Interrogating function, regulation, and interactions in a clade of prevalent human gut microbes

项目摘要/摘要 与人类相关的微生物与免疫调节、保护等各种过程有因果关系 抗病原体和动脉粥样硬化。这些联系中的许多都是通过生化转化来调节的 饮食、药物或宿主化合物的。这使得微生物组成为一个有希望的治疗靶点，特别是作为 我们可以通过控制新陈代谢输入来潜在地影响下游过程。然而，为了 要有效干预，我们必须首先了解这些投入的确切变化是如何导致差异的 生长、基因表达和新陈代谢的调节。这很有挑战性，因为我们的微生物群不是 不仅在基因和生理上不同，而且与最常见的模式也非常不同 生物体，有许多未知功能的基因。 在接下来的五年里，我的研究小组将使用计算和实验相结合的策略 为了表征基因功能、代谢调节和微生物相互作用，在最普遍和 肠道细菌的丰富分支--类杆菌。具体地说，我们试图确定1)哪些拟杆菌 基因在不同的营养物质和压力下参与生长；2)类杆菌属基因是如何调控的， 以及这如何影响它们的代谢产出；以及3)类杆菌属如何与其他微生物相互作用 肠子里的居民。我们将通过从不同的组收集高通量的体外数据来实现这一点 微生物组分离和合成群落，开发更强大和特定的统计工具来 分析这些数据，并使用这些新数据和工具重新分析从 活体病例对照研究。我设想，这条调查路线将提供缺失的基本知识 这一微生物支系，最终将帮助我们解释微生物组和 支持制定更精准的干预措施。

PROJECT SUMMARY/ABSTRACT Human-associated microbes are causally linked to processes as diverse as immunomodulation, protection against pathogens, and atherosclerosis. Many of these links are mediated through biochemical transformations of dietary, drug, or host compounds. This makes the microbiome a promising therapeutic target, especially as we could potentially affect downstream processes by controlling metabolic inputs. However, in order to effectively intervene, we must first understand how exactly changes in these inputs lead to differential regulation of growth, gene expression, and metabolism. This is challenging because our microbiomes are not only genetically and physiologically diverse, but are also highly diverged from the most common model organisms, with many genes of unknown function. Over the next five years, my research group will use a combined computational and experimental strategy to characterize gene function, metabolic regulation, and microbial interactions in one of the most prevalent and abundant clades of gut bacteria, the Bacteroidales. Specifically, we seek to determine 1) which Bacteroidales genes are involved in growth on different nutrients and stressors; 2) how Bacteroidales genes are regulated, and how this affects their metabolic outputs; and 3) how Bacteroidales interact with the other microbial inhabitants of the gut. We will accomplish this by gathering high-throughput in vitro data from diverse sets of microbiome isolates and synthetic communities, developing more powerful and specific statistical tools to analyze these data, and using these new data and tools to re-analyze metagenomics data gathered from in vivo case-control studies. I envision that this line of inquiry will provide missing fundamental knowledge about this clade of microbes, which will ultimately help us interpret case-control studies of the microbiome and support the development of more precise interventions.