Yanjun Wei

SEA: a super-enhancer archive

Super-enhancers are large clusters of transcriptional enhancers regarded as having essential roles in driving the expression of genes that control cell identity during development and tumorigenesis. The construction of a genome-wide super-enhancer database is urgently needed to better understand super-enhancer-directed gene expression regulation for a given biology process. Here, we present a specifically designed web-accessible database, Super-Enhancer Archive (SEA, http://sea.edbc.org). SEA focuses on integrating super-enhancers in multiple species and annotating their potential roles in the regulation of cell identity gene expression. The current release of SEA incorporates 83 996 super-enhancers computationally or experimentally identified in 134 cell types/tissues/diseases, including human (75 439, three of which were experimentally identified), mouse (5879, five of which were experimentally identified), Drosophila melanogaster (1774) and Caenorhabditis elegans (904). To facilitate data extraction, SEA supports multiple search options, including species, genome location, gene name, cell type/tissue and super-enhancer name. The response provides detailed (epi)genetic information, incorporating cell type specificity, nearby genes, transcriptional factor binding sites, CRISPR/Cas9 target sites, evolutionary conservation, SNPs, H3K27ac, DNA methylation, gene expression and TF ChIP-seq data. Moreover, analytical tools and a genome browser were developed for users to explore super-enhancers and their roles in defining cell identity and disease processes in depth.

Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes.

DNA methylation is a key epigenetic mark that is critical for gene regulation in multicellular eukaryotes. Although various human cell types may have the same genome, these cells have different methylomes. The systematic identification and characterization of methylation marks across cell types are crucial to understand the complex regulatory network for cell fate determination. In this study, we proposed an entropy-based framework termed SMART to integrate the whole genome bisulfite sequencing methylomes across 42 human tissues/cells and identified 757 887 genome segments. Nearly 75% of the segments showed uniform methylation across all cell types. From the remaining 25% of the segments, we identified cell type-specific hypo/hypermethylation marks that were specifically hypo/hypermethylated in a minority of cell types using a statistical approach and presented an atlas of the human methylation marks. Further analysis revealed that the cell type-specific hypomethylation marks were enriched through H3K27ac and transcription factor binding sites in cell type-specific manner. In particular, we observed that the cell type-specific hypomethylation marks are associated with the cell type-specific super-enhancers that drive the expression of cell identity genes. This framework provides a complementary, functional annotation of the human genome and helps to elucidate the critical features and functions of cell type-specific hypomethylation.

The Identification of Specific Methylation Patterns across Different Cancers

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.

Detection of type 2 diabetes related modules and genes based on epigenetic networks

BackgroundType 2 diabetes (T2D) is one of the most common chronic metabolic diseases characterized by insulin resistance and the decrease of insulin secretion. Genetic variation can only explain part of the heritability of T2D, so there need new methods to detect the susceptibility genes of the disease. Epigenetics could establish the interface between the environmental factor and the T2D Pathological mechanism.ResultsBased on the network theory and by combining epigenetic characteristics with human interactome, the weighted human DNA methylation network (WMPN) was constructed, and a T2D-related subnetwork (TMSN) was obtained through T2D-related differentially methylated genes. It is found that TMSN had a T2D specific network structure that non-fatal metabolic disease causing genes were often located in the topological and functional periphery of network. Combined with chromatin modifications, the weighted chromatin modification network (WCPN) was built, and a T2D-related chromatin modification pattern subnetwork was obtained by the TMSN gene set. TCSN had a densely connected network community, indicating that TMSN and TCSN could represent a collection of T2D-related epigenetic dysregulated sub-pathways. Using the cumulative hypergeometric test, 24 interplay modules of DNA methylation and chromatin modifications were identified. By the analysis of gene expression in human T2D islet tissue, it is found that there existed genes with the variant expression level caused by the aberrant DNA methylation and (or) chromatin modifications, which might affect and promote the development of T2D.ConclusionsHere we have detected the potential interplay modules of DNA methylation and chromatin modifications for T2D. The study of T2D epigenetic networks provides a new way for understanding the pathogenic mechanism of T2D caused by epigenetic disorders.

MetaImprint: an information repository of mammalian imprinted genes

Genomic imprinting is a complex genetic and epigenetic phenomenon that plays important roles in mammalian development and diseases. Mammalian imprinted genes have been identified widely by experimental strategies or predicted using computational methods. Systematic information for these genes would be necessary for the identification of novel imprinted genes and the analysis of their regulatory mechanisms and functions. Here, a well-designed information repository, MetaImprint (http://bioinfo.hrbmu.edu.cn/MetaImprint), is presented, which focuses on the collection of information concerning mammalian imprinted genes. The current version of MetaImprint incorporates 539 imprinted genes, including 255 experimentally confirmed genes, and their detailed research courses from eight mammalian species. MetaImprint also hosts genome-wide genetic and epigenetic information of imprinted genes, including imprinting control regions, single nucleotide polymorphisms, non-coding RNAs, DNA methylation and histone modifications. Information related to human diseases and functional annotation was also integrated into MetaImprint. To facilitate data extraction, MetaImprint supports multiple search options, such as by gene ID and disease name. Moreover, a configurable Imprinted Gene Browser was developed to visualize the information on imprinted genes in a genomic context. In addition, an Epigenetic Changes Analysis Tool is provided for online analysis of DNA methylation and histone modification differences of imprinted genes among multiple tissues and cell types. MetaImprint provides a comprehensive information repository of imprinted genes, allowing researchers to investigate systematically the genetic and epigenetic regulatory mechanisms of imprinted genes and their functions in development and diseases.

DiseaseMeth version 2.0: a major expansion and update of the human disease methylation database.

The human disease methylation database (DiseaseMeth, http://bioinfo.hrbmu.edu.cn/diseasemeth/) is an interactive database that aims to present the most complete collection and annotation of aberrant DNA methylation in human diseases, especially various cancers. Recently, the high-throughput microarray and sequencing technologies have promoted the production of methylome data that contain comprehensive knowledge of human diseases. In this DiseaseMeth update, we have increased the number of samples from 3610 to 32 701, the number of diseases from 72 to 88 and the disease–gene associations from 216 201 to 679 602. DiseaseMeth version 2.0 provides an expanded comprehensive list of disease–gene associations based on manual curation from experimental studies and computational identification from high-throughput methylome data. Besides the data expansion, we also updated the search engine and visualization tools. In particular, we enhanced the differential analysis tools, which now enable online automated identification of DNA methylation abnormalities in human disease in a case-control or disease–disease manner. To facilitate further mining of the disease methylome, three new web tools were developed for cluster analysis, functional annotation and survival analysis. DiseaseMeth version 2.0 should be a useful resource platform for further understanding the molecular mechanisms of human diseases.

DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes

Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.

Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature

Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlass identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.

Revealing the architecture of genetic and epigenetic regulation: a maximum likelihood model

Gene expression is modulated by multiple mechanisms, including genetic and/or epigenetic regulation, and associated with the processes of cellular differentiation and morphogenesis. Single nucleotide polymorphisms (SNPs) and DNA methylation play important roles in regulating gene expression. In this study, we focused on revealing the relationship between SNPs, DNA methylation and gene expression in two human populations genome-wide through proposing four regulation patterns and developed maximum likelihood estimate models. Using simulated data with different correlation coefficients between any two traits, the power of our approach showed a favourable performance and relative stability. In all, 6733 SNP-CpG-gene pairs including 957 genes were obtained in Northern European ancestry (CEU) population. As the results showed, SNPs and DNA methylation had approximately the same effect on expression regulation of 49% genes, which was termed cooperative/antagonistic regulation pattern. Less than 30% of genes are controlled only by one of the factors (SNP/DNA methylation). The others showed SNPs that affect methylation have no consequent effects or crosstalk regulation on gene expression. Similar result was shown in Yourba (YRI) population. Specific genes were inferred using the different mechanisms of gene regulation involved in complex diseases by combining literature. This approach provides a method to comprehensively assess regulation patterns of gene expression in the whole genome.

Survival differences of CIMP subtypes integrated with CNA information in human breast cancer

CpG island methylator phenotype of breast cancer is associated with widespread aberrant methylation at specified CpG islands and distinct patient outcomes. However, the influence of copy number contributing to the prognosis of tumors with different CpG island methylator phenotypes is still unclear. We analyzed both genetic (copy number) and epigenetic alterations in 765 breast cancers from The Cancer Genome Atlas data portal and got a panel of 15 biomarkers for copy number and methylation status evaluation. The gene panel identified two groups corresponding to distinct copy number profiles. In status of mere-loss copy number, patients were faced with a greater risk if they presented a higher CpG islands methylation pattern in biomarker panels. But for samples presenting merely-gained copy number, higher methylation level of CpG islands was associated with improved viability. In all, the integration of copy number alteration and methylation information enhanced the classification power on prognosis. Moreover, we found the molecular subtypes of breast cancer presented different distributions in two CpG island methylation phenotypes. Generated by the same set of human methylation 450K data, additional copy number information could provide insights into survival prediction of cancers with less heterogeneity and might help to determine the biomarkers for diagnosis and treatment for breast cancer patients in a more personalized approach.