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Microbiome Sequencing Methods for Studying Human Diseases

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Disease Gene Identification

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1706))

Abstract

Over the last decade, biologists have come to appreciate that the human body is inhabited by thousands of bacterial species in diverse communities unique to each body site. Moreover, due to high-throughput sequencing methods for microbial characterization in a culture-independent manner, it is becoming evident that the microbiome plays an important role in human health and disease. This chapter focuses on the most common form of bacterial microbiome profiling, targeted amplicon sequencing of the 16S ribosomal RNA (rRNA) subunit encoded by 16S rDNA. We discuss important features for designing and performing microbiome experiments on human specimens, including experimental design, sample collection, DNA preparation, and selection of the 16S rDNA sequencing target. We also provide details for designing fusion primers required for targeted amplicon sequencing and selecting the most appropriate high-throughput sequencing platform. We conclude with a review of the fundamental concepts of data analysis and interpretation for these kinds of experiments. Our goal is to provide the reader with the essential knowledge needed to undertake microbiome experiments for application to human disease research questions.

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References

  1. Bertelli C, Greub G (2013) Rapid bacterial genome sequencing: methods and applications in clinical microbiology. Clin Microbiol Infect 19(9):803–813. https://doi.org/10.1111/1469-0691.12217

    Article  CAS  PubMed  Google Scholar 

  2. Gilbert JA, Jansson JK, Knight R (2014) The earth microbiome project: successes and aspirations. BMC Biol 12:69. https://doi.org/10.1186/s12915-014-0069-1

    Article  PubMed  PubMed Central  Google Scholar 

  3. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI (2007) The human microbiome project. Nature 449(7164):804–810. https://doi.org/10.1038/nature06244

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Human Microbiome Project C (2012) Structure, function and diversity of the healthy human microbiome. Nature 486(7402):207–214. https://doi.org/10.1038/nature11234

    Article  Google Scholar 

  5. Feazel LM, Baumgartner LK, Peterson KL, Frank DN, Harris JK, Pace NR (2009) Opportunistic pathogens enriched in showerhead biofilms. Proc Natl Acad Sci U S A 106(38):16393–16399. https://doi.org/10.1073/pnas.0908446106. 0908446106 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ghurye JS, Cepeda-Espinoza V, Pop M (2016) Metagenomic assembly: overview, challenges and applications. Yale J Biol Med 89(3):353–362

    PubMed  PubMed Central  Google Scholar 

  7. Yang B, Wang Y, Qian PY (2016) Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinformatics 17:135. https://doi.org/10.1186/s12859-016-0992-y

    Article  PubMed  PubMed Central  Google Scholar 

  8. Claesson MJ, Wang Q, O'Sullivan O, Greene-Diniz R, Cole JR, Ross RP, O'Toole PW (2010) Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucleic Acids Res 38(22):e200. https://doi.org/10.1093/nar/gkq873

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chakravorty S, Helb D, Burday M, Connell N, Alland D (2007) A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods 69(2):330–339. https://doi.org/10.1016/j.mimet.2007.02.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Barb JJ, Oler AJ, Kim HS, Chalmers N, Wallen GR, Cashion A, Munson PJ, Ames NJ (2016) Development of an analysis pipeline characterizing multiple hypervariable regions of 16S rRNA using mock samples. PLoS One 11(2):e0148047. https://doi.org/10.1371/journal.pone.0148047

    Article  PubMed  PubMed Central  Google Scholar 

  11. Human Microbiome Project C (2012) A framework for human microbiome research. Nature 486(7402):215–221. https://doi.org/10.1038/nature11209

    Article  Google Scholar 

  12. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6(8):1621–1624. https://doi.org/10.1038/ismej.2012.8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Whiteley AS, Jenkins S, Waite I, Kresoje N, Payne H, Mullan B, Allcock R, O'Donnell A (2012) Microbial 16S rRNA ion tag and community metagenome sequencing using the ion torrent (PGM) platform. J Microbiol Methods 91(1):80–88. https://doi.org/10.1016/j.mimet.2012.07.008

    Article  CAS  PubMed  Google Scholar 

  14. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD (2013) Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol 79(17):5112–5120. https://doi.org/10.1128/AEM.01043-13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Goodrich JK, Di Rienzi SC, Poole AC, Koren O, Walters WA, Caporaso JG, Knight R, Ley RE (2014) Conducting a microbiome study. Cell 158(2):250–262. https://doi.org/10.1016/j.cell.2014.06.037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Meisel JS, Hannigan GD, Tyldsley AS, SanMiguel AJ, Hodkinson BP, Zheng Q, Grice EA (2016) Skin microbiome surveys are strongly influenced by experimental design. J Invest Dermatol 136(5):947–956. https://doi.org/10.1016/j.jid.2016.01.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Laukens D, Brinkman BM, Raes J, De Vos M, Vandenabeele P (2016) Heterogeneity of the gut microbiome in mice: guidelines for optimizing experimental design. FEMS Microbiol Rev 40(1):117–132. https://doi.org/10.1093/femsre/fuv036

    Article  CAS  PubMed  Google Scholar 

  18. Moore RJ, Stanley D (2016) Experimental design considerations in microbiota/inflammation studies. Clin Transl Immunol 5(7):e92. https://doi.org/10.1038/cti.2016.41

    Article  Google Scholar 

  19. Weiss S, Amir A, Hyde ER, Metcalf JL, Song SJ, Knight R (2014) Tracking down the sources of experimental contamination in microbiome studies. Genome Biol 15(12):564. https://doi.org/10.1186/s13059-014-0564-2

    Article  PubMed  PubMed Central  Google Scholar 

  20. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, Turner P, Parkhill J, Loman NJ, Walker AW (2014) Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 12:87. https://doi.org/10.1186/s12915-014-0087-z

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yuan S, Cohen DB, Ravel J, Abdo Z, Forney LJ (2012) Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One 7(3):e33865. https://doi.org/10.1371/journal.pone.0033865

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kaeser M, Ruf MT, Hauser J, Pluschke G (2010) Optimized DNA preparation from mycobacteria. Cold Spring Harb Protoc 2010(4):prot5408. https://doi.org/10.1101/pdb.prot5408

    Google Scholar 

  23. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336. https://doi.org/10.1038/nmeth.f.303. nmeth.f.303 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541. https://doi.org/10.1128/AEM.01541-09

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ciccarelli FD, Doerks T, von Mering C, Creevey CJ, Snel B, Bork P (2006) Toward automatic reconstruction of a highly resolved tree of life. Science 311(5765):1283–1287. https://doi.org/10.1126/science.1123061

    Article  CAS  PubMed  Google Scholar 

  26. McDonald D, Clemente JC, Kuczynski J, Rideout JR, Stombaugh J, Wendel D, Wilke A, Huse S, Hufnagle J, Meyer F, Knight R, Caporaso JG (2012) The biological observation matrix (BIOM) format. Gigascience 1(1):7. https://doi.org/10.1186/2047-217X-1-7

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wong RG, Wu JR, Gloor GB (2016) Expanding the unifrac toolbox. PLoS One 11(9):e0161196. https://doi.org/10.1371/journal.pone.0161196

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: an effective distance metric for microbial community comparison. ISME J 5(2):169–172. https://doi.org/10.1038/ismej.2010.133

    Article  PubMed  Google Scholar 

  29. McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8(4):e61217. https://doi.org/10.1371/journal.pone.0061217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yilmaz P, Kottmann R, Field D, Knight R, Cole JR, Amaral-Zettler L, Gilbert JA, Karsch-Mizrachi I, Johnston A, Cochrane G, Vaughan R, Hunter C, Park J, Morrison N, Rocca-Serra P, Sterk P, Arumugam M, Bailey M, Baumgartner L, Birren BW, Blaser MJ, Bonazzi V, Booth T, Bork P, Bushman FD, Buttigieg PL, Chain PS, Charlson E, Costello EK, Huot-Creasy H, Dawyndt P, DeSantis T, Fierer N, Fuhrman JA, Gallery RE, Gevers D, Gibbs RA, San Gil I, Gonzalez A, Gordon JI, Guralnick R, Hankeln W, Highlander S, Hugenholtz P, Jansson J, Kau AL, Kelley ST, Kennedy J, Knights D, Koren O, Kuczynski J, Kyrpides N, Larsen R, Lauber CL, Legg T, Ley RE, Lozupone CA, Ludwig W, Lyons D, Maguire E, Methe BA, Meyer F, Muegge B, Nakielny S, Nelson KE, Nemergut D, Neufeld JD, Newbold LK, Oliver AE, Pace NR, Palanisamy G, Peplies J, Petrosino J, Proctor L, Pruesse E, Quast C, Raes J, Ratnasingham S, Ravel J, Relman DA, Assunta-Sansone S, Schloss PD, Schriml L, Sinha R, Smith MI, Sodergren E, Spo A, Stombaugh J, Tiedje JM, Ward DV, Weinstock GM, Wendel D, White O, Whiteley A, Wilke A, Wortman JR, Yatsunenko T, Glockner FO (2011) Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat Biotechnol 29(5):415–420. https://doi.org/10.1038/nbt.1823

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rosser EC, Mauri C (2016) A clinical update on the significance of the gut microbiota in systemic autoimmunity. J Autoimmun 74:85–93. https://doi.org/10.1016/j.jaut.2016.06.009

    Article  CAS  PubMed  Google Scholar 

  32. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, Bertalan M, Borruel N, Casellas F, Fernandez L, Gautier L, Hansen T, Hattori M, Hayashi T, Kleerebezem M, Kurokawa K, Leclerc M, Levenez F, Manichanh C, Nielsen HB, Nielsen T, Pons N, Poulain J, Qin J, Sicheritz-Ponten T, Tims S, Torrents D, Ugarte E, Zoetendal EG, Wang J, Guarner F, Pedersen O, de Vos WM, Brunak S, Dore J, Meta HITC, Antolin M, Artiguenave F, Blottiere HM, Almeida M, Brechot C, Cara C, Chervaux C, Cultrone A, Delorme C, Denariaz G, Dervyn R, Foerstner KU, Friss C, van de Guchte M, Guedon E, Haimet F, Huber W, van Hylckama-Vlieg J, Jamet A, Juste C, Kaci G, Knol J, Lakhdari O, Layec S, Le Roux K, Maguin E, Merieux A, Melo Minardi R, M'Rini C, Muller J, Oozeer R, Parkhill J, Renault P, Rescigno M, Sanchez N, Sunagawa S, Torrejon A, Turner K, Vandemeulebrouck G, Varela E, Winogradsky Y, Zeller G, Weissenbach J, Ehrlich SD, Bork P (2011) Enterotypes of the human gut microbiome. Nature 473(7346):174–180. https://doi.org/10.1038/nature09944

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Honda K, Littman DR (2012) The microbiome in infectious disease and inflammation. Annu Rev Immunol 30:759–795. https://doi.org/10.1146/annurev-immunol-020711-074937

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ishikawa D, Sasaki T, Osada T, Kuwahara-Arai K, Haga K, Shibuya T, Hiramatsu K, Watanabe S (2016) Changes in intestinal microbiota following combination therapy with fecal microbial transplantation and antibiotics for ulcerative colitis. Inflamm Bowel Dis 23(1):116–125. https://doi.org/10.1097/MIB.0000000000000975

    Article  Google Scholar 

  35. Khoruts A, Dicksved J, Jansson JK, Sadowsky MJ (2010) Changes in the composition of the human fecal microbiome after bacteriotherapy for recurrent Clostridium Difficile-associated diarrhea. J Clin Gastroenterol 44(5):354–360. https://doi.org/10.1097/MCG.0b013e3181c87e02

    PubMed  Google Scholar 

  36. Carmody LA, Zhao J, Kalikin LM, LeBar W, Simon RH, Venkataraman A, Schmidt TM, Abdo Z, Schloss PD, LiPuma JJ (2015) The daily dynamics of cystic fibrosis airway microbiota during clinical stability and at exacerbation. Microbiome 3:12. https://doi.org/10.1186/s40168-015-0074-9

    Article  PubMed  PubMed Central  Google Scholar 

  37. Zemanick ET, Wagner BD, Robertson CE, Stevens MJ, Szefler SJ, Accurso FJ, Sagel SD, Harris JK (2014) Assessment of airway microbiota and inflammation in cystic fibrosis using multiple sampling methods. Ann Am Thorac Soc 12(2):221–229. https://doi.org/10.1513/AnnalsATS.201407-310OC

    Article  Google Scholar 

  38. Huffnagle GB (2016) Another piece in the “research mosaic” that describes the role of the lung microbiome in COPD. Thorax 71(9):777–778. https://doi.org/10.1136/thoraxjnl-2015-207415

    Article  PubMed  Google Scholar 

  39. Stokell JR, Gharaibeh RZ, Hamp TJ, Zapata MJ, Fodor AA, Steck TR (2015) Analysis of changes in diversity and abundance of the microbial community in a cystic fibrosis patient over a multiyear period. J Clin Microbiol 53(1):237–247. https://doi.org/10.1128/JCM.02555-14.JCM.02555-14[pii]

  40. Coburn B, Wang PW, Diaz Caballero J, Clark ST, Brahma V, Donaldson S, Zhang Y, Surendra A, Gong Y, Elizabeth Tullis D, Yau YC, Waters VJ, Hwang DM, Guttman DS (2015) Lung microbiota across age and disease stage in cystic fibrosis. Sci Rep 5:10241. https://doi.org/10.1038/srep10241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cox MJ, Allgaier M, Taylor B, Baek MS, Huang YJ, Daly RA, Karaoz U, Andersen GL, Brown R, Fujimura KE, Wu B, Tran D, Koff J, Kleinhenz ME, Nielson D, Brodie EL, Lynch SV (2010) Airway microbiota and pathogen abundance in age-stratified cystic fibrosis patients. PLoS One 5(6):e11044. https://doi.org/10.1371/journal.pone.0011044

    Article  PubMed  PubMed Central  Google Scholar 

  42. Beck JM, Young VB, Huffnagle GB (2012) The microbiome of the lung. Transl Res 160(4):258–266. https://doi.org/10.1016/j.trsl.2012.02.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Noval Rivas M, Crother TR, Arditi M (2016) The microbiome in asthma. Curr Opin Pediatr 135(1):25–30. https://doi.org/10.1097/MOP.0000000000000419

    Google Scholar 

  44. Hanson BM, Weinstock GM (2016) The importance of the microbiome in epidemiologic research. Ann Epidemiol 26(5):301–305. https://doi.org/10.1016/j.annepidem.2016.03.008

    Article  PubMed  Google Scholar 

  45. Wu H, Tremaroli V, Backhed F (2015) Linking microbiota to human diseases: a systems biology perspective. Trends Endocrinol Metab 26(12):758–770. https://doi.org/10.1016/j.tem.2015.09.011

    Article  CAS  PubMed  Google Scholar 

  46. Blekhman R, Goodrich JK, Huang K, Sun Q, Bukowski R, Bell JT, Spector TD, Keinan A, Ley RE, Gevers D, Clark AG (2015) Host genetic variation impacts microbiome composition across human body sites. Genome Biol 16:191. https://doi.org/10.1186/s13059-015-0759-1

    Article  PubMed  PubMed Central  Google Scholar 

  47. Dabrowska K, Witkiewicz W (2016) Correlations of host genetics and gut microbiome composition. Front Microbiol 7:1357. https://doi.org/10.3389/fmicb.2016.01357

    Article  PubMed  PubMed Central  Google Scholar 

  48. Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, Beaumont M, Van Treuren W, Knight R, Bell JT, Spector TD, Clark AG, Ley RE (2014) Human genetics shape the gut microbiome. Cell 159(4):789–799. https://doi.org/10.1016/j.cell.2014.09.053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Research, Center for Genes, Environment and Health, National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA

    Rebecca M. Davidson & L. Elaine Epperson

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  1. Rebecca M. Davidson
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  2. L. Elaine Epperson
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Correspondence to Rebecca M. Davidson .

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Editors and Affiliations

  1. Translational Genomics Research Institute, Phoenix, Arizona, USA

    Johanna K. DiStefano

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Davidson, R.M., Epperson, L.E. (2018). Microbiome Sequencing Methods for Studying Human Diseases. In: DiStefano, J. (eds) Disease Gene Identification. Methods in Molecular Biology, vol 1706. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7471-9_5

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  • DOI: https://doi.org/10.1007/978-1-4939-7471-9_5

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