The packaging of chromatin into the nucleus of a eukaryotic cell requires an extraordinary degree of compaction and physical organization. In recent years, it has been shown that this organization is dynamically orchestrated to regulate responses to exogenous stimuli as well as to guide complex cell-type-specific developmental programs. Gene expression is regulated by the compartmentalization of functional domains within the nucleus, by distinct nucleosome compositions accomplished via differential modifications on the histone tails and through the replacement of core histones by histone variants. In this review, we focus on these aspects of chromatin organization and discuss novel approaches such as live cell imaging and photobleaching as important tools likely to give significant insights into our understanding of the very dynamic nature of chromatin and chromatin regulatory processes. We highlight the contribution plant studies have made in this area showing the potential advantages of plants as models in understanding this fundamental aspect of biology. In eukaryotic cells the nuclear DNA does not appear naked but is associated with many proteins to form the complex called chromatin. The most prominent proteins in chromatin are the histone proteins—basic proteins responsible for the vast degree of packaging of the DNA within the confines of the eukaryotic nucleus. Approximately 146 base pairs of DNA are wrapped around an octamer of histones containing two copies of each of the four core histones  forming the most fundamental unit of chromatin—the nucleosome [.Core histones have been structurally conserved through evolution and have evolved to accomplish two conflicting and yet vital tasks: on one hand the long DNA molecules have to be packaged within the limits of the eukaryotic nucleus, preventing knots and tangles and protecting the genome from physical damage; on the other hand the information that is encoded in the DNA needs to be accessed at appropriate times. These functions are, at least in part, regulated by local changes in nucleosome and chromatin structure by complex mechanisms that have only recently begun to be understoodThe nucleosomes are positioned every ~200 bp on a typical DNA strand, and can be linked together by the linker histone H1. Electron microscopy of isolated polynucleosomes clearly reveals the open “beads-on-a-string” structure of the so-called 10 nm DNA fibre ,4. It wa. In nuclei of mammals and some other eukaryotes these domains are organized in the nucleus with heterochromatin mainly localized towards the nuclear periphery and euchromatin in the interior of the nucleus [Chromatin has traditionally been divided in two distinct classes: euchromatin and heterochromatin . Origina nucleus ,19,20. The ability of a gene to be transcribed is thought to depend on its accessibility to the transcription machinery. In fact nucleosomes represent a barrier to many protein complexes that need to contact the DNA and regulate gene expression . The mecIn this review we consider two main aspects of chromatin regulation at the nucleosome level (histone modifications and histone variants) as well as the spatial distribution of genes within the three-dimensional space of the nucleus as a means to regulate transcriptional activity.DNA methylation at 5-methylcytosine (5-mC) is one of the best studied epigenetic marks implicated in regulation of gene expression. The function of DNA methylation includes roles in development and in genome defense against transposons. DNA methylation is confined to relatively specific regions of the genome. Highly methylated regions comprise: transposons, rDNA arrays and centromeric repeats. Heterochromatin knobs  and periIn mammals, methylation is found almost exclusively in CG dinucleotides , with exRecent advances in high-throughput sequencing technologies combined with bisulfite treatment of Arabidopsis genomic DNA allow analyses and the generation of high resolution genome-wide methylation maps and are now revealing in unprecedented detail the patterns and dynamic changes of DNA methylation in plants. We will not discuss it further for the purpose of this article but there is currently a great body of literature on this subject ,42,43,44Histone proteins contain flexible N-terminal tails that extend outward from the nucleosome core and that are subject to diverse post-translational modifications (PTMs). Although histone tails are the main targets for post-translational modifications, the histone core domains can also be modified (reviewed in ). For th. This suggests that the regulation is highly complex, rather than being a simple and predictable code in which each modification has a unique effect.In recent years the number of modifications that have been identified on histones has increased quite dramatically. Over eight different classes have been characterized to date and many sites have been described within each class. An additional degree of complexity is that multiple modifications can be added simultaneously in the same nucleosome or even on the same histone tail  creatingHistone modifications are caused by the direct action of histone-modifying enzymes. During the last few years, enzymes have been identified for almost all types of modification (reviewed in ). A numbRapid progress in recent years has brought plenty of data about the diverse roles and mechanisms of histone modifications in regulating gene expression. For instance the arrival of genome sequencing brought the possibility to map genome modifications at high resolution and led ultimately to the concept of the “epigenome”. There are now several examples of epigenomic profiling in Arabidopsis ,55,56,57Not much is known, however, about the interplay between the mechanisms responsible for the addition and removal of these marks and how they are involved in orchestrating development. Further studies to reveal the molecular role of these factors will be necessary to understand the mechanisms underlying cell differentiation and development. The best characterized post-translational modification regulating chromatin structure is the acetylation of one or more of the lysine residues in the histone N-terminal tails. Its regulatory role was proposed about 50 years ago . Histonein vitro [Of all modifications acetylation has the highest potential to induce chromatin unfolding. The neutralization of the positive charge of lysine at the histone tails has a strong effect in destabilizing inter-nucleosome contacts and therefore the chromatin structure itself. Experiments in which tail residues were chemically modified on recombinant histone core preparations showed that acetylation of histone H4 on lysine 16 (H4-K16Ac) has a negative effect on the formation a higher order chromatin structures in vitro . The enzymes that acetylate histones, the histone acetyltransferases, are classified into three main families, GNAT, MYST and CBP/p300 . In geneTriticum aestivum) inhibition of HDAC activity by trichostatin-A (TSA) was shown to affect gene positioning and the overall structure of interphase chromosomes [In wheat  [In plants the LIKE HETEROCHROMATIN PROTEIN 1 (LHP1), also known as TERMINAL FLOWER 2 and considered to accomplish the role of Polycombs (PcG) in plants, has the ability to bind to H3K27me3 ,76). LHP C (FLC) .Histones can also be mono- or dimethylated on arginines ; howeverHistone modifications and their implications for chromatin structure and regulatory processes that act on DNA, such as replication, repair and regulation of gene expression, have long been recognized. Similarly to histone modifications, the incorporation of histone variants into chromatin contributes to its regulatory repertoire. The expression of most of histones is tightly coupled to S phase of the cell cycle, when the newly synthesized DNA requires additional histone molecules . These hWith the exception of histone H4, all of the core histones have a number of variants, which probably arose through gene duplication . Here, wThe cenH3 is an H3 variant that is specialized for packaging chromatin at eukaryotic centromeres and is essential for the assembly of kinetochores ,84,85,86CenH3 variants have unusual features. Relative to the canonical H3, which is one of the most conserved proteins, cenH3 sequences are surprisingly divergent. Sequence analysis has revealed 50%–60% similarity with the canonical form at the C-terminal histone-fold domain (HFD) and little or no conservation at the N-terminal region . In spitThe dynamic behaviour of cenH3 was studied in Arabidopsis by developing transgenic lines in which this variant was tagged with GFP . Using fDrosophila melanogaster cenH3) nucleosomes have only half of the height of canonical nucleosomes. Together these observations support a model in which cenH3-containing nucleosomes consist of stable heterotypic tetramers —the “hemisome” model [CenH3-containing nucleosomes have been traditionally considered as octamers; however recent studies suggest that they might be organized as tetramers. Evidence for that comes from MNase assays in Drosophila . Unlike e” model . Howevere” model .Another potentially important feature of cenH3 variants is that in common with archeal histones, they induce positive supercoils in DNA . This poBesides cenH3, other H3 variants have been reported. H3.3 differs from the canonical H3 only at four amino acid residues and is incorporated into chromatin in a replication independent fashion . Three oin vivo into a transgene array as the array undergoes activation [Additionally, while the canonical H3 is incorporated in chromatin by the chromatin-assembly factor 1 (CAF1) during DNA replication and repair, H3.3 is assembled into chromatin by another complex—histone regulator A (HIRA)—independently of DNA synthesis . In spittivation .D. melanogaster revealed that H3.3 is turned over more rapidly than H3 [Studies in  than H3 , which m than H3 . A possi than H3 . On thatH2A.Z is a highly conserved variant of the canonical histone H2A. Phylogenetic analysis indicates that H2A.Z diverged from the canonical form early on during the evolution of eukaryotes but since then it has been fairly well conserved. In fact, the H2A.Z variants of different organisms show a higher degree of sequence similarity (approximately 90%) than the H2A.Z and H2A within the same organism ,102. Saccharomyces cerevisiae it is not essential for viability but has a function that cannot be substituted by the canonical H2A [Arabidopsis thaliana there are three different H2A.Z genes , which are redundant to some extent and differ in cell-cycle regulation [The H2A.Z variant has been widely studied and has been shown to be essential in both mammals and in Drosophila ,104. In ical H2A . Unlike ical H2A . In Arabgulation .The H2A.Z variant is incorporated into nucleosomes by the action of a specific multi-subunit complex termed SWR1 in yeast and SRCAP in humans. SWR1 is an ATP-dependent remodelling complex that exchanges H2A-H2B dimers for H2A.Z-H2B in the nucleosomes ,110,111.Tetrahymena thermophila H2A.Z was found to be associated with the transcriptionally active macronucleus but absent in the transcriptionally inert micronucleus [. The crystal structure of H2A.Z-containing nucleosomes suggested that its incorporation destabilises the interface between H2A.Z-H2B and the H3-H4 dimers [In onucleus . These o4 dimers . Accordi4 dimers . A separ4 dimers ,122.Saccharomyces cerevisiae H2A.Z has been implicated in diverse biological processes including the control of gene expression [Studies of the properties of H2A.Z-containing nucleosomes have led to conflicting conclusions over the past few years. It appears that in spite of great sequence conservation, the function might have diverged considerably, with different functions described for different organisms ,124. In pression ,128,129,pression , chromospression , DNA reppression  and prevpression . In highpression , embryonpression  and antapression . Its funH2A.Z can show contradictory functions even within the same organism. In differentiated mouse fibroblasts H2A.Z is distributed across the entire interphase nucleus but is excluded from transcriptionally silent and HP1 enriched pericentromeric regions . HoweverRecent studies have suggested one reason why H2A.Z function shows such contrasting effects. Genome-wide ChIP sequencing or ChIP-microarray experiments showed that H2A.Z preferentially localizes in the 5' regions of genes within euchromatic regions ,141,142.Finally, research in Arabidopsis presented a new concept where H2A.Z-containing nucleosomes provide thermosensory information for coordinating temperature responses in plants . H2A.Z nAs well as modification either covalently by histone post-translational modifications or by incorporation of histone variants, nucleosomes can also be mobilized to different positions along the DNA, or evicted by ATP-dependent remodelling enzymes. ATP-dependent remodelling factors play an essential role in gene expression by regulating the access of the transcription machinery to DNA sequences . GeneralCurrent data from studies in animals suggest that the biochemical mechanism by which each type of remodeller acts is distinct and may account for their unique biological functions. For example SWI/SNF complexes are able to induce ATP-dependent disruption of nucleosome structure, facilitating the binding of transcription factors to their target sites on nucleosomal templates . In contAn obvious question is how these ATP remodellers are targeted to the appropriate genomic regions. Different mechanisms have been proposed; the best studied is based on targeting via DNA-sequence-specific factors . For insBromodomain factor 1) as a member of the SWR1 complex [Another way by which ATP remodelers regulate chromatin structure and gene regulation is via incorporation of histone variants. One example, mentioned previously, is the ATP-dependent remodelling enzyme responsible exchanging the canonical histone H2A with the variant H2A.Z. The SWR1 complex is named after its ATPase sub-unit Swr1 (for Swi2/Snf2 related). This complex seems to be targeted to specific sites by a mechanism that involves the recognition of histone acetylation. This conclusion came from the identification of Bdf1 , sorghum (Sorghum bicolor) and Arabidopsis this arrangement is not observed, and chromosomes seem to lose their polarized anaphase configuration after entering interphase. In Arabidopsis, the telomeres have a strong tendency to be associated with the nucleolar periphery, and this association has been suggested to be an initial stage in meiotic homologue pairing [While not much is known about what controls the organization of CT in interphase it is believed that several factors might be involved, including content and distribution of heterochomatin and repetitive regions, association with the nuclear envelope among others . In many pairing .Oryza sativa) the Rabl congfiguration is only observed in certain tissues [In rice  the activation of the avenacin gene cluster is accompanied by chromatin decondensation [Sad1 and Sad2 genes and used mRNA and DNA in situ hybridization techniques to show that the two genes moved apart from approximately 0.45 to 0.90 µm upon activation in a cell type-specific manner. They also showed that individual genes elongated on activation to the order of about 20-fold compaction compared to naked B-DNA. This is more compact than the 10 nm fibre, but less than the proposed 30 nm fibre  and immunofluorescence-FISH, and in molecular approaches such as chromosome conformation capture (3C) and related methods [Another possibility is the occurrence of discrete sites for transcription in the nucleus, which have been termed transcription factories . Accordi methods ,183 that methods ). The development of new molecular, genomic and computational approaches based on chromosome conformation capture technology  are currently helping us to study the spatial organization of genomes at an extraordinary level resolution. In 3C-based methods cells are initially cross-linked with formaldehyde to preserve chromatin structure and subsequently digested with restriction enzymes or sonication. Cross-linked fragments are then ligated resulting in segments that contain sequences that were in close spatial proximity. The DNA is then finally purified and analysed.The different 3C-based methods differ essentially in the way the DNA segments are detected and quantified. In conventional 3C techniques, single ligation products are detected by PCR one at a time using primers from specific sequences. 4C, chromosome conformation capture-on-chip, combines 3C technology with microarrays to analyse the contacts of a specific genomic site with all of the genomic fragments present on the array generating therefore a genome-wide interaction profiles for single loci ,186. In in vivo. Recently, a combination of molecular biology and live cell microscopy techniques has allowed observation of the dynamic behaviour of chromatin in vivo within the natural environment of the nucleus. For many years the majority of studies that examined the mechanisms by which chromatin regulates transcriptional activity focused on measuring steady-state levels. A typical biochemical approach is chromatin immunoprecipitation, in which chromatin fragments still associated with histones or other chromatin proteins  are precipitated using a specific antibody, and the DNA or proteins associated with the target are then analysed. The trouble with these types of approach is that often they do not take into account the dynamic properties of chromatin, leading to rigid assumptions about stable interactions between chromatin and chromatin proteins in vivo.The first experimental evidence to indicate that the protein composition of chromatin was not static came from radioactive isotope labelling studies in the early 1980s ,193,194.fluorescence recovery after photobleaching (FRAP) confirmed that most of the functionally important characteristics of chromatin show a dynamic behaviour. With the development of confocal microscopes and their laser illumination systems, FRAP became a very accessible technique for analysing the kinetics of molecules in living cells . FRThe first results of FRAP experiments on histone H1-GFP fusions appeared in 2000 ,198. Bott1/2≅ 130 min) independent of ongoing DNA replication and transcription, and another (>50%) that remains bound stably (t1/2 > 8.5 h) [Apart from histone H1 the mobility of many other histones has also been assessed by FRAP (reviewed in ). Compar> 8.5 h) . in vitro studies showed that the transfer of H2A–H2B from nucleosomes to histone chaperone Nap1 (nucleosomes assembly protein 1) is facilitated by p300-mediated acetylation [The mechanism that accounts for the differences in kinetics between different histones and between the different pools within the same type of histone is related to differences in biochemical modifications. For instance it is known that acetylation of histone tails alters the stability of nucleosomes and also has been shown to alter the dynamics of H2B in living cells . Acetylatylation . Althougin vivo [Recently the development of photoswitchable or photoactivatable tags has made them an optimal tool for studying the spatial and temporal dynamics of proteins in vivo . As the Drosophila melanogaster S2 cells with the methionine (Met) surrogate azidohomoalanine (Aha). The surrogate amino acid is incorporated into histones, and Aha coupled with biotin. Streptavidin beads are then used to affinity purify nucleosomes containing newly synthesized histones. The nucleosomal DNA is finally hybridized to a high-density tiling microarray to create a map of nucleosomes that have turned over during the pulse. The great advantage of this strategy is the possibility of assaying native histones, overcoming limitations related to requirements for transgenics and tags. The method should be applicable across species; however; there are only a few reports available so far.An alternative method for measuring histone turnover showing great promise has recently been developed . The metWhile it is clear that spatial organization of chromatin in the nucleus is highly correlated with gene expression, the majority of the techniques used to study chromatin structure are unable to reveal a real dynamic view, providing instead a static analysis under a particular condition. The observation of chromatin motion requires the labelling of specific chromatin loci with techniques that are compatible with live cell imaging.lac operator/repressor (lacO/LacI) [tetO/TetR) [Recently, taking advantage of the exploitation of bacterial repressor/operator interactions in conjugation with GFP technology, significant breakthroughs in this field have been possible. Two systems are currently in use: one is based on the cO/LacI) ,207,208,tO/TetR) ,210. BottO/TetR) ,212. TheDrosophila nuclei [The initial surprise from this approach was the dynamic behaviour that chromatin exhibits in both yeast and a nuclei ,214. Thea nuclei . In anota nuclei . Studiesa nuclei . These oExperiments in yeast showed that early and late origins of replication were more mobile in G1 than in S phase, and that the movement in G1 was highly sensitive to ATP depletion and changes in metabolic status . This colac operator system has been used to analyse the repositioning of alleles of a Polycomb target gene, FLOWERING LOCUS C (FLC), during an environmentally-triggered silencing process involved in the control of flowering time—vernalization [FLC locus physically associate and reposition in the nucleus during in the silencing process (see In plants a few studies have used this approach. The first use  showed tlization . This stcess see , and theOne drawback of this type of approach is the high sensitivity to photodamage and bleaching. It was shown for both yeast and mammalian cells that the movement of the tagged sequence is affected by long time-course imaging ,225. ThuIn summary, as the field of chromatin dynamics has developed, it has become increasingly clear that a large number of underlying physical and molecular phenomena is involved, and that to fully understand them, information at many different levels is required. The study of chromatin dynamics and its effect on gene expression has opened new and exciting perspectives and one important goal of current research is to be able to relate these different levels of regulation. Advances and new strategies that allow “super” resolution in light microscopy will soon be in common use and may open the way to this type of correlative work.


Recent evidence has proven the relevance of epigenetic changes in the development of hepatocellular carcinoma (HCC), the major adult liver malignancy. Moreover, HCC onset and progression correlate with the deregulation of several long noncoding RNAs (lncRNAs), exhibiting great biological significance. As discussed in this review, many of these transcripts are able to specifically act as tumor suppressors or oncogenes by means of their role as molecular platforms. Indeed, these lncRNAs are able to bind and recruit epigenetic modifiers on specific genomic loci, ultimately resulting in regulation of the gene expression relevant in cancer development. The evidence presented in this review highlights that lncRNAs-mediated epigenetic regulation should be taken into account for potential targeted therapeutic approaches. Mammalian genomes produce thousands of long nonprotein coding transcripts currently referred to as long noncoding RNAs (lncRNAs) , 2. ThesWide range analysis of cellular transcription by deep sequencing unveiled a large and continuously expanding number of lncRNAs. The GENCODE consortium in the framework of ENCODE (encyclopedia of DNA elements) project estimated, already in 2012, the human catalog of lncRNAs comprising 9277 manually annotated genes and producing 14880 transcripts [LncRNAs can exhibit subcellular localization in precise compartments and, although they are expressed in lower amount with respect to mRNA , these tIn the last years, increasing evidence showed that lncRNAs do not represent a “transcriptional noise,” having instead great biological significance.These transcripts, in fact, play a key role in various cellular contexts and are involved in almost every stage of gene expression, in both physiological and pathological cellular conditions. Different lncRNAs control epigenetic processes, such as expression of specific genes, as well as imprinting, and chromosome dosage-compensation, and also transcription, splicing, transport, and translation . Thus, l cis (at the site of transcription) or in trans (at distantly located genes)   .LncRNAs often display either tumor suppressor or oncogenic activities that frequently have to be ascribed to their capacity to control gene expression by acting at epigenetic level.In this review, we focus on lncRNAs involved in the epigenetic modifications influencing onset and progression of hepatocellular carcinoma (HCC). Firstly, we summarize the state of the art of research on DNA and histone epigenetic modifications in HCC; secondly, we discuss the biological roles and the molecular functions of known chromatin-associated lncRNAs whose expression is deregulated in HCC stages, highlighting that lncRNAs activities in epigenetic regulation should be taken into account for potential therapeutic approaches.Levels of chromatin compaction depend on complex mechanisms, including epigenetic modifications that affect either DNA, by methylation and hydroxymethylation of cytosine residues, or histones, by posttranslational additions of several chemical groups . All these posttranslational modifications (PTMs) are tightly controlled by specific enzymes and directly affect chromatin condensation or act as signals for other chromatin-modifying or chromatin-remodeling activities, resulting in transcription regulation .Recent findings point to the involvement of epigenetic mechanisms in the pathogenesis of HCC.This tumor type represents the major form of adult primary liver cancers and one of the most frequent cancers worldwide. Poor understanding of HCC pathogenesis mechanisms limits diagnosis and treatment at early stages and current therapies, despite recent advances, are essentially unsuccessful. Thus, liver transplantation is still the most efficient treatment, with the lowest risk of tumor recurrence, even if surgical resection and chemoembolization can be valid alternatives in some circumstances . ProgresApart from several genetic causes, all HCC stages closely correlate to changes in epigenetic patterns of both DNA and histones on several genes crucial for cancer onset and progression.In line with evidence regarding other tumor types, DNA hypermethylation at specific loci has been correlated to inactivation of tumor suppressor genes also in HCC. For example, epigenetic silencing of Ras pathway inhibitors was found, resulting in Ras and its downstream effectors ERK, AKT, and RAL activation. Similarly, an inactivation of angiogenesis inhibitors has been shown in HCC with poor prognosis . MoreoveMoreover, a wide range analysis of genome methylation in HCC compared with normal liver provided a methylation “signature” useful in diagnosis and prognosis of tumor.For example, some tumor suppressor genes   showed s de novo methylation of DNA sequences [DNA methylation is orchestrated by the DNA methyltransferase (DNMT) enzymes; DNMT1 is mainly involved in the maintenance of established methylation patterns , while Dequences ; thus thequences . Notablyequences . Moreoveequences . Interesequences .Several studies also revealed the correlation between HCC and aberrant histone modifications.Chromatin accessible to transcriptional factors is generally characterized by the acetylation of histone tails controlled by histone acetyltransferases (HATs). Concerning, instead, histone methylation, its effects depend on (i) which amino acid residue is methylated and (ii) which is the methylation form, because one, two, or three methyl groups  can be dynamically added to the histone tail. In particular, the trimethylation on lysine 27 of histone H3 (H3K27me3) is a key repressive mark.Polycomb group proteins (PcG) are the main regulators of repressive epigenetic modifications of chromatin, initiating and maintaining the transcriptional repression of target genes. These proteins are classified in two groups, the polycomb repressive complexes (PRC) 1 and 2, taking into account the multimeric complexes with which they associate. PRC2, composed of Eed, Suz12, Ezh2, and RBBP7/4, initiates gene silencing by methylation of histone H3 lysine 27 (H3K27); PRC1, composed of Ring1a/1b, Nph1, and Bmi1, maintains gene silencing by monoubiquitinating histone H2A lysine 119 (H2AK119) .In a recent study, a number of HCC was characterized for both acetylation and trimethylation state of H3K27  and a coIn accordance with these findings, the deregulation of some histone modifiers has been observed in HCC, suggesting a key role of these molecules in the pathogenesis of the tumor. In particular, members of PRC2 and PRC1 complexes are frequently deregulated in tumor cells and the consequent aberrant gene expression drives progression of hepatocarcinogenesis towards metastatic stages. Among PRC subunits, considerable evidence concerns the methyltransferase Ezh2, directly responsible for H3K27 trimethylation. Invasive properties of HCC tumors were strongly associated with Ezh2 upregulation , 40; conβ-catenin signaling by epigenetic repression of different negative regulators of Wnt pathway  implying a control on cellular proliferation; downregulation of Ezh2, indeed, reduced HCC cell growth [Moreover, Ezh2 was found to activate the l growth .Concerning the subunit of PRC2 complex Suz12, while it appeared upregulated in HCC as well as other PRC2 components , its proWith regard to the PRC1 member Bmi1, its deregulation in HCC has been correlated to both early-stage HCC  and progHCC is also characterized by deregulation of other chromatin modifying enzymes. For example, Patt1, a GNA family acetyltransferase exhibiting proapoptotic activity in hepatoma cells, was downregulated . Tumor cMoreover, in hepatocarcinogenesis of chronic viral hepatitis protein phosphatase 2A (PP2A) and the protein arginine methyltransferase 1 (PRMT1) are both dysregulated .Deregulation of lncRNAs is related to several human diseases and a broad range of cancers . In diffThe HOX TranscriptAntisense Intergenic RNA (HOTAIR) is a polyadenylated and spliced transcript, antisense to the mammalian homeobox transcription factor C (HOXC) locus, identified by Rinn and colleagues . Based oSequence analyses proved that HOTAIR exists in mammals and evolved faster than nearby HoxC genes . HOTAIR Chiyomaru and colleagues  recentlyHowever, the fundamental and better-characterized functions of HOTAIR are its roles as “guide” and “scaffold.” in trans recruitment of this complex on several distantly located genes [Pertaining to the role of guide, HOTAIR was found to interact through its 5′-end with PRC2 and specifically lead toed genes . The inted genes . in trans the expression of the HOXD locus on a different chromosome, but also acts in a genome-wide manner, controlling H3K27 trimethylation, then epigenetic silencing, of several target sequences [The HOTAIR-mediated trans-recruitment is consistent with recent finding that lincRNAs can couple DNA and chromatin modifying activities by binding the enzymes and triggering their specific targeting , 53, 61.Regarding the HOTAIR role as scaffold, this depends on its capacity to recruit several chromatin modifiers organizing a molecular platform. Apart from the PRC2 binding at its 5′-end, indeed, HOTAIR binds through its 3′-end the lysine-specific demethylase 1 (LSD1), the enzyme that, complexed with the corepressors REST (RE1-silencing transcription factor) and CoREST (corepressor for element 1-silencing transcription factor), erases the activating H3K4 trimethylation. Thus, HOTAIR assembles and addresses different histone modifiers to chromatin: the positioning of PRC2 and LSD1 complexes on specific chromosomal loci couples the H3K27 trimethylation and the H3K4 demethylation, fundamental chromatin modifications for gene silencing  Table 1Table 1. HOTAIR overexpression was proven to promote metastasis and was correlated to poor prognosis of several tumor types, for example, breast , colorecNotably, its knockdown significantly affects migratory and invasive properties  as well β treatment, inducing EMT, increased HOTAIR levels. Interestingly, HOTAIR silencing by a siRNA approach prevented the transitional program and the acquisition of stem properties by colon and breast cancer cells [Two recent reports proposed that the role of HOTAIR in tumor progression and acquisition of invasiveness can involve an EMT process. Xu and colleagues demonstrated that HOTAIR knockdown concurrently suppressed invasion and reversed EMT of gastric cancer cells . Alves aer cells .Moreover, enforced expression of HOTAIR in epithelial cancer cells determined the retargeting of PRC2 complex causing an occupancy pattern on genome resembling that of fibroblasts, in parallel with metastatic properties acquisition . In spitIn conclusion, HOTAIR is one of the well-studied PRC2 interacting lncRNAs and its expression appears to be a driving force for the acquisition of malignant properties by HCC, as well as other tumor types. It is able to recruit different players in controlling gene expression, organizing a molecular platform able to specifically retrieve target sequences.Nevertheless, a comprehensive characterization of HOTAIR mechanism of function is still needed and further studies of its related pathways and partners could be useful to identify new possible therapeutic targets.The human H19 gene encodes a ~2.3-kilobase long, spliced, and polyadenylated lncRNA . The fir in trans the expression of Igf2 and IGN genes and restores the normal phenotype [The H19 gene belongs to a conserved gene cluster that plays an important role in embryo development and growth control. H19 locus is imprinted in both humans and mice and is expressed from the maternal allele . The cluhenotype , 75. Thehenotype . They deH19 role in tumorigenesis is controversial, since it has been described as either protumorigenic or tumor suppressor depending on the context.Upregulation of H19 and Igf2 expression has been shown in HBV-associated HCC  and an i in vivo and in vitro between H19 and the levels of the key epithelial adhesion molecule E-cadherin, in which repression is causal to the EMT [βcatenin pathway that positively regulates EMT. In fact, this lncRNA facilitates the recruitment of Ezh2 on the promoter of Nkd1, thus impairing the expression of this inhibitor of the Wnt/βcatenin signaling. As a result, in bladder cell lines, H19 promotes invasiveness by indirectly activating Wnt/βcatenin pathway and directly inhibiting E-cadherin transcription [β, hypoxia and HGF/SF also increase the expression levels of H19 gene. They also observed that ectopic expression of H19 enhanced Hep3B invasiveness in vitro and H358 (lung cancer cell line) metastasizing capability in vivo, concluding that H19 is involved in enhancing invasion and metastasis [Invasiveness and metastatic potential of tumor cells are associated with the process of EMT and H19 has been demonstrated to influence different players involved in this trans-differentiation phenomenon. Indeed, a significant negative correlation was observed both the EMT . H19 was the EMT . Meanwhicription . Consistcription  also foutastasis .Interestingly, H19 was demonstrated to also play a role in MDR1-associated drug resistance in human hepatocarcinoma cell lines HepG2 and Hep3B. H19 was found overexpressed in doxorubicin-resistant cells and its overexpression correlated with MDR1 promoter demethylation, higher P-glycoprotein levels, and doxorubicin resistance. H19 knockdown leads to MDR1 promoter hypermethylation, decreased expression of P-glycoprotein, and doxorubicin sensitization. As a consequence, H19 may contribute to multidrug resistance in liver cancer cell lines by inducing demethylation of MDR1 promoter and overexpression of P-glycoprotein . in vitro end in vivo while H19 ectopic expression inhibited invasion. Moreover, H19 knockdown decreased the expression of epithelial markers including E-cadherin and increased the expression levels of mesenchymal markers such as N-cadherin, Snail1, Vimentin, and Twist1, thus indicating that H19 could suppress the EMT process that is involved in tumor progression and invasiveness. Indeed, ectopic H19 upregulated the expression of miR-200 family members that play a crucial role in EMT inhibition and whose expression is downregulated in HCC. H19 activated miR-200 family expression by associating with the HnRNPU/PCAF/RNA PolII complex and favoring the binding of the complex and consequent histone H3 acetylation on the promoter of miR-200 family [Conversely, H19 has been found to regulate the rate of tumor metastasis in advanced stages of HCC via the epigenetic activation of the miR-200 family . The aut0 family  . In vivo tumors from lncRNA HEIH-downregulated xenografts showed, indeed, a reduced growth compared with that of tumors formed from controls. lncRNA HEIH has been shown to associate with EZH2 and to increase the binding of EZH2 to p16 and p21 promoters; however, H3K27 levels were increased only across p16 promoter  the alternative splicing, by interacting with splicing factors so influencing their distribution and levels , and , resulting in gene repression or activation  Table 1Table 1.Recent findings correlated MALAT1 to HCC. In particular, Lai and colleagues  evaluateThe maternally expressed gene 3 (MEG3) lncRNA was found strongly downregulated in several (HCC) cell lines and in HCC samples. Its enforced expression in HCC cells counteracts the transformed phenotype by reducing the uncontrolled growth and inducing apoptosis . in vitro as well as the JARID2-mediated recruitment of PRC2 to chromatin in vivo [With respect to its specific role in epigenetic control of gene expression, a recent study unveiled as MEG3, together with other lncRNAs, stimulates the interaction between JARID2, an accessory component of PRC2, and EZH2 in vivo   is a lncRNA significantly upregulated in HCC. Its gene is located in physical proximity to the gene locus HOXA, whose deregulation was, in turn, described in hepatocarcinogenesis. In particular, levels of HOTTIP and HOXA13 were recently related to the clinical progression of HCC and predictive for prognosis . HOTTIP The knowledge about mechanisms governing gene expression in tumor onset and progression appears essential for efficient therapies setting.In HCC, some deregulated lncRNAs are predictive markers for precise tumor stages and, most importantly, they have been characterized as tumor suppressor and/or oncogenes. These functional roles depend on their ability to specifically control gene expression as part of a broad epigenetic regulatory network .More efforts are needed to better elucidate the roles of these molecules in all HCC stages, as well as in other tumor types, but increasing evidence already proved the potential role of these transcripts as targets for possible anticancer treatments, similarly to currently well-utilized microRNAs. Proposed HCC epigenetic drugs are represented by inhibitors of DNMTs –97 and h


NR1H4) is a ligand-activated transcription factor, belonging to the nuclear receptor superfamily. FXR is highly expressed in the liver and is essential in regulating bile acid homeostasis. FXR deficiency is implicated in numerous liver diseases and mice with modulation of FXR have been used as animal models to study liver physiology and pathology. We have reported genome-wide binding of FXR in mice by chromatin immunoprecipitation - deep sequencing (ChIP-seq), with results indicating that FXR may be involved in regulating diverse pathways in liver. However, limited information exists for the functions of human FXR and the suitability of using murine models to study human FXR functions.Farnesoid X receptor  treated with a synthetic FXR agonist, GW4064 or DMSO control. In parallel, RNA deep sequencing (RNA-seq) and RNA microarray were performed for GW4064 or control treated PHHs and wild type mouse livers, respectively.ChIP-seq showed similar profiles of genome-wide FXR binding in humans and mice in terms of motif analysis and pathway prediction. However, RNA-seq and microarray showed more different transcriptome profiles between PHHs and mouse livers upon GW4064 treatment.In summary, we have established genome-wide human FXR binding and transcriptome profiles. These results will aid in determining the human FXR functions, as well as judging to what level the mouse models could be used to study human FXR functions. NR1H4) is a ligand activated transcription factor belonging to the nuclear receptor (NR) superfamily Farnesoid X receptor  analysis has been widely used to study the functions of various NRs, including androgen receptor (AR), estrogen receptor alpha (ERα), glucocorticoid receptor (GR) etc. st, FXR could bind to multiple sites within a known FXR target gene. For example, FXR binds to both the promoter and 3′ gene regulatory regions of the Nr0b2 gene, which encodes small heterodimer partner (SHP) nd, many new target genes of FXR are identified in the liver and/or intestine, including the Sqstm1 gene, which encodes the protein p62, an important component of autophagy rd, FXR cooperates with other transcription factors, most likely orphan nuclear receptors, to modulate transcription of genes involved in specific biological processes. For exp., FXR and LRH-1 (liver receptor homolog-1) co-regulate genes involved in lipid homeostasis th, FXR elicits tissue-specific binding patterns, indicating differential regulation of chromatin structures as well as FXR functions among different organs/cells. 5th, FXR binding could suppress gene expression, which could be altered during disease state, such as obesity We and others have reported the genome-wide binding analysis of FXR in mice ABCB4 , ABCB11, FGF19 (fibroblast growth factor 19), ICAM1 (intercellular adhesion molecule 1), and NR0B2To date, the binding of human FXR in primary human hepatocytes (PHHs) or hepatoma cell lines has been characterized to limited genes, including In this study, using ChIP-seq and RNA-seq techniques, we determined the genome-wide binding and transcriptome profiling of human FXR in PHHs. We compared and contrasted the binding patterns and gene regulation profiles of FXR between human and mouse livers. PHHs were cultured in 37°C, 5% CO2 upon arrival. Three hours later, medium were refreshed with serum-free HMM Hepatocyte Maintenance Medium supplemented with dexamethasone, insulin, and GA-1000 . Next morning, cells were treated with 5 µM GW4064 Table S2.Primary human hepatocytes (PHHs) used in this study were obtained through the Liver Tissue Cell Distribution System from the University of Pittsburgh BSEP  and OST-β (organic solute transporter beta) as well as the negative control (promoter region of IL-8 (interleukin-8). Primer sequences were listed in Table S2. PHH samples from four donors, with good FXR activation and pull-down efficiency, were selected to pool together for the generation of sequencing libraries (Table S1).One hour after GW4064 treatment, cells were fixed in 1% formaldehyde for 10 minutes, followed by quenching with glycine and rinsing with cold PBS. Afterwards, cells were collected and lysed. Nuclei were released and sonicated into 200–700 base-pair (bp) DNA fragments. Aliquot chromatin was incubated overnight with 5 µg anti-FXR antibody  , or control rabbit immunoglobulin G  . Chromatin-antibody complex were pulled down with prewashed Dyna beads , washed and eluted. DNA fragments associated with FXR or control antibodies were eluted and purified. Input genomic DNA was obtained through similar elution and purification procedures. Quality of ChIP assay was confirmed by qPCR with primers amplifying known FXRREs of human FXR target genes , respectively. The quality of all library samples was confirmed by Agilent Bioanalyzer  before the sequencing reactions. For ChIP-seq, purified library DNA ranging from 400 to 500 bp was fractionated on an agarose gel, followed by extraction and purification before sequencing. All libraries were sequenced 100 bp paired-end on Illumina HiSeq2000 sequencing system.Homo sapiens version 19 (hg19) reference genome using bowtie (version 0.12.7) http://www.ebi.ac.uk/bertone/software.html) and filtered by p-value of Poisson distribution lower than 10−5. Peaks were annotated using R packages (http://www.r-project.org) based on the ENSEMBL version 65 human genes.Genome Analyzer Pipeline Software  were used for both primary image data files processing and base calling. All sequenced paired-end reads were aligned to For each ChIP-seq dataset, the sequences for the summit regions (201 bp), spanning 100 bp up and downstream from the summit of each peak, were retrieved. The top 500 sequences with the highest peak score were selected for motif analysis based on MEME (Multiple Em for Motif Elicitation) Table S2.To validate the ChIP-seq results, we first identified the IR-1 site for each peak, which was located inside the peak summit regions (201 bps) used for motif analysis. Then the genome sequences spanning the IR-1 sites were retrieved to design qPCR primers. ChIPed DNA from individual donors and pooled samples were used for qPCR validation. The primer sequences were listed in http://www.affymetrix.com).C57BL/6J (hereafter referred to as wild type (WT)) mice (n = 3) were treated with either GW4064 (100 mg per kg body weight) or vehicle three times as previously described Homo Sapiens reference genome (hg19) using TopHat (version 2.0.0) For RNA-seq in PHHs, RNA was pooled from selected PHH donors. After sequencing, the obtained reads were aligned to the http://www.david.niaid.nih.gov). For a pathway or process to be defined, the threshold count was set at 2 with a minimum EASE  score, a modified Fisher Exact Test, of 0.1. Categories from DAVID with false discovery rates (FDRs) less than or equal to 0.1 were considered as statistically significant.Functional genes from ChIP-seq and RNA-seq were selected and analyzed using the Functional Annotation Tool in DAVID ((http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57312)All sequencing data files discussed in this publication have been deposited in NCBI's Gene Expression Omnibus For RT-qRCR experiments, due to the difficulty of repeating sample collection from individual PHH donors, PHHs from different donors served as experimental replicates to validate FXR activation for the pooled PHH samples.Figure S1A). Pooled chromatin samples, which were collected after 1 hour GW4064 treatment, showed significant enrichment of FXR binding to known FXR targets (promoter regions of BSEP and OST-β), but not the negative control (promoter region of IL-8) . ChIPed-DNA, generated from pooled chromatin from selected donors (Table S1), as well as pooled RNA was then used to generate DNA and RNA sequencing libraries. Indeed, many known human FXRREs were detected with relatively high peak values in this study, in both DMSO and GW4064 treated PHHs  were induced in PHHs from individual donors  and GW4064 treated PHHs (PHH-GW), respectively. And the peak summits in the two datasets were 3665 and 3695 bps downstream from the TSS of FGF19, respectively. Nevertheless, these peaks were indicating the same binding site.In ChIP-seq, the peak summit of each peak (binding site) was a single bp position within the peak with the highest coverage given by the MACS analysis. Due to the relatively large size of fragmented library DNA obtained in this study (average 350 bp for ChIP-seq), and the subtle differences in the local chromatin environment in different samples, we saw slightly different peak width and peak summit values for the same binding sites in the two datasets. For example, for the binding site located in the intron of st, genomic distributions of FXR binding sites were similar in PHHs compared to those in mice  of the associated RefSeq genes, and FXR's intron binding patterns were both similar to those in mice as well. The highest frequency of total binding events was located within 0–10 kb up and downstream of TSSs  for ChIP-seq data analysis was set as 20, a total of 2759 and 5235 FXR binding sites were identified from PHH-DMSO and PHH-GW, respectively. Human and mouse FXR binding profiles in livers were compared between these human data with our previous genome-wide mouse FXR binding data, which were obtained from WT mice treated with GW4064   in mice Figure 1ABCB11, FGF19, NR0B2, and OST-βThe most commonly reported FXR binding motif is an IR1 in both mice and humans. This motif has been reported in many human FXR target genes, such as After motif analysis, we were able to precisely locate the IR-1 site associated with each peak summit for most peaks in our datasets. The subtle position difference (mostly around 50 bp) from the IR-1 site to the peak summit for each FXR target was most likely caused by a combination of relatively large DNA fragments used for our sequencing analysis and the technical limitation of the sequencing processing and data analysis. Since most classic FXR targets were presented correctly in the datasets Table 1,BSEP and most selected novel targets, but not the negative controls. And for those FXR targets, of which FXR pull-down was not further enhanced upon GW4064 treatment, we were able to calculate their FXR pull-down efficiency after normalizing to rIgG control from pooled PHH samples , we obtained 102 different genes with altered expression levels, up or down more than or equal to 1.5 fold. From RNA-seq for GW4064-treated PHHs (R-PHH-GW), which was normalized to DMSO control, we obtained 143 genes with log2 fold enrichment ≥2  and p-value <0.05. The percentage of genes found in microarray and RNA-seq, which were also bound by FXR from ChIP-seq, was plotted in FGF19 and CYP7A1  were the top up- and down-regulated FXR targets in R-PHH-GW, respectively.Using microarray, we obtained a gene expression profile from WT mouse livers treated with GW4064 (M-mLiver-GW) normalized to vehicle control. When set cut off fold induction (CO fold) as 1.5 and A major difference between the mLiver-GW dataset and the PHH-GW dataset was that, more than 5,000 genes and 10,000 peaks were found in mLiver-GW compared to 5,231 peaks identified in PHH-GW. The lack of IgG control for the mLiver-GW may well explain this major difference as non-specific peaks may present in the mouse FXR binding study. Another difference was that the majority of the peaks in mLiver-GW were associated with RefSeq genes, which encode proteins with known functions, whereas in PHH-GW only around 50% peaks were.Table S3A) obtained were listed, whereas most non-redundant categories (with gene count >5) from GO-BP were presented (3B). From DAVID analyses, we could see overall similar pathways enriched from mLiver-GW and PHH-GW, though the number of genes retrieved from PHH-GW was smaller. Nevertheless, the percentage of genes found in each category was similar between the two datasets.In order to compare and contrast the functional detail of FXR binding in humans and mice, two major analyses from DAVID were performed, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Gene Ontology Biological Process (GO-BP) analysis. Similar to our previous study, functional genes associated with peaks located in the upstream 0-10 kb promoter regions were selected to run DAVID analyses. Overall, more categories were enriched in PHH-GW than PHH-DMSO. And most categories enriched in PHH-DMSO were also presented in PHH-GW. In this regard, only categories and their corresponding FXR targets from PHH-GW were presented and compared with mLiver-GW. All significantly enriched KEGG categories (with FDR <0.1) Table 2A2 fold ≥1 , and p-value ≤0.05 were retrieved for KEGG and GO-BP analyses. Note that the cut off fold change used for pathway analysis is smaller than the cut off used for the ChIP-seq/RNA-seq correlation analysis. For pathway analysis, the cumulative effect from many altered genes in a single pathway could be of functional importance as well, though the level of fold change for individual gene was relatively low. All categories from KEGG and non-redundant categories from GO-BP, and the corresponding genes were presented . Furthermore, similar pathways were enriched from PHH-GW compared to mLive-GW. More genes were obtained from mLiver-GW than PHH-GW. As a result, more pathways were enriched in mLiver-GW Using multiple pathway analysis tools (KEGG and GO-BP from DAVID), the current study predicts that human FXR could participate in the regulation of diverse physiological processes . Indeed, both the magnitude of relative fold change levels and the number of altered genes were much larger in R-PHH-GW than M-mLiver-GW. Third, gene expression in the mouse livers could be affected by whole body physiology, such as circadian rhythms, hormones, fast/feeding cycles, physical activity and energy level, etc. Fourth, for microarray study, we cannot completely rule out the contributions from other cell types in mouse livers, such as endothelial cells and liver Kupffer cells, while in the enriched PHHs, the number of other liver cell types was minimal. Finally, technical differences between RNA-seq and microarray could be another minor factor as well. Among all these factors, the first two that affecting the baseline expression levels of many FXR target genes, could be the major causes of the differences we saw between the in vivo and in vitro studies. While certain technical limitations existed, the correlation/comparison study still provides valuable information for human FXR function. For R-PHH-GW, mRNA level down-regulation was observed for only a few FXR target genes found in ChIP-seq. But for M-mLiver-GW, the percentage of up- and down- regulated genes also presented in ChIP-seq was similar. Lee et al have shown that direct gene suppression by FXR is common in mice When correlating the results from ChIP-seq with RNA-seq for PHHs, and microarray for mouse livers, different trends were observed for genes down-regulated following GW4064 treatment Figure 5The FXR gene sequence is highly conserved across species, and the protein sequence is very similar between humans and mice In summary, we have obtained valuable information of genome-wide binding and transcriptome analyses of human FXR in PHHs. Detailed analysis of the ChIP-seq data indicates that the global binding patterns of FXR in PHHs are similar to those in mouse livers. In addition, similar biological pathways were enriched from genes bound by FXR in PHHs compared to those enriched in mouse livers. We also identified and validated novel FXR target genes, with and without alteration of mRNA levels. Species differences were found for specific pathways and within gene families involved in similar pathways. In a major extent, mouse model is a suitable model for studying human FXR functions.Figure S1Validation of FXR activation in individual PHH donors and pooled PHH chromatin. (A) Relative mRNA levels of FXR and FXR targets  in the selected four PHH donors  upon 24 hr GW4064 treatment normalized to DMSO control by RT-qPCR analysis. For each PHH donors, we treated 3 wells of cells with GW4064, 3 with DMSO control. RNA from each well was collected and analyzed individually. Human 18S was used as the normalization control. (B) ChIP-qPCR analysis of FXR antibody pull-down for the promoter regions of BSEP, OST-β and IL-8 upon 1 hr DMSO or GW4064 treatment for pooled chromatin from the selected 4 donors in (A). Fold enrichment of FXR binding was normalized to rabbit immunoglobulin-G control antibody.(TIF)Click here for additional data file.Table S1Summary of PHH Donors.(DOCX)Click here for additional data file.Table S2Primers Used for Quantitative PCR.(DOCX)Click here for additional data file.Table S3Comparison of Genes from Selected Categories in DAVID Anotation for ChIP-seq.(DOCX)Click here for additional data file.


Accurate identification of DNA regulatory elements becomes an urgent need in the post-genomic era. Recent genome-wide chromatin states mapping efforts revealed that DNA elements are associated with characteristic chromatin modification signatures, based on which several approaches have been developed to predict transcriptional enhancers. However, their practical application is limited by incomplete extraction of chromatin features and model inconsistency for predicting enhancers across different cell types. To address these issues, we define a set of non-redundant shape features of histone modifications, which shows high consistency across cell types and can greatly reduce the dimensionality of feature vectors. Integrating shape features with a machine-learning algorithm AdaBoost, we developed an enhancer predicting method, DELTA . We show that DELTA significantly outperforms current enhancer prediction methods in prediction accuracy on different datasets and can predict enhancers in one cell type using models trained in other cell types without loss of accuracy. Overall, our study presents a novel framework for accurately identifying enhancers from epigenetic data across multiple cell types. Transsulators . Howeversulators ,4,5. Earsulators –10. Expesulators , as theysulators ,13. Sincsulators ,15.et al. found that promoters showed an enrichment of H3K4me3, H3ac and H4ac and a depletion of H3K4me1 and enhancers were enriched with H3K4me1, H3K4me2, H3ac and H4ac but lack of H3K4me3 [+ T and other cell types showed that H3K4me2 and H3K4me3 at transcription start sites (TSSs) followed a bimodal distribution asymmetrically skewing towards the downstream coding region while their distribution around enhancers is unimodal and symmetric [Recently, it was shown that different elements, such as promoters and enhancers, were marked by distinct patterns of chromatin modifications. Specifically, Heintzman  H3K4me3 . In addiymmetric –17.Supervised and unsupervised computational approaches were developed for identifying enhancers on the basis of chromatin modification mapping data. The supervised approaches used chromatin signatures surrounding known enhancers to predict new enhancers. Existing supervised approaches include profiling method (PM) , artificHowever, the practical application of these methods is limited by two primary constraints. First, feature extraction of chromatin signatures is usually lacking. Most previous methods predict enhancers only using the intensity of histone modification, which refers to the total number of sequencing reads or the maximum coverage depth in a genomic region. However, the distributions of chromatin modifications around regulatory elements are sequential profiles with distinct shape features. Constructing intensity vectors by dividing genomic regions into small bins, namely the “binned-vector” method, is a straightforward way to keep the distributional information of histone modifications around regulatory elements . HoweverTo address these issues, here we present a novel method for enhancer prediction based on AdaBoost algorithm  and the Kurtosis, Skewness and Bimodality, to quantify the shape features of chromatin modification profiles surrounding regulatory elements. Specifically, Kurtosis is introduced to quantify the “sharpness” of a ChIP-seq signal, Skewness quantifies the extent of a ChIP-seq signal “leans” to one side of the mean, and Bimodality is a parameter that quantifies a bimodally distributed ChIP-seq signal, as illustrated in Max is also introduced to describe the strength of a ChIP-seq signal. The definitions and calculation methods are described in detail in Materials and Methods.We know from the technical principles of ChIP-seq that the number of sequencing reads mapped to a genomic region is proportional to the density of protein binding sites or chromatin modifications . Therefo+ T and H1 cells to investigate the shape and intensity parameters at enhancers and promoters. p300 binding sites overlapping with DNase-I hypersensitive sites (DHSs) and distal (>2.5 kb) to UCSC TSSs are treated as representative of active enhancers, and UCSC TSSs overlapped with DHSs are treated as representative of active promoters. A total of 1983 and 6693 active and distal p300 sites and 9476 and 10663 active TSSs were included in this survey in CD4+ T and H1 cells, respectively.Because enhancers and promoters are known to have distinct chromatin signatures, we first examined whether the defined parameters could accurately describe the characteristic patterns of histone modifications surrounding the two types of elements. We used the datasets of 38 and 26 histone modifications in CD4Skewness of H3K4me1, H3K4me2, H3K4me3 and H3K9ac at promoters is negative, indicating their distribution is asymmetrically biased to the left  of TSS. In contrast, their average Skewness at enhancers is very close to zero, indicating their unbiased distribution at enhancers , indicating their distribution is generally bimodal  than those at promoters. In addition, we found that a subset of histone modifications displayed very distinct shape and intensity parameters between enhancers and promoters. Among all modifications, H3K4me1, H3K4me2 and H3K4me3 are three modifications exhibiting the most significant differences, as shown in Max does, suggesting the potential capability of shape features of histone modification in enhancer prediction.We found that the shape parameters accurately quantify the known chromatin signatures of promoter and enhancer. The skewed distribution of histone modifications is an important feature of promoters, and several histone modifications enriched at promoters were reported to be skewed towards the downstream (transcribed region) of TSS ,16. We fnhancers  Tables.  bimodal . Their aKurtosis, Skewness, Bimodality and Max) showed good consistency, and no significant difference were found between p300 and MED1-binding sites  for all three important modifications H3K4me1, H3K4me2 and H3K4me3, indicating p300-positive/negative enhancers share similar histone modification profiles.We also investigated whether p300-positive and p300-negative enhancers have different histone modification profiles. We examined the shape and intensity parameters at a p300-independent transcriptional coactivator MED1 (TRAP220), which has been shown to occupy enhancers as well as promoters  in ENCODKurtosis, Skewness, Bimodality and Max as the input features of AdaBoost, we developed a new method for predicting transcriptional enhancers: DELTA or a Distal Enhancer Locating Tool based on AdaBoost algorithm.AdaBoost, short for “Adaptive Boosting”, is a machine learning meta-algorithm . The corTo train DELTA, we selected active and distal p300 binding sites overlapping DNase-I hypersensitive sites (DHSs) and distal (>2.5 kb) to UCSC TSSs as representative of enhancers (positive class set). The background set (negative class set) is composed of two types of genomic regions: UCSC TSSs overlapping DHSs as well as random genomic regions distal (>2.5 kb) to UCSC TSSs and p300 binding sites. We used a sliding window approach to predict enhancers along the genome with a step size of 100 bp. For each window, the shape and intensity parameters are calculated in both directions (forward and backward). A true enhancer with symmetric modification profiles will be assigned to high probabilities in both directions, while promoters and noise signals will be assigned to low probabilities in at least one direction. Thus, the lower probability in the two directions is treated as the final probability of a window.+ T (AUC = 0.9606) and H1 (AUC = 0.9726), as shown in We used a 5-fold cross-validation approach and Receiver Operating Characteristic (ROC) curves to determine the optimal window size and the ratio of p300 sites to background regions in training dataset. For window size determination, we evaluated four different window sizes  and found that a window size of 2 kb shows the highest Area Under the Curve (AUC) both in CD4+ T and 150 for H1 cells . A measurement of importance for predictor variables of a decision tree has been proposed previously . We chos+ T and H1 cells were used to train the AdaBoost models, and both a clean and a noisy non-enhancer background were constructed. The clean background contains only random genomic regions distal (>2.5 kb) from UCSC TSSs and p300 binding sites, and the noisy background contains both random genomic regions and UCSC TSSs overlapping DHSs. Heatmaps  showed significantly higher importance than in the case of clean background, demonstrating that Skewness is a specific and important shape feature for discriminating functional enhancers from noisy genome background with promoters.To comprehensively assess the variable importance of histone modifications for enhancer prediction, chromatin profiles of 38 and 26 histone modifications in CD4Heatmaps  were useT and H1 . The KurT and H1 . The SkeK-means clustering method to automatically divide these modifications into clusters on the basis of their variable importance. An average silhouette width method was used to estimate the optimal number of clusters [+ T and H1, the optimal numbers of clusters were estimated to be 3. Accordingly, histone modifications in CD4+ T and H1 were grouped into three clusters, termed “strong predictor”, “weak predictor” and “irrelevant predictor”, respectively. Nine histone modifications in CD4+ T and five modifications in H1 were classified as strong predictors. Among all histone modifications, H3K4me1, H3K4me2 and H3K4me3 were the sole three modifications consistently presented in “strong predictor” sets in all four cases. Our findings as well as the similar results obtained in H1 and IMR90 by Rajagopal et al. [To obtain an informative set of histone modifications for enhancer prediction, we used a clusters . For botl et al.  confirme+ T and H1 cells. We ranked 23 histone modifications that presented in both datasets by their overall variable importance and examined the correlation of their rankings between two cell types. For the clean background case, we observed a correlation of 0.688  between the importance rankings  between the rankings . Rajagopal et al. ranked 24 shared modification between H1 and IMR90 using RFECS and obtained a correlation of 0.645  with noisy background training data [We then investigated the relationship between the variable importance of histone modifications in CD4rankings . Becauserankings , indicating data , also siWe next assessed the prediction accuracy of AdaBoost models with different feature sets. In the case of enhancer predictions, the definition of true negatives is not straightforward, because we can never be certain that the randomly selected TSS- and p300-distal genomic regions are all non-functional. Therefore, we determined the predictive power of model by counting the percentage of predicted enhancers that overlap markers of enhancers  or active TSSs (misclassification rate) as in the previous studies ,21. In a+ T and H1 cells. We applied AdaBoost models with three different sets of histone modifications: all histone modifications in the datasets , modifications classified as strong predictors , and three core modifications H3K4me1/H3K4me2/H3K4me3 and computed validation and misclassification rate and F-score at different numbers of enhancers determined by taking different probability cutoffs. As shown in + T and H1 , as listed in We next compared the performance of DELTA with three currently available methods for enhancer prediction: CSI-ANN, ChromaGenSVM and RFECS. The three existing methods have been applied on histone modification datasets of CD4 studies ,20,21. UIn H1, histone modifications H3K4me1, H3K4me2 and H3K4me3 were used for enhancer prediction. CSI-ANN made 30996 predictions  and Chro+ T was excluded from this analysis because the dataset of sequence-specific TFs binding sites is not available for this cell type.Because the importance rankings of histone modifications in AdaBoost models are highly correlated across different cell types, we anticipated that DELTA could accurately predict enhancers in one cell type using models trained on other cell types. To test this hypothesis, the dataset of H3K4me1, H3K4me2 and H3K4me3 of five human cell types: H1, GM12878, HeLaS3, K562 and HepG2 was downloaded from the ENCODE project for model training and enhancer prediction. We also downloaded the corresponding p300 binding sites, sequence-specific TFs binding sites and DHS regions to validate the predictions. In order to validate prediction in a uniform criterion across cell types, CD4Enhancers were predicted in each of the five ENCODE cell types using two AdaBoost models: one trained on the same cell type and the other trained on a combined dataset of the other four cell types. For the combined dataset, 1000 representative enhancers  and 8000 background regions  were randomly sampled from each of the four cell types and then constituted a “multicell” training set. Validation rate, misclassification rate and F-score were calculated at each cutoff as described previously to evaluate prediction accuracy. We compared the predictions made by the two AdaBoost models and found that the two prediction sets exhibited very similar validation rate, misclassification rate and F-score curves in all five cell types, as shown in P-value = 0.35, Spearman’s rank correlation test).A set of high-confidence enhancer predictions in multiple cell types would contribute substantially to the functional annotation of human non-coding genomic regions and to the reliable construction of genetic regulatory networks (GRNs). We predicted enhancers in the five ENCODE cell types using DELTA models trained on the same cell type. When predicting enhancers across multiple cell types, it is preferable to have enhancer predictions with the same level of confidence. We calculate a False Discovery Rate (FDR) to determine the appropriate cutoff for each cell type. Two cases are treated as false discovery evens: declaration as enhancer at permuted genomic regions and declaration as enhancer at active TSSs. The ratio of false discovery evens/enhancers predicted in real data is computed as FDR at various cutoffs . The conWe next compared these predictions with those of ChromHMM in four shared cell types. We found the numbers of enhancers predicted by ChromHMM  are one magnitude order larger than those predicted by DELTA with a FDR of 0.05. Despite the great difference in the number of predictions, we found the vast majority of the predictions by DELTA overlapped with Weak/Strong enhancers states of ChromHMM . Moreover, we examined the validation rates and misclassification rates of ChromHMM in the four cell types and found it can achieve validation rates of 60.5–69.6% and misclassification rates of 9.4–10.5%, as shown in In addition, we found enhancers predicted by both the same-cell type model and cross-cell type model are highly cell-type specific. As shown in In summary, we developed a method for accurately predicting enhancers across different cell types and obtained a high-confidence set of enhancer predictions in multiple ENCODE cell lines with the same level of confidence. This will enable us to systemically locate and annotate the regulatory non-coding genomic regions in a wide range of human cell lines.Discussion In recent years, the chromatin modification data of a growing number of cell types have been obtained with the progress of ENCODE and Roadmap epigenomics projects, but accurately predicting DNA regulatory elements from these chromatin data remains a great challenge, as current prediction tools are limited in their prediction accuracy and ability to reliably predict regulatory elements in new cell types. In this paper, we described a novel method for predicting transcriptional enhancers on the basis of shape features of histone modifications and AdaBoost algorithm. There are several advantages of our method that will significantly contribute to the field of enhancer prediction.Initially, we developed an efficient dimension reduction method by systemically introducing and assessing a set of shape parameters of histone modifications profiles. We showed that these parameters can accurately reflect the distributional characteristics of histone modifications at promoters and enhancers and that the combined shape and intensity parameters could achieve significantly higher prediction accuracy than the previously used feature sets (intensity features and binned-vector features), indicating it is a more advanced method for feature extraction of chromatin modification profiles than the current techniques.In addition, we developed a new automatic approach for selecting informative histone modifications for enhancer prediction from a variety of modifications. AdaBoost models with small decision trees also gave information on the variable importance of histone modifications for making enhancer predictions. We used an unsupervised clustering method to automatically classify histone modifications into different groups according to their variable importance for enhancer prediction. We found that predictions made by AdaBoost using modifications termed as “strong predictor” closely resembled those made by the full set of modifications in prediction accuracy. We also confirmed that H3K4me1/2/3 constitute a robust modification set that keeps most of the information for enhancer prediction in different datasets. These findings indicate combining AdaBoost algorithm with automatic clustering method is an efficient way to select importance variables from a variety of histone modifications.+ T and H1 cells.Furthermore, our enhancer prediction method significantly outperformed existing methods in prediction accuracy. Three experimental markers of enhancers, including p300 binding sites, sequence-specific TF binding sites and Dnase-I hypersensitive sites, were used to evaluate the prediction accuracy. We found DELTA improves the validation rates of at least one marker on the previous methods RFECS, CSI-ANN and ChromaGenSVM by 13.3–105% relatively and the validation rates of three markers by 87.5–300% relatively in CD4Moreover, DELTA is the first model that successfully predicts enhancers in one cell type using models trained in other cell types without loss of accuracy. We attribute this success to the non-redundant shape parameters of histone modification that are highly consistent across different cell types, because the AdaBoost models with binned-vector features didn’t show such property. This finding also indicates a general mechanism of histone modifications acting on transcriptional enhancers.In summary, all above features make DELTA a powerful and reliable tool for predicting enhancers in a variety of cell types. We also want to mention that the entire workflow of this study, including shape feature extraction, variable importance assessment and model training and prediction, can be conveniently applied to identify other types of DNA elements with distinct chromatin signatures.+ T cells, the ChIP-seq data of 38 histone modifications were obtained from Wang et al. [et al. [et al. [et al. [http://www.genboree.org/EdaccData/) [http://hgdownload.cse.ucsc.edu/ goldenPath/hg19/encodeDCC/wgEncodeSydhTfbs/). We obtained sequence-specific TFs binding sites from FactorBook Version 3 dataset at UCSC ENCODE website (http://hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeRegTfbsClustered/). The numbers of available TFs in H1, HeLas3, HepG2, GM12878 and K562 are 28, 30, 40, 47 and 50 respectively. [http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeUwDnase/). Any data mapped to hg18 or hg17 genome version was converted to hg19 using UCSC liftover tool [For CD4g et al. . The p30 [et al. , the DNa [et al. . For H1 ccData/)  and the ccData/) . For celccData/)  under thctively. . Narrow ver tool .To obtain histone modification profiles, the ChIP-seq reads were first binned into short intervals (100 bp). The number of reads in each interval was normalized against the total read count by using an RPKM-like measure . In ordeTo determine the binding sites of p300 and other five TFs: CBP, ETS1, FOXP3, RUNX1 and STAT5, we used MACS  softwareTo describe the shape features of a ChIP-seq signal, we employed three descriptive parameters defining three distinct aspects of a probability distribution. Their definitions are listed as follows:Kurtosis: Kurtosis measures the “sharpness” of a probability distribution, ranging from 1 for uniform distribution to infinity for extremely sharp distribution. It can be quantified in different ways, and one common measure is based on a scaled version of the fourth moment, which is defined as:Kurtosis to quantify the sharpness of a ChIP-seq signal. Let xi index the relatively position of the i-th read in a sliding window, n reads in the window. The Kurtosis of the ChIP-seq signal in this window can be calculated as follows:In this study, we used a sample Skewness: Skewness measures the asymmetry of the probability distribution of a random variable about its mean, ranging from -1 for extremely right-skewed distribution to +1 for extremely left-skewed distribution, and a zero value indicates symmetric distribution. The population Skewness can be defined as:Skewness as follows to quantify the extent of a ChIP-seq signal “leans” to one side of the mean:We used a sample Bimodality: Bimodality is a describer to indicate a probability distribution is bimodal or unimodal. Here we use Sarle’s bimodality coefficient to represent the Bimodality, which is defined as:Bimodality ranges from 0 to 1, and value greater than 5/9 indicate a bimodal distribution. We used the following equation to calculate the Bimodality of the ChIP-seq signal in a window:Max parameter to measure the abundance of histone modification. It is defined as the maximum RPKM value of bins in a window.The intensity of a ChIP-seq signal is a feature frequently used in previous enhancer prediction methods. We used a In order to train AdaBoost models, p300 binding sites overlapping with DNase-I hypersensitive sites and distal to annotated TSS were used as representative of positive enhancers (true positives). For negative training data, we constructed both a clean and a noisy non-enhancer background. The clean background only contains random genomic regions distal from known TSSs and enhancers, while the noisy background contains both random genomic regions and annotated TSSs overlapping with DNase-I locations. The clean background was only used in the variable importance assessment procedure.http://www.factorbook.org/) [http://genome.ucsc.edu/ENCODE/) [In the case of enhancer predictions, the definition of true negatives is not straightforward, because we can never be certain that the randomly selected TSS- and p300-distal genomic regions are all non-functional. Therefore, to compare model performances, we used three types of experimental markers of enhancers, including p300 binding sites (excluding those used in training), a wide range of sequence-specific transcription factors binding sites (TFBSs) obtained from FactorBook (ok.org/)  and otheok.org/) ,39, and ENCODE/) . The binENCODE/) . We recoENCODE/)  as misclhttps://github.com/drlu/delta).The DELTA software was implemented in Python and R. Specifically, reads counting, FPKM normalization and profile smoothing was implemented in Python using the “pybedtools” package . Shape pS1 FigP-value) of the difference between two means was calculated by Wilcoxon rank-sum difference test.Significance ((TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigA) Validation rates, B) Misclassification rates and C) F-scores of enhancer predictions using AdaBoost models trained in the same cell type (solid lines) and other four cell types (dashed lines) in five cell types. Validation rates were measured as overlap with either p300 binding sites, DNase-I hypersensitive sites (DHS) or sequence-specific TF binding sites from FactorBook, and misclassification rates were measured as overlap with UCSC TSSs, versus total number of enhancers determined by taking different probability cutoffs.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigValidation rates were measured as overlap with either p300 binding sites, DHSs or sequence-specific TF binding sites from FactorBook, and misclassification rates were measured as overlap with UCSC TSSs.(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Heart progenitor cells differentiate into various cell types including pacemaker and working cardiomyocytes. Cell-type specific gene expression is achieved by combinatorial interactions between tissue-specific transcription factors (TFs), co-factors, and chromatin remodelers and DNA binding elements in regulatory regions. Dysfunction of these transcriptional networks may result in congenital heart defects. Functional analysis of the regulatory DNA sequences has contributed substantially to the identification of the transcriptional network components and combinatorial interactions regulating the tissue-specific gene programs. GATA TFs have been identified as central players in these networks. In particular, GATA binding elements have emerged as a platform to recruit broadly active histone modification enzymes and cell-type-specific co-factors to drive cell-type-specific gene programs. Here, we discuss the role of GATA factors in cell fate decisions and differentiation in the developing heart. GATA4 and its family member GATA6 in patients have been associated with arrhythmias and defects in atrial, ventricular, and AV septation [Congenital heart defects affect nearly one percent of live births and are found in up to one-tenth of spontaneously aborted fetuses , 2. The eptation . In thisIn vertebrates, the heart arises from cells in the anterior lateral plate mesoderm of the early embryo (stage E7–7.5 in mouse). By E7.5–8.0, during folding of the embryo and formation of the foregut, these cells migrate to the midline to form the primary heart tube. The embryonic myocardium of the tube is characterized by a primitive phenotype, i.e., limited cell proliferation, a poorly developed contractile apparatus, and slow conduction , 13. DurBmp2) is expressed in the primary heart tube where it stimulates the expression of T-box 2 (Tbx2), required for the development of the AV canal [Bmp2 is selectively expressed in the AV canal myocardium precursors and conditional deletion of the BMP type I receptor ALK3 in the conduction system using specific Cre driver mice (cGata6-Cre) demonstrated that BMP signaling regulates the proper development of the AV conduction system [Gata6 (cGata6) in the mouse genome leads to transgene activity in the cardiac crescent (E7.5) where both Bmp2 and Tbx2 are expressed [Tbx2, support the notion that the AV canal myocardial cells are already specified at this early stage [Embryonic myocardial cells are confronted with a critical decision: differentiation into pacemaker-like cardiomyocytes of the AV canal or into working cardiomyocytes of the atrial and ventricular chambers. Bone morphogenetic protein 2 , Gja1 , and Gja5  [Hey1 and -2 [Hey1 is expressed in the atria and Hey2 in the ventricles, but neither gene is found to be expressed in the AV canal myocardium [More broadly expressed cardiac transcription factors such as Tbx5, Tbx20, and Nkx2.5 are also required for the development of the conduction system , 32. Chaexin 40) –35. The 1 and -2 , 36. Heyocardium .Gata4, -5, and -6 are detected in the precardiac mesoderm (stage E7.5) [Gata4 and -6 have been reported to be identical throughout the primary heart tube (E8.0–8.5). As the tube loops (E8.5), Gata5 becomes restricted to the endocardium, while Gata4 and -6 are maintained in the myocardium. Gata4 is detected in the endocardium of the atria and ventricles and endocardial cushions of the AV canal and outflow tract. Gata4 and -6 are expressed in both the AV canal and chamber myocardium, with no evidence for differential expression levels and subcellular compartmentalization [The family of GATA transcription factors consists of six members, Gata1-6. In vertebrates, most tissues of either mesodermal or endodermal origin express one or more Gata4-6 factor during development. In the primitive streak, ge E7.5) , 39. Thelization , 40–43. Gata4 die between E8 and 9 because of defects in heart morphogenesis and ventral closure of the foregut. Aberrant heart formation in these mice is mainly secondary to defects in the extra-embryonic and definitive endoderm that affects migration and folding morphogenesis of the precardiogenic splanchnic mesodermal cells [Gata4 and Gata6 mutants demonstrated that both factors are critical for general cellular processes including cardiac cell commitment, differentiation, proliferation, and survival, hampering the analysis of their function in the formation of structures that arise later [GATA6 mutations cause persistent truncus arteriosus in human [Gata6 is downregulated in Gata4 null mice and Gata6 null embryos show down regulation of Gata4, indicating that these factors cross regulate each other [Gata6 compensates the loss of Gata4 in inducing the myocardial gene program [Gata4 and Gata6 double heterozygote mice die very early during embryonic development [Gata4 and Gata6 mutants show defects of the venous pole region, suggesting involvement of these factors in embryonic inflow tract development and the venous blood connection [Mice null for al cells –46. Usinal cells . Elegantse later , 48. These later , 48. Embin human . Gata6 ain human . Wnt2, ain human . Gata6 ich other . In addi program , 48. Thinnection . Gata5 innection .Nppa and Nppb  [Myh6 (α-MHC) and Myh7 (β-MHC) [Gja5 (Cx40) [Ncx1 [Kv4.2 [Nkx2.5, one of the earliest markers of the cardiac lineage in vertebrates, depends on GATA binding sites [Mefc2c in outflow tract and right ventricular myocardium [cyclin D2 and Cdk4. Gata4 physically and functionally interacts with CDK4 [Gata4 is known to activate many cardiac genes displaying regionally restricted expression, including the chamber-specific genes  factor)  and the  (β-MHC) , 56. Gat5 (Cx40)  that pla5 (Cx40) . Gata6 a0) [Ncx1  and the 1 [Kv4.2 . Expressng sites . GATA faocardium . Gata4 iith CDK4  and is rith CDK4 . Gata4 cith CDK4 . This unApproximately seven million GATA motifs are present in the human genome . This raNppb [There is no doubt that emerging molecular technologies will help in understanding the function of GATA TFs in cardiac lineage specification. GATA TFs regulate transcription during differentiation by mediating long-range DNA looping. Whether this is the case in the context of heart development is unknown. Chromosome conformation capture technologies  were developed to identify long-range chromatin interactions . The ENCNppb  leading Nppb . ChIP-seNppb . These aFrom studies in cell culture, two distinct DNA binding modes of GATA transcription factors have been deduced . In the Mechanisms underlying the function of governing the decision of whether GATA TFs function as activators or repressors of a targets gene have been studied in depth in the context of the hematopoietic system. Using the aforementioned approaches, it has been shown that in this lineage context, Gata1 uses Fog1 to activate and or repress the majority of its target genes , 83. FogChromatin remodeling enzymes regulate Gata4 target genes by post-translational modifications of Gata4 , 88. GatNppa [Id2 [Id2 [Id2 in the conduction system and whether a Gata4/6-Smad4-Id2 network module participates in the normal control of conduction system gene expression and specification. Studies in postnatal cardiomyocytes demonstrated that Gata4 and Gata6 can functionally interact with each other, at a single GATA element, to synergistically activate the Nppa and Nppb promoters [Nppa and Nppb genes via direct interaction with Gata4 and its co-repressor, Fog2 [GATA factors have a dual finger module which is highly conserved among the six mammalian proteins. The C-terminal finger mediates sequence-specific DNA binding, while the N-terminal finger modulates DNA binding and contacts DNA. The zinc fingers interact with multiple coregulators. Disruption of these interactions has been reported to underlie congenital heart defects . A preciNppa . Gata6 aNppa . CombinaNppa , 68, 99.Nppa . Gata6 rppa [Id2 , a transppa [Id2 . Gata4 aId2 [Id2 . Furtherromoters . Retinoior, Fog2 . Mutatioor, Fog2 .Tbx2 [Tbx3 [Gjd3  involved in conduction slowing [cTnI [Gjd3-AV canal enhancer also depends on the broadly expressed factor Tbx5 [DNA elements conferring cell-type-specific gene expression are ideal to analyze the molecular mechanisms that underlie the localized gene expression of conduction system genes. For example, one of the enhancers that flank the cGata6 gene directs transgene expression throughout the entire primary heart tube and in all regions of primary myocardium that persist at later stages Fig.  27, 43,,43, 105.Tbx2 , Tbx3 [1x2 [Tbx3 , Gjd3 , suggesting that Hey factors mediate repression via GATA protein interaction [Tbx2 [In chamber myocardium, Gata4 represses these enhancers through pan-cardiac HDACs and chamber-specific Notch target genes Hey1 and -2 . The cGaeraction . We speceraction . This imon [Tbx2 . These fGata4, Bmp2, and cGata6-lacZ expression patterns revealed that they are already expressed in the cardiac crescent, it suggests that already at the primitive streak stage, the GATA-Smad-HAT complex may occupy primary myocardial gene loci. This could lead to acetylation of H3K27 at this early stage. Subsequently upon chamber formation, these loci may become deacetylated in cells receiving appropriate local guidance cues.GATA sites in the AV canal regulatory elements thus serve as a platform to recruit broadly active histone modification enzymes and localized co-factors to drive AV canal-specific gene activity Fig. . ObviousDisease, aging, or gene defects may cause conduction system dysfunction, a common cardiac disorder that currently requires the implantation of an electronic pacemaker. Recent advances in the stem cell field indicate that the creation of induced pacemaker cells may offer a promising alternative to repair diseased pacemaker myocardium . DevelopIn many of the reprogramming studies, multiple factors are required for the cellular conversion, emphasizing the role of cooperativity among factors in the starting population of cells. Binding cooperatively could be sufficient to simultaneously engage a target site in chromatin and activate gene expression. Stoichiometry of Gata4 and its co-factors influences the efficiency and quality of cardiomyocyte reprogramming . AlthougIn summary, the cooperation of GATA factors with active histone modification enzymes and localized co-factors ensures proper spatial–temporal expression of cardiac genes during normal embryonic development Fig. . Definin