ENCODE Consortium Research Articles

The long-range interaction landscape of gene promoters.

The vast non-coding portion of the human genome is awash in functional elements and disease-causing regulatory variants. The principles defining the relationships between these elements and distal target genes remain unknown. Promoters and distal elements can engage in looping interactions that have been implicated in gene regulation1. Here we have applied chromosome conformation capture carbon copy, 5C2, to comprehensively interrogate interactions between transcription start sites (TSSs) and distal elements in 1% of the human genome representing the ENCODE pilot project regions3. 5C maps were generated for GM12878, K562 and HeLa-S3 cells and results were integrated with data from the ENCODE consortium4. In each cell line we discovered >1,000 long-range interactions between promoters and distal sites that include elements resembling enhancers, promoters and CTCF-bound sites. We observed significant correlations between gene expression, promoter-enhancer interactions and the presence of enhancer RNAs. Long-range interactions display striking asymmetry with a bias for interactions with elements located ~120 Kb upstream of the TSS. Long-range interactions are often not blocked by sites bound by CTCF and cohesin implying that many of these sites do not demarcate physically insulated gene domains. Further, only ~7% of looping interactions are with the nearest gene, suggesting that genomic proximity is not a simple predictor for long-range interactions. Finally, promoters and distal elements are engaged in multiple long-range interactions to form complex networks. Our results start to place genes and regulatory elements in three-dimensional context, revealing their functional relationships.

Sequence and chromatin determinants of cell-type–specific transcription factor binding

Gene regulatory programs in distinct cell types are maintained in large part through the cell-type–specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type–specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line–specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type–specific binding patterns, four display distinct cell-type–specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type–specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type–specific binding profiles.

Ubiquitous heterogeneity and asymmetry of the chromatin environment at regulatory elements

Gene regulation at functional elements (e.g., enhancers, promoters, insulators) is governed by an interplay of nucleosome remodeling, histone modifications, and transcription factor binding. To enhance our understanding of gene regulation, the ENCODE Consortium has generated a wealth of ChIP-seq data on DNA-binding proteins and histone modifications. We additionally generated nucleosome positioning data on two cell lines, K562 and GM12878, by MNase digestion and high-depth sequencing. Here we relate 14 chromatin signals (12 histone marks, DNase, and nucleosome positioning) to the binding sites of 119 DNA-binding proteins across a large number of cell lines. We developed a new method for unsupervised pattern discovery, the Clustered AGgregation Tool (CAGT), which accounts for the inherent heterogeneity in signal magnitude, shape, and implicit strand orientation of chromatin marks. We applied CAGT on a total of 5084 data set pairs to obtain an exhaustive catalog of high-resolution patterns of histone modifications and nucleosome positioning signals around bound transcription factors. Our analyses reveal extensive heterogeneity in how histone modifications are deposited, and how nucleosomes are positioned around binding sites. With the exception of the CTCF/cohesin complex, asymmetry of nucleosome positioning is predominant. Asymmetry of histone modifications is also widespread, for all types of chromatin marks examined, including promoter, enhancer, elongation, and repressive marks. The fine-resolution signal shapes discovered by CAGT unveiled novel correlation patterns between chromatin marks, nucleosome positioning, and sequence content. Meta-analyses of the signal profiles revealed a common vocabulary of chromatin signals shared across multiple cell lines and binding proteins.

Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors

Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) has become the dominant technique for mapping transcription factor (TF) binding regions genome-wide. We performed an integrative analysis centered around 457 ChIP-seq data sets on 119 human TFs generated by the ENCODE Consortium. We identified highly enriched sequence motifs in most data sets, revealing new motifs and validating known ones. The motif sites (TF binding sites) are highly conserved evolutionarily and show distinct footprints upon DNase I digestion. We frequently detected secondary motifs in addition to the canonical motifs of the TFs, indicating tethered binding and cobinding between multiple TFs. We observed significant position and orientation preferences between many cobinding TFs. Genes specifically expressed in a cell line are often associated with a greater occurrence of nearby TF binding in that cell line. We observed cell-line–specific secondary motifs that mediate the binding of the histone deacetylase HDAC2 and the enhancer-binding protein EP300. TF binding sites are located in GC-rich, nucleosome-depleted, and DNase I sensitive regions, flanked by well-positioned nucleosomes, and many of these features show cell type specificity. The GC-richness may be beneficial for regulating TF binding because, when unoccupied by a TF, these regions are occupied by nucleosomes in vivo. We present the results of our analysis in a TF-centric web repository Factorbook (http://factorbook.org) and will continually update this repository as more ENCODE data are generated.

Combining RT-PCR-seq and RNA-seq to catalog all genic elements encoded in the human genome.

Within the ENCODE Consortium, GENCODE aimed to accurately annotate all protein-coding genes, pseudogenes, and noncoding transcribed loci in the human genome through manual curation and computational methods. Annotated transcript structures were assessed, and less well-supported loci were systematically, experimentally validated. Predicted exon–exon junctions were evaluated by RT-PCR amplification followed by highly multiplexed sequencing readout, a method we called RT-PCR-seq. Seventy-nine percent of all assessed junctions are confirmed by this evaluation procedure, demonstrating the high quality of the GENCODE gene set. RT-PCR-seq was also efficient to screen gene models predicted using the Human Body Map (HBM) RNA-seq data. We validated 73% of these predictions, thus confirming 1168 novel genes, mostly noncoding, which will further complement the GENCODE annotation. Our novel experimental validation pipeline is extremely sensitive, far more than unbiased transcriptome profiling through RNA sequencing, which is becoming the norm. For example, exon–exon junctions unique to GENCODE annotated transcripts are five times more likely to be corroborated with our targeted approach than with extensive large human transcriptome profiling. Data sets such as the HBM and ENCODE RNA-seq data fail sampling of low-expressed transcripts. Our RT-PCR-seq targeted approach also has the advantage of identifying novel exons of known genes, as we discovered unannotated exons in ∼11% of assessed introns. We thus estimate that at least 18% of known loci have yet-unannotated exons. Our work demonstrates that the cataloging of all of the genic elements encoded in the human genome will necessitate a coordinated effort between unbiased and targeted approaches, like RNA-seq and RT-PCR-seq.

Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages

We have analyzed publicly available K562 Hi-C data, which enable genome-wide unbiased capturing of chromatin interactions, using a Mixture Poisson Regression Model and a power-law decay background to define a highly specific set of interacting genomic regions. We integrated multiple ENCODE Consortium resources with the Hi-C data, using DNase-seq data and ChIP-seq data for 45 transcription factors and 9 histone modifications. We classified 12 different sets (clusters) of interacting loci that can be distinguished by their chromatin modifications and which can be categorized into two types of chromatin linkages. The different clusters of loci display very different relationships with transcription factor-binding sites. As expected, many of the transcription factors show binding patterns specific to clusters composed of interacting loci that encompass promoters or enhancers. However, cluster 9, which is distinguished by marks of open chromatin but not by active enhancer or promoter marks, was not bound by most transcription factors but was highly enriched for three transcription factors (GATA1, GATA2 and c-Jun) and three chromatin modifiers (BRG1, INI1 and SIRT6). To investigate the impact of chromatin organization on gene regulation, we performed ribonucleicacid-seq analyses before and after knockdown of GATA1 or GATA2. We found that knockdown of the GATA factors not only alters the expression of genes having a nearby bound GATA but also affects expression of genes in interacting loci. Our work, in combination with previous studies linking regulation by GATA factors with c-Jun and BRG1, provides genome-wide evidence that Hi-C data identify sets of biologically relevant interacting loci.

Linking the Epigenome to the Genome: Correlation of Different Features to DNA Methylation of CpG Islands

DNA methylation of CpG islands plays a crucial role in the regulation of gene expression. More than half of all human promoters contain CpG islands with a tissue-specific methylation pattern in differentiated cells. Still today, the whole process of how DNA methyltransferases determine which region should be methylated is not completely revealed. There are many hypotheses of which genomic features are correlated to the epigenome that have not yet been evaluated. Furthermore, many explorative approaches of measuring DNA methylation are limited to a subset of the genome and thus, cannot be employed, e.g., for genome-wide biomarker prediction methods. In this study, we evaluated the correlation of genetic, epigenetic and hypothesis-driven features to DNA methylation of CpG islands. To this end, various binary classifiers were trained and evaluated by cross-validation on a dataset comprising DNA methylation data for 190 CpG islands in HEPG2, HEK293, fibroblasts and leukocytes. We achieved an accuracy of up to 91% with an MCC of 0.8 using ten-fold cross-validation and ten repetitions. With these models, we extended the existing dataset to the whole genome and thus, predicted the methylation landscape for the given cell types. The method used for these predictions is also validated on another external whole-genome dataset. Our results reveal features correlated to DNA methylation and confirm or disprove various hypotheses of DNA methylation related features. This study confirms correlations between DNA methylation and histone modifications, DNA structure, DNA sequence, genomic attributes and CpG island properties. Furthermore, the method has been validated on a genome-wide dataset from the ENCODE consortium. The developed software, as well as the predicted datasets and a web-service to compare methylation states of CpG islands are available at http://www.cogsys.cs.uni-tuebingen.de/software/dna-methylation/.

Why is DNA methylation of Igf2 CpG island shore affected during ageing?

In post-genomic era attention has been focused on the characterization of the human DNA “methylome” at single nucleotide resolution. It is a particularly difficult goal to achieve as methylation profiling is different in tissues and cells of the same individual. 5- Methylcytosine is considered the “fifth base” of DNA as it introduces an epigenetic code on genome which is defined during development. Nutrition, environment and style of life have an impact on the “methylome” and the introduction of anomalous methyl groups in sequences that have to be maintained unmethylated and/or demethylation of chromatin regions which are usually methylated drive the biological events associated with pathologies. The same anomalies in methylation patterns occur with age so an in-depth knowledge of these changes will help us to individuate what leads to the passage from successful ageing to one in which there is the onset of disease [1]. Data from Franceschi's group are focused on ageing and the cohorts of individuals are selected in order to establish whether variability in DNA methylation patterns is to ascribe to age of individuals rather than to their geographical dimension. Notably, in this study monozygotic and dizygotic twins of ages ranging from 22 to 97 years are also enrolled. Four regions, located at the Igf2/H19 imprinted locus, were analyzed including the CpG island present in differentially methylated region 2 (DMR2) and the nearby 5’ shore, two fragments never investigated up to now. Authors conclude that the shore is the one in which the scatter in methylation variability during ageing is more evident. Shore regions, which localize in proximity of CpG islands at 200-2000 kb away from them, are of particular interest as involved in gene expression regulation through the DMRs often located within them [2]. A long-range looping interaction is involved in the control of imprinting at maternally inherited allele at the Igf2/H19 gene locus. An interesting hypothesis for susceptibility to epimutation of this shore region in aged individuals is that its position inside the loop is topologically responsible for its availability to change the methylation profile. Although the higher order chromatin organization at Igf2/H19 locus is well assessed in mice, less information is available in humans. Anyway, the analysis of data about chromatin structure from the ENCODE consortium, accessible at the UCSC Genome Browser (http://genome.ucsc.edu/) [3, 4], indicates a distinctive chromatin feature of this shore region which corresponds to an open, DNAaseI hypersensitive chromatin domain characterized by an early S-phase replication. As chromatin structure affects the susceptibility to DNA damage, the chromatin shape of the shore could be the reason for its susceptibility to epimutation. An important link was found between DNA damage, homology directed repair and DNA methylation [5], suggesting that DNA-methyltransferase 1(DNMT1), which is recruited to repair sites following double-strand DNA breaks [6], marks the repaired segments superimposing novel methylation profile. Another interesting hypothesis rises from the early replication timing of this shore. The maintenance of DNA methylation patterns during DNA replication requires the function of DNMT1 which is brought to the replication fork through a direct interaction with Proliferating Cell Nuclear Antigen protein (PCNA). Interestingly, DNMT1 forms a complex at replication foci only during late stages of S-phase when methylated DNA is replicated. Thus, the controlled recruitment of DNMT1 into replication forks is an important component in the perpetuation of DNA methylation patterns and S-phase abnormalities could affect methylation patterns by altering the replication timing of sequences. The biological event able to disrupt DNMT1 function in S phase could be poly(ADP-ribosyl)ation. It has been found that competitive inhibition of PARPs leads to hyperexpression of DNMT1 in G1/early S phase, increases the amount of DNMT1 that co-immunoprecipitates with PCNA in this phase and causes DNA hypermethylation [7]. Interestingly, reduced levels of poly(ADP-ribosyl)ation enzymes PARP1 and 2 and poly(ADP-ribosyl)ation activity have been associated with ageing [8, 9]. In conclusion, changes of methylation profile at Igf2 CpG island shore observed during ageing could be due to the loss of local epigenetic control of this region. The understanding of molecular mechanism(s) involved in the protection of the methylation pattern of this shore could provide important information about the participation of epigenetics in ageing and disease.

Conserved cis-regulatory regions in a large genomic landscape control SHH and BMP-regulated Gremlin1expression in mouse limb buds

BackgroundMouse limb bud is a prime model to study the regulatory interactions that control vertebrate organogenesis. Major aspects of limb bud development are controlled by feedback loops that define a self-regulatory signalling system. The SHH/GREM1/AER-FGF feedback loop forms the core of this signalling system that operates between the posterior mesenchymal organiser and the ectodermal signalling centre. The BMP antagonist Gremlin1 (GREM1) is a critical node in this system, whose dynamic expression is controlled by BMP, SHH, and FGF signalling and key to normal progression of limb bud development. Previous analysis identified a distant cis-regulatory landscape within the neighbouring Formin1 (Fmn1) locus that is required for Grem1 expression, reminiscent of the genomic landscapes controlling HoxD and Shh expression in limb buds.ResultsThree highly conserved regions (HMCO1-3) were identified within the previously defined critical genomic region and tested for their ability to regulate Grem1 expression in mouse limb buds. Using a combination of BAC and conventional transgenic approaches, a 9 kb region located ~70 kb downstream of the Grem1 transcription unit was identified. This region, termed Grem1 Regulatory Sequence 1 (GRS1), is able to recapitulate major aspects of Grem1 expression, as it drives expression of a LacZ reporter into the posterior and, to a lesser extent, in the distal-anterior mesenchyme. Crossing the GRS1 transgene into embryos with alterations in the SHH and BMP pathways established that GRS1 depends on SHH and is modulated by BMP signalling, i.e. integrates inputs from these pathways. Chromatin immunoprecipitation revealed interaction of endogenous GLI3 proteins with the core cis-regulatory elements in the GRS1 region. As GLI3 is a mediator of SHH signal transduction, these results indicated that SHH directly controls Grem1 expression through the GRS1 region. Finally, all cis-regulatory regions within the Grem1 genomic landscape locate to the DNAse I hypersensitive sites identified in this genomic region by the ENCODE consortium.ConclusionsThis study establishes that distant cis-regulatory regions scattered through a larger genomic landscape control the highly dynamic expression of Grem1, which is key to normal progression of mouse limb bud development.

Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression

BackgroundIn the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a complex network that regulate the state of chromatin and processes that depend on it including transcription and DNA replication. Currently, a large number of these epigenetic modifications are being mapped in a variety of cell lines at different stages of development using high throughput sequencing by members of the ENCODE consortium, the NIH Roadmap Epigenomics Program and the Human Epigenome Project. An extremely promising and underexplored area of research is the application of machine learning methods, which are designed to construct predictive network models, to these large-scale epigenomic data sets.ResultsUsing a ChIP-Seq data set of 20 histone lysine and arginine methylations and histone variant H2A.Z in human CD4+ T-cells, we built predictive models of gene expression as a function of histone modification/variant levels using Multilinear (ML) Regression and Multivariate Adaptive Regression Splines (MARS). Along with extensive crosstalk among the 20 histone methylations, we found H4R3me2 was the most and second most globally repressive histone methylation among the 20 studied in the ML and MARS models, respectively. In support of our finding, a number of experimental studies show that PRMT5-catalyzed symmetric dimethylation of H4R3 is associated with repression of gene expression. This includes a recent study, which demonstrated that H4R3me2 is required for DNMT3A-mediated DNA methylation--a known global repressor of gene expression.ConclusionIn stark contrast to univariate analysis of the relationship between H4R3me2 and gene expression levels, our study showed that the regulatory role of some modifications like H4R3me2 is masked by confounding variables, but can be elucidated by multivariate/systems-level approaches.

Identifying determinants of chromosomal domains as defined by time of replication

The genome is organized into functional domains in which gene expression, chromatin modifications, and DNA replication is coordinately regulated. The time at which a genomic locus replicates during S phase appears to be dependent on subnuclear positioning and chromatin conformation. Recently, our lab generated a high resolution replication timing profile for 30 Mb of the human genome (1%) as part of the ENCODE consortium. Using a computational approach, we have identified regions of rapid transition in replication timing that coincide with changes in chromatin modifications. We hypothesize that these regions contain genomic elements, such as nuclear matrix attachment regions and binding sites for insulator proteins, that that are responsible for the organization of chromatin into functional domains. To test this hypothesis, we are mapping matrix attachment sites in HeLa cells and utilizing enhancer‐blocking insulator assays to identify potential boundary elements in these regions. These results will yield a better understanding of how replication domains are organized and insight to the mechanisms that regulate chromatin domain structure. This work is supported by NIH R01 CA089406 to A.D.

The role of local transcription and chromatin structure in establishing DNA replication origins

In order to duplicate the genome within S‐phase, DNA replication must initiate at multiple start sites. We are using genomic approaches in the model organism Drosophila to elucidate how origins of replication are selected and regulated to maintain genomic stability. As part of the model organism ENCODE consortium we have characterized the average time of replication for all unique sequences, mapped early origins of replication and identified sites of preRC localization in multiple cell lines. We have found that ORC preferentially localizes to open chromatin near the transcription start site of active genes. ORC binding sites are enriched for the histone variant H3.3 which marks open and dynamic chromatin. The large number of ORC binding sites has provided us the opportunity to use machine learning algorithms to identify complex sequence elements that direct ORC localization. We have also found that the single X chromosome of male cell lines replicates significantly earlier than the autosomes, suggesting a link between DNA replication and dosage compensation. Funding from NIH grant HG004279.

Efficient Algorithms for Probing the RNA Mutation Landscape

The diversity and importance of the role played by RNAs in the regulation and development of the cell are now well-known and well-documented. This broad range of functions is achieved through specific structures that have been (presumably) optimized through evolution. State-of-the-art methods, such as McCaskill's algorithm, use a statistical mechanics framework based on the computation of the partition function over the canonical ensemble of all possible secondary structures on a given sequence. Although secondary structure predictions from thermodynamics-based algorithms are not as accurate as methods employing comparative genomics, the former methods are the only available tools to investigate novel RNAs, such as the many RNAs of unknown function recently reported by the ENCODE consortium. In this paper, we generalize the McCaskill partition function algorithm to sum over the grand canonical ensemble of all secondary structures of all mutants of the given sequence. Specifically, our new program, RNAmutants, simultaneously computes for each integer k the minimum free energy structure MFE(k) and the partition function Z(k) over all secondary structures of all k-point mutants, even allowing the user to specify certain positions required not to mutate and certain positions required to base-pair or remain unpaired. This technically important extension allows us to study the resilience of an RNA molecule to pointwise mutations. By computing the mutation profile of a sequence, a novel graphical representation of the mutational tendency of nucleotide positions, we analyze the deleterious nature of mutating specific nucleotide positions or groups of positions. We have successfully applied RNAmutants to investigate deleterious mutations (mutations that radically modify the secondary structure) in the Hepatitis C virus cis-acting replication element and to evaluate the evolutionary pressure applied on different regions of the HIV trans-activation response element. In particular, we show qualitative agreement between published Hepatitis C and HIV experimental mutagenesis studies and our analysis of deleterious mutations using RNAmutants. Our work also predicts other deleterious mutations, which could be verified experimentally. Finally, we provide evidence that the 3′ UTR of the GB RNA virus C has been optimized to preserve evolutionarily conserved stem regions from a deleterious effect of pointwise mutations. We hope that there will be long-term potential applications of RNAmutants in de novo RNA design and drug design against RNA viruses. This work also suggests potential applications for large-scale exploration of the RNA sequence-structure network. Binary distributions are available at http://RNAmutants.csail.mit.edu/.

Encyclopaedia of humble DNA

Researchers of the ENCODE consortium have analysed 1% of the human genome. Their findings bring us a step closer to understanding the role of the vast amount of obscure DNA that does not function as genes.

PhenCode: connecting ENCODE data with mutations and phenotype

PhenCode (Phenotypes for ENCODE; http://www.bx.psu.edu/phencode) is a collaborative, exploratory project to help understand phenotypes of human mutations in the context of sequence and functional data from genome projects. Currently, it connects human phenotype and clinical data in various locus-specific databases (LSDBs) with data on genome sequences, evolutionary history, and function from the ENCODE project and other resources in the UCSC Genome Browser. Initially, we focused on a few selected LSDBs covering genes encoding alpha- and beta-globins (HBA, HBB), phenylalanine hydroxylase (PAH), blood group antigens (various genes), androgen receptor (AR), cystic fibrosis transmembrane conductance regulator (CFTR), and Bruton's tyrosine kinase (BTK), but we plan to include additional loci of clinical importance, ultimately genomewide. We have also imported variant data and associated OMIM links from Swiss-Prot. Users can find interesting mutations in the UCSC Genome Browser (in a new Locus Variants track) and follow links back to the LSDBs for more detailed information. Alternatively, they can start with queries on mutations or phenotypes at an LSDB and then display the results at the Genome Browser to view complementary information such as functional data (e.g., chromatin modifications and protein binding from the ENCODE consortium), evolutionary constraint, regulatory potential, and/or any other tracks they choose. We present several examples illustrating the power of these connections for exploring phenotypes associated with functional elements, and for identifying genomic data that could help to explain clinical phenotypes.

Early History of Mammals Is Elucidated with the ENCODE Multiple Species Sequencing Data

Understanding the early evolution of placental mammals is one of the most challenging issues in mammalian phylogeny. Here, we addressed this question by using the sequence data of the ENCODE consortium, which include 1% of mammalian genomes in 18 species belonging to all main mammalian lineages. Phylogenetic reconstructions based on an unprecedented amount of coding sequences taken from 218 genes resulted in a highly supported tree placing the root of Placentalia between Afrotheria and Exafroplacentalia (Afrotheria hypothesis). This topology was validated by the phylogenetic analysis of a new class of genomic phylogenetic markers, the conserved noncoding sequences. Applying the tests of alternative topologies on the coding sequence dataset resulted in the rejection of the Atlantogenata hypothesis (Xenarthra grouping with Afrotheria), while this test rejected the second alternative scenario, the Epitheria hypothesis (Xenarthra at the base), when using the noncoding sequence dataset. Thus, the two datasets support the Afrotheria hypothesis; however, none can reject both of the remaining topological alternatives.

Genomic Distribution of Transcripts and DNA Associated Proteins in One Percent of the Genome of Erythroid and Myeloid Cells.

The NIHGRI has sponsored a multi-group enterprise to use a variety of approaches to map DNA elements in one per cent of the human genome-the ENCODE Project. As part of this effort we have mapped sites of active transcription (transcriptionally active regions or TARS) for myeloid cells including the undifferentiated and retinoic acid treated promyelocytic NB4 cell line and normal neutrophils, as well as the erythroid cell line K562. The results show a large number of intergenic and intronic transcripts, particularly in neutrophils. One feature of note is the amount of cell type specific intergenic transcription in the HoxA cluster. In addition we have been developing simple methods to map the sites of polyadenylation of RNAs and applied this to several cell types. This method also indicates the presence of polyadenylated transcripts derived from the multiple introns of known genes as well as intergenic regions.In parallel with the transcript mapping, we have been performing microarray based chromatin immunoprecipitation studies (Chip-chip) on several factors, analyzing them on oligonucleotide arrays tiling the ENCODE selected genomic regions. These include various phosphorylated forms of RNA polymerase, various methylated forms of histone H3, certain DNA sequence specific transcription factors, and some other factors either broadly involved in chromatin remodeling or specifically found to be associated with certain sequences in the beta globin cluster. Among other things, our results as well as those of other members of the ENCODE consortium, provide a signature motif commonly associated with promoters.

An integrated map of p53-binding sites and histone modification in the human ENCODE regions

TP53 (tumor protein p53; p53) regulates its target genes under various cellular stresses. By combining chromatin immunoprecipitation with oligonucleotide microarrays, we have mapped binding sites of p53 (p53-BS) in the genome of HCT116 human colon carcinoma cells, along with those of acetylated H3, acetylated H4, and methylated H3-K4. We analyzed a 30-Mb portion of the human genome selected as a representative model by the ENCODE Consortium. In the region, we found 37 p53-BS, of which the p53-binding motif was present in 32 (86%). Acetylated histone H3 and H4 were detected at 14 (38%) and 33 (89%) of the p53-BS, respectively. A significant portion (58%) of H4 acetylation in the p53-BS was not accompanied by H3 acetylation. Acetyl H3 were preferentially located at the 5′ and 3′ ends of genes, whereas acetyl H4 were distributed widely across the genome. These results provide novel insights into how p53 binding coordinates with histone modification in human.