Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Abstract

Advances in sequencing technologies have enabled the identification of mutations acquired by bacterial pathogens during infection1,2,3,4,5,6,7,8,9,10. However, it remains unclear whether adaptive mutations fix in the population or lead to pathogen diversification within the patient11,12. 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.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Alternative models of within-patient evolution.
Figure 2: Two methods for studying genomic intraspecies diversity.
Figure 3: Within-patient evolution leads to diversification not substitution.
Figure 4: Sublineages coexist within a patient for many years after divergence.
Figure 5: Coexistence of alternative adaptive mutations in the same sample highlights specific genes as drivers of within-host evolution.

Accession codes

Primary accessions

Sequence Read Archive

Referenced accessions

NCBI Reference Sequence

References

  1. Mwangi, M.M. et al. Tracking the in vivo evolution of multidrug resistance in Staphylococcus aureus by whole-genome sequencing. Proc. Natl. Acad. Sci. USA 104, 9451–9456 (2007).

    CAS  Article  Google Scholar 

  2. Comas, I. et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nat. Genet. 44, 106–110 (2012).

    CAS  Article  Google Scholar 

  3. Ford, C.B. et al. Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat. Genet. 43, 482–486 (2011).

    CAS  Article  Google Scholar 

  4. Kennemann, L. et al. Helicobacter pylori genome evolution during human infection. Proc. Natl. Acad. Sci. USA 108, 5033–5038 (2011).

    CAS  Article  Google Scholar 

  5. Young, B.C. et al. Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease. Proc. Natl. Acad. Sci. USA 109, 4550–4555 (2012).

    CAS  Article  Google Scholar 

  6. Huse, H.K. et al. Parallel evolution in Pseudomonas aeruginosa over 39,000 generations in vivo. MBio 1, e00199–10 (2010).

    Article  Google Scholar 

  7. Snitkin, E.S. et al. Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Science Transl. Med. 4, 148ra116 (2012).

    Article  Google Scholar 

  8. Lieberman, T.D. et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nat. Genet. 43, 1275–1280 (2011).

    CAS  Article  Google Scholar 

  9. Didelot, X. et al. Genomic evolution and transmission of Helicobacter pylori in two South African families. Proc. Natl. Acad. Sci. USA 110, 13880–13885 (2013).

    CAS  Article  Google Scholar 

  10. Wilson, D.J. Insights from genomics into bacterial pathogen populations. PLoS Pathog. 8, e1002874 (2012).

    CAS  Article  Google Scholar 

  11. Workentine, M. & Surette, M.G. Complex Pseudomonas population structure in cystic fibrosis airway infections. Am. J. Respir. Crit. Care Med. 183, 1581–1583 (2011).

    Article  Google Scholar 

  12. Nguyen, D. & Singh, P.K. Evolving stealth: genetic adaptation of Pseudomonas aeruginosa during cystic fibrosis infections. Proc. Natl. Acad. Sci. USA 103, 8305–8306 (2006).

    CAS  Article  Google Scholar 

  13. Chung, J.C. et al. Genomic variation among contemporary Pseudomonas aeruginosa isolates from chronically infected cystic fibrosis patients. J. Bacteriol. 194, 4857–4866 (2012).

    CAS  Article  Google Scholar 

  14. Workentine, M.L. et al. Phenotypic heterogeneity of Pseudomonas aeruginosa populations in a cystic fibrosis patient. PLoS ONE 8, e60225 (2013).

    CAS  Article  Google Scholar 

  15. Foweraker, J.E., Laughton, C.R., Brown, D.F. & Bilton, D. Phenotypic variability of Pseudomonas aeruginosa in sputa from patients with acute infective exacerbation of cystic fibrosis and its impact on the validity of antimicrobial susceptibility testing. J. Antimicrob. Chemother. 55, 921–927 (2005).

    CAS  Article  Google Scholar 

  16. Sun, G. et al. Dynamic population changes in Mycobacterium tuberculosis during acquisition and fixation of drug resistance in patients. J. Infect. Dis. 206, 1724–1733 (2012).

    CAS  Article  Google Scholar 

  17. Harris, S.R. et al. Whole-genome sequencing for analysis of an outbreak of meticillin-resistant Staphylococcus aureus: a descriptive study. Lancet Infect. Dis. 13, 130–136 (2013).

    CAS  Article  Google Scholar 

  18. Walker, T.M. et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect. Dis. 13, 137–146 (2013).

    CAS  Article  Google Scholar 

  19. Hansen, S.K. et al. Evolution and diversification of Pseudomonas aeruginosa in the paranasal sinuses of cystic fibrosis children have implications for chronic lung infection. ISME J. 6, 31–45 (2012).

    Article  Google Scholar 

  20. Vermis, K. et al. Proposal to accommodate Burkholderia cepacia genomovar VI as Burkholderia dolosa sp. nov. Int. J. Syst. Evol. Microbiol. 54, 689–691 (2004).

    CAS  Article  Google Scholar 

  21. Kalish, L.A. et al. Impact of Burkholderia dolosa on lung function and survival in cystic fibrosis. Am. J. Respir. Crit. Care Med. 173, 421–425 (2006).

    Article  Google Scholar 

  22. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    CAS  Article  Google Scholar 

  23. Barrick, J.E. & Lenski, R.E. Genome-wide mutational diversity in an evolving population of Escherichia coli. Cold Spring Harb. Symp. Quant. Biol. 74, 119–129 (2009).

    CAS  Article  Google Scholar 

  24. Pickrell, J.K., Gilad, Y. & Pritchard, J.K. Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science 335, 1302 (2012).

    CAS  Article  Google Scholar 

  25. Nakamura, K. et al. Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res. 39, e90 (2011).

    CAS  Article  Google Scholar 

  26. Oliver, A. & Mena, A. Bacterial hypermutation in cystic fibrosis, not only for antibiotic resistance. Clin. Microbiol. Infect. 16, 798–808 (2010).

    CAS  Article  Google Scholar 

  27. Oliver, A., Canton, R., Campo, P., Baquero, F. & Blazquez, J. High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288, 1251–1254 (2000).

    CAS  Article  Google Scholar 

  28. Jolivet-Gougeon, A. et al. Bacterial hypermutation: clinical implications. J. Med. Microbiol. 60, 563–573 (2011).

    CAS  Article  Google Scholar 

  29. Hoboth, C. et al. Dynamics of adaptive microevolution of hypermutable Pseudomonas aeruginosa during chronic pulmonary infection in patients with cystic fibrosis. J. Infect. Dis. 200, 118–130 (2009).

    CAS  Article  Google Scholar 

  30. Marvig, R.L., Johansen, H.K., Molin, S. & Jelsbak, L. Genome analysis of a transmissible lineage of Pseudomonas aeruginosa reveals pathoadaptive mutations and distinct evolutionary paths of hypermutators. PLoS Genet. 9, e1003741 (2013).

    CAS  Article  Google Scholar 

  31. Pope, C.F., Gillespie, S.H., Moore, J.E. & McHugh, T.D. Approaches to measure the fitness of Burkholderia cepacia complex isolates. J. Med. Microbiol. 59, 679–686 (2010).

    CAS  Article  Google Scholar 

  32. Kingman, J.F.C. On the genealogy of large populations. J. Appl. Probab. 19, 27–43 (1982).

    Article  Google Scholar 

  33. Fogle, C.A., Nagle, J.L. & Desai, M.M. Clonal interference, multiple mutations and adaptation in large asexual populations. Genetics 180, 2163–2173 (2008).

    Article  Google Scholar 

  34. Gerrish, P.J. & Lenski, R.E. The fate of competing beneficial mutations in an asexual population. Genetica 102–103, 127–144 (1998).

    Article  Google Scholar 

  35. Hegreness, M., Shoresh, N., Hartl, D. & Kishony, R. An equivalence principle for the incorporation of favorable mutations in asexual populations. Science 311, 1615–1617 (2006).

    CAS  Article  Google Scholar 

  36. Mowat, E. et al. Pseudomonas aeruginosa population diversity and turnover in cystic fibrosis chronic infections. Am. J. Respir. Crit. Care Med. 183, 1674–1679 (2011).

    Article  Google Scholar 

  37. Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).

    Article  Google Scholar 

  38. Ashish, A. et al. Extensive diversification is a common feature of Pseudomonas aeruginosa populations during respiratory infections in cystic fibrosis. J. Cyst. Fibros. 10.1016/j.jcf.2013.04.003 (1 May 2013).

  39. Menard, A., de Los Santos, P.E., Graindorge, A. & Cournoyer, B. Architecture of Burkholderia cepacia complex σ70 gene family: evidence of alternative primary and clade-specific factors, and genomic instability. BMC Genomics 8, 308 (2007).

    Article  Google Scholar 

  40. Guss, A.M. et al. Phylogenetic and metabolic diversity of bacteria associated with cystic fibrosis. ISME J. 5, 20–29 (2011).

    Article  Google Scholar 

  41. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  42. Felsenstein, J. PHYLIP-Phylogeny Inference Package (Version 3.2). Cladistics 5, 164–166 (1989).

    Google Scholar 

  43. Aziz, R.K. et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9, 75 (2008).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to J.-B. Michel and members of the Kishony laboratory for insightful discussions and support, to the team at the Partners HealthCare Center for Personalized Genetic Medicine (PCPGM) for Illumina sequencing, to L. Williams and A. Palmer for discussions and technical assistance, and to Y. Gerardin, J. Meyer, L. Stone and R. Ward for their comments on the manuscript. T.D.L. and G.P.P. were supported in part by grants from the Cystic Fibrosis Foundation (LIEBER12H0 to T.D.L. and PRIEBE1310 to G.P.P.). This work was funded in part by the US National Institutes of Health (GM081617 to R.K.), the New England Regional Center of Excellence for Biodefense and Emerging Infectious Diseases (NERCE; U54 AI057159 to R.K.) and Hoffman-LaRoche.

Author information

Authors and Affiliations

Authors

Contributions

T.D.L., A.J.M., G.P.P. and R.K. designed the study. A.J.M. and T.R.M. collected clinical samples. K.B.F., T.R.M., A.J.M. and G.P.P. conducted chart review and provided medical information. T.D.L. performed experiments. T.D.L., I.Y. and R.K. wrote the sequence analysis scripts. T.D.L. and R.K. analyzed the data. T.D.L., A.J.M., G.P.P. and R.K. interpreted the results and wrote the manuscript.

Corresponding authors

Correspondence to Alexander J McAdam, Gregory P Priebe or Roy Kishony.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 1–4 and Supplementary Note (PDF 2544 kb)

Supplementary Table 5

Mutations found in isolates (XLSX 63 kb)

Supplementary Table 6

Mutations found in deep population sequencing (XLSX 126 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lieberman, T., Flett, K., Yelin, I. et al. Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures. Nat Genet 46, 82–87 (2014). https://doi.org/10.1038/ng.2848

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.2848

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing