1887

Abstract

Chronic bacterial airway infections in people with cystic fibrosis (CF) are often caused by Pseudomonas aeruginosa, typically showing high phenotypic diversity amongst co-isolates from the same sputum sample. Whilst adaptive evolution during chronic infections has been reported, the genetic mechanisms underlying the observed rapid within-population diversification are not well understood. Two recent conflicting reports described very high and low rates of homologous recombination in two closely related P. aeruginosa populations from the lungs of different chronically infected CF patients. To investigate the underlying cause of these contrasting observations, we combined the short read datasets from both studies and applied a new comparative analysis. We inferred low rates of recombination in both populations. The discrepancy in the findings of the two previous studies can be explained by differences in the application of variant calling techniques. Two novel algorithms were developed that filter false-positive variants. The first algorithm filters variants on the basis of ambiguity within duplications in the reference genome. The second omits probable false-positive variants at regions of non-homology between reference and sample caused by structural rearrangements. As gains and losses of prophage or genomic islands are frequent causes of chromosomal rearrangements within microbial populations, this filter has broad appeal for mitigating false-positive variant calls. Both algorithms are available in a Python package.

Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000051
2016-03-02
2019-10-20
Loading full text...

Full text loading...

/deliver/fulltext/mgen/2/3/mgen000051.html?itemId=/content/journal/mgen/10.1099/mgen.0.000051&mimeType=html&fmt=ahah

References

  1. Bankevich A., Nurk S., Antipov D., Gurevich A. A., Dvorkin M., Kulikov A. S., Lesin V. M., Nikolenko S. I., Pham S., other authors. 2012; SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol19:455–477 [CrossRef][PubMed]
    [Google Scholar]
  2. Chapman B.. 2014; Validating generalized incremental joint variant calling with GATK HaplotypeCaller, FreeBayes, Platypus and samtools. https://bcbio.wordpress.com//10/07/joint-calling/
  3. Cramer N., Klockgether J., Wrasman K., Schmidt M., Davenport C. F., Tümmler B.. 2011; Microevolution of the major common Pseudomonas aeruginosa clones C and PA14 in cystic fibrosis lungs. Environ Microbiol13:1690–1704 [CrossRef][PubMed]
    [Google Scholar]
  4. Darch S. E., McNally A., Harrison F., Corander J., Barr H. L., Paszkiewicz K., Holden S., Fogarty A., Crusz S. A., Diggle S. P.. 2015; Recombination is a key driver of genomic and phenotypic diversity in a Pseudomonas aeruginosa population during cystic fibrosis infection. Sci Rep5:7649 [CrossRef][PubMed]
    [Google Scholar]
  5. DePristo M. A., Banks E., Poplin R., Garimella K. V., Maguire J. R., Hartl C., Philippakis A. A., del Angel G., Rivas M. A., other authors. 2011; A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet43:491–498 [CrossRef][PubMed]
    [Google Scholar]
  6. Didelot X., Wilson D. J.. 2015; ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLOS Comput Biol11:e1004041 [CrossRef][PubMed]
    [Google Scholar]
  7. Fothergill J. L., Walshaw M. J., Winstanley C.. 2012; Transmissible strains of Pseudomonas aeruginosa in cystic fibrosis lung infections. Eur Respir J40:227–238 [CrossRef][PubMed]
    [Google Scholar]
  8. Jeukens J., Boyle B., Kukavica-Ibrulj I., Ouellet M. M., Aaron S. D., Charette S. J., Fothergill J. L., Tucker N. P., Winstanley C., Levesque R. C.. 2014; Comparative genomics of isolates of a Pseudomonas aeruginosa epidemic strain associated with chronic lung infections of cystic fibrosis patients. PLoS One9:e87611 [CrossRef][PubMed]
    [Google Scholar]
  9. Jorth P., Staudinger B. J., Wu X., Hisert K. B., Hayden H., Garudathri J., Harding C. L., Radey M. C., Rezayat A., other authors. 2015; Regional isolation drives bacterial diversification within cystic fibrosis lungs. Cell Host Microbe18:307–319 [CrossRef][PubMed]
    [Google Scholar]
  10. Kenna D. T., Doherty C. J., Foweraker J., Macaskill L., Barcus V. A., Govan J. R. W.. 2007; Hypermutability in environmental Pseudomonas aeruginosa and in populations causing pulmonary infection in individuals with cystic fibrosis. Microbiology153:1852–1859 [CrossRef][PubMed]
    [Google Scholar]
  11. Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R., 1000 Genome Project Data Processing Subgroup. 2009; The Sequence Alignment/Map format and SAMtools. Bioinformatics25:2078–2079 [CrossRef][PubMed]
    [Google Scholar]
  12. Martin K., Baddal B., Mustafa N., Perry C., Underwood A., Constantidou C., Loman N., Kenna D. T., Turton J. F.. 2013; Clusters of genetically similar isolates of Pseudomonas aeruginosa from multiple hospitals in the UK. J Med Microbiol62:988–1000 [CrossRef][PubMed]
    [Google Scholar]
  13. Marttinen P., Hanage W. P., Croucher N. J., Connor T. R., Harris S. R., Bentley S. D., Corander J.. 2012; Detection of recombination events in bacterial genomes from large population samples. Nucleic Acids Res40:e6 [CrossRef][PubMed]
    [Google Scholar]
  14. Marvig R. L., Johansen H. K., Molin S., Jelsbak L.. 2013; Genome analysis of a transmissible lineage of Pseudomonas aeruginosa reveals pathoadaptive mutations and distinct evolutionary paths of hypermutators. PLoS Genet9:e1003741 [CrossRef][PubMed]
    [Google Scholar]
  15. McElroy K. E., Luciani F., Thomas T.. 2012; GemSIM: general, error-model based simulator of next-generation sequencing data. BMC Genomics13:74 [CrossRef][PubMed]
    [Google Scholar]
  16. McKenna A., Hanna M., Banks E., Sivachenko A., Cibulskis K., Kernytsky A., Garimella K., Altshuler D., Gabriel S., other authors. 2010; The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res20:1297–1303 [CrossRef][PubMed]
    [Google Scholar]
  17. Mowat E., Paterson S., Fothergill J. L., Wright E. A., Ledson M. J., Walshaw M. J., Brockhurst M. A., Winstanley C.. 2011; Pseudomonas aeruginosa population diversity and turnover in cystic fibrosis chronic infections. Am J Respir Crit Care Med183:1674–1679 [CrossRef][PubMed]
    [Google Scholar]
  18. Oliver A., Cantón R., Campo P., Baquero F., Blázquez J.. 2000; High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science288:1251–1253 [CrossRef][PubMed]
    [Google Scholar]
  19. Olson N. D., Lund S. P., Colman R. E., Foster J. T., Sahl J. W., Schupp J. M., Keim P., Morrow J. B., Salit M. L., Zook J. M.. 2015; Best practices for evaluating single nucleotide variant calling methods for microbial genomics. Frontiers in Genetics6:235[CrossRef]
    [Google Scholar]
  20. Robinson D. F., Foulds L. R.. 1981; Comparison of Phylogenetic Trees. Mathematical Biosciences53:131–147[PubMed][CrossRef]
    [Google Scholar]
  21. Rau M. H., Hansen S. K., Johansen H. K., Thomsen L. E., Workman C. T., Nielsen K. F., Jelsbak L., Høiby N., Yang L., Molin S.. 2010; Early adaptive developments of Pseudomonas aeruginosa after the transition from life in the environment to persistent colonization in the airways of human cystic fibrosis hosts. Environ Microbiol12:1643–1658[PubMed]
    [Google Scholar]
  22. Smith E. E., Buckley D. G., Wu Z., Saenphimmachak C., Hoffman L. R., D'Argenio D. A., Miller S. I., Ramsey B. W., Speert D. P., other authors. 2006; Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proc Natl Acad Sci U S A103:8487–8492 [CrossRef][PubMed]
    [Google Scholar]
  23. Van der Auwera G. A., Carneiro M. O., Hartl C., Poplin R., Del Angel G., Levy-Moonshine A., Jordan T., Shakir K., Roazen D., other authors. 2013; From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics43:11.10.1–11.10.33[PubMed]
    [Google Scholar]
  24. Williams D., Evans B., Haldenby S., Walshaw M. J., Brockhurst M. A., Winstanley C., Paterson S.. 2015; Divergent, coexisting Pseudomonas aeruginosa lineages in chronic cystic fibrosis lung infections. Am J Respir Crit Care Med191:775–785 [CrossRef][PubMed]
    [Google Scholar]
  25. Winstanley C., Langille M. G., Fothergill J. L., Kukavica-Ibrulj I., Paradis-Bleau C., Sanschagrin F., Thomson N. R., Winsor G. L., Quail M. A., other authors. 2009; Newly introduced genomic prophage islands are critical determinants of in vivo competitiveness in the Liverpool Epidemic Strain of Pseudomonas aeruginosa. Genome Res19:12–23 [CrossRef][PubMed]
    [Google Scholar]
  26. Workentine M. L., Sibley C. D., Glezerson B., Purighalla S., Norgaard-Gron J. C., Parkins M. D., Rabin H. R., Surette M. G.. 2013; Phenotypic heterogeneity of Pseudomonas aeruginosa populations in a cystic fibrosis patient. PLoS One8:e60225 [CrossRef][PubMed]
    [Google Scholar]
  27. Zhou Y., Liang Y., Lynch K. H., Dennis J. J., Wishart D. S.. 2011; phast: a fast phage search tool. Nucleic Acids Res39:(Suppl)W347–W352 [CrossRef][PubMed]
    [Google Scholar]
  28. 1. Darch, S. E., McNally, A., Harrison, F., Corander, J., Barr, H. L., Paszkiewicz, K., Holden, S., Fogarty, A., Crusz, S. A. & Diggle, S. P. (2014). European Nucleotide Archive: ERP005188.
  29. 2. Williams, D., Evans, B., Haldenby, S., Walshaw, M. J., Brockhurst, M. A., Winstanley, C. & Paterson, S. (2014). European Nucleotide Archive: ERR953477–ERR953516.
  30. 3. Winstanley, C., Langille, M. G., Fothergill, J. L., Kukavica-Ibrulj, I., Paradis-Bleau, C., Sanschagrin, F., Thomson, N. R., Winsor, G. L., Quail, M. A. & other authors. (2008). RefSeq: NC_011770.1.
  31. 4. Jeukens, J., Boyle, B., Kukavica-Ibrulj, I., Ouellet, M. M., Aaron, S. D., Charette, S. J., Fothergill, J. L., Tucker, N. P., Winstanley, C. & Levesque, R. C. (2013). RefSeq: NZ_CP006981.1.
  32. 5. Williams, D., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2015). FigShare: http://dx.doi.org/10.6084/m9.figshare.2056350
  33. 6. Williams, D., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2015). FigShare: http://dx.doi.org/10.6084/m9.figshare.2056359
  34. 7. Williams, D., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2015). FigShare: http://dx.doi.org/10.6084/m9.figshare.2056365
  35. 8. Williams, D., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2015). FigShare: http://dx.doi.org/10.6084/m9.figshare.2056326
  36. 9. Williams, D., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2015). FigShare: http://dx.doi.org/10.6084/m9.figshare.2056356
  37. 10. Williams, D., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2015). FigShare: http://dx.doi.org/10.6084/m9.figshare.2056344
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000051
Loading
/content/journal/mgen/10.1099/mgen.0.000051
Loading

Data & Media loading...

Supplements

Supplementary Data

PDF

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error