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. 2014 Sep 16;5(5):e01592-14.
doi: 10.1128/mBio.01592-14.

Environmental heterogeneity drives within-host diversification and evolution of Pseudomonas aeruginosa

Trine Markussen  1 , Rasmus Lykke Marvig  1 , María Gómez-Lozano  1 , Kasper Aanæs  2 , Alexandra E Burleigh  1 , Niels Høiby, Helle Krogh Johansen  3 , Søren Molin  4 , Lars Jelsbak  4
Affiliations

Environmental heterogeneity drives within-host diversification and evolution of Pseudomonas aeruginosa

Trine Markussen et al. mBio. .

Abstract

Microbial population polymorphisms are commonly observed in natural environments, including long-term infected hosts. However, the underlying processes promoting and stabilizing diversity are difficult to unravel and are not well understood. Here, we use chronic infection of cystic fibrosis airways by the opportunistic pathogen Pseudomonas aeruginosa as a system for investigating bacterial diversification processes during the course of infection. We analyze clonal bacterial isolates sampled during a 32-year period and map temporal and spatial variations in population diversity to different infection sites within the infected host. We show that the ancestral infecting strain diverged into distinct sublineages, each with their own functional and genomic signatures and rates of adaptation, immediately after initial colonization. The sublineages coexisted in the host for decades, suggesting rapid evolution of stable population polymorphisms. Critically, the observed generation and maintenance of population diversity was the result of partitioning of the sublineages into physically separated niches in the CF airway. The results reveal a complex within-host population structure not previously realized and provide evidence that the heterogeneity of the highly structured and complex host environment promotes the evolution and long-term stability of pathogen population diversity during infection.

Importance: Within-host pathogen evolution and diversification during the course of chronic infections is of importance in relation to therapeutic intervention strategies, yet our understanding of these processes is limited. Here, we investigate intraclonal population diversity in P. aeruginosa during chronic airway infections in cystic fibrosis patients. We show the evolution of a diverse population structure immediately after initial colonization, with divergence into multiple distinct sublineages that coexisted for decades and occupied distinct niches. Our results suggest that the spatial heterogeneity in CF airways plays a major role in relation to the generation and maintenance of population diversity and emphasize that a single isolate in sputum may not represent the entire pathogen population in the infected individual. A more complete understanding of the evolution of distinct clonal variants and their distribution in different niches could have positive implications for efficient therapy.

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Figures

FIG 1
FIG 1
Patient origin and sampling time of genome-sequenced P. aeruginosa DK1 isolates. DK1 P. aeruginosa isolates were sampled between 1973 and 2012 from five different CF patients (P30M0, P33F0, P50F0, P13F1, and P43M1). Bacterial isolates from different patients are specified by symbols as indicated. Multiple symbols indicate that multiple isolates were sampled in the same year from the same patient.
FIG 2
FIG 2
Evolutionary trajectory of the DK1 lineage. (A) Maximum-parsimony reconstruction of the phylogeny of DK1 clones isolated from CF patients P30M0, P33F0, P50F0, P13F1, and P43M1. The tree is based on 160 SNPs (identified from genome sequencing) that accumulated in a highly parsimonious fashion (parsimony consistency of 0.99). The bacterial isolates are named according to their clone type, the patient from whom they were isolated, and their year of isolation (e.g., DK1-P30M0-1980). Capital letters indicate branch names, and lengths of branches are proportional to the numbers of SNPs. The specific mutations that have accumulated during each specific branching are listed in Table S1 in the supplemental material. The three phylogenetic clusters in P30M0 are labeled A, B, and C. Median estimates of divergence dates (in calendar years) are given in red for the major nodes. Numbers at nodes indicate bootstrap values of ≥90%. (B) Mutation rates (SNPs per year) for each of the three clusters and for all DK1 isolates. (C) Prevalence of mutations in pathoadaptive genes in the three DK1 sublineages. The genes hit by nonsynonymous SNPs are indicated by filled circles for each of the three DK1 sublineages in P30M0.
FIG 3
FIG 3
Phenotypic characterization of DK1 isolates. (A) For morphology, “n” indicates nonmucoid and “m” indicates mucoid cells. Motility is relative to that of strain PAO1. Quorum sensing (QS) was assayed by determining the production of acylated homoserine lactones (HSL), detected by inspection of the bioluminescence of the monitor strain (++, high level; +, low level; −, not detectable). Protease secretion on skim milk plates was determined (+++, high level; +, low level; −, not detectable). (B) Doubling times of historical isolates and P30M0 isolates measured in LB medium. (C) Dendrogram showing the hierarchical cluster analysis (Wards linkage, Euclidean distance) of global catabolic function. Colors represent each cluster; cluster A is blue; cluster B is green; and cluster C is purple.
FIG 4
FIG 4
Transcriptome analysis. (A) Comparison of gene expression of the two mucoid isolates DK1-P30M0-2001b and DK1-P30M0-2007b from cluster A. (B) Comparison of gene expression of the two nonmucoid isolates DK1-P30M0-2001a and DK1-P30M0-2009a from cluster B. (C) Comparison of gene expression of the two mucoid isolates DK1-P30M0-2006 and DK1-P30M0-2011 from cluster C. Red circles represent genes with significantly increased expression (fold change of ≥2; P ≤ 0.05), and green circles indicate genes with decreased expression. The r (Pearson correlation) values indicate the strength and direction of the linear relationship between the expression levels of the isolates. (D) Results of single value decomposition (SVD) analysis of the gene expression relationships among isolates from clusters A, B, and C. Each dot represents the mean of duplicates. Blue dots represent isolates from cluster A, green dots represent isolates from cluster B, and isolates from cluster C are shown as purple dots.
FIG 5
FIG 5
Distribution of the three sublineages at different sites in patient P30M0. (A) Analysis of P. aeruginosa isolates sampled from patient P30M0 during paranasal sinus surgery in 2012. Each randomly chosen isolate was inspected using a cluster-specific PCR method to determine if it was part of either cluster A (blue), B (green), or C (red). All isolates belonged to these three clusters. With the exception of the sample from the right sinus, 64 random isolates from each site were tested. (B) A total of 1,025 isolates from sputum samples collected in 2010, 2011, and 2012 were picked randomly and association with one of the three clusters determined.
FIG 6
FIG 6
Model of the evolutionary history of DK1 in patient P30M0. Reconstruction of the evolutionary history of DK1 in patient P30M0 on the basis of the phylogenetic relationship among the studied isolates and their distribution in different niches in the infected airway. The patient was first colonized in the paranasal sinuses with isolates belonging to cluster A. Soon after initial colonization, the population diverged and formed a genetically and phenotypically distinct sublineage (cluster B) in the lower airway. A decade later, another sublineage (cluster C) diverged from the population in the paranasal sinuses and migrated to the lower airway. The precise locations of clusters B and C in the lower airway (e.g., left versus right lung or the conductive zone versus the respiratory zone) are not known and for illustrative purposes are shown as being segregated into the left and right lung. However, the differences between clusters B and C in relation to phenotypes such as mucoidity and in the number of pathoadaptive mutations suggest that they inhabit distinct niches with different selective pressures in the lower airway.
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