Chromatin signatures at Notch-regulated enhancers reveal large-scale changes in H3K56ac upon activation.

The conserved Notch pathway functions in diverse developmental and disease-related processes, requiring mechanisms to ensure appropriate target selection and gene activation in each context. To investigate the influence of chromatin organisation and dynamics on the response to Notch signalling, we partitioned Drosophila chromatin using histone modifications and established the preferred chromatin conditions for binding of Su(H), the Notch pathway transcription factor. By manipulating activity of a co-operating factor, Lozenge/Runx, we showed that it can help facilitate these conditions. While many histone modifications were unchanged by Su(H) binding or Notch activation, we detected rapid changes in acetylation of H3K56 at Notch-regulated enhancers. This modification extended over large regions, required the histone acetyl-transferase CBP and was independent of transcription. Such rapid changes in H3K56 acetylation appear to be a conserved indicator of enhancer activation as they also occurred at the mammalian Notch-regulated Hey1 gene and at Drosophila ecdysone-regulated genes. This intriguing example of a core histone modification increasing over short timescales may therefore underpin changes in chromatin accessibility needed to promote transcription following signalling activation.

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics.

Estimating binding properties of transcription factors from genome-wide binding profiles.

The binding of transcription factors (TFs) is essential for gene expression. One important characteristic is the actual occupancy of a putative binding site in the genome. In this study, we propose an analytical model to predict genomic occupancy that incorporates the preferred target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in the case of eukaryotes), the number of TF molecules expected to be bound specifically to the DNA and a parameter that modulates the specificity of the TF. Given actual occupancy data in the form of ChIP-seq profiles, we backwards inferred copy number and specificity for five Drosophila TFs during early embryonic development: Bicoid, Caudal, Giant, Hunchback and Kruppel. Our results suggest that these TFs display thousands of molecules that are specifically bound to the DNA and that whilst Bicoid and Caudal display a higher specificity, the other three TFs (Giant, Hunchback and Kruppel) display lower specificity in their binding (despite having PWMs with higher information content). This study gives further weight to earlier investigations into TF copy numbers that suggest a significant proportion of molecules are not bound specifically to the DNA.

Physical constraints determine the logic of bacterial promoter architectures.

Site-specific transcription factors (TFs) bind to their target sites on the DNA, where they regulate the rate at which genes are transcribed. Bacterial TFs undergo facilitated diffusion (a combination of 3D diffusion around and 1D random walk on the DNA) when searching for their target sites. Using computer simulations of this search process, we show that the organization of the binding sites, in conjunction with TF copy number and binding site affinity, plays an important role in determining not only the steady state of promoter occupancy, but also the order at which TFs bind. These effects can be captured by facilitated diffusion-based models, but not by standard thermodynamics. We show that the spacing of binding sites encodes complex logic, which can be derived from combinations of three basic building blocks: switches, barriers and clusters, whose response alone and in higher orders of organization we characterize in detail. Effective promoter organizations are commonly found in the E. coli genome and are highly conserved between strains. This will allow studies of gene regulation at a previously unprecedented level of detail, where our framework can create testable hypothesis of promoter logic.

A combination of computational and experimental approaches identifies DNA sequence constraints associated with target site binding specificity of the transcription factor CSL.

Regulation of transcription is fundamental to development and physiology, and occurs through binding of transcription factors to specific DNA sequences in the genome. CSL (CBF1/Suppressor of Hairless/LAG-1), a core component of the Notch signaling pathway, is one such transcription factor that acts in concert with co-activators or co-repressors to control the activity of associated target genes. One fundamental question is how CSL can recognize and select among different DNA sequences available in vivo and whether variations between selected sequences can influence its function. We have therefore investigated CSL-DNA recognition using computational approaches to analyze the energetics of CSL bound to different DNAs and tested the in silico predictions with in vitro and in vivo assays. Our results reveal novel aspects of CSL binding that may help explain the range of binding observed in vivo. In addition, using molecular dynamics simulations, we show that domain-domain correlations within CSL differ significantly depending on the DNA sequence bound, suggesting that different DNA sequences may directly influence CSL function. Taken together, our results, based on computational chemistry approaches, provide valuable insights into transcription factor-DNA binding, in this particular case increasing our understanding of CSL-DNA interactions and how these may impact on its transcriptional control.

Genome-wide screens for in vivo Tinman binding sites identify cardiac enhancers with diverse functional architectures.

The NK homeodomain factor Tinman is a crucial regulator of early mesoderm patterning and, together with the GATA factor Pannier and the Dorsocross T-box factors, serves as one of the key cardiogenic factors during specification and differentiation of heart cells. Although the basic framework of regulatory interactions driving heart development has been worked out, only about a dozen genes involved in heart development have been designated as direct Tinman target genes to date, and detailed information about the functional architectures of their cardiac enhancers is lacking. We have used immunoprecipitation of chromatin (ChIP) from embryos at two different stages of early cardiogenesis to obtain a global overview of the sequences bound by Tinman in vivo and their linked genes. Our data from the analysis of ~50 sequences with high Tinman occupancy show that the majority of such sequences act as enhancers in various mesodermal tissues in which Tinman is active. All of the dorsal mesodermal and cardiac enhancers, but not some of the others, require tinman function. The cardiac enhancers feature diverse arrangements of binding motifs for Tinman, Pannier, and Dorsocross. By employing these cardiac and non-cardiac enhancers in machine learning approaches, we identify a novel motif, termed CEE, as a classifier for cardiac enhancers. In vivo assays for the requirement of the binding motifs of Tinman, Pannier, and Dorsocross, as well as the CEE motifs in a set of cardiac enhancers, show that the Tinman sites are essential in all but one of the tested enhancers; although on occasion they can be functionally redundant with Dorsocross sites. The enhancers differ widely with respect to their requirement for Pannier, Dorsocross, and CEE sites, which we ascribe to their different position in the regulatory circuitry, their distinct temporal and spatial activities during cardiogenesis, and functional redundancies among different factor binding sites.

Reliable scaling of position weight matrices for binding strength comparisons between transcription factors.

BACKGROUND: Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748-7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities. RESULTS: Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches. CONCLUSION: These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases.

The androgen receptor fuels prostate cancer by regulating central metabolism and biosynthesis.

The androgen receptor (AR) is a key regulator of prostate growth and the principal drug target for the treatment of prostate cancer. Previous studies have mapped AR targets and identified some candidates which may contribute to cancer progression, but did not characterize AR biology in an integrated manner. In this study, we took an interdisciplinary approach, integrating detailed genomic studies with metabolomic profiling and identify an anabolic transcriptional network involving AR as the core regulator. Restricting flux through anabolic pathways is an attractive approach to deprive tumours of the building blocks needed to sustain tumour growth. Therefore, we searched for targets of the AR that may contribute to these anabolic processes and could be amenable to therapeutic intervention by virtue of differential expression in prostate tumours. This highlighted calcium/calmodulin-dependent protein kinase kinase 2, which we show is overexpressed in prostate cancer and regulates cancer cell growth via its unexpected role as a hormone-dependent modulator of anabolic metabolism. In conclusion, it is possible to progress from transcriptional studies to a promising therapeutic target by taking an unbiased interdisciplinary approach.

Specificity of Notch pathway activation: twist controls the transcriptional output in adult muscle progenitors.

Cell-cell signalling mediated by Notch regulates many different developmental and physiological processes and is involved in a variety of human diseases. Activation of Notch impinges directly on gene expression through the Suppressor of Hairless [Su(H)] DNA-binding protein. A major question that remains to be elucidated is how the same Notch signalling pathway can result in different transcriptional responses depending on the cellular context and environment. Here, we have investigated the factors required to confer this specific response in Drosophila adult myogenic progenitor-related cells. Our analysis identifies Twist (Twi) as a crucial co-operating factor. Enhancers from several direct Notch targets require a combination of Twi and Notch activities for expression in vivo; neither alone is sufficient. Twi is bound at target enhancers prior to Notch activation and enhances Su(H) binding to these regulatory regions. To determine the breadth of the combinatorial regulation we mapped Twi occupancy genome-wide in DmD8 myogenic progenitor-related cells by chromatin immunoprecipitation. Comparing the sites bound by Su(H) and by Twi in these cells revealed a strong association, identifying a large spectrum of co-regulated genes. We conclude that Twi is an essential Notch co-regulator in myogenic progenitor cells and has the potential to confer specificity on Notch signalling at over 170 genes, showing that a single factor can have a profound effect on the output of the pathway.

GRiP: a computational tool to simulate transcription factor binding in prokaryotes.

MOTIVATION: Transcription factors (TFs) are proteins that regulate gene activity by binding to specific sites on the DNA. Understanding the way these molecules locate their target site is of great importance in understanding gene regulation. We developed a comprehensive computational model of this process and estimated the model parameters in (N.R.Zabet and B.Adryan, submitted for publication). RESULTS: GRiP (gene regulation in prokaryotes) is a highly versatile implementation of this model and simulates the search process in a computationally efficient way. This program aims to provide researchers in the field with a flexible and highly customizable simulation framework. Its features include representation of DNA sequence, TFs and the interaction between TFs and the DNA (facilitated diffusion mechanism), or between various TFs (cooperative behaviour). The software will record both information on the dynamics associated with the search process (locations of molecules) and also steady-state results (affinity landscape, occupancy-bias and collision hotspots). AVAILABILITY: http://logic.sysbiol.cam.ac.uk/grip, program and source code

A comprehensive computational model of facilitated diffusion in prokaryotes.

MOTIVATION: Gene activity is mediated by site-specific transcription factors (TFs). Their binding to defined regions in the genome determines the rate at which their target genes are transcribed. RESULTS: We present a comprehensive computational model of the search process of TF for their genomic target site(s). The computational model considers: the DNA sequence, various TF species and the interaction of the individual molecules with the DNA or between themselves. We also demonstrate a systematic approach how to parametrize the system using available experimental data.

A graphical modelling approach to the dissection of highly correlated transcription factor binding site profiles.

Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF binding profiles is challenging. A major reason is that TF binding profiles significantly overlap and are therefore highly correlated. Clustered occurrence of multiple TFs at genomic sites may arise from chromatin accessibility and local cooperation between TFs, or binding sites may simply appear clustered if the profiles are generated from diverse cell populations. Overlaps in TF binding profiles may also result from measurements taken at closely related time intervals. It is thus of great interest to distinguish TFs that directly regulate gene expression from those that are indirectly associated with gene expression. Graphical models, in particular Bayesian networks, provide a powerful mathematical framework to infer different types of dependencies. However, existing methods do not perform well when the features (here: TF binding profiles) are highly correlated, when their association with the biological outcome is weak, and when the sample size is small. Here, we develop a novel computational method, the Neighbourhood Consistent PC (NCPC) algorithms, which deal with these scenarios much more effectively than existing methods do. We further present a novel graphical representation, the Direct Dependence Graph (DDGraph), to better display the complex interactions among variables. NCPC and DDGraph can also be applied to other problems involving highly correlated biological features. Both methods are implemented in the R package ddgraph, available as part of Bioconductor (http://bioconductor.org/packages/2.11/bioc/html/ddgraph.html). Applied to real data, our method identified TFs that specify different classes of cis-regulatory modules (CRMs) in Drosophila mesoderm differentiation. Our analysis also found depletion of the early transcription factor Twist binding at the CRMs regulating expression in visceral and somatic muscle cells at later stages, which suggests a CRM-specific repression mechanism that so far has not been characterised for this class of mesodermal CRMs.

Evolution of genes and genomes on the Drosophila phylogeny.

Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

Construction of a large extracellular protein interaction network and its resolution by spatiotemporal expression profiling.

Extracellular interactions involving both secreted and membrane-tethered receptor proteins are essential to initiate signaling pathways that orchestrate cellular behaviors within biological systems. Because of the biochemical properties of these proteins and their interactions, identifying novel extracellular interactions remains experimentally challenging. To address this, we have recently developed an assay, AVEXIS (avidity-based extracellular interaction screen) to detect low affinity extracellular interactions on a large scale and have begun to construct interaction networks between zebrafish receptors belonging to the immunoglobulin and leucine-rich repeat protein families to identify novel signaling pathways important for early development. Here, we expanded our zebrafish protein library to include other domain families and many more secreted proteins and performed our largest screen to date totaling 16,544 potential unique interactions. We report 111 interactions of which 96 are novel and include the first documented extracellular ligands for 15 proteins. By including 77 interactions from previous screens, we assembled an expanded network of 188 extracellular interactions between 92 proteins and used it to show that secreted proteins have twice as many interaction partners as membrane-tethered receptors and that the connectivity of the extracellular network behaves as a power law. To try to understand the functional role of these interactions, we determined new expression patterns for 164 genes within our clone library by using whole embryo in situ hybridization at five key stages of zebrafish embryonic development. These expression data were integrated with the binding network to reveal where each interaction was likely to function within the embryo and were used to resolve the static interaction network into dynamic tissue- and stage-specific subnetworks within the developing zebrafish embryo. All these data were organized into a freely accessible on-line database called ARNIE (AVEXIS Receptor Network with Integrated Expression; www.sanger.ac.uk/arnie) and provide a valuable resource of new extracellular signaling interactions for developmental biology.

The impact of gene expression regulation on evolution of extracellular signaling pathways.

Extracellular protein interactions are crucial to the development of multicellular organisms because they initiate signaling pathways and enable cellular recognition cues. Despite their importance, extracellular protein interactions are often under-represented in large scale protein interaction data sets because most high throughput assays are not designed to detect low affinity extracellular interactions. Due to the lack of a comprehensive data set, the evolution of extracellular signaling pathways has remained largely a mystery. We investigated this question using a combined data set of physical pairwise interactions between zebrafish extracellular proteins, mainly from the immunoglobulin superfamily and leucine-rich repeat families, and their spatiotemporal expression profiles. We took advantage of known homology between proteins to estimate the relative rates of changes of four parameters after gene duplication, namely extracellular protein interaction, expression pattern, and the divergence of extracellular and intracellular protein sequences. We showed that change in expression profile is a major contributor to the evolution of signaling pathways followed by divergence in intracellular protein sequence, whereas extracellular sequence and interaction profiles were relatively more conserved. Rapidly evolving expression profiles will eventually drive other parameters to diverge more quickly because differentially expressed proteins get exposed to different environments and potential binding partners. This allows homologous extracellular receptors to attain specialized functions and become specific to tissues and/or developmental stages.

FlyTF: improved annotation and enhanced functionality of the Drosophila transcription factor database.

FlyTF (http://www.flytf.org) is a database of computationally predicted and/or experimentally verified site-specific transcription factors (TFs) in the fruit fly Drosophila melanogaster. The manual classification of TFs in the initial version of FlyTF that concentrated primarily on the DNA-binding characteristics of the proteins has now been extended to a more fine-grained annotation of both DNA binding and regulatory properties in the new release. Furthermore, experimental evidence from the literature was classified into a defined vocabulary, and in collaboration with FlyBase, translated into Gene Ontology (GO) annotation. While our GO annotations will also be available through FlyBase as they will be incorporated into the genes' official GO annotation in the future, the entire evidence used for classification including computational predictions and quotes from the literature can be accessed through FlyTF. The FlyTF website now builds upon the InterMine framework, which provides experimental and computational biologists with powerful search and filter functionality, list management tools and access to genomic information associated with the TFs.

Analysis of differentially expressed proteins in oral squamous cell carcinoma by MALDI-TOF MS.

PURPOSE: To explore the presence of differentially expressed proteins in OSCC for discrimination of tumour and normal mucosa to establish potential biomarkers and therapeutic targets. EXPERIMENTAL DESIGN: Paired protein samples of 12 individuals (tongue cancer and non-cancerous mucosa) were separated by two-dimensional polyacrylamid gel electrophoresis. The protein patterns were compared pairwise and protein spots were quantified. We identified about 70 regulated proteins which we subsequently identified by MALDI-TOF mass spectrometry. RESULTS: Cancerous and non-cancerous tissues could be most precisely distinguished by a panel of proteins. They include the heat shock proteins (hsp)70 and 90, keratins (ck) 5, 6, 13, 14, 16, 17 and 19, beta globin, alpha-2-actin, stratifin, tropomyosin, calreticulin precursor, beta-2-tubulin, galectin7, thioredoxin, involucrin, adenylyl-cyclase-associated protein, disulfide isomerase-associated protein, thyrosine 3-monooxygenase, MYL2 and the s100 calcium binding protein. MYL3, cardiac muscle alpha actin 1 proprotein and transferrin were under-represented in OSCC. Six biomarkers, ck6 und ck13, beta globin, alpha-2-actin, hsp70 and hsp90 discriminated best between cancerous and non-cancerous oral tissues. All over-expressed proteins were analysed by STRING-analysis to highlight experimentally determined and computationally predicted interactions between the proteins. Especially involucrin, hsp70, calreticulin precursor, stratifin, (ck) 5, 6, 14, 19, tyrosine 3-monooxygenase, beta-2-tubulin and disulfide isomerase associated protein showed multiple relations. CONCLUSION: We identified six proteins which are differentially expressed in most OSCC compared to healthy tissues. Of those, by string analysis, multiple interaction partners are assumed for hsp70. This protein is supposed to be the most promising candidate as marker molecule and target for OSCC therapy.

Stability and dynamics of polycomb target sites in Drosophila development.

Polycomb-group (PcG) and Trithorax-group proteins together form a maintenance machinery that is responsible for stable heritable states of gene activity. While the best-studied target genes are the Hox genes of the Antennapedia and Bithorax complexes, a large number of key developmental genes are also Polycomb (Pc) targets, indicating a widespread role for this maintenance machinery in cell fate determination. We have studied the linkage between the binding of PcG proteins and the developmental regulation of gene expression using whole-genome mapping to identify sites bound by the PcG proteins, Pc and Pleiohomeotic (Pho), in the Drosophila embryo and in a more restricted tissue, the imaginal discs of the third thoracic segment. Our data provide support for the idea that Pho is a general component of the maintenance machinery, since the majority of Pc targets are also associated with Pho binding. We find, in general, considerable developmental stability of Pc and Pho binding at target genes and observe that Pc/Pho binding can be associated with both expressed and inactive genes. In particular, at the Hox complexes, both active and inactive genes have significant Pc and Pho binding. However, in comparison to inactive genes, the active Hox genes show reduced and altered binding profiles. During development, Pc target genes are not simply constantly associated with Pc/Pho binding, and we identify sets of genes with clear differential binding between embryo and imaginal disc. Using existing datasets, we show that for specific fate-determining genes of the haemocyte lineage, the active state is characterised by lack of Pc binding. Overall, our analysis suggests a dynamic relationship between Pc/Pho binding and gene transcription. Pc/Pho binding does not preclude transcription, but levels of Pc/Pho binding change during development, and loss of Pc/Pho binding can be associated with both stable gene activity and inactivity.

Cytoskeletal scaffolding proteins interact with Lynch-Syndrome associated mismatch repair protein MLH1.

The involvement of MLH1 in several mismatch repair-independent cellular processes has been reported. In an attempt to gain further insight into the protein's cellular functions, we screened for novel interacting partners of MLH1 utilizing a bacterial two-hybrid system. Numerous unknown interacting proteins were identified, suggesting novel biological roles of MLH1. The network of MLH1 and its partner proteins involves a multitude of cellular processes. Integration of our data with the "General Repository for Interaction Datasets" highlighted that MLH1 exhibits relationships to three interacting pairs of proteins involved in cytoskeletal and filament organization: Thymosin beta 4 and Actin gamma, Cathepsin B and Annexin A2 as well as Spectrin alpha and Desmin. Coimmunoprecipitation and colocalization experiments validated the interaction of MLH1 with these proteins. Differential mRNA levels of many of the identified proteins, detected by microarray analysis comparing MLH1-deficient and -proficient cell lines, support the assumed interplay of MLH1 and the identified candidate proteins. By siRNA knock down of MLH1, we demonstrated the functional impact of MLH1-Actin interaction on filament organization and propose that dysregulation of MLH1 plays an essential role in cytoskeleton dynamics. Our data suggest novel roles of MLH1 in cellular organization and colorectal cancerogenesis.

CTCF genomic binding sites in Drosophila and the organisation of the bithorax complex.

Insulator or enhancer-blocking elements are proposed to play an important role in the regulation of transcription by preventing inappropriate enhancer/promoter interaction. The zinc-finger protein CTCF is well studied in vertebrates as an enhancer blocking factor, but Drosophila CTCF has only been characterised recently. To date only one endogenous binding location for CTCF has been identified in the Drosophila genome, the Fab-8 insulator in the Abdominal-B locus in the Bithorax complex (BX-C). We carried out chromatin immunopurification coupled with genomic microarray analysis to identify CTCF binding sites within representative regions of the Drosophila genome, including the 3-Mb Adh region, the BX-C, and the Antennapedia complex. Location of in vivo CTCF binding within these regions enabled us to construct a robust CTCF binding-site consensus sequence. CTCF binding sites identified in the BX-C map precisely to the known insulator elements Mcp, Fab-6, and Fab-8. Other CTCF binding sites correlate with boundaries of regulatory domains allowing us to locate three additional presumptive insulator elements; "Fab-2," "Fab-3," and "Fab-4." With the exception of Fab-7, our data indicate that CTCF is directly associated with all known or predicted insulators in the BX-C, suggesting that the functioning of these insulators involves a common CTCF-dependent mechanism. Comparison of the locations of the CTCF sites with characterised Polycomb target sites and histone modification provides support for the domain model of BX-C regulation.