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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
  • 6,299 View
  • 131 Download
  • 19 Web of Science
  • 19 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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
  • Research progress of circular RNA molecules in aging and age-related diseases
    Zhidan Zhang, Yuling Huang, AYao Guo, Lina Yang
    Ageing Research Reviews.2023; 87: 101913.     CrossRef
  • CircRNAs and RNA-Binding Proteins Involved in the Pathogenesis of Cancers or Central Nervous System Disorders
    Yuka Ikeda, Sae Morikawa, Moeka Nakashima, Sayuri Yoshikawa, Kurumi Taniguchi, Haruka Sawamura, Naoko Suga, Ai Tsuji, Satoru Matsuda
    Non-Coding RNA.2023; 9(2): 23.     CrossRef
  • 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
    Bioengineered.2022; 13(3): 5724.     CrossRef
  • 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
    BioMed Research International.2022; 2022: 1.     CrossRef
  • An intriguing role of circular RNA in insulin resistance and endothelial dysfunction: the future perspectives
    Monisha Prasad, Selvaraj Jayaraman, Vishnu Priya Veeraraghavan
    Hypertension Research.2022; 45(11): 1843.     CrossRef
  • Circular RNAs in Diabetic Nephropathy: Updates and Perspectives
    Miao Liu, Junli Zhao
    Aging and disease.2022; 13(5): 1365.     CrossRef
  • CircRNAs: Key molecules in the prevention and treatment of ischemic stroke
    Zeyu Liu, Yanhong Zhou, Jian Xia
    Biomedicine & Pharmacotherapy.2022; 156: 113845.     CrossRef
  • Pro-Inflammatory Cytokines Promote the Transcription of Circular RNAs in Human Pancreatic β Cells
    Simranjeet Kaur, Caroline Frørup, Aashiq H. Mirza, Tina Fløyel, Reza Yarani, Maikel L. Colli, Jesper Johannesen, Joachim Størling, Decio L. Eizirik, Flemming Pociot
    Non-Coding RNA.2022; 8(5): 69.     CrossRef
  • 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
    Journal of Immunology Research.2022; 2022: 1.     CrossRef
  • Circular RNAs in diabetes and its complications: Current knowledge and future prospects
    Wenfeng Yin, Ziwei Zhang, Zilin Xiao, Xia Li, Shuoming Luo, Zhiguang Zhou
    Frontiers in Genetics.2022;[Epub]     CrossRef
  • 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
    Journal of Pharmacy and Pharmacology.2022;[Epub]     CrossRef
  • Emerging roles of circular RNAs in systemic lupus erythematosus
    Xin Wang, Rui Ma, Weimin Shi, Zhouwei Wu, Yuling Shi
    Molecular Therapy - Nucleic Acids.2021; 24: 212.     CrossRef
  • Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
    Xuanzi Yi, Xu Cheng
    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 3865.     CrossRef
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
  • 5,524 View
  • 128 Download
  • 12 Web of Science
  • 13 Crossref
AbstractAbstract PDFPubReader   
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  
  • 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2024
    Nuha A. ElSayed, Grazia Aleppo, Raveendhara R. Bannuru, Dennis Bruemmer, Billy S. Collins, Laya Ekhlaspour, Jason L. Gaglia, Marisa E. Hilliard, Eric L. Johnson, Kamlesh Khunti, Ildiko Lingvay, Glenn Matfin, Rozalina G. McCoy, Mary Lou Perry, Scott J. Pil
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    Nuha A. ElSayed, Grazia Aleppo, Vanita R. Aroda, Raveendhara R. Bannuru, Florence M. Brown, Dennis Bruemmer, Billy S. Collins, Jason L. Gaglia, Marisa E. Hilliard, Diana Isaacs, Eric L. Johnson, Scott Kahan, Kamlesh Khunti, Jose Leon, Sarah K. Lyons, Mary
    Diabetes Care.2023; 46(Supplement): S19.     CrossRef
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    Johnny Ludvigsson
    Diabetologia.2023; 66(5): 955.     CrossRef
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    Diabetes Care.2022; 45(Supplement): S17.     CrossRef
  • Screening Strategy for Islet Autoantibodies in Diabetes Patients of Different Ages
    Xixi Nan, Xia Li, Yufei Xiang, Xiang Yan, Houde Zhou, Xiaohan Tang, Jin Cheng, Xiaohong Niu, Jing Liu, Qiuhe Ji, Linong Ji, Gan Huang, Zhiguang Zhou
    Diabetes Technology & Therapeutics.2022; 24(3): 212.     CrossRef
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    Shivajirao Prakash Patil
    Primary Care: Clinics in Office Practice.2022; 49(2): 225.     CrossRef
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    Wenfeng Yin, Shuoming Luo, Zilin Xiao, Ziwei Zhang, Bingwen Liu, Zhiguang Zhou
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
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    Frontiers in Endocrinology.2022;[Epub]     CrossRef
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    Li Qian, Yuxiao Zhu, Yan Luo, Mu Zhang, Liping Yu, Yu Liu, Tao Yang
    Endocrine Connections.2022;[Epub]     CrossRef
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    Diabetes Care.2021; 44(Supplement): S15.     CrossRef
  • High-Affinity ZnT8 Autoantibodies by Electrochemiluminescence Assay Improve Risk Prediction for Type 1 Diabetes
    Xiaofan Jia, Ling He, Dongmei Miao, Kathleen Waugh, Cristy Geno Rasmussen, Fran Dong, Andrea K Steck, Marian Rewers, Liping Yu
    The Journal of Clinical Endocrinology & Metabolism.2021;[Epub]     CrossRef
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    Lucia Mihaela Custură, Oana Deteşan, Raluca Maria Tilinca, Reka Annamaria Schmiedt, Brigitta Irén Bacso, Mariana Cornelia Tilinca
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    Messripour Manoochehr, Mesripour Azadeh
    Open Journal of Analytical and Bioanalytical Chemistry.2020; : 007.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal