Molecular mechanisms behind microbiota regulation of host amino acid and glucose homeostasis

2型糖尿病(T2D)的患病率随着年龄的增长而增加，影响美国超过13.2%的人 到2020年的人口数量。研究表明，微生物区系组成及其代谢基因是 正常人和T2D患者之间的差异。然而，要弄清它们之间的因果关系是具有挑战性的。 由于缺乏有效的操作系统，对宿主葡萄糖稳态和T2D的影响 它们在体内的水平。最近，我们能够切换微生物区系氨基酸代谢途径 在体内发现了一些影响宿主葡萄糖动态平衡的途径。我们假设 肠道细菌影响宿主氨基酸库，这将进一步调节宿主葡萄糖 动态平衡。在这里，我们将鉴定活跃发酵日粮氨基酸和 评估它们如何影响患病小鼠模型的宿主葡萄糖动态平衡。我们会学到 更多关于微生物区系介导的AA代谢在T2D进展中的作用。我们的工作将 还为生成合成和工程肠道微生物群落奠定了基础 明确了预防和治疗T2D的代谢功能。有三个趋同的动机： 首先，许多微生物区系分子特征在健康和T2D患者之间是不同的，并且 我们需要功能研究来将它们与T2D因果联系起来。我们将结合生物信息学， 代谢组学、细菌遗传学和调节微生物群的诺生菌小鼠模型 寄主体内的代谢途径。这种方法将促进对T2D的系统识别- 导致微生物区系基因和途径；我们的发现也将促进新的治疗策略 通过瞄准这些以前未知的微生物代谢途径。 其次，肠道微生物区系代谢显著影响宿主代谢健康。然而， 微生物区系调控宿主氨基酸动态平衡的分子机制 下游生物在很大程度上仍未被探索。我们相信，这种方法具有巨大的潜力： 它可用于调节宿主体内不同部位的微生物代谢功能。 微生物相互作用。我们的发现也将打开审问的大门--最终 控制-肠道细菌对宿主生物学最具体的贡献之一。 第三，具有明确和可编程代谢功能的合成微生物群落具有 T2D的治疗潜力。肠道微生物群是我们‘泛基因组’的一部分，它的代谢 通过基因操作或调整微生物区系组成，功能更容易处理。我们的 该方法将加快微生物组代谢基因的基因组和生物学特征， 为具有明确和可编程的代谢功能的合成社区奠定基础 防治T2D和其他与年龄相关的代谢性疾病。

Type 2 Diabetes (T2D) prevalence increases with age and affects over 13.2% of the US population by 2020. Studies show that microbiota composition and their metabolic genes are different between healthy and T2D patients. However, it is challenging to unravel their causal effects on host glucose homeostasis and T2D due to the lack of an efficient system to manipulate their levels in vivo. Recently, we were able to toggle microbiota amino acid metabolic pathways in vivo and found that some pathways affect host glucose homeostasis. We hypothesize that the gut bacteria impact the host amino acid pool, which will further modulate host glucose homeostasis. Herein we will identify the microbes that actively ferment dietary amino acids and evaluate how they affect host glucose homeostasis in diseased mouse models. We will learn more about the role of microbiota-mediated AA metabolism in the progress of T2D. Our work will also lay the ground for generating a synthetic and engineered gut microbial community with defined metabolic functions to prevent and cure T2D. There are three convergent motivations: First, many microbiota molecular features are different between healthy and T2D patients, and we need functional studies to causally connect them with T2D. We will combine bioinformatics, metabolomics, bacterial genetics, and a gnotobiotic mouse model to modulate microbiome metabolic pathways in the host. This approach will boost a systematic identification of T2D- causing microbiota genes and pathways; our findings will also promote new therapeutic strategies by targeting these previously unknown microbial metabolic avenues. Second, gut microbiota metabolism significantly impacts host metabolic health. However, the molecular mechanisms behind how microbiota regulates host amino acid homeostasis and downstream biology remain largely unexplored. We believe that this approach has huge potential: it can be used to regulate the microbiome metabolic functions at different body sites where host and microbes interact. Our finding would also open the door to interrogating – and ultimately controlling – one of the most concrete contributions that gut bacteria make to host biology. Third, a synthetic microbial community with a defined and programmable metabolic function has therapeutic potential for T2D. The gut microbiome is part of our ‘pan-genome,’ whose metabolic functions are more tractable by genetic manipulation or adjusting microbiota composition. Our approach will expedite the genomic and biological characterization of microbiota metabolic genes, laying the basis for a synthetic community with a defined and programmable metabolic function to prevent and cure T2D and other age-related metabolic diseases.