Precise nucleosome positioning is an increasingly recognized feature of promoters and enhancers, reflecting complex contributions of DNA sequence, nucleosome positioning, histone modification and transcription factor binding to enhancer activity and regulation of gene expression. Changes in nucleosome position and occupancy, histone variants and modifications, and chromatin remodeling are also critical elements of dynamic transcriptional regulation, but poorly understood at enhancers. We investigated glucocorticoid receptor-associated (GR) nucleosome dynamics at enhancers in acute lymphoblastic leukemia.For the first time, we demonstrate functionally distinct modes of nucleosome remodeling upon chromatin binding by GR, which we term central, non-central, phased, and minimal. Central and non-central remodeling reflect nucleosome eviction by GR and cofactors, respectively. Phased remodeling involves nucleosome repositioning and is associated with rapidly activated enhancers and induction of gene expression. Minimal remodeling sites initially have low levels of enhancer-associated histone modification, but the majority of these regions gain H3K4me2 or H3K27Ac to become de novo enhancers. Minimal remodeling regions are associated with gene ontologies specific to decreased B cell number and mTOR inhibition and may make unique contributions to glucocorticoid-induced leukemia cell death.Our findings form a novel framework for understanding the dynamic interplay between transcription factor binding, nucleosome remodeling, enhancer function, and gene expression in the leukemia response to glucocorticoids.The online version of this article (doi:10.1186/s13072-015-0046-0) contains supplementary material, which is available to authorized users. Glucocorticoids have been mainstays of treatment for a variety of malignant, autoimmune, and inflammatory diseases for many decades. Glucocorticoids are critical components of therapy for acute lymphoblastic leukemia (ALL), and resistance to glucocorticoid-induced cell death is associated with poor prognosis in childhood ALL , 2. DexaEnhancers are generally defined as non-promoter DNA elements that contribute to modulation of gene expression. The human genome is estimated to harbor hundreds of thousands enhancers, and the enhancers participating in transcriptional regulation differ extensively among cell types and stages of differentiation. Among the many unresolved questions surrounding enhancers are those of how nucleosome positioning and chromatin remodeling contribute to their modulation of gene expression . In mammMany groups have circumvented this difficulty by measuring chromatin “accessibility” typified by hypersensitivity to cleavage by DNaseI (DHS) or insertion of a transposon (ATAC-Seq). These techniques require far fewer sequencing reads than MNase-Seq, and chromatin accessibility is often used to approximate decreased nucleosome occupancy with relatively low resolution and sensitivity. Although DHS offers limited insights into nucleosome dynamics, it identifies active enhancers and promoters very well. Increased DHS is a widely accepted indicator of enhancer activation, and many groups have developed computational models that use changes in DHS to predict transcription factor binding –15.Active enhancers are also characterized by histone modifications that include histone 3 lysine 4 mono- and di-methylation (H3K4me1 and H3K4me2), and H3K27 acetylation , 17. He We wished to better understand nucleosome dynamics and their relationships to transcription factor binding, enhancer activity, and gene expression. We performed ChIP-Seq for GR and multiple enhancer-defining histone modifications, as well as DHS and gene expression analysis both before and after the addition of dexamethasone. Using a number of open-source computational packages, we have developed an analytic approach to ChIP-Seq that captures rapid changes in nucleosome position and occupancy associated with chromatin binding by GR. We find that nucleosome remodeling is highly variable among GR binding sites, but can be computationally resolved into four predominant modes that we have dubbed “central”, “non-central”, “phased”, and “minimal”. Moreover, each class of nucleosome remodeling represents functionally distinct contributions to enhancer activation and changes in gene expression. We propose that patterns of nucleosome remodeling offer important new insights into dynamic changes of the enhancer landscape and transcriptional outcomes following glucocorticoid treatment in ALL.We first sought to identify enhancers in the RS4; 11 acute lymphoblastic leukemia cell line, and to characterize GR binding with respect to this existing chromatin landscape. We performed ChIP-Seq of GR and three different chromatin features typically used to define enhancers . Cells were cultured in charcoal-dextran-treated serum for 2 days prior to each experiment, effectively depriving them of hormone exposure. GR ChIP-Seq was performed on fixed and sonicated chromatin, whereas histone modification ChIP-Seq was performed on native chromatin fragmented by MNase digestion. As expected, very few GR–chromatin interactions were detected in cells not treated with dexamethasone. In contrast, 1 h after treatment with 10 nM dexamethasone, 9058 GR binding events were detected, with virtually no overlap between binding sites in the unstimulated and dexamethasone-stimulated cells. As in other cell types, promoter-bound GR peaks in RS4; 11 are a small minority (5.3 %). Among GR binding sites, we found that 46 % occur at enhancers having all three marks. We find that 45 % of GR binding events in RS4; 11 cells occur at sites not having pre-existing DHS. Additionally, 13 % of GR binding occurs at sites bearing none of the canonical enhancer marks, where GR may function as a true pioneer factor . On average, GR binding sites unique to A549 do not show nucleosome depletion in RS4; 11  or demethylation , as well as nucleosome remodeling. In an effort to mitigate the changes in ChIP-Seq signal resulting from histone modification, we calculated the sum of the deltaH3K4me1, deltaH3K4me2, and deltaH3K4me3 signals, henceforth referred to as the deltaH3K4methyls signal. The result of this summation is exemplified in Fig. 8 reads/condition). As seen in Fig. k-means clustering to partition GR binding sites according to the magnitude and position of change in ChIP-Seq signal. Results of k-means clustering (k = 27) of the deltaH3K4methyls signal in a 3-kb window centered on the GR binding summit are illustrated in Fig. We next wished to assess the relationship between the deltaH3K4methyls ChIP-Seq signal and changes in nucleosome occupancy. We performed deep sequencing of the MNase-digested input chromatin that was used for our ChIP-Seq  distinguished by subtle shifts in the location of their central signal loss. Strikingly, clustering also reveals previously unrecognized patterns of nucleosome remodeling occurring at and near GR binding sites. When these clusters are applied to the deltaMNase signal Fig. , we see As seen in Fig. For each GR peak, we examined a 3-kb window centered on its summit. By measuring the area under the curve, we were able to capture changes in ChIP-Seq signal reflecting both the height and width of the signal peak. We first evaluated the GR binding and enhancer properties of sites belonging to each mode of nucleosome remodeling. The degree of GR binding differs dramatically between modes Fig. a. Sites r2 = 0.18, data not shown).Prior to GR binding, the enhancers belonging to each mode of remodeling also have very different properties. In unstimulated cells, regions that will later demonstrate non-central remodeling have the highest H3K27Ac and most frequent DHS. In contrast, sites of minimal remodeling have very low H3K27Ac and infrequent DHS Fig. c, d. Givk-means clusters described above. Using histone modification signal to approximate nucleosome position, as in Kundaje et al. [Heatmaps in Fig. e et al. , we see Nucleosome remodeling that overlaps transcription factor binding, which we call “central” remodeling, has been previously described. For example, in their study of AR-modulated nucleosome remodeling, He et al. describe nucleosome remodeling using changes in H3K4me2 signal. In their analysis, three well-positioned nucleosomes having similar occupancy were present prior to androgen stimulation, one directly overlying, and two immediately flanking the AR binding site. AR binding was shown to result in destabilization of the central nucleosome and stabilization of flanking nucleosomes without changes in flanking nucleosome position , 18.Our clustering analysis reveals that considerable diversity of nucleosome position, occupancy, and remodeling underlies the central mode of remodeling. GR binding diminishes occupancy of one to three underlying nucleosomes. Flanking nucleosomes become more precisely positioned and have stable or decreased occupancy , which is essential for B cell differentiation, and one of the most highly expressed members of the ETS family in pre-B cells . The oveApproximately 6 % of GR binding sites demonstrate a pattern of remodeling that can also be described as “phased”, where the deltaK4methyls signal shows distinct, alternating peaks and valleys. Phased remodeling sites are not the most highly “activated” enhancers Fig. b, c, havRegions lacking enhancer marks comprise the majority of sites having minimal remodeling upon GR binding 57 %). The remaining regions of minimal remodeling also have low levels of enhancer activation, bearing less H3K27Ac and DHS relative to other modes of remodeling . This distance constraint associates nearly all peaks with a gene (95 %), and many genes have multiple associated peaks (40 %). For genes associated with multiple proximal GR peaks, we considered only those genes for which all peaks have the same mode of remodeling. As seen in Fig. Phased remodeling is associated with the most significant induction of gene expression, and very few genes (<9 %) having proximal phased sites show decreased gene expression. Central, non-central, and minimal remodeling are also significantly associated with induction of gene expression relative to genes without proximal GR peaks. Remarkably, genes associated with minimally remodeled sites are induced as strongly as those having central or non-central remodeling. In contrast, only non-central remodeling is associated with genes repressed more significantly than genes having no proximal GR binding. In examining gene expression at the 24-h time point, we again find that each mode of remodeling remains significantly associated with induced gene expression, but none are associated with repression.Finally, we used the Genomic Regions Enrichment of Annotations Tool  to applMany groups have demonstrated well-positioned nucleosomes in relation to transcriptional start sites and RNA polymerase II binding in eukaryotic cells , 17, 33.Much less is understood about the dynamics of nucleosome organization and their relationships to transcriptional regulation. He et al. investigated nucleosome dynamics relative to chromatin binding by androgen receptor and used H3K4me2 ChIP-Seq to describe nucleosome depletion overlapping transcription factor binding that is similar to the “central” mode of remodeling we define here . This chWe find that sites of central remodeling are characterized by (1) predominant eviction, rather than repositioning, of nucleosomes; (2) increased positioning of flanking nucleosomes, and (3) frequent shifts in position of flanking nucleosomes. Non-central remodeling sites are defined by GR-proximal nucleosome eviction not overlapping the GR peak. We propose that this nucleosome eviction reflects binding of transcriptional cofactors, exemplified by the ETS-family member PU.1, which can serve as both pioneer factor and tethering factor for GR. Interestingly, sites of non-central remodeling are also enriched for RUNX1 binding motifs. RUNX1 translocations producing chimeric fusion proteins with the ETS-family transcription factor ETV6 are found in 20–25 % of childhood ALL and are associated with a favorable prognosis, whereas RUNX1 amplifications are seen in ~2 % of childhood ALL and are associated with high-risk disease , 35. Morr2 = 0.14) is poor relative to its converse (r2 = 0.41) [Many sites of non-central remodeling show decreased H3K27Ac, and a subset shows modest correlation with repression of gene expression. Given the many prior studies showing association of increased H3K27Ac, enhancer activation, and induction of gene expression , it is t = 0.41) . We thinA minority of GR binding events is accompanied by a phased pattern of nucleosome remodeling characterized by predominant repositioning, rather than eviction, of central nucleosomes. This repositioning results in increased occupancy and positioning of one or more nucleosomes flanking the GR binding site. Phased remodeling is not only associated with enhancer activation via increased H3K27Ac, but also with rapid induction of proximal gene expression. Given the previously reported dependence of dexamethasone-induced gene expression on the SWI/SNF chromatin remodeling complex , 20, it Finally, we define a subset of GR binding sites characterized by minimal nucleosome remodeling that is highly enriched for non-enhancer and weak enhancer regions. Many of these regions acquire H3K4methylation, H3K27Ac, and/or DHS only upon dexamethasone stimulation and GR binding, suggesting that GR can act as a true pioneer factor. We speculate that sites of pioneering represent de novo, “GR-specific” enhancers and speculate that they are among the most important for the leukemia cell death effect of glucocorticoid treatment. Contrary to the prevailing logic, our hypothesis suggests that the critical components of glucocorticoid-mediated transcriptional modulation are among the weakest GR binding sites, weakest enhancers, and least dynamic regions of early nucleosome remodeling. However, it is consistent with observations that prolonged glucocorticoid exposure is required to induce apoptosis in many ALL cell lines  and thatIn summary, we have developed a novel method for using histone modification-directed ChIP-Seq to reveal dynamic nucleosome occupancy and position and describe, for the first time, functionally distinct modes of nucleosome remodeling upon chromatin binding by GR. Our approach can be readily adapted to study nucleosome and enhancer dynamics mediated by other transcription factors, and we look forward to a burgeoning understanding of how nucleosome remodeling affects gene expression.6 cells/mL of medium . Two days prior to each experiment, cells were transferred to RPMI medium containing 10 % charcoal-dextran treated serum , again supplemented with 1 % penicillin/streptomycin. For dexamethasone treatment, cells were adjusted to a density of 2 × 106 cells/mL in CDT-containing medium and treated by addition of 1 mM dexamethasone (Sigma) dissolved in 100 % ethanol for a final dexamethasone concentration of 10 nM and a final ethanol content of 0.001 %. Dexamethasone treatment duration was 1 h for ChIP-Seq and DNase-Seq experiments.The RS4;11 cell line was obtained from ATCC (CRL-1873). Cultures were maintained at a density of 1.5–3 × 10For gene expression experiments, cells were treated with dexamethasone as above for 0, 8, or 24 h. Five replicates were performed for each time point. RNA was isolated using Qiagen RNeasy Plus Mini columns. Transcripts were quantitated via hybridization to Agilent hgu133a2 microarrays. Quality control assessment of microarray data was performed using the arrayQualityMetrics  package.For ChIP-Seq experiments, antibodies against the following targets were as follows: GR , PU.1 , H3K4me1 , H3K4me2 , H3K4me3 , H3K27Ac .7 cells) were resuspended in 5 mL of MNase digestion buffer . For digestion, 10 μL of MNase (New England Biolabs) was added and nuclei incubated at 37 °C for 2′45′′. Digestion was then stopped by addition of 100 μL of 0.5 M EDTA. Nuclei were pelleted and DNA-containing supernatant was removed. The remaining nuclear pellet was resuspended in 500 μL of 1 mM Tris–HCl, 0.2 mM EDTA, pH 7.6 and incubated at 4 °C for 30 min to extract larger DNA fragments. Nuclei were again pelleted and this supernatant was combined with the previous one. 100 μL of supernatant was removed from each sample and saved as input DNA for later analysis. The remaining supernatant was diluted eightfold using N-ChIP dilution buffer . One-fourth of the diluted supernatant was used for each IP by addition of 20 μL antibody-coated protein G Dynabeads (Invitrogen) followed by incubation overnight at 4 °C. Chromatin-containing beads were isolated by magnetic separation and serially washed with 1 mL of cold (1) low-salt wash buffer , (2) high-salt wash buffer , (3) LiCl wash buffer , and (4) Tris–EDTA buffer. Beads were resuspended in 100 μL of elution buffer . Input and ChIP samples were then subjected to RNase digestion by addition of 4 μL of 5 M NaCl and 1 μL DNase-free RNase A  with incubation at 56 °C for 30 min. Protein digestion was then accomplished by addition of 1 μL proteinase K (NEB) and further incubation at 56 °C for 1 h. DNA was isolated using Qiagen MinElute PCR Purification Kit per manufacturer’s directions.Isolation of nuclei was performed by sucrose gradient centrifugation as previously described . Nuclei 6 cells/mL. Fixation was accomplished by addition of EGS (ethylene glycol-bis(succinic acid N-hydroxysuccinimide ester), 1.5 mM final concentration, Sigma) with incubation at room temperature for 30 min, followed by addition of formaldehyde , and incubation at room temperature for 10 min. Quenching and isolation of nuclei were performed as previously described [6 cells/mL. 25 × 106 cells were placed in each sonication tube  and sonicated using a Covaris E220 Focused Ultrasonicator . 10 μL of sonicated chromatin was removed from each sample and saved as input DNA for later analysis. Sonicated samples were then diluted fivefold with RIPA buffer. Immunoprecipitation was done by addition of 100 μL antibody-coated protein G Dynabeads (Invitrogen) followed by incubation overnight at 4 °C. Chromatin-containing beads were isolated by magnetic separation and serially washed three times with 1 mL of cold high-salt LiCl buffer  followed by 1 mL of cold TE. Elution, RNA digestion, and protein digestion were performed as described above. Reversal of cross-linking was done by incubation at 65 °C overnight, followed by DNA isolation as described above.For ChIP of transcription factor targets , cells were resuspended in fixing buffer  at a concentration of 5 × 10escribed . Nuclei 2, 1 mM CaCl2, pH 7.9) at a concentration of 5 × 106 cells/mL. DNase (Roche) digestion was performed using five different enzyme concentrations  for each sample by incubation at 37 °C for 5 min. An additional cell sample was incubated without addition of exogenous DNase. Digestion was stopped by addition of an equal volume of stop-lysis buffer . For sequencing purposes, 5 μg of genomic DNA isolated from the control sample was digested in a volume of 50 μL using 4 U/mL of DNase. This control digestion was stopped by adding 5 μL of 25 mM EDTA. DNA was isolated by phenol–chloroform extraction and ethanol precipitation.Isolation of nuclei for DNase hypersensitivity assays was performed by sucrose gradient centrifugation as described above with the addition of 0.15 mM spermine and 0.05 % spermidine to buffers. The nuclear pellet was resuspended in DNase digestion buffer  was verified by visualization on a 2 % agarose E-Gel (Life Technologies). DNA was size-selected using a Pippin Prep agarose gel system  with an average fragment size of 150 bp for MNase-digested samples, 200 bp for sonicated samples, and 75 bp for DNase-digested samples. DNA yield was quantified using Quant-iT PicoGreen reagent (Life Technologies) and NanoDrop 3300 fluorospectrometer (Thermo Scientific). 1–2 ng of size-selected DNA was used for each library. ChIP DNA was end-repaired and adenylated as previously described . Adenylap value threshold of 1 × 10−5 [Sequences were aligned to the hg19 genome assembly with Bowtie 0.12.8 allowing one sequence mismatch and requiring unique alignments . Peak-ca1 × 10−5 . Genome 1 × 10−5 . Non-nor1 × 10−5 . These n1 × 10−5 . Area-un1 × 10−5 . Gene on1 × 10−5 .K-means clustering was performed using the computeMatrix and heatmapper functions of deepTools [k value by plotting the total within-groups sum of squares against the number of centroids, using a range of 5–80 centroids. Based upon this curve, we estimated that 25–30 centroids would provide a good fit to the data. As described by Kundaje et al. [eepTools . We firse et al. , we founIn generating figures, we used the multiIntersectBed tool of the BEDTools2 package  and euleAll microarray and sequencing data have been deposited in GEO (and SRA) with reference series GSE71617, comprised of subseries GSE71615 (microarray) and GSE71616 (ChIP-Seq and DHS).


Beyond its role in genomic organization and compaction, the nucleosome is believed to participate in the regulation of gene transcription. Here, we report a computational method to evaluate the nucleosome sensitivity for a transcription factor over a given stretch of the genome. Sensitive factors are predicted to be those with binding sites preferentially contained within nucleosome boundaries and lacking 10 bp periodicity. Based on these criteria, the Acute Myeloid Leukemia-1a (AML-1a) transcription factor, a regulator of immune gene expression, was identified as potentially sensitive to nucleosomal regulation within the mouse Ly49 gene family. This result was confirmed in RMA, a cell line with natural expression of Ly49, using MNase-Seq to generate a nucleosome map of chromosome 6, where the Ly49 gene family is located. Analysis of this map revealed a specific depletion of nucleosomes at AML-1a binding sites in the expressed Ly49A when compared to the other, silent Ly49 genes. Our data suggest that nucleosome-based regulation contributes to the expression of Ly49 genes, and we propose that this method of predicting nucleosome sensitivity could aid in dissecting the regulatory role of nucleosomes in general. The nucleosome—a large protein complex with DNA wound around it—is the fundamental unit of genomic organization in the eukaryotic cell. More than just a DNA organizer, however, nucleosomes may control gene expression by interfering with the cell’s ability to access the wound-up DNA, as shown by recent research. In this report, we demonstrate a computational method for predicting which elements of the genome are sensitive to regulation by nucleosomes. As a proof-of-concept, we identify AML-1a binding sites—important sequences in DNA regulation—as being specifically nucleosome sensitive. We then show that AML-1a sites are specifically depleted of nucleosomes when a gene is expressed, indicating the ability for nucleosomes to suppress the expression of that gene. This finding confirms that nucleosomes are likely involved in genome regulation, and provides a method for predicting which areas of the genome are probably affected most by nucleosomes. This paper also highlights the usefulness of the Ly49 gene family in testing computer-derived genomic predictions, and is of interest to anyone studying how gene expression is regulated from cell to cell. The nucleosome, comprising an octameric protein core surrounded by ~147 bp of DNA, is the fundamental unit of genomic organization in the eukaryotic cell . This orin silico mapping of the ‘default’ nucleosome landscape of the genome [The role of histone modifications in regulating gene expression has received considerable attention since the discovery of the epigenome, and considerably more attention is still required to understand this complex regulatory network. Alongside these histone modifications, however, nucleosomes are also believed to regulate gene expression simply by their steric effects on the accessibility of a given sequence of DNA . Originae genome ,17—that C. elegans [In more specific terms, nucleosomes are understood to be positioned either statistically or specifically . Statist elegans . Specifi elegans –27. Many elegans , DNA def elegans , a therm elegans , or the  elegans , were usOur goal has been to study how changes to this ‘default’ sequence-determined nucleosome landscape correlate with gene expression. To that end, we have investigated the effects of nucleosome positioning on the expression of a family of immune genes, the Ly49 receptors. Ly49 receptors—and their human analogues, the killer-cell immunoglobulin-like receptors (KIR)—are expressed on natural killer (NK) and other immune cells. These receptors interact with the class-I major histocompatibility complex (MHC-I). This sensitivity to MHC-I expression levels allows NK cells to distinguish healthy cells from cancer or virus-infected cells, and is vital to the innate immunosurveillance performed by NK cells ,31.Aside from its importance in immunology, the Ly49 family has several traits that make it an ideal model for our investigation of transcriptional regulation by nucleosomes. The Ly49 genes all reside in a 500,000 bp region of chromosome 6, and have very similar transcription factor requirements. However, expression of an individual Ly49 gene is stochastic, such that each NK cell acquires a unique repertoire of Ly49 receptors during development and then maintains this repertoire throughout its life –34. ThisIn mice, this stochastic expression is achieved in part by the coordinated activity of two or three promoters for each gene . ImmaturWhile the above model nicely describes the observed Ly49 expression patterns on NK cells, a recent report has shown that Pro1 affinity does not always correlate with that Ly49’s expression level . This fiin vivo.In this report, we identify AML-1a as a transcription factor with binding sites displaying both this preferential nucleosome coverage and lack of 10 bp periodicity. We then confirm, in a cell line that naturally expresses Ly49, that AML-1a sites are preferentially depleted of nucleosomes throughout the promoter/enhancer regions of expressed Ly49 genes when compared to the unexpressed genes within the same population, implicating nucleosome positioning as a possible mechanism to affect Ly49 expression Based on sequences generated previously in our lab, we predicted the basal nucleosome affinity of the C57BL/6 mouse Ly49 gene family. Using a hidden Markov model of nucleosome occupancy, we determined the probable nucleosome map for these regions . These pHaving generated this sequence-based map of the default genomic nucleosome affinity, we next asked whether nucleosome positions, and their interaction with transcription factor binding sites, might contribute to Ly49 gene transcriptional regulation. For each of the Ly49 genes in the C57BL/6 genome with promoter annotations, we determined whether a pattern emerged when examining whether the promoter was predominantly nucleosome-bound or nucleosome-free . We alsoWe next sought to determine whether certain TF binding sites within the Ly49 gene cluster are particularly sensitive to nucleosome coverage. First, since Ly49 receptors are thus far known only to be expressed in immune cells, and predominantly in natural killer lymphocytes, we selected 17 transcription factors with well-known functions in regulating lymphocyte gene expression . FactorsAbove, we identified five transcription factor binding sites which are predicted to be covered by nucleosomes in the Ly49 gene family; these transcription factors are the ones we hypothesize to be most sensitive to nucleosome interference. However, as mentioned previously, factors which display a strong 10 bp periodicity are able to exist in-phase with the nucleosome—and therefore be accessible even when covered—while those specifically lacking 10 bp periodicity will be disrupted by the turning of the DNA in the nucleosome . To measTo determine whether AML-1a’s pattern of nucleosome co-occupancy and a lack of 10 bp periodicity is unique to the Ly49 family, we extended these two analyses to the entirety of mouse chromosome 6  Figs. ToThe Ly49 gene family presents a powerful and convenient tool to test whether these patterns can be detected in an experimental setting. It is a large gene family, whose members all exist in the same region of chromosome 6, and which have closely related promoter sequences and similar transcriptional regulation in terms of transcription factor binding sites. Despite these similarities, individual Ly49 genes can display remarkably different expression patterns. Indeed, RMA, a mouse NK-T cell line with a C57BL/6-derived Ly49 gene cluster, expresses the inhibitory Ly49A receptor, but no other Ly49 for which there is a detecting antibody . We usedNext, we asked whether specific TF binding sites, for each promoter individually, diverge significantly in nucleosome occupancy, compared to the rest of that promoter. Taking the overall predictive convergence for each Ly49 promoter  and upstream of exon 2 (corresponding to Pro3). As these are much smaller genetic elements with only a few nucleosomes and AML-1a binding sites, statistical analysis similar to the one performed above was not possible. However, for each of Pro2 and Pro3, we identified each AML-1a binding site and whether it was preferentially covered or not, again determining the degree of convergence at these sites . SurprisIn silico, we have shown that, within the Ly49 gene cluster, AML-1a is predicted to be exquisitely sensitive to steric hindrance by nucleosome occupancy. The AML-1a binding sites tend to be located within a nucleosome boundary, meaning that AML-1a cannot simply avoid nucleosomes. Moreover, unlike other covered TF binding sites, AML-1a binding sites lack 10 bp periodicity, meaning it must exist ‘out-of-phase’ with the nucleosomes and so cannot adopt a conformation where many AML-1a sites are exposed on the surface of the nucleosome. These predictions were confirmed in a cell line, where we have shown that expressed Ly49 genes show preferential nucleosome depletion specifically at AML-1a binding sites compared to the inactive Ly49 genes. Furthermore, this depletion at AML-1a sites is not likely to be the result of depletion at some confounding TF binding site, since knowledge of the nucleosome depletion of AML-1a was as good or better at predicting the depletion of all other highly depleted TF binding sites than those sites were at predicting AML-1a. Taken together, these results suggest that Ly49 expression is partially regulated by nucleosome occupancy at AML-1a binding sites.In this report, we have shown that nucleosome positioning may be involved in regulating Ly49 expression, not just by condensing the gene complex, but by specifically interfering with AML-1a transcription factor binding sites. This conclusion is based on two complementary studies. cis-elements and transcription factors involved in Ly49 regulation, there has been little attention to date given to the role of nucleosome positioning.We chose the Ly49 gene family for this analysis for both immunological and bioinformational reasons. Immunologically, Ly49 gene expression on NK cells is an incredibly complex phenomenon, with its mostly stochastic gene expression giving rise to myriad functionally distinct subpopulations of NK cells. This heterogeneity allows a population of NK cells to have members able to respond to virtually any alteration to homeostasis, giving rise to phenomena like missing-self rejection of foreign or diseased cells . As thisFrom a bioinformatics viewpoint, the Ly49 family presents an attractive subject of study thanks to its genetic isolation, homology, and independent expression. This provides a model system where comparisons can be made between related expressed and silent genes, using one group to control for the other, while any large-scale genetic factors, such as the action of locus control regions or chromosome looping, are likely to impact the entire family equally, regardless of expression state. Additionally, we have taken advantage of this fact to compare expressed and silent Ly49 genes within the same dataset, effectively mitigating the impact of technical errors on our results. We suggest that other predictive informatics endeavours would benefit from performing their analyses on the Ly49  gene family.ex vivo, which among other things would allow for the study of the differences between inhibitory and activating Ly49 receptors. Unfortunately, such a complex study goes beyond the scope of the present report.Unfortunately, this complex expression of Ly49 does also introduce hindrances into the present study. Not only are the Ly49 genes expressed randomly in a pool of NK cells, but most of them—the inhibitory receptors—display a mono-allelic expression pattern ,43. TherThat our analysis identifies and confirms AML-1a binding sites—specifically within the Pro1/enhancer region—as the most significant in terms of nucleosomal regulation is intriguing, thanks to AML-1’s known role in Ly49 transcriptional control. One of the features of the Pro1 region in many Ly49 genes is a central AML-1a binding site, identified as being absolutely required for Pro1 to function in either direction . Thus, dFinally, it will be of interest to study the nucleosome sensitivity of AML-1a binding sites at other, non-Ly49 genes, to determine whether AML-1a is ubiquitously sensitive to nucleosome occupancy, or whether this sensitivity is a selected trait for Ly49 regulation only. Our whole-chromosome analysis of nucleosome-transcription factor interactions revealed the surprising result that, on this scale, many other transcription factors display the nucleosome coverage and lack of 10 bp periodicity that identified AML-1a as a sensitive factor in the Ly49 cluster. This suggests that, on a genome-wide scale, many different transcription factors have the potential to be nucleosome-sensitive. However, within a given gene or gene family, only a small number of factors—such as AML-1a in Ly49—retain this sensitivity, and may act as nucleosome ‘linchpin’ factors, while the other factors lose their sensitivity either by avoiding nucleosomes or by getting in-phase with them. A genome-wide characterization and identification of these linchpin factors and their associated genes could provide a novel, informative method of functionally grouping genes.It is our hope that this analysis of TF distribution and periodicity will assist in achieving a greater understanding of how chromatin compaction and nucleosome positioning shapes the gene expression landscape.The genomic DNA sequence of the Ly49 gene cluster from C57BL/6 mice was previously available ; sequencThe same four genomic DNA sequences of the Ly49 gene clusters of the four mouse strains were used to predict transcription factor binding sites. The Position Weight Matrix (PWM) of the 18 transcription factors ) were retrieved from the TRANSFAC or JASPAR public databases . DetectiThe promoter architecture was presented around the Pro 1 region including Pro1 and exon -1a. The landscape of the nucleosome binding sites and the TF binding sites in the promoters were examined to find relationships. Nucleosome predictions were aligned to exon -1a for each Ly49 gene in the C57BL/6 mouse. For genes without an exon -1a annotation, the average distance between the Pro1 and exon -1a for each annotated gene was used to approximate the location of exon -1a. If a transcription factor binding site was overlapped with a nucleosome position, then the transcription factor binding site was marked as closed. Otherwise, it was marked as open.The distribution of transcription factor binding sites around the proximal nucleosome was explored for each transcription factor. First, the output files of the transcription factor binding sites and the Viterbi prediction of the nucleosome positions were converted to BED format. Then for each transcription factor binding site, the proximal nucleosome was identified by the BEDtools suite  using thFor all transcription factor binding sites located within the predicted nucleosome-bound regions, we examined the periodicity of the distances between the transcription factor binding sites and the proximal nucleosome by Fourier transform. For each nucleosome-bound region of the Ly49 cluster, the distance between each transcription factor and its proximal nucleosome center was selected and pooled. The spectral density of the pooled distance counts from the 17 transcription factors was generated as a baseline diagram. Then, the spectral density of the distance counts from leave-one-out samples, which are the counts from 16 transcription factors by excluding one transcription factor at a time, were computed. The periodicities from the leave-one-out samples were compared with the baseline periodicities. Deviation from the baseline indicated that the left-out factor contributed to the periodicity of the baseline—with the factor gone, a reduction in periodicity at the 10 bp peak indicates that the left-out factor contributed to the overall 10 bp periodicity.The predicted binding positions were compared with the experimental positions by MNase-seq results obtained on the mouse NK-T cell line, RMA, as previously described , with thA chi-square analysis was performed to detect specific transcription factors enriched for nucleosome deviancy compared to the rest of the gene. Based on each gene’s overall true-positive rate, expected true-positive counts were generated for each Ly49 region of interest. These expected values were compared to the observed using the chi-square test. To highlight individual TF contributions to the region of interest’s overall chi-square statistic, individual chi-square statistics were presented in a heat-map.Odds ratios were estimated by logistic regression for each of the indicated TFs and AML-1a. Regression was performed using R by comparing the state  at each predictor site to the nearest reporter site in the Ly49 cluster, after accounting for whether the two sites were within 147bp or not. Higher estimated odds ratios indicate that knowledge of the state of the predictor site is more able to predict the state of the reporter site.RMA cells or isolated mouse splenocytes were analyzed by flow cytometry after staining with antibodies against NK1.1, TCRβ, and the Ly49 receptors such as Ly49A/D (clone 4E5), Ly49D, Ly49C/I (5E6), Ly49E/F, Ly49G (4D11), and Ly49H (3D10). Antibodies were purchased from eBioscience  or Becton Dickinson , and samples were acquired using a Beckman Coulter CyAN-ADP and analyzed using FlowJo .Fragmented chromatin from RMA cells was prepared by MNase digest as above, except that 150 bp fragments were not selected. Chromatin was then incubated with a rabbit polyclonal antibody raised against AML-1 or against immunoglobulin M (as an isotype control) overnight (Abcam). Antibody complexes were collected using protein A agarose beads, and DNA was purified using a high pH chelating solution, as previously described ,56. The Ly49A promoter: AGGCCAGGGAAACCTGGTGTAAAGAGGTGGGGCACTGGACTGLy49A 6 kb up: ACAGAACTCAGAGGGCAAAGGAAATGGGCCACTTGGCCATTTATCT2 Mastercycler thermal cycler.Real-time PCR was performed using an Eppendorf RealplexS1 Figst track) and probable occupancy (2nd track) of the Ly49G pro1 region (3rd track). Transcription factor binding sites from the TransFac database (4th track) are also shown. (B) Analysis of %GC bias introduced by nucleosome prediction. (C) Prediction validation by comparison to a published MNase-Seq dataset. Nucleosome predictions (in black) were tested against a published MNase-Seq dataset from the Gene Expression Ontology project , presented in blue. Nucleosomes having at least 50% overlap with the prediction were taken to represent a true prediction (green), while other nucleosomes were taken as false (red). An example window is shown, with a total accuracy of 48.8% correctly identified nucleosomes.(A) Example of an alignment showing the raw genomic affinity (1(TIF)Click here for additional data file.S2 FigProbable nucleosome position across the Ly49 gene family is shown as in (TIF)Click here for additional data file.S3 FigPosition weight matrices obtained from the TRANSFAC or JASPAR public databases are shown as sequence logos. Logos were generated using the seqLogo R package from Bioconductor.(TIF)Click here for additional data file.S4 FigOf the 17 transcription factors studied in this report, 8 were excluded due to insufficient binding sites (< 300) in the Ly49 gene family. The (A) nucleosome proximity and (B) nucleosome-bound periodicity for these excluded factors are indicated here.(TIF)Click here for additional data file.S5 FigFactor-nucleosome distances were calculated and presented as in (TIF)Click here for additional data file.S6 FigFactor periodicities were calculated and presented as in (TIF)Click here for additional data file.


IRF1 is a transcription factor involved in interferon signaling and has been shown to harbor tumor suppressor activity. In order to comprehensively identify pathways regulated by IRF1, we used chromatin immunoprecipitation followed by massive-parallel sequencing (ChIP-seq) to evaluate the gene targets of IRF1 genome-wide. We identified 17,416 total binding events in breast cancer cells. Functional categorization of the binding sites after IFN-gamma (interferon-gamma) treatment determined that ‘apoptosis’ or ‘cell death’ is the most enriched target process. Motif discovery analysis of the chromosomal regions bound by IRF1 identified a number of unique motifs correlated with apoptosis, DNA damage and immune processes. Analysis of GEO transcriptome data from IRF1-transduced cells or IFN-gamma treated fibroblasts indicates that IRF1-bound targets in IFN-treated cells are associated with a positive transcriptional response. Many of the enriched target genes from the expression analysis are associated with apoptosis. Importantly, this data indicates that a significant function of IRF1 is the regulation of anti-cancer apoptotic pathways and this reinforces IRF1’s role as a tumor suppressor. Interferon regulatory factor 1 (IRF1) was the first interferon regulatory factor family protein identified (family members consist of IRF1-9) . It was Leishmania major which is thought to be due to a defective T helper 1 (Th1) response. T cells from IRF1 mice fail to mount a Th1 response and are skewed toward a Th2 response with impaired production of IL-12 [IRF1 is involved in the development and differentiation of immune cells. Using gene knockouts, IRF1 was shown to be important for Natural Killer (NK) cell development. IL-15 is a gene target of IRF1 and the absence of IRF1 results in a lack of IL-15 production important in the microenvironment supporting the maturation of NK cells. Specifically IRF1 −/− mice showed dramatically reduced NK1.1+TCR α/β and decreased NK-cell-mediated cytolytic activity -9. IRF1 of IL-12 ,11. ThusIRF1 has also been shown to be involved in a number of other diverse cellular processes such as DNA damage, cell cycle and oncogenesis. IRF1 −/− MEFs are deficient in their ability to undergo DNA damage-induced cell cycle arrest similar to MEFs lacking the tumor suppressor, p53. Also, IRF1 is important for the DNA damage-induced apoptosis of mitogenically activated mature T lymphocytes ,13 TheseA number of targets for IRF1 have been identified in single gene studies. IL-15 (7), IL-12 10,11,18), MHC class II transactivator CIITA, TAP-1 and LMP-2 are IRF1 targets involved in immune responses , MHC cla. IRF-1 iWe undertook a high-throughput ChIP-sequencing study of IRF1 chromatin bound regions in unstimulated and IFN-gamma stimulated breast cancer cells in order to increase our understanding of pathways regulated by IRF1 in cells. Gene ontology analysis identified cell death as a major target pathway of IRF1 in IFN stimulated cells. De novo motif discovery identified a set of DNA sequences enriched in specific functional pathways such as DNA damage or antigen presentation. Transcriptomic analysis suggests that binding of IRF1 to target regions results in up-regulation of gene expression in IFN-gamma stimulated cells.Chromatin immunoprecipitation was performed on H3396 breast cancer cells as previously described. H3396 cells were maintained in culture and treated with 1000 units/mL of IFN-gamma for three hours as previously described . ChromatBriefly, sequence data generated by the Illumina GAIIx platform are outputted in FASTQ format. FASTQ allowed quality scores to be assigned to each base in the read as Phred scores. An additional quality assessment measure was performed to give each read an average quality read (passing a purity filter). Reads were aligned to the hg18 genome build using the GERALD program. Output read alignments were then analyzed using the Illumina Genome Studio software, where regions and peaks of high binding density for IRF1 were identified. Region detection consisted of three sub-routines, moving sum determination, thresholding and region identification. Moving sum determination took a sequence of reads, each specified by a starting location and a read length, and a window size W. An array of summed base counts was returned. Thresholding then took this array, and a threshold value T, to filter the array so that only values above the given threshold value were used. A region was then defined as a maximal run of summed base counts above this threshold. Once regions were identified, peaks within these regions were determined.http://support.illumina.com/array/array_software/gnomestudio/documentation.ilmn.Detailed explanations and algorithms used by the GenomeStudio ChIP Sequencing Module can be found at: Peak regions were used as seeds for motif discovery. Bed files of chromosomal peak regions were converted to FASTA format and submitted to MEME-ChIP. MEME-ChIP searches for motif enrichment in input sequences and returns motifs as position-dependent letter-probability matrices .Expression data contained in the Gene Expression Omnibus were submitted to GEO2R for calculation of differential expression between two conditions. Raw GEO datasets were assigned to control or treated groups. Data submitted is compared using GEOquery and limma  R packages ,29. The Annotation of peak regions was performed by submitting a bed file of chromosomal coordinates to GPAT. Genes were annotated based on distance from the transcriptional start site of coding regions (within 10 kb of a TSS), in addition any genes directly mapped to a chromosomal coordinate were annotated. GO was performed on gene data sets from the ChIP-sequencing annotated gene list by submitting lists to DAVID for identification of overrepresented biological processes. Pathway analysis was performed using GREAT by submitting bed files of ChIP-seq peak regions. Gene ontologies associated with enriched DNA motifs from MEME-ChIP were derived from GOMO -32.In order to identify new IRF1 gene targets genome-wide we performed a ChIP-sequencing study in human breast cancer cells, H3396. Cells were left unstimulated or stimulated with IFN-gamma for 3 hours. Chromatin immunoprecipitated with IRF1 antibody (3 independent samples for each condition) was sequenced and analyzed using Illumina software. There was total of 36 million reads generated for both control and IFN-gamma stimulated chip samples. Reads were quality filtered and then aligned to the hg18 human genome build. Peak regions for each condition (control or IFN-treated) were identified by moving sum determination followed by thresholding (Illumina Genome Studio package). A total of 17, 416 peak regions were identified, which corresponded to 11, 429 peak regions in control (unstimulated) cells and 12, 538 peak regions in IFN-gamma stimulated cells . There iPeak regions were annotated using GPAT  within 1As we were interested in defining IRF1 pathways important for its’ tumor suppressor activity, we performed analysis of enriched functional categories for the annotated genes bound in control (unstimulated)  or IFN-gFurther pathway analysis (GREAT)  was perfNext, the distance of peak regions from known TSS’s  were mapped (using GPAT). Peak regions within 10 kb upstream and downstream of the TSS of a gene coding region were binned and the number of binding sites (counts) were mapped against the distance from the TSS . The datDNA motif analysis of peak regions in control (unstimulated) cells did not produce any significantly enriched motifs. Analysis of the interferon stimulated peak regions (IFN-gamma unique) yielded a number of DNA motifs that slightly differs (at position 5) from the consensus IRF1 binding site in the transcription factor database contained in MEME-ChIP (JASPAR) . InteresIn order to correlate binding of IRF1 to a transcriptional output, re-analysis of transcriptomic data in the GEO (Gene Expression Omnibus) was undertaken. Two data sets were analysed using GEO2R  to identify differentially expressed genes. We analyzed the GEO dataset, GSE26817 that contains data from a study on IRF1 transduction in Huh-7 human hepatoma cells . In ordeIn order to look more closely at the DNA motifs identified from the ChIP-seq, we analysed gene ontology associated with these motifs using GOMO . GOMO scWe undertook a high-throughput study to identify IRF1 binding sites in breast cancer cells in order to comprehensively identify target pathways that may contribute to IRF1’s tumor suppressor activity. IRF1 has been shown in numerous studies to inhibit the growth of transformed cells, in vivo and in vitro -44 but dWe have found that IRF1 bound targets in IFN-stimulated cells are more likely to be up-regulated. Thus, IFN signaling may contribute to the ability of IRF1 to induce target genes. We hypothesize that this may be due to a number of reasons such as cis-acting factors working in cooperation with IRF1 in IFN-stimulated cells, chromatin environment in control vs. IFN-stimulated cells or modification of IRF1 in IFN-stimulated cells. Also, plotting of the number of IRF1 peak regions near transcriptional start sites increases after IFN stimulation and apoptotic targets are enriched in this pool of putative targets. Binding of transcription factors near the TSS indicates in many cases that there will be a direct affect on its transcription ,46. ThusA significant portion of the peak regions mapping to apoptotic genes are unique to IFN-stimulated cells and it may be that these genes only require the recruitment of IRF1 to their regulatory domains in IFN-stimulated cells to induce expression. A number of these genes map to the extrinsic cell death pathway and we found that this was indeed a significantly enriched GO biological process found uniquely in IFN-gamma stimulated cells . TRAIL, Another interesting putative target pathway identified in IFN-gamma stimulated cells is the ATR signaling pathway. This pathway is important for the DNA damage response. A ChIP-chip study determined that IRF1 potentially regulates the Fanconi anemia pathway via its regulation of BRIP1 protein expression. Interestingly, the fanconi anemia pathway interacts with the ATR signaling pathway -49. A nuA large number of the putative target genes are categorized under immune or interferon responses. As IRF1 is an interferon-regulated gene and has been shown to bind interferon-stimulated genes, it indicates that the ChIP-sequencing was successful in finding bona fide IRF1 targets. The presence of the core consensus IRF-E within all the DNA motifs overrepresented in our target set also strengthens this conclusion. We found that all of the longer motifs (15-20bp) were associated with the promoter regions of genes involved in T-helper cell differentiation. This is important as IRF1 is known to be essential for proper differentiation of naïve T cells into Th1 cells . DNA damIRF1 inhibits cancer cell growth. Our study has added to our understanding of pathways regulated by IRF1 after IFN stimulation. Many of the putative target genes fall under processes known to be regulated by IRF1 such as apoptosis and DNA damage. Here, we have shown that IRF1 binding correlates with increased expression of genes in IFN-stimulated cells or IRF1-transduced cells. Expression targets are enriched in apoptotic genes which is the most over-represented biological process associated with IRF1 binding. These data indicate that the major anti-cancer pathway regulated by IRF1 is cell death and solidifies IRF1’s role as a tumor suppressor.Supplementary DataSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4


TRIM28 is a multidomain protein with versatile functions in transcription and DNA repair. Recently it was shown that this factor plays unanticipated roles in transcriptional elongation. TRIM28 was shown to stabilize the pausing of RNA polymerase II (Pol II) close to the transcriptional start site in many unactivated genes, permitting Pol II accumulation and readying genes for induction. In addition, the factor was shown to respond rapidly to signals accompanying transcriptional activation permitting the productive elongation of RNA by previously paused Pol II. We discuss here critical regulatory mechanisms of TRIM28 in transcriptional control and DNA repair that may illuminate the novel roles of this factor in pausing and elongation of Pol II. Transcription is one of the major cellular processes to access the genome and regulate gene expression. Finely controlled gene expression is crucial to determine cell identity and maintain normal cell growth and homeostasis. During transcription, the RNA polymerase II (Pol II) complex carries out the generation of messenger RNAs and the majority of non-coding genes in eukaryotic cells , 2, and Transcriptional initiation is the initial checkpoint in gene expression, where Pol II and general transcription factors (GTFs) are recruited to the promoter region upon activation . Then, oTRIM28 (TRIpartate motif-containing protein 28), also known as KAP1 (KRAB-associated protein 1) and transcription intermediary factor 1β (TIF1β), was first discovered as a polypeptide interacting with zinc finger family members of the Kruppel transcription factor family (KRAB) –21. (Fortrans-repression of target genes. These are the PHD (plant homeo domain) and the C-terminal BR (BRomo domain). The BR domain of TRIM28 is atypical in that it does not bind to acetyl lysine residues [TRIM28 was shown to contain an N terminal RBCC domain that is comprised of a RING  finger, two B-box zinc fingers and a coiled coil domain through which it interacts with KRAB proteins and is recruited to DNA , 23–25 . The RINresidues . The PHDresidues . Sumoylaresidues  . TRIM28 was shown to be recruited to the region of DNA double strand breaks in association with HP1 and to be rapidly phosphorylated close to the C terminus (on S824) by the DNA damage response kinase ATM , located close to the HP1 binding-motif inhibited association of the factor with HP1 and reversed gene silencing mediated through binding to HP1  gene, TRIM28 was identified [HSPA1B close to the transcriptional start site (TSS) at around +70. Using an in vitro transcription assay, it was then shown that TRIM28 could stabilize pausing of Pol II on HSPA1B and that depletion of the factor from HeLa nuclear extracts used in the assay led to increased transcriptional elongation [HSPA1B. Indeed, knockdown of TRIM28 led to increases in HSPA1B RNA and protein levels in vivo as well as levels of other proteins regulated by Pol II pausing such as NFB and ERK1 [HSPA1B, ERK1, JUN and EGR1 genes [Recently, in an unbiased screen for proteins that bound at the pausing site to regulate Pol II pausing on the human entified . TRIM28 ongation . It couland ERK1 . ChIP-seR1 genes . These dHSPA1B during heat shock showed little evidence of TRIM28 release [trans-activator HSF1 was shown to bind to HSP genes [HSPA1B, suggesting an important role of this phosphorylation signaling on transcriptional elongation. The significance of TRIM28 phosphorylation in Pol II pause release was confirmed in in vitro transcription experiments showing that phosphomimetic mutation of S824 by aspartate substitution abolished the ability of TRIM28 to mediate Pol II pausing on HSPA1B. These findings established a role for TRIM28 in Pol II pausing regulation and a mechanism for pausing release involving DNA damage-triggered kinases DNA-PK and ATM. In this model, unphosphorylated TRIM28 stabilizes Pol II pausing at the pausing site. Upon transcriptional activation, ATM and DNA-PK become activated to phosphorylate TRIM28 at S824, potentially leading to more favorable nucleosome architecture for Pol II processive elongation  by P-TEFb, disengagement of NELF, and subsequent elongation . ChIP Setrans-activation in androgen receptor and estrogen receptor-regulated genes have shown association of target genes with the catalytic subunit of DNA-PK, DNA-PK associated proteins Ku70 and Ku80, ATM, topoisomerase II and DNA repair intermediate poly (ADP-ribose polymerase (PARP1), an association leading to transcription through a mechanism that may involve generation of DNA double strand breaks in the activated gene. [HSP genes [HSP70 in heat shocked cells and subsequent processive modification of histones by ADP ribosylation in Drosophila. This effect was triggered by HSF induced recruitment of Tip60 and histone acetylation on histone H2A, an effect required for activation of PARP1 residues pre-existing at the 5′ of the unactivated gene and triggering HSP70 transcription [Many unresolved questions await further experimentation regarding TRIM28-mediated Pol II pausing regulation. For instance, a mechanism for the activation of the PIKK kinases ATM and DNA-PK prior to phosphorylation of TRIM28 at S824 needs to be established. How such principle signaling molecules in the DDR response could become activated in productive elongation is not clear. Previous studies of ed gene. –54. In aSP genes , 56. Precription . These fcription .Another question is—by what mechanism does TRIM28 interact with the TSS of the gene bodies of paused genes? TRIM28 may operate in pause regulation in a “hit and run” manner or might associate stably with chromatin. As mentioned earlier, it was shown that TRIM28 could associate with target areas of chromatin through: 1) binding to KRAB transcription factors through its RBCC domain , (2) ass binding In conclusion therefore, TRIM28 appears to play a unique and essential role in transcriptional elongation. We anticipate future investigation of upstream and downstream signaling and the regulatory mechanisms that underlie the role of TRIM28 in transcriptional pausing and elongation.


Transcription factors regulate gene expression by binding regulatory DNA. Understanding the rules governing such binding is an essential step in describing the network of regulatory interactions, and its pathological alterations. We show that describing regulatory regions in terms of their profile of total binding affinities for transcription factors leads to increased predictive power compared to methods based on the identification of discrete binding sites. This applies both to the prediction of transcription factor binding as revealed by ChIP-seq experiments and to the prediction of gene expression through RNA-seq. Further significant improvements in predictive power are obtained when regulatory regions are defined based on chromatin states inferred from histone modification data. A major goal of molecular biology in the post-genomic era is to understand the network of regulatory interactions that allow a single genome to produce hundreds of distinct cell types, and to react to external stimuli with the appropriate gene expression response. The most fundamental layer of gene regulation happens at the transcriptional level, where transcription factors (TFs) bind regulatory regions located in the proximity of their target genes and affect the binding of the transcriptional machinery and their level of expression.The first step in understanding transcriptional regulation is to predict the DNA sequences to which a TF is able to bind, so as to identify its targets. Most TFs bind sequences that are relatively short and degenerate, making this prediction quite challenging. The degeneracy of the binding sites is reflected in the use of a Positional Weight Matrix (PWM) to describe the binding preferences of a TF. A PWM specifies the frequency distribution of the 4 nucleotides in each position of a binding site, and is typically used to assign a score to each DNA sequence. The score expresses the degree of similarity between the observed sequence and the PWM. In the most common approach, a sequence is then predicted to be a transcription factor binding site (TFBS) if it scores above a given cutoff.The need for a cutoff is unsatisfactory not only because it introduces an arbitrary parameter, but also and especially because recent detailed investigations of transcription factor binding have shown it to be a thermodynamic process in which transient binding to low-affinity sequences plays an important role . In thisRecently we used TBA profiles to study the evolution of cis-regulatory regions in humans : indeed However a systematic investigation on the use of TBA profiles in studying gene regulation is still lacking. Indeed in the studies cited above no systematic attempt was made to predict the functional outcome of TF binding, that is gene expression, using TBA. Moreover, knowledge of chromatin states as provided by histone modification marks was not used to improve the definition of regulatory sequences and thus obtain more accurate TBA profiles.In this work we fill these gaps and show that TBA profiles of regulatory regions are systematically superior to cutoff-based methods in predicting both TF binding and gene expression in human cells. Including information about chromatin state in the definition of the regulatory regions further improves the predictive power of TBA.ChIP-seq peaks were downloaded from the hg19 UCSC Track “Transcription Factor ChIP-seq Uniform Peaks from ENCODE/Analysis”. This track represents a collection of all the ChIP-seq experiments performed in human cell lines for the ENCODE project re-analyzed with a uniform pipeline. We obtained 690 files of ChIP-seq peaks performed with 91 human cell lines and representing 161 unique regulatory factors.To define a wide collection of human PWMs we used the Bioconductor Package MotifDB , that cEntrez IDs were used to link the target of a given antibody used for a ChIP-seq with its corresponding PWM, resulting in 689 peak files associated with at least a PWM. Due to redundancies in the MotifDB collection of matrices and in the ENCODE ChIP-seq antibodies we end up with 992 experiment/PWM pairs, representing 104 different antibodies, 85 Entrez IDs and 203 PWMs.As negative controls for determining Receiver Operating Characteristic (ROC) curves we used the peaks obtained in HeLa cells with total mouse IgG used in the IP step, obtained from another UCSC track . To ensure that our results do not depend on this choice of negative controls we performed some tests using as negatives the total input derived peaks and random sequences following the same size distribution of total mouse IgG peaks or of the real ChIP-seq peaks, and the results were always comparable.arw of a PWM w for a sequence r is given by:l is the length of the PWM w, L is the length of the sequence r, ri is the nucleotide at the position i of the sequence r on the plus strand, P is the probability to observe the given nucleotide ri at the position j of the PWM w and P is the background probability to observe the same nucleotide ri.Total affinity is defined as in : arw ofl asIn the usual approach aimed at identifying discrete binding sites one usually defines the score of a sequence of length Smax(w) is the maximum possible score for the PWM, binding sites are then defined as all sites scoring better than C ⋅ Smax, with 0 < C < 1.If occupancy which is computed like the TBA, but limiting the sum to sites scoring better than C ⋅ Smax:To compare TBA to cutoff-based methods we defined a cutoff-dependent C ⋅ Smax. Background bases frequencies were obtained from intergenic regions in the hg19 release of the human genome.Therefore the occupancy is computed exactly like the TBA, but ignoring all contribution from sequences scoring less than −6), smaller than the smallest non-zero probability. PWMs directly downloaded from Jaspar CORE have counts, in this case we transformed zeroes to ones before converting counts to probabilities.MotifDB PWMs are expressed as probabilities and in some cases are equal to zero: we applied a standard “pseudocount” of (1 × 10C from 0.1 to 1) for all PWMs were computed over human proximal promoters, defined as sequences 1500 bases upstream and 500 downstream of the transcription start sites (TSS) as reported in the RefSeq track of the UCSC genome browser . To focus on genes unambiguosly associated to a single TSS we restricted our analyses to 12,681 genes associated with a single transcript (according to the GENCODE gene ID—Refseq mapping obtained from Ensembl BioMart 73).The TBA and occupancy values  or cell extracts  datasets were downloaded from the ENCODE/Cold Spring Harbor Lab Long RNA-seq track, obtaining expression level estimates at the gene level. We chose the 9 cell lines for which a Hidden Markov Model (HMM) is available to define regulatory regions using chromatin states (see below). Values were normalized by adding a pseudo-count, namely the smallest expression value different from zero, to each value and then log2-transformed.15-state HMM datasets were downloaded from the release 1 of Broad ChromHMM UCSC track, converting hg18 genomic coordinates to hg19 via liftover. These chromatin predictions are available for the same nine cell lines for which we downloaded gene expression data. Using this segmentation model we classified the 15 states into 13 “open chromatin” states including promoter, enhancers and transcriptionally active states; and two “closed” states .We then used these chromatin state predictions to better define our proximal promoters: masking  positions overlapping “closed chromatin” regions and expanding each masked proximal promoter region to the maximal overlapping “open” region. The resulting sequences were used as regulatory regions based on chromatin states.ROC package [ROC and AUC analyses were performed using the R  package —to compalm from the stats package. Each linear model is characterized by a coefficient of determination (R2), that reflects the fraction of variance in gene expression that can be explained with a linear composition of the TBA values , and a P-value related to the statistical significance of the of the whole model. More precisely, the P-value reflects the statistical significance of the improvement in predictive power obtained by using the TBA values as independent variables compared with a model with intercept only . All the R2 values we quote are adjusted for the number of predictors in the model.To understand if TBA could be used to model gene expression we fitted linear models with gene expression values as the dependent variable and TBA or occupancies as independent ones. Every gene was associated with its expression value (in a specific cell line) and TBA values calculated on its promoter or on its chromatin-defined regulatory regions (as described above). A linear model was then fitted with the R function In addition to these measures referring to the complete model, multivariate linear regression also yields a coefficient and a significance value for each of the dependent variables (i.e. TBA for different PWMs), reflecting the influence of its values on the independent one (i.e. gene expression) and taking into account the global effect of all the other variables. In this context it is advisable to use a small set of well defined PWMs without redundancies to avoid to set up an overdetermined model and to be able to easily interpret the coefficients—therefore for these models we used a reduced collection of 130 PWMs from Jaspar CORE.Cross validation and lasso penalized regression where used to test for possible overfitting and redundance of the regressors. We performed a standard 10 fold cross validation and lasso regression respectively with the R packages bootstrap and glmnet , 19.In order to be able to compute total affinities and occupancy values on many sequences in an efficient manner we developed the MatrixRider Bioconductor package  that givin vivo binding of TFs to regulatory regions and to compare it to methods using score cutoffs to define discrete candidate binding sites. We used a large collection of ChIP-seq datasets available from the UCSC genome browser, and associated PWMs to TFs as described in the Methods.We set out to determine the effectiveness of TBA in predicting L we computed the likelihood score of all the subsequences of length L. These were used as described in the Methods section to compute the TBA and the occupancy of the sequence at various values of the score cutoff. The TBA sums the contributions of all subsequences of the original sequences, weighted by their likelihood ratio, while the occupancy limits the sum to subsequences scoring better than a given cutoff C, expressed as a fraction of the maximum possible score.Given a sequence, and a PWM of length C, from 0.1 to 1 (taking C = 1 means considering only the contribution of the highest scoring sequence).We then sought to compare the predictive power of the TBA and occupancy at various cutoff values, as measured by the area under the ROC curve (AUC). For each ChIP-seq dataset the bound sequences that we want to predict are given by the peaks provided in the UCSC track. As negative data for all the datasets we used the peaks obtained in Hela cells with total mouse IgG used in the IP step, which are available from the Yale track. We then computed the AUC for the TBA and 10 equally spaced values of the score cutoff C = 0.1 the distribution of the difference in AUC between TBA and occupancy for the 188 PWMs is shown in The AUC for the 188 PWMs we could analyze, each averaged over all relevant ChIP-seq experiments, are shown together as a boxplot in C = 0.1) occupancy gets contribution from a rather small portion of the sequence: for example for a PWM of intermediate information content such as the JASPAR Core matrix representing NFI CCAAT-binding (MA0161.1) we find an average of 27 sites per ChIP-seq peak, covering 8.9% of the peaks.Note that even with the lowest cutoff we examined (We compared our results with those of another tool (“MotifLocator” ) which pThese results show that binding sites of arbitrarily low affinity contribute to TF binding as revealed by ChIP-seq data. The TBA of a sequence takes into account all binding sites and weighs them based on a physical model of TF:DNA interaction, and turns out to be a better predictor of TF binding than quantities based on the identification of a discrete set of binding sites.eg of gene g is given byg for PWM i, ci and b are parameters to be fitted and rg is the residual. The index i runs over a suitable collection of PWMs. The model is fitted separately for each cell line, so that the fitted ci’s reflect the relevance of PWM i for each cell type (or rather the relevance of the TFs whose binding sites are described by the PWM). This is a radically simplified model based on the following assumptions:We then asked whether TBA can be used to predict gene expression in human cells, and how its predictive power compares to methods based on discrete binding sites. We used RNA-seq experiments performed within the ENCODE project on 9 human cell lines, and we modeled the dependence of gene expression on TBA profiles with log-linear regression . Howevevs occupancy defined with a C = 0.8 cutoff, that is 80% of the maximum score. The fraction of variance explained by the TBA model varies between 0.338 and 0.449 (median 0.378). Overfitting is not a serious issue since in a 10-fold cross-validation experiment we obtained a median R2 of 0.371. In all cases this is strongly significant, as all P-values are <2.2 ⋅ 10−16. For comparison, using MotifLocator with the same PWMs and a 0.8 cutoff and summing over the score of all binding sites produces a median R2 of 0.267.To avoid using redundant PWMs describing the same TFs we chose the JASPAR “Core Vertebrate” collection (2009 release) containing 130 PWMs. We considered only genes associated to a single transcript so as to avoid ambiguities in the definition of the Transcription Start Site (TSS). As regulatory region we took 1500 bps upstream and 500 bps downstream of the TSS. R2 values slightly lower than the full model. The PWMs selected by lasso are in all cases a large subset of the original ones , suggesting that the JASPAR Core set has limited redundance. The PWMs selected by lasso regression for the various cell lines are shown in We asked how many of the 130 PWMs actually contribute to the prediction of gene expression. Using lasso regression on the same data, with penalization coefficient chosen to minimize cross-validation error, we obtained These data show that TBA profiles are good predictors of both TF binding and gene expression, but we cannot conclude that they predict gene expression through predicting TF binding. Indeed TBA profiles could represent generic sequence features associated to expression levels, largely independent from the ability to bind the specific TFs represented by the PWMs. To gain some insight into this we generated shuffled PWMs which retain the base composition, but not the position specificity, of the original 130 PWMs, and used the TBA for these matrices as regressors of gene expression.P = 0.0039, Wilcoxon paired test), the effect is rather small: shuffled PWMs achive a median R2 of 0.353 compared to 0.378 of the original ones. This suggests that much, but not all, the predictive power of TBA profiles is due to their capturing sequence features that are not directly related to the binding sites of the TF they describe. It is an interesting open problem to understand exactly what these features are: indeed these are not trivial, since a model based on base and CpG composition obtained a median R2 of 0.248.While true PWMs have significantly higher predictive power than shuffled ones . HisFor the 9 cell lines used above the genome has been classified  into 15 we start with the regulatory regions used above, that is 1500 bps upstream and 500 bps downstream of the TSSof these we consider only the parts that are classified into one of the “open” states by if the upstream end of the regulatory regions falls into an open state, we extend the region until the end of the open state; the same extension is performed downstreamR2 of 0.46 for the Gm12878 cells, that we also study, based on a regression model in which the independent variables are histone modifications and ChIP-seq binding data for a set of TFs chosen for their relevance to the cells under investigation. It is noteworthy that a better R2  TBA profiles for a set of PWMs describing the binding preferences of ∼130 TFs, (b) a classification of chromatin states derived from histone modification data.Importantly, we used the same set of PWMs for all cell lines, so that the TBA profiles are independent of the cellular context. Chromatin states are instead context-dependent, but they are derived from a standard set of histone modification data. Therefore no specific knowledge about the biology of each cell line is used in modeling gene expression. In this respect our work differs from several other approaches in which ChIP-seq data for transcription factors known to be relevant in a given context are used to predict expression However it is clear that the predictive power of TBA profiles is not simply due to their ability to predict TF binding. Indeed much, but not all, of such predictive power is retained by shuffled PWMs which are not describing the binding preferences of known TFs. Presumably TBA profiles capture, beside TF binding affinities, other sequence characteristics that are strong predictors of gene expression.While using chromatin data greatly improves the ability to predict gene expression, using the “correct” chromatin data  is not crucial, as a significant improvement is obtained using the same chromatin data for all cell lines. This is important in the perspective of applying this type of modeling to data for which chromatin data for each individual sample are not available  and to infer which transcription factors are involved in such changes, as was done for example in Refs. , 33 baseS1 FigFor each PWM we show the AUC value averaged over all relevant ChIP-seq experiments. These data are the ones used to produce the boxplots of (PDF)Click here for additional data file.S2 FigBlue: positive coefficient. Red: negative coefficient. White: zero coefficient.(PDF)Click here for additional data file.S1 DatasetSheet 1: For each PWM we show the AUC value averaged over all relevant ChIP-seq experiments. Sheet 2: AUCs and P-values of individual PWM/experimet pairs. The P-values are obtained from a Mann-Whitney U test comparing the TBA/occupancy of positive vs negative sequences.(XLSX)Click here for additional data file.S2 DatasetCoefficient of each PWM in a lasso regression of gene expression in 9 ENCODE cell lines.(XLSX)Click here for additional data file.