Skip to main content

Compositional Analysis of the Human Microbiome in Cancer Research

  • Protocol
  • First Online:
Cancer Metabolism

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1928))

Abstract

Gut microbial composition has shown to be associated with obesity, diabetes mellitus, inflammatory bowel disease, colitis, autoimmune disorders, and cancer, among other diseases. Microbiome research has significantly evolved through the years and continues to advance as we develop new and better strategies to more accurately measure its composition and function. Careful selection of study design, inclusion and exclusion criteria of participants, and methodology are paramount to accurately analyze microbial structure. Here we present the most up-to-date available information on methods for gut microbial collection and analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. NRC (2007) The new science of metagenomics—revealing the secrets of our microbial planet. The National Academies Press, Washington, DC

    Google Scholar 

  2. Schmidt TM (2006) The maturing of microbial ecology. Int Microbiol 9(3):217–223

    CAS  PubMed  Google Scholar 

  3. Brock TD (1987) The study of microorganisms in situ: progress and problems. Symp Soc Gen Microbiol 41:1–17

    Google Scholar 

  4. Amann RI, Ludwig W, Schleifer KH (1995) Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol Rev 59(1):143–169

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Woese CR, Fox GE (1977) Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc Natl Acad Sci U S A 74(11):5088–5090

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pace NR, Stahl DA, Lane DJ, Olsen GJ (1986) The Analysis of Natural Microbial Populations by Ribosomal RNA Sequences. In: Marshall K.C. (eds) Adv Microb Ecol vol 9. Springer, Boston, MA

    Google Scholar 

  7. Maron PA, Ranjard L, Mougel C, Lemanceau P (2007) Metaproteomics: a new approach for studying functional microbial ecology. Microb Ecol 53(3):486–493. https://doi.org/10.1007/s00248-006-9196-8

    Article  CAS  PubMed  Google Scholar 

  8. Behjati S, Tarpey PS (2013) What is next generation sequencing? Arch Dis Child Educ Pract Ed 98(6):236–238. https://doi.org/10.1136/archdischild-2013-304340

    Article  PubMed  PubMed Central  Google Scholar 

  9. Heintz-Buschart A, May P, Laczny CC, Lebrun LA, Bellora C, Krishna A, Wampach L, Schneider JG, Hogan A, Beaufort C, Wilmes P (2016) Erratum: Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol 2:16227. https://doi.org/10.1038/nmicrobiol.2016.227

    Article  CAS  PubMed  Google Scholar 

  10. Mallick H, Ma S, Franzosa EA, Vatanen T, Morgan XC, Huttenhower C (2017) Experimental design and quantitative analysis of microbial community multiomics. Genome Biol 18(1):228. https://doi.org/10.1186/s13059-017-1359-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Snitkin ES, Zelazny AM, Thomas PJ, Stock F, Group NCSP, Henderson DK, Palmore TN, Segre JA (2012) Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Sci Transl Med 4(148):148ra116. https://doi.org/10.1126/scitranslmed.3004129

    Article  PubMed  PubMed Central  Google Scholar 

  12. Shibata T (2015) Current and future molecular profiling of cancer by next-generation sequencing. Jpn J Clin Oncol 45(10):895–899. https://doi.org/10.1093/jjco/hyv122

    Article  PubMed  Google Scholar 

  13. Vogtmann E, Goedert JJ (2016) Epidemiologic studies of the human microbiome and cancer. Br J Cancer 114(3):237–242. https://doi.org/10.1038/bjc.2015.465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Drewes JL, White JR, Dejea CM, Fathi P, Iyadorai T, Vadivelu J, Roslani AC, Wick EC, Mongodin EF, Loke MF, Thulasi K, Gan HM, Goh KL, Chong HY, Kumar S, Wanyiri JW, Sears CL (2017) High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia. NPJ Biofilms Microbiomes 3:34. https://doi.org/10.1038/s41522-017-0040-3

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bullman S, Pedamallu CS, Sicinska E, Clancy TE, Zhang X, Cai D, Neuberg D, Huang K, Guevara F, Nelson T, Chipashvili O, Hagan T, Walker M, Ramachandran A, Diosdado B, Serna G, Mulet N, Landolfi S, Ramon YCS, Fasani R, Aguirre AJ, Ng K, Elez E, Ogino S, Tabernero J, Fuchs CS, Hahn WC, Nuciforo P, Meyerson M (2017) Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 358(6369):1443–1448. https://doi.org/10.1126/science.aal5240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zackular JP, Baxter NT, Chen GY, Schloss PD (2016) Manipulation of the gut microbiota reveals role in colon tumorigenesis. mSphere 1(1):e00001-15. https://doi.org/10.1128/mSphere.00001-15

    Article  PubMed  Google Scholar 

  17. Zackular JP, Baxter NT, Iverson KD, Sadler WD, Petrosino JF, Chen GY, Schloss PD (2013) The gut microbiome modulates colon tumorigenesis. MBio 4(6):e00692–e00613. https://doi.org/10.1128/mBio.00692-13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Peters BA, Wu J, Pei Z, Yang L, Purdue MP, Freedman ND, Jacobs EJ, Gapstur SM, Hayes RB, Ahn J (2017) Oral microbiome composition reflects prospective risk for esophageal cancers. Cancer Res 77(23):6777–6787. https://doi.org/10.1158/0008-5472.CAN-17-1296

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yoshimoto S, Loo TM, Atarashi K, Kanda H, Sato S, Oyadomari S, Iwakura Y, Oshima K, Morita H, Hattori M, Honda K, Ishikawa Y, Hara E, Ohtani N (2013) Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499(7456):97–101. https://doi.org/10.1038/nature12347

    Article  CAS  PubMed  Google Scholar 

  20. Kostic AD, Chun E, Robertson L, Glickman JN, Gallini CA, Michaud M, Clancy TE, Chung DC, Lochhead P, Hold GL, El-Omar EM, Brenner D, Fuchs CS, Meyerson M, Garrett WS (2013) Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14(2):207–215. https://doi.org/10.1016/j.chom.2013.07.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kostic AD, Gevers D, Pedamallu CS, Michaud M, Duke F, Earl AM, Ojesina AI, Jung J, Bass AJ, Tabernero J, Baselga J, Liu C, Shivdasani RA, Ogino S, Birren BW, Huttenhower C, Garrett WS, Meyerson M (2012) Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res 22(2):292–298. https://doi.org/10.1101/gr.126573.111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. McDermott AJ, Huffnagle GB (2014) The microbiome and regulation of mucosal immunity. Immunology 142(1):24–31. https://doi.org/10.1111/imm.12231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC, Karpinets TV, Prieto PA, Vicente D, Hoffman K, Wei SC, Cogdill AP, Zhao L, Hudgens CW, Hutchinson DS, Manzo T, Petaccia de Macedo M, Cotechini T, Kumar T, Chen WS, Reddy SM, Szczepaniak Sloane R, Galloway-Pena J, Jiang H, Chen PL, Shpall EJ, Rezvani K, Alousi AM, Chemaly RF, Shelburne S, Vence LM, Okhuysen PC, Jensen VB, Swennes AG, McAllister F, Marcelo Riquelme Sanchez E, Zhang Y, Le Chatelier E, Zitvogel L, Pons N, Austin-Breneman JL, Haydu LE, Burton EM, Gardner JM, Sirmans E, Hu J, Lazar AJ, Tsujikawa T, Diab A, Tawbi H, Glitza IC, Hwu WJ, Patel SP, Woodman SE, Amaria RN, Davies MA, Gershenwald JE, Hwu P, Lee JE, Zhang J, Coussens LM, Cooper ZA, Futreal PA, Daniel CR, Ajami NJ, Petrosino JF, Tetzlaff MT, Sharma P, Allison JP, Jenq RR, Wargo JA (2018) Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359(6371):97–103. https://doi.org/10.1126/science.aan4236

    Article  CAS  PubMed  Google Scholar 

  24. Dejea CM, Fathi P, Craig JM, Boleij A, Taddese R, Geis AL, Wu X, DeStefano Shields CE, Hechenbleikner EM, Huso DL, Anders RA, Giardiello FM, Wick EC, Wang H, Wu S, Pardoll DM, Housseau F, Sears CL (2018) Patients with familial adenomatous polyposis harbor colonic biofilms containing tumorigenic bacteria. Science 359(6375):592–597. https://doi.org/10.1126/science.aah3648

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gagniere J, Bonnin V, Jarrousse AS, Cardamone E, Agus A, Uhrhammer N, Sauvanet P, Dechelotte P, Barnich N, Bonnet R, Pezet D, Bonnet M (2017) Interactions between microsatellite instability and human gut colonization by Escherichia coli in colorectal cancer. Clin Sci (Lond) 131(6):471–485. https://doi.org/10.1042/CS20160876

    Article  CAS  Google Scholar 

  26. Wang X, Yang Y, Huycke MM (2015) Commensal bacteria drive endogenous transformation and tumour stem cell marker expression through a bystander effect. Gut 64(3):459–468. https://doi.org/10.1136/gutjnl-2014-307213

    Article  CAS  PubMed  Google Scholar 

  27. Purcell RV, Pearson J, Aitchison A, Dixon L, Frizelle FA, Keenan JI (2017) Colonization with enterotoxigenic Bacteroides fragilis is associated with early-stage colorectal neoplasia. PLoS One 12(2):e0171602. https://doi.org/10.1371/journal.pone.0171602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Raisch J, Buc E, Bonnet M, Sauvanet P, Vazeille E, de Vallee A, Dechelotte P, Darcha C, Pezet D, Bonnet R, Bringer MA, Darfeuille-Michaud A (2014) Colon cancer-associated B2 Escherichia coli colonize gut mucosa and promote cell proliferation. World J Gastroenterol 20(21):6560–6572. https://doi.org/10.3748/wjg.v20.i21.6560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Castellarin M, Warren RL, Freeman JD, Dreolini L, Krzywinski M, Strauss J, Barnes R, Watson P, Allen-Vercoe E, Moore RA, Holt RA (2012) Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Genome Res 22(2):299–306. https://doi.org/10.1101/gr.126516.111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Strauss J, Kaplan GG, Beck PL, Rioux K, Panaccione R, Devinney R, Lynch T, Allen-Vercoe E (2011) Invasive potential of gut mucosa-derived Fusobacterium nucleatum positively correlates with IBD status of the host. Inflamm Bowel Dis 17(9):1971–1978. https://doi.org/10.1002/ibd.21606

    Article  PubMed  Google Scholar 

  31. Baxter NT, Ruffin MT, Rogers MA, Schloss PD (2016) Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med 8(1):37. https://doi.org/10.1186/s13073-016-0290-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Iida N, Dzutsev A, Stewart CA, Smith L, Bouladoux N, Weingarten RA, Molina DA, Salcedo R, Back T, Cramer S, Dai RM, Kiu H, Cardone M, Naik S, Patri AK, Wang E, Marincola FM, Frank KM, Belkaid Y, Trinchieri G, Goldszmid RS (2013) Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342(6161):967–970. https://doi.org/10.1126/science.1240527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zitvogel L, Kroemer G (2012) Targeting PD-1/PD-L1 interactions for cancer immunotherapy. Oncoimmunology 1(8):1223–1225. https://doi.org/10.4161/onci.21335

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sinha R, Abu-Ali G, Vogtmann E, Fodor AA, Ren B, Amir A, Schwager E, Crabtree J, Ma S Microbiome Quality Control Project C, Abnet CC, Knight R, White O, Huttenhower C(2017) Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat Biotechnol 35(11):1077–1086. https://doi.org/10.1038/nbt.3981

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Costea PI, Zeller G, Sunagawa S, Pelletier E, Alberti A, Levenez F, Tramontano M, Driessen M, Hercog R, Jung FE, Kultima JR, Hayward MR, Coelho LP, Allen-Vercoe E, Bertrand L, Blaut M, Brown JRM, Carton T, Cools-Portier S, Daigneault M, Derrien M, Druesne A, de Vos WM, Finlay BB, Flint HJ, Guarner F, Hattori M, Heilig H, Luna RA, van Hylckama Vlieg J, Junick J, Klymiuk I, Langella P, Le Chatelier E, Mai V, Manichanh C, Martin JC, Mery C, Morita H, O’Toole PW, Orvain C, Patil KR, Penders J, Persson S, Pons N, Popova M, Salonen A, Saulnier D, Scott KP, Singh B, Slezak K, Veiga P, Versalovic J, Zhao L, Zoetendal EG, Ehrlich SD, Dore J, Bork P (2017) Towards standards for human fecal sample processing in metagenomic studies. Nat Biotechnol 35(11):1069–1076. https://doi.org/10.1038/nbt.3960

    Article  CAS  PubMed  Google Scholar 

  36. Clarke SF, Murphy EF, O’Sullivan O, Lucey AJ, Humphreys M, Hogan A, Hayes P, O’Reilly M, Jeffery IB, Wood-Martin R, Kerins DM, Quigley E, Ross RP, O’Toole PW, Molloy MG, Falvey E, Shanahan F, Cotter PD (2014) Exercise and associated dietary extremes impact on gut microbial diversity. Gut 63(12):1913–1920. https://doi.org/10.1136/gutjnl-2013-306541

    Article  CAS  PubMed  Google Scholar 

  37. Biedermann L, Zeitz J, Mwinyi J, Sutter-Minder E, Rehman A, Ott SJ, Steurer-Stey C, Frei A, Frei P, Scharl M, Loessner MJ, Vavricka SR, Fried M, Schreiber S, Schuppler M, Rogler G (2013) Smoking cessation induces profound changes in the composition of the intestinal microbiota in humans. PLoS One 8(3):e59260. https://doi.org/10.1371/journal.pone.0059260

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505(7484):559–563. https://doi.org/10.1038/nature12820

    Article  CAS  PubMed  Google Scholar 

  39. Flores R, Shi J, Fuhrman B, Xu X, Veenstra TD, Gail MH, Gajer P, Ravel J, Goedert JJ (2012) Fecal microbial determinants of fecal and systemic estrogens and estrogen metabolites: a cross-sectional study. J Transl Med 10:253. https://doi.org/10.1186/1479-5876-10-253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Salazar N, Arboleya S, Valdes L, Stanton C, Ross P, Ruiz L, Gueimonde M, de Los Reyes-Gavilan CG (2014) The human intestinal microbiome at extreme ages of life. Dietary intervention as a way to counteract alterations. Front Genet 5:406. https://doi.org/10.3389/fgene.2014.00406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Macfarlane S (2014) Antibiotic treatments and microbes in the gut. Environ Microbiol 16(4):919–924. https://doi.org/10.1111/1462-2920.12399

    Article  CAS  PubMed  Google Scholar 

  42. Seto CT, Jeraldo P, Orenstein R, Chia N, DiBaise JK (2014) Prolonged use of a proton pump inhibitor reduces microbial diversity: implications for Clostridium difficile susceptibility. Microbiome 2:42. https://doi.org/10.1186/2049-2618-2-42

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lee H, Ko G (2014) Effect of metformin on metabolic improvement and gut microbiota. Appl Environ Microbiol 80(19):5935–5943. https://doi.org/10.1128/AEM.01357-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hill CJ, Brown JR, Lynch DB, Jeffery IB, Ryan CA, Ross RP, Stanton C, O’Toole PW (2016) Effect of room temperature transport vials on DNA quality and phylogenetic composition of faecal microbiota of elderly adults and infants. Microbiome 4(1):19. https://doi.org/10.1186/s40168-016-0164-3

    Article  PubMed  PubMed Central  Google Scholar 

  45. Thomas V, Clark J, Dore J (2015) Fecal microbiota analysis: an overview of sample collection methods and sequencing strategies. Future Microbiol 10(9):1485–1504. https://doi.org/10.2217/fmb.15.87

    Article  CAS  PubMed  Google Scholar 

  46. Sze MA, Schloss PD (2016) Looking for a signal in the noise: revisiting obesity and the microbiome. MBio 7(4):e01018–e01016. https://doi.org/10.1128/mBio.01018-16

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kelly BJ, Gross R, Bittinger K, Sherrill-Mix S, Lewis JD, Collman RG, Bushman FD, Li H (2015) Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics 31(15):2461–2468. https://doi.org/10.1093/bioinformatics/btv183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. La Rosa PS, Brooks JP, Deych E, Boone EL, Edwards DJ, Wang Q, Sodergren E, Weinstock G, Shannon WD (2012) Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS One 7(12):e52078. https://doi.org/10.1371/journal.pone.0052078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mattiello F, Verbist B, Faust K, Raes J, Shannon WD, Bijnens L, Thas O (2016) A web application for sample size and power calculation in case-control microbiome studies. Bioinformatics 32(13):2038–2040. https://doi.org/10.1093/bioinformatics/btw099

    Article  CAS  PubMed  Google Scholar 

  50. Schirmer M, Franzosa EA, Lloyd-Price J, McIver LJ, Schwager R, Poon TW, Ananthakrishnan AN, Andrews E, Barron G, Lake K, Prasad M, Sauk J, Stevens B, Wilson RG, Braun J, Denson LA, Kugathasan S, McGovern DPB, Vlamakis H, Xavier RJ, Huttenhower C (2018) Dynamics of metatranscription in the inflammatory bowel disease gut microbiome. Nat Microbiol 3(3):337–346. https://doi.org/10.1038/s41564-017-0089-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sinha R, Chen J, Amir A, Vogtmann E, Shi J, Inman KS, Flores R, Sampson J, Knight R, Chia N (2016) Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol Biomarkers Prev 25(2):407–416. https://doi.org/10.1158/1055-9965.EPI-15-0951

    Article  PubMed  Google Scholar 

  52. Bassis CM, Moore NM, Lolans K, Seekatz AM, Weinstein RA, Young VB, Hayden MK, Program CDCPE (2017) Comparison of stool versus rectal swab samples and storage conditions on bacterial community profiles. BMC Microbiol 17(1):78. https://doi.org/10.1186/s12866-017-0983-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Abrahamson M, Hooker E, Ajami NJ, Petrosino JF, Orwoll ES (2017) Successful collection of stool samples for microbiome analyses from a large community-based population of elderly men. Contemp Clin Trials Commun 7:158–162. https://doi.org/10.1016/j.conctc.2017.07.002

    Article  PubMed  PubMed Central  Google Scholar 

  54. Song SJ, Amir A, Metcalf JL, Amato KR, Xu ZZ, Humphrey G, Knight R (2016) Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1(3):e00021-16. https://doi.org/10.1128/mSystems.00021-16

    Article  PubMed  PubMed Central  Google Scholar 

  55. Choo JM, Leong LE, Rogers GB (2015) Sample storage conditions significantly influence faecal microbiome profiles. Sci Rep 5:16350. https://doi.org/10.1038/srep16350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Vogtmann E, Chen J, Kibriya MG, Chen Y, Islam T, Eunes M, Ahmed A, Naher J, Rahman A, Amir A, Shi J, Abnet CC, Nelson H, Knight R, Chia N, Ahsan H, Sinha R (2017) Comparison of fecal collection methods for microbiota studies in Bangladesh. Appl Environ Microbiol 83(10):e00361-17. https://doi.org/10.1128/AEM.00361-17

    Article  PubMed  PubMed Central  Google Scholar 

  57. Bahl MI, Bergstrom A, Licht TR (2012) Freezing fecal samples prior to DNA extraction affects the Firmicutes to Bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol Lett 329(2):193–197. https://doi.org/10.1111/j.1574-6968.2012.02523.x

    Article  CAS  PubMed  Google Scholar 

  58. 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 

  59. Wesolowska-Andersen A, Bahl MI, Carvalho V, Kristiansen K, Sicheritz-Ponten T, Gupta R, Licht TR (2014) Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2:19. https://doi.org/10.1186/2049-2618-2-19

    Article  PubMed  PubMed Central  Google Scholar 

  60. Hsieh YH, Peterson CM, Raggio A, Keenan MJ, Martin RJ, Ravussin E, Marco ML (2016) Impact of different fecal processing methods on assessments of bacterial diversity in the human intestine. Front Microbiol 7:1643. https://doi.org/10.3389/fmicb.2016.01643

    Article  PubMed  PubMed Central  Google Scholar 

  61. Kumar J, Kumar M, Gupta S, Ahmed V, Bhambi M, Pandey R, Chauhan NS (2016) An improved methodology to overcome key issues in human fecal metagenomic DNA extraction. Genomics Proteomics Bioinformatics 14(6):371–378. https://doi.org/10.1016/j.gpb.2016.06.002

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bag S, Saha B, Mehta O, Anbumani D, Kumar N, Dayal M, Pant A, Kumar P, Saxena S, Allin KH, Hansen T, Arumugam M, Vestergaard H, Pedersen O, Pereira V, Abraham P, Tripathi R, Wadhwa N, Bhatnagar S, Prakash VG, Radha V, Anjana RM, Mohan V, Takeda K, Kurakawa T, Nair GB, Das B (2016) An improved method for high quality metagenomics DNA extraction from human and environmental samples. Sci Rep 6:26775. https://doi.org/10.1038/srep26775

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Olson ND, Morrow JB (2012) DNA extract characterization process for microbial detection methods development and validation. BMC Res Notes 5:668. https://doi.org/10.1186/1756-0500-5-668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Shiba T, Harada S, Sugawara H, Naitow H, Kai Y, Satow Y (2000) Crystallization and preliminary X-ray analysis of a bacterial lysozyme produced by Streptomyces globisporus. Acta Crystallogr D Biol Crystallogr 56(Pt 11):1462–1463

    Article  CAS  PubMed  Google Scholar 

  65. Gill C, van de Wijgert JH, Blow F, Darby AC (2016) Evaluation of lysis methods for the extraction of bacterial DNA for analysis of the vaginal microbiota. PLoS One 11(9):e0163148. https://doi.org/10.1371/journal.pone.0163148

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Sohrabi M, Nair RG, Samaranayake LP, Zhang L, Zulfiker AH, Ahmetagic A, Good D, Wei MQ (2016) The yield and quality of cellular and bacterial DNA extracts from human oral rinse samples are variably affected by the cell lysis methodology. J Microbiol Methods 122:64–72. https://doi.org/10.1016/j.mimet.2016.01.013

    Article  CAS  PubMed  Google Scholar 

  67. 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  CAS  PubMed  PubMed Central  Google Scholar 

  68. Rintala A, Pietila S, Munukka E, Eerola E, Pursiheimo JP, Laiho A, Pekkala S, Huovinen P (2017) Gut microbiota analysis results are highly dependent on the 16S rRNA gene target region, whereas the impact of DNA extraction is minor. J Biomol Tech 28(1):19–30. https://doi.org/10.7171/jbt.17-2801-003

    Article  PubMed  PubMed Central  Google Scholar 

  69. Comeau A, Douglas G, Langille M (2017) Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research. https://github.com/LangilleLab/microbiome_helper/wiki/Microbiome-Amplicon-Sequencing-Workflow. Accessed 15 Mar 2018

  70. Fuks G, Elgart M, Amir A, Zeisel A, Turnbaugh PJ, Soen Y, Shental N (2018) Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling. Microbiome 6(1):17. https://doi.org/10.1186/s40168-017-0396-x

    Article  PubMed  PubMed Central  Google Scholar 

  71. Cox MJ, Cookson WO, Moffatt MF (2013) Sequencing the human microbiome in health and disease. Hum Mol Genet 22(R1):R88–R94. https://doi.org/10.1093/hmg/ddt398

    Article  CAS  PubMed  Google Scholar 

  72. Vetrovsky T, Baldrian P (2013) The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One 8(2):e57923. https://doi.org/10.1371/journal.pone.0057923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Taylor DL, Walters WA, Lennon NJ, Bochicchio J, Krohn A, Caporaso JG, Pennanen T (2016) Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for Illumina amplicon sequencing. Appl Environ Microbiol 82(24):7217–7226. https://doi.org/10.1128/AEM.02576-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Louca S, Doebeli M, Parfrey LW (2018) Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome 6(1):41. https://doi.org/10.1186/s40168-018-0420-9

    Article  PubMed  PubMed Central  Google Scholar 

  75. 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 

  76. Clooney AG, Fouhy F, Sleator RD, O’Driscoll A, Stanton C, Cotter PD, Claesson MJ (2016) Comparing apples and oranges?: next generation sequencing and its impact on microbiome analysis. PLoS One 11(2):e0148028. https://doi.org/10.1371/journal.pone.0148028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Whelan FJ (2014) Isolation of DNA from Clinical Samples (Genomic Prep). Surette Laboratory—Microbiome & Polymicrobial Research

    Google Scholar 

  78. Surette MG (2014) Isolation of DNA from clinical samples (GENOMIC PREP)

    Google Scholar 

  79. Li H (2015) Microbiome, metagenomics, and high-dimensional compositional data analysis. Annu Rev Statist Appl 2(1):73–94. https://doi.org/10.1146/annurev-statistics-010814-020351

    Article  Google Scholar 

  80. Gilbert JA, Quinn RA, Debelius J, Xu ZZ, Morton J, Garg N, Jansson JK, Dorrestein PC, Knight R (2016) Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535(7610):94–103. https://doi.org/10.1038/nature18850

    Article  CAS  PubMed  Google Scholar 

  81. Kaul A, Mandal S, Davidov O, Peddada SD (2017) Analysis of microbiome data in the presence of excess zeros. Front Microbiol 8:2114. https://doi.org/10.3389/fmicb.2017.02114

    Article  PubMed  PubMed Central  Google Scholar 

  82. 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 

  83. Xiao J, Cao H, Chen J (2017) False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing. Bioinformatics 33(18):2873–2881. https://doi.org/10.1093/bioinformatics/btx311

    Article  CAS  PubMed  Google Scholar 

  84. Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R (2013) EMPeror: a tool for visualizing high-throughput microbial community data. GigaScience 2(1):1–4. https://doi.org/10.1186/2047-217X-2-16

    Article  Google Scholar 

  85. Callahan BJ, DiGiulio DB, Goltsman DSA, Sun CL, Costello EK, Jeganathan P, Biggio JR, Wong RJ, Druzin ML, Shaw GM, Stevenson DK, Holmes SP, Relman DA (2017) Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women. Proc Natl Acad Sci U S A 114(37):9966–9971. https://doi.org/10.1073/pnas.1705899114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Kuczynski J, Liu Z, Lozupone C, McDonald D, Fierer N, Knight R (2010) Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nat Methods 7(10):813–819. https://doi.org/10.1038/nmeth.1499

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H (2012) Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 28(16):2106–2113. https://doi.org/10.1093/bioinformatics/bts342

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84(1):45–67. https://doi.org/10.1890/13-0133.1

    Article  Google Scholar 

  89. McCoy CO, Matsen FA IV (2013) Abundance-weighted phylogenetic diversity measures distinguish microbial community states and are robust to sampling depth. PeerJ 1(6):e157. https://doi.org/10.7717/peerj.157

    Article  PubMed  PubMed Central  Google Scholar 

  90. Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, Lozupone C, Zaneveld JR, Vázquez-Baeza Y, Birmingham A, Hyde ER, Knight R (2017) Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5(1):59. https://doi.org/10.1186/s40168-017-0237-y

    Article  Google Scholar 

  91. McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82(1):290–297. https://doi.org/10.2307/2680104?ref=no-x-route:eace2e52c544dc8ef8f1c463e5849bd9

    Article  Google Scholar 

  92. Alekseyenko AV (2016) Multivariate Welch t-test on distances. Bioinformatics 32(23):3552–3558. https://doi.org/10.1093/bioinformatics/btw524

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Tang Z-Z, Chen G, Alekseyenko AV (2016) PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances. Bioinformatics 32(17):2618–2625. https://doi.org/10.1093/bioinformatics/btw311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, Zhou JJ, Ringel Y, Li H, Wu MC (2015) Testing in microbiome-profiling studies with MiRKAT, the microbiome regression-based kernel association test. Am J Hum Genet 96(5):797–807. https://doi.org/10.1016/j.ajhg.2015.04.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. White JR, Nagarajan N, Pop M (2009) Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol 5(4):e1000352. https://doi.org/10.1371/journal.pcbi.1000352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. https://doi.org/10.1093/bioinformatics/btp616

    Article  CAS  PubMed  Google Scholar 

  98. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12(6):R60. https://doi.org/10.1186/gb-2011-12-6-r60

    Article  PubMed  PubMed Central  Google Scholar 

  99. Paulson JN, Stine OC, Bravo HC, Pop M (2013) Differential abundance analysis for microbial marker-gene surveys. Nat Methods 10(12):1200–1202. https://doi.org/10.1038/nmeth.2658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB (2014) Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2(1):15. https://doi.org/10.1186/2049-2618-2-15

    Article  PubMed  PubMed Central  Google Scholar 

  101. Sohn MB, Du R, An L (2015) A robust approach for identifying differentially abundant features in metagenomic samples. Bioinformatics 31(14):2269–2275. https://doi.org/10.1093/bioinformatics/btv165

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD (2015) Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 26(0):27663. https://doi.org/10.3402/mehd.v26.27663

    Article  PubMed  Google Scholar 

  103. Chen J, King E, King E, Deek R, Deek R, Wei Z, Yu Y, Grill D, Grill D, Ballman K, Stegle O (2018) An omnibus test for differential distribution analysis of microbiome sequencing data. Bioinformatics 34(4):643–651. https://doi.org/10.1093/bioinformatics/btx650

    Article  CAS  PubMed  Google Scholar 

  104. Tsilimigras MCB, Fodor AA (2016) Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann Epidemiol 26(5):330–335. https://doi.org/10.1016/j.annepidem.2016.03.002

    Article  PubMed  Google Scholar 

  105. Jonsson V, Österlund T, Nerman O, Kristiansson E (2016) Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics. BMC Genomics 17(1):1. https://doi.org/10.1186/s12864-016-2386-y

    Article  CAS  Google Scholar 

  106. Thorsen J, Brejnrod A, Mortensen M, Rasmussen MA, Stokholm J, Al-Soud WA, Sørensen S, Bisgaard H, Waage J (2016) Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies. Microbiome 4(1):62. https://doi.org/10.1186/s40168-016-0208-8

    Article  PubMed  PubMed Central  Google Scholar 

  107. Waldron L (2018) Data and statistical methods to analyze the human microbiome. mSystems 3(2):e00194–e00117. https://doi.org/10.1128/mSystems.00194-17

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Leigh Greathouse .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Morales, E., Chen, J., Greathouse, K.L. (2019). Compositional Analysis of the Human Microbiome in Cancer Research. In: Haznadar, M. (eds) Cancer Metabolism. Methods in Molecular Biology, vol 1928. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9027-6_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9027-6_16

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9026-9

  • Online ISBN: 978-1-4939-9027-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics