癌症DNA甲基化调控位点的识别

DNA甲基化是一种重要的表观遗传学修饰,在基因的转录调控方面具有重要的作用。异常的DNA甲基化可以导致癌症等复杂疾病发生,癌基因相关的DNA甲基化调控位点的识别对于解析癌症的发生发展机制及识别新的癌症标记具有重要意义。本研究通过整合The Cancer Genome Atlas(TCGA)的泛癌症基因组的高通量甲基化谱和基因表达谱,识别癌基因相关的DNA甲基化调控位点。对于每种癌症分批次计算Cp G位点甲基化与相关基因表达之间的相关性,并筛选调控下游基因的Cp G位点(包括强调控位点、弱调控位点和不调控位点),结果表明仅有一半的Cp G位点对下游基因具有调控作用;对癌症间共享的调控位点的分析发现不同癌症间共享的调控位点不尽相同,表明癌症特异的甲基化调控位点的存在。进一步地,对差异甲基化和差异表达基因的功能富集分析揭示了受甲基化调控的基因确实参与了癌症发生发展相关的功能。本研究的结果是对当前甲基化调控位点集的重要补充,也是识别癌症新型分子标记特征的重要资源。     

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.     

Methylation-mediated silencing of TMS1/ASC is accompanied by histone hypoacetylation and CpG island-localized changes in chromatin architecture.

Aberrant methylation of CpG-dense islands in the promoter regions of genes is an acquired epigenetic alteration associated with the silencing of tumor suppressor genes in human cancers. In a screen for endogenous targets of methylation-mediated gene silencing, we identified a novel CpG island-associated gene, TMS1, which is aberrantly methylated and silenced in response to the ectopic expression of DNA methyltransferase-1. TMS1 functions in the regulation of apoptosis and is frequently methylated and silenced in human breast cancers. In this study, we characterized the methylation pattern and chromatin architecture of the TMS1 locus in normal fibroblasts and determined the changes associated with its progressive methylation. In normal fibroblasts expressing TMS1, the CpG island is defined by an unmethylated domain that is separated from densely methylated flanking DNA by distinct 5' and 3' boundaries. Analysis of the nucleoprotein architecture of the locus in intact nuclei revealed three DNase I-hypersensitive sites that map within the CpG island. Strikingly, two of these sites coincided with the 5'- and 3'-methylation boundaries. Methylation of the TMS1 CpG island was accompanied by loss of hypersensitive site formation, hypoacetylation of histones H3 and H4, and gene silencing. This altered chromatin structure was confined to the CpG island and occurred without significant changes in methylation, histone acetylation, or hypersensitive site formation at a fourth DNase I-hypersensitive site 2 kb downstream of the TMS1 CpG island. The data indicate that there are sites of protein binding and/or structural transitions that define the boundaries of the unmethylated CpG island in normal cells and that aberrant methylation overcomes these boundaries to direct a local change in chromatin structure, resulting in gene silencing.     

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/.     

Quantitative DNA Methylation Analysis of Candidate Genes in Cervical Cancer

Aberrant DNA methylation has been observed in cervical cancer; however, most studies have used non-quantitative approaches to measure DNA methylation. The objective of this study was to quantify methylation within a select panel of genes previously identified as targets for epigenetic silencing in cervical cancer and to identify genes with elevated methylation that can distinguish cancer from normal cervical tissues. We identified 49 women with invasive squamous cell cancer of the cervix and 22 women with normal cytology specimens. Bisulfite-modified genomic DNA was amplified and quantitative pyrosequencing completed for 10 genes (APC, CCNA, CDH1, CDH13, WIF1, TIMP3, DAPK1, RARB, FHIT, and SLIT2). A Methylation Index was calculated as the mean percent methylation across all CpG sites analyzed per gene (~4-9 CpG site) per sequence. A binary cut-point was defined at >15% methylation. Sensitivity, specificity and area under ROC curve (AUC) of methylation in individual genes or a panel was examined. The median methylation index was significantly higher in cases compared to controls in 8 genes, whereas there was no difference in median methylation for 2 genes. Compared to HPV and age, the combination of DNA methylation level of DAPK1, SLIT2, WIF1 and RARB with HPV and age significantly improved the AUC from 0.79 to 0.99 (95% CI: 0.97–1.00, p-value = 0.003). Pyrosequencing analysis confirmed that several genes are common targets for aberrant methylation in cervical cancer and DNA methylation level of four genes appears to increase specificity to identify cancer compared to HPV detection alone. Alterations in DNA methylation of specific genes in cervical cancers, such as DAPK1, RARB, WIF1, and SLIT2, may also occur early in cervical carcinogenesis and should be evaluated.     

结直肠癌中DNA甲基化研究进展

CpG岛是人类基因组中富含CpG二核苷酸的DNA序列,主要位于基因启动子区,大小约为100-1000bp,与约60％编码基因相关。DNA中CpG岛甲基化可导致抑癌基因的表观遗传学转录失活,直接参与肿瘤的发生机制。近年来,甲基化已成为表观遗传学研究的焦点。我们简要综述了DNA甲基化在结直肠癌中的研究进展。     

Identification and Comparison of Aberrant Key Regulatory Networks in Breast,Colon, Liver,Lung, and Stomach Cancers through Methylome Database Analysis

Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the “cancer” related network or the “cancer” related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.     

Genome-wide profiling of DNA methylation reveals a class of normally methylated CpG island promoters

The role of CpG island methylation in normal development and cell differentiation is of keen interest, but remains poorly understood. We performed comprehensive DNA methylation profiling of promoter regions in normal peripheral blood by methylated CpG island amplification in combination with microarrays. This technique allowed us to simultaneously determine the methylation status of 6,177 genes, 92% of which include dense CpG islands. Among these 5,549 autosomal genes with dense CpG island promoters, we have identified 4.0% genes that are nearly completely methylated in normal blood, providing another exception to the general rule that CpG island methylation in normal tissue is limited to X inactivation and imprinted genes. We examined seven genes in detail, including ANKRD30A, FLJ40201, INSL6, SOHLH2, FTMT, C12orf12, and DPPA5. Dense promoter CpG island methylation and gene silencing were found in normal tissues studied except testis and sperm. In both tissues, bisulfite cloning and sequencing identified cells carrying unmethylated alleles. Interestingly, hypomethylation of several genes was associated with gene activation in cancer. Furthermore, reactivation of silenced genes could be induced after treatment with a DNA demethylating agent or in a cell line lacking DNMT1 and/or DNMT3b. Sequence analysis identified five motifs significantly enriched in this class of genes, suggesting that cis-regulatory elements may facilitate preferential methylation at these promoter CpG islands. We have identified a group of non-X-linked bona fide promoter CpG islands that are densely methylated in normal somatic tissues, escape methylation in germline cells, and for which DNA methylation is a primary mechanism of tissue-specific gene silencing.     

Identification of New Differentially Methylated Genes That Have Potential Functional Consequences in Prostate Cancer

Many differentially methylated genes have been identified in prostate cancer (PCa), primarily using candidate gene-based assays. Recently, several global DNA methylation profiles have been reported in PCa, however, each of these has weaknesses in terms of ability to observe global DNA methylation alterations in PCa. We hypothesize that there remains unidentified aberrant DNA methylation in PCa, which may be identified using higher resolution assay methods. We used the newly developed Illumina HumanMethylation450 BeadChip in PCa (n = 19) and adjacent normal tissues (n = 4) and combined these with gene expression data for identifying new DNA methylation that may have functional consequences in PCa development and progression. We also confirmed our methylation results in an independent data set. Two aberrant DNA methylation genes were validated among an additional 56 PCa samples and 55 adjacent normal tissues. A total 28,735 CpG sites showed significant differences in DNA methylation (FDR adjusted P<0.05), defined as a mean methylation difference of at least 20% between PCa and normal samples. Furthermore, a total of 122 genes had more than one differentially methylated CpG site in their promoter region and a gene expression pattern that was inverse to the direction of change in DNA methylation (e.g. decreased expression with increased methylation, and vice-versa). Aberrant DNA methylation of two genes, AOX1 and SPON2, were confirmed via bisulfate sequencing, with most of the respective CpG sites showing significant differences between tumor samples and normal tissues. The AOX1 promoter region showed hypermethylation in 92.6% of 54 tested PCa samples in contrast to only three out of 53 tested normal tissues. This study used a new BeadChip combined with gene expression data in PCa to identify novel differentially methylated CpG sites located within genes. The newly identified differentially methylated genes may be used as biomarkers for PCa diagnosis.     

Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape

Background

DNA methylation is an epigenetic modification that changes with age in human tissues, although the mechanisms and specificity of this process are still poorly understood. We compared CpG methylation changes with age across 283 human blood, brain, kidney, and skeletal muscle samples using methylation arrays to identify tissue-specific age effects.

Results

We found age-associated CpGs (ageCGs) that are both tissue-specific and common across tissues. Tissue-specific ageCGs are frequently located outside CpG islands with decreased methylation, and common ageCGs show the opposite trend. AgeCGs are significantly associated with poorly expressed genes, but those with decreasing methylation are linked with higher tissue-specific expression levels compared with increasing methylation. Therefore, tissue-specific gene expression may protect against common age-dependent methylation. Distinguished from other tissues, skeletal muscle ageCGs are more associated with expression, enriched near genes related to myofiber contraction, and closer to muscle-specific CTCF binding sites. Kidney-specific ageCGs are more increasingly methylated compared to other tissues as measured by affiliation with kidney-specific expressed genes. Underlying chromatin features also mark common and tissue-specific age effects reflective of poised and active chromatin states, respectively. In contrast with decreasingly methylated ageCGs, increasingly methylated ageCGs are also generally further from CTCF binding sites and enriched within lamina associated domains.

Conclusions

Our data identified common and tissue-specific DNA methylation changes with age that are reflective of CpG landscape and suggests both common and unique alterations within human tissues. Our findings also indicate that a simple epigenetic drift model is insufficient to explain all age-related changes in DNA methylation.