National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.
Section of Endocrinology, Department of Internal Medicine, School of Medicine, Yale University, New Haven, CT, U.S.A.
Biochem Soc Trans. 2022 Jun 30;50(3):1133-1142. doi: 10.1042/BST20220004.
Type 1 diabetes (T1D) is an organ-specific autoimmune disease characterized by progressive pancreatic β-cell loss. Both a predisposing genetic background, that may encompass mutations in several genes, as well as exposure to environmental factors can affect the progression of autoimmune responses to multiple pancreatic islet autoantigens. Many genetic variants that increase the risk of T1D are found in immunity genes involved in sensing and responding to microorganisms. Although increasing evidence indicates that the gut microbiome composition may promote or prevent T1D development, little is known about the link between gut microbiota and T1D susceptibility genes in patients with T1D. Recent studies in the inbred non-obese diabetic (NOD) mouse, a widely used model of T1D, have suggested that many genetic loci can influence gut microbiome composition to modulate islet autoimmunity. This review summarizes evidence that examines the effect of host genes on gut microbiota diversity and function during T1D development. Knowledge of the host gene-gut microbiota interactions at play during T1D progression may help us identify new diagnostic and prognostic tools and help also design effective strategies for disease treatment.
1 型糖尿病（T1D）是一种以胰腺β细胞进行性丧失为特征的器官特异性自身免疫性疾病。易患遗传背景（可能包括几个基因的突变）以及环境因素的暴露都可能影响到对多种胰岛自身抗原的自身免疫反应的进展。许多增加 T1D 风险的遗传变异存在于参与感知和对微生物作出反应的免疫基因中。尽管越来越多的证据表明肠道微生物组组成可能促进或预防 T1D 的发生，但对于 T1D 患者的肠道微生物组与 T1D 易感性基因之间的联系知之甚少。最近在近交非肥胖型糖尿病（NOD）小鼠中的研究，一种广泛使用的 T1D 模型，提示许多遗传位点可以影响肠道微生物组组成，从而调节胰岛自身免疫。这篇综述总结了宿主基因对 T1D 发展过程中肠道微生物多样性和功能的影响的证据。了解宿主基因-肠道微生物组在 T1D 进展过程中的相互作用可能有助于我们识别新的诊断和预后工具，并有助于设计有效的疾病治疗策略。
