Biogeography of the ecosystems of the healthy human body

Yanjiao Zhou, Hongyu Gao, Kathie A Mihindukulasuriya, Patricio S La Rosa, Kristine M Wylie, Tatiana Vishnivetskaya, Mircea Podar, Barb Warner, Phillip I Tarr, David E Nelson, J Dennis Fortenberry, Martin J Holland, Sarah E Burr, William D Shannon, Erica Sodergren, George M Weinstock, Yanjiao Zhou, Hongyu Gao, Kathie A Mihindukulasuriya, Patricio S La Rosa, Kristine M Wylie, Tatiana Vishnivetskaya, Mircea Podar, Barb Warner, Phillip I Tarr, David E Nelson, J Dennis Fortenberry, Martin J Holland, Sarah E Burr, William D Shannon, Erica Sodergren, George M Weinstock

Abstract

Background: Characterizing the biogeography of the microbiome of healthy humans is essential for understanding microbial associated diseases. Previous studies mainly focused on a single body habitat from a limited set of subjects. Here, we analyzed one of the largest microbiome datasets to date and generated a biogeographical map that annotates the biodiversity, spatial relationships, and temporal stability of 22 habitats from 279 healthy humans.

Results: We identified 929 genera from more than 24 million 16S rRNA gene sequences of 22 habitats, and we provide a baseline of inter-subject variation for healthy adults. The oral habitat has the most stable microbiota with the highest alpha diversity, while the skin and vaginal microbiota are less stable and show lower alpha diversity. The level of biodiversity in one habitat is independent of the biodiversity of other habitats in the same individual. The abundances of a given genus at a body site in which it dominates do not correlate with the abundances at body sites where it is not dominant. Additionally, we observed the human microbiota exhibit both cosmopolitan and endemic features. Finally, comparing datasets of different projects revealed a project-based clustering pattern, emphasizing the significance of standardization of metagenomic studies.

Conclusions: The data presented here extend the definition of the human microbiome by providing a more complete and accurate picture of human microbiome biogeography, addressing questions best answered by a large dataset of subjects and body sites that are deeply sampled by sequencing.

Figures

Figure 1
Figure 1
Accumulation curves of 22 habitats. Each line represents the accumulative richness from all subjects. All the reads were included in the analysis to have a full view of the genera revealed by the HMP and other datasets. At the genus level, oral, vaginal, and stool habitats become asymptotically flat at the current sampling depth and sampling efforts. More subjects are needed to reach the saturation for skin and skin-associated habitats.
Figure 2
Figure 2
Shannon diversity comparisons of 22 habitats. One thousand reads were rarefied from each sample. The boxplot shows the minimum, 25th percentile, median, 75th percentile, and the maximum of the data from bottom to top. Oral habitats in general have more even bacterial communities, and vaginal habitats have the lowest Shannon diversity.
Figure 3
Figure 3
Bacterial distribution patterns viewed by rank abundance curves. The genus distributions are illustrated by rank abundance curves. The x-axis represents the ranked genera from high to low. The y-axis shows the average relative abundance for a given genus. Twenty-two different line shapes and nine different colors represent the 22 habitats in this study. One or a few genera dominate each habitat with a long tail representing rare genera. This bacterial distribution pattern agrees with the species abundance pattern in other environments.
Figure 4
Figure 4
Cosmopolitan and endemic aspects of human microbiota. The relative abundance and prevalence of each taxon from anterior nares is plotted to indicate the cosmopolitan and endemic features of human microbiota. Each dot represents a genus. The x-axis represents the fraction of subjects carrying a given genus (prevalence). The y-axis shows the average fractional abundance (m ± se) of that genus in those subjects. In general, highly abundant genera tend to be found in more subjects while lower abundance genera are less widely distributed. However, some high abundance genera are harbored by only a subset of subjects
Figure 5
Figure 5
Biodiversity correlation between habitats. The symmetric plot was used to show the association of biodiversity between paired habitats. The size and the redness of the circle represent the degree of correlation. Large size and deep redness indicate strong correlation. The large red circles on the diagonal line represent self-comparisons. Proximal habitats have similar alpha and beta diversity. (A) Alpha diversity association. The association of bacterial richness of different habitats from the same individuals is expressed by the Spearman correlation coefficient. (B) Beta diversity association. Mantel correlation was used to compare the Bray-Curtis dissimilarity matrix.
Figure 6
Figure 6
Bacterial community variation. Overall the variation of bacterial community for each habitat was evaluated by the over-dispersion parameter theta (m ± sd) from the Dirichlet-multinomial model. Higher theta indicates higher variation and vice versa. The variation within a bacterial community was calculated at 1,000, 3,000, 6,000, and 9,000 read depths as shown by different colors. No significant difference was found in bacterial community variation at different read depths.
Figure 7
Figure 7
Taxa distribution across habitats. Thirty-nine genera present in at least one subject for all 18 HMP habitats are plotted. Each symbol represents a habitat. The y-axis shows the sample prevalence of the genera calculated as the number of samples who harbor the genus divided by the total number of subjects. Using this criterion, 12 of these genera are spread across all 22 habitats.
Figure 8
Figure 8
Community stability over time. The longitudinal studies were based on 18 habitats from HMP. The similarity of the bacterial communities of each subject between two sampling points was evaluated by Spearman correlation coefficient (y-axis). Oral habitats and stool showed higher correlation between visits; skin and vaginal habitats showed lower correlation between visits. The variation of community stability between subjects is especially high in the skin habitats.

References

    1. Fierer N. Microbial biogeography: patterns in microbial diversity across space and time. Washington, DC: ASM Press; 2008.
    1. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;14:1022–1023. doi: 10.1038/4441022a.
    1. Smith B, Bode S, Petersen BL, Jensen TK, Pipper C, Kloppenborg J, Boye M, Krogfelt KA, Molbak L. Community analysis of bacteria colonizing intestinal tissue of neonates with necrotizing enterocolitis. BMC Microbiol. 2011;14:73. doi: 10.1186/1471-2180-11-73.
    1. Donders GG, Bosmans E, Dekeersmaecker A, Vereecken A, Van Bulck B, Spitz B. Pathogenesis of abnormal vaginal bacterial flora. Am J Obstet Gynecol. 2000;14:872–878. doi: 10.1016/S0002-9378(00)70338-3.
    1. Parracho HM, Bingham MO, Gibson GR, McCartney AL. Differences between the gut microflora of children with autistic spectrum disorders and that of healthy children. J Med Microbiol. 2005;14:987–991. doi: 10.1099/jmm.0.46101-0.
    1. Fava F, Lovegrove JA, Gitau R, Jackson KG, Tuohy KM. The gut microbiota and lipid metabolism: implications for human health and coronary heart disease. Curr Med Chem. 2006;14:3005–3021. doi: 10.2174/092986706778521814.
    1. Nelson DE, Van Der Pol B, Dong Q, Revanna KV, Fan B, Easwaran S, Sodergren E, Weinstock GM, Diao L, Fortenberry JD. Characteristic male urine microbiomes associate with asymptomatic sexually transmitted infection. PLoS One. 2010;14:e14116. doi: 10.1371/journal.pone.0014116.
    1. Kostic AD, Gevers D, Pedamallu CS, Michaud M, Duke F, Earl AM, Ojesina AI, Jung J, Bass AJ, Tabernero J, Liu C, Shivdasani RA, Ogino S, Birren BW, Huttenhower C, Garrett WS, Meyerson M. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 2011;14:292–298.
    1. Castellarin M, Warren RL, Freeman JD, Dreolini L, Krzywinski M, Strauss J, Barnes R, Watson P, Allen-Vercoe E, Moore RA, Holt RA. Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Genome Res. 2011;14:299–306.
    1. Zarco M, Vess T, Ginsburg G. The oral microbiome in health and disease and the potential impact on personalized dental medicine. Oral Dis. 2012;14:109–120. doi: 10.1111/j.1601-0825.2011.01851.x.
    1. Fava F, Danese S. Intestinal microbiota in inflammatory bowel disease: friend of foe? World J Gastroenterol. 2011;14:557–566. doi: 10.3748/wjg.v17.i5.557.
    1. Spencer MD, Hamp TJ, Reid RW, M FL, Zeisel SH, Fodor AA. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology. 2011;14:976–986. doi: 10.1053/j.gastro.2010.11.049.
    1. Hooper LV, Gordon JI. Commensal host-bacterial relationships in the gut. Science. 2001;14:1115–1118. doi: 10.1126/science.1058709.
    1. Fierer N, Hamady M, Lauber CL, Knight R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc Natl Acad Sci USA. 2008;14:17994–17999. doi: 10.1073/pnas.0807920105.
    1. Chen T, Yu WH, Izard J, Baranova OV, Lakshmanan A, Dewhirst FE. The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information. Database (Oxford) 2010;14:baq013. doi: 10.1093/database/baq013.
    1. Dewhirst FE, Chen T, Izard J, Paster BJ, Tanner AC, Yu WH, Lakshmanan A, Wade WG. The human oral microbiome. J Bacteriol. 2010;14:5002–5017. doi: 10.1128/JB.00542-10.
    1. Pozhitkov AE, Beikler T, Flemmig T, Noble PA. High-throughput methods for analysis of the human oral microbiome. Periodontol 2000. 2011;14:70–86. doi: 10.1111/j.1600-0757.2010.00380.x.
    1. Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, Bouffard GG, Blakesley RW, Murray PR, Green ED, Turner ML, Segre JA. Topographical and temporal diversity of the human skin microbiome. Science. 2009;14:1190–1192. doi: 10.1126/science.1171700.
    1. Frank DN, Feazel LM, Bessesen MT, Price CS, Janoff EN, Pace NR. The human nasal microbiota and Staphylococcus aureus carriage. PLoS One. 2010;14:e10598. doi: 10.1371/journal.pone.0010598.
    1. Wos-Oxley ML, Plumeier I, von Eiff C, Taudien S, Platzer M, Vilchez-Vargas R, Becker K, Pieper DH. A poke into the diversity and associations within human anterior nare microbial communities. ISME J. 2010;14:839–851. doi: 10.1038/ismej.2010.15.
    1. Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SS, McCulle SL, Karlebach S, Gorle R, Russell J, Tacket CO, R M, Davis CC, Ault K, Peralta L, Forney LJ. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci USA. 2011. pp. 4680–4687.
    1. Zaura E, Keijser BJ, Huse SM, Crielaard W. Defining the healthy "core microbiome" of oral microbial communities. BMC Microbiol. 2009;14:259. doi: 10.1186/1471-2180-9-259.
    1. Peterson J, Garges S, Giovanni M, McInnes P, Wang L, Schloss JA, Bonazzi V, McEwen JE, Wetterstrand KA, Deal C, Baker CC, Di Francesco V, Howcroft TK, Karp RW, Lunsford RD, Wellington CR, Belachew T, Wright M, Giblin C, David H, Mills M, Salomon R, Mullins C, Akolkar B, Begg L, Davis C, Grandison L, Humble M, Khalsa J, Little AR. et al.The NIH Human Microbiome Project. Genome Res. 2009;14:2317–2323.
    1. The Human Microbiome Consortium. A framework for human microbiome research. Nature. 2012;14:215–221. doi: 10.1038/nature11209.
    1. The Human Microbiome Consortium. Structure, Function and Diversity of the Human Microbiome in an Adult Reference Population. Nature. 2012;14:207–214. doi: 10.1038/nature11234.
    1. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, Huttenhower C. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;14:e1002606. doi: 10.1371/journal.pcbi.1002606.
    1. Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;14:e1002687. doi: 10.1371/journal.pcbi.1002687.
    1. Huse SM, Ye Y, Zhou Y, Fodor AA. A Core Human Microbiome as Viewed Through 16S rRNA Sequences Clusters. PLoS One. 2012;14:e34242. doi: 10.1371/journal.pone.0034242.
    1. Li K, Bihan M, Yooseph S, Methe BA. Analyses of the microbial diversity across the human microbiome. Plos One. 2012;14:e32118. doi: 10.1371/journal.pone.0032118.
    1. Hamady M, Knight R. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Res. 2009;14:1141–1152. doi: 10.1101/gr.085464.108.
    1. The Jumpstart Consortium Human Microbiome Project Data Generation Working Group. Evaluation of 16S rDNA-based community profiling for human microbiome research. PLos One. 2012;14:e39315. doi: 10.1371/journal.pone.0039315.
    1. Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, Kulam-Syed-Mohideen AS, McGarrell DM, Marsh T, Garrity GM, Tiedje JM. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 2009;14:D141–145. doi: 10.1093/nar/gkn879.
    1. Huse SM, Welch DM, Morrison HG, Sogin ML. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ Microbiol. 2010;14:1889–1898. doi: 10.1111/j.1462-2920.2010.02193.x.
    1. Pace NR. Mapping the tree of life: progress and prospects. Microbiol Mol Biol Rev. 2009;14:565–576. doi: 10.1128/MMBR.00033-09.
    1. Janssen PH. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl Environ Microbiol. 2006;14:1719–1728. doi: 10.1128/AEM.72.3.1719-1728.2006.
    1. Schauer R, Bienhold C, Ramette A, Harder J. Bacterial diversity and biogeography in deep-sea surface sediments of the South Atlantic Ocean. ISME J. 2010;14:159–170. doi: 10.1038/ismej.2009.106.
    1. Quince C, Lanzen A, Curtis TP, Davenport RJ, Hall N, Head IM, Read LF, Sloan WT. Accurate determination of microbial diversity from 454 pyrosequencing data. Nat Methods. 2009;14:639–641. doi: 10.1038/nmeth.1361.
    1. Reeder J, Knight R. The 'rare biosphere': a reality check. Nat Methods. 2009;14:636–637. doi: 10.1038/nmeth0909-636.
    1. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. Bacterial community variation in human body habitats across space and time. Science. 2009;14:1694–1697. doi: 10.1126/science.1177486.
    1. Holmes I, Harris K, Quince C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLos One. 2012;14:e30126. doi: 10.1371/journal.pone.0030126.
    1. La Rosa PS, Brooks JP, Deych E, Boone EL, Edwards DJ, Wang Q, Sodergen E, Weinstock GM, Shannon WD. Hypothesis Testing and Power Calculations for Taxonomic-based Human Microbiome Data. Plos One. 2012;14:e52078. doi: 10.1371/journal.pone.0052078.
    1. Wertz J, Isaacs-Cosgrove N, Holzman C, Marsh TL. Temporal Shifts in Microbial Communities in Nonpregnant African-American Women with and without Bacterial Vaginosis. Interdiscip Perspect Infect Dis. 2008;14:181253.
    1. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R, Gordon JI. A core gut microbiome in obese and lean twins. Nature. 2009;14:480–484. doi: 10.1038/nature07540.
    1. Yang F, Zeng X, Ning K, Liu KL, Lo CC, Wang W, Chen J, Wang D, Huang R, Chang X, Chain PS, Xie G, Ling J, Xu J. Saliva microbiomes distinguish caries-active from healthy human populations. ISME J. 2012;14:1–10. doi: 10.1038/ismej.2011.71.
    1. Wu GD, Lewis JD, Hoffmann C, Chen YY, Knight R, Bittinger K, Hwang J, Chen J, Berkowsky R, Nessel L, Li H, Bushman FD. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiol. 2010;14:206. doi: 10.1186/1471-2180-10-206.
    1. Fredricks DN, Fiedler TL, Marrazzo JM. Molecular identification of bacteria associated with bacterial vaginosis. N Engl J Med. 2005;14:1899–1911. doi: 10.1056/NEJMoa043802.
    1. Hendley JO, Hayden FG, Winther B. Weekly point prevalence of Streptococcus pneumoniae, Hemophilus influenzae and Moraxella catarrhalis in the upper airways of normal young children: effect of respiratory illness and season. APMIS. 2005;14:213–220. doi: 10.1111/j.1600-0463.2005.apm1130310.x.
    1. Becking LGMB. Geobiologie of inleiding tot de milieukunde. Den Haag: W.P. Van Stockum & Zoon; 1934.
    1. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods. 2012;14:811–814. doi: 10.1038/nmeth.2066.
    1. Smith T, Brown HR, Walker EL. The Fermentation Tube in the Study of Anaërobic Bacteria with Special reference to Gas Production and the use of Milk as a Culture medium. J Med Res. 1905;14:193–206.
    1. Tarr PI, Warner B, Sodergren E, Shannon W, Hamvas A, Magrini V, Weinstock G. The Neonatal Microbiome and Necrotizing Enterocolitis. Nature Precedings. 2010.
    1. Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G, Ciulla D, Tabbaa D, Highlander SK, Sodergren E, Methe B, DeSantis TZ, Petrosino JF, Knight R, Birren BW. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 2011;14:494–504. doi: 10.1101/gr.112730.110.
    1. Oksanen J, Blanchet FG, Kindt R, Legendre P, O'Hara RB, Simpson GL, Solymos P, Henry M, Stevens H, Wagner H. Vegan: Community Ecology Package. 2011.
    1. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies.
    1. 16S rRNA Trimmed Data Set.
    1. NIH Human Microbiome Project - Core Microbiome Sampling Protocol A (HMP-A).

Source: PubMed

3
Abonnieren