

Key Points Across the diverse biological systems discussed in this Review, the underlying principles concerning the mechanisms and dynamics of resistance development are similar. Drug resistance has emerged in all biological systems in which drugs are used as a standard therapeutic strategy to control infections or cancer. There is an urgent need not only to develop new drugs to support effective therapy but also to develop a better understanding of the underlying mechanisms and forces that drive resistance development. Large population sizes and/or high mutation rates ensure that the emergence of drug resistance is not limited by mutation supply in HIV, in many bacterial infections or in human cancers. Mutation supply may be a limiting factor for fungal and parasitic infections. Horizontal gene transfer (HGT) from a very broad gene pool substantially contributes to the emergence of drug resistance in bacteria but is absent as a source of genetic variation in the other systems discussed. We currently know very little about the dynamics and trajectories of HGT events and have a very poor ability to make predictions. The study and understanding of the dynamics of growth and competition within complex populations subjected to drug therapy are being advanced by the increasing application of next-generation sequencing technologies. In biological systems in which resistance emergence has long been acknowledged to be a problem (particularly HIV infection and human cancer), therapy with combinations of drugs is standard of care. The systematic use of drug combinations in the treatment of bacterial, fungal and parasitic infections might be the most effective short-term means to slow resistance emergence. The emergence of drug resistance is a major challenge for controlling diverse infectious diseases and cancer. In this Review, the authors discuss the mechanisms and evolutionary consequences of drug resistance. They highlight commonalities and distinctions across diverse pathogens and systems, and the implications for optimizing the current use and future development of drug therapies. Drug therapy has a crucial role in the treatment of viral, bacterial, fungal and protozoan infections, as well as the control of human cancer. The success of therapy is being threatened by the increasing prevalence of resistance. We examine and compare mechanisms of drug resistance in these diverse biological systems (using HIV and Plasmodium falciparum as examples of viral and protozoan pathogens, respectively) and discuss how factors — such as mutation rates, fitness effects of resistance, epistasis and clonal interference — influence the evolutionary trajectories of drug-resistant clones. We describe commonalities and differences related to resistance development that could guide strategies to improve therapeutic effectiveness and the development of a new generation of drugs.

Antibiotic resistance increasingly limits the success of antibiotic treatments, and physicians require new ways to achieve efficient treatment despite resistance. Resistance mechanisms against a specific antibiotic class frequently confer increased susceptibility to other antibiotic classes, a phenomenon designated collateral sensitivity (CS). An informed switch of antibiotic may thus enable the efficient treatment of resistant strains. CS occurs in many pathogens, but the mechanisms that generate hypersusceptibility are largely unknown. We identified several molecular mechanisms of CS against the antibiotic nitrofurantoin (NIT). Mutants that are resistant against tigecycline (tetracycline), mecillinam (β-lactam), and protamine (antimicrobial peptide) all show CS against NIT. Their hypersusceptibility is explained by the overexpression of nitroreductase enzymes combined with increased drug uptake rates, or increased drug toxicity. Increased toxicity occurs through interference of the native drug-response system for NIT, the SOS response, with growth. A mechanistic understanding of CS will help to develop drug switches that combat resistance.