Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Jan;46(1):82-7.
doi: 10.1038/ng.2848. Epub 2013 Dec 8.

Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures

Tami D Lieberman  1 , Kelly B Flett  2 , Idan Yelin  3 , Thomas R Martin  4 , Alexander J McAdam  5 , Gregory P Priebe  6 , Roy Kishony  7
Affiliations

Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures

Tami D Lieberman et al. Nat Genet. 2014 Jan.

Abstract

Advances in sequencing technologies have enabled the identification of mutations acquired by bacterial pathogens during infection. However, it remains unclear whether adaptive mutations fix in the population or lead to pathogen diversification within the patient. Here we study the genotypic diversity of Burkholderia dolosa within individuals with cystic fibrosis by resequencing individual colonies and whole populations from single sputum samples. We find extensive intrasample diversity, suggesting that mutations rarely fix in a patient's pathogen population--instead, diversifying lineages coexist for many years. Under strong selection, multiple adaptive mutations arise, but none of these sweep to fixation, generating lasting allele diversity that provides a recorded signature of past selection. Genes involved in outer-membrane components, iron scavenging and antibiotic resistance all showed this signature of within-patient selection. These results offer a general and rapid approach for identifying the selective pressures acting on a pathogen in individual patients based on single clinical samples.

PubMed Disclaimer

Figures

Figure 1
Figure 1. Alternative models of within-patient evolution
(a) In the dominant-lineage model of within-host evolution, lineages with beneficial mutations sweep to fixation (green lines), eliminating their less fit ancestors or other temporarily arising genotypes (dashed lines). In this model, most observed mutations will be fixed and polymorphic mutations will be rare, representing only recent mutational events (magenta lines). (b) In the diverse community-model, lineages coexist and compete for long stretches of time. In this model, most sampled mutations will be polymorphic.
Figure 2
Figure 2. Two methods for studying genomic intraspecies diversity
(a) To study within-patient evolution, we cultured sputum samples from patients with cystic fibrosis on selective media. In the colony re-sequencing approach (solid arrows, performed for one patient), we isolated multiple individual colonies from the same single sample, independently called variants for each isolate via alignment of reads, and compared variants among the isolates. In the deep population sequencing approach (dashed arrow, performed for five patients), we pool hundreds of colonies from the same plate and analyze the pool's genomic DNA. We identified positions on the genome where some reads, originating from different colonies on the plate, disagree with an inferred ancestral genome (Online Methods). (b) Allele frequency estimates in the population sequencing (y-axis) versus the colony re-sequencing (x-axis) from the same sputum sample (P1) for each mutated position. Mutations are classified as either fixed (green circles) or polymorphic (magenta circles). Some mutations found in the colony-based approach are sub-threshold in frequency or confidence in the pool-based approach (open squares). Slight jitter is added in the X and Y locations for each point to improve visibility (up to 2% change). As an example, the insets at top and at right display a summary of the raw data at the indicated genomic position. The population sequencing (right) at this position shows 70% aligned reads supporting a T (orange) and 30% supporting a G (black), consistent with the corresponding number of colonies in the individual isolates (22, T; 7, G). Reads from each isolate (top) are mostly of identical calls (all T, or all G). Green indicates a single read in one isolate supporting an A, likely a sequencing error. For further comparison of the two methods, see Supplementary Figure 7.
Figure 3
Figure 3. Within-patient evolution leads to diversification, not substitution
Mutations found in B. dolosa within-patient populations relative to the outgroup are classified as fixed (green), or polymorphic (magenta). An excess of polymorphic versus fixed mutations supports the diverse-community model over the dominant-lineage model. (a) A maximum-parsimony phylogeny of 29 isolates from the same sputum sample (P1) shows the coexistence of diverse sub-lineages separated by many single nucleotide mutations accumulating since the last common ancestor (LCA) of this patient. Each isolate is represented by a dotted line. (b) The diverse-community model is also supported by the distribution of allele frequencies from the population sequencing in 5 patients' samples.
Figure 4
Figure 4. Sublineages coexist within a patient for many years after divergence
(a) A histogram of the number (dLCA) of single nucleotide mutations found in isolates from Patient 1, relative to their LCA. The black bar indicates the mean value of dLCA across the isolates. (b) The value of <dLCA> from the population sequencing data for patients Patients 1 through 4 (Online Methods). In both panels, the axis at top shows the relationship between dLCA and years to LCA, as calculated via the molecular clock (2.1 SNPs/yr).
Figure 5
Figure 5. Coexistence of alternative adaptive mutations in the same sample highlights specific genes as drivers of within-host evolution
(a) Number of multi-diverse genes observed in samples from Patients 1-5 (P1-P5, blue bars) relative to a null expectation in which diverse sites are randomly distributed across the genome (histogram, 1000 simulations). For P5, the number of multi-diverse genes observed is not significant. (b) The canonical signal for selection, dN/dS, across the set of 16 genes and 3 operons showing a multi-diverse signature in at least one patient (P1-P4, 21 genes total, blue) versus dNdS across the set of genes not showing this signature (black). dN/dS >1 indicates positive selection for amino acid change. Error bars indicate 95% CIs. See Online Methods for details on the calculation of dNdS. (c-d) Linkage between nearby polymorphisms based on jointly overlapping short reads. Percentages of reads supporting the ancestral genotype, each of the single mutants, and the double mutant are plotted. No reads supporting the double mutant were found (c, n=524; d, n=415; See Supplementary Fig. 5 for exception). (e) A network of patients and genes showing a multi-diverse signature at least once in P1-P4. A gene is connected to a patient if it was mutated multiple times (solid line), had a single polymorphic mutation (dashed lined), or single fixed mutation (dotted line) within that patient. Genes closer to the center of the network are mutated in more patients, representing common targets of in vivo pathogen selection, while genes connected to single patients may indicate patient-specific adaptation. Genes are labeled with their closest homolog and predicted biological role. The biological role of rpoD is unclassified because it is recently duplicated in the B. cepacia complex (see Supplementary Note, Supplementary Table 2).
See this image and copyright information in PMC

Comment in

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Mwangi MM, et al. Tracking the in vivo evolution of multidrug resistance in Staphylococcus aureus by whole-genome sequencing. Proc Natl Acad Sci U S A. 2007;104:9451–6. - PMC - PubMed
    1. Comas I, et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nat Genet. 2012;44:106–10. - PMC - PubMed
    1. Ford CB, et al. Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat Genet. 2011;43:482–6. - PMC - PubMed
    1. Kennemann L, et al. Helicobacter pylori genome evolution during human infection. Proc Natl Acad Sci U S A. 2011;108:5033–8. - PMC - PubMed
    1. Young BC, et al. Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease. Proc Natl Acad Sci U S A. 2012;109:4550–5. - PMC - PubMed

Publication types

Associated data