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基于全基因组表观基因组和转录组谱,采用多组学 WGCNA 鉴定妊娠期糖尿病患者的枢纽甲基化差异表达基因。

Identification of hub-methylated differentially expressed genes in patients with gestational diabetes mellitus by multi-omic WGCNA basing epigenome-wide and transcriptome-wide profiling.

机构信息

Department of Obstetrics, Fujian Provincial Maternity and Children's Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.

出版信息

J Cell Biochem. 2020 Jun;121(5-6):3173-3184. doi: 10.1002/jcb.29584. Epub 2019 Dec 30.

Abstract

Gestational diabetes mellitus (GDM), defined as dysglycaemia that is detected during pregnancy for the first time, has become a global health burden. GDM was found to be correlated to epigenetic changes, which would cause abnormal expression of placental genes. In the present study, we performed multi-omic weighted gene coexpression network analysis (WGCNA) to systematically identify the hub genes for GDM using both epigenome- and transcriptome-wide microarray data. Two microarray datasets (GSE70493 and GSE70494) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to screen differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between normal and GDM samples, separately. The results of WGCNA found that 15 modules were identified and the MEblack module had a significantly negative correlation with GDM (r = -.28, P = .03). GO enrichment analysis by BinGO of the MEblack module showed that genes were primarily enriched for the presentation of antigen processing, regulation of interferon-α production and interferon-γ-mediated signaling pathway. By comparing the DEGs, DMGs and hub genes in the coexpression network, we identified five hypermethylated, lowly expressed genes (ABLIM1, GRHL1, HLA-F, NDRG1, and SASH1) and one hypomethylated, highly expressed gene (EIF3F) as GDM-related hub DMGs. Moreover, the expression levels of ABLIM1, GRHL1, HLA-F, NDRG1, and SASH11 in the GDM patients and healthy controls were validated by a real-time quantitative polymerase chain reaction. Finally, gene set enrichment analysis showed that the biological function of cardiac muscle contraction was enriched for four GDM-related hub DMGs (ABLIM1, GRHL1, NDRG1, and SASH1). Analysis of this study revealed that dysmethylated hub genes in GDM placentas might affect the placental function and thus, take part in GDM pathogenesis and fetal cardiac development.

摘要

妊娠期糖尿病(GDM)定义为首次在怀孕期间检测到的血糖异常,已成为全球健康负担。已经发现 GDM 与表观遗传变化有关,这会导致胎盘基因的异常表达。在本研究中,我们使用表观基因组和转录组全基因组微阵列数据进行了多组学加权基因共表达网络分析(WGCNA),以系统地识别 GDM 的枢纽基因。从基因表达综合(GEO)数据库中下载了两个微阵列数据集(GSE70493 和 GSE70494)。使用 GEO2R 分别筛选正常和 GDM 样本之间的差异表达基因(DEGs)和差异甲基化基因(DMGs)。WGCNA 的结果发现鉴定了 15 个模块,并且 MEblack 模块与 GDM 呈显著负相关(r = -.28,P =.03)。通过 BinGO 对 MEblack 模块进行的 GO 富集分析表明,基因主要富集在抗原加工呈递、调节干扰素-α产生和干扰素-γ介导的信号通路。通过比较共表达网络中的 DEGs、DMGs 和枢纽基因,我们确定了五个高甲基化、低表达基因(ABLIM1、GRHL1、HLA-F、NDRG1 和 SASH1)和一个低甲基化、高表达基因(EIF3F)作为与 GDM 相关的枢纽 DMGs。此外,通过实时定量聚合酶链反应验证了 GDM 患者和健康对照者中 ABLIM1、GRHL1、HLA-F、NDRG1 和 SASH1 的表达水平。最后,基因集富集分析表明,四个与 GDM 相关的枢纽 DMGs(ABLIM1、GRHL1、NDRG1 和 SASH1)的心肌收缩生物学功能富集。这项研究的分析表明,GDM 胎盘中的失调枢纽基因可能会影响胎盘功能,从而参与 GDM 的发病机制和胎儿心脏发育。

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