Juan Huang

Dr. Juan Huang

orcid

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 May 2020 in JCI Insight
Verified
Journal Article
Open access
PDF file
JHJuan Huang
Juan Huang
JPJames A. Pearson
James A. Pearson
JPJian Peng
Jian Peng
YHYoujia Hu
Youjia Hu
SSSha Sha
Sha Sha
LWLi Wen
Li Wen
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.

9 March 2023 in Frontiers in Microbiology
Verified
Journal Article
Open access
KGKeyu Guo
Keyu Guo
JLJiaqi Li
Jiaqi Li
XLXia Li
Xia Li
JHJuan Huang
Juan Huang
ZZZhiguang Zhou
Zhiguang Zhou
Objective: To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D).Methods: A literature search for research studies on gut microbiota and T1D was conducted using the Web of Science Core Collection (WoSCC) database on 24 September 2022. VOSviewer software and the packages Bibliometrix R and ggplot used in RStudio were applied to perform the bibliometric and visualization analysis.Results: A total of 639 publications was extracted using the terms “gut microbiota” and “type 1 diabetes” (and their synonyms in MeSH). Ultimately, 324 articles were included in the bibliometric analysis. The United States and European countries are the main contributors to this field, and the top 10 most influential institutions are all based in the United States, Finland and Denmark. The three most influential researchers in this field are Li Wen, Jorma Ilonen and Mikael Knip. Historical direct citation analysis showed the evolution of the most cited papers in the field of T1D and gut microbiota. Clustering analysis defined seven clusters, covering the current main topics in both basic and clinical research on T1D and gut microbiota. The most commonly found high-frequency keywords in the period from 2018 to 2021 were “metagenomics,” “neutrophils” and “machine learning.”Conclusion: The application of multi-omics and machine learning approaches will be a necessary future step for better understanding gut microbiota in T1D. Finally, the future outlook for customized therapy toward reshaping gut microbiota of T1D patients remains promising.

20 June 2022 in Frontiers in Immunology
Verified
Journal Article
Open access
MYMin Yin
Min Yin
YZYan Zhang
Yan Zhang
SLShanshan Liu
Shanshan Liu
JHJuan Huang
Juan Huang
XLXia Li
Xia Li
Type 1 diabetes (T1D) patients are at heightened risk for other autoimmune disorders, particularly Hashimoto’s thyroiditis (HT) and celiac disease (CD). Recent evidence suggests that target tissues of autoimmune diseases engage in a harmful dialogue with the immune system. However, it is unclear whether shared mechanisms drive similar molecular signatures at the target tissues among T1D, HT, and CD. In our current study, microarray datasets were obtained and mined to identify gene signatures from disease-specific targeted tissues including the pancreas, thyroid, and intestine from individuals with T1D, HT, and CD, as well as their matched controls. Further, the threshold-free algorithm rank-rank hypergeometric overlap analysis (RRHO) was used to compare the genomic signatures of the target tissues of the three autoimmune diseases. Next, promising drugs that could potentially reverse the observed signatures in patients with two or more autoimmune disorders were identified using the cloud-based CLUE software platform. Finally, microarray data of auto-antibody positive individuals but not diagnosed with T1D and single cell sequencing data of patients with T1D and HT were used to validate the shared transcriptomic fingerprint. Our findings revealed significant common gene expression changes in target tissues of the three autoimmune diseases studied, many of which are associated with virus infections, including influenza A, human T-lymphotropic virus type 1, and herpes simplex infection. These findings support the importance of common environmental factors in the pathogenesis of T1D, HT, and CD.

6 May 2022 in Biochemical Society Transactions
Verified
Journal Article
Open access
PDF file
KGKeyu Guo
Keyu Guo
JHJuan Huang
Juan Huang
ZZZhiguang Zhou
Zhiguang Zhou
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.