Letter | Published:

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

Nature Genetics volume 46, pages 8287 (2014) | Download Citation

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Sequence Read Archive

Referenced accessions

NCBI Reference Sequence

References

  1. 1.

    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).

  2. 2.

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

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 15.

    , , & 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).

  16. 16.

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

  17. 17.

    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).

  18. 18.

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

  19. 19.

    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).

  20. 20.

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

  21. 21.

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

  22. 22.

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

  23. 23.

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

  24. 24.

    , & Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science 335, 1302 (2012).

  25. 25.

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

  26. 26.

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

  27. 27.

    , , , & High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288, 1251–1254 (2000).

  28. 28.

    et al. Bacterial hypermutation: clinical implications. J. Med. Microbiol. 60, 563–573 (2011).

  29. 29.

    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).

  30. 30.

    , , & Genome analysis of a transmissible lineage of Pseudomonas aeruginosa reveals pathoadaptive mutations and distinct evolutionary paths of hypermutators. PLoS Genet. 9, e1003741 (2013).

  31. 31.

    , , & Approaches to measure the fitness of Burkholderia cepacia complex isolates. J. Med. Microbiol. 59, 679–686 (2010).

  32. 32.

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

  33. 33.

    , & Clonal interference, multiple mutations and adaptation in large asexual populations. Genetics 180, 2163–2173 (2008).

  34. 34.

    & The fate of competing beneficial mutations in an asexual population. Genetica 102–103, 127–144 (1998).

  35. 35.

    , , & An equivalence principle for the incorporation of favorable mutations in asexual populations. Science 311, 1615–1617 (2006).

  36. 36.

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

  37. 37.

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

  38. 38.

    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. 39.

    , , & Architecture of Burkholderia cepacia complex σ70 gene family: evidence of alternative primary and clade-specific factors, and genomic instability. BMC Genomics 8, 308 (2007).

  40. 40.

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

  41. 41.

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

  42. 42.

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

  43. 43.

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

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

Affiliations

  1. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.

    • Tami D Lieberman
    •  & Roy Kishony
  2. Department of Medicine, Division of Infectious Diseases, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Kelly B Flett
    •  & Gregory P Priebe
  3. Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel.

    • Idan Yelin
    •  & Roy Kishony
  4. Department of Medicine, Division of Respiratory Diseases, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Thomas R Martin
  5. Department of Laboratory Medicine, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Alexander J McAdam
  6. Department of Anesthesiology, Perioperative and Pain Medicine, Division of Critical Care Medicine, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Gregory P Priebe
  7. Department of Medicine, Division of Infectious Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Gregory P Priebe

Authors

  1. Search for Tami D Lieberman in:

  2. Search for Kelly B Flett in:

  3. Search for Idan Yelin in:

  4. Search for Thomas R Martin in:

  5. Search for Alexander J McAdam in:

  6. Search for Gregory P Priebe in:

  7. Search for Roy Kishony in:

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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

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

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8, Supplementary Tables 1–4 and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 5

    Mutations found in isolates

  2. 2.

    Supplementary Table 6

    Mutations found in deep population sequencing

About this article

Publication history

Received

Accepted

Published

DOI

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

Further reading