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

总结 现在人们认识到，生理上重要的社区，如 作为肠道微生物组，可能导致广泛的人类疾病，从免疫紊乱 到精神病表型再到肥胖症这是一个非常重要的医学问题， 饮食不平衡，塑造居民细菌的身份和丰度，影响健康， 每天都有数百万美国人。尽管潜在的复杂性，我们的初步 分析表明，肠道细菌的动力学实际上可以用几个强大的 统计关系。此外，表征微生物群波动的关系是 与以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的高通量实验和 在微米尺度上表征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一个综合的计算和实验的方法 全面研究饮食依赖的动力学和肠道微生物群的稳定性：Aim1. 开发和实施一套互补的计算方法， 微生物代谢表型的预测。目标2。收集绝对细菌计数的时间数据 在几种与健康相关的饮食和常见的益生元补充剂中， 小鼠模型。应用定量生态学框架， 微生物群的稳定性和动力学的不同饮食。目标3。收集空间共定位 在微米尺度上和多种饮食中的信息。将联合收割机协同定位与 概率代谢注释，以调查潜在的合作和 肠道中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- 通过验证几十个高置信度的体外相互作用来实现计算循环。

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.!