BioMiCo: a supervised Bayesian model for inference of microbial community structure
Tóm tắt
Microbiome samples often represent mixtures of communities, where each community is composed of overlapping assemblages of species. Such mixtures are complex, the number of species is huge and abundance information for many species is often sparse. Classical methods have a limited value for identifying complex features within such data. Here, we describe a novel hierarchical model for Bayesian inference of microbial communities (BioMiCo). The model takes abundance data derived from environmental DNA, and models the composition of each sample by a two-level hierarchy of mixture distributions constrained by Dirichlet priors. BioMiCo is supervised, using known features for samples and appropriate prior constraints to overcome the challenges posed by many variables, sparse data, and large numbers of rare species. The model is trained on a portion of the data, where it learns how assemblages of species are mixed to form communities and how assemblages are related to the known features of each sample. Training yields a model that can predict the features of new samples. We used BioMiCo to build models for three serially sampled datasets and tested their predictive accuracy across different time points. The first model was trained to predict both body site (hand, mouth, and gut) and individual human host. It was able to reliably distinguish these features across different time points. The second was trained on vaginal microbiomes to predict both the Nugent score and individual human host. We found that women having normal and elevated Nugent scores had distinct microbiome structures that persisted over time, with additional structure within women having elevated scores. The third was trained for the purpose of assessing seasonal transitions in a coastal bacterial community. Application of this model to a high-resolution time series permitted us to track the rate and time of community succession and accurately predict known ecosystem-level events. BioMiCo provides a framework for learning the structure of microbial communities and for making predictions based on microbial assemblages. By training on carefully chosen features (abiotic or biotic), BioMiCo can be used to understand and predict transitions between complex communities composed of hundreds of microbial species.
Tài liệu tham khảo
Caporaso JG, Lauber CL, Costello EK, Berg-Lyons D, Gonzalez A, Stombaugh J, et al. Moving pictures of the human microbiome. Genome Biol. 2011;12:R50.
Gilbert JA, Steele JA, Caporaso JG, Steinbrück L, Reeder J, Temperton B, et al. Defining seasonal marine microbial community dynamics. ISME J. 2012;6:298–308.
Boon E, Meehan CJ, Whidden C, Wong DH, Langille MG, Beiko RG. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiol Rev. 2014;38:90–118.
Faith JJ, Guruge JK, Charbonneau M, Subramanian S, Seedorf H, Goodman AL, et al. The long-term stability of the human gut microbiota. Science. 2013;341:1237439.
Britton RA, Young VB. Role of the intestinal microbiota in resistance to colonization by Clostridium difficile. Gastroenterology. 2014;146:1547–53.
Halm H, Lam P, Ferdelman TG, Lavik G, Dittmar T, LaRoche J, et al. Heterotrophic organisms dominate nitrogen fixation in the South Pacific Gyre. ISME J. 2012;6:1238–49.
Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe. 2014;15:382–92.
Korpela K, Flint HJ, Johnstone AM, Lappi J, Poutanen K, Dewulf E, et al. Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. PLoS One. 2014;9:e90702.
Knights D, Kuczynski J, Charlson ES, Zaneveld J, Mozer MC, Collman RG, et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods. 2011;8:761–3.
Zarraonaindia I, Smith DP, Gilbert JA. Beyond the genome: community-level analysis of the microbial world. Biol Philos. 2013;28:261–82.
Hastie T, Tibshiriani R, Freidman J. The elements of statistical learning. Data mining, inference, and prediction. Springer series in statistics: Springer, New York; 2001.
Knights D, Costello EK, Knight R. Supervised classification of human microbiota. FEMS Microbiol Rev. 2011;35:343–59.
Holmes I, Harris K, Quince C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One. 2012;7:e30126.
Blei DM, McAuliffe JD. Supervised topic models. Adv Neural Inf Process Syst. 2007;21:1–8.
Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, et al. The metacommunity concept: a framework for multi- scale community ecology. Ecol Lett. 2004;7:601–13.
Burke C, Steinberg P, Rusch D, Kjelleberg S, Thomas T. Bacterial community assembly based on functional genes rather than species. Proc Natl Acad Sci. 2011;108:14288–93.
Patel PV, Gianoulis TA, Bjornson RD, Yip KY, Engelman DM, Gerstein MB. Analysis of membrane proteins in metagenomics: networks of correlated environmental features and protein families. Genome Res. 2010;20:960–71.
Koropatkin NM, Cameron EA, Martens EC. How glycan metabolism shapes the human gut microbiota. Nat Rev Microbiol. 2012;10:323–35.
Giovannoni SJ, Vergin KL. Seasonality in ocean microbial communities. Science. 2012;335:671–6.
Liu JS. The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem. J Am Stat Assoc. 1994;89:958–66.
Martínez I, Muller CE, Walter J. Long-term temporal analysis of the human fecal microbiota revealed a stable core of dominant bacterial species. PLoS One. 2013;8:e69621.
Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, et al. Genomic variation landscape of the human gut microbiome. Nature. 2013;493:45–50.
Nardis C, Mosca L, Mastromarino P. Vaginal microbiota and viral sexually transmitted diseases. Ann Ig. 2013;25:443–56.
Donati L, Di Vico A, Nucci M, Quagliozzi L, Spagnuolo T, Labianca A, et al. Vaginal microbial flora and outcome of pregnancy. Arch Gynecol Obstet. 2010;281:589–600.
Nugent RP, Krohn MA, Hillier SL. Reliability of diagnosing bacterial vaginosis is improved by a standardized method of gram stain interpretation. J Clin Microbiol. 1991;29:297–301.
Gajer P, Brotman RM, Bai G, Sakamoto J, Schütte UM, Zhong X, et al. Temporal dynamics of the human vaginal microbiota. Sci Transl Med. 2012;4:132ra52.
Ravel J, Brotman RM, Gajer P, Ma B, Nandy M, Fadrosh DW, et al. Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis. Microbiome. 2013;1:29.
Legendre P, Legendre L. Numerical Ecology, 3rd English ed. Amsterdam: Elsevier Science BV; 2012.
Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SS, McCulle SL, et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci. 2011;108 Suppl 1:4680–7.
Sogin ML, Morrison HG, Huber JA, Mark Welch D, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci. 2006;103:12115–20.
Zinger L, Amaral-Zettler LA, Fuhrman JA, Horner-Devine MC, Huse SM, Mark Welch DB, et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS One. 2011;6:e24570.
Fuhrman J, Steele J. Community structure of marine bacterioplankton: patterns, networks, and relationships to function. Aquat Microb Ecol. 2008;53:69–81.
Fuhrman J, Hewson I, Schwalbach M, Steele J, Brown M, Naeem S. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc Natl Acad Sci. 2006;103:13104–9.
Steele JA, Countway PD, Xia L, Vigil PD, Beman JM, Kim DY, et al. Marine bacterial, archaeal and protistan association networks reveal ecological linkages. ISME J. 2011;5:1414–25.
El-Swais H, Dunn KA, Bielawski JP, Li WKW, Walsh DA: Seasonal assemblages and short-lived blooms in coastal north-west Atlantic Ocean bacterioplankton. Environ microbiol. doi: 10.1111/1462-2920.12629 (Epub ahead of print).
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.
Tada Y, Taniguchi A, Nagao I, Miki T, Uematsu M, Tsuda A, et al. Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the western North Pacific Ocean. Appl Environ Microbiol. 2011;77:4055–65.
Li WKW, Dickie P. Monitoring phytoplankton, bacterioplankton, and virioplankton in a coastal inlet (Bedford Basin) by flow cytometry. Cytometry. 2001;44:236–46.
Dupont CL, Rusch DB, Yooseph S, Lombardo MJ, Richter RA, Valas R, et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 2012;6:1186–99.
Rappé MS, Connon SA, Vergin KL, Giovannoni SJ. Cultivation of the ubiquitous SAR 11 marine bacterioplankton clade. Nature. 2002;418:630–3.
Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
Shafiei M, Dunn KA, Chipman H, Gu H, Bielawski JP. BiomeNet: A Bayesian model for inference of metabolic divergence among microbial communities. PLoS Comput Biol. 2014;10:e1003918.