Metabolic model-based integrative study of the relationship between the gut microbiome, metabolome, and diet

基于代谢模型的肠道微生物组、代谢组和饮食之间关系的综合研究

• 批准号: 9365005
• 负责人: Elhanan Borenstein
• 金额: $ 29.41万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9365005
• 关键词: Address; Autistic Disorder; Biochemical Pathway; Biological; Biological Assay; Communities; Complex; Coupled; Data; Data Set; Diet; Dietary Fiber; Dietary Intervention; Dietary Practices; Disease; Ecology; Ecosystem; Energy Metabolism; Energy-Generating Resources; Fatty acid glycerol esters; Fermentation; Future; Genes; Genomics; Growth; Health; Human; Immunity; Individual; Insulin Resistance; Intake; Internet; Knowledge; Link; Measures; Metabolic; Metabolic syndrome; Metabolism; Metagenomics; Methods; Microbe; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Nutritional; Obesity; Play; Polysaccharides; Protocols documentation; Research; Sampling; Shapes; System; Taxonomy; Time; Variant; Volatile Fatty Acids; autistic behaviour; base; cardiovascular risk factor; computer framework; gastrointestinal; gut microbiome; high throughput technology; improved; member; metabolome; metabolomics; microbial; microbiome; model development; network models; novel; response; web-based tool

Project Summary/Abstract The human gut microbiome is a tremendously complex ecosystem with a marked impact on our health. The specific mix of species in this ecosystem varies substantially across individuals, over time, and in association with disease. These gut-dwelling species are endowed with diverse metabolic capacities and continuously break down and synthesize numerous metabolites. These capacities, however, and consequently the synthesis of metabolites in the gut, depend on the composition of species in the microbiome and on the web of metabolic interactions between microbiome members. Another important factor that modulates metabolism in the gut is the host diet. Indeed, diet has been shown to play a key role in shaping both the microbiome’s composition and the abundance of various gut metabolites. Yet, the relationship between the composition of the microbiome, the gut metabolites, and the host diet is nontrivial and extremely complex. To address this challenge, numerous studies are now taking a multi-omic approach, harnessing the progress in high-throughput technologies to profile different facets (such as species, gene, and metabolite compositions) of each microbiome sample and to identify associations between these facets. This approach, however, ignores the wealth of knowledge about the mechanisms that link microbiome ecology and metabolism and fails to model such mechanisms, and therefore the findings obtained often lack clear mechanistic interpretations. Our proposed research aims to introduce two novel computational frameworks, integrating multi-omic data with various metabolic modeling approaches, to model the link between microbiome composition, gut metabolites, and diet and to provide a more mechanistic, comprehensive understanding of these relationships. Our first framework will focus on the relationship between the composition of the microbiome and its impact on gut metabolites. It will use taxonomic, genomic, and enzymatic data to model community-wide metabolism and to estimate the community’s potential to synthesize/degrade each metabolite. We will analyze several large-scale datasets pairing microbiome and metabolomic assays to examine how well communities’ estimated metabolic potentials explain observed variation in the gut metabolome. We will further develop methods for identifying species that drive this variation and universal mechanisms governing this relationship. Our second framework will focus on the impact of diet and dietary interventions on microbiome composition. This framework will use taxonomic, genomic, and nutritional data to construct models of community members, to convert dietary information to metabolite intake, and to utilize a novel multi-species dynamic metabolic modeling approach, aiming to predict the growth and metabolism of community members over time on a given diet. We will apply this framework to predict diet-induced microbiome compositions using data from several studies that assayed the microbiome response to well-defined diets. We will further use this framework to explore the metabolic mechanisms that underlie this complex diet-microbiome relationship.

项目总结/摘要 人类肠道微生物组是一个非常复杂的生态系统，对我们的健康有着显著的影响。的 在这个生态系统中，物种的特定混合在个体之间、在时间上和在联合体中有很大的差异 疾病。这些肠道栖息物种被赋予不同的代谢能力， 分解并合成大量的代谢物。然而，这些能力， 肠道中代谢物的合成，取决于微生物组中物种的组成和 微生物组成员之间的代谢相互作用。另一个调节新陈代谢的重要因素是 肠道是宿主的饮食。事实上，饮食已被证明在塑造微生物组和 各种肠道代谢物的组成和丰度。然而，成分之间的关系 微生物组、肠道代谢物和宿主饮食的关系是非常复杂的。 为了应对这一挑战，许多研究正在采取多组学方法，利用这一进展 在高通量技术中分析不同方面（如物种，基因和代谢物组成） 每个微生物组样本的数据，并确定这些方面之间的关联。然而，这种方法， 忽视了关于微生物组生态学和代谢机制的丰富知识， 因此，所获得的研究结果往往缺乏明确的机制解释。 我们提出的研究旨在引入两个新的计算框架，整合多组学 数据与各种代谢建模方法，以模拟微生物组组成，肠道 代谢物和饮食，并提供这些关系的更机械，全面的理解。 我们的第一个框架将侧重于微生物组的组成与其 对肠道代谢物的影响它将使用分类学，基因组学和酶数据来模拟整个社区 代谢和估计社区的潜力，合成/降解每种代谢物。我们将分析 几个大规模的数据集配对微生物组和代谢组学测定，以检查社区的 估计的代谢潜力解释了观察到的肠道代谢组的变化。我们将进一步发展 确定驱动这种变异的物种的方法以及管理这种关系的普遍机制。 我们的第二个框架将侧重于饮食和饮食干预对微生物组的影响 混合物.该框架将使用分类学、基因组学和营养学数据来构建 社区成员，将饮食信息转换为代谢物摄入量，并利用新的多物种 动态代谢建模方法，旨在预测群落成员的生长和代谢 随着时间的推移，在给定的饮食。我们将应用这个框架来预测饮食诱导的微生物组组成， 来自几项研究的数据，这些研究分析了微生物对明确定义的饮食的反应。我们将进一步利用这一 该框架旨在探索这种复杂的饮食-微生物组关系背后的代谢机制。