Ecological dynamics and metabolic interactions in thegut microbiome across space and time

肠道微生物组跨空间和时间的生态动力学和代谢相互作用

• 批准号: 10392399
• 负责人: Dennis Vitkup
• 金额: $ 66.29万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10392399
• 关键词: Affect; American; Bacteria; Collaborations; Communities; Complex; Data; Diet; Dietary Intervention; Disease; Economics; Grant; Health; High-Throughput Nucleotide Sequencing; Immune; Immune System Diseases; In Vitro; Joints; Manuscripts; Medical; Metabolic; Metabolism; Microbe; Modeling; Monitor; Nature; Obesity; Pattern; Phenotype; Physiological; Recording of previous events; Regimen; Research; Resolution; Shapes; System; Testing; Time; Universities; Work; bacterial community; computerized tools; dietary; dietary supplements; dysbiosis; feeding; gut bacteria; gut microbiome; gut microbiota; human disease; innovation; member; metabolic phenotype; microbial; microbiome; microbiota; mouse model; multimodality; next generation; novel; prebiotics; user-friendly; web interface

SUMMARY It is now recognized that instability and dysbiosis of physiologically important communities, such as the gut microbiome, may contribute to a wide range of human disease, from immune disorders to psychiatric phenotypes to obesity. This is a problem of preeminent medical importance as dietary imbalances, shaping the identity and abundance of resident bacteria, affect the health of many millions of Americans on a daily basis. Despite the underlying complexity, our preliminary analyses revealed that the dynamics of gut bacteria can be in fact described by several robust statistical relationships. Moreover, the relationships characterizing microbiota fluctuations are strikingly similar to patterns previously observed across multiple other ecological and economic systems. We have also recently developed novel high-throughput experimental and computational approaches to characterize likely metabolic interactions at the micron scale and across different diets. We propose to use an integrated computational and experimental approach to comprehensively investigate diet-dependent dynamics and stability of gut microbiota: Aim1. Develop and implement a set of complementary computational approaches for probabilistic prediction of microbial metabolic phenotypes. Aim2. Collect temporal data on absolute bacterial abundances in the gut across several health-related diets and common prebiotic supplements in mice models. Apply a quantitative ecological framework to comprehensively investigate microbiota stability and dynamics on different diets. Aim 3. Collect spatial co-localization information on the micron scale and across multiple diets. Combine co-localization with probabilistic metabolic annotations to investigate the nature of potential cooperative and competitive metabolic interactions between microbial species in the gut. Investigate the diet- dependent stability of bacterial interactions in space and time. Close the experimental- computational loop by validating several dozens of high-confident interactions in vitro.!

概括 现在人们认识到，具有重要生理意义的群落的不稳定性和生态失调，例如 作为肠道微生物组，可能会导致多种人类疾病，例如免疫紊乱 从精神表型到肥胖。这是一个具有极其重要医学意义的问题 饮食不平衡，影响常驻细菌的特性和丰度，影响健康 每天有数百万美国人。尽管潜在的复杂性，我们的初步 分析表明，肠道细菌的动态实际上可以用几种可靠的方法来描述 统计关系。此外，表征微生物群波动的关系是 与之前在多个其他生态和经济领域观察到的模式惊人相似 系统。我们最近还开发了新颖的高通量实验和 计算方法来表征微米尺度上可能的代谢相互作用 跨越不同的饮食。我们建议使用综合计算和实验方法 全面研究肠道微生物群的饮食依赖性动态和稳定性：Aim1。 开发并实施一套互补的概率计算方法 微生物代谢表型的预测。目标2。收集绝对细菌的时间数据 几种与健康相关的饮食和常见的益生元补充剂在肠道中的丰度 小鼠模型。应用定量生态框架进行全面调查 不同饮食中微生物群的稳定性和动态。目标 3. 收集空间共定位 微米级和多种饮食的信息。将共定位与 概率代谢注释来研究潜在合作和 肠道微生物物种之间的竞争性代谢相互作用。调查饮食—— 细菌相互作用在空间和时间上的依赖稳定性。关闭实验- 通过在体外验证数十个高度可信的相互作用来进行计算循环。！