Structure, function and diversity of the healthy human microbiome (2024)

The diversity of microbes within a given body habitat can be defined as the number and abundance distribution of distinct types of organisms, which has been linked to several human diseases: low diversity in the gut to obesity and inflammatory bowel disease^2,3, for example, and high diversity in the vagin* to bacterial vaginosis⁴. For this large study involving microbiome samples collected from healthy volunteers at two distinct geographic locations in the United States, we have defined the microbial communities at each body habitat, encountering 81–99% of predicted genera and saturating the range of overall community configurations (Fig. 1, Supplementary Fig. 1 and Supplementary Table 1; see also Fig. 4). Oral and stool communities were especially diverse in terms of community membership, expanding prior observations⁵, and vagin*l sites harboured particularly simple communities (Fig. 1a). This study established that these patterns of alpha diversity (within samples) differed markedly from comparisons between samples from the same habitat among subjects (beta diversity, Fig. 1b). For example, the saliva had among the highest median alpha diversities of operational taxonomic units (OTUs, roughly species level classification, see http://hmpdacc.org/HMQCP), but one of the lowest beta diversities—so although each individual’s saliva was ecologically rich, members of the population shared similar organisms. Conversely, the antecubital fossae (skin) had the highest beta diversity but were intermediate in alpha diversity. The vagin* had the lowest alpha diversity, with quite low beta diversity at the genus level but very high among OTUs due to the presence of distinct Lactobacillus spp. (Fig. 1b). The primary patterns of variation in community structure followed the major body habitat groups (oral, skin, gut and vagin*l), defining as a result the complete range of population-wide between-subject variation in human microbiome habitats (Fig. 1c). Within-subject variation over time was consistently lower than between-subject variation, both in organismal composition and in metabolic function (Fig. 1d). The uniqueness of each individual’s microbial community thus seems to be stable over time (relative to the population as a whole), which may be another feature of the human microbiome specifically associated with health.

No taxa were observed to be universally present among all body habitats and individuals at the sequencing depth employed here, unlike several pathways (Fig. 2 and Supplementary Fig. 2, see below), although several clades demonstrated broad prevalence and relatively abundant carriage patterns^6,7. Instead, as suggested by individually focused studies^2,3,5,8,9, each body habitat in almost every subject was characterized by one or a few signature taxa making up the plurality of the community (Fig. 3). Signature clades at the genus level formed on average anywhere from 17% to 84% of their respective body habitats, completely absent in some communities (0% at this level of detection) and representing the entire population (100%) in others. Notably, less dominant taxa were also highly personalized, both among individuals and body habitats; in the oral cavity, for example, most habitats are dominated by Streptococcus, but these are followed in abundance by Haemophilus in the buccal mucosa, Actinomyces in the supragingival plaque, and Prevotella in the immediately adjacent (but low oxygen) subgingival plaque¹⁰.

Additional taxonomic detail of the human microbiome was provided by identifying unique marker sequences in metagenomic data¹¹ (Fig. 3a) to complement 16S profiling (Fig. 3b). These two profiles were typically in close agreement (Supplementary Fig. 3), with the former in some cases offering more specific information on members of signature genera differentially present within habitats (for example, vagin*l Prevotella amnii and gut Prevotella copri) or among individuals (for example, vagin*l Lactobacillus spp.) One application of this specificity was to confirm the absence of NIAID (National Institute of Allergy and Infectious Diseases) class A–C pathogens above 0.1% abundance (aside from Staphylococcus aureus and Escherichia coli) from the healthy microbiome, but the near-ubiquity and broad distribution of opportunistic ‘pathogens’ as defined by PATRIC¹². Canonical pathogens including Vibrio cholerae, Mycobacterium avium, Campylobacter jejuni and Salmonella enterica were not detected at this level of sensitivity. Helicobacter pylori was found in only two stool samples, both at <0.01%, and E. coli was present at >0.1% abundance in 15% of stool microbiomes (>0% abundance in 61%). Similar species-level observations were obtained for a small subset of stool samples with 454 pyrosequencing metagenomics data using PhylOTU^13,14. In total 56 of 327 PATRIC pathogens were detected in the healthy microbiome (at >1% prevalence of >0.1% abundance, Supplementary Table 2), all opportunistic and, strikingly, typically prevalent both among hosts and habitats. The latter is in contrast to many of the most abundant signature taxa, which were usually more habitat-specific and variable among hosts (Fig. 3a, b). This overall absence of particularly detrimental microbes supports the hypothesis that even given this cohort’s high diversity, the microbiota tend to occupy a range of configurations in health distinct from many of the disease perturbations studied to date^3,15.

Inter-individual variation in the microbiome proved to be specific, functionally relevant and personalized. One example of this is illustrated by the Streptococcus spp. of the oral cavity. The genus dominates the oropharynx¹⁶, with different species abundant within each sampled body habitat (see http://hmpdacc.org/HMSMCP) and, even at the species level, marked differences in carriage within each habitat among individuals (Fig. 4a). As the ratio of pan- to core-genomes is high in many human-associated microbes¹⁷, this variation in abundance could be due to selective pressures acting on pathways differentially present among Streptococcus species or strains (Fig. 4b). Indeed, we observed extensive strain-level genomic variation within microbial species in this population, enriched for host-specific structural variants around genomic islands (Fig. 4c). Even with respect to the single Streptococcus mitis strain B6, gene losses associated with these events were common, for example differentially eliminating S. mitis carriage of the V-type ATPase or choline binding proteins cbp6 and cbp12 among subsets of the host population (Fig. 4d). These losses were easily observable by comparison to reference isolate genomes, and these initial findings indicate that microbial strain- and host-specific gene gains and polymorphisms may be similarly ubiquitous.

Other examples of functionally relevant inter-individual variation at the species and strain levels occurred throughout the microbiome. In the gut, Bacteroides fragilis has been shown to prime T-cell responses in animal models via the capsular polysaccharide A¹⁸, and in the HMP stool samples this taxon was carried at a level of at least 0.1% in 16% of samples (over 1% abundance in 3%). Bacteroides thetaiotaomicron has been studied for its effect on host gastrointestinal metabolism¹⁹ and was likewise common at 46% prevalence. On the skin, S. aureus, of particular interest as the cause of methicillin-resistant S. aureus (MRSA) infections, had 29% nasal and 4% skin carriage rates, roughly as expected²⁰. Close phylogenetic relatives such as Staphylococcus epidermidis (itself considered commensal) were, in contrast, universal on the skin and present in 93% of nares samples, and at the opposite extreme Pseudomonas aeruginosa (a representative Gram-negative skin pathogen) was completely absent from both body habitats (0% at this level of detection). These and the data above suggest that the carriage pattern of some species in the human microbiome may be analogous to genetic traits, where recessive alleles of modest risk are maintained in a population. In the case of the human microbiome, high-risk pathogens remain absent, whereas species that pose a modest degree of risk also seem to be stably maintained in this ecological niche.

Finally, microorganisms within and among body habitats exhibited relationships suggestive of driving physical factors such as oxygen, moisture and pH, host immunological factors, and microbial interactions such as mutualism or competition²¹ (Supplementary Fig. 4). Both overall community similarity and microbial co-occurrence and co-exclusion across the human microbiome grouped the 18 body habitats together into four clusters corresponding to the five target body areas (Supplementary Fig. 4a, b). There was little distinction among different vagin*l sites, with Lactobacillus spp. dominating all three and correlating in abundance. However, Lactobacillus varied inversely with the Actinobacteria and Bacteroidetes (see Supplementary Fig. 4c and Figs 2 and 3), as also observed in a previous cohort⁹. Gut microbiota relationships primarily comprised inverse associations with the Bacteroides, which ranged from dominant in some subjects to a minority in others who carried a greater diversity of Firmicutes. A similar progression was evident in the skin communities, dominated by one of Staphylococcus (phylum Firmicutes), Propionibacterium, or Corynebacterium (both phylum Actinobacteria), with a continuum of oral organisms (for example, Streptococcus) appearing in nares communities (Supplementary Fig. 4c). These observations suggest that microbial community structure in these individuals may sometimes occupy discrete configurations and under other circ*mstances vary continuously, a topic addressed in more detail by several HMP investigations (ref. 6 and unpublished results). An individual’s location within such configurations is indicative of current microbial carriage (including pathogens) and of the community’s ability to resist future pathogen acquisition or dysbiosis; it may thus prove to be associated with disease susceptibility or other phenotypic characteristics.

As the first study to include both marker gene and metagenomic data across body habitats from a large human population, we additionally assessed the ecology of microbial metabolic and functional pathways in these communities. We reconstructed the relative abundances of pathways in community metagenomes²², which were much more constant and evenly diverse than were organismal abundances (Fig. 2b, see also Fig. 1), confirming this as an ecological property of the entire human microbiome². We were likewise able to determine for the first time that taxonomic and functional alpha diversity across microbial communities significantly correlate (Spearman of inverse Simpson’s r = 0.60, P = 3.6 × 10⁻⁶⁷, n = 661), the latter within a more proscribed range of community configurations (Supplementary Fig. 5).

Unlike microbial taxa, several pathways were ubiquitous among individuals and body habitats. The most abundant of these ‘core’ pathways include the ribosome and translational machinery, nucleotide charging and ATP synthesis, and glycolysis, and reflect the basics of host-associated microbial life. Also in contrast to taxa, few pathways were highly variable among subjects within any body habitat; exceptions included the Sec (orally, pathway relative abundance s.d. = 0.0052; total mean of oral standard deviations = 0.0011 with s.d. = 0.0016) and Tat (globally, pathway s.d. = 0.0055; mean of global standard deviations = 0.0023 with s.d. = 0.0033) secretion systems, indicating a high degree of host–microbe and microbe–microbe interactions in the healthy human microbiota. This high variability was particularly present in the oral cavity; for phosphate, mono- and di-saccharide, and amino acid transport in the mucosa; and also for lipopolysaccharide biosynthesis and spermidine/putrescine synthesis and transport on the plaque and tongue (http://hmpdacc.org/HMMRC). The stability and high metagenomic abundance of this housekeeping ‘core’ contrasts with the greater variability and lower abundance of niche-specific functionality in rare but consistently present pathways; for example, spermidine biosynthesis, methionine degradation and hydrogen sulphide production, all examples highly prevalent in gastrointestinal body sites (non-zero in >92% of samples) but at very low abundance (median relative abundance < 0.0052). This ‘long tail’ of low-abundance genes and pathways also probably encodes much of the uncharacterized biomolecular function and metabolism of these metagenomes, the expression levels of which remain to be explored in future metatranscriptomic studies.

Protein families showed diversity and prevalence trends similar to those of full pathways, ranging from maxima of only ∼16,000 unique families per community in the vagin* to almost 400,000 in the oral cavity (Fig. 1a, b; http://hmpdacc.org/HMGI). A remarkable fraction of these families were indeed functionally uncharacterized, including those detected by read mapping, with a minimum in the oral cavity (mean 58% s.d. 6.8%) and maximum in the nares (mean 77% s.d. 11%). Likewise, many genes annotated from assemblies could not be assigned a metabolic function, with a minimum in the vagin* (mean 78% s.d. 3.4%) and maximum in the gut (mean 86% s.d. 0.9%). The latter range did not differ substantially by body habitat and is in close agreement with previous comprehensive gene catalogues of the gut metagenome³. Taken together with the microbial variation observed above throughout the human microbiome, functional variation among individuals might indicate pathways of particular importance in maintaining community structure in the face of personalized immune, environmental or dietary exposures among these subjects. Determining the functions of uncharacterized core and variable protein families will be especially essential in understanding role of the microbiota in health and disease.

We finally examined relationships associating both clades and metabolism in the microbiota with host properties such as age, gender, body mass index (BMI), and other available clinical metadata (Fig. 5 and Supplementary Table 3). Using a sparse multivariate model, 960 microbial, enzymatic or pathway abundances were significantly associated with one or more of 15 subject phenotype and sample metadata features. A wide variety of taxa, gene families and metabolic pathways were differentially distributed with subject ethnicity at every body habitat (Fig. 5a), representing the phenotype with the greatest number (266 at false discovery rate (FDR) q < 0.2) of total associations with the microbiome. vagin*l pH has also been observed to correlate with microbiome composition⁹, and we detected in this population both the expected reduction in Lactobacillus at high pH and a corresponding increase in metabolic diversity (Fig. 5b). Intriguingly, and not previously observed, subject age was most associated with a collection of highly differential metagenomically encoded pathways on the skin (Fig. 5c), as well as shifts in skin clades including retroauricular Firmicutes (P = 1.0 × 10⁻⁴, q = 0.033). The examples of associations with ethnicity and vagin*l pH are among the strongest associations with the microbiome, however, and most correlates (for example, with subject BMI, Fig. 5d) are more representatively modest. This lower degree of correlation held for most available biometrics (gender, temperature, blood pressure, etc.), with even the most significant associations possessing generally low effect sizes and considerable unexplained variance. We conclude that most variation in the human microbiome is not well explained by these phenotypic metadata, and other potentially important factors such as short- and long-term diet, daily cycles, founder effects such as mode of delivery, and host genetics should be considered in future analyses.

This extensive sampling of the human microbiome across many subjects and body habitats provides an initial characterization of the normal microbiota of healthy adults in a Western population. The large sample size and consistent sampling of many sites from the same individuals allows for the first time an understanding of the relationships among microbes, and between the microbiome and clinical parameters, that underpin the basis for individual variation—variation that may ultimately be critical for understanding microbiome-based disorders. Clinical studies of the microbiome will be able to leverage the resulting extensive catalogues of taxa, pathways and genes¹, although they must also still include carefully matched internal controls. The uniqueness of each individual’s microbiome even in this reference population argues for future studies to consider prospective within-subjects designs where possible. The HMP’s unique combination of organismal and functional data across body habitats, encompassing both 16S and metagenomic profiling, together with detailed characterization of each subject, has allowed us and subsequent studies to move beyond the observation of variability in the human microbiome to ask how and why these microbial communities vary so extensively.

Many details remain for further work to fill in, building on this reference study. How do early colonization and lifelong change vary among body habitats? Do epidemiological patterns of transmission of beneficial or harmless microbes mirror patterns of transmission of pathogens? Which co-occurrences among microbes reflect shared response to the environment, as opposed to competitive or mutualistic interactions? How large a role does host immunity or genetics play in shaping patterns of diversity, and how do the patterns observed in this North American population compare to those around the world? Future studies building on the gene and organism catalogues established by the Human Microbiome Project, including increasingly detailed investigations of metatranscriptomes and metaproteomes, will help to unravel these open questions and allow us to more fully understand the links between the human microbiome, health and disease.

Author notes

1. Curtis Huttenhower and Dirk Gevers: These authors contributed equally to this work.

Authors and Affiliations

1. Biostatistics, Harvard School of Public Health, Boston, 02115, Massachusetts, USA

   Curtis Huttenhower,Curtis Huttenhower,J. Fah Sathirapongsasuti&Nicola Segata

2. The Broad Institute of MIT and Harvard, Cambridge, 02142, Massachusetts, USA

   Curtis Huttenhower,Dirk Gevers,Ashlee M. Earl,Michael G. FitzGerald,Sarah K. Young,Qiandong Zeng,Eric J. Alm,Lucia Alvarado,Scott Anderson,Harindra M. Arachchi,Toby Bloom,Dawn M. Ciulla,Rachel L. Erlich,Michael Feldgarden,Sheila Fisher,Dennis C. Friedrich,Georgia Giannoukos,Jonathan M. Goldberg,Allison Griggs,Sharvari Gujja,Brian J. Haas,Theresa A. Hepburn,Clinton Howarth,Katherine H. Huang,Cristyn Kells,Niall Lennon,Teena Mehta,Chad Nusbaum,Matthew Pearson,Margaret E. Priest,Carsten Russ,Narmada Shenoy,Sean M. Sykes,Diana G. Tabbaa,Doyle V. Ward,Chandri Yandava,Jeremy D. Zucker&Bruce W. Birren

3. Department of Chemistry and Biochemistry, University of Colorado, Boulder, 80309, Colorado, USA

   Rob Knight,Jose C. Clemente,Catherine A. Lozupone&Daniel McDonald

4. Howard Hughes Medical Institute, Boulder, 80309, Colorado, USA

   Rob Knight

5. The Genome Institute, Washington University School of Medicine, St. Louis, 63108, Missouri, USA

   Sahar Abubucker,Asif T. Chinwalla,Robert S. Fulton,Kymberlie Hallsworth-Pepin,Elizabeth A. Lobos,Vincent Magrini,John C. Martin,Makedonka Mitreva,Erica J. Sodergren,Aye M. Wollam,Elizabeth Appelbaum,Veena Bhonagiri,Lei Chen,Sandra W. Clifton,Kimberley D. Delehaunty,David J. Dooling,Candace N. Farmer,Catrina C. Fronick,Lucinda L. Fulton,Hongyu Gao,Brandi Herter,Karthik C. Kota,Elaine R. Mardis,Kathie A. Mihindukulasuriya,Patrick J. Minx,Michelle O’Laughlin,Craig Pohl,Chad M. Tomlinson,Jason Walker,Zhengyuan Wang,Wesley Warren,Kristine M. Wylie,Todd Wylie,Liang Ye,Yanjiao Zhou,George M. Weinstock&Richard K. Wilson

6. J. Craig Venter Institute, Rockville, Maryland 20850, USA.,

   Jonathan H. Badger,Ramana Madupu,Monika Bihan,Dana A. Busam,A. Scott Durkin,Leslie Foster,Johannes Goll,Kelvin Li,Jamison M. McCorrison,Jason R. Miller,Yu-Hui Rogers,Ravi K. Sanka,Indresh Singh,Granger G. Sutton,Mathangi Thiagarajan,Manolito Torralba,Barbara A. Methé&Karen E. Nelson

7. Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, 21201, Maryland, USA

   Heather H. Creasy,Michelle G. Giglio,Jennifer R. Wortman,Olukemi O. Abolude,Cesar A. Arze,Brandi L. Cantarel,Jonathan Crabtree,Noam J. Davidovics,Victor M. Felix,Catherine Jordan,Anup A. Mahurkar,Joshua Orvis,Jacques Ravel,Lynn Schriml,James R. White&Owen White

8. Human Genome Sequencing Center, Baylor College of Medicine, Houston, 77030, Texas, USA

   Donna M. Muzny,Kim C. Worley,Christian J. Buhay,Yan Ding,Shannon P. Dugan,Michael E. Holder,Huaiyang Jiang,Vandita Joshi,Christie L. Kovar,Sandra L. Lee,Lora Lewis,Yue Liu,Irene Newsham,Xiang Qin,Jeffrey G. Reid,Katarzyna Wilczek-Boney,YuanQing Wu,Lan Zhang,Yiming Zhu,Richard A. Gibbs,Sarah K. Highlander&Joseph F. Petrosino

9. Department of Pathology & Immunology, Baylor College of Medicine, Houston, 77030, Texas, USA

   James Versalovic

10. Department of Pathology, Texas Children’s Hospital, Houston, 77030, Texas, USA

    James Versalovic

11. Department of Obstetrics & Gynecology, Division of Maternal-Fetal Medicine, Baylor College of Medicine, Houston, 77030, Texas, USA

    Kjersti M. Aagaard

12. Molecular and Cellular Biology, University of Guelph, Guleph, Ontario N1G 2W1, Canada.,

    Emma Allen-Vercoe

13. Department of Civil & Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Eric J. Alm

14. Center for Environmental Biotechnology, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

    Gary L. Andersen

15. School of Dentistry, University of California, San Francisco, San Francisco, 94143, California, USA

    Gary Armitage

16. Molecular Virology and Microbiology, Baylor College of Medicine, Houston, 77030, Texas, USA

    Tulin Ayvaz,Wendy A. Keitel,Matthew C. Ross,Bonnie P. Youmans,Sarah K. Highlander&Joseph F. Petrosino

17. National Institute of Arthritis and Musculoskeletal and Skin, National Institutes of Health, Bethesda, 20892, Maryland, USA

    Carl C. Baker

18. Office of Research on Women’s Health, National Institutes of Health, Bethesda, 20892, Maryland, USA

    Lisa Begg

19. National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, Maryland, USA

    Tsegahiwot Belachew,Joseph L. Campbell,Carolyn Deal,Valentina Di Francesco,Christina Giblin&Maria Y. Giovanni

20. Department of Medicine, New York University Langone Medical Center, New York, 10016, New York, USA

    Martin J. Blaser

21. National Human Genome Research Institute, National Institutes of Health, Bethesda, 20892, Maryland, USA

    Vivien Bonazzi,Joseph L. Campbell,Shaila Chhibba,Jean McEwen,Jane Peterson,Lita M. Proctor,Jeffery A. Schloss,Lu Wang,Christopher Wellington&Kris A. Wetterstrand

22. Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, 23284, Virginia, USA

    J. Paul Brooks

23. Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, 23284, Virginia, USA

    J. Paul Brooks,Gregory A. Buck,Maria C. Rivera&Nihar U. Sheth

24. Department of Biology, Virginia Commonwealth University, Richmond, 23284, Virginia, USA

    Gregory A. Buck&Maria C. Rivera

25. Technology Integration Group, National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

    Shane R. Canon

26. Bioscience Division, Genome Science Group, Los Alamos National Laboratory, Los Alamos, 87545, New Mexico, USA

    Patrick S. G. Chain,Chien-Chi Lo&Matthew Scholz

27. Joint Genome Institute, Walnut Creek, 94598, California, USA

    Patrick S. G. Chain,Nikos C. Kyrpides,Konstantinos Liolios,Victor M. Markowitz,Konstantinos Mavromatis&Ioanna Pagani

28. Computational Research Division, Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

    I-Min A. Chen,Ken Chu,Victor M. Markowitz&Krishna Palaniappan

29. National Institute of Dental and Craniofacial Research (NIDCR), National Institutes of Health, Bethesda, 20892, Maryland, USA

    Mary A. Cutting,Holli A. Hamilton,Emily L. Harris,R. Dwayne Lunsford&Pamela McInnes

30. FemCare Product Safety and Regulatory Affairs, The Procter & Gamble Company, Cincinnati, 45224, Ohio, USA

    Catherine C. Davis

31. Bioinformatics Department, Second Genome, Inc., San Bruno, 94066, California, USA

    Todd Z. DeSantis

32. Department of Molecular Genetics, Forsyth Institute, Cambridge, 02142, Massachusetts, USA

    Floyd E. Dewhirst,Jacques Izard&Katherine P. Lemon

33. Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, 02115, Massachusetts, USA

    Floyd E. Dewhirst&Jacques Izard

34. Department of Medicine, Division of General Medical Science, Washington University School of Medicine, St. Louis, 63110, Missouri, USA

    Elena Deych,Patricio S. La Rosa&William D. Shannon

35. Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, 63110, Missouri, USA

    Wm Michael Dunne&Mark A. Watson

36. bioMerieux, Inc., Durham, 27712, South Carolina, USA

    Wm Michael Dunne

37. drive5.com, Tiburon, 94920, California, USA

    Robert C. Edgar

38. Center for Ethics, Humanities and Spiritual Care, Cleveland Clinic, Cleveland, 44195, Ohio, USA

    Ruth M. Farrell&Richard R. Sharp

39. Department of Structural Biology, VIB, Belgium, 1050 Ixelles, Belgium.,

    Karoline Faust&Jeroen Raes

40. Department of Applied Biological Sciences (DBIT), Vrije Universiteit Brussel, 1050 Ixelles, Belgium.,

    Karoline Faust&Jeroen Raes

41. Department of Bioinformatics and Genomics, University of North Carolina - Charlotte, Charlotte, 28223, North Carolina, USA

    Anthony A. Fodor

42. Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844, USA.,

    Larry J. Forney

43. Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,

    Jonathan Friedman&Christopher S. Smillie

44. Center for Advanced Dental Education, Saint Louis University, St. Louis, 63104, Missouri, USA

    Nathalia Garcia

45. Department of Computer Science, University of Colorado, Boulder, 80309, Colorado, USA

    Antonio Gonzalez&Dan Knights

46. Division of Associated Clinical Specialties and Dental Research Institute, UCLA School of Dentistry, Los Angeles, California 90095, USA.,

    Susan Kinder Haake

47. University of Maryland Francis King Carey School of Law, Baltimore, 21201, Maryland, USA

    Diane E. Hoffmann

48. Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, 02543, Massachusetts, USA

    Susan M. Huse

49. Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

    Janet K. Jansson

50. Department of Periodontics, University of Texas Health Science Center School of Dentistry, Houston, 77030, Texas, USA

    James A. Katancik

51. Department of Biology, San Diego State University, San Diego, 92182, California, USA

    Scott T. Kelley&Beltran Rodriguez-Mueller

52. Faculty of Medicine, McGill University, 3647 Peel St, Montreal, Ouebec H3A 1X1, Canada.,

    Nicholas B. King

53. Dermatology Branch, CCR, National Cancer Institute, Bethesda, 20892, Maryland, USA

    Heidi H. Kong

54. Department of Microbiology, Cornell University, Ithaca, 14853, New York, USA

    Omry Koren&Ruth E. Ley

55. Center for Bioinformatics and Computational Biology, University of Maryland, College Park, 20742, Maryland, USA

    Sergey Koren,Bo Liu,Mihai Pop&Daniel D. Sommer

56. Division of Infectious Diseases, Children’s Hospital Boston, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Katherine P. Lemon

57. Department of Anthropology, University of Oklahoma, Norman, 73019, Oklahoma, USA

    Cecil M. Lewis&Paul Spicer

58. Department of Obstetrics and Gynecology, Washington University School of Medicine, Saint Louis, 63110, Missouri, USA

    Tessa Madden

59. Division of Gastroenterology and Hepatology, University of Alabama at Birmingham, Birmingham, 35294, Alabama, USA

    Peter J. Mannon

60. Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, 77030, Texas, USA

    Amy L. McGuire

61. Medicine-Infectious Disease, Baylor College of Medicine, Houston, 77030, Texas, USA

    sh*tal M. Patel

62. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, 37831, Tennessee, USA

    Mircea Podar&Tatiana A. Vishnivetskaya

63. Gladstone Institutes, University of California, San Francisco, San Francisco, 94158, California, USA

    Katherine S. Pollard,Thomas J. Sharpton&Rebecca M. Truty

64. Institute for Human Genetics, University of California, San Francisco, San Francisco, 94158, California, USA

    Katherine S. Pollard

65. Division of Biostatistics, University of California, San Francisco, San Francisco, 94158, California, USA

    Katherine S. Pollard

66. Department of Computer Science, University of Maryland, College Park, 20742, Maryland, USA

    Mihai Pop

67. School of Informatics and Computing, Indiana University, Bloomington, 47405, Indiana, USA

    Mina Rho&Yuzhen Ye

68. Mount Sinai School of Medicine, New York, 10029, New York, USA

    Rosamond Rhodes

69. Molecular & Human Genetics, Baylor College of Medicine, Houston, 77030, Texas, USA

    Kevin P. Riehle

70. Center for Bioethics and Department of Medical Ethics, University of Pennsylvania, Philadelphia, 19104, Pennsylviana, USA

    Pamela Sankar

71. Department of Microbiology & Immunology, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Patrick D. Schloss&Alyxandria M. Schubert

72. Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, 48824, Michigan, USA

    Thomas M. Schmidt

73. The EMMES Corporation, Rockville, 20850, Maryland, USA

    Gina A. Simone

74. Harper University Hospital, Wayne State University School of Medicine, Detroit, 48201, Michigan, USA

    Jack D. Sobel

75. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    Todd J. Treangen

76. J. Craig Venter Institute, San Diego, 92121, California, USA

    Shibu Yooseph

77. Feinberg School of Medicine, Northwestern University, Chicago, 60611, Illinois, USA

    Laurie Zoloth

78. Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, 77030, Texas, USA

    Joseph F. Petrosino

79. Genetics and Molecular Biology Branch, National Human Genome Research Institute, Bethesda, 20892, Maryland, USA

    Sean Conlan&Julia A. Segre

Consortia

Contributions

Principal investigators: B.W.B., R.A.G., S.K.H., B.A.M., K.E.N., J.F.P., G.M.W., O.W., R.K.W. Manuscript preparation: D.G., C.H., R.K., O.W. Funding agency management: C.C.B., T.B., V.R.B., J.L.C., S.C., C.D., V.D.F., C.G., M.Y.G., R.D.L., J.M., P.M., J.P., L.M.P., J.A.S., L.W., C.W., K.A.W. Project leadership: S.A., J.H.B., B.W.B., A.T.C., H.H.C., A.M.E., M.G.F., R.S.F., D.G., M.G.G., K.H., S.K.H., C.H., E.A.L., R.M., V.M., J.C.M., B.A.M., M.M., D.M.M., K.E.N., J.F.P., E.J.S., J.V., G.M.W., O.W., A.M.W., K.C.W., J.R.W., S.K.Y., Q.Z. Analysis preparation for manuscript: J.C.C., K.F., D.G., A.G., K.H.H., C.H., R.K., D.K., H.H.K., O.K., K.P.L., R.E.L., J.R., J.F.S., P.D.S., N.S. Data release: L.A., T.B., I.A.C., K.C., H.H.C., N.J.D., D.J.D., A.M.E., V.M.F., L.F., J.M.G., S.G., S.K.H., M.E.H., C.J., V.J., C.K., A.A.M., V.M.M., T.M., M.M., D.M.M., J.O., K.P., J.F.P., C.P., X.Q., R.K.S., N.S., I.S., E.J.S., D.V.W., O.W., K.W., K.C.W., C.Y., B.P.Y., Q.Z. Methods and research development: S.A., H.M.A., M.B., D.M.C., A.M.E., R.L.E., M.F., S.F., M.G.F., D.C.F., D.G., G.G., B.J.H., S.K.H., M.E.H., W.A.K., N.L., K.L., V.M., E.R.M., B.A.M., M.M., D.M.M., C.N., J.F.P., M.E.P., X.Q., M.C.R., C.R., E.J.S., S.M.S., D.G.T., D.V.W., G.M.W., Y.W., K.M.W., S.Y., B.P.Y., S.K.Y., Q.Z. DNA sequence production: S.A., E.A., T.A., T.B., C.J.B., D.A.B., K.D.D., S.P.D., A.M.E., R.L.E., C.N.F., S.F., C.C.F., L.L.F., R.S.F., B.H., S.K.H., M.E.H., V.J., C.L.K., S.L.L., N.L., L.L., D.M.M., I.N., C.N., M.O., J.F.P., X.Q., J.G.R., Y.R., M.C.R., D.V.W., Y.W., B.P.Y., Y.Z. Clinical sample collection: K.M.A., M.A.C., W.M.D., L.L.F., N.G., H.A.H., E.L.H., J.A.K., W.A.K., T.M., A.L.M., P.M., S.M.P., J.F.P., G.A.S., J.V., M.A.W., G.M.W. Body site experts: K.M.A., E.A.V., G.A., L.B., M.J.B., C.C.D., F.E.D., L.F., J.I., J.A.K., S.K.H., H.H.K., K.P.L., P.J.M., J. Ravel, T.M.S., J.A.S., J.D.S., J.V. Ethical, legal and social implications: R.M.F., D.E.H., W.A.K., N.B.K., C.M.L., A.L.M., R.R., P. Sankar, R.R.S., P. Spicer, L.Z. Strain management: E.A.V., J.H.B., I.A.C., K.C., S.W.C., H.H.C., T.Z.D., A.S.D., A.M.E., M.G.F., M.G.G., S.K.H., V.J., N.C.K., S.L.L., L.L., K.L., E.A.L., V.M.M., B.A.M., D.M.M., K.E.N., I.N., I.P., L.S., E.J.S., C.M.T., M.T., D.V.W., G.M.W., A.M.W., Y.W., K.M.W., B.P.Y., L.Z., Y.Z. 16S data analysis: K.M.A., E.J.A., G.L.A., C.A.A., M.B., B.W.B., J.P.B., G.A.B., S.R.C., S.C., J.C., T.Z.D., F.E.D., E.D., A.M.E., R.C.E., K.F., M.F., A.A.F., J.F., H.G., D.G., B.J.H., T.A.H., S.M.H., C.H., J.I., J.K.J., S.T.K., S.K.H., R.K., H.H.K., O.K., P.S.L., R.E.L., K.L., C.A.L., D.M., B.A.M., K.A.M., M.M., M.P., J.F.P., M.P., K.S.P., X.Q., J. Raes, K.P.R., M.C.R., B.R., J.F.S., P.D.S., T.M.S., N.S., J.A.S., W.D.S., T.J.S., C.S.S., E.J.S., R.M.T., J.V., T.A.V., Z.W., D.V.W., G.M.W., J.R.W., K.M.W., Y.Y., S.Y., Y.Z. Shotgun data processing and alignments: C.J.B., J.C.C., E.D., D.G., A.G., M.E.H., H.J., D.K., K.C.K., C.L.K., Y.L., J.C.M., B.A.M., M.M., D.M.M., J.O., J.F.P., X.Q., J.G.R., R.K.S., N.U.S., I.S., E.J.S., G.G.S., S.M.S., J.W., Z.W., G.M.W., O.W., K.C.W., T.W., S.K.Y., L.Z. Assembly: H.M.A., C.J.B., P.S.C., L.C., Y.D., S.P.D., M.G.F., M.E.H., H.J., S.K., B.L., Y.L., C.L., J.C.M., J.M.M., J.R.M., P.J.M., M.M., J.F.P., M.P., M.E.P., X.Q., M.R., R.K.S., M.S., D.D.S., G.G.S., S.M.S., C.M.T., T.J.T., W.W., G.M.W., K.C.W., L.Y., Y.Y., S.K.Y., L.Z. Annotation: O.O.A., V.B., C.J.B., I.A.C., A.T.C., K.C., H.H.C., A.S.D., M.G.G., J.M.G., J.G., A.G., S.G., B.J.H., K.H., S.K.H., C.H., H.J., N.C.K., R.M., V.M.M., K.M., T.M., M.M., J.O., K.P., M.P., X.Q., N.S., E.J.S., G.G.S., S.M.S., M.T., G.M.W., K.C.W., J.R.W., C.Y., S.K.Y., Q.Z., L.Z., W.G.S. Metabolic reconstruction: S.A., B.L.C., J.G., C.H., J.I., B.A.M., M.M., B.R., A.M.S., N.S., M.T., G.M.W., S.Y., Q.Z., J.D.Z.

Corresponding author

Correspondence to Curtis Huttenhower.

Competing interests

The author declare no competing financial interests.

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