A problem of particular interest is the identification of regions of the genome where different cell types from the same organism exhibit different patterns of histone enrichment. This problem turns out to be surprisingly difficult, even in simple pairwise comparisons, because of the significant level of noise in ChIP-seq data. In this paper we propose a two-stage statistical method, called ChIPnorm, to normalize ChIP-seq data, and to find differential regions in the genome, given two libraries of histone modifications of different cell types. We show that the ChIPnorm method removes most of the noise and bias in the data and outperforms other normalization methods. We correlate the histone marks with gene expression data and confirm that histone modifications H3K27me3 and H3K4me3 act as respectively a repressor and an activator of genes. Compared to what was previously reported in the literature, we find that a substantially higher fraction of bivalent marks in ES cells for H3K27me3 and H3K4me3 move into a K27-only state. We find that most of the promoter regions in protein-coding genes have differential histone-modification sites. The software for this work can be downloaded from  Histones are proteins that package the DNA into chromosomes etal. 2009 Genome-wide chromatin maps (using ChIP-seq technology) for three mouse cell types – embryonic stem (ES) cells, neural progenitor (NP) cells, and embryonic fibroblasts (EF) – have been published A significant impediment to the analysis of ChIP-seq data is the high level of noise. Noise or systematic distortion can enter at various stages of the procedure: variations in the number of cells used in the experiment, variation in the amount of antibody that attaches to the DNA fragments, tandem repeats, uneven rates of success in sequencing different fragments, etc. The type of histone modification and the cell types can also affect the level of noise. For example, we show in this paper that histone modification H3K27me3 (K27) in ES cells has less background noise and a better signal-to-noise (S/N) ratio than the same modification in NP cells. In addition, the signal tends to be found mostly in gene-rich regions of the genome. Therefore computational methods may produce many false positives. In the case of modification K27 for ES and NP cells, for instance, false positives are likely in gene-poor regions for NP cells and in gene-rich regions for ES cells. Such bias problems are present in microarray data and many authors have addressed this issue To address the problems of noise and bias in finding differential regions, we propose a two-stage statistical method, called ChIPnorm, to remove the noise and the bias from two ChIP-seq libraries and to normalize the data so as to enable a direct comparison between the two libraries to identify differential regions. Our normalization step is similar to quantile normalization To motivate our work, we examine data for histone modification H3K27me3 (K27) in mouse ES and NP cells etal. 2008 etal. 2008 In the following, we use a notation similar to that of Xu An observed fragment count Function We will address each source of error separately and proceed in two main stages. In the first stage, we address the removal of stochastic background noise and local genomic bias in each library. In the second stage, we address the problem of normalization.To solve the problem of stochastic background noise, both Bayesian modelling methods To understand the rationale for choosing the amplified binomial distribution (ABD) as the random distribution for the bincounts of ChIP-seq library, we must examine the ChIP-seq process as illustrated in To estimate the null distribution of a ChIP-seq library, we assume that the fragmented DNA from the sonicated whole cell is treated with an antibody that randomly captures fragments without any specificity. The bincounts of captured fragments then follow a binomial distribution. The captured fragments are amplified, sequenced, and binned to get the null distribution of the ChIP-seq library. This binned data follows a random distribution, in which each of the fragments following the binomial distribution is amplified; we refer to it as the amplified binomial distribution (ABD). We assume that each fragment is amplified by a constant amplification factor.An ABD can be defined by two parameters: the total number of fragments The ABD is thus estimated as We calculated the false discovery rate (FDR) for a ChIP-seq data using its corresponding estimated null distribution (ABD) ChIP-seq data contains many local genomic biases corresponding to open chromatin regions, over-amplified satellite repeats, GC-rich regions, and unmappable perfect repeat regions. Some of these biases depend on experimental conditions and others may vary among the cell lines in a systematic manner. If such cell-specific biases are not taken care of while comparing ChIP-seq libraries it will give many false positive differential regions. Some of these biases can be reduced by using an input DNA control library. To find differential regions, we must consider only those regions that are significantly enriched with respect to the input DNA control. The input DNA control are the DNA fragments in the ChIP-seq experiment prior to the application of the histone specific antibody. To find the enriched bins, we need to normalize the input DNA control with respect to the data.etal. 2002 Yang In the first stage of iterative normalization, we normalize the DNA control with respect to the data using quantile normalization. For illustration purposes, we first show the LOESS curve of the MA plot in Bins which are declared both as significant in the ABD approach and as enriched in the iterative approach, are declared as ‘enriched-significant’. Bins which are declared as enriched-significant in either of the two libraries are passed on to the second stage, with their original bincount values.Since our first stage removed the majority of bins with low S/N ratios and genomic bias, and since we expect interesting regions to have good S/N ratios, we now make the simplifying assumption: With this assumption the next step is to normalize the data to the same scale so that bin values in the two libraries are comparable. Mathematically, given two observed data et al. 2003 We propose a quantile normalization method . Bins which are above 50% in etal. 2006 The normalization method also facilitates the comparison of more than two libraries. Our method is easily extended to handle multiple types of histone modifications in multiple cell types. Such analyses can give more insight into combinatorial patterns of histone modifications, sometimes referred to as “histone language” etal. 2007 We carried out a series of experiments with the two libraries for H3K27me3 and H3K4me3 histone modifications (ES and NP cells), including experiments for bias and sensitivity. Since H3K4me3 has sharper peaks than H3K27me3, it needs a finer resolution, and smaller bin sizes are used. Using the replicate data analysis described earlier, we chose a bin size of 1000 bp for H3K27me3 (K27) and a bin size of 200 bp for H3K4me3 (K4). The bin size of 1000 bp for H3K27me3 has also been used previously in literature The five methods not yet described are as follows:unit mean normalization: is the standard Affymetrix scaling method for microarray data • quantile normalization: the two libraries are quantile normalized, and a fold change threshold . Gene poor regions have a high number of inactive regions. We noticed that most of the earlier methods for comparing two libraries suffer from bias with respect to gene density. In order to study this bias quantitatively, we divided the whole genome . We tested the experiments on ES and GM12878 cell lines for H3K27me3 histone modifications etal. 2007 We next plot receiver operating characteristics (ROC) for H3K27me3 histone modification data  is declared differentially enriched (i.e. above threshold) in Class 1 (since H3K27me3 is a repressor).FN – rest of the genes which fall in Class 1.FP – genes were ES (ChIP-seq) is declared differentially enriched (i.e. above threshold) in Class 0.TN – rest of the genes which fall in Class 0.True positive rate (TPR)  =  sensitivity  =  TP/(TP + FN).False positive rate (FPR)  =  (1 – specificity)  =  FP/(FP+ TN).Now Class 1 is defined as four-fold ES differentially over-expressed genes compared to NP; Class 0 is rest of the genes. And the rest of the parameters are defined for NP differentially enriched (opposite of the previous case).The two ROC curves are shown in Since the ENCODE Broad database for human histone modifications data etal. 2007 We analyze the effect of presence of K27 and K4 sites  which were previously analyzed by Bernstein etal. 2006 The “A–B” in a label indicates the presence of modification “A” in ES cells and of modification “B” in NP cells; as before, detected but depleted modifications are treated as if they were absent; labels marked with an asterisk denote bivalent regions. We found that about 20% of these 333 genes had bivalent regions within 1 kb from the promoter in ES cells – a result consistent with Bernstein etal. 2006 etal. 2009 Finally we studied the connection between bivalent regions and gene expression levels. We divided the UCSC known genes in the same A–E groups according to the log ratio of expression levels in ES and NP cells. Within each class we further classified genes into 16 classes according to the presence of one or both histone modifications in ES and NP cells. We analyzed the human ENCODE ES and GM12878 H3K27me3 ChIP-seq data We have presented an approach for the analysis of ChIP-seq data, with particular emphasis on the discovery of differentially enriched histone-modification sites. The problem of the bias inherent in the comparison of two sets of data with different noise backgrounds is biologically more relevant because such bias shows a false correlation between computationally identified differential regions with gene density. The ChIPnorm removes most of this bias and provides a normalization that enables direct comparison of values. We have conducted experiments that demonstrate that this new approach improves significantly on the state of the art. Finally, we have used our approach to highlight some aspects of K27 modifications in mouse embryonic stem cells and neural progenitor cells, including a so far unnoticed transition of bivalent mark of K4 and K27 in embryonic stem cell to a K27-only state in differentiated cells, possibly reflecting permanent repression of developmental genes. For the human ENCODE H3K27me3 data for ES and GM12878 cells, when we look at protein-coding genes, we provide evidence that most of the promoter regions have differentially enriched histone-modification sites. Recently, the ChIPnorm approach has also been used to study histone methylation changes associated with leaf senescence in Arabidopsis Our approach is not restricted to the identification of differentially enriched sites nor is it limited to pairwise comparisons. A natural next step, therefore, is to apply it to more complex data , to verify its efficacy, and to use it to shed light on the complex interactions described in the “histone language”.Figure S1Iterative normalization of input DNA. (a) before first iteration. (b) after first iteration, post removal of outliers.(PDF)Click here for additional data file.Supporting Information S1Supplementary material containing Supplementary figures, Supplementary table, Supplementary methods, Supplementary results, and Supplementary references (PDF)Click here for additional data file.


The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification. Epidermal growth factor is an important mitogen for hepatocytes for its ability to modulate proto-oncogene as well as liver specific gene expression. To better understand EGF's role in malignant transformation a transgenic mouse model was developed where EGF was targeted to the liver. Notably, transgenic mice developed liver cancer around 7–8 months and a tumour stage-dependent network of EGF-regulated genes was identified, as previously reported Overall this study aims for a better understanding of the EGF transforming capacity and combines different lines of evidence for a possible mechanism of disease.ExPlain 2.3 mapped Affymetrix probe sets to 10,262 mouse genes. Differential expression analysis with Ebarrays detected 303 and 355 up regulated genes as well as 95 and 141 down regulated genes in transgenic and tumor cells, respectively. Transgenic and tumor states shared 144 up regulated genes and 25 down regulated genes  With defined sets of differentially expressed (DE) genes at hand, we set out to identify functional changes that accompany EGF-induced hepatocarcinogenesis. For this purpose, we calculated enrichment P-values for all GO Biological Process categories associated with at least one gene in transgenic or tumour gene sets and applied them as a measure of relative importance of a particular biological function for a given gene set. To limit the effect of false negative findings during differential expression analysis, enrichment P-values were calculated for extended gene sets composed of sets 1–4 (up regulated in transgenic cells), 2–5 (up regulated in tumor cells), 6–9 (down regulated in transgenic cells), and 7–10 (down regulated in tumour cells) . ResultsCell-cycle dysregulation was previously identified as one causal mechanism of nongenotoxic carcinogenicity Networks of interacting proteins exert a large part of cellular functions. Analysis of differentially expressed molecules in the context of known signalling pathways enables identification of molecular networks targeted by observed expression changes. We applied network cluster analysis to propose functional context for signaling components encoded by differentially expressed genes of transgenic and tumor cells along with supporting network topologies constructed from experimentally proven signaling reactions. Networks were constructed for extended upregulation and downregulation gene sets described above. As a result, we obtained one cluster each for upregulated genes of transgenic cells and for upregulated genes of tumor cells. A small network of downregulated genes was found in tumor cells, in which EGF and beta-c interact with Shc and a complex comprising EGF, ErbB1, Shc-1, Grb2, and Sos (not shown). The two networks of upregulated genes were constituted by 85 and 88 components including 39 and 41 molecules upregulated in transgenic or tumor cells, respectively. Diagrams of transgenic and tumor network clusters are shown in Both networks feature a mix of functional categories such as growth factor signaling, e.g. through ErbB receptors, InsR, or FGFR-1, cell cycle-regulatory cascades, e.g. involving cyclin B1, Cdk1, Aurora-A, or p53, as well as cytokine and chemokine signaling through IL-1RI and CXCR4. The combined list of upregulated components consists of 48 proteins of which 7 are specific to the transgenic network and 9 are only part of the tumor network . For 19 The presented network clusters reveal potential effects on the activity of upregulated TFs like c-Fos, c-Jun, c-Myc, PPAR-gamma1, Smad3, Egr-1, and c-Ets-2. C-Myc and PPAR-gamma1 were restricted to the transgenic and the tumor network, respectively. Each factor integrates signals from several upstream molecules with altered expression. Interestingly, well-known cancer-associated TFs like c-Fos, c-Jun, Smad3, and c-Ets-2 exhibited rather constant levels of upregulation throughout tumorigenesis. Furthermore, a relatively high number of both activating and inhibitory reactions target p53, which was void of detectable differential expression in either progression state.A cascade involving VCAM-1, alpha4-integrin, and IAP was found specifically in the transgenic network . Alpha4-The network clusters reveal regulatory circuitries that might be explored for novel therapeutic interventions. Indeed, PPAR-gamma antagonists are being investigated as treatment of various malignancies including liver cancers. Regulatory cascades targeting PPAR-gamma through upstream kinases and phosphatases, such as M3/6, JNK1, MEKK2, MKP3, MEK2, or ERK2, of which M3/6, MEKK2, and MFP-3 were induced during carcinogenesis, suggest additional possibilities for drug development. Furthermore, the ligand of insulin and insulin-like receptors, IGF-2, was strongly upregulated in tumor cells, whereas there were moderate changes in transgenic cells . The potential role of this ligand in autocrine regulation of cancer cell growth was previously discussed in the literature q-value, estimates the value of the odds ratio, so that the true ratio is higher with 99% probability  and PPAR-gamma with the tumor state . Of thesWhile many of the aforementioned transcription factors are well-known proto-oncogenes, such as Jun, Myc, or E2f3, and the link between HNF6, PPARgamma and lipid metabolism is comprehensible, other factors revealed by our analysis are novel with respect to their role in liver carcinogenesis. Binding sites of Kaiso (Zbtb33) were most strongly overrepresented in downregulated tumor genes. Kaiso was shown to silence tumor suppressor genes in colorectal cancer Given transcription factors whose binding sites were enriched in transgenic or tumor gene sets, we sought upstream regulators of these TFs that may contribute to their activation or inhibition through signal transduction pathways. We extracted TFs linked to overrepresented TRANSFAC motifs . The ECPNotably, components of the EGF signaling network, Adam10, a metallopeptidase that processes EGF, and Grb2, an adaptor protein that binds directly to the EGF receptor, exhibited a greater importance for transgenic than for tumor TFs , red dotTo examine the role of IGF-2 signaling as a surrogate for EGF, we superposed the key node/TF networks of Adam10, Grb2 and EGF with the IGF-2 network . The merTo further investigate the EGF-induced switch from transgenic to tumor state, we performed another step of promoter analysis, where we focused on TFs, whose regulation of activity can be mechanistically explained by signal transduction cascades downstream of an EGF receptor ligand or an EGF receptor. Hence, our goal was to identify EGF-dependent transcription regulators that could exert the switch from the transgenic to the tumor state. Therefore, we sought TFs downstream from EGF whose binding sites were enriched in promoters of upregulated tumor genes. This was done using the key node functionality of ExPlain. Co-occurrence analysis of TF binding sites was chosen to achieve higher specificity in selecting potentially regulated promoter regions in foreground and background sets than is possible by considering individual PWMs only.−3 in the tumor complete and specific gene sets, respectively. Identified pairs grouped by TF classes are provided in We retrieved all TFs that were inferred in the signal transduction network downstream from the EGF ligands and receptors: HB-EGF, EGF, ErbB1, ErbB2, or ErbB3. In addition, we included PWMs of upregulated tumor factors. TRANSFAC matrices linked to all such TFs were used for further analysis. In total, we obtained 266 “tumor-EGF-network” PWMs yielding 31,306 pairwise combinations. To avoid pairs of similar matrices, we considered only combinations of PWMs, which overlapped in less than 10% of the hits of the PWM with the lower total number of matches at a score P-value threshold of 0.001. Score P-values were estimated on the basis of predictions in promoters of the background subset. As a result, 34 and 45 PWM pairs were reported at a P-value below 10The TF pair analysis results provide evidence for involvement of several upregulated TFs such as Atf3, E2f3, Egr1, Fos, Hnf1b, Jun, Maff, Mef2a, Nfe2, Nfe2l3, Nr2f1 or Nr2f2, Pbx3. Pparg, Smad3, and Tcf7. These factors were found in different combinations in the pair analysis . We addiSynergistic binding of multiple transcription factors to certain combinations of binding sites is an important mechanism of achieving highly specific regulation of genes in particular cellular conditions. We can hypothesize, that tumor cells are characterized by particular pattern of activated transcription factors, which enabled them to evolve from pre-tumor to tumor state by escaping multiple checkpoints and by establishing signal independent uncontrolled proliferation. We sought to identify such patterns of multiple activated transcription factors, which synergistically bind to their target genes and may be responsible for the transgenic-tumor switch. For this, we focused our attention, first of all, on genes specifically upregulated in tumor compared to transgenic state, that encode components of the revealed EGF and IGF-2 pathways. Such genes can provide complex feedback mechanisms in the network helping to maintain the tumor status of the cells. All highly upregulated genes (log-Fold change in tumor > 2.0) were mapped on the TRANSPATH network and signal transduction chains propagating the activation signal downstream to transcription factors were constructed using ExPlain tools. Among the components of such downstream signal transduction network we identified 12 proteins whose genes switched expression in the transgenic-tumor transition (transgenic fold change < tumor fold change). We speculate that the change of expression of these 12 genes in tumor compared to transgenic contributes to self-maintenance of the tumor state, and the set of transcription factors regulating activity of these genes might be the best candidates for understanding the regulatory mechanism of this switch. In addition, we analyzed 34 genes, that were downregulated in tumors compared to transgenic state and that were associated with the most significantly downregulated functional category “organ development” as revealed by GSEA analysis. We analyzed promoters of these upregulated and downregulated genes using the ExPlain tool in order to reconstruct maps of multiple TF binding sites in promoters of these genes, which could help us to understand the molecular mechanisms of the switch to the tumor state. TF site maps of upregulated gene promoters are depicted in A total of 5 upregulated and 14 downregulated genes were selected based on highest scores for predicted TF binding sites while EMSA band shift assays were carried out with specific antibodies to fully validate the predicted DNA binding activity. As shown in This study aimed at an improved understanding of molecular events associated with EGF-induced, nongenotoxic hepatocarcinogenicity. We report on results of computational as well as experimental analyses carried out on gene expression profiles of successive disease stages, which we described previously To further elucidate the biological and molecular functions targeted by expression responses, we analyzed associations between GO biological processes and DE genes in transgenic and tumor cells and constructed network clusters, which connected signaling molecules in pathways with a minimal proportion of differentially expressed components. Since we noticed that genes were often detected in only one progression state by statistical differential expression analysis while fold changes indicated a difference to the normal condition in both states, we constructed so-called extended gene sets to reduce the effect of false negative findings in the statistical analysis step. This was done in a careful manner by taking detection of differential expression in one progression state as evidence for the other and included a gene in the extended set if the detected microarray probe set suggested more than 2-fold expression change. By this process we obtained enlarged up- and downregulated gene sets, which eliminated some differences seen otherwise and led to a more stringent definition of specific up- or downregulation. This was in line with our goals to perform comparative analyses of GO category enrichment and to compare clusters of differentially expressed signaling molecules, where we sought to minimize the possibility of falsely identifying differences.Comparison of GO biological enrichment pinpointed regulation of genes involved in cell cycle, developmental pathways, lipid metabolism, and protein deubiquitination. Other scientific works supported our findings. In contrast to cell cycle and developmental categories whose enrichment in both transgenic and tumor gene sets reflects progressive regulatory alterations during carcinogenesis, downregulation of genes associated with protein deubiquitination, a relatively new target for cancer therapy, was first observed in tumor cells. Focusing analyses on the identified cellular functions could thus help to further dissect the causal mechanisms of switching from pre-tumor to tumor state. A first step in this direction was undertaken in this work by analyzing and comparing overrepresentation of transcription factor binding sites in promoters of cell cycle as well as lipid metabolism gene sets as further discussed below.The interplay of differentially expressed signaling molecules was examined in more detail by network analysis. ExPlain generated the network clusters in a data driven process, which is guided by the specified input set of molecules. The resulting clusters represent context-specific subdistricts of the entire cellular network, which are densely populated with molecules targeted by observed expression changes. The network clusters constructed for upregulated transgenic and tumor molecules connected components of growth factor signaling, cell cycle regulation, as well as chemokine and cytokine signaling. We assume that the interplay of molecules with different canonical functions provides for a realistic view on cellular control mechanisms. These are implemented by a cellular network that connects thousands of molecules, many of which exert a regulatory role in a multiplicity of biological contexts. Comparison of transgenic and tumor networks supported our hypothesis of progressive alterations of cell cycle regulation as well as of lipid metabolism during hepatocarcinogenesis. Moreover, this analysis yielded testable hypotheses about relevant molecular cascades involving p107, p130, and p15INK4b as well as survivin, Cdk1, cyclin B1, Plk1, and Bub1. In the transgenic network we found a cascade of molecules regulating cell migration and adhesion, which included VCAM-1, alpha4-integrin, TSP-1, and IAP. This finding complied with decreased enrichment of cell motility components in tumor revealed by GO analysis. Hence, the constructed network clusters complemented the results of our GO analysis by facilitating detailed insight into the molecular pathways targeted by carcinogenic expression changes. Taking into account the presence of EGF-receptors ErbB1-3 in both transgenic and tumor clusters, the networks reveal in addition how components of biological processes proposed by GO analysis were tied to EGF-signaling in the context of hepatocarcinogenesis.After elaborating on downstream effects of EGF-induced tumor development, we reconstructed causes of observed expression changes. First, we analyzed overrepresentation of transcription factor binding sites in promoters of upregulated and downregulated transgenic as well as tumor gene sets. For this part of the work, we developed a novel statistic for binding site enrichment analysis that compares foreground/background binding site proportions with promoter proportions and quantifies overrepresentation with the ratio quantile of corresponding Beta distributions. The statistic was proposed for several reasons. PWM-based binding site prediction typically requires specification of a score threshold that determines true and false positive rates. In a comparative method, e.g. like F-MATCH, the threshold at which a weight matrix optimally detects overrepresentation is usually not known a-priori. Therefore, F-MATCH adopts the strategy of iterating over score thresholds to optimize the overrepresentation of predicted binding sites for each specified PWM. However, at low score cut-offs, where high numbers of binding sites are predicted in foreground and background promoters, statistical tests like the exact binomial test can report highly significant P-values for small “fold” differences when these are supported by high counts of binding site instances. Consequently, one cannot start from arbitrarily low PWM score thresholds in order to find the best, most likely higher cut-off. Eventually, one might also like to prioritize binding site motifs and would naturally assign highest priority to motifs with strongest enrichment. In this case, simply calculated odds ratios may result in a different ordering of motifs than corresponding enrichment P-values. We therefore developed an approach that focuses on the magnitude of overrepresentation expressed as a probabilistic estimate of the odds ratio of binding sites and promoter sequences in foreground and background gene sets. This quantity cannot be trapped by highly significant P-values associated with small odds ratios, because it focuses on a  estimate of the odds ratio itself. Finally, we assume that the proposed statistic enables more intuitive prioritization of motifs by expressing their importance as ”relative enrichment” in foreground promoters. It may however by perceived as potential drawback to specify the quantile interval as a free parameter, which was set here to 1%.In this study, we applied the new promoter analysis method to transgenic and tumor promoter sets and subsequently compared the results of both progression states. Similar to comparison of GO analyses, this setup enabled us to not only identify highly enriched binding sites in promoters of DE genes, but also to observe differences in importance of certain motifs for transgenic and tumor gene sets. As described in the results section, promoter analysis supported stronger regulation of cell cycle and of lipid metabolism in the tumor state by associating motifs of Atf3, Jun, E2f3, and Pparg with corresponding tumor genes and thus supported our previous findings. Importantly, this part of our study revealed overrepresentation of POU motifs predominantly in transgenic promoter sets. Expression profiles of POU factors as well as of HMG and Forkhead factors whose motifs were also identified through promoter analysis led us to propose Oct4 (Pou5f1), Tcf7, Lef1, and Foxc1 as regulators of a transcriptional network potentially under control of Wnt signaling. We speculate that the factors contribute to regulation of developmental pathways, which were enriched in both up- and downregulation according to GO analysis. Although network analyses carried out in this work did not reveal a link between Oct4 and EGF-signaling on the level of protein-protein interactions, in-vivo binding fragments for c-Myc, c-Jun, and c-Fos were previously located in the vicinity of the human POU5F1 gene -4. We used actual promoter sequences to estimate respective PWM score thresholds corresponding to the chosen background frequency. However, promoter sequences contain both functional and non-functional binding sites. Moreover, the proportion of functional sites among all predicted sites is expected to increase with higher, more stringent PWM scores, so that the background frequency of site predictions could be overestimated by using sequences of regulated genes. Yet, gene expression data allow for selection of sequences in which binding sites should occur at a background rate in the context of the particular biological condition. Thus, we composed background sets of genes with unaltered expression in the tumor state to adjust score cut-offs. In order to focus our analysis on TFs whose activity can be modulated by EGF signaling, we selected motifs of TFs downstream of EGF according to network analysis and of TFs upregulated in the tumor state. As a result, we obtained significantly enriched  pairs of TRANSFAC motifs representing several upregulated and EGF-associated TFs. The set of weight matrices selected by co-occurrence analysis was subsequently used as starting point to derive more complex promoter models. Although Fisher test P-values indicated enrichment of several PWM pairs at the chosen significance level, we noticed that each particular combination was present in only few promoters. We attribute this to the stringent threshold that was imposed on individual binding site predictions. Furthermore, we assume that the foreground sets comprised promoters controlled by a number of different regulatory processes, so that higher order TF combinations were more suitable to describe subsets of coregulated promoters.To further examine the molecular causes of EGF-induced malignant transformation, we searched promoters of upregulated tumor genes for pair wise combinations of TF binding sites. Paired site searches started out from individual site predictions adjusted to a P-value of 10Based on more complex TF modules, we selected a small set of factors for experimental validation. Results of the EMSA analysis are provided in We identified Stat5 transcription factor binding site (TFBS) enrichment in genes regulated in HCCs. Several studies have demonstrated an important role of STAT5 in liver fibrosis and cancer development through TGF-beta and STAT3 activation. We observed loss of STAT5 protein expression in HCC and demonstrated STAT5 DNA-binding activity in Igfbp6 . Indeed,Furthermore, we detected regulation of genes by C/EBPalpha. Overall we examined 9 novel gene candidates targeted by C/EBPalpha. Strikingly, all TFBS could be confirmed by EMSA band shift assay and in Western blotting experiments with nuclear extracts of tumor liver tissue C/EBPalpha was significantly repressed. This agrees well with its function as a key regulator of p21. Indeed cell cycle progression is regulated, at least in part by protein-protein interaction of C/EBPalpha with p21. It is of no surprise that tumors display less C/EBPalpha expression and we found all novel gene targets of C/EBPalpha to be repressed in tumors .Finally, there is a wealth of information on the role of p53 in liver cancer. Here, we demonstrate p53 binding sites in genes encoding components downstream EGF/IGF-2. Notably, p53 was still identified by promoter analysis although its expression level was similar to normal controls.In order to summarize the results obtained in this study we have build a hypothetical model-diagram of the EGF/IGF-2 regulatory circuit functioning during the transition from transgenic to tumor state . This moIn conclusion, promoter analysis of the differentially expressed genes enabled us to identify transcription factor binding sites. Such integration of sequence information into signal transduction networks enabled an identification of key nodes upstream of the identified transcription factors. By searching for pairs of TF sites and integration of this information into the network analysis robust information can be retrieved in an unbiased manner that clearly identifies keynodes and molecules acting in concert in defined biological conditions. Therefore, we propose a sequence of events whereby the insulin-like growth factor (IGF) pathway represents an important molecular switch in malignantly transformed liver cells. Possibly, an initial upregulation of EGF is followed by a subsequent and sustained activation of IGF2 signaling cascades. Overall, we hypothesize a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF. In this regard c-Myb is considered to be an important factor of the IGF2 positive feedback loop. Notably, we identified c-Myb binding sites in the promoter IGF2 gene and c-Myb to be a downstream partner of the IGF2 signaling cascade. Therefore our analysis demonstrates the knowledge gain form promoter analysis combined with upstream key node identification.http://www.laves.niedersachsen.de). The approval ID is Az: 33.9-42502-04-06/1204.All animal work followed strictly the Public Health Service (PHS) Policy on Humane Care and Use of Laboratory Animals. Formal approval to carry out animal studies was granted by the ethical review board of Hannover/Lower Saxony, Germany (“Niedersächsische Landesamt für Verbraucherschutz und Lebensmittelsicherheit (LAVES)”, We analyzed gene expression data from murine primary hepatocytes in normal cells as well as different states of disease progression, which were measured on Affymetrix MG_U74Av2 chips. These data were previously described in Computational analyses used version 2.3 of the BIOBASE ExPlain system P(X ≥ x) of finding purely by chance at least x out of K genes associated with a category comprising M genes given a total database size of N genes (1).The Gene Ontology (GO) 10-P-values in order to guide towards cellular functions most strongly affected by disease progression.To gain insight into functional changes accompanying tumor development in non-genotoxic hepatocarcinogenesis we compared enrichment P-values of GO biological process terms calculated for differentially expressed genes of transgenic and tumor states. Fisher test P-values were calculated for all GO terms linked to at least one gene in either gene set or otherwise assigned a value of 1.0. Functional categories were then attributed to the gene set (transgenic or tumor) achieving the highest significance of enrichment and ranked by the difference of logSignal transduction through a network of molecules is an important part of the cellular regulatory system. We applied network analysis methods to elucidate the molecular context of differentially expressed signaling molecules (network clusters) and to reconstruct pathways between transcription factors and upstream regulators of their activity (key nodes). Network construction in ExPlain implements the Dijkstra algorithm as core to find the shortest path  tree whose reaction cascades are weighted by the sum of the involved edge costs. We initialized the edge costs using three different values. Cost 0 was used to represent hierarchical classification links between molecules in the database. For direct reactions and indirect reactions we used cost 1 and 3, resp. A path cost is implicitly defined as the sum of the costs of its edges.The network cluster algorithm constructs cascades of maximally three reactions to connect each pair of input vertices (molecules). As a result, input molecules are joined into clusters of one or more proteins linked by corresponding pathways.xV and constructs the shortest path to all nodes of the complete network V within a given maximal path cost maxd . The search can be conducted in reverse direction of the edges leading to input molecules (upstream) or in the same direction (downstream). For each node i of V, the algorithm counts iN, the number of nodes of xV (number of true positives) that can be reached by a path satisfying maxd. The list of all possible key nodes that can reach at least one input molecule is sorted according to the specificity score is.In key node analysis, we searched for signaling molecules (key nodes) and corresponding networks that can transmit a signal to or receive a signal from several of input molecules within a certain path cost limit iM is the number of molecules in the whole network, which are reachable from node i within maxd steps, yet are not part of the input set of molecules . The parameter α is a penalty (0<α<1) that adjusts the balance between true positives and false positives. As described earlier h .In equation (2), P is defined as a graph P  = G , which is a sub-graph of the complete signaling graph G =  . Let PijS be the graph of the shortest paths between i,jPV within pathway P. Further, let PijC be the cost of the shortest paths, where PijC  =  ∞ if PijS  =  Ø. We combine PG and G yielding the final graph G' =  by introducing additional edges Pij ≠ Ø}E'  =  E U { | S and by extending the cost function for them by Pij)| f(CPij) ≤ Cij} U {Cij| f(CPij) > Cij}C'  =  {f(C. As function f we use f(x)  =  hx, with h . The aim of f is to make the cost function of any new edge  sublinearly dependent from the cost of the corresponding original shortest path PijS. The effect is that the costs of paths within a known pathway have decreased due to the cheaper short-cuts. This algorithm prioritizes the selection of the paths persisting inside known pathways during the search of the key nodes. The effect is maximal when h = 0 and absent when h = 1. Therefore we call h “rigidity parameter” which the balance between rigidity and sensitivity during the search. As demonstrated earlier, the pathway persistence has an advantageous effect with regard to sensitivity and specificity of identifying correct pathway components in a key node network In details, the application of verified cascades (pathways) to the key node search is done as follows: A pathway Due to the strong connectivity in signaling networks, a key node search is prone to yield many false-positive key nodes. In this study, we therefore calculated an empirical cumulative probability  for each key node identified, based on the score rank of the key node. The ECP value of a key node is calculated as ECP = RK*1/NK, where, RK is the rank of the key node and NK is the total number of the key nodes found in the analysis. ECP estimates a cumulative probability of a given key node to be found at the rank RK and higher by random chance. We then contrasted ECP values of key nodes obtained for the transgenic state with ECP values of key nodes of the tumor state. This was done to highlight those key nodes with strongest differences regarding their importance for transgenic and tumor TFs as well as expression changes and thereby rely less on selection of key nodes based on statistical error rates. A framework to automatize handling of the various free parameters required by the key node analysis algorithm and to estimate false discovery rate using random shuffling of the input nodes shall be described elsewhere (manuscript in preparation).n denotes the number of predicted sites in the foreground and N is the total number of sites. The relative proportion of foreground sequences among both foreground and background sequences gives the parameter p. Hence, the F-MATCH algorithm compares the proportion of foreground sites among all predicted binding sites to the proportion of foreground promoters among all promoters.We analyzed overrepresentation of transcription factor binding sites in promoters of differentially expressed genes and compared the importance of transcription factors for transgenic and tumor gene sets. It is well known that promoters of co-regulated genes are enriched with binding sites of relevant transcription factors Beta, promoters were assumed to come from the distribution pBeta, where fp and bp denote the number of promoters in foreground and background, respectively. Likewise, the site distribution (at a certain PWM score threshold) was set to sBeta, with fn and bn denoting counts of predicted binding sites in foreground and background. These two Beta distributions were used to assess the uncertainty about true proportions of sites as well as promoters.We adopted this strategy to juxtapose the relative importance of transcription factors (represented by their PWM) for corresponding transgenic and tumor gene sets. Instead of using P-values to quantify enrichment of binding sites in a foreground promoter set, we expressed the relative importance of a TF by a probabilistic estimate of the odds ratio of site and promoter proportions. As in the F-MATCH algorithm, PWM score thresholds were optimized to yield a maximal odds ratio. Given counts of predicted binding sites as well as counts of promoters, we assumed independent Beta distributions (4) for the foreground proportions.s/xp)P was derived by Pham-Gia For two independent distributions the probability 2F1 near w = 1, we calculated P(w) by numerical integration of equation (6).α's and β's. Eventually, we used equation (6) to compute quantile (F−1) estimates of the ratio of site and promoter proportions by numerical integration. The p-quantile yields the value for which the cumulative probability is at most p. In this work, we chose p = 0.01, so that the ratio of corresponding Beta distributions was assumed to be at least as high with 99% probability. The computer program sought a PWM score threshold to maximize this quantile value. Hence, a value F−1(0.01) = 2 for a certain PWM means that the proportion of foreground sites among all predicted sites (foreground + background) was at least two times higher than the proportion of foreground promoters among all promoters with 99% probability which indicates enrichment of sites in foreground promoters.To avoid numerical difficulties with evaluating the Gauss hypergeometric function PWM score thresholds for initial MATCH searches were adjusted to a common background frequency as baseline. Single binding site analyses used initial theoretically calculated cut-offs corresponding to a P-value of 0.05. P-values for PWM scores were computed with the standard method ik)Q  =    =  R  =  π  =   satisfies a given threshold. The reverse PWM Q' is defined in (7) and (8).ik,q'ik) for the sequence segment. As previously shown, the probability distribution of PWM scores can be efficiently calculated by convolution f by the convolution defined in (9). Equation 9 gives the joint probability density of scores is and is' up to site position i by convolution of ik,q'ik)ghg.We further extended the standard method to apply a dinucleotide background model. By conditioning score functions on the terminal residue j associated with a score pair -4). Furthermore, the frequency of sequences with both site types was compared with the expected frequency in the foreground, given the numbers of promoters with either TFBS, and calculated a coefficient of independence (11), which is similar to the mutual information of two random variables.f, f(i) and f(k) denote proportions of promoters with the TFBS pair, site i, and site k, respectively. To further consider a pair, it was required for this value to be at least 1.3. Finally, dependence of PWMs with similar sequence specificity was eliminated, because this property would inevitably produce high co-occurrence rates. This problem was addressed by considering only pairs of PWMs showing overlap in less than 10% of predicted sites. This approach was fully data-driven since admissible PWM combinations, baseline thresholds, and final score cut-offs, for individual PWMs and for the pair, were estimated on the basis of a background promoter set that was defined by the gene expression data.An analysis of individual TFBS with a test for pair wise co-occurrences of binding sites was performed. The approach represents a variant of the F-MATCH algorithm for binding site pairs and quantifies overrepresentation of promoter sequences with sites of both PWMs in the foreground set using the Fisher test. Like F-MATCH, the site co-occurrence method starts with externally defined score thresholds that can be further optimized using minimization of the Fisher test P-value as objective. While sequences containing a certain binding site pair can be significantly overrepresented in the foreground set, the association of the two sites might not be significant per se, e.g. enrichment may be determined by only one type of sites. Therefore, the signal conveyed by the event of co-occurrence itself was controlled using a stringent site score cut-off Click here for additional data file.Table S2Biological processes selected by comparison of their enrichment P-values in transgenic and tumor state.(XLS)Click here for additional data file.Table S3Upregulated molecules connected in network clusters shown in (XLS)Click here for additional data file.Table S4TRANSFAC weight matrices with highest q-values in transgenic and tumor gene sets.(XLS)Click here for additional data file.Table S5Transcription factors represented by enriched TRANSFAC motifs.(XLS)Click here for additional data file.Table S6TRANSFAC PWM pairs enriched in upregulated tumor genes (P < 0.001).(XLS)Click here for additional data file.Table S7Transcription factors identified by PWM co-occurrence analysis.(XLS)Click here for additional data file.Table S8Experimental validation of predicted binding sites in promoters of up- and downregulated genes.(XLS)Click here for additional data file.Figure S1Multiple alignment of TRANSPro promoters (-1000 to +500) of murine Bcl2a1a-d.(TIF)Click here for additional data file.Figure S2www.genexplain.com) and adapted with the Inkscape SVG editor (inkscape.org). The SBGN diagram illustrates the feedback loops triggered by EGF and IGF-2 signaling. The endpoints of regulation – multiple transcription factors (shown in light blue) that are activated through upstream signaling events, regulate expression of their target genes (shown in light blue) whose products are the key components of the signaling network (shown in red) upstream of the transcription factors.Representation of EGF/IGF-2 regulatory circuit in SBGN notation. This diagram was constructed using the geneXplain platform for systems biology ((PNG)Click here for additional data file.


Brassica napus , an oil crop of world-wide importance, originated from interspecific hybridization of B. rapa  and B. oleracea , and has six FLOWERING LOCUS T (FT) paralogues. Two located on the homeologous chromosomes A2 and C2 arose from a lineage distinct from four located on A7 and C6. A set of three conserved blocks A, B and C, which were found to be essential for FT activation by CONSTANS (CO) in Arabidopsis, was identified within the FT upstream region in B. napus and its progenitor diploids. However, on chromosome C2, insertion of a DNA transposable element (TE) and a retro-element in FT upstream blocks A and B contributed to significant structural divergence between the A and C genome orthologues. Phylogenetic analysis of upstream block A indicated the conserved evolutionary relationships of distinct FT genes within Brassicaceae. We conclude that the ancient At-α whole genome duplication contributed to distinct ancestral lineages for this key adaptive gene, which co-exist within the same genus. FT-A2 was found to be transcribed in all leaf samples from different developmental stages in both B. rapa and B. napus, whereas FT-C2 was not transcribed in either B. napus or B. oleracea. Silencing of FT-C2 appeared to result from TE insertion and consequent high levels of cytosine methylation in TE sequences within upstream block A. Interestingly, FT-A7/C6 paralogues were specifically silenced in winter type B. napus but abundantly expressed in spring type cultivars under vernalization-free conditions. Motif prediction indicated the presence of two CO protein binding sites within all Brassica block A and additional sites for FT activation in block C. We propose that the ancestral whole genome duplications have contributed to more complex mechanisms of floral regulation and niche adaptation in Brassica compared to Arabidopsis. Arabidopsis, FLOWERING LOCUS T (FT) expression causes early flowering independent of environmental and endogenous signals, whereas loss of FT function results in late flowering under long day (LD) conditions FT plays a central and indispensable role for floral induction, as an integrator of different pathways CO binds a unique sequence element containing the TGTG (N2-3) ATG motif within the FT promoter to induce expression in Arabidopsis under long day conditions FT intron 1 to repress transcription Arabidopsis FT activation by CO binding APETALA 1 (AP1) and FRUITFUL (FUL) to promote flowering Initiation of flowering is a key to the reproduction, evolution and survival of plants, and is determined by complex genetic pathways interacting with environmental and developmental signals. These are mediated through primary photoperiod, vernalization, gibberellin and autonomous pathways Brassica species belong to the monophyletic Brassiceae tribe within the Brassicaceae family Brassica crops in the U-triangle B. carinata , B. juncea  and B. napus  are allotetraploids, which originated from pairwise hybridization of the three diploid species, B. nigra , B. oleracea  and B. rapa . Comparative mapping have demonstrated the presence of numerous chromosomal regions homologous to the Arabidopsis genome, which are effectively triplicated within the diploid Brassica species, consistent with a common hexaploid ancestor in the evolution of Brassica and the tribe BrassiceaeArabidopsisArabidopsis were recently found to be retained as systemic orthologues in each of the triplicated A subgenomes (Brassica rapa Genome Sequencing Project Consortium The cultivated B. oleraceaB. oleracea transposon 1 (Bot1), a C genome-specific CACTA element, is believed to play a major role in the recent divergence of the B. rapa and B. oleracea genomes Arabidopsis, the major effect of TE DNA methylation is to silence transposition. However, methylated sequences may also affect the transcription of the flanking genes, typically reducing their expression Transposable elements (TEs) are ubiquitous amongst eukaryotic genomes. Of the most prevalent classes, retroelements and DNA elements BnFT paralogues in B. napusBnC6.FT.a and BnC6.FT.b; BnA7.FT.a and BnA7.FT.b). The remaining two paralogues were located on homoeologous chromosomes A2 and C2 (BnA2.FT and BnC2.FT) with deletion or variation of a putative CArG box for the FLC protein binding site that had been identified in Arabidopsis within intron 1 BnFT paralogues share a common ancestor with Arabidopsis FT, but had undergone triplication events consistent with the hypothesis of a common hexaploid ancestor for the Brassica species.We previously identified six FT promoter sequences from A2 and C2 that have a common lineage, and explore their evolutionary history both within the genus and the Brassicaceae family. FT transcripts from the distinct A2/C2 and A7/C6 lineages were then analyzed from a sample of eleven Brassica diploid and tetraploid cultivars at different developmental stages and environments, revealing divergence of expression patterns. Finally, the potential regulatory mechanisms, including DNA methylation, are further explored and discussed.Here, we report the isolation and comparison of the Brassica FT orthologues on chromosome A2 and C2 arose from a common lineage FT-A2 and partial promoter of FT-C2 from two BACs of B. napus Tapidor DH, JBnB045N08 and JBnB006F10. Based on the established upstream blocks A, B and C required for FT activation in ArabidopsisFT-A2 and FT-C2 from B. napus, FT-A2 from B. rapa BAC KBrB070J11 and FT-C2 from B. oleracea Scaffold000001 . This demonstrated the conservation of these blocks in the FT upstream regions of the Brassica homoeologous chromosomes A2 and C2 , JX193766 (FT-C2), JX193767 (FT-C6a) and JX193768 (FT-C6b)).Our previous results indicated that 2 and C2 . Interesm region . The seqFT-A2 and FT-C2 from B. rapa, B. oleracea and B. napus, as well as the orthologous region from Arabidopsis. This indicated that TEs were more abundant in the neighboring region of FT-C2 than the homologous region of FT-A2. Unfortunately, we had limited access to data from the corresponding regions of FT-C2 in B. napus compared with B. oleracea , B.oleracea (17), B. napus (51), B. nigra (1), B. juncea (8), B. carinata (6) and 9 species representing a range of taxa from the Brassicaceae. Based on the evidence from these marker assays, TEs were only detected within the Brassica C genome, and no insertion was found in the homologous regions of Brassica A and B genomes, nor from other Brassicaceae  and Ningyou7DH  FT-A2 was transcribed in all leaf samples from different developmental stages and different photoperiod treatments, whereas FT-C2 was not transcribed  and Chinese cabbage (Chiifu-401), whereas FT-C2 was not transcribed in B. oleracea var. alboglabra, where TEs were inserted in block A and B  ATG motif within the FT promoter ATTGTGGTTATGATT” in Arabidopsis, type II = “ATTGTGGTGATGAGT” in the Brassica FT-A2/C2 lineage, and type III = “ATTGCGGTGATGAGC” within the inverted duplication blocks of Brassica FT-A7/C6 lineage  identified, six were universal within Brassicaceae, and another (C-2) specific to the Brassica FT-A2/C2 lineage  gene led to a new allele conferring a novel phenotype of stable gray trichome color. The 20.5 kb insertion has the molecular structure of a putative autonomous transposon of the CACTA family We previously established that all extant n events  which arFT genes are located in genomic regions corresponding to Arabidopsis chromosome 1 block E Brassica species, extensive comparative genomic studies have shown that copies of block E are found on chromosomes A2, B2, C2, A7, B7 and C6 FT lineages could be inferred within the B genome, with FT-A2/C2/B2 representing homeologues from one lineage and FT-A7/B7/C6 from the other.FT-A2/C2 upstream block A were grouped according to genomic content of Brassica diploid and tetraploid species within the U triangle  events leading to paleopolyploidy At-α (∼40 MYA) duplication was considered to explain the drastically increased species number in the core BrassicaceaeAt-α duplication was followed by later lineage-specific mesopolyploid WGD events. In Brassicaceae, at least four independent lineage-specific WGD events have been revealed and the whole genome triplication (Br-α) in Brassica was first proposed and then shown to have occurred before the radiation of the whole tribe BrassiceaeBrassiceae, as shown by our results above.The sequences of triangle , which st groups , furtherBrassica FT copies is challenging due to high sequence similarity of transcripts, especially for the four FT paralogues within the duplication blocks of A7/C6 FT-C2 in all accessions including B. napus and B. oleracea contrasted with the constitutive expression of FT-A2 in B. rapa and B. napus  and seven PSY (Phytoene synthase) paralogues in B. napus and its two progenitors found that all were expressed, but with tissue specific expression exhibiting overlapping redundancy and signs of sub-functionalization FT genes differed with developmental stage and in different species/cultivars, consistent with selection for different behaviors for this key regulator of the primary adaptive trait of flowering time, associated with the A and C genome divergence and more recent formation of B. napus. This behavior provides a wider range of options for adaption to different environments and domestication selection, such as the divergence between FT-A7/C6 which may correspond to the formation of winter and spring B. napus crop types. Interestingly, FT-A2 was constitutively expressed in all materials and developmental stages tested, and notably in winter-type B. napus before vernalization. FT genes have recently been found not only to regulate flowering but stomatal opening in Arabidopsis, vegetative growth in perennial poplar and storage organ formation in potato FT have evolved antagonistic roles in the control of flowering, with the one functionally conserved with Arabidopsis FT being essential for flowering. However, the second paralogue represses flowering, with its down-regulation crucial for the vernalization response FT-A2 may participate in other physiological processes at least in winter-type B. napus, alternatively still in regulating flowering but in a different manner.Recent characterization of 12 FT have been found to be essential for activation by CO in Arabidopsis, with block A subsequently found as a cis-element for promoting gene expression, block B probably enhancing FT expression in response to blue light COBrassica FT-A7/C6 lineage although these FT genes could be expressed normally , has been identified that binds directly to the FT promoter near the transcript start site to regulate FT expression in ArabidopsisFT by PIF4 may also exist in Brassica species which requires further investigation. We found that block C was significantly more conserved across the Brassica species and other species of Brassicaceae, even when located about 4.5–7.5 kb upstream of the FT start codon  at 18°C under LDs (16 h light/8 h dark) for 3 weeks, vernalized (8 h light/16 h dark) at 5°C for 7 weeks and 3 weeks, respectively, with half of the plants then transferred to LDs , and another plants to SDs  conditions.Ninety-nine plant accessions representing diploid and tetraploid asshouse . No specB. rapa var. trilocularis R-o-18 and var. pekinensis Chiifu-401, and B. oleracea var. alboglabra Bailey were grown in cabinets  at 22° under LDs (16 h light/8 h dark). Tissue was collected from leaves at different growth stages  for transcript and DNA methylation analysis. Leaves were excised from the plant, the midrib removed, and the left hand side lamina used for RNA, the right hand side for DNA.The plants of B. rapa gene models were obtained from the Brassica database (BRAD) (http://brassicadb.org/brad/index.php) and coding sequences of B. oleracea and B. napus were compared to B. rapa and Arabidopsis genomes using Blastn and WU-BLAST against the BRAD and TIGR database (www.tigr.org/tdb/e2k1/bog1/). Transposable elements were predicted and located using “RepeatMasker” (http://www.repeatmasker.org). Primers used for isolating the FT upstream blocks and estimating the insertion events are given in http://www.ebi.ac.uk/). Phylogenetic trees were constructed using MEGA version 4.1 GenScan software optimized for Arabidopsis was used to identify protein-coding genes within TapidorDH BAC sequences Brassica FT transcripts (30 cycles). The thermal cycling program was 94°C for 3 min followed by 30 cycles of 94°C for 30 s, annealing for 30 s, and extension at 72°C for 30 s, ending with a 10 min extension at 72°C. The primer sets used for RT-PCR detection are listed in Total RNA was extracted from leaves using the RNeasy Plant Mini Kit (Qiagen). cDNA was reverse-transcribed from total RNA (1 µg) with RevertAid™ First Strand cDNA Synthesis Kit (Fermentas) in a 20 µl reaction. Gene-specific primers were used for RT-PCR to detect Actin (30 cycles) and http://katahdin.mssm.edu/kismeth) and listed in DNA was extracted from leaves using the DNeasy Plant Mini Kit (Qiagen). 450 ng genomic DNA was subjected to two successive treatments of sodium bisulphite conversion using the EpiTect Bisulphite kit (Qiagen) according to the manufacturer's instructions. The reaction was then purified once more using the PCR purification kit (Qiagen). Forward (F) and reverse (R) primers for bisulphite sequencing PCR were designed using Kismeth Click here for additional data file.Table S2Description of primer sets used in this study and the resultant PCR products.(XLS)Click here for additional data file.


Despite its critical role for mammalian gene regulation, the basic structural landscape of chromatin in living cells remains largely unknown within chromosomal territories below the megabase scale.cis-interactions suggest that it may underlie the dynamics of a significant number of gene-rich domains in mammals, thus contributing to genome evolution. Finally, using an original model derived from polymer physics, we show that this modulation can be understood as a fundamental helix shape that chromatin tends to adopt in gene-rich domains when no significant locus-specific interaction takes place.Here, using the 3C-qPCR method, we investigate contact frequencies at high resolution within interphase chromatin at several mouse loci. We find that, at several gene-rich loci, contact frequencies undergo a periodical modulation (every 90 to 100 kb) that affects chromatin dynamics over large genomic distances (a few hundred kilobases). Interestingly, this modulation appears to be conserved in human cells, and bioinformatic analyses of locus-specific, long-range Altogether, our work unveils a fundamental aspect of chromatin dynamics in mammals and contributes to a better understanding of genome organization within chromosomal territories. Within the interphasic cell nucleus, the mammalian genome, packed into the chromatin, is spatially restrained into specific chromosomal territories ,2 and iscis and trans chromatin interactions in their native genomic context. Subsequently, several 3C-based methods have been developed that allow the unbiased large-scale identification of such interactions [At the supranucleosomal level (approximately 10 to 500 kb), it is largely accepted that one essential determinant in relation to gene expression and other chromosomal activities is chromatin looping . Howeverractions ,7,13-16.ractions ,18, combractions , provideractions ,19. FurtHere, using a 3C-qPCR approach , we deteUsp22  locus, on chromosome 11, displays such characteristics. Two intergenic HindIII sites . We then investigated four additional gene-rich loci that displayed no evidence for long-range specific interactions in the postnatal mouse liver: the Dlk1 (Delta-like 1 homologue) locus on chromosome 12 [Lnp  and Mtx2 (Metaxine 2) loci on chromosome 2, and the Emb (Embigin) locus on chromosome 13 , followed by a very significant decrease between 100 and 140 kb . Very interestingly, random collision frequencies stabilized between 140 and 180 kb before finally dropping for distances above 180 kb  located one potential modulation away (91.3 kb upstream from site F1 and 109.9 kb from site F7) . We then repeated experiments at the anchor site F1 of the ) Figure . These eWe conclude that several gene-rich mouse loci display an unexpected 90-kb modulation that affects contact frequencies over large genomic distances. To simplify further statistical analyses, we decided to describe this 90-kb modulation as consecutive supranucleosomal domains encompassing separation distances where random collision frequencies alternate between high and low values Figure .Previous 3C studies in yeast  and humaβ-globin locus [P = 0.022, Mann-Whitney U-test) followed by a very significant decrease for larger site separations   data obtained at the human in locus  from expcis-regulatory sequences that (for mechanistic reasons) should interact together over long distances will tend to accumulate at preferred relative separation distances where the collision dynamics is fundamentally the most prone to such contacts. According to this proposal, cis-interacting sequences should position into supranucleosomal domain I (less than 35 kb) or domain III (around 90 kb), and eventually in domain V (around 180 kb), since the higher basal collision levels are found in these domains. Using the READ Riken Expression Array Database [cis  and showed that, around such co-expressed genes, conserved sequences are significantly over-represented in both domain III (+7.9%) and domain V (+6.6%)   in domain IV (between the first and second modulations) where the lower random collisions frequencies were observed. We conclude that, as a predicted consequence of our findings, conserved intergenic sequences of clustered co-expressed genes are significantly over-represented within supranucleosomal domains III and V corresponding to the first and second modulations of random collision frequencies.Modulations in contact frequencies, as observed here for gene-rich regions, should have fundamental implications for gene regulation and mammalian genome evolution. Indeed, if, as demonstrated in this work, the frequency of random collisions does not regularly decrease according to genomic distances but displays a periodical modulation, then Database , we iden) Figure . The numInterestingly, recent genome-wide mapping of chromosomal interactions in human by Hi-C experiments also provides direct experimental validation of our proposal. Indeed, these data confirm that long-range interactions in Giemsa-negative bands, containing gene-rich regions, are favored for site separations around 90 kb (domain III) relative to Giemsa-positive bands, which are gene-poor regions Figure . TherefoWe reasoned that the modulations of contacts frequencies observed at several gene-rich loci may reflect a preferential statistical shape that the chromatin tends to adopt when no strong locus-specific interactions take place. Since this constraint appears to be independent of the genomic position at all five gene-rich loci investigated, this preferential non-linear shape should possess a long-range translational symmetry. This led us to postulate that this statistical shape may correspond to a simple helix organization.X(s) (in mol × liter-1 × nm3) and site separation s (in kb):The dynamics of chromatin has been successfully modeled in yeast ,24 usingβ term represents the number of Kuhn's statistical segments and depends on polymer shape. Equations 2a and 2b  provide the β terms used for linear and circular polymers, respectively. For a polymer folded into a circular helix, we developed the following β term :The D is the diameter of the helix (in nm) and P its step (in nm). In the above equations, S is the length of the Kuhn's statistical segment in kb, which is a measure of the flexibility of the chromatin, and k is the crosslinking efficiency, which reflects experimental variations. The linear mass density L is the length of the chromatin in nm that contains 1 kb of genomic DNA.where β terms, we fitted our experimental data to three polymer models. The linear model fits appropriately only for site separations lower than 35 kb , the circular polymer model [c  .Using Equation 1 and the appropriate  regions . These pIt is important to stress that the shape of the chromatin described by these parameters is averaged over the whole population of cells analyzed (5 million nuclei in each 3C sample) and thus is more likely to represent a statistical shape arising from the global dynamics of the chromatin than a fixed organization  and F14 (62.7 kb away); at the Usp22 locus, it takes place at site F-28 as well as sites F1 (91.4 kb away) and F7 (109.9 kb away). Secondly, this 90-kb modulation was found at five distinct gene-rich loci located on four different mouse chromosomes. Finally, using published 5C data [β-globin locus in cells where very weak interactions were found. Interestingly, this modulation was not revealed in previous 3C experiments that we, and many others, performed in mouse or human. There are at least two reasons why this phenomenon went unnoticed. Firstly, the amplitude of the modulation is very weak and could only be significantly revealed when a relatively large number of experimental points were obtained from a highly quantitative method and combined together into a single graph after accurate normalization of the data [β-globin locus [cis- or trans-interactions . Interestingly, this modulation is not a simple consequence of gene expression per se since RT-qPCR analysis indicated that, in the samples investigated (30-day-old mouse liver), some loci are completely repressed (Dlk1 locus), or display very low expression levels (Emb and Lnp loci), while others contain expressed genes (UspP22 and Mtx2 loci) , compared to silent loci (D = 278.83 ± 7.65 nm and P = 149.20 ± 13.67 nm)  at the mouse Hoxd (Homeobox d) locus, which is located at one or two modulations away from the genes that it regulates. It was suggested that 'the GCR would have concentrated, in the course of evolution, several important enhancers, due to an intrinsic property to work at a distance' [As previously mentioned, one consequence of modulated contact frequency is that long-range interacting istance' . The modSaccharomyces cerevisiae [Our work suggests that modulated contact frequencies arise from an intrinsic constraint that applies to the chromatin. This led us to wonder about the nature of this constraint and to propose that it may result from a preferential statistical shape that the chromatin tends to adopt in gene-rich regions when no strong locus-specific interactions take place. This hypothesis is supported by the finding that modulated contact frequencies can be described by polymer models as if, in these regions, the chromatin was statistically shaped into a helix Figure . Interesrevisiae , we showVs) of the statistical helix. In mammals, Vs = 1.02 × 105 ± 0.05 × 105 nm3/kb . Therefore, the folding of a putative 30-nm chromatin fiber into a statistical helix would result in a 15.00 ± 0.73-fold increase (Vs/V) of the volume that it occupies. Finally, if the entire diploid genome had a helical chromatin organization as shown in Figure 3 (the volume occupied by such a helix encompassing two times 3 × 109 bp), which is higher than the volume of a regular mammalian nucleus (approximately 520 μm3 for a nuclear diameter of 10 μm). Therefore, in addition to the helix-shaped organization described above, other types of dynamic folding should exist that achieve higher levels of chromatin compaction. This hypothesis is supported by our finding showing that the dynamics of random collisions in gene-desert regions is completely different to that observed in gene-rich domains.One consequence of folding the chromatin into a helix-shaped structure is that the volume it occupies increases dramatically. This increase can be estimated by calculating the volumetric mass density . Our work suggests that the upper limit for such linear polymer models may occur, in gene-rich regions, for separation distances around approximately 35 kb  experiments. For separation distances below 1 Mb, spatial distances predicted from the statistical helix model  SDS . Step 10: added 50 μl of 12% (v/v) Triton X-100 diluted in 1 × ligase buffer from Fermentas . Step 13: added 450 U of restriction enzyme (EcoRI for the Dlk1 locus or HindIII for the other loci). Step 16: incubated 30 minutes at 37°C; shake at 900 rpm. Step 34: additional digestions were performed using BamHI for the Dlk1 locus and StyI for the other loci. Step 39: adjusted 3C assays with H2O to 25 ng.μl-1. 3C products were quantified (during the linear amplification phase) on a LighCycler 480 II apparatus ; 10 minutes at 95°C followed by 45 cycles 10 s at 95°C/8 s at 69°C/14 s at 72°C) using the Hot-Start Taq Platinum Polymerase from Invitrogen  (10966-34) and a standard 10 × qPCR mix [Usp22 locus; RP23 117C15 for the Dlk1 locus; and a subclone derived from RP23 3D5 for the gene-desert region. For 3C-qPCR analyses of site F-28 at Usp22 locus, a PCR product encompassing 733 bp around site F-28 was generated from genomic DNA (FA4 gccatactcagccacagggac and RA2 cctgatctcacgaatcaccctc). This PCR product (0.1 μg) was mixed with 3.4 μg of the RP23 55I2 BAC before HindIII digestion and ligation to generate standard curves. Data obtained from these experiments are included in Additional file Usp22 locus, for anchor sites F1 and F7, 34 and 40 sites, respectively; Dlk1 locus, for anchor sites F14/F5 and F3, 23/17 and 9 sites, respectively; Emb locus, for anchor sites R4 and R7, 31 and 30 sites, respectively; Lnp locus, for anchor sites R41 and R46, 27 and 25 sites, respectively; Mtx2 locus, for anchor sites R2 and R56, 52 sites for each anchor; and for the gene-desert locus, for anchor sites R9/F25/F35 and F48, 36/40/40 and 38 sites, respectively.The 3C-qPCR assays were performed as previously described  with a fqPCR mix  where thqPCR mix : RP23 552O at 12.5 ng.μl-1. Each reaction was done as follows: 0.1 μM of extension primer was added to a 10-μl reaction containing 1 × qPCR mix [For each biological sample and each extension primer , six reactions were performed, pooled, purified with a QiaQuick PCR purification kit and diluted in HqPCR mix  and 1 μlTotal RNA extraction and RT-qPCR quantification were performed as previously described ,21 usingSupranucleosomal domains (D.I to D.VI) were defined from statistical analyses (Mann-Whitney U tests) performed on data shown in Figure X(s) (in mol × liter-1 × nm3) and the site separation s (in kb):We used the Freely Jointed Chain/Kratky-Porod worm-like chain model . This mowith, for a linear polymer:S is the length of the Kuhn's statistical segment in kb, which is a measure of the flexibility of the chromatin, and k is the efficiency of crosslinking, which reflects experimental variations. The linear mass density L is the length of the chromatin in nm that contains 1 kb of genomic DNA. For the following analyses, we used a value L = 9.6 nm/kb [c giving the 'apparent circle size' in kb into the β term of Equation 2a, Dekker et al. [In Equation 1, .6 nm/kb  estimater et al.  derived β term in Equation 1 corresponds to the number n of Kuhn's statistical segments [R〉 in nm and the length of the statistical segment S as given in Equation 3:The segments , which iR〉 parameter in Equation 3 and using the resulting β term in Equation 1, one can simulate spatial constraints that 'fold' the intrinsically linear polymer. Such modifications help us to model the dynamics of random collisions within a chromatin that possesses higher levels of organization. For a linear polymer, the average spatial distance 〈R〉 is directly linked to site separation s as given in Equation 4a:Interestingly, by setting appropriately the 〈β term given in Equation 2a. For a circular polymer, the average spatial distance 〈R〉 can be linked to site separation s by introducing the previously described [c as given in Equation 4b:and thus substitution of Equation 4a in Equation 3 yields the escribed  apparentβ term given in Equation 2b.and thus substitution of Equation 4b in Equation 3 yields the R〉 (in nm) is related to site separation s (in kb), to the mean diameter D of the helix in nm and the mean step P in nm as given in Equation 4c:For a polymer folded into a circular helix the average spatial distance 〈β term given in Equation 5:Substitution of Equation 4c in Equation 3 yields the β term given in Equation 5 can be used in Equation 1 to provide a model that describes random collisions within a circular helix polymer. . The length of one turn on the statistical helix Sh was calculated from the best-fit curve , which determines the nonlinear (weighted) least-squares estimates of the parameters of nonlinear models.Best-fit analyses were implemented under the R software . We usedβ-globin locus in the EBV-transformed lymphoblastoid cell line GM06990 were downloaded from [Contact frequencies at the human ded from  .Co-expressed genes were selected from the READ Riken Expression Array Database , which cd Aitken , houseker) from their relative expression levels in at least 15 tissues. Co-expressed genes were defined as those having either similar (r ≥ 0.8) or opposite (r ≤ -0.8) tissue-specific expression patterns. This finally provided a set of 130 strongly co-expressed/co-regulated genes. We then determined the relative site separations between the transcriptional start sites of these co-regulated genes and conserved intergenic sequences. Conserved sequences were downloaded from the mouse genome  on the UCSC server. We limited our analysis to a maximal separation distance of 250 kb covering the six previously defined supranucleosomal domains  from the following equation:For each possible gene pair, co-expression levels were determined by calculating the Pearson correlation coefficient , μ is the mean number of counts, σ is the standard deviation and N is the number of randomizations performed (here 30). Error bars represent the 95% confidence interval for counts in each domain.where trans-interactions). Pairs of sequence-tags were classified by chromosome. We extracted the positions of all HindIII and NcoI sites from the human genome (hg18). Restriction fragments were numbered and, for each restriction fragment, we specified the positions of the 5' and 3' ends. The downloaded positions of the tags were replaced by the position of the corresponding restriction site. For this operation we used the restriction fragment numbers provided in the downloaded files. Direction '0' corresponds to a restriction site located at the 3' end of the restriction fragment (sense reading of the sequence-tag) while direction '1' corresponds to the 5' end (antisense reading of the sequence-tag). We then assembled datasets generated from lanes 1 and 2 of each experiment. We extracted from the UCSC server the positions of chromosomal bands . We selected all pairs of sequence-tags for which both partners are located within the same chromosomal band (to remove long-range/inter-band interactions). Data were pooled into two separate sets: a first set corresponding to all pairs of sequence-tags located in Giemsa-negative bands and a second one corresponding to pairs of sequence tags located in Giemsa-positive bands . For each set, we selected interactions that are represented by at least four pairs of sequence tags  and calculated for each interaction the separation distance between the restriction sites . In each set (Giemsa-negative and Giemsa-positive), the number of interactions were counted in each supranucleosomal domain  at two Usp22 genomic sites (sites F1 and F-28)  in liver samples from 16.5-days-post-coitus embryos (grey data points) or 30-day-old mice (white data points). Data analysis was as described in the legend of Figure 1b. Red squares represent the floating mean . We determined the higher and the lower points of the floating mean for site separations above 40 kb and calculated the average random collision frequencies  of sites located 40 kb around these points . P-values (Mann-Whitney U-test) account for the significance of the differences observed between these averages. Error bars are standard error of the mean.Click here for fileβ-globin locusCollision frequencies at the human . Collision frequencies at the human β-globin locus (a gene-rich region on chromosome 11p15.4) were obtained from several published 5C experiments performed in GM06990 cells, an EBV-transformed lymphoblastoid cell line where this locus is not expressed and where only a very weak/residual interaction was detected . Data fand 7 in  and plotClick here for fileFitting the circular polymer model to mouse gene-rich loci. The circular polymer model (Equations 1 and 2b) was fitted to 3C-qPCR data obtained at gene-rich loci. The best fit curve is shown in red and best fit parameters are as follows: R2 = 0.50 with K = 725,785 ± 66,540; S = 2.515 ± 0.092 kb; c = 110.515 ± 2.028 kb. The black curve depicts the best fit obtained with the linear polymer model .Click here for fileGene expression at loci investigated by 3C-qPCR. Total RNA from 30-day-old mouse liver was prepared and mRNA levels were determined by RT-qPCR relative to Gapdh mRNA level. The Usp22, LnP and Mtx2 genes were found to be expressed. Very low levels of expression were found for the Gtlf3b, Aldh3a2 and Emb genes. The other genes  are fully repressed.Click here for fileRandom collisions at silent versus expressed loci. Data points represent collision frequencies determined at silent  or expressed  loci. Best fit of the statistical helix model (Equations 1 and 5) was performed for each dataset . The values of best fit parameters for each data set are indicated in the graph. Both the diameter (D) and the step (P) of the helix are larger in the expressed loci compared to the silent ones.Click here for fileSaccharomyces cerevisiae genomeFitting the statistical helix model to the yeast . In order to test whether a statistical helix organization may be valid for other organisms, we fitted the statistical helix polymer model to the 3C data obtained in the yeast S. cerevisiae [R2 = 0.82 and 0.80, respectively) were similar to those obtained from published models  [S. cerevisiae genome.revisiae . For botctively) . For AT-ctively) , the stactively) . TherefoClick here for fileSaccharomyces cerevisiae genomeFitting the statistical helix model to the yeast . Data published by Dekker for the yeast S. cerevisiae [(a) For AT-rich regions, consistent with previous findings [D and the step P are not relevant, as indicated by large standard deviations. (b) In GC-rich regions, the statistical helix model (red curve), fits with a distended helical shape. Best-fit parameters are indicated above the graph. They were calculated using a linear mass density of 11.1 nm/kb [S = 6.060 ± 0.519 kb and 4.558 ± 0.503 kb for AT-rich and GC-rich domains, respectively) are compatible with the parameters previously obtained with the linear or circular polymer models  [(b) Using the best-fit parameters obtained for the yeast S. cerevisiae (b), we calculated the expected mean spatial distances (in nm) for increasing site separation distances (0 to 140 kb) for both the statistical helix  and the linear polymer  models. The experimental spatial distances (in nm) obtained by Bystricky et al.  . (b) UsiTable of ) from hiClick here for fileAn upper limit of validity for the statistical helix model. Expected spatial distances (in nm) were calculated as a function of increasing genomic distances (in kb) using either Equation 4a  or Equation 4c and the biophysical parameter given in Figure 4 . Dashed lines represent the expected deviations due to standard errors on the measured biophysical parameters . Details about mathematical equations are given in the Materials and methods section. Data points (blue diamonds) depict spatial distances measured by FISH experiments as reported by van den Engh et al. [h et al. . These dClick here for file3C-qPCR dataset for gene-rich regions.Click here for file3C-qPCR dataset for the gene-desert region.Click here for file3C-qPCR primers.Click here for file


Additional sex combs (Asx) gene and its mammalian homologs exhibit PcG function in genetic assays; however, the mechanism by which Asx family proteins mediate gene repression is not well understood. ASXL2, one of three mammalian homologs for Asx, is highly expressed in the mammalian heart and is required for the maintenance of cardiac function. We have previously shown that Asxl2 deficiency results in a reduction in the bulk level of histone H3 lysine 27 trimethylation (H3K27me3), a repressive mark generated by the Polycomb Repressive Complex 2 (PRC2). Here we identify several ASXL2 target genes in the heart and investigate the mechanism by which ASXL2 facilitates their repression. We show that the Asxl2-deficient heart is defective in converting H3K27me2 to H3K27me3 and in removing ubiquitin from mono-ubiquitinated histone H2A. ASXL2 and PRC2 interact in the adult heart and co-localize to target promoters. ASXL2 is required for the binding of PRC2 and for the enrichment of H3K27me3 at target promoters. These results add a new perspective to our understanding of the mechanisms that regulate PcG activity and gene repression.Polycomb Group (PcG) proteins are epigenetic repressors of gene expression. The Drosophila  Hox genes) . We have previously generated an pression . Initialhe heart . Interesactivity . Here weDrosophila Asx is a chromatin-associated protein . ImmunosAsxl2-/- hearts is correlated with de-repression of myosin heavy chain β (β-MHC), the fetal form of MHC that has lower ATPase activity than the adult alpha form [-MHC promoter. This, along with our previous observation that the level of bulk H3K27me3 is significantly reduced in Asxl2-/- hearts, led us to hypothesize that ASXL2 and PRC2 may act together to regulate the expression of β-MHC and other target genes. To investigate this hypothesis, we first sought to identify additional targets of ASXL2 in the murine heart. We performed a microarray analysis on 1-month-old wild-type and Asxl2-/- hearts and identified 753 genes that are either induced or repressed more than 2 fold in Asxl2-/- hearts ; Actin, alpha 1, skeletal muscle (Acta1); and Gprotein-coupled receptor kinase 5 (Grk5). First, query of the Broad Institute ChIP-seq database revealed that the promoters of these genes are enriched for PRC2 components and H3K27me3 in embryonic stem (ES) cells  cells . This suroblasts . Second,roblasts –26, suggectively .Sfrp2, Acta1 and Grk5 are directly repressed by ASXL2 and PRC2, we examined enrichment of ASXL2 and PRC2 components at these loci by ChIP-qPCR assays, focusing on regions between -2 kb and +2 kb of the TSS. For each locus, we selected 2-3 genomic sites that are conserved between mouse, rat and human (Genome-wide studies have consistently found PRC2 components to be enriched at chromatin regions near the transcription start sites (TSSs) of target genes –34. To dnd human . ASXL2 wnd human . Most ofnd human .Sfrp2 and Grk5 and examined the level of ASXL2 enrichment by ChIP-qPCR assays. For both genes, ASXL2 was most highly enriched at the promoter, and the level of enrichment decreases from 5’ to 3’ of the gene , suggesting that it does not require ASXL2 for repression. These results suggest that ASXL2 is specifically involved in the regulation of a subset of PcG targets.We have previously shown that the bulk level of H3K27me3 is decreased in - hearts . This isactivity ,35,36. Wromoters , confirm- hearts . AdditioAsxl2 may result in depletion of nucleosomes or indiscriminate reduction of all histone modifications at target loci, we examined the enrichment of AcH3, an active histone mark [Asxl2, the level of AcH3 enrichment increased significantly at β-MHC, Sfrp2, Acta1 and Grk5 – loci that are dependent on ASXL2 for repression , which interacts with EZH2 ,41,42 anFirst, PHF1 can be an integral component of PRC2 and co-purifies with the core components ,39,42. AAsxl2 deficiency on bulk H3K27me2/3 levels suggests that in the adult heart, most PRC2 targets require ASXL2. In contrast, PHF1 may be required for the regulation of just a small number of targets.Secondly, PHF1 deficiency did not affect the level of bulk H3K27me2 or H3K27me3 . Thus, AFinally, although a GAL4-PHF1 fusion protein is able to recruit PRC2 to transgenic UAS sites, EZH2 enrichment at target chromatin is independent of PHF1 . In compAsxl2 resulted in a decrease in the level of bulk H3K27me3 [ASXL1 disrupted PRC2 and H3K27me3 enrichment at the HOXA gene cluster without disrupting the level of uH2A [HOXA genes as observed in ASXL1-deficient cells [HOXA cluster, and that the loss of PRC2 recruitment in ASXL1-deficient cells did not result from inactivation of PR–DUB. A comprehensive study of more gene loci is needed to answer whether there is a functional relationship between histone H2A deubiquitination and H3K27 trimethylation. It is also possible that this relationship is different in heart tissue and in blood cells.Asx and ASXL proteins are core components of the PR–DUB complex, which specifically removes ubiquitin from histone H2A that is mono-ubiquitinated at lysine 119 . The dis of uH2A . Furthernt cells . This sePho mutants, suggesting a high degree of functional conservation [Hoxc8 and Hoxa5 [ASXL1/2 are large proteins that interact with multiple proteins other than BAP1 –45. Inteervation . In mousnd Hoxa5 . ThroughUbx in the wing primordia [Asx and all ASXL proteins contain a highly conserved plant homeo domain (PHD) at the C-terminus . The PHDrimordia . PHD finrimordia . For exarimordia , and tharimordia ,56. If tA recent computational modeling study of ASXL proteins identified an N-terminal winged helix-turn-helix (wHTH) domain that is predicted to bind DNA . wHTH doAsx [trx severely disrupted binding of Asx, but not of PRC2, to polytene chromosomes [The level of bulk H3K27me3 was dependent on ASXL1/2 in mammalian cells but was unaffected in Drosophila embryos carrying a homozygous null mutation of Asx . Moreoveomosomes , suggestWhile the mechanism that regulates PRC2 binding is far from well understood, differences between mammals and Drosophila have been observed . ASXL prAsxl2 for H3K27 trimethylation in the heart  at the University of Illinois at Chicago.Total RNA was extracted from 1-month-old hearts and real-time RT-PCRs were performed using the SuperScript™ III Platinum SYBR Green One-Step qRT-PCR kit (Invitrogen).β-Actin in the same sample. Primer sequences are provided in the Supplementary Material.Gene expression levels were normalized against that of 18S rRNA or Whole hearts were cut into pieces and homogenized in Buffer A  using a Tissue Master homogenizer . Biochemical fractionation was performed as previously described .Nuclei were harvested from 1-month-old hearts that had been fixed in formaldehyde and homogenized. Chromatin was sheared by sonication andimmunoprecipitated with KC17 anti-ASXL2 antibody , anti-EZ3VO4). Immunoprecipitations were performed using antibodies against proteins of interest and Dynabeads Protein G (Invitrogen). After washing, beads were boiled in Laemmli Buffer and IPed proteins were analyzed by Western blots.Nuclear pellet was prepared from homogenized whole hearts and extracted in high salt buffer Click here for additional data file.Figure S1Sfrp2, Acta1 and Grk5 loci in ES cells.Epigenetic profiles at  The Broad Institute ChIP-seq database (http://www.broadinstitute.org/scientific-community/science/programs/epigenomics/chip-seqdata) was queried for the enrichment of H3K27me3, SUZ12, and EZH2 at the loci of interest. For each gene, only the genomic region around the TSS is shown. The scale bar for each panel is shown at the bottom of the panel. Arrow points to the direction of transcription. The y axis is the relative level of enrichment. (A–C) Representative epigenetic profiles for three types of genes in ES cells: those that are repressed by PcG activity, those that are constitutively expressed and not regulated by PcG activity, and those that are repressed via PcG-independent mechanism. (A) The chromatin region near the TSS of Hoxa3, a classical PcG target gene, displays high levels of enrichment of H3K27me3, SUZ12 and EZH2. (B) The profile for Polr2d, a housekeeping gene that encodes an RNA polymerase II subunit, shows no enrichment of H3K27me3, SUZ12 or EZH2. (C) H3K27me3 and PRC2 components are not enriched near the TSS of Cp, a gene that is repressed in ES cells. (D–F) The epigenetic profiles around the TSS of Sfrp2, Acta1 and Grk5 resemble that for Hoxa3.(TIF)Click here for additional data file.Figure S2S100a10 locus.ASXL2 is not enriched at the S100a10 encodes a calcium binding protein and is highly expressed in both wild-type and Asxl2-/- hearts. Shown are anti-ASXL2 ChIP-PCR results for six chromatin sites (a1-a6) within -5kb to +5kb of S100a10 TSS. Mock ChIP was performed with normal rabbit IgG. Input: PCR assay of 1:100 diluted total input chromatin.(TIF)Click here for additional data file.Figure S3-MHC (A–B), Grk5 (C–D), Sfrp2 (E–F) and Acta1 (G–H) loci, shown as percentages of total input.ChIP-qPCR analysis of H3K27me3 enrichment at β  H3K27me3 ChIP.  Mock IgG ChIP. Each column represents the mean value of data from three independent samples. *p<0.05; **p<0.01; Error bar: standard deviation.(TIF)Click here for additional data file.Figure S4Hoxb5 locus, shown as percentages of total input.ChIP-qPCR analysis of H3K27me3 enrichment at the  (A) Alignment of mouse, rat and human genomic sequences from -3kb to +3kb of Hoxb5. H1 and H2 are two highly conserved regions that were selected for ChIP-qPCR analysis. (B) H3K27me3 ChIP. (C) Mock IgG ChIP. Each column represents the mean value of data from three independent samples. Error bar: standard deviation.(TIF)Click here for additional data file.Figure S5Asxl2-/- heartsComparison of EZH2 protein level in wild-type and . Serial dilutions of heart extracts were subjected to SDS-PAGE and then probed with anti-EZH2 antibody. Western blot of TBP was used as a loading control.(TIF)Click here for additional data file.Figure S6-MHC (A–B), Grk5 (C–D), Sfrp2 (E–F) and Acta1 (G–H) loci, shown as percentages of total input.ChIP-qPCR analysis of EZH2 enrichment at β  EZH2 ChIP.  Mock IgG ChIP. Each column represents the mean value of data from three independent samples. *p<0.05; **p<0.01; Error bar: standard deviation.(TIF)Click here for additional data file.Figure S7Asxl genes in the adult mouse heart.Expression of  The mRNA levels of Asxl1, Asxl2, and Asxl3 in wild-type and Asxl2-/- hearts were analyzed by real-time RT-PCR. Each column shown is the mean value of data generated from three independent samples. *p<0.05; Error bar: standard deviation.(TIF)Click here for additional data file.Methods S1Supporting Methods.(DOC)Click here for additional data file.