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。这些疾病的一个核心组成部分是胰岛素抵抗。胰岛素抵抗是基因-环境相互作用的产物。最近发现的这些基因-环境相互作用的主要媒介是肠道微生物组。为了开始剖析微生物组在2型糖尿病和肥胖发病机制中基因-环境相互作用中的作用，我们利用三个品系的实验小鼠：Jax的C57BL6/J和129S1小鼠(B6J和129J)以及Taconic的129S6小鼠(129T)开发了一个新的模型。当被高脂饮食(HFD)挑战时，B6J小鼠具有胰岛素抵抗和肥胖和糖尿病倾向，而129J小鼠则对胰岛素敏感、肥胖和糖尿病抵抗。另一方面，在基因上与129J相似的129T小鼠，在服用HFD后体重增加几乎与B6J小鼠相同，但仍然对胰岛素敏感，而且没有糖尿病，也就是说，是一种“代谢健康”的肥胖症模型。虽然遗传学在这些表型差异中发挥了作用，但微生物组也起到了作用。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来改变微生物群来减少或改变。这些表型上的差异与分子水平上胰岛素信号的差异是平行的。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身上。利用非靶向代谢组学，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。这个项目的主要目标是识别微生物区系和代谢物，这些微生物区系和代谢物被变化的微生物群改变，并导致胰岛素抵抗和代谢失调。具体目标是：1)利用我们三种不同遗传背景的小鼠模型，我们将通过元基因组分析确定高脂肪和高碳水化合物饮食以及运动后肠道微生物区系的变化与胰岛素信号和代谢表型的变化如何相关；我们还将确定宿主遗传学如何与肠道微生物区系相互作用，通过将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体内来影响代谢组。2)确定微生物群落及其元基因组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)整合所有模型的代谢数据，以确定与胰岛素抵抗相关的未知代谢物的优先顺序；并确定已知的和新发现的与胰岛素抵抗相关的未知代谢物如何在体外和体内改变胰岛素信号。结合这些数据，我们将能够确定微生物组及其相关代谢体在胰岛素中的作用 抗性和代谢失调，以及它们在这一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.