The C-terminal domain (CTD) of RNA polymerase II (Pol II) consists of conserved heptapeptide repeats that function as a binding platform for different protein complexes involved in transcription, RNA processing, export, and chromatin remodeling. The CTD repeats are subject to sequential waves of posttranslational modifications during specific stages of the transcription cycle. These patterned modifications have led to the postulation of the “CTD code” hypothesis, where stage-specific patterns define a spatiotemporal code that is recognized by the appropriate interacting partners. Here, we highlight the role of CTD modifications in directing transcription initiation, elongation, and termination. We examine the major readers, writers, and erasers of the CTD code and examine the relevance of describing patterns of posttranslational modifications as a “code.” Finally, we discuss major questions regarding the function of the newly discovered CTD modifications and the fundamental insights into transcription regulation that will necessarily emerge upon addressing those challenges. The transcription of DNA to RNA in eukaryotes is catalyzed by three structurally related RNA polymerases, with each acting on a different class of genes . RNA pol1S2P3T4S5P6S7 [Saccharomyces cerevisiae, to 52 repeats in humans. When fully extended, the yeast CTD can span a distance of up to 650 Å, over 4 times the diameter of the core polymerase , general transcription factors (GTFs), the Mediator complex, and Pol II. These factors self-assemble into a pre-initiation complex (PIC) at the promoters of Pol II-transcribed genes , 32. Recleosomes –45. Therleosomes , 47. Theleosomes –51. Thisleosomes –57. The leosomes –60. Howeleosomes , 62. Polleosomes . It is pleosomes . The holleosomes . In thisleosomes –68. Thesleosomes . Importaleosomes .in vivo, with Kin28/Cdk7 being the dominant kinase [Two complexes of the PIC, TFIIH and the Mediator, contain important kinases that phosphorylate the CTD. TFIIH is a ten-subunit complex containing two helicases, an ATPase, a ubiquitin ligase, a neddylation regulator, and a cyclin-dependent kinase  –118. Bott kinase , 119–124Phosphorylation of Ser5 is involved in coordinating the placement of several key posttranslational modifications on chromatin that constitute the histone code   and possibly the methyltransferase (Abd1 in cerevisiae) directly interact with both the Ser5-P and the core polymerase [Schizosaccharomyces pombe [GAL1 gene is significantly compromised by the inhibition [An especially important role of Ser5-P is the recruitment of the capping enzyme complex. The capping complex places the mreaction , 131. Thlymerase , 133. Allymerase , 131. A lymerase , 135. Ines pombe . Intereshibition . No suchhibition  made thehibition .We and others have recently demonstrated that Kin28/Cdk7 is also the primary kinase that phosphorylates Ser7 (Ser7-P) –141. Thelongation factor (NELF) complex [Following promoter clearance, transcription initiation factors are exchanged for transcription elongation factors required for RNA processing, passage through chromatin, and suppressing cryptic transcripts. In budding yeast, this exchange occurs immediately after the +1 nucleosome . The ass complex , 159. It complex . Bur1 al complex , 163. H2 complex , 129. Bu complex . Initial complex , 166. In complex .Most Ser5-P marks are removed near the +1 nucleosome through the action of the newly characterized CTD phosphatase Rtr1 . This phSer2-P is critically important for the interaction between the CTD and many histone modifying and RNA processing machines , 172–178The role of CTD modifications in orchestrating transcription termination is better described in recent reviews , 188. InProcessing of Pol II transcripts occurs via one of two distinct, gene class-specific pathways in yeast. Many small mRNAs (<550 bp), CUTs, snRNA, and snoRNAs (non-coding genes) are processed via the Nrd1-Nab3 pathway , while lThe second pathway, used for the processing of most mRNA transcripts, involves the cleavage and polyadenylation factor (CPF) complex, cleavage factor IA and IB (CFIA and CFIB) complexes, and the exosome  31, 195195, 197.cis-conformation [in vitro, Fcp1 is considered a Ser2-P-specific phosphatase in vivo [In both pathways, the CTD is hypophosphorylated by the combined action of two essential phosphatases at the end of transcription: Ssu72 and Fcp1. Ssu72 is a member of the Associated with Pta1 (APT) complex, which is present at both gene classes and is involved in 3′ processing of non-coding RNAs . As suchormation , 74. In ormation , 230. Al in vivo , 232. Fc in vivo . Defects in vivo . Though  in vivo . Removal in vivo .Global dephosphorylation of the CTD facilitates the release of Pol II from DNA, which can then recycle to promoters for the next cycle of transcription , 236. ItIn addition to its many roles in transcription initiation, elongation, and termination, the CTD has been implicated in a variety of transcription-extrinsic processes, such as mRNA export and stress response. mRNA export (reviewed in –242) reqThe CTD is also involved in stress response. The ubiquitin ligase Rsp5 binds the CTD and ubiquitylates Pol II in response to DNA damage , 246. SiThe concept of the CTD code was first proposed due to the enormous amount of information that can be encoded via post-translational modification of the CTD repeats , 248. Thtrans-conformation while Ssu72 prefers a cis-CTD as substrate [cis-elements encountered by Pol II to the protein complexes necessary for successful progression through the transcription cycle. Further investigation into the mechanism of this information transfer will resolve the controversy over the existence of a CTD code.In addition to the various phosphorylation marks, the isomerization state of the CTD also contributes to the complexity of the code. For example, Pcf11 binds the CTD in the ubstrate –75. ManyS. pombe [Extraordinarily rapid progress has been made over the last several years in the field of CTD research; however, many important questions remain unanswered. Although the profiles of Ser7-P have been mapped and several of its kinases discovered, its function at protein coding genes remains unclear. Additionally, most of the kinases identified are established members of the transcription initiation or elongation complexes. One could expect to find new enzymes that could modulate the CTD in response to signals, as post-translational modifications are often used as a mechanism for cells to respond to external stimuli. The recent discovery of Ser7-P at elongating Pol II has also prompted the question of whether Tyr1 and Thr4 phosphorylation (Tyr1-P and Thr4-P) occurs? Tyr1 can be phosphorylated by c-Abl in mammals, but no homolog is present in yeast . In addiS. pombe . Interesrpb1 in the human CTD is methylated by the coactivator-associated methyltransferase1 (CARM1) [The role of non-canonical residues and their modification states on mammalian CTD remain to be explored. In mammals, the Ser7 residue is only weakly conserved in polymerase-distal repeats of the CTD, often changed to lysine or arginine . Interes (CARM1) . This me (CARM1) .in vivo at different points during the transcription cycle. Recent mutational analysis suggests that the minimal functional unit of the CTD consists of three consecutive Ser-Pro dipeptide residues in a S2-S5-S2 configuration [Besides the characterization of novel marks, significant structural challenges remain for understanding the known phosphomarks. One limitation of ChIP is its inability to identify the exact phosphorylation patterns across individual CTD repeats guration , but it guration . However


In hepatocytes, HNF4α regulates a large number of genes important for nutrient/xenobiotic metabolism and cell differentiation and proliferation. Currently, little is known about the epigenetic mechanism of gene regulation by HNF4α. In this study, the global and specific alterations at the selected gene loci of representative histone modifications and DNA methylations were investigated in Hnf4a-deficient female mouse livers using the improved MeDIP-, hMeDIP- and ChIP-qPCR assay. Hnf4a deficiency significantly increased hepatic total IPed DNA fragments for histone H3 lysine-4 dimethylation (H3K4me2), H3K4me3, H3K9me2, H3K27me3 and H3K4 acetylation, but not for H3K9me3, 5-methylcytosine,or 5-hydroxymethylcytosine. At specific gene loci, the relative enrichments of histone and DNA modifications were changed to different degree in Hnf4a-deficient mouse liver. Among the epigenetic signatures investigated, changes in H3K4me3 correlated the best with mRNA expression. Additionally, Hnf4a-deficient livers had increased mRNA expression of histone H1.2 and H3.3 as well as epigenetic modifiers Dnmt1, Tet3, Setd7, Kmt2c, Ehmt2, and Ezh2. In conclusion, the present study provides convenient improved (h)MeDIP- and ChIP-qPCR assays for epigenetic study. Hnf4a deficiency in young-adult mouse liver markedly alters histone methylation and acetylation, with fewer effects on DNA methylation and 5-hydroxymethylation. The underlying mechanism may be the induction of epigenetic enzymes responsible for the addition/removal of the epigenetic signatures, and/or the loss of HNF4αper se as a key coordinator for epigenetic modifiers.Hepatocyte nuclear factor 4 HNF4A gene causes maturity-onset diabetes of the young in humans (MODY) HNF4α is a conserved member of the nuclear receptor superfamily of ligand-dependent transcription factors Chromatin is the complex of DNA and histone proteins, which provides the scaffold for the packaging of entire genome α regulating gene expression may be mediated by its influence on epigenetic modifications There are limited studies on the chromatin-related alterations by HNF4α, although previous study has suggested that HNF4At present, many platforms are available for investigation of DNA and histone modifications, including ChIP, N-ChIP, biotin-tag affinity purification and DNA adenine methyltransferase identification for histone modifications Hnf4a-deficient female mice. Our results demonstrate that HNF4α is essential in establishment and/or maintenance of epigenetic modifications in liver.To our best knowledge, no previous studies have been reported to spike external positive and negative controls in ChIP and hMeDIP coupled with qPCR, which are critical to ensure the reproducibility and robustness of the assays by normalizing variations in the processes of immunoprecipitation (IP), preparation of IPed DNA, and/or qPCR quantification. Thus, the aim of the present study was to develop improved ChIP and (h)MeDIP assays by incorporation of spiked DNA fragments as positive and negative controls, and to use the assays to elucidate alterations of hepatic DNA methylation (5 mC and 5 hmC) and representative histone modifications  in Hnf4a (Hnf4a-LivKO)  and age-matched wild-type  littermates at the age of 45 days were collected in the previous studies The livers of female young-adult mice with liver-specific knockout of TATGATCCAGAACAAAGGAAACG and reverse: GAAGTTCAAACCATGCAGTAAGA; negative control primers, forward: GGAGAATAACTTCTTCGTGGAAA and reverse: TCAGTATTAGGAAACTTCTTGGC), and JumpStart® PCR master mix . The PCR conditions were as follow: 95°C for 10 min; 95°C for 30 s, 60°C for 30 s and 72°C for 30 s for 40 cycles; 72°C for 4 min, in a total volume of 20 µl. Combined PCR products from several tubes were purified with Gel Extraction Kit according to the manufacturer's instructions . The positive control (P2) for MeDIP was derived from P1 methylated by CpG methyltransferase  followed by purification  and the negative control (forward: GGAGAATAACTTCTTCGTGGAAA and reverse: GCCATGATAATGTTGGACGAC) were assessed by gel electrophoresis after PCR reactions in the presence of human and mouse genomic DNA and input DNA fragments. The primers for mouse Ugt2b36 promoter (forward: CATGATTTTCCACCAACACAGTA and reverse: GTAATCCATCTGTCACTGCTTG) and human MDR1 promoter (forward: TGGAGCCATAGTCATGTACTCA and reverse: ACAAGTGTTTATCCCAGTACCA) were used as controls for mouse and human samples, respectively. The PCR conditions for above primers were as follows: 95°C for 10 min; 95°C for 30 s, 60°C for 30 s and 72°C for 30 s for 35 cycles; 72°C for 4 min, in a total volume of 20 µl. The potential mutual interferences between P1 and Input DNA fragments in RT-PCR reaction were also evaluated by comparing one's Cq values in the absence and presence of the other one at low, medium, and high concentrations or dilutions.The external positive  and negative (303 bp) control DNA fragments for the ChIP assay were generated by PCR reactions from pRL-CMV vector   with minor modifications . Briefly, frozen liver tissues (n = 4) were homogenized in RNA STAT-60, and RNA was precipitated by the addition of isopropanol. After washing with 75% ethanol, the RNA pellet was air-dried and dissolved in RNase-free water. cDNA was produced by the use of High Capacity cDNA Reverse Transcription Kit  according to the manufacturer's instructions. The resultant cDNA was used as the templates for RT-PCR reactions.Table S3 in File S1) and iQ™ SYBR® Green Supermix  by MyiQ2™ Two-Color Real-Time PCR Detection System (Bio-Rad). The PCR conditions, unless otherwise specified in Table S3 in File S1, were as follows: 95°C for 3 min; 95°C for 15 s, 60°C for 30 s and 72°C for 30 s for 40 cycles in a total volume of 10 µl. Percentage of enrichment of epigenetic signatures at loci of interest normalized by positive control was calculated based on the following formula: The enriched DNA fragments in ChIP, MeDIP, and hMeDIP were quantified with RT-PCR reaction containing DNA fragments, 500 nM primers  for ChIP, MeDIP, and hMeDIP.The prepared cDNA from mouse livers was quantified by RT-PCR under the same conditions as IPed samples. Amounts of mRNAs were calculated using the comparative CT method, which determines the amount of target normalized to β-actin, with the wild-type control value set at 1.0. Each Cq value used for the calculation was the mean of triplicates.2 and protease inhibitor). After centrifugation at 1500g for 5min, the supernatant was removed. The pelleted nuclei were incubated at 4°C with gentle mixing for 1h in PBS supplemented with 0.1% Triton X-100. The DNA-bound and –unbound histones were fractionated by centrifuging at 12000g for 10min. The resultant pellets were incubated with RIPA buffer followed by centrifugation to release the histones from chromatin. Histones in the resultant supernatant were resolved in sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). Western blot quantification of histones was carried out with the primary antibodies same as those in ChIP assay and the anti-H3 antibody (D1H2) from Cell Signaling Technologies. Primary antibodies were revealed with HRP-conjugated secondary antibodies  or anti-mouse IgG ) and ECL Western Blotting Substrate . ChemiDoc™ XRS+ System (Bio-Rad) and Image Lab 4.0 software (Bio-Rad) were used to capture signals and determine signal intensities.Nuclear fractions of histones were prepared as reported previously Hnf4a-LivKO and wild-type samples (SigmaPlot 12.0). Statistical significance was set at p<0.05.All values are expressed as mean ± S.E. The student's t-test was used to determine the statistical difference between The positive and negative controls in ChIP, MeDIP, and hMeDIP assays were generated by PCR amplification of fragments of Renilla luciferase cDNA using the vector pRL-CMV (Promega) as a template to avoid the homologies to human and mouse genomic DNA, the context in which the assays will be carried out. The lengths of the positive controls and negative control are 307 and 303bp, respectively, which are the average sizes of sonicated DNA fragments in three assays. Gel electrophoresis in The positive control P2 and the positive control P3 were generated by the methylation of P1 and PCR reaction for MeDIP and hMeDIP, respectively. To validate P2 and P3, the controls were digested by McrBC endonuclease which can cleave methylated and hydroxymethylated DNA but not unmethylated DNA. In contrast to the unmethylated P1 (positive control for ChIP) that had no degradation after digestion, more than 97% of P2 and P3 were destroyed as shown in 6, which is beneficial for downstream analysis such as microarray and deep sequencing. The potential interference of the positive control with the determination of target locus exemplified by UDP glucuronosyltransferase 2 family, polypeptide b36 (Ugt2b36) promoter in the context of mouse liver DNA fragments was evaluated by comparing the Cq values of Ugt2b36 promoter in the presence/absence of the positive control. As illustrated in Table S1 in File S1, the positive control did not have a significant effect on the RT-PCR reaction of Ugt2b36 promoter. The efficiencies were consistent across four groups with values of 1.91–1.96. Likewise, the existence of mouse genomic DNA fragments at all dilution factors did not affect the PCR efficiencies of the positive control, with efficiency values of 1.90–1.94. In addition, the coefficient of variation (CV) values of Cq for all groups were less than 2%, indicating that the RT-PCR system worked well and could be utilized for subsequent quantitative experiments.The amplification efficiencies in the RT-PCR reactions for all positive and negative controls were derived from the standard plots each generated with 5 orders of magnitude ranging from 0.01fg to 100 fg. The values of PCR efficiency were 1.93, 1.83, 2.01, and 1.93 for P1, P2, P3, and negative controls, respectively, demonstrating that it is suitable to quantify P1, P2, and P3 by using the same pair of primers (primers 2). The final amounts of 3 positive and 1 negative controls used in RT-PCR were approximately 40 fg, which resulted in Cq values around 20. The ratio of added controls relative to the total DNA fragments in the IP step was less than 1∶10The enrichment ratio of positive to negative control was about 600∶1 and 1800∶1 in MeDIP and hMeDIP assay, respectively, further confirming the validity of our methylated (P2) and hydroxymethylated (P3) controls. The CV values of P2 and P3 enrichment in six technical replicates were more than 20%, underscoring the technical deviations from the procedures and the importance of incorporation of an external control to normalize the MeDIP and hMeDIP assays. The average CV values for loci of interest in 3 technical replicates were decreased from 21% and 41% to 13% and 17% for MedIP and hMeDIP, respectively, after the normalization by the corresponding positive control.In ChIP assay, the enrichment for negative control was less than 0.02% and the positive control was recovered more than 43%. P1 in ChIP had a relatively lower CV of recovery equal to 17.5%, compared to P2 and P3 in MeDIP and hMeDIP, accounting for the technical deviations from the procedures after the washing step in ChIP. The average CV value for loci of interest in 3 technical replicates was slightly decreased, from 25% to 21%, after the normalization by the positive control.Hnf4a-LivKO mice and wild-type littermates. We interrogated the particular loci of enrichment normalized by the positive control in MeDIPed and hMeDIPed samples. The loci chosen for determination were based on their potentially alterable 5 mC and 5 hmC status during development that was proven or speculated by previous studies Slc10a1/Ntcp) and H+/peptide transporter 2 (Slc15a2/Pept2) is strongly negatively associated with their tissue-specific expression in liver and kidney Rgl3) and exon3 of SRY-box containing gene 9 (Sox9) have much higher 5 mC in adult mouse livers than in embryonic mouse livers. Conversely, the loci in promoter of Caspase 9 (Casp9) and exon1 of Z-DNA binding protein 1 (Zbp1) have much higher 5 mC in embryonic mouse livers than adult mouse livers Hnf4a-LivKO and wild-type livers, indicating that Hnf4a deficiency in young-adult mice does not extensively affect DNA methylation and hydroxymethylation. For specific loci, Rgl3 intron2-3 and Sox9 exon3 had higher 5 mC than Casp 9 promoter and Zbp1 exon1 in wild-type mouse livers. Nevertheless, Hnf4a deficiency had no significant effects on 5 mC and 5 hmC of these four loci , organic anion transporter 1 (Slc22a6/Oat1), microsomal glutathione S-transferase 3 (Mgst3), Slc15a2, and sulfotransferase 2a1 (Sult2a1) encode enzymes and transporters responsible for drug metabolism and disposition in the liver and kidney Gtf2h2) encodes TFIIH which has a central role in the transcription of RNA polymerases Hnf4a deficiency did not affect the 5 mC and 5 hmC of loci in Slc10a1 promoter and exon1, Slc47a1 exon1, Slc15a2 promoter, and Sult2a1 exon2, as well as the promoter of the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (Gapdh). In contrast to the above gene sites, the loci in Slc22a6 exon1, Gtf2h2 promoter, and Mgst3 intron1-2 were methylated at higher levels in Hnf4a-LivKO livers than those in wild-type livers. Intriguingly, the 5 hmC of Slc22a6 exon1 and Mgst3 intron1–2 were decreased accordingly due to Hnf4a deficiency. Gtf2h2 promoter displayed slight but insignificant decrease of 5 hmC in Hnf4a-LivKO Liver  determined in previous studies by Quantigene Plex assay or microarray Hnf4a-LivKO model as ours in the present study. The proteins encoded by the selected genes are important in HNF4α-regulated processes in liver, such as xenobiotic metabolism, cellular defense, or apoptosis. Among these genes, Perp has been reported as a direct HNF4α-target gene in mouse liver PDZK1 is also a direct HNF4α-target gene in human cell line Hnf4a-deficient female mouse livers , but down-regulated Asgr1, Cyp2c44, Cyp3a11, Gas2, Slc47a1, Slc10a1, Pdzk1, Perp, Sult1b1, and Ugt2b1 (down-regulated group). For other genes Celsr1, Lifr, Ppara, and Ugt2b36 (unchanged group), Hnf4a deficiency slightly but did not significantly decrease their mRNA expression. The trends of alterations of gene expression due to Hnf4a deficiency are generally in agreement with the previous data To elucidate the effect of e livers Fig. 4. Hnf4a-LivKO and wild-type mouse livers. As shown in Hnf4a deficiency significantly increased the total ChIPed DNA fragments by 73%, 51%, 36%, 74%, and 89% for H3K4me2, H3K4me3, H3K9me2, H3K27me3 and H3K4ac, respectively, compared with wild-type. In contrast, the total enrichment for H3K9me3 was not significantly changed. To validate the findings in ChIP, we further used Western blot to quantify these histone marks in DNA-bound histones prepared from wild-type and Hnf4a-LivKO mouse livers. We used 0.1% Triton to remove the soluble histones to prepare insoluble histones (assembled to chromatin) which are the targets of antibodies in ChIP assay. In general, the alterations of the histone modifications in Hnf4a-LivKO from Western blot are consistent with those of total ChIPed fragments in ChIP. The total DNA-bound H3K4me2, H3K4me3, H3K9me2, and H3K27me3 in Hnf4a-LivKO mice were 2.1, 2.7, 5.2, and 6.7 fold higher, respectively, than those in wild-type mice  total DNA-bound H3K4ac than wild-type mice  harbored in the down-regulated genes had more enrichment of H3K4me2 in Hnf4a-LivKO mouse livers than in wild-type livers. However, all 3 up-regulated genes  had no changes in H3K4me2 at the promoter. Unchanged genes including Celsr1, Ppara, and Ugt2b36 also had more H3K4me2 in their promoters in Hnf4a-LivKO livers.Next we interrogated the enrichment of each histone modifications tested at specific loci. The results are summarized in Hnf4a deficiency than that of H3K4me2. At the same loci, only those in the promoters of Defb1, Gadd45β, Slc10a1, Pdzk1, and Perp had changes in enrichment of H3K4me3 in Hnf4a-LivKO livers. Intriguingly, the loci harbored in up-regulated genes Defb1 and Gadd45β had increased, whereas the loci in down-regulated genes such as Slc10a1 and Pdzk1 had decreased enrichment of H3K4me3 due to Hnf4a deficiency. In addition, we also determined the enrichment of H3K4me3 in additional loci located in the gene body, including Cyp2c44, Sult1b1, Sult1e1, and Ugt2b1 exons. Cyp2c44 exon1 but not exon2 as well as Ugt2b1 exon1 and exon2 had significantly lower H3K4me3 enrichment in Hnf4a-LivKO than in wild-type livers, although the promoters of these genes had no changes in H3K4me3. In contrast, Hnf4a-LivKO mouse livers had no changes in H3K4me3 in exon1 and exon2 of Sult1b1 or exon 2 of Sult1e1, similar to those in the promoters of Sult1b1 and Sult1e1.The enrichment of H3K4me3 in the gene loci investigated was affected to a lesser extent by Hnf4a deficiency, whereas the up-regulated loci were not affected. In contrast, H3K9me3 was not changed in the majority of loci investigated, with the exception of increases in the Cyp2c44 and Ugt2b36 promoters in Hnf4a-LivKO mice.The enrichment of H3K9me2 in most down-regulated and unchanged loci was increased by Ugt2b1 and Ugt2b36, were enriched more for H3K27me3 in Hnf4a-LivKO than in wild-type livers, whereas the up-regulated gene loci were not affected. In regard to H3K4ac, Hnf4a deficiency increased its enrichment at the majority of tested loci regardless of up-, down-regulated, or unchanged genes, with the exception of loci in the Defb1 promoter, the Gadd45β promoter, and Slc47a1 exon1. The promoter of housekeeping gene Gapdh was used as a negative reference, which expectedly had no changes in the six histone modifications in Hnf4a-LivKO mice. Collectively, Hnf4a deficiency affects H3K4me2, H3K4me3, H3K9me2, H3K9me3, H3K27me3 and H3K4 acetylation, to different degree, at the loci of these tested genes.Most of the down-regulated and unchanged genes, except Hnf4a deficiency, we determined the alteration of mRNA expression of genes encoding a set of epigenetic modifiers in the Hnf4a-LivKO mouse liver (Setd7), mixed-lineage leukemia 3 (Kmt2c/Mll3), WD repeat domain 5 (Wdr5), euchromatic histone lysine N-methyltransferase 2 (Ehmt2/G9a), suppressor of variegation 3-9 homolog 1 (Suv39h1), enhancer of zeste homolog 2 (Ezh2), histone deacetylase 3 (Hdac3), Hdac6, DNA methyltransferase (cytosine-5) 1 (Dnmt1), tet methylcytosine dioxygenase 2 (Tet2), Tet3, isocitrate dehydrogenase 1 (Idh1), Idh2, and Idh3a which are important for dynamically laying down and/or removing modifications to DNA and histones Hnf4a deficiency significantly induced mRNA expression of Setd7, Kmt2c, Ehmt2, Ezh2, Dnmt1, and Tet3, but not Wdr5, Suv39h1, Hdac3, Hdac6, Tet2, Idh1, Idh2, and Idh3a. In addition, the expression of Hist1h1c encoding H1.2 H3f3b encoding H3.3 Hist1h1d encoding H1.3 Hnf4a-LivKO livers  and repressive signatures (e.g. H3K9me2) at the same loci. The promoter of a gene locus with unchanged mRNA expression, such as Ugt2b36, has both increased active signature H3K4ac and increased repressive signatures H3K4me2, H3K9me2, and H3K9me3 due to Hnf4a deficiency, further confirming the complex nature of histone modifications in regulating gene expression. The final impact on gene expression may be determined by the combination of these active/suppressive marks, the dominant histone modification (if exists) and/or other factors such as the coactivators or corepressors that recognize these histone modifications. In the present study, Hnf4a deficiency increases the expression of histone H1 isoform H1.2 and Histone H3 variant H3.3. The histone linker H1 stimulates H3K27me3 by EZH2 Hnf4a-deficient liver.The redundant combination of multiple active/suppressive histone modifications to ensure robust chromatin regulation and the existence of bivalent domains indicate the complex nature of gene regulation by histone modifications Hnf4a deficiency in the young-adult mouse liver causes a global increase in H3K4me3. The present data suggests a role of induced Setd7 and Kmt2c, two H3K4 methyltransferases Hnf4a deficiency. In contrast to other histone modifications investigated, H3K4me3 alterations correlate bidirectionally with gene expression, at least for the loci investigated, in that the increases in H3K4me3 correspond with increased gene expression, and decreases in H3K4me3 correspond with decreased gene expression. H3K4me3 interacts directly with the TAF3 subunit of the general initiation factor TFIID to facilitate the recruitment of TFIID, and H3K4me3 cooperates with the TATA Box to enhance the assembly of the preinitiation complex and initiation of transcription Hnf4a deficiency affects H3K4me3 in exon1 of Cyp2c44 as well as exon1 and exon2 of Ugt2b1 but not in the corresponding promoters suggests that the alteration of H3K4me3 may be gene locus specific. Future genome-wide mapping will provide comprehensive information regarding gene locus-specific H3K4me3 alteration in response to Hnf4a deficiency as well as the role of H3K4me3 in hepatic gene expression regulated by HNF4α.Hnf4a deficiency, and HNF4α has gender-divergent expression, with 5 fold more protein in male than female rat livers Hnf4a deficiency on DNA and histone modifications in male mouse liver might be more dramatic, which warrants further investigation.It is noteworthy that sex differences exist in the response to liver-specific Hnf4a deficiency in young-adult mouse hepatocytes alters H3K4me2, H3K4me3, H3K9me2, H3K9me3, H3K27me3, and H3K4ac, as well as DNA methylation and hydroxymethylation to differential extents, at least for the loci investigated. The underlying mechanism may be induction of epigenetic enzymes responsible for the addition/removal of the epigenetic signatures, and/or the loss of HNF4α per se as a key coordinator for epigenetic modifiers. In addition to its key role in the regulation of transcriptome, HNF4α has a major role in regulating the epigenome in hepatocytes.In conclusion, the present study provides convenient improved (h)MeDIP- and ChIP-qPCR assays for epigenetic study. File S1Hnf4a-LivKO and wild-type mice was normalized by the positive control. Insets, the percentage of total ChIPed DNA fragments relative to input for H3K4me2 and H3K4me3 in livers from female Hnf4a-LivKO and wild-type mice. Negative = negative control. Mean ± S.E., N = 3 biological replicates. *, p<0.05 versus wild-type. Figure S2, Effects of Hnf4a deficiency on histone H3 lysine 9 dimethyl (H3K9me2) and histone H3 lysine 9 trimethyl (H3K9me3) in female mouse livers. Enrichment of H3K9me2 and H3K9me3 at specific loci relative to input in ChIPed DNA fragments in livers from female Hnf4a-LivKO and wild-type mice was normalized by the positive control. Insets, the percentage of total ChIPed DNA fragments relative to input for H3K9me2 and H3K9me3 in livers from female Hnf4a-LivKO and wild-type mice. Negative = negative control. Mean ± S.E., N = 3 biological replicates. *, p<0.05 versus wild-type. Figure S3, Effects of Hnf4a deficiency on histone H3 lysine 27 trimethyl (H3K27me3) and histone H3 lysine 4 acetylation (H3K4ac) in female mouse livers. Enrichment of H3K27me3 and H3K4acat specific loci relative to input for ChIPed DNA fragments in livers from female Hnf4a-LivKO and wild-type mice was normalized by the positive control. Insets, the percentage of total ChIPed DNA fragments relative to input for H3K27me3 and H3K4ac in livers from female Hnf4a-LivKO and wild-type mice. Negative = negative control. Mean ± S.E., N = 3 biological replicates. *, p<0.05 versus wild-type. Table S1, The efficiency (E) and coefficient of variation (CV) for Cq values in real-time PCR reactions in the presence/absence of input DNA sample and positive control. Table S2, The list of functions of genes tested in histone modifications due to Hnf4a deficiency. Table S3, List of qPCR primers used for IPed DNA fragments and cDNA.Figure S1, Effects of Hnf4a deficiency on histone H3 lysine 4 dimethyl (H3K4me2) and histone H3 lysine 4 trimethyl (H3K4me3) in female mouse livers. Enrichment of H3K4me2 and H3K4me3 at specific loci relative to input in ChIPed DNA fragments in livers from female (PDF)Click here for additional data file.


PIK3CG gene expression in platelets and macrophages. A protein–protein interaction network including up- and down-regulated genes in Pik3cg knockout mice indicated that PIK3CG is associated with gene pathways with an established role in platelet membrane biogenesis and thrombus formation. Thus, rs342293 is the functional common variant at this locus; to the best of our knowledge this is the first such variant to be elucidated among the known platelet quantitative trait loci (QTLs). Our data suggested a molecular mechanism by which a non-coding GWA index SNP modulates platelet phenotype.Turning genetic discoveries identified in genome-wide association (GWA) studies into biological mechanisms is an important challenge in human genetics. Many GWA signals map outside exons, suggesting that the associated variants may lie within regulatory regions. We applied the formaldehyde-assisted isolation of regulatory elements (FAIRE) method in a megakaryocytic and an erythroblastoid cell line to map active regulatory elements at known loci associated with hematological quantitative traits, coronary artery disease, and myocardial infarction. We showed that the two cell types exhibit distinct patterns of open chromatin and that cell-specific open chromatin can guide the finding of functional variants. We identified an open chromatin region at chromosome 7q22.3 in megakaryocytes but not erythroblasts, which harbors the common non-coding sequence variant rs342293 known to be associated with platelet volume and function. Resequencing of this open chromatin region in 643 individuals provided strong evidence that rs342293 is the only putative causative variant in this region. We demonstrated that the C- and G-alleles differentially bind the transcription factor EVI1 affecting  Genome-wide scans have revealed multiple genetic regions underlying complex traits. However, the transition from an initial association signal to identifying the functional DNA change(s) has proved challenging. Many of the DNA changes discovered are located outside protein-coding regions and may exert their effects through gene regulation. We screened genetic regions associated with hematological traits in erythroblasts (red blood cells) and megakaryocytes (platelet-producing cells) and mapped sites of open chromatin, which harbor active gene regulatory elements. We investigated a DNA sequence change located within a site of open chromatin at chromosome 7 in megakaryocytes, but not erythroblasts, known to be associated with platelet volume. We showed that this DNA change is functional due to alteration of the binding site of a transcription factor, which regulates the expression of a gene that affects platelet characteristics. Mice lacking this gene revealed significant differences in expression of several important platelet genes compared to wild-type mice. The approach described here can be applied in different cell types to functionally follow-up association signals with many other biological traits by identification of the causative base change and how it affects gene function, thus paving the way to clinical benefit. In recent years, genome-wide association (GWA) studies have driven the discovery of genetic loci associated with a multitude of complex traits and diseases. However, due to linkage disequilibrium (LD), the single-nucleotide polymorphisms (SNPs) assayed in large-scale GWA studies typically yield a proxy for the actual causative variant(s) and often fail to even pinpoint the underlying gene Several strategies have been proposed to identify causative sequence variants underlying GWA signals, including fine-mapping  and functional annotation using biochemical assays (e.g. gene expression studies) Integrated studies have illustrated how particular trait-associated alleles are mechanistically relevant to cellular functions and pathways SH2B3–ATXN2 and PTPN11, have been reported to be associated with coronary artery disease (CAD) and myocardial infarction (MI) suggesting a possible role for platelets as an intermediate phenotype Heritable hematological traits are of particular interest in studying the architecture of complex traits, since relevant cell types for functional assays are easily accessible. In addition, genetic loci associated with platelet counts, Here we describe maps of open chromatin generated by FAIRE in a megakaryocytic and an erythroblastoid cell line at known GWA loci associated with hematological quantitative traits, CAD and MI. We report substantial differences in chromatin architecture among the two cell types and assess the phenotypic causality of variants that fall into NDRs specific to either cell type. We demonstrate the procedure with the locus at chromosome 7q22.3, which is associated with mean platelet volume and function, where we identify a site of open chromatin in megakaryocytes but not erythroblasts and elucidate the molecular mechanism that contributes to the platelet phenotype.We profiled chromatin at 62 non-redundant genetic loci representing all known associations, as of November 2009, with 11 cardiovascular traits  in a megWe then analyzed the 254 and 251 NDRs in MK and EB cells, respectively, in context of their genomic location , overlap 3′-UTR or exonic; TBX3 gene locus). Open chromatin observed in MK but not EB cells was located on average 2.80 kb upstream of a TSS. NDRs found in EB but not MK cells were located on average 1.77 kb, whereas NDRs common to both cell types were located on average 0.98 kb upstream of a TSS.At the interrogated loci, NDRs clustered around transcription start sites (TSS). In MK and EB cells, 70.5% and 76.1% of all FAIRE peaks were located within 20 kb of a TSS, respectively . HoweverP=0.0225). A similar trend of enrichment was observed in EB lineage-specific genes in EB cells (P=0.0781). This result highlights the importance of studying chromatin architecture and gene regulatory circuits in a cell type-dependent manner.To assess the cell type specificity of NDRs marked by FAIRE in our experiment, we determined the number of peaks in lineage-specific genes for MK and EB cells present on the array . A signi2≥0.8, Phase II HapMap, CEU population) located within a NDR  HBS1L–MYB gene cluster . At the four NDRs common to both cell types, we found variants associated with: platelet signaling (PLS) located at the promoter regions of PEAR1  and its best proxy rs342294 . Data from the 1000 Genomes Project 2≥0.8) with rs342293, and confirmed that only rs342293 and rs342294 fall into the MK-specific NDR. This NDR was absent in both FAIRE-chip data sets  in EB cells  in 643 healthy individuals. No other common or low-frequency variants were detected, although a rare, possibly private, SNP was detected in one individual as a heterozygous position  located in a polyA-region . Based oin vitro binding of GATA1 transcription factors to this site. We also confirmed RUNX1 transcription factor binding in vitro by demonstrating a supershift for both probes with a RUNX1 antibody (We then performed electrophoretic mobility shift assays (EMSA) in nuclear extracts from the megakaryocytic cell line CHRF-288-11. We observed a band shift with probes surrounding rs342293 for both the ancestral allele rs342293-C and the alternative allele rs342293-G . Howeverantibody . We valiantibody .PIK3CG transcript levels (permutation P=0.047) P=0.0542; PRKAR2B, HBP1, PBEF1 and COG5, had a statistically significant eQTL with rs342293 . Next, we assessed the PIK3CG-eQTL in three different types of white blood cells, macrophages, monocytes and B cells , as well as in different tissues, fat and skin  and wild type mice (n=3) with a fold-change of at least ±1.5 (P=2.97×10−14), and ‘blood coagulation’ . Notably, this gene list includes Gp1bb (fold-change of −2.203 in −/−Pik3cg compared to wild type mice), Gp5 (−3.211), Gp9 (−0.996), Gp6 (−0.711) and Vwf (−1.381). All five genes are transcribed in the megakaryocytic lineage in humans, based on the HaemAtlas data. The platelet glycoproteins GP1BB, GP5 and GP9, together with GP1BA constitute the platelet membrane receptor for the plasma protein Von Willebrand Factor (VWF), which is encoded by VWFTo further our understanding of the role of PIK3CG in platelets, we performed whole-genome gene expression profiling in whole blood of ast ±1.5 . Functioast ±1.5  revealed−/−Pik3cg and wild type mice. Canonical pathway enrichment analysis based on the curated gene sets of the Molecular Signatures Database (MSigDB) v3.6 P=3.44×10−8), ‘platelet activation’ (P=5.05×10−8), ‘formation of platelet plug’ (P=2.85×10−7), ‘hemostasis’ (P=7.48×10−6), ‘formation of fibrin clot and clotting cascade’ (P=1.39×10−5) and ‘platelet adhesion to exposed collagen’ (P=1.86×10−5).To explore the signaling pathways of PIK3CG in humans, we analyzed 191 human orthologs of the 220 differentially expressed genes between We constructed a protein-protein interaction network centered on the proteins encoded by the 191 transcripts described above. First-order interactors of these ‘core’ proteins were obtained from Reactome, an open-source manually curated database of human biological pathways The intersection of maps of open chromatin with variants identified through GWA studies can facilitate the search for underlying functional variant(s). We applied the FAIRE assay to generate a catalog of NDRs in a megakaryocytic and an erythroblastoid cell line at 62 selected genetic loci associated with hematological and cardiovascular traits. We provided evidence that open chromatin profiles exhibit distinct patterns among different cell types and that cell-specific NDRs can be useful in prioritizing regions for further functional analysis. As proof, we elucidated the molecular basis of the association with mean platelet volume and function at chromosome 7q22.3. We identified a NDR in MK but not EB cells containing the index SNP rs342293 for this association and demonstrated that the alleles of rs342293 differentially bind the transcription factor EVI1. Thus, to the best of our knowledge this is the first functional variant to be elucidated among the known platelet QTLs.PIK3CG gene expression levels. PIK3CG is transcribed in megakaryocytes but only weakly expressed in erythroblasts FLJ36031, which spans only 2 kb, has no reported protein product and function and requires further characterization. PIK3CG is located 134 kb downstream of rs342293 and encodes the phosphoinositide-3-kinase γ-catalytic subunit. The lipid kinase PIK3CG (PI3Kγ) is a member of the class I PI3Ks and catalyzes the conversion of phosphatidylinositol-4,5-bisphosphate P2; PIP2) to phosphatidylinositol-3,4,5-trisphosphate P3; PIP3) downstream of cell surface receptor activation Pik3cg knockout mice have indicated a role in wound healing Expression QTL data in platelets and macrophages provided statistical support that rs342293 affects Pik3cg−/− mice showed differential expression of genes involved in important platelet-related pathways compared to wild type mice, most notably Vwf and its platelet membrane receptor components. Our recent meta-analysis of GWA studies in 68,000 Northern Europeans showed that common sequence variation at the VWF and GP1BA loci exert an effect on platelet volume and counts, respectively . This recent finding together with the established knowledge that Mendelian mutations in the genes encoding the platelet VWF  and its receptor  are causative of giant platelets, give biological credence to the observed effects of Pik3cg knockout on the transcription of these platelet genes. The protein-protein interaction network centered on PIK3CG highlighted additional proteins implicated in severe platelet disorders, including TUBB1 , F5  and P2RY12 . Therefore, it is plausible to assume that differences in the PIK3CG transcript levels in humans, based on the different alleles of rs342293, may lead to changes in the abundance of platelet membrane proteins that are key regulators of platelet formation.Whole-genome gene expression profiling in GP62=0.87, Phase II HapMap, CEU) making rs342293 the putative causative variant underlying both functional associations. The observation that platelet functional events triggered via an immunoreceptor tyrosine-based activation motif on the Fc receptor γ-chain (ITAM-FcR-γ) or G-protein-coupled receptor, for collagen and epinephrine, respectively, are both modified by differences in the PIK3CG transcript level is compatible with the notion that both signaling cascades require PIP3 and that silencing of Pik3cg in mice reduces the expression of the Gp6 gene.We previously showed an association of rs342293-G with decreased platelet reactivity in humans, assessed as the proportion of binding to annexin V and fibrinogen, as well as P-selectin expression, after activation of platelets with collagen-related peptide (CRP-XL) PIK3CG in megakaryocytes and ultimately affects platelet phenotype.Based on the above data, we propose a model  in whichEVI1  encodes a protein with two zinc-finger domains (ZF1 and ZF2), which feature distinct DNA-binding specificities RUNX1 leads to the destabilization of the DNA-RUNX1 complex and subsequent loss of RUNX1 function By converting the read-out system from microarrays to high-throughput next-generation sequencing, genome-wide open chromatin profiles can be interrogated. This would scale up analysis to the whole genome generating a catalog of open chromatin profiles to annotate association loci identified in past and future GWA studies. Furthermore, resources such as the completed 1000 Genomes Project will make the requirement for resequencing any identified NDR redundant. However, access to cell types relevant to the studied trait is not always feasible and can be a limitation for functional studies. For instance, we did not observe any intersection of FAIRE peaks with variants associated with CAD/MI or hypertension indicating that cell types other than the megakaryocytic and erythroblastoid cell lines analyzed here may be more suitable.DNM3) transcript with expression restricted to the megakaryocytic lineage . Correlation of signatures of open chromatin with experimentally determined transcription factor binding sites studied in different cell types can systematically and rapidly translate GWA signals into functional components and biological mechanisms, thus paving the way to clinical benefit.In addition to rs342293, we identified six putative functional variants associated with hematological and cardiovascular traits that fall into open chromatin . Follow-http://www.cambridgebioresource.org.uk/). The study has ethical approval from the NHS Cambridgeshire Research Ethics Committee. The care and use of all mice in this study was carried out in accordance with the UK Home Office regulations under the Animals (Scientific Procedures) Act 1986.All human subjects were recruited with appropriate informed consent in Cambridgeshire and enrolled in the Cambridge BioResource (http://www.genome.gov/gwastudies/). We considered only genetic loci that reached genome-wide significance with the threshold of P<5×10−8  using 72 known genetic loci associated with the traits listed in 2 and 100% humidified atmosphere. FAIRE experiments were carried out as previously described 6 cells were cross-linked with 1% formaldehyde for 8 or 12 min. Chromatin was extracted and subjected to 12 sonication cycles  using the Bioruptor UCD-200 (Diagenode). DNA fragments depleted of proteins (‘open chromatin’) were phenol-chloroform extracted and ethanol precipitated. The samples were treated with RNase A and cleaned-up using the MinElute PCR Purification Kit (QIAGEN). A DNA amplification step was not applied.We performed FAIRE in the human cell lines CHRF-288-11 and K562 on tiling arrays. Both cell lines exhibit markers characteristic for megakaryocytes/platelets DNA extracted from cross-linked cells  and uncross-linked cells (reference sample) were labeled with Cy5 and Cy3 dye, respectively, and subsequently co-hybridized to the tiling array. Dual-color sample labeling, array hybridization, washing and scanning were performed using the dual-channel microarray platform by Roche NimbleGen according to the manufacturer's protocol .2-ratio values for all features from each log2-ratio value. In the scaled log2-ratio data, peaks were identified using a sliding window approach 2-ratio cut-off value was calculated as the percentage of a hypothetical maximum (Pmax=arithmetic mean+6× standard deviation). The peak finding process was repeated using a series of log2-ratio cut-off values from Pstart to Pend. The following settings for the peak-finding analysis were applied: sliding window: 300 bp; min. probes>cut-off in peak=4; all probes in peak>cut-off=2; Pstart=90%, Pend=15%, Pstep=0.5, number of steps: 100. Log2-ratio and peak data sets were displayed as UCSC Genome Browser (http://genome.ucsc.edu/) custom tracks. For the genomic characterization of FAIRE peaks, we extracted all annotation from the Ensembl database v54 (build: hg18). Proxy-SNPs to reported GWA index SNPs were gathered using the Genome-wide Linkage Disequilibrium Repository and Search Engine (GLIDERS) 2 limit≥0.8; no distance limits. The FAIRE microarray data sets are available online in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE25716.Experimental data were analyzed using the software NimbleScan v2.5 (Roche NimbleGen). The two-channel raw signal intensities were scaled between channels by subtracting the Tukey bi-weight mean for the loghttp://www.sanger.ac.uk/resources/downloads/human/exoseq.html). DNA was amplified by PCR and applied to bi-directional sequencing using Big Dye chemistry on 3730 DNA sequencers (Applied Biosystems). We used the following primer pairs (for two sequence-tagged sites): 5′-TGG AAA ATT ACA AAA GTC CCA AA, 5′-GAG AAA GGA TCA TGA GGG AGA A; and 5′-ACA AAA GTC CCA AAA TTT CAC A, 5′-GAG AAA GGA TCA TGA GGG AGA. The resulting products map to the following genomic location: chr7:106,159,328–106,159,998 (671 bp) and chr7:106,159,337–106,159,998 (662 bp). Pre-processed sequence traces were analyzed using the semi-automated analysis software ExoTrace, developed at the Wellcome Trust Sanger Institute (http://www.sanger.ac.uk/resources/downloads/human/exoseq.html). Potential SNP positions were indicated by the software and then reviewed manually.DNA samples from a total of 643 individuals of Northern European ancestry were subjected to capillary DNA sequencing of the targeted locus at chromosome 7q22.3. Details of the sequencing protocol are described online  according to the manufacturer's protocol. Each 20-µl binding reaction contained 1× binding buffer, 75 ng/µl poly(dI/dC), 2.5% glycerol, 0.05% NP-40, 87.5 mM KCl and 6.25 mM MgCl2. For competition assays, we used 200-fold molar excess of the unlabeled probe. Reactions were incubated for 2 hr at room temperature. Supershift experiments were performed with EVI1 , GATA1  and RUNX1  antibodies.Nuclear extract was prepared from CHRF-288-11 cells with the NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher Scientific). Oligonucleotides were designed based on the genomic sequence surrounding rs342293; the SNP position is shown in bold: 5′-biotin-PIK3CG is the same across all chips (probe-ID: ILMN_1770433). Genotyping was performed on DNA extracted from whole blood using TaqMan (Applied Biosystems) SNP genotyping assays (platelets study) and Illumina SNP arrays  following the manufacturer's instructions. Expression QTL analysis with rs342293 or its proxy-SNP was performed with the software Genevar (Gene Expression Variation) using a window of ±1 Mb centered on the SNP P-values.Gene expression profiling and genotypic data sets were obtained from different sources depending on the studied cell type. RNA from all studied cell types was isolated using a TRIZOL standard protocol. Total RNA was quantified using the NanoDrop  and quality-checked using the 2100 Bioanalyzer (Agilent Technologies). Standard protocols were applied to generate biotinylated cRNA and hybridize to Illumina BeadChips . Then, arrays were washed and scanned. All experimental procedures were carried out according to the manufacturer's protocol. Even though different versions of Illumina platforms were used, the probe for Pik3cg knockout mice were obtained from sources previously described tm1Pngr) and then maintained as a closed colony by intercrossing from within the colony (C57BL/6J Jax contribution: 99.6%). We performed PCR genotyping using a standard protocol with the following primer pairs: 5′-TCA GGC TCG GAG ATT AGG TA, 5′-GCC CAA TCG GTG GTA GAA CT (wild type); 5′-GGA CAC GGC TTT GAT TAC AAT C, 5′-GGG GTG GGA TTA GAT AAA TG (mutant) −/−Pik3cg and three C57BL/6J Jax wild type mice ) was collected from terminally anesthetized mice via the retro-orbital sinus. We extracted total RNA using the Mouse RiboPure-Blood RNA Isolation Kit (Ambion). Total RNA was quantified using the NanoDrop and quality-checked using the 2100 Bioanalyzer. Standard protocols were used to generate biotinylated cRNA and hybridize to MouseWG-6 v2 Expression BeadChips. Arrays were washed, detected and scanned. All experimental procedures were carried out according to the manufacturer's protocol. On the raw expression data, we performed background subtraction, variance-stabilizing transformation and quantile normalization across all samples with the R package lumi (http://bioconductor.org/packages/release/bioc/html/lumi.html). Technical replicates were averaged and the differentially expressed transcripts between wild type and knockout mice were identified by calculating the log2-fold changes of the averaged expression values. P-values were calculated by 1-way analysis of variance (ANOVA). We performed gene ontology term enrichment analysis using AmiGO v1.7 P-value cut-off: 1×10−5; minimum number of gene products: 10. The whole-genome gene expression data sets of −/−Pik3cg and wild type mice are available online in the GEO database under accession number GSE26111.−/−Pik3cg and wild type mice, we retrieved 191 orthologous human genes using BioMart (http://www.ensembl.org/biomart/martview/) and their respective proteins using UniProt (http://www.uniprot.org/). These ‘core’ proteins were used as primary seeds to develop the protein-protein interaction network. We determined first-order interactors of core proteins using Reactome v36. Only clustered non-redundant first-level interactions between human proteins that were connected to the largest connected component were considered. Based on the HaemAtlas data, we neglected interactors that are not expressed in MK cells (P>0.01). The described network is available for download in Cytoscape v2.8.0 format . In order to reduce experimental error and achieve higher stringency, replicated and overlapping peaks were merged for each cell type and subjected to further analysis.(TIF)Click here for additional data file.Figure S2Location of the open chromatin sites in respect to the closest transcription start site (TSS) at the selected association loci.(TIF)Click here for additional data file.Figure S3Average number of FAIRE peaks in lineage-specific genes in MK and EB cells. The number of open chromatin sites in lineage-specific genes (±2 kb) was averaged and normalized for the length of the gene.(TIF)Click here for additional data file.Figure S4FLJ36031–PIK3CG; (B) DNM3; (C) HBS1L–MYB; (D) PEAR1; (E) RAF1; (F) TMCC2; (G) CYP17A1–C10orf32. Shown are the scaled log2-ratio and the called peaks from FAIRE experiments in an erythroblastoid (blue) and a megakaryocytic cell line (red). Only the data sets using a formaldehyde fixation time of 12 min are shown for both cell types. The putative regulatory SNP located within a site of open chromatin is shown below each track.Maps of open chromatin at the selected genetic loci in MK and EB cells displayed as UCSC Genome Browser custom tracks. (A) (PDF)Click here for additional data file.Figure S5Gene expression profiles. The heat maps show normalized gene expression profiles in differentiated human blood cells according to the HaemAtlas . (A) Gen(TIF)Click here for additional data file.Figure S6in vitro. Reactions were incubated for 1 hr at room temperature.Gel shift assays in CHRF-288-11 nuclear extracts using GATA1 and RUNX1 antibodies. No supershift was observed when incubating CHRF-288-11 nuclear extract with GATA1 antibodies for probes containing either rs342293-C or -G. However, we showed evidence for RUNX1 transcription factor binding (TIF)Click here for additional data file.Figure S75 in CellGro SCGM (CellGenix) in the presence of 100 ng/ml recombinant human thrombopoietin (CellGenix) and 10 ng/ml interleukin-1β (Miltenyi Biotec) for 10 days, after which 71% of cells expressed CD41. ChIP was performed as previously described in vivo binding of GATA1, but weak RUNX1 binding. In total, the ChIP-seq data sets comprised 4,722 and 7,345 peaks for GATA1 and RUNX1, respectively No significant GATA1 but weak RUNX1 binding at the MK-specific open chromatin region at chromosome 7q22.3. Cord blood-derived CD34-positive hematopoietic progenitor cells were seeded at 1×10(TIF)Click here for additional data file.Table S1FAIRE peak data sets. The genomic positions of FAIRE peaks are reported at (A) the association loci and (B) the lineage-specific reference genes.(PDF)Click here for additional data file.Table S2Comparison of the FAIRE peak density between the ENCODE and the here presented data sets.(PDF)Click here for additional data file.Table S3Characterization of open chromatin regions in relation to gene annotations.(PDF)Click here for additional data file.Table S4Investigation of the functional role of platelet volume-associated variants at chromosome 7q22.3.(PDF)Click here for additional data file.Table S5Resequencing of the MK-specific open chromatin region at chromosome 7q22.3.(PDF)Click here for additional data file.Table S6PIK3CG gene locus in platelets, macrophages, monocytes, B cells (LCLs), fat, and skin.Expression QTL associations at the (PDF)Click here for additional data file.Table S7−/−Pik3cg and wild-type mice.Differentially expressed genes between (PDF)Click here for additional data file.Table S8−/−Pik3cg and wild-type mice.Functional ontology classification of differentially expressed genes between (PDF)Click here for additional data file.Table S9Genetic loci selected for the high-density DNA tiling array.(PDF)Click here for additional data file.Dataset S1PIK3CG protein–protein interaction network.(BZ2)Click here for additional data file.


Modularity is a crucial issue in the engineering world, as it enables engineers to achieve predictable outcomes when different components are interconnected. Synthetic Biology aims to apply key concepts of engineering to design and construct new biological systems that exhibit a predictable behaviour. Even if physical and measurement standards have been recently proposed to facilitate the assembly and characterization of biological components, real modularity is still a major research issue. The success of the bottom-up approach strictly depends on the clear definition of the limits in which biological functions can be predictable.Escherichia coli via different measurement systems  relative to an in vivo reference promoter. Second, promoter activity variation was measured when two independent gene expression cassettes were assembled in the same system. Third, the interchangeability of input modules (a set of constitutive promoters and two regulated promoters) connected to a fixed output device (a logic inverter) expressing GFP was evaluated. The three input modules provide tunable transcriptional signals that drive the output device. If modularity persists, identical transcriptional signals trigger identical GFP outputs. To verify this, all the input devices were individually characterized and then the input-output characteristic of the logic inverter was derived in the different configurations.The modularity of transcription-based biological components has been investigated in several conditions. First, the activity of a set of promoters was quantified in Promoters activities (referred to a standard promoter) can vary when they are measured via different reporter devices (up to 22%), when they are used within a two-expression-cassette system (up to 35%) and when they drive another device in a functionally interconnected circuit (up to 44%). This paper provides a significant contribution to the study of modularity limitations in building biological systems by providing useful data on context-dependent variability of biological components. Standardization of components, abstraction, modularity and predictability are the main engineering principles for which the emerging field of Synthetic Biology lays the foundations in vivo in the same experimental conditions. RPUs have been recently used in the literature to characterize many promoters However, the tremendous potential of Synthetic Biology is limited by the intrinsic complexity of living systems The central challenge about quantitative characterization is the modularity of components Crosstalk or incompatibility among components As Synthetic Biology key concepts are based on engineering, the success of this emerging field depends on the definition of the working boundaries in which biological functions can be predictable, in order to enable the bottom-up composition of customized systems. Recently, some experimental studies have been reported to assess modularity of biological components. Davis et al. ad-hoc constructed model systems. To study the context-dependent variability of biological parts, the quantitative behaviour of promoters was studied in three increasingly complex conditions in E. coli, a popular chassis in Synthetic Biology. First, the activity of a set of promoters was quantified in vivo via different biological measurement systems  relative to a reference promoter in identical conditions, using the Relative Promoter Unit (RPU) approach. Second, promoter activity variation was measured when two independent gene expression cassettes were assembled in the same system. Third, the modularity of input devices was tested in a functionally interconnected framework, composed by a variable input device connected to a fixed output device (a logic inverter) expressing GFP.Here, the modularity of transcription-based biological parts and devices has been investigated in In order to estimate the activity variation when a promoter is characterized via different biological measurement systems, a representative set of five widely used promoters was assembled to three different reporter expression devices in two different plasmid vectors and characterized in terms of RPU in the TOP10 strain see . RelyingResults are shown in The observed variability may be caused by downstream sequence-dependent promoter activity change. The maximum activity variability found in this set of promoters is relatively low and it is smaller than previously reported in other downstream sequence-dependent case studies Promoters with GFP32 and RFP34 reporter devices were also tested in a high copy number plasmid. Results showed that the RPU activity of the strongest promoters . The activity of the individually characterized promoter, via the same measurement system, is also reported for each group. Among the groups where at least one of the mean activities showed statistical difference , CVs of 33%, 7%, 33% , but the promoter driving the ‘downstream’ cassette has a different upstream sequence . If the data of the promoters driving the ‘downstream’ cassette are excluded from the variability analysis see , only th1, X2, …, XN) and a fixed output block (Z) downstream, if the signals provided by the input blocks are the same (in1 = in2 = … = inN), the output signals should be identical (out1 = out2 = … = outN) even if the input blocks are structurally different.The modularity of biological devices was studied when dealing with functionally interconnected circuits. The basic idea driving this part of the study is illustrated in E. coli strain, which over-expresses the lacI repressor. The circuits are composed by different input devices interconnected to the same output device. The considered input modules were: i) a set of constitutive promoters of different strengths , ii) a lacI-regulated promoter , while the KSubsequently, the interconnected systems composed by the different input devices and the NOT gate downstream were considered. The transcriptional activity of the different inputs was tuned either by changing the constitutive promoter (INPUT1) or by exogenously adding different inducer amounts to the bacterial cultures (IPTG for INPUT2 and HSL for INPUT3). The response of the logic inverter output device was measured in terms of RPU via GFP, thus yielding an input-output transfer function for each of the three input devices used. If the modularity hypothesis is valid, the three transfer functions should be identical, as input systems should be functionally interchangeable. hard truths, recently captured as the major challenges for Synthetic Biology, that nowadays prevent the fully rational design of biological systems following engineering principles ad-hoc constructed model systems in E. coli. The RPU approach was applied in order to generate reproducible results among the experiments and to enable the sharing of the data presented in this work, as they are expressed in standard measurement units. To compute RPU, absolute promoters activity was divided by the absolute activity of a reference promoter.The apparent unpredictability of genetic circuitry is one of the five in vivo. The reference promoter was quantified via the same measurement system, so if promoters activity does not change with the reporter device assembled downstream, the studied promoters should theoretically yield the same RPU activity values when measured via different devices. Only one of the five promoters showed a significant activity difference among the three tested reporter devices, yielding a CV of 22%. Plasmid copy number dependence was also studied for the tested promoters and results showed that the RPU activity of the strongest promoters is significantly weaker (up to 4.4-fold) in high copy than in low copy context. This result is in accordance with other studies The first preliminary goal was to estimate the activity variation for a set of five popular promoters when measured in TOP10 strain via different measurement systems: three different reporter devices were assembled to the promoters in a low copy vector and were used to quantify their activity The second goal was to estimate how much the RPU activity changes when a promoter with a given reporter device is moved from the individual context to a complex system composed by two non-interacting gene expression cassettes in the same plasmid. Results showed a maximum activity variation of 33% and 27% for TOP10 and KRX strains respectively.In these two studies, some of the activity variations could be explained by the physical un-insulation of promoters, in fact it is known that upstream and downstream sequences can significantly change the activity of a promoter. This can explain the variation in the RPU activity of individually characterized promoters when the downstream reporter device is varied; it can also explain the activity variation of a promoter  when the upstream sequence is changed by assembling another expression cassette in this position. However, not all the observed variability could be explained by un-insulation, in fact, considering the systems with two combined cassettes in the same plasmid, the activity of the promoter driving the ‘upstream’ cassette could differ from the one in the individually characterized context, even if the promoter had the same upstream and downstream sequences. Such effect has also been found by Hajimorad et al. The third goal of this work was to evaluate the interchangeability of different input modules driving the same output device. The transcriptional signals generated by the investigated input components were measured in terms of RPU via RFP and then the components were used to regulate a tetR-based logic inverter with GFP downstream. Three input modules were tested: four constitutive promoters of different strengths , an IPTG-inducible promoter and an HSL-inducible device. In a modular framework, the three steady-state input-output transfer functions of the logic inverter should be the same, as devices driven by identical signals should yield identical outputs. However, although the obtained input-output curves had similar shapes , their switch point showed a CV of 44% among the conditions. One or more of the above mentioned factors could be responsible for this variability, e.g. the different RBS-gene downstream of the promoter in characterization and test stages could cause a downstream sequence-dependent activity change of the input.Previously published works also reported successful modularity of input devices in interconnected systems, such as logic gates, actuators or feed-forward circuits The use of M9 supplemented medium in the performed characterization experiments most probably contributed to the reproducibility of many results obtained in this study, as it is a standard growth medium with an extremely low background fluorescence. Medium composition can significantly affect gene expression and protein synthesis rates, so experiments performed in different growth media can yield highly different outcomes Finally, it is worth noting that in the model systems considered here, promoters have very similar predicted transcription start sites, so, given a specific downstream device, the produced mRNA should be the same for all the promoters BioBrick™ Standard Assembly was used to construct all the plasmids of this study, following a number of conventional molecular biology techniques. As a result, all the DNA junctions between parts had the TACTAGAG sequence (or TACTAG when the downstream part was a coding sequence) TOP10 and KRX (Promega) strains were used as chassis for all quantitative experiments. Enzymes, purification kits and competent cells were used according to manufacturer's instructions. All the plasmids realized in this study were assembled from basic or composite parts from the Registry 2009 or 2010 DNA Distribution reference culture) were also assayed, so that strain, plasmid copy number, antibiotic resistance, reporter gene and RBS were exactly the same as in the cultures of interest.Recombinant bacteria from a long-term glycerol stock were streaked on an LB agar plate and grown for about 20 hours at 37°C. 1 ml of selective M9 supplemented medium  was inoculated with a single colony from the streaked plate and incubated at 37°C, 220 rpm shaking for about 20 hours. Bacteria were diluted 1∶500 in 2 ml of fresh selective M9 supplemented medium and grown for 6 hours under the same conditions as before. When required, properly diluted isopropyl ϕ is the culture bearing the promoter of interest and ref is the reference culture.Data were analyzed as described in n is the Hill coefficient. All the data were processed either with Microsoft Excel 2007 or with the MATLAB 2007b suite . In particular, the fitting of the Hill functions was performed through the MATLAB lsqnonlin routine which implements the least squares method.The induction curves of the regulated input devices and the input-output transfer curves of the logic inverter were fitted with the Hill function All the coefficients of variation were corrected for small samples: for N samples, To assess the statistical difference among the mean promoter activity values in a group, ANOVA test was performed. If a difference was detected in the group, individual t-tests were performed to compare the mean values of the group members to identify statistically different sub-groups in order to compute the CV. The p-values (P) were corrected for multiple comparisons with the Bonferroni method. The mean values of the non-significantly different sub-groups were averaged and the final CV within the group was computed on the mean values of the statistically different sub-groups. If ANOVA highlighted a statistical difference, but multiple comparisons showed no evident sub-groups, the CV was computed on all the mean activities among the group.Figure S1Measured RPU values for individual promoters characterized in a high or low copy vector. Promoters were characterized via GFP32 (panel A) and RFP34 (panel B) reporter devices. Error bars represent the standard deviation of the mean activity computed on three clones. Strains with (EPS)Click here for additional data file.Figure S2Composition of the constructed plasmids. BioBrick™ composition is shown for the plasmid inserts used in this paper to study the context-dependent variability of promoters as a function of the biological measurement system (A), when independent expression cassettes are assembled in the same plasmid (B) and in an interconnected genetic circuit containing a logic inverter (C). All the shown inserts were cloned and tested in the pSB4C5 vector backbone and all the inserts of panel A were also cloned and tested in pSB1A2. Green, red and yellow groups in panel A include the promoters that have been individually tested with the GFP32, RFP34 and RFP32 reporter device respectively. Cyan group in panel B includes the constructs in which two independent expression cassettes are tested, while the grey group in the same panel includes the individual expression cassettes. Violet group in panel C includes the measurement constructs of the input devices , while the pink group in the same panel includes the model systems where input devices are interconnected with a logic inverter. Inserts highlighted with dashed line in the three panels represent the reference constructs, containing the BBa_J23101 promoter with an appropriate reporter device downstream, used to compute the RPUs of the promoters of interest.(EPS)Click here for additional data file.Figure S3Measured RPU values for the five investigated promoters in LB medium. Promoters were individually characterized via three reporter devices: GFP32, RFP34 and RFP32 in pSB4C5 vector backbone in TOP10 strain grown in selective LB medium. Error bars represent the standard deviation of the mean activity computed on three clones. For each promoter, statistical analysis was performed via ANOVA test to compare the RPU activities measured via the three different reporter devices. Promoters showing a statistical difference (P<0.05) in the mean activities among the three conditions are marked with a ‘+’ sign, while promoters not showing any significant difference (P(EPS)Click here for additional data file.Text S1Crosstalk estimation between GFP and RFP spectra; preliminary design of the interconnected system; characterization of individual promoters in a high copy vector.(PDF)Click here for additional data file.


Regulation of gene expression has been shown to involve not only the binding of transcription factor at target gene promoters but also the characterization of histone around which DNA is wrapped around. Some histone modification, for example di-methylated histone H3 at lysine 4 (H3K4me2), has been shown to bind to promoters and activate target genes. However, no clear pattern has been shown to predict human promoters. This paper proposed a novel quantitative approach to characterize patterns of promoter regions and predict novel and alternative promoters. We utilized high-throughput data generated using chromatin immunoprecipitation methods followed by massively parallel sequencing (ChIP-seq) technology on RNA Polymerase II (Pol-II) and H3K4me2. Common patterns of promoter regions are modeled using a mixture model involving double-exponential and uniform distributions. The fitted model obtained were then used to search for regions displaying similar patterns over the entire genome to find novel and alternative promoters. Regions with high correlations with the common patterns are identified as putative novel promoters. We used this proposed algorithm, RNA-seq data and several transcripts databases to find alternative promoters in MCF7  cell line. We found 7,235 high-confidence regions that display the identified promoter patterns. Of these, 4,167 regions (58%) can be mapped to RefSeq regions. 2,444 regions are in a gene body or overlap with transcripts . Some of these maybe potential alternative promoters. We also found 193 regions that map to enhancer regions (represented by androgen and estrogen receptor binding sites) and other regulatory regions such as CTCF (CCCTC binding factor) and CpG island. Around 5% (431 regions) of these correlated regions do not overlap with any transcripts or regulatory regions suggesting that these might be potential new promoters or markers for other annotation which are currently undiscovered. Because of the multitude of cell types, the regulation of gene expression in complex genomes, such as the human genome, is known to be an extremely complicated process. It is now well accepted that apart from sequence polymorphism and variations, gene regulation in human plays an important role in many disease onset and progression. By matching the gene expression profiles to those of known tumors, researchers can type cancer cells of unknown tissue origin. As such, understanding the mechanism governing regulation of genes is very crucial. For many genes, their expression levels are controlled by attachment of specific proteins known as transcription factors to locations on the DNA to activate or suppress expression of the target genes. The location where transcription factor binds to is known as promoter region. Recent discoveries show that regulation of gene expression not only involve the binding of transcription factors in target gene promoters but it also depends on the characterization of the epigenetic events such as histone marks around which DNA is wrapped around [Multicellular organism consists of hundreds of different cell types. A cell typically expresses only a fraction of its genes. Each type of cells become different from others because they activate different sets of genes whose activities turn on and off various biological processes. The process in which a cell determines which genes it will express and when is called d around -3. Certad around . Specifid around . Thus, cd around . TherefoWith the rapid availability of high-throughput technologies such as chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq), scientists can now observe the binding patterns of the protein of interest in the entire genome. Genome-wide identification of promoter is commonly done using antibody against RNA polymerase II (enzyme that are required for gene transcription) . HoweverThus, development of a better promoter identification algorithm is needed to account for these different situations. It is conceivable that promoter regions display unique combination of chromatin and Pol-II patterns. Condition such as Pol-II stalling may display different patterns than those of transcribing genes. As an attempt to address this problem, in this article, we propose a computational method using a finite mixture model to identify promoter signature profiles based on both Pol-II and H3K4me2 binding patterns. We choose to use H3K4me2 pattern because H3K4 di-methylation has been shown to promote transcriptional activities of genes . We scanTwo ChIP-seq data sets are used to identify patterns of promoters, RNA Pol-II ChIP-seq data and H3K4me2 ChIP-seq data, both from MCF7 . RNA-seq (RNA sequencing) data also from MCF7 are used to identify transcripts in the breast cancer cell line including alternative splicing. Genome annotation databases such as non-coding RNA (ie. snoRNA and miRNA), ESTs (Expressed Sequence Tags), CpG island and CTCF (CCCTC binding factor) tracks are downloaded from UCSC genome browser. ER/AR (Estrogen and Androgen Receptor) binding sites are retrieved from HRTBLDb (Hormone Receptor Target Binding Loci) database .st step, we characterize Pol-II binding and chromatin mark patterns by performing k-means clustering around the gene transcription starting site (TSS) of known genes. This is then followed in the second step of fitting a double-exponential and uniform mixture model. At the end of the two step procedure the patterns identified will be used to scan the genome to identify putative promoter regions  regions contains read counts in bins of size 100-bp. In pre-processing step, we smooth the data using a moving average filter replacing each count in each bin with the average of three consecutive bins. Next, in order to prevent interference from neighboring genes, we exclude genes with TSSs within 10-kb of each other. Furthermore, to prevent degenerate clustering, we remove regions with low binding intensities and low variance. Low binding intensities regions are regions with maximum read counts less than 4 among the 100-bp bins over the 10-kb regions. Low variance regions are defined as regions with variance less than 10th percentile over all 10-kb regions. These filtering criteria result in a dataset consisting a total of 9,859 10-kb regions. K-means clustering using correlation as distance measurement is then performed to find sets of common patterns. The optimal number of cluster is determined using e values . Larger y1(t) and y2(t) be the read counts of Pol-II and H3K4me2 ChIP-seq in the 10-kb region around TSS of a gene where t is an indicator variables denoting the bin index, respectively. If we quantify the data into bins of size = 100-bp, then t ∈ T = {-50,- 49, ..., 49, 50}. Let R(t) be the chromosomal region relative to the TSS of the gene. Thus, for t = -50, R(t) denotes region 4901-bp to 5000-bp upstream of TSS. The mixture model for each profile (i.e. Pol-II and H3K4me2) can be defined as follows:Let μ and β are the location and scale parameters of the double exponential distribution, respectively. π is the mixing proportion  as follows:where distance  to fk(t)-6, (4) After a successful poll, the difference between the function value at the previous best point and the function value at the current best point is less than 10-6. The search algorithm is repeated 16 times with different initial points. Using this strategy, we obtained four distinct models of Pol-II and H3K4me2 signatures representing the majority of the patterns exist at promoter region of known genes. Each model is a mixture of double exponential and uniform components. Figure using generalized pattern search (GPS) algorithm . GPS metz. The threshold z is chosen as the 95th percentile of the asymptotic distribution of the test statistics. These genomic locations which display these specific patterns of Pol-II and H3K4me2 are designated as potential promoters. For brevity, we will refer to the fitted Pol-II and H3K4me2 patterns as promoter patterns. We further annotate these correlated regions as known promoters and predicted alternative promoters using RNA-seq data in MCF7 along with transcripts databases such as ESTs, snoRNA/miRNA downloaded from USCS genome browser.Finally, we scan the whole genome using the fitted models to find regions that display these Pol-II and H3K4me2 patterns . Genes expression are determined using FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values derived from RNA-seq data on MCF7 using CuffLink [Scanning the entire genomic region for promoter patterns, we found 7,235 highly correlated regions. These are the regions that show high similarity with any of the four patterns modeled by the double-exponential and uniform mixture models. Around 58% 4167) of these matched regions overlapped with known promoter regions (1-kb upstream and downstream of the RefSeq TSSs). Although these regions only represent 22% of the entire known promoters, it is not surprising as it has been known that not all genes are expressed at the same time. Hence, these promoter patterns may represent those that are currently active in the breast cancer model MCF7 cell line. Indeed as shown in Figure 167 of thCuffLink .TANK on chromosome 2 has been found to have isoforms. Interestingly, as shown in Figure MAT2B also display the promoter pattern  which cannot be mapped to known genes, we found 1,104 of them falls inside known gene bodies. Some of them are known isoforms. For example gene For the rest of correlated regions , we went to find whether these regions can be associated with any transcripts. In order to do this we first find whether there is overlap between these correlated regions and non-coding RNA tracks (i.e. snoRNA and miRNA) from UCSC genome browser as the RNA-seq protocol does not yield data for small RNAs. We found only 6 regions overlap with the location of non-coding RNA in human genome. One example of this region is shown in Figure Recently there has been new discovery on the presence of RNA polymerase II at enhancer regions. These regions which are found to affect genes far away can manufactured their own RNA molecules. Thus, we try to find whether the same promoter pattern can be found at enhancer regions. We used the binding sites of ER (Estrogen Receptor) and AR (Androgen Receptor) as representative of the enhancer regions since both of these protein have been shown to bind at distal enhancer region. Overlapping unmapped region with ER binding sites, we found 120 regions with similar promoter patterns. This region is shown in Figure We found 73 out of the rest of the correlated region (504) can be further mapped to other regulatory regions such as CpG island and CCCTC binding factor (CTCF). We used CpG island tracks downloaded from UCSC genome browser to annotate CpG island location. For CTCF, we used the CTCF binding sites that are present in three different cell lines  since it has been shown that these sites are conserved . ExampleRPS6KB1 on chromosome 17. The Pol-II and H3K4me2 patterns are very prominent around the TSS of this gene with the combination of unimodal Pol-II peak and the bimodal H3K4me2 peak. Figure We investigated the overlap of these correlated regions with more than one genome annotation  . Four coThe authors declare that they have no competing interests.taslim.2@osu.edu), KH (kun.huang@osumc.edu) or SL (shili@stat.osu.edu).CT collected datasets, performed all the data analyses and drafted the manuscript. SL, KH and CT designed the study and wrote the manuscript. SL, KH and THMH conceived the study and directed the whole research work. THMH provided the ChIP-seq data. All authors read and approved the manuscript. Correspondence and requests for materials should be addressed to CT (