Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units.de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units  from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts.Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.The online version of this article (doi:10.1186/s12859-015-0656-3) contains supplementary material, which is available to authorized users. For example, with adequate sequencing depth, short read RNA-seq can provide a reasonably complete and unbiased identification of protein-coding mRNA isoforms, as well as many types of non-coding RNAs, which can be improved with the use of long read lengths –3. Howevd others , 8  at the promoters and gene bodies of expressed coding and non-coding RNA genes, which correlates with the expression of the cognate RNAs –16  [Given that GRO-seq is a direct measure of transcriptional output, it is well suited for calling active transcription units. However, other approaches, such as RNA-seq and ChIP-seq can be used as well. RNA-seq, which measures steady-state accumulation of RNA, is limited with respect to the calling of transcription units because most mRNAs and lncRNAs are processed to mature forms lacking introns, thus generating sequencing reads that do not cover the entire transcription unit Fig. . ChIP-seA genes) , 20, 25,A genes) , 21, 24,Here, we introduce groHMM, a complete pipeline for the (1) accurate identification of the boundaries of transcriptional activity across the genome using GRO-seq data and (2) classification of these transcription units using a database of available annotations, which is provided as an R package in Bioconductor . In addide novo from GRO-seq data using a two-state hidden Markov model (HMM). Our tool, which we call groHMM, is available as an R package in Bioconductor [Transcription units can be identified from various types of genomic data, including GRO-seq, RNA-seq, and ChIP-seq for RNA Pol II, H3K4me3 (promoters), and H3K36me3 (gene bodies) Fig. . We deveonductor . GroHMM T, θT) and the transition probability from the non-transcribed to the transcribed state (N) were trained using the Baum-Welch expectation maximization (EM) algorithm. Because GRO-seq has a low background level, we constrained the gamma distribution for the non-transcribed state such that the mean of the distribution became 1 after adding pseudocounts to every window. Self-transition parameters for the transcribed and non-transcribed states are, by definition, 1-T and 1-N, respectively.Our HMM is parameterized by probability distributions representing the number of GRO-seq reads each hidden state emits across the genome and by a 2x2 matrix of transition probabilities between the hidden states Fig. . We usedT, as a penalty to control the length of the called transcription units. We chose the second tuning parameter, σ2, to control the variance of the GRO-seq background signal . Between the two tuning parameters, changing T has a larger effect on the length of transcription units than the variance of the constrained gamma distribution (see below). In most of the analyses shown herein, these two tuning parameters were set for mammalian genomes. For non-mammalian genomes with smaller genome sizes and higher gene densities , a higher range of probabilities for the transition of the transcribed state to the non-transcribed state were more effective (see below). Also, in the groHMM package, users can optimize the tuning parameters by comparing to existing annotations. Details for optimizing the parameters are provided in the tutorial associated with the groHMM tool in Bioconductor [Two parameters of the HMM, representing the transition probability of the transcribed state to the non-transcribed state (T) and the variance of the non-transcribed state (σ2) were held out for tuning using known gene annotations Fig. . We chosonductor .After optimizing the values of the tuning parameters, we used the Viterbi algorithm to obtain a set of called primary transcription units across the genome. Browser track representations of raw strand-specific GRO-seq data from MCF-7 breast cancer cells  showing The quality of the boundaries of transcription units called by groHMM from the MCF-7 cell GRO-seq data was evaluated by comparison to known gene annotations. For these analyses, we assumed that the transcripts called by groHMM should largely be in agreement with annotations, when available. Two types of error are naturally defined in this comparison, including cases in which (1) distinct neighboring annotations are merged into a single transcription unit (“merged annotation error”) and (2) a single annotation is broken up into multiple distinct transcription units (“dissociated annotation error”) Fig. . GroHMM i.e., area) of overlapped transcription units relative to the area of expressed annotations by associating each gene with a best matched groHMM-called transcription unit. We represented these areas as To compare transcription units called by groHMM with previously annotated genes more rigorously, we devised a measure of mismatch between transcription units and gene annotations where: (1) overlap between the called transcription unit and the annotated gene body, and (2) a lack of overlap between the called transcription unit and the upstream or downstream regions of the annotated gene, provide information on the accuracy of the transcription unit predictions. We divided non-overlapping gene annotations into three distinct regions, all scaled to a uniform gene size: (1) upstream of the transcription start site (TSS), (2) within the gene body, and (3) downstream of the transcription termination site (TTS) Fig. . For eacely Fig. . We assui.e., i.e., non-redundant annotations from RefSeq and GENCODE), which have non-overlapping genomic coordinates, were used for comparing called transcripts with annotations . No such input exists for GRO-seq data due to the nature of the assay [When comparing approaches for calling transcription units from GRO-seq data using tools designed for ChIP-seq data, one must consider the following: First, transcription units are more akin to broad features  for each method, we selected 2,060 highly-expressed mRNAs from the consensus annotations based on a filtering criterion that we call EDR . Using the optimal parameters for each transcription unit caller . We found that the TUA values were monotonically increased for groHMM, but not SICER and HOMER, which reached a plateau at EDR = 10 or EDR = 20, respectively  [C. elegans) [T) because of the high gene density in D. melanogaster (~76 genes per Mb) and C. elegans (~200 genes per Mb) compared to humans (11 genes per Mb)   and wormelegans) . We usedelegans) , 36. As Mb) Fig. . We plotMb) Fig. . In addiMb) Fig. , c and dMb) Fig.  and somei.e., “enhancer transcripts”) were defined as those that are short (<9 kb), bidirectional , and partially overlapping, with transcription units whose TSS is located >10 kb from the TSSs or TTSs of annotated genes, as described previously [Many transcriptomic applications require defining the specific ‘biotype’ of the primary transcripts from predicted transcription units. To illustrate how groHMM can be applied to this task, we classified transcription units called from MCF-7 GRO-seq data  Additio. Transcreviously . Althougeviously , short beviously . This anetc.) was similar across the four cell types , indicating differences in biology or groHMM sensitivity across cell types. In addition, protein-coding transcripts were significantly depleted from the cell type-specific fraction of the transcriptome in all cell types , whereas enhancer transcripts were significantly enriched   and transformed cells (LNCaP prostate cancer cells)  Fig.  and b. Ti.e., identified in one cell type only), while the remaining enhancers were detected in two or more cell lines  versus the other cell types  also illustrate the cell type-specificity of many of the enhancers  for each enhancer. Next, we performed hierarchical clustering analysis, displaying the normalized GSEA enrichment scores for each enhancer  . To do sGRO-seq data provides a wealth of information about the cellular transcriptome. Accurately defining various types of transcripts and systematically assigning these transcripts into functional categories poses a great challenge to the research community. In this study, we describe groHMM, a transcription unit identification software package designed for GRO-seq data. In addition, we describe novel metrics for determining the accuracy of transcription unit annotation, which show that groHMM substantially outperforms alternative approaches for identifying both coding and non-coding transcription units. Finally, to demonstrate the utility of our approach, we used groHMM to annotate four GRO-seq data sets derived from cells representing a variety of different human tissue types, focusing on enhancer transcription. Our analyses reveal new insights into cell type-specific transcription.C. elegans, frequently merging separate annotations into a single called transcription unit.We used the TUA metric, which compares the overlap of predicted transcription units to protein coding genes, to evaluate the accuracy of groHMM and two alternative transcription unit identification tools. We found that groHMM achieved superior performance compared to both SICER and HOMER when using optimized models for each method. For both mRNA and lncRNA transcription units with lower levels of expression, groHMM was the only method among those tested that showed monotonically increasing performance as a function of the ‘smoothness’ of expression, indicating a high correlation of the groHMM model with gene expression patterns determined from GRO-seq data. A full comparison with HOMER was challenging because of the large number of free parameters that were difficult to explore completely. Therefore, it is possible that parameters which were not fully explored might produce a better outcome. However, adjusting many parameters may not be ideal for typical users since it could easily lead to overfitting. We successfully annotated many transcript types from GRO-seq data sets generated by different labs under a various conditions, including GRO-seq data from non-mammalian cells, further validating the usefulness and generality of groHMM. Together, these results demonstrate that groHMM is the most versatile and accurate of the tools currently available for transcription unit identification using GRO-seq data. There are limitations however; groHMM struggled with the gene dense genome of Cell type-specific transcription can be a useful indicator of the biology of different cell types or states, and can even be used to classify cell types into groups with related biology. Our analysis of GRO-seq data across four different human cell lines revealed a considerable amount of cell type-specific transcription for both coding and non-coding transcription units. Cell type-specific transcription was particularly enriched for lncRNA genes, repeat sequences, other genic – antisense sequences, intergenic regions, and enhancers. The cell type-specific transcription of repeat sequences, other genic-antisense sequences, or intergenic regions suggests a further layer of regulatory complexity in the genome that should be explored further. The cell type-specific transcription of enhancers is consistent with observations made in previous studies , 11.A strength of GRO-seq is that activation of gene expression can be measured within the same assay in which one assesses the function of regulatory elements, like enhancers . We used this feature of GRO-seq to assess the functional role of cell type-specific enhancers in the GSEA analysis shown in Additional file Recent studies have placed great emphasis on the identification of cell type-specific enhancers, since they likely control most of the cell type-specific transcription in the cell, including protein-coding and regulatory RNAs . One approach for identifying candidate enhancers is to use DNA sequence features, such as sequence conservation or transcription factor binding motifs , 41. EnhWe have previously shown that enhancer transcription, as detected by GRO-seq, can be a powerful tool for identifying putative active enhancers , 18, 20.de novo, performs favorably when compared to two existing peak-calling methods tuned to identify broad regions (SICER and HOMER). To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units  from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells). In particular, we have used groHMM to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts. Collectively, our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.GRO-seq provides extensive information on the location and function of coding and non-coding transcripts, including primary miRNAs, long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units. GroHMM, a computational tool in R, which defines the boundaries of transcription units The groHMM package was developed in house and is available for download from Bioconductor . The tooThe groHMM tool does not automatically take read mappability into an account. It uses mapped reads from any read mapping tool, with parameters  set by the tool. The analyses presented herein manuscript do not take mappability in account for two reasons: (1) the performance gain in transcript calling when considering mappability was minimal (data not shown) and (2) SICER does not consider mappability, so to put the comparisons on equal footing, we did not consider mappability for groHMM and HOMER. To consider mappability in groHMM, one can use existing genome mappability files to mask reads that fall in unmappable regions. The remaining reads can then be used as input to groHMM.D. melanogaster: GSM1020091, GSM1020092, GSM1020093, GSM1020094; C. elegans: GSM1056279, GSM1056282, GSM1056283. The dm3 and ce10 genome assemblies were used for unique mapping of reads in D. melanogaster and C. elegans data, respectively.Publicly available GRO-seq data sets from MCF-7, LNCaP, IMR90, and AC16 cells were downloaded from the NCBI GEO repository using the following accession numbers: MCF-7: GSM678535, GSM678536, GSM678537, GSM678538, GSM678539, GSM678540, GSM678541, GSM678542; LNCaP: GSM686948, GSM686949, GSM686950; AC16: GSM1240738, GSM1240739, GSM1240740, GSM1240741, GSM1240742, GSM1240743, GSM1240744, GSM1240745; and IMR90: GSM340901, GSM340902 . Thus, further overlapping intervals were trimmed at the 3′ end in order to preserve the 5′ transcription start site (TSS) of the interval. The protein-coding genes of RefSeq hg19 , GENCODE v. 19 Basic , and UCSC Genes  were downloaded, combined, and collapsed into a set of 19,834 non-overlapping consensus annotations for all unique gene symbols. These consensus annotations were used for calculating the TUA metrics and for fixing the boundaries of called transcripts with merged annotation errors or dissociated annotation errors. All called transcripts were mapped to annotations one-to-one; if more than one transcript overlapped a single annotation, the best overlapping called transcript was chosen so that dissociated annotation errors were accommodated in the TUA metrics.Called transcripts were assigned to one of the following ten functional classes, according to the rules defined below, as described previously .(1) Protein coding: A transcript with more than 20 % of its sequence overlapping any well annotated protein-coding gene from RefSeq and GENCODE release 19.(2) Non-coding transcript: A transcript overlapping an annotated non-coding RNA gene, such as those encoding a miRNA, tRNA, or snRNA, without any restrictions on the size of the transcript or the quality of the overlap using the RNA Genes table from the UCSC genome browser.(3) lncRNA: A transcript with more than 20 % of its sequence overlapping any well annotated lncRNA from LNCipedia 2.1.(4) Enhancer: A pair of short (< 10 kb) bidirectionally transcribed intergenic transcripts that do not significantly overlap annotated transcripts.(5) Divergent: A transcript that overlaps the 5′ promoter driving expression of a primary transcript, such as an mRNA or a lncRNA. Divergent transcripts were only included if (1) >10 % of the transcript overlapped the proximal region of a promoter (±500 bp relative to the TSS) driving expression of a primary transcript >1 kb in size on the opposite strand and (2) the transcript was <50 % of the size of the primary transcript, which effectively excluded divergent enhancer-transcript pairs.(6) Antisense: A transcript that runs antisense to a protein-coding gene or lncRNA gene and has >20 % of its sequence overlapping >20 % of an annotated protein-coding gene or lncRNA gene on the opposite strand.(7) Repeat: A transcript with more than 50 % of its sequence overlapping genomic regions identified in the RepeatMasker track in the UCSC Genome Browser.(9) Other genic – sense/antisense: A transcript that has a poor match to existing annotations. Transcripts in this category overlap any segment of a gene annotation on either strand, but show <20 % matching to the annotation on the same strand (sense) or on the opposite (antisense).(10) Intergenic: A transcript does not belong to any defined categories above.Enhancer transcription heatmaps were generated with Java Treeview, v.1.1.6r4 . Cell tyhttp://download.baderlab.org/EM_Genesets/September_02_2011/Human/symbol/GO/Human_GO_bp_no_GO_iea_symbol.gmt. Normalized enrichment scores (NESs) were used for the subsequent hierarchical clustering analysis after filtering the terms whose NESs were zero for more than 20 enhancers. The heatmap.2 function in the gplots package in R was used for clustering both terms and enhancers using the average linkage option.Gene Set Enrichment Analysis (GSEA), v. 2.0.14 , was useRegulation in response to hormone treatments was determined using the edgeR package in R  with a F


Krüppel-like factors (KLFs) are a group of master regulators of gene expression conserved from flies to human. However, scant information is available on either the mechanisms or functional impact of the coupling of KLF proteins to chromatin remodeling machines, a deterministic step in transcriptional regulation.In the current study, we use genome-wide analyses of chromatin immunoprecipitation (ChIP-on-Chip) and Affymetrix-based expression profiling to gain insight into how KLF11, a human transcription factor involved in tumor suppression and metabolic diseases, works by coupling to three co-factor groups: the Sin3-histone deacetylase system, WD40-domain containing proteins, and the HP1-histone methyltransferase system. Our results reveal that KLF11 regulates distinct gene networks involved in metabolism and growth by using single or combinatorial coupling events.This study, the first of its type for any KLF protein, reveals that interactions with multiple chromatin systems are required for the full gene regulatory function of these proteins. The traditional strategy for studying the role of transcription factors in gene regulation relies on either small or large-scale expression analysis following overexpression, somatic knockdown, or germ line deletion. While these approaches permit the identification of gene networks regulated by transcription factors, little information is gathered about the chromatin coupling events that ultimately drive gene expression. This gap in knowledge is unfortunate as transcription factors are often modular proteins armed with multiple sites for potential interactions with chromatin cofactors. Thus, knocking-out a transcription factor will, in theory, disrupt all chromatin-coupling events. For this reason, it is important to dissect whether different transcriptional regulators use single or combinatorial coupling events to regulate distinct gene expression networks.This knowledge is particularly important for proteins in which dysfunction contributes to disease. Impairment in each of the coupling events may yield differential effects, affecting disease penetrance as well as progression. Moreover, with the development of novel chromatin-centric pharmacology, different drugs can partially inactivate certain gene networks regulated by a transcription factor while leaving others intact. Therefore, the dissection of individual and combinatorial chromatin coupling events possesses both biological and medical relevance.Consequently, the current study focuses on KLF11 as a model system for addressing these important questions. KLF11 is a well characterized human disease-causing gene that couples to several chromatin partners -4. KLF11In the current study, we have employed a combination of genome-wide ChIP-on-Chip and gene expression profiles to reconstruct both the direct and indirect effects of KLF11 on the regulation of different gene networks. Furthermore, we used site-directed mutants to disrupt individual chromatin coupling domains to generate genome-wide expression for the identification of the genes regulated by KLF11 in response to binding to each of its chromatin cofactors. Our results demonstrate that certain gene programs require a single chromatin machine interaction with KLF11 while others require interaction with multiple chromatin systems. Accordingly, we provide analyses of the distinct gene networks regulated by KLF11 and its coupling to chromatin partners. This type of comprehensive genome-wide analysis has never been performed for any member of the KLF family. When applied to KLF11, our analysis demonstrates that this protein behaves in a modular fashion with chromatin machinery to alter gene expression and impacts a variety of biological processes.Recent studies support a model whereby KLF11 functions by binding to GC-rich sites within promoters of different gene networks involved in the regulation of metabolism and cell growth -4,7-12. To address this question, we first preformed genome-wide promoter binding analysis of wild type KLF11. We then limited our analysis to 5 kb upstream and downstream of the transcription start site of known gene promoters. Examination of the distance of KLF11-bound peaks from each TSS revealed that the majority (~60%) was within 500–1000 base pairs of the TSS  shows that 97% of transcripts that are modulated by the wild type KLF11 are also regulated by any of the three mutations, with the A347S and Δ486 mutations sharing the largest number of genes with the wild type Figure A. In facSubsequently, we examined genes for which expression might be regulated by KLF11 in a manner that does not involve the chromatin remodeling machines examined above. A small percentage of genes are still significantly modulated uniquely by wild type KLF11 Figure G. OntoloGene Ontology (GO) enrichment analysis of the genes modulated by wild type KLF11 and each of the three mutations show various common biological processes Figure A. IndeedTwo of the wild type KLF11 biological processes, the G2/M transition during DNA damage checkpoint and cilium assembly, are also conserved in the A347S mutation. There are 16 biological processes exclusively conserved between wild type KLF11 and the Δ486 mutant. These include cellular metabolic processes, such as aldehyde and nitrogen compound metabolism, isocitrate and 2-oxoglutarate metabolism, glycosaminoglycan catabolic processes, and fatty acid beta-oxidation.Interestingly, the A347S and Δ486 mutations share more than a quarter of biological processes (n = 39) that are absent in wild type KLF11 and the EAPP mutant. Between these 39 GO terms, we found enrichment of terms associated to the monitoring of the DNA transcription, RNA polymerase I transcription promoter regulation, and epigenetic control of gene expression, such as histone H3 acetylation, chromatin remodeling, protein acetylation and deacetylation, and regulation of phosphorylation. Wild type KLF11 and EAPP share only two GO-terms in common: branched chain amino acid family catabolic processes and negative regulation of TOR and signaling cascade.Overall, 22 of 57 38.6%) of the biological processes enriched in the wild type KLF11 are not present any of the three mutants. Among the 22 most enriched biological process found only in the wild type KLF11, we observed several metabolic processes, such as glucose, carbohydrate, and very long-chain fatty acid metabolism, oxaloacetate metabolism, fatty acid homeostasis, and oligosaccharide metabolism. Top processes associated with wild type KLF11 expression are shown in Figure 8.6% of tOf the mutants, A347S displays the highest number of unique biological process that are not enriched in the wild type (n = 93). Top processes associated with A347S mutant expression are shown in Figure In turn, the Δ486 mutant exclusively enriches 45 biological processes that are mediated by the wild type KLF11 or in other mutants. Top processes associated with Δ486 expression are shown in Figure The EAPP mutant did not display robust gene expression changes within the experiment. However, utilizing a more permissive cutoff yields a list of 53 biological categories, although each only contains a single gene. We find enrichment of biological processes involving nitric oxide transport, catabolic processes, and developmental morphogenesis function, among others. Top processes associated with EAPP expression are shown in Figure Subsequently, we wished to further assess the relatedness between the gene targets significantly modulated by KLF11 and its mutants utilizing a semantic-based approach and the Ingenuity Global Canonical Pathways (GPC) algorithm. The input dataset of significantly regulated gene targets was compared against the IPA canonical pathways, which are curated from published literature. The significance of the associations between the data set and a given canonical pathway is determined by the ratio of the number of genes mapping to the pathway divided by the total number of pathway genes. A p-value is calculated using Fischer's Exact Test determining the probability that the association between the data set and the pathway occurs by chance alone. GPC analysis of genes modulated by the wild type KLF11 and the three mutants shows a variety of significantly enriched pathways . The wild type KLF11 shares about 71% (30/42) of its pathways with at least one of the mutants  are metabolic biosynthetic pathways.Mutant A347S possesses the most exclusive pathways. Indeed, 28 pathways are enriched in the A347S mutant compared to wild type KLF11 and other mutants Figure C. IntereWe next explored the degree of connectivity between KLF11 and mutant-regulated genes and their pathobiological associations. Networks of significantly regulated genes were algorithmically generated based on their connectivity and assigned a score that encapsulates the relevance of the generated network to the original list of focus genes. All edges are supported by at least one literature reference of direct physical, transcriptional, and enzymatic interactions, or from canonical information stored in the Ingenuity Pathways Knowledge Base. A right-tailed Fisher’s Exact Test was used to calculate the p-value for networks and a threshold of p < 0.05 used to determine significance. A functional analysis of a network then determined the biological functions and/or diseases that are most significant to the genes contained within the network Figure .Of the identified processes, only three were common to wild type KLF11 and all three mutants: cell death and survival, hereditary disorders, and molecular transport mechanisms. Wild type KLF11 possessed the most unique networks (n = 10) and included a number of characterized biological and disease networks, including cellular growth and proliferation , endocriFinally, we wished to identify the upstream transcriptional regulators using Ingenuity’s Upstream Regulator (UR) analysis. The UR analytic is based on prior knowledge of the expected effects of transcriptional regulators and their target genes stored in the Ingenuity Public Knowledge Base. The algorithm examines how many known targets of each transcriptional regulator are present in the list of significantly regulated targets and compares direction of expression to that expected from previously published data. For each potential regulator, an overlap p-value using Fisher’s Exact Test and an activation z-score was computed, the latter of which determines the activation state. A threshold p-value of <0.01 and activation score of +/- 2 was considered significant for our purposes. The end result was the identification of upstream regulators of KLF11 or its mutants that permit the generation of plausible signaling cascades mediated by these upstream regulators through KLF11 chromatin coupling.The results of the UR analysis are presented in Additional file In summary, here we have identified the precise interplay between KLF11 and three of its chromatin coupling systems, namely Sin3/HDAC, HP1/HMTs, and WD40 proteins, on a genome-wide scale. The functional repertoire of KLF11 appears to be dictated by both combinatorial effects between these systems and singular chromatin coupling events. Redundancy, particularly in critical biological processes such as metabolism and organismal development, occurs between systems to ensure preservation of critical functions. On the other hand, singular chromatin coupling events exist to ensure precision in processes requiring strict spatial and temporal parameters to avoid inappropriate signaling.Until recently, the relationship between chromatin and transcription factors appeared largely passive. However, the evidence that overexpression of four transcription factors—Oct4, Sox2, m-Myc, and KLF4—is sufficient to reprogram fully differentiated fibroblasts into pluripotent ES-like cells suggests an active role for transcription factors in the epigenetic regulation of chromatin state . KLF proKLF proteins deliver epigenetic information to gene promoters through three primary mechanisms: (i) sensing and translating environmental stimuli into a program of gene expression, (ii) sequence-specific targeting of chromatin remodeling complexes to gene promoters, and (iii) transactivation of other transcription factors to assist in the regulation of large networks of interdependent genes. Transient regulation of gene expression occurs through association with histone acetyltransferases (HATs) and histone deacetyltransferases (HDACs), while long-term gene silencing is enacted by interactions with histone methyltransferases (HMTs) and DNA methyltransferases (DNMTs). No single classification scheme may accurately characterize the function of KLF11 in its entirety. However, the classification of family members by virtue of its co-factors provides a framework to understand the functional differences in the manner in which it engages chromatin to activate or repress transcription in a dynamic and reversible manner.The association between KLF11 and chromatin coupling is well established. Initial biochemical characterization of KLF11 revealed that its N-terminus possesses a domain that adopts an α-helical conformation and that mutations within this domain significantly disrupted its binding to Sin3 and the subsequent recruitment of HDACs ,27. SubsThe investigation presented here is the first to attempt to dissect the roles of KLF11 and chromatin coupling on a genome-wide level to ascertain the combinatorial or unique effects of each type of previously identified interacting chromatin system. Utilizing a single cell type with overexpression of wild type KLF11 and the three previously described mutants, A347S, Δ486, and EAPP, we were able to examine the interplay between coupling of the transcription factor to the WD40, HP1/HMT, and Sin3/HDAC systems, respectively. By introducing saturating amounts of each mutant into pancreatic epithelial cells, we were able to tease apart the relative and overlapping contributions of each chromatin coupling mechanism to the regulation of pathobiological gene networks. Our results reveal that in the pancreatic epithelial cell utilized as our model, a large number of the affected genes, networks, and signaling cascades are modulated by the coupling of KLF11 to its variety of chromatin systems. The number of genes identified as regulated independently of these systems represented <1% of the total genes mediated in the presence of wild type or mutant proteins.It is important to underscore that the study reported here is unique as it uses genome-wide approaches to dissect the function of several different chromatin-based pathways in the transcriptional function of a single KLF protein. The KLF family of transcription factors consists of 17 members , the degMore than fifteen years ago, our research team hypothesized that the discovery and study of KLF proteins and their chromatin cofactors would assist in unraveling complex human diseases. In the intervening years, our understanding of the interplay between KLF proteins and epigenetic machinery in transient and long-term gene regulation has grown exponentially. With knowledge inferred from the predictive power of rationally derived computational models, we propose a new paradigm for KLF regulation of gene networks through the translation of input from cellular milieu into epigenetic information to affect changes in chromatin structure. KLF proteins currently provide the best model for understanding the interactions between mechanisms of “hard inheritance”  and “soft inheritance” (epigenetic variation) in underscoring the phenotypic variability observed in complex disease mechanisms. Perhaps most importantly, these new pathways open up infinite possibilities for targeting new molecules and processes in therapeutic intervention and disease management.Panc1 cell lines were obtained from the American Type Culture Collection . Cells were cultured as described previously ,36.Standard molecular biology techniques were used to clone full length KLF11, KLF11-A347S, KLF11-EAPP, and KLF11-486 into pcDNA3.1/His . All constructs were verified by sequencing at the Mayo Clinic Molecular Biology Core Facility. Epitope-tagged (6XHis-Xpress™) KLF11, KLF11-A347S, KLF11-EAPP, KLF11Δ486 variants as well as empty vector (Ad5CMV) were generated as recombinant adenovirus in collaboration with the Gene Transfer Vector Core at the University of Iowa.6 cells/100 mm dish and transduced with empty vector, KLF11, KLF11-A347S, KLF11-Δ486, or KLF11-EAPP at an MOI of 150. Relative expression levels were confirmed by Western Blot  and corrected using Bayes approach [http://www.ingenuity.com) for annotation, redundancy checks, canonical pathway, biological network, and upstream regulator analysis using default parameters.Data analysis, background subtraction and intensity normalization was performed using Robust Multiarray Analysis (RMA) . Genes tapproach ,44. A thapproach . SelecteThe data sets supporting the results of this article are available in the NCBI repository, GSE56778.The authors declare that no competing interests exist.EC, AG, GL, and AM carried out the molecular, promoter binding, and gene expression studies, participated in the bioinformatics analysis, and drafted the manuscript. JI and RU conceived of the study, participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.6-Xpress™) wild type KLF11 and the A347S, Δ486, and EAPP mutants demonstrating similar overexpression in Panc1 cells. α-Tubulin was used as a loading control.Analysis of wild type KLF11 and mutant protein expression. Protein expression of epitope-tagged .Click here for fileBiological processes unique to the decoupling of KLF11 from HP1a/HMT (Δ486 mutant).Click here for fileUpstream regulators of KLF11 and mutants.Click here for fileqPCR validation of Affymetrix gene expression data. A small subset of significantly regulated genes identified by Affymetrix whole-genome microarray for wild type KLF11 or the A347S, Δ486, and EAPP mutants were validated by qPCR.Click here for file


The hsi2-4 mutant harbors a point mutation that affects the plant homeodomain (PHD)-like domain in HIGH-LEVEL EXPRESSION OF SUGAR-INDUCIBLE GENE2 (HSI2)/VIVIPAROUS1/ABI3-LIKE1 (VAL1). In hsi2-4 seedlings, expression of this LUC transgene and certain endogenous seed-maturation genes is constitutively enhanced. The parental reporter line (WTLUC) that was used for mutagenesis harbors two independent transgene loci, KanR and KanS. Both loci express luciferase whereas only the KanR locus confers resistance to kanamycin.The novel mutant allele KanR and KanS loci. Expression of LUC and Neomycin Phosphotransferase II transgenes is associated with dynamic changes in H3K27me3 levels, and the activation marks H3K4me3 and H3K36me3 but does not appear to involve repressive H3K9me2 marks, DNA methylation or histone deacetylation. However, hsi2-2 and hsi2-4 mutants are partially resistant to growth inhibition associated with exposure to the DNA methylation inhibitor 5-aza-2′-deoxycytidine. HSI2 is also required for the repression of a subset of regulatory and structural seed maturation genes in vegetative tissues and H3K27me3 marks associated with most of these genes are also HSI2-dependent.Here we show that both transgene loci harbor multiple tandem insertions at single sites. Luciferase expression from these sites is regulated by the HSI2 PHD-like domain, which is required for the deposition of repressive histone methylation marks (H3K27me3) at both These data implicate HSI2 PHD-like domain in the regulation of gene expression involving histone modifications and DNA methylation-mediated epigenetic mechanisms.The online version of this article (doi:10.1186/s12870-014-0293-4) contains supplementary material, which is available to authorized users. Seed n stress . Recentl mutants . These rDevelopmental regulation of gene expression in plants is affected by chromatin mediated epigenetic mechanisms that include DNA methylation, chromatin remodeling, histone variants, and histone modifications ,21. DNA in vitro and, recently, it was reported that the HSL1 CW domain interacts with the histone deacetylase HDA19 to repress the “LAFL network” genes, including LEC1 and LEC2, by promoting histone deacetylation and the addition of H3K27me3 marks [HSI2 and HSL1 proteins were predicted to contain a PHD-like domain, a B3-DNA binding domain, a conserved cysteine and tryptophan residue-containing (CW) domain and an ethylene-responsive element binding factor-associated amphiphilic repression (EAR) motif ,17,25,26e3 marks . HoweverHSI2 allele, hsi2-4, in Arabidopsis that harbors a point mutation resulting in an amino acid substitution (C66Y) in the second zinc finger of the HSI2 PHD-like domain. The hsi2-4 mutant seedlings that carry a glutathione S-transferase F8::luciferase (GSTF8::LUC) reporter gene showed constitutively elevated transgene expression [LUC transgene, HSI2 PHD-like domain is required for the non-redundant repression of several seed-maturation genes in seedlings. These genes include those that encode both regulatory factors such as FUS3, and AGAMOUS-Like 15 (AGL15) and structural proteins that include cupin family storage protein, oleosins, late-embryogenesis-related proteins and seed storage albumins. Moreover, seed-specific genes that are de-repressed in hsi2-4 mutant seedlings are targets of H3K27me3 marks. Chromatin immunoprecipitation and quantitative PCR (ChIP-qPCR) analyses indicated that HSI2 PHD-like domain promotes H3K27me3 marks on transgene GSTF8 promoter and LUC coding sequences to repress transgene expression in parental GSTF8::LUC reporter (WTLUC) seedlings [LUC and hsi2-4LUC mutant plants harbor two independent transgene loci [KanR and KanS, respectively [Previously, we reported a novel pression . In addieedlings . Both WTene loci . One locectively .LUC transgene expression from both KanR and KanS loci by promoting H3K27me3 marks but not DNA methylation and siRNA associated H3K9me2 marks. Expression of Neomycin Phosphotransferase II (NPTII) from the KanR locus is also partially suppressed in an HSI2-dependent mechanism. However, while our data indicate that DNA methylation and histone deacetylation are not involved in the transcriptional repression of transgene loci in WTLUC, the HSI2 PHD-like domain may play a role in the inhibition of seedling growth and development caused by DNA methylation inhibitor 5-aza-2′-deoxycytidine (5-azadC).In this work, we show that HSI2 PHD-like domain represses GSTF8::LUC reporter construct contains a GSTF8 promoter sequence that controls the transcription of a luciferase expression cassette, along with an NPTII gene under control of the nopaline synthase promoter and terminator sequences, which confers kanamycin resistance in plants  was performed using genomic DNA from WTLUC, KanR and KanS plants. Since both KanR and KanS loci confer luminescence expression, we used PCR primers that are specific to the LUC coding sequences to estimate the copy numbers. The results show that KanS plants contain 2 copies of LUC whereas the KanR locus harbors 5 LUC copies. Independent analysis of WTLUC plants, which contain both KanR and KanS loci, showed seven copies of the LUC transgene  and GSTF8-Short (GSTF8-S) [GSTF8 expression is altered in hsi2 mutant alleles, we performed qRT-PCR using various wild type lines  and hsi2 mutant lines . hsi2-2 is a loss-of-function mutant allele that carries a T-DNA insertion in the seventh exon of HSI2 gene (SALK_088606) [hsi2-2LUC line, GSTF8::LUC transgenes were introgressed into the hsi2-2 mutant background by genetic crossing. Expression of GSTF8-Total (GSTF8-T) represents both GSTF8-L and GSTF8-S transcripts whereas GSTF8-Long expression represents GSTF8-L transcripts only. As shown in Figure GSTF8-L and GSTF8-T transcripts were not significantly affected in hsi2-2LUC and hsi2-4LUC plants and NPTII expression was not detected in the KanS reporter line, consistent with the kanamycin sensitivity of these plants. NPTII transcripts were expressed at similar levels in the WTLUC and hsi2-4LUC seedlings but expression of NPTII in KanR seedlings was responsive to the HSI2 PHD-like domain point mutation  . To dete_088606) –14,17. TGSTF8::LUC transgene loci in the WTLUC plants are targets of DNA methylation, treatment of these seedlings with an inhibitor of DNA methylation should derepress the luminescence expression similar to that seen in hsi2-4 seedlings. Previous reports showed that treatment with 5 μM/mL of the DNA methylation inhibitor 5-aza-2′-deoxycytidine (5-azadC) was effective in derepressing the transcriptional silencing of auxin-responsive ß-GUS reporter lines [GSTF8::LUC transgene expression, WTLUC, hsi2-2LUC and hsi2-4LUC seedlings were grown on media containing 5-azadC at various concentrations. Luminescence expression in WTLUC seedlings was not affected at either 1 or 5 μM/mL 5-azadC concentrations  was used as a negative control for H3K27me3 and as a positive control for H3K4me3 and H3K36me3. TA2 was used as a positive control for H3K9me2 and as a negative control for H3K4me3 and H3K36me3. In agreement with our previous report [GSTF8 promoter region, and both 5′ and 3′ ends of the LUC coding sequences was highly enriched in H3K27me3 marks in WTLUC seedlings  and transgene as used  in WTLUC seedlings was significantly decreased in both hsi2-2LUC and hsi2-4LUC backgrounds, which is consistent with the increased expression of AGL15 in these mutants [AGL15 locus.Some members of the LAFL clade of regulatory genes that control the expression of seed maturation genes  are misreedlings . LEC1 an-4 lines . These r mutants . Therefohsi2-4 mutant seedlings [hsi2 mutant seedlings correlates with changes in associated H3K27me3 marks, ChIP-qPCR analyses were performed on chromatin from WTLUC, hsi2-2LUC and hsi2-4LUC mutant seedlings using primers specific to At2g41260 (Late-embryogenesis-abundant (LEA) protein), At3g22640 (cupin family seed storage protein), At1g04660 (glycine-rich protein), At2g29370 (NAD(P)-binding Rossmann-fold superfamily protein), At5g45830  and At2g34700 (proline-rich glycoprotein). These seed maturation genes were previously reported to be targets of H3K27 hypermethylation [LUC, significant decreases in H3K27me3 enrichments were detected in both hsi2-2LUC and hsi2-4LUC mutant seedlings on chromatin associated with 5′ genomic sequences of DOG1 and At2g34700, and at both 5′ and 3′ sequences of At3g22640 . No significant differences in luminescence were apparent between Col-L and hsi2-4-L plants but LUC expression driven by the short GSTF8 promoter in hsi2-4-S plants was substantially elevated relative to that detected in Col-S plants.In contrast to the r et al. , could cin vitro [Polycomb group (PcG) proteins are evolutionarily conserved multi-protein complexes required for developmental repression of gene expression by chromatin based mechanisms. PcG proteins in plants comprise of two major complexes: Polycomb Repressive Complex 1 (PRC1) and PRC2 –6,55. Arin vitro ,56,57, win vitro –60.GSTF8 promoter sequence used in the GSTF8::LUC reporter contains an octopine synthase (OCS) sequence element at −460 and OCS element binding proteins 1 (OBP1) elements that were shown to bind proteins of the DNA binding with One Finger (DOF) family of transcription factors and are reported to act either as positive or negative regulatory factors in various plant genes [myeloblastosis2 (MYB2) binding element is also located at −311 but its potential function is unknown. Sequence elements with a potential role in PRC2-based silencing can also be identified in the GSTF8 promoter. By analyzing the co-distribution of the ubiquitous Arabidopsis PRC2 protein FERTILIZATION INDEPENDENT ENDOSPERM (FIE) and H3K27me3 marks, Deng et al. [GSTF8 promoter, a putative GAGA box located between −123 and −131 relative to the transcription start site and a TTC repeat element located in the 5′ untranslated region between +36 and +56. GAGA elements were found to be specifically associated with FIE and H3K27me3 enriched sites and these sequences are also found in polycomb response elements (PREs) of Drosophila [The nt genes . A putatg et al.  identifiosophila . Howeverosophila .LEC2 gene was confirmed by Berger et al. [cis-activating element, which was associated with a distinct cis-repressing element, termed repressive LEC2 element (RLE) that apparently consists of two component sequences. Comparison of the LEC2 RLE sequence with the GSTF8 promoter identified a duplicated element identical to the 5′ component of the LEC2 RLE. As in the LEC2 gene, the putative GSTF8 RLE-like sequence is located immediately downstream of the GAGA element , DOG1 (At5g45830) and a proline-rich glycoprotein (At2g34700) are similarly up-regulated in hsi2-2LUC and hsi2-4LUC plants and decreased H3K27me3 marks are also apparent in both mutant lines.Similar patterns can be seen in the structural (non-regulatory) seed maturation gene sample. The putative glycine-rich protein gene (At1g04660) is more strongly expressed in C plants  and H3K2FUS3 and the LEA protein gene At2g41260, are expressed at elevated levels in both hsi2-2LUC and hsi2-4LUC backgrounds and accumulate substantial H3K27me3 in WTLUC plants but these marks are not diminished in either hsi2 mutant. Therefore, derepression of these genes in hsi2 mutant plants does not appear to require the depletion of H3K27me3. It seems likely that, in addition to the PRC2-mediated accumulation of H3K27me3 marks, other mechanisms are involved in the repression of these genes.Two of the genes in our sample group do not show correlations between HSI2-dependent transcriptional repression and H3K27me3 marks. These genes, AGL15 results in enhanced somatic embryogenesis [LAFL network genes and several LAFL genes are direct regulatory targets of AGL15 [DOG1 is a seed-specific gene and plays a critical role in promoting seed dormancy by integrating environmental signals [AGL15 and DOG1 in seedlings by promoting H3K27me3 marks possibly via PRC1-PRC2 complex which requires HSI2 PHD-like domain.AGL15 is a member of the MIKC subfamily of MADS domain transcription factors that is preferentially expressed in developing embryos. Ectopic overexpression of ogenesis ,66. AGL1of AGL15 ,68. DOG1 signals ,70. Rece signals ,71. Our LEC1, FUS3 and ABI3 [GSTF8::LUC transgene loci. AtRING1a was also shown to physically interact with the PRC2 core component CLF [GSTF8::LUC transgene loci to repress their expression during the seed to seedling developmental phase transition.HSI2 was shown to directly interact with PRC1 complex proteins AtBMI1A/B/C and is required for the deposition of H2Aubi and H3K27me3 marks on “LAFL network” seed-maturation genes including and ABI3 . Disruptand ABI3 ,72,73. Tnent CLF  and sevenent CLF ,71,75. SLUC and NPTII transgenes from two independent transgene loci. Transcriptional repression of both of these transgene loci by HSI2 PHD-like domain is associated with repressive histone methylation marks H3K27me3 but not siRNA and DNA methylation associated H3K9me2 marks. In addition to the transgenes, HSI2 is also required for the repression of a subset of seed maturation genes in seedlings by promoting H3K27me3 marks in a PHD-like domain dependent and independent manner.HSI2 contains a putative PHD domain, which could act as a “reader” of histone methylation marks. In this work, we show that HSI2 PHD-like domain regulates both Arabidopsis thaliana Columbia-0  wild-type was obtained from Arabidopsis Biological Resources Center. WTLUC and hsi2-4LUC, which contain both KanR and KanS transgene loci in Col-0 background were described before [KanR, KanS, hsi2-4-KanR and hsi2-4-KanS were obtained by crossing the WTLUC and hsi2-4LUC into the Col-0 wild-type. The hsi2-2LUC line harbors GSTF8::LUC transgenes in the HSI2 T-DNA knock-out allele hsi2-2 (SALK_088606) background. For all the experiments described here, plants were grown under continuous illumination at 24°C on 0.3% Phytagel plates containing 0.5X Murashige and Skoog (MS) salt, 0.5 g/L MES (2-(N-morpholino) ethanesulfonic acid, 1X Gamborg vitamin mix and 1 % sucrose (pH adjusted to 5.7). 5-Aza-2′-deoxycytidine  and Trichostatin A  stocks were prepared using dimethyl sulfoxide and methanol respectively, and added directly to the MS media plates. Hypocotyl and root length measurements were made using Image J software (http://rsbweb.nih.gov/ij/). Digital photos of seedlings grown on 5-azadC plates were taken along with a ruler of known length. Hypocotyl length was measured from the tip of the apical meristem to the junction between hypocotyl and root, while root length was measured from the hypocotyl/root junction to the tip of the primary root.d before . The othKanR and KanS loci, WTLUC and hsi2-4LUC mutant were crossed into Col-0 wild-type plants. Successful crosses were identified based on luciferase imaging in the F1 generation and plants were allowed to self-pollinate. Progeny lines homozygous for the KanS locus were identified based on kanamycin sensitivity whereas plants homozygous for the KanR locus were identified by PCR genotyping using T-DNA and genomic primers. To genotype hsi2-4 mutation, a previously described CAPS marker [Luminescence imaging was performed using Andor iKON-M DU934N-BV CCD camera (Andor Technology). After spraying with 1 mM D-luciferin potassium salt (Gold Biotechnology) containing 0.01% Triton X-100 solution, seedlings were kept in the dark for 5 minutes and imaging was performed with a 5 minute exposure. Andor SOLIS (I) imaging software (Andor Technology) was used for the acquisition of luminescence images and processing. To separate S marker  was usedTotal RNA extraction and real-time reverse transcription quantitative-PCR (RT-qPCR) analysis was performed as described in Veerappan et al. . PrimersLUC transgenes in WTLUC, KanR and KanS lines, real-time quantitative PCR (qPCR) was performed as described before [http://cels.uri.edu/gsc/cndna.html), applying the formula: number of copies = (amount * 6.022×1023)/(length * 1×109 * 650). The calibration curves for LUC were created using the plasmid DNA pBI121-GSTF8::LUC as a template. At5g47480, a single copy gene from Arabidopsis, was used as an internal control for normalization of the data. PCR primers used in the estimation of transgene copy numbers can be found in Additional file To determine the copy numbers of d before ,33. All Chromatin immunoprecipitation (ChIP) and quantitative PCR (qPCR) analyses were performed as described by Veerappan et al. . Percent


Recently, unique areas of transcriptional regulation termed super-enhancers have been identified and implicated in human disease. Defined by their magnitude of size, transcription factor density, and binding of transcriptional machinery, super-enhancers have been associated with genes driving cell differentiation. While their functions are not completely understood, it is clear that these regions driving high-level transcription are susceptible to perturbation, and trait-associated single nucleotide polymorphisms (SNPs) occur within super-enhancers of disease-relevant cell types. Here we review evidence for super-enhancer involvement in cancers, complex diseases, and developmental disorders and discuss interactions between super-enhancers and cofactors/chromatin regulators. Classic enhancers were initially defined in SV40 by their ability to modify gene expression in an orientation and position-independent manner by recruiting coactivators and RNA Polymerase II to target genes . Ov. Ov10]. Complex interactions between components of this large complex and constituent enhancers all contribute to their transcriptional regulation of cell identity and function via developmental signaling pathways. Many super-enhancer associated genes undergo loss of expression upon loss of ESC differentiation ,16,17,27MED12, the major component of Mediator Complex, cause several identifiable syndromes in humans, including FG, Lujan, and Ohdo Syndromes . Intereyndromes . These dDefective cohesin or NIBPL results in autosomal dominant Cornelia de Lange syndrome (CdLS), a complex developmental disorder characterized by a constellation of abnormalities including growth and developmental delays, upper limb malformations, microcephaly, craniofacial dysmorphisms, and other structural birth defects. Loss of one copy of various components of the Mediator-cohesin complex can also lead to transcriptional dysregulation and disease, suggesting complex dosage sensitive effects.AFF4, a component of the super-elongation complex, were recently identified in children with phenotypes overlapping with CdLS [In addition to cohesin and NIPBL, mutations in ith CdLS . ChiP-seith CdLS . AFF4, Rith CdLS . RNAPII ith CdLS . By exteCHD7, exhibits widely variable clinical expressivity and penetrance of craniofacial anomalies, vision and hearing losses, cardiac malformations, and neuronal developmental defects in both peripheral and central nervous systems [EP300 and the closely related CREBBP cause Rubenstein-Taybi syndrome, characterized by microcephaly, broad thumbs and toes, and characteristic craniofacial anomalies [SOX11, mutated in Coffin-Siris syndrome, was also recently identified as a downstream genetic target of CHD7 [ARID1A, ARID1B, SMARCA4 and SMARCB1 also cause Coffin-Siris, suggesting broad genetic heterogeneity [Mutations in many different transcription factors and chromatin remodeling proteins lead to developmental disorders in humans. CHARGE Syndrome, a multiple congenital anomaly condition caused by heterozygous mutations in the chromatin remodeler  systems . CHD7 bi systems ,32. Intenomalies . The tra of CHD7 . Coffin-ogeneity . It is tMuch work thus far has focused on undifferentiated ESCs, yet the vast array of differentiated cell types and their associated diseases may also be targets of super-enhancers. To date, super-enhancers have been implicated in complex hematological, endocrine, and autoimmune disorders. Not surprisingly, evidence for association between super-enhancers and common complex diseases came from analysis of the distribution of 5303 trait-associated SNPs identified by GWAS . Most  disease associated SNPs in brain tissue, 13 of 67 (19%) disease associated SNPs in T-helper-cells, and 22 of 67 (33%) disease associated SNPs in B-cells . Similaret al. explored the epigenomic dynamics of activated endothelial cells [CCL2) that contribute to the inflammatory response, whereas genes associated with resting endothelial cell states were found to have reduced BRD4 binding [Although the precise mechanisms that control the inflammatory response are poorly understood, many complex diseases exhibit features of underlying chronic inflammation. To further dissect how pro-inflammatory signaling contributes to pathogenesis, Brown al cells . Upon st binding . These oet al. showed that in mouse models for Huntington Disease (HD), RNAPII and H3K27ac are preferentially decreased in the brain striatum, a region known to exhibit extensive transcriptional misregulation in humans with HD [While previous studies have largely investigated associations between SNPs and enhancers or transitions between active super-enhancers, loss of super-enhancers in disease-associated tissues has also been observed. Achour  with HD . Super-e with HD . The pro with HD . If trueet al. began investigating why cancer therapies targeting ubiquitous chromatin regulators result in specific genetic effects on tumor cells [c-MYC transcription was attained by inhibition of BRD4, a ubiquitous bromodomain chromatin remodeling protein that recruits the positive transcription elongation factor P-TEFb, thereby regulating transcriptional elongation. Because the multiple myeloma cell line used to study the effects of BRD4 inhibition contains a rearrangement resulting in MYC expression driven by an IgH enhancer, Loven et al. hypothesized that features of the IgH enhancer are responsible for the target selectivity of BRD4 inhibition. BRD4 displays similar binding patterns to Mediator, localizing to regulatory regions of actively transcribed genes, especially super-enhancers. Unsurprisingly, these regions are associated with genes having a role in multiple myeloma biology, including c-MYC. In addition, BRD4 inhibition results in preferential loss of BRD4 at super-enhancers, resulting in a corresponding decrease in MED1 binding and transcription. Together, these observations support the hypothesis that MYC inhibition is attained by BRD4 depletion at its enhancers [Tumorigenesis was first associated with super-enhancers shortly after their characterization in 2013, when Loven nhancers .c-MYC yet are absent in healthy cells [MYC expression including multiple myeloma, Burkitt’s lymphoma, acute myeloid leukemia, and acute lymphoblastic leukemia. In particular, for multiple myeloma it is estimated that nearly half of cases carry a MYC rearrangement and that most of these reposition MYC near a super-enhancer [Interestingly, comparison of multiple myeloma tumor cells and related healthy cells suggests that cancer cells “acquire” specific super-enhancers near oncogenes, since they tend to occur in a gene desert near hy cells . This achy cells . These nenhancer .Many studies have been conducted to investigate cancer associations with super-enhancers and can be generally divided largely into two categories: those using super-enhancers to identify related genes which may play a role in cancer biology, and those identifying super-enhancers that drive known oncogenes . Super-ePAX5, an essential player in B-cell differentiation. CRISPR/Cas9-based genome editing of this region demonstrated that inactivation of the enhancer could be achieved through point mutation or deletion, resulting in a 40% reduction in the expression of PAX5 [Whole-genome sequencing has also resulted in the identification of variants in putative enhancer regions, which may be super-enhancers. Recently, the mutational load of patients with CLL was expanded to include mutations in enhancer regions enriched for both lymphocyte-specific transcription factor binding sites and histone marks related to enhancer elements only in a lymphoblastoid B-cell line. This region was found to exert control over vs. 71% reduction in typical enhancers [MYC [OCA-B [BCL6 [EVI1 [in vitro models of inflammation, JQ1 treatment also suppressed rolling and adhesion properties of leukocytes, and transmigration of neutrophils. Further, JQ1 treatment in in vivo models of murine atherosclerosis attenuated early atherogenesis [Associations between super-enhancers and cancers, together with the observation that super-enhancers are sensitive to the amount of bound transcription factors and coactivators, led to the use of inhibitors to reduce super-enhancer transcriptional effects. JQ1, a BET bromodomain inhibitor, reduced BRD4 occupancy at super-enhancers (97% reduction nhancers ), reduceers [MYC ,11, OCA-C [OCA-B , BCL6 [1-B [BCL6 , and EVIL6 [EVI1 . In in vogenesis . These sogenesis . A similMYC family [In addition to BET inhibitors, other treatments have targeted cyclin-dependent kinases that regulate RNAPII initiation and elongation. One such inhibitor targets CDK7, which primarily affects transcripts with short half-lives such as anti-apoptosis and cell cycle regulators . Use of C family . TogetheWhile these methods of potential therapy have targeted transcription machinery acting at super-enhancers, another therapeutic approach has been proposed to target genes associated with super-enhancers. In particular, the enrichment of STAT at super-enhancers suggested that use of Tofacitinib, an upstream inhibitor of JAK, would have a targeted influence on super-enhancers . Indeed,


All viruses target host cell factors for successful life cycle completion. Transcriptional control of DNA viruses by host cell factors is important in the temporal and spatial regulation of virus gene expression. Many of these factors are recruited to enhance virus gene expression and thereby increase virus production, but host cell factors can also restrict virus gene expression and productivity of infection. CCCTC binding factor (CTCF) is a host cell DNA binding protein important for the regulation of genomic chromatin boundaries, transcriptional control and enhancer element usage. CTCF also functions in RNA polymerase II regulation and in doing so can influence co-transcriptional splicing events. Several DNA viruses, including Kaposi’s sarcoma-associated herpesvirus (KSHV), Epstein-Barr virus (EBV) and human papillomavirus (HPV) utilize CTCF to control virus gene expression and many studies have highlighted a role for CTCF in the persistence of these diverse oncogenic viruses. CTCF can both enhance and repress virus gene expression and in some cases CTCF increases the complexity of alternatively spliced transcripts. This review article will discuss the function of CTCF in the life cycle of DNA viruses in the context of known host cell CTCF functions. CCCTC-binding factor (CTCF) is a ubiquitously expressed DNA binding protein that is highly conserved in bilaterian metazoans . The proThe association of CTCF with the human genome is important for genomic organisation and the control of gene expression. In particular, CTCF is essential for genomic imprinting, chromatin insulation, and transcriptional activation and repression. Furthermore, association of CTCF with specific regions of DNA has been linked to DNA looping, nucleosome organization, and the control of RNA Polymerase II (RNA Pol II) progression to co-ordinate co-transcriptional gene splicing events. Adding to the complexity of CTCF-mediated chromatin organization and the regulation of gene expression, DNA CpG methylation can influence CTCF DNA binding. Up to 41% of CTCF binding sites have been shown to be sensitive to CpG methylation . InteresThese important functions of CTCF in the control of host cell gene expression have been extensively reviewed elsewhere. We will therefore focus this article on emerging evidence that CTCF is utilised by diverse DNA viruses to control viral gene expression, genome organisation, virus replication and persistence.c-myc gene promoter to repress transcription. A subsequent study showed that binding of CTCF to a core sequence located at the 5′ end of the chicken β-globin locus conferred strong enhancer blocking activity [The role of CTCF in transcriptional silencing and activation was first described in the 1990s ,12,13. Tactivity . Furtheractivity ,16. In tactivity ,17,18.The involvement of CTCF in the control of viral gene transcription has been demonstrated in several DNA viruses. In studies of Kaposi’s sarcoma-associated herpesvirus (KSHV), CTCF was shown to associate with several regions within the viral genome, the strongest of these binding regions was located at an intergenic site between the divergent ORF73 and K14 open reading frames (ORFs), which are active in the latent and lytic phases of the virus life cycle, respectively. The association of CTCF at this strong binding region occurs in a cell cycle dependent manner, specifically during mid-S phase to repress transcription of lytic genes . MutatioInterestingly, dynamic binding of CTCF at some sites within the KSHV genome has been demonstrated, and rather than a global eviction of CTCF upon lytic cycle activation, CTCF was gradually reduced at the majority of binding sites but maintained at others . In thisIn the Epstein-Barr virus (EBV) genome, CTCF binds to a site between the viral origin of replication (OriP) and the C promoter (Cp). Deletion of this CTCF binding site results in an increase in EBNA2 transcription levels, which is interesting considering EBV latency types are distinguished by their expression of EBNA2 levels. Furthermore, there was more total CTCF protein and mRNA detected in the type-I EBV cells compared to type-III. Overall, the presence of CTCF binding in EBV was shown to negatively affect transcription at Cp . AdditioThere is also evidence to support a role for CTCF in regulating adenovirus replication and late gene expression as siRNA-mediated depletion of CTCF represses late gene expression but had no effect on early gene expression . In addiet al. [CTCF is the major transcriptional insulator in mammals and, by binding to specific sites within the cellular genome, creates a physical barrier between active and repressive chromatin, forming chromatin boundaries. Genome-wide chromatin immunoprecipitation (ChIP)-Seq analysis has demonstrated a significant enrichment of CTCF binding at regions located between active and repressive chromatin domains  and deplet al.  describeet al. . Converset al. .These data elegantly highlight the ability of CTCF to function as a chromatin barrier within the context of a viral genome. Whether CTCF functions as a barrier to prevent the spread of inactive chromatin in small DNA viruses remains to be determined but it is interesting to note that loss of CpG methylation within the HPV16 genome has been demonstrated upon differentiation of host cells . While tOne of the ways in which CTCF contributes to genome wide organization of chromatin is in the formation of loops that mediate long-range interactions between distant loci. Exactly how CTCF co-ordinates loop formation is not entirely understood. Evidence suggests that CTCF is able to form homodimers and complexes with proteins known to associate with insulator sites, such as the nucleolar protein nucleophosmin, providing evidence that loop formation is mediated by CTCF-associated protein complexes . In addic-myc locus [In support of this hypothesis, CTCF has been shown to co-localize with cohesin in the KSHV episome on a region of the major latency control transcript during latent infection. Subsequent deletion of this CTCF binding site resulted in a loss of cohesin binding and a reduction in stable colony formation . Co-inciyc locus . Interesyc locus . Similaryc locus . These sTo address the role of CTCF in mediating chromatin loop formation in EBV, chromatin conformation capture (3C) was used to reveal loop formation between CTCF binding sites at the type I latency promoter Cp and type III latency promoter Qp with the OriP enhancer. As predicted, loop formation was demonstrated between OriP and Qp during type I latency and between OriP and Cp during type III latency . Subsequvia co-transcriptional RNA splicing [It has been demonstrated that CTCF can bind to the large subunit of RNA Pol II, which is needed for transcriptional initiation and elongation, and recruit RNA Pol II to target genes . A recensplicing . The potsplicing ,53. Expesplicing . It appeStudies in KSHV have also shown that CTCF binding influences alternative splicing of the major latency transcripts , althougCohesin and CTCF organize genomic DNA to create discrete nuclear regions to support efficient DNA replication . WhetherIt is clear that CTCF plays an integral role in virus genome organization and gene regulation, in both large and small DNA viruses. Studies have demonstrated that CTCF organizes viral genomes by creating boundaries between active and inactive chromatin and can regulate viral gene expression by altering RNA pol II recruitment and progression, and nucleosome positioning. The co-localization of CTCF with cohesin in EBV and KSHV has highlighted the complex regulation of chromatin structure and the CTCF mediated DNA looping required to confer different genomic outcomes. The ability of CTCF to alter nucleosome positioning and RNA Pol II function adds further evidence to its role in altering gene splicing events and chromatin arrangement, in order to facilitate gene expression across the genome. It is unlikely that CTCF mediates separate functions alone but instead uses them in concert to co-ordinate complex genomic processes. It is intriguing that CTCF recruitment to some sites within viral genomes appears to be restrictive to virus replication, while recruitment to other sites results in activation of virus gene expression. The completion of a virus life cycle is without doubt a finely tuned balancing act and it is likely that DNA viruses have evolved to utilize CTCF to maintain this balance for efficient life cycle completion; CTCF thereby behaves as a restriction factor and an activating factor depending on virus life cycle stage and the specific context of CTCF binding. The continued study of viral interactions with CTCF will allow us to further understand the functions and implications of the vast array of CTCF-mediated events.


RNA polymerase I (RNAPI)-dependent promoters controlling the ribosomal RNA (rRNA) genes have been well identified. Although the RNAPI transcription machinery recognizes the DNA conformation instead of the DNA sequence of promoters, no conformational study has been reported for these promoters. Here we present the in silico analysis of the intrinsic DNA curvature of the rRNA gene core promoters in Trypanosoma brucei, Trypanosoma cruzi, and Leishmania major. We found that, in spite of the absence of sequence conservation, these promoters hold conformational properties similar to other eukaryotic rRNA promoters. Our results also indicated that the intrinsic DNA curvature pattern is conserved within the Leishmania genus and also among strains of T. cruzi and T. brucei. Furthermore, we analyzed the impact of point mutations on the intrinsic curvature and their impact on the promoter activity. Furthermore, we found that the core promoters of protein-coding genes transcribed by RNAPI in T. brucei show the same conserved conformational characteristics. Overall, our results indicate that DNA intrinsic curvature of the rRNA gene core promoters is conserved in these ancient eukaryotes and such conserved curvature might be a requirement of RNAPI machinery for transcription of not only rRNA genes but also protein-coding genes.In trypanosomatids, the  Trypanosoma cruzi, Trypanosoma brucei, and Leishmania major, which are collectively known as Tritryps, are the etiologic agents of Chagas disease, sleeping sickness, and leishmaniasis, respectively, causing millions of human deaths in tropical and subtropical countries trans-splicing, a process that involves the addition of a small conserved RNA called the spliced leader (SL) and the polyadenylation at their 3′ ends In trypanosomatids, the protein-coding genes are arranged in large directional gene clusters (DGCs) cis-acting elements and trans-acting factors) are evolutionary conserved As in other eukaryotes, the trypanosomatid RNA polymerase I (RNAPI) drives rRNA gene transcription T. brucei RNAPI has the unusual function of directing the transcription of the gene families encoding the developmentally-regulated variant surface glycoproteins (VSGs) and genes encoding the less variable EP/GPEET procyclinss VSG can be transcribed as a polycistron mRNA together with the so-called expression site associated genes (ESAGs), while only one VSG variant is expressed at a time VSGs is silenced and VSGs at the cell surface are replaced by procyclins, another type of stage-specific glycoproteins. These procyclin proteins can be classified into two different types: one containing a domain of tandem pentapeptide repeats (GPEET) and the other characterized by a dipeptide repeat (EP) GPEET/PAG3 and EP1/PAG1-2, each harboring a single independent promoter VSG genes, which are distinctively transcribed into monocistronic mRNAs VSG expression sites, sequence data are limited due to the gene assembly difficulties caused by their telomere location Interestingly, the Leishmania and also to strains variants of T. cruzi and T. brucei. Furthermore, the analysis of the previously-reported transcriptionally-deficient single nucleotide mutants at these core promoters showed that changes in curvature produced by base substitutions is sufficient to explain the observed transcriptional failure. Finally, we examined the intrinsic DNA curvature at the core promoter of the protein-coding genes that are distinctively transcribed by RNAPI in T. brucei. We found that regardless of the nature of the encoded products (either protein or RNA), the core promoters of the genes transcribed by the trypanosomatid RNAPI are characterized by the presence of the conserved pattern of curvature, as described for other eukaryotes. Altogether, these observations support the importance of promoter curvature for rRNA transcriptional machinery and, for the first time, extend this finding to protein-coding genes.To further the current understanding of the molecular mechanisms involved in transcription initiation by RNAPI in trypanosomatids, we examined whether the core promoters of Tritryp rRNA gene share the conformational characteristics conserved in eukaryotes. To evaluate the conservation of this phenomenon, we extended the analyses to the genus T. brucei and T. cruzi rRNA promoters showed the conserved eukaryotic DNA structural feature that has been previously described for the corresponding promoters in human Figure 1A). They are characterized by a region of high curvature, peaking consensually at position −20 relative to the TSP, which is followed by a gradual decrease of curvature toward the TSP . Nevertheless, the pattern displayed by L. major is dissimilar . Similarly, core promoters of 35 randomly-selected eukaryotic sequences transcribed by RNAPII also exhibited a monotonous consensus curvature . Particularly, a peak or shoulder of intrinsic curvature is observed at position −10 of the different strains and is conspicuous in the consensus (B). As a proof of principle, we compared the curvature of an early proposed TSP of the T. cruzi Cl strain In the case of  strains A. Particonsensus B. As a pT. cruzi I) Figure 4A), with obvious loss of intrinsic curvature at the position −10. Meanwhile, the point mutation at position −3 (Construct 17), which increases the promoter activity (124%), shows an increased intrinsic curvature at this position , reinforcing the relevance of the intrinsic curvature at this position. In addition, these mutations cause a reduction of the deepness of the intrinsic curvature valley observed between positions +1 and +15 in comparison with the wild type sequence. Concordantly, two nucleotide substitutions that do not markedly modify the wild type intrinsic curvature consensus (Constructs 25 and 26) only slightly affect the promoter activity   . The remctively) , suggestT. cruzi, analysis of T. brucei genomic data  showed that rRNA core promoters have highly similar sequence . In order to examine whether this curvature profile is also conserved at the promoters of the protein-coding genes that are transcribed by RNAPI, we analyzed the intrinsic curvature of the core promoters of the genes encoding major surface proteins: the VSGs of the metacyclic and bloodstream form of the parasite, and the acidic repetitive proteins GPEET and EP1 of the procyclic form. We found that the core promoters of the genes encoding the metacyclic and bloodstream VSGs, and the EP1, share a curvature pattern that is closely similar to that of the rRNA promoters , in spite of the lack of nucleotide sequence conservation GPEET promoter is different, which lacks the valley of curvature toward the TSP as observed in the other promoters  is known to be transcribed by RNAPI. The trypanosomatid RNAPI promoters of the rRNA and of the T. brucei RNAPI dependent protein-coding genes have been defined and well characterized  and leads to the increased intrinsic curvature at position −10. On the other hand, five downregulating mutations  are also transcribed by RNAPI in T. brucei, where the promoters for rRNA and protein-coding genes have extensively diverged in sequence composition. However, our results suggest that a selection pressure acts to strictly maintain their conformation. Indeed, different variants with no consequences on the intrinsic curvature are naturally present in the T. brucei VSG core promoters. These constitute “conformationally-silent single nucleotide variants”. Meanwhile, artificially-induced single point mutations affecting the intrinsic curvature pattern of the T. cruzi rRNA gene promoter have been proved to disturb the transcriptional activity.Early studies have shown that, in spite of the absence of sequence conservation, functional RNAPI hybrid promoters could be obtained in trypanosomatids depending on nucleotide spacing constraints While intrinsic curved DNA most likely functions as a signal for TF recognition or interaction and/or facilitates the formation of the open promoter complex In summary, our results indicate that the RNAPI core promoters of kinetoplastids conserve the eukaryotic intrinsic curvature features, not only for the rRNA genes, but also for the protein-coding genes transcribed by RNAPI.http://www.icgeb.trieste.it/dna). The curvature is expressed as degrees per helical turn .Promoter sequences were aligned and the similarities were calculated by using the Emboss suite A summary of the accession numbers of the sequences used in the present study is given in T. brucei Lister strain 427 from the NCBI (GenBank accession No. AF416290.1), whose TSP was previously mapped T. brucei TREU927 and T. brucei gambiense DAL972 were obtained by blast homology search in the TritrypDB database .We obtained the rRNA promoter region for T. cruzi strains CL Brener, CL, Y, 150zd, Tulahuen, Basilieu, Nr cl3, SO3 cl4, Cuica, OPS, G3, Colombiana, and Dm28c T. cruzi promoter mapped by Figueroa-Angulo et al. was used Promoter sequences described for Leishmania promoter sequences used in this study were retrieved from the NCBI database as well  for the analysis with TSP experimentally determined previously GPEET (GenBank accession No. S60066.1) and EP1 (GenBank accession No. M38222.1) were analyzed For the analysis of the X01547http://epd.vital-it.ch/) as nonspecific controls .The previously-characterized human rRNA promoter (GenBank accession No. BG conceived and designed the study. PS obtained the data, whereas PS, MD, and BG analyzed and/or interpreted the data. All authors participated in manuscript writing, read and approved the final manuscript.The authors have declared no competing interests.