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Antibiotic resistance encoded on plasmids is a pressing global health problem. Predicting which plasmids spread in the long term remains very challenging, even though some key parameters influencing plasmid stability have been identified, such as plasmid growth costs and horizontal transfer rates. Here, we show these parameters evolve in a strain-specific way among clinical plasmids and bacteria, and this occurs rapidly enough to alter the relative likelihoods of different bacterium—plasmid combinations spreading. We used experiments with Escherichia coli and antibiotic-resistance plasmids isolated from patients, paired with a mathematical model, to track long-term plasmid stability (beyond antibiotic exposure). Explaining variable stability across six bacterium—plasmid combinations required accounting for evolutionary changes in plasmid stability traits, whereas initial variation of these parameters was a relatively poor predictor of long-term outcomes. Evolutionary trajectories were specific to particular bacterium—plasmid combinations, as evidenced by genome sequencing and genetic manipulation. This revealed epistatic (here, strain-dependent) effects of key genetic changes affecting horizontal plasmid transfer. Several genetic changes involved mobile elements and pathogenicity islands. Rapid strain-specific evolution can thus outweigh ancestral phenotypes as a predictor of plasmid stability. Accounting for strain-specific plasmid evolution in natural populations could improve our ability to anticipate and manage successful bacterium—plasmid combinations.

Countering the rise of antibiotic-resistant pathogens requires improved understanding of how resistance emerges and spreads in individual species, which are often embedded in complex microbial communities such as the human gut microbiome. Interactions with other microorganisms in such communities might suppress growth and resistance evolution of individual species (e.g., via resource competition) but could also potentially accelerate resistance evolution via horizontal transfer of resistance genes. It remains unclear how these different effects balance out, partly because it is difficult to observe them directly. Here, we used a gut microcosm approach to quantify the effect of three human gut microbiome communities on growth and resistance evolution of a focal strain of Escherichia coli. We found the resident microbial communities not only suppressed growth and colonisation by focal E. coli but also prevented it from evolving antibiotic resistance upon exposure to a beta-lactam antibiotic. With samples from all three human donors, our focal E. coli strain only evolved antibiotic resistance in the absence of the resident microbial community, even though we found resistance genes, including a highly effective resistance plasmid, in resident microbial communities. We identified physical constraints on plasmid transfer that can explain why our focal strain failed to acquire some of these beneficial resistance genes, and we found some chromosomal resistance mutations were only beneficial in the absence of the resident microbiota. This suggests, depending on in situ gene transfer dynamics, interactions with resident microbiota can inhibit antibiotic-resistance evolution of individual species.

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of the rates and spectra of spontaneous mutations. However, demonstrating the role of transition bias in adaptive evolution remains challenging. In particular, it is unclear whether such biases direct the evolution of bacterial pathogens adapting to treatment. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis (MTB). We found strong evidence for transition bias in two independently curated data sets comprising 152 and 208 antibiotic-resistance mutations. This was true at the level of mutational paths (distinct adaptive DNA sequence changes) and events (individual instances of the adaptive DNA sequence changes) and across different genes and gene promoters conferring resistance to a diversity of antibiotics. It was also true for mutations that do not code for amino acid changes (in gene promoters and the 16S ribosomal RNA gene rrs) and for mutations that are synonymous to each other and are therefore likely to have similar fitness effects, suggesting that transition bias can be caused by a bias in mutation supply. These results point to a central role for transition bias in determining which mutations drive adaptive antibiotic resistance evolution in a key pathogen.

The spread of antibiotic-resistant bacteria in the gut depends on their ability to establish within complex microbial communities. However, the role of various ecological factors in modulating this process, particularly in the absence of antibiotic selection, remains poorly understood. We hypothesize that different strains within the same species vary in their ability to colonize due to distinct interactions with resident microbiota. Using human gut-microbiome samples in replicated anaerobic microcosms with and without antibiotics, we test multiple clinically relevant and phylogenetically distinct Escherichia coli strains carrying extended-spectrum beta-lactamase (ESBL) or carbapenemase plasmids. While antibiotics influence the growth of incoming resistant strains, some are successful even without antibiotics. Growth outcomes depend on a combination of intrinsic growth capacities in relevant abiotic conditions, competition with resident E. coli , and strain-specific shifts in resident community composition. We also detect horizontal transfer of resistance plasmids in some conditions, but transconjugants remain rare across treatments. Here, we show that the success of antibiotic-resistant bacteria depends on strain-specific ecological interactions, helping to explain the spread and persistence of resistance in human microbiomes. The role of ecological factors in modulating the spread of antibiotic-resistance bacteria in the gut remains unclear. Here, the authors use anaerobic microcosms to study the population growth of four clinical E. coli strains in the gut microbiome.

Key Points The fitness effects of antibiotic-resistance mutations and of mutations that compensate for the cost of resistance depends on the molecular basis of resistance and the ecological (or treatment) context in which resistance evolves. The distribution of fitness effects of resistance mutations is determined by antibiotic dose and drug–target interactions. Resistance mutations impose a fitness cost that varies widely among mutations. It may be possible to predict costs of resistance by considering the effects of resistance mutations on protein stability. Compensatory mutations alleviate the cost of resistance, allowing resistant strains to persist in the absence of antibiotics. The opportunity for compensation varies among resistant mutants and it may be possible to predict this variability by explicitly considering the mechanistic basis of the costs of resistance. Physiological interactions between antibiotics and genetic interactions between resistance mutations are crucial for the evolution of multidrug resistance by modifying the benefits associated with resistance and compensatory mutation. Immigration from antibiotic-free populations is important in the evolution of resistance. Immigration accelerates the evolution of resistance when resistance mutations are rare and immigration can reverse resistance following the cessation of antibiotic use. Spatial and temporal patterns of antibiotic use play a key part in the evolution of resistance. Resistance evolves most slowly under maximal levels of environmental heterogeneity. Future work should concentrate on developing predictive models of resistance evolution by integrating molecular mechanisms of resistance with treatment context. This may help develop improved treatment strategies for preventing resistance evolution in pathogen populations. The authors discuss the evolutionary dynamics of antibiotic resistance in bacteria in relation to the complex interplay between population genetic factors and the spatial and temporal pattern of antibiotic use. Despite efforts from a range of disciplines, our ability to predict and combat the evolution of antibiotic resistance in pathogenic bacteria is limited. This is because resistance evolution involves a complex interplay between the specific drug, bacterial genetics and both natural and treatment ecology. Incorporating details of the molecular mechanisms of drug resistance and ecology into evolutionary models has proved useful in predicting the dynamics of resistance evolution. However, putting these models to practical use will require extensive collaboration between mathematicians, molecular biologists, evolutionary ecologists and clinicians.

Understanding the genetic constraints on pathogen evolution will help to predict the emergence of generalist pathogens that can infect a range of different host genotypes. Here we show that generalist viral pathogens are more likely to emerge during coevolution between the bacteriumPseudomonas fluorescensand the lytic phage SBW25Φ2 than when the same pathogen is challenged to adapt to a nonevolving population of novel hosts. When phages were able to adapt to nonevolving novel hosts, the resulting phenotypes had relatively narrow host ranges compared with coevolved phages. Evolved (rather than coevolved) phages also had lower virulence, although they attained virulence similar to that of coevolved phages after continued adaptation to a nonevolving population of the same host. We explain these results by using sequence data showing that the evolution of broad host range is associated with several different amino acid substitutions and therefore occurs only through repeated rounds of selection for novel infectivity alleles. These findings suggest that generalist bacteriophages are more likely to emerge through long‐term coevolution with their hosts than through spontaneous adaptation to a single novel host. These results are likely to be relevant to host‐parasite systems where parasite generalism can evolve through the acquisition of multiple mutations or alleles, as appears to be the case for many plant‐bacteria and bacteria‐virus interactions.

The spread of antibiotic resistance is driving interest in new approaches to control bacterial pathogens. This includes applying multiple antibiotics strategically, using bacteriophages against antibiotic-resistant bacteria, and combining both types of antibacterial agents. All these approaches rely on or are impacted by associations among resistance phenotypes (where bacteria resistant to one antibacterial agent are also relatively susceptible or resistant to others). Experiments with laboratory strains have shown strong associations between some resistance phenotypes, but we lack a quantitative understanding of associations among antibiotic and phage resistance phenotypes in natural and clinical populations. To address this, we measured resistance to various antibiotics and bacteriophages for 94 natural and clinical Escherichia coli isolates. We found several positive associations between resistance phenotypes across isolates. Associations were on average stronger for antibacterial agents of the same type (antibiotic-antibiotic or phage-phage) than different types (antibiotic-phage). Plasmid profiles and genetic knockouts suggested that such associations can result from both colocalization of resistance genes and pleiotropic effects of individual resistance mechanisms, including one case of antibiotic-phage cross-resistance. Antibiotic resistance was predicted by core genome phylogeny and plasmid profile, but phage resistance was predicted only by core genome phylogeny. Finally, we used observed associations to predict genes involved in a previously uncharacterized phage resistance mechanism, which we verified using experimental evolution. Our data suggest that susceptibility to phages and antibiotics are evolving largely independently, and unlike in experiments with lab strains, negative associations between antibiotic resistance phenotypes in nature are rare. This is relevant for treatment scenarios where bacteria encounter multiple antibacterial agents. IMPORTANCE Rising antibiotic resistance is making it harder to treat bacterial infections. Whether resistance to a given antibiotic spreads or declines is influenced by whether it is associated with altered susceptibility to other antibiotics or other stressors that bacteria encounter in nature, such as bacteriophages (viruses that infect bacteria). We used natural and clinical isolates of Escherichia coli , an abundant species and key pathogen, to characterize associations among resistance phenotypes to various antibiotics and bacteriophages. We found associations between some resistance phenotypes, and in contrast to past work with laboratory strains, they were exclusively positive. Analysis of bacterial genome sequences and horizontally transferred genetic elements (plasmids) helped to explain this, as well as our finding that there was no overall association between antibiotic resistance and bacteriophage resistance profiles across isolates. This improves our understanding of resistance evolution in nature, potentially informing new rational therapies that combine different antibacterials, including bacteriophages. Rising antibiotic resistance is making it harder to treat bacterial infections. Whether resistance to a given antibiotic spreads or declines is influenced by whether it is associated with altered susceptibility to other antibiotics or other stressors that bacteria encounter in nature, such as bacteriophages (viruses that infect bacteria). We used natural and clinical isolates of Escherichia coli , an abundant species and key pathogen, to characterize associations among resistance phenotypes to various antibiotics and bacteriophages. We found associations between some resistance phenotypes, and in contrast to past work with laboratory strains, they were exclusively positive. Analysis of bacterial genome sequences and horizontally transferred genetic elements (plasmids) helped to explain this, as well as our finding that there was no overall association between antibiotic resistance and bacteriophage resistance profiles across isolates. This improves our understanding of resistance evolution in nature, potentially informing new rational therapies that combine different antibacterials, including bacteriophages.

Parasitism creates selection for resistance mechanisms in host populations and is hypothesized to promote increased host evolvability. However, the influence of these traits on host evolution when parasites are no longer present is unclear. We used experimental evolution and whole-genome sequencing of Escherichia coli to determine the effects of past and present exposure to parasitic viruses (phages) on the spread of mutator alleles, resistance, and bacterial competitive fitness. We found that mutator alleles spread rapidly during adaptation to any of four different phage species, and this pattern was even more pronounced with multiple phages present simultaneously. However, hypermutability did not detectably accelerate adaptation in the absence of phages and recovery of fitness costs associated with resistance. Several lineages evolved phage resistance through elevated mucoidy, and during subsequent evolution in phage-free conditions they rapidly reverted to nonmucoid, phage-susceptible phenotypes. Genome sequencing revealed that this phenotypic reversion was achieved by additional genetic changes rather than by genotypic reversion of the initial resistance mutations. Insertion sequence (IS) elements played a key role in both the acquisition of resistance and adaptation in the absence of parasites; unlike single nucleotide polymorphisms, IS insertions were not more frequent in mutator lineages. Our results provide a genetic explanation for rapid reversion of mucoidy, a phenotype observed in other bacterial species including human pathogens. Moreover, this demonstrates that the types of genetic change underlying adaptation to fitness costs, and consequently the impact of evolvability mechanisms such as increased point-mutation rates, depend critically on the mechanism of resistance.

Understanding the role of horizontal gene transfer (HGT) in adaptation is a key challenge in evolutionary biology. In microbes, an important mechanism of HGT is prophage acquisition (phage genomes integrated into bacterial chromosomes). Prophages can influence bacterial fitness via the transfer of beneficial genes (including antibiotic-resistance genes, ARGs), protection from superinfecting phages, or switching to a lytic lifecycle that releases free phages infectious to competitors. We expect these effects to depend on environmental conditions because of, for example, environment-dependent induction of the lytic lifecycle. However, it remains unclear how costs/benefits of prophages vary across environments. Here, studying prophages with/without ARGs in Escherichia coli, we disentangled the effects of prophages alone and adaptive genes they carry. In competition with prophage-free strains, benefits from prophages and ARGs peaked in different environments. Prophages were most beneficial when induction of the lytic lifecycle was common, whereas ARGs were more beneficial upon antibiotic exposure and with reduced prophage induction. Acquisition of prophage-encoded ARGs by competing strains was most common when prophage induction, and therefore free phages, were common. Thus, selection on prophages and adaptive genes they carry varies independently across environments, which is important for predicting the spread of mobile/integrating genetic elements and their role in evolution.

With rising antibiotic resistance, alternative treatments for communicable diseases are increasingly relevant. One possible alternative for some types of infections is honey, used in wound care since before 2000 BCE and more recently in licensed, medical‐grade products. However, it is unclear whether medical application of honey results in the evolution of bacterial honey resistance and whether this has collateral effects on other bacterial traits such as antibiotic resistance. Here, we used single‐step screening assays and serial transfer at increasing concentrations to isolate honey‐resistant mutants of Escherichia coli. We only detected bacteria with consistently increased resistance to the honey they evolved in for two of the four tested honey products, and the observed increases were small (maximum twofold increase in IC90). Genomic sequencing and experiments with single‐gene knockouts showed a key mechanism by which bacteria increased their honey resistance was by mutating genes involved in detoxifying methylglyoxal, which contributes to the antibacterial activity of Leptospermum honeys. Crucially, we found no evidence that honey adaptation conferred cross‐resistance or collateral sensitivity against nine antibiotics from six different classes. These results reveal constraints on bacterial adaptation to different types of honey, improving our ability to predict downstream consequences of wider honey application in medicine.