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.

  • Article
  • Published:

Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis

Nature Genetics volume 47, pages 57–64 (2015)Cite this article

Subjects

Abstract

Little is known about how within-host evolution compares between genotypically different strains of the same pathogenic species. We sequenced the whole genomes of 474 longitudinally collected clinical isolates of Pseudomonas aeruginosa sampled from 34 children and young individuals with cystic fibrosis. Our analysis of 36 P. aeruginosa lineages identified convergent molecular evolution in 52 genes. This list of genes suggests a role in host adaptation for remodeling of regulatory networks and central metabolism, acquisition of antibiotic resistance and loss of extracellular virulence factors. Furthermore, we find an ordered succession of mutations in key regulatory networks. Accordingly, mutations in downstream transcriptional regulators were contingent upon mutations in upstream regulators, suggesting that remodeling of regulatory networks might be important in adaptation. The characterization of genes involved in host adaptation may help in predicting bacterial evolution in patients with cystic fibrosis and in the design of future intervention strategies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Overview of the present investigations.
Figure 2: Overview of the 474 genome-sequenced P. aeruginosa isolates.
Figure 3: Clone types found in more than one patient.
Figure 4: Pathoadaptive genes (n = 52).
Figure 5: The most frequently mutated functional classes and genes.
Figure 6: The order of mutations in mutants with two nonsynonymous mutations in the same regulatory pathway.

Similar content being viewed by others

Accession codes

Primary accessions

Sequence Read Archive

Referenced accessions

NCBI Reference Sequence

References

  1. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Smith, E.E. et al. Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proc. Natl. Acad. Sci. USA 103, 8487–8492 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Cramer, N. et al. Microevolution of the major common Pseudomonas aeruginosa clones C and PA14 in cystic fibrosis lungs. Environ. Microbiol. 13, 1690–1704 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Yang, L. et al. Evolutionary dynamics of bacteria in a human host environment. Proc. Natl. Acad. Sci. USA 108, 7481–7486 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Holt, K.E. et al. Tracking the establishment of local endemic populations of an emergent enteric pathogen. Proc. Natl. Acad. Sci. USA 110, 17522–17527 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Eyre, D.W. et al. Diverse sources of C. difficile infection identified on whole-genome sequencing. N. Engl. J. Med. 369, 1195–1205 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Chewapreecha, C. et al. Dense genomic sampling identifies highways of pneumococcal recombination. Nat. Genet. 46, 305–309 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Comas, I. et al. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat. Genet. 45, 1176–1182 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Casali, N. et al. Evolution and transmission of drug-resistant tuberculosis in a Russian population. Nat. Genet. 46, 279–286 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mather, A.E. et al. Distinguishable epidemics of multidrug-resistant Salmonella Typhimurium DT104 in different hosts. Science 341, 1514–1517 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Grad, Y.H. et al. Genomic epidemiology of Neisseria gonorrhoeae with reduced susceptibility to cefixime in the USA: a retrospective observational study. Lancet Infect. Dis. 14, 220–226 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Croucher, N.J. et al. Rapid pneumococcal evolution in response to clinical interventions. Science 331, 430–434 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sokurenko, E.V., Hasty, D.L. & Dykhuizen, D.E. Pathoadaptive mutations: gene loss and variation in bacterial pathogens. Trends Microbiol. 7, 191–195 (1999).

    Article  CAS  PubMed  Google Scholar 

  14. Folkesson, A. et al. Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective. Nat. Rev. Microbiol. 10, 841–851 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Johansen, H.K., Moskowitz, S.M., Ciofu, O., Pressler, T. & Hoiby, N. Spread of colistin resistant non-mucoid Pseudomonas aeruginosa among chronically infected Danish cystic fibrosis patients. J. Cyst. Fibros. 7, 391–397 (2008).

    Article  PubMed  Google Scholar 

  16. Wiehlmann, L. et al. Population structure of Pseudomonas aeruginosa. Proc. Natl. Acad. Sci. USA 104, 8101–8106 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jelsbak, L. et al. Molecular epidemiology and dynamics of Pseudomonas aeruginosa populations in lungs of cystic fibrosis patients. Infect. Immun. 75, 2214–2224 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zimakoff, J., Hoiby, N., Rosendal, K. & Guilbert, J.P. Epidemiology of Pseudomonas aeruginosa infection and the role of contamination of the environment in a cystic fibrosis clinic. J. Hosp. Infect. 4, 31–40 (1983).

    Article  CAS  PubMed  Google Scholar 

  19. Worby, C.J., Lipsitch, M. & Hanage, W.P. Within-host bacterial diversity hinders accurate reconstruction of transmission networks from genomic distance data. PLoS Comput. Biol. 10, e1003549 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. 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).

    Article  CAS  PubMed  Google Scholar 

  21. Winsor, G.L. et al. Pseudomonas Genome Database: improved comparative analysis and population genomics capability for Pseudomonas genomes. Nucleic Acids Res. 39, D596–D600 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Luzar, M.A. & Montie, T.C. Avirulence and altered physiological properties of cystic fibrosis strains of Pseudomonas aeruginosa. Infect. Immun. 50, 572–576 (1985).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Damkiær, S., Yang, L., Molin, S. & Jelsbak, L. Evolutionary remodeling of global regulatory networks during long-term bacterial adaptation to human hosts. Proc. Natl. Acad. Sci. USA 110, 7766–7771 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Pai, H. et al. Carbapenem resistance mechanisms in Pseudomonas aeruginosa clinical isolates. Antimicrob. Agents Chemother. 45, 480–484 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ballestero, S. et al. Carbapenem resistance in Pseudomonas aeruginosa from cystic fibrosis patients. J. Antimicrob. Chemother. 38, 39–45 (1996).

    Article  CAS  PubMed  Google Scholar 

  26. Schurek, K.N. et al. Involvement of pmrAB and phoPQ in polymyxin B adaptation and inducible resistance in non-cystic fibrosis clinical isolates of Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 53, 4345–4351 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cabot, G. et al. Genetic markers of widespread extensively drug-resistant Pseudomonas aeruginosa high-risk clones. Antimicrob. Agents Chemother. 56, 6349–6357 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Juan, C. et al. Molecular mechanisms of β-lactam resistance mediated by AmpC hyperproduction in Pseudomonas aeruginosa clinical strains. Antimicrob. Agents Chemother. 49, 4733–4738 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mahenthiralingam, E., Campbell, M.E. & Speert, D.P. Nonmotility and phagocytic resistance of Pseudomonas aeruginosa isolates from chronically colonized patients with cystic fibrosis. Infect. Immun. 62, 596–605 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Sobel, M.L., Hocquet, D., Cao, L., Plesiat, P. & Poole, K. Mutations in PA3574 (nalD) lead to increased MexAB-OprM expression and multidrug resistance in laboratory and clinical isolates of Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 49, 1782–1786 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hancock, R.E. Resistance mechanisms in Pseudomonas aeruginosa and other nonfermentative Gram-negative bacteria. Clin. Infect. Dis. 27 (suppl. 1), S93–S99 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Strateva, T. & Yordanov, D. Pseudomonas aeruginosa—a phenomenon of bacterial resistance. J. Med. Microbiol. 58, 1133–1148 (2009).

    Article  CAS  PubMed  Google Scholar 

  33. Pasca, M.R. et al. Evaluation of fluoroquinolone resistance mechanisms in Pseudomonas aeruginosa multidrug resistance clinical isolates. Microb. Drug Resist. 18, 23–32 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Huse, H.K. et al. Pseudomonas aeruginosa enhances production of a non-alginate exopolysaccharide during long-term colonization of the cystic fibrosis lung. PLoS ONE 8, e82621 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Kuchma, S.L. et al. BifA, a cyclic-Di-GMP phosphodiesterase, inversely regulates biofilm formation and swarming motility by Pseudomonas aeruginosa PA14. J. Bacteriol. 189, 8165–8178 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Davies, D.G. et al. The involvement of cell-to-cell signals in the development of a bacterial biofilm. Science 280, 295–298 (1998).

    Article  CAS  PubMed  Google Scholar 

  37. Choy, W.K., Zhou, L., Syn, C.K., Zhang, L.H. & Swarup, S. MorA defines a new class of regulators affecting flagellar development and biofilm formation in diverse Pseudomonas species. J. Bacteriol. 186, 7221–7228 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. An, S., Wu, J. & Zhang, L.H. Modulation of Pseudomonas aeruginosa biofilm dispersal by a cyclic-Di-GMP phosphodiesterase with a putative hypoxia-sensing domain. Appl. Environ. Microbiol. 76, 8160–8173 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Goodman, A.L. et al. A signaling network reciprocally regulates genes associated with acute infection and chronic persistence in Pseudomonas aeruginosa. Dev. Cell 7, 745–754 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Hickman, J.W., Tifrea, D.F. & Harwood, C.S. A chemosensory system that regulates biofilm formation through modulation of cyclic diguanylate levels. Proc. Natl. Acad. Sci. USA 102, 14422–14427 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Behrends, V. et al. Metabolite profiling to characterize disease-related bacteria: gluconate excretion by Pseudomonas aeruginosa mutants and clinical isolates from cystic fibrosis patients. J. Biol. Chem. 288, 15098–15109 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Eschbach, M. et al. Long-term anaerobic survival of the opportunistic pathogen Pseudomonas aeruginosa via pyruvate fermentation. J. Bacteriol. 186, 4596–4604 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Aires, J.R., Kohler, T., Nikaido, H. & Plesiat, P. Involvement of an active efflux system in the natural resistance of Pseudomonas aeruginosa to aminoglycosides. Antimicrob. Agents Chemother. 43, 2624–2628 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Westbrock-Wadman, S. et al. Characterization of a Pseudomonas aeruginosa efflux pump contributing to aminoglycoside impermeability. Antimicrob. Agents Chemother. 43, 2975–2983 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sobel, M.L., McKay, G.A. & Poole, K. Contribution of the MexXY multidrug transporter to aminoglycoside resistance in Pseudomonas aeruginosa clinical isolates. Antimicrob. Agents Chemother. 47, 3202–3207 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Islam, S., Jalal, S. & Wretlind, B. Expression of the MexXY efflux pump in amikacin-resistant isolates of Pseudomonas aeruginosa. Clin. Microbiol. Infect. 10, 877–883 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Yu, H., Schurr, M.J. & Deretic, V. Functional equivalence of Escherichia coli σE and Pseudomonas aeruginosa AlgU: E. coli rpoE restores mucoidy and reduces sensitivity to reactive oxygen intermediates in algU mutants of P. aeruginosa. J. Bacteriol. 177, 3259–3268 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. DeVries, C.A. & Ohman, D.E. Mucoid-to-nonmucoid conversion in alginate-producing Pseudomonas aeruginosa often results from spontaneous mutations in algT, encoding a putative alternate sigma factor, and shows evidence for autoregulation. J. Bacteriol. 176, 6677–6687 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Schurr, M.J., Martin, D.W., Mudd, M.H. & Deretic, V. Gene cluster controlling conversion to alginate-overproducing phenotype in Pseudomonas aeruginosa: functional analysis in a heterologous host and role in the instability of mucoidy. J. Bacteriol. 176, 3375–3382 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ciofu, O. et al. Investigation of the algT operon sequence in mucoid and non-mucoid Pseudomonas aeruginosa isolates from 115 Scandinavian patients with cystic fibrosis and in 88 in vitro non-mucoid revertants. Microbiology 154, 103–113 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Heeb, S. et al. Functional analysis of the post-transcriptional regulator RsmA reveals a novel RNA-binding site. J. Mol. Biol. 355, 1026–1036 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Yang, L. et al. In situ growth rates and biofilm development of Pseudomonas aeruginosa populations in chronic lung infections. J. Bacteriol. 190, 2767–2776 (2008).

    Article  CAS  PubMed  Google Scholar 

  53. Barrick, J.E. et al. Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461, 1243–1247 (2009).

    Article  CAS  PubMed  Google Scholar 

  54. Lee, D.G. et al. Genomic analysis reveals that Pseudomonas aeruginosa virulence is combinatorial. Genome Biol. 7, R90 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Lieberman, T.D. 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).

    Article  CAS  PubMed  Google Scholar 

  56. Ciofu, O., Riis, B., Pressler, T., Poulsen, H.E. & Hoiby, N. Occurrence of hypermutable Pseudomonas aeruginosa in cystic fibrosis patients is associated with the oxidative stress caused by chronic lung inflammation. Antimicrob. Agents Chemother. 49, 2276–2282 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Waine, D.J., Honeybourne, D., Smith, E.G., Whitehouse, J.L. & Dowson, C.G. Association between hypermutator phenotype, clinical variables, mucoid phenotype, and antimicrobial resistance in Pseudomonas aeruginosa. J. Clin. Microbiol. 46, 3491–3493 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hoiby, N. & Frederiksen, B. in Cystic Fibrosis (eds. Hodson, M. & Geddes, D.) 83–107 (Arnold, London, 2000).

  59. Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  64. Swofford, D.L. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4 (Sinauer Associates, Sunderland, MA, 2003).

Download references

Acknowledgements

We thank U.R. Johansen, P. Poss, H. Nordbjerg, N. Kirkby, K. Bloksted and B.H. Erichsen for excellent technical assistance and T. Pressler and M. Skov for information about the patients' visits to the hospital. This work was supported by the Lundbeck Foundation, and H.K.J. was supported by a clinical research stipend from the Novo Nordisk Foundation.

Author information

Authors and Affiliations

  1. Department of Clinical Microbiology, Rigshospitalet, Copenhagen, Denmark

    Rasmus Lykke Marvig & Helle Krogh Johansen

  2. Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark

    Rasmus Lykke Marvig, Lea Mette Sommer & Søren Molin

  3. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

    Lea Mette Sommer, Søren Molin & Helle Krogh Johansen

Authors
  1. Rasmus Lykke Marvig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lea Mette Sommer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Søren Molin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Helle Krogh Johansen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.M. and H.K.J. jointly supervised the study. R.L.M., S.M. and H.K.J. conceived and designed the experiments. H.K.J. collected clinical samples and provided clinical information. L.M.S. prepared the genomic libraries for whole-genome sequencing. R.L.M. designed the bioinformatics workflows for the analysis. R.L.M. and L.M.S. conducted whole-genome sequence analysis. R.L.M., L.M.S., S.M. and H.K.J. analyzed and interpreted the data. R.L.M. wrote the manuscript. L.M.S., S.M. and H.K.J. helped write the manuscript and provided revisions.

Corresponding author

Correspondence to Rasmus Lykke Marvig.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Patient origin and clone type of the 474 genome-sequenced isolates of P. aeruginosa.

Numbers in white squares denote the number of isolates of the respective clone type that have been isolated from the patient. Red numbers indicate the most prevalent clone type in the patient, and underlined numbers indicate that the clone type is the most recent clone type to be found in the respective patient. Names of clone types framed by blue boxes indicate that the clone type has been found in more than one patient. Patient IDs framed by blue boxes indicate that more than one clone type has been sampled from the patient.

Supplementary Figure 2 Overview of the time between the earliest and latest isolates of P. aeruginosa from each patient.

The bars indicate the time in years between the first and last genome-sequenced isolates from the respective patient.

Supplementary Figure 3 Number of SNPs accumulated since the MRCA of clonal isolates from the same patient.

We evaluated the within-patient diversity of clonal isolates by counting the number of SNPs that isolates have accumulated since the MRCA of clonal isolates from the same patient. The distribution of genetic distances was visualized using a box plot. Points that were beyond the quartiles by 1.5 times the interquartile range were considered to be outliers.

Supplementary Figure 4 Maximum-parsimonious phylogeny of 23 P. aeruginosa isolates of the DK12 clone type.

The phylogeny has a consistency of 0.95 and is based on 576 SNPs identified from whole-genome comparison of the isolates. Branch lengths are drawn to scale, and the scale bar indicates the number of SNPs that have accumulated over the branch. Alleles of P. aeruginosa reference strain PAO1 were used to determine the MRCA. The overall transition-to-transversion ratio was 3.6 (452 transitions, 125 transversions), but a mutational skew towards transition substitutions (88 transitions, 3 transversions) was observed in the branch leading to hypermutable isolate 104, which carries a 24-bp deletion in the DNA mismatch repair gene mutL.

Supplementary Figure 5 Maximum-parsimonious phylogeny of three P. aeruginosa isolates of the DK40 clone type.

The phylogeny has a perfect consistency of 1 and is based on 400 SNPs identified from whole-genome comparison of the isolates. The transition-to-transversion ratio of the SNPs was 2.1 (269 transitions, 131 transversions). Branch lengths are drawn to scale, and the scale bar indicates the number of SNPs that have accumulated over the branch. Alleles of P. aeruginosa reference strain PAO1 were used to determine the MRCA.

Supplementary Figure 6 Order of mutations in genotypes with multiple nonsynonymous mutations in the retS-gacA/S-rsmA/Z signaling pathway.

All mutations were acquired since the MRCA of each of the genotypes. The order of mutations was unambiguously inferred from maximum-parsimonious phylogenetic reconstructions (Online Methods).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Tables 1, 4–6, 8 and 9. (PDF 1667 kb)

List of SNPs found to have accumulated in the recent evolutionary history of 36 clone types of P. aeruginosa.

The SNPs are described according to their position in the genome of P. aeruginosa reference strain PAO1. The SNPs are sorted according to the clone type in which they are found, and the rightmost columns indicate whether the mutation is present in the isolates of the given clone type. (XLSX 1370 kb)

List of indels found to have accumulated in the recent evolutionary history of 36 clone types of P. aeruginosa.

The indels are described according to their position in the genome of P. aeruginosa reference strain PAO1. The indels are sorted according to the clone type in which they are found, and the rightmost columns indicate whether the mutation is present in the isolates of the given clone type. (XLSX 511 kb)

Information about whole genome–sequenced clinical isolates.

(XLSX 75 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marvig, R., Sommer, L., Molin, S. et al. Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis . Nat Genet 47, 57–64 (2015). https://doi.org/10.1038/ng.3148

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3148

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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