The relationship between host diet, the gut microbiota, and host transcription

宿主饮食、肠道微生物群和宿主转录之间的关系

• 批准号: 8605657
• 负责人: Jeremiah James Faith
• 金额: $ 38.14万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8605657
• 关键词: Adult; Affect; Animals; Bacteria; Biological Models; Calculi; Cereals; Chemicals; Clinical Research; Colon; Communities; Complex; Computer Simulation; Computing Methodologies; Confounding Factors (Epidemiology); DNA Sequence; Data; Data Set; Diet; Digestive Physiology; Donor person; Eating; Ecosystem; Environmental Risk Factor; Escherichia coli; Food; Fruit; Gene Components; Genes; Genetic Transcription; Genotype; Germ-Free; Glean; Gnotobiotic; Goals; Grant; Health; Human; Individual; Intestines; Life; Liver; Meat; Metabolic; Microbe; Modeling; Mus; Nutrient; Organ; Organism; Persons; Phylogeny; Physiology; Play; Relative (related person); Role; Series; Shapes; Small Intestines; Statistical Models; Study models; System; Technology; Time; Variant; Vegetables; Veins; base; design; gut microbiota; insight; interest; microbial; microbial community; microbiome; mouse model; operation; postnatal; predictive modeling; public health relevance; research study; response; success; tool

DESCRIPTION (provided by applicant): The set of microbes in our gut (i.e., our microbiota) as well as the food we ingest, both represent environmental perturbations to our physiology. The abundance of each species in our gut is dramatically altered as we consume different foods, while the chemical composition of these of foods is likewise uniquely modified in our intestines by the specific metabolic capabilities of the group of microbial organisms that resides there. Thus our diets, our microbiota, and our physiology represent a unique and complex system of interacting genes, organisms, and nutrients. Computational modeling provides a series of tools to understand and control complex systems; it is in this vein that our lab continues to develop statistical models to understand the relationship between our diet, our microbiota, and our health. Towards these long-term goals, this application has two specific aims focused on predictive models involving six defined human-derived microbiotas isolated from different individuals. Aim 1 - Building on our previous success in using gnotobiotic mice harboring a defined community of 10 human-gut bacteria to predict the abundance of each species in response to specific ingredient perturbations to the host diet, we aim to understand the quantitative relationship between our diets and our gut microbes by modeling the responses of six defined microbiota, isolated from different human donors and transplanted into gnotobiotic mice, to diet perturbations of eight commonly eaten human foods. These models will provide insight into the interpersonal variations governing the rules between diet and the microbiota (e.g., does an Escherichia coli species isolated from two different donors response to similar ways to the same food). In addition, Aim 1 will provide valuable preliminary data about the relationship between diet and the microbiota that will be used to optimize the design of Aim 2 where we will define the interrelationship between diet, the gut microbiota, and host transcriptional variation. By systematically perturbing the host diet in the context of germ-free animals and animals harboring the defined microbiotas from Aim 1, we plan to model and quantify the transcriptional changes in the host small intestine, colon, and liver that are influenced by specific diet ingredients, the microbiota, or a combination of diet and the microbiota.

描述(申请人提供)：我们肠道中的一组微生物(即我们的微生物区系)以及我们摄入的食物，都代表着对我们生理的环境干扰。当我们摄入不同的食物时，我们肠道中每种生物的丰度都会发生戏剧性的变化，而这些食物的化学成分也同样在我们的肠道中被居住在那里的微生物群体的特定代谢能力所独特地改变。因此，我们的饮食、微生物区系和生理系统代表了一个由基因、有机体和营养物质相互作用的独特而复杂的系统。计算建模提供了一系列工具来理解和控制复杂的系统；正是在这种背景下，我们的实验室继续开发统计模型，以了解我们的饮食、微生物群和我们的健康之间的关系。对于这些长期目标，这项申请有两个具体目标，重点是预测模型，涉及从不同个体分离的六个已定义的人类来源微生物群。目标1-在我们之前成功地使用携带10个人类肠道细菌的确定群落的诺生菌小鼠来预测每个物种的丰度，以响应宿主饮食的特定成分扰动，我们的目标是通过模拟从不同人类捐赠者分离并移植到诺生菌鼠体内的6个已定义微生物区系对8种常见人类食物的饮食扰动的反应，了解我们的饮食和肠道微生物之间的定量关系。这些模型将提供对支配饮食和微生物区系之间规则的人际差异的洞察(例如，从两个不同捐赠者分离的大肠杆菌物种是否对相同的食物做出类似的反应)。此外，目标1将提供关于饮食和微生物区系之间关系的有价值的初步数据，这些数据将用于优化目标2的设计，在那里我们将定义饮食、肠道微生物区系和宿主转录变异之间的相互关系。通过在无菌动物和携带目标1中定义的微生物群的动物的背景下系统地扰动宿主饮食，我们计划对宿主小肠、结肠和肝脏中受特定饮食成分、微生物区系或饮食和微生物区系组合影响的转录变化进行建模和量化。