Legionella pp 429-443 | Cite as

Analysis of the Pulmonary Microbiome Composition of Legionella pneumophila-Infected Patients

Part of the Methods in Molecular Biology book series (MIMB, volume 1921)


The analysis of the lung microbiome composition is a field of research that recently emerged. It gained great interest in pulmonary diseases such as pneumonia since the microbiome seems to be involved in host immune responses, inflammation, and protection against pathogens. Thus, it is possible that the microbial communities living in the lungs play a role in the outcome and severity of lung infections such as Legionella-caused pneumonia and in the response to antibiotic therapy. In this chapter, all steps necessary for the characterization of the bacterial and fungal fraction of the lung microbiome using high-throughput sequencing approaches are explained, starting from the selection of clinical samples to the analysis of the taxonomic composition, diversity, and ecology of the microbiome.

Key words

Pulmonary microbiome Legionella pneumophila 16S rRNA gene ITS High-throughput sequencing Bioinformatics Microbial ecology 



Work in the CB laboratory is financed by the Institut Pasteur and AECP was fianced by a fellowship from grant ANR-10-LABX-62-IBEID and grant ANR 15 CE17 0014 03.


  1. 1.
    Lozupone CA, Knight R (2008) Species divergence and the measurement of microbial diversity. FEMS Microbiol Rev 32(4):557–578CrossRefGoogle Scholar
  2. 2.
    Andrews S (2010) FastQC a quality control tool for high throughput sequence data. Accessed 26 Apr 2010
  3. 3.
    Hannon Lab (2009) FASTX toolkit. Accessed 2 Feb 2010
  4. 4.
    Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27(6):863–864CrossRefGoogle Scholar
  5. 5.
    Aronesty E (2011) ea-utils: “Command-line tools for processing biological sequencing data”. Accessed 20 June 2017
  6. 6.
    Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336CrossRefGoogle Scholar
  7. 7.
    Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541CrossRefGoogle Scholar
  8. 8.
    Wang Q, Garrity GM, Tiedje JM et al (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261–5267CrossRefGoogle Scholar
  9. 9.
    Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460–2461CrossRefGoogle Scholar
  10. 10.
    Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410CrossRefGoogle Scholar
  11. 11.
    Cole JR, Wang Q, Fish JA et al (2014) Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42(Database issue):D633–D642CrossRefGoogle Scholar
  12. 12.
    DeSantis TZ, Hugenholtz P, Larsen N et al (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72(7):5069–5072CrossRefGoogle Scholar
  13. 13.
    Kõljalg U, Larsson KH, Abarenkov K et al (2005) UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166(3):1063–1068CrossRefGoogle Scholar
  14. 14.
    Deshpande V, Wang Q, Greenfield P et al (2016) Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences. Mycologia 108(1):1–5CrossRefGoogle Scholar
  15. 15.
    Stoddard SF, Smith BJ, Hein R et al (2015) rrnDB: improved tools for interpreting and rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res 43:D593–D598. Scholar
  16. 16.
    Angly FE, Dennis PG, Skarshewski A et al (2014) CopyRighter: a rapid tool for improving the accuracy of microbial community profiles through lineage-specific gene copy number correction. Microbiome 2:11. Scholar
  17. 17.
    R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Accessed 28 Sept 2017
  18. 18.
    McMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8(4):e61217. Scholar
  19. 19.
    Oksanen J, Guillaume F, Friendly M et al (2017) vegan: Community Ecology Package. R package version 2.4-4. Accessed 24 Aug 2017
  20. 20.
    Goodrich JK, Di Rienzi SC, Poole AC (2014) Conducting a microbiome study. Cell 158(2):250–262CrossRefGoogle Scholar
  21. 21.
    Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71(12):8228–8235CrossRefGoogle Scholar
  22. 22.
    Segata N, Izard J, Waldron L et al (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12(6):R60. Scholar
  23. 23.
    Oksanen J (2015) Multivariate analysis of ecological communities in R: vegan tutorial.
  24. 24.
    Dickson RP, Erb-Downward JR, Freeman CM et al (2017) Bacterial topography of the healthy human lower respiratory tract. MBio 8(1):e02287-16. Scholar
  25. 25.
    Mizrahi H, Peretz A, Lesnik R et al (2017) Comparison of sputum microbiome of legionellosis-associated patients and other pneumonia patients: indications for polybacterial infections. Sci Rep 7:40114. Scholar
  26. 26.
    Wesolowska-Andersen A, Bahl MI, Carvalho V et al (2014) Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2:19. Scholar
  27. 27.
    Stämmler F, Gläsner J, Hiergeist A et al (2016) Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4(1):28. Scholar
  28. 28.
    Tourlousse DM, Yoshiike S, Ohashi A et al (2017) Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res 45(4):e23. Scholar
  29. 29.
  30. 30.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institut Pasteur, Biologie des Bactéries IntracellulairesParisFrance
  2. 2.CNRS UMR 3525ParisFrance

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