Epigenomic analysis of multilineage differentiation of human embryonic stem cells.

Epigenetic mechanisms have been proposed to play crucial roles in mammalian development, but their precise functions are only partially understood. To investigate epigenetic regulation of embryonic development, we differentiated human embryonic stem cells into mesendoderm, neural progenitor cells, trophoblast-like cells, and mesenchymal stem cells and systematically characterized DNA methylation, chromatin modifications, and the transcriptome in each lineage. We found that promoters that are active in early developmental stages tend to be CG rich and mainly engage H3K27me3 upon silencing in nonexpressing lineages. By contrast, promoters for genes expressed preferentially at later stages are often CG poor and primarily employ DNA methylation upon repression. Interestingly, the early developmental regulatory genes are often located in large genomic domains that are generally devoid of DNA methylation in most lineages, which we termed DNA methylation valleys (DMVs). Our results suggest that distinct epigenetic mechanisms regulate early and late stages of ES cell differentiation.

Epigenomic analysis reveals DNA motifs regulating histone modifications in human and mouse.

Histones are modified by enzymes that act in a locus, cell-type, and developmental stage-specific manner. The recruitment of enzymes to chromatin is regulated at multiple levels, including interaction with sequence-specific DNA-binding factors. However, the DNA-binding specificity of the regulatory factors that orchestrate specific histone modifications has not been broadly mapped. We have analyzed 6 histone marks (H3K4me1, H3K4me3, H3K27ac, H3K27me3, K3H9me3, H3K36me3) across 121 human cell types and tissues from the NIH Roadmap Epigenomics Project as well as 8 histone marks (with addition of H3K4me2 and H3K9ac) from the mouse ENCODE Consortium. We have identified 361 and 369 DNA motifs in human and mouse, respectively, that are the most predictive of each histone mark. Interestingly, 107 human motifs are conserved between the two species. In human embryonic cell line H1, we mutated only the found DNA motifs at particular loci and the significant reduction of H3K27ac levels validated the regulatory roles of the perturbed motifs. The functionality of these motifs was also supported by the evidence that histone-associated motifs, especially H3K4me3 motifs, significantly overlap with the expression of quantitative trait loci SNPs in cancer patients more than the known and random motifs. Furthermore, we observed possible feedbacks to control chromatin dynamics as the found motifs appear in the promoters or enhancers associated with various histone modification enzymes. These results pave the way toward revealing the molecular mechanisms of epigenetic events, such as histone modification dynamics and epigenetic priming.

Human body epigenome maps reveal noncanonical DNA methylation variation.

Understanding the diversity of human tissues is fundamental to disease and requires linking genetic information, which is identical in most of an individual's cells, with epigenetic mechanisms that could have tissue-specific roles. Surveys of DNA methylation in human tissues have established a complex landscape including both tissue-specific and invariant methylation patterns. Here we report high coverage methylomes that catalogue cytosine methylation in all contexts for the major human organ systems, integrated with matched transcriptomes and genomic sequence. By combining these diverse data types with each individuals' phased genome, we identified widespread tissue-specific differential CG methylation (mCG), partially methylated domains, allele-specific methylation and transcription, and the unexpected presence of non-CG methylation (mCH) in almost all human tissues. mCH correlated with tissue-specific functions, and using this mark, we made novel predictions of genes that escape X-chromosome inactivation in specific tissues. Overall, DNA methylation in several genomic contexts varies substantially among human tissues.

Integrative analysis of 111 reference human epigenomes.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Identification of DNA motifs that regulate DNA methylation.

DNA methylation is an important epigenetic mark but how its locus-specificity is decided in relation to DNA sequence is not fully understood. Here, we have analyzed 34 diverse whole-genome bisulfite sequencing datasets in human and identified 313 motifs, including 92 and 221 associated with methylation (methylation motifs, MMs) and unmethylation (unmethylation motifs, UMs), respectively. The functionality of these motifs is supported by multiple lines of evidence. First, the methylation levels at the MM and UM motifs are respectively higher and lower than the genomic background. Second, these motifs are enriched at the binding sites of methylation modifying enzymes including DNMT3A and TET1, indicating their possible roles of recruiting these enzymes. Third, these motifs significantly overlap with "somatic QTLs" (quantitative trait loci) of methylation and expression. Fourth, disruption of these motifs by mutation is associated with significantly altered methylation level of the CpGs in the neighbor regions. Furthermore, these motifs together with somatic mutations are predictive of cancer subtypes and patient survival. We revealed some of these motifs were also associated with histone modifications, suggesting a possible interplay between the two types of epigenetic modifications. We also found some motifs form feed forward loops to contribute to DNA methylation dynamics.

Epipolymorphisms associated with the clinical outcome of autoimmune arthritis affect CD4+ T cell activation pathways.

Multifactorial diseases, including autoimmune juvenile idiopathic arthritis (JIA), result from a complex interplay between genetics and environment. Epigenetic mechanisms are believed to integrate such gene-environment interactions, fine-tuning gene expression, and possibly contributing to immune system dysregulation. Although anti-TNF therapy has strongly increased JIA remission rates, it is not curative and up to 80% of patients flare upon treatment withdrawal. Thus, a crucial unmet medical and scientific need is to understand the immunological mechanisms associated with remission or flare to inform clinical decisions. Here, we explored the CD4+ T-cell DNA methylome of 68 poly-articular and extended oligo-articular JIA patients, before and after anti-TNF therapy withdrawal, to identify features associated with maintenance of inactive disease. Individual CpG sites were clustered in coherent modules without a priori knowledge of their function through network analysis. The methylation level of several CpG modules, specifically those enriched in CpG sites belonging to genes that mediate T-cell activation, uniquely correlated with clinical activity. Differences in DNA methylation were already detectable at the time of therapy discontinuation, suggesting epigenetic predisposition. RNA profiling also detected differences in T-cell activation markers (including HLA-DR) but, overall, its sensitivity was lower than epigenetic profiling. Changes to the T-cell activation signature at the protein level were detectable by flow cytometry, confirming the biological relevance of the observed alterations in methylation. Our work proposes epigenetic discrimination between clinical activity states, and reveals T-cell-related biological functions tied to, and possibly predicting or causing, clinical outcome.

Predicting the human epigenome from DNA motifs.

The epigenome is established and maintained by the site-specific recruitment of chromatin-modifying enzymes and their cofactors. Identifying the cis elements that regulate epigenomic modification is critical for understanding the regulatory mechanisms that control gene expression patterns. We present Epigram, an analysis pipeline that predicts histone modification and DNA methylation patterns from DNA motifs. The identified cis elements represent interactions with the site-specific DNA-binding factors that establish and maintain epigenomic modifications. We cataloged the cis elements in embryonic stem cells and four derived lineages and found numerous motifs that have location preference, such as at the center of H3K27ac or at the edges of H3K4me3 and H3K9me3, which provides mechanistic insight about the shaping of the epigenome. The Epigram pipeline and predictive motifs are at http://wanglab.ucsd.edu/star/epigram/.

Computational schemes for the prediction and annotation of enhancers from epigenomic assays.

Identifying and annotating distal regulatory enhancers is critical to understand the mechanisms that control gene expression and cell-type-specific activities. Next-generation sequencing techniques have provided us an exciting toolkit of genome-wide assays that can be used to predict and annotate enhancers. However, each assay comes with its own specific set of analytical needs if enhancer prediction is to be optimal. Furthermore, integration of multiple genome-wide assays allows for different genomic features to be combined, and can improve predictive performance. Herein, we review the genome-wide assays and analysis schemes that are used to predict and annotate enhancers. In particular, we focus on three key computational topics: predicting enhancer locations, determining the cell-type-specific activity of enhancers, and linking enhancers to their target genes.

Predicting enhancer transcription and activity from chromatin modifications.

Enhancers play a pivotal role in regulating the transcription of distal genes. Although certain chromatin features, such as the histone acetyltransferase P300 and the histone modification H3K4me1, indicate the presence of enhancers, only a fraction of enhancers are functionally active. Individual chromatin marks, such as H3K27ac and H3K27me3, have been identified to distinguish active from inactive enhancers. However, the systematic identification of the most informative single modification, or combination thereof, is still lacking. Furthermore, the discovery of enhancer RNAs (eRNAs) provides an alternative approach to directly predicting enhancer activity. However, it remains challenging to link chromatin modifications to eRNA transcription. Herein, we develop a logistic regression model to unravel the relationship between chromatin modifications and eRNA synthesis. We perform a systematic assessment of 24 chromatin modifications in fetal lung fibroblast and demonstrate that a combination of four modifications is sufficient to accurately predict eRNA transcription. Furthermore, we compare the ability of eRNAs and H3K27ac to discriminate enhancer activity. We demonstrate that eRNA is more indicative of enhancer activity. Finally, we apply our fibroblast trained model to six other cell-types and successfully predict eRNA synthesis. Thus, we demonstrate the learned relationships are general and independent of cell-type. We provided a powerful tool to identify active enhancers and reveal the relationship between chromatin modifications, eRNA production and enhancer activity.

STAR: an integrated solution to management and visualization of sequencing data.

MOTIVATION: Easily visualization of complex data features is a necessary step to conduct studies on next-generation sequencing (NGS) data. We developed STAR, an integrated web application that enables online management, visualization and track-based analysis of NGS data. RESULTS: STAR is a multilayer web service system. On the client side, STAR leverages JavaScript, HTML5 Canvas and asynchronous communications to deliver a smoothly scrolling desktop-like graphical user interface with a suite of in-browser analysis tools that range from providing simple track configuration controls to sophisticated feature detection within datasets. On the server side, STAR supports private session state retention via an account management system and provides data management modules that enable collection, visualization and analysis of third-party sequencing data from the public domain with over thousands of tracks hosted to date. Overall, STAR represents a next-generation data exploration solution to match the requirements of NGS data, enabling both intuitive visualization and dynamic analysis of data. AVAILABILITY AND IMPLEMENTATION: STAR browser system is freely available on the web at http://wanglab.ucsd.edu/star/browser and https://github.com/angell1117/STAR-genome-browser.

Global identification of transcriptional regulators of pluripotency and differentiation in embryonic stem cells.

Human embryonic stem cells (hESCs) hold great promise for regenerative medicine because they can undergo unlimited self-renewal and retain the capability to differentiate into all cell types in the body. Although numerous genes/proteins such as Oct4 and Gata6 have been identified to play critical regulatory roles in self-renewal and differentiation of hESC, the majority of the regulators in these cellular processes and more importantly how these regulators co-operate with each other and/or with epigenetic modifications are still largely unknown. We propose here a systematic approach to integrate genomic and epigenomic data for identification of direct regulatory interactions. This approach allows reconstruction of cell-type-specific transcription networks in embryonic stem cells (ESCs) and fibroblasts at an unprecedented scale. Many links in the reconstructed networks coincide with known regulatory interactions or literature evidence. Systems-level analyses of these networks not only uncover novel regulators for pluripotency and differentiation, but also reveal extensive interplays between transcription factor binding and epigenetic modifications. Especially, we observed poised enhancers characterized by both active (H3K4me1) and repressive (H3K27me3) histone marks that contain enriched Oct4- and Suz12-binding sites. The success of such a systems biology approach is further supported by experimental validation of the predicted interactions.

Skin Cancer Risk Is Increased by Somatic Mutations Detected Noninvasively in Healthy-Appearing Sun-Exposed Skin.

Skin cancer risk is increased by exposure to ultraviolet radiation (UVR). Because UVR exposure accumulates over time and lighter skin is more susceptible to UVR, age and skin tone are risk factors for skin cancer. However, measurements of somatic mutations in healthy-appearing skin have not been used to calculate skin cancer risk. In this study, we developed a noninvasive test that quantifies somatic mutations in healthy-appearing sun-exposed skin and applied it to a 1038-subject cohort. Somatic mutations were combined with other known skin cancer risk factors to train a model to calculate risk. The final model (DNA-Skin Cancer Assessment of Risk) was trained to predict personal history of skin cancer from age, family history, skin tone, and mutation count. The addition of mutation count significantly improved model performance (OR = 1.3, 95% confidence interval = 1.14-1.48; P = 5.3 × 10-6) and made a more significant contribution than skin tone. Calculations of skin cancer risk matched the known United States population prevalence, indicating that DNA-Skin Cancer Assessment of Risk was well-calibrated. In conclusion, somatic mutations in healthy-appearing sun-exposed skin increase skin cancer risk, and mutations capture risk information that is not accounted for by other risk factors. Clinical utility is supported by the noninvasive nature of skin sample collection through adhesive patches.

DNA methylome signature in rheumatoid arthritis.

OBJECTIVES: Epigenetics can influence disease susceptibility and severity. While DNA methylation of individual genes has been explored in autoimmunity, no unbiased systematic analyses have been reported. Therefore, a genome-wide evaluation of DNA methylation loci in fibroblast-like synoviocytes (FLS) isolated from the site of disease in rheumatoid arthritis (RA) was performed. METHODS: Genomic DNA was isolated from six RA and five osteoarthritis (OA) FLS lines and evaluated using the Illumina HumanMethylation450 chip. Cluster analysis of data was performed and corrected using Benjamini-Hochberg adjustment for multiple comparisons. Methylation was confirmed by pyrosequencing and gene expression was determined by qPCR. Pathway analysis was performed using the Kyoto Encyclopedia of Genes and Genomes. RESULTS: RA and control FLS segregated based on DNA methylation, with 1859 differentially methylated loci. Hypomethylated loci were identified in key genes relevant to RA, such as CHI3L1, CASP1, STAT3, MAP3K5, MEFV and WISP3. Hypermethylation was also observed, including TGFBR2 and FOXO1. Hypomethylation of individual genes was associated with increased gene expression. Grouped analysis identified 207 hypermethylated or hypomethylated genes with multiple differentially methylated loci, including COL1A1, MEFV and TNF. Hypomethylation was increased in multiple pathways related to cell migration, including focal adhesion, cell adhesion, transendothelial migration and extracellular matrix interactions. Confirmatory studies with OA and normal FLS also demonstrated segregation of RA from control FLS based on methylation pattern. CONCLUSIONS: Differentially methylated genes could alter FLS gene expression and contribute to the pathogenesis of RA. DNA methylation of critical genes suggests that RA FLS are imprinted and implicate epigenetic contributions to inflammatory arthritis.

metaTIGER: a metabolic evolution resource.

Metabolic networks are a subject that has received much attention, but existing web resources do not include extensive phylogenetic information. Phylogenomic approaches (phylogenetics on a genomic scale) have been shown to be effective in the study of evolution and processes like horizontal gene transfer (HGT). To address the lack of phylogenomic information relating to eukaryotic metabolism, metaTIGER (www.bioinformatics.leeds.ac.uk/metatiger) has been created, using genomic information from 121 eukaryotes and 404 prokaryotes and sensitive sequence search techniques to predict the presence of metabolic enzymes. These enzyme sequences were used to create a comprehensive database of 2257 maximum-likelihood phylogenetic trees, some containing over 500 organisms. The trees can be viewed using iTOL, an advanced interactive tree viewer, enabling straightforward interpretation of large trees. Complex high-throughput tree analysis is also available through user-defined queries, allowing the rapid identification of trees of interest, e.g. containing putative HGT events. metaTIGER also provides novel and easy-to-use facilities for viewing and comparing the metabolic networks in different organisms via highlighted pathway images and tables. metaTIGER is demonstrated through evolutionary analysis of Plasmodium, including identification of genes horizontally transferred from chlamydia.

Prediction of horizontal gene transfers in eukaryotes: approaches and challenges.

HGT (horizontal gene transfer) is recognized as an important force in bacterial evolution. Now that many eukaryotic genomes have been sequenced, it has become possible to carry out studies of HGT in eukaryotes. The present review compares the different approaches that exist for identifying HGT genes and assess them in the context of studying eukaryotic evolution. The metabolic evolution resource metaTIGER is then described, with discussion of its application in identification of HGT in eukaryotes.

Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast-like synoviocytes.

Epigenetics contributes to the pathogenesis of immune-mediated diseases like rheumatoid arthritis (RA). Here we show the first comprehensive epigenomic characterization of RA fibroblast-like synoviocytes (FLS), including histone modifications (H3K27ac, H3K4me1, H3K4me3, H3K36me3, H3K27me3, and H3K9me3), open chromatin, RNA expression and whole-genome DNA methylation. To address complex multidimensional relationship and reveal epigenetic regulation of RA, we perform integrative analyses using a novel unbiased method to identify genomic regions with similar profiles. Epigenomically similar regions exist in RA cells and are associated with active enhancers and promoters and specific transcription factor binding motifs. Differentially marked genes are enriched for immunological and unexpected pathways, with "Huntington's Disease Signaling" identified as particularly prominent. We validate the relevance of this pathway to RA by showing that Huntingtin-interacting protein-1 regulates FLS invasion into matrix. This work establishes a high-resolution epigenomic landscape of RA and demonstrates the potential for integrative analyses to identify unanticipated therapeutic targets.

The Transcriptional Response to DNA-Double-Strand Breaks in Physcomitrella patens.

The model bryophyte Physcomitrella patens is unique among plants in supporting the generation of mutant alleles by facile homologous recombination-mediated gene targeting (GT). Reasoning that targeted transgene integration occurs through the capture of transforming DNA by the homology-dependent pathway for DNA double-strand break (DNA-DSB) repair, we analysed the genome-wide transcriptomic response to bleomycin-induced DNA damage and generated mutants in candidate DNA repair genes. Massively parallel (Illumina) cDNA sequencing identified potential participants in gene targeting. Transcripts encoding DNA repair proteins active in multiple repair pathways were significantly up-regulated. These included Rad51, CtIP, DNA ligase 1, Replication protein A and ATR in homology-dependent repair, Xrcc4, DNA ligase 4, Ku70 and Ku80 in non-homologous end-joining and Rad1, Tebichi/polymerase theta, PARP in microhomology-mediated end-joining. Differentially regulated cell-cycle components included up-regulated Rad9 and Hus1 DNA-damage-related checkpoint proteins and down-regulated D-type cyclins and B-type CDKs, commensurate with the imposition of a checkpoint at G2 of the cell cycle characteristic of homology-dependent DNA-DSB repair. Candidate genes, including ATP-dependent chromatin remodelling helicases associated with repair and recombination, were knocked out and analysed for growth defects, hypersensitivity to DNA damage and reduced GT efficiency. Targeted knockout of PpCtIP, a cell-cycle activated mediator of homology-dependent DSB resection, resulted in bleomycin-hypersensitivity and greatly reduced GT efficiency.

Constructing 3D interaction maps from 1D epigenomes.

The human genome is tightly packaged into chromatin whose functional output depends on both one-dimensional (1D) local chromatin states and three-dimensional (3D) genome organization. Currently, chromatin modifications and 3D genome organization are measured by distinct assays. An emerging question is whether it is possible to deduce 3D interactions by integrative analysis of 1D epigenomic data and associate 3D contacts to functionality of the interacting loci. Here we present EpiTensor, an algorithm to identify 3D spatial associations within topologically associating domains (TADs) from 1D maps of histone modifications, chromatin accessibility and RNA-seq. We demonstrate that active promoter-promoter, promoter-enhancer and enhancer-enhancer associations identified by EpiTensor are highly concordant with those detected by Hi-C, ChIA-PET and eQTL analyses at 200 bp resolution. Moreover, EpiTensor has identified a set of interaction hotspots, characterized by higher chromatin and transcriptional activity as well as enriched TF and ncRNA binding across diverse cell types, which may be critical for stabilizing the local 3D interactions.