肠道微生物群落可塑性的大数据建模和深度理解

Human microbial communities are complex ecosystems, which were affected by various environmental factors such as diet, and have been in dynamic status. Such dynamics status has shown the plastic pattern: the community structures on taxonomical and functional levels could change, largely driven by environmental factors such as diet. And such changes might be reversed if diets were shifted back. However, what pattern does such dynamic change follow remain unclear. For a better understanding of microbial plasticity and the ecological patterns behind it, microbiome big-data modeling is essential...In this project, we aim to model the gut microbiome plasticity based on a massive amount of microbiome samples and datasets. Firstly, we will profile microbiome samples collected from different time-points. Secondly, we will analyze the pattern of such plasticity on macro-scale (whole community) and micro-scale (species or genes) levels. Thirdly, we will combine dietary information, time-series information as well as microbiome profiles, and establish plasticity models from different angles such as enterotypes and species networks. Finally, we will combine wet-lab and dry-lab experiments to verify a selected sets of gut microbial plasticity models. ..The conduction of this project would build a set of plastic models, that would help to provide a deeper understanding of the plastic pattern of gut microbial communities.

肠道微生物群落受到饮食等环境因素的影响，长期处在动态的变化过程中。这种动态变化具有高度的时空可塑性特征，即在菌群物种结构和功能组成等方面，容易受环境因素的影响而产生相应较有规律的变化。肠道菌群可塑性的研究是深入理解肠道菌群微生态和功能的重要方向，然而肠道菌群动态变化在时空范围内遵循何种规律目前还不是十分清楚，需要通过肠道微生物组大数据建模进行深入理解。.本项目计划基于大量在时空范围内相关的微生物组样本和测序数据，构建肠道菌群可塑性模型。首先，解析环境因素对肠道菌群的影响；其次，肠道菌群的宏观和微观可塑性分析；再次，从时空动态性、肠型差异性和物种网络等不同角度建立肠道菌群可塑性模型。本项目还计划通过数据分析和生物实验相结合的办法，验证一部分肠道菌群可塑性模型。.本项目将通过针对肠道菌群时空可塑性的大数据建模，在更深的层次理解肠道菌群受环境影响发生动态变化所遵循的规律。

肠道微生物群落受到饮食等环境因素的影响，长期处在动态的变化过程中。这种动态变化具有高度的时空可塑性特征，即在菌群物种结构和功能组成等方面，容易受环境因素的影响而产生相应较有规律的变化。肠道菌群可塑性的研究是深入理解肠道菌群微生态和功能的重要方向，然而肠道菌群动态变化在时空范围内遵循何种规律目前还不是十分清楚，需要通过肠道微生物组大数据建模进行深入理解。..本项目已完成的内容包括：基于大量在时空范围内相关的微生物组样本和测序数据，构建肠道菌群可塑性模型。首先，解析了环境因素对肠道菌群的影响；其次，完成了肠道菌群的宏观和微观可塑性分析；再次，从时空动态性、肠型差异性和物种网络等不同角度建立了肠道菌群可塑性模型。此外，本项目还通过数据分析和生物实验相结合的办法，验证了一部分肠道菌群可塑性模型。..本项目通过针对肠道菌群时空可塑性的大数据建模，在更深的层次加深理解了肠道菌群受环境影响发生动态变化所遵循的规律。已完成的工作发表于Gut （3篇），Microbiome，Gut Microbiome，Annals of the Rheumatic Diseases，PNAS，Genome Biology，Genome Medicine，Briefings in Bioinformatics等领域内顶尖杂志上。

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