We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor–binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor–binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.

Epigenetic reprogramming is commonly observed in cancer, and is hypothesized to involve multiple mechanisms, including DNA methylation and Polycomb repressive complexes (PRCs). Here we devise a new experimental and analytical strategy using customized highdensity tiling arrays to investigate coordinated patterns of gene expression, DNA methylation, and Polycomb marks which differentiate prostate cancer cells from their normal counterparts. Three major changes in the epigenomic landscape distinguish the two cell types. Developmentally significant genes containing CpG islands which are silenced by PRCs in the normal cells acquire DNA methylation silencing and lose their PRC marks (epigenetic switching). Because these genes are normally silent this switch does not cause de novo repression but might significantly reduce epigenetic plasticity. Two other groups of genes are silenced by either de novo DNA methylation without PRC occupancy (5mC reprogramming) or by de novo PRC occupancy without DNA methylation (PRC reprogramming). Our data suggest that the two silencing mechanisms act in parallel to reprogram the cancer epigenome and that DNA hypermethylation may replace Polycomb-based repression near key regulatory genes, possibly reducing their regulatory plasticity.spatial clustering ͉ DNA methylation ͉ MeDIP normalization ͉ Polycomb B iochemical processes including DNA cytosine methylation and histone modifications interact with each other to ensure the stability of epigenetic states. These processes are clearly altered in cancer and contribute to the establishment and maintenance of the malignant phenotype (1). Recent discoveries and technological developments have greatly expanded our understanding of the cell's epigenetic makeup, revealing a rich repertoire of histone modifications and protein complexes that regulate them (2, 3). Chromatin regulating complexes, and most notably the family of Polycomb repressive complexes (PRCs), which mediate trimethylation at H3K27, are highly active in cancer cells (4). Studies of Polycomb activity in pluripotent embryonic stem cells (ESC) (5-7) revealed broad patterns of Polycomb-based repression near key developmental regulators, many of which are known DNA hypermethylation targets in cancer (8). It is therefore pertinent to take an integrated look at both DNA methylation and histone modifications simultaneously in a coherent cell set in which the appropriate presumed normal cell counterpart is compared to its malignant state.The evidence on interaction between PRCs and the DNA methylation machinery is partial and sometimes conflicting. The correlations between ESC PRC targets and cancer hypermethylation (8), or the reported enrichment of H3K27me3 marks at specific hypermethylated CpG islands (9), have led to multiple mechanistic hypotheses on the underlying process. The lack of DNA hypermethylation at PRC occupied regions in embryonic carcinoma cells (10) and of DNA hypomethylation after knockdown of a PRC2 component (EZH2) in cancer cells (11) further demonstrate that the i...

The most widely used method for detecting genome-wide protein–DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and “spike-ins” comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated.

The technique of chromatin immunoprecipitation (ChIP) is a powerful method for identifying in vivo DNA binding sites of transcription factors and for studying chromatin modifications. Unfortunately, the large number of cells needed for the standard ChIP protocol has hindered the analysis of many biologically interesting cell populations that are difficult to obtain in large numbers. New ChIP methods involving the use of carrier chromatin have been developed that allow the one-gene-at-a-time analysis of very small numbers of cells. However such methods are not useful if the resultant sample will be applied to genomic microarrays or used in ChIP-sequencing assays. Therefore, we have miniaturized the ChIP protocol such that as few as 10,000 cells (without the addition of carrier reagents) can be used to obtain enough sample material to analyze the entire human genome. We demonstrate the reproducibility of this MicroChIP technique using 2.1 million feature high-density oligonucleotide arrays and antibodies to RNA polymerase II and to histone H3 trimethylated on lysine 27 or lysine 9.

Pancreatic cancer is a highly lethal cancer with few well established risk factors. A genome wide association study (GWAS) of pancreatic cancer (PanScan) is being conducted within the framework of the NCI-sponsored Cohort Consortium and the Pancreatic Cancer Case-Control Consortium (PANC4). Susceptibility loci discovered to date in PanScan are non-coding variants that lie in intronic or intergenic regions, suggesting that the underlying signals may function through regulatory mechanisms that influence gene expression or splicing. Alternatively, these may lie in unannotated transcripts and directly affect their function. Epigenetic mechanisms, such as DNA methylation, can affect the regulation of gene expression and plays a critical role in the development of many human diseases including cancer. Powerful methods exist to analyze DNA methylation patterns in higher eukaryotes including methylated DNA immunoprecipitation (MeDIP), an affinity based approach to enrich methylated DNA regions from genomic DNA, which can be combined with microarrays to profile genomic DNA methylation patterns. We created a new semi-custom MeDIP-optimized array design based on our Human DNA Methylation 3×720K CpG Island Plus RefSeq Promoter array by tiling additional regions associated with pancreatic cancer that were identified in the PanScan study. To complement DNA methylation data a genome-wide transcriptome analysis was performed with RNA-sequencing (RNA-seq). Here we describe a comprehensive genome wide analysis of 8 pancreatic cancer cell lines to examine methylation patterns of Refseq promoters and annotated CpG islands as well as transcribed sequences. One of the susceptibility loci from PanScan is in the vicinity of the ABO gene on Chr9q34 where four SNPs (rs505922, rs495828, rs657152 and rs630014) are associated with a significantly increased risk of pancreatic cancer. As a pilot study, DNA methylation patterns and expressed sequences in this locus were investigated in cell lines derived from pancreatic tumors and normal pancreatic tissues. Our genome wide DNA methylation and RNA-seq analysis aims at establishing a comprehensive catalog of epigenetic patterns in pancreatic cell lines that could provide plausibility for the association signal in the ABO gene and other GWAS regions and thereby, initiate the characterization of the molecular phenotype of the susceptibility alleles for pancreatic cancer risk. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 164.