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Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota

Abstract

Amplicon sequencing (for example, of the 16S rRNA gene) identifies the presence and relative abundance of microbial community members. However, metagenomic sequencing is needed to identify the genetic content and functional potential of a community. Metagenomics is challenging in samples dominated by host DNA, such as those from the skin, tissue and respiratory tract. Here, we combine advances in amplicon and metagenomic sequencing with culture-enriched molecular profiling to study the human microbiota. Using the cystic fibrosis lung as an example, we cultured an average of 82.13% of the operational taxonomic units representing 99.3% of the relative abundance identified in direct sequencing of sputum samples; importantly, culture enrichment identified 63.3% more operational taxonomic units than direct sequencing. We developed the PLate Coverage Algorithm (PLCA) to determine a representative subset of culture plates on which to conduct culture-enriched metagenomics, resulting in the recovery of greater taxonomic diversity—including of low-abundance taxa—with better metagenome-assembled genomes, longer contigs and better functional annotations when compared to culture-independent methods. The PLCA is also applied as a proof of principle to a previously published gut microbiota dataset. Culture-enriched molecular profiling can be used to better understand the role of the human microbiota in health and disease.

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Fig. 1: The culture-enriched metagenomic sequencing workflow.
Fig. 2: The majority of the cystic fibrosis lung microbiota is culturable.
Fig. 3: Taxonomic diversity captured across culture enrichment conditions.
Fig. 4: The PLCA determines an optimal plate set for culture-enriched metagenomics.
Fig. 5: MAG and non-MAG bins resulting from culture-enriched and direct metagenomic sequencing of the first sample in the dataset.
Fig. 6: The taxonomic and functional diversity of direct and culture-enriched metagenomic sequencing.

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Data availability

All sequencing results are publicly available (BioProject ID PRJNA503799). The PLCA algorithm is available from https://github.com/fwhelan/PLCA.

Code availability

All code developed by the authors is available under a GNU licence at http://github.com/fwhelan/PLCA and https://github.com/shekas3/BinTaxaAssigner.

References

  1. Van Leeuwenhoek, A. Microscopical observations about animals in the scurf of the teeth. Philos. Trans. R Soc. Lond. B Biol. Sci. 14, 568–574 (1683).

    Google Scholar 

  2. Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009).

    PubMed  PubMed Central  Google Scholar 

  3. Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    CAS  Google Scholar 

  4. Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290 (2011).

    CAS  PubMed  Google Scholar 

  5. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    CAS  PubMed  Google Scholar 

  6. Olesen, S. W. & Alm, E. J. Dysbiosis is not an answer. Nat. Microbiol. 1, 16228 (2016).

    CAS  PubMed  Google Scholar 

  7. Shade, A. Diversity is the question, not the answer. ISME J. 11, 1–6 (2017).

    PubMed  Google Scholar 

  8. Finegold, S. M., Attebery, H. R. & Sutter, V. L. Effect of diet on human fecal flora: comparison of Japanese and American diets. Am. J. Clin. Nutr. 27, 1456–1469 (1974).

    CAS  PubMed  Google Scholar 

  9. Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Lagier, J.-C. et al. Microbial culturomics: paradigm shift in the human gut microbiome study. Clin. Microbiol. Infect. 18, 1185–1193 (2012).

    CAS  PubMed  Google Scholar 

  11. Rettedal, E. A., Gumpert, H. & Sommer, M. O. A. Cultivation based multiplex phenotyping of human gut microbiota allows targeted recovery of previously uncultured bacteria. Nat. Commun. 5, 4714 (2014).

    CAS  PubMed  Google Scholar 

  12. Lau, J. T. et al. Capturing the diversity of the human gut microbiota through culture-enriched molecular profiling. Genome Med. 8, 72 (2016).

    PubMed  PubMed Central  Google Scholar 

  13. Hilt, E. E. et al. Urine is not sterile: use of enhanced urine culture techniques to detect resident bacterial flora in the adult female bladder. J. Clin. Microbiol. 52, 871–876 (2014).

    PubMed  PubMed Central  Google Scholar 

  14. Myles, I. A. et al. A method for culturing Gram-negative skin microbiota. BMC Microbiol. 16, 60 (2016).

    PubMed  PubMed Central  Google Scholar 

  15. Thompson, H., Rybalka, A., Moazzez, R., Dewhirst, F. E. & Wade, W. G. In-vitro culture of previously uncultured oral bacterial phylotypes. Appl. Environ. Microbiol. 81, 8307–8314 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Sibley, C. D. et al. Culture enriched molecular profiling of the cystic fibrosis airway microbiome. PLoS ONE 6, e22702 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Oh, J. et al. Biogeography and individuality shape function in the human skin metagenome. Nature 514, 59–64 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang, W.-L. et al. Application of metagenomics in the human gut microbiome. World J. Gastroenterol. 21, 803–814 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. Zhang, C. et al. Identification of low abundance microbiome in clinical samples using whole genome sequencing. Genome Biol. 16, 265 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lim, Y. W. et al. Metagenomics and metatranscriptomics: windows on CF-associated viral and microbial communities. J. Cyst. Fibros. 12, 154–164 (2013).

    CAS  PubMed  Google Scholar 

  21. Huang, Y. J. & LiPuma, J. J. The microbiome in cystic fibrosis. Clin. Chest Med. 37, 59–67 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhao, J. et al. Decade-long bacterial community dynamics in cystic fibrosis airways. Proc. Natl Acad. Sci. USA 109, 5809–5814 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Whelan, F. J. et al. Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis. PLoS ONE 12, e0172811 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. Lagier, J.-C. et al. Culture of previously uncultured members of the human gut microbiota by culturomics. Nat. Microbiol. 1, 16203 (2016).

    CAS  PubMed  Google Scholar 

  25. Surette, M. G. The cystic fibrosis lung microbiome. Ann. Am. Thorac. Soc. 11(Suppl. 1), S61–S65 (2014).

    PubMed  Google Scholar 

  26. Field, T. R., Sibley, C. D., Parkins, M. D., Rabin, H. R. & Surette, M. G. The genus Prevotella in cystic fibrosis airways. Anaerobe 16, 337–344 (2010).

    CAS  PubMed  Google Scholar 

  27. van der Gast, C. J. et al. Partitioning core and satellite taxa from within cystic fibrosis lung bacterial communities. ISME J. 5, 780–791 (2011).

    PubMed  Google Scholar 

  28. Tunney, M. M. et al. Detection of anaerobic bacteria in high numbers in sputum from patients with cystic fibrosis. Am. J. Respir. Crit. Care Med. 177, 995–1001 (2008).

    PubMed  Google Scholar 

  29. Parkins, M. D. & Floto, R. A. Emerging bacterial pathogens and changing concepts of bacterial pathogenesis in cystic fibrosis. J. Cyst. Fibros. 14, 293–304 (2015).

    CAS  PubMed  Google Scholar 

  30. Pop, M. et al. Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment. BMC Genomics 17, 440 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. Coleman, F. T. et al. Hypersusceptibility of cystic fibrosis mice to chronic Pseudomonas aeruginosa oropharyngeal colonization and lung infection. Proc. Natl Acad. Sci. USA 100, 1949–1954 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Jorth, P. et al. Regional isolation drives bacterial diversification within cystic fibrosis lungs. Cell Host Microbe 18, 307–319 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    CAS  PubMed  Google Scholar 

  34. Pompilio, A. et al. Stenotrophomonas maltophilia phenotypic and genotypic diversity during a 10-year colonization in the lungs of a cystic fibrosis patient. Front. Microbiol. 7, 1551 (2016).

    PubMed  PubMed Central  Google Scholar 

  35. Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Li, S. S. et al. Durable coexistence of donor and recipient strains after fecal microbiota transplantation. Science 352, 586–589 (2016).

    CAS  PubMed  Google Scholar 

  37. Nicholls, S. M. et al. Probabilistic recovery of cryptic haplotypes from metagenomic data. Preprint at https://www.biorxiv.org/content/10.1101/117838v1 (2017).

  38. Creevey, C. J., Doerks, T., Fitzpatrick, D. A., Raes, J. & Bork, P. Universally distributed single-copy genes indicate a constant rate of horizontal transfer. PLoS ONE 6, e22099 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Frank, D. N. et al. Molecular–phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Collins, S. M. A role for the gut microbiota in IBS. Nat. Rev. Gastroenterol. Hepatol. 11, 497–505 (2014).

    CAS  PubMed  Google Scholar 

  41. Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).

    CAS  PubMed  Google Scholar 

  42. Gohir, W., Whelan, F. J., Surette, M. G., Moore, C. & Jonathan, D. Pregnancy-related changes in the maternal gut microbiota are dependent upon the mother’s periconceptional diet. Gut Microbes 6, 310–320 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1095 (2015).

    CAS  PubMed  Google Scholar 

  44. Naseribafrouei, A. et al. Correlation between the human fecal microbiota and depression. Neurogastroenterol. Motil. 26, 1155–1162 (2014).

    CAS  PubMed  Google Scholar 

  45. Grice, E. A. & Segre, J. A. The skin microbiome. Nat. Rev. Microbiol. 9, 244–253 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, J. et al. Metagenomic sequencing reveals microbiota and its functional potential associated with periodontal disease. Sci. Rep. 3, 1843 (2013).

    PubMed  PubMed Central  Google Scholar 

  47. Dickson, R. P., Erb-Downward, J. R., Martinez, F. J. & Huffnagle, G. B. The microbiome and the respiratory tract. Annu. Rev. Physiol. 78, 481–504 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. Chi, B., Chauhan, S. & Kuramitsu, H. Development of a system for expressing heterologous genes in the oral spirochete treponema denticola and its use in expression of the treponema pallidum flaA gene. Infect. Immun. 67, 3653–3656 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Camanocha, A. & Dewhirst, F. E. Host-associated bacterial taxa from Chlorobi, Chloroflexi, GN02, Synergistetes, SR1, TM7 and WPS-2 phyla/candidate divisions. J. Oral Microbiol. 6, 25468 (2014).

    Google Scholar 

  50. Marcy, Y. et al. Dissecting biological ‘dark matter’ with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc. Natl Acad. Sci. USA 104, 11889–11894 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Meyer, K. C., Sharma, A., Rosenthal, N. S., Peterson, K. & Brennan, L. Regional variability of lung inflammation in cystic fibrosis. Am. J. Respir. Crit. Care Med. 156, 1536–1540 (1997).

    CAS  PubMed  Google Scholar 

  52. Stressmann, F. A. et al. Does bacterial density in cystic fibrosis sputum increase prior to pulmonary exacerbation? J. Cyst. Fibros. 10, 357–365 (2011).

    PubMed  Google Scholar 

  53. Quigley, E. M. Gut bacteria in health and disease. Gastroenterol. Hepatol. 9, 560–569 (2013).

    Google Scholar 

  54. Whelan, F. J. & Surette, M. G. A comprehensive evaluation of the sl1p pipeline for 16S rRNA gene sequencing analysis. Microbiome 5, 100 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. Fuchs, H. J. et al. Effect of aerosolized recombinant human DNase on exacerbations of respiratory symptoms and on pulmonary function in patients with cystic fibrosis. The Pulmozyme Study Group. N. Engl. J. Med. 331, 637–642 (1994).

    CAS  PubMed  Google Scholar 

  56. Sibley, C. D. et al. McKay agar enables routine quantification of the ‘Streptococcus milleri’ group in cystic fibrosis patients. J. Med. Microbiol. 59, 534–540 (2010).

    PubMed  Google Scholar 

  57. Whelan, F. J., Rossi, L., Stearns, J. C. & Surette, M. G. Culture and molecular profiling of the respiratory tract microbiota. Methods Mol. Biol. 1894, 49–61 (2018).

    Google Scholar 

  58. Whelan, F. J. et al. The loss of topography in the microbial communities of the upper respiratory tract in the elderly. Ann. Am. Thorac. Soc. 11, 513–521 (2014).

    PubMed  Google Scholar 

  59. Bartram, A. K., Lynch, M. D. J., Stearns, J. C., Moreno-Hagelsieb, G. & Neufeld, J. D. Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired end illumina reads. Appl. Environ. Microbiol. 77, 3846–3852 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Google Scholar 

  61. Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13, 31 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Ye, Y. Identification and quantification of abundant species from pyrosequences of 16S rRNA by consensus alignment. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine 153–157 (IEEE, 2011).

  63. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  PubMed  Google Scholar 

  64. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  69. McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    PubMed  PubMed Central  Google Scholar 

  70. Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029 (2015).

    PubMed  PubMed Central  Google Scholar 

  71. Pheatmap: pretty heatmaps. R Package v.1.0.12 (CRAN, 2012).

  72. Denton, M., Hall, M., Todd, N., Kerr, K. & Littlewood, J. Improved isolation of Stenotrophomonas maltophilia from the sputa of patients with cystic fibrosis using a selective medium. Clin. Microbiol. Infect. 6, 395–396 (2000).

    Google Scholar 

  73. Schmieder, R. & Edwards, R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE 6, e17288 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    CAS  PubMed  Google Scholar 

  75. Wu, Y.-W., Tang, Y.-H., Tringe, S. G., Simmons, B. A. & Singer, S. W. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2, 26 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Sczyrba, A. et al. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells and metagenomes. Genome Res. 25, 1043–1055 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Breitwieser, F. P. & Salzberg, S. L. KrakenHLL: confident and fast metagenomics classification using unique k-mer counts. Genome Biol. 19, 198 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Lee, S. T. M. et al. Tracking microbial colonization in fecal microbiota transplantation experiments via genome-resolved metagenomics. Microbiome 5, 50 (2017).

    PubMed  PubMed Central  Google Scholar 

  80. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).

    PubMed  PubMed Central  Google Scholar 

  81. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  PubMed  Google Scholar 

  82. Mao, C. et al. Curation, integration and visualization of bacterial virulence factors in PATRIC. Bioinformatics 31, 252–258 (2015).

    CAS  PubMed  Google Scholar 

  83. Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293 (2016).

    CAS  PubMed  Google Scholar 

  85. Arndt, D. et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44, W16–W21 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Jia, B. et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573 (2017).

    CAS  PubMed  Google Scholar 

  87. Skinnider, M. A. et al. Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM). Nucleic Acids Res. 43, 9645–9662 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Bland, C. et al. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 8, 209 (2007).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was funded in part by a Canadian Institutes of Health Research (CIHR) Doctoral Scholarship, a Cystic Fibrosis Canada (CFC) Studentship and a Marie Skłodowska-Curie Individual Fellowship (GA no. 793818) awarded to F.J.W., and grants from CIHR, CFC and a Tier 1 Canada Research Chair to M.G.S. We thank the patients and healthcare professionals at the Calgary Adult Cystic Fibrosis Clinic for their participation and assistance with this study. We acknowledge critical intellectual conversations with J.T. Lau and J.C. Szamosi. We wish to acknowledge that this research was conducted on traditional territory shared by the Haudenosaunee confederacy and the Anishinaabe nations as well as the peoples of the Treaty 7 region in Southern Alberta.

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F.J.W. is the primary author of this prepared manuscript. H.R.R. and M.D.P. collected patient information and enrolled willing participants for this study. B.W. collected, processed and cultured all sputum samples in addition to all biological and technical controls. F.J.W. and S.A.S. isolated DNA from culture/sputum material, and ran PCR reactions to amplify the 16S rRNA gene variable 3 region. S.A.S. performed the enrichment of Stenotrophomonas. F.J.W. prepared DNA for metagenomic sequencing. F.J.W. processed and analysed all 16S rRNA gene and metagenomic sequencing results. S.S. provided code for the taxonomic assignment of metagenomic bins. F.J.W., M.D.P. and M.G.S. conceptualized the experimental outline. F.J.W. conducted all data analyses and wrote the manuscript. All authors edited and approved the manuscript.

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Correspondence to Michael G. Surette.

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Extended data

Extended Data Fig. 1

Media used for culture enrichment.

Source data

Extended Data Fig. 2 Using more stringent abundance thresholds, the vast majority of the cystic fibrosis lung microbiota is still captured by culture-enriched methods. Culture-enriched 16S rRNA gene sequencing increases OTU recovery.

Using a cutoff of >= 0.1% relative abundance, 65.6% of the lung microbiota was identified only by culture across samples (a), with only 4.3% of OTUs not being cultured (b). c. In each sample, more OTUs were recovered via culture-enriched sequencing than by direct sequencing. d. Overall, an average of 65.5% of all OTUs were identified only via culture. e. The α-diversity of the original samples subject to culture enrichment.

Source data

Extended Data Fig. 3 Culture enriches for low abundance taxa.

The first sample in our dataset was re-sequenced to a depth of 972,834 reads. A rank order curve, and associated quantification of the recovered OTUs (inset), illustrates the number of OTUs present in the original (41,199) and re-sequenced sample as well as at rarefied depths, indicating the increased recovery of cultured OTUs by direct sequencing as sequencing depth increases. The inset shows, at two different read cutoffs, that with increasing sequencing depth, the number of OTUs seen only in culture decreases.

Extended Data Fig. 4 No specific culture condition consistently recapitulates the originating sputum sample.

Principle coordinate analyses based on the Bray Curtis β-diversity metric indicates that, across all 20 samples, no culture condition consistently recapitulates the microbial community of the originating sputum sample. For a list of media abbreviations see Extended Data Table 1; Aer = aerobic, Ana = anaerobic.

Extended Data Fig. 5 The importance of selective and non-selective culture conditions in capturing the genus-level diversity of the cystic fibrosis microbiome.

This Figure is a labelled version of Fig. 3c.

Extended Data Fig. 6 The importance of selective and non-selective culture conditions in capturing the OTU-level diversity of the cystic fibrosis microbiome.

A heatmap indicates the breadth of culture conditions necessary to culture this community at the OTU level.

Source data

Extended Data Fig. 7

The optimal plate sets needed to sequence all samples (S) in this dataset, and the number of OTUs which would be obtained.

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Extended Data Fig. 8 Culture-enriched metagenomic sequencing finds similar communities to 16S rRNA gene sequencing.

Comparisons of the bacterial composition of 16S rRNA gene sequencing to the 16S rRNA gene sequences obtained via whole-genome metagenomics reveal similar communities in the culture conditions amplified as part of the de novo PLCA (a-b) and adjusted PLCA plate sets. (c-d), Communities are compared visually using taxonomic summaries (a,c) and quantitatively using PCoA of the Bray Curtis β-diversity metric (b,d). 16S=16S rRNA gene sequencing; MG=shotgun metagenomic sequencing.

Source data

Extended Data Fig. 9 The PLCA consistently recovers targeted OTUs and is not specific to the cystic fibrosis lung microbiota.

a, When the adjusted PLCA was applied to the 20 samples in this cystic fibrosis dataset, it consistently recovered the targeted OTUs (orange), though some (gray) were not recovered as metagenomic bins due to inadequate sequencing depth, or the inability to separate species into separate bins. The overlaying numbers represent the number of OTUs in each category. Additional species obtained as a consequence of being present on a plate with a targeted OTU are shown as yellow dots. b, The PLCA is not specific to the cystic fibrosis lung microbiota or to a particular set of culture conditions; here, we apply the PLCA to previously published culture-enriched gut microbiota data (reference 12). Even through the culture conditions used by Lau et al. differ from those used in this study, the PLCA still predicts successful recovery of almost all species at abundances above the PLCA thresholds (dotted line).

Source data

Extended Data Fig. 10 Biological (a-b) and technical (c-d) replicates of sputum and culture-enriched 16S rRNA gene sequencing.

PCoA plots of the Bray Curtis distance between sputum profiles show close clustering of 3 sputum biological replicates prior to plating, n=6 (a), and cultured replicates (technical replicates after plating of 3 sputum samples x 2 culture replicates of 6 representative media types, n=108, c). Polygons/lines connect the replicates in each plot; specifically between duplicate sputum sampling of 3 individuals (labelled A-C) in a and between triplicate platings of sputums in c. In c, the legend labels refer to the media type (for example BHI), environmental condition (Aer, aerobic or Ana, anaerobic), and replicate number (1–6). Taxonomic summaries of biological samples prior to plating (b) and 3 technical replicates each of 2 sputum samples from 3 patients (d) show the consistency of these techniques. Labelling of biological (A-C) and technical (1–6) replicates is consistent with parts a and c. In cases of no visible growth (that is MAC_Aer_3 and MAC_Aer_4) no samples were collected. In some cases, there was some difference in abundance of organisms between replicates (for example KVLB 5,6 and McKay 5,6) on which there were low bacterial counts and only at the lowest dilution plated (the cultured organisms representing ~104 CFU/ml and less than 1% in the original sputum sample). These will be subject to more variability in plating.

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Whelan, F.J., Waddell, B., Syed, S.A. et al. Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota. Nat Microbiol 5, 379–390 (2020). https://doi.org/10.1038/s41564-019-0643-y

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