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Aberrant DNA Methylation Involved in Obese Women with Systemic Insulin

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Aberrant DNA Methylation Involved in Obese Women with Systemic Insulin Open Life Sci. 2018; 13: 201–207 Research Article Shao-Jun Zhang, Yan Wang, Yan-Lan Yang, Hong Zheng* Aberrant DNA methylation involved in obese women with systemic insulin resistance https://doi.org/10.1515/biol-2018-0024 epigenetic biomarkers that may be related to obesity- Received September 4, 2017; accepted January 8, 2018 associated insulin resistance. Our results provide new Abstract: Background: Epigenetics has been recognized insights into the epigenetic regulation of disease etiology as a significant regulator in many diseases. White adipose and also identify novel targets for insulin resistance tissue (WAT) epigenetic dysregulation is associated with treatment in obese women. systemic insulin resistance (IR). The aim of this study was to survey the differential methylation of genes in Keywords: obesity; insulin resistance; DNA methylation; obese women with systemic insulin resistance by DNA bioinformatics methylation microarray. Methods: The genome-wide methylation profile of systemic insulin resistant obese women was obtained from Gene Expression Omnibus 1 Introduction database. After data preprocessing, differing methylation patterns between insulin resistant and sensitive obese Obesity, an emerging nutritional problem globally, has women were identified by Student’s t-test and methylation become the fifth leading risk for global deaths [1]. It is value differences. Network analysis was then performed associated with low-grade inflammation resulting from to reveal co-regulated genes of differentially methylated chronic activation of the innate immune system, and genes. Functional analysis was also implemented to can often lead to insulin resistance [2], a condition that reveal the underlying biological processes related to is characterized by elevated circulating levels of insulin systemic insulin resistance in obese women. Results: during fasting, despite normal or elevated glucose levels Relative to insulin sensitive obese women, we initially in the blood [3, 4]. The impaired ability of insulin to induce screened 10,874 differentially methylated CpGs, including cellular responses is a pathophysiological mechanism 7402 hyper-methylated sites and 6073 hypo-methylated that links obesity to metabolic disorders, such as type CpGs. Our analysis identified 4 significantly differentially 2 diabetes and cardiovascular disease [5-7]. Recently, methylated genes, including SMYD3, UST, BCL11A, and epigenetics has been recognized as a significant regulator BAI3. Network and functional analyses found that these in complex diseases. Both genetic and epigenetic factors differentially methylated genes were mainly involved in have been implicated in the development of systemic chondroitin and dermatan sulfate biosynthetic processes. insulin resistance. The association between insulin Conclusion: Based on our study, we propose several resistance and excess abdominal fat, particularly in intra- abdominal or visceral adipose tissue (VAT), is believed to be mediated by increased spontaneous hydrolysis of lipids *Corresponding author: Hong Zheng, Department of Endocrinology, (i.e. adipocyte lipolysis) [8, 9]. In addition, systemic IR is The Second Affiliated Hospital of Dalian Medical University, No. 467 characterized by ectopic triacylglycerol accumulation in Zhongshan Road, Shahekou District, Dalian, Liaoning 116023, China, skeletal muscle and the liver [10-12]. E-mail: [email protected] As the best-studied epigenetic modification, DNA Shao-Jun Zhang, Yan-Lan Yang, Department of Endocrinology, The People's Hospital of Shanxi Province , Taiyuan, Shanxi 030012, methylation is closely involved in several key biological China processes [13]. Growing evidence has indicated that DNA Shao-Jun Zhang, Department of Endocrinology, The Sixth Division methylation contributes to obesity associated insulin Hospital of Xinjiang Production and Construction Corps, Wujiaqu, resistance [14-16]. Candidate gene methylation studies Xinjiang 830025, China in animal models and humans have demonstrated Yan Wang, Medical Laboratory Diagnosis Center, Jinan Central Hospital, Jinan, Shandong 250013, China methylation changes in the promoters of various genes that are implicated in obesity, appetite control and Open Access. © 2018 Shao-Jun Zhang et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution- NonCommercial-NoDerivs 4.0 License. 202 S.J. Zhang, et al. metabolism, insulin signaling, immunity, growth and interfere with hybridization, probes containing SNPs with circadian clock regulation [3, 17, 18]. Moreover, epigenome- minimum allelic frequency < 0.05 were removed from the wide association studies have been used to identify data, leaving 427,909 probes for subsequent analysis. novel genes and pathways related to obesity and obesity- induced complications. Previous studies have identified several obesity-associated inflammatory genes, such as 2.2 Differential methylation analysis UBASH3, TRIM3, HIF3A, and LY86 [19, 20]. Most recently, Fradin et al. [21] revealed 18 and 138 specific differentially In this study, the raw methylation score for each probe methylated CpGs in moderately and severely obese was expressed as methylation β-values. The β-value is a children, respectively, relative to lean children. Global and quantitative methylation measure for CpGs, ranging from site-specific differences in CpG methylation have been 0 (complete unmethylation) to 1 (full methylation). The associated with obesity and insulin resistance [22-24]. β-values of individual CpGs from the two conditions were However, to our knowledge, few studies have focused on obtained, and the mean β-value difference of each CpG the candidate methylated genes associated with systemic between two conditions was measured. CpG sites with insulin resistance in obese women. absolute mean β-value differences < 0.05 were removed Hence, in this study, we attempted to identify from this study. Moreover, a Student’s t-test was employed differential DNA methylation of genes and the underlying to compare the β-values of CpGs between obese women biological processes involved in systemic insulin with IR and those without IR. A threshold of p < 0.01 was resistance in obese women based on the genome-wide used in this study. Statistically significant differences methylation data. We anticipate that our work contribute between groups were required to show a false discovery to a better understanding of the underlying molecular rate (FDR) <0.05 following multiple-test correction by the mechanisms, and provide novel insights for the treatment Benjamini–Hochberg method. of obesity-related systemic insulin resistance. It is well known that methylation changes preferentially exist in genomic locations at intermediate methylation levels (0.2 < β <0.8) [27]. To reduce non- 2 Materials and Methods variable CpGs, only CpGs with 0.2 < β-values <0.8 were retained for subsequent analysis. In addition, a previous 2.1 Genome-wide DNA methylation data study indicated that many CpGs with β-value differences < 0.2 were difficult to reproduce [28]. Thus, only CpGs The global CpG methylation profile of omental adipose with mean β-value differences ≥ 0.2 were considered as tissue in obese women with systemic insulin resistance significantly different in our study. was downloaded from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database of NCBI, with the accession number of GSE76394 [24]. In 2.3 Network analysis this dataset, there were a total of 79 samples available, including 39 visceral adipose tissue specimens from After obtaining differentially methylated genes in IR obese obese women with systemic insulin resistance (IR group) women, we performed a network analysis to explore and 40 visceral adipose tissue samples from obese women co-regulated genes and reveal the biological functions without systemic insulin resistance (control group). DNA of those proteins. In this study, the protein-protein was assayed using the Illumina Human Methylation 450 interaction (PPI) data were obtained from the Search Tool Bead Chips (Illumina, San Diego, CA, USA). Detailed for the Retrieval of Interacting Genes (STRING, http:// sample characteristics have been described in the string-db.org/) database. Each interaction in STRING is previous study [24]. given a combined-score, reflecting the reliability of the Before analysis of differential methylation sites, interaction [29]. Here only the interactions with combined- quantitative normalization and adjustment of DNA score ≥ 0.4 were selected to construct the PPI sub-network, methylation data were performed using the Bioconductor and the network was visualized by Cytoscape (v3.4.0, Lumi [25] and BMIQ packages [26]. This dataset contained http://www.cytoscape.org/). 485,577 probes (covering 21,231 genes). Since probes overlapping single nucleotide polymorphisms (SNPs) Aberrant methylation in insulin resistant obese women 203 2.4 Functional analysis 21,231 genes). Following methylation data preprocessing, the methylation data for 427,909 autosomal CpG sites Generally, co-regulated genes tend to perform similar in 79 individuals remained for differential methylation biological functions. To analyze the underlying biological analysis. Initial analysis identified 10,874 differentially functions of genes involved in the differential methylation methylated CpGs (covering 5940 genes), including 7402 of CpGs, function analyses were implemented based on hyper-methylated sites and 3472 hypo-methylated CpGs, Gene Ontology (GO)
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