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

摘要 现在人们认识到，具有重要生理意义的群落，如 作为肠道微生物群，可能会导致人类多种疾病，从免疫紊乱 从精神表型到肥胖。这是一个极其重要的医学问题，因为 饮食不平衡，塑造了细菌的身份和丰富程度，影响了 每天都有数以百万计的美国人。尽管潜在的复杂性，我们初步的 分析表明，肠道细菌的动态实际上可以用几个健壮的 统计关系。此外，表征微生物区系波动的关系是 与之前在多个其他生态和经济领域观察到的模式惊人地相似 系统。我们最近还开发了新颖的高通量实验和 描述微米级和微米级可能的代谢相互作用的计算方法 在不同的饮食中。我们建议使用计算和实验相结合的方法 全面研究肠道微生物区系的动态和稳定性：AIM1。 开发和实现一套互补的概率计算方法 微生物代谢表型的预测。AIM2.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菌补充剂在肠道中的丰度 小鼠模型。运用定量生态框架全面考察 微生物区系在不同饲料中的稳定性和动态。目标3.收集空间协同定位 关于微米级和多种饮食的信息。将协同本地化与 概率新陈代谢注释，以研究潜在的合作和 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- 通过在体外验证几十个高可信的相互作用来计算循环。

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.!