Interaction between genes, environment, the microbiome and metabolome in type 2 diabetes and metabolic syndrome

我们正处于糖尿病和肥胖症的全球流行之中。这些疾病的核心组成部分是胰岛素抵抗。胰岛素抵抗是基因与环境相互作用的产物。最近确定的这些基因-环境相互作用的主要介质是肠道微生物组。为了开始剖析微生物组在2型糖尿病和肥胖症发病机制中的基因-环境相互作用中的作用，我们开发了一种新型模型，该模型利用了三种实验室小鼠品系：来自Jax的C57 B16/J和129 S1小鼠（B6 J和129 J）以及来自Taconic的129 S6小鼠（129 T）。当用高脂肪饮食（HFD）挑战时，B6 J小鼠是胰岛素抵抗的并且肥胖和糖尿病倾向，而129 J小鼠是胰岛素敏感的并且肥胖和糖尿病抵抗。另一方面，与129 J遗传相似的129 T小鼠在HFD上增加的体重几乎与B6 J小鼠一样多，但保持胰岛素敏感性和非糖尿病，即，是“代谢健康”肥胖的典型。虽然遗传学在这些表型差异中起作用，但微生物组也有贡献。因此，这些差异中的一些可以通过在相同的环境中饲养小鼠或通过用抗生素治疗小鼠以改变微生物组来减少或改变。这些表型上的差异被分子水平上胰岛素信号传导的差异所掩盖。重要的是，代谢综合征和胰岛素信号异常的倾向可以通过粪便移植部分转移到无菌小鼠。使用非靶向代谢组学，我们已经证明微生物组的这些影响与多种循环代谢物水平的急剧变化有关，包括已知和未知的代谢物。该项目的主要目标是确定微生物群和代谢物，这些微生物群和代谢物因微生物组的变化而改变，并导致胰岛素抵抗和代谢失调。具体目标是：1）使用我们在三种不同遗传背景下的小鼠的稳健模型，我们将定义肠道微生物群的变化，如通过宏基因组分析评估的，响应于高脂肪和高碳水化合物饮食以及运动，与胰岛素信号传导和代谢表型的改变有关;我们也将决定如何主持-遗传学与肠道微生物群相互作用，通过微生物群转移到具有不同糖尿病和代谢综合征遗传风险的小鼠中来影响代谢组。2)定义微生物群及其宏基因组表达的变化如何与所有模型中血浆/盲肠代谢组的变化相关，以及这些变化如何导致这些模型中的胰岛素抵抗。我们还将整合代谢组学数据，以创建完整的代谢网络。3)整合所有模型中的代谢组学数据，以优先考虑与胰岛素抵抗相关的未知代谢物进行鉴定;并确定与胰岛素抵抗相关的已知和新鉴定的未知代谢物如何在体外和体内改变胰岛素信号传导。这些数据将使我们能够确定微生物组及其相关代谢组在胰岛素中的作用 抗性和代谢失调以及这些在这个过程中如何与宿主遗传学相互作用。

We are in the midst of a worldwide epidemic of diabetes and obesity. A central component of these disorders is insulin resistance. Insulin resistance is the product of gene-environment interactions. A recently identified major mediator of these gene-environment interactions is the gut microbiome. To begin to dissect the role of the microbiome in gene-environment interactions in the pathogenesis of type 2 diabetes and obesity, we have developed a novel model taking advantage of three strains of laboratory mice: C57Bl6/J and 129S1 mice from Jax (B6J and 129J) and 129S6 mice from Taconic (129T). When challenged with high fat diet (HFD), B6J mice are insulin resistant and obesity- and diabetes-prone, while 129J mice are insulin sensitive and obesity- and diabetes-resistant. 129T mice, which are similar genetically to 129J, on the other hand, gain almost as much weight as B6J mice on HFD, but remain insulin sensitive and non-diabetic, i.e., are a model of “metabolically healthy” obesity. While genetics plays a role in these phenotypic differences, the microbiome also contributes. Thus, some of these differences can be reduced or modified by breeding the mice in the same environment or by treating the mice with antibiotics to alter the microbiome. These differences in phenotype are paralleled by differences in insulin signaling at the molecular level. Importantly, the propensity to metabolic syndrome and abnormalities in insulin signaling can be transferred in part to germ-free mice by fecal transplant. Using non-targeted metabolomics, we have shown that these effects of the microbiome are associated with dramatic changes in the levels of multiple circulating metabolites, including both known and unknowns. The major goal of this project is to identify microbiota and metabolites which are altered by the changing microbiome and contribute to insulin resistance and metabolic dysregulation. The specific aims are: 1) Using our robust model of mice on three different genetic backgrounds, we will define how changes in gut microbiota, as assessed by metagenomic analysis, in response to high fat and high carbohydrate diets, as well as exercise, are related to alterations in insulin signaling and metabolic phenotype; we will also determine how host-genetics interacts with gut microbiota to affect the metabolome by microbiome transfer into mice with different genetic risk of diabetes and metabolic syndrome. 2) Define how changes in the community of microbiota and their metagenomic representation relate to changes in the plasma/cecal metabolome across all models, and how these contribute to the insulin resistance in these models. We will also integrate the metabolomics data to create complete metabolic networks. 3) Integrate metabolomic data across all models to prioritize the unknown metabolites linked to insulin resistance for identification; and determine how both the known and the newly-identified unknown metabolites linked to insulin resistance alter insulin signaling in vitro and in vivo. Together these data will allow us to define the role of the microbiome and its associated metabolome in insulin resistance and metabolic dysregulation and how these interact with host genetics in this process.