Within-host evolution of bacterial pathogens

Abstract

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.

Figure 1. Within-host evolution of Staphylococccus aureus.

A Staphylococcus aureus infection begins with the transmission of a S. aureus population (blue circles)_from a donor host to a new host. Owing to the transmission bottleneck, only a subset of the within-host diversity in the donor host is transmitted to the newly infected host. Colonization starts at a given site — typically somewhere on the skin — and the pathogen population initially increases in size, with a new mutation arising occasionally (orange circle). The infection may also spread to other sites in the host, such as from skin to nose. When adaptive mutations (red circles) arise — that is, mutations that confer a selective advantage — they can quickly become fixed in the pathogen population through a selective sweep . When a secondary infection from another donor transmits a second S. aureus population (green circles) to the host, the two infecting populations may recombine to form a new genotype (purple circles). Transmission to other hosts may occur at any stage of colonization, as can spread to the blood stream, where the pathogen can cause life-threatening septicaemia.

Figure 2. Effect of wihtin-host evloution on the reconstruction of transmission events .

This example illustrates the benefit of sampling several genomes, rather than a single genome, from each host when reconstructing a transmission network . In this example, three hosts (A, B and C) are infected with a bacterial pathogen, with host A having become infected first and having directly transmitted to B and then to C, as indicated by the vertical arrows. If only a single genome is sampled from each host (see inset), their ancestry is only weakly informative about who infected whom. In the example shown, the ancestry of the genome sampled from host A is such that the genomes sampled from host B and host C are more closely related to one another than with the genome sampled from host A, which might be mistaken as evidence for host B having infected host C (or vice versa) . This error can be avoided if within-host evolution is accounted for, but doing so with only a single genome sampled from each host results in substantial uncertainty in reconstructions of transmission . However, when several genomes are available for each host (coloured circles in the transmission tree), an ancestry analysis becomes much more informative about the transmission events . In the example shown, the three genomes sampled from host B form a clade that is closely related to a clade of genomes sampled in host A, as do the four genomes sampled from host C, correctly suggesting that A infected both B and C . Different colour circles represent genetic variation in the pathogen genome following within-host mutations .

Figure 3. Within-host adaptive potential during antibiotic exposure .

A susceptible bacterial strain (blue circle), when exposed to an antibiotic, will be highly likely to be killed, but may occasionally survive by evolving into a resistant strain (orange circle). Resistance usually has a high fitness cost (inset), so that resistant strains disappear when not exposed to the antibiotic. However, resistant strains can evolve compensatory mutations (green circle) so that they remain resistant without the associated fitness cost. Such compensated strains pose a serious danger to public health, because they do not disappear simply as a result of antibiotic disuse. Alternatively, strains may evolve adaptability (yellow circle), allowing them to quickly switch resistance on or off and therefore avoid the associated fitness cost, presenting a similar risk to public health as that presented by compensated strains.

Figure 4. Within-host evolution of pathogens in the lungs of a cystic fibrosis patients.

Infection of the lungs of a cystic fibrosis patient begins with transmission from the environment or the skin of a donor, and progresses with a rapid increase in the size of the pathogen population . Mutations occasionally occur, some of which may be adaptative mutations that spread through the pathogen population in a selective sweep (red circles). Another important mechanism of genome evolution for these pathogens is hypermutation, whereby a strain loses the function of its mismatch repair machinery and thus becomes a hypermutator, with a mutation rate that is increased several fold, thus greatly increasing its evolutionary potential . As the infection progresses, the pathogen colonizes all ecological niches within the cystic fibrosis lungs, and separate adaptation to each niche leads to the coexistence of differentially adapted lineages.