Histone acetylation has been implicated in learning and memory in the brain, however, its function at the level of the genome and at individual genetic loci remains poorly investigated. This study examines a key acetylation mark, histone H4 lysine 5 acetylation (H4K5ac), genome-wide and its role in activity-dependent gene transcription in the adult mouse hippocampus following contextual fear conditioning.Using ChIP-Seq, we identified 23,235 genes in which H4K5ac correlates with absolute gene expression in the hippocampus. However, in the absence of transcription factor binding sites 150 bp upstream of the transcription start site, genes were associated with higher H4K5ac and expression levels. We further establish H4K5ac as a ubiquitous modification across the genome. Approximately one-third of all genes have above average H4K5ac, of which ~15% are specific to memory formation and ~65% are co-acetylated for H4K12. Although H4K5ac is prevalent across the genome, enrichment of H4K5ac at specific regions in the promoter and coding region are associated with different levels of gene expression. Additionally, unbiased peak calling for genes differentially acetylated for H4K5ac identified 114 unique genes specific to fear memory, over half of which have not previously been associated with memory processes.Our data provide novel insights into potential mechanisms of gene priming and bookmarking by histone acetylation following hippocampal memory activation. Specifically, we propose that hyperacetylation of H4K5 may prime genes for rapid expression following activity. More broadly, this study strengthens the importance of histone posttranslational modifications for the differential regulation of transcriptional programs in cognitive processes. The formation of memory requires highly orchestrated gene expression programs for the establishment and the stabilization of memory traces over time. These programs are initiated during learning and can persist for several hours ,2. WholeWith the emergence of the post-genomic era, recent studies in the field of learning and memory have investigated the implication of chromatin remodeling in cognitive processes. Several studies have revealed that chromatin remodeling plays a critical role in memory formation ,11-14. CAlthough these studies provide strong evidence that histone acetylation is modulated by memory formation, a global assessment of histone acetylation at the level of the genome and the mechanism with which it regulates gene expression in memory processes is lacking. Using a genome-wide approach, we examined the distribution of H4K5ac, a mark of active chromatin implicated in transcriptional re-activation of post-mitotic cells through gene bookmarking , and itsTo examine the epigenetic and transcriptional profile of genes associated with memory formation in the hippocampus, we trained adult mice on a CFC paradigm Figure A. We choFC has been associated with transcriptional programs that are activated within 1 hour after conditioning, and that persist for up to 6 hours ,29. Subsde novo identification of genes associated with H4K5ac after CFC (after 2 training sessions) in the mouse hippocampus.Previous studies have shown the association of histone acetylation at promoters of a restricted set of canonical genes involved in memory ,9,13, buAnalysis of H4K5ac distribution showed enrichment of reads in the promoter and coding sequence (CDS) of H4K5ac-ChIP samples compared to IgG-IP samples in both FC Figure A and 2B To determine whether the observed profile was specific for H4K5ac, we compared it with H4K12ac, another histone PTM associated with fear memory, from a publicly available dataset .We then examined the relationship between H4K5ac and gene transcription using a publicly available whole mouse genome microarray dataset (Agilent) for gene expression immediately after CFC in the mouse hippocampus ,29-32,35We then identified genes acetylated above average and performed a cross-wise comparison between experimental groups. Based on the average promoter read count of 45 in our dataset, we considered genes with more than 50 reads in the promoter as above average. From a total of 23,235 genes in the dataset, 7,103 genes were identified in the FC group, and 7,708 genes in the control   and Pisd-ps1/3 (14.72) but not for Eif4enif1 (11.48) or Drg1 (12.44), which had lower enrichment for H4K5ac. For Phactr3, H4K5ac coverage was lower in intergenic and CDS of neighboring genes Zfp931, Sycp2, and Ppp1r3d  and neighboring genes, Zfp931 (11.42), Sycp2 (3.97), and Ppp1r3d (11.51), which show lower expression levels. This provides further evidence that the level of H4K5ac enrichment in the CDS is directly proportional to the level of gene transcription.These findings suggest that H4K5ac in the promoter and/or CDS may be a feature of highly expressed genes. To validate this observation, we examined the profile of H4K5ac in rocesses ,40, respWe next examined whether high levels of gene expression associated with H4K5ac is linked to permissible TF binding. We scanned the promoter region 2 kb upstream of the TSS for conserved TFBS, and computed the percentage of expressed genes with H4K5ac at that position , also known as Scapinin (scaffold-associated PP1-inhibiting proteins), is an interesting candidate with respect to memory as it is transcribed primarily in the brain and in tumors but has been relatively unstudied in the context of memory . Ma. Ma71]. ad libitum. All animals were maintained in accordance with the Federation of Swiss Cantonal Veterinary Office and European Community Council Directive (86/609/EEC) guidelines.Experiments were conducted using adult C57Bl6/J males (4–7 months old). Mice were housed under standard conditions with a 12 hour reversed light–dark cycle and access to food and water Mice were habituated to the testing room and handled for three days prior to training and testing. They were then trained in a contextual fear-conditioning paradigm using a TSE Fear Conditioning System. The training consisted of a 3 min. exposure to the conditioning context followed by a brief electric shock (0.7 mA for 1s), then left for an additional 3 min. in the conditioning context. Mice that were not re-conditioned were euthanized 1 hour after the initial fear-conditioning session. Mice that were to be further fear-conditioned were trained on the second day and the memory test performed 24 hours later on the third day. Single trial CFC is known to produce a robust, long-lasting memory, however subsequent training has been shown to strengthen the memory and prevent random association of shock with re-exposure -10. Furtpost hoc analysis, where appropriate. GraphPad Prism was used for statistical analysis and significance was set at *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001. All data are shown as mean ± SEM.Comparisons between groups were analyzed by paired student’s t-test or one-way ANOVA with Tukey Nuclear protein extraction was performed as previously described with the following modifications . Hippoca15 μg of proteins from nuclear extracts was mixed with 4× LDS sample buffer  and 10% β-Mercaptoethanol to a final volume of 20 μl and loaded on a Novex 4-12% Bis-Tris Gel . Proteins were then transferred onto a nitrocellulose membrane (Bio-Rad), blocked , and incubated with primary and secondary antibodies. Whole purified histones were run in parallel to confirm histone subunits (ImmunoVision) and Precision Plus protein dual color standards were used to determine molecular weights (Bio-Rad Laboratories). Bands were identified and quantified using an Odyssey IR scanner (LI-COR Biosciences) and the H4K5ac (11 kDa) signal was normalized to β-actin (42 kDa). Primary antibodies used were anti-acetyl H4K5  . Crossing point (Cp) values, which are more reliable and reproducible than Ct values, were obtained using the second derivative maximum method (Roche AG). Comparative analysis on Cp values was performed and expressed as fold change over the average of controls [Total RNA was extracted from hippocampus using TRIzol reagent and 1 μg of RNA was reverse-transcribed using the SuperScript First Strand Synthesis II system (Invitrogen). Equal amounts of cDNA from each sample were run in duplicate along with an endogenous control, controls . Mean anChromatin immunoprecipitation (ChIP) was performed as previously described , with thLibrary preparation was according to recommended guidelines (Life Technologies). From both ChIP and input control samples, 200 ng of DNA was further sonicated at 4°C to a mean fragment size of between 100 to 150 bp using the Covaris S2 sonicator. The DNA was then end-repaired using end-polishing enzymes such that damaged DNA with protruding 5’ or 3’ ends were blunt-ended and phosphorylated. Following repair, the samples were purified using a column purification kit and the blunt ends were ligated with 1 μl of multiplex adaptors. The ligated samples were then nick translated and amplified according to the SOLiD Fragment Library Barcoding protocol and column purified separately. The libraries were then quantitated using a Qubit fluorometer. 20 μl of each library was size-selected for ligation products of 170–230 bp using 2% E-gels and pooled following gel purification. Finally, equimolar amounts of each barcoded library were mixed together before ePCR followed by sequencing.http://www.ncbi.nlm.nih.gov/geo/) and assigned the identifier (accession no. GSE30325).Sequencing was performed on an Applied Biosystems SOLiD 3 platform. Image acquisition and base calling was automated on the SOLiD Instrument Control Software system. The color space reads were mapped and aligned to the current assembly of the mouse genome  using the mapping tool of the Bioscope v1.2.1 software suite (Applied Biosystems). Only reads with a maximum of 4 failed color calls and quality values larger than 8 were considered for contiguous mapping. The reads were mapped allowing a maximum of 6 color mismatches and reads with up to 10 mappings on the genome were reported in a SAM file. This file was used for subsequent identification of enriched regions. Sequence data from this study has been submitted to NCBI Gene Expression Omnibus database  50-bp ChIP-seq reads, 230 million  were uniquely mapped to the current mouse reference genome with a mismatch allowance of ≤ 6 per 50 consecutive bases  algorithm . To detehttp://david.abcc.ncifcrf.gov) for the analysis of functionally-enriched genes in our respective gene lists [http://bioinfo.vanderbilt.edu/webgestalt) for the analysis of functionally-enriched genes in our respective gene lists [http://www.genome.jp/kegg/kegg2.html).To determine functional gene enrichment and interaction networks of genes differentially acetylated in fear-conditioned compared to non-fear-conditioned controls, we used the genes identified in MACS for functional annotation. From the 241 differentially acetylated regions identified in fear-conditioned over control, 115 unique peaks were associated in the promoter or coding region of genes. From the 77 differentially acetylated regions identified in control over fear-conditioned, 42 unique peaks were associated with gene bodies. We used The Database for Annotation, Visualization and Integrated Discovery , and processed and analyzed with R/Bioconductor. Spot intensities were normalized using quantile normalization. For the comparison with the acetylation levels, the genes were subdivided in ten equally sized bins according to the average expression.Gene expression data was obtained from the Gene Expression Omnibus repository at NCBI (Known transcription factor binding sites (TFBS) were downloaded from the CisRED database . A totalWe computed acetylation profiles in the ± 2 kb region around the transcription start site (TSS) and used k-means clustering to subdivide the profiles into 5 clusters. We subsequently built cross-tabulation tables to check whether cluster membership correlates with the expression level and/or with the presence of a known TFBS in certain regions. Clusters were generated in an unsupervised fashion and correlation between acetylation scores and gene expression was computed using Spearman's rank correlation.CDS: Coding sequence; CFC: Contextual fear conditioning; ChIP-Seq: Chromatin immunoprecipitation followed by sequencing; ChIP: Chromatin immunoprecipitation; Cp: Crossing point; DAVID: The database for annotation, visualization and integrated discovery; FC: Fear conditioning; FDR: False discovery rate; GO: Gene ontology; H2BK5ac: Histone H2B lysine 5 acetylation; H2BK12ac: Histone H2B lysine 12 acetylation; H2BK15ac: Histone H2B lysine 15 acetylation; H2BK20ac: Histone H2B lysine20 acetylation; H3K14ac: Histone H3 lysine 14 acetylation; H3K9ac: Histone H3 lysine 9 acetylation; H4K12ac: Histone H4 lysine 12 acetylation; H4K5ac: Histone H4 lysine 5 acetylation; H4K8ac: Histone H4 lysine 8 acetylation; HDAC: Histone deacetylases; IgG-IP: IgG-immunoprecipitated; KEGG: Kyoto encyclopedia of genes and genomes; MACS: Model-based analysis of ChIP-Seq; PCR: Polymerase chain reaction; PP1: Protein phosphatase 1; PTM: Posttranslational modification; SAHA: Suberoylanilide hydroxamic acid; SICER: Statistical model for the identification of chip-enriched regions; TF: Transcription factor; TFBS: Transcription factor binding site; TSS: Transcription start site; TTS: Transcription termination site; UTR: Untranslated region; WebGestalt: Web-based gene set analysis toolkit V2.The authors declare they have no competing interests.CSP initiated and designed the study. CSP performed and/or oversaw fear conditioning experiments, ChIP for sequencing, peak-finding analysis, raw data analysis, GO analysis, RT-PCR validations, and wrote the manuscript. HR provided analysis of mapped reads, sequence read annotation and classification, gene expression correlation, and transcription factor binding site analysis. IMM provided interpretation of the data and critical revision of the manuscript. All authors have read and approved the manuscript.Supplementary figures and tables. This file contains Supplementary Figures 1 to 5 and Supplementary Tables 1 to 6.Click here for file


Long-range regulatory elements, such as enhancers, exert substantial control over tissue-specific gene expression patterns. Genome-wide discovery of functional enhancers in different cell types is important for our understanding of genome function as well as human disease etiology.in silico approach to model the previously reported phenomenon of transcriptional pausing, accompanied by divergent transcription, at active promoters. We then used this model for large-scale prediction of non-promoter-associated bidirectional expression of short transcripts. Our predictions were significantly enriched for DNase hypersensitive sites, histone H3 lysine 27 acetylation (H3K27ac), and other chromatin marks associated with active rather than poised or repressed enhancers. We also detected modest bidirectional expression at binding sites of the CCCTC-factor (CTCF) genome-wide, particularly those that overlap H3K27ac.In this study, we developed an Our findings indicate that the signature of bidirectional expression of short transcripts, learned from promoter-proximal transcriptional pausing, can be used to predict active long-range regulatory elements genome-wide, likely due in part to specific association of RNA polymerase with enhancer regions. Cellular identity and function are defined in large part by regulatory networks that determine gene expression profiles. Control of gene expression is complex, multi-faceted, and coordinated ,2. Over The first step in gene expression is the recruitment to gene promoters of a multi-protein transcription initiation complex , which iOne of these methods, global nuclear run-on followed by high-throughput sequencing (GRO-seq), provides a density map of transcriptionally engaged RNAP across the genome by purifying, sequencing, and mapping nascent RNAs . When apThe functional consequences of promoter-proximal RNAP pausing are likely diverse -17. One In this study, we first sought to assess whether BEST is a common feature in human cells. We analyzed published IMR90 GRO-seq data  to definet al. [To confirm BEST at active promoters, we first sought to define from IMR90 GRO-seq data a set of transcriptionally active genes . We calculated the average reads per kilobase normalized for mapability for every known human gene longer than 3 kb, and performed a receiver operating characteristic (ROC) analysis using 1,522 expressed genes and 2,046 non-expressed genes from an IMR90 microarray expression dataset . The moset al. , althouget al. ,28 was iWe next sought to use GRO-seq data aligning to the promoter regions of each of the active genes to train a probabilistic model (NBC) for genome-wide prediction of BEST events . Specifically, for each of three categories of interest , we computed the probability distributions for six distinct features Figure . Then fon = 6,770/9,662) of the genome-wide BEST predictions overlap IMR90 open chromatin loci  of these also overlap regions significantly enriched for H3K27ac in IMR90 cells (the most reliable chromatin marker to-date of active enhancers [Approximately 70% (nhancers ), which To further characterize BEST at DHS regions, we compared the profiles of GRO-seq sense/anti-sense read density at non-promoter-associated DHS and non-DHS control sites within transcribed intragenic Figure , non-traTo analyze the chromatin landscape at the predicted non-promoter-associated BEST loci more comprehensively, we assessed the representation of ten different histone modifications for which IMR90 data from chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) and corresponding control (input) data were available for two samples from the Epigenome Atlas . The most strongly enriched modification was H3K27ac Figure , which i+/H3K27ac+) and for those that do not (DHS+/H3K27ac-)  than poised enhancers (DHS+/H3K27ac-).We repeated the above-described analysis for non-promoter-associated DHSs that overlap H3K27ac peaks (DHS) Figure . Expectes Figure . DHS+/H32 Figure . Most imP < 0.0001) for BEST relative to poised enhancers  of the candidate enhancers that were validated by an in vitro reporter gene assay [To confirm this finding in another cell type, we turned to mouse embryonic stem cells (mESCs), which is the only other cell type in which both the nascent transcriptome (GRO-seq)  and enhane assay  were pre+/H3K27ac+), only a very modest signal at sites that overlap DHS peaks alone (CTCF+/DHS+), and no signal at sites that overlap neither  that are associated with active long-range regulatory elements.n = 35,983) were downloaded in hg18 coordinates from the UCSC Table Browser, build 36 [n = 30,326). For each transcript longer than 3 kb , we defined the body of the transcript as 1 kb downstream of the transcription start site to the annotated gene end. The level of transcription for each transcript body in IMR90 cells was determined by computing the average GRO-seq sense reads/kb/mapability using the previously published IMR90 GRO-seq data [n = 1,522) and true negative genes  from a published IMR90 microarray dataset [All human RefSeq transcripts , (ii) active transcript bodies , and (iii) randomly selected intergenic regions  were used to train a NBC to identify BEST, transcriptional elongation, and non-transcribed regions, respectively. First, 2-kb windows were centered at all active start sites of transcription, mid-points of active transcripts, and mid-points of the randomly selected intergenic regions. Then, for all 2-kb test windows in each class, the values for the following six features were calculated: (i) sense strand reads/kb/mapability (hereafter 'read density'), (ii) antisense strand read density, (iii) ratio of the sense strand read density to that in a 2-kb window immediately 3' of the test window, (iv) ratio of the antisense strand read density to that in a 2-kb window immediately 3' of the test window, (v) ratio of the sense strand read density to that in a 2-kb window immediately 5' of the test window, and (vi) ratio of the antisense strand read density to that in a 2-kb window immediately 5' of the test window. The values of these features were used to compute a probability distribution for each feature for each class. These distributions were utilized by the NBC, according to the following equation, to differentiate BEST events from productive transcriptional elongation and transcriptional noise:GRO-seq data aligning to (i) active promoters , were set to be equal for all classes. The six described features are represented by 1 fto 6f. The NBC was applied genome-wide, on both strands, avoiding regions associated with promoters or gene ends . On the plus strand, plus strand GRO-seq reads are interpreted as 'sense' and minus strand GRO-seq reads are interpreted as 'antisense'; on the minus strand, plus strand GRO-seq reads are interpreted as 'antisense' and minus-strand GRO-seq reads are interpreted as 'sense'. On each strand, for each non-overlapping 2-kb test window in the search space, a LOD score was computed comparing the probability of BEST with that of the other two classes:where the three classes Test windows with LOD scores > 2.5 on one or both strands were set as high-confidence BEST loci.IMR90 DNase-seq read data for four biological replicates were downloaded from the Epigenome Atlas, release 3 . MACS 4 version DHS/CTCF peaks were categorized as located within actively transcribed intragenic regions, inactive intragenic regions, or intergenic regions, with respect to the RefSeq dataset used in this study . To avoid promoter-associated peaks, DHS/CTCF peaks + 5 kb flanking regions that were within 2 kb of known transcription start sites, annotated gene ends, or IMR90 H3K4me3 peaks were discarded. For each of the remaining DHS/CTCF peaks within each category, GRO-seq sense and anti-sense reads/kb/mapability were computed in 150-bp windows from the start of the DHS/CTCF peak to the end of 5-kb flanking regions on either side. Then, for each DHS/CTCF peak and flanking region, nucleotide distance was converted to proportional distance. For example, for a DHS/CTCF peak that is 300 bp in length, the first 150 bp immediately upstream of the peak corresponds to '-0.5 to 0', the first 150 bp within the peak corresponds to '0 to 0.5', the second 150 bp within the peak corresponds to '0.5 to 1', the first 150 bp immediately downstream of the peak corresponds to '1 to 1.5', and so on.IMR90 ChIP-seq read data for ten different histone modifications, each with at least two biological replicates, and corresponding control (input) data, were downloaded from the Epigenome Atlas, release 3. For each histone modification dataset, the read density (reads/bp) was computed at predicted, high-confidence BEST loci, and then divided by the read density at randomly generated background (control) regions (2 kb in length and drawn from the same genomic locations as BEST loci), to yield an enrichment value. The enrichment value was then divided by the enrichment value for input, to yield a normalized enrichment value.et al. [in vitro validated mESC enhancers were downloaded from Schnetz et al. [To perform genome-wide prediction of BEST loci in an additional cell type, the NBC was trained and applied on publicly available mESC GRO-seq data in exactly the same manner as was done using GRO-seq data from IMR90 cells. Genome-wide candidate mESC enhancers  were downloaded from Zentner et al.  and in vz et al. . In bothBEST: bidirectional expression of short transcripts; ChIP-seq: chromatin immunoprecipitation followed by high-throughput sequencing; CTCF: CCCTC binding factor; DHS: DNase hypersensitive site; eRNA: enhancer RNA; GRO-seq: global nuclear run-on assay followed by high-throughput sequencing; H3K18ac: histone H3 lysine 18 acetylation; H3K27ac: histone H3 lysine 27 acetylation; H3K4me1: histone H3 lysine 4 mono-methylation; H3K4me3: histone H3 lysine 4 tri-methylation; IMR90: human lung fibroblasts; LOD: logarithm of odds; mESC: mouse embryonic stem cell; NBC: Naïve Bayes classifier; RNAP: RNA polymerase.The authors declare that they have no competing interests.PS conceived of, designed, and co-supervised the study, carried out computational analyses, and wrote the manuscript. MFM carried out computational analyses and participated in manuscript preparation. FSC co-supervised the study and edited the manuscript. All authors read and approved the final manuscript.Receiver operating characteristic (ROC) curve depicting the sensitivity and specificity at various IMR90 GRO-seq read density cutoffs for gene activity. This figure shows that a cutoff of 5 reads/kb/mapability achieves the best combination of sensitivity and specificity, according to the maximal accuracy metric.Click here for fileInverse correlation between promoter-proximal pausing index and level of gene transcription in IMR90 cells. This figure shows that promoter-proximal pausing of RNA polymerase is high for lowly expressed genes and low for highly expressed genes.Click here for fileRepresentation of BEST at three different enhancer subtypes in mouse embryonic stem cells. This figure shows that signal for BEST  is approximately two-fold and approximately eight-fold enriched at strong enhancers relative to weak enhancers and poised enhancers, respectively.Click here for file


Lin28 is a developmentally regulated RNA-binding protein that plays important roles in diverse physiological and pathological processes including oncogenesis and brain synaptic function. These pleiotropic roles of Lin28 are mechanistically linked both to its ability to directly stimulate translation of genes involved primarily in cell growth and metabolism and to its ability to block biogenesis of a subset of miRNAs including the let-7 family of miRNAs. In the case of direct stimulation of gene expression, Lin28 binds to targeted mRNAs through recognition of Lin28-responsive elements (LREs) within mRNAs and recruits RNA helicase A (RHA) to promote translation. RHA belongs to the DEAD-box protein family of RNA helicases, which generally catalyze ATP-dependent unwinding of RNA duplexes or remodeling of ribonucleoprotein complexes (RNPs). Since any given mRNA can potentially be inhibited by miRNAs bearing complementary sequences, we hypothesize that binding of Lin28 to LREs not only nucleates the binding of multiple Lin28 molecules to the same mRNA, but also leads to remodeling of RNPs through recruitment of RHA and causes release of inhibitory miRNA-induced silencing complexes bound to the mRNA. This mode of action may contribute to Lin28-mediated stimulation of translation in both tumor and neuronal cells. Lin28 is an evolutionarily conserved RNA-binding protein that has been implicated in diverse processes ranging from development, pluripotency, metabolism and cancer, to brain synaptic function , which is counteracted by Lin28 via blockage of let-7 production . Specifically, in terminally differentiated neurons, BDNF elevates Lin28 protein levels rapidly, and independently of transcription. This leads to selective loss of Lin28-regulated miRNAs including let-7, thus leading to increased translation of a specific set of genes targeted by these miRNAs  of Lin28 is required for interaction with RHA at both its N- and C-terminal regions  was first uncovered in a co-immunoprecipitation and mass spectrometry study using human ES cells, where RHA was found to be significantly enriched in Lin28-containing protein complexes  family of RNA helicases that function in diverse aspects of RNA metabolism including transcription, splicing, nuclear export, and translation  may overcome the inhibition by removing these structures, similar to the manner in which RHA disrupts the structures of PCEs Figure A. An altin vitro systems have suggested that this HuR-mediated derepression likely occurs via oligomerization along the RNA strand which leads to miRISC dissociation  acts in an analogous way to displace miRISC from Lin28 target mRNAs Figure B.in vitro and in vivo (Desjardins et al., An alternative possibility is that Lin28 oligomerizes along its target mRNA following initial binding to an LRE and contributes to miRISC dissociation Figure C. SeveraIn summary, we propose that the above three mechanisms may contribute to Lin28-mediated stimulation of translation, either singly or in combination, and dependent on cell type, developmental stage, and/or cell condition. Further studies will be required to thoroughly test this hypothesis.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Organisation of RNAs into functional subgroups that are translated in response to extrinsic and intrinsic factors underlines a relatively unexplored gene expression modulation that drives cell fate in the same manner as regulation of the transcriptome by transcription factors. Recent studies on the molecular mechanisms of inflammatory responses and haematological disorders indicate clearly that the regulation of mRNA translation at the level of translation initiation, mRNA stability, and protein isoform synthesis is implicated in the tight regulation of gene expression. This paper outlines how these posttranscriptional control mechanisms, including control at the level of translation initiation factors and the role of RNA binding proteins, affect hematopoiesis. The clinical relevance of these mechanisms in haematological disorders indicates clearly the potential therapeutic implications and the need of molecular tools that allow measurement at the level of translational control. Although the importance of miRNAs in translation control is well recognised and studied extensively, this paper will exclude detailed account of this level of control. Hematopoietic stem cells (HSCs) have a life-long capacity to replenish the stem cell compartment and give rise to multipotent progenitors. These progenitors expand to maintain the hematopoietic compartment and differentiate into various blood lineage progenitors. Lineage positive progenitors are committed for differentiation into mature blood cells.Transcription factors have a pivotal role in hematopoiesis to maintain a gene expression program that endows self-renewal properties to HSCs and enables commitment and differentiation into different blood cell lineages . The upr11 erythrocytes daily under normal physiological erythropoiesis. Gene expression is regulated at the transcription level, producing a cell-specific mRNA pool. Subsequent control of mRNA translation enables the cells to further adapt to environmental and developmental cues. Translation regulation (i) permits fast cellular responses to growth factors, inducing specific proteins to be expressed, (ii) selective expression of different protein isoforms from a given transcript, and (iii) induction of expression of pro-apoptotic proteins when the transcription program is inhibited. Translation Initiation is an important level of translation control. Cap-dependent translation initiation depends on two major limiting steps: (i) the formation of the initiation complex by release of the cap-binding initiation factor eIF4E from its binding factor 4E-BP and (ii) binding of the ternary complex (TC) consisting of GTP-loaded translation initiation factor 2 (eIF2:GTP) plus a methionine-loaded initiator tRNA (tRNAimet) to the 40S ribosome subunit. Cap-independent translation initiation depends on an internal ribosomal entry site (IRES) in the transcript that has to bind IRES transactivating factors (ITAFs). The biochemistry of translation initiation has been extensively reviewed [Upon commitment, the balance between proliferation and differentiation of lineage-specific progenitors is under tight control, to maintain the progenitor pool and ensure maturation in response to physiological demand. The production of increased numbers of mature blood cells during stress situations such as inflammation or hypoxia requires higher progenitor proliferation rates. Concurrently, feedback mechanisms must be closely coordinated to repress progenitor proliferation when the stress is over . The humreviewed . We willThe main regulator of erythropoiesis is the glycoprotein hormone Erythropoietin (Epo), produced in the kidney in response to oxygen tension in the blood. The function of Epo initiates from the specific interaction to its cell surface receptor (EpoR). In stress erythropoiesis, stem cell factor (cKit ligand) and glucocorticoids (GR) work in concert with Epo to induce expansion of progenitors in the mouse spleen , 7. The Epo and SCF transduce signals via multiple cooperating pathways in erythroid progenitors –10, amonimet [4EBP hyperphosphorylation results in the release of the mRNA cap-binding factor eIF4E (eukaryotic Initiation Factor 4E) . Subsequimet . This primet . There timet , 17. Theimet  and therimet , 20 in rimet . The rolAlthough the preinitiation complex consists of general translation factors, it scans mRNAs with a short and simple 5′ Untranslated Region (UTR) much more efficiently than mRNAs with a long and structured 5′UTR . Structuα4 complex sequestrating the phosphatase activity away from its own downstream targets 4EBP and S6K [Proliferation of erythroid progenitors under conditions that mimic stress erythropoiesis is strictly dependent on PI3K activity , 27. Ove and S6K –31. Cons and S6K . This fu and S6K , 32, 33  and S6K .cKIT, but not in normal human cord-blood-derived mast cells. This implies that inhibitors targeting translation initiation regulators are therapeutic candidates in the treatment of aggressive systemic mastocytosis (associated with cKIT D816V) and in AML harbouring the D816V cKIT mutant (present in 10 to 40% of core-binding factor leukaemia [ Mutations that enhance translation initiation efficiency have been implicated in the aggressiveness of various human cancers including Acute Myeloid Leukemia (AML) , 36. Coneukaemia ).α4 [Enhanced eIF4E-dependent translation of transcripts with a structured 5′UTR also contributes to chronic myelogenous leukaemia (CML). Leukemic transformation of hematopoietic progenitors by the BCR/ABL fusion protein depends on PI3K activation, which will enhance 4EBP phosphorylation and eIF4E release. In addition, BCR/ABL induces expression of SET, which subsequently acts as an inhibitory regulatory subunit of PP2A similar to α4 , 40. Notα4 .α4, reactivation of the phosphatase, pp2a, offers a potential alternative treatment to therapy-resistant patients [Pharmacological inactivation of mTOR with rapamycin reduces neoplastic proliferation in PTEN deficient mice, and reverses tumour growth in cancer cells characterised by activated PKB . In AML,patients .imet is known as the ternary complex (TC) [α subunit. Phosphorylation inhibits the recovery of eIF2:GTP from eIF2:GDP by protein eIF2B, and thereby the reassociation with tRNAimet. The kinases involved in eIF2 phosphorylation are activated by lack of haem (HRI), unfolded proteins in the ER (PERK), double strand RNA , or lack of amino acids (GCN2) [The second limiting translation factor next to eIF4E is eIF2, a GTPase that is associated with methionine-loaded initiator tRNA only in its GTP-bound state. This complex of eIF2:GTP/tRNAlex (TC) . The GTPlex (TC) . This thlex (TC) . The eIFs (GCN2) . In the s (GCN2) . Phosphos (GCN2) .α4. Translation of these transcripts is hypersensitive not only to growth factor signalling but also to inhibition by oxidative stress, or iron availability.Similar to eIF4E, also eIF2 availability has consequences for both the overall protein synthesis rate and the translation of transcripts that carry regulatory sequences in their 5′UTR. The phosphorylation level of eIF2 specifically controls translation of transcripts with upstream open reading frames (uORFs). Following the translation of a short uORF, the preinitiation complex lacking eIF2 continues scanning. During scanning the TC will reassociate to enable translation initiation at the next AUG startcodon in an appropriate context. The availability of TC will determine how fast translation can reinitiate. Thus, eIF2 phosphorylation renders translation dependent on the distance between an uORF and the AUG start codon. Notably, the majority of transcripts that are dependent on SCF-induced PI3K activity and on availability of eIF4E in erythroblasts also contain several uORFs, including the previously mentioned transcripts encoding Nme2, Use1, and The transcript encoding thrombopoietin (TPO) contains 7 uORFs of which the last uORF overlaps with the TPO start codon . In hereα and β contain an upstream ORF (uORF) that overlaps and is out of frame of the proper AUG start codon of the full-length isoform of the transcription factor [α translation initiation at a downstream AUG results in a truncated transcription factor protein that acts as a dominant negative isoform [Notably, the transcripts of several Ets-family members, Scl/Tal, and C/EBPn factor , 51. The isoform .α isoforms is evidenced by the occurrence of mutations in acute myeloid leukaemia (AML) cases that inhibit translation of the full-length C/EBPα protein [α is required for the generation of the GMP compartment and its expression also denotes selectivity in differential commitment to monocytic lineage [α is mutated [α but allows for translation from the downstream AUG start codon, which contributes to the phenotype of the AML [The importance of the relative abundance of C/EBP protein . The fun protein . In addi lineage . In a su mutated . Strikin the AML .Increasing evidence supports the importance of expressing different isoforms that modulate lineage commitment in hematopoietic cells. The expression of truncated forms of the transcription factor, Stem Cell Leukaemia (Scl), is regulated by differential initiation of translation  and resuThe list of transcripts regulated at the level of alternative AUG usage in hematopoietic regulation is increasing. A novel technique to identify translation start codons indicated that 65% of all transcripts expressed in mouse ES cells are translated from at least 2 alternative start sites . Translation initiation inhibition of growth regulatory proteins  is a known phenomenon  and thisPDGF2) is long (1022 bp), is structured and contains upstream ORFs, it is efficiently translated during megakaryocytic differentiation via binding and activation of the IRES by hnRNP C . The structural complexity of IRES elements argue in favour of their role as translation inhibitors, although it is more correct to define these structures as modulators of translation. For instance, although the 5′UTR of platelet-derived growth factor . Expression profiling of polyribosome-bound mRNAs from erythroblasts identified a specific set of transcripts that are selectively lost from polyribosomes upon reduced expression of Rps19 . Among tnslation .γ (IFNγ) suppresses the survival and expansion of T-helper 17 (Th17) cells by inducing expression of TTP, resulting in the destabilisation of the p19 mRNA, coding for a subunit of IL23 [mRNA translation is regulated by activities of the translation machinery components as described previously but also via regulation of proteins that bind to specific mRNAs. RNA binding protein complexes attenuate expression by modulating stability, degradation, cellular localisation, and silencing of specific RNAs or subgroups of mRNAs. The most studied RNA-binding proteins present in ribonucleoprotein (RNP) particles are the heterogeneous nuclear ribonucleoproteins (hnRNPs) that recognize AU-rich elements (ARE) and coordinate expression of mRNAs at the level of nuclear-cytoplasmic shuttling , cytopla of IL23 , 72. The of IL23 .In contrast to TTP and Brf1, members of the ELAV family of RNA-binding proteins  bind and stabilize ARE-containing transcripts . InteresModification of RNA binding-proteins by signalling can be another level of regulating translation efficiency at the proper AUG. Silencing of transcripts by binding of hnRNP K and hnRNP E1 to differentiation control element (DICE) in the 3′ UTR of 15-lipoxygenase (LOX) mRNA transcript  can be rAnother prototype example in which RNA-binding proteins regulate translation in hematopoietic cells is the iron-response element (IRE) . It is aCeruloplasmin is involved in iron metabolism and is translationally regulated by interferon gamma . Phosphoα (The expression of RNA-binding proteins that attenuate translation of specific subsets of mRNAs has been implicated in the transition from chronic CML to blast crisis events , 85. Forα  and G-CSRegulation of gene expression has been studied extensively in disease models and patient groups giving detailed annotations of the differential expression at the level of transcription regulation. Differential expression between the transcriptome and the proteome supports the importance of posttranscriptional regulation. Increasing evidence supports the prominent and so far underestimated regulation of mRNA translation, which depends on the availability and activity of the translation machinery, the structure of the transcript, and the expression of RNA binding proteins. Growth factor signaling enhances polysome recruitment of specific RNA transcripts, with marginal effect on the transcription regulation . Similar


Thyroid hormones play an essential role in early vertebrate development as well as other key processes. One of its modes of action is to bind to the thyroid hormone receptor (TR) which, in turn, binds to thyroid response elements (TREs) in promoter regions of target genes. The sequence motif for TREs remains largely undefined as does the precise chromosomal location of the TR binding sites. A chromatin immunoprecipitation on microarray (ChIP-chip) experiment was conducted using mouse cerebellum post natal day (PND) 4 and PND15 for the thyroid hormone receptor (TR) beta 1 to map its binding sites on over 5000 gene promoter regions. We have performed a detailed computational analysis of these data.By analysing a recent spike-in study, the optimal normalization and peak identification approaches were determined for our dataset. Application of these techniques led to the identification of 211 ChIP-chip peaks enriched for TR binding in cerebellum samples. ChIP-PCR validation of 25 peaks led to the identification of 16 true positive TREs. Following a detailed literature review to identify all known mouse TREs, a position weight matrix (PWM) was created representing the classic TRE sequence motif. Various classes of promoter regions were investigated for the presence of this PWM, including permuted sequences, randomly selected promoter sequences, and genes known to be regulated by TH. We found that while the occurrence of the TRE motif is strongly correlated with gene regulation by TH for some genes, other TH-regulated genes do not exhibit an increased density of TRE half-site motifs. Furthermore, we demonstrate that an increase in the rate of occurrence of the half-site motifs does not always indicate the specific location of the TRE within the promoter region. To account for the fact that TR often operates as a dimer, we introduce a novel dual-threshold PWM scanning approach for identifying TREs with a true positive rate of 0.73 and a false positive rate of 0.2. Application of this approach to ChIP-chip peak regions revealed the presence of 85 putative TREs suitable for further in vitro validation.This study further elucidates TRβ gene regulation in mouse cerebellum, with 211 promoter regions identified to bind to TR. While we have identified 85 putative TREs within these regions, future work will study other mechanisms of action that may mediate the remaining observed TR-binding activity. Thra and Thrb that are expressed as TRα and TRβ. Each of these has alternative splice variants, i.e. TRα1, TRα2, THRβ1 and TRβ2 [Thyroid hormones (TH), triidothyronine T3) and thyroxine T4), are produced by the thyroid gland and are critically important for normal fetal and early neonatal development. T4 is a pro-hormone, which is converted to T3 by deiodinase enzymes . T3 bind and thyr, are proand TRβ2 .TREs can be divided into positive and negative TREs, which promote or inhibit gene transcription, respectively. TREs show considerable sequence variation and are therefore considered to be degenerate. Thus, precise characterization of the TRE sequence elements has been extremely difficult relative to more conserved motifs. In general, “classic” TREs are composed of 2 hexamers  in direct repeat (DR), inverted repeat (IR) or everted repeat (ER) order. TR homodimers or heterodimers with RXR interact with the TRE hexamer motif. There are multiple factors, in addition to the consensus sequence, that determine whether a TR will bind to a DNA sequence. The orientation of hexamers and the number of nucleotides separating hexamer pairs in the response element is a discriminating factor for nuclear receptor binding to DNA . In contIn an attempt to identify TREs involved in mitigating the neurotoxic effects of perturbations in thyroid hormone levels, our previous work screened TRβ-bound DNA prepared by chromatin immunoprecipitation from juvenile mouse cerebellum DNA using promoter microarrays . This work examined ages bracketing a period of dynamic cerebellum development including an age immediately prior to (PND4) and at the peak of (PND15) a dramatic increase in circulating thyroid hormone levels in rodents (reviewed in Howdeshell ). In conet al.[et al. mimic sheared immunoprecipitated DNA with the advantage of knowing the precise chromosomal location and concentration. We used the spike-ins from the Whitehead Institute raw dataset to evaluate the sensitivity of peak identification using the normalization and peak finding analyses that we applied. In addition, we used the DNA sequence information associated with peaks to identify TRE hexamer characteristics and to conduct classic TRE mining. We then examined the frequency and distribution of TRE hexamers across known TH-responsive and non-responsive promoter regions, as well as random regions of the genome. Lastly, a number of predicted TR-binding regions were confirmed via ChIP-PCR. Our work describes a robust method for in silico prospecting for putative TREs for future confirmation, and enhances our understanding of the nature of TH regulation of gene expression.In order to optimize methodological approaches for identifying TRE-containing DNA regions, we applied bioinformatics analyses to the ChIP-chip data acquired in our previous work . We evalet al. compreheEuthyroid mice were sampled on post-natal days 4 or 15 and DNA from cerebella samples was subjected to ChIP using antibodies against TRβ. ChIP DNA was hybridized against total input DNA on Agilent custom gene promoter tiled DNA microarrays. Full ChIP-chip procedures are described in .There are a number of sources of bias that may confound a microarray signal. For example, microarray probes with high G-C content – particularly if C/G residues occur at or near the probe ends – will tend to hybridize more effectively than probes with lower GC. We investigated methods of normalization to reduce the influences of these biases on microarray data.A range of contemporary gene expression normalization methods were evaluated for their relevance to ChIP-chip data. Quantile  and Loweet al. [Various normalization techniques have been developed specifically for ChIP-chip data. However, many of these are designed for single colour arrays. The method of Song et al.  was evalet al. ), our daet al. [et al.[A linear-based model was developed by Potter et al.  to corre. [et al.  was notTo optimize the Splitter intensity threshold parameter for the Agilent platform, we conducted a series of analyses over a range of log ratio cut-off intensities from 2.25 to 2.75 standard deviations of the distribution, with and without prior application of normalization, to the Whitehead dataset. Normalization was also applied to each individual channel prior to peak finding. However, no improvement in peak recovery accuracy was observed. Accuracy was measured in terms of true positives (TP), false positives (FP), and false negatives (FN). Here, a predicted peak is identified as a TP if a predicted binding site overlaps with the true starting or ending location of a spike-in DNA segment , which plot  /  versus  /  using the Whitehead dataset [We have plotted performance curves as in Johnson  dataset . Figure  dataset  was founFor each predicted peak, Splitter outputs a chromosome number, a starting location, and an ending location. Using the optimal threshold value of the Whitehead dataset, 68 out of 100 true peaks were successfully predicted by Splitter. For each predicted peak, the difference between the predicted and actual start and end of the genome location were calculated to check for systematic over- or under-estimation of peak width. Since a true positive was labelled when any overlap occurred between true and predicted peak regions Figure , histogret al.[et al. note that the distribution of the length of shredded DNA is left shifted, leading to the expectation that many true hits will have fewer than three probes per peak. Therefore, we have set the MINRUN parameter to 2 in this study.The last two Splitter parameters to establish for the analysis are MINRUN and GAPMAX. MINRUN specifies the minimum number of adjacent probes that must have intensities above threshold to constitute a “peak”. This value will depend on the density/tiling of the probes across the promoter region and the expected length of DNA fragments following sonication. MINRUN must be set to at least two in order to minimize false positives. In contrast, setting the MINRUN to 3 may be overly conservative and lead to too many missed positives. Using the Keles et al. equationet al. study, the GAPMAX applied by the Whitehead Institute was set to 200 to cover the mapping of 3 probes (60 bp each) plus 2 gaps (10 bp each). Since probe mapping intervals are variable in length  it is prudent to extend GAPMAX to allow for one omitted probe. Using our own media characteristics  model was built using all previously reported half sites Table a vs. b. The above findings led us to examine the promoter regions of the published genes that are known to be regulated by TH in more detail and to determine if the sub-sequence most likely to be TR-binding can be determined by examining half site frequency. A sliding window of 250 bp was used to examine the number of half sites within the promoter regions of TH-regulated genes from Table By using a leave-one-out (LOO) protocol over all half sites from the known TREs in Table 1, and the one half site with a PWM score exceeding a higher threshold, t2. If the two scores comply with this rule, the putative site has a high probability of being a true TRE. Approaches have been developed in the literature to find bi partie binding sites [Based on the results above, we propose a new dual-threshold model for identifying potential TREs. For a given putative TRE site, the score of each half site should be computed. A putative TRE should be composed of one half site whose PWM score exceeds a lower threshold, tng sites -37. ThesA cross-validation experiment was conducted to optimize the two threshold values in order to minimize the false positive rate while maximizing the sensitivity. The test dataset was composed of sequences taken from the genomic regions surrounding previously reported TREs found in the literature Table . For the1 = {3, 3.05, 3.1…..5} and t2 = {5, 5.05, 5.1….8} to identify TREs in the 19 sequences described above. The optimal parameter values found by the LOO cross validation were t1 =7 3.76 and t2 = 6.0, obtaining a true positive rate of 0.73 and a false positive rate of 0.2.The LOO cross validation experiment was conducted by evaluating our dual-threshold PWM scanning model using every possible pair of threshold values  over the ranges tTo further validate our dual-threshold PWM method, we attempted to identify the known mouse TREs in the sequences included in Table Once the optimal parameter values were determined, our novel dual-threshold PWM scanning model was applied to the two sets (PND4 & PND15) of TRE ChIP-chip data to scan for putative TREs. Additional files We then applied our dual-threshold PWM method to all six classes of sequences used to plot half-site frequency in Figure et al.[, for correcting for nucleotide composition and position within the probe. Although normalization appears to correct for bias when applied in isolation, it was found to not be beneficial when combined with the Splitter peak finding algorithm. The Splitter algorithm for peak finding was modified to our dataset, and revealed 230 high confidence predicted peaks in PND4 and PND15. Of 25 peaks selected for ChIP-PCR validation, 16 peaks were determined to be truly TR-binding.We used ChIP-chip normalization techniques to evaluate a slightly modified version of Potter et al., for corWe tested the hypothesis that half site density within a promoter may be indicative of TH-gene regulation. Randomly selected promoter regions had a significantly higher density of half site sequence motifs than permutated sequences. However, genes known to be regulated by TH and ES appear to show a bimodal distribution, where one set of sequences appears to be enriched with half sites, whereas the other set does not exhibit significant enrichment. The fact that many TH-regulated promoter sequences had significantly fewer half sites than Splitter predicted peak regions may be explained by the fact that TRE motifs may only be enriched near the actual TR-binding site, and that the enrichment observed within the relatively short and focused Splitter predicted peak regions reflects this observation. Another possible explanation is that the known TH-regulated genes are not, in fact, mediated by direct binding of TR at a TRE .Considering that TR tends to operate as a dimer, a novel dual-threshold model was developed to scan genomic sequences for putative TREs. Analysis of classification performance estimated the true positive rate of the dual-threshold model to be 0.73 and the false positive rate to be 0.2. Applying this model we identified 85 novel putative TREs in the developing cerebellum.Overall, we have established a novel pipeline for the identification of TREs and provide a list of candidate binding sites that may be critical for normal TR-driven neurodevelopment. Future research will include wet-laboratory binding site validation, and examination of the other possible binding mechanisms that may be responsible for TR-binding regions not found to contain a putative TRE.This study was designed using 2 groups of 5 mice each; sacrificed either on post natal day 4 (PND4) or on PND15. ChIP was conducted using an anti-TR antibody and was analyzed alongside total input control DNA. The 10 sample ChIP-chip experiment was conducted on individual Agilent 44k custom tiled promoter region microarrays  containing 5000 gene promoter regions, with 60 nucleotide long probes mapping the -8 to + 2 kb region of the transcription start site. Probes corresponded to genomic locations every 200 bp on average. All details about the laboratory protocol used to conduct the ChIP-chip experiment and preliminary findings have been described previously .http://genome.ucsc.edu/cgi-bin/hgLiftOver) utility provided by the UCSC genome browser.The probe mapping was first updated for the TRE ChIP-chip dataset. Probe chromosomal locations were updated to the (NCBI 37 / MM9) build using the liftOver .Popular gene expression normalization methods . By comparing our observed value of d* relative to the distribution of d, we are unable to reject the null hypothesis (p > 0.01). An identical test was performed on TREs from Human, Rat, Mouse, and Chicken reported in Williams [Using the list of known half sites , the median signal intensity across probes within the peak, the ChIP-PCR validation results , the location of the peak with respect to the closest mRNA mapped by the UCSC genome browser on MM9 genome build , the ID of the mRNA mapped, the mRNA symbol, the distance from the closest RNA for outside peaks, and the number of references relating the mRNA symbol to the terms “thyroid hormone” on Pubmed. The microarray data can be obtained at MIAMExpress http://www.ebi.ac.uk/arrayexpress/; accession number E-MEXP-180.Click here for fileList of Peaks Identified by Splitter in the Dong PND 15 Dataset. Additional file 3 shows the peaks found by applying the strategy elaborated in this paper to the ChIP-chip data from PND15. The file includes the following columns; the chromosome of the peak, the starting location of the peak, the corrected ending location of the peak, the number of probes in a peak, the gap value for the peak (highest distance between probes of a peak), the median signal intensity across probes in the peak, the ChIP-PCR validation results , the location of the peak with respect to the closest mRNA mapped by the UCSC genome browser on MM9 genome build , the ID of the mRNA mapped, the mRNA symbol, the distance from the closest RNA for outside peaks, and the number of references relating the mRNA symbol to the terms “thyroid hormone” on Pubmed. The microarray data can be obtained at MIAMExpress http://www.ebi.ac.uk/arrayexpress/.Click here for fileList of peaks appearing in both PND4 and PND15 and overlapping with Dong et al [.g et al .Click here for fileHalf site density (y-axis) along the length of the promoter region sequence (x-axis) with the actual TRE location highlighted using an arrow for the eight remaining mouse TH-regulated genes from Table 2that were not plotted in Figure 6.Click here for filePutative TREs in Splitter Peaks for PND4. The table below show the results of our novel dual-threshold PWM scanning approach for PND4. Columns are described as the peakID which is the corresponding identifier to the peakID identifier for PND4 in Additional file Click here for filePutative TREs in Splitter Peaks for PND15. The table below show the results of the known TRE motif scanning for PND15.Columns are described as the peakID which is the corresponding identifier to the peakID identifier for PND15 in Additional file Click here for fileDistribution of putative TRE frequency as determined by our dual-threshold TRE scanning algorithm for the six classes of sequences plotted in Figure 5.Click here for file


MHC class I polypeptide-related sequence A (MICA) gene with the risk of HCV-induced HCC. In the current study, we performed detailed functional analysis of 12 candidate SNPs in the promoter region and found that a SNP rs2596538 located at 2.8 kb upstream of the MICA gene affected the binding of a nuclear protein(s) to the genomic segment including this SNP. By electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP) assay, we identified that transcription factor Specificity Protein 1 (SP1) can bind to the protective G allele, but not to the risk A allele. In addition, reporter construct containing the G allele was found to exhibit higher transcriptional activity than that containing the A allele. Moreover, SNP rs2596538 showed stronger association with HCV-induced HCC (P = 1.82×10−5 and OR = 1.34) than the previously identified SNP rs2596542. We also found significantly higher serum level of soluble MICA (sMICA) in HCV-induced HCC patients carrying the G allele than those carrying the A allele (P = 0.00616). In summary, we have identified a functional SNP that is associated with the expression of MICA and the risk for HCV-induced HCC.Hepatitis C virus (HCV) infection is the major cause of hepatocellular carcinoma (HCC) in Japan. We previously identified the association of SNP rs2596542 in the 5' flanking region of the  We also identified that the risk A allele of SNP rs2596542 was strongly associated with the low expression of soluble MICA (sMICA) in the serum of HCV-related HCC patients Hepatocellular carcinoma (HCC) is one of the common cancers in the world. It is well-known to be associated with the chronic infection of Hepatitis B (HBV) and Hepatitis C (HCV) viruses. In Japan, nearly 70% of HCC patients are infected with HCV MICA is a membrane protein which is up-regulated in various tumor cells and also induced in response to various cellular stresses such as infection, hypoxia, and heat shock MICA variations could affect sMICA level by either one or both of the following two possible mechanisms: (1) the genetic variation(s) in the coding region affecting the protein stability and (2) the transcriptional regulation. Previously, variable numbers of tandem repeats (VNTRs) in exon 5 of MICA were identified to affect MICA subcellular localization and serum MICA level MICA encodes the transmembrane domain and the insertion of an extra G nucleotide in the domain would result in a premature stop codon that would generate MICA protein without a transmembrane domain and subsequently affect sMICA level MICA variations would affect the MICA transcription in the liver cancer cells. Through the functional analysis of genetic variations in the MICA promoter region, we here report a causative SNP rs2596538 that increases the binding affinity of the transcription factor Specificity Protein 1 (SP1) and the risk of progression of the disease.We hypothesize that DNA samples for direct sequencing (50 HCV-related HCC cases), imputation analysis , and serum samples for sMICA ELISA (246 HCV-related HCC) were obtained from BioBank Japan The imputation study was performed by using a hidden Markov model programmed in MACH 2.Human liver cancer cell lines HLE and HepG2 were purchased from JHSF  and ATCC. These cells were grown in Dulbecco's modified Eagle's medium (Invitrogen) with 10% fetal bovine serum. Cells were cultured at 37°C with 5% COnd Generation (Roche) according to the manufacturer's instructions. The sequences of the 12 probes were listed in the HLE cells were grown in 15 cm culture plate until they reached 95% confluency. The plate was then sealed with parafilm and immersed in a water bath at 42.5°C for 1.5 hours The HLE cells  and HepG2 cells (heterozygote) were used in the ChIP assay. The plasmid pCAGGS-SP1 was transfected into both cells by using FuGENE6 Transfection Reagent (Roche). The ChIP assays were carried out using Chromatin Immunoprecipitation Assay Kit (Millipore) according to the manufacturer's protocol. In brief, the cells were treated with formaldehyde to crosslink DNA-protein complexes at 48 hours post-transfection. DNA-protein complexes were then sheared by sonication and immunoprecipitated by rabbit polyclonal anti-SP1 antibody . The resulting DNAs were analyzed by PCR . In ordeThree copies of 31 bp DNA fragments equivalent to the EMSA oligonucleotides of SNP rs2596538 were cloned into pGL3-promoter vector (Promega). The plasmids were co-transfected with pCAGGS-SP1 and pRL-TK plasmids (Promega) into HLE cells by FuGENE6 Transfection Reagent (Roche). The pCAGGS-SP1 plasmid provided the expression of transcription factor SP1, and pRL-TK plasmid served as internal control for transfection efficiency Cancer cell lysates were prepared by using pre-chilled RIPA buffer, and 25 μg of each lysate was loaded into the gel and separated by SDS-PAGE. Western blotting was performed according to the standard protocol. Rabbit anti-MICA antibody  and rabbit anti-SP1 antibody  were used in the experiment.t-test and Fisher's exact test as appropriate. The association of allele dependent sMICA expression was studied by Kruskal-Wallis test using R statistical environment version 2.8.1. The LD and coefficients (D' and r2) were calculated by Haploview version 4.2 The case-control association was analyzed by Student's MICA with both liver fibrosis and hepatocellular carcinogenesis.Since the development of HCC consists of multiple steps, we investigated the role of SNP rs2596542 with disease progression. SNP rs2596542 was genotyped in patients at three different disease categories of CHC (chronic hepatitis C) without liver cirrhosis (LC) or HCC, LC without HCC, and HCC. The statistical analysis indicated that SNP rs2596542 was significantly associated with disease progression from CHC to LC with P-value of 0.048 and odds ratio of 1.17 . The risMICA locus in 3.2% of Japanese population MICA mRNA expression, we analyzed the relation between the MICA copy number variation (CNV) and the HCC susceptibility. We quantified this CNV by real-time PCR in 375 HCV-related HCC patients and 350 HCV-negative controls. As shown in A previous report has indicated the deletion of the entire MICA gene which may be associated with its promoter activity. We had conducted direct DNA sequencing of the 5-kb promoter region which included the marker SNP rs2596542 using genomic DNAs of 50 HCC subjects and identified 11 SNPs showing strong linkage disequilibrium with the marker SNP rs2596542 (D'>0.953 and r2>0.832) (We then focused on the variations in the 5' flanking region of the 2>0.832) , Table 2To investigate whether these genetic variations would affect the binding affinity of some transcription factors, we had conducted the electrophoretic mobility shift assay (EMSA) using the nuclear extract of HLE human hepatocellular carcinoma cells. Since MICA is a stress-inducible protein in silico analysis identified a putative GC box in a protective G allele but not in a risk A allele  into these cell lines, the cell extracts were subjected to ChIP assay using anti-SP1 antibody  was not immunoprecipitated with anti-SP1 antibody. To further evaluate the binding ability of SP1 to each allele in vivo, we sub-cloned the DNA fragment that amplified from genomic DNA of HepG2 cells before and after immunoprecipitation by anti-SP1 antibody. The subsequent sequencing results showed that 26 out of 29 tested clones contained the G allele, demonstrating the preferential binding of SP1 to the G allele  assay to confirm the binding of SP1 to this genomic region antibody . SubsequG allele .To further investigate the physiological role of the interaction between SP1 and this genomic region, we performed reporter gene assay. Three copies of 31-bp DNA fragments flanking the candidate functional SNP rs2596538 were subcloned into the multiple cloning sites of the pGL3 promoter vector. The relative luciferase activity of the plasmid including the G allele was significantly higher than that including the A allele . Further−5). The functional SNP rs2596538 exhibited a stronger association with the HCC risk than the marker SNP rs2596542 (2.46×10−5). We also analyzed the relationship between the SNP rs2596538 and the sMICA level among 246 HCV-induced HCC patients and found a significant association with the P-value of 0.00616  and activates the transcription of various viral and cellular genes MICA expression. The membrane-bound MICA (mMICA) serves as a ligand for NKG2D to activate the immune system and results in the elimination of viral-infected cells by NK cells and CD8+ T cells Approximately 160 million people (2.35% of the worldwide population) are estimated to have HCV infection MICA would be attributable to the difference in downstream pathway between HBV and HCV. HBV virus encodes hepatitis B virus X protein (HBx) that is pathogenic and promotes tumor formation. It had been reported that HBx protein was associated with an elevated expression of MT1-MMP, MMP2, and MMP3 In contrast to HCV-induced HCC, our group had previously identified that higher sMICA level was associated with poor prognosis in HBV-induced HCC patients MICA and MICBMICB promoter region was found to be associated with MICB transcription level MICA transcription is directly influenced by functional variant. Moreover, this functional SNP is significantly associated with HCV-induced HCC. Our findings provide an insight that MICA genetic variation is a promising prognostic biomarker for CHC patients.SP1 was previously identified as a transcriptional regulator of both Figure S1Pairwise LD map between marker SNP and 11 candidates SNP. Black color boxes represent regions of high pairwise 2r value. The LD was determined by direct DNA sequencing of MICA promoter region from 50 randomly selected HCV-HCC patients.(TIF)Click here for additional data file.Table S1Characteristics of samples and methods used in this study.(DOCX)Click here for additional data file.Table S2The sequences of each oligo used in the EMSA and ChIP assay.(DOCX)Click here for additional data file.Table S3Copy number variation between HCV-HCC and control samples.(DOCX)Click here for additional data file.


Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context—the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding?We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity.Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data—ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure—we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts. Transcription factors (TFs) are proteins that bind sequence elements in DNA and thereby affect expression of neighboring or distal genes. Depending on cellular contexts, such as hormone stimulus or the cell’s differentiation state or cell type, a TF can bind to different subsets of the TF’s potential binding sites and regulate different gene expression programs. InvestiThere are three main factors that determine a TF’s binding activity at a potential binding site. First, TFs bind to specific sequence motifs that favin silico. Some of these methods rely on context-dependent data, such as experimentally determined gene expression, TF binding, or chromatin structure[The TF binding activities that result from a given cellular context form in sum a transcription regulatory network. There are many different methods of inferring the structure of such regulatory networks tructure, and thetructure,14, and tructure-18.In comparison, many traditional methods for inferring regulatory networks are context-indifferent, typically relying on sequence motifs to map putative transcription factor binding sites (TFBS). Some of these methods use additional data such as a putative site’s conservation level in related species,20 and min vivo, so using ChIP-seq data alone or as a basis in more integrative methods for modeling gene regulation will result in context-specific predictions[Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) is the current high-throughput experimental technique of choice for mapping the genome-wide state of chromatin, and this technique is also used for experimentally identifying TFBS. ChIP-sedictions,18. But The few studies that have investigated cell-type specificity of TFBS show that in general, binding differences increase with functional and evolutionary distance. A study investigating MyoD-binding in the highly related cell types myoblasts and myotubes found the majority of predicted binding sites to be common in both tissues. AnotherTo address this question, we used ChIP-seq data from two ENCODE cell lines to examiRecent genome-wide analyses of TF binding have shown that for a few individual TFs, binding sites can vary substantially between cell-types-31. But p = 0.34 on a two-sided Wilcoxon signed rank test).Nearly half of the K562 peaks (47%) and more than a third of the HeLa-S3 peaks (34%) were associated with promoter regions; specifically, the 2000bp upstream and 200bp downstream of RefSeq genes’ transcription start sites plus their first introns FigureB. We incp = 0.52).Given the high variability in putative TFBS, we wondered to what extent these binding sites were cell-type specific. By comparing the genomic loci of peaks across cell types, we found that 33% of K562 and 30% of HeLa-S3 peaks overlapped with at least one peak of the same TF in the other cell type. Taking into account that the relative overlap is limited by the cell type having the fewest peaks for the TF, we found that the potential overlap varied between 16% (TAF1) and 93% (BRF2)  in HeLa-S3. A large fraction (81%) of the relatively few E2F4 peaks in HeLa-S3 overlapped with K562 peaks. Others using ChIP-chip have previously found E2F4 to have between 500 and 700 target genes with little cell-type specific binding. This cop = 0.0052 for K562 and p = 0.0034 for HeLa-S3, one-sided Wilcoxon signed-rank test). Promoter regions were again defined as the region 2000 bp upstream to 200 bp downstream of transcription start sites of RefSeq genes, together with the complete first intron of the genes. Consistent with Pol III factors preferentially associating with regions near functional Pol II promoters[p = 0.012 for K562 and p = 0.020 for HeLa-S3).Because of the unexpected differences between the two cell lines for the E2F4 peaks, we wondered whether overlapping peaks were enriched in genomic regions known to have limited tissue-specific activity. A previous study has indicated that TFs primarily mediate cell-type specific regulation through enhancers located far from core promoter regions. In accoromoters three ofp = 0.0093 for K562 and p = 0.0049 for HeLa-S3). Housekeeping genes frequently have high CpG promoters, whereas tissue-specific genes tend to have low CpG promoters[p = 2.4∗10−4 for K562 and p = 4.9∗10−4 for HeLa-S3). Thus, as expected, peaks mapping to tissue-independent regulatory regions showed a higher degree of overlap between cell types than did peaks mapping to other genomic regions.Housekeeping genes tend to maintain similar expression levels across cell types and are therefore also likely to have similar regulation within different cell types. Indeed, the overlap was greater for peaks mapping to promoters of housekeeping genes comparedromoters,41. AccoSome of the “missing” overlap could perhaps be explained by alternative transcription factor binding sites, where a binding site active in only one cell type could have another nearby site active in the other cell type. To test this possibility, we counted the number of overlaps when allowing increasingly larger regions surrounding the original peak regions FigureD. We four = 0.46, two-sided t-test p-value p = 0.21 in K562; r = 0.54, p = 0.11 in HeLa-S3). Using the differences in expression and peak count between the two cell types gave similar results , but removing the expression outliers c-Fos and c-Myc gave a large and significant correlation . This suggested that the difference in peak count could partly be related to difference in mRNA expression levels of the TFs.The variability in peak numbers could suggest that some TFs have more binding activity in one cell type compared to the other. As one important determinant of binding activity could be TF availability, we looked at how the TF mRNA expression levels, as measured by RNA sequencing, correlated with the number of peaks. We found some, but insignificant, correlation between TF expression and peak numbers  and CpG-rich regions (10 TFs significant). The TFs that showed little or no association were not expected to be associated with Pol II promoters; CTCF acts primarily in intergenic and intronic regions), whereaEven though others have demonstrated poor conservation of binding sites across species for some TFs,32, we nHigh peaks were also generally more associated with sequence motifs (peaks for 5 of 7 TFs in K562 and 7 of 7 TFs in HeLa-S3 were positively correlated with PWM scores). However, c-Fos peak heights in K562 correlated negatively with PWM scores for the AP-1 motif, which c-Fos recognizes as a dimer with co-factor Jun. One possible explanation could be that competition with Jun homodimers and Jun-ARF2 heterodimers for the AP-1 motif may have pushed c-Fos to bind more in non-canonical motif regions. AlternaUsing discriminative motif discovery on the 10% highest and 10% lowest c-Fos peaks (see Methods), we found that the highest c-Fos peaks in K562 had motif sequences such as CCAAT and CGCGG, which resemble binding profiles for NF-Y and parts of AP-2, but we did not find any AP-1 binding motif or variant thereof. When using sequences from another TF (CTCF) as negative data and running motif discovery with high and low c-Fos peaks separately as positive data, we again could not find the AP-1 motif in the high peaks , as K562 and HeLa-S3 are established from two different individuals with different genotypes. We aligned available DNA sequencing data for K562 and HeLa-S3 to the reference genome (hg18) and by using SNPs from the HapMap database and the aligned sequence reads, we calculated the most likely genotype for each cell type for the SNPs in peak regions (see Methods).In total, 12% of the peaks harboured at least one SNP. Relative to peak count, more overlapping peaks had SNPs (13.9%) than K562-specific (11.8%) or HeLa-S3-specific (12.1%) peaks. But a relatively higher number of SNPs in cell-type specific peaks were homozygous with different alleles in the cell types .p = 1.9∗10−4 and p = 1.1∗10−5 on one-sided paired t-tests for K562 and HeLa-specific peaks, respectively). Among 73 K562-specific peaks that fitted the SNP and motif criteria stated above, 49 had higher motif scores in K562 and 24 had higher scores in HeLa-S3. Among 82 HeLa-S3-specific peaks, 60 had higher motif scores in HeLa-S3 and 22 in K562.We reasoned that the SNPs that were homozygous for one allele in one cell type and homozygous for another allele in the other cell type could affect TF binding if the SNP resided in a high-scoring TF motif FigureA. Indeedr = 0.22, p = 0.09). This correlation was especially strong and significant for the 10% highest peaks, both for the peaks that had SNPs in high scoring motif regions  and for the peaks that had SNPs in any location within the peak region . Thus, differences in genotypes do affect TF binding and can explain some of the binding site differences between cell lines.Genotype-related differences were also evident in peaks common between the two cell types. Specifically, for the SNPs that had different homozygous genotypes and were located in high-scoring motif regions, the difference in motif score between the genotypes correlated positively with the difference in peak height between the cell lines  classifiers to separTo assess which SVM features were most important for correct classification, we removed a single feature group at a time from the SVM and measured how this affected the SVM’s cross-validation performance FigureA. AlthouGiven the major importance of peak height and clustering for classifier performance, and in light of previous results Figure, we reasPeak height and clustering were the dominant factors in explaining cell-type specificity and could therefore confound the effects of other features when analyzing the total set of all peaks. We therefore did a separate analysis of the highest and most clustered peaks, as differences between cell types for these peaks should be due to other factors than peak height and clustering. Specifically, we first identified the subset of peaks that were among the 10% highest and that also occurred among the top 20% largest peak clusters; that is, in clusters containing peaks from six or more TFs. Second, we did a feature removal analysis on the five TFs that had more than 100 remaining peaks in both the overlapping and the cell-type specific datasets. Although none of the features on this reduced dataset showed the same consistent pattern as the height and cluster features did on the complete dataset, five of the eight features  had a positive impact on the prediction for the majority of the five TFs . The HPP model was also significantly better than Height (p = 7.8∗10−3). The Height model only had a higher ROC-score than the full SVM model on the BRF1 dataset. This is likely due to a low number of training examples for the more complicated SVM method, as there were only 81 BRF1 peaks in K562. Consistent with previous results  used during training, but would the models generalize to a different cell type? To address this question, we used a stratified 10-fold cross-validation procedure where we first trained the models on a subset of K562 peaks that were common and cell-type specific compared with HeLa-S3 peaks. We then tested the models on a different subset of K562 peaks that were common and cell-type specific compared to peaks in the GM12878 ENCODE cell line; that is, we trained the models on K562 and HeLa-S3 data and tested the models on K562 and GM12878 data. Importantly, none of the peaks in these train and test sets overlapped and the cell-type specific features  were derived from two different cell types in the train (HeLa-S3) and test (GM12878) sets. We trained and tested the models on the five TFs with ChIP-seq data available in all three cell types.p = 0.22 and p = 0.43, respectively; one-sided Wilcoxon signed rank test), the full SVM model had a significant drop in performance (p = 0.031); see FigureWhereas the Height and HPP models had similar performance in the new cell type  having fewest peaks for the given TF. Promoter regions were defined as the region 2000 bp upstream to 200 bp downstream of transcription start sites of RefSeq genes (as of October 22. 2009), together with the first intron of the gene. Pol III promoters were defined as the regions -2000bp upstream and +200bp downstream of tRNA transcription start sites from the tRNAscan-SE Genomic tRNA Database[% and CpG frequency > 75%; CpG poor regions having CpG frequency < 48%. The list of housekeeping genes were downloaded from[CpG frequencies and CpG region types were computed as in. Specifided from.Peaks were clustered by first extending each peak region to a total length of 2000bp. Peaks overlapping within the extended region were then identified as belonging to the same cluster.Paired-end RNA-seq reads were downloaded from the ENCODE Caltech RNA-Seq track in the UCSC Genome Browser. Both available replicates were used and the number of reads mapping to each RefSeq exon was counted. The count was normalized on exon length and averaged, to get the expression for a given RefSeq. Counts were also normalized on total number of reads within one experiment when comparing across experiments. The expression for a TF was averaged from all RefSeqs whose gene symbol mapped to the TF and had at least 1 read mapping to an exon.To investigate how peak height correlated with other genomic features, we binned peaks in 10 approximately equally-sized groups according to peak height (i.e. number of tags mapping to peak region). Each peak got a measure of chromatin accessibility using data on DNaseI hypersensitivity from the ENCODE Open Chromatin track available from the UCSC Genome Browser: Using the tag files with aligned reads from the DNaseI hypersensitivity sequencing (DNase-seq) experiments, we counted the number of tags that overlapped with each peak region and divided by the peak region length and total number of tags (in millions) in the experiment to control for variation in peak region length and differences in sequencing depth between experiments. We used version 2 of the dataset and all available experimental replicates. Likewise, data on H3K4me3 and H3K27me3 was taken from the ENCODE UW Histone ChIP track. The average phyloP score in each peak region was calculated from the phyloP28Way placental mammals multiple alignment scores, also available from UCSC.An in-house developed program was used to calculate PWM scores. The maxRaw reads from ENCODE ChIP-seqp≤0.05) in the distribution of PWM scores for the known co-factor PWMs in a 1000 bp region surrounding the cell-type specific peaks of K562 and HeLa-S3.A list of potential co-factors for each TF was made by combining data from the Fantom consortium and the Discriminative motif discovery in 500 bp regions centered on cell-type specific peaks was done with DREME, using aThe list of features for the SVM method is available in TableThe chromatin and histone data for each peak were calculated as explained above. For the features we calculated the average read count in the peak region in two cell types, and also the difference in read count between the cell types. CpG features were computed as explained above (in “Defining overlap regions and peak clusters”). The phyloP feature was calculated for each peak region by using a weighted average of all the signal values that overlapped with a peak region, weighting each value by the length of the signal region.Two 10-fold crossvalidation tests were performed on all three methods using peaks from the K562 cell type. The first test investigated the ability to recognize which K562 peaks overlapped with HeLa-S3 peaks when trained and tested on different parts of the K562 dataset. The second tested how the methods could predict which K562 peaks overlapped with GM12878 peaks when being trained on data from comparing K562 peaks with HeLa-S3.When doing feature elimination on single features, all features within a single feature group were removed from the dataset before training and testing. Feature elimination was also done with larger groups of features, as explained in Results. All of the tested methods were based on the same Support Vector Machine setup for consistency. The classification was performed using a combination of own code and the PyML machine learning framework,67. We cThe ChIP-seq peak region data are available upon request.The authors declare that they have no competing interests.Analyzed the data and drafted the article (TH). Created the peak region datasets and cluster regions from ChIP-seq tag count data (MR). Calculated genotype for SNPs in the cell types (RM). Conceived the study . Helped prepare the final article and supervised the work (PS). All authors read and approved the final manuscript.This tab-separated file includes all peaks and the SNPs mapping to the peak regions. The fields are peakID, cell type, TF, chromosome, peak start, peak stop, height, overlapK562, overlapHeLa-S3, overlapGM12878, overlapPromoter, SNPs. The SNP field is further delimited by a dash (−) for each SNP in the peak region. Each SNP is described by ID, genotype , genotyping quality score estimated by SAMtools[Click here for fileHigher peaks in HeLa-S3 have more consistent support in other data marking regulatory regions. As FigureClick here for fileHigher peaks in GM12878 have more consistent support in other data marking regulatory regions. As FigureClick here for filePol III promoter overlap correlates with peak height for Pol III-associated factors. Similar to Promoter data in FigureClick here for fileA) The canonical AP-1 motif taken from the Transfac database[B) The highest scoring motif discovered in low c-Fos peaks in K562 is similar to the canonical motif. C) The 23rd highest scoring motif in high c-Fos peaks in K562 has the best resemble to the canonical motif, but is still quite different from the motif depicted in A).Different motifs in low and high c-Fos peaks. database  Peaks were binned in equally-sized bins sorted on peak height. The upper panel (K562) shows the difference in median DNase-seq read count pr bin between K562 and HeLa-S3 for K562 specific peaks, whereas the lower panel shows the difference between HeLa-S3 and K562 for HeLa-S3 specific peaks. B) As A), but for H3K4me3 signal instead of DNase accessibility. Within the set of peaks that are unique to a cell type, the higher peaks have larger difference in chromatin accessibility and active marks between the cell types than lower peaks. This suggests that higher cell-type specific peaks are more likely to be due to cell-type specific regulation of the chromatin.High cell-type specific peaks have a larger difference in chromatin signal than low peaks. Click here for fileA) ROC curves on 10-fold stratified cross-validation on K562 and HeLa-S3 training and testing data. X-axis is true positive rate, Y-axis is 1-false positive rate. B) ROC curves on 10-fold stratified cross-validation on K562/HeLa-S3 training and GM12878 testing data.ROC curves. Click here for fileMost important feature group for classification. Difference in ROC score after first removing confounding factors (peak height and clustering), and grouping features into three groups  and then removing a given feature group. Error bars show average ROC score change on 10 cross-validation folds plus/minus one standard deviation.Click here for fileK562 peaks overlap. Overlap in different cell types for all K562 peaks. CTCF and GABP have many common peaks between all the cell types, whereas c-Fos have few common peaks between all cell types.Click here for fileSignificant co-factors. This table shows the co-factors having significant expression differences between K562 and HeLa-S3. Co-factors shown in bold text also had a PWM available and significant difference in PWM score distributions between cell-type specific peaks.Click here for filePWMs. The position weight-matrix identifiers of the PWMs taken from Transfac Professional (6) and Click here for file