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Usage Information

Gut microbial metabolites alter IgA immunity in type 1 diabetes
Juan Huang, … , Zhiguang Zhou, Li Wen
Juan Huang, … , Zhiguang Zhou, Li Wen
Published April 16, 2020
Citation Information: JCI Insight. 2020;5(10):e135718. https://doi.org/10.1172/jci.insight.135718.
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Research Article

Gut microbial metabolites alter IgA immunity in type 1 diabetes

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Abstract

The incidence of type 1 diabetes (T1D) has been increasing among children and adolescents, in which environmental factors, including gut microbiota, play an important role. However, the underlying mechanisms are yet to be determined. Here, we show that patients with newly diagnosed T1D displayed not only a distinct profile of gut microbiota associated with decreased short-chain fatty acids (SCFAs) production, but also an altered IgA-mediated immunity compared with healthy control subjects. Using germ-free NOD mice, we demonstrate that gut microbiota from patients with T1D promoted different IgA-mediated immune responses compared with healthy control gut microbiota. Treatment with the SCFA, acetate, reduced gut bacteria–induced IgA response accompanied by decreased severity of insulitis in NOD mice. We believe our study provides new insights into the functional effects of gut microbiota on inducing IgA immune response in T1D, suggesting that SCFAs might be potential therapeutic agents in T1D prevention and/or treatment.

Authors

Juan Huang, James A. Pearson, Jian Peng, Youjia Hu, Sha Sha, Yanpeng Xing, Gan Huang, Xia Li, Fang Hu, Zhiguo Xie, Yang Xiao, Shuoming Luo, Chen Chao, F. Susan Wong, Zhiguang Zhou, Li Wen

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Usage data is cumulative from September 2022 through September 2023.

Usage JCI PMC
Text version 626 434
PDF 74 114
Figure 211 23
Table 19 0
Supplemental data 39 4
Citation downloads 21 0
Totals 990 575
Total Views 1,565

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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