Towards standards for human fecal sample processing in metagenomic studies (original) (raw)

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References

  1. Meyer, F. et al. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  2. Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5, e9085 (2010).
    Article PubMed PubMed Central Google Scholar
  3. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).
    Article CAS PubMed Google Scholar
  4. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  5. Manichanh, C. et al. Reduced diversity of faecal microbiota in Crohn's disease revealed by a metagenomic approach. Gut 55, 205–211 (2006).
    Article CAS PubMed PubMed Central Google Scholar
  6. Carroll, I.M. et al. Molecular analysis of the luminal- and mucosal-associated intestinal microbiota in diarrhea-predominant irritable bowel syndrome. Am. J. Physiol. Gastrointest. Liver Physiol. 301, G799–G807 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  7. Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).
    Article PubMed PubMed Central Google Scholar
  8. Dethlefsen, L., McFall-Ngai, M. & Relman, D.A. An ecological and evolutionary perspective on human-microbe mutualism and disease. Nature 449, 811–818 (2007).
    Article CAS PubMed Google Scholar
  9. Dominguez-Bello, M.G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl. Acad. Sci. USA 107, 11971–11975 (2010).
    Article PubMed PubMed Central Google Scholar
  10. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  11. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
    Article CAS PubMed Google Scholar
  12. Wesolowska-Andersen, A. et al. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2, 19 (2014).
    Article PubMed PubMed Central Google Scholar
  13. McOrist, A.L., Jackson, M. & Bird, A.R. A comparison of five methods for extraction of bacterial DNA from human faecal samples. J. Microbiol. Methods 50, 131–139 (2002).
    Article CAS PubMed Google Scholar
  14. Smith, B., Li, N., Andersen, A.S., Slotved, H.C. & Krogfelt, K.A. Optimising bacterial DNA extraction from faecal samples: comparison of three methods. Open Microbiol. J. 5, 14–17 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  15. Maukonen, J., Simões, C. & Saarela, M. The currently used commercial DNA-extraction methods give different results of clostridial and actinobacterial populations derived from human fecal samples. FEMS Microbiol. Ecol. 79, 697–708 (2012).
    Article CAS PubMed Google Scholar
  16. Kennedy, N.A. et al. The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing. PLoS One 9, e88982 (2014).
    Article PubMed PubMed Central Google Scholar
  17. Salonen, A. et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J. Microbiol. Methods 81, 127–134 (2010).
    Article CAS PubMed Google Scholar
  18. Ariefdjohan, M.W., Savaiano, D.A. & Nakatsu, C.H. Comparison of DNA extraction kits for PCR-DGGE analysis of human intestinal microbial communities from fecal specimens. Nutr. J. 9, 23 (2010).
    Article PubMed PubMed Central Google Scholar
  19. Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).
    Article CAS PubMed Google Scholar
  20. Manichanh, C., Borruel, N., Casellas, F. & Guarner, F. The gut microbiota in IBD. Nat. Rev. Gastroenterol. Hepatol. 9, 599–608 (2012).
    Article CAS PubMed Google Scholar
  21. Lozupone, C.A. et al. Meta-analyses of studies of the human microbiota. Genome Res. 23, 1704–1714 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  22. Raes, J. & Bork, P. Molecular eco-systems biology: towards an understanding of community function. Nat. Rev. Microbiol. 6, 693–699 (2008).
    Article CAS PubMed Google Scholar
  23. Voigt, A.Y. et al. Temporal and technical variability of human gut metagenomes. Genome Biol. 16, 73 (2015).
    Article PubMed PubMed Central Google Scholar
  24. Franzosa, E.A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl. Acad. Sci. USA 111, E2329–E2338 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  25. Song, S.J. et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems https://dx.doi.org/10.1128/mSystems.00021-16 (2016).
  26. Gohl, D.M. et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–949 (2016).
    Article CAS PubMed Google Scholar
  27. Claassen, S. et al. A comparison of the efficiency of five different commercial DNA extraction kits for extraction of DNA from faecal samples. J. Microbiol. Methods 94, 103–110 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  28. Yuan, S., Cohen, D.B., Ravel, J., Abdo, Z. & Forney, L.J. Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One 7, e33865 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  29. Kultima, J.R. et al. MOCAT: a metagenomics assembly and gene prediction toolkit. PLoS One 7, e47656 (2012).
    Article PubMed PubMed Central Google Scholar
  30. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  31. Huttenhower, C. et al. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
    Article CAS Google Scholar
  32. Franzosa, E.A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Natl. Acad. Sci. USA 112, E2930–E2938 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  33. Powell, S. et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–D289 (2012).
    Article CAS PubMed Google Scholar
  34. Lozupone, C.A., Stombaugh, J.I., Gordon, J.I., Jansson, J.K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  35. Santiago, A. et al. Processing faecal samples: a step forward for standards in microbial community analysis. BMC Microbiol. 14, 112 (2014).
    Article PubMed PubMed Central Google Scholar
  36. InhibitEx Tablets - QIAGEN Online Shop. Available at: https://www.qiagen.com/fr/shop/lab-basics/buffers-and-reagents/inhibitex-tablets/.
  37. Henderson, G. et al. Effect of DNA extraction methods and sampling techniques on the apparent structure of cow and sheep rumen microbial communities. PLoS One 8, e74787 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  38. Jones, M.B. et al. Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proc. Natl. Acad. Sci. USA 112, 14024–14029 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  39. Salter, S.J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
    Article PubMed PubMed Central Google Scholar

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Acknowledgements

We thank S. Burz and K. Weizer for editing and web-posting the SOPs. We thank D. Ordonez and N.P. Gabrielli Lopez for advice on flow cytometry, which was provided by the Flow Cytometry Core Facility, EMBL. This study was funded by the European Community's Seventh Framework Programme via International Human Microbiome Standards (HEALTH-F4-2010-261376) grant. We also received support from Scottish Government Rural and Environmental Science and Analytical Services as well as from EMBL.

Author information

Authors and Affiliations

  1. Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
    Paul I Costea, Georg Zeller, Shinichi Sunagawa, Melanie Tramontano, Marja Driessen, Rajna Hercog, Ferris-Elias Jung, Jens Roat Kultima, Matthew R Hayward, Luis Pedro Coelho, Kiran Raosaheb Patil & Peer Bork
  2. Department of Biology, Institute of Microbiology, ETH Zurich, Zurich, Switzerland
    Shinichi Sunagawa
  3. CEA - Institut François Jacob - Genoscope, Evry, France
    Eric Pelletier, Adriana Alberti, Laurie Bertrand & Céline Orvain
  4. CNRS UMR-8030, Evry, France
    Eric Pelletier
  5. Université Evry Val d'Essonne, Evry, France
    Eric Pelletier
  6. Metagenopolis, Institut National de la Recherche Agronomique, Jouy en Josas, France
    Florence Levenez, Michelle Daigneault, Philippe Langella, Emmanuelle Le Chatelier, Nicolas Pons, S Dusko Ehrlich & Joel Dore
  7. Department of Molecular and Cellular Biology, The University of Guelph, Guelph, Ontario, Canada.,
    Emma Allen-Vercoe
  8. Department of Gastrointestinal Microbiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
    Michael Blaut, Jana Junick, Delphine Saulnier & Kathleen Slezak
  9. School of Microbiology & APC Microbiome Institute, University College Cork, Cork, Ireland
    Jillian R M Brown & Paul W O'Toole
  10. Biofortis, Mérieux NutriSciences, Nantes, France
    Thomas Carton, Clémentine Mery & Milena Popova
  11. Danone Nutricia Research, Palaiseau, France
    Stéphanie Cools-Portier, Muriel Derrien, Anne Druesne, Johan van Hylckama Vlieg & Patrick Veiga
  12. Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands
    Willem M de Vos, Hans Heilig & Erwin G Zoetendal
  13. Department of Bacteriology and Immunology, Immunobiology Research Program, University of Helsinki, Helsinki, Finland
    Willem M de Vos & Anne Salonen
  14. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
    B Brett Finlay
  15. Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK
    Harry J Flint, Jennifer C Martin & Karen P Scott
  16. Digestive System Research Unit, Vall d'Hebron Research Institute, Barcelona, Spain
    Francisco Guarner & Chaysavanh Manichanh
  17. Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
    Masahira Hattori
  18. Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan
    Masahira Hattori
  19. Texas Children's Hospital, Feigin Center, Houston, Texas, USA
    Ruth Ann Luna & James Versalovic
  20. Center for Medical Research, Medical University of Graz, Graz, Austria
    Ingeborg Klymiuk
  21. Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
    Volker Mai
  22. Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan
    Hidetoshi Morita
  23. Department of Medical Microbiology, School of Nutrition and Translational Research in Metabolism (NUTRIM) and Care and Public Health Research Institute (Caphri), Maastricht University Medical Center, Maastricht, the Netherlands
    John Penders
  24. Department of Bacteria, Unit of Foodborne Infections, Parasites & Fungi, Statens Serum Institut, Copenhagen, Denmark
    Søren Persson
  25. Department of Microbiology & Immunology and Robarts Research Institute, Centre for Human Immunology, University of Western Ontario, London, Ontario, Canada
    Bhagirath Singh
  26. Ministry of Education Key Laboratory for Systems Biomedicine, Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, PR China
    Liping Zhao
  27. King's College London, Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy's Hospital, London, UK
    S Dusko Ehrlich
  28. Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
    Peer Bork
  29. Molecular Medicine Partnership Unit, Heidelberg, Germany
    Peer Bork
  30. Max-Delbrück-Centre for Molecular Medicine, Berlin, Germany
    Peer Bork

Authors

  1. Paul I Costea
  2. Georg Zeller
  3. Shinichi Sunagawa
  4. Eric Pelletier
  5. Adriana Alberti
  6. Florence Levenez
  7. Melanie Tramontano
  8. Marja Driessen
  9. Rajna Hercog
  10. Ferris-Elias Jung
  11. Jens Roat Kultima
  12. Matthew R Hayward
  13. Luis Pedro Coelho
  14. Emma Allen-Vercoe
  15. Laurie Bertrand
  16. Michael Blaut
  17. Jillian R M Brown
  18. Thomas Carton
  19. Stéphanie Cools-Portier
  20. Michelle Daigneault
  21. Muriel Derrien
  22. Anne Druesne
  23. Willem M de Vos
  24. B Brett Finlay
  25. Harry J Flint
  26. Francisco Guarner
  27. Masahira Hattori
  28. Hans Heilig
  29. Ruth Ann Luna
  30. Johan van Hylckama Vlieg
  31. Jana Junick
  32. Ingeborg Klymiuk
  33. Philippe Langella
  34. Emmanuelle Le Chatelier
  35. Volker Mai
  36. Chaysavanh Manichanh
  37. Jennifer C Martin
  38. Clémentine Mery
  39. Hidetoshi Morita
  40. Paul W O'Toole
  41. Céline Orvain
  42. Kiran Raosaheb Patil
  43. John Penders
  44. Nicolas Pons
  45. Milena Popova
  46. Anne Salonen
  47. Delphine Saulnier
  48. Karen P Scott
  49. Bhagirath Singh
  50. Kathleen Slezak
  51. Patrick Veiga
  52. James Versalovic
  53. Liping Zhao
  54. Erwin G Zoetendal
  55. S Dusko Ehrlich
  56. Joel Dore
  57. Peer Bork

Contributions

P.I.C., S.S. and G.Z. analyzed data and drafted and finalized the manuscript. E.P. and A.A. analyzed data, sequenced samples and wrote the manuscript. F.L., J.R.K., M.R.H., L.P.C. and E.A.-V. analyzed data and wrote the manuscript. M.T., M. Driessen, R.H., F.-E.J. and K.R.P. created and quantified the mock community. M.B., J.R.M.B., L.B., T.C., S.C.-P., M. Derrien, A.D., M. Daigneault, R.A.L., W.M.d.V., B.B.F., H.J.F., F.G., M.H., H.H., J.v.H.V., J.J., I.K., P.L., E.L.C., V.M., C. Manichanh, J.C.M., C. Mery, H.M., C.O., P.W.O., J.P., S.P., N.P., M.P., A.S., D.S., K.P.S., B.S., K.S., P.V., J.V., L.Z. and E.G.Z. extracted samples and wrote the manuscript. S.D.E., J.D. and P.B. designed the study and wrote the manuscript.

Corresponding authors

Correspondence toS Dusko Ehrlich, Joel Dore or Peer Bork.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Inter-individual distance dependence on study.

Similar to Figure 3, we show the estimated effect sizes of different parameters in the context of inter-individual distance assessed within the different studies used. It is clear that while small, there are clear differences in the median distance within studies, with HMP samples appearing to be more homogenous that MetaHIT ones.

Supplementary Figure 2 Extraction bias across the two samples.

Extraction bias is consistent across the two samples, independent of the distance measure that was used. (a) shows a PCoA projection of the species abundances for each sample, independently, using a Spearman ranked correlation as well as a Euclidean distance. Most of the variation is captured by the first two principal coordinates and the clustering of extraction methods is easily observable. (b) shows a PCoA projection of the functional distance, both Spearman ranked and Euclidean.

Supplementary Figure 3 Lysis of Gram-positive bacteria positively correlates with Shannon diversity.

Recovery of Gram-positive bacteria correlates with overall Shannon diversity. Considering only the top 20 most abundant species within each sample, ratios were computed between all Gram-positive and Gram-negative bacteria as well as Gram-negative to Gram-negative bacteria. The top panel shows the correlation of these ratios with the Shannon diversity index, while the lower panel exemplifies this correlation on the most abundant Gram-positive and Gram-negative bacteria that are common to both samples A and B, indicating the strong positive relation between recovery of Gram-positive bacteria and observed Shannon diversity.

Supplementary Figure 4 Shannon diversity of sample composition.

Observed Shannon diversity is consistently influenced by extraction method, as illustrated in both samples. Furthermore, there is a considerable difference in diversity between the two samples, which is not overwritten by extraction bias.

Supplementary Figure 5 Extraction bias of best performing protocols considered in Phase II.

Extraction variation is the same in Phase II replicates as that of Phase I (bars 1 and 2, respectively). Furthermore, the three protocols that have been merged into protocol Q for Phase II, namely 6, 9 and 15 produce similar results and present extraction bias below the biological replicate variation. The tree Phase II protocols (H, W and Q), when applied in different laboratories, with no previous experience in the particular protocol used, produce comparable abundance estimates, with errors below the level of biological variation within one specimen.

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Costea, P., Zeller, G., Sunagawa, S. et al. Towards standards for human fecal sample processing in metagenomic studies.Nat Biotechnol 35, 1069–1076 (2017). https://doi.org/10.1038/nbt.3960

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