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Noecker C, Eng A, Srinivasan S, et al. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation. mSystems. 2016;1(1)doi: 10.1128/mSystems.00013-15.
Noecker, C., Eng, A., Srinivasan, S., Theriot, C. M., Young, V. B., Jansson, J. K., Fredricks, D. N., & Borenstein, E. (2016). Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation. mSystems, 1(1), . https://doi.org/10.1128/mSystems.00013-15
Noecker, Cecilia, et al. "Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation." mSystems vol. 1,1 (2016). doi: https://doi.org/10.1128/mSystems.00013-15
Noecker C, Eng A, Srinivasan S, Theriot CM, Young VB, Jansson JK, Fredricks DN, Borenstein E. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation. mSystems. 2016 Jan-Feb;1(1). doi: 10.1128/mSystems.00013-15. Epub 2016 Jan 19. PMID: 27239563; PMCID: PMC4883586.
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mSystems. 2016 Jan-Feb;1(1). doi: 10.1128/mSystems.00013-15. Epub 2016 Jan 19.

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation.

mSystems

Cecilia Noecker, Alexander Eng, Sujatha Srinivasan, Casey M Theriot, Vincent B Young, Janet K Jansson, David N Fredricks, Elhanan Borenstein

Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
  2. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
  3. Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA.
  4. Department of Internal Medicine, Division of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.
  5. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA.
  6. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; Division of Allergy and Infectious Diseases, University of Washington, Seattle, Washington, USA; Department of Microbiology, University of Washington, Seattle, Washington, USA.
  7. Department of Genome Sciences, University of Washington, Seattle, Washington, USA; Department of Computer Science and Engineering, University of Washington, Seattle, Washington, USA; Santa Fe Institute, Santa Fe, New Mexico, USA.

PMID: 27239563 PMCID: PMC4883586 DOI: 10.1128/mSystems.00013-15

Abstract

Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease.

IMPORTANCE: Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.

Keywords: community composition; metabolic modeling; metabolomics; microbiome; multi-omic

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