Within-host evolution of bacterial pathogens

Key Points Whole-genome sequencing of several isolates from single hosts has revealed previously unsuspected within-host diversity of many bacterial pathogens. Within-host bacterial populations are subject to multifarious evolutionary forces including mutation, genetic drift, natural selection and fluctuating population size. Within-host evolution limits the utility of sampling a single genome per host for reconstructing transmission relationships, conferring a benefit to sequencing several genomes per host. Resistance to some antimicrobials frequently evolves independently in individual hosts, revealing the substantial potential of bacteria to adapt in the human body. Within-host adaptation has a major role in the evolution of opportunistic infections in immunocompromised patients by otherwise free-living bacteria. The study of within-host genomic evolution promises to shed light on whether pathogens tend to become more or less virulent within the host, and the selective pressures underlying this evolution. Advances in whole-genome sequencing have enabled within-host genome evolution to be studied with unprecedented detail. In this Review article, Didelot, Wilson and colleagues discuss how these studies have altered our view of host adaptation and antibiotic resistance during bacterial infection. Whole-genome sequencing has opened the way for investigating the dynamics and genomic evolution of bacterial pathogens during the colonization and infection of humans. The application of this technology to the longitudinal study of adaptation in an infected host — in particular, the evolution of drug resistance and host adaptation in patients who are chronically infected with opportunistic pathogens — has revealed remarkable patterns of convergent evolution, suggestive of an inherent repeatability of evolution. In this Review, we describe how these studies have advanced our understanding of the mechanisms and principles of within-host genome evolution, and we consider the consequences of findings such as a potent adaptive potential for pathogenicity. Finally, we discuss the possibility that genomics may be used in the future to predict the clinical progression of bacterial infections and to suggest the best option for treatment.