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Genetics
The rs2304256 Polymorphism in TYK2 Gene Is Associated with Protection for Type 1 Diabetes Mellitus
Felipe Mateus Pellenz, Cristine Dieter, Guilherme Coutinho Kullmann Duarte, Luís Henrique Canani, Bianca Marmontel de Souza, Daisy Crispim
Diabetes Metab J. 2021;45(6):899-908.   Published online May 24, 2021
DOI: https://doi.org/10.4093/dmj.2020.0194
  • 4,750 View
  • 157 Download
  • 1 Web of Science
  • 3 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFPubReader   ePub   
Background
Tyrosine kinase 2 (TYK2) is a candidate gene for type 1 diabetes mellitus (T1DM) since it plays an important role in regulating apoptotic and pro-inflammatory pathways in pancreatic β-cells through modulation of the type I interferon signaling pathway. The rs2304256 single nucleotide polymorphism (SNP) in TYK2 gene has been associated with protection for different autoimmune diseases. However, to date, only two studies have evaluated the association between this SNP and T1DM, with discordant results. This study thus aimed to investigate the association between the TYK2 rs2304256 SNP and T1DM in a Southern Brazilian population.
Methods
This case-control study comprised 478 patients with T1DM and 518 non-diabetic subjects. The rs2304256 (C/A) SNP was genotyped by real-time polymerase chain reaction technique using TaqMan minor groove binder (MGB) probes.
Results
Genotype and allele frequencies of the rs2304256 SNP differed between T1DM patients and non-diabetic subjects (P<0.0001 and P=0.001, respectively). Furthermore, the A allele was associated with protection against T1DM under recessive (odds ratio [OR], 0.482; 95% confidence interval [CI], 0.288 to 0.806) and additive (OR, 0.470; 95% CI, 0.278 to 0.794) inheritance models, adjusting for human leukocyte antigen (HLA) DR/DQ genotypes, gender, and ethnicity.
Conclusion
The A/A genotype of TYK2 rs2304256 SNP is associated with protection against T1DM in a Southern Brazilian population.

Citations

Citations to this article as recorded by  
  • Associations of genetic variants within TYK2 with pulmonary tuberculosis among Chinese population
    Mingwu Zhang, Zhengwei Liu, Yelei Zhu, Kunyang Wu, Lin Zhou, Ying Peng, Junhang Pan, Bin Chen, Xiaomeng Wang, Songhua Chen
    Molecular Genetics & Genomic Medicine.2024;[Epub]     CrossRef
  • Host genetic variants associated with COVID-19 reconsidered in a Slovak cohort
    Maria Skerenova, Michal Cibulka, Zuzana Dankova, Veronika Holubekova, Zuzana Kolkova, Vincent Lucansky, Dana Dvorska, Andrea Kapinova, Michaela Krivosova, Martin Petras, Eva Baranovicova, Ivana Baranova, Elena Novakova, Peter Liptak, Peter Banovcin, Anna
    Advances in Medical Sciences.2024; 69(1): 198.     CrossRef
  • Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease
    Krutika Patidar, Jennifer H. Deng, Cassie S. Mitchell, Ashlee N. Ford Versypt
    International Journal of Molecular Sciences.2024; 25(8): 4503.     CrossRef
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
  • 6,170 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  
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    Yuhong Zhong, Juan Xia, Li Liao, Mohammad Reza Momeni
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    Molecular Biotechnology.2023;[Epub]     CrossRef
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    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.2023;[Epub]     CrossRef
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    Lei Ren
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    Wenqi Fan, Haipeng Pang, Zhiguo Xie, Gan Huang, Zhiguang Zhou
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
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    Jianni Chen, Guanfei Jia, Xue Lv, Shufa Li, Christos K. Kontos
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    Monisha Prasad, Selvaraj Jayaraman, Vishnu Priya Veeraraghavan
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    Miao Liu, Junli Zhao
    Aging and disease.2022; 13(5): 1365.     CrossRef
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    Simranjeet Kaur, Caroline Frørup, Aashiq H. Mirza, Tina Fløyel, Reza Yarani, Maikel L. Colli, Jesper Johannesen, Joachim Størling, Decio L. Eizirik, Flemming Pociot
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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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    Xin Wang, Rui Ma, Weimin Shi, Zhouwei Wu, Yuling Shi
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    Xuanzi Yi, Xu Cheng
    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 3865.     CrossRef
Review
Basic Research
Histone Deacetylase 9: Its Role in the Pathogenesis of Diabetes and Other Chronic Diseases
Siqi Hu, Eun-Hee Cho, Ji-Young Lee
Diabetes Metab J. 2020;44(2):234-244.   Published online March 24, 2020
DOI: https://doi.org/10.4093/dmj.2019.0243
  • 6,386 View
  • 161 Download
  • 20 Web of Science
  • 21 Crossref
AbstractAbstract PDFPubReader   

As a member of the class IIa histone deacetylases (HDACs), HDAC9 catalyzes the deacetylation of histones and transcription factors, commonly leading to the suppression of gene transcription. The activity of HDAC9 is regulated transcriptionally and post-translationally. HDAC9 is known to play an essential role in regulating myocyte and adipocyte differentiation and cardiac muscle development. Also, recent studies have suggested that HDAC9 is involved in the pathogenesis of chronic diseases, including cardiovascular diseases, osteoporosis, autoimmune disease, cancer, obesity, insulin resistance, and liver fibrosis. HDAC9 modulates the expression of genes related to the pathogenesis of chronic diseases by altering chromatin structure in their promotor region or reducing the transcriptional activity of their respective transcription factors. This review summarizes the current knowledge of the regulation of HDAC9 expression and activity. Also, the roles of HDAC9 in the pathogenesis of chronic diseases are discussed, along with potential underlying mechanisms.

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Original Article
Clinical Features and Causes of Endogenous Hyperinsulinemic Hypoglycemia in Korea
Chang-Yun Woo, Ji Yun Jeong, Jung Eun Jang, Jaechan Leem, Chang Hee Jung, Eun Hee Koh, Woo Je Lee, Min-Seon Kim, Joong-Yeol Park, Jung Bok Lee, Ki-Up Lee
Diabetes Metab J. 2015;39(2):126-131.   Published online March 9, 2015
DOI: https://doi.org/10.4093/dmj.2015.39.2.126
  • 5,223 View
  • 85 Download
  • 22 Web of Science
  • 19 Crossref
AbstractAbstract PDFPubReader   
Background

Endogenous hyperinsulinemic hypoglycemia (EHH) is characterized by an inappropriately high plasma insulin level, despite a low plasma glucose level. Most of the EHH cases are caused by insulinoma, whereas nesidioblastosis and insulin autoimmune syndrome (IAS) are relatively rare.

Methods

To evaluate the relative frequencies of various causes of EHH in Korea, we retrospectively analyzed 84 patients who were diagnosed with EHH from 1998 to 2012 in a university hospital.

Results

Among the 84 EHH patients, 74 patients (88%), five (6%), and five (6%) were diagnosed with insulinoma, nesidioblastosis or IAS, respectively. The most common clinical manifestation of EHH was neuroglycopenic symptoms. Symptom duration before diagnosis was 14.5 months (range, 1 to 120 months) for insulinoma, 1.0 months (range, 6 days to 7 months) for nesidioblastosis, and 2.0 months (range, 1 to 12 months) for IAS. One patient, who was diagnosed with nesidioblastosis in 2006, underwent distal pancreatectomy but was later determined to be positive for insulin autoantibodies. Except for one patient who was diagnosed in 2007, the remaining three patients with nesidioblastosis demonstrated severe hyperinsulinemia (157 to 2,719 µIU/mL), which suggests that these patients might have had IAS, rather than nesidioblastosis.

Conclusion

The results of this study suggest that the prevalence of IAS may be higher in Korea than previously thought. Therefore, measurement of insulin autoantibody levels is warranted for EHH patients, especially in patients with very high plasma insulin levels.

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Diabetes Metab J : Diabetes & Metabolism Journal