The evolution and spread of antibiotic resistance in bacterial pathogens is a grow- ing threat to public health. The frequency of antibiotic resistance in many bacterial pathogens is increasing around the world, and the resulting failures of antibiotic ther- apy cause hundreds of thousands of deaths annually1. The hope of addressing this crisis by developing new antibiotics is diminished both by the low rate of novel antibiotic dis- covery and by the likelihood that pathogens will evolve resistance to novel antibiotics just as they have to existing antibiotics. The long- term threat, therefore, is just as much the process of evolution as the microbial patho- gens themselves. Although the use of anti- biotics inevitably promotes resistance, the rate of evolution depends on the genomic background and treatment strategies. Thus, understanding the genomics and evolution- ary biology of antibiotic resistance could inform therapeutic strategies that are both effective and mitigate the future potential to evolve resistance.
Antibiotic resistance can be acquired either by mutation or by the horizontal transfer of resistance-conferring genes, often in mobile genetic cassettes. The rela- tive contribution of these factors depends
on the class of antibiotic and on the genetic plasticity of the bacterial species. For exam- ple, Mycobacterium tuberculosis primarily acquires antibiotic resistance through nucleo- tide changes, whereas hospital-acquired Enterobacteriaceae infections often pos- sess multidrug resistance cassettes and may also acquire nucleotide changes that confer resistance to drugs that are not often resisted by mobile elements, such as quinolones2.
Progress in DNA sequencing and other genotyping technologies means that the genotypes of pathogens will soon be widely available in clinical as well as research settings. Genotype-based antibiotic resist- ance profiling is already faster and more economical than phenotypic profiling in select cases (for example, rifampicin resist- ance in M. tuberculosis caused by nucleotide substitutions, and methicillin resistance in Staphylococcus aureus caused by a resistance cassette), and over time therapeutic and infection control strategies will more heavily rely on information derived from genome sequencing of the infecting agents3.
Importantly, genotypes can inform not only on the current drug susceptibility of a pathogen but also on its future potential to evolve resistance and spread. For example,
sequencing could determine whether a drug- susceptible strain carries precursors to resist- ance genes (which are termed proto-resistance genes4), such as drug-degrading enzymes or efflux pumps, that might be mutated to increase expression or to strengthen activity. Sequencing could also determine whether resistance cassettes may be only one mutation away from increased potency or from the capacity to resist other drugs related to the originally resisted drug4,5. Even a mod- est predictive power might improve thera- peutic outcomes by informing the selection of drugs, the preference between monotherapy or combination therapy and the temporal dosing regimen to select genotype-based treatments that are most resilient to evolu- tion of resistance. To realize such a potential will require new tools to explore how differ- ent treatment regimes affect the genotypic and phenotypic evolutionary paths to anti- biotic resistance in the laboratory and in the clinic. Here we discuss new tools to select for drug resistance, strategies for identifying and characterizing adaptive mutations in the evolved genotypes, and approaches to study the genetic constraints on the evolution of resistance.
Selection for drug resistance Drug resistance in laboratory experiments. Laboratory evolution6 can investigate how the rate and genotypic path to resistance varies across different controlled drug treat- ment regimens. In a traditional selection experiment, bacteria are exposed to fixed drug doses that permit only the growth of resistant mutants. Typically, this approach identifies only a single adaptive step and does not reveal how multiple mutations can accrue sequentially to confer strong resist- ance (FIG. 1a). Technological innovations now facilitate rapid multistep experimental evolution, revealing long-term evolutionary paths. Recurrent evolutionary patterns, such as the appearance of mutations in a preferred order, provide some level of predictability to a seemingly stochastic evolutionary process7. Devices for establishing spatial or temporal gradients of drug concentration allow evolving populations to be continu- ously challenged by effectively increasing the drug dosage to maintain selective pressure
Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance Adam C. Palmer and Roy Kishony
Abstract | The evolution of antibiotic resistance can now be rapidly tracked with high-throughput technologies for bacterial genotyping and phenotyping. Combined with new approaches to evolve resistance in the laboratory and to characterize clinically evolved resistant pathogens, these methods are revealing the molecular basis and rate of evolution of antibiotic resistance under treatment regimens of single drugs or drug combinations. In this Progress article, we review these new tools for studying the evolution of antibiotic resistance and discuss how the genomic and evolutionary insights they provide could transform the diagnosis, treatment and predictability of antibiotic resistance in bacterial infections.
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as stronger antibiotic resistance evolves. Continuous culture devices (for example, turbidostats) can be modified to increase drug dose steadily over time8, to imple- ment automated feedback control of drug
dosage in response to increasing levels of resistance7 or to mimic the antibiotic dos- ing regime experienced within a patient (FIG. 1b). Multistep experimental evolution can also be carried out in spatial drug gra- dients, as was demonstrated by a microfluidic device of connected chambers implementing a spatial drug gradient, allowing bacteria to expand throughout the device only as they evolve increasing levels of antibiotic resistance9 (FIG. 1c). These experiments have revealed that although the evolution of resistance can follow similar phenotypic paths in replicate experiments, the underly- ing genotypic process can be variable for some drugs (for example, chloramphenicol and doxycycline) but reproducible for other drugs (for example, trimethoprim and cip- rofloxacin)7,9. Substantial variability in rate is also observed: resistance to some drugs increases 1,000-fold over 20 days, whereas resistance to other drugs might increase only tenfold over the same period7. Therefore, for any specific genotype there could be vast differences between drugs in the propensity for resistance and the mechanisms by which resistance is acquired; these factors are cru- cial to the design of combination treatments that inhibit the evolution of resistance.
Combination therapy has the potential to slow the evolution of resistance, as a bacterial subpopulation with a mutation that renders it resistant to one drug may still be inhibited by a second drug, preventing the growth of a large drug-resistant population (that might subsequently evolve multidrug resistance)10. However, the choice of an optimal combina- tion to slow evolution can crucially depend on the details of the treatment regimen, drug interactions and cross-resistance10–13. Experimental evolution has facilitated the systematic analysis of evolution under differ- ent combination therapies and is revealing the principles behind their ability to slow down and possibly even to reverse the evolu- tion of resistance12–15 (reviewed in REF. 16). Several approaches have been used to select for drug resistance in multidrug environ- ments: mutants can be selected from a grid of drug concentrations across multiple agar dishes12 or in a microtitre plate13. Multiple mutations that confer strong multidrug resistance can be selected by serial passaging across such gradients13 or through the use of drug combinations in the continuous culture devices described above7.
Many questions about the evolution of multidrug resistance remain, including: to what extent is resistance acquired by a series of drug-specific mutations versus mutations that each confer resistance to multiple drugs
(that is, positive cross-resistance); in which cases can resistance to one drug lead to sensitivity to another (that is, negative cross- resistance); and, even when resistance to one drug does not immediately confer positive or negative cross-resistance to a second drug, can it affect the future evolution of resistance to the second drug? These and other ques- tions about the evolution of resistance to single or multiple drug treatments are being addressed by the systematic selection meth- odologies outlined above. Although these methods are often first applied to model organisms, they can and should be more widely applied to study pathogens isolated from human infections.
Drug resistance in clinical isolates. The increasing capacity to sequence whole bacte- rial genomes has allowed detailed analyses of large collections of clinical isolates. Various sampling approaches are available to view the evolution and spread of antibiotic- resistant bacteria over different scales (FIG. 2). Isolates collected from individual patients over the course of acute and chronic infec- tions have revealed the within-patient evo- lution of antibiotic resistance, instances of cross-resistance between antibiotics, the evo- lution of compensatory mutations that alle- viate the fitness costs of resistance, and the transmission of specific antibiotic-resistant clones between organs17–19. Sampling dur- ing the spread of an epidemic has been used to identify the likely patient-to-patient transmission of antibiotic-sensitive or antibiotic-resistant bacteria and may reveal trade-offs between infectivity and antibiotic resistance18. At the largest scale, worldwide sampling of endemic infections over decades has been used to determine long-term trends in the evolution of antibiotic resistance and pathogenicity and to determine transmission patterns across continents20,21.
Finding the genotypic basis Identifying adaptive mutations. Comparing the genomes of ancestral and evolved strains identifies the precise genetic changes that underlie adaptive evolution. However, separating adaptive mutations from neutral or passenger mutations is challenging, par- ticularly for clinical strains that may have been evolving antibiotic resistance over decades. In the context of contemporary bacterial evolution, tests for adaptive evolu- tion based on rates of nonsynonymous and synonymous substitutions (dN/dS) cannot be applied on a per-gene basis as there are typi- cally too few mutations for statistical power; also, they should not be applied to a whole
Figure 1 | Selection of antibiotic-resistant bac- teria from experimental evolution. Gradients of drug concentration over time or space facili- tate multistep experimental evolution. a | In a classical selection for antibiotic resistance, a uniform drug concentration selects for only a single mutation. b | A continuous culture device can select for multiple resistance-conferring mutations by dynamically increasing drug con- centration in response to increasing drug resist- ance. c | If bacteria can migrate over a spatial gradient of drug concentration then they can explore larger regions of space only as they evolve increasing levels of drug resistance.
Concentration of antibiotic
High Medium None
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