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
Source: PubMed


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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Available from: Peter J Turnbaugh
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    • "Microbial dysbiosis has been implicated in many human diseases including diabetes, autism, and obesity. A particularly strong relationship between disease and microbiota exists for Crohn disease (CD) and ulcerative colitis, the two major subtypes of inflammatory bowel disease (IBD) (Mazmanian et al., 2008;Greenblum et al., 2012;Manichanh et al., 2012), characterized by chronic inflammation of the gastrointestinal tract, which causes significant morbidity and can lead to colorectal cancer or death (Card et al., 2003). With more than 1.4 million people affected in the United States (CCFA, 2015), IBD poses an urgent challenge to understand the link between microbiota and human health. "
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    ABSTRACT: The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environments are highly variable. We have developed a set of techniques based on population dynamics and information theory to address this challenge. These methods identify additional bacterial taxa associated with pediatric Crohn disease and can detect significant changes in microbial communities with fewer samples than previous statistical approaches required. We have also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples, and we found that the ecological niche of a microbe predicts its role in Crohn disease. Bacteria typically residing in the lumen of healthy individuals decrease in disease, whereas bacteria typically residing on the mucosa of healthy individuals increase in disease. Our results also show that the associations with Crohn disease are evolutionarily conserved and provide a mutual information-based method to depict dysbiosis.
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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.
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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.
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