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1 HISTONE MODIFICATION OF STRESS RESPONSIVE REGULATORY REGIONS, A BIOINFORMATICS STUDY By GUANGYAO LI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FO R THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013
2 2013 Guangyao Li
3 To my dear wife Jingyi and our baby Sophia
4 ACKNOWLEDGMENTS This remarkable journey of achieving the Ph.D. degree fulfills with my most valuable memories. It would not have been possible for me to complete this doctoral dissertati on without the help of the following individuals. First, I would like to express my appreciation to my advisor Dr. Lei Zhou for his continuous support of my Ph.D. study His patience inspiration and motivation made my achievements possible He has show n me the curiosity, enthusiasm and persistence a real scientist should carry, and he always encourage s me t o overcome all obstacles an d gui d e s me to become independent in every aspect o f my life. I also want to express a special thanks to my dissertation committee: Dr. Thomas Yang Dr. Suming Huang Dr. Luciano Brocchieri, Dr. Samuel Wu and my former committee Dr. Alberto Riva, for their valuable suggestions inspiration and continuous support My sincere thanks also go to the formal and current members of Zhou lab oratory especially Dr. Yanp ing Zhang, Dr. Nianwei Lin, Dr. Can Zhang, Dr. Bo Li u Michelle Chung, Jordan Reuter, Denis Titov f or their kind and patient help. I also would lik e to thank my classmates and best friends, including Jianxing Zhang, Yihai Wang, Qingchun Shi, Tong Lin, Shaojun Tang, Yajie Yang, Ming Tang, Yuanqing Yan, Ruli Gao, Bing Yao, Weiyi Ni, Shuibin Lin, Chen Ling, Yi Guo, Wei Wang Shanjun Helian Liangjie Yin Mei Zhang and so on. I am so grateful to have met them in Gainesville and become lifetime friends. Last and most importantly, I want to thank m y parents and my family, whose love and encourageme nt allow ed me to finish this journey. A special thanks to m y beloved
5 wife Jingyi He and our litt le daughter Sophia Li, who fulfills my life and makes everything meaningful to me
6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF ABBREVIATIONS ................................ ................................ ........................... 12 ABSTRACT ................................ ................................ ................................ ................... 14 CHAPTER 1 BACKGROUND AND INTRODUCTION ................................ ................................ 16 Polycomb Group Proteins and Polycomb Silencing ................................ ................ 16 PcG Proteins ................................ ................................ ................................ .... 16 Targeting of PcG Silencing ................................ ................................ ............... 17 TrxG Proteins ................................ ................................ ................................ ... 18 Chromatin Insulators ................................ ................................ ............................... 19 Two Functions of Chromatin Insulators ................................ ............................ 19 Models for Chromatin In sulator Functions ................................ ........................ 20 Epigenetic Regulation of Gene Expression ................................ ............................. 21 DNA Methylation ................................ ................................ .............................. 21 Histone Modification ................................ ................................ ......................... 22 The Coordination between DNA Methylation and Histone Modifications in Gene Repression ................................ ................................ .......................... 23 Noncoding RNAs ................................ ................................ .............................. 25 Epigenetic Regulation and Cancer ................................ ................................ ......... 26 Dys Regulation of Distinct Epigenetic Mechanisms Leads to Cancer .............. 26 PcG Proteins and Cancer ................................ ................................ ................. 27 P53 and Cancer ................................ ................................ ................................ ...... 28 Tumor Suppression Fu nction of P53 ................................ ................................ 29 P53 and Cancer Therapy ................................ ................................ ................. 30 ChIP Seq: Genome Wide Monitoring of Epigenetic Regulation .............................. 31 Bioinformatics Programs for ChIP Seq Data Analysis ................................ ...... 32 2 HISTONE MODIFICATIONS, DNA ACCESSIBILITY, AND P53 BINDING PROFILES FOLLOWING DNA DAMAGE ................................ ............................... 34 Introduction ................................ ................................ ................................ ............. 34 Materials and Methods ................................ ................................ ............................ 38 Dataset ................................ ................................ ................................ ............. 38 Data Processing ................................ ................................ ............................... 38 Mean Signals around P53 Binding Sites ................................ .......................... 38
7 Grouping of P53 Bindi ng Sites Based on DNA Accessibility ............................ 38 Predict P53 Binding Sites using TransFac P53 Motif ................................ ....... 39 Results ................................ ................................ ................................ .................... 39 Constitutive and Conditional Intergenic P53 Binding Sites in Mouse ES Cells ................................ ................................ ................................ .............. 39 Correlation between Histone Modification and DNA Accessibility in the C57 BL/6 Mouse ES Cells ................................ ................................ .............. 40 Lack of Correlation between DNA Accessibility and P53 Binding ..................... 41 Correlation between Active Enhancer Mark er H3K27ac and Constitutive P53 Binding Sites ................................ ................................ .......................... 42 P53 Binding Sites with Lower DNA Accessibility Tend to Contain Consensus P53 Binding Motif ................................ ................................ ....... 42 Discussions ................................ ................................ ................................ ............. 43 3 GENOME WIDE IDENTIFICATION OF CHROMATIN TRANSITIONAL REGIONS REVEALS DIVERSE MECHANISMS DEFINING THE BOUNDARY OF FACULTATIVE HETEROCHROMATIN ................................ ............................ 53 Introduction ................................ ................................ ................................ ............. 53 Materials and Methods ................................ ................................ ............................ 56 CTRICS (Chromatin Transitional Regions Inference from ChIP Seq) Algorithm ................................ ................................ ................................ ....... 56 Dataset ................................ ................................ ................................ ............. 58 Parameters Used for Predicting CTRs and/or H3K27me3 Domains ................ 58 Statistical Analysis ................................ ................................ ............................ 58 Motif Discovery ................................ ................................ ................................ 59 Calculation of Nucleotides Content ................................ ................................ .. 59 Results ................................ ................................ ................................ .................... 59 Localize the Chromatin Transitional Regions (CTRs) Based on H3K27me3 ChIP Seq Data ................................ ................................ .............................. 59 Genome Wide Identification of CTRs in S2 Cells ................................ ............. 61 The Spatial Relationships between CTRs and Known Boundary Setting Proteins ................................ ................................ ................................ ......... 63 The Diversity of Facultative Heterochromatin Boundaries ................................ 64 Strong Co Factor Binding Distinguishes dCTCF and Su(Hw) Binding Associated with CTR vs. Those in H3 K27me3 Enriched Regions ................ 66 Poly(dA:dT) Tracts and Decreased Nucleosome Density around the Insulator Binding Sites associated with CTR ................................ ................. 68 Poly(dA:dT) Tracts and Increased Sensitivity to MNase are Associated with CTRs that do not Bind with Known Insulator Proteins ................................ ... 69 Enrichment of H3.3 but Decreased Nucleosome Turnover a t CTR Associated dCTCF Binding Sites ................................ ................................ .. 71 Chromatin Transitional Regions in the HeLa Cell Line ................................ ..... 73 Discussions ................................ ................................ ................................ ............. 74 Fixed vs. Variable Boundary for Facultative Heterochromatin .......................... 75 Binding of Insulator Protein Alone is not Sufficient for Establishing the H3K27me3 Boundary ................................ ................................ .................... 76
8 Nucleosome Dynamics, Histone Variants, and H3K27me3 Boundary ............. 79 4 DISCUSSIONS, EXPLORATIVE WORKS, AND PERSPECTIVES ...................... 102 Epigenomics Era: New Opportunities and New Challenges ................................ 102 Potential Opportunities from Large Scale Epigenomics ................................ 103 Challenges with Large Scale Epigenomics ................................ .................... 104 Application of Machine Learning to the Prediction of Chromatin Boundaries ........ 105 Experimental Verification of the Predicted Chromatin Boundaries ........................ 106 LIST OF REFERENCES ................................ ................................ ............................. 110 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 127
9 LIST OF TABLES Table page 2 1 List of datasets used in this study ................................ ................................ ....... 45 2 2 Percentage of p53 binding sites that contain consensus p53 binding motif ........ 46 3 1 The list of ChIP Chip profiles used in the clustering analysis ............................. 81 3 2 List of datasets used in this study ................................ ................................ ....... 82
10 LIST OF FIGURES Figure page 2 1 Schematic diagram summarizing previous findings regarding the IRER and ILB in our lab ................................ ................................ ................................ ...... 47 2 2 Constitutive and conditional p53 binding sites. ................................ ................... 48 2 3 DNA acces sibility around p53 binding sites ................................ ........................ 49 2 4 Histone modifications around p53 binding sites ................................ .................. 50 2 5 Binding intensities of conditiona l and constitutive p53 in untreated and Adr treated conditions ................................ ................................ ............................... 51 2 6 H3K27me3 ChIP Seq profiles from different laboratories are not comparable ... 52 3 1 Histone modifications and gene expression levels on the euchromatic vs. heterochromatic side of the CTRs in Drosophila S2 cell line .............................. 83 3 2 CTRs and the known insulator p roteins in Drosophila S2 cell line ...................... 84 3 3 Subgroups of CTRs based on associated proteins in Drosophila S2 cell line .... 85 3 4 Bindi ng intensity and patterns of insulator proteins and co factors associated with CTRs in Drosophila S2 cell line ................................ ................................ ... 87 3 5 Cis elements associated with CTRs in Drosophila S2 cell line ........................... 88 3 6 Multi A (AAAA/TTTT) content and nucleosome density around individual subgroup of CTRs in Drosophila S2 cell line ................................ ...................... 89 3 7 Contrasting pa tterns of H3.3 enrichment and nucleosome turnover rate associated with subgroups of CTRs in Drosophila S2 cell line ........................... 91 3 8 Chromatin transitional regions in human HeLa cell line ................................ ...... 92 3 9 Proposed models for facultative heterochromatin boundary ............................... 93 3 10 Construction of empirical positive and negative evaluation datasets. ................. 94 3 11 Comparison of CTRICS with SICER and RSEG ................................ ................. 95 3 12 Sequencing depth analysis ................................ ................................ ................. 96 3 13 Principal component analysis of CTRs based on association with the 15 proteins ................................ ................................ ................................ ............... 97
11 3 14 Genomic distribution of CTRs ................................ ................................ ............. 98 3 15 An example of 2 CTRs predicted by CTRICS in human HeLa cells ................... 99 3 16 Binding patterns of co factors are different for CTR associated and euchromatic binding sites ................................ ................................ ................. 100 3 17 Flowchart of CTRICS ................................ ................................ ........................ 101 4 1 Application of support vector machine (SVM) to predict chromatin boundaries 108 4 2 Experimental verification of chromatin boundaries ................................ ........... 109
12 LIST OF ABBREVIATION S BEAF 32 Boundary element associated factor of 32kD CATCH IT Covalent attachment of tags to cap ture histones and identify turnover C H IP Chromatin Immuno precipitation CP190 Centrosomal protein 190kD CTCF CCCTC binding factor CTR Chromatin transitional region D CTCF Drosophila ortholog of mammalian CCCTC binding factor ENCODE ENCyclopedia of DNA ele ment GAF GAGA factor H3K9 ME 3 Trimethylated histone 3 lysine 9 H3K27 ME 3 Trimethylated histone 3 lysine 27 IRER Irradiation responsive enhancer region ILB IRER left boundary MN ASE Micrococcal nuclease MOD ENCODE Model organism ENCyclopedia of DNA element M OD ( M DG 4) Modifier of mdg4 NURF Nucleosome remodeling factor P C G Polycomb group PRE Polycomb response elements PUMA p53 upregulated modulator of apoptosis QPCR Quantitative polymerase chain reaction S U (H W ) Suppressor of h airy w ing TRX G T rithorax group TSS Tran scription start site
13 UAS Upstream activation sequence USF1 Upstream stimulatory factor 1
14 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Do ctor of Philosophy HISTONE MODIFICATION OF STRESS RESPONSIVE REGULATORY REGIONS, A BIOINFORMATICS STUDY By Guangyao Li December 2013 Chair: Lei Zhou Major: Genetics and Genomics The juxtaposed distribution of euchromatic and heter o chromatic chromatin domains defines the expressivity of genes and thus the identi t y of individual cells. Due to the self propagating nature of the heterochromatic modification H3K27me3 (tri methylation of lysine 27 on histone H3 tail) chromatin barrier activities are require d to demarcate the boundary and prevent it from encroaching into euchromatic regions. Studies in Drosophila and vertebrate systems have revealed several important chromatin barrier elements and the ir respective binding factors. However, epigenomic data ind icate that the binding of these factors are not exclusive to chromatin boundaries To gain a comprehensive understanding of facultative heterochromatin boundaries we developed a two tiered method to identify the Chromatin Transitional Region (CTR) i.e. t he 200bp region that shows the greatest transition rate of the H3K27me3 modification as revealed by ChIP Seq (chromatin immunoprecipitation followed by high throughput sequencing ) This approach was applied to identify CTRs in Drosophila S2 cells and huma n HeLa cells Although many insulator proteins have been characterized in Drosophila less than half of the CTRs in S2 cells are associated with known insulator proteins, indicating unknown mechanisms remain to be characterized. O ur analysis also
15 reveal ed that the peak binding of insulator proteins are usually 200 ~ 600bp away from the CTR. Comparison of CTR associated insulator protein binding sites vs. those in heterochromatic region revealed that boundary associated binding sites are distinctively flanked by nucleosome destabilizing sequences, which correlates with significantly decreased nucleosome density and increased binding intensities of co factors A subgroup of facultative heterochromatin boundaries have enhanced H3.3 incorporation but red uced nucl eosome turnover rate. Together, o ur genome wide study reveals that diverse mechanisms are employed to defin e the boundaries of facultative heterochromatin
16 CHAPTER 1 BACKGROUND AND INTRO DUCTION Polycomb Group Proteins and Polycomb Silencing In eukaryoti c genomes, DN A is compacted into nucleus in the high order structure called chromatin. As the structural unit of chromatin, nucleosome is composed of a histone octamer and 147bp of DNA sequence wrapped around it. The histone octamer consists of two copies of each of the histones H2A, H2B, H3 and H4. The nucleosomes are li nucleosomes are condensed together by the linker histone H1 to form t he 30nm chromatin fiber (Allan et al., 1980) With the function of scaffold proteins, chromatin further forms the higher order looping structure. The N terminal tails of histones can protrude out of the nucleosome particle and are subject to different post transcripti onal modifications, such as methylation, acetylation, phosphorylation, ubiquitination and so on. The different combinations of histone modifications, known as histone code (Jenuw ein and Allis, 2001) will result in different chromatin structure and subsequently, distinct expression pattern of associated genes. PcG P roteins Polycomb group (PcG) proteins are originally discovered in Drosophila as chromatin structure remod elers to p revent inappropriate expression of homeotic (Hox) genes during embryonic development (Lewis, 1978) PcG proteins begin to fun ction in 3 hour old fly embryo in which the expression pattern of Hox genes has been shaped by ups t ream transcription factors (Pirrotta, 1998; Zhang and Bienz, 1992) After early embryogenesis, PcG silencing takes place and Hox genes will maintain their state of expression throughout the rest of development.
17 PcG mediated silencing in Drosophila involves at least three multiprotein complexes, which are PRC1, PRC2 and PhoRC complexes The key components of PRC1 (Polyc omb repressive complex 1) and PRC2 (Polycomb repressive complex 2) are the polycomb protein PC (Polycomb) and E(z) (Enhancer of zeste) respectively (Czermin et al., 2002; Shao et al., 1999) Polycomb mediated sile ncing is usually associated with trimethylation of lysine 27 of histone 3 (H3K27) which is catalyzed by the PRC2 complex This histone modifying activity requires a minimum of three components in PRC2 E(z), Esc and Su(z)12 (Cao and Zhang, 2004; Nekrasov et al., 2005) The chromodomain of PC can recognize and specifically bind to trimethylated H3K27 (Fischle et al., 2003) then PRC2 is recruited and the neighboring nucleoso me is trimethylated by histone methyltransferase (HMTase) activity of PRC2 The polycomb silencing will self propagate until the chain reaction is broken. The core components of the other PcG complex PhoRC (Pleiohomeotic repressive complex) is PHO (Pleioho meotic) and PHOL (Pleiohomeotic like), which are the only known DNA binding PcG proteins (Brown et al., 2003; Brown et al., 1998; Fritsch et al., 1999) The role of PhoRC o n polycomb silencing is unclear, as the mut ation of PHO and PHOL did n ot significantly abolish the binding of PRC1 and PRC2 on polytene chromosomes (Brown et al., 2003) But the direct binding of PhoRC to DNA may mediate the initiation of PcG repression (Mohd Sarip et al., 2005) Targeting of PcG Silencing How the PcG mediated silencing gets initiated still remains to be fully understood In Drosophila the specific regulatory region named Polycomb Response Element (PRE) which serves as docking platforms for PcG proteins and initiates PcG repression, have long been discovered. Although some sequence specific DNA binding proteins have
18 been found at PREs, including PHO, PHO L, GAGA factor, Zeste and so on. The PREs share little sim ilarity at the sequence level Based on the clustered pairs of these transcription factors binding sites, an algorithm has been developed and more than a hundred of PREs have been predicted in Drosophila genome (Ringrose et al., 2003) However, the result was challenged by recent genome wide ChI P Chip analyses where only limited overlap were found between PcG protein binding sites and the predicted PREs (Negre et al., 2006; Schwartz et al., 2006; Tolhuis et al., 2006) Nowadays, more and more evidences in dicate the polycomb silencing is a complex progress, its initiation may involve different mechanisms such as non coding RNA and RNAi machinery (Petruk et al., 2006; Rinn et al., 2007; Sanchez Elsner et al., 2006) TrxG P roteins The identification of Trithorax group (TrxG) proteins was also originated from the early study of Hox genes in Drosophila Compared to PcG silencing, TrxG proteins set up the active state for Hox genes. For example, in the absence of Trithora x (TRX), multiple Hox genes become repressed in early stage embryos where they normally express, and flies show segmental transformations consequently (Breen and Harte, 1991; Orlando and Paro, 1995) The maintenanc e of active chromatin state by TrxG proteins was achieved through direct histone modification (Strahl and Allis, 2000) or ATP dependent nucleosome remodeling (Vignali et al., 2000) The main Drosophila TrxG proteins, TRX, is itself a histone methyltransferase which catalyzes H3K4 trimethylation (Santos Rosa et al., 2002) And the SWI SNF nucleosome remodeling complex in TrxG family contains ATP dependent nucleosome remodeling proteins that can change chromatin structure to facilitate transcription machinery (Smith and Peterson, 2005) The appropriate regulation between PcG and TrxG proteins, through
19 catalyzing either repressive or active histone modifications, respectively, is crucial for the temporal and spatial expression pat tern for Hox genes as well as other target genes. Chromatin Insulators Chromatin insulators are regulatory DNA elements that are bound by insulator proteins and cofactors to prevent inappropriate communication between different chromatin domains Two Func tions of C hromatin I nsulators Two types of functions have been attributed to insulators. T he first is called enhancer blocking activity, i.e. blocking the interaction between enhancer and promoter when locate in between to prevent inappropriate gene expres sion. The other is chromatin barrier activity, which antagonizes the propagation of heterochromatin silencing (G aszner and Felsenfeld, 2006) Insulators, such as the gypsy insulator, w ere originally identified for their enhancer blocking activity (Geyer et al., 1986) Later, it was revealed that most of them also have barri er activity It was not clear whether the two activities are separable until the characterization of the cHS4 insulator globin locus. The complete cHS4 has both enhancer blocking and barrier activity. However, a series of mechanistic studi es indicated that the two activities are separable and carried out by dis tinct DNA elements. The enhancer blocking activity of cHS4 is mediated by CTCF, while its barrier activity against heterochromatin formation requires a binding site for USF1 (Upstream S timulatory Factor 1). Binding of USF1 to cHS4 recruits chromatin modifying enzymes that catalyze histone modifications incompatible with heterochromatin formation, thus
20 preventing the propagation of suppressive histone modification (Huang et al., 2007; West et al., 2004) Recently, a novel chromatin barrier that lacks any detectable enhancer blocking function has also been identified in Drosophila (Lin et al., 2011) This ~200bp element is located at the left boundary of IRER ( I rradiation R esponsive E nhancer R egion), a 33 kb intergenic regulator y region controlling stress induced expression of multiple pro apoptotic genes (Zhang et al., 2008a) When tested in transgenic animals, ILB (IRER Left Boundary) is fully capable of blocking the propagation of H3K2 7me3 initiated by a strong Polycomb response element (PRE) (Lin et al., 2011) The c hromatin barrier function of ILB is evolutionarily conserved. When tested in a vertebrate system, it blocked heterochromatin propagation as effectively as the cHS4 (Lin et al., 2011) Although many insulator/boundary associated proteins have been characterized in Drosophila including Su(Hw), d CTCF, BEAF 32, GAF, CP190 and Mod(mdg4) (Gurud atta and Corces, 2009) none of those was found associated with ILB. Models for Chromatin Insulator F unctions Although the molecular mechanisms of insulators are not as well studied as promoters or enhancers, several models have been proposed for the two functions of chromatin insulators based on recent investigations on yeast, Drosophila and vertebrates. The three models supporting the enhancer blocking activity are called promoter decoy model, physical barrier model, and loop domain model (Bushey et al., 2008; Raab and Kamakaka, 2010) T hree models have also been proposed for the chromatin barrier activity. The first model, called nucleosome gap model, got inspiration from the studies in yeast (Bi and Broach, 1999, 2001) This model propose s that a nucleosome free region forms at
21 chromatin insulator, and it disrupts the spread of repressive histone modifications since the modifiers need to interact with the adjacent nucleoso me. The second model proposes that the recruited acetytransferases can compete with the spreading deacetylation and methylation activities to counteract the propagation of heterochromatin. heteroch romatin boundaries in yeast leads to the third model. In this model, rapid and constant replacement of nucleosomes erases the repressive histone modification, before the repressive modifications can spread any further (Dion et al., 2007) Although different models have been proposed, it was observed in vivo that multiple mechanisms are in volved in demarcating the boundaries between heterochromatin and euchromatin (Oki et a l., 2004) Epigenetic Regulation of Gene Expression In contrast to genetics, which studies the phenotype variation due to the changes of primary DNA sequences, epigenetics is the study of heritable and reversible changes in accessibility and expression st atus of the underlying genetic information Major epigenetic mechanisms include DNA methylation, histone modification, and noncoding RNAs (Henikoff, 2008) DN A Methylation DNA methylation in mammals is predominantly found on cytosine residues of CpG dinucleotides between 60% and 90% of all CpGs are methylated (Ehrlich et al., 1982; Tucker, 2001) The genomic regions wi th high density of CpGs are referred to as CpG islands, and DNA methylation of these CpG islands correlates with transcriptional repression (Goll and Bestor, 2005)
22 regulatory regions of active or poised genes, where by the underlying chromatins have H3K4 methylation DNA methylation may achieves the tra nscriptional repression state by two mechanisms. First, DNA methylation itself can prevent the binding of transcriptional factors to gene regulatory regions (Choy et al., 2010) And second, methylated DNA can be bound by the methyl CpG binding proteins (MBDs), which further recruit histone deacetylases and chromatin remodeling proteins, results in the compact and inactive chromatin structure called heterochromatin (Zhang et al., 1998a) DNA methylation involves in many biological processes like centromere silencing, X chromosome inactivation in female mammals, and mammalian imprinting (Yang and Kuroda, 2007) Abnormal DNA methylation like hypermethylation at promoter CpG islands of tumor suppressor genes are associated with tumorigenesis (Esteller et al., 2001) Histone Modification As a fore mentioned, nucleosome consists of an octamer of two copies of the histone proteins H2A, H2B, H3, H4, and 147bp of DNA sequences wra p p ed around the octamer complex The histone tails are su bject to a variety of post translational modifications, among which methylation, acetylation and phosphorylation are the best studied. The histone modifications pattern can be extremely complex not only because the variety of modifications, but also becaus e that lots of amino acids on the histone tails can be modified. For instance, lysine 4, 9, 14, 18, 27, 36, and arginine 2, 3, 8, 17, 26 on histone H3 tail can accept methyl or acetyl groups Histone modifications have a direct impact on gene expression pa ttern, it is now understand that transcription activators will recruit histone acetyltransferases to acetylate histones, while transcription
23 repressors will recruit histone deacetyltransferases to deacetylate histones (Allis et al., 2007) Recent advantages in ChIP Chip and ChIP Seq technologies (chromatin immunoprecipitation followed by microarray or high throughput sequencing) have allowed the study of histone modifications on a genome wide scale. A nd these studies have shown that certain histone modifications are consistently associated with certain expression patterns at certain regulatory regions. For example, H3K4me3 is associated with active promoters, H3K4me1 is associated with active and poise d enhancers, H3K27ac is associated with active enhancers, and H3K36me3 is enriched in gene body specifically in exons. Whereas H3K27m e3 is associated with repressed regions, and H3K9me3 is enriched at centromere and telomere regions (Guttman et al., 2009; Heintzman et al., 2009a; Heintzman et al., 2007; Ozsolak et al., 2008a; Won et al., 2008) The Coordination between DNA Methylation and Histone Modifications in Gene Repression Chromatin structure is well known to have a large impact on the pattern of gene expression during animal development. DNA methylation and histone modifications are both involved in the establishment of chromatin structure as well as gene expression regulation. Repressive histone methylations, like H3K27me3 and H3K9me3, will cause local formation of inaccessible heterochromatin structure, which is reversible due to developmental signals and environmental stresses, whereas DNA methylation will lead to stable repression pattern (Cedar and Bergman, 2009) The appropriate coordination between the transient and stable repression forces is crucial for animal development a nd responses to stimuli. F or example, the early developmental genes like Hox genes
24 need to remain inactive after certain stages, whereas the tissue specific genes and tumor supp ressor genes need to be reactivated in c ertain cell types According to the recent model that the establishment of DNA methylation in early development is mediated by histone modification (Ooi et al., 2007) the H3K4 methylation at promoters and en hancers might be formed before de novo DNA methylation. It was proposed that H3K4 methylation is mediated by direct binding of RNA polymerase II, which recruits H3K4 methyltransferases (Guenther et al., 2007) As a result, de novo DNA methylation, which is carried out by the DNA methyltransferases enzymes, happens exclusively at CpG sites that do not have H3K4 methylation since DNA methylat ion and H3K4 methylation are strongly anti correlated (Meissner et al., 2008; Mohn et al., 2008; Weber et al., 2007) Maintaining the repressed state of pluripotency genes during stem cell differentiation provides a good example for illustrating the coordination between histone m odification and DNA methylation. T he repression stability is achieved through three steps. In the first step, transcription is shut down by the repressor molecules which bind to promoter reg ions of pluripotency genes. This initial repression is reversible once the repressors are removed. In the next step, the histone deacetylation and histone methyltransferase lead to the formation of local heterochromatin regions marked by methylated H3K9. T his new layer of repression provided by the change of chromatin structure is much more stable than repressor binding alone. H owever, it is not sufficient to maintain the repression stability against reprogramming, as the pluripotency genes which are silenc ed by H3K9 methylation alone can be reactivated during differentiation (Epsztejn Litman et al., 2008; Feldman et al., 2006) The final step of pluripotency gene
25 inactivation involves DNA methylation to their promot ers, after which the reactivation becomes almost impossible if the key factors are not artificially altered (Cedar and Bergman, 2009) Noncoding RNAs The mechanisms of noncoding RNAs in epigenetic regulation are less well understood than DNA methylation and histone modification. A generally accepted classification of noncoding RNAs is based on the length, which divides noncoding RNAs into small (less than 200 nucleotides) and long (more than 200 nucleotides) categories. Small noncoding RNAs usually derive from large RNA precursors, and include microR NA (miRNA), short interfering RNA (siRNA), and so on. miRNAs are about 22 nucleotides in length, and are the products of imperfect hairpin structures in long noncoding RNA precursors or introns. They function by base pairing with the complementary sequence s within target mRNAs, and usually resulting in gene silencing through translational inhibition or mRNA degradation. It was recently reported that miRNA can regulate de novo DNA methylation in mouse embryonic stem cells (Benetti et al., 2008; Sinkkonen et al., 2008) Long noncoding RNAs can sometimes reach more than 10kb in length, and they were initially considered as noisy transcription or artifacts from the contamination with genomic DNA or pre mRNA (Mattick, 2005; Struhl, 2007) However it is now generally accepted that large amounts of such long noncoding RNAs exi st in eukaryotes genomes. Long noncoding RNAs have an effect on transcription by regulating the activity or localization of transcription factors within cells, and by processing to various types of small regulatory RNAs. For example, a s the first well characterized long noncoding RNA, HOTAIR was identified in human HOXC locus, it functions in trans on
26 HOXD locus by targeting PRC2 complex to HOXD and generating a transcriptional ly repressed chromosomal domain (Rinn et al., 2007) Epigenetic Regulation and Cancer The concerted coordination of epigenetic mechanisms includin g DNA methylation, histone modification, noncoding RNA and so on, forms an epigenetic regulatory network which dynamically adjusts gene expression pattern and cellular properties according to developmental signals and environmental stresses. Dys regulatio n of this network will lead to various of diseases including cancer (Baylin and Ohm, 2006; Feinberg, 2007; Jirtle and Skinner, 2007; Rodriguez Paredes and Esteller, 2011) Dys R egulation of D istinct E pigenetic Mech anisms L eads to C ancer Cancer has been previously considered as a pure genetic disease. Due to the consistent study over the last decade, it becomes clear that epigenetic regulation is implicated in tumorigenesis. Aberrant DNA methylation, histone modifica tion and nucleosome remodeling are the common epigenetic processes which take place during cancer development (Rodriguez Paredes a nd Esteller, 2011) These abnormal disruptions of epigenome have been shown to lead to tumorigenesis by silencing tumor suppressor genes and reactivating oncogenic retroviruses (Perry et al., 2010) It has been widely observed that hypermethylation of si te specific CpG island promoters is responsible for the silencing of numerous tumor suppressor genes (Esteller, 2007) Global DNA hypomethylation, which preferentially appears at repetitive sequences, can account for genomic instability (Esteller, 2008a) and the resuscitation of proto oncogenes (Rodriguez Paredes and Esteller, 2011) Global mis organization of histone modification is another hallmark of cancer. One of the most well studied examples is that the global reduction of monoacetylation at
27 H4K16 and trimethylation at H4K20 appear early and accumulate in multiple primary tumors (Fraga et al., 2005) Abnormalities in global histone modification levels can also serve as a prediction for cancer recurrence and prognosis For instance, lower global levels of H3K4me2 and H3K18ac are associated with higher risk of prostate cancer recurrence (Seligson et al., 2009) as well as lower survival probabilities in both lung and kidney cancer patients And reduced global level of H3K9me2 is also prognostic of poorer outcome for prostate or kidney cancer patients (Seligson et al., 2009) An increasing number of cases have shown that nucleosome remodeling caused by the loss of specific landmarks or proteins is another reason for the aberrations in epigenomic landscape of cancer. For example, recent e vidences have demonstrated that the loss of the insulator protein CTCF binding will lead to the spreading of facultative heterochromatin into the genebody of tumor suppressor genes p16 (Witcher and Emerson, 2009) and p53 (Soto Reyes and Recillas Targa, 2010) eventually cause the sil encing of these tum or suppressor genes This kind of cancer progression through the loss of protection against heterochromatin propagation has no direct correlation with DNA methylation (Soto Reyes and Recillas Targa, 2010) And trad itional epigenetic AZA deoxycytidine can neither permanently restore p16 expression (Esteller, 2007) nor reverse CTCF binding (Witcher and Emerson, 2009) PcG Proteins and C ancer Dysregulation of PcG components were also found in variety of cancers. For example, EZH2 was up regulated in a wide range of hematopoietic and solid human malignancies, such as lymphoma, breast ca ncer, prostate cancer colon cancer, and so on (Kleer et al., 2003; Mimori et al., 2005; van Kemenade et al., 2001; Varambally et al.,
28 2002; Visser et al., 2001) And EZH2 was also found to consistently over expres s in metastatic prostate cancer compared to localized prostate cancer or normal tissues (Varambally et al., 2002) The elevated activity of EZH2 causes tumorigenesis by ectopic repression of tumor suppressor genes, for example, DAB2IP and MSMB were silenced by EZH2 in prostate cancer (Beke et al., 2007; Chen et al., 2005) Several recent large scale transcriptome/exome studies, which aimed at identifying the tumor genetic ab normalities, have found that histone modifiers, such as the histone H3K27 demethylase UTX, were frequently mutated in a variety of cancers (Dalgliesh et al., 2010; Gui et al., 2011; van Haaften et al., 2009) In a ddition, tumor cells may be mis specified by PcG proteins to adopt stem cell properties likely through the PcG mediated silencing of genes responsible for differentiation and lineage specification (Bernstein et al. 2006a; Caretti et al., 2004; Ezhkova et al., 2009; Lee et al., 2006) The well known phenomena that tumor cells and stem cells share some common properties like extensive proliferation capacity and (Pardal et al., 2003; Sparmann and van Lohuizen, 2006) Although this hypothesis is still debatable, it does not affect the fact that tumorigenesis can be initiated by PcG silencing of developmental g enes. P 53 and Cancer The origin of mammalian p53 family proteins (p53, p63 and p73) can probably go back as far as the divergence of animalia and fungi approximately 2 billion year ago (Fernandes and Atchley, 2008) p53 is unique than the other two mammalian p53 family proteins because of its p rominent role as tumor suppressor. In addition, p53 also involves in regulating cellular responses upon stress and DNA damage as well as many
29 other physiological processes like stem cell maintenance, development, etc (Junttila and Evan, 2009) Although the origin of mammalian p53 is early, its role as tumor suppressor is likely a relative recent adaption, which is only needed for large, long lived organisms. Several mysteries about its tumor suppr ession function are still need to be addressed, such as how p53 distinguish tumor cells from normal cells, when p53 is activated during tumorigenesis, and what signals cause the loss of p53 function in cancers. The understanding of these questions will imp rove the therapeutic efficacy in the cancer treatment. Tumor Suppression F unction of P 53 In normal cells, the interaction between p53 and MDM2, MDM4 results in the steady and low activity level of p53, which is sufficient for most of the physiological func tions of p53. In contrast, oncogenic and DNA damage signals will interrupt the interaction between p53 and MDM2, MDM4, which triggers the transcriptional activity of p53 and causes its accumulation and dramatically increased activity. The rapid induction o f p53 activity by interrupting its interaction with MDM2 and MDM4 is vertebrate specific, as no counterparts of MDM2 or MDM4 exist in invertebrates (Brodsky et al., 2000; Nordstrom and Abrams, 2000) It is still un clear whether the roles of p53 in tumor suppression, stress or DNA damage responses and other physiological processes are independent or not. The to the concept that the tumor suppression role of p53 is achieved by antagonizing DNA damage and so preserving the genome integrity and preventing the accumulation of oncogenic mutations (Junttila and Evan, 2009) The i dea was further sponsored by the evidences that dramatic genome instability shown in many cancer cells (Donehower et
30 al., 1995) and that oncogenic signals can induce DNA damage in some circumstances (Di Micco et al., 2006) However, the concept that DNA damage functions as the principal trigger of p53 mediated tumor suppression has recently been challenged by the work in mouse shown that the tumor suppression function regu lated by p53 is entirely p19 ARF dependent (Christophorou et al., 2006) p19 ARF i s encoded by an alternative open reading frame within the Ink4a Afr locus (Quelle et al., 1995) and can activate p53 activity by inhibiting its interaction with MDM2 (Pomerantz et al., 1998; Stott et al., 1998; Zhang et al., 1998b) More importantly, only oncogenic signaling can specifically induce p19 ARF but DNA damage could not (Christophorou et al., 2006; Kamijo et al., 1997; Zindy et al., 2003) which implies that oncogenic signaling is the mechanistic feature of tumor cells whereas DNA damage is dispensable. p53 is a sequence specific transcription factor that regulates many downstream genes, among which the induction of apoptotic genes is the predominant role p53 plays in tumor suppression and DNA damage respo nses. The mechanisms include p53 mediated induction of proteins (Marchenko et al., 2000) such as Bax, N OXA, PUMA. In addition, p53 may also induce apoptosis through relocation of death receptors, such as Fas and DR5 to the cell surface (Bennett et al., 1998) ; and regulation of translation by (El Deiry, 1998) P 53 a nd Cancer T herapy The predominant role of p53 in tumor suppression makes it a potential target for cancer therapy. Most of the early efforts focused on gene therapy approach that transfers p53 to cancer patients that lack functional p53 by virus vectors (Peng, 2005; Roth et al., 1996; Senzer and Nemunaitis, 2009) A different strategy focusing on the
31 development of low molecular mass compounds that restore p53 activity is also under conduct. To this end, efforts hav e evolved to produce compounds that interact with mutant p53 proteins in tumor cells and restore their function by altering their conformation (Boeckler et al., 2008; Bykov et al., 2002) ; as well as to produce c omp ounds that enhance p53 activ ity by disrupting its interaction with MDM2 (Vassilev, 2007; Vassilev et al., 2004) p53 mutations can also potentially predict patients prognosis (Aas et al., 1996; Young et al., 2008) however these expectations have not been fulfilled because of the genetic complexity and extensive diversity of individual tumors (Levine and Oren, 2009) ChIP Seq: Genome W ide Monitoring of Epigenetic Regulation ChIP Seq combines chromatin immunoprecipitation with next generation high throughp ut parallel DNA sequencing to identify the binding sites of transcription factors and effective domains of histone modifications. Compared with ChIP Chip (chromatin immunoprecipitation followed by microarray), ChIP Seq has several advantages such as higher throughput, higher resolution, and lower systematic bias. Due to the recent development in next generation sequencing, a lot of genome wide epigenetic datasets became available for the model organisms as well as others. Among these, ENCODE (encyclopedia o f DNA elements) (2004) and modENCODE (model organism ENCODE ) (Roy et al., 2010) projects provide a huge amount of whole genome ChIP Seq as well as other types of datasets for human, mouse, Drosophila and C.elegans in different cell lines, tissues and developmental stages (Bernstein et al., 2012; Djebali et al., 2012; Gerstein et al., 2012; Neph et al., 2012; Sanyal et al., 2012; Thurman et al., 2012)
32 Bioinformatics Programs for ChIP Seq Data A nalysis ChIP S eq read s enrichment regions or ChIP S eq peaks can generally be classified into three categories: sharp, broad and mixed. Sharp peaks of a few hundred base pairs width usually appear in DNA binding protein profiles, which are usually used for the identification of transcription factor binding site s While broad ChIP S eq enrichment regions, which can span from several kilobases to even hundreds of kilobases covering tens of genes, are usually found in the histone modification profiles that mark transcription active or repressed regions. Most of the current algorithms such as MACS (Zhang et al., 2008b) PeakSeq (Rozowsky et al., 2009) etc, are specifically developed to search for peaks or transcrip tion factor binding sites in the DNA binding protein profiles. Several comprehensive comparisons have been conducted among these methods, but no consensus has been reached, partially due to the lack of well defined evaluation dataset s (Laajala et al., 2009; Pepke et al., 2009; Qin et al., 2010; Wilbanks and Facciotti, 2010) Although it is possible to apply these peak calling methods to histone modificat ion profiles, there are obvious drawbacks. For example, the foc us of the analysis of histone modification data sets is to identify a specific chromatin pattern t hat often spans a long range (Hon et al., 2008) However, peak calling for transcription factors is usually focused on much narrow peaks. This kin d of problem can be clearly seen as the average width of the peaks identified by the peak calling methods are in the range of 100bp to 3000bp when dealing with H3K27me3 profiles (Qin et al., 2010) while the real enrichment regions should be tens of kilobases wide. Besides the methods specific for the detection of p eaks in DNA binding protein profiles, there are a few algo rithms developed to identify the broad, low intensity
33 enrichment region s in histone modification ChIP S eq data, such as SICER (Zang et al., 2009) RSEG (Song and Smith, 2011) ChIPDiff (Xu et al., 2008) CCAT (Xu et al., 2010) ChromaSig (Hon et al., 2008) and Models 1 3 (Johannes et al., 2010) However, ChIPDiff and Models 1 3 are aimed to characterize the differential histone modification sit es, and they both requir e ChIP S eq data from multiple cell lines or developmental stages. CCAT is specified to identify the weak ChIP signals from background noise a nd it requires a n input control profile. SICER (spatial clustering approach for the identification of ChIP enriche d regions) is the mostly cited method to identify broad histone modification enrichment domains. It utilizes the clustering approach to identify candidate spatial islands of enriched signals and report s the islands with significant scores by comparing to a random background model The other widely used program RSEG instead applies the two state HMM (hidden Markov model) and also provides specific boundary calling function.
34 CHAPTER 2 HIS TONE MODIFICATIONS DNA ACCESSIBILITY AND P53 BINDING PROFILES FOLLOWI NG DNA DAMAGE Introduction The origin of mammalian p53 family proteins (p53, p63 and p73) can probably go back as far as the divergence of animalia and fungi approximately 2 billion year ago (Fernandes and Atchley, 2008) p53 is unique than the other two mammalian p53 family member s (p63 and p7 3) because of its prominent role as tumor suppressor. It is involve d in the regulat ion of cellular responses upon oncogenic stress es such as DNA damage as well as many other physiological processes like stem cell maintenance, development, etc (Junttila and Evan, 2009) In normal cells, the interaction between p53 and MDM2 / MDM4 results in the steady and low activity level of p53, which is sufficient for most of the physiological functions of p53. I n contrast, oncogenic and DNA damage signals will interrupt the interaction between p53 and MDM2 / MDM4, which triggers the transcriptional activity of p53 and causes its accumulation and dramatically increased activity. The rapid induction of p53 activity b y interrupting its interaction with MDM2 and MDM4 could be vertebrate specific, as no counterparts of MDM2 or MDM4 has been identified in invertebrates (Brodsky et al., 2000; Nordstrom and Abrams, 2000) p53 is a se quence specific transcription factor that regulates many downstream genes, among which the induction of apoptotic genes is the predominant role p53 plays in tumor suppression and DNA damage responses. N ucleosome consists of a histone octamer which has two copies of the histone proteins H2A, H2B, H3, H4, and 147bp of DNA sequences wrapped around the complex The histone tails are subject to a variety of post translational modifications, whose pattern can be extremely comp licated not only because the variety of
35 modifications there exist but also because that lots of amino acids on the histone tails can be modified Although single histone modification can change the chromatin property the combination of histone modifications is believed to be the ultimate d eterminant of the chromatin structure. Recent advantages in ChIP Chip and ChIP Seq technologies (chromatin immunoprecipitation followed by microarray or sequencing) have allowed the study of histone modifications on a genome wide scale. And these studies h ave shown that certain histone modifications are consistently associated with regulatory regions that controls or influences gene expression For example, H3K4me3 is associated with active promoters, H3K4me1 is associated with active and poised enhancers, H3K27ac is associated with active enhancers, and H3K36me3 is enriched in gene body specifically in exons. Whereas H3K27me3 is mostly associated with repressed regions, and H3K9me3 is typically enriched at centromere and telomere regions (Guttman et al., 2009; Heintzman et al., 2009a; Heintzman et al., 2007; Ozsolak et al., 2008a; Won et al., 2008) In our quest to understand what controls cellular sensitivity to p53 mediated pro apoptotic response following DNA damag e, we found that epigenetic regulation plays a pivotal role in controlling the responsiveness of pro apoptotic genes. It has long been observed in Drosophila that while cells in early embryogenesis (stage 9 11) are extremely sensitive to irradiation induce d apoptosis, cells in late stage embryos became very resistant, even though development cell death can still occur (Fig ure 2 1 ). Three pro apoptotic genes, reaper hid and sickle are rapidly induced within 15 20 minutes following DNA damage in early stag e embryo s (Lin et al., 2011; Zhang et al., 2008a) This induction is dependent on p 53 and the regulatory region IRER (Irradiation
36 Responsive Enhancer Region) (Zhang et al., 20 08a) (Fi gure 2 1 ). Remarkably, IRER is not only required for irradiation induced expression of reaper and sick le but also required for the responsiveness of hid which is located ~210 kb away from reaper In Df(IRER) mutant embryos, none of the three pro apoptotic genes are responsive to DNA damage although the overall development expression patterns of the three genes are largely unchanged. consequently the three genes are very sensitive to irradiation and activated p 53. However, during late embryogenesis, when most cells enter into post mitotic differentiation, IRER becomes enriched for suppressive histone modifications H3K27me3 and H3K9me3. Correspondingly, this region is boun d by Polycomb group proteins and HP1 (heterochromatin protein 1). As a consequence, DNA in IRER became inaccessible as measured by DNase I sensitivity assay. This epigenetic blocking of IRER renders all three pro apoptotic genes unresponsive to irradiation even though the responsiveness of other p 53 target genes, such as Ku70 & Ku80, are unaffected at this stage (Zhang et al., 2008a) In embryos lacking the function of key Polycomb group proteins, this sensitive to resistant transition is significantly delayed. Interestingly, the epigenetic blocking of the IRER is strictly limited to the IRER but never reaches the transcribed region of reaper which is expressed in neural stem cells at later stage embryos (Lin et al., 2011; Zhang et al., 2008a) This is in sharp contrast with canonic PcG mediated epigenetic regulation of homeotic genes, in which the whole transcribed region of the targeted gene is marked by repressive histone modifications. This restriction of heterochromatin formation is functionally significant. While reaper
37 becomes unresponsive to irradiation in later stage embryos, it is expressed in response to developmental cues and required for programmed neuroblast cell death in late developmental expression of reaper is mediated by the intergenic Neuroblast Regulatory Region (NBRR) located downstream of reaper (Fig ure 2 1 ) (Tan et al., 2011) Our data indicated that epigenetic regulation of the enhancer region mediating p53 indued pro apoptotic gene expression could serve as an important mechanism to regulate cellular sensitivity to DNA damage d uring cel lular differentiation. Given the fact that the role of p53 in mediating DNA damage induced cell death is highly conserved, we are curious as to whether the enhancer specific epigenetic regulation also plays a role in controlling cellular response to activa ted p53 in mammalian cells. A recent study of p53 mediated DNA damage signaling provide d a whole genome map of p53 binding profiles in mouse ES cells (genotype 129X1/129S1) in both normal condition and DNA damaged condition induced by adriamycin (Adr) t reatment (Li et al., 2012) Unfortunately, DNA accessibility and histone modification data was not available for mES cells of the same genotype. However, genome wide DNA accessibility and histone modification profi les were available for the C57BL/6 genotype, generated by the ENCODE project (Shen et al., 2012) This provide d us an opportunity to test whether histone modifications and DNA accessibility affect the binding profil e of p53 in the mES cells which has been rarely studied before. In this work, we tried to use these datasets to ask whether and how the his tone modifications and DNA accessibility correlate with the binding profile of p53 upon DNA damage.
38 Materials and M ethods Dataset The datasets used in this study are listed in Table 3 1. Data P rocessing For histone modificati on ChIP Seq and MNase S eq dataset s we first applied MACS (Zhang et al., 2008b) to count the number of tags that fell within the 10bp non overlapping bins. Then we normalized the reads number in each bin to the total reads number, and the n subtracted the normalized input signal from the normalized ChIP Seq signal. Mean Signals aroun d P53 Binding S ites Bx python (Blankenberg et al., 2011) function was applied to calculate the average ChIP Se q signal around p53 binding sites with window size as 200bp and step size as 20bp. Grouping of P53 Binding Sites B ased on DNA A ccessibility In order to robustly divide p53 binding sites based on DNA accessibility, we used two independent MNase Seq dataset s GSM769010 (Shen et al., 2012) and GSM849958 (Hu et al., 2013) which both measures the DNA accessibility in mouse ES cells with high resolution. And we used the ir average signals in the 800bp region surrounding the midpoint of a p53 binding site as a measure of its DNA accessibility The high DNA accessibility group was defined as the p53 binding sites whose average MNase Seq signals are both below their respective 10% quantile while the low DNA accessibility group should have both signals in th eir respective top 10% percent And the medi um DNA accessibility gro up should satisfy that the first MNase Seq
39 (GSM769010) signal is between 30% and 70% quantile, and the second MNase Seq (GSM849958) signal is not below 10% or above 90% quantile Predic t P53 Binding Sites u sing TransFac P53 M otif The consensus p53 binding motif (V$P53_05) was extracted from TRANSFAC database (Wingender et al., 1996) And 67425 p53 binding sites were pre dicted in mouse genome (mm9) by MATCH (Kel et al., 2003) when setting both matrix and core cutoffs as 0.8. Results Constitutive and Conditio nal Intergenic P53 Binding Sites in Mouse ES C ells A recent whole genome study of p53 mediated DNA damage signaling provides a comprehensive map of p53 binding profiles in mouse ES cells in both normal condition and DNA damage condition induced by adriamyc in (Adr) treatment (Li et al., 2012) With the peak calling software MACS, Li et al. identified 7820 and 54564 p53 binding sites in normal and Adr treated conditions, respectively (Li et al., 2012) We found that the majority (7778 99.7% ) of p53 binding sites in normal condition have a corresponding binding site in the Adr treated condition (Figure 2 2A). We thus named these as constitutive p53 binding sites. T he remaining (4 6786) p53 binding sites were only detectable following Adr treatment. For the clarity of discussion we will henceforth refer those as conditional (Adr induced) p53 binding sites. Since p53 binding in promoter region s and transcribed regions can be affecte d by other transcription factors and the transcription process we chose to focus on intergenic p53 binding sites that are at least 5 kb away from any TSS and not overlap with any a nnotated transcribed region. A total of 3640 constitutive and 19391 conditi onal p53 binding sites were identifie d as intergenic (Figure 2 2B). The number of intergenic
40 constitutive sites is about half of all p53 binding site s identified by the ChIP Seq experiments. We believe that this set of binding sites provide a relatively cl ean context for us to probe how p53 binding may correlate with histone modifications and DNA accessibility in the genome scale. Correlation between Histone Modification and DNA A ccessibility in the C 57BL/6 Mouse ES C ells We first tried to assess whether w e can verify the correlation between DNA accessibility and histone modi fication in the same mouse ES cell line. As show in table 2 1, the DNA accessibility data and histone modification data were generated by the Ren group as part of the ENCODE project (Shen et al., 2012) There is also MNase Seq dataset in mouse ES cells from the Z hao group (Hu et al., 2013) (see Methods for detail) Based on these two data sets, we grouped the p53 binding sites that fall into high (top 10%), medium (30 70 percentile), and low (bottom 10%) DNA accessibility groups (Fig ure 2 3 ). Instead of directly using histone modification ChIP Seq signal, w e applied the data processing method of ENCODE project (Shen et al., 2012) as we first normalized the IP and input signal to their total number of reads respectively, then subtracted the normalized input signal fro m the normalized IP signal. With this extra step, we eliminated the bias in histone modification signal introduced by the inconsistency in the underlying DNA accessibility. When we plotted the histone modification ChIP Seq va lue around the three groups of p53 b inding sites, we found that DNA accessibility in those sites positively correlates with the active histone modifications like H3K27ac, H3K4 me1/3 but negatively correlates with repressive marks like H3K27 me3 and H3K9me3 ( Figure 2 4 ). Overall,
41 the cor relation between DNA accessibility and different histone modifications can be verified when using the datasets generated by the same laboratory with the mouse ES cells that have the same genetic background. Lack of Correlation between DNA A ccessibility an d P53 Binding We then tried to assess whether there is a correlation between DNA accessibility and p53 binding by extrapolating the DNA accessibility data to compare with the p 53 binding data W e grouped the p53 binding sites into high (top 10%), medium ( 30 70 percentile), and low (bottom 10%) DNA accessibility groups as mentioned above (Fig ure 2 3 ). Interestingly, when DNA accessibility surrounding constitutive sites were compared against those surr ounding the conditional sites, there was little differenc e between the two for any of the three groups of DNA accessibility (Fig ure 2 3) This lack of correlation is surprising since the level of p 53 binding following Adr treatment was significantly higher at the constitutive sites (Figure 2 5 ) We then proceede d to see whether DNA accessibility have an impact on DNA damage induced p53 binding by plotting and compare the number of p53 ChIP Seq reads following Adr treatment for these three different accessibility groups. Although 19391 more p53 binding sites cou ld be detected following DNA damage, their average binding intensity is much lower than that of the cons titutive binding sites (Figure 2 5 ). More interestingly, the average intensity curves for the three DNA accessibility group s are almost overlapping in A dr treated condition (Fig ure 2 5 B ). This suggests that, for the dataset we analyzed, the number of p53 ChIP Seq reads following DNA damage has no significant difference between those that are in high accessibility region vs. those that are in low accessibi lity region.
42 Correlation between Active Enhancer M arker H3K27ac and Constitutive P 53 Binding S ites The active histone modification H3K27ac has been found to distinguish active enhancers from inactive/poised enhancers containing H3K4me1 alone (Creyghton et al., 2010) We reasoned that the constitut ive and conditional p53 binding sites contain active and poised enhancer elements respectively, although it requires intensive experiments to decide the exact identity. Indeed, the signal of active enhancer marker H3K27ac is higher around constitutive p53 binding sites than conditional ones, and the difference is most prominent in the high DNA accessibility group (Fig ure 2 4 ). Comparatively, the enhancer marker H3K4me1, promoter marker H3K4me3 and suppressive histone modifications H3K27me3 and H3K9me3 do no t have significant difference between constitutive and conditional p53 binding sites (Fig ure 2 4 ). P53 Binding Sites with Lower DNA A ccessibility T end to C ontai n Consensus P 53 Binding M otif We also interested in whether the underlying DNA sequence has d ifference for the differen t groups of p53 binding sites. We identified consensus p53 binding sites in the mouse genome using the p53 binding site matrices from TRANSFAC database (Wingender et al., 1996) In general greater portion of constitutive p53 binding sites contain consensus p53 motif than the conditional ones for all the three p53 groups based on DNA accessibility (Table 3 2 ). Interestingly, we found that the p53 binding sites with in low DNA accessibility region are more likely to contain the consensus p53 motif This is true for both cond itional and constitutive binding sites (Table 3 2 ). These observations imply that conditional p53 binding sites, especially those with high DNA ac cessibility may interact with DNA through different binding motif s or even through indirect interaction.
43 Discussions The repressive histone modifications like H3K27me3 and H3K9me3 are supposed to mark the heterochromatin regions, which have compact chroma tin structure and prevent the binding of transcription factors. However, with the data set that were available to us at the time of our work, we found that only the active enhancer mark H3K27ac shows significant differen ce between conditional and constitut ive p53 binding sites in DNA accessibility high and medium groups, which is consistent with the previous finding that H3K27ac distinguishes active enhancers from poised ones (Creyghton et al., 2010) The intensities of other histone modifications, like the repressive H3K27me3, H3K9me3 and the active H3K4me1/3 have no detectable difference between conditional and constitutive p53 binding sites, no matter the DNA accessibility level Also, DNA accessibility itself does not seem to have significant impact on p53 binding intensity. The main limitation o f our analysis is that the histone modification and DNA accessibility profiles were generated from the mouse strain that is genetically different with the one used to study p53 binding prof iles. In addition, different experimental protocols and fragmentati on methods were used. We note that histone modification profiles like H3K27me3 generated for the mES cells with different genotype and produced by different labs can have dramatic difference (Figure 2 6). So it would give us more confidence about any concl usion if the p53 and histone modification profiles generated from the same laboratory based on the same genetic background are available in the future. With the limitation standing, our analysis failed to support the hypothesis that heterochromatin region s with low DNA accessibility prevent p53 binding upon DNA
44 damage Since p53 binding sites are constrained to a relatively narrow space, not like other transcription factors, for example CTCF, wh ose binding occupies much longer DNA sequences. The chromatin structure may not have a significant impact on this kind of localized narrow binding.
45 Table 2 1. List of datasets used in this study Dataset GEO # Cell line Genetic background Reference p53 control GSM647224 mES 129X1 x 129S1 (Li et al., 2012) P53 Adr8h GSM647225 mES 129X1 x 129S1 (Li et al., 2012) Mnase Seq GSM769010 mES Bruce4 C57BL/6 (Shen et al., 2012) M n ase Seq GSM 849958 mES 129S6/SvEvTac (Hu et al., 2013) H3K27me3 GSM1000089 mES Bruce4 C57BL/6 (Shen et al., 2012) H3K9me3 GSM1000147 mES Bruce4 C57BL/6 (Shen et al., 2012) H3K27ac GSM1000099 mES Bruce4 C57BL/6 (Shen et al., 2012) H3K4me1 GSM769009 mES Bruce4 C57BL/6 (Shen et al., 2012) H3K4me3 GSM769008 mES Bruce4 C57BL/6 (Shen et al., 2012) H3K27me3 GSM9 70531 mES 129SvJae/C57BL/6 (Jia et al., 2012)
46 Table 2 2. Percentage of p53 binding sites that contain consensus p53 binding motif p53 binding sites DNA accessibility Group Low Medium High Conditional p53 239 ( 31.5%) 1250 (18.1%) 67 (10.6%) Constitutive p53 69 (56.6%) 654 (50.0%) 37 (35.2%)
47 Figure 2 1 Schematic diagram summarizing previous findings regarding the IRER and ILB in our lab. Previous work from our lab has mapped the irradiation responsive enh ancer region (IRER) to the 33kb intergenic sequence on the 3rd chromosome. It is located between two pro apoptotic genes reaper and sickle including the putative p53 response element (P53 RE). This enhancer region is subject to PcG mediated epigenetic reg ulation and undergoes an open to closed transition during embryonic stage 11 12. The open chromatin structure in early stage embryos facilitates irradiation induced transcription of reaper and hid and leads to apoptosis; whereas the condensed chromatin in late staged embryos precludes transcription and blocks apoptosis. The facultative heterochromatin formation is restricted to IRER by the IRER left barrier (ILB), which allows the reaper promoter to remain open throughout development and accessible to regul ation. The barrier activity requires binding of the Cut protein, which may recruit chromatin modifying enzymes such as CBP; mechanistically, much remains to be elucidated. Modified from (Lin et al., 2011; Tan et al., 2011; Zhang et al., 2008a)
48 Figure 2 2 Constitutive and conditional p53 binding sites. Venn diagrams show the number of p53 binding sites in untreated (ctr) condition and Adr treated conditions, and the definition of constitutive and conditional p53 binding sites genome wide (A), or in intergenic regions (B).
49 Figure 2 3 DNA accessibility around p53 binding sites. The signals from two independent MNase Seq datasets (A) GSM769010, and (B) GSM849958, around conditional and constitutive p53 binding sites in intergenic regions. The solid, dashed and dotted lines indicate the DNA accessibility high, medium and low groups respectively.
50 Figure 2 4 Histone modifications around p53 binding sites. Histone modifications, including the active enhancer ma rk H3K27ac (A), repressive marks H3K27me3, H3K9me3 (B), and active marks H3K4me1/3 (C) around conditional and constitutive p53 binding sites in intergenic regions. The solid, dashed and dotted lines indicate the DNA accessibility high, medium and low group s respectively.
51 Figure 2 5 Binding intensities of conditional and constitutive p53 in untreated and Adr treated conditions. Average p53 ChIP Seq reads number for conditional and constitutive p53 binding sites in control (A) and Adr treated (B) conditio ns. The solid, dashed and dotted lines indicate the DNA accessibility high, medium and low groups respectively.
52 Figure 2 6 H3K27me3 ChIP Seq profiles from different laboratories are not comparable (A) Venn diagram shows how many H3K27me3 enriched doma ins are overlapping between the two datasets. (B) Scatter plot of ChIP Seq signals from the two datasets in 200bp windows. Linear regression shows the coefficie nt of determination is only 0.13 (C) A representative region shows how different the two H3K27m e3 datasets could be.
53 CHAPTER 3 GENOME WIDE IDENTIFICATION OF CHROMATIN TRANSITIONAL REGIONS REVEALS DIVERSE MECHANISMS DEFINING THE BOUNDARY OF FACULTATIVE HETEROCHROMATIN Introduction Site specific formation of facultative heterochromatin, mediated b y PcG (Polycomb group ) protein s, plays a fundamentally important role in controlling cellular differentiation and in defining the pro perty of differentiated cells. The suppressive histone modification mark, H3K27me3, is catalyzed by Polycom b repressive com plex 2 (PRC2). This suppressive modification has strong affinity to, and is usually bound by, Polycom b repressive complex 1 (PRC1). The interaction between PRC1 and PRC2 lead s to the propensity to spread this suppressive histone modification until it is an tagonized (reviewed in (Muller and Verrijzer, 2009; Schwartz and Pirrotta, 2007) ). Although certain strong promoters of active genes can prevent the formation of facultative heterochromatin (Raab et al., 2012) under many circumstances, specialized DNA elements called chromatin barriers or barrier insulators are needed to demarcate the boundary of facultative heterochromatin (reviewed in (Gaszner and Felsenfeld, 2006) ). Insulators, such as the gypsy insulator, w ere originally identified for their enhancer blocking ac tivity, i.e. blocking the interaction between the enhancer and promoter when placed in between (Geyer et al., 1986) Later, it was revealed that most of them also have barrier activity (Kahn et al., 2006; Roseman et al., 1993) i.e. blocking the propagation of repressive histone mod ifications. It was not clear whether the two activities are separable until the characterization of the cHS4 insulator globin loc us. The complete cHS4 has both enhancer blocking and barrier activity. However, a series of mechanistic studies indicated that the two activities are separable
54 and carried out by distinct DNA elements. The enhancer blocking activity of cHS4 is mediated by CTCF, while its barrier activity against heterochromatin formation requires a binding site for USF1 (Upstr eam Stimulatory Factor 1). Binding of USF1 to cHS4 recruits chromatin modifying enzymes that catalyze histone modifications incompatible with heteroch romatin formation, thus preventing the propagation of suppressive histone modification (Huang et al., 2007; West et al., 2004) Recently, a novel chromatin barrier that lacks any detectable enhancer blocking funct ion has also been identified in Drosophila (Lin et al., 2011) This ~200bp element i s located at the left boundary of IRER ( I rradiation R esponsive E nhancer R egion), a 33 kb intergenic regulatory region controlling stress induced expression of multiple pro apoptotic genes (Zhang et al., 2008a) Whe n tested in transgenic animals, ILB (IRER Left Boundary) is fully capable of blocking the propagation of H3K27me3 initiated by a strong Polycomb response element (PRE) (Lin et al., 2011) The chromatin barrier function of IL B is evolutionarily conserved. When tested in a vertebrate system, it blocked heterochromatin propagation as effecti vely as the cHS4 (Lin et al., 2011) Although many insulator/boundary associated pro teins have been characterized in Drosophila including Su(Hw), d CTCF, BEAF 32, GAF, CP190 and Mod(mdg4) (reviewed in (Gurudatta and Corces, 2009) ), none of those was found associated with ILB. The presence and prevalence of novel boundary setting mechanism s were also implicated by epigenomic studies conducted in Drosophila and mammalian systems, which revealed that the majority of H3K27me3 boundaries are not associated with characterized insulator proteins.
55 Although lots of efforts have been directed toward s partitioning the genome into large domains based on multiple histone modifications (Hon et al., 2008; Kharchenko et al., 2011) or protein binding profiles (Filion et al., 2010) there is much less focus on understanding how individual repressive histone modification is demarcated by chromatin barrier elements. To gain a comprehensive understanding of boundaries of facultative heterochromatin, we developed a novel bioinform atics approach to identify the chromatin transition al regions (CTRs). We reasoned that if the propagation of heterochromatin formation is stopped by a counter acting mechanism as revealed by the models proposed by Felsenfeld and colleagues (Gaszner and Felsenfeld, 2006) then the boundary of the facultative heterochromatin should manifest as a rapid transitional re gion where the level of H3K27me3 show s dramatic changes. Using a two tiered approach, we demonstrate d that it is feasible to identify the CTRs based on H3K27me3 ChI P Seq data from both Drosophila and mammalian cell lines. By locating CTRs to single nucleos ome resolution, we found that CTRs are usually 200 ~ 600bp away from the binding sites of known insulato r/boundary associated factors. However, the majority of CTRs are not associated with any known insulator proteins. Conversely, only a small portion of ins ulator protein binding s ites are associated with CTRs. Comparing insulator protein bindings associated with CTRs vs. those in H3K27me3 enriched region s revealed interesting distinctions in co factor binding as well as in DNA sequences flanking the binding sites. Overall, our analysis suggests that diverse mechanisms can be employed to establish the boundaries of facultative heterochromatin (Li and Zhou, 2013)
56 Materials and Methods CTRICS (Chromatin Transitional R e g ions Inference from ChIP Seq) A lgorithm The program will take H3K27me3 ChIP Seq data as input (and control dataset measures the input DNA level, if available). The datasets should be in BED (Browser Extensible Data) format. Redundant tags which map to the same genomic region will be kept as a single tag in order to minimize potential PCR bias. CTRICS then divides the whole genome into non overlapping windows of size w (default is 200bp), counts ChIP Seq tags in each window and generates a bedGRAPH file whi ch can be viewed with the UCSC Genome Browser (Kent et al., 2002) We measure the rate of chromatin transition with T score as, (2 1) where L(N) and R(N) denote the average of ChIP Seq tags (when there is no input control file), or normalized ChIP Seq tags in the N (default =20) windows upstream and downstream of the giv en window, respectively. Because H3K27me3 generally forms broad regions covering repressive genes and intergenic regions (Barski et al., 2007; Pauler et al., 2009) the long genom ic region used in this initial eval uation should minimize the impact of enrichment level fluctuation observed for H3K27me3 enriched regions. Similar to other stud ies analyzing the change of chromatin modifications (He et al., 2010; Meyer et al., 2011 ) we t ook the square root transformation to minimize the variance introduced by higher counts. We will take the denominator as 1/ N if the min(L(N), R(N)) wa s zero. A T score greater than the threshold (see below) and has one side show significant enrich ment of ChIP Seq tags, will be taken as a candidate region where a transition exist
57 In the next step, in seeking to pinpoint the CTR location we calculate the T score for each window around the candidate CTRs, and the region that has the highest T sco re i.e. the highest enrichment transition rate, will be reported as the predicted CTRs. T score is defined as, (2 2) which is the product of the T score for the long genome region ( N windows) and the absolute T score for a s hort genome region ( n windows ; default=3 ). To assess the statistical significance of each CTR (the probability that the observed T score is by chance), we need to derive the distribution of T score in the background model. In this program, we chose not to make any assumption about the background distribution of the ChIP Seq tags because different datasets have variations and will not always follow a certain assumed distribution. Instead we applied a bootstrapping approach to get the background distribution of T score. The bootstrapping was conducted by randomly choosing (with replacement ) N windows from the whole genome as left windows and N windows as right windows, then calculating T score with the randomly chosen windows. The T score distribution in the random background model is obtained by repeating (with replacement ) the above process for a large number of times (10 6 ). Based on the T score distribution and the runtime input p value, we will get the T score threshold. In the presence of the input contr ol file an extra step is needed before the estimation of T score threshold and prediction of CTRs. We named this step as background correction which normalizes the tag number of each window in ChIP file to the tag number of the same window in control f ile by the following formula,
58 where n t n c are the tag numbers of a given window in ChIP file and control file (again n c will be set as one if it is zero), N t N c are the total tag counts in ChIP and control files. After the bac kground correction, the program will use the normalized tag counts to estimate T sc ore cutoff and to predict CTRs. Fig ure 3 17 shows the workflow of CTRICS. CTRICS has been implemented in Perl, and it can be downloaded from http://22.214.171.124/CTRICS/home.htm Dataset T he datasets used in this study are listed in Table 3 1 and 3 2 Parameters U sed for P redicting CTRs and /or H3K27me3 D omains CTRs were predicted in Drosophila S2 and human HeLa cells by CTRI CS with default parameters (expect that the p value was set to 0.005 for HeLa cells). We ran SICER without control file using default parameters suggested by the authors (window size = 200bp, gap size = 600bp, E value = 100, p value = 0.2), and we took the effective Drosophila genome size as 71.6%. RSEG was also run with default parameters defined by the program. The two boundaries of an H3K27me3 enrichment domain predicted by these programs were taken as two CTRs, and we discarded the boundaries which are less than 4kb to an unmappable region. Statistical A nalysis The two way hierarchical clustering (Ward method) and p rincipal component analysis w ere carried out using JMP Genomics 5.0 (SAS Institute, Cary, NC) A binding
59 is considered positively associated with a CTR if the midpoint of the binding is within 1 kb of the CTR. Wilcoxon rank sum test was performed in R programming environment (R version 2.9.2, R Development Core Team, 2009) to compare the gene expression levels on different sides of CTRs, as well as the binding intensity and width of binding sites of insulator proteins and co factors. Motif D iscovery The insulator protein binding motif s were identified using CisFinder (Sharov and Ko, 2009) with default setting. 400bp region s centered on the midpoint of the binding s were used as input The predicted motifs were depicted as color logos using WebLogo (Crooks et al., 2004) The discriminative motifs were discovered usi ng MEME (web version 4.8.1) (Bailey et al., 2010) Calculation of Nucleotides C ontent Poly(dA:dT) (AAAA/TTTT) level w as calculated using a sliding window approach with window size of 200bp and step of 25bp. The contents were fur ther normalized to the genome average. Results Localize the Chromatin T ransition al Regions (CTRs) B ased on H3K27me3 ChIP Seq D ata At the time of our study, several methodologies, such as SICER (Zang et al., 2009) and RSEG [ (Song and Smith, 2011) have been developed to analyze genomic profiles of H3K27me3, the signature marker o f facultative heterochromatin. Most of these methodologies focus on identifying broad domains enriched for a particular histone modification Although these methodologies are very useful for identifying
60 H3K27me3 enriched regions, they were not designed for the purpose of specifying the boundary o f facultative heterochromatin. The fact that there is a lack of experimentally verified data set of H3K27me3 boundaries also prevented objective comparison of these methodologies. Drosophila melanogaster provides the best system for studying the boundaries o f facultative heterochromatin. Several insulator proteins, such as Su(Hw) (Harrison et al., 1993) BEAF 32 (Gilbert et al., 2006) and dCTCF (Mohan et al., 2007) have been very well characterized in Drosophila The genome wide binding profiles for these proteins, as well as many other genomic and epigenomic information, are available for the Drosophila Schneider 2 (S2) cells due to the efforts of the modENCODE project (Roy et al., 2010) and many other individual la bs. Taking the advantage of these data, we generated an empirical set of chrom atin transition al regions for H3K27me3 (Figure 3 10 ) In essence, we selected regions where clear changes in H3K27me3 enrichment, as revealed by ChIP Seq, were accompanied by experimentally verified binding of the insulator proteins and their respective co factors (such as CP190). Testing of the two popular H3K27me3 enrichment calling algorithms with this empirical H3K27me3 boundary data set reveal ed inconsistency in precisely d efining the transition region. We noticed that the enrichment calling algorithms such as SICER is sensitive to the fluctuation of H3K27me 3 enrichment levels in continuous facultative heterochromatin regions, and consequently where the enrichment level of H3K27me3 fluctuated (Figure 3 1 Figure 3 11 ) On the other hand, methodology such as RSEG, which based on the two state hidden Markov model and provided specific boundary calling func tion, seems to miss some putative
61 boundaries in our empirical data set (Fig ure 3 11 ). It is worth noting that RSEG also failed to predict a boundary at the ILB locus, which has been experimentally verified to function as chromatin barrier against Polycomb group (PcG) mediated spreading of H3K27me3 (Lin et al., 2011) To pinpoint the locat ion of CTR, we developed a two tiered analysis methodology called CTRICS (Chromatin Transitional Regions Inference from ChIP Seq) (see Methods for detail) First, the existence of a transition was detected by comparing the enrichment of H3 K 27me3 in relativ ely large genomic intervals (4kb). The relatively large interval helps to minimize the false positives due to the fluctuation of H3K27me 3 enrichment levels in facult ative heterochromatin regions. After a transitional event has been identified, a secondary analysis is performed with short intervals to identify a 200bp region where the enrichment of H3K27me 3 display s the most significant change. The number of CTRs identified by CTRIC S is comparable to the boundaries identified by RSEG, and both are much less when compar ed with the boundaries predicted by SICER (Figure 3 11A ). The majority of CTRs we identified overlap (i.e. within 2 kb) with the boundaries predicted by RSEG (Figure 3 11A ). However, unlike RSEG, our method was able to identify more putative bou ndaries in ou r empirical data set (Figure 3 11B) as well as the ILB. Visual inspection indicated that some of the CTRs identified by CTRIC S but missed by RSEG can be corroborated with other evidences such as RNA Seq or H3K4me3 data (Figure 3 11C ). Thus we resorted to use CTRIC S for genome wide analysis of CTRs in S2 cells. Genome Wide I dentification of CTRs in S2 C ells A pplying CTRICS to the H3K27me3 ChIP Seq dataset derived from the Drosophila S2 cell line (Gan et al., 2010) identified a total of 2082 CTRs. From
62 sequencing depth analysis, we noticed that the H3K27me3 ChIP Seq dataset with a total of ~2.8 million uniquely mapped reads, ha d already reached saturation plateau for CTR detection (Figure 3 12 ) Since CTRs define the boundaries between repressive facultative heterochromatin and accessible euchromatin, the active and repressive histone marks should have contrasting patterns aro und CTRs. Indeed, active histone marks, such as H3K4me1, H3K4me2, H3K4me3, H3K9ac and H3K27ac, are enriched on the euchromatic side of CTRs, while depleted on the heterochromatic side (Figure 3 1 A). We noticed that the enrichment levels of H3K9me3, which mostly associate with constitutive heterochromatin in centromer ic and telomer ic regions (Schones and Zhao, 2008) do not change significantly around the identif ied CTR s. This indicate d that the CTRs we identified are specific to facultative heterochromatin. Although the two repressive histone marks overlap at some loci (Bilodeau et al., 2009; Hon et al., 2009; Lin et al., 2011) their global localization s are largely independent of each other Our analysis also suggest ed that in most loci, the change of H3K27me3 level at the boundary was not associated with significant changes in H3K9me3. In addition, we reasoned that gen es locate on the heterochromatic side s of CTRs should in general be repressed compared to those on the euchromatic side. When the expression profile was evaluated using a companying RNA Seq dataset from S2 cells (Gan et al., 2010) the difference was indeed obvious for genes on different sides of the CTRs. Compared with the global average, genes whose entire transcribed regions locate within the 4kb regions on the euchromatic side of CTRs had significantly higher level of expression, whereas genes on the heterochromatic sides were significantly
63 repressed ( Figure 3 1B). Corresponding with the difference in gene expression levels, the binding o f Pol II as well as active histone modification H3K4me3 show specific enrich ment on the euchromatic side of CTRs ( Figure 3 1C). These evidences all support that the CTRs identified by our method indeed are sharp boundaries interface H3K27me3 enriched and d epleted regions The Spatial Relationships b etween CTRs and Known B ou ndary S etting P roteins The global binding profiles of the major insulator proteins Su(Hw), BEAF 32, dCTCF, GAF, and their important co factors (such as CP190 and Mod(mdg4)) are availabl e for the S2 ce lls. Comparison of H3K27me3 CTRs and the binding profiles indicated that less than 15% of the insulator proteins binding sites are within 1 kb of the identified CTRs ( Figure 3 2 A ). The majority of the binding sites of these known insulator p rotein s are not in close association with CTRs. For instance, many (~49%) Su(Hw) binding sites are found in continuous H3K27me3 domains ( Figure 3 2 B). Conversely, less than half (~42%) of the H3K27me3 CTRs are associated with any of the four DNA binding insulator proteins i.e. located within 1 kb ( Figure 3 2 C ). However, for those that do associate with a binding site for the insulator proteins, the binding site is always preferentially located at the euchromatic side of the CTR ( Figure 3 2D,E), which agr ees very well with a recent genome wide study of chromatin boundary elements conducted in human CD4 + cells (Wang et al., 2012) When the intensity of these proteins were plotted, the peaks of insulator binding is located at about 200 ~ 600bp away from the CTR ( Figure 3 2 D ) Very similar spatial relationship between CTRs an d insulator protein binding was observed for BEAF 32, Su(Hw), and dCTCF. Compared with these insulator proteins, the spatial relationship between GAF binding and the correlated CTRs was somewhat different, with the enrichment region
64 more spread out and the peak of binding intensity about 1 more nucleosome space away from the CTR ( Figure 3 2 D). Figure 3 2E illustrates a CTR as an example, it is associated with BEAF 32 and CP190, and the peaks of both protein binding sites are located on the euchromatic side of this CTR with about 400bp between the peaks of binding and the CTR The Diversity of Facultative Heterochromatin B oundar ies As mentioned above, the spatial relationship between CTR and the binding of known insulator proteins suggests that the CTRs obse rved for S2 cells are due to the barrier a ctivity of insulator proteins. However, more than half of the CTRs are not co localized with any of the known insulator proteins ( Figure 3 2C). To gain a comprehensive understanding of the H3K27me3 CTRs identified in S2 cells, we expanded our analysis to include the binding profiles for other chromatin associated proteins all of which were generated by the modENCODE project (Roy et al., 2010) with the S2 cells In this anal ysis we ex clude d proteins which have been shown to be directly involved in the establishment or maintenance of the facultative heterochromatin such as the polycomb group proteins, the trithorax group proteins and heterochromatin binding proteins. A total of 15 binding profiles were selected ( Figure 3 3, Table 3 1 ), and the binding call was processed as described (Kharchenko et al., 2011) Similar to previous association studies (Cuddapah et al., 2009) we consider ed a binding within 1kb of a CTR as a positive association. We then conducted unsupervised hierarchical clustering to classify CTRs based on the association with these chromatin associated proteins. From the cluste ring analysis, the predicted CTRs can be clearly divided into eight groups ( Figure 3 3A). We also performed principal component analysis on the 15 proteins and the first three
65 principal components turned out to account for 25.1%, 12.2% and 10.5% of the tot al variance respectively ( Figure 3 13 ). After projecting the predicted CTRs on the first three principal components, the eight distinct CTR groups were also clearly separated ( Figure 3 13 ), demonstrating that the grouping of CTRs was robust to different cl assification methods. The protein occupancy in distinct CTR groups clearly suggested that the majority of CTRS in groups A, B and C are associated with the insulator protein CP190 whereas the other five groups are CP190 independent ( Figure 3 3B) For the 3 CP190 assoc ia ted groups, about 30% of CTRs in Group A are also associated with the insulator protein dCTCF, which requires CP190 as a co factor (Mohan et al., 2007) The majority of CTR s in group B are also boun d by insulator proteins Su(Hw), Mod(mdg4), which are the required trans factors for the gypsy insulator (Ghosh et al., 2001; Harrison et al., 1993; Pai et al., 2004) CTRs in Group C are enriched for ins ulator prote in BEAF 32 Interestingly CTRs in this group are also associated with the chromatin remodeling protein NURF, which has been shown to be required for establishing the chromatin barrier activity of cHS4 at the chicken globin locus (Li et al., 2011) Taken together, our unsupervised hierarchical clustering agrees very well with the model put forward based on genetic analysis of three insulator proteins, i.e. while the three insulator proteins Su(Hw), BEAF 32, and dC TCF barely overlap with each other, they all co localize with CP190 (Bushey et al., 2009) Interestingly, t he majority of CTRs in group E are associated with JIL1, which can maintain euchromatic state by terminating the constitutive heterochromatin spreading (Bao et al., 2 007; Zhang et al., 2006) T he colocalization suggests that JIL1 may also
66 antagoniz e facultative heterochromatin through a mechanism which is different from the other CTR groups. The separation of group D from group E is due to the presence of RNA p olymera se II. However, CTRs in group D were not associated with annotated TSSs ( T ranscription S tart S ite s ) while the majority of CTRs in groups A, C and G are located close to TSS ( Figure 3 14 ) In depth analysis indicated that the Pol II binding ated with CTRs in group D are much smaller than those associated with bona fide TSS and they do not correl ate with H3K4me2/3 enrichment. Close inspection suggested that the association of Pol II binding with this group was questionable and could be du e to artifact of peak calling. Since all of the peak calling were generated by modENCOD E with unifying standard (Kharchenko et al., 2011) we refrained from changing the calling specifically for the Pol II data. Group F is associated with the insulator protein GAF (Schweinsberg et al., 2004) In groups G and H most of the CTRs have no clear association with any of the investigated proteins, which suggest s the existence of other proteins functioning at these CTRs. Interestingly, the novel chromatin barrier ILB we have recently identified (Lin et al., 2011) does not co localize with any of the 15 proteins, and belongs to group H. Strong Co Factor Binding Distinguishes dCTCF and Su(Hw) Binding A ssociated with CTR vs. T hose in H3K27 me3 Enriched R egions The strict spatial relationship between the binding of insulat or proteins and the identified CTRs strongly suggests a cause effect relationship between insulator protein binding and the formation of the boundary for the H3K27me3 modification. However, analysis of the global profiles indicated that only a small portio n of binding sites for dCTCF and Su(Hw) are associated with CTRs. To reconcile the two seemingly
67 conflicting observations, we first asked wh ether there is any difference in terms of binding intensity by the respective insulator proteins. To address this qu estion, we compared the binding profiles at sites associated with CTRs against those in regions enriched for H3K27me3, which are clearly not associated with any chromatin barrier activity. We found that for dCTCF, Su(Hw), and GAF, there was no significant difference in terms of enrichment levels at the peaks of the binding ( Figure 3 4A, 2 2B) There was only marginal difference for BEAF 32, where the peak intensity was about 50% higher in sites associated with CTR ( Figure 3 4A) These findings suggest ed tha t insulator proteins such as dCTCF and Su(Hw) can bind with similar affinity to euchromatic regions associated with CTRs and facultative heterochromatic regions enriched for H3K27me3. Although the intensities at the peak were similar for both dCTCF and Su( Hw), we did notice that the binding for these two insulator proteins was more spread in heterochromatic regions and more constrained in binding site s associated with CTRs ( Figure 3 4 A, C 2 2B ). The functional significance of this difference is unclear. It has been well documented that the binding of co factors such as CP190 is required for the enhancer block ing function of Su(Hw) and dCTCF (Mohan et al., 2007; Pai et al., 2004) We found that the intensity of CP190 b inding was much higher at sites associated with CTRs. This is true for both dCTCF and Su(Hw), where the CP190 binding intensities at sites associated with CTRs were significantly higher than those that are in H3K 27me3 enriched regions ( Figure 3 4B, 2 2B) Significant difference in binding intensity was also observed for another co factor of Su(Hw), i.e. Mod(mdg4) (Ghosh et al., 2001) for which the binding intensity for sites associated with CTRs was
68 2.98 fold of those in heterochromatic regions ( Figure 3 4B). These observations strongly suggest ed that co factors such as CP190 and Mod(mdg4) are involved in establishing the chromatin barrier activity of dCTCF and Su(Hw). Poly(dA:dT) Tracts an d Decreased Nucleosome Density a round the Insulator B i n ding Sites a ssociated with CTR We next asked whether there is any difference between the DNA sequences underlying the insulator protein binding sites associate with CTRs and those in H3K27me3 enriched region s We inputted the 400bp regions around the CTR associated binding sites to CisFinder (Sharov and Ko, 2009) to identify statistically overrepresented DNA motifs, w hich w ere then compared with the motifs obtained with the 400bp sequence s surrounding the heterochromatic (H3K 27me3 enriched) binding sites. T here was no significant difference between the motif s identified from CTR associated binding sites vs. those identified fro m the binding sites in heterochromatic region ( Figure 3 5A ). In fact, motifs identified from the aforementioned two subsets resemble d the motifs identified using all binding sites identified in the S2 cells This suggested that the DNA sequences interactin g with the insulator proteins do not d istinguish whether the association of the respective insulator protein can function as chromatin barrier or not. We then asked whether sequences surrounding the CTR associated insulator protein binding sites have disc riminative patterns comparing to th ose surrounding the heterochromatic binding sites. Interestingly, when we supplied MEME (Bailey et al., 2010) with the CTR associated binding sites as positive regions and the heterochromatic b inding sites as negative regions to identify discriminative motifs, a motif with continues deoxyadenosine (multi A) show ed up for all of the four insulator
69 proteins ( Figure 3 5B ). S imilar results were ob tained when using the CisFinder program (Sharov and Ko, 2009) This indicated that a key distinction of insulator protein binding sites associated with CTR s was that they tend to be in close proximity to sequences with long stretch of dA/dTs ( poly(dA:dT) tracts). DNA sequences with poly(dA:dT) tracts where n(A/T) 4 has been found to be rigid and discourage nucleosome binding (Mavrich et al., 2008a; Suter et al., 2000) When we compiled the levels of poly(dA:dT) (frequency of A AAA/TTTT) and the nucleosome density around the binding sites of the four known insulator proteins (Su(Hw), BEAF 32, GAF, dCTCF), we found that the binding sites associated with CTRs were strongly associated with increased poly(dA:dT) levels as well as dra matically decreased nucleosome occupation (increased sensitivity to MNase) ( Figure 3 5C ). In contrast, such an association was not observed for binding sites in H3K27me3 enriched regions. We concluded that the CTR associated insulator protein binding sites tend to be surrounded by DNA sequences characterized with nucleosome d e s tabilizing poly(dA:dT) tracts and manifest as hypersensitiv e to MNase. Poly(dA:dT) Tracts and Increased Sensitivity to MNase are A ssociated with CTRs that do n ot Bind with Known Insu lator P roteins To see how general are poly(dA:dT) tracts and increased MNase sensitivity associated with CTRs of H3K27me3, we plotted the distribution for each of the groups identified by the hierarchical clustering ( Figure 3 3A ). We found that for CTRs in groups A, B, and C, which are all enriched for the binding of CP190, there is a clear trend of increased level of poly(dA:dT) (n 4) and d ecreased nucleosome occupancy. The region of increased poly(dA:dT) levels roughly correlates with that of the increase d sensitivity to MNase, and both peak at the euchromatic side of CTRs ( Figure 3 6). CTRs in group
70 A, B, and C are enriched for the presence of binding of dCTCF, Su(H w), and BEAF 32, respectively. However, not all of the CTRs in each group are associated wi th the corresponding insulator protein ( Figure 3 3B). We could not observe clear increase of poly(dA:dT) level, nor decreased nucleosome density, associated with the CTRs in groups D and E, which are associated with PolII and JIL1 respectively. While t her e is a slightly increased level of poly(dA:dT) and a decreased level of nucleosome density for CTRs in group F. However, the distribution is somewhat different from those observed for groups A, B, and C, in that there is no clear peak. Interestingly, f or CTRs in groups G and H, which are not enriched for the binding of any known insulator proteins or other chromatin associated protein s investigated here there is a clear trend of increased poly(dA:dT) level on the euchromatic side of CTRs. For group G, there is also a significant decrease of nucleosome density correlates with the increased level of poly(dA:dT) It is well known that nucleosome positioning sequences, including poly(dA:dT), are associated with promoters (Mavrich et al., 2008b) However, the majority of CTRs in group H are not close to TSS ( Figure 3 14 ) or associated with Pol II binding the increased multi A level in the two groups is un likely due to the nucleosome positioning sequences associated w ith promoters. This indicated that the presence of poly(dA:dT) tract and decreased nucleosome density is a general feature of CTRs beyond those that associate with the characterized insulator proteins.
71 Enrichment of H3.3 but Decreased Nucleosome Turnover a t CTR Associated dCTCF Binding S ite s It has been shown in mammalian systems that the binding of insulator proteins such as CTCF results in dynamic (unstable) nucleosomes and manifest as sites with increased enrichment of histone variants such as H3.3/H2A.Z at low salt isolation condition (Jin et al., 2009) The dynamics of nucleosomes in S2 cells has also been assayed with the r ate of histone variant H3.3 replacement (Mito et al., 2005) and more recently, with the CATCH IT technology (Deal et al., 2010) The latter is based on metabolic labeling of histones and is thus a direct measurement of nucleosome turnover rate independent of the composition of nucleosom e. It has been shown that in general the profiles obtained with CATCH IT correlate very well with the one based on H3.3 incorporation (Deal et al., 2010) In addition to the CATCH IT profile, datasets for H3.3 enrichment at low vs. high salt isolation conditions (Henikoff et al., 2009) nucleosome density (ratio of nucleosomal/genomic) (Henikoff et al., 2009) and DNA a ccessibility evaluated with methylation footprinting (Bell et al., 2010) were also available for the same cell line. When these profiles were evaluated around all of the H3K27me3 CTRs identified for S2 cells, we found that there was a conspicuous decrease of nucleosome density at the euchromatic side of CTRs ( Figure 3 7A ). The lowest point of nucleosome density is about 200 ~ 600bp away from the CTR, which corresponds well with the peak of the binding sites for known insulator proteins ( Figure 3 2 D), as well as the region enriched for poly(dA:dT) trac t s ( Figure 3 6) Correspondingly, consistent increase of DNA accessibility was also observed at the same relative position.
72 However, when the nucleosome dynamic s data was evaluated, we noticed an appare nt discrepancy between the H3.3 incorporation measureme nts and the CATCH IT profiles. At the same relative location to CTRs, there is a significant increase of H3.3 incorporation ( at low salt condition ) which would have indicated an increased dynamics (tu rnover rate ) at the in sulator protein binding sites. However, this was contradicted by the CATCH IT profile at these sites, which showed a sharp drop at the same relative position ( Figure 3 7A) To understand the cause of this discrepancy between the H3. 3 incorporation and the turnover rate measure d with CATCH IT, we looked at these profiles associated with each individual insulator proteins ( Figure 3 7 B). We found that for the GAF bi n ding sites, whether associated with CTRs or not, there is a consistent incre ase of both H3.3 and CATCH IT. This agrees well with previous findings that GAF binding sites are marked by increased nucleosome dynamics (Deal et al., 2010) This also indicates that in terms of nucleosome dynamics, there is no difference between GAF binding sites associated with CTRs vs. those that are not associated. However, a contrasting pattern was specifically observed between the H3.3 and CATCH IT profiles around CTR a ssociated dCTCF bindi ng sites. While there is a significant increase of H3.3 in these sites, the CATCH IT data indicated that the turnover rate at these sites is not higher, but rather lower than the neighboring region ( Figure 3 7B). This contrasting trend of H3.3 incorporatio n and nucleosome turnover rate suggest ed that, unlike GAF binding sites, the increased level of H3.3 incorporation is accompanied by decreased level of nucleosome turnover at the dCTC F binding sites close to CTRs. Interestingly, this contrasting trend was only obvious with dCTCF
73 binding sites associated with CTR s, but was not observed around dCTCF binding sites not associated with a CTR ( more than 1kb away from the closest CTR) ( Figure 3 7B). As aforementioned, the contrasting pattern between the enrichme nt of H3.3 and decreased nucleosome turnover rate was obvious when the two profiles w ere evaluated for all H3K27m e3 CTRs identified in S2 cells. For the groups of CTRs associated with known insulator factors, we found that this contrasting pattern is most prominent for group A ( Figure 3 7C ). About 30 % of CTRs in this group has verified binding of dCTCF ( Figure 3 3 B ). In addition, the enrichment of H3.3 was also prominent for CTRs in group G, which has no clear association with any of the known insulator pro teins. Chromatin Transitional R egions in the HeLa Cell L ine A pplying the CTRICS program to H3K27me3 ChIP Seq dataset derived from human HeLa cells (Cuddapah et al., 2009) identified a total of 10710 CTRs. The majo rity ( 8047 ) of which overlaps with the boundaries of H3K27me3 domains identified by Cuddapah et al (Cuddapah et al., 2009) which identified a total of 32,704 H3k27me3 domains in HeLa cells ( Figure 3 8A ). The diff erence in the number of H3k27me3 boundaries identified by CTRICS and the H3K27me3 domain approach is likely due to the combined effect of 1.) the CTRICS methodology is less sensitive to fluctuation of H3K27me3 enrichment levels within H3K 27me3 enriched dom ains ( Figure 3 1C) ; and 2.) CTRIC S is more stringent in that it will only identify boundaries with a significant drop of H3K27me3 level ( Figure 3 15 ) With this stringent set of CTRs in HeLa cells, there is a significant increase of DNA accessibility ( DNas e Seq data set from (Thurman et al., 2012) ) in at the immediate euchromatic side of the CTRs ( Figure 3 8B). This is very similar to what we observed in th e S2 cells. There is also a significant change of nucleosome density (MNase Seq data
74 from (Tolstorukov et al., 2012) ) around the predicted CTRs, which confirms that our method is identifying well defined facultative heteroc hromatin boundaries. Similar to what was observed for S2 ce lls, CTCF was also enriched on the euchromatic side of CTRs with about 2 nucleosome space in between ( Figure 3 8 C). However, unlike dCTCF, there was a minor peak of the polled CTCF binding signal on the heterochromatic side of CTRs ( Figure 3 8C ). Interesti ngly, a similar major peak and minor peak pattern of CTCF binding was also observed independently for facultative heterochromatin boundaries in human CD4 + cells identified with a consortium of histone modification profiles and a maximal segment algorithm (Wang et al., 2012) Overall the binding intensity for CTCF was moderately, but significantly, higher for binding sties associated wit h CTR than those in heterochromatic regions ( Figure 3 8D). Since no co factor such as CP190 was identified in mammalian systems, which prevented us to test whether similar distinction of co factor bindin g also applies to human CTRs. Discussions In this wor k, we showed that it was possible to i dentify the boundaries of facultative heterochromatin based on H3K27me3 ChIP Seq da ta. Our two tiered method first identifies a heterochromatin to euchromatin transition event by considering the enrichment value for a relatively large region. Following that, the 200bp region that shows the greatest transition rate of enrichment values is designated as the CTR. The validity of this simple strategy was firstly verified by the dramatic difference in active/repressive histo ne modifications and gene expression levels on the heterochromatic vs. euchromatic side of the predicted CTRs (Figure 2 1) More importantly, the validity of this strategy was vindicated by the fact that, for CTRs
75 associated with the binding of known insul ator proteins, there is a strict spatial relationship between the CTRs and the insula tor protein binding sites. Fixed vs. Variable B ound ary for Facultative H eterochromatin The method developed in this study is specifically suitable for the identification of fixed boundaries fo r facultative heterochromatin. Visual inspection of H3K27me3 profile has suggested that certain H3K27me3 domains do not have a fixed boundary (Schwartz et al., 2012) It is clear that for constitutive heterochromat in close to centromere, the boundary of heterochromatin marked by H3K9me2/3 can vary in diffe rent cells of the same tissue. reporter/marker genes located close to centromere, i.e. position effect variegation (PEV) (reviewed in (Girton and Johansen, 2008; Karpen, 1994) ). It is possible that our method for which the p ooled ChIP Seq data will lack a sharp transition region. It is conceivable that due to its close association with euchromatic region, the boundaries of facultative heterochromatin need to be precisely defined to avoid the disruption of the transcriptio na l regulation of adjacent genes. In the case of the cHS4 chromatin barrier in the chicken globin locus, the binding of USF 1 was responsible for recruiting histone modifying enzymes which in turn catalyze euchromatic histone modifications on adjacent nucleosomes (Huang et al., 2007; West et al., 2004) Th e USF 1 directed euchromatic histone modifications effectively block the propagation of heterochromatic marks and results in a shar p transition of histone marks. Interestingly, a recent study revealed that NURF is recruited by USF1 to cHS4 and is required for establishing the chromatin barrier (Li et al., 2011) Our analysis indicated that the binding of NURF (N URF 301, Figure 2 3) is associated with CTRs in groups A, B, C, and
76 G. It has been shown that Drosophila N URF is required for the enhancer blocking activity of several insulators (Li et al., 2010) Our results suggest that its role in establishing chromatin barrier is also likely conserved over long evolutionary distan ce. Our analysis of genome wide H3K27me3 CTRs in S2 cells indicated that at least in this cell line, many boundaries of facultative heterochromatin, marked by the transition of H3K27me3 enrichment level can be clearly identified. However, formation of fac ultative heterochromatin is, by definition, c ell type specific. We found that clear boundaries cannot be reliably identified from H3K27me3 data obtained from homogenize d animals (embryos or larvae). Since the binding profile of many insulator proteins as w ell as other epigenomic profile has been well studied in the S2 cells, the genome wide identification of CTRs in this cell line allowed us to address several interesting questions in regards t o chromatin barriers. Binding of Insulator Protein Alone is n ot Sufficient for E stablishing the H3K27me3 Boundary Our analysis indicated that only a small portion of genome wide binding sites for insulator proteins such as dCTCF and Su(Hw ) are associated with the CTRs. This was not surprising, given that a genomic s tudy conducted in mammalian cells also revealed that for CTCF binding sites observed for CD4+ T cells and HeLa cells, only a small percent (about 5.6% and 4.1%, respectively) are associated with the boundaries of H3K27me3 enriched domains (Cuddapah et al., 2009) Our results indicated that similar to what was observed for CTCF in mammalian cells, the majority binding sites for insulator proteins such as dCTCF and Su(Hw) do not co reside with the boundaries o f facult ative heterochromatin. The same mammalian study also revealed that only a very small portion (less than 5%) of the H3K27me3 boundaries in those cells have a CTCF
77 binding site within 1 kb of distance. Although many more insulator proteins have been characte rized in Drosophila less than half of all H3K27me3 CTRs identified in S2 cells are associated with any of the known insulator proteins. This indicates that uncharacterized mechanisms, which do not involve any of those proteins known to play a role in this process, is responsible for establishing more than half of the facultative heterochromatin boundaries in S2 cells. In this study, by narrowing down the transitional region to 200bp, we were able to reveal some very interesting relationships between the bi nding of insulator proteins and the CTRs. Central to these findings are the observation that there is a clear spatial relationship between the binding sites of i nsulator proteins and the CTRs. The binding of insulator proteins is at the euchromatic side of CTRs and the peak of binding is about 200 ~ 600bp away from the CTR s This strict spatial relationship suggests that there is a functional relationship between the binding of these insulator proteins and the establishment of the sharp transition at the CTRs A prominent question in regards to insulator proteins binding and the formation of chromatin boundary is what distinguishes those sites associated with a chromatin boundary versus those do not. We found that compared with dCTCF and Su(Hw) binding sites i n heterochromatic regions, the binding sites associated with CTRs were bound by higher levels of co factors such as CP190 or/and Mod(mdg4). In contrast, such a distinction was not observed for CTRs associated with BEAF 32 A recent work revealed that, unli ke dCTCF and Su(Hw), binding of CP190 at BEAF 32 binding sites was not affected when the insulator protein was knocked down (Schwartz et al., 2012) The same work also suggested that the inherited binding preferences of, not the
78 interac tion between, the two proteins could be responsible for the observed coloca lization of BEAF 32 and CP190. Our observation s further support their argument. The increased binding intensities of co factors at dCTCF and Su(Hw) sites associated with CTR s were n ot simply because those binding sites are located on the euchromatic side of the CTRs, since the intensity at CTR associated sites was significantly higher when compared with that in euchromatic regions (Figure 2 16 ) These observations strongly suggest ed that there is a significant difference in co factor binding between CTR associated binding of dCTCF and Su(Hw) vs. those that are in heterochromatic regions. Besides the difference in co factor binding, the underlying DNA sequences surrounding CTR associa ted binding sites are enriched for poly(dA:dT) tracts. Poly(dA:dT) trac t s have been found to form rigid structures and discourage nucleosome formation (Mavrich et al., 2008b; Suter et al., 2000) The fact that poly (dA:dT) trac t s distinguish CTR associated insulator protein binding sites from those in heterochromatic region suggest ed that it plays a role in establishing/encouraging the barrier function of dCTCF and Su(Hw). One hypothetic model come out of our analysi s is that the presence of nucleosome destabilizing sequences flanking the insulator protein binding site associated with CTRs could change the dynamics of nucleosome formation as well as facilitate increased binding of co factors. However, the enrichment o f poly(dA:dT) tracts surrounding CTR associated binding sites could simply be an indicator of nucleosome depletion, instead of playing a role in the formation of nucleosome depletion regions, as suggested by a recent study that these regions favor G/C to A /T mutations (Chen et al., 2012)
79 N ucleosome Dynamics, Histone V ariants, and H3K27me3 Boundary Increased nucleosome dynamics, often manifested as increased enrichment level of histone variants such as H3.3, has bee n linked with transcriptionally active genes in both Drosophila and mammalian systems. Our analyses indicate d that distinctive patterns of nucleosome dynamics and histone variants incorporation are associated with different subgroups of CTRs. For CTRs asso ciated with GAF, there is an increased nucleosome turnover rate (measured by CATCH IT) as well as an enrichment of H3.3 incorporation (Figure 2 7B) This agreement between turnover rate measured by CATCH IT and H3.3 incorporation has been observed globally for TSSs (transcription start sites) and several important chromatin landmarks such as binding sites for ploycomb group proteins (Deal et al., 2010) It is conceivable that the dynamic nucleosome loc ated at the binding site of GAF could serve to discourage the propagation of repressive histone modifications (Fig ure 2 9 A) which is in consistent with the model proposed in yeast (Dion et al., 2007) However, a surprising phenomenon was identified for those CTRs that are as sociated with dCTCF. Instead of increased turnover rate, the nucleo somes close to the binding sites actually showed decreased level of turnover as measured by CATCH IT (Fig ure 2 7B). This reduced turnover rate at H3K27me3 CTRs was not limited to those th at have binding of dCTCF. It was also prominent for CTRs in group G ( Figure 2 7C) Intriguingly, the decreased level of turnover is accompanied by increased incorporation of H3.3 in those CTRs. This suggest ed that for certain subgroups of CTRs, the nucleosome at the boundary has reduced turnover rate but nonetheless has str ong preference for the histone variant H3.3 (Fig ure 2 9 B). The preference of H3.3 could potential serve as a deterrent for the spreading of H3K27me3. However, this
80 mechanism, if indeed contributes to the formation of H3K27me3 boundary, is lik ely redundant and dispensable. Since the deletion of H3.3 did not have significant impact on facultative heterochromatin formation and can be compensated by overexpression of H3 (Sakai et al., 2009)
81 Table 3 1. The list of ChIP Chip profiles used in the clustering analysis Protein modENCODE Title DCCid Public Release Date Antibody Platform Su(Hw) Su(Hw) VC.S2 modENCODE_331 10/11/2009 Su(Hw) VC Affymetrix Drosophila Tiling Arrays v2.0R Mod(mdg4) mod2.2 VC.S2 mo dENCODE_2674 02/15/2010 mod2.2 VC Affymetrix Drosophila Tiling Arrays v2.0R dCTCF CTCF VC.S2 modENCODE_283 10/11/2009 CTCF VC Affymetrix Drosophila Tiling Arrays v2.0R GAF GAF.S2 modENCODE_285 10/11/2009 GAF Affymetrix Drosophila Tiling Arrays v2.0R dMi 2 dMi 2_Q2626.S2 modENCODE_926 10/11/2009 dMi 2_Q2626 Affymetrix Drosophila Tiling Arrays v2.0R SPT16 SPT16_Q2583.S2 modENCODE_3058 09/27/2010 SPT16_Q2583 Affymetrix Drosophila Tiling Arrays v2.0R MRG15 MRG15_Q2481.S2 modENCODE_3047 09/27/2010 MRG15_Q24 81 Affymetrix Drosophila Tiling Arrays v2.0R JIL1 JIL1_Q3433.S2 modENCODE_945 10/12/2009 JIL1_Q3433 Affymetrix Drosophila Tiling Arrays v2.0R RNAPolII RNA pol II (ALG).S2 modENCODE_329 10/11/2009 RNA pol II (ALG) Affymetrix Drosophila Tiling Arrays v2.0R BRE1 BRE1_Q2539.S2 modENCODE_923 10/11/2009 BRE1_Q2539 Affymetrix Drosophila Tiling Arrays v2.0R CP190 CP190 VC.S2 modENCODE_280 10/11/2009 CP190 VC Affymetrix Drosophila Tiling Arrays v2.0R NURF301 NURF301_Q2602.S2 modENCODE_947 10/12/2009 NURF301_Q26 02 Affymetrix Drosophila Tiling Arrays v2.0R Chriz Chro(Chriz)BR.S2 modENCODE_278 10/11/2009 Chro(Chriz)BR Affymetrix Drosophila Tiling Arrays v2.0R BEAF BEAF HB.S2 modENCODE_274 10/11/2009 BEAF HB Affymetrix Drosophila Tiling Arrays v2.0R WDS WDS_Q2691 .S2 modENCODE_953 10/12/2009 WDS_Q2691 Affymetrix Drosophila Tiling Arrays v2.0R
82 Table 3 2. List of datasets used in this study Dataset Cell line Platform GEO # H3K27me3 S2 ChIP Seq GSM480157 Gene expression S2 RNA Seq GSM480160 H3K4me1 S2 ChIP Chip GSE20786 H3K4me2 S2 ChIP Chip GSE20838 H3K4me3 S2 ChIP Chip GSE20787 H3K9ac S2 ChIP Chip GSE20790 H3K9me3 H3K27ac S2 S2 ChIP Chip ChIP Chip GSE20794 GSE20779 H3K27me3 S2 ChIP Chip GSE20781 RNA Polymerase II S2 ChIP Seq GSM480159 H3.3 (low salt) S2 ChIP Chip GSM333869 H3.3 (high salt) S2 ChIP Chip GSM333871 Nucleosome density S2 MNase Chip GSM333835 GSM333840 GSM333844 DNA accessibility Nucleosome turnover H3K27me3 CTCF S2 S2 HeLa HeLa Methylation footprinting CATCH IT ChIP Seq ChIP Seq GSM44128 2 GSM494308 GSM325898 GSM325897 DNA accessibility HeLa DNase Seq GSM816643 Nucleosome density HeLa MNase Seq GSM937970
83 Figure 3 1 Histone modifications and gene expression levels on the euchromatic vs. heterochromatic side of the CTRs in Drosophila S2 cell line. (A) E nrichment levels of active (solid lines) and repressive (dashed lines) histone modification s around the H3K27me3 CTRs identified in S2 cells Negative and positive distance s indicate e u chromatic and heterochromatic side s of the identifi ed CTRs, respectively (B) Expression levels of genes on the euchromatic or heterochromatic side of CTRs Barplots represent Mean S E for all genes (grey), genes within the 4kb region on the euchromatic side (yellow) or the heterochromatic side (green) of C TRs. The expression level s for genes on euchromatic side of CTRs are significantly greater than those of the genes on the heterochromatic side (p<2.2E 16, Wilcoxon rank sum test). (C) An example of 7 CTRs (red bars) predicted by CTRICS. Bar height reflects T score, top and bottom rows denotes the orientation of the CTRs. The panel below CTR show s H3K27me3 domains called by SICER. RNA Seq signal, RNA Pol II binding, as well as active histone modification (H3K4me3) are depleted in heterochromatic regions whic h have high H3K27me3, while they are enriched in euchromatic regions.
84 Figure 3 2 CTRs and the known insulator proteins in Drosophila S2 cell line. (A) Percentages of insulator protein binding sites are associated with a CTR. The x axis shows the distan ce between insulator protein binding site and the nearest CTR, and y axis shows the percentage of binding sites that are within a certain distance from the nearest CTR. The d ashed line indicates the distance cutoff of 1kb which is used for association ana lysis (B) A 200kb region on chromosome 2L as an example. There are five Su(Hw) binding sites in this region, one is associated with a CTR (red bar, highlighted region), the others locate in regions enriched for H3K27me3 The intensities of co factors (CP1 90, Mod(mdg4)) are relatively high at the CTR associated binding site, and lower at the binding sites in the H3K27me3 enriched region. (C) Venn diagram shows the number of CTRs that are associated with four insulator proteins. Note that more than half (120 3/2082) of the CTRs are not associated with any of the four insulator proteins. (D) Enrichment of insulator protein s in the + 5 kb region around corresponding CTRs. The negative and positive distances also indicate the euchromatic and heterochromatic side of CTR respectively (E) An ex ample illustrates the relative positions of a predicted CTR and the binding profiles of BEAF 32 and CP190. The peaks of the binding sites locate on the euchromatic side of the CTR, and the distance between the peak s of binding site s and the CTR midpoint is about 400bp.
85 Figure 3 3 Subgroups of CTRs based on associated proteins in Drosophila S2 cell line. (A) Heat map of the hierarchical clustering analysis result. Each column denotes a single CTR, and each row represents one protein included in the association analysis The red and blue bars denote the presence or absence of an association with the corresponding CTR, respectively Capital letters within colored boxes highlight the different sub groups of CTRs. (B) Proportions o f CTRs in each subgroup (identified in ( A ) ) that are associated with individual protein. The width of the bar indicates the percentage of CTRs in each group that are bound by the respective protein.
87 Figure 3 4 Binding intensity and patterns of insulat or proteins and co factors associated with CTRs in Drosophila S2 cell line The enrichment levels of respective insulator proteins (A) and co factors (B) around binding sites associated with CTR (solid lines) or located in H3K27me3 enriched region (dashed lines). For CTR associated binding sites, negative and positive distances denote euchromatic and heterochromatic side. Box plots show the peak values for individual insulator proteins (A) and co factors (B) at binding sites associated with CTR (open box) o r in heterochromatic regions (shaded box). (C) Box plot s of the width of insulator proteins binding patterns at binding sites associated with CTR (open box) or in heterochromatic regions (shaded box). P values were all calculated by Wilcoxon rank sum test.
88 Figure 3 5 Cis elements associated with CTRs in Drosophila S2 cell line (A) Logos representation of motif s identified from DNA sequences underlying insulator protein binding sites associated with CTRs ( CTR associated ) or in H3K27me3 enriched (Hetero chromatic) regions. Motifs obtained with all binding sites are represented at the bottom (B) Multi A motifs are the discriminative motif identified by MEME for CTR associated binding sites vs. heterochromatic binding sites. (C) Multi A (AAAA/TTTT) content (normalized to genome average, red curve) and nucleosome density (blue curve) around CTR associated insulator protein binding sites (solid line) and heterochromatic binding sites (dashed line). Data presents combined value for all the insulator proteins, dCTCF, Su(Hw), GAF, and BEAF 32. For CTR associated binding sites, negative and positive distances denote euchromatic and heterochromatic side.
89 Figure 3 6 Multi A (AAAA/TTTT) content (normalized to genome average, red curve) and nucleosome density (blu e curve) around individual subgroup of CTRs in Drosophila S2 cell line (For group F only those co localized with GAF were included). The negative and positive distances denote the euchromatic and heterochromatic sides of CTR, respectively.
91 Figure 3 7 Contrasting p atterns of H3.3 enrichment and nucleosome turnover rate associated with subgroups of CTRs in Drosophila S2 cell line. (A) Composite plot for all CTRs. H3.3 (low salt) incorporation is enriched on the euchromatic side of CTRs (red arrow), whil e nucleosome turnover rate (CATCH IT) is drops down sharply at the same region (green arrow). (B) H3.3 enrichment and CATCH IT measurements of nucleosome turnover rate moves to the same direction for GAF (both CTR associated and others) In contrast, for C TR associated dCTCF binding sites the enrichment of H3.3 is accompanied by decreased turnover ra te. (C) Plots of H3.3 enrichment (red) nucleosome turnover rate (green, measured with CATCH IT), and nucleosome density (purple) for each subgroup of the CTRs (for group F only those co localized with GAF were included). Note the contrasting pattern between H3.3 enrichment and CATCH IT profile in subgroups A, B, C, G, but not in subgroups D and E.
92 Figure 3 8 Chromatin transitional re gions in human HeLa cell line. (A) 6852 predicted CTRs in HeLa cells are overlapping (within 1kb) with the chromatin barrier regions in the previous study. (B) DNA accessibility (measured by DNase Seq) and nucleosome density (measured by MNase Seq) around all the predicted CTRs i n HeLa cell line (normalized to genome average). T he negative and positive distances denote the euchromatic and heterochromatic sides of CTR, respectively (C) Binding pattern of insulator protein CTCF around the CTRs which co localize with CTCF. The negat ive and positive distances also denote the euchromatic and heterochromatic sides of CTR, respectively (D) The enrichment level of CTCF around CTR associated (red) and heteroc hromatic (blue) binding sites. Box plots show the peak values of CTCF at the CTR associated (red) and heterochromatic (blue) binding sites. The peak values of CTCF at CTR associated binding sites were significantly greater than that at the heterochromatic binding sites (p value = 6.097e 6, Wilcoxon rank sum test).
93 Figure 3 9 Propo sed models for facult ative heterochromatin boundary. Models represent distinct features of GAF associated (A) vs. dCTCF associated (B) CTRs. The red and blue dashed lines denote the position of CTR and chromatin barrier, respectively. The blue circles at t he bottom of each model indicate the nucleosome turnover rate, the bigger the circles, the faster the nucleosomes turnover. For dCTCF associated CTRs, the increased enrichment of H3.3 is coupled with decreased turnover rate.
94 Figure 3 10 Construction of empirical p ositive and negative evaluation data sets
95 Figure 3 11 Comparison of CTRICS with SICER and RSEG. (A) The Venn diagram shows the number of CTRs (predicted by CTRICS) that are overlapping with the chromatin boundaries predicted by the other two methods in S2 cells. (B) False positive and false negative rates for different methods based on the empirical evaluation datasets. (C) A region shows several examples of CTRs predicted by CTRICS (red bar), H3K27me3 boundaries predicted by RSEG (grey b ar), and H3K27m e3 domains predicted by SICER. RSEG missed the three boundaries shown in the blue dashed block, which can be corroborated with RNA Seq and H3K4me3 ChIP chip data.
96 Figure 3 12 Sequencing depth analysis. In order to test if the H3K27me3 Ch IP Seq dataset has reached saturation status and if sequencing depth has any influence on the CTR prediction, we conducted the sequencing depth analysis. We first randomly extracted a series of subsamples (10%, 20%, 30%, and so on until 90% of the original tags) from the H3K27me3 ChIP Seq dataset without replacement. We then identified chromatin transitional regions in each subsample using CTRICS with default parameters. The x axis of the plot represents the percentage of subsample tags compared to the tota l tags (~2.8 x10 6 ), and y axis indicates the number of CTRs identified.
97 Figure 3 13 Principal component analysis of CTRs based on association with the 15 proteins. (A) Percentage of total variance accounted for by individual principal components. (B) Two dimensional projections onto the first three principal components. Different colors of the dots represent different groups of CTRs corresponding to the groups shown in the hierarchical clustering result ( Figure 3 3 ).
98 Figure 3 14 Genomic distribu tion of CTRs. The average intensities of RNA polymerase II (A) and H3K4me3 (B) around individual groups of CTR. (C) The distribution of CTRs in each group.
99 Figure 3 15 An example of 2 CTRs (red bars) predicted by CTRICS in human HeLa cells The panel below CTR show s H3K27me3 domains predicted in Cuddapah et al. 2009 CTRICS identifies the boundaries with a significant drop of H3K27me3 level, but ignores the boundaries with minor changes in H3K27me3 signal.
100 Figure 3 16 Binding patterns of co fact ors are different for CTR associated and euchromatic binding sites. Binding patterns of insulator proteins (A) and their co factors (B) around CTR associated (solid curve), heterochromatic (dotted curve) and euchromatic (break curve) binding sites in Droso phila S2 cells. For CTR associated binding sites, negative and positive distances denote euchromatic and heterochromatic side.
101 Figure 3 17 Flowchart of CTRICS. The green characters are the parameters needed in each step.
102 CHAPTER 4 DISCUSSIONS, EXP LORATIVE WORKS, AND PERSPECTIVES More and more evidences have demonstrated the crucial role of epigenetic regulation in multiple biological processes such as apoptosis, development, cell differentiation and tumorigenesis. The high order chromatin structur e is not only an efficient way to compact DNA into nucleus, but also an elaborate approach to store the heritable information of epigenomic landscapes. In specific, the close coordination between appropriate chromatin modifications and systematic changes o f chromatin state will help to maintain the stable gene expression profile in particular developmental stages, as well as to dynamically adjust the gene expression pattern in response to developmental and environmental stimuli. The series of studies in ou r lab have shown in vitro and in vivo that c hromatin barriers and enhancer elements play a fundamentally crucial role in the regulation of chromatin landscapes. My in silico work takes advantage of the current available genome wide datasets to expand and verify our observations and hypothesis in the genome scale. Epigenomics Era: New Opportunities and New Challenges Since the development of new techniques, especially next generation high throughput sequencing, we en tered into the era of epigenomics, which studies DNA methylation and histone modifications layer on top of the genome. One representative feature of epigenomics research is the generation of tons of genome scale, high resolution datasets. Several nation al and international collaborat ive project s such as
103 greatly advanced epigenomics study by producing high quality datasets and state of the art analysis tools and algorithms. Potential Opportunities from Large Scale Epi genomics The understanding of genetic information of many organisms has been well established, but the epigenomic landscapes have not been systematically studied in the genome wide scale. The major hurdle is the large number of epigenomes there may exist e ven within a single individual. Each individual has essentially one genome, whereas the individual is believed to have a distinct epigenome in each cell type and tissue. Given the extreme complexity involved in cellular regulation, the emerging epigenome maps will help us reveal new principles in the process. For example, the genome wide epigenomics datasets will provide us a comprehensive chromatin map, which will facilitate the identification of transcription factors, regulatory molecules and pathways, t arget genes of epigenetic features (Ernst and Kellis, 2010) Mapping of different epigene tic marks, such as histone modifications, DNA methylation and non coding RNAs simultaneously in the same cell type will help us to better understand the coordination among these regulatory mechanisms. Epigenomic datasets also have the valuable power to ide ntify cis regulatory elements like enhancers, insulators, and loci poised for activation (Bernstein et al., 2006b; Dindot et al., 2009; Heintzman et al., 2009b; Ozsolak et al., 2008b) With respect to disease s epi genomic maps will also provide a unique resource allowing us to identify respon sible factors that contribute to the disease as well as downstream genes affected by the disease. In addition, epigenetic states can serve as
104 biomarkers for certain disease s and maybe useful for disease diagnosis and prognosis (Esteller, 2008b) Challenges with Large Scale Epigenomics The possible combinations of cell types, disease states, individual variations and environmental stresses make the number of possible distinct epigenomes seems astronomical. Thus the effective a pplication and interpretation of these datasets generated from diverse laboratories requires the development of standards, which will help in improving comparability between datasets, avoiding duplication of effort, and so on. The epigenomics community is going to benefit most from a systematic and organized collaboration in which similar epigenetic features are investigated in a defined set of cell types and tissues, using a standardized protocols and quality controls, eventually generating high quality da tasets that are comparable with one another (Satterlee et al., 2010) The standardized approach would guarantee the reliable identification of epigenomic features in particular cell states. Another challenge is the development and standardization of computational methods to process and display the large epigenomics datasets. As the scale of epigenomic datasets continues to increase, more sophis ticated methods, such as statistical modeling and machine learning techniques, are needed to uncover the underlying patterns behind the massive and complicated data. In addition, user friendly data visualizing tools are also extremely welcomed by not only experimental biologists, since they will provide the valuable direct visualization and interpretation of the datasets.
105 Application of Machine Learning to the Prediction of Chromatin Boundaries As mentioned above, more sophisticated methods, like machine l earning are needed to elucidate the mysteries underlying large biological datasets. In Chapter 3, it has been shown that the predicted CTRs in Drosophila S2 cells can be divided into 8 subgroups based on the binding patterns of 15 proteins (Figure 3 3). I also tried to apply the machine learning method called Support Vector Machine (SVM) to study the relationship between the 15 proteins and CTRs, and to predict chromatin boundaries solely based on the 15 proteins or even less information (Figure 4 1). Suppo rt vector machines are supervised learning models that can be applied to classification (binary output) and regression (continuous output). The essential of SVM is to use the kernel functions to map the inputs (of dimension n) into a higher dimensional spa ce ( dimension >n) so that different classes can be classified with a linear hyperplane I t has been proved that this hyperplane always exists when using the appropriate kernel functions So in practice, the most important thing is to choose the right kernel function with the most suitable parameters, in order to get the best prediction accuracy as well as not to over fit the model. Over fitting, which means the model fits perfectly with training data but has little prediction power with new data, is a common drawback should be carefully examined and avoid. In order to apply SVM to predict chromatin boundaries, the independent and dependent variables need to be specified. Here the binary CTR status was defined as dependent variable, where the 2082 CTRs in Dro sophila S2 cells were taken as positive set (chromatin boundaries), and the binding sites of the 4 insulator binding proteins (including BEAF 32, dCTCF, GAF, Su(Hw)) in heterochromatic regions were taken as negative set (not chromatin boundaries). The peak or mean signals of the 15 proteins
106 (Figure 3 3) or the 6 insulator proteins (including BEAF 32, dCTCF, GAF, Su(Hw), CP190, Mod(mdg2)) in the 2kb region centered on CTR were taken as independent variables (Figure 4 1A). We are aiming to differentiate the c hromatin boundaries from the non boundaries binding sites based on the signals of the proteins using support vector machine. In addition, the 10 fold cross validation was used to evaluate the model, where positive and negative sets were divided into 10 sub groups respectively, and 9 positive and 9 negative subgroups were taken as training set, whereas the other subgroups were taken as testing set. Then repeat this process 10 times and each time using a different subgroup as testing set. As a result, we find that the models generated by SVM can successfully differentiate chromatin boundaries from the non boundary regions. The area under the ROC (receiver operating characteristic) curves for the models using peak or mean signals of the 15 proteins can reach as high as 0.971, and model with only 6 insulator proteins also has great prediction power (AUC=0.936) (Figure 4 1B). In the future stud ies the SVM models may be applied to predict chromatin boundaries in other Drosophila cell types and developmental stages And it may even be applied to mammalian systems, but of course other independent variables should be utilized since some of the 15 proteins do not have homologies in mammals. Experimental Verification of the Predicted Chromatin Boundaries In Chapter 3, it has been observed that for some groups of CTR, the signal of nucleosome density has a dip while DNA accessibility signal has a peak on the euchromatic side of CTRs (Figure 3 6, 3 7). This means for these CTRs, the regions on their euchromatic side are o pen and accessible to DNase I digestion. In order to experimentally verify this observation, I conducted DNase I sensitivity assay on
107 Drosophila S2 cells, followed by qPCR assay on 5 selected CTRs. As a result, 4 of the selected CTRs are DNase I hypersensi tivity sites (Figure 4 2). It is of great benefit for computational biologists to be directly involved in molecular biology experiments, since such a practice can help the understanding of biological processes, promote the generation of biological hypothe sis, and facilitate the interpretation of computational outcomes. During my Ph.D. training, I tried to be actively involved in the bench work and to establish an approach to experimentally verify the findings from my bioinformatics analysis These experien ces taught me that reasoning in a biological ly meaningful way is crucial for the success of computational biologists.
108 Figure 4 1 Application of support vector machine (SVM) to predict chromatin boundaries. (A) Schematic figure shows how the indepe ndent and dependent variables were chosen. (B) Receiver operating characteristic (ROC) curves for different models. The AUC (area under curve) for the three models are 0.971, 0.967, 0.936 respectively.
109 Figure 4 2 Experimental verification of chromat in boundaries. I performed DNase I sensiti vity assay on Drosophila S2 cells, and five intergenic CTRs predicted by CTRICS were selected for qPCR verification. Among them, four CTRs near genes Neu2 MED30 CG12523 and Or30a were proved to be DNase I hyperse nsitive sites. H23 and bxd PRE we re used as negative and p ositive controls, respectively. Barplot shows MeanSEM.
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127 BIOGRAPHICAL SKETCH Guangyao Li was born in Zhengzhou, Henan province, P.R. China. He is currently a Ph.D. candidate at the University of Florida, majoring in Genetics and Genomics. He received the Bachelor of Science degree in Mathematics and Applied Mathematics from Sun Yat sen University in Guangzhou in 2008, and immediate ly started the graduate study in Bioinformatics at the same university following his interest in applying mathematics and statistics to biological sciences. In 2009, he was admitted to the Ph.D. program in Genetics and Genomics at the University of Florida In 2010, he joined the laboratory of Dr. Lei Zhou in the Genetics Institute and Department of Molecular Genetics and Microbiology, with the dissertation topic focusing on revealing chromatin structure and epigenetic regulation using bioinformatics approa ches. He passed the qualifying exam and became a Ph.D. candidate in September 2011. In May 2013, he received the Master of Statistics degree from Department of Statistics at the University of Florida. He finished his Ph.D. dissertation and received the Ph. D. degree in Genetics and Genomics in December 2013.