Greenblum S, Turnbaugh PJ, Borenstein E.. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci USA 109: 594-599

Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 12/2011; 109(2):594-9. DOI: 10.1073/pnas.1116053109
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The human microbiome plays a key role in a wide range of host-related processes and has a profound effect on human health. Comparative analyses of the human microbiome have revealed substantial variation in species and gene composition associated with a variety of disease states but may fall short of providing a comprehensive understanding of the impact of this variation on the community and on the host. Here, we introduce a metagenomic systems biology computational framework, integrating metagenomic data with an in silico systems-level analysis of metabolic networks. Focusing on the gut microbiome, we analyze fecal metagenomic data from 124 unrelated individuals, as well as six monozygotic twin pairs and their mothers, and generate community-level metabolic networks of the microbiome. Placing variations in gene abundance in the context of these networks, we identify both gene-level and network-level topological differences associated with obesity and inflammatory bowel disease (IBD). We show that genes associated with either of these host states tend to be located at the periphery of the metabolic network and are enriched for topologically derived metabolic "inputs." These findings may indicate that lean and obese microbiomes differ primarily in their interface with the host and in the way they interact with host metabolism. We further demonstrate that obese microbiomes are less modular, a hallmark of adaptation to low-diversity environments. We additionally link these topological variations to community species composition. The system-level approach presented here lays the foundation for a unique framework for studying the human microbiome, its organization, and its impact on human health.

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    • "16S rRNA gene sequencing is commonly used for the identification , classification and quantification of microbes in samples from different environmental regions (Statnikov et al., 2013; Cox et al., 2013). 16S rRNA gene marker is used in a vast variety of studies like identifying the diverse patterns of the microbes in human colorectal cancer (Geng et al., 2013), identifying changes in the gut microbes causing diseases like obesity and chronic inflammation (Korecka and Arulampalam, 2012), studying changes in the composition of microbes in obesity and inflammatory bowel disease and analysis of human oral microbiome (Greenblum et al., 2012). Operational taxonomic units (OTUs) are identified through clustering from 16S rRNA sequence data and then sequences are classified. "
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    ABSTRACT: Human body is the home for large number of microbes. The complexity of enterotype depends on the body site. Microbial communities in various samples from different regions are being classified on the basis of 16S rRNA gene sequences. With the improvement in sequencing technologies various computational methods have been used for the analysis of microbiome data. Despite of several available machine learning techniques there is no single platform available which could provide several techniques for clustering, multiclass classification, comparative analysis and the most significantly the identification of the subgroups present within larger groups of human microbial communities. We present a tool named MCaVoH for this purpose which performs clustering and classification of 16S rRNA sequence data and highlight various groups. Our tool has an added facility of biclustering which produces local group of communities present within larger groups (clusters). The core objective of our work was to identify the interaction between various bacterial species along with monitoring the composition and variations in microbial communities. MCaVoH also evaluates the performance and efficiency of different techniques using comparative analysis. The results are visualized through different plots and graphs. We implemented our tool in MATLAB. We tested our tool on several real and simulated 16S rRNA data sets and it outperforms several existing methods. Our tool provides a single platform for using multiple clustering, classification algorithms, local community identification along with their comparison which has not been done so far. Tool is available at Copyright © 2015. Published by Elsevier B.V.
    Journal of microbiological methods 07/2015; 117. DOI:10.1016/j.mimet.2015.07.015 · 2.03 Impact Factor
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    • "For example, fecal metagenomic data obtained from WGS of 124 unrelated individuals along with six monozygotic twin pairs and their mothers were analyzed by the construction of community level metabolic networks of the microbiome. It was observed that gene-level and network-level topological differences are strongly associated with obesity and IBD [79]. WGS of 252 fecal metagenomic samples in another study showed huge variations at the metagenomic level, in which authors identified 107,991 short insertions/deletions, 10.3 million single nucleotide polymorphisms (SNPs) and 1051 structural variants. "
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    ABSTRACT: Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational taxonomic unit (OTU) levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ultimately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interaction among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe-microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe-microbe interaction networks. Copyright © 2015. Production and hosting by Elsevier Ltd.
    07/2015; 13(3). DOI:10.1016/j.gpb.2015.02.005
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    • "e gut microbiota as a whole ( Figure 2 ; Taffs et al . , 2009 ; Abubucker et al . , 2012 ; Thiele et al . , 2013a ) . Generally , the community - level metabolic network can be reconstructed directly from shotgun metagenomics data by ignoring cell boundaries and the exchange of metabolites between species ( Borenstein , 2012 ) . In an early work ( Greenblum et al . , 2012 ) , by integrating such microbiome - level metabolic network with corresponding gene abundances , topological differences in both gene - level and network - level were identified as being associated with obesity and IBD . Ultimately , such community - based approaches ignore the boundaries between species and compartmentalization of var"
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    ABSTRACT: Changes in the human gut microbiome are associated with altered human metabolism and health, yet the mechanisms of interactions between microbial species and human metabolism have not been clearly elucidated. Next-generation sequencing has revolutionized the human gut microbiome research, but most current applications concentrate on studying the microbial diversity of communities and have at best provided associations between specific gut bacteria and human health. However, little is known about the inner metabolic mechanisms in the gut ecosystem. Here we review recent progress in modeling the metabolic interactions of gut microbiome, with special focus on the utilization of metabolic modeling to infer host-microbe interactions and microbial species interactions. The systematic modeling of metabolic interactions could provide a predictive understanding of gut microbiome, and pave the way to synthetic microbiota design and personalized-microbiome medicine and healthcare. Finally, we discuss the integration of metabolic modeling and gut microbiome engineering, which offer a new way to explore metabolic interactions across members of the gut microbiota.
    Frontiers in Genetics 06/2015; 6:219. DOI:10.3389/fgene.2015.00219
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