Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease
Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease
复制标题
DOI：
10.1073/pnas.1116053109
复制
发表时间：
2012-01-10
影响因子：
11.1
通讯作者：
Borenstein, Elhanan
中科院分区：
文献类型：
--
作者：
Greenblum, Sharon;Turnbaugh, Peter J.;Borenstein, Elhanan
人体微生物组在广泛的宿主相关过程中起着关键作用，对人类健康有着深远的影响。对人类微生物组的比较分析揭示了与各种疾病状态相关的物种和基因组成的实质性变化，但可能无法全面了解这种变化对社区和宿主的影响。在这里，我们介绍了一个宏基因组系统生物学计算框架，将宏基因组数据与代谢网络的计算机系统级分析相结合。专注于肠道微生物组，我们分析了来自124个无关个体的粪便宏基因组数据，以及6个同卵双胞胎及其母亲，并生成了微生物组的社区级代谢网络。将基因丰度的变化置于这些网络的背景下，我们确定了与肥胖和炎症性肠病（IBD）相关的基因水平和网络水平的拓扑差异。我们发现，与这些宿主状态中的任何一个相关的基因往往位于代谢网络的外围，并且富含拓扑衍生的代谢“输入”。“这些发现可能表明，瘦和肥胖的微生物组主要在它们与宿主的界面以及它们与宿主代谢相互作用的方式上有所不同。我们进一步证明，肥胖微生物组的模块化程度较低，这是适应低多样性环境的标志。我们还将这些拓扑变化与群落物种组成联系起来。本文提出的系统级方法为研究人类微生物组、其组织及其对人类健康的影响奠定了基础。
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.