Diabetes & Metabolism Journal

Original Articles

Type 1 Diabetes
Differential Profile of Plasma Circular RNAs in Type 1 Diabetes Mellitus: Yangyang Li, Ying Zhou, Minghui Zhao, Jing Zou, Yuxiao Zhu, Xuewen Yuan, Qianqi Liu, Hanqing Cai, Cong-Qiu Chu, Yu Liu; Diabetes Metab J. 2020;44(6):854-865. Published online July 13, 2020; DOI: https://doi.org/10.4093/dmj.2019.0151

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Background

No currently available biomarkers or treatment regimens fully meet therapeutic needs of type 1 diabetes mellitus (T1DM). Circular RNA (circRNA) is a recently identified class of stable noncoding RNA that have been documented as potential biomarkers for various diseases. Our objective was to identify and analyze plasma circRNAs altered in T1DM.

Methods

We used microarray to screen differentially expressed plasma circRNAs in patients with new onset T1DM (n=3) and age-/gender-matched healthy controls (n=3). Then, we selected six candidates with highest fold-change and validated them by quantitative real-time polymerase chain reaction in independent human cohort samples (n=12). Bioinformatic tools were adopted to predict putative microRNAs (miRNAs) sponged by these validated circRNAs and their downstream messenger RNAs (mRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to gain further insights into T1DM pathogenesis.

Results

We identified 68 differentially expressed circRNAs, with 61 and seven being up- and downregulated respectively. Four of the six selected candidates were successfully validated. Curations of their predicted interacting miRNAs revealed critical roles in inflammation and pathogenesis of autoimmune disorders. Functional relations were visualized by a circRNA-miRNA-mRNA network. GO and KEGG analyses identified multiple inflammation-related processes that could be potentially associated with T1DM pathogenesis, including cytokine-cytokine receptor interaction, inflammatory mediator regulation of transient receptor potential channels and leukocyte activation involved in immune response.

Conclusion

Our study report, for the first time, a profile of differentially expressed plasma circRNAs in new onset T1DM. Further in silico annotations and bioinformatics analyses supported future application of circRNAs as novel biomarkers of T1DM.

Citations

Citations to this article as recorded by

Non-coding RNAs and exosomal non-coding RNAs in diabetic retinopathy: A narrative review
Yuhong Zhong, Juan Xia, Li Liao, Mohammad Reza Momeni
International Journal of Biological Macromolecules.2024; 259: 128182. CrossRef
Circular RNAs: Potential biomarkers and therapeutic targets for autoimmune diseases
Ren-Jie Zhao, Wan-Ying Zhang, Xing-Xing Fan
Heliyon.2024; 10(1): e23694. CrossRef
Hsa_circRNA_405498 and hsa_circRNA_100033 Serve as Potential Biomarkers for Differential Diagnosis of Type 1 Diabetes
Ziwei Zhang, Shuoming Luo, Zilin Xiao, Wenfeng Yin, Xiajie Shi, Hongzhi Chen, Zhiguo Xie, Zhenqi Liu, Xia Li, Zhiguang Zhou
The Journal of Clinical Endocrinology & Metabolism.2024; 109(6): 1464. CrossRef
A Comprehensive Review on Circulating cfRNA in Plasma: Implications for Disease Diagnosis and Beyond
Pengqiang Zhong, Lu Bai, Mengzhi Hong, Juan Ouyang, Ruizhi Wang, Xiaoli Zhang, Peisong Chen
Diagnostics.2024; 14(10): 1045. CrossRef
Circulating non-coding RNA in type 1 diabetes mellitus as a source of potential biomarkers – An emerging role of sex difference
Lucyna Stachowiak, Weronika Kraczkowska, Aleksandra Świercz, Paweł Piotr Jagodziński
Biochemical and Biophysical Research Communications.2024; 736: 150482. CrossRef
Importance of Studying Non-Coding RNA in Children and Adolescents with Type 1 Diabetes
Manuela Cabiati, Giovanni Federico, Silvia Del Ry
Biomedicines.2024; 12(9): 1988. CrossRef
Circular RNAs in human diseases
Yuanyong Wang, Jin Zhang, Yuchen Yang, Zhuofeng Liu, Sijia Sun, Rui Li, Hui Zhu, Tian Li, Jin Zheng, Jie Li, Litian Ma
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Circular RNA in autoimmune diseases: special emphasis on regulation mechanism in RA and SLE
Yurong Huang, Qiuyun Xue, Chenglong Cheng, Yuting Wang, Xiao Wang, Jun Chang, Chenggui Miao
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Zhidan Zhang, Yuling Huang, AYao Guo, Lina Yang
Ageing Research Reviews.2023; 87: 101913. CrossRef
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Yuka Ikeda, Sae Morikawa, Moeka Nakashima, Sayuri Yoshikawa, Kurumi Taniguchi, Haruka Sawamura, Naoko Suga, Ai Tsuji, Satoru Matsuda
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Decrypting the circular RNAs does a favor for us: Understanding, diagnosing and treating diabetes mellitus and its complications
Zi Li, Yuanyuan Ren, Ziwei Lv, Man Li, Yujia Li, Xiaobin Fan, Yuyan Xiong, Lu Qian
Biomedicine & Pharmacotherapy.2023; 168: 115744. CrossRef
Circular RNA PIP5K1A Promotes Glucose and Lipid Metabolism Disorders and Inflammation in Type 2 Diabetes Mellitus
Ge Song, YiQian Zhang, YiHua Jiang, Huan Zhang, Wen Gu, Xiu Xu, Jing Yao, ZhengFang Chen
Molecular Biotechnology.2023;[Epub] CrossRef
Circular RNA PIP5K1A act as microRNA-552-3p sponge to regulates inflammation, oxidative damage in glucolipotoxicity-induced pancreatic INS-1 β-cells via Janus kinase 1
Lei Ren
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Circular RNAs in diabetes mellitus and its complications
Wenqi Fan, Haipeng Pang, Zhiguo Xie, Gan Huang, Zhiguang Zhou
Frontiers in Endocrinology.2022;[Epub] CrossRef
Type 1 Diabetes Mellitus-Related circRNAs Regulate CD4+ T Cell Functions
Jianni Chen, Guanfei Jia, Xue Lv, Shufa Li, Christos K. Kontos
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Differential Expression and Bioinformatics Analysis of Plasma-Derived Exosomal circRNA in Type 1 Diabetes Mellitus
Haipeng Pang, Wenqi Fan, Xiajie Shi, Shuoming Luo, Yimeng Wang, Jian Lin, Yang Xiao, Xia Li, Gan Huang, Zhiguo Xie, Zhiguang Zhou, Jinhui Liu
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Wenfeng Yin, Ziwei Zhang, Zilin Xiao, Xia Li, Shuoming Luo, Zhiguang Zhou
Frontiers in Genetics.2022;[Epub] CrossRef
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Xin Wang, Rui Ma, Weimin Shi, Zhouwei Wu, Yuling Shi
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Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
Xuanzi Yi, Xu Cheng
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Technology/Device
Glutamic Acid Decarboxylase Autoantibody Detection by Electrochemiluminescence Assay Identifies Latent Autoimmune Diabetes in Adults with Poor Islet Function: Yuxiao Zhu, Li Qian, Qing Liu, Jing Zou, Ying Zhou, Tao Yang, Gan Huang, Zhiguang Zhou, Yu Liu; Diabetes Metab J. 2020;44(2):260-266. Published online November 12, 2019; DOI: https://doi.org/10.4093/dmj.2019.0007

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140 Download
12 Web of Science
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Background

The detection of glutamic acid decarboxylase 65 (GAD65) autoantibodies is essential for the prediction and diagnosis of latent autoimmune diabetes in adults (LADA). The aim of the current study was to compare a newly developed electrochemiluminescence (ECL)-GAD65 antibody assay with the established radiobinding assay, and to explore whether the new assay could be used to define LADA more precisely.

Methods

Serum samples were harvested from 141 patients with LADA, 95 with type 1 diabetes mellitus, and 99 with type 2 diabetes mellitus, and tested for GAD65 autoantibodies using both the radiobinding assay and ECL assay. A glutamic acid decarboxylase antibodies (GADA) competition assay was also performed to assess antibody affinity. Furthermore, the clinical features of these patients were compared.

Results

Eighty-eight out of 141 serum samples (62.4%) from LADA patients were GAD65 antibody-positive by ECL assay. Compared with ECL-GAD65 antibody-negative patients, ECL-GAD65 antibody-positive patients were leaner (P<0.0001), had poorer β-cell function (P<0.05), and were more likely to have other diabetes-associated autoantibodies. The β-cell function of ECL-GAD65 antibody-positive patients was similar to that of type 1 diabetes mellitus patients, whereas ECL-GAD65 antibody-negative patients were more similar to type 2 diabetes mellitus patients.

Conclusion

Patients with ECL-GAD65 antibody-negative share a similar phenotype with type 2 diabetes mellitus patients, whereas patients with ECL-GAD65 antibody-positive resemble those with type 1 diabetes mellitus. Thus, the detection of GADA using ECL may help to identify the subtype of LADA.

Citations

Citations to this article as recorded by

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