Rebecca F. Lowdon

Identification of functional elements and regulatory circuits by Drosophila modENCODE

From Genome to Regulatory Networks For biologists, having a genome in hand is only the beginning—much more investigation is still needed to characterize how the genome is used to help to produce a functional organism (see the Perspective by Blaxter). In this vein, Gerstein et al. (p. 1775) summarize for the Caenorhabditis elegans genome, and The modENCODE Consortium (p. 1787) summarize for the Drosophila melanogaster genome, full transcriptome analyses over developmental stages, genome-wide identification of transcription factor binding sites, and high-resolution maps of chromatin organization. Both studies identified regions of the nematode and fly genomes that show highly occupied targets (or HOT) regions where DNA was bound by more than 15 of the transcription factors analyzed and the expression of related genes were characterized. Overall, the studies provide insights into the organization, structure, and function of the two genomes and provide basic information needed to guide and correlate both focused and genome-wide studies. The Drosophila modENCODE project demonstrates the functional regulatory network of flies. To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.

An encyclopedia of mouse DNA elements (Mouse ENCODE)

To complement the human Encyclopedia of DNA Elements (ENCODE) project and to enable a broad range of mouse genomics efforts, the Mouse ENCODE Consortium is applying the same experimental pipelines developed for human ENCODE to annotate the mouse genome.

DNA hypomethylation within specific transposable element families associates with tissue-specific enhancer landscape

Transposable element (TE)-derived sequences comprise half of the human genome and DNA methylome and are presumed to be densely methylated and inactive. Examination of genome-wide DNA methylation status within 928 TE subfamilies in human embryonic and adult tissues identified unexpected tissue-specific and subfamily-specific hypomethylation signatures. Genes proximal to tissue-specific hypomethylated TE sequences were enriched for functions important for the relevant tissue type, and their expression correlated strongly with hypomethylation within the TEs. When hypomethylated, these TE sequences gained tissue-specific enhancer marks, including monomethylation of histone H3 at lysine 4 (H3K4me1) and occupancy by p300, and a majority exhibited enhancer activity in reporter gene assays. Many such TEs also harbored binding sites for transcription factors that are important for tissue-specific functions and showed evidence of evolutionary selection. These data suggest that sequences derived from TEs may be responsible for wiring tissue type–specific regulatory networks and may have acquired tissue-specific epigenetic regulation.

Exploring long-range genome interactions using the WashU Epigenome Browser

To the Editor : Eukar yotic chromosomes are a highly organized three-dimensional entity folded through a tightly regulated process1,2 with important functions that include bringing distal regulatory elements into the vicinity of their target gene promoters and arranging the chromosomes into distinct compartments3–6. Recent technological innovations, including chromosome conformation capture carbon copy (5C), Hi-C and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), have facilitated the discovery of chromosomal organization principles and folding architectures at unprecedented scales and resolution. Each technology also comes with corresponding computational tools7,8 to process and visualize its specific data type (Supplementary Note 1). However, visualizing and navigating long-range interaction data, as well as integrating these interactions with other epigenomics data, remains a much-desired capability and a daunting challenge9. We have extended the WashU Epigenome Browser10 (http:// epigenomegateway.wustl.edu/), which currently hosts thousands of epigenome and transcriptome data sets for multiple cell types, tissues, individuals and species, to support multiple types of long-range genome interaction data. This enables investigators to explore epigenomic data in the context of higher-order chromosomal domains and to generate multiple types of intuitive, publication-quality figures of interactions (see tutorial in Supplementary Note 2). In Figure 1 we display the histonemodification profile and long-range interaction data of two human cell lines (IMR90 and K562) side by side and note that regions can exhibit similar interaction patterns while showing different histone modifications (such as the boundary region between domains 1 and 2) (Supplementary Methods). These observations are consistent with the hypothesis that chromatin domains are stable across cell types but can have different epigenetic profiles in different cells. Genes within each domain are regulated epigenetically in a cell type– specific manner5. Integrating higherorder chromatin interaction data with other genomic data could potentially reveal novel insights about mechanisms underlying gene and genome regulation. Pairs of interacting regions can be joined by arcs (Fig. 1b)—a suitable representation for sparse interactions—as is common with ChIA-PET and sometimes found in 5C data sets, or they can be indicated by filled rectangles in a heat map (Fig. 1c) for dense interactions (as is typical for Hi-C and some regions in 5C data sets). Investigators can click on the arcs or heat-map cells to invoke a companion Browser panel (Supplementary Fig. 1), which displays epigenomic data over the distal interacting locus. This companion panel can be navigated independently, enabling comparison of data patterns of interacting loci in the same view. Thus, investigators can observe several loci that are distant in their genomic coordinates but are inferred to be spatially close to each other in the nucleus. Both the “arc” and “heat-map” modes display only interactions that are contained within the current browsing range while omitting interactions beyond the range. To visualize the complete set of interactions, investigators can invoke the “Circlet View” (Supplementary Fig. 2), in which the chromosomal axis curls to form a circle and interactions are displayed as arcs inside the circle. Investigators can choose to display a single chromosome or to include interacting chromosomes to achieve a wholegenome perspective of the interactions. Investigators can also toggle between “thin,” “full” or “density” mode for any data track (Supplementary Figs. 3–5). “Gene Set View” and genomic juxtaposition can be combined with the long-range interaction function to focus on the interaction events in a subregion of the genome (Fig. 1). An investigator’s own long-range interaction data can be displayed on the Browser via the custom track or Data Hub function. The Browser currently hosts over 100 genomewide chromatin interaction data sets for human, mouse and fly. We expect such data to become increasingly available, and the browser will be a helpful tool for exploring how eukaryotic genomes function as nonlinear systems.

Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm

DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. This includes one of the most widely applied technologies for measuring DNA methylation: methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been unavailable. Here, we present a novel integrative statistical framework M&M (for integration of MeDIP-seq and MRE-seq) that dynamically scales, normalizes, and combines MeDIP-seq and MRE-seq data to detect differentially methylated regions. Using sample-matched whole-genome bisulfite sequencing (WGBS) as a gold standard, we demonstrate superior accuracy and reproducibility of M&M compared to existing analytical methods for MeDIP-seq data alone. M&M leverages the complementary nature of MeDIP-seq and MRE-seq data to allow rapid comparative analysis between whole methylomes at a fraction of the cost of WGBS. Comprehensive analysis of nineteen human DNA methylomes with M&M reveals distinct DNA methylation patterns among different tissue types, cell types, and individuals, potentially underscoring divergent epigenetic regulation at different scales of phenotypic diversity. We find that differential DNA methylation at enhancer elements, with concurrent changes in histone modifications and transcription factor binding, is common at the cell, tissue, and individual levels, whereas promoter methylation is more prominent in reinforcing fundamental tissue identities.

Developmental enhancers revealed by extensive DNA methylome maps of zebrafish early embryos

DNA methylation undergoes dynamic changes during development and cell differentiation. Recent genome-wide studies discovered that tissue-specific differentially methylated regions (DMRs) often overlap tissue-specific distal cis-regulatory elements. However, developmental DNA methylation dynamics of the majority of the genomic CpGs outside gene promoters and CpG islands has not been extensively characterized. Here we generate and compare comprehensive DNA methylome maps of zebrafish developing embryos. From these maps we identify thousands of developmental stage-specific DMRs (dsDMR) across zebrafish developmental stages. The dsDMRs contain evolutionarily conserved sequences, are associated with developmental genes, and are marked with active enhancer histone post-translational modifications. Their methylation pattern correlates much stronger than promoter methylation with expression of putative target genes. When tested in vivo using a transgenic zebrafish assay, 20 out of 20 selected candidate dsDMRs exhibit functional enhancer activities. Our data suggest that developmental enhancers are a major target of DNA methylation changes during embryogenesis.

Comparative DNA methylome analysis of endometrial carcinoma reveals complex and distinct deregulation of cancer promoters and enhancers

BackgroundAberrant DNA methylation is a hallmark of many cancers. Classically there are two types of endometrial cancer, endometrioid adenocarcinoma (EAC), or Type I, and uterine papillary serous carcinoma (UPSC), or Type II. However, the whole genome DNA methylation changes in these two classical types of endometrial cancer is still unknown.ResultsHere we described complete genome-wide DNA methylome maps of EAC, UPSC, and normal endometrium by applying a combined strategy of methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation-sensitive restriction enzyme digestion sequencing (MRE-seq). We discovered distinct genome-wide DNA methylation patterns in EAC and UPSC: 27,009 and 15,676 recurrent differentially methylated regions (DMRs) were identified respectively, compared with normal endometrium. Over 80% of DMRs were in intergenic and intronic regions. The majority of these DMRs were not interrogated on the commonly used Infinium 450K array platform. Large-scale demethylation of chromosome X was detected in UPSC, accompanied by decreased XIST expression. Importantly, we discovered that the majority of the DMRs harbored promoter or enhancer functions and are specifically associated with genes related to uterine development and disease. Among these, abnormal methylation of transposable elements (TEs) may provide a novel mechanism to deregulate normal endometrium-specific enhancers derived from specific TEs.ConclusionsDNA methylation changes are an important signature of endometrial cancer and regulate gene expression by affecting not only proximal promoters but also distal enhancers.

Evolution of Epigenetic Regulation in Vertebrate Genomes

Empirical models of sequence evolution have spurred progress in the field of evolutionary genetics for decades. We are now realizing the importance and complexity of the eukaryotic epigenome. While epigenome analysis has been applied to genomes from single-cell eukaryotes to human, comparative analyses are still relatively few and computational algorithms to quantify epigenome evolution remain scarce. Accordingly, a quantitative model of epigenome evolution remains to be established. We review here the comparative epigenomics literature and synthesize its overarching themes. We also suggest one mechanism, transcription factor binding site (TFBS) turnover, which relates sequence evolution to epigenetic conservation or divergence. Lastly, we propose a framework for how the field can move forward to build a coherent quantitative model of epigenome evolution.

Regulatory network decoded from epigenomes of surface ectoderm-derived cell types.

Developmental history shapes the epigenome and biological function of differentiated cells. Epigenomic patterns have been broadly attributed to the three embryonic germ layers. Here we investigate how developmental origin influences epigenomes. We compare key epigenomes of cell types derived from surface ectoderm (SE), including keratinocytes and breast luminal and myoepithelial cells, against neural crest-derived melanocytes and mesoderm-derived dermal fibroblasts to identify SE differentially methylated regions (SE-DMRs). DNA methylomes of neonatal keratinocytes share many more DMRs with adult breast luminal and myoepithelial cells than with melanocytes and fibroblasts from the same neonatal skin. This suggests that SE origin contributes to DNA methylation patterning, while shared skin tissue environment has limited effect on epidermal keratinocytes. Hypomethylated SE-DMRs are in proximity to genes with SE relevant functions. They are also enriched for enhancer- and promoter-associated histone modifications in SE-derived cells, and for binding motifs of transcription factors important in keratinocyte and mammary gland biology. Thus, epigenomic analysis of cell types with common developmental origin reveals an epigenetic signature that underlies a shared gene regulatory network.

methylC Track: visual integration of single-base resolution DNA methylation data on the WashU EpiGenome Browser

SUMMARY We present methylC track, an efficient mechanism for visualizing single-base resolution DNA methylation data on a genome browser. The methylC track dynamically integrates the level of methylation, the position and context of the methylated cytosine (i.e. CG, CHG and CHH), strand and confidence level (e.g. read coverage depth in the case of whole-genome bisulfite sequencing data). Investigators can access and integrate these information visually at specific locus or at the genome-wide level on the WashU EpiGenome Browser in the context of other rich epigenomic datasets. AVAILABILITY AND IMPLEMENTATION The methylC track is part of the WashU EpiGenome Browser, which is open source and freely available at http://epigenomegateway.wustl.edu/browser/. The most up-to-date instructions and tools for preparing methylC track are available at http://epigenomegateway.wustl.edu/+/cmtk. CONTACT twang@genetics.wustl.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.