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不同黏膜微生物群的评估导致基于肠道微生物群的 NOD 小鼠 1 型糖尿病预测。

Evaluation of different mucosal microbiota leads to gut microbiota-based prediction of type 1 diabetes in NOD mice.

机构信息

Section of Endocrinology, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, 06510, USA.

Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, CT, 06510, USA.

出版信息

Sci Rep. 2018 Oct 18;8(1):15451. doi: 10.1038/s41598-018-33571-z.

Abstract

Type 1 diabetes (T1D) is a progressive autoimmune disease in which the insulin-producing beta cells are destroyed by auto-reactive T cells. Recent studies suggest that microbiota are closely associated with disease development. We studied gut, oral and vaginal microbiota longitudinally in non-obese diabetic (NOD) mice. We showed that the composition of microbiota is very different at the different mucosal sites and between young and adult mice. Gut microbiota are more diverse than oral or vaginal microbiota and the changes were more evident in the mice before and after onset of diabetes. Using alpha-diversity, Gram-positive/Gram-negative ratio as well as the relative abundance of Bacteroidetes and Erysipelotrichaceae in the gut microbiota, at 8 weeks of age, we formulated a predictive algorithm for T1D development in a cohort of 63 female NOD mice. Using this algorithm, we obtained 80% accuracy of prediction of diabetes onset, in two independent experiments, totaling 29 mice, with Area Under the Curve of 0.776 by ROC analysis. Interestingly, we did not find differences in peripheral blood mononuclear cells of the mice at 8 weeks of age, regardless of later diabetes development. Our results suggest that the algorithm could potentially be used in early prediction of future T1D development.

摘要

1 型糖尿病(T1D)是一种进行性自身免疫性疾病,其中胰岛素产生的β细胞被自身反应性 T 细胞破坏。最近的研究表明,微生物群与疾病的发展密切相关。我们纵向研究了非肥胖型糖尿病(NOD)小鼠的肠道、口腔和阴道微生物群。我们表明,微生物群的组成在不同的粘膜部位和幼鼠与成年鼠之间差异很大。肠道微生物群比口腔或阴道微生物群更具多样性,并且在糖尿病发病前和发病后小鼠中的变化更为明显。使用α多样性、革兰氏阳性菌/革兰氏阴性菌比值以及肠道微生物群中拟杆菌门和肠杆菌科的相对丰度,我们在 63 只雌性 NOD 小鼠的队列中制定了 T1D 发展的预测算法。使用该算法,我们在两项独立的实验中,共 29 只小鼠中获得了 80%的糖尿病发病预测准确性,ROC 分析的曲线下面积为 0.776。有趣的是,我们在 8 周龄时,无论以后是否发生糖尿病,小鼠的外周血单核细胞均未发现差异。我们的研究结果表明,该算法有可能用于早期预测未来 T1D 的发展。

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