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

微生物群调节宿主氨基酸和葡萄糖稳态背后的分子机制

• 批准号: 10639042
• 负责人: Chun-Jun Guo
• 金额: $ 49.15万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10639042
• 关键词: Affect; Age; Amino Acid Metabolism Pathway; Amino Acids; Aryl Hydrocarbon Receptor; Bioinformatics; Biological; Biological Assay; Biological Availability; Biology; Cells; Clinical; Code; Collection; Communities; Complex; Consumption; Data; Diabetic mouse; Diet; Disease; Economic Burden; Engineering; Enterochromaffin Cells; Environmental Risk Factor; Epigenetic Process; Etiology; Fermentation; Functional disorder; Gene Modified; Genes; Genetic; Genomics; Germ-Free; Glucose; Gnotobiotic; Health; Homeostasis; Human; Human body; In Vitro; Intestines; Investigation; Knockout Mice; Kynurenine; Learning; Mediating; Metabolic; Metabolic Diseases; Metabolic Pathway; Metabolism; Microbe; Microbial Genetics; Molecular; Motivation; Mus; Non-Insulin-Dependent Diabetes Mellitus; Nutrient; Pathway interactions; Patients; Peripheral; Phylogenetic Analysis; Play; Population; Prevalence; Public Health; Publishing; Receptor Signaling; Regulation; Role; Sampling; Serotonin; Severity of illness; Signal Pathway; Signal Transduction; Site; System; Testing; Therapeutic; Tissues; Tryptophan; Work; age related; amino acid metabolism; aryl hydrocarbon receptor ligand; bacterial genetics; blood glucose regulation; dietary; gene synthesis; genetic manipulation; glucose metabolism; glucose tolerance; gut bacteria; gut microbes; gut microbiome; gut microbiota; human microbiota; in vivo; inhibitor; insight; longitudinal dataset; member; metabolic phenotype; metabolomics; microbial; microbial community; microbiome; microbiota; mouse model; multiple omics; mutant; novel therapeutic intervention; pan-genome; prevent; public health relevance; synthetic biology

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.

2型糖尿病（T2 D）患病率随年龄增长而增加，影响超过13.2%的美国人 到2020年人口。研究表明，微生物群组成及其代谢基因是 健康人和T2 D患者之间的差异。然而，要解开它们的因果关系是具有挑战性的。 对宿主葡萄糖稳态和T2 D的影响，由于缺乏有效的系统来操纵 体内的水平。最近，我们能够切换微生物群氨基酸代谢途径， 并发现一些途径影响宿主葡萄糖稳态。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 体内平衡在此，我们将确定积极发酵膳食氨基酸的微生物， 评估它们如何影响患病小鼠模型中宿主的葡萄糖稳态。我们将学习 更多关于微生物介导的AA代谢在T2 D进展中的作用。我们的工作将 也为产生合成和工程化的肠道微生物群落奠定了基础， 定义代谢功能来预防和治疗T2 D。有三种趋同的动机： 首先，健康和T2 D患者之间的许多微生物群分子特征是不同的， 我们需要功能研究来将它们与T2 D联系起来。我们将结合联合收割机生物信息学， 代谢组学、细菌遗传学和调节微生物组的gnotobiotic小鼠模型 宿主体内的代谢途径这种方法将促进T2 D的系统识别- 导致微生物群基因和途径;我们的发现也将促进新的治疗策略 通过这些以前未知的微生物代谢途径。 其次，肠道微生物群代谢显著影响宿主代谢健康。但 微生物群如何调节宿主氨基酸稳态的分子机制， 下游生物学仍然在很大程度上未被探索。我们认为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组代谢功能， 微生物相互作用。我们的发现也为审讯打开了大门--最终 控制-肠道细菌对宿主生物学的最具体贡献之一。 第三，具有确定的和可编程的代谢功能的合成微生物群落具有 T2 D的治疗潜力。肠道微生物组是我们“泛基因组”的一部分， 通过基因操作或调整微生物群组成，功能更容易处理。我们 该方法将加快微生物群代谢基因的基因组和生物学表征， 为具有确定和可编程代谢功能的合成社区奠定基础 预防和治疗T2 D和其他与年龄相关的代谢性疾病。