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1.  Epigenome Mapping Reveals Distinct Modes of Gene Regulation and Widespread Enhancer Reprogramming by the Oncogenic Fusion Protein EWS-FLI1 
Cell Reports  2015;10(7):1082-1095.
Summary
Transcription factor fusion proteins can transform cells by inducing global changes of the transcriptome, often creating a state of oncogene addiction. Here, we investigate the role of epigenetic mechanisms in this process, focusing on Ewing sarcoma cells that are dependent on the EWS-FLI1 fusion protein. We established reference epigenome maps comprising DNA methylation, seven histone marks, open chromatin states, and RNA levels, and we analyzed the epigenome dynamics upon downregulation of the driving oncogene. Reduced EWS-FLI1 expression led to widespread epigenetic changes in promoters, enhancers, and super-enhancers, and we identified histone H3K27 acetylation as the most strongly affected mark. Clustering of epigenetic promoter signatures defined classes of EWS-FLI1-regulated genes that responded differently to low-dose treatment with histone deacetylase inhibitors. Furthermore, we observed strong and opposing enrichment patterns for E2F and AP-1 among EWS-FLI1-correlated and anticorrelated genes. Our data describe extensive genome-wide rewiring of epigenetic cell states driven by an oncogenic fusion protein.
Graphical Abstract
Highlights
•Reference epigenome maps identify widespread epigenetic change in Ewing sarcoma cells•EWS-FLI1-regulated genes fall into clusters with characteristic chromatin signatures•Transcriptome response to HDAC inhibitors depends on promoter-specific histone marks•EWS-FLI1 induces global changes in H3K27ac and genome-wide enhancer reprogramming
EWS-FLI1 is an oncogenic fusion protein and the main driver of Ewing sarcoma. Tomazou et al. establish comprehensive epigenome maps for an EWS-FLI1-dependent cell line. Based on these data, they identify clusters of epigenetically regulated genes and a unique enhancer signature that is associated with EWS-FLI1 oncogene addiction.
doi:10.1016/j.celrep.2015.01.042
PMCID: PMC4542316  PMID: 25704812
2.  Genome-wide mapping of DNA methylation: a quantitative technology comparison 
Nature biotechnology  2010;28(10):1106-1114.
DNA methylation is a key component of mammalian gene regulation and the most classical example of an epigenetic mark. DNA methylation patterns are mitotically heritable and stable over time, but they undergo considerable changes in response to cell differentiation, diseases and environmental influences. Several methods have been developed for DNA methylation profiling on a genomic scale. Here, we benchmark four of these methods on two sample pairs, comparing their accuracy and power to detect DNA methylation differences. The results show that all evaluated methods (MeDIP-seq: methylated DNA immunoprecipitation, MethylCap-seq: methylated DNA capture by affinity purification, RRBS: reduced representation bisulfite sequencing, and the Infinium HumanMethylation27 assay) produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies.
doi:10.1038/nbt.1681
PMCID: PMC3066564  PMID: 20852634
Epigenome profiling; epigenetics; sequencing; differentially methylated regions; molecular diagnostics; biomarker discovery; cancer
3.  A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis 
Nature biotechnology  2008;26(7):779-785.
DNA methylation is an indispensible epigenetic modification of mammalian genomes. Consequently there is great interest in strategies for genome-wide/whole-genome DNA methylation analysis, and immunoprecipitation-based methods have proven to be a powerful option. Such methods are rapidly shifting the bottleneck from data generation to data analysis, necessitating the development of better analytical tools. Until now, a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling has been the inability to estimate absolute methylation levels. Here we report the development of a novel cross-platform algorithm – Bayesian Tool for Methylation Analysis (Batman) – for analyzing Methylated DNA Immunoprecipitation (MeDIP) profiles generated using arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). The latter is an approach we have developed to elucidate the first high-resolution whole-genome DNA methylation profile (DNA methylome) of any mammalian genome. MeDIP-seq/MeDIP-chip combined with Batman represent robust, quantitative, and cost-effective functional genomic strategies for elucidating the function of DNA methylation.
doi:10.1038/nbt1414
PMCID: PMC2644410  PMID: 18612301
4.  Generation of a genomic tiling array of the human Major Histocompatibility Complex (MHC) and its application for DNA methylation analysis 
BMC Medical Genomics  2008;1:19.
Background
The major histocompatibility complex (MHC) is essential for human immunity and is highly associated with common diseases, including cancer. While the genetics of the MHC has been studied intensively for many decades, very little is known about the epigenetics of this most polymorphic and disease-associated region of the genome.
Methods
To facilitate comprehensive epigenetic analyses of this region, we have generated a genomic tiling array of 2 Kb resolution covering the entire 4 Mb MHC region. The array has been designed to be compatible with chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), array comparative genomic hybridization (aCGH) and expression profiling, including of non-coding RNAs. The array comprises 7832 features, consisting of two replicates of both forward and reverse strands of MHC amplicons and appropriate controls.
Results
Using MeDIP, we demonstrate the application of the MHC array for DNA methylation profiling and the identification of tissue-specific differentially methylated regions (tDMRs). Based on the analysis of two tissues and two cell types, we identified 90 tDMRs within the MHC and describe their characterisation.
Conclusion
A tiling array covering the MHC region was developed and validated. Its successful application for DNA methylation profiling indicates that this array represents a useful tool for molecular analyses of the MHC in the context of medical genomics.
doi:10.1186/1755-8794-1-19
PMCID: PMC2430202  PMID: 18513384

Results 1-4 (4)