癌症相关的DNA甲基化连锁区域

DNA甲基化是重要的表观遗传标记之一,在转录调控中起直接作用。DNA甲基化的异常与癌症的发生发展密切相关。高通量测序使得在单碱基分辨率下检测全基因组的DNA甲基化水平成为可能。本文基于临近CpGs位点甲基化水平的相关性挖掘DNA甲基化连锁区域。结果发现DNA甲基化连锁区域的甲基化水平和模式在癌症中存在异常,而且显著富集到分化/发育相关的生物学功能。DNA甲基化连锁区域的挖掘有助于对具有生物学功能的表观遗传标记的进一步理解,有助于对癌症诊断的表观遗传标记的挖掘。     

基因组DNA甲基化及组蛋白甲基化

在真核生物中,DNA甲基化是一种非常重要的表观遗传学标记,能影响染色质的结构和基因的表达。随着全基因组甲基化测序的发展,全基因组范围内的DNA甲基化水平得以了解。文章概述了基因组中启动子、基因本体、增强子、沉默子和转座子等不同元件的DNA甲基化的研究进展,以及DNA甲基化与基因表达调控间的关系。启动子的DNA甲基化对基因的表达有抑制作用,而基因本体的DNA甲基化与基因的表达关系因物种或细胞类型不同而异。增强子的DNA甲基化状态与基因活性呈反比关系,沉默子则相反呈正相关。转座子的DNA高度甲基化抑制其转座活性,从而维持基因组的稳定性。文章还探讨了DNA甲基化与组蛋白甲基化间的相互作用及其对基因表达、可变剪切、转录的调控作用,以及本领域的未来研究方向。     

DNA甲基化与乳腺癌的关系研究进展

表观遗传学分子机制,其中包括DNA甲基化,通过细胞分裂逐代遗传,在基因转录调控中发挥着重要作用.尽管DNA甲基化是正常哺乳动物胚胎形成所必需的,但在致癌作用中却经常观察到DNA的低-和高-甲基化现象.DNA甲基化在癌症的发生和发展中起着重要作用,它使得许多抑癌基因转录沉默,最终导致癌基因的无限增殖化.许多肿瘤抑制基因启动子区异常甲基化与乳腺癌的形成密切相关,比如ER,p16,APC,RASSFlA,BRCAl等.DNA甲基化是可逆的过程,通过DNA去甲基化制剂,可以使基因恢复正常表达功能.因此,DNA去甲基化制剂在乳腺癌的治疗中具有重要临床意义.     

人类基因组DNA甲基化数据分析的研究现状

作为人类基因组最为典型的表观遗传现象,DNA甲基化在多种关键生理活动中扮演重要角色.系统分析基因组尺度的DNA甲基化概况意义重大.从Cp G岛等基本定义出发,阐述了高通量DNA甲基化的检测技术以及针对芯片技术与下一代测序技术的低水平数据处理方法;重点对比了基于机器学习理论对Cp G位点及Cp G岛甲基化水平的预测算法,以及所利用的特征对预测效果的影响与发展趋势;并对DNA差异甲基化在组织特异性、癌症等多种疾病中的计算分析进行了全面的综述.     

基于hMeDIP-seq分析肝癌小鼠癌组织中肝癌相关信号分子基因的羟甲基化

本研究通过hMeDIP-Seq技术和生物信息学分析,从肝细胞癌模型小鼠肝癌组织和肝组织基因组羟甲基化的角度研究肝癌相关信号通路,以期探讨肝癌相关信号分子基因的羟甲基化分布及其与基因表达的关系。研究结果表明,肝癌组织中共有52个具有显著统计学意义的肝癌相关信号分子基因发生了羟(FDR0.05),而肝组织的则没有出现有统计学意义的相关羟甲基化信号分子基因。而且,这些信号分子基因的羟甲基化位点全部分布在基因表达的调控区域(外显子外),包括启动子-转录启始位点区、内含子区和基因间区。由于DNA羟甲基化是激活基因表达的重要手段,因此,这些区域的羟甲基化表明这些基因在肝癌组织中处于激活状态,并得到了RT-q PCR实验的抽样证明。由此可见,肝癌组织基因组上发生的羟甲基化与肝癌相关;它们通过激活大量的信号分子基因引起肝癌相关信号通路的异常活化,从而引起肝癌的发生或迁移。     

MBD结构域和SRA结构域识别甲基化DNA的结构机理

DNA胞嘧啶5-甲基化修饰是表观遗传重要的修饰之一,其对基因表达的调控依赖下游的识别蛋白识别和传递甲基化信号.本文围绕两种主要的甲基化DNA识别结构域——MBD结构域和SRA结构域,综述了它们识别不同修饰形式DNA的结构基础以及发挥功能的分子机理.     

沉默Dnmt1基因表达诱导大鼠骨髓间充质干细胞向心肌样细胞分化

骨髓间充质干细胞(MSCs)具有向心肌样细胞分化的潜能.本室前期研究发现,MSCs在体外经DNA甲基转移酶(Dnmt)抑制剂5-氮胞苷诱导可分化为心肌样细胞.本研究证明,沉默DNA甲基化转移酶1(Dnmt1)基因表达,可诱导大鼠MSCs向心肌样细胞分化.本文采用表达Dnmt1 siRNA慢病毒感染MSCs,沉默Dnmt1表达.DNA甲基化分析显示,随着沉默Dnmt1时间延长(7-28 d),Gata-4基因上游DNA调控序列的Cp G甲基化水平明显降低,而Gata-4 mRNA的转录水平明显上调,说明敲减Dnmt1表达导致Gata-4基因激活.蛋白质印迹和/或免疫细胞化学揭示,与对照组比较,心肌相关基因MHC和c Tn T表达上调,而骨髓干细胞标志物CD90和CD29随转染时间延长表达下调.同时,实时定量PCR显示,心肌早期发育调控基因Nkx2.5 mRNA水平与Gata-4 mRNA相同,随表达Dnmt1 siRNA的慢病毒感染而上调.上述结果提示,敲减Dnmt1可降低心肌发育调控基因Gata-4启动子Cp G岛的甲基化水平,上调Gata-4基因的表达,诱导骨髓间充质干细胞向心肌样分化.     

癌症相关microRNA与靶基因的生物信息学分析

MicroRNAs(miRNAs)是一类长度约为22nt的内源性非编码RNA,通过与靶基因转录本互补结合调控基因的表达。近年来,研究发现miRNA与癌症发生密切相关,miRNA可以直接充当癌基因或者抑癌基因而影响肿瘤的发生和生长。为更进一步揭示癌症相关miRNA的特征及靶基因的功能,文章通过数据库搜索及文献检索,在人类基因组中发现了475个癌症相关miRNA,系统地比较了癌症相关miRNA与非癌症miRNA以及基因内和基因间区癌症相关miRNA在保守性、SNP位点分布、癌谱及转录调控等特性。研究发现,癌症相关miRNA比非癌症miRNA保守性要强,发生SNP概率比较低,同时发现miRNA所涉及癌症数目与保守性成正相关。基因组定位分析发现,癌症相关miRNA比非癌症miRNA更倾向于成簇存在。进一步对宿主基因、癌症相关miRNA及作用的靶基因与癌症发生进行关联分析,发现一些非癌症miRNA的宿主基因倾向于被癌症miRNA作用。本研究结果为深入理解miRNA与癌症之间的关系,以及进一步为miRNA作为癌症诊断指示物提供理论依据。     

DNA甲基化与基因表达调控研究进展

表观遗传修饰是指不改变DNA序列的、可遗传的对碱基和组蛋白的化学修饰,主要包括DNA甲基化、组蛋白修饰、染色质重塑以及非编码RNA等.表观遗传修饰是更高层次的基因表达调控手段.DNA甲基化是一种重要的表观遗传修饰,参与基因表达调控、基因印记、转座子沉默、X染色体失活以及癌症发生等重要生物学过程.近年来随着研究方法和技术的进步,全基因组DNA甲基化的研究广泛兴起,多个物种全基因组甲基化图谱被破译,全局水平对DNA甲基化的研究不仅利于在宏观层面上了解DNA甲基化的特性与规律,同时也为深入分析DNA甲基化的生物学功能与调控奠定了基础.结合最新研究进展综述DNA甲基化在基因组中的分布模式、规律以及和基因转录的关系等.     

牛早期胚胎发育中AID基因的表达及其调节区DNA甲基化的动态变化北大核心CSCD

以具有DNA主动去甲基化作用的活化诱导胞苷脱氨酶(Activation-induced cytidine deaminase,AID,亦称为AICDA)基因为研究对象,检测其在牛卵母细胞及体外受精胚胎发育不同阶段的表达变化及其调节方式,揭示细胞重编程分子机制。应用Real-time PCR、BSP(Bisulfite Sequencing PCR)和免疫荧光化学等方法分析DNA甲基化对牛早期胚胎发育中AID基因表达的影响。结果显示,AID基因在牛早期胚胎发育中受DNA甲基化的调控,AID基因的T-DMR(tissue-dependent and differentially methylated region)位于其转录起始位点-88 bp--431 bp。在牛卵母细胞成熟过程中,T-DMR第2和第3号Cp G位点的DNA甲基化明显去除,而其他位点都未发生变化。卵母细胞在成熟过程中AID基因的积累与DNA甲基化状态变化相关。在牛体外受精胚发育早期的各阶段,尽管AID基因的表达不同,但AID基因T-DMR除第2和第3号CpG位点一直都维持去甲基化状态外,其他位点始终维持甲基化状态。推测其表达的变化可能是胚胎基因组激活有关。以上研究表明,AID基因T-DMR的低甲基化与其表达存在一定的相关性。通过免疫荧光检测发现,从卵母细胞成熟期到桑椹胚期,AID蛋白都是均匀分布于细胞核和细胞质中。而囊胚时期大量AID蛋白集中于内细胞团,这可能对于内细胞团多能性的维持起重要作用。综上所述,牛卵母细胞成熟中积累的AID作用于受精过程,其启动子区的DNA去甲基化与AID基因的表达有关,胚胎基因组激活后AID基因的表达可能与胚胎发育有关。     

Genetic Control of Individual Differences in Gene-Specific Methylation in Human Brain

We have observed extensive interindividual differences in DNA methylation of 8590 CpG sites of 6229 genes in 153 human adult cerebellum samples, enriched in CpG island “shores” and at further distances from CpG islands. To search for genetic factors that regulate this variation, we performed a genome-wide association study (GWAS) mapping of methylation quantitative trait loci (mQTLs) for the 8590 testable CpG sites. cis association refers to correlation of methylation with SNPs within 1 Mb of a CpG site. 736 CpG sites showed phenotype-wide significant cis association with 2878 SNPs (after permutation correction for all tested markers and methylation phenotypes). In trans analysis of methylation, which tests for distant regulation effects, associations of 12 CpG sites and 38 SNPs remained significant after phenotype-wide correction. To examine the functional effects of mQTLs, we analyzed 85 genes that were with genetically regulated methylation we observed and for which we had quality gene expression data. Ten genes showed SNP-methylation-expression three-way associations—the same SNP simultaneously showed significant association with both DNA methylation and gene expression, while DNA methylation was significantly correlated with gene expression. Thus, we demonstrated that DNA methylation is frequently a heritable continuous quantitatively variable trait in human brain. Unlike allele-specific methylation, genetic polymorphisms mark both cis- and trans-regulatory genetic sites at measurable distances from their CpG sites. Some of the genetically regulated DNA methylation is directly connected with genetically regulated gene expression variation.     

New findings showing how DNA methylation influences diseases

In 1975, Holliday and Pugh as well as Riggs independently hypothesized that DNA methylation in eukaryotes could act as a hereditary regulation mechanism that influences gene expression and cell differentiation. Interest in the study of epigenetic processes has been inspired by their reversibility as well as their potentially preventable or treatable consequences. Recently, we have begun to understand that the features of DNA methylation are not the same for all cells.Major differences have been found between differentiated cells and stem cells.Methylation influences various pathologies, and it is very important to improve the understanding of the pathogenic mechanisms. Epigenetic modifications may take place throughout life and have been related to cancer, brain aging, memory disturbances, changes in synaptic plasticity, and neurodegenerative diseases,such as Parkinson's disease and Huntington's disease. DNA methylation also has a very important role in tumor biology. Many oncogenes are activated by mutations in carcinogenesis. However, many genes with tumor-suppressor functions are "silenced" by the methylation of CpG sites in some of their regions.Moreover, the role of epigenetic alterations has been demonstrated in neurological diseases. In neuronal precursors, many genes associated with development and differentiation are silenced by CpG methylation. In addition,recent studies show that DNA methylation can also influence diseases that do not appear to be related to the environment, such as IgA nephropathy, thus affecting,the expression of some genes involved in the T-cell receptor signaling. In conclusion, DNA methylation provides a whole series of fundamental information for the cell to regulate gene expression, including how and when the genes are read, and it does not depend on the DNA sequence.     

Identification of thresholds for dichotomizing DNA methylation data

DNA methylation plays an important role in many biological processes by regulating gene expression. It is commonly accepted that turning on the DNA methylation leads to silencing of the expression of the corresponding genes. While methylation is often described as a binary on-off signal, it is typically measured using beta values derived from either microarray or sequencing technologies, which takes continuous values between 0 and 1. If we would like to interpret methylation in a binary fashion, appropriate thresholds are needed to dichotomize the continuous measurements. In this paper, we use data from The Cancer Genome Atlas project. For a total of 992 samples across five cancer types, both methylation and gene expression data are available. A bivariate extension of the StepMiner algorithm is used to identify thresholds for dichotomizing both methylation and expression data. Hypergeometric test is applied to identify CpG sites whose methylation status is significantly associated to silencing of the expression of their corresponding genes. The test is performed on either all five cancer types together or individual cancer types separately. We notice that the appropriate thresholds vary across different CpG sites. In addition, the negative association between methylation and expression is highly tissue specific.     

COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis

COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data produced by either an Illumina methylation array or targeted bisulfite sequencing. The goal of the COHCAP algorithm is to identify CpG islands that show a consistent pattern of methylation among CpG sites. COHCAP is currently the only DNA methylation package that provides integration with gene expression data to identify a subset of CpG islands that are most likely to regulate downstream gene expression, and it can generate lists of differentially methylated CpG islands with ∼50% concordance with gene expression from both cell line data and heterogeneous patient data. For example, this article describes known breast cancer biomarkers (such as estrogen receptor) with a negative correlation between DNA methylation and gene expression. COHCAP also provides visualization for quality control metrics, regions of differential methylation and correlation between methylation and gene expression. This software is freely available at https://sourceforge.net/projects/cohcap/.